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+{"text": "Background: Mounting evidence has demonstrated that circular RNA (circRNA) plays crucial roles in the occurrence and development of hepatocellular carcinoma (HCC). However, the expression pattern and clinical application value of plasma circRNA in HCC are still largely unknown. Herein, we explored the role of plasma hsa_circ_0005397 in diagnosis and prognosis of HCC.Methods: The expression level of plasma hsa_circ_0005397 was measured by quantitative real-time polymerase chain reaction (qRT-PCR). The identification and origin of plasma hsa_circ_0005397 were confirmed by RNase R assay, Sanger sequencing and HCC cell culture. In addition, its diagnostic value was assessed by receiver operating characteristic (ROC) curve and prognostic value was evaluated by dynamics monitoring and Kaplan\u2013Meier curve analyses in HCC patients.Results: The expression of plasma hsa_circ_0005397 was higher in patients with HCC than that in patients with benign liver diseases and healthy controls (both p < 0.05). Moreover, it was closely correlated with tumor size (p = 0.020) and TNM stage (p = 0.006) of HCC patients. The area under the ROC curve of plasma hsa_circ_0005397 was 0.737 and 95% confidence interval was 0.671\u20130.795. Furthermore, the combination of plasma hsa_cic_0005397, serum AFP and AFP-L3 could improve the diagnostic sensitivity of HCC. Additionally, dynamic monitoring plasma hsa_cic_0005397 might help us predict recurrence or metastasis in HCC patients after surgical resection. Besides, the increased plasma hsa_cic_0005397 was closely correlated with shorter overall survival of HCC patients (p = 0.007).Conclusion: Plasma has_circ_0005397 represents a novel noninvasive biomarker for HCC. Moreover, the combination of plasma hsa_cic_0005397, serum AFP and AFP-L3 might improve the diagnostic value for HCC. Hepatocellular carcinoma (HCC), with high morbidity and high mortality, remains one of the most common malignant tumors worldwide. In China, Hepatitis B or C viral (HBV or HCV) infection contributes to the important risk factor for HCC and accounts for a great proportion of cancer-related death every year . In the Circular RNA (circRNA), a special class of non-coding RNA molecules, can be widely expressed in various cells and tissues. Unlike linear RNA, circRNA has not 5\u2032-end cap and 3\u2032-end poly (A) tail, but owns a closed-loop structure. Thus, it is more stable and difficult to degrade than linear RNA, due to its less susceptible to exonucleases . Recent http://www.circbank.cn/). Moreover, it has been reported that circRHOT1 as a conserved circRNA is dramatically over-expressed in HCC and promotes cancer progression by initiation of NR2F6 expression , including 4 human HCC cell lines  and a normal liver cell line LO2. Cells were cultured in DMEM medium  containing 10% fetal bovine serum (FBS) and 1% penicillin-streptomycin, maintained in a humidified incubator with 5% CO\u2212\u0394\u0394CT method.Total plasma RNAs were extracted using the Trizol LS  in accordance with the manufacturer\u2019s instructions. RNA concentration was detected with NanoDrop 2000 ultra-micro spectrophotometer . Reverse transcription of plasma total RNA was performed by the Revert Aid First Strand cDNA Synthesis Kit . Plasma circRNAs were measured with Plus SYBR real-time PCR mixture  by qRT-PCR analyses. All reactions were performed using Roche LightCycler 480  according to the following protocol: 95\u00b0C for 15\u00a0s, then 45 cycles of 60\u00b0C for 30\u00a0s and 72\u00b0C for 30\u00a0s. The internal reference was 18S rRNA. The sequence of primers are as follows: hsa_circ_0005397, 5\u2032-GACAAAGACAGCA GGTTCCT-3\u2032 (forward) and 5\u2032-CTC\u200bTGT\u200bTCT\u200bGCT\u200bTCT\u200bGAG\u200bTA-3\u2032 (reverse); hsa_ circ_0006302, 5\u2032-GCC\u200bTAC\u200bATG\u200bATC\u200bGAG\u200bGAT\u200bA-3\u2032 (forward) and 5\u2032-GGATCTG GGTGTTCCTTTAC-3\u2032 (reverse); hsa_circ_0088494, 5\u2032-GCCCACTCCCTAGCAA CTGA-3\u2032 (forward) and 5\u2032-CCA\u200bACT\u200bCCA\u200bGCA\u200bCAA\u200bTGT\u200bTC-3\u2032 (reverse); hsa_circ_ 0083766, 5\u2032-AGA\u200bACC\u200bTGA\u200bGTC\u200bGGA\u200bCTT\u200bTCA-3\u2032 (forward) and 5\u2032- GGGGACATG TTGGGATTTGC-3\u2032 (reverse); 18S rRNA, 5\u2032-GTA\u200bACC\u200bCGT\u200bTGA\u200bACC\u200bCCA\u200bTT-3\u2032 (forward) and 5\u2032-CCATCCAATCGGTA GTAGCG-3\u2032 (reverse). The relative expression levels of plasma circRNAs were calculated using the 2Plasma total RNA was treated with or without RNase R , and purified with RNeasy Min Elute Cleanup Kit . Reverse transcription was conducted with random 6-mers or oligo (dT) primers. After qRT-PCR, the products were subjected to 2% agarose gel electrophoresis and sent for Sanger sequencing .Serum AFP-L3 levels were measured using the AFP-L3 detection kit  and serum AFP concentration was determined by chemiluminescence immunoassay using ARCHITECT i2000SR analyzer .t-test, one-way analysis of variance (ANOVA) and Fisher\u2019s exact test, as appropriate. Receiver operator characteristic (ROC) curve was drawn to evaluate the diagnostic value of plasma hsa_circ_0005397 for HCC. In addition, Kaplan\u2013Meier survival curve was constructed to evaluate survival data. A two-sided p < 0.05 was considered as statistically significant.Statistical analyses were performed using SPSS 20.0 and graphs were drawn using GraphPad Prism 7.0 software . Differences in plasma circRNA concentrations were estimated by independent samples p = 0.012, p > 0.05, To screen the candidate plasma circRNA in patients with HCC, we investigated 4 dysregulated circRNAs from GSE97332 database and GSE97508 database, hsa_circ_0005397, hsa_circ_0006302, hsa_circ_0088494 and hsa_circ_0083766, by qRT-PCR analyses in the plasma of 15 patients with HCC and 15 healthy volunlteers. The results showed that plasma hsa_circ_0005397 was over-expressed in patients with HCC . The Fisher's exact test statistical analysis revealed that the expression level of plasma hsa_cic_0005397 was positively correlated with tumor size (p = 0.020) and TNM stage (p = 0.006). However, there was no significant correlation of plasma hsa_cic_0005397 with other clinicopathological features, such as age, gender, HBV infection, hepatocirrhosis, serum AFP and tumor differentiation, all p > 0.05  infection, hepatocirrhosis, serum AFP, tumor size, differentiation degree, and TNM stage. According to the median level = 1.87 of plasma hsa_cic_0005397, 89 HCC patients were subdivided into two groups  and 0.872 , respectively, both of them were slightly better than plasma hsa_cic_0005397  primers were performed to identify the plasma hsa_circ_0005397. The results showed that hsa_circ_0005397 had a complete closed loop structure and was stable in the plasma of HCC patients. In addition, we further analyzed the origin of circulating hsa_circ_0005397 and found that hsa_circ_0005397 expression was very often boosted in HCC tumor tissues, HCC cell lines and increased in cell culture medium in a time-dependent manner, indicating that plasma hsa_circ_0005397 might be released from tumor cells into the blood circulatory system.Next, ROC curve analysis showed that plasma hsa_cic_0005397 yielded an AUC of 0.737 , with the sensitivity of 82.0% and the specificity of 58.8%. As we all know, serum AFP and AFP-L3 have been widely used in clinical practice for screening and auxiliary diagnosing of HCC over the past few decades . AlthougAdditionally, we also surveyed whether plasma hsa_circ_0005397 was associated with clinicopathological characteristics of HCC. The results displayed that high level of plasma hsa_circ_0005397 was closely associated with tumor size and TNM stage of HCC patients, suggesting that plasma hsa_circ_0005397 could help us predict the degree of malignancy and judge the progression of HCC. Except this, accumulating evidence has revealed that dynamic detection of circulating free nucleic acid could evaluate the postoperative status, including remission, recurrence or metastasis in patients with malignancy . Our dynTaken together, our current study, for the first time, has described in detail the clinical value of plasma hsa_cic_0005397 for HCC. We demonstrated that overexpressed plasma hsa_cic_0005397 might serve as a novel noninvasive biomarker to improve clinical diagnosis, as well as help us predict progression and assess prognosis of HCC. However, this was a preliminary estimation about the clinical utility of plasma hsa_cic_0005397 in HCC. Several limitations should be taken more attention, such as 1) all cases enrolled in this study only from a single institution; 2) relatively small sample size; 3) lack of long-term follow-up; 4) standardized detection of the circRNA by qRT-PCR. Consequently, in order to comprehensively explore potential value of plasma hsa_cic_0005397 in HCC clinical practice, further studies should be concentrated on multi-centered collaboration, a larger cohort of cases collection, long-term follow-up and methodological evaluation of circRNA detection. To sum up, plasma hsa_cic_0005397 warrants a promising candidate for early detection or screening AFP-negative HCC, monitoring treatment and prognosis of this deadly disease."}
+{"text": "Complete and accurate genome assemblies form the basis of most downstream genomic analyses and are of critical importance. Recent genome assembly projects have relied on a combination of noisy long-read sequencing and accurate short-read sequencing, with the former offering greater assembly continuity and the latter providing higher consensus accuracy. The recently introduced Pacific Biosciences (PacBio) HiFi sequencing technology bridges this divide by delivering long reads (>10 kbp) with high per-base accuracy (>99.9%). Here we present HiCanu, a modification of the Canu assembler designed to leverage the full potential of HiFi reads via homopolymer compression, overlap-based error correction, and aggressive false overlap filtering. We benchmark HiCanu with a focus on the recovery of haplotype diversity, major histocompatibility complex (MHC) variants, satellite DNAs, and segmental duplications. For diploid human genomes sequenced to 30\u00d7 HiFi coverage, HiCanu achieved superior accuracy and allele recovery compared to the current state of the art. On the effectively haploid CHM13 human cell line, HiCanu achieved an NG50 contig size of 77 Mbp with a per-base consensus accuracy of 99.999% (QV50), surpassing recent assemblies of high-coverage, ultralong Oxford Nanopore Technologies (ONT) reads in terms of both accuracy and continuity. This HiCanu assembly correctly resolves 337 out of 341 validation BACs sampled from known segmental duplications and provides the first preliminary assemblies of nine complete human centromeric regions. Although gaps and errors still remain within the most challenging regions of the genome, these results represent a significant advance toward the complete assembly of human genomes. Genome assembly is the process of reconstructing continuous genomic regions from shorter overlapping fragments, called reads . RecentlRecently, Pacific Biosciences (PacBio) introduced a new data type, referred to as HiFi reads . The proAlthough the resulting read lengths are modest by the modern long-read sequencing standards\u2014PacBio CLR reads frequently exceed 50 kbp, and ultralong Oxford Nanopore Technologies (ONT) reads can exceed even 100 kbp , HiFi isEarly studies adopting HiFi sequencing showed improved variant calling and repeat resolution . HoweverIn the following sections, we present HiCanu, a modification of the Canu assembler  designedHiCanu builds on the original Canu pipeline, replacing or significantly modifying its core methods. Here we provide an overview of the new pipeline, highlighting the introduced changes, although a more detailed description of individual steps can be found in the Methods section. Whereas the original Canu pipeline starts with read self-correction, which can homogenize reads from different alleles or near-identical repeat instances, HiCanu begins by compressing all consecutive copies of the same nucleotide to a single base . In accordance with the earlier observation that misestimation of homopolymer length is the primary error mode of HiFi technology , the resDrosophila melanogaster F1 hybrid . To match typical coverage, the HiFi data set was down-sampled to 40\u00d7 and assembled with the HiFi-specific tools, HiCanu and Peregrine . To facilitate like-for-like comparison of all assemblies, we ran Purge_dups .Total assembly size varied between HiCanu (301 Mbp), Canu (293 Mbp), Peregrine (162 Mbp), and CLR (294 Mbp). Besides Peregrine, the assembly sizes were more than twice that of the 144-Mbp e genome , suggestrge_dups  to identrge_dups  with IllThe primary contig sets across all assemblies reported high BUSCO completeness (>98%). BUSCO duplication values were <2% across all contig sets. The HiCanu primary contig set was noticeably more continuous than any other assembly as measured by NG50 . Canu and HiCanu showed very similar per-base consensus accuracy, radically improving on both Peregrine and CLR assemblies. The Peregrine assembly collapsed both haplotypes together and output few alternate contigs . HiCanu improved over all other assemblies with respect to the total size, BUSCO completeness, and continuity of the alternate set (including a threefold improvement in NG50 over Canu).D. melanogaster ISO1 reference. Because Purge_dups can split and/or trim the initial contigs but has a negligible effect on continuity, we report structural correctness of the original assemblies. Considering that one of the haplotypes  is expected to differ significantly from the reference, we adjusted QUAST's parameters to detect only large-scale genomic differences (Methods). Although the HiCanu assembly reported three more structural discrepancies than Canu (seven vs. four), it maintained the highest NG50 and alternate contig BUSCO completeness.To assess the integrity of the assemblies, we used QUAST v5.02  to compak-mer markers inferred from parental Illumina reads . As a baseline, we considered a haplotype-resolved assembly produced by TrioCanu  represent \u201cpseudohaplotypes,\u201d which may switch between haplotypes. However, for highly heterozygous genomes with short regions of homozygosity, HiCanu is expected to produce a low number of haplotype switches and mostly preserve long-range phasing. We used Merqury  to splitTrioCanu  generateTrioCanu .We next ran HiCanu, Canu, and Peregrine on three different human data sets (see Data access): a 20-kbp library of the completely homozygous cell line CHM13 , a 15-kbSupplemental Note 2; Supplemental Table S3). To assess the structural correctness of the assemblies, we followed the methodology of Supplemental Table S3). As before, because Purge_dups may introduce or correct misassemblies as it modifies the contigs, the structural correctness assessment was performed on the original assemblies.Per-base consensus quality was again estimated by Merqury  using IlPrimary contig summary statistics for the three human genomes are presented in The total length of the HiCanu alternate contig sets exceeded 2 Gbp, highlighting its ability to separate human alleles . The following section, \u201cHuman haplotype phasing,\u201d further explores allele separation and phasing across these assemblies. The drastic improvements in consensus accuracy and allele separation for Canu versus HiCanu assemblies of HG002 is likely owing to Canu improvements and bug fixes made during the HiCanu development process, whereas the CHM13 and HG00733 assemblies represent the latest Canu version and the differences are less pronounced.Supplemental Tables S4, S5).For CHM13 and HG00733 genomes, we additionally validated the assemblies against long continuous fragments of the corresponding genome, earlier reconstructed via bacterial artificial chromosome (BAC) sequencing . Many of these so-called \u201cchallenge\u201d BACs were deliberately selected from genomic regions that pose significant assembly challenges , making them useful for assembly benchmarking . Table 3Supplemental Note 3; Supplemental Figs. S2\u2013S5). Manual inspection of HiFi read alignments did not reveal any standard misassembly signatures in the corresponding regions of the HiCanu assembly, providing evidence that HiCanu was correct in these cases and able to resolve 337 out of 341 (99%) of the CHM13 challenge BACs .A deeper investigation of the unresolved CHM13 BAC sequences indicated that 11 BACs likely contain assembly errors or cloning artifacts themselves . A complementary mapping-based analysis confirmed the comparatively high completeness of the HiCanu assembly and classified the majority (80%) of missing sequence as satellite repeats, suggesting good recovery of all other human repeat classes .Although the challenge BACs are useful for validation, they do not represent the full landscape of human repeats. To further assess the ability of HiFi reads and different assemblers to resolve genomic repeats, we used the method of When assembling a diploid genome, an assembler must choose to either collapse alleles into a single sequence or preserve them as two separate sequences. Collapsing heterozygosity results in a mosaic consensus that may not faithfully represent any allele and can introduce frameshifting errors within coding sequence.Supplemental Table S8). We again used Merqury . The phase block NG50s of HiCanu primary (0.6 Mbp) and alternate (0.1 Mbp) contig sets were the highest across all considered assemblies (2.5-fold higher than next best) . Note that the human phase block NG50s are significantly shorter than for the D. melanogaster F1 hybrid but are longer than a typical human gene. For comparison, Supplemental Table S8 also includes statistics for the recently obtained haplotype-resolved assemblies of HG002   . The HiCanu contigs expectedly switch between the haplotypes, but there is only one switch in the MHC region. The Hi-C-phased HG002 assembly from To assess the phasing accuracy, we used a gold-standard variant set from the Genome in a Bottle (GIAB) consortium ( (97.7%) . Finally (97.7%)  to the kSupplemental Note 5; Supplemental Fig. S7). To validate this result, we focused on the performance of HiCanu within some of the most difficult-to-assemble regions of the genome, namely, centromeres and SDs. Unlike past assemblies of the human genome, including clone-based assemblies, HiCanu generated several contigs spanning mega-bases of satellite DNA. The CHM13 HiCanu assembly contains nine of 23 (39%) expected centromere regions: Chromosomes 2, 3, 7, 8, 10, 12, 16, 19, and 20 . The structure of these regions was consistent with an expectation of one or more higher-order repeat (HOR) array(s) flanked by more divergent tracts of monomeric satellite DNA . The structure and length of the centromeric HOR array(s) in each contig is highly concordant with those reported in the literature . This contig revealed a more complete representation of the HOR structure of the D19Z1 HOR unit (13-mer vs. 10-mer) . Alignment of HiFi sequence data to the corresponding HiCanu contig did not reveal any coverage anomalies  that could indicate the presence of structural errors. However, marker-assisted alignment of ultralong Oxford Nanopore data  and the smallest overall fraction of contig ends mapping to SDs (49%) . These results indicate that SDs are better resolved using HiCanu; however, SDs still contribute disproportionately to the overall number of assembly breaks.Beyond the obvious challenge of centromere assembly, SDs represent another significant impediment and have been estimated to account for 68% of misassemblies and contig breaks in recent long-read genome assemblies, irrespective of the platform or assembly algorithm . To estiDs (49%) . Of thesDEFB136, DEFB135, etc.) mapping to two locations on Chromosome 8p23.1 (which we refer to as the defensin beta cluster), is a case in point. This \u223c6-Mbp region plays an important role in immune function and disease , with a coverage drop present in both the 10- and 20-kbp HiFi libraries. This coverage drop is flanked by a >250-bp simple-sequence repeat (AAAGG). Suspecting a possible bias in the HiFi datatype, we further examined Chromosome X, for which we have a complete CHM13 reference sequence available , this coverage bias appears to be a current weakness of the HiFi chemistry.The rightmost contig breaks in the HiFi assemblies are likely owing to the presence of long, nearly identical repeats, which would require either longer reads or a careful examination of repeat copy number to resolve. We also investigated the fragmentation of HiCanu and Canu contigs at position 10.4 Mbp, which is not part of any observed repeat structure. Alignment of the raw HiFi reads onto this region with minimap2  revealedvailable . On thisSupplemental Fig. S6). In particular, We have shown that HiCanu is capable of generating the most accurate and complete human genome assemblies to date and is able to achieve the resolution of repeats that are up to 99.99% identical. As a result, HiCanu surpasses prior HiFi and Nanopore ultra-long-read assemblies in terms of both repeat resolution and per-base consensus accuracy. HiFi data excels in resolving large highly-similar  repeat instances. The remaining unresolved sequences seem to primarily represent satellite repeats . Most prior long-read assemblers have also required a final \u201cpolishing\u201d step to improve consensus accuracy, which requires additional computation but can also introduce errors in repeat instances owing to ambiguous read mappings , one should consider the relative importance of read length versus accuracy. A metagenomic project may aim for shorter, higher accuracy reads to confidently identify low-abundance strains, whereas a vertebrate genome project may benefit from longer reads to span midsized identical repeats. We also identified an apparent bias in the current HiFi chemistry at low-complexity A/G (T/C) repeats, leading to coverage drops and assembly fragmentation. This issue warrants further investigation and may limit the applicability of HiFi sequencing to genomes with large stretches of such repeats. Thus, identifying optimal sequencing strategies and developing methods that can combine multiple technologies remains an area for future research.HiCanu's diploid assemblies accurately capture both alleles in long haplotype blocks of very high quality (QV50). In particular, HiCanu consistently recovered both haplotypes for the six canonical MHC typing genes in the human genome, improving upon recently developed HiFi-based methods for haplotype-resolved assembly . HoweverAlthough HiFi reads are highly accurate compared with other long-read sequencing technologies, they are not error free, which complicates the identification of reads originating from the same genomic loci during assembly. To identify and remove false read overlaps, we sought to increase the accuracy of HiFi data via read correction.https://github.com/human-pangenomics/HG002_Data_Freeze_v1.0) and does not appear to be necessary for newer HiFi data sets, we chose to enable it by default on all assemblies in this paper for consistency. This improvement suggests that a significant fraction of reads was structurally incorrect in the poorly performing library owing to a low-quality sequencing library. Because other libraries did not show this problem, it is likely future versions of the HiCanu pipeline can skip this step and reduce runtime by >60%.Although the transition to homopolymer-compressed sequence space can reduce the specificity of the read alignment search, the corresponding reduction in the number of observed errors in the reads allow for a more restrictive alignment identity threshold . Subsequent steps are performed on the homopolymer-compressed sequences, whereas the detailed correspondence between positions of original and compressed versions is generated on the fly when necessary. Compressed reads are first subjected to overlap-based trimming . AlthougTo further reduce the influence of the errors in compressed HiFi reads, we have updated the OEA module of Canu . This mok-mers are the same for any k ranging between two and six on either side (starting at zero to k \u2212 1 bp from the difference). We note that this phenomenon deserves a deeper investigation, and our strategy can be improved to capture additional genomic differences, which are ignored by the current approach.Manual investigation of read alignments during HiCanu development revealed a previously unreported error mode in HiFi reads: incorrect repeat unit counts within microsatellite repeat arrays. Because the incorrect repeat counts are systematic and often supported by multiple reads, the conservative strategy described above is not able to correct them. Recognizing this, we modified the OEA procedure for recomputing overlap alignment scores to ignore sequence differences flanked by a microsatellite repeat in either read. Namely, the difference is ignored if five out of six nonoverlapping flanking Supplemental Note 1). Supplemental Table S1; Supplemental Fig. S14). Although almost no (<1%) raw HiFi reads map error free, 97.23% of the compressed, corrected, and masked reads map without a single difference. Without correction (compression + masking only), reads have similar median error to just compression, and less than half have perfect alignment. As we did not control for reads mapping from other chromosomes and as the Chromosome X sequence itself is not error free, this likely represents a lower bound on the percentage of error-free reads. To extend beyond Chr X, we also estimated read accuracy using k-mers from short-read data for all human genomes and found correction improved read accuracy across all data sets .We evaluated the contribution of each of the above corrections using the recently completed CHM13 Chromosome X  as a refThe Bogart module constructs a set of draft contigs from read overlap information. A detailed description is given by Canu's initial overlap search uses a relaxed identity threshold to account for varying error rates between samples. Because overlap identities are changed by OEA and because we wished to avoid considering false-positive overlaps, Bogart first attempts to select a higher overlap identity threshold. Previously, Canu computed the identity of the best-scoring overlap on each side of every read  and set a threshold based on the median and MAD of the computed values . Howeverbest overlap graph . In Canu, this behavior had been affecting only genomes with >1% heterozygosity, because below this threshold most heterozygous differences were implicitly hidden by the relatively permissive threshold on overlap identity. With the high-accuracy HiFi data, and a correspondingly high overlap identity threshold, this overfragmentation became an issue even for human levels of heterozygosity.HiCanu aims to reconstruct long pseudohaplotype contigs \u2014potentiabubble contigs. As suggested by the name, the bubble contigs are related to the bubble subgraphs, typically considered by most assemblers. Candidate bubbles are found by identifying reads in each contig that have overlaps to some other, larger, contig. A read within a smaller contig can be placed in the larger contig if the overlaps between it and the reads in the larger contig are below a specified threshold of similar quality to the previously incorporated overlaps (0.1% by default). If the placements for both the first and last reads of a candidate contig are correctly oriented and placed at approximately the correct distance in the larger contig (75%\u2013125% of the candidate contig size), the candidate contig is flagged as a bubble and its reads are excluded from later repeat detection. This avoids fragmentation of otherwise structurally correct pseudohaplotype contigs. Similar strategies have previously been used in short-read assembly . Canu originally used the layout produced by Bogart to estimate the position of each read within the contig and align it only to that location. Because the read layouts are now in homopolymer-compressed space, this strategy is unable to locate the read in uncompressed space. Instead, we compute the correspondence of each position in the compressed read to the original. This is used to update the read positions within the contig and expand the layout to uncompressed space. A modified version of the PBdagcon algorithm , with imCurrently, HiCanu will exclude erroneous reads from large contigs, but these erroneous reads may form their own short, low-coverage contigs. This can slightly reduce average assembly accuracy for homozygous genomes versus a more permissive strategy like that in Canu. However, Canu's more permissive approach will incorrectly mix haplotypes and similar repeat copies. Further HiCanu consensus gains are possible with better handling of erroneous reads and a more sophisticated approach for predicting homopolymer run length, similar to MarginPolish .When available, previously published assemblies were downloaded and used. This included Oxford Nanopore UL Canu assemblies presented by D. melanogaster HiFi data are available from NCBI BioProject database (https://www.ncbi.nlm.nih.gov/bioproject/) at PRJNA573706  and CLR . Because of the high coverage, this data set was down-sampled to 40\u00d7 HiFi data and 200\u00d7 CLR data. These coverages represent \u223c25% of the full run output. Because the exact parents of the F1 were not available, we used the previously generated short-read sequencing for binning and analysis . The CHM13 Nanopore data are available at https:// s3.amazonaws.com/nanopore-human-wgs/chm13/nanopore/rel3/rel3.fastq.gz and Illumina at GitHub (https://github.com/nanopore-wgs-consortium/CHM13#10x-genomics-data). The HG002 Nanopore data are available at https://s3-us-west-2.amazonaws.com/human-pangenomics/index.html, HiFi at SRX5327410. HG002 and parent Illumina data are available from GIAB (https://github.com/genome-in-a-bottle/giab_data_indexes), we only used the 2\u00d7250 data sets. The HG00733 Nanopore data are available at https://s3-us-west-2.amazonaws.com/human-pangenomics/index.html, HiFi at ERX3831682. The Illumina data for HG00733 and parents were downloaded from the 1000 Genomes Project Consortium at https://www.internationalgenome.org/data-portal/sample (https://github.com/nanopore-wgs-consortium/CHM13#downloads).The rom GIAB  at GitHul/sample . The CHMcanu -assemble -p asm -d asm genomeSize=G -pacbio-hifi reads.fastq.gzHiCanu was run using Canu branch hicanu_rc with the following commands:D. melanogaster. This required 131 CPU hours and 16 GB of memory for D. melanogaster, 2780 CPU hours and 66 GB of memory for the CHM13 10-kbp library, 5000 CPU hours and 119 GB of memory for the CHM13 20-kbp library, 3999 CPU hours and 62 GB of memory for HG002, and 5233 CPU hours and 50 GB of memory for HG00733.with G = 3.1 g for human and 150 m for canu -p asm -d asm genomeSize=G correctedErrorRate=0.015 batOptions=\u201c-eg 0.01 -eM 0.01 -dg 6 -db 6 -dr 1 -ca 50 -cp 5\u201d -pacbio-corrected reads.fastq.gzFor the standard Canu assembles, Canu branch hicanu_rc ran with the following command:D. melanogaster. This required 232 CPU hours and 12 GB of memory for D. melanogaster, 3524 CPU hours and 80 GB of memory for the CHM13 20-kbp library, and 3836 CPU hours and 47 GB of memory for HG00733.with G = 3.1 g for human and 150 m for canu -p asm -d asm genomeSize=150m corOutCoverage=100 batOptions=\u201c-dg 6 -db 6 -dr 1 -ca 500 -cp 50\u201d -pacbio-raw reads.fastq.gzFor CLR data Canu branch hicanu_rc was run with the following command:All HiFi assemblies required less than 12 wall-clock hours on the NIH Biowulf cluster quick partition with all jobs using <120 GB RAM. We reran HG002 on our cluster limiting the maximum concurrent CPUs to 288, which required 30 h. We estimated the cost of an AWS run using the c5d.18xlarge instance, which costs $3.456/h. Assuming four reserved nodes  and an average runtime of 4200 CPU hours with perfect parallelization, the run would complete in 14.5 h. We increase this by a factor of 2.0 to account for any nonparallelized steps based on the experiments above for a cost of $3.456 \u00d7 4 \u00d7 29 = $401. We note these estimates limited by differences in CPU and I/O between our cluster and AWS, as well as the overhead of waiting for a job to be scheduled on our cluster. The cost could also be reduced if additional nodes were spun up on-demand for the parallel portions of compute and spun down when not needed (as performed in Canu's DNAnexus implementation). We omit this from the estimate for simplicity. We also note that the assemblies could be completed faster if more nodes were allocated in parallel.yes yes | python3 /data/korens/devel/Peregrine/bin/pg_run.py asm \\\u2003\u2003\u2003\u2003\u2003chm13.list 24 24 24 24 24 24 24 24 24 \\\u2003\u2003\u2003\u2003\u2003\u2010\u2010with-consensus \u2010\u2010shimmer-r 3 \u2010\u2010best_n_ovlp 8 \\\u2003\u2003\u2003\u2003\u2003\u2010\u2010output ./Peregrine assembler and SHIMMER ASMKit (0.1.5.3) was run with the commandD. melanogaster, 32 CPU hours and 347 GB of memory for the CHM13 10-kbp library, 58 CPU hours and 449 GB of memory for the CHM13 20-kbp library, 55 CPU hours and 407 GB for HG0002, and 63 CPU hours and 477 GB for HG00733.This required 7 CPU hours and 29 GB of memory for nucmer \u2010\u2010maxmatch \u2010\u2010nosimplifydelta-filter -i 98 -l 10000HiCanu contigs flagged as bubbles were excluded from the analysis. MUMmer  3.23 wasnucmer \u2010\u2010maxmatch \u2010\u2010noextend \u2010\u2010nosimplify -l 500 -c 1000delta-filter -i 99.9 -l 10000and high-stringency repeatsquast.py -t 20 \u2010\u2010large \u2010\u2010skip-unaligned-mis-contigs \u2010\u2010min-alignment 10000 \u2010\u2010min-identity 98.0 \u2010\u2010extensive-mis-size 5000 \u2010\u2010min-contig 50000QUAST alignments were generated asIcarus was patched not to show breaks at \u201csmall indels\u201d and \u201cstretches of mismatches,\u201d and used to visualize the resulting alignments.RepeatMasker -pa 8 -q -species=mammal -xm -dir=asm.out asm.fastaRepeatMasker version 4.1.0 was run with the commandsSupplemental Table S12.on each contig \u226550 kbp in the assembly. Centromeric arrays were identified by taking all hits marked as Satellite/centr and merging any hits within 100 bp of each other using BEDTools . ResultiHLA-ASM.pl \u2010\u2010use_minimap2 1 \u2010\u2010assembly_fasta $asm \u2010\u2010sampleID $prefix \u2010\u2010workingDir `pwd`/$prefix \u2010\u2010truth reference_HLA_ASM/$truthHLA*LA version commit 24930adadb0d2b6bcd69a271401dfc88a5d09f4d was run with the commandswhere $asm was the assembly, $prefix was a unique identifier, and $truth was either truth_HG002.txt or truth_HG00733.txt.python pd_config.py asm.fasta `pwd` <pb folder> <10x folder left blank> asmminimap2 -I6G -xasm5 -DP asm.split asm.split > asm.split.self.pafminimap2 -I6G -xmap-pb asm.fasta $line > pb.$jobid.paf (for each HiFi cell)pbcstat pb.*.pafcalcuts PB.stat > cutoffs 2>calcults.logpurge_dups -2 -T cutoffs -c PB.base.cov asm.split.self.paf > dups.bed 2> purge_dups.logget_seqs dups.bed asm.fasta > purged.fa 2> hap.faPurge_dups version commit 8f580b41e6aa20c99383d6ff19b8689e93d7490e was run with the commandsD. melanogaster, an incorrect threshold was computed for the cutoffs owing to the entire genome being separated and so the cutoffs were manually adjusted to be50\u20031\u20031\u2003115\u20032\u2003200.For D. melanogaster, 59 CPU hours and 24 MB of memory for HG0002, and 74 CPU hours and 24 MB of memory for HG00733.The purged.fa output was then used as the primary set reported in the tables. To obtain the alternate set, we ran a second round of Purge_dups using hap.fa as the input assembly instead. This required an average of 20 CPU hours and 7 MB of memory for k-mers were built using meryl available as a binary within Canu:k-size> <reads.fastq.gz> output <genome>.k<k-size>.merylmeryl count k=<k-size> <asm.fasta> output <asm>.k<k-size>.merylmeryl count k=<Merqury version commit 154610d19ee6f4fead77da077af1ed7abdbe8866 was used. For each assembly and read set, canonical k = 21 for humans and k = 18 for D. melanogaster based on  withtrio/hapmers.shtrio/phased_block.shwhich converts escribed  and to aescribed . HaplotySupplemental Note 2 and https:// github.com/marbl/merqury/wiki.For further information see quast.py <asm> -o quast_results/<asm> -r <reference> -t 16 -s \u2010\u2010largeQUAST 5.0.2 ran with the commandpython3 reference/quast_sv_extractor.py -q quast_results/<asm>/contigs_reports/all_alignments*tsv -c reference/centromere.bed -d reference/GRCh38_marked_regions.bed -s reference/emptyVariants were filtered using the pipeline from  to filtepython3 reference/quast_sv_extractor.py -q quast_results/<asm>/contigs_reports/all_alignments*tsv -c reference/centromere.bed -d reference/GRCh38_marked_regions.bed -s reference/HG002_SVs_Tier1plusTier2_v0.6.1.bedandhttps://www.ncbi.nlm.nih.gov/assembly/GCF_000001215.4 filtered to remove any unassigned sequences for D. melanogaster  and https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz filtered to exclude alts and unaligned sequences . Because no filtering file was available for D. melanogaster, we modified QUAST parameters to try to avoid false-positive misassembly counts with the commandquast.py <asm> -o quast_results/<asm> -r <reference> \u2010\u2010large \u2010\u2010min-alignment 20000 \u2010\u2010extensive-mis-size 500000 \u2010\u2010min-identity 90for HG002. We used https://github.com/skoren/bacValidation) run with default parameters. This pipeline aligns reads using minimap2 . HiFi read alignments to the assembly and BAC sequences were visualized with the Integrative Genomics Viewer (IGV)  .Supplemental Table S8) with the commandsrun-dipcall hg002_purge GRCh38_full_analysis_ set_plus_ decoy_hla.fa primary.fasta alts.fasta > hg002.makmake -j1 -f hg002.mak# exclude chrX/Y since there are no GIAB variants on themgunzip -c hg002_purge.dip.vcf.gz |grep -v chrX |grep -v chrY |bgzip -c > hg002_purge.dip_ nohom.vcf.gz# mark calls as homozygous alt in regions where only primary calls a variant and no alts mapgunzip -c hg002_purge.dip_nohom.vcf.gz | sed 's/GAP2/./;s/1|\\./1|1/;s/ID=\\./ID=GAP2/' | grep -v 'HET\\|GAP\\|DIP' | bgzip -c > hg002_purge.dip.vcf.gztabix hg002_purge.dip_nohom.vcf.gztabix hg002_purge.dip.vcf.gz# measure statisticsrtg vcfeval -b HG002_GRCh38_GIAB_highconf_CG-Illfb-IllsentieonHC-Ion-10XsentieonHC-SOLIDgatkHC_CHROM1-22_v.3.3.2_highconf_triophased.vcf.gz -c hg002_purge.dip.vcf.gz -e HG002_GRCh38_GIAB_highconf_CG-Illfb-IllsentieonHC-Ion-10XsentieonHC-SOLIDgatkHC_CHROM1-22_v.3.3.2_highconf_noinconsistent.bed -t GRCh38_hs38d1.sdf -m annotate -o homWe downloaded trio-phased GIAB  variant Supplemental Note 8).To evaluate phasing, we evaluated the number of maternal and paternal variant calls out of the true positive calls in each contig and reported the total fraction of misphased variants  under accession number PRJNA530776 .All raw and processed sequencing data generated in this study have been submitted to the NCBI BioProject database .We have posted the down-sampled data sets, generated assemblies, and corrected CHM13 BAC sequences at R.G. is an employee and shareholder of Pacific Biosciences. E.E.E. is on the scientific advisory board of DNAnexus. All other authors have no competing interests to declare."}
+{"text": "Infinium Human Methylation BeadChip is an array platform for complex evaluation of DNA methylation at an individual CpG locus in the human genome based on Illumina\u2019s bead technology and is one of the most common techniques used in epigenome-wide association studies. Finding associations between epigenetic variation and phenotype is a significant challenge in biomedical research. The newest version, HumanMethylationEPIC, quantifies the DNA methylation level of 850,000 CpG sites, while the previous versions, HumanMethylation450 and HumanMethylation27, measured >450,000 and 27,000 loci, respectively. Although a number of bioinformatics tools have been developed to analyse this assay, they require some programming skills and experience in order to be usable.http://galaxyproject.org), a web-based platform. This allows users to analyse data from the Infinium Human Methylation BeadChip in the easiest possible way.We have developed a pipeline for the Galaxy platform for those without experience aimed at DNA methylation analysis using the Infinium Human Methylation BeadChip. Our tool is integrated into Galaxy (The pipeline provides a group of integrated analytical methods wrapped into an easy-to-use interface. Our tool is available from the Galaxy ToolShed, GitHub repository, and also as a Docker image. The aim of this project is to make Infinium Human Methylation BeadChip analysis more flexible and accessible to everyone. Over the past several years comprehensive sequencing datasets have been generated, allowing analysis of genome-wide activity in cohorts of different individuals to be increasingly available. Infinium Human Methylation BeadChip requires only a few days to produce methylome profiles of human samples with a low sample input requirement (as low as 500\u00a0ng of genomic DNA) for the starting material . StudiesThe workflow combines 5 main steps see Fig.\u00a0, startinRRID:SCR_010973) solution converts the data into plain-text ASCII files, losing a large amount of information during this process [The Infinium Human Methylation BeadChip assay interrogates fluorescent signals (green and red) from the methylated and unmethylated sites into binary values that can be read directly as IDAT files . Illumin process . To prevGreen and red channel signals from .IDAT files can be converted into methylated and unmethylated signals assigned to methylation levels or \u03b2 values. \u03b2 are built in RatioSet object, and they estimate the methylation level using channel ratios in a range between 0 and 1, with 0 being unmethylated and 1 being fully methylated . HoweverData quality assurance is an important step in Infinium Human Methylation BeadChip analysis. The quality control function extracts and plots the data frame with 2 columns mMed and uMed, which are the medians of MethylSet signals (Meth and Unmeth). Comparing these against one another allows users to detect and remove low-quality samples that normalization cannot correct .SNP regions may affect results of downstream analysis. The Remove SNPs step returns data frames containing the SNP information of unwanted probes and removes them from the dataset .t-statistic at each methylated locus location, with optional smoothing, then groups probes into clusters with a maximum location gap and a cut-off size to refer the lowest possible value of genomic profile hunted by our tool [The main goal of the Infinium Human Methylation BeadChip tool is to simplify the way differentially methylated locus sites are detected. The workflow contains a function detecting DMPs with respect to the phenotype covariate, and a method for finding DMRs . DMRs caour tool .RRID:SCR_012828) annoPeaks tool [In addition to downstream analysis, users can access annotations provided via Illumina by ChIPpeakAnno  project Github repository [We have also provided training sessions and interactive tours for user self-learning. The training materials are freely accessible at the Galaxy , which is suggested to be responsible for RAS/MAPK pathway signaling. Trough activation may regulate the MAPKi mechanism in non-responsive tumours. The methylation regulation of this altered status of PDGFR requires additional studies . The PITWith the rapidly increasing volume of epigenetics data available, computer-based analysis of heritable changes in gene expression becomes more and more feasible. Many genome-wide epigenetics studies have focused on generation of data, with data interpretation now being the challenge. Risk evaluation, disease management, and novel therapeutic development are prompting researchers to find new bioinformatic frameworks and approaches. In this regard we propose a user-friendly tool suite available via Galaxy platform. Ewastools allows life scientists to run complex epigenetics analysis . The casProject name: Ewastools: Infinium Human Methylation BeadChip pipeline for population epigenetics integrated into Galaxyhttps://github.com/kpbioteam/ewas_galaxyProject home page: Operating system(s): Linux (recommended), MacProgramming language: R programming language Other requirements: Galaxy , Docker License: MIT Licensehttps://bio.tools/ewastoolsbiotoolsID identifier: RRID:SCR_018085https://github.com/kpbioteam/ewastools-case_study). All tools described here are available in the Galaxy ToolShed (https://toolshed.g2.bx.psu.edu). The Dockerfile required to automatically deploy the pre-built Docker image is available at https://galaxyproject.org/use/ewas-galaxy/. Archival snapshots of the code are available in the GigaScience GigaDB repository [The test dataset in this article is available in the GEO database under accession GSE65186. The results of the re-analysis of the GSE65186 dataset are available in the GitHub repository (pository .DMP: differentially methylated position; DMR: differentially methylated region; EWAS: epigenome-wide association study; GEO: Gene Expression Omnibus; GO: Gene Ontology; MAPKi: mitogen-activated protein kinase inhibitor; PDGFR: platelet-derived growth factor receptor; SNP: single-nucleotide polymorphism; UCSC: University of California Santa Cruz.The authors declare that they have no competing interests.K.P. conceived and designed the study, K.P., K.M. and B.G. developed the software, K.P., K.M., P.W.P. and B.G. did testing, K.P., K.M. and P.W.P. performed the analyses, K.P, K.M, D.J.T and G.W. provided biological interpretation. All authors wrote the manuscript. All authors read and approved the final manuscript.giaa049_GIGA-D-19-00088_Original_SubmissionClick here for additional data file.giaa049_GIGA-D-19-00088_Revision_1Click here for additional data file.giaa049_GIGA-D-19-00088_Revision_2Click here for additional data file.giaa049_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giaa049_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giaa049_Reviewer_1_Report_Original_SubmissionMallory Freeberg, Ph.D. -- 4/10/2019 ReviewedClick here for additional data file.giaa049_Reviewer_1_Report_Revision_1Mallory Freeberg, Ph.D. -- 3/14/2020 ReviewedClick here for additional data file.giaa049_Reviewer_2_Report_Original_SubmissionDaniel Blankenberg -- 10/28/2019 ReviewedClick here for additional data file."}
+{"text": "Vibrio harveyi is a Gram-negative marine bacterium that causes major disease outbreaks and economic losses in aquaculture. Phage therapy has been considered as a potential alternative to antibiotics however, candidate bacteriophages require comprehensive characterization for a safe and practical phage therapy. In this work, a lytic novel jumbo bacteriophage, vB_VhaM_pir03 belonging to the Myoviridae family was isolated and characterized against V. harveyi type strain DSM19623. It had broad host lytic activity against 31 antibiotic-resistant strains of V. harveyi, V. alginolyticus, V. campbellii and V. owensii. Adsorption time of vB_VhaM_pir03 was determined at 6 min while the latent-phase was at 40 min and burst-size at 75 pfu/mL. vB_VhaM_pir03 was able to lyse several host strains at multiplicity-of-infections (MOI) 0.1 to 10. The genome of vB_VhaM_pir03 consists of 286,284 base pairs with 334 predicted open reading frames (ORFs). No virulence, antibiotic resistance, integrase encoding genes and transducing potential were detected. Phylogenetic and phylogenomic analysis showed that vB_VhaM_pir03 is a novel bacteriophage displaying the highest similarity to another jumbo phage, vB_BONAISHI infecting Vibrio coralliilyticus. Experimental phage therapy trial using brine shrimp, Artemia salina infected with V. harveyi demonstrated that vB_VhaM_pir03 was able to significantly reduce mortality 24 h post infection when administered at MOI 0.1 which suggests that it can be an excellent candidate for phage therapy. Vibrio harveyi, which is a halophilic Gram-negative bacterium causing vibriosis disease in marine finfish, crustacean and molluscan species [Vibrio harveyi is ubiquitous in the aquatic environment and can survive without a host. It is an opportunistic pathogen that will induce disease when the water temperature is optimal for its growth and at the same time its hosts are stressed [Vibrio harveyi has also been increasingly reported in the Mediterranean aquaculture [Vibrio spp. detection in the environment [Flavobacterium psychrophilum and Vibrio spp. [V. harveyi that could be used for phage therapy in aquaculture.The financial losses in aquaculture due to outbreaks of bacterial diseases are estimated to be in the range of billion US dollars globally. Disease outbreaks are among the most important threats for the economic sustainability of the aquaculture sector ,2. An im species ,4. Vibristressed . Vibrio aculture ,7,8. Intironment ,10,11,12ironment . As a coironment ,17,18,19ironment ,21,22. Prio spp. ,25,26,27rio spp. . This stVibrio harveyi type strain DSM19623. A single plaque of vB_VhaM_pir03 was carefully isolated and purified through six times propagation. Throughout the propagation steps, vB_VhaM_pir03 showed a consistent plaque morphology. In the double layer agar plating assay, vB_VhaM_pir03 produced a pinhole-type plaque formation with a diameter of 0.27 \u00b1 0.05 mm. We found that a comparison between the use of LB agar and diluted (LB/2) agar as the bottom layer for plating showed that a higher bacteriological nutrient composition reduced the visibility and plaque size of vB_VhaM_pir03 but not the actual count (data not shown). Transmission electron microscopy (TEM) showed that vB_VhaM_pir03 has a short neck, contractile tail and an icosahedral capsid  of its titer was observed between 40 to 45 \u00b0C while complete inactivation of vB_VhaM_pir03 was observed from 50 \u00b0C and above. When exposed to 0.001% benzalkonium chloride, BKC (p < 0.05) compared to the control. However, vB_VhaM_pir03 was complete inactivated when exposed to other organic solvents.Exposure to different temperatures a showed ide, BKC b, vB_VhaIn the adsorption time assay a, it wasIn vitro lysis assay with DSM19623  showed tThe sequenced genome of vB_VhaM_pir03 produced 41,500,540 clean reads with an average read length of 150 bp and 96.13% correct base calls. The GC content (%) was 43.6%. The per base calls scores produced good per sequence quality scores with a median of 36 for 150 bp reads. The per base sequence content and per sequence GC content showed that there was no bias in the proportion of each base position calls for four normal DNA bases or contamination during library preparation for vB_VhaM_pir03 sequencing. Finally, the per base N content result showed that no N substitutions were made which indicated that the sequencer had sufficient confidence to make base call. The genome of vB_VhaM_pir03 was assembled into a single contig with a minimum genome coverage of 5\u00d7. The total genome length of vB_VhaM_pir03 was 286,284 bp. A total of 99.91% of the raw reads were mapped back to the assembled genome resulting to an average coverage depth of 21,669\u00d7. In addition, the vB_VhaM_pir03 genome does not have any termini and was found to be terminally redundant and circularly permuted.\u22123) with an average similarity of 55.8%. 71 ORFs (21.3%) were determined to have best hits with a jumbo Vibrio phage, vB_BONAISHI MH595538 which infects Vibrio coralliilyticus [Vibrio phages; vB_VmeM-Yong MS31 MK308676.1, vB_VmeM-Yong MS32 MK308677.1, vB_VmeM-Yong XC31 MK308674.1 and vB_VmeM-Yong XC32 MK308675.1. In addition, protein structural homolog search for the predicted ORFs showed 26 hits in the Gene Ontology database, 35 hits with InterPro, 38 hits with the NCBI CDD and 61 hits with the HHPRED search tool. Overall, 137 (41.0%) ORFs were annotated based on amino acid sequence and protein structural homologies. No homologs of integrase, virulence or antibiotic-resistance encoding genes were found in vB_VhaM_pir03.The genome size of vB_VhaM_pir03  indicated that it is a jumbo phage . The gene-coding potential of the global genome is 96.85% with 1.17 genes per kbp which suggests a dense genome arrangement. A total of 336 ORFs were identified with Rapid Annotation using Subsystem Technology (RASTk) server, 282 ORFs by Glimmer.hmm 2.0 and 286 ORFs by GeneMark. Comparison of the predicted ORFs showed that all ORFs called by Glimmer.hmm 2.0 and GeneMark were also called by RASTk. Manual inspection of each predicted ORF and gap between ORFs, and subsequent alignment in the NCBI nr database showed that 334 ORFS were present in vB_VhaM_pir03 genome. No tRNA was found in the genome. 303 ORFs used a start codon of ATG, 17 ORFs used GTG and 14 used TTG. A search on NCBI nr database showed that 119 ORFs (35.6%) had significant hits , tail protein (ORF 4), membrane puncturing device (ORF 8), tail-tube (ORF 51), tail-sheath (ORF 52), capsid protein (ORF 143), internal head protein (ORF 148), major capsid protein (ORF 156), portal protein (ORF 167) and other virion structural proteins . The large terminase subunit involved for DNA packaging for tailed phages was identified at ORF 57. Interestingly, Proline-Alanine-Alanine-aRginine (PAAR) repeat proteins (ORF 10 and 11), a sharp conical structure for penetration of host cells  were alsProteins for DNA replication, recombination and repair were also identified; ribonuclease HI (ORF 16), RecA (ORF 18), HNH endonuclease , homing endonuclease (ORF 35), DNA polymerases , SbcD nuclease (ORF 115), DNA helicases , Holliday junction resolvase (ORF 164), HNH catalytic motif (ORF 185), UV-damage endonuclease (ORF 207), ribonuclease E/G (ORF 267), NAD-dependent DNA ligase LigA (ORF 275), ribonucleotide reductase  and PIN protein (ORF 299). Glutaredoxin 2, a reducing agent for ribonucleotide reductase was identified at ORF 310. A DNA polymerase accessory protein for ATP hydrolase for DNA replication was also found at ORF 263.For nucleotide metabolism, enzymes such as putative nucleotidyl transferase (ORF 223), putative N-acetyltransferase (ORF 225), thymidylate kinase (ORF 281) and thymidylate synthase ORF 323). DNA modification enzymes were also identified such as polynucleotide kinase (ORF193) and phosphagen kinase (ORF 262). For DNA transcription, multiple RNA polymerases  and RNA binding proteins  were identified. Two ORFs encoding transcription factor type II for site-specific DNA binding [3. DNA moSeveral enzymes for lysis of host bacteria were also identified as glycoside hydrolase  which functions to degrade the host bacterial cell wall prior to phage burst. Additionally, two ORFs with no previous phage-associated descriptions were identified in vB_VhaM_pir03 genome which are ORF 200 with a structural homolog to palindromic amphipathic repeat coding elements (PARCEL) protein and ORF 64 with a structural homologs to Ro60-related proteins.Salmonella phage SKML 39. Alignment with another four similar jumbo phages; vB_VmeM-Yong_MS32; vB_VmeM-Yong_XC31; vB_VmeM-Yong_XC32 and vB_VmeM-Yong_MS31; showed shared collinear blocks of different length with no synteny and very low sequence similarities.Following whole genome alignment with the most similar phage genomes obtained from the NCBI nr database , vB_VhaMMyoviridae taxonomic family however, it was observed that the position of vB_VhaM_pir03 was in a subcluster within the Siphoviridae family. In addition, vB_VhaM_pir03 was also determined to infect a host from the Gammaproteobacteria class which includes Vibrionaceae family.Wide genome proteomic tree analysis  showed tPhylogeny using large terminase subunits of jumbo phages  showed tVibrio harveyi-infected Artemia was approximately 30% which was lower than the phage-treated groups (except for MOI 10) and comparable to the untreated control group  between all treatments (data not shown). No colony forming units were observed in the control group at both times of measurement.At 24 h post infection, the survival of the ol group . At 48 h\u00aeYRS against Yersinia ruckeri [Phage therapy is a very promising alternative to antibiotics. While scientific publications about isolation of phages have increased in the last decade, there is only a small group of phages that have been applied for commercial use  and only ruckeri . One of  ruckeri . These c ruckeri ,38. TherVibrio harveyi type strain DSM19623 and analyzed its therapeutic potential for aquaculture. Transmission electron microscopy revealed that vB_VhaM_pir03 is related to the Myoviridae family based on the presence of an icosahedral head and long contractile tail [Vibrio spp. [In the present study, we have isolated and characterized a novel jumbo bacteriophage, vB_VhaM_pir03 with broad host lytic activity against ile tail . In addirio spp. ,41. The rio spp. . In relario spp. .Vibrio phages [Myoviridae and Podoviridae families showed reduction in phage titer after exposure to chloroform. In addition, vB_VhaM_pir03 was also completely inactivated after exposure to organic solvents in this study except for benzalkonium chloride, BKC. This suggests that vB_VhaM_pir03 applications can be controlled for contaminations and unintentional transfers such as observed in inactivation of Lactobacillus phages during milk productions [Several factors affect the reproductivity and stability of phages. In our study, the significant reduction of vB_VhaM_pir03 titers was first observed at 40 \u00b0C which was lower than that of previously described o phages ,25,43,44o phages  have shoo phages ,47 have o phages  and are o phages  however ductions .Vibrio and jumbo phages [vB_VhaM_pir03 showed a rapid adsorption time to its host compared to previously reported o phages ,51,52,53o phages , applicao phages . In one-o phages ,56,57. Ao phages . Nonetheo phages  which maIn small phages , the arrangement of core genes that encode for head, tail, DNA replication and nucleotide metabolism proteins are conserved in a modular order for maintenance of functions throughout its replication cycles ,59 howevVibrio coralliilyticus, was determined to be the only similar phage to vB_VhaM_pir03 with an average ORF similarity of 52.3%. Analysis of genomic synteny in this study revealed that both phages shared similar genomic arrangements but with low nucleotide sequence similarities. In addition, phylogenetic analysis using large terminase subunits of vB_VhaM_pir03 and other described jumbo phages produced strong bootstrap support to the evolutionary relationship between vB_VhaM_pir03 and vB_BONAISHI. This suggested that both phages may have diverged from a common ancestor but have since undergone multiple nucleotide substitution events [Phages have been associated with risks of horizontal gene transfer  thereforn events . Since, n events , the phyVibrio species within the Harveyi clade [Vibrio phage, KVP40 was reported to infect multiple Vibrio species [Vibrio species are ubiquitous and diverse [Vibrio population. It has been previously reported that there is an inverse relationship between bacterial antibiotic and phage resistance due to the high biological cost to maintain each resistance mechanism [vB_VhaM_pir03 was shown to have a broad host lytic activity against different yi clade ,70. To o species . The abi species . For jum species ,74. In o species  therefor diverse ,77 thus echanism . In thisin vivo, pharmacokinetics and pharmacodynamics of the phage therapy are the major factors in the treatment efficacy [Pseudomonas aeruginosa in biofilms and new colonization of Campylobacter jejunii in chickens [In vitro lysis is typically carried out as an intermediate step to large scale applications. In this step, the therapeutic effects of phages at different MOIs and the host resistance development are measured concurrently against time . In the efficacy . Based oefficacy . This suefficacy . In addiefficacy . While tchickens ,83.Artemia nauplii, we found that a single dosage of vB_VhaM_pir03 was effective in increasing survival of Artemia nauplii infected with Vibrio harveyi strain Vh5 at 24 h post infection even at MOI 0.1. This result was showed that vB_VhaM_pir03 performed slightly better than a previously reported phage therapy trial with Artemia spp. in which the survival was measured at 50% [Artemia nauplii at 48 h post infection. Nonetheless, Artemia nauplii population still showed a higher percentage of survival to the untreated population which suggested a residual effect of protection. Despite the short protective period provided by vB_VhaM_pir03, a single dosage of vB_VhaM_pir03 can also be considered as a potential live feed disinfectant since Artemia nauplii are fed to fish within 24 h post hatch. For the infected Artemia nauplii that received a delayed treatment, vB_VhaM_pir03 was unable to provide protection which suggested that the damage caused by vibriosis was irreversible similarly to the reported results of Diaz et al. [In the in vivo trial with d at 50%  However,z et al. . Phages z et al. , howeverz et al. ,86.Vibrio harveyi. The biological characterization of vB_VhaM_pir03 showed that it can rapidly locate and adsorb to a host and produce a high burst size within a short latent phase. Further characterization showed that vB_VhaM_pir03 has a broad lytic activity against thirty-one multiple antibiotic resistant strains of species belonging to the Harveyi clade. This is a unique ability of vB_VhaM_pir03 which has only been reported in only one other Vibrio phage, KVP40. Genomic analysis revealed a wealth of diverse gene functions that contributed to the efficacy of vB_VhaM_pir03. Furthermore, we also suggest that vB_VhaM_pir03 is not a temperate phage, does not harbor virulence or antibiotic resistance genes and does not exhibit transduction potential. Evaluation of the performance vB_VhaM_pir03 in vitro showed that it can inhibit several host bacterial growths at low MOI which supports its application in phage therapy. Finally, in the in vivo trial, vB_VhaM_pir03 was able to provide some protection to Artemia nauplii against vibriosis at 24 h post infection at all MOIs. Further characterization of its genome to understand its underlying mechanism is suggested to be carried out. In addition, we also suggest that large scale phage therapy trial with vB_VhaM_pir03 which includes investigations into different types of delivery methods. Finally, we would also like to emphasize that phage characterizations should be comprehensive to ensure a safe and practical phage therapy. This is very important towards the progress of phage therapy from the regulatory perspective.In this study, we have provided a comprehensive biological and genomic characterization of vB_VhaM_pir03 as a candidate for phage therapy against Vibrio harveyi, V. alginolyticus, V. owensii, V. anguillarum, V. campbellii, V. parahaemolyticus and other Vibrio spp.  at \u221280 \u00b0C and were grown in Lysogeny Broth  at 25 \u00b0C when used.Thirty-one strains of rio spp.  used in 600. The diluted bacterial suspensions were then plated on Mueller-Hinton agar  with 2% NaCl. Antimicrobial susceptibility disks    were plaVibrio harveyi type strain DSM19623 liquid culture. The enriched water sample were incubated at 25 \u00b0C with a shaking speed of 70 rpm for 24 h. Following filtration through a 0.22 \u00b5m sterile filter , 10 \u00b5L of each sample were spotted on bacterial lawns of the host strain. Following 24 h incubation at 25 \u2103, the clearest plaque formations were collected. Serial propagations for phage purification were then made for the collected plaques against its host by double agar layer method according to Clokie et al. [Water samples were collected from three locations: (a) the Port of Piraeus, Athens, (b) the Karavolas beach, Heraklion, Crete and (c) a fish tank in the broodstock section of HCMR in Heraklion. 250 mL of the collected water samples were then enriched with 25 mL of concentrated LB and 2.5 mL of the host strain, e et al. . A singl10 PFU/mL was prepared and negatively stained with 4% w/v uranyl acetate (pH 7.2). The phage was observed using a JEOL transmission electron microscopy operated at 80 kV at the Electron Microscopy Laboratory in the University of Crete. From the obtained digital micrographs, structural dimensions of individual phages were measured with ImageJ software version 1.52t [For transmission electron microscopy, aliquot of vB_VhaM_pir03 suspension with a titer of ~10on 1.52t  for caps7 CFU/mL and were then mixed with top molten LB agar (0.75% agar) and poured on bottom LB/2 agar  which only contained half of the tryptone and yeast content from the LB agar. After solidification of top agar, 10 \u00b5L of vB_VhaM_pir03 were spotted on the host lawn. The phage titer was determined after the agar plates were incubated at 25 \u00b0C for 24 h.For determination of host range for the purified phage, fresh cultures of bacterial strains used in this study  were gro0, 10\u22121, 10\u22122, 10\u22123, 10\u22124 and 10\u22125 and spotted on the bacterial lawns of the 31 susceptible strains. The phage titer was determined after the agar plates were incubated at 25 \u00b0C for 24 h. The EOP was calculated as a percentage of the number of plaque-forming units formed on a bacterial strain against the number of plaque-forming units formed on the host DSM19623. EOP more than 10 was categorized as high, EOP between 9.9 and 0.5 was considered medium while EOP less than 0.5 was considered low.Efficiency of plating (EOP) was performed in this study according to . The pha7 PFU/mL to different temperatures  for 1 h before being rested at 25 \u00b0C for 10 min. The aliquots were then serially diluted and spotted on host bacterial lawn. The phage titer was determined after the agar plates were incubated at 25 \u00b0C for 24 h. vB_VhaM_pir03 stored at 4 \u00b0C for 24 h was used as a control.Phage thermal stability was measured by exposing the phage aliquots at ~107 PFU/mL of the phage aliquots to 10% chloroform at 4 \u00b0C for 1 h while the stability of the vB_VhaM_pir03 against commonly used disinfectants in aquaculture was measured by exposing ~107 PFU/mL of vB_VhaM_pir03 to 0.001% benzalkonium chloride, BKC; 3% hydrogen peroxide, H2O2; 1% sodium hypochlorite, NaOCl; 70% ethanol, EtOH and; 1% formaldehyde, CH2O at 25 \u00b0C for 1 h. vB_VhaM_pir03 incubated at 25 \u00b0C for 1 h were used as control. Each treatment was serially diluted and spotted on host bacterial lawn. The phage titer was determined after the agar plates were incubated at 25 \u00b0C for 24 h. All assays were done with triplicates.The sensitivity of vB_VhaM_pir03 to chloroform was determined by exposing ~108 CFU/mL) was infected with vB_VhaM_pir03 at multiplicity of infection (MOI) 0.01. Aliquots of the infected culture were then collected and transferred to chilled Eppendorf tubes at 0, 2, 4, 6, 10, 15, 20, 30 min, and kept in ice. The aliquots were centrifuged at 13,000 rpm for 3 min and supernatants were collected and serially diluted. The serial dilutions were then spotted on the host bacterial lawn on LB/2 agar plates. The phage titer was determined after the agar plates were incubated at 25 \u00b0C for 24 h.Adsorption time and one-step growth of vB_VhaM_pir03 was determined according to Kutter  with som8 CFU/mL) was centrifuged at 13,000 rpm for 3 min. The supernatant was then discarded and the pellet was washed and resuspended in 1 mL of SM buffer . This step was then repeated twice before the pellet was finally resuspended in 1 mL of LB. The fresh host culture was then infected with vB_VhaM_pir03 at MOI 0.01. After incubation for 10 min at 25 \u00b0C, the infected DSM19623 culture was then transferred to LB with the final volume of 30 mL. Afterwards, 1 mL aliquots were then collected from the infected host culture and immediately transferred to chilled Eppendorf tubes. The aliquots were then centrifuged for 13,000 rpm for 3 min. Subsequently, the supernatants were collected and serially diluted. The serial dilutions were then spotted on the host bacterial lawn on LB/2 agar plates. This step was repeated at 10 min intervals. The phage titer was determined after the agar plates were incubated at 25 \u00b0C for 24 h.For one-step growth, 1 mL of host culture in exponential phase (~10600 using TECAN microplate reader (Infinite PRO 200) at 25 \u00b0C with orbital shaking. A total of 20 \u00b5L of vB_VhaM_pir03 was then added at MOIs 0.1, 1 and 10 when host culture was at exponential phase (~108 CFU/mL). Phages added to LB without host bacteria served as control. The assay was also carried out for the remaining 30 susceptible hosts of vB_VhaM_pir03. The growth curves of the cultures were then measured every 10 min for 18 h. All assays were done in triplicates.The in vitro cell lysis of vB_VhaM_pir03 against DSM19623 was carried out by loading 180 \u00b5L of fresh host bacterial culture in each well of sterile 96-well plates. The plates were then read at OD\u00ae Reference Water  was used as a negative control. The extracted DNA of vB_VhaM_pir03was then stored in \u221220 \u00b0C.The DNA extraction of vB_VhaM_pir03 was carried out using the phenol-chloroform method according to Higuera et al. . The ext\u22123. The genome of vB_VhaM_pir03 with annotated predicted ORFs was then visualized in a circular representation with Geneious software .The whole genome of vB_VhaM_pir03 was sequenced, assembled, and annotated previously as described in Misol et al. . The genThe whole proteome of vB_VhaM_pir03 was searched for similarity to other phages using the NCBI BLASTP nr protein database. The phage genomes with significant similarities were then downloaded and aligned with vB_VhaM_pir03 using the progressiveMauve: Multiple Genome Alignment  for analVibrio harveyi strain VH5 in brine shrimp, Artemia salina nauplii. The first two treatments were control groups: a negative control containing Artemia nauplii only and a positive control of Artemia nauplii with V. harveyi. The other three treatments were single doses of vB_VhaM_pir03 at MOI 0.1, 1 and 10. The final treatment was a group that received a delayed single dose of vB_VhaM_pir03 at MOI 10 at 24 h post infection. The V. harveyi strain Vh5 was determined earlier to be the most pathogenic to Artemia nauplii (data not shown). Newly hatched Artemia nauplii were obtained from the live feed section of IMBBC, HCMR and were disinfected according to the protocol by Gomez-Gil et al. [Artemia nauplii were then washed three times with autoclaved and filtered borehole water (T: 25 \u00b0C). Afterwards, 50 Artemia nauplii were transferred to each well in Thermo Scientific\u2122 Nunc\u2122 Cell-Culture 6-well plates  with 10 mL autoclaved and filtered borehole water. Aliquots from the washed Artemia nauplii were spotted on a bacterial lawn to observe presence of sodium hypochlorite residues. All treatments except for the negative control were inoculated with Vh5 at ~10\u22125 CFU/mL. At 2 h post infection, the single dose vB_VhaM_pir03 treatments were inoculated. For the delayed treatment, inoculation of vB_VhaM_pir03 was only carried out at 24 h post infection. The Artemia nauplii were fed with autoclaved Aeromonas hydrophila at 10 cells per individuals daily [A. hydrophila was tested earlier by transferring 10 \u00b5L from its suspension to LB and streaking on a LB agar. Individual counts of the Artemia nauplii were carried out by visual counting using a NIKON SMZ-800 Stereomicroscope  at 24 and 48 h post infection to determine survival percentage. 100 \u00b5L of water from each treatment were taken and serially diluted before spotted on TCBS agar at 24 and 48 h post infection to determine the total Vibrio load. All treatments were done in triplicates at 25 \u00b0C.Six different treatments were investigated to assess the efficacy of vB_VhaM_pir03 in controlling pathogenic l et al. . The Artls daily . The steArtemia nauplii  and total Vibrio load . Tukey\u2019s HSD post hoc test [One-way ANOVA was performed for the thermal stability and effects of organic solvents assays. Two-way ANOVA was performed for calculation the survival of hoc test  was usedhoc test ."}
+{"text": "Biogas production with anaerobic digestion (AD) is one of the most promising solutions for both renewable energy production and resolving the environmental problem caused by the worldwide increase in organic waste. However, the complex structure of the microbiome in AD is poorly understood.In this study, we constructed a microbial gene catalog of AD  based on 1,817 Gb metagenomic data derived from digestate samples of 56 full-scale biogas plants fed with diverse feedstocks. Among the gene catalog, 73.63% and 2.32% of genes were taxonomically annotated to Bacteria and Archaea, respectively, and 57.07% of genes were functionally annotated with KEGG orthologous groups. Our results confirmed the existence of core microbiome in AD and showed that the type of feedstock  has a great influence on carbohydrate hydrolysis and methanogenesis. In addition, 2,426 metagenome-assembled genomes were recovered from all digestate samples, and all genomes were estimated to be \u226580% complete with \u226410% contamination.This study deepens our understanding of the microbial composition and function in the AD process and also provides a huge number of reference genome and gene resources for analysis of anaerobic microbiota. In the context of global climate change, in recent years the use of biogas as a renewable form of energy has increasingly drawn the world's attention. While the vast amount of organic waste caused by population expansion, urbanization expansion, and agriculture intensification continues to severely threaten the environment , at the AD includes 4 sequential metabolic steps, namely, hydrolysis, acidogenesis, acetogenesis, and methanogenesis, and is performed by a complex consortium of bacteria and archaea . The firCulture-independent technologies based on high-throughput sequencing enable the deep investigation of microbial compositions and functions. High-throughput 16S ribosomal RNA gene sequencing has been frequently used to analyze the taxonomic profile of AD microbial communities , 9. Meta3  to southwest   were inv3 .Among these BGPs, 46 were in mono-digestion process, treating a single type of livestock manure , and the remaining 10 BGPs treated other animal manures alone or a mixture of livestock manure and other substrates, such as straw, vegetables, or sewage water . AccordiDigestate samples were collected from the fermentation tank or sampling valve. Before sampling, the reactor content was stirred and the sampling valve was opened for 5\u00a0min to flush the sampling valve and tubes. Approximately 300\u00a0mL of digestate was sampled from each BGP and transferred into 6 sterile, gastight tubes (50\u00a0mL) and frozen immediately in a cooler with dry ice, and then transported to the laboratory. Frozen samples were stored at \u221280\u00b0C\u00a0before DNA extraction. In total, 59 digestate samples were collected, including 53 samples from 53 single-stage BGPs and 6 samples from each stage of 3 different 2-stage BGPs  .Frozen digestate samples were removed from the \u221280\u00b0C\u00a0freezer and thawed at room temperature. Genomic DNA was extracted in triplicate using the PowerSoil DNA Isolation Kit  according to the manufacturer's protocol. To increase DNA yield, an extra physical cell disruption step of repeated freeze-thaw  was used prior to the standard protocol. The integrity of DNA extracts was checked on 0.7% (w/v) agarose gel with GelRed nucleic acid gel stain . DNA samples showing obvious concentrated DNA bands >15\u00a0kb in size were used for further analysis . The quaSequencing libraries were prepared for each sample using Illumina TruSeq DNA PCR-Free Library Preparation Kit  according to the manufacturer's instructions. In brief, a total of 1.5\u00a0\u00b5g metagenomic DNA was sheared to 350-bp fragments using Covaris S220 , and the sheared DNA fragments were purified, blunt-end repaired, and size selected. Subsequently, a single \u201cA\u201d nucleotide was added to the 3\u2032 end of the blunt fragments, and then multiple indexing adapters were ligated to the A-tailed fragments by a complementary pairing single \u201cT\u201d nucleotide on the 3\u2032 end. All 59 prepared sequencing libraries were first checked for quality and quantity and then paired-end sequenced (2\u00a0\u00d7 150\u00a0bp) using Illumina Hiseq X10 platform  by Cloud Health Genomics Ltd. . In total, 1,817 Gb of raw data were generated with 30.80\u00a0\u00b1 3.77 Gb per sample (Table\u00a0 \u00a0RRID:SCR_018551) [RRID:SCR_011936) [RRID:SCR_010910) [The Illumina raw reads were cleaned by trimming the adapter sequences and low-quality regions using 2 in-house software packages, clean_adapter and clean_lowqual  with def_018551)  under pa_011936)  with par_011936) . As a reRRID:SCR_007105) [All the obtained genes were pooled  and then clustered to construct an initial non-redundant gene catalog  using CD-HIT-EST v4.6.6   with parThe relative gene abundances of the MGCA were calculated using the qualified reads , 21. BriRRID:SCR_011919) [To assess to what extent the present MGCA could represent the microbial genes in full-scale BGPs, a more comprehensive microbial gene catalog of AD (C-MGCA) of full-scale BGPs was constructed. Besides the 59 metagenomes that were generated in this study , 39 other metagenomes (580 Gb) derived from full-scale BGPs, which were located in Germany 22 samples), United Kingdom (12 samples), Spain (4 samples), and Sweden (1 sample), were downloaded from NCBI, ENA, or MGnify database . All dat samples,_011919)  were proRRID:SCR_004999) [RRID:SCR_016071) [Methanosarcina (0.16%), Methanosaeta (0.14%), Methanoculleus (0.14%), Methanoregula (0.13%), and Methanobrevibacter (0.10%)   on the b_016071)  alignmen_016071) . Of the 0%) Fig.\u00a0. To calcRRID:SCR_005305) [Functional annotation was performed by aligning all protein sequences in the gene catalog against the KEGG  database_005305)  and takiIdentifying the core microbial populations across different full-scale BGPs is important to elucidate the essential process in AD, and multiple studies have sought to define the core AD microbiome , 35, 36.Bacteroides and Clostridium , 4 , 1 (Methanosarcina), and 4  genera were identified as core microbes for MCA, MCH, MPI, and OTH, respectively . As a result, only ium Fig.\u00a0, within  pig gut , 32, 37. pig gut . To compP <\u00a00.05) higher than those of MCA and MCH  oxidation, and methanogenesis were compared. For genes involved in carbohydrate hydrolysis, we selected the CAZyme families involved in lignocellulose and starch hydrolysis and categorized them in accordance with the CAZy database and previous studies  Supplem. The genP <\u00a00.05), and significantly  higher in MCA than in MPI  higher relative gene abundance involved in acetate, propionate, and butyrate oxidation than those of MPI  higher than MCH and MPI  degradation, the relative gene abundances were higher in MCA than in MCH , physicochemical characteristics of feedstock , and intermediate metabolites  for all BGPs  from theRRID:SCR_016965) [RRID:SCR_002105) [RRID:SCR_019134) [RRID:SCR_016646) [To reconstruct the metagenome-assembled genomes (MAGs), all 59 digestate samples were included. Metagenome binning was applied to single-sample assemblies, which were performed in the \u201cMetagenome assembly\u201d step, and the contigs with length <1,000\u00a0bp were filtered out. BBmap v38.50   was used_002105)  was used_016646)  with optRRID:SCR_019135) [MAG de-replication was performed using Mash v2.2   on the e_019135) , and MAG_019135) . MAGs we_019135) . As a reRRID:SCR_019136) [To estimate the degree of novelty of our study, we performed a comparison with 1,401 MAGs (Cp \u2265 70% and Ct <\u00a010%) recovered from a previous study , which u_019136) , and 96.Here, we present a microbial gene catalog of AD, by using in-depth sequencing of the digestate samples from 56 full-scale BGPs treating diverse feedstocks, and provide >22.8 million taxonomically and functionally annotated genes. Our results confirmed the existence of core microbiome in AD and showed that the type of feedstock  has a great influence on carbohydrate hydrolysis, VFAs oxidation, and methanogenesis. Additionally, we also provided 2,426 MAGs derived from full-scale BGPs. Compared to previously published microbial gene catalogs of different ecosystems such as soil, ocean, and animal gut and rumen , 56\u201359, PRJNA533495. For details, see SRR8925713\u2013SRR8925730, SRR8925732\u2013SRR8925742, SRR8925747\u2013SRR8925748, SRR8925751\u2013SRR8925758, SRR8925797\u2013SRR8925806, SRR8925817\u2013SRR8925824, and SRR8925826\u2013SRR8925827 for metagenome sequencing data of 59 digestate samples. Other supporting data, including the files of gene sequences, taxonomic and functional annotations, the abundance profile tables of the 2 gene catalogs (MGCA and C-MGCA), and metagenome-assembled genomes (MAGs) generated in this study are available in the GigaScience GigaDB repository [All\u00a0raw sequencing data generated during the present study have been deposited at DDBJ/ENA/GenBank under project accession pository .Supplementary Figure S1: Geographic distribution of 56 full-scale biogas plants (BGPs) from which the digestate samples were collected. The sampling BGPs ranged in location from the northeast  to the southwest  of China, including cattle manure BGPs (MCA), chicken manure BGPs (MCH), pig manure BGPs (MPI), and BGPs with other feedstocks (OTH).Supplementary Table S1: Background information of the investigated 56 full-scale biogas plants (BGPs).Supplementary Figure S2: Electrophoresis graph of DNA samples.Supplementary\u00a0Table S2: Information of the sequencing data downloaded from public database.Supplementary Figure S3: Rarefaction analysis of gene catalogs MGCA and C-MGCA. The gene number of a given number of samples was calculated after 100 random samplings with replacement.Supplementary Table S3: Overlap of genes between gene sets of public metagenome sequencing data and MGCA and C-MGCA.Supplementary\u00a0Figure S4: The KEGG methane metabolism pathway. The enzymes present in 100% of digestate samples (59 samples) are highlighted in red, the enzymes present in >90% of digestate samples are highlighted in light blue, and other enzymes annotated in the gene catalog are shown in green. The enzymes were analyzed on the basis of the KO annotation.Supplementary\u00a0Figure S5: The number of shared genera and KOs among biogas plants (BGPs) at different frequency thresholds.Supplementary\u00a0Figure S6: Shannon index of MCA, MCH, and MPI at the genus level. MCA: cattle manure biogas plants (BGPs); MCH: chicken manure BGPs; MPI: pig manure BGPs. Box plots show median \u00b1 interquartile range (IQR) and 1.5 IQR ranges (whiskers), with outliers denoted by circles. Wilcoxon rank-sum test among different groups was performed. *P <\u00a00.05 between the 2 groups.Supplementary\u00a0Table S4: Categories of CAZyme families.Supplementary\u00a0Table S5: Genes selected for the analysis of the acetate, propionate, and butyrate oxidation pathways.Supplementary\u00a0Figure S7: Redundancy analysis (RDA) of microbial communities and operational parameters. Red arrows indicate the influence of process parameters , physicochemical characteristics of feedstock , and intermediate metabolites  on microbial communities. Colored dots indicate samples of different groups of BGPs.Supplementary\u00a0Table S6: Statistics and taxonomic annotation of metagenome-assembled genomes (MAGs).ABR: anaerobic baffled reactor; AD: anaerobic digestion; BGP: biogas plant; bp: base pairs; BWA: Burrows-Wheeler Aligner; CAZyme: carbohydrate-active enzyme; C-MGCA: comprehensive microbial gene catalog of AD; Cp: completeness; CSTR: continuous stirred tank reactor; Ct: contamination; DDBJ: DNA Data Bank of Japan; Gb: gigabase pairs; kb: kilobase pairs; KEGG: Kyoto Encyclopedia of Genes and Genomes; KO: KEGG orthologous group; MAG: metagenome-assembled genome; MCA: cattle manure biogas plants; MCH: chicken manure biogas plants; MGCA: microbial gene catalog of AD; MPI: pig manure biogas plants; NCBI: National Center for Biotechnology Information; OTH: biogas plants with other feedstocks; USR: upflow solids reactor; VFA: volatile fatty acid.The authors declare that they have no competing interests.This project was supported by grants from Shenzhen Science and Technology Program (JCYJ20190814163805604), Agricultural Science and Technology Innovation Program (ASTIP), Chinese Academy of Agricultural Sciences (CAAS-ASTIP-2016-BIOMA), the Agricultural Science and Technology Innovation Program & The Elite Young Scientists Program of CAAS, Fundamental Research Funds for Central Non-profit Scientific Institution (No. Y2017JC01), Science and Technology Program of Sichuan Province, China (2017JY0242), the Agricultural Science and Technology Innovation Program Cooperation and Innovation Mission (CAAS-XTCX2016), the Fund of Key Laboratory of Shenzhen (ZDSYS20141118170111640), the Fundamental Research Funds for Central Non-profit Scientific Institution, China (1610012016023), and the Infrastructure and Facility Development Program of Sichuan Province (2019JDPT0012). The sponsors had no role in the design or conduct of the study; the collection, management, analysis, or interpretation of the data; the preparation, review, or approval of the manuscript; or the decision to submit the manuscript for publication.S.M., Y.H., H.F., and Q.L. collected the samples, and F.J., Y.Z., L.Y., and S.L. extracted the DNA and constructed the Illumina sequencing libraries. S.M., F.J., Y.H., Y.Z., S.W., B.L., and H.W. analyzed the data. H.L. and Y.R. provided helpful suggestions. S.M., F.J., Y.H., Y.Z., and S.W. wrote the manuscript. W.F., Y.D., and L.C. conceived the study, designed the experiments, and revised the manuscript. All authors read and approved the final manuscript.giaa164_GIGA-D-20-00207_Original_SubmissionClick here for additional data file.giaa164_GIGA-D-20-00207_Revision_2Click here for additional data file.giaa164_GIGA-D-20-00207_Revision_3Click here for additional data file.giaa164_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giaa164_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giaa164_Reviewer_1_Report_Original_SubmissionChristopher Hunter, Ph.D. -- 7/17/2020 ReviewedClick here for additional data file.giaa164_Reviewer_1_Report_Revision_1Christopher Hunter, Ph.D. -- 11/13/2020 ReviewedClick here for additional data file.giaa164_Reviewer_2_Report_Original_SubmissionJames P. J. Chong -- 8/5/2020 ReviewedClick here for additional data file.giaa164_Reviewer_3_Report_Original_SubmissionKorn\u00c3\u00a9l L. Kov\u00c3\u00a1cs -- 8/17/2020 ReviewedClick here for additional data file.giaa164_Supplemental_FilesClick here for additional data file."}
+{"text": "Streptomyces by altering the activity of the pleiotropic regulator BldD. Here we report a role of the heme-binding diguanylate cyclase SSFG_02181 from Streptomyces ghanaensis in the regulation of the peptidoglycan glycosyltransferase inhibitor moenomycin A biosynthesis. Deletion of ssfg_02181 reduced the moenomycin A accumulation and led to a precocious sporulation, while the overexpression of the gene blocked sporogenesis and remarkably improved antibiotic titer. We also demonstrate that BldD negatively controls the expression of ssfg_02181, which stems from direct binding of BldD to the ssfg_02181 promoter. Notably, the heterologous expression of ssfg_02181 in model Streptomyces spp. arrested morphological progression at aerial mycelium level and strongly altered the production of secondary metabolites. Altogether, our work underscores the significance of c-di-GMP-mediated signaling in natural product biosynthesis and pointed to extensively applicable approach to increase antibiotic production levels in streptomycetes.Streptomycetes are filamentous bacteria famous for their ability to produce a vast majority of clinically important secondary metabolites. Both complex morphogenesis and onset of antibiotic biosynthesis are tightly linked in streptomycetes and require series of specific signals for initiation. Cyclic dimeric 3\u2032\u20135\u2032 guanosine monophosphate, c-di-GMP, one of the well-known bacterial second messengers, has been recently shown to govern morphogenesis and natural product synthesis in  Since that time, its role has been expanded to control various cellular processes including biofilm formation, planktonic to sessile state transition, cell progression and expression of virulence genes2. Alteration of c-di-GMP intracellular levels crucially affect the life cycle of microorganisms. Two classes of enzymes with opposite activities are responsible for the c-di-GMP turnover. Diguanylate cyclases (DGCs) catalyze the synthesis of the second messenger by homodimerization, using two molecules of GTP. Their active site consists of the highly-conservative GG(D/E)EF [Gly-Gly-(Asp/Glu)-Glu-Phe] domain4. Phosphodiesterases (PDEs) are responsible for c-di-GMP degradation through the catalytic EAL (Glu-Ala-Leu) domain, yielding the linear dinucleotide 5\u2032-phosphoguanylyl-(3\u2032\u2009\u2192\u20095\u2032)-guanosine (pGpG)6. Additionally, the less frequent PDEs containing HD-GYP (His-Asp)-(Gly-Tyr-Pro) domain hydrolyze c-di-GMP into two GMP molecules7.Cyclic dimeric (3\u2032\u2009\u2192\u20095\u2032) GMP (c-di-GMP) was initially reported by Ross et al.GMP-regulated PDEs, Anabaenaadenylyl cyclases and Escherichia coli transcription activator FhlA) domains are the most spread among c-di-GMP metabolizing enzymes2. Due to their high plasticity, PAS domains are able to bind a large variety of ligands, such as heme, divalent cations and small molecules. In turn, these cofactors empower proteins to be responsive to redox potentials, gaseous ligands and visible light8.Moreover, sensor domains are often associated with catalytic domains, making these proteins responsive to environmental stimuli. PAS (Per-Arnt-Sim) and GAF 17 as well as several secondary messengers18. The first evidence that c-di-GMP is involved in morphological development and antibiotic production in streptomycetes appeared in 2010. In Streptomyces coelicolor, den Hengst et al.13 identified a gene (cdgA) encoding a GGDEF-EAL protein. Upon overexpression, the resulting strain displayed a \u201cbald\u201d phenotype  and reduced production of the blue-pigmented antibiotic actinorhodin. Since that time, few more DGC and PDE encoding genes were investigated in S. coelicolor. Overproduction of the DGCs CdgB and CdgD resembled the phenotype of cdgA-overexpressed mutant strain, where morphological development was blocked at aerial mycelium level10. Analogous, deletion of two genes encoding for PDEs (rmdA and rmdB), caused an increase in c-di-GMP levels followed by the appearance of a \u201cbald\u201d phenotype11. Recently, studies in Streptomyces venezuelae revealed that orthologues of CdgA, CdgB, RmdA, RmdB and a newly identified DGC CdgC control morphological transitions15. In 2014, Tschowri et al. described a detailed mechanism regulating morphogenesis in streptomycetes. It was shown that tetrameric form of c-di-GMP mediates the dimerization of the master pleiotropic regulator BldD. Then homodimeric BldD binds to its target promoter sequences, controlling the transcription of developmental-related genes during the vegetative growth14. Interestingly, it was also demonstrated in S. coelicolor that cdgA and cdgB are direct targets of BldD13, revealing another layer of regulation for c-di-GMP metabolizing enzymes. Finally, a recent work published by Gallagher et al.19 showed that in S. venezuelae a dimer of c-di-GMP is required for the binding between the sporulation-specific \u03c3WhiG factor and its anti-\u03c3 (RsiG), leading to the block of differentiation of aerial hyphae into spore chains.Although the influence of c-di-GMP on bacterial lifestyle has been mostly investigated in unicellular, motile bacteria, recent studies pointed out the crucial role of the cyclic dinucleotide in the Gram-positive genus ssfg_02181, a gene encoding a putative DGC enzyme in Streptomyces ghanaensis ATCC14672. This strain is famous for the production of a mixture of phosphoglycolipids known as moenomycins20. Among them, moenomycin A (MmA) is considered as the founding member of this class of antibiotics. Broad spectrum of activity and its unique mechanism of action make MmA a promising lead against multidrug-resistant pathogenic bacteria (e.g. VRS and MRSA)21. In recent years, many studies have been directed to understand the regulatory mechanisms of MmA biosynthesis with the final aim of generating overproducer strains. Structural genes responsible for antibiotic production are located in two moe clusters. Uncommonly for streptomycetes biosynthetic gene clusters, no cluster-situated regulators were identified within moe clusters20. Interestingly, a fine-tuned control over MmA biosynthesis is directly governed by the pleiotropic regulators AdpA, BldA, AbsB22 and WblA23. AdpA is a well-known transcriptional regulator for morphological differentiation and secondary metabolism in Streptomyces26. The bldA gene encodes tRNALeuUAA, the only tRNA able to translate the rare UUA codon in GC-rich streptomycetes27. WblA is involved in sporogenesis28 and was shown to negatively influence MmA production23. Additionally, expression of adpA, bldA and wblA is controlled by the master regulator BldD13. Recently, we showed that manipulation of genes involved in c-di-GMP metabolism in S. ghanaensis caused variations of intracellular c-di-GMP levels, thus affecting the binding of BldD to its target promoters. Consequently, MmA production as well as morphogenesis were severely altered12.In this study, we focused our attention on ssfg_02181 remarkably influences antibiotic biosynthesis and morphological differentiation in S. ghanaensis. Deletion of ssfg_02181 causes a significant decrease of MmA levels and precocious sporulation of the mutant strain. In contrast, overexpression of the gene leads to a substantial increment of antibiotic production and to a morphological arrest at the aerial mycelium stage. In agreement with in silico analysis, we proved that SSFG_02181 effectively acts as a diguanylate cyclase enzyme in vitro and it is able to bind heme. In addition, we showed that transcription of ssfg_02181 is negatively regulated by BldD. Finally, heterologous expression of ssfg_02181 in S. coelicolor and Streptomyces albus displayed a phenotype similar to that gained in S. ghanaensis, suggesting that c-di-GMP may play a conservative role among Streptomyces spp.In this work, we show that the expression of Streptomyces life cycle15. Interestingly, c-di-GMP metabolizing enzymes were found to be broadly conserved and omnipresent in streptomycetes15. Bioinformatic analysis of the S. ghanaensis genome identified ssfg_02181 out of nine genes encoding for putative DGC/PDE enzymes. SSFG_02181 is a 1079-aa protein consisting of a highly-conserved GGDEF domain, followed by a degenerated EAL domain. An N-terminal PAS sensor domain is located upstream the putative DGC active site (A-site), preceded by nine transmembrane-spanning helices  and 72% (sequence coverage 89%) of identity, respectively.In past years, several studies have marked the importance of the second messenger c-di-GMP in Xanthomonas campestris29, PleD from Caulobacter crescentus3 and AxDGC2 from Acetobacter xylinum30 were chosen. As shown in Fig.\u00a02+ or Mn2+) coordination3. Asp (in same cases Glu) at the third position is also involved in metal coordination and is crucial for catalysis32. Additionally, SSFG_02181 GGDEF domain contains the canonical RxxD motif (I-site), responsible for binding of dimeric c-di-GMP and thus mediating its own inhibition. Another feature of an active GGDEF domain is the presence of the invariant DxLT motif, which is required for the formation of a stabilizing \u201cwide-turn\u201d structure and represents the starting point of the catalytic site33. Other residues in the vicinity of the active site and required for the activity were also found to be highly conserved in SSFG_02181 GGDEF domain33, suggesting that this enzyme functions as DGC EF domains of XCC4471 from 2+ coordination. In fact, sequence alignment with the enzymatically active PDEs from E. coli (EcDos)34, Pseudomonas aeruginosa (RocR)35 and Acetobacter xylinum (AxPDEA1)36 showed Glu and Leu residues (of the EAL site) substituted with Cys and Gln, respectively 35 is mutated to Arg in SSFG_02181 EAL sequence, further suggesting that this domain is likely enzymatically inactive. Based on these evidences, we predicted SSFG_02181 to work solely as DGC enzyme.The predicted EAL domain of SSFG_02181 lacks key residues for the PDE activity and Mgssfg_02181 in E. coli. Despite all our attempts to gain the full protein, it remained insoluble most likely due to the presence of the nine transmembrane helices. A truncated version of the protein (named SSFG_02181460) lacking the first 459 amino acids was purified to homogeneity  carrying both G682A and G683A substitutions in the GGDEF domain was purified and tested for cyclase activity. As shown in Fig.\u00a0m/z\u2009=\u2009689.1 [M-H]\u2013) was detected in the reaction sample but not in the negative controls  and UV\u2013vis absorption spectra were recorded  and Fe(III) forms. No significant changes in the DGC activity were observed comparing the reaction products of the apoprotein to the heme-protein complexes  complex, whereas the heme Fe(II) and the heme Fe(II)-NO complexes result inactive38. Conversely, the PDEA1 from Acetobacter xylinum decreases its enzymatic activity when O2 is bound to the protein36. To date, just one c-di-GMP metabolizing enzyme in Streptomyces was proven to be a hemoprotein. The PDE RmdA from S. coelicolor binds hemin through its PAS9 N-terminal domain and it is able to respond to O2 and CO, albeit its PDE activity does not change upon the binding11. Finally, a truncated version of SSFG_02181 was used for the in vitro assay. In fact, SSFG_02181460 lacks nine N-terminal transmembrane domains which might also be involved in signals transduction upon sensing environmental stimuli. Whether the activity of SSFG_02181 is affected by gaseous ligands and/or by inputs of the transmembrane domains remains obscure and requires further investigations.Most likely, the activity of SSFG_02181 might be influenced by sensing gaseous ligands. Several DGC/PDE enzymes have been found to vary their activity upon binding of gases such as OS. ghanaensis development and MmA production, we constructed an ssfg_02181 marker-free null-mutant through homologous recombination. Analysis of moenomycin production revealed a 2-fold decrease of antibiotic production in \u0394ssfg_02181 in comparison to the wild-type strain  and integrated into S. ghanaensis \u0394ssfg_02181 chromosome. The \u0394ssfg_02181 mutant carrying the empty vector was used as a control. As shown in Fig.\u00a0ssfg_02181 deletion on MmA production were abolished upon gene complementation. However, a substantial increase of antibiotic accumulation was observed in \u0394ssfg_02181 pSET02181+ in comparison to both mutant and wild-type strains. This phenomenon might be due to the integration of an additional copy of the plasmid into pseudo \u03c6C31-sites in the genome of S. ghanaensis \u0394ssfg_02181, as it was already shown for others Streptomyces spp.39. Next, we studied the influence of ssfg_02181 deletion on morphological development. The mutant strain was cultivated on SFM agar medium for 5\u00a0days at 37\u00a0\u00b0C. After 2\u00a0days of growth, \u0394ssfg_02181 displayed the characteristic green pigment associated with spores formation on its surface, whereas the surface of a control strain still remained white . In addition, overexpression of ssfg_02181 led to a delay in morphological development, as it was shown for cdgA, cdgB and cdgD overexpressing mutants in S. coelicolor13.Our results report the involvement of a c-di-GMP metabolizing enzyme in S. ghanaensis severely affect secondary metabolites production. Indeed, c-di-GMP mediates the dimerization of BldD, which in turn activates adpAgh expression and inhibits wblAgh transcription12. AdpA was shown to act as a positive transcriptional regulator of key structural genes in moe cluster22, whereas WblA was shown to negatively regulate MmA production23. In this study, deletion of ssfg_02181 results in a significant decrease of antibiotic production. Similarly to the \u0394cdgBgh mutant12, we assume that the loss of SSFG_02181 causes a reduction of intracellular c-di-GMP pool and thus the dissociation of BldD dimer from the target promoters (e.g. adpA and wblA).Recently, we showed that manipulations of c-di-GMP metabolizing enzymes in WhiG factor19. However, Streptomyces spp. possess more than one c-di-GMP-metabolizing enzymes, suggesting that these proteins might be active at different time points and spatial locations during the cell life. Finally, the majority of these enzymes carry one or more sensor domains whose functions have not yet been fully clarified. Most likely, these domains regulate the catalytic activity by responding to either external or internal stimuli, thus maintaining a well-tuned control over intracellular c-di-GMP levels in the cell.In the past years, several studies were conducted to fully understand the functions and mechanisms of action of DGCs and PDEs in streptomycetes. Genetic manipulation and biochemical analysis confirmed that these enzymes are responsible for the metabolism of c-di-GMP, which intracellular level is crucially balanced over the entire bacterial life cycle. Pool of this second messenger in turn regulates both morphogenesis and secondary metabolite production by controlling both BldD and the \u03c3ssfg_02181 could affect secondary metabolites production and morphological development in other streptomycetes, we introduced an additional copy of the gene controlled by the strong constitutive promoter ermEp (plasmid pTES02181) into two model strains S. coelicolor M145 and S. albus J1074. As a control, strains carrying an empty copy of the vector pTES were used. After 3\u00a0days of growth, S. coelicolor pTES02181 exhibited a significant increase of actinorhodin production on SFM agar, in comparison to the control strain , S. venezuelae (SVEN_5187), S. griseus (SGR_2001) and S. albus (XNR_1322) showed that these proteins possess a degenerated EAL domain but share the conservative GGDEF domain GnGTnAn, named BldD-box. In S. coelicolor, BldD-box was found to be located also in the promoter regions of three genes encoding for c-di-GMP metabolizing enzymes 13. Likewise, bioinformatic analysis12 on ssfg_02181 promoter region identified two sequences (TCTACGCTCCGTAAC and ATGTCCCTGAGTGAC)  Fig.\u00a0a resemblS. ghanaensis and its orthologs from S. coelicolor, S. venezuelae and S. albus revealed an identical DNA binding domain (DBD) and the presence of both c-di-GMP binding motifs RxD-X8-RxxD . An N-terminally His6-tagged BldD protein was produced in E. coli and purified. Increasing amounts of BldD were incubated with the 33P-radiolabeled promoter region of ssfg_02181 at the presence of 1\u00a0\u00b5M c-di-GMP. As shown in Fig.\u00a0ssfg_02181-promoter DNA was used to compete with the radiolabeled one for BldD  and ATGTCCCTGAGTGAC (here named BldD-box II) effectively corresponds to the major BldD-box in the ssfg_02181 by semiquantitative (sq)RT-PCR and GusA reporter assay41. For sqRT-PCR, the cDNA from S. ghanaensis wild-type and S. ghanaensis \u0394bldD12 were used as template. As depicted in Fig.\u00a0ssfg_02181 were found to increase in S. ghanaensis \u0394bldD, in comparison to the wild-type strain. This observation was further confirmed by the GusA reporter system, showing a 1.7-fold increase in the transcriptional activity of the ssfg_02181 promoter in the bldD mutant  is under control of BldD. These genes were found to be omnipresent among streptomycetes15, indicating that such mechanism might be universally present across Streptomyces. Likely, the transcription of these genes is blocked by the BldD dimer when c-di-GMP concentrations reach a certain threshold. As result, the homeostasis of the second messenger is retained during the entire bacterial life cycle. However, it can not be excluded that the binding of BldD might be also required to temporally control the transcription of these genes. Studies in S. venezuelae revealed that cdgB is expressed in all developmental stages, whereas expression of cdgC (ortholog of ssfg_02181) increases over the time and reaches the highest levels during sporulation. Finally, cdgA is the least transcribed among all DGCs15. Therefore, all these findings point to the conclusion that regulatory mechanisms governing the c-di-GMP levels are immensely intricate and well-tuned to permit a coordinate morphological progression and antibiotic synthesis in actinobacteria.In addition, we hypothesize that intracellular c-di-GMP levels undergo a fine-tuned regulation through a negative feedback mechanism. Notably, the expression of the DGC-encoding genes  and oatmeal agar media and in TSB liquid medium at 37\u00a0\u00b0C. S. coelicolor M145 was grown on SFM and R2YE agar media at 28\u00a0\u00b0C. S. albus J1074 was cultivated on SFM at 28\u00a0\u00b0C. Where necessary, appropriate antibiotics were added to the media.The bacteria strains and plasmids used in this work are listed in Supplementary Table 42. All plasmids were verified by enzymatic digestion, PCR or sequencing. Conjugal transfer of plasmids from E. coli to Streptomyces spp. was achieved using the dam dcm mutant strain E. coli ET12567, carrying the helper plasmid pUZ8002 and performed as described previously43.Polymerase chain reactions (PCRs) were performed using Phusion polymerase (ThermoFisher). All primers used in this work are listed in Supplementary Table S. ghanaensis strains was recovered from the SFM agar plates and placed on a metal stub. Each sample was introduced into a Quanta 250 environmental scanning electron microscope (SEM) and images were captured at magnification 12,800\u00a0\u00d7.For morphological evaluation, a slice of https://smart.embl-heidelberg.de/). Multiple sequence alignments of BldD and the DGC and PDE domains were constructed using the Clustal Omega software  and the figures were generated using the ESPript 2.2 software (https://espript.ibcp.fr/ESPript/ESPript/). BLAST software was employed to identify a putative heme-binding site in SSFG_02181 sequence. Finally, the identification of a BldD-binding site on ssfg_02181 promoter was done as described elsewhere12 and the alignment of ssfg_02181 promoter and its orthologs was performed using Clustal Omega software.SSFG_02181 protein sequence was analyzed by the SMART domain database (ssfg_02181 coding sequence and its flanking regions was amplified from the genomic DNA of S. ghanaensis by PCR (primers 02181_del_for and 02181_del_rev) and cloned into the EcoRV-digested pBluescriptKS vector, yielding pBlue02181. The apramycin resistance cassette (aac(3)IV), flanked by loxP sites, was amplified from pLERECJ using 02181_kn_for and 02181_kn_rev primers. pBlue02181 and aac(3)IV were introduced into E. coli BW25113/pIJ790 and ssfg_02181 was replaced with the gene marker by REDIRECT technology44. The resulting fragment 02181::aac(3)IV was amplified by PCR using the same primer pair and cloned into the suicide EcoRV-digested pKGLP2 vector (hygromycin resistance), yielding pKG02181::aac(3)IV. The latter was transferred into S. ghanaensis by conjugation. Double-crossover mutants were selected for apramycin resistance and hygromycin sensitivity. The replacement of ssfg_02181 with the aac(3)IV gene marker in the genome of S. ghanaensis \u039402181::aac(3)IV was confirmed by PCR. In order to excise the apramycin cassette and yield a marker-free mutant, the Cre-expressing helper plasmid pUWLCre was introduced into S. ghanaensis \u039402181::aac(3)IV by conjugation45. The exconjugants were selected for resistance to thiostrepton followed by screening for sensitivity to apramycin, yielding S. ghanaensis \u0394ssfg_02181. The deletion of apramycin cassette from the genome of S. ghanaensis \u0394ssfg_02181 was confirmed by PCR.The DNA fragment containing ssfg_02181 coding sequence, along with its own promoter region, was amplified by PCR (primers 02181_compl_for and 02181_compl_rev) and digested with XbaI and EcoRI. The fragment was cloned into the XbaI-EcoRI digested pSET152 vector, to gain pSET02181. The latter was then transferred into S. ghanaensis \u0394ssfg_02181 by conjugation.For complementation experiment, the ssfg_02181-overexpressed S. ghanaensis strain, the DNA fragment comprising only the coding sequence of ssfg_02181 was amplified by PCR (primers 02181_exp_for and 02181_exp_rev) and digested with XbaI and EcoRI. Next, the resulting fragment was cloned under the control of the strong, constitutive promoter ermEp in the integrative pTES vector, linearized by XbaI and EcoRI. The resulting plasmid pTES02181 was transferred into S. ghanaensis, S. coelicolor M145 and S. albus J1074 strains by conjugation.In order to create the 47. Briefly, S. ghanaensis strains were grown in 50\u00a0ml TSB medium for 4\u00a0days, in triplicate. The cellular pellet was collected by centrifugation and mixed with 10\u00a0ml of methanol overnight. The resulting extracts were concentrated in vacuo and dissolved in methanol prior analysis by HPLC\u2013MS. The analysis was performed as established previously12. In this work, moenomycin refers to the mixture of the following main compounds: MmA  and the precursor nosokomycin B . The experiments were repeated at least three times to ensure reproducibility of results and the levels of antibiotic were referred back to the equal amount of dry biomass (10\u00a0mg) in different strains. The data shown in Fig.\u00a0S. ghanaensis wild-type was taken as 100%. Error bars indicate the standard deviations.The quantification of moenomycin production levels was done as described previously460) was amplified by PCR (primers 02181_460_for and 02181_460_rev) and digested with BamHI and HindIII. The digested fragment was cloned into BamHI-HindIII linearized pET51b vector, yielding pET02181460. To generate a mutated version of SSFG_02181460 (named SSFG_02181460 AADEF), PCR mutagenesis was applied to amplify a 7\u00a0kb DNA fragment from pET02181460 using primers designed to bring G682A and G683A substitutions (02181_460_aadef_for and 02181_460_aadef_rev). The resulting amplicon was treated with T4 Polynucleotide kinase and then self-ligated, yielding pET02181460_AADEF. pET02181460 and pET02181460_AADEF were individually introduced into E. coli BL21 Star (DE3) pLysS. The strain was grown at 37\u00a0\u00b0C to OD600 of 0.5 and the protein production was induced with 0.2\u00a0mM IPTG. Following 5\u00a0h of incubation at 22\u00a0\u00b0C, cells were harvested by centrifugation and the pellet was resuspended in Step-tag equilibration buffer . Cell lysis was achieved using three passages through French-press and the lysate was centrifuged at 14.000\u00a0rpm for 35\u00a0min at 4\u00a0\u00b0C. The soluble cell extract was loaded onto a column containing 4\u00a0ml of pre-equilibrated Strep-Tactin resin (IBA). Fractions were eluted using Strep-tag equilibration buffer containing 2.5\u00a0mM desthiobiotin and pooled together. Size-exclusion chromatografy was performed using the \u00c4KTA fast protein liquid chromatography (FPLC) system, equipped with a Superdex 200\u00a0h 16/60 column. After gel filtration, the protein was stored at \u2212\u00a080\u00a0\u00b0C in a buffer containing 50\u00a0mM Tris-HCl, 0.5\u00a0M NaCl, 1\u00a0mM MgCl2, 1\u00a0mM dithiotreitol and 5% glycerol, pH 7.5.The truncated N-terminal Strep-tagged version of SSFG_02181 was produced as following. DNA sequence encoding the PAS-GGDEF-EAL domains of SSFG_02181  containing 200\u00a0\u00b5M GTP, 50\u00a0mM Tris-HCl [pH 8.0], 50\u00a0mM NaCl and 10\u00a0mM MgCl2. The mixture was incubated at 37\u00a0\u00b0C for 2\u00a0h. The reaction was stopped by adding 10\u00a0mM CaCl2, followed by incubation at 70\u00a0\u00b0C for 5\u00a0min. LC\u2013MS was used to analyze the reaction products, as described previously12. C-di-GMP and GTP standards were purchased from InvivoGen and Sigma-Aldrich, respectively.Enzymatic in vitro activity assay was performed by adding 5\u00a0\u00b5M Strep-tagged SSFG_02181460 protein could bind heme, hemin (stock solution 1\u00a0mM in DMSO) was added to the purified protein (10\u00a0\u00b5M) at equimolar concentrations. Following a 30\u00a0min incubation at room temperature, the UV\u2013vis absorption spectra were measured with 100\u00a0\u00b5l protein-hemin complex using a Jasco V-650 spectrophotometer. A solution of 10\u00a0\u00b5M hemin in a protein-storage buffer was prepared as a control. All experiments were performed under anaerobic conditions in a glove-box at room temperature.In order to determine whether Strep-tagged SSFG_02181ssfg_02181 was amplified by PCR using primers 02181_EMSA_for and 02181_EMSA_rev and then 5\u2032-end labelled with [\u03b3-33P] using T4 polynucleotide kinase. 20\u00a0fmol of labelled DNA was incubated with increasing concentrations of His6-tagged BldD12 in 15\u00a0\u00b5l buffer  containing 1\u00a0\u00b5g poly(dI-dC) and 1\u00a0\u00b5M c-di-GMP. The reaction products were separated on 8% native polyacrylamide gel in TBE buffer. The bands were visualized by phosphorimaging. Competition assay was performed in reaction sample containing 20\u00a0fmol labelled ssfg_02181 promoter incubated with 0.5\u00a0\u00b5M BldD, 1\u00a0\u00b5M c-di-GMP and 10-, 50-, 100- and 250-fold molar excess of unlabeled probe in binding buffer, as described above. Finally, to evaluate whether c-di-GMP might affect the binding efficiency between BldD and ssfg_02181 promoter, the latter (20\u00a0fmol) was labelled and incubated with 0.1\u00a0\u00b5M BldD and increasing concentrations of c-di-GMP .The DNA fragment corresponding to the promoter region of ssfg_02181 promoter region, three pairs of 53\u00a0bp complementary oligonuleotide strands were designed where BldD-boxes were replaced by a poly(A) non-sense sequence. Particularly, PmutI_for and PmutI_rev primers were used for mutating BldD-box I, PmutII_for and PmutII_rev for mutating BldD-box II and PmutIII_for and PmutIII_rev for mutating both boxes simultaneously. As a control, a pair of complementary oligonuclotide strands (Pnat_for and Pnat_rev) carrying both native sequences was used. Complementary oligonucleotides were mixed together at a 1:1 molar ratio and diluted in a buffer  to a final concentration 1\u00a0pmol/\u00b5l. To anneal, the tube with oligonucleotides was incubated in a boiling water for 2\u00a0min and let slowly cool down to room temperature. Hybridized oligonucleotides were then 5\u2032-end labelled with [\u03b3-33P]-ATP using T4 polynucleotide kinase and applied for electrophoretic mobility shift assay experiments.In order to identify the major BldD binding site in ssfg_02181 promoter, the DNA fragment containing the promoter region of ssfg_02181 was amplified by PCR (primers 02181_script_for and 02181_script_rev) and digested with XbaI and KpnI. The obtained fragment was cloned into XbaI, KpnI-linearized pGUS vector, yielding pGUS02181. The latter was transferred into S. ghanaensis strains by conjugation. \u03b2-glucuronidase activity was measured as described previously41. The experiments were carried out at least three times. The values refer back to equal amount of dry biomass (10\u00a0mg) and correspond to the mean of independent experiments. Error bars indicate the standard deviations.To investigate the transcriptional activity of ssfg_02181 between S. ghanaensis strains. sqRT-PCR experiments were performed as described previously12. Briefly, total RNA samples were isolated in triplicate from streptomycetes cultures grown in TSB for 48\u00a0h. One mcg of total RNA per reaction was used to synthesize cDNA using Photoscript II Reverse Transcriptase (NEB). Two hundred ng of cDNA were used for PCR (primers 02181_check_for and 02181_RT_rev) and the expression levels of the gene were estimated by visual examination. As a control, primers specific to the sequence of hrdB encoding the RNA polymerase principal sigma factor were used12.Semiquantitative RT-PCR was used to determine differences in the transcription levels of Supplementary Information."}
+{"text": "Idiopathic pulmonary fibrosis is a progressive and debilitating lung disease with large unmet medical need and few treatment options. We describe an analysis connecting single cell gene expression with bulk gene expression-based subsetting of patient cohorts to identify IPF patient subsets with different underlying pathogenesis and cellular changes. We reproduced earlier findings indicating the existence of two major subsets in IPF and showed that these subsets display different alterations in cellular composition of the lung. We developed classifiers based on the cellular changes in disease to distinguish subsets. Specifically, we showed that one subset of IPF patients had significant increases in gene signature scores for myeloid cells versus a second subset that had significantly increased gene signature scores for ciliated epithelial cells, suggesting a differential pathogenesis among IPF subsets. Ligand-receptor analyses suggested there was a monocyte-macrophage chemoattractant axis  among the myeloid-enriched IPF subset and a ciliated epithelium-derived chemokine axis (e.g. CCL15) among the ciliated epithelium-enriched IPF subset. We also found that these IPF subsets had differential expression of pirfenidone-responsive genes suggesting that our findings may provide an approach to identify patients with differential responses to pirfenidone and other drugs. We believe this work is an important step towards targeted therapies and biomarkers of response. Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive fibrosing disease of the lung with a median survival time of <5 years after diagnosis , 2. IPF IPF is a heterogeneous disease with differences in clinical outcome and rates of disease worsening, suggesting that there are subsets of IPF patients with different molecular mechanisms of pathogenesis , 9. As sIn the current study, we found that the subsets described by Yang et al.   were rephttps://github.com/JKarmanAbbVie/IPFproject2020).We downloaded and reprocessed microarray data from Schwartz and colleagues   were dowhttps://CRAN.R-project.org/package=diceR). This score has been reported as the best performing metric to assess performance of consensus clustering [We used the R package \u2018consensusClusterPlus\u2019  to perfoustering .http://pages.ingenuity.com/rs/ingenuity/images/0812%20upstream_regulator_analysis_whitepaper.pdf). KEGG, Gene Ontology and Reactome pathway analyses were performed using \u2018clusterProfiler\u2019 and \u2018ReactomePA\u2019 R packages [Principal component analysis (PCA) on the same 5,000 most variable genes used for consensus clustering was performed using \u2018FactoMineR\u2019 and \u2018factoextra\u2019 R packages , 28. R ppackages , 31.https://github.com/RGLab/MAST/) and by maximizing the power of the gene signature to discriminate a particular cell type from the other cell types using an AUROC-based metric  , Habermaeference ). To sumeference . This ineference . This mehttps://github.com/kassambara/ggcorrplot).We created two sets of gene expression signatures for GSE132771 (Sheppard-UCSF single cell cohort) : one forGene signature scores from bulk IPF RNA microarray  GSVA-derived gene signature scores from GSE47460 (Kaminski-LGRC bulk expression cohort) \u201317, 22 aWe calculated cell type percentages in GSE135893 (Kropski-Vanderbilt Univ single cell cohort)  by dividhttps://github.com/topepo/caret/) was used to build classifiers and feature selection from the GSE47460 (Kaminski-LGRC bulk expression cohort) dataset [The R package \u2018caret\u2019  . TherefoWe performed a PCA of the GSE47460 (Kaminski-LGRC bulk expression cohort) \u201317 datasAs the set of samples between GSE32537 (Schwartz-Univ of Colorado bulk expression cohort)  and GSE4For the first approach, we used Gene Expression Omnibus records to analyze both non-overlapping IPF data from GSE32537 (Schwartz-Univ of Colorado bulk expression cohort)  and GSE4We next determined whether the subsets identified by consensus clustering in GSE47460 (Kaminski-LGRC bulk expression cohort) \u201317 overlTo further substantiate our results, we analyzed an additional independent cohort of IPF patients (GSE134692 (BMS bulk RNA-seq cohort)  to validlCO), forced expiratory volume in 1 second (FEV1) or forced vital capacity (FVC) between the two subsets of patients in GSE47460 (Kaminski-LGRC bulk expression cohort) [GREM1, MMP7, CTHRC1 and FHL2) identified by Kaminski and colleagues [lCO and as markers that separate IPF patients by disease severity were expressed at a higher level in Subset 2 of GSE47460 (Kaminski-LGRC bulk expression cohort) [MMP7 and FHL2 were also reported to be different between subsets in the study by Yang et al.  [We next determined whether markers of fibrosis and clinical data associated with the severity of IPF were different between the two subsets identified in GSE47460 (Kaminski-LGRC bulk expression cohort) \u201317. We d cohort) \u201317  , in whic cohort) \u201317, did  cohort)  related lleagues  as havin cohort) \u201317 (S2 F cohort) . We did  cohort) .The pathological process in IPF leads to marked changes in cellular composition of lung tissue and is associated with alterations in both hematopoietic and non-hematopoietic cell populations . AdvanceWe applied the cellular signatures derived from Tsukui et al. GSE132771 (Sheppard-UCSF single cell cohort)  to the dNext, we calculated gene signature scores using Gene Set Variation Analysis (GSVA) as described in \u2018Methods\u2019 and observed strong, coordinated changes in gene signature scores for epithelial and endothelial cell populations that differed significantly between Subsets 1 and 2 . Our worTo confirm the findings using cellular signatures developed from the data of Tsukui et al.  , we usedAdditionally, to further validate this approach in additional IPF cohorts with bulk transcriptomic data, we repeated consensus clustering and cell type signature analysis of an additional IPF cohort in which bulk RNAseq data was available ) . This an+ fibroblasts were not different between the two subsets. Taken together, this data indicated that the two subsets identified in GSE47460 (Kaminski-LGRC bulk expression cohort) [The scRNAseq data allowed identification of novel subtypes of fibroblasts in IPF as reported by Tsukui et al. . Importa cohort) \u201317, base cohort) \u201317. BaseWe hypothesized that the differential cellular make-up in the Myeloid-enriched IPF subset as compared to the Ciliated epithelium-enriched IPF subset was likely due to the differential activation of chemokine and chemokine receptor networks. To test this hypothesis, first, we compared the expression patterns of chemokine ligands across the two subsets identified in GSE47460 (Kaminski-LGRC bulk expression cohort) \u201317. As sAdditionally, we also conducted a transcriptome-wide analysis of single cell RNA seq data from Habermann et al.   using thth percentile of ligand-receptor pairs from macrophages and epithelial cells (based on z score output provided by PyMiner) in each subset and determined the expression of matching receptors from each condition for visualization purposes. As expected, significant differences in the profiles of inferred active ligand-receptor networks were detected. We confirmed these results using another ligand-receptor network approach, NicheNet [In our ligand-receptor analysis using PyMiner, we focused on differential ligands produced by cell populations that were significantly different in percentage between \u2018Ciliated_low\u2019 and \u2018Ciliated_high\u2019 subsets . NicheNet  S9 Fig)th percenBecause the two IPF patient subsets we identified in GSE47460 (Kaminski-LGRC bulk expression cohort) \u201317 may hWe used support vector machines with a linear kernel, elastic net and a gradient boosting machine to create models for cell type-based classifiers and gene expression values. We trained our models on a randomly selected 70% of IPF patients in GSE47460 (Kaminski-LGRC bulk expression cohort) \u201317 and udownregulated in response to pirfenidone in lung homogenates [Finally, we asked whether the two subsets of IPF patients would respond differentially to approved IPF therapies. Currently, there are two FDA-approved therapies available for IPF patients, pirfenidone and nintedanib , 4. We dogenates  Fig 9A)downregulogenates \u201317, we fWe used a data-driven, unsupervised clustering of RNA expression data from IPF patient lung samples, that was reproducible across patient cohorts and was associated with changes in the cellular composition of the lungs in IPF. We believe this study provides novel ideas on differential mechanisms of pathogenesis in this heterogeneous disease. We used single cell RNA sequencing data to uncover subpopulations of both mesenchymal and hematopoietic cell populations associated with disease pathogenesis , 45\u201347. decrease in cytotoxic T cells and helper T cells. Similar to increases in B cells/plasma cells, decreases in T cell responses have been shown to be associated with a poor prognosis in IPF [GREM1, MMP7, CTHRC1 and FHL2) identified by Kaminski and colleagues [lCO and as markers that separate IPF patients by disease severity and predicted progression were expressed at higher levels in \u2018Ciliated epithelium-enriched\u2019 patients of GSE47460 (Kaminski-LGRC bulk expression cohort) [Prior studies suggested that both plasma cells and T cells are associated with disease progression \u201313, and s in IPF , 51. Evas in IPF  suggestes in IPF . Additiolleagues  as havin cohort) \u201317 . Adic lungs  and wereets Figs \u20135. Besid subsets . Additio subsets , \u2018MC_27\u2019+ cells were increased as compared to control samples in the Myeloid-enriched IPF subset but not in the Ciliated epithelium-enriched IPF subset  score, ), determAlso, we used scores determined by gene set enrichment to estimate levels of cell type enrichment; as such, the scores calculated represent cell type-specific signatures and are not a direct measurement of each cell type. Despite this potential limitation, we believe that we used the most relevant GSVA method to determine cell type-specific gene signature scores; GSVA offers distinct advantages over calculating gene signature scores due to its efficient ranking and outlier smoothing algorithms , 34.In addition, we believe that the conclusions we made using gene signatures developed from the scRNAseq datasets are also supported by the observation that the correlation between GSVA-signature scores calculated from total lung suspension datasets GSE132771 (Sheppard-UCSF single cell cohort) and GSE136893 (Kropski-Vanderbilt Univ single cell cohort) are low across the dataset  Figs. AlAnother limitation of our study is that it represents \u2018hypothesis generation\u2019 and lacks experimental validation. Unfortunately, we were unable to link and validate our findings to histopathological and longitudinal clinical and gene expression data. Future datasets may answer the question of whether the changes we observed based on gene expression are reflected in cellular changes observable by other methods and if gene expression differences are relevant to prediction of clinical disease course in IPF. However, we believe that our hypotheses have generated valuable insights despite this shortcoming as our results provide testable ideas with suggested associated biomarkers.In conclusion, we developed an analysis pipeline to subset IPF patients in a data-driven, unsupervised manner and demonstrated an association of cellular changes with gene expression in the two identified subsets. We believe this work provides novel insights into the pathogenesis of IPF and provides testable hypothesesabout differential alterations of cellular composition of the lung in subsets of IPF patients in this difficult-to-treat disease.S1 FigA. PAC scores as a function of number of clusters (k) calculated based on consensus clustering results in GSE47460 (Kaminski-LGRC bulk expression cohort) \u201317. B. P(TIF)Click here for additional data file.S2 FigAdjusted p values are reported on plots.(TIF)Click here for additional data file.S3 Fig+ monocytes; Infl_monocytes_1: Inflammatory monocytes; ACKR1pos_endo_2: ACKR1+ endothelial cells; ACKR1neg_endo_3: ACKR1- endothelial cells; Fibroblasts_4: Fibroblasts; AT2_5 and AT2_23: Alveolar epithelial cell type II subpopulations; Th_6: helper T cells; Pericytes_7 and Pericytes_22: Pericyte subpopulations; HLAhigh_mac_8 and HLAhigh_mac_10: HLA class II high macrophage subpopulations; Sm_9: smooth muscle cells; Bcells_11 and Bcells_21: B cell subpopulations; Tc_12: cytotoxic T cells; AT1_13: Alveolar epithelial cell type I; PC_14: Plasma cells; Endo_15 and Endo_24: endothelial cell subpopulations; Ciliated_16: ciliated epithelial cells; Monocytes_17 and Monocytes_18: Monocyte subpopulations. B. Lineage sorted cells. THY1high_alv_fib_0: THY1 high alveolar fibroblasts; THY1pos_sm_1: THY1+ smooth muscle; THY1neg_sm_2: THY1- smooth muscle; CTHRC1pos_3: CTHRC1+ fibroblasts; Adventitial_4: Adventitial fibroblasts; THY1neg_alv_fib_5: THY1- alveolar fibroblasts; Pericytes_6: Pericytes; Peribronchial_7: Peribronchial fibroblasts; Sm_8 and Sm_13: smooth muscle cell subpopulations; Alveolar_9 and Alveolar_10: Alveolar fibroblast subpopulations; Epi_11: Epithelial cells; Hematopoietic_12 and Hematopoietic_14: Hematopoietic cells. C. Heatmap (left panel) and correlation matrix (right panel) in GSE47460 of genes included in the signature derived from the \u2018Total lung cell suspension\u2019 (shown in panel A) dataset across each cluster shown in panel A. D. Heatmap (left panel) and correlation matrix (right panel) in GSE47460 of genes included in the signature derived from the \u2018Lineage sorted\u2019 (shown in panel B) dataset across each cluster shown in panel B.Clustering was performed using R package Seurat and cell types were identified using known markers. A. Total lung cell suspension. SPP1_monocytes_0: SPP1(ZIP)Click here for additional data file.S4 FigA. Expression of various B cell, plasma cell and myeloid markers in GSE47460 (Kaminski-LGRC bulk expression cohort) \u201317 subse(TIF)Click here for additional data file.S5 Fig+ monocytes/macrophages; C1QA_mac_4, C1QA_mac_5, C1QA_mac_9, C1QA_mac_12: C1QA+ macrophage subpopulations; Mono_7, Mono_21: Monocyte subpopulations; Tc_8: cytotoxic T cells; Th_10: helper T cells; AT1_11, MUC5Bpos_AT1_15, Basal_AT1_17: Alveolar epithelial cell type I subpopulations; ACKR1_pos_endo_14: ACKR1+ endothelial cells; ACKR1_neg_endo_16 and ACKR1_neg_endo_20: ACKR1- endothelial cell subpopulations; Diff_cil_18: Differentiating ciliated epithelial cells; Fibroblasts_19 and Fibroblasts_23: Fibroblast subpopulations; Sm_26: smooth muscle; Prolif_mac_22: Proliferating macrophages; Ly_endo_24: Lymphatic endothelium; Bcells_25: B cells; PC_28: Plasma cells; MC_27: mast cells; Mesothelial_31: mesothelial cells. B. Heatmap (left panel) and correlation matrix (right panel) in GSE47460 (Kaminski-LGRC bulk expression cohort) of genes included in the signature derived from the dataset shown in panel A.A. Cell type labels used based on re-analysis of IPF and healthy control data from GSE135893 (Kropski-Vanderbilt Univ single cell cohort) . Cluster(ZIP)Click here for additional data file.S6 FigOnly cell types with relevance to subsetting are shown. Nomenclature of cell types follows (TIF)Click here for additional data file.S7 FigA. Non-hematopoietic populations from B. Hematopoietic populations from C. Cell populations from Only cell types with relevance to subsetting shown. Nomenclature of cell types follows (ZIP)Click here for additional data file.S8 Fig+ monocytes/macrophages; Ciliated_1, Ciliated_3 and Ciliated_28: Ciliated epithelial cell subpopulations; C1QA_mac_2 and C1QA_mac_6: C1QA positive macrophage subpopulations; AT1_4, AT1_9, AT1_11, AT1_26: Alveolar epithelial cell type I subpopulations; AT2_5 and AT2_24: Alveolar epithelial cell type II subpopulations; ACKR1pos_endo_7: ACKR1+ endothelial cells; Monocytes_8: monocytes; Th10: helper T cells; Macs_12, Macs_22 and Macs_27: Macrophage subpopulations; Tc_13: cytotoxic T cells; HAS1_fibro_14: HAS1 positive fibroblasts; Diff_ciliated_15: differentiating ciliated epithelial cells; ACKR1neg_endo_16: ACKR1- endothelial cells; Fibroblasts_17 and Fibroblasts_29: Fibroblast subpopulations; Prolif_macs_18: Proliferating macrophages; Ly_endo_19: Lymphatic endothelium; Sm_20: smooth muscle; Bcells_21: B cells; PC_23: Plasma cells; MC_25: Mast cells. B. Differences in the percentage of Ciliated cells, Total myeloid cells and Endothelial cells between \u2018Ciliated_low\u2019 and \u2018Ciliated_high\u2019 subsets in GSE135893 (Kropski-Vanderbilt Univ single cell cohort) [A. IPF samples in GSE135893 (Kropski-Vanderbilt Univ single cell cohort)  divided (TIF)Click here for additional data file.S9 FigSize of circle indicated percent of cells gene on x axis is expressed in; color represents relative expression level. Nomenclature of cell clusters follows (TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file."}
+{"text": "Circular RNAs (circRNAs) belong to noncoding RNAs and are widely expressed in a variety of cell species, including cancers. However, the function and mechanism of circRNAs in colorectal cancer (CRC) has not been well investigated. Here, we firstly downloaded and analyzed the circRNA expression profile of CRC from the Gene Expression Omnibus (GEO) database. And we identified 181 differentially expressed circRNAs between 10 pairs of CRC and adjacent normal tissues. Interestingly, we observed that the expression of hsa_circRNA_000166 was the top increased among these circRNAs. Then, we confirmed an upregulation of hsa_circRNA_000166 in CRC tissues and cell lines and observed that higher expression of hsa_circRNA_000166 was associated with poor 5-year survival rate of patients with CRC. Next, we investigated the function of hsa_circRNA_000166 during CRC progression by knocking down its expression. Cell growth and apoptosis assay revealed that hsa_circRNA_000166 regulated the cell growth and apoptosis in CRC cell lines. Furthermore, we identified that hsa_circRNA_000166 targeted the miR-326/LASP1 pathway using bioinformatics analysis and luciferase reporter assay. Finally, suppression of miR-326 or overexpression of LASP1 could sufficiently rescue the aberrant cell growth and apoptosis in CRC cell lines. Taken together, our results indicated that downregulation of hsa_circRNA_000166 inhibited the cell growth and facilitated apoptosis during CRC development by sponging the miR-326/LASP1 pathway. Colorectal cancer (CRC) is one of the malignant cancers with the highest incidence and the fourth leading cause of cancer-related mortality around the world . RecentlCircular RNAs (circRNAs), a subgroup of noncoding RNAs, have a crucial role in regulating gene expression and function in distinct biological processes \u201312. DiffIn our study, we downloaded the circRNA expression profile of CRC from the Gene Expression Omnibus (GEO) database. After analysis, we identified 181 differentially expressed circRNAs and observed a top overexpression of hsa_circRNA_000166 among them. Subsequently, we revealed the ectopic expression of hsa_circRNA_000166 in CRC tissues and cell lines, which was associated with the 5-year survival rate of patients with CRC. Next, we knocked down the expression of hsa_circRNA_000166 using small interfering RNA (siRNA) to explore the potential roles of hsa_circRNA_000166 during CRC progression. We observed that hsa_circRNA_000166 inhibited the cell proliferation and promoted apoptosis in CRC cell lines. Moreover, we evidenced that hsa_circRNA_000166 directly regulated the miR-326/LASP1 pathway and the aberrant cell growth and apoptosis could be rescued after forced expression of miR-326 in CRC cell lines. In summary, our findings revealed that hsa_circRNA_000166 promoted the cell growth and repressed apoptosis via inducing the miR-326/LASP1 pathway during CRC tumorigenesis, which might be a promising candidate for diagnostic and therapeutic application in CRC treatment.The CRC tissues and adjacent normal colon tissues were obtained from Inner Mongolia Medical University Affiliated Hospital between 2015 and 2018. Totally, 40 pairs of tissues were analyzed in the study. Patients with CRC did not experience systemic treatment of chemotherapy or radiotherapy before surgery. All of the patients had got the written informed consent. The study followed the ethics committee of Inner Mongolia Medical University Affiliated Hospital guidance. All specimens were stored at \u221280\u00b0C until use.2 at 37\u00b0C, the incubation of the cell lines mentioned above was performed. We purchased the cell lines from the Institute of Biochemistry and Cell Biology at the Chinese Academy of Sciences .We cultured CRC cell lines SW1116, DLD-1, HCT116, SW480, and SW620 and human normal colonic epithelial cells HCoEpiC in minimum essential medium (MEM)  with 1% Glutamax , 1% Nonessential Amino Acids, 100x , and 10% fetal bovine serum (FBS). In a humidified atmosphere containing 5% COhttps://www.ncbi.nlm.nih.gov/geo/), a publicly available genomics database, is queried for all datasets. We downloaded the dataset of CRC, which was the circRNA expression profile from GEO. The selected dataset was in accordance with the following criteria. (1) They employed CRC tissue samples. (2) They took the adjacent normal tissues as control. (3) They utilized information on technology and platform.Gene Expression Omnibus (GEO) , following the manufacturer's instructions. For quantitative real-time PCR (qRT-PCR), the reverse transcription kit  was used to reverse transcribe total RNA into cDNA according to the manufacturer's protocol, while a stem-loop RT-qPCR method was used to generate miRNAs. qRT-PCR was conducted in ABI StepOnePlus\u2122 real-time PCR system . U6 and GAPDH were applied as internal controls. The gene-specific primers are listed in The siRNA-negative control (NC), siRNA-1, siRNA-2, siRNA-3, miR-NC, miR-326 mimics, miR-326 inhibitor (miR-326 I), hsa_circRNA_000166 wild-type (WT) plasmid, hsa_circRNA_000166 mutant (Mut) plasmid, LASP1 wild-type (WT) plasmid, mutant (Mut) plasmid, and LASP1 overexpression plasmid were constructed by GenePharma . According to the manufacturer's instructions, we transfected the plasmids into HCT116 and SW480 cells using the Lipofectamine 2000 Transfection Reagent (Invitrogen).The Cell Counting Kit-8 (CCK-8) assay was used to detect cell growth of HCT116 and SW480 cells. Each group was incubated with a density of 104 cells in 96-well plates. Cells in each well were incubated which lasted for 2\u2009h at 1, 2, and 3 days with CCK-8 reagent . We measured the optical density at 450\u2009nm using an automatic microplate reader .We seeded the transfected cells into 6-well plates and cultured for 14 days and then fixed the cells with methanol and stained them with 0.5% crystal violet (Beyotime Biotechnology) for 30\u2009min. Colonies with more than 10 cells were counted under a light microscope.For apoptosis detection, the HCT116 and SW480 cells were transfected with different plasmids for 24 hours (h) before collection. Then, we used an Annexin V-FITC/PI apoptosis detection kit (Invitrogen) to label the HCT116 and SW480 cells with Annexin V and PI. Flow cytometry  was used to detect and analyze the fluorescence (FL1) and red fluorescence (FL2).http://www.circbase.org). starBase v2.0 (http://starbase.sysu.edu.cn) and CircInteractome (https://circinteractome.nia.nih.gov) were utilized to predict the binding sites between hsa_circRNA_000166 and miRNAs.We obtained the sequence of hsa_circRNA_000166 from circBase  to transfect the luciferase reporter with hsa_circRNA_000166 WT with wild binding site (CCCAGAG) and hsa_circRNA_000166 Mut with mutant binding site (GGGUCCU) into the HCT116 and SW480 cells. The firefly luciferase activity was detected at 48\u2009h after transfection using the Dual Luciferase Reporter Assay system (Promega).For protein isolation after transfection, cells were lysed in the RIPA buffer . The SDS-PAGE gel assay was utilized to separate the proteins, and then, we transferred the separated proteins onto nitrocellulose membranes . Primary antibodies were used to incubate the membranes overnight at 4\u00b0C, followed by washing the membranes for 5 times using phosphate-buffered saline supplemented with Tween 20 (PBST). Subsequently, the corresponding horseradish peroxidase-conjugated secondary antibodies (Santa Cruz) were used to incubate the membranes for 2\u2009h at room temperature. Finally, the SuperSignal West Femto kit  was utilized to bring the bands on the membranes into visualization in the final. The primary antibodies and secondary antibody were used as follows: rabbit anti-LASP1 , rabbit anti-GAPDH , and goat anti-rabbit IgG H&L (HRP) . We used GAPDH as the endogenous control in this assay.t-test and shown as the mean \u00b1 SD; \u2217P < 0.05, \u2217\u2217P < 0.01, and \u2217\u2217\u2217P < 0.001.For significant difference analysis, GraphPad Prism 5.0 software was used to perform all the data. All results were analyzed using the two-tailed Student We downloaded the circRNA expression microarray dataset GSE126094 associated with colorectal cancer from the Gene Expression Omnibus (GEO) database and normalized . Then, wTo figure out the potential function of hsa_circRNA_000166 during CRC development, we firstly detected the expression level of hsa_circRNA_000166 in CRC tissues and adjacent normal colonic tissues. And we observed the overexpression of hsa_circRNA_000166 in CRC tissues compared with the matched normal tissues using qRT-PCR assay . Then, wDownregulation of hsa_circRNA_000166 suppressed cell growth and promoted apoptosis in CRC cells.According to the overexpression of hsa_circRNA_000166 in CRC cells, we knocked down the transcriptional level of hsa_circRNA_000166 using small interfering RNA (siRNA) to study the role of hsa_circRNA_000166 during CRC tumorigenesis. After transfection with siRNAs, we observed that the expression of hsa_circRNA_000166 was obviously decreased in siRNA-1- and siRNA-2-treated CRC cells and no significant changes in siRNA-3-treated CRC cells compared with controls by qRT-PCR analysis , which cTo elucidate the mechanism of hsa_circRNA_000166 in controlling CRC cell proliferation and apoptosis, we predicted that miR-326 was the candidate target of hsa_circRNA_000166 using the target prediction tool. Firstly, the Venn analysis between the predicted target miRNAs of hsa_circRNA_000166 and differential expressed miRNAs in CRC cells indicated that 3 miRNAs, containing miR-326, were involved in CRC using starBase and CircInteractome . To certTo further confirm that miR-326 and LASP1 mediated the function of hsa_circRNA_000166 during CRC development, we conducted codepletion of both siRNA-1 and miR-326 I or depletion of siRNA-1 while there was overexpression of LASP1 and inspected the role of miR-326 and LASP1 in the regulation of hsa_circRNA_000166 in CRC development. CCK-8 and colony formation assay demonstrated that miR-326 downregulation or LASP1 overexpression in siRNA-1-transfected cells could restore cell growth compared with the controls in HCT116 cells Figures . CorrespColorectal cancer (CRC) is one of the solid tumors with a higher mortality among cancer-related deaths worldwide. Though advanced surgery technologies and medicine treatments have been applied in treating patients with CRC, the survival rate of patients with CRC is still poor. Therefore, there is an urgent demand for understanding the mechanisms underlying the development of CRC. In the past decades, circRNAs are discovered to be a subgroup of noncoding RNAs and play an essential role in regulating gene expression and function associated with cancers \u201319. In tGenerally, circRNAs are sponging miRNAs to play its function in multiple biological processes, including tumorigenesis \u201323. PrevIn this study, we used bioinformatics analysis to screen the GSE126094 dataset in CRC and identified that hsa_circRNA_000166 was the top 1 among all upregulated circRNAs. We further confirmed the overexpressions of hsa_circRNA_000166 in CRC tissues and cell lines and found that 5-year survival rate of CRC patients was highly related to the expression levels of hsa_circRNA_000166. Importantly, we confirmed that the miR-326/LASP1 pathway functions downstream of hsa_circRNA_000166 for the circRNA function during CRC progression. Together, our findings manifested that hsa_circRNA_000166 had a vital role in regulating CRC tumorigenesis, which implied that hsa_circRNA_000166 had a promising value in early diagnosis and prevention of CRC."}
+{"text": "In this paper, we consider the application of the matching pursuit algorithm (MPA) for spectral analysis of non-stationary signals. First, we estimate the approximation error and the performance time for various MPA modifications and parameters using central processor unit and graphics processing unit (GPU) to identify possible ways to improve the algorithm. Next, we propose the modifications of discrete wavelet transform (DWT) and package wavelet decomposition (PWD) for further use in MPA. We explicitly show that the optimal decomposition level, defined as a level with minimum entropy, in DWT and PWD provides the minimum approximation error and the smallest execution time when applied in MPA as a rough estimate in the case of using wavelets as basis functions (atoms). We provide an example of entropy-based estimation for optimal decomposition level in spectral analysis of seismic signals. The proposed modification of the algorithm significantly reduces its computational costs. Results of spectral analysis obtained with MPA can be used for various signal processing applications, including denoising, clustering, classification, and parameter estimation. Most of the real signals  are non-stationary. Processing of such signals includes denoising, randomness degree estimation, short-term local features extraction, filtering, etc. Despite the fact that the processing of non-stationary signals has been studied for a long time (wavelets were described in the late 1980s), there are several unsolved problems, as follows: Working in conditions of a priori uncertainty of signal parameters, processing complex non-stationary signals with multiple local features, and multi-component signal analysis ,3. Current advances in applied mathematics and digital signal processing, along with the development of high-performance hardware, allow the effective application of numerous mathematical techniques, including continuous and discrete wavelet transforms. Wavelets are an effective tool for signal processing due to their adaptability, availability of fast computational algorithms, and diversity of wavelet bases.Detection of foreign objects, size estimation of these objects, hazard assessment based on analyzing local signal features;Signal detection and classification based on the analysis of local signal features;Signal detection in the presence of background noise; andEfficient signal visualization and processing based on multiscale wavelet spectrograms.For example, using wavelets for non-stationary signal analysis provides the following possibilities :DetectioWavelet transform uses wavelets as basis functions. An arbitrary function can be obtained from one function (\u201cmother\u201d wavelet) by using translations and dilations in the time domain. The wavelet transform is commonly used for analyzing non-stationary signals, usually together with various spectral analysis algorithms. The topic of this study is the matching pursuit algorithm (MPA), which is common technique of spectral analysis . This alOne of the modifications of MPA was developed for geoacoustic emission signals. The geoacoustic emission signals consist of a sequence of impulses whose shape is close to the modulated Berlage functions. It is possible to use the sparse approximation method to analyze this type of signal. This method decomposes the signal into the sum of the modulated Berlage functions describing the impulses and the modulated Gaussian functions approximating the noise. Lukovenkova et al.  developeTwo of the basic prototypes of greedy optimization are abovementioned MPA and the Frank-Wolfe method. Locatello, in Reference , took a Marapulets et al.  providedThe vibration signal measured from the mechanical equipment is associated with the operation of the key structure, such as the rolling bearing and gear. The development of effective signal processing methods for early fault detection has attracted a lot of academic attention. Such methods are of vital importance in reliability monitoring and fault diagnosis . The newThe problem of overfitting arises due to the high dimension of the system, making regularization vital. Although classic regularizations provide sparsity, they fail to return highly accurate models. On the contrary, state-of-the-art group-lasso regularizations provide better results, but the cost is the low sparsity. In Reference , KonstanA review of relevant sources allows us to conclude that the main problems of the MPA are basis function selection and dictionary optimization. Since the choice of basis functions is made either empirically or by a priori information on the spectral composition of the signal, the focus of our study is the optimization procedures. We discuss a new way to optimize the vocabulary by automatically determining an optimal level of wavelet decomposition based on the entropy analysis. We define an optimal dictionary as a dictionary with a maximum correlation to the analyzed signal, compared to other dictionaries. We also define the optimal decomposition level as the level with minimal entropy when using DWT and PWD. When applying in MPA as a rough estimate in case of using wavelets as basis functions (atoms), the optimal decomposition level provides the minimum approximation error and the smallest execution time. The last of the paper is organized as follows: In Let We search for the vector For lgorithm .The main difference between OMP and BMP is that, after every OMP step, all the coefficients extracted so far are updated by computing the orthogonal projection of the signal onto a set of currently selected atoms. This can lead to better results than BMP, but requires more computations.The choice of vector Steps If Otherwise The final decomposition is as follows :(4)f=\u2211n=The algorithm is as follows:f that is in the space spanned by the dictionary and the error One can use several criteria  for stopThe main drawback of the MPA is its computational complexity and the main advantage is the possibility of choosing an arbitrary basis. MPA can be applied to signal, image, and video processing, shape representation and recognition, 3D objects coding, and in interdisciplinary applications like structural health monitoring .To estimate the computational complexity of MPA we performed the experiments using noised data with a default basis that includes several harmonic functions and wavelets. The simulation was performed on a PC under Windows 10 64-bit with CPU Intel Core i7 Skylake 4.0 GHz, RAM DDR4 Kingston HyperX Fury 64 GB 2.4 GHz, GPU NVIDIA GeForce GTX 1080 1.7 GHz DDR5 8 GB 10 GHz, and CUDA kernel 2560 with MATLAB R2018b 64-bit software.N is signal length, The test signal is non-stationary, as follows:j, n, k), where j = 0 \u2026 J is the decomposition level ; J is determined by the expression The basis wavelet functions can be determined by a triplet  optimal tree. All of them are based on the introduction of a function (\u201centropy\u201d), which allows estimation of the informativeness of a set of coefficients. The methodology is as follows. First, a complete decomposition tree is built, and then pairs of nodes with a common root are analyzed from the bottom up. If entropy does not decrease from root to node, this pair is replaced by the root. The simplified variant is to choose the optimal level, i.e., the height of the complete tree, at which the entropy is minimal ,18.The main problem in DWT and PWD applications is the determination of the decomposition level which determines computational costs . To solvEntropy is a common indicator characterizing the degree of uncertainty. Larger entropy values correspond to the greater randomness of the system and the smaller ones indicate the ordering of the system ,18.s, and its coefficients of expansion in some orthonormal basis, Let there be a signal, 1.2.Shannon EntropyLog EnergyThreshold entropySURENormThere are several known formulas for entropy, as follows:f, reflects the number of essential coefficients in the expansion. If we have a set of orthonormal bases, we can choose the one that provides the lowest entropy.The entropy of a function, We propose to do the decomposition recursively on one level for the low-frequency component (approximation coefficients) in the case of DWT and for low-frequency/high-frequency components (approximation and detail coefficients) in the case of a PWD. In each recursive call, we compare the entropy of the derived descendant nodes with the entropy of the ancestor node.The entropy difference, as follows:Saturation condition:Increase condition:E denotes entropy and Decrease condition:If the entropy went into saturation (minimum is achieved), or if the limiting decomposition level is reached, decomposition stops and we thus find all the coefficients and the optimal decomposition level. The form of entropy, the limiting level, and the accuracy of the expansion are set.N is signal length and Let us test the given approach by applying it to the processing of real seismic signals. The experiments have been carried out using MATLAB and seismic signals from the MATLAB library . Figure N = 2048, Shannon entropy curves for wavelet  basis (c) using the optimal decomposition level X = 8, the execution time for wavelet (Daubechies) basis (d) using the optimal decomposition level X = 8, the approximation error for extended basis (e) using the optimal decomposition level X = 8, and the execution time for extended basis (f) using the optimal decomposition level X = 8.N = 2048, entropy (log energy) curves for wavelet (Daubechies) basis (b) with the estimated optimal decomposition level X = 8, the approximation error for wavelet (Daubechies) basis (c) using the optimal decomposition level X = 8, the execution time for wavelet (Daubechies) basis (d) using the optimal decomposition level X = 8, the approximation error for extended basis (e) using the optimal decomposition level X = 8, and the execution time for extended basis (f) using the optimal decomposition level X = 8.N = 2048, entropy  curves for wavelet (Daubechies) basis (b) with the estimated optimal decomposition level X = 7, the approximation error for wavelet (Daubechies) basis (c) using the optimal decomposition level X = 7, the execution time for wavelet (Daubechies) basis (d) using the optimal decomposition level X = 7, the approximation error for extended basis (e) using the optimal decomposition level X = 7, and the execution time for extended basis (f) using the optimal decomposition level X = 7.N = 2048, entropy  curves for wavelet  basis (c) using the optimal decomposition level X = 7, the execution time for wavelet (Meyer) basis (d) using the optimal decomposition level X = 7, the approximation error for extended basis (e), and the execution time for extended basis (f) using the optimal decomposition level X = 7.N = 2048, entropy  curves for wavelet (Meyer) basis (b) with the estimated optimal decomposition level X = 7, the approximation error for wavelet (Meyer) basis (c) using the optimal decomposition level X = 7, the execution time for wavelet (Meyer) basis (d) using the optimal decomposition level X = 7, the approximation error for extended basis (e) using the optimal decomposition level X = 7, and the execution time for extended basis (f) using the optimal decomposition level X = 7.The entropy concept comes from the information theory (Shannon entropy). In the Shannon formula, probabilities are used to calculate the entropy  the minus sign in front of the sum sign is to compensate for the negative value resulting from the probabilities logarithm. However, in the case of using the entropy criteria in digital signal processing, the signal samples in the general case can go beyond the range 0\u20131 and, therefore, the logarithm will not give a negative result, so the Shannon entropy will be negative. Depending on the use of one or another entropy criterion, we look for the minimum or maximum of its value (minimum modulus).In From Execution time depends on the decomposition basis  .Approximation error depends on the decomposition basis  .Using the wavelet basis provides a gain in time, but increases the approximation error c\u2013d.Using the extended basis reduces the approximation error, but eliminates the time gain e\u2013f.There are some known approaches to construct an optimal decomposition tree based on entropy in various applications ,21. To oThe various results obtained for different bases and entropy criteria can be explained by the properties of the mother wavelets and the different entropy formulas. There is no straight dependence of the results of the proposed method on the choice of the type of entropy.The proposed approach has no specificity and can be used in the processing of various non-stationary signals .The MPA has been studied for spectral analysis applications and its software implementation has been performed for signals with different parameters. The approximation accuracy and execution time of the algorithm were estimated using CPU and GPU platforms.The novel modifications of DWT and PWD have been proposed for further implementation in MPA. We explicitly show that the optimal decomposition level  in DWT and PWD provides a minimal approximation error and the shortest execution time applied in MPA as a rough estimate in the case of using wavelets as basis functions.All of the entropy curves for the chosen range of the decomposition level  in MATLAB:function CF = wavedec_adaptive%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Adaptive DWT function% Input:% x-signal% wname-wavelet name% max_level-max decomposition level% entropy-entropy criterion% threshold-entropy threshold% epsilon-entropy decay rate% Output:% CF-decomposition coefficients\u2003%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%global level E_L E_R E_S E_RT lvleps = 10;\u2003 = dwt;\u2003% decomposition into 1 levelCF = [{A} {D}];\u2003\u2003\u2003\u2003% unionlevel = level + 1;\u2003\u2003\u2003% tree depth (stack)\u2003E_RT (level) = wentropy;\u2003\u2003% root entropyE_L(level) = wentropy;\u2003\u2003\u2003% left node entropyE_R(level) = wentropy;\u2003\u2003% right node entropyE_S(level) = E_L(level) + E_R(level);\u2003\u2003\u2003\u2003% total entropy\u2003if level ==1\u2003\u2003delta = E_L(level)-E_RT(level);else\u2003\u2003delta = E_L(level)-E_L(level-1);end\u2003if(level > = max_level || (delta > 0 && abs(delta) > eps) || abs(delta) < = eps)\u2003% stop criterion\u2003\u2003lvl = level;\u2003\u2003level = level-1;\u2003\u2003% tree depth (stack)\u2003\u2003return;end\u2003CF = [wavedec_adaptive {D}]; % further decompositionlevel = level-1;\u2003\u2003\u2003% tree depth (stack)\u2003end\u2003\u2003function [CF] = wpdec_adaptive\u2003%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Adaptive PWD function% Input:% x-signal% wname-wavelet name% max_level-max decomposition level% entropy-entropy criterion% threshold-entropy threshold% epsilon-entropy decay rate% Output:% CF-decomposition coefficients\u2003%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\u2003global level E_L E_R E_S E_RT lvleps = 10;\u2003 = dwt;\u2003\u2003% decomposition into 1 levelCF = [{A} {D}];\u2003\u2003\u2003\u2003% union\u2003level = level+1;\u2003\u2003\u2003\u2003% tree depth (stack)\u2003E_RT (level) = wentropy;\u2003\u2003% root entropyE_L(level) = wentropy;\u2003\u2003\u2003% left node entropyE_R(level) = wentropy;\u2003\u2003% right node entropyE_S(level) = E_L(level) + E_R(level);\u2003\u2003\u2003\u2003% total entropy\u2003if level == 1\u2003\u2003delta = E_L(level)-E_RT(level);else\u2003\u2003delta = E_L(level)-E_L(level-1);end\u2003if(level > = max_level || (delta > 0 && abs(delta) > eps) || abs(delta) < = eps)\u2003% stop criterion\u2003\u2003level = level-1; \u2003\u2003% tree depth (stack)\u2003\u2003return;end\u2003CF1=wpdec_adaptive; % further decomposition\u2003if level == 1\u2003\u2003delta = E_R(level)-E_RT(level);else\u2003\u2003delta = E_R(level)-E_R(level-1);end\u2003if(level > = max_level || (delta > 0 && abs(delta) > eps) || abs(delta) < = eps) % stop criterion\u2003\u2003level = level-1;\u2003\u2003\u2003% tree depth (stack)\u2003\u2003return;end\u2003CF2 = wpdec_adaptive; % further decomposition\u2003CF = [CF1 CF2];level = level-1;\u2003\u2003\u2003% tree depth (stack)\u2003end"}
+{"text": "Anser cygnoides) and the graylag goose (Anser anser), we selected a female Tianfu goose for genome sequencing. We generated a chromosome-level goose genome assembly by adopting a hybrid de novo assembly approach that combined Pacific Biosciences single-molecule real-time sequencing, high-throughput chromatin conformation capture mapping, and Illumina short-read sequencing.The domestic goose is an economically important and scientifically valuable waterfowl; however, a lack of high-quality genomic data has hindered research concerning its genome, genetics, and breeding. As domestic geese breeds derive from both the swan goose  accounting for \u223c88.36% of the goose genome. Compared with previous goose assemblies, our assembly has more continuity, completeness, and accuracy; the annotation of core eukaryotic genes and universal single-copy orthologs has also been improved. We have identified 17,568 protein-coding genes and a repeat content of 8.67% (96.57 Mb) in this genome assembly. We also explored the spatial organization of chromatin and gene expression in the goose liver tissues, in terms of inter-pseudo-chromosomal interaction patterns, compartments, topologically associating domains, and promoter-enhancer interactions.We present the first chromosome-level assembly of the goose genome. This will be a valuable resource for future genetic and genomic studies on geese. Anser cygnoides) and the graylag goose (Anser anser) [The goose is a member of the family Anatidae and is an economically important waterfowl with distinctive characters. Domesticated geese derive from the swan goose (r anser) , and \u223c6,r anser) . Currentr anser) . The domr anser) . Its excde novo assembly approaches to improve the quality of genome assemblies. This is because short, accurate reads from the Illumina platform can be combined with the longer, less accurate reads generated by the single-molecule real-time (SMRT) sequencing platform [Aedes aegypti [The goose was one of the earliest animals to be domesticated , 7, and platform . With Hiplatform . This applatform , goats [platform , rockfisplatform , Aedes a aegypti , and bar aegypti .de novo assembly approach using a combination of short reads from the Illumina platform, long reads from the Pacific Biosciences (PacBio) platform, and Hi-C\u2013based chromatin interaction maps. Our chromosome-level goose genome comprises longer scaffolds than currently available goose genome assemblies, and these scaffolds are of a higher quality and are more continuous and accurate. Our new genome assembly thus provides a valuable resource for exploring the molecular basis of the morphological and physiological features of the goose and will facilitate further genomic, genetic, and breeding studies of this domesticated waterfowl.Here, we have generated a chromosome-level goose assembly with chromosome-length scaffolds by adopting a hybrid We extracted genomic DNA from the liver tissue of a healthy adult female (136 days old) from the Tianfu goose maternal line (NCBI:txid381198), which was provided by the Experimental Farm of Waterfowl Breeding of Sichuan Agricultural University  [RRID:SCR_002942) software to assemble the genome sequence, which resulted in a draft assembly with a contig N50 of 1.72 Mb (RRID:SCR_005056) [RRID:SCR_012091) [RRID:SCR_014731) [RRID:SCR_017644) [The size of the goose genome was estimated by  1.72 Mb . Next, w_005056)  and PBJe_012091) , respect_014731)  software_017644) . With thDe novo methods and homology-based approaches were used to annotate the repeat content of the goose genome. First, we used ab initio prediction software, including LTR-finder  [RRID:SCR_015027) [RRID:SCR_014653) [de novo annotation of the genome. For homology-based predictions, we identified repeat regions across species in published RepBase sequences [RRID:SCR_012954) [RRID:SCR_012954) [ab initio\u2013based, homology-based, and RNA sequencing (RNA-seq)-based prediction methods. First, GenScan  [RRID:SCR_002473) [RRID:SCR_008417) [ab initio\u2013based predictions. Next, we selected 6 chromosome-level genomes, namely, Homo sapiens (GCF_000001405.39), Mus musculus (GCF_000001635.26), Gallus gallus (GCF_000002315.6), Anas platyrhynchos (GCF_003850225.1), Meleagris gallopavo (GCF_000146605.3), and Taeniopygia guttata (GCF_003957565.1), to use for homology-based annotation of our goose chromosome-level assembly genome using TBLASTN  [RRID:SCR_015054) [RRID:SCR_013048) [RRID:SCR_013035) [RRID:SCR_014659) [RRID:SCR_015899) [RRID:SCR_014597) [RRID:SCR_002764) [RRID:SCR_004726) [ab initio assembly method and 542 transcripts of uncertain coding potential (TUCP) were identified; the long reads will be helpful to improve the identification and annotation of the lncRNA and TUCP in goose genome._005659) , RepeatM_015027) , and Rep_014653) , to perfequences  using Re_012954)  and Repe_012954) , 9. Long_012902) , Geneid _008417)  were use_015054)  software_013048)  and TopH_013035) . Finally_015899)  and subj_014597)  and TACOos GCF_0050225.1, Our assembly has more scaffolds and fewer contigs, and significantly improved contig and scaffold N50 values, than the goose genome assemblies presented in 2 previous studies Fig.\u00a0 and exonRRID:SCR_015055) and using a set of BUSCO . We found that 211 of the 248 (85.08%) core eukaryotic genes and 2,586 (97%) of the universal single-copy orthologs were assembled in our genome. Compared with previous studies, this suggests that our genome assembly is more complete than previous drafts of the goose genome [To evaluate the completeness of our chromosome-level genome assembly, we determined the number of conserved eukaryotic and universal genes present in our assembly by applying the CEGMA software  searches of our newly assembled goose genome. We found no sequences similar to leptin in our chromosome-level goose assembly. Furthermore, although the human genome region that contains the leptin gene  aligned with the goose genome, we did not find a sequence similar to the leptin gene in this region. These results confirm the previous finding that the leptin gene is not present in the goose genome [To explore the hypothesis that the leptin gene was lost from goose , we downe genome .RRID:SCR_007839) [RRID:SCR_006086) [P = 3.85 \u00d7 10\u221224), G protein-coupled receptor activity , and integral component of membrane  , 16,157 _006086) . This reRRID:SCR_018924) [P = 8.58 \u00d7 10\u221251), G protein-coupled receptor activity , and integral component of membrane , which is consistent with the results from our analysis of lineage-specific genes (P = 1.96 \u00d7 10\u221206), NAD(P)+-protein-arginine ADP-ribosyl transferase activity , ATPase activity , and aspartic-type endopeptidase activity , ion channel activity , ion transmembrane transport , and ATPase-coupled intramembrane lipid transporter activity  . We founic genes . This fuRRID:SCR_004542) (GCH1 (GTP-cyclohydrolase I), are associated with Parkinsonism, dystonia, and phenylketonuria disease in humans [GCH1 divergence between human populations living at different altitudes [GCH1 in goose is likely to be related to their adaption to high-altitude or migratory habitats. SNW1 (SNW1 domain containing 1) is involved in the nuclear factor \u03baB pathway and is associated with oculopharyngeal muscular dystrophy disease [SNW1 in goose suggests that it may confer protection from diseases and aid adaptation in changeable environments. POU2F3 (POU domain class 2 transcription factor 3) is pivotal in the discrimination of taste qualities, such as sweet, umami, and bitter characteristics. Deficiency in this gene in mice alters their electrophysiology and behavioral responses to taste characters [POU2F3 in goose is likely to be related to a requirement for seeking food in variable migratory habitats.We identified 52 positively selected genes (PSGs) in the goose genome based on orthologous genes from the 17 aforementioned species, using a branch-site model and F3 \u00d7 4 codon frequencies in Codeml  . Some ofn humans , 50. Theltitudes . Selecti disease . The deparacters , 56. SelR = 0.39, P = 2.2 \u00d7 10\u221216; P = 2.2 \u00d7 10\u221216; We analyzed the inter-pseudo-chromosomal interaction pattern , compartWTSS00000000; the high-quality Hi-C data are available through the NCBI SRA database under accession No. SRR10483522. The PacBio long-read sequencing data have been deposited in the NCBI SRA (SRR10483521). The high-quality Illumina short-read sequencing data are available through NCBI SRA accession Nos. SRR10483516, SRR10483517, SRR10483518, and SRR10483520. The transcriptome data are available through NCBI SRR10483519. The chromosome-level goose genome assembly, annotation files, and other supporting data are available via the GigaScience GigaDB database [The chromosome-level goose genome assembly sequence is available at NCBI GenBank through accession No. database .Supplementary Figure S1. The Hi-C interaction contact heatmap of goose pseudochromosome genome assembly (bin size is 1Mb).Supplementary Figure S2. The shared homologous gene families across the six species .Supplementary Figure S3. The distribution of gene density in the goose genome. Number of PCGs in each 1Mb bins was counted.Supplementary Figure S4. Divergence of time and the expansion, contraction gene families in the seventeen species.Supplementary Figure S5. Resolution evaluation showing that the Hi-C data attained 2 Kb.Supplementary Figure S6. Vioplot of PC1 values in 100 Kb bins with various number of PCGs. PC1 value indicates the chromatin activity.Supplementary Figure S7. TPMs of PCGs located in A compartment were consistently higher than PCGs in B compartment both at 25 Kb and 100 Kb resolution.Supplementary Figure S8. TAD distribution across the goose genome assembly.Supplementary Figure S9. TSSs of PCGs were enriched in TAD boundary regions.Supplementary Figure S10. Gene expression levels positively correlated with the number of its associated enhancers in all three liver tissues.Supplementary Table S1. Summary of the PacBio initial assembly and Hi-C read mapping used for goose genome assembly process.Supplementary Table S2. Summary of the length of pseudo-chromosomes in goose genome.Supplementary Table S3. A comparative summary of assembled repeat content in this study and previous studies.Supplementary Table S4. Summary of the map rates of the wild goose resequencing data.Supplementary Table S5. Gene ontology (GO) enrichment analysis for the lineage-specific gene annotation in goose genome.Supplementary Table S6. Functional gene categories enriched for the goose genome\u2013specific expansion gene families.Supplementary Table S7. Functional gene categories enriched for the contraction of gene families in goose genome.Supplementary Table S8. Positively selected genes (PSGs) identified in the goose genome.Supplementary Table S9. The PC1 values (100 kb) through principal component analysis (PCA) and A-B index values (25 kb).Supplementary Table S10. TAD in genome coordinates of our goose genome by using method of Directionality Index values.Supplementary Table S11. Detailed information on promoter-enhancer interactions (PEIs) identified in goose genome.ATP: adenosine triphosphate; BLAST: Basic Local Alignment Search Tool; bp: base pairs; BUSCO: Benchmarking Universal Single-Copy Orthologs; CHMP1B: charged multivesicular body protein 1B; CEGMA: Core Eukaryotic Genes Mapping Approach; EVM: EVidenceModeler; Gb: gigabase pairs; GC: guanine-cytosine; GCH1: GTP cyclohydrolase 1; Hi-C: chromosome conformation capture; kb: kilobase pairs; LINE: long interspersed nuclear element; lncRNA: long noncoding RNA; Mb: megabase pairs; NCBI: National Center for Biotechnology Information; PacBio: Pacific Biosciences; PCG: protein-coding gene; PEI: promoter-enhancer interaction; POU2F3: POU domain class 2 transcription factor 3; PSG: positively selected gene; RAxML: Randomized Axelerated Maximum Likelihood; RNA-seq: RNA sequencing; SMRT: single-molecule real-time; SRA: Sequence Read Archive; TAD: topological associated domain; TPM: transcripts per kilobase million; TUCP: transcripts of uncertain coding potential.All animal experiments were approved and reviewed by the Animal Care and Use Committee Institutional of Sichuan Agricultural University  and the Ministry of Science and Technology of the People's Republic of China .The authors declare that they have no competing interests.M.L. and G.G. designed and supervised the project. Y. Li, Y. Lin, Q.T., and S.H. performed bioinformatics analyses. J.W., Y. Li, G.Wang, and Y. Luo contributed to collecting the samples. M.L., Q.W., G.G., Y. Luo, G.Wang, and L.J. were involved in the data analyses and wrote the manuscript.giaa114_GIGA-D-20-00133_Original_SubmissionClick here for additional data file.giaa114_GIGA-D-20-00133_Revision_1Click here for additional data file.giaa114_GIGA-D-20-00133_Revision_2Click here for additional data file.giaa114_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giaa114_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giaa114_Reviewer_1_Report_Original_SubmissionDavid Burt -- 6/2/2020 ReviewedClick here for additional data file.giaa114_Reviewer_1_Report_Revision_1David Burt -- 8/19/2020 ReviewedClick here for additional data file.giaa114_Reviewer_2_Report_Original_SubmissionMartien Groenen -- 6/10/2020 ReviewedClick here for additional data file.giaa114_Reviewer_2_Report_Revision_1Martien Groenen -- 8/18/2020 ReviewedClick here for additional data file.giaa114_Supplemental_FilesClick here for additional data file."}
+{"text": "The package facilitates the search of the Rfam database by keywords or sequences, as well as the retrieval of all available information about specific Rfam families, such as member sequences, multiple sequence alignments, secondary structures and covariance models. By providing such programmatic access to the Rfam database, rfaRm enables genomic workflows to incorporate information about non-coding RNA, whose potential cannot be fully exploited just through interactive access to the database. The features of rfaRm are demonstrated by using it to analyze the SARS-CoV-2 genome as an example case. The Rfam database  is a colThe database can be used to identify non-coding RNA elements within a nucleotide sequence of interest by searching it against the Rfam library of covariance models with the Infernal software . AdditioHere, we present a client-side interface to the Rfam database, enabling its programmatic access and therefore expanding the scope of the genomic analysis that can be carried out with the information provided by the Rfam database. The language of choice was R. This choice is based on the large number of tools already available in the Bioconductor project for the analysis of high-throughput genomic data. The software presented here complements these tools and facilitates the integration of the data retrieved via rfaRm within existing genomic workflows .rfaRm provides two types of functionalities: searches within the Rfam database, and retrieval of data associated to specific RNA families.In its current version, rfaRm allows two types of searches within the Rfam database: by keyword, and by sequence. In a keyword search, the user can provide a keyword that will be matched against family descriptions and identifiers. Matching families are returned as a list of Rfam accession numbers. In a sequence search, the user submits an RNA sequence and the list of RNA families present in this sequence is returned. While the current implementation of the Rfam web server allows for queries on sequences of up to 10,000 nucleotides, rfaRm imposes no limit on the length of sequences to be analyzed. Instead, if a sequence longer than 10,000 nucleotides is provided as input, it is internally split into smaller, overlapping fragments that are then used to perform individual searches. Found hits are mapped back into the original sequence before being returned to the user.Furthermore, rfaRm also provides a functionality analogous to the \u201cclan competition\u201d feature employed by the Rfam web server to ensure hit quality. If such functionality is enabled, groups (clans) of related Rfam families are defined. If two hits overlap by a user-defined length , and they belong to the same clan, only the hit with best score is kept. rfaRm allows clan competition to be disabled, which in some cases might be preferrable to identify nested non-coding RNA hits.After identifying a set of RNA families of interest, rfaRm allows to retrieve and plot different data about each family by providing their Rfam accession number or ID. The data that can be retrieved for each family include: a descriptive summary, the consensus sequence and secondary structure (in extended dot-bracket or WUSS notations) , the covR4RNA R package ]## Obtain the corresponding Rfam family IDrfamFamilyAccessionToID(rfamFamilyAccession = testAccession)Purpose. Converts an Rfam family ID to the corresponding family accession.Arguments.rfamFamilyID: string with the Rfam family ID to be converted to a family accession.Example of usage## Extract the Rfam family accession of the first hit## detected in the mitochondrial DNA of Ashbya gossypii$rfamIDtestID <- a_gossypii_MT_hits_clanCompetition[[1]]## Obtain the corresponding Rfam family IDrfamFamilyAccessionToID(rfamFamilyID = testID)Purpose. Retrieves a brief summary describing the specified Rfam family.ArgumentsrfamFamily: string with the Rfam family accession or ID for which a descriptive summary should be retrieved.Example of usage## Obtain a summary for the Rfam family with accession RF00005,## of which several instances were identified in the mitochondrial## DNA of Ashbya gossypii. It corresponds to tRNA.rfamFamilySummary(rfamFamily = \"RF00005\")Purpose. Retrieves the consensus secondary structure and sequence of the specified Rfam family.ArgumentsrfamFamily: string with the Rfam family accession or ID for which a descriptive summary should be retrieved.filename: optional string specifying the name of a file. If provided, the consensus secondary structure and sequence will be saved to the specified file.format: string indicating the notation to be used for the RNA secondary structure. It can be either \"DB\"  or \"WUSS\" (Washington University Secondary Structure notation).Example of usage## Obtain the consensus secondary structure and sequence## for the Rfam family with accession RF00005 (tRNA)## in the extended Dot-Bracket formatrfamConsensusSecondaryStructurePurpose. Plots a diagram of the specified type of the secondary structure of an Rfam family.ArgumentsrfamFamily: string with the Rfam family accession or ID for which the secondary structure should be plotted.filename: optional string specifying the name of a file. If provided, the plot will be saved to the specified file.plotType: string indicating the desired type of secondary structure diagram. Possible values are \u201cnorm\u201d , \u201ccons\u201d (sequence conservation), \u201cfcbp\u201d (basepair conservation), \u201ccov\u201d (covariation), \u201cent\u201d (relative entropy), \u201cmaxcm\u201d (maximum covariance model parse), \u201crscape\u201d  and \u201crscape-cyk\u201d (secondary structure predicted by R-scape).Example of usage## Generate a diagram of the secondary structure of## the Rfam family with accession RF00005 (tRNA), colored## by basepair conservationrfamSecondaryStructurePlotPurpose. Obtain an SVG file (in XML format) with a representation of the secondary structure of the specified Rfam family.ArgumentsrfamFamily: string with the Rfam family accession or ID for which an SVG file the secondary structure should be plotted.filename: string specifying the path to which the SVG file should be saved.plotType: string indicating the desired type of secondary structure diagram. Possible values are \u201cnorm\u201d , \u201ccons\u201d (sequence conservation), \u201cfcbp\u201d (basepair conservation), \u201ccov\u201d (covariation), \u201cent\u201d (relative entropy), \u201cmaxcm\u201d (maximum covariance model parse), \u201crscape\u201d  and \u201crscape-cyk\u201d (secondary structure predicted by R-scape).Example of usage## Save an SVG file with a diagram of the secondary structure## of the Rfam family with accession RF00005 (tRNA), colored## by sequence conservationrfamSecondaryStructureXMLSVGPurpose. Retrieves the seed multiple alignment of the specified Rfam family. The seed alignment is used to determine the covariance model defining each Rfam family, and comprises only a subset of all the members of each family.ArgumentsrfamFamily: string with the Rfam family accession or ID whose seed alignment should be retrieved.filename: optional string specifying a file to which the seed alignment will be saved if provided.format: string indicating the desired format for the seed alignment. Possible values are \u201cstockholm\u201d (standard Stockholm format), \u201cpfam\u201d , \u201cfasta\u201d (gapped FASTA format) and \u201cfastau\u201d (ungapped FASTA format).Example of usage## Obtain the seed alignment of the Rfam family with## accession RF00005 (tRNA) in the Stockholm format and## save it to a filerfamSeedAlignmentPurpose. Retrieves the phylogenetic tree of the seed multiple alignment associated to the specified Rfam family. The tree is retrieved in the NHX format (New Hampshire extended) and saved to a file.ArgumentsrfamFamily: string with the Rfam family accession or ID for which the phylogenetic tree of the seed alignment should be retrieved.filename: string specifying a file to which the phylogenetic tree will be saved.Example of usage## Obtain the phylogenetic tree of seed alignment of the## Rfam family with accession RF00005 (tRNA) and save it## to a filerfamSeedTreePurpose. Plots the phylogenetic tree of the seed multiple alignment associated to the specified Rfam family.ArgumentsrfamFamily: string with the Rfam family accession or ID for which the phylogenetic tree of the seed alignment should be plotted.filename: optional string specifying a file to which the plot of the phylogenetic tree will be saved if provided.label: string indicating the type of labels that should be added to the plot of the phylogenetic tree. Can be either \u201cspecies\u201d (for labeling with species names) or \u201cacc\u201d (for labeling with sequence accessions).Example of usage## Plot the phylogenetic tree of seed alignment of the## Rfam family with accession RF00005 (tRNA) labelled with## species namesrfamSeedTreeImagePurpose. Retrieves the covariance model of the specified Rfam family .ArgumentsrfamFamily: string with the Rfam family accession or ID for which the covariance model should be retrieved.filename: string specifying a file to which the covariance model will be saved.Example of usage## Retrieve the covariance model of the Rfam family with## accession RF00005 (tRNA) and save it to a filerfamCovarianceModelPurpose. Retrieves all sequence regions encoding an RNA assigned to be a member of the specified Rfam family.ArgumentsrfamFamily: string with the Rfam family accession or ID for which the member sequence regions should be retrieved.filename: optional string specifying the name of a file. If provided, the sequence regions will be saved to the specified file in tab-delimited format.Example of usage## Retrieve the sequence regions belonging to the Rfam## family with accession RF00177 rfamSequenceRegions(rfamFamily = \"RF00177\")Purpose. Retrieves entries of the PDB database with the experimentally solved 3D structure of members of the specified Rfam family, with correspondences between residues of the PDB structure and positions in the covariance model of the Rfam family.ArgumentsrfamFamily: string with the Rfam family accession or ID for which the matching PDB entries should be retrieved.filename: optional string specifying the name of a file. If provided, the matching PDB entries will be saved to the specified file in tab-delimited format.Example of usage## Retrieve the PDB entries with structures of members## of the Rfam family with accession RF00005 (tRNA)rfamPDBMapping(rfamFamily = \"RF00005\")In order to demonstrate the functionalities of our package, we used rfaRm to analyze the reference genome of SARS-CoV-2 (RefSeq accession number NC_045512.2). For this, we first identified the non-coding RNA elements present in the SARS-CoV-2 genome. We then extracted and plotted information concerning the RNA families found to be present in the genome and showed how such information can be further processed with other Bioconductor packages.Even though the genome is 29,903 bases long, such a sequence can be directly processed by rfaRm thanks to the internal splitting into fragments of 10,000 bases. We chose an overlap between consecutive fragments of 3,000 nucleotides to minimize the risk of missing hits present at the boundaries between fragments. We performed the search with and without clan competition to compare the results. An illustration of these queries implemented using R is provided hereafter:library(rfaRm)## Read genome from FASTA filelibrary(seqinr)unlist)## Convert DNA string to RNA stringgsubsars_cov_2_RNA_genome <- ## Search for Rfam families hits in the whole genome without clan competitionrfamSequenceSearchlength(sars_cov_2_Rfam_hits)## Search for Rfam families hits in the whole genome with clan competitionrfamSequenceSearchsars_cov_2_Rfam_hits_2 <- length(sars_cov_2_Rfam_hits_2)## Search for Rfam families hits in the whole genome without clan competitionrfamSequenceSearchsars_cov_2_Rfam_hits_2 <- length(sars_cov_2_Rfam_hits_2)The search without clan competition returned a total of 7 RNA families in the SARS-CoV-2 genome .After extracting the IDs of all families, we performed queries for all of them to obtain a brief description with relevant information about the type and role of each RNA family:## Extract Rfam IDsas.character)rfamIDs_noClanCompetition <- as.character)rfamIDs_ClanCompetition <- ## Iterate over set of Rfam IDs and retrieve a summary for each onelistsummary_list <- for (id in rfamIDs_noClanCompetition) {list(rfamFamilySummary(id))summary <- names(summary) <- idcsummary_list <- }All identified families were non-coding RNA elements typically found in the genome of beta-coronaviruses:bCoV-5UTR: 5\u2019 untranslated region comprising 150\u2013200 nucleotides found in beta-coronaviruses.Sarbecovirus-5UTR: 5\u2019 untranslated region specific of SARS beta-coronaviruses.Corona_FSE: stem loop conserved amongst coronaviruses that can promote ribosomal frameshifting.bCoV-3UTR: 3\u2019 untranslated region comprising 300\u2013500 nucleotides found in beta-coronaviruses.Sarbecovirus-3UTR: 3\u2019 untranslated region specific of SARS beta-coronaviruses.Corona_pk3: conserved pseudoknot of approximately 55 nucleotides found in the 3\u2019 untranslated region of coronaviruses.s2m: motif of unknown function found in the 3\u2019 untranslated region of astroviruses, coronaviruses and equine rhinoviruses.On the other hand, the search with clan competition enabled only returned 3 hits, corresponding to Sarbecovirus-5UTR, Corona_FSE and Sarbecovirus-3UTR. bCoV-5UTR and bCoV-3UTR are discarded because they are essentially the same hit as the Sarbecovirus-5UTR and Sarbecovirus-3UTR respectively, with the only difference that the latter are versions found specifically in SARS beta-coronaviruses. The Corona_pk3 and s2m motifs were also omitted, since they are comprised within the larger Sarbecovirus-3UTR.Knowing the IDs of the RNA families of interest, more detailed information can be easily retrieved through a set of query functions. As an example, we acquired detailed information about the Sarbecovirus-5UTR (with Rfam accession number RF03120).First, we extracted the consensus sequence and secondary structure for the family:## Retrieve consensus sequence and secondary structure and save them to a file in the## extended dot-bracket formatrfamConsensusSecondaryStructure## [1] \"AuauuAgGcuuuuACCuaccCaGGaa..aagCcAAccAA.uuUcGauCuCUUGUaGauCUGuuCUcUAAAcGa.aCUUUAAAA\u2026\u2026UCuGcGuggCuGUCgCucgGCUGcAUGCcuaGcGCacccaCgCaGUAUAAauAaUAAuaAAuUUUAcUGuCGuuGaCagGgaaCgaGUAACuCGuCcauCuuCuGCAGgCuGCUcaCGGUUUCGUCCGugUUGCaGcCGAUCAUCaGCacacCcAGGUUUcGUCCgGguguGaCCGAAAGGuaaGaUgGaGaGCCucGucCcuGGuuuCaaCGaGAAAA\"## [2] \"\u2026\u2026<<<<<<<.<<<. . . .>>>>>..>>>>>. . . .. . .. . ..<<<<<. . . ..>>>>>.<<<<. . .. . ..>>.>>. . . .. . . .. . .. . .<<<<<<<<.<<.<<<<.<<<. . . ..>>>.>>>>>>.>>>>>>>>. . . .. . . .. . . .. . . .. . . .. . . .((((((((((((.(((((. . .(((.(((.((((<<<..<<<<<<.<<<<<. . .. . .>>>>>..>>>>>>. . .. . .>>><<<<<<<.<<. . .. . .>>>>>>>>><<<. . . .>>>)))).)))))).))))))))))\u2026)))))))\u2026 ..\"Next, we extracted the seed multiple sequence alignment of the family:## Retrieve seed multiple sequence alignment and save it to a FASTA filerfamSeedAlignmentThe information from the consensus secondary structure and the multiple seed alignment can be combined and visualized with the R4RNA package :## Read consensus secondary structure and multiple alignmentlibrary(R4RNA)library(Biostrings)readVienna(\"RF03120_cons.txt\")secondaryStructureTable <- readBStringSet(\"RF03120_seedAlgn.fasta\")seedAlignment <- ## Make a helix plot of the consensus secondary structure and annotate it with information## from the seed alignmentplotCovarianceIt is also possible to generate plots of the secondary structure annotated with different types of information by specifying the \u201cformat\u201d argument. Possible values are:norm: default type with no annotationcons: sequence conservationfcbp: basepair conservationcov: covariationent: relative entropymaxcm: maximum covariance model parserscape: R-scape analysis of the seed alignment of the familyrscape-cyk: secondary structure predicted by R-scape from the seed alignment of the familyFor example, in order to generate a plot of the secondary structure of the Sarbecovirus-5UTR annotated with sequence conservation , the fol## Generate a plot of the secondary structure of Rfam family RF03120 annotated with## sequence conservation and save it to a PNG filerfamSecondaryStructurePlot## The plot can also be saved to an SVG file, which is useful to save images into an## editable vector-based formatrfamSecondaryStructureXMLSVGA plot of the phylogenetic tree of the seed alignment can be easily generated and labeled with species names or sequence accession numbers :## Generate a plot of the phylogenetic tree of Rfam family RF03120 labeled with species## names and save it to a GIF filerfamSeedTreeImageAdditionally, the phylogenetic tree can be retrieved in the New Hampshire Extended (NHX) format. The tree can then be read and processed with other software, such as the treeio R package:## Save the phylogenetic tree of Rfam family RF03120 to a file in the NHX formatrfamSeedTree## Read the tree as a treedata objectlibrary(treeio)read.nhx(\"RF03120_treeNHX.nhx\")treeioTree <- ## Print a summary of the treeas.phylo(treeioTree)#### Phylogenetic tree with 19 tips and 17 internal nodes.#### Tip labels:## _DQ022305.2/1-295_Bat_SARS_coronavirus_HK\u20261, _DQ648857.1/1-297_Bat_CoV_279/2005.1, _MG772934.1/1-298_Bat_SARS-like_coronavirus_{}.1, _MT345841.1/1-293_Severe_acute_respiratory_syndrome_coronavirus_2.6, _MT344963.1/1-299_Severe_acute_respiratory_syndrome_coronavirus_2.2, _MT345869.1/1-293_Severe_acute_respiratory_syndrome_coronavirus_2.5,\u2026## Node labels:##, 0.780, 0.940, 0.800, 0.890, 1.000,\u2026#### Unrooted; includes branch lengths.The rfaRm R package provides an easy-to-use client-side interface to the Rfam database, enabling users to access it programmatically and therefore bypassing the limitations of interactive access through the web interface or the requirement to install and search the database locally. Programmatic access allows the identification of non-coding RNA across entire genomes. The package is designed to interoperate with existing software by returning data into formats directly readable by other tools and R packages and is available as part of the Bioconductor project, which facilitates its integration within workflows and pipelines for the analysis of genomic data. We believe the package will provide a useful resource for the community of RNA Bioinformatics, whose interest in the tool has been demonstrated by the considerable number of downloads of the package in spite of its still short lifetime. 21 Dec 2020PONE-D-20-33168rfaRm: an R client-side interface to facilitate the analysis of the Rfam database of RNA familiesPLOS ONEDear Dr. Selles Vidal,Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE\u2019s publication criteria as it currently stands. 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If there are restrictions on publicly sharing data\u2014e.g. participant privacy or use of data from a third party\u2014those must be specified.The Reviewer #1:\u00a0Yes**********4. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1:\u00a0Yes**********5. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1:\u00a01. It is necessary to replace high-resolution figures.2. in addition to the case study, you had better to add descriptions for specific functions of your package, such as usage and arguments of rfamSequenceSearch, rfamFamilySummary, rfamSeedAlignment.**********what does this mean?). If published, this will include your full peer review and any attached files.6. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool,\u00a0 22 Dec 2020The editor requested the addition of some more examples. These have been added as individual examples of usage for each function available in the package. More usage examples with real, publicly available data can be seen at the manual and vignette of the packageThe reviewer had two comments:1) replacing of figures by high resolution tif files. This has been done.2) addition of descriptions of each function of the package (including arguments and usage). This has been done.AttachmentResponse to Reviewers.docxSubmitted filename: Click here for additional data file. 28 Dec 2020rfaRm: an R client-side interface to facilitate the analysis of the Rfam database of RNA familiesPONE-D-20-33168R1Dear Dr. Selles Vidal,We\u2019re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you\u2019ll receive an e-mail detailing the required amendments. When these have been addressed, you\u2019ll receive a formal acceptance letter and your manuscript will be scheduled for publication.http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. 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For more information, please contact Kind regards,Zhong-Hua Chen, Ph.D.Academic EditorPLOS ONEAdditional Editor Comments :Reviewers' comments: 29 Dec 2020PONE-D-20-33168R1 rfaRm: an R client-side interface to facilitate the analysis of the Rfam database of RNA families Dear Dr. Selles Vidal:I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. onepress@plos.org.If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact plosone@plos.org. 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+{"text": "The bulk of social neuroscience takes a \u2018stimulus-brain\u2019 approach, typically comparing brain responses to different types of social stimuli, but most of the time in the absence of direct social interaction. Over the last two decades, a growing number of researchers have adopted a \u2018brain-to-brain\u2019 approach, exploring similarities between brain patterns across participants as a novel way to gain insight into the social brain. This methodological shift has facilitated the introduction of naturalistic social stimuli into the study design (e.g. movies) and, crucially, has spurred the development of new tools to directly study social interaction, both in controlled experimental settings and in more ecologically valid environments. Specifically, \u2018hyperscanning\u2019 setups, which allow the simultaneous recording of brain activity from two or more individuals during social tasks, has gained popularity in recent years. However, currently, there is no agreed-upon approach to carry out such \u2018inter-brain connectivity analysis\u2019, resulting in a scattered landscape of analysis techniques. To accommodate a growing demand to standardize analysis approaches in this fast-growing research field, we have developed Hyperscanning Python Pipeline, a comprehensive and easy open-source software package that allows  neuroscientists to carry-out and to interpret inter-brain connectivity analyses. Social cognition involves the integration of biological, behavioral and social processes at both intra- and inter-individual levels . Paradoxvs online social cognition (vs complementary (i.e. asymmetric) roles of people engaged in the interaction , electroencephalography (EEG), magneto- encephalography and functional near-infrared spectroscopy (fNIRS). This was initially triggered by two needs: capturing the social brain in the context of daily life and capturing how being actively engaged in interaction is different from passively processing social stimuli . Variousognition , recogniBeyond this \u2018interactive turn\u2019, social neuroscience has also followed a call for more naturalistic studies and ecological validity, to bring daily life into the lab, and even the lab into daily life . This moGiven these developments, it is no surprise that the hyperscanning technique . The asshttps://github.com/GHFC/HyPyP.The HyPyP library provides a suite of Python tools to manipulate hyperscanning data and inter-brain connectivity measures. Using a community-driven perspective, the code is open source, licensed under a three-clause Berkeley Software Distribution (BDS) license and editable at this address:  Running HyPyP requires Python 3.7 (or higher) with major data science libraries, such as Scipy  and Matphttps://hypyp.readthedocs.io). A detailed tutorial with a toy-dataset is also available on the Github page (http://github.com/GHFC/HyPyP) and illustrates what the current version of HyPyP allows researchers in social neuroscience to do. It especially covers the following analysis steps for multi-person datasets , preload=True)epochs2 = mne.read_epochs, preload=True)HyPyP includes tools to automatically pre-process data, but researchers are still strongly encouraged to manually inspect data and determine the most appropriate pre-processing strategy. Users can also import already pre-processed data (from EEGLAB for exemple) and proceed directly to the analysis steps with HyPyP.  is an adaption of MNE-Python ,random_state=42)# selecting components semi-automatically and remove themcleaned_epochs_ICA = prep.ICA_choice_compThis is followed by a prompt asking which participant should be used as a template and which IC from this participant should be used as a template.# Applying local AR for each participant rejecting bad epochs, rejecting or interpolating partially bad channels per participant, and removing the same bad channels and epochs across participants.cleaned_epochs_AR = prep.AR_local[utils.merge] takes epochs from each participant to align and merge them into a single data file (whether participant data were recorded in one file or in separate files). This is particularly important when users have loaded their data into MNE and just want to concatenate multiple participants in the same MNE structure. For creating a hyper-dataset combining two recordings stored in epochs called epochs1 and epochs2, this is as simple as:hyper_epo = merge[utils.merge] also takes previously pre-processed recordings: users can load data directly to visualize them (bad channels are still taken into account).Respectively, [utils.split] takes a single hyper-epoch with both participants\u2019 data merged and channel names indicating the participants 1 & 2 with \u201c_1\u201d and \u201c_2\u201d, and split it into two single-participant epochs:epochs1, epochs2 = split(hyper_epo)[analyses.pow] computes Welch power spectral density (PSD) from pre-processed epochs. fmin and fmax set the minimum (fmin) and maximum (fmax) frequencies over which PSD is computed. The parameter n_fft states the length of the fast Fourier transform (FFT), and n_per_seg states the length of each Welch segment. The exact frequency bins are calculated based on FFT parameters and are returned in freq_list. Here is a guide for determining n_fft and n_per_seg: when n_fft is None, n_per_seg determines the sample count of each segment for computing the PSD. A longer segment means higher frequency resolution and lower time resolution. N_fft is used only if a zero-padded FFT is desired, and it has to be bigger or equal to n_per_seg. The segment length for FFT is thus n_per_seg if n_fft is None or n_fft if it is not. To estimate the power in the frequencies of interest, the segment length should be set to at least four times the period of the minimum frequency . The user can either average PSD values over epochs (epochs_average\u2009=\u2009True) or preserve the complete time course. In the example below, the frequency-band-of-interest is restricted to Alpha_Low, frequencies for which PSD is actually computed are returned in freq_list and PSD values are averaged across epochs:psd1 = analyses.powpsd2 = analyses.powdata_psd = np.array[analyses.compute_freq_bands] and [analyses.compute_sync] take pre-processed epochs, the analytic signal, and return different measures of inter-individual brain connectivity  is used. The results are then sliced to generate the inter-brain part of the matrix. This is exemplified below using Alpha_Low (frequencies); Cohens\u2019 D is computed for further analyses.complex_signal = analyses.compute_freq_bandsresult = analyses.compute_syncn_ch = len(epochs1.info['ch_names'])theta, alpha_low, alpha_high, beta, gamma = resultvalues = alpha_lowvalues -= np.diag)C = ) / np.stdThis process can also be applied to intra-individual brain connectivity to support single participant analysis. In addition, the mode argument in the function can take different connectivity measurements (see section \u2018Inter-brain connectivity measures\u2019). This generates connectivity matrices for each epoch .Similar to the inter-brain analyses, results are sliced to generate the intra-brain part of the matrix, exemplified here with Alpha_Low and Cohens\u2019 D.for i in :\u2003theta, alpha_low, alpha_high, beta, gamma = result\u2003values_intra = alpha_low\u2003values_intra -= np.diag)\u2003C_intra = )/np.stdCross-spectral density (CSD) values can also be sampled directly for statistical analyses:result_intra.append(C_intra)t-test corrected for multiple comparisons and a non-parametric cluster-level statistical permutation test using a pre-defined threshold , corrected with channel connectivity across space and frequencies . Both functions take PSD, intra- or inter-individual brain connectivity measurements (result or data) and return statistical values. Permutation tests can be leveraged to test a number of null hypotheses, ranging from modulation of inter-brain synchronization within dyads to between groups of participants (t-test for independent or paired samples (\u2018ind ttest\u2019 or \u2018rel ttest\u2019), a one-way ANOVA test (\u2018f oneway\u2019) or a multiple-way ANOVA test (\u2018f multipleway\u2019) .[stats.statsCond] and [stats.statscluster] are adapted from MNE-Python statistical tests: a parametric icipants . Permutaicipants . Clustert-test is based on the MNE function stats.permutations_t_test to which we added a false discovery rate correction for multiple comparisons.The HyPyP simple parametric statsCondTuple =stats.statsCondFor non-parametric cluster-based permutations, we created a matrix of a priori channel connectivity within individuals based on the channels\u2019 position. In HyPyP, the permutation test can be used for comparing either two groups of PSD matrices or two groups of inter-brain synchrony matrices. Note that for both types of comparison, we are using the same matrix as adjacency prior, assuming both EEG were recorded with the same montage. This means that in the case of inter-brain connectivity matrix comparison, clusters of inter-brain connections are counted based on the distance between their ends on each brain according to the channel locations; in the case of PSD matrix comparison, clusters are counted based on the channel locations on one brain.The following example is for comparing two groups\u2019 PSD in the Alpha Low band.con_matrixTuple = stats.con_matrixch_con_freq = con_matrixTuple.ch_con_freqBelow two fake groups are created for PSD comparison: one with two instances of \u2018participant1\u2019 and the other with two instances of \u2018participant2\u2019.data_group = statscluster=stats.statscluster, tail=0, n_permutations=5000, alpha=0.05)The HyPyP non-parametric cluster-based permutations test can also be used to compare intra-brain connectivity values between participants. To that end, a matrix is created of a priori connectivity between channels across space and frequencies based on their position.con_matrixTuple = stats.con_matrixNote that for inter-brain connectivity measures, the resulting frequency bins are every integer frequency between fmin and fmax with a 1 Hz spectral resolution regardless of the data structure because the FFT window parameters are adaptive. For PSD, however, the resulting frequency bins are determined based on the specific FFT window parameters and are returned in \u2018freq_list\u2019. The spectral resolution may thus differ from 1 Hz depending on the parameters.For CSD, values are averaged across each frequency, so you do not need to take frequency into account to correct clusters.ch_con = con_matrixTuple.ch_conHere again, two fake groups are created with twice the \u2018participant1\u2019 and twice the \u2018participant2\u2019. Here we have, for example, in the Alpha_Low band:Alpha_low = statscluster_intra = stats.statscluster, tail=0,n_permutations=5000, alpha=0.05)Finally, intra-brain connectivity values can be compared to a surrogate signal. For now, creating a surrogate signal has not been implemented in HyPyP, but the user can compare intra-connectivity between subjects. No a priori connectivity between channels is considered between the two participants. In the Alpha_Low band, for example (see earlier), two fake groups are again created with twice the \u2018participant1\u2019 and twice the \u2018participant2\u2019:data = statscluster = stats.statsclusterT values for statistical analyses can be visualized for all channels or for significant channels only.For example:# visualize T values for channels for HyPyP parametric t test with FDR correctionviz.plot_significant_sensors# visualize T values for significant channel only for HyPyP parametric t test with FDR correctionviz.plot_significant_sensorsStatistical modulation of the inter-brain connectivity can also be visualized.  take channel locations and the matrix of inter-individual brain connectivity to visualize inter-brain links projected on either 2D topographic maps or 3D head models. Links are represented by 10th order Bezier curves; shape can be modulated with the [steps] parameter. Only values over the user-defined threshold are plotted. The sequential red color map is used for positive connectivity values, the blue for negative. Line thickness increases with the strength of the connectivity. Bad channels are excluded after pre-processing and are displayed on the visualization models with a cross (as opposed to the points that are used for good channels) to clearly distinguish between channels for which there was no significant inter-brain link and channels that were excluded prior to analysis # Visualization of inter-brain connectivity in 3Dviz.viz_3D_interhttps://pypi.org/project/HyPyP/), with the ultimate goal of formulating testable linking hypotheses between connectivity metrics and psychological processes, as such promoting consensus within the hyperscanning research field with regard to analysis choices. Here, we briefly describe the core connectivity measures that are implemented in HyPyP.To measure the connectivity between two signals, we can measure the similarity in their power, phase or both . AmplituAlthough inter-brain connectivity metrics are often grounded in functional connectivity measures used in single-brain studies, the experimental design and underlying mechanism differ significantly. Unlike intra-brain synchrony, inter-brain synchronization is not driven by physical connections between brain sources and cannot be explained by information transfer through neuronal oscillations  signals in hyperscanning fMRI studies have been found to characterize joint attention : the amount of spectral information in the future of y that can be predicted from the past of TE: equivalent to GC under the Gaussian distribution, can better handle non-linearity.To our knowledge, HyPyP is the first comprehensive toolbox dedicated to quantifying brain connectivity across multiple participants. Tables We aim to integrate the study of behavioral variables from inter to intra-brain level and extend statistical and visualization functions to group analyses. Also, taking advantage of MNE source level functionality, we aim to integrate source-level hyperscanning to complement our channel-level visualization. In addition to streamlining and implementing these functionalities  neuroscientists interested in comparing brain data across two or more participants. It already integrates the core tools to run inter-brain connectivity measures from pre-processing to visualization and will continue to be improved upon in a community-driven fashion. The specific tools provided in HyPyP will facilitate standardized inter-individual neurophysiological analyses to support scientific progress and replicability in social neuroscience research."}
+{"text": "Objective: Osteoporosis is the most common skeletal disease world-wide. The aim of this study is to identify potential circRNA biomarkers for osteoporosis diagnosis and treatment, as well as their roles in regulating osteogenic differentiation.Results: Hsa_circ_0076690 expression was significantly decreased in osteoporosis patients compared to control and showed an acceptable diagnostic value in clinical samples. Subsequently, hsa_circ_0076690 was identified to act as a sponge of miR-152. The expression of hsa_circ_0076690 was gradually increased during osteogenic differentiation while miR-152 showed a decreased expression trend. Moreover, osteogenic differentiation was promoted by hsa_circ_0076690 over-expression and remain unchanged by miR-152/hsa_circ_0076690 co-overexpression.Conclusions: In conclusion, our study revealed that hsa_circ_0076690 may act as a potential diagnostic biomarker for osteoporosis patients and hsa_circ_0076690 could regulate osteogenic differentiation of hBMSCs via sponging miR-152.Materials and methods: A total of 114 participants were enrolled in this study with ethics approvals. CircRNAs were identified by means of RNA-sequencing and qRT-PCR experiment. The clinical significance was measured by ROC curve analysis. Target relationship was validated by luciferase reporter assay. The osteogenic-associated biomarkers and ALP activity were detected by western blots. Osteoporosis (OP) is a systemic skeletal disease with the main feature of reduction of bone density, low bone mass and increased risk of bone fracture \u20133. The oCircular RNAs (circRNAs) are a novel class of non-coding RNAs that characterized by a covalently closed continuous loop structure . NumerouThe present study characterized circRNAs expression profile in osteoporosis patient samples. Candidate circRNAs with abnormal expression were further validated by qRT-PCR experiment in 57 pairs of osteoporosis patients and non-osteoporosis healthy subjects. Subsequently, receiver operating characteristic (ROC) curve analysis was conducted to assess the diagnostic value of candidate circRNAs and found that hsa_circ_0076690 acted as a potential diagnostic biomarker for osteoporosis. Furthermore, the molecular mechanism of hsa_circ_0076690 was investigated in hBMSC cells and the results demonstrated that hsa_circ_0076690 might regulate osteogenic differentiation of hBMSCs through targeting miR-152.2FC| \u2265 1 and P-value < 0.05  compared to control. The expression level of hsa_circ_0111433 in OP showed a similar trend with hsa_circ_0076690 , while had no significant correlation with age  and Body Mass Index   with 79% sensitivity and 85% specificity for hsa_circ_0076690, while hsa_circ_0111433 did not show an acceptable diagnostic value in clinical samples (data not shown).To examine the clinical significance of hsa_circ_0076690 and hsa_circ_0111433 for OP, the Pearson correlation analysis was conducted to measure the correlation between circRNAs expression and clinical parameters of OP patients. The statistical results showed that both the expression of hsa_circ_0076690 and hsa_circ_0111433 was significantly correlated with bone mineral density (BMD) and T-score (ctively) . SubsequP-value < 0.01, To investigate the molecular mechanism of hsa_circ_0076690 underlying osteoporosis pathogenesis, bioinformatics analyses was conducted. As predicted by miRanda and TargetScan software, hsa-miR-152-5p and hsa-miR-3678-3p were identified to have potential binding sites of hsa_circ_0076690 sequence . Dual-luWe further predicted the downstream targets of miR-152 by bioinformatics algorithm and found that RUNX2 was a potential target . A subseDuring the osteogenic differentiation of hBMSCs at 0, 7, 14 and 21 day, osteogenic-associated biomarkers were measured . We notiTo further investigate the function of hsa_circ_0076690/miR-152/RUNX2 axis during osteogenic differentiation, qRT-PCR assays were performed in transfected hBMSCs . As a reWith the development of RNA sequencing technology, a large number of circRNAs have been identified in mammalian cells . IncreasRecently, studies have focused on the potential regulatory role of circRNAs in biological events . One hypIn conclusion, our study revealed that hsa_circ_0076690 may act as a potential diagnostic biomarker in osteoporosis patients. Moreover, hsa_circ_0076690 could regulate osteogenic differentiation of hBMSCs through targeting miR-152.In total, 57 pairs of osteoporosis patients and non-osteoporosis healthy subjects were enrolled in this study. None of the subjects had metabolic diseases that affect bone metabolism or received medical treatment before. Clinical parameters were recorded from all the participants, including age, body mass index (BMI), BMD, T-score, \u03b2-crosslaps (\u03b2-CROSSL), N-terminal osteocalcin (OSTEOC) and Total Procollagen Type1 Intact N-terminal Propeptide (TPINP), etc . This st2FC \u2265 1 and P-value < 0.01).Five pairs of serum or plasma samples from osteoporosis patients and healthy controls were subjected to circRNA sequencing using Illumina Hiseq 3000 . Firstly, total RNAs were extracted from samples using  following the instruction. After measuring the quantity and quality of RNAs, Rnase R was used to remove linear transcripts. Subsequently, cDNA library was built for circRNA sequencing following the instruction of TruSeq RNA Sample Prep Kit . FastQC and cutadapt software were used to obtain high quality clean reads for further analysis. GRCh38 genome and circBase were used for circRNAs annotation. EdgeR package was used to filter the differentially expressed circRNAs between osteoporosis and control group  were calculated by R. Target prediction was performed by TargetScan and miRanda software. All experiments data are representative of independent triplicate experiments and the statistical analysis was performed using R. Statistically significant was set to The hBMSC cells were purchased from Ribio company  cultured in culture medium supplemented with 12% fetal bovine serum . The hBMSCs induction process was conducted by supplying the cells with osteogenic differentiation medium and changed every three days. HBMSC cells were cultured for twenty-one days and the osteogenic associated biomarkers including ALP, Osteocalcin, \u03b2-catenin, BMP2 and RUNX2 were detected to measure the osteogenic differentiation process.HBMSC cells were cultured in six-well plates and transfected with Lipofectamine 3000 for 6h according to the manufacturer\u2019s instruction. The negative controls (NC), miR-152 mimics and si-hsa_circ_0076690 were purchased from Genechem company . The ALP activity was measured after transfected for 14 days using a commercial kit according to the instruction. Absorption was measured at 405 nm.-\u0394\u0394Ct method, GAPDH and U6 were used as references.Total RNAs were extracted from osteoporosis samples, control samples and transfected hBMSCs using TRIzol reagent  according to the protocol. The reverse-transcription reaction was conducted using Reverse Transcriptase kit . The Real-time PCR was performed to detect the expression of candidate circRNAs in tissue samples and specific molecule in transfected hBMSC cells. A SYBR Green PCR kit  was used following the instrument: denaturation for 180 s at 93 \u00b0C and followed by 30 cycles at a Real-Time PCR Applied Biosystems. The relative expression level was determined by 2The hBMSC cells from different groups were lysed with protein-extraction reagent  following the instrument. The BCA Protein Assay Kit  was used to measure protein concentration. Then proteins were separated by 12% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and transferred to PVDF membrane to incubate with primary antibody. Finally, the membrane was washed (three times) and further incubated with secondary antibody. The bands were visualized by ECL (Millipore) following the instrument. ARS staining was performed at 14d and 21d during osteogenic differentiation by treating cells with 0.1% ARS-Tris-Hcl , then the mixture was washed by distilled water and ARS level was examined by microscope.The potential binding sequences between RNA molecules were synthesized and cloned into pGL3-Basic luciferase vector. Luciferase plasmid and miR-152 NC/mimics were transiently transfected into cells with Lipofectamine 3000 for 24h following the protocol. The luciferase activities were then measured using an applied system . The RNA immunoprecipitation (RIP) experiment was conducted in transfected cells using an RNA Binding Protein Kit  and human anti-Ago2 antibody following the protocols. IgG was used as negative control and binding output was measured by qRT-PCR.Supplementary Figure 1Supplementary TablesSupplementary Table 2"}
+{"text": "Lactococcus lactis strains are important components in industrial starter cultures for cheese manufacturing. They have many strain-dependent properties, which affect the final product. Here, we explored the use of machine learning to create systematic, high-throughput screening methods for these properties. Fast acidification of milk is such a strain-dependent property. To predict the maximum hourly acidification rate (Vmax), we trained Random Forest (RF) models on four different genomic representations: Presence/absence of gene families, counts of Pfam domains, the 8 nucleotide long subsequences of their DNA (8-mers), and the 9 nucleotide long subsequences of their DNA (9-mers). Vmax was measured at different temperatures, volumes, and in the presence or absence of yeast extract. These conditions were added as features in each RF model. The four models were trained on 257 strains, and the correlation between the measured Vmax and the predicted Vmax was evaluated with Pearson Correlation Coefficients (PC) on a separate dataset of 85 strains. The models all had high PC scores: 0.83 (gene presence/absence model), 0.84 (Pfam domain model), 0.76 (8-mer model), and 0.85 (9-mer model). The models all based their predictions on relevant genetic features and showed consensus on systems for lactose metabolism, degradation of casein, and pH stress response. Each model also predicted a set of features not found by the other models. It is important to understand the genomic basis for microbial traits in order to produce better microorganisms for food cultures, probiotics, and production of enzymes, medicine, and chemicals. While the arrival of whole-genome sequencing has revealed the DNA sequences of microorganisms, the functions of many microbial genes are as yet unknown. Furthermore, many phenotypes are complex and depend on the interactions of multiple genes. Phenotypes may therefore still be difficult to predict even with good knowledge of the genes directly involved and their individual functions. When little previous knowledge of the phenotype is available, machine learning provides a fast and cheap way to model the phenotype; simultaneously providing a valuable screening tool and insights into the mode of action. We here demonstrate how machine learning can be applied to genome data in order to predict complex traits and identify genes involved in causing them.Lactococcus lactis is widely used in industrial starter cultures for cheese and butter-milk production. The choice of starter culture affects both the texture, flavour, and aroma of the cheese  OR \u201cLactococcus lactis subsp. cremoris\u201d [Organism]) AND \u201cComplete Genome\u201d[Assembly level] AND \u201clatest\u201d[filter]. These genomes were included in the pangenome for annotation purposes, but not included in any of the analyses.Thirty-five complete RefSeq genome sequences of rom NCBI . Of thesThe genes were predicted and annotated with Prokka version 1.14.1 .lactis and cremoris.Roary 3.12.0  was usedLactococcus proteins were downloaded from Uniprot (organism:\u201cLactococcus\u201d), and a BLAST protein database was created. A BLASTp search was run for representative sequences randomly selected from each gene group. The search was done with an E-value of 0.01 and all matches with identity or coverage below 50% were discarded. The pangenome included some tRNA genes. As these do not encode proteins, they were annotated by BLASTn search against the bacterial tRNA sequences of max_depth=min_samples_split=min_samples_leaf=oob_score = max_features parameter was kept to the default auto since there was a very large number of features, many of which were noisy. The bootstrap parameter was also left at the default True to use sampling with replacement. The parameters of the gene model found by randomized 3-fold cross validated grid-search were \u2018n_estimators\u2019: 450, \u2018max_depth\u2019: None, \u2018min_samples_split\u2019: 2, \u2018min_samples_leaf\u2019: 1, \u2018oob_score\u2019: True for the gene model. For the Pfam model they were \u2018n_estimators\u2019: 650, \u2018max_depth\u2019: 70, \u2018min_samples_split\u2019: 6, \u2018min_samples_leaf\u2019: 4, \u2018oob_score\u2019: False. For the 8-mer model they were \u2018n_estimators\u2019: 650, \u2018max_depth\u2019: 110, \u2018min_samples_split\u2019: 4, \u2018min_samples_leaf\u2019: 2, \u2018oob_score\u2019: False. For the 9-mer model they were \u2018n_estimators\u2019: 550, \u2018max_depth\u2019: 120, \u2018min_samples_split\u2019: 4, \u2018min_samples_leaf\u2019: 1, \u2018oob_score\u2019: False. The models were trained on the training data using the identified optimal parameters.The https://github.com/signetang/bacterial_phenotype_genotype_matching_RFR.Scripts and software requirements can be found at S1 FigMade using Shap . The SHA(TIF)Click here for additional data file.S2 FigMade using Shap . The SHA(TIF)Click here for additional data file.S3 FigMade using Shap . The SHA(TIF)Click here for additional data file.S4 Figy = x. The distributions of the predicted values and the actual values are shown above and to the right of the plot respectively. L. lactis subsp. lactis strains are colored green and L. lactis subsp. cremoris strains are colored blue. For each model, three scores evaluate the accuracy of the predictions: The Explained Variance, the Pearson Correlation, and the Root Mean Square Error.Predicted training set values plotted against the actual values of the maximum hourly acidification rate. Perfect predictions would produce a line where (TIF)Click here for additional data file.S1 File(PDF)Click here for additional data file.S2 File(PDF)Click here for additional data file.S3 File(PDF)Click here for additional data file.S4 File(PDF)Click here for additional data file.S5 File(PDF)Click here for additional data file.S6 FileInput matrix for the Random Forest. This text file contains 6759 semicolon-separated columns in 343 lines. The first line is a header. The first column contains the strain IDs, the second column contains the subspecies annotations, and the following columns contain presence/absence of genes (1s and 0s). A column can represent multiple genes if their occurrence profiles are identical\u2014in those cases the IDs in the column header are comma-separated.(TXT)Click here for additional data file.S7 FileInput matrix for the Random Forest. This text file contains 563 semicolon-separated columns in 343 lines. The first line is a header. The first column contains the strain IDs, the second column contains the subspecies annotations, and the following columns contain counts of Pfam domains. A column can represent multiple Pfam domains if their occurrence profiles are identical\u2014in those cases the IDs in the column header are comma-separated.(TXT)Click here for additional data file.S8 FileInput matrix for the Random Forest. This text file contains 774 semicolon-separated columns in 343 lines. The first line is a header. The first column contains the strain IDs, the second column contains the subspecies annotations, and the following columns contain counts of reverse-complement 8-mer pairs. A column can represent multiple such pairs if their occurrence profiles are identical\u2014in those cases the IDs in the column header are comma-separated. Only the alphabetically first 8-mer in the pairs are used in the column headers.(TXT)Click here for additional data file.S9 FileInput matrix for the Random Forest. This text file contains 12615 semicolon-separated columns in 343 lines. The first line is a header. The first column contains the strain IDs, the second column contains the subspecies annotations, and the following columns contain counts of reverse-complement 9-mer pairs. A column can represent multiple such pairs if their occurrence profiles are identical\u2014in those cases the IDs in the column header are comma-separated. Only the alphabetically first 9-mer in the pairs are used in the column headers.(TXT)Click here for additional data file.S10 Filemax values under the different conditions.Input matrix for the Random Forest. This file contains 13 semicolon-separated columns in 243 lines. The first line is a header. The first column contains the strain IDs and the following columns contain V(CSV)Click here for additional data file.S11 FileGene names and corresponding Uniprot IDs found in the strains.(PDF)Click here for additional data file."}
+{"text": "ChiRA, a generic framework for sensitive annotation of these chimeric reads, which in turn can be used to predict the sequenced hybrids.With the advances in next-generation sequencing technologies, it is possible to determine RNA-RNA interaction and RNA structure predictions on a genome-wide level. The reads from these experiments usually are chimeric, with each arm generated from one of the interaction partners. Owing to short read lengths, often these sequenced arms ambiguously map to multiple locations. Thus, inferring the origin of these can be quite complicated. Here we present ChiRA improved the number of correct alignments to the reference up to 3-fold. It is shown that the genes that belong to the common read loci share the same protein families or similar pathways. In published data, ChiRA could detect 3\u00a0times more new interactions compared to existing approaches. In addition, ChiRAViz can be used to visualize and filter large chimeric datasets intuitively.Grouping reference loci on the basis of aligned common reads and quantification improved the handling of the multi-mapped reads in contrast to common strategies such as the selection of the longest hit or a random choice among all hits. On benchmark data ChiRA tool suite provides a complete analysis and visualization framework along with ready-to-use Galaxy workflows and tutorials for RNA-RNA interactome and structurome datasets. Common read loci built by ChiRA can rescue multi-mapped reads on paralogous genes without requiring any information on gene relations. We showed that ChiRA is sensitive in detecting new RNA-RNA interactions from published RNA-RNA interactome datasets. Many non-coding RNAs (ncRNAs) regulate gene expression, post-transcriptionally, via mechanisms such as activation or inhibition of translation, destabilization, localization, and processing. For example, a microRNA (miRNA) can downregulate target expression via translational inhibition or transcript destabilization, initiated by the formation of base pairs between the mature miRNA (~22\u00a0nt long) and the target RNA transcript . For sucMicroRNAs have been a subject of avid research in the past decade owing mostly to 2 reasons: (i) it is proposed that each miRNA can regulate up to several hundred targets and that a substantial proportion of protein-coding genes are targeted by miRNAs at some stage  and (ii)Bowtie2  that a read segment s actually stemmed from CRL c. We denote with \u03c1 the vector of all \u03c1c. Note that when the CRLs have a similar length as in our case, length normalization can be omitted; i.e., \u03c1c are then direct estimates for CRL abundances. In the case of multiple mapping, we define 2 indicator variable matrices to model the read segment selection process. We have an N \u00d7 K indicator matrix Let g et\u00a0al.  in the aZ exactly 1 entry with 1, whereas in Y we can have several such entries. Furthermore, s,cy = 0 implies s,cz = 0. We call Z the committed categorization and Y the uncommitted categorization. In the case of multiple mappings, we have many different Z-matrices that are compatible with Y  and are unobservable. Then, the likelihood of the observation  However, this is not directly observable in the case that the reads map to different CRLs. This can be overcome by introducing another matrix t) \u2208 c\u2194c\u2032] that a chimeric read ..s..s\u2032.. is an interaction between CRLs c and c\u2032:s (respectively s\u2032) and Let \u03c1z:e\u22125. The expression levels of the CRLs are reported in transcripts per million (TPM). Calculation of TPM is explained in The M-Step is simply the maximum likelihood estimate, given the hidden values IntaRNA  and the DFG-funded Collaborative Research Centre 992 Medical Epigenetics [SFB 992/1 2012 awarded to R.B.]. The article processing charge was funded by the Baden-W\u00fcrttemberg Ministry of Science, Research and Art and the University of Freiburg in the funding programme Open Access Publishing.ChiRA tool suite, integrated into Galaxy, created training material, analyzed the data, and wrote the major portion of the manuscript. A.K. developed the ChiRAViz Galaxy visualization and was involved in writing corresponding sections of the manuscript. B.A.G. and O.Z. supported in Galaxy integration and deployment. All authors were involved in reviewing the manuscript.P.V. implemented the giaa158_GIGA-D-20-00250_Original_SubmissionClick here for additional data file.giaa158_GIGA-D-20-00250_Revision_1Click here for additional data file.giaa158_GIGA-D-20-00250_Revision_2Click here for additional data file.giaa158_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giaa158_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giaa158_Reviewer_1_Report_Original_SubmissionAnil Thanki -- 9/16/2020 ReviewedClick here for additional data file.giaa158_Reviewer_2_Report_Original_SubmissionMallory Freeberg, Ph.D. -- 10/11/2020 ReviewedClick here for additional data file.giaa158_Supplemental_FileClick here for additional data file."}
+{"text": "Background: This study aimed to investigate the aberrant expression of hsa_circ_0002874 in non-small cell lung cancer (NSCLC) and elucidate associated molecular mechanisms that influence apoptosis and induce paclitaxel (PTX) resistance.Methods: Inhibitors were used to downregulate circRNA or miRNA expression. pCDNA plasmid transfection and mimics were used to upregulate circRNA or miRNA expression. Dual-luciferase reporter assays were conducted to evaluate interactions between miR1273f and MDM2. Xenograft tumor models were used to assess the effect of hsa_circ_0002874 and miR1273f on tumor growth. NSCLC tissues and matched non-cancerous tissues were also collected for correlation analysis.Results: hsa_circ_0002874 acts as a sponge for miR1273f which targets MDM2/P53. The stability of the hsa_circ_0002874/miR1273f/MDM2/P53 pathway was verified by upregulating and downregulating the expression of hsa_circ_0002874 and miR1273f. hsa_circ_0002874 downregulation or miR1273f upregulation reversed the resistance of the A549/Taxol cells in xenograft models. The expression of hsa_circ_0002874 was high, and the level of MDM2 was low in NSCLC tissues. P53 was only weakly expressed in NSCLC tissues with high expression of MDM2.in vitro and vivo.Conclusions: hsa_circ_0002874 is strongly expressed in NSCLC tissues and maybe a potential marker for PTX resistance. hsa_circ_0002874 downregulation could regulate miR1273f/MDM2/P53 signaling pathway to reverse the PTX resistance of NSCLC and induce apoptosis  Lung cancer (LC) is one of the most common causes of cancer-related death worldwide \u20133 and raUnlike linear RNA, circular RNA (circRNA) forms a covalently closed continuous loop. That is, the 3' and 5' ends usually present in RNA molecules are connected in circular RNA . In moleIn this study, we investigated the aberrant expression of hsa_circ_0002874 in NSCLC and elucidated the molecular mechanisms underlying its influence on apoptosis and PTX resistance induction. Our findings provide novel viewpoints for the anti-tumor mechanisms of PTX and the molecular mechanism of PTX resistance in NSCLC.We designed primers for 18 circRNAs based on the reported microarray results from the doxorubicin-resistant breast cancer cell line MCF-7  Table 1Table 1. To accomplish this, we first excluded candidates with cycle threshold values  of circRNA >30, which is considered too small when the Ct value of GAPDH is 18-20. Among the 18 circRNAs screened, 10 failed this quality control step, namely, hsa_circ_ 0006528, hsa_circ_ 0007769, hsa_circ_ 0092276, hsa_circ_ 0044556, hsa_circ_0003183, hsa_circ_ 0008131, hsa_circ_ 0003838, hsa_circ_ 0007551, hsa_circ_0006903 and hsa_circ_0018293. As shown in Next, after identifying the target circRNA, target miRNAs were predicted by circMir 1.0, RegRNA 2.0 and MirTrap as follows: hsa-miR-1273f, hsa-miR-4726-5p, hsa-miR-2115-5p and hsa-miR-4649-5p . As showTheir target genes were predicted by reviewing the literature and miRBase and Targetscan web page analysis. They were identified as MDM2, SHP-1, Erbb2, and MLLT6 . ChangedFurther bioinformatics prediction analysis was undertaken to explore the combination of hsa_circ_0002874 and hsa-miR-1273f, or miR1273f and MDM2. As shown in To investigate the hsa_circ_0002874/miR1273f/MDM2/P53 pathway and its association with A549/Taxol PTX resistance, relationships between PTX and hsa_circ_0002874, miR1273f, MDM2, or P53 were respectively verified.50 value=17.18 \u03bcM and 55.47 \u03bcM respectively)  . Intracectively) . Expressctively) .Second, the regulation of MDM2 and P53 levels were analyzed by Western blotting. It was found that the amount of MDM2 protein was significantly decreased in A549 cells after treatment with PTX (P=0.004) , 2F, whiThird, based on the above results, the negative regulatory activity of miR1273f on MDM2 expression was verified by dual-luciferase reporter gene assays. It was predicted by miRBase and TargetScan that the target gene of miR1273f is MDM2. To test this, the predicted binding sites were mutated , and a lTo further analyze relationships among hsa_circ_0002874, miR1273f, MDM2, and P53, expression of hsa_circ_0002874 was downregulated or upregulated in A549 cells by siRNAs-ciR interference or pCD25-ciR plasmid transfection. siRNAs-ciR is a group of short interfering RNAs used to down-regulate the expression of circular RNAs. pCD25-ciR is the fifth generation circRNA expression vector, which is used to overexpress circRNA. Thereafter, changes in the expression of miR1273f, MDM2 and P53 were analyzed by qPCR and western blotting.First, as depicted in Second, intracellular RNA levels were analyzed via qPCR. As shown in Next, MDM2 and P53 levels were analyzed by Western blotting. As shown in Firstly, to further validate the hsa_circ_0002874/miR1273f/MDM2/P53 pathway, miR1273f in A549 cells was downregulated or upregulated via transfection of inhibitor-miR1273f or mimic-miR1273f, and the expression changes of MDM2 and P53 were analyzed by qPCR and Western blot analysis to validate the miR1273f/MDM2/P53 pathway further. As shown in Secondly, to further analyze the association between hsa_circ_0002874/miR1273f/MDM2/P53 pathway and PTX resistance, we down- and upregulated miR1273f in A549 and A549/Taxol cells via the transfection of inhibitor- and mimic-miR1273f, respectively. Colony formation and CCK-8 assays were then employed to explore the changes in cell proliferation and cell viability in A549 and A549/Taxol cells after transfection. in vivo.To determine the important role of hsa_circ_0002874/miR1273f on PTX resistance in LC, we constructed drug-resistant xenografts by subcutaneously injecting A549/Taxol cells. Agomir is a small double-stranded RNA that has been specially labeled and chemically modified. It modulates the biological functions of target genes by simulating endogenous miRNA , 17. As To further validate the role of the hsa_circ_0002874/miR1273f/MDM2/P53 pathway in NSCLC, in addition to the above cellular and molecular experiments, we also collected 20 samples of resected NSCLC tissues. We matched paired non-cancerous tissues from patients diagnosed between September 2018 and May 2019.First, qPCR was used to quantify hsa_circ_0002874, miR1273f, and MDM2 mRNA in these 20 paired NSCLC and neighboring non-cancerous tissues \u20137C. As sSecond, correlations between hsa_circ_0002874/miR1273f/MDM2 expression levels and other clinicopathological parameters in these NSCLC patients were also analyzed. The mean values of hsa_circ_0002874, miR1273f, or MDM2 in NSCLC tissues were used as the cut-off threshold for distinguishing high from low expression groups. It was found that increased expression of hsa_circ_0002874 was clearly related to advanced TNM stage P=0.045, . BesidesRegarding MDM2, the increased expression of MDM2 was evidently related to advanced T stage P=0.020, . In summThird, based on the qPCR results in The focus of this study was to determine whether the expression of hsa_circ_0002874 in NSCLC was aberrant and to elucidate molecular mechanisms influencing apoptosis and PTX resistance. Our results suggest that PTX exerted its effects in the A549 cell line by down-regulating the expression of hsa_circ_0002874, which in turn could regulate the expression of MDM2 and P53 via acting as a sponge for miR1273f. High expression of hsa_circ_0002874 is associated with PTX resistance in NSCLC cells, and downregulation of hsa_circ_0002874 or upregulation of miR1273f could increase the chemosensitivity of A549/Taxol to PTX in xenograft models. According to our analysis of NSCLC and paired matched non-cancerous tissues, hsa_circ_0002874 was upregulated in NSCLC and correlated with poor TNM staging. These findings offer a new vista for research into the role of circular RNA in the development of NSCLC, and provide a new perspective for analyzing PTX resistance in this cancer. This study also explored the use of siRNAs-ciRs or mimic-miRs as new approaches to reverse PTX resistance.The previous research of our group found that PTX depends on P53 protein to promote the apoptosis of A549 cells and can up-regulate P53 protein expression via up-regulating the level of long-chain ncRNA MEG3 . AssociaIn studies of lncRNAs, it was recently reported that knockdown of LINC00511 could reduce the resistance of cervical cancer cells to PTX . HoweverAn increased understanding of the biological roles of circRNA has resulted in questions on the relationship between circRNA and NSCLC becoming a hot topic. In 2017, some scholars analyzed the circRNA expression profile of patients with early lung adenocarcinoma . A totalIn our study, the level of miR1273f, a downstream target of hsa_circ_0002874, did not differ significantly between NSCLC tissues and paired non-cancerous matched tissues. However, this could be due to the small sample size in this pilot study, with only 20 pairs of samples, or other pathways affecting the expression of miR1273f. There are many studies in the literature on the relationship between miRNA and NSCLC. A recent report suggested that miRNA-621 is closely related to the pathological grade and poor prognosis of NSCLC. Furthermore, miRNA-621 could inhibit the malignant progression of NSCLC by modulating SIX4 expression . miRNA cRegarding the relationship between MDM2 protein and NSCLC tissues, MDM2 is highly expressed as a proto-oncogene in cancer tissues. MDM2 is significantly upregulated in lung adenocarcinoma tissues compared with adjacent tissues . Howeverin vivo (E3 ubiquitin ligase of p53) [Regarding the relationship between P53 protein and NSCLC tissues, the most common mutated gene in lung adenocarcinoma and lung squamous cell carcinoma is p53, found in 45%-70% of adenocarcinomas and 60%-80% of squamous cells cancer . Due to  of p53) , 33. Mam of p53) , 35. RET of p53) , 37. Hen of p53) . This reThere are some limitations to our analysis that deserve discussion. First, the mechanism study was carried out in a single pair of cell lines A549 and A549/Taxol, and more studies on other cell lines need to be further studied. Second, the mechanism of paclitaxel treatment regulating the expression of hsa_circ_0002874 needs further study. Third, only 20 NSCLC tissues and paired non-cancerous matched tissues were available for study. Limited sample size weakens conclusions on the abnormal expression of hsa_circ_0002874 in NSCLC. Forth, also due to the small sample size, there are many false positives in the chi-square test and the Student t-test in CircRNA hsa_circ_0002874 is strongly expressed in NSCLC tissues and maybe a potential marker of PTX resistance. CircRNA hsa_circ_0002874 acts as a sponge for miR1273f and thereby affects the level of MDM2, eventually acting as a tumor promoter in NSCLC.PTX was purchased from Aladdin. Dulbecco's Modified Eagle Medium (DMEM) /F12 was purchased from Gibco. Fetal bovine serum (FBS) was purchased from Biological Industries. Trypsin, crystal violet, 3-  -2,5-diphenyltetrazolium bromide (MTT) kit, Cell Counting Kit-8, and P53 antibody were purchased from Beyotime Biotechnology. Trizol was purchased from Ambion. RevertAid First Strand complementary DNA (cDNA) Synthesis Kit was purchased from Thermo. SYBR Green Polymerase Chain Reaction (PCR) kit was purchased from Biomake. Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) antibody was purchased from Signalway Antibody. Goat anti-mouse IgG horseradish peroxidase horseradish peroxidase (HRP) -conjugated secondary antibody was purchased from Santa Cruz Biotecnology. MDM2 antibody was purchased from Affinity. Polyvinylidene difluoride (PVDF) membrane was purchased from Immobilon. 0.3% triton-X was purchased from Vetec. Lipofectamine 3000 transfection reagent was purchased from Invitrogen. Mimic-1273f and Inhibitor-1273f were synthesized by GenePharma . siRNAs-ciR and pCD25-ciR were synthesized by Geneseed . The siRNAs-ciR used in this study is a mixture of 3 types of siRNA, and their sequences are 5\u2019-AATCCTGGGAAAGGCTTAT-3\u2019, 5\u2019-ATCCTGGGA AAGGCTTATA-3\u2019, and 5\u2019-CTGGGAAAGGCTTATAACC-3\u2019. Dual luciferase reporter vector plasmid (miR1273f) was purchased by GenePharma . Dual luciferase reporter gene fluorescence detection kit was purchased by Promega. Agomir-1273f  was purchased by GenePharma .A total of 20 NSCLC tissues and paired non-cancerous matched tissues were collected through surgical resection from patients diagnosed between September 2018 and May 2019 at the The Second Affiliated Hospital of Suzhou University . With the guidance of a skillful pathologist, we collected normal lung samples with a distance of \u22652 cm from the edge of cancer tissue. All patients did not receive radiotherapy and chemotherapy before surgery. All specimens were collected under the guidance of the HIPAA protocol. The study was approved by the Ethics Committee of Second Affiliated Hospital of Suzhou University, and written informed consent was obtained from all the patients. TNM stage classification complied with the NCCN Clinical Practice Guidelines in Oncology: Non-Small Cell Lung Cancer (Version 2.2019).Human A549 cell lines were supplied by the Cell Bank of Type Culture Collection of the Chinese Academy of Sciences, Shanghai, China (CBP60084). Human A549/Taxol cell lines were purchased from Yaji Biotechnology Company, Shanghai, China (YS421C). A549 cells were cultured in DMEM/F12 medium supplemented with 10% fetal bovine serum (which contained 100 U/ml penicillin and 100 mg/ml streptomycin). A549/Taxol cells were cultured in DMEM/F12 medium supplemented with 10% fetal bovine serum .Primers for 18 circRNAs resistant to breast cancer cell MCF-7 doxorubicin  were desHsa_circ_0002874 was screened and its target miRNAs predicted by circMir 1.0, RegRNA 2.0 and MirTrap software were hsa-miR-1273f, hsa-miR-4726-5p, hsa-miR-2115-5p, and hsa-miR-4649-5p. The literature review, miRBase and TargetScan web analysis were used to predict the target genes of the four, and the downregulated protein expressions were: MDM2, SHP-1, Erbb2, MLLT6. The expressions of predicted miRNAs after the administration of PTX were verified by qPCR to estimate the true target miRNA of hsa_circ_0002874 .-\u0394\u0394CT. The primers used are shown in According to the manufacturer's protocol, total RNA was extracted with Trizol reagent. According to OD260/280 readings, the purity and concentration of RNA were determined by NanoDrop ND-1000 spectrophotometer. Total RNA (500ng) was reverse transcribed into cDNA with a final volume of 20 \u03bcl. RevertAid First Strand cDNA Synthesis Kit (Thermo) was used under standard conditions with random primers and oligo dT primers. Purity and concentration of DNA were determined by NanoDrop ND-1000 spectrophotometer. Then, the SYBR Green PCR kit was used for qPCR. The reaction was set as follows: 94\u00b0 C for 3 min, 30 cycles at 94\u00b0 C for 30 s, 55\u00b0 C for 30 s, and 72\u00b0 C for 30 s. Final extension was performed at 72\u00b0 C for 7 min. The results of qPCR normalized to the expression of GAPDH. The results of qPCR were analyzed relative to the threshold cycle (Ct) value and converted into multiple values according to the rule of 23 cells/well in 200 \u03bcl culture medium. After treatment, the cells were incubated in 200 ml DMEM/F12 containing 0.5 mg/ml MTT at 37\u00b0 C for 4 hours. Afterward, the supernatant was removed, and the cells were lysed in 200 \u03bcl dimethyl sulfoxide (DMSO) for 10 min at 37\u00b0 C. Optical density (OD) values were detected at 490 nm. The obtained values were presented as folds of the control group.The cells were seeded into a 96-well plate (Corning) at a density of 5\u00d710Western blot analysis was performed using standard procedures. Briefly, total protein was extracted and isolated by 10% sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and transferred to a PVDF membrane. To block non-specifically bound, the membrane was incubated with 5% skim milk powder for 1 hour at room temperature. Membranes were then incubated with primary antibody against MDM2 or P53 (1:1000) followed by HRP labeled secondary antibody and detected by chemiluminescence. An anti-GAPDH antibody (1:1000) was used as a protein loading control.MDM2 3\u2019 UTR was amplified from cDNA of 293 cells and inserted into pGL-3 . The 293 cells  were cotransfected with the wild-type 3\u2019UTR of MDM2 containing the putative miR1273f binding site (Site 1: 2709-2715) and mutant MDM2 3\u2019 UTR with either NC mimics or miR1273f mimics via Lipofectamine 3000. After transfection, the cells were cultivated at 37\u00b0 C, 5% CO2 for 4 h. Then, the luciferase activities were confirmed using a dual-luciferase reporter assay system according to the manufacturer\u2019s protocol.A549 cells were seeded into 6-well plates, incubated overnight and transfected with siRNAs-ciR/pCD25-ciR plasmid or miR1273f inhibitor/negative control. A549/Taxol cells were transfected with the miR1273f mimic/negative control under the same conditions. The sequences used for transfection were listed in 3 cells per well. After incubation for 36 hours at 37\u00b0 C in a 5% CO2 humidified incubator, the cells were incubated with medium supplemented with PTX (2\u03bcM) and cultured at 37\u00b0 C in a 5% CO2 humidified incubator for 7 days. After colony formation was observed, the medium was removed. The cells were washed twice with phosphate buffered saline (PBS), fixed with 4% formaldehyde for 10 minutes, and stained with 5% crystal violet for 10 minutes. The stained cell area ratio was calculated by randomly photographing 15 fields per well under a 10\u00d7 microscope. Finally, after dissolving crystal violet with 10% glacial acetic acid, OD values were detected at 595 nm. The obtained values were presented as folds of the control group.Transfected A549 or A549/Taxol cells were seeded in 6-well plates at 5\u00d710After 48 hours of transfection in 96-well plates, the freshly prepared medium contained PTX at a final concentration of 10\u03bcM. The medium was added to the wells with 7 replicate wells per set. After 48 hours of incubation, cell viability was measured using CCK-8 kit according to the manufacturer's instructions. The absorbance at 450 nm was measured using NanoDrop ND-1000 spectrophotometer.6 cells per animal). From the 10th day after cell injection, the engraftment of tumor was confirmed and the baseline tumor size was evaluated. The xenograft-bearing mouse models were randomized into four groups (n=5), five mice were were intraperitoneally injected with PTX (15mg/kg each time) and intratumorally injected with agomir-1273f (5 nmol each time); five mice were intraperitoneally injected with PTX and intratumorally injected with siRNAs-ciR; five mice were intraperitoneally injected with PTX and intratumorally injected with PBS; and the remaining five mice were intratumorally and intraperitoneally injected with PBS as a control once every 3 days. Tumor formations were monitored by measuring the length (L) and width (W) with calipers every 2 days, and the volumes were calculated using the following formula: (L\u00d7W\u00d7W)/2. All mice were sacrificed on 10 days, and the tumors were neatly excised. Tumor tissues were then subjected to RNA isolation for qPCR analysis.All experimental protocols were approved by the Animal Ethics Committee of Second Affiliated Hospital of Soochow University. A total of 20 BALB/c nude mice (4 weeks old) weighing 20.75\u00b11.2g were fed a pellet diet and housed under controlled environment with a temperature of 24\u00b12 C and air humidity of 60\u00b12%. For the drug-resistant xenograft model, A549/Taxol cells were subcutaneously injected into the armpits of nude mice  and Graph pad Prism 5.0. Differences between NSCLC tissues and paired non-cancerous matched tissues were analyzed using the Student\u2019s t test. One-way ANOVA further analyzed the correlations between hsa_circ_0002874 expression levels and clinicopathological factors. The correlations among hsa_circ_0002874 expression, miR1273f expression, and MDM2 expression were explored by Pearson correlation analysis. P <0.05 was considered statistically significant."}
+{"text": "Advances in high-throughput sequencing accessibility have democratized small subunit ribosomal RNA gene sequence data collection, coincident with an increasing availability of computational tools for sequence data processing, multivariate statistics, and data visualization. However, existing tools often require programming ability and frequent user intervention that may not be suitable for fast-paced and large-scale data analysis by end user microbiologists who are unfamiliar with the Linux command line environment or who prefer interactions with a GUI. Here we present AXIOME3, which is a completely redeveloped AXIOME pipeline that streamlines small subunit ribosomal RNA data analysis by managing QIIME2, R, and Python-associated analyses through an interactive web interface.AXIOME3 comes with web GUI to improve usability by simplifying configuration processes and task status tracking. Internally, it uses an automated pipeline that is wrapped around QIIME2 to generate a range of outputs including amplicon sequence variant tables, taxonomic classifications, phylogenetic trees, biodiversity metrics, and ordinations. The extension module for AXIOME3 provides advanced data visualization tools such as principal coordinate analysis, bubble plots, and triplot ordinations that can be used to visualize interactions between a distance matrix, amplicon sequence variant taxonomy, and sample metadata.https://github.com/neufeld/AXIOME3, https://github.com/neufeld/AXIOME3-GUI), and researchers are encouraged to modify and redistribute the package.Because repeat analysis of small subunit ribosomal RNA amplicon sequence data is challenging for those who have limited experience in command line environments, AXIOME3 now offers rapid and user-friendly options within an automated pipeline, with advanced data visualization tools and the ability for users to incorporate additional analyses easily through extension. AXIOME3 is completely open source ( Advances in high-throughput DNA sequencing technologies have facilitated large-scale small subunit (SSU) ribosomal RNA (rRNA) data collection, which consequently increased the need for efficient computational tools. Although existing pipelines and databases such as QIIME2 , mothur Previously, we developed the Automation, eXtension, and Integration Of Microbial Ecology (AXIOME) pipeline that enabled researchers to automate the analysis of SSU rRNA gene amplicon data easily , with moThe AXIOME3 web GUI was designed to accommodate researchers who are unfamiliar with the Linux operating system environment, eliminating a potentially steep learning curve associated with traditional bioinformatics tools. Users can easily configure various options and start the automated analysis pipeline via a straightforward web interface. All usage-related information is embedded in the web interface so that users can avoid navigating to different resources in search of relevant information. Because a typical SSU rRNA gene amplicon data analysis may take several hours for a relatively large sample size, AXIOME3 assigns a unique session identifier to each analysis, which can be monitored and reloaded at any time. Users may optionally receive email notifications upon task queueing and completion.AXIOME3 resolves potential installation conflicts by containerizing its software and operating system dependencies using Docker  and DockThe AXIOME3 pipeline makes computational microbial research accessible to researchers who are unfamiliar with the Linux command line environment by automating common microbial research workflows. The core functionality of the pipeline relies on the QIIME2 package  and usesThe extension module is unique to AXIOME3, with custom Python and R scripts used to visualize outputs of the interactive pipeline Fig.\u00a0. CurrentAdditional data exploration, visualization, and statistical tools will be added in future releases of AXIOME3, and users are invited to participate in the development process.A typical AXIOME3 workflow involves four modular analyses: Input Upload, Denoise, Analysis, and Extension/Visualization Fig.\u00a0. For theAXIOME3 offers several improvements compared to its predecessor AXIOME  and AXIOAlthough QIIME2 Studio provides a user-friendly GUI for amplicon sequence analysis, it has several limitations that are addressed by AXIOME3. First, QIIME2 data visualization files (.qzv) require the QIIME2 viewer, which adds an extra layer of complexity for end user microbiologists. Instead, AXIOME3 implements analysis and visualization within the same front-end interface, which simplifies the process for users. Second, QIIME2 Studio requires that users manually assemble individual workflow components. This limitation is addressed by AXIOME3 by enabling an automated workflow in which individual components are chained together into a single pipeline. This increased automation benefits users by simplifying repetitive workflows. The AXIOME3 pipeline also enables iterative data visualization and analysis, which allows users to easily interact with their data while customizing data visualizations. Importantly, AXIOME3 is not intended as a replacement for QIIME2 and QIIME2 Studio but is rather an automation tool that manages and extends QIIME2 capabilities.https://github.com/neufeld/AXIOME3; https://github.com/neufeld/AXIOME3-GUI). Note that only the AXIOME3 GUI (https://github.com/neufeld/AXIOME3-GUI) needs to be installed for end users and doing so will automatically install the pipeline as well. The AXIOME3 pipeline repository (https://github.com/neufeld/AXIOME3) is exclusively intended for developers and collaborators. AXIOME3 is cross-platform compatible and the web GUI currently supports Chrome, Firefox, and Edge. The only other software requirement for AXIOME3 is Docker and Docker Compose, which is required to ensure a consistent build environment. A tutorial and sample dataset, as well as instructions about collaboration, are included in the AXIOME3 GUI project home page.AXIOME3 is an actively maintained and developed open-source project and is available from GitHub (Project name: AXIOME3https://github.com/neufeld/AXIOME3 (AXIOME3 pipeline), https://github.com/neufeld/AXIOME3-GUI (AXIOME3 GUI)Project home page: Operating system(s): Platform independentBrowser support: Chrome, Firefox, Edge (AXIOME3 GUI)Programming language: Python, JavascriptOther requirements: Docker (1.13.0+), Docker Compose (Version 3+)License: BSD 3-ClauseAny restrictions to use by non-academics: NoSnapshots of our code and other data further supporting this work are openly available in the GigaScience repository, GigaDB .ASV: amplicon sequence variant; GUI: graphical user interface; rRNA: ribosomal RNA; SSU: small subunit.The authors declare that they have no competing interests.In addition to Discovery grants to A.C.D. and J.D.N. from the Natural Sciences and Engineering Research Council of Canada (NSERC), this research was supported by an Ontario Research Fund: Research Excellence (ORF-RE) grant and a Collaborative Research and Development (CRD) grant from NSERC, both in partnership with the Nuclear Waste Management Organization (NWMO).D.M. designed and implemented AXIOME3 and prepared the manuscript. A.C.D. and J.D.N. contributed to design and coordination of AXIOME3 and manuscript preparation. All authors read and approved the final manuscript.giab006_GIGA-D-20-00273_Original_SubmissionClick here for additional data file.giab006_GIGA-D-20-00273_Revision_1Click here for additional data file.giab006_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giab006_Reviewer_1_Report_Original_SubmissionJulien Tremblay -- 10/14/2020 ReviewedClick here for additional data file.giab006_Reviewer_1_Report_Revision_1Julien Tremblay -- 2/22/2020 ReviewedClick here for additional data file.giab006_Reviewer_2_Report_Original_SubmissionTodd Treangen -- 10/20/2020 ReviewedClick here for additional data file.giab006_Reviewer_2_Report_Revision_1Todd Treangen -- 12/9/2020 ReviewedClick here for additional data file."}
+{"text": "This study aimed to identify the differentially expressed circular RNAs (circRNAs) between human abdominal aortic aneurysm (AAA) and the\u00a0control group.High-throughput sequencing was applied to determine the circRNA expression profiles of 4 paired aortic samples. Real-time quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was carried out to testify 6 randomly selected dysregulated circRNAs. Kyoto Encyclopedia of Genes and Genomes and Gene ontology (GO) analysis were conducted for functional annotation of the\u00a0parental genes. Additionally, interaction networks between circRNA and 5 putative microRNA (miRNA) partners were constructed.Homo sapiens) _circ_0005360 (LDLR) and hsa_circ_0002168 (TMEM189) were proved significantly lower in the AAA group by qRT-PCR. Regarding upregulated circRNAs, the most enriched GO molecular function, biological process and cellular component terms were poly(A) RNA binding, negative regulation of transcription from RNA polymerase II promoter and nucleoplasm, respectively. Moreover, circRNA/miRNA interaction networks showed that hsa_circ_0005360/miR-181b and hsa_circ_0002168/miR-15a axis might have a regulative role in human AAA.Finally, 411 differentially expressed circRNAs were discovered, including 266 downregulated and 145 upregulated circRNAs. Compared with the control group, the expression level of hsa (This study revealed new circRNAs potentially related to the pathogenesis of AAA. Further experimental studies are warranted to clarify the potential molecular mechanisms. Ruptured abdominal aortic aneurysm (AAA) is an important cause of cardiovascular mortality in men over the age of 65\u2009years . In SwedCircular RNAs (circRNAs) represent a new type of endogenous non-coding RNAs produced by non-colinear reverse splicing. They are generated by an incorporation of the 3\u2032 end and 5\u2032 end and highly stable in vivo because of covalently closed loop structures . NumerouIn this study, we intended to identify the differentially expressed circRNAs between the AAA and the\u00a0control group. Computational analysis was performed to predict the circRNA/miRNA interaction networks. Several dysregulated circRNAs expression levels were further testified by real-time quantitative reverse transcription-polymerase chain reaction (qRT-PCR).P\u2009=\u20090.057).This study was approved by the Institutional Ethics Committees of Zhongshan Hospital, Fudan University B2018-040R) and complied with the Declaration of Helsinki. From March 2018 to September 2018, 4 consecutive patients with AAA, who were unsuitable for endovascular repair, underwent open surgery at our center. All the 4 patients received computed tomography angiography examinations preoperatively and were not found with underlying connective tissue diseases. Written informed consent was obtained from all enrolled patients and donors next-of-kin. Full thickness AAA specimens were obtained from the aneurysmal segment of abdominal aorta and stored at \u2212\u200980\u2009\u00b0C until assayed. Over the same period, abdominal aortic samples just below the aortic trunk from 4 heart-beating brain-dead organ donors were used as the controls. Clinical characteristics and maximal infrarenal aortic diameter were recorded for patients with AAA, but this information was unavailable for donors . NanoDrop ND-1000  was utilized to assess the quality and quantification of the total RNA. The OD260/280 ratios of our samples were located between 1.8 and 2.1, which were acceptable. Then, RNA integrity and genomic DNA contamination were evaluated by electrophoresis on a denaturing agarose gel. RNase R  was used to degrade the linear and ribosomal RNAs.The enriched circRNAs were amplified and transcribed into fluorescence labeling complementary RNA (cRNA) . RNeasy Mini Kit  was applied to purify the labeled cRNAs. Subsequently, the fragmentation mixture, consisting of 1\u2009\u03bcg of each labeled cRNA, 5\u2009\u03bcl 10\u00d7 blocking agent and 1\u2009\u03bcl 25\u00d7 fragmentation buffer, was incubated at 60\u2009\u00b0C for 30\u2009min. The fragmented labeled cRNAs were hybridized onto the circRNA expression microarray slide (Arraystar Human circRNA Array V2). The slides were incubated for 17\u2009h at 65\u2009\u00b0C and then washed, fixed and scanned.https://www.r-project.org/). Dysregulated circRNAs between 4 paired aortic samples were determined using the Limma package. The statistical significance was set as |log2 fold change (FC)|\u2009\u2265\u20091 with P value <\u20090.05 estimated by t-test. A scatter plot and heat map were generated to assess the variation in circRNAs expression profiles between the AAA and the\u00a0control group. The dysregulated circRNAs were showed via a Volcano Plot.The scanned images were collected and imported into Agilent Feature Extraction software . Quantile normalization and subsequent data processing were carried out using the R software packages  was utilized to conduct Gene Ontology (GO) analysis, including molecular function (MF), biological process (BP) and cellular component (CC). Further, pathway enrichment analysis was performed by Kyoto Encyclopedia of Genes and Genomes (KEGG) (http://www.genome.jp/kegg/kegg2.html) to detect the biological pathways of the involved parental genes [P value <\u20090.05) were ranked by -log10 .For the functional annotation of parental genes of the dysregulated circRNAs, the Database for Annotation, Visualization and Integrated Discovery . A Reverse Transcription kit (Takara Bio Inc.) was utilized to synthesize the complementary DNA. Circular RNAs of interest were amplified according to the manufacturer\u2019s protocol of SYBR-Green PCR Mix (Takara Bio Inc.). The PCR primer sequences are shown in Table\u00a0equation .Table 2P value <\u20090.05 was considered statistically significant. Statistical analyzes were carried out using Stata version 14.0 .The relative expression level was compared by Student\u2019s t-test. Two tailed 2 FC|\u2009\u2265\u20091 and P value <\u20090.05  _circ_0060063 (UQCC1), hsa_circ_0070382 (AFF1), hsa_circ_0027446 (HMGA2), hsa_circ_0028198 (ANAPC7), hsa_circ_0005360 (LDLR) and hsa_circ_0002168 (TMEM189), were selected for further qRT-PCR validation. Compared with the control group, the expression level of hsa_circ_0005360 (LDLR) and hsa_circ_0002168 (TMEM189) were proved significantly lower in the AAA group , including hsa . As a result, the most enriched MF, BP and CC terms were poly(A) RNA binding, negative regulation of transcription from RNA polymerase II promoter and nucleoplasm, respectively. Moreover, KEGG analysis presented that only one pathway significantly related to these upregulated circRNAs (P\u2009=\u20090.020), namely transcriptional misregulation in cancer. However, the downregulated circRNAs failed to be enriched in any GO terms or KEGG pathways.For the upregulated circRNAs, the top 8 enriched GO terms were showed in Fig.\u00a0circRNAs usually function as an inhibitor of their interacting miRNA partners. To assess the potential function of these dysregulated circRNAs, 5 putative miRNA partners were predicted for each circRNA. In total, 2055 circRNA/miRNA pairs having one or more binding regions were generated. Additionally, interaction networks between circRNA and their top 5 predicted miRNAs were constructed for the above qRT-PCR confirmed circRNAs Fig.\u00a0.Fig. 4TIt is well established that AAAs are associated with smooth muscle cell apoptosis, local inflammatory cells infiltration, and extracellular matrix degradation in the aortic media layer at the aneurysm site , 16\u201318. In this study, we identified 411 differentially expressed circRNAs, of which 145 circRNAs were significantly upregulated and 266 circRNAs were significantly downregulated in AAA samples compared with controls. Six randomly selected circRNAs, including hsa_circ_0060063, hsa_circ_0070382, hsa_circ_0027446, hsa_circ_0028198, hsa_circ_0005360 and hsa_circ_0002168, were testified by qRT-PCR. The expression level of hsa_circ_0005360 and hsa_circ_0002168 were confirmed in accordance with the microarray analysis. Specially, the parental gene of hsa_circ_0005360 is LDLR, whose variant is proved associated with AAA in a genome-wide association study based on population . ConsideTo further detect the regulative role of circRNAs in AAA, KEGG and GO analysis were performed for the functional annotation of parental genes. The most enriched MF, BP and CC terms were associated with poly(A) RNA binding, negative regulation of transcription from RNA polymerase II promoter and nucleoplasm, respectively. In addition, KEGG pathway analysis determined that transcriptional misregulation in cancer was the only significantly enriched pathway. These processes indicated that the parental genes of dysregulated circRNAs may participate in the transcriptional regulation of AAA. Similar with their parental gene function, circRNAs can also regulate transcriptional and posttranscriptional gene expression, especially functioning as miRNA sponges.Previously, Zheng et al. found that hsa_circ_000595 was upregulated in human AAA tissues, which would reduce the expression of miR-19a and subsequently promote human vascular smooth muscle cells (VSMC) apoptosis . SimilarThis study has several potential limitations. First, the sample size is relatively small and the results should be cautious to interpret. A multicenter study with large sample size may reduce the ethical biases and improve the reliability of the microarray data. Second, not all circRNAs function as the inhibitor of miRNAs. In fact, circRNAs can also interact with RNA-binding proteins, modulate transcription and alternative splicing, and even be translated, which are not mentioned in this study. Third, all the functional annotation of circRNAs and interaction networks were predicted based on bioinformatics analysis. Further experimental studies are warranted to clarify the potential mechanisms.In summary, the dysregulated circRNAs identified by our study may have a regulative role in the initiation and progression of AAA. Additionally, circRNA/miRNA interaction networks provide new insights into the molecular mechanisms and potential therapeutic targets for AAA."}
+{"text": "Temozolomide (TMZ) is the internationally recognized and preferred drug for glioma chemotherapy treatment. However, TMZ resistance in glioma appears after long-term use and is an urgent problem that needs to be solved. Circular RNAs (circRNAs) are noncoding RNAs and play an important role in the pathogenesis and progression of tumors. Hsa_circ_0110757 was identified in TMZ-resistant glioma cells by high-throughput sequencing analysis and was derived from reverse splicing of myeloid cell leukemia-1 (Mcl-1) exons. The role of hsa_circ_0110757 in TMZ-resistant glioma was evaluated both in vitro and in vivo. It was found that hsa_circ_0110757 and ITGA1 are more highly expressed in TMZ-resistant glioma than in TMZ-sensitive glioma. The overexpression of hsa_circ_0110757 in glioma patients treated with TMZ was obviously associated with tumor invasion. This study indicates that hsa_circ_0110757 inhibits glioma cell apoptosis by sponging hsa-miR-1298-5p to promote ITGA1 expression. Thus, hsa_circ_0110757/hsa-miR-1298-5p/ITGA could be a potential therapeutic target for reversing the resistance of glioma to TMZ. The main characteristics are diffuse invasive growth of tumor cells, unclear boundaries, relatively unlimited proliferation, and high invasiveness, all of which seriously affect human health3. Despite advances in the treatment of gliomas, the 5-year survival rate of glioma patients remains low4. For advanced glioma patients, systemic chemotherapy is main form of treatment5. However, drug resistance is still an important cause of chemotherapy failure in many cancers including glioma7. Therefore, it is an urgent problem to overcome the chemotherapy drug resistance in glioma.Glioma, originating from the neuroepithelium and accounting for 40\u201350% of intracranial tumors, is the most common malignant tumor of the central nervous system9. TMZ is the second generation of alkalization agent that can be widely distributed throughout the body without liver metabolism and can enter the brain through the blood\u2013brain barrier (BBB), achieving high drug concentrations in the brain11. However, long-term clinical research has found that TMZ can only prolong survival time to a small extent, and a considerable number of gliomas are still insensitive to TMZ and gradually develop drug resistance12. Therefore, the resistance of glioma to TMZ is considered to be the fundamental cause of chemotherapy failure and glioma recurrence13.Currently, temozolomide (TMZ) is the internationally recognized and preferred drug for glioma chemotherapy14. circRNAs competitively inhibit the expression of endogenous RNAs by sponging microRNAs (miRNAs), which is a novel mechanism of regulating miRNA expression16. Due to many biological processes adjusted by miRNAs, circRNAs can alter biological processes by sponging miRNAs17. miRNAs are a ubiquitous kind of short noncoding RNA (~22\u2009nt) that can be directly paired with target bases in the mRNA and regulate gene expression after transcription18. circRNAs can influence miRNA function by competitively binding to miRNA sites19. Nevertheless, the role of circRNAs as miRNA sponges in TMZ-resistant glioma has not been fully clarified.Circular RNAs (circRNAs) are noncoding RNAs that can promote or inhibit tumorigenesisTo explore the regulatory effect of circRNAs on TMZ-resistant glioma, high-throughput sequencing was performed, and there were many different circRNAs in TMZ-resistant and TMZ-sensitive glioma tissues. Through many experiments, we found that hsa_circ_0110757, originating from myeloid cell leukemia-1 (Mcl-1) exons, was obviously overexpressed in TMZ-resistant and TMZ-sensitive glioma tissues and cells. In addition, we found that hsa_circ_0110757 facilitates TMZ resistance modulation by sponging hsa-miR-1298-5p, which inhibits Integrin subunit alpha 1 (ITGA1) expression by activating the PI3K/AKT pathway in glioma.RNA-Seq assays were conducted to identify differentially expressed circRNAs in TMZ-sensitive patients and TMZ-resistant patients. As shown in Fig. First, two siRNAs targeting hsa_circ_0110757 were constructed Fig. . Hsa_cirTo identify miRNAs that hsa_circ_0110757 could sponge in glioma, eight miRNAs were chosen by overlapping the prediction results using miRanda, PITA, and RNAhybrid Fig. . A pull-Further high-throughput sequencing was performed on U87 and U87/R cells to identify the differentially expressed genes Fig. . The topAnti-hsa-miR-1298-5p offset the downregulation of ITGA1 induced by si-hsa_circ_0110757 in U87/R cells Fig. . Hsa_cirTo study the effect of hsa_circ_0110757 in vivo, U87/R cells with or without hsa_circ_0110757 knockdown were subcutaneously injected into BALB/c nude mice. As shown in Fig. 20. It can pass through the BBB and is widely used for the treatment of glioma21. However, long-term application is likely to induce acquired drug resistance, and drug resistance is still one of the problems that cannot be ignored in the treatment of tumors22. In high-throughput sequencing results, we found that hsa_circ_0110757 of Mcl-1 was overexpressed in TMZ-resistant glioma patient tissues and cells. Furthermore, compared with other circRNAs, hsa_circ_0110757 was obviously upregulated in TMZ-resistant glioma patients and may play a larger role in glioma. In this study, inhibition of hsa_circ_0110757 reduced the viability of U87/R cells and the number of invading cells and promoted cell apoptosis upon TMZ application.TMZ is an alkane antitumor drug that does not need to go through liver activation and metabolism and is widely distributed throughout the body23. Abnormal miRNA expression is related to the occurrence and development of tumors and can be used as both proto-oncogenes and tumor suppressor genes and is an important regulatory factor for the occurrence and development of tumors24. To identify miRNAs that hsa_circ_0110757 could sponge in glioma, eight miRNAs were chosen by overlapping the prediction results using miRanda, PITA, and RNAhybrid. Pull-down, PCR, and FISH assays indicated that hsa_circ_0110757 plays a role by sponging hsa-miR-1298-5p. Further experiments revealed that ITGA1 was the direct target of hsa-miR-1298-5p.miRNAs are endogenous noncoding RNAs with regulatory functions that can recognize target mRNAs by base complementary pairing and then degrade target mRNAs or inhibit the translation of target mRNAs25. ITGA1 was highly expressed in TMZ-resistant pancreatic cancer, and downregulated ITGA1 restored the sensitivity of the above cells to TMZ26. According to the KEGG pathway database (www.kegg.jp/kegg/pathway.html), ITGA plays an important role in apoptosis by the PI3K/AKT pathway. In this study, the ITGA1 mRNA and protein levels were much higher in TMZ-resistant U87 cells than in TMZ-sensitive cells. Furthermore, hsa-miR-1298-5p mimics obviously decreased the expression of ITGA1, reduced cell viability, and induced apoptosis of U87/R cells.ITGA1, also known as CD49a or VLA1, encodes the \u03b1 subunit of integrin receptors and plays an important role in cell\u2013cell adhesion27. In this study, hsa_circ_0110757 may strengthen TMZ resistance by the PI3K/AKT pathway in glioma.According to the above results, a series of experiments proved that hsa_circ_0110757 weakened TMZ-induced apoptosis by activating PI3K/AKT, resulting in resistance to TMZ application. At the molecular level, hsa_circ_0110757 can sponge hsa-miR-1298-5p to eliminate the suppressive effect of hsa-miR-1298-5p on ITGA1, which then excites the PI3K/AKT signaling pathway and inhibits apoptosis in glioma cells. It was reported that upregulation of the PI3K/AKT pathway induced the upregulation of the antiapoptotic protein Bcl-2 and resulted in castration-resistant prostate cancerIn summary, we found that hsa_circ_0110757 is overexpressed in TMZ-resistant glioma tissues and cells and can effectively sponge hsa-miR-1298-5p to increase the expression of ITGA1. It was also indicated that downregulation of hsa_circ_0110757 can significantly enhance TMZ sensitivity by regulating hsa-miR-1298-5p/ITGA1. This study demonstrates a novel theoretical basis that circRNAs exert function by sponging miRNAs and provides a new therapeutic strategy for glioma resistance.28. A total of 5 TMZ-resistant and 5 TMZ-sensitive glioma tissues were used for high-throughput sequencing, and 12 TMZ-resistant and 12 TMZ-sensitive glioma tissues were used to quantify ITGA1.Seventeen TMZ-resistant and 17 TMZ-sensitive glioma samples were obtained from Xiangya Hospital Central South University. TMZ resistance was defined as tumor recurrence at the time of TMZ application, and TMZ sensitivity was defined as no tumor relapse at the time of TMZ application2 at 37\u2009\u00b0C. To obtain TMZ  resistant cell lines, we gradually increased the exposure of U87 cells from 5 to 100\u2009\u03bcmol/l of TMZ. The TMZ-resistant U87 cells were named U87/R cells29.The human glioma cell line U87 was acquired from the Advanced Research Center of Central South University. U87 cells were cultured in 1640 medium . Fetal bovine serum  was diluted to a concentration of 10% in 1640 medium, and penicillin\u2013streptomycin  was added at a concentration of 1%. U87 cells were cultured in an incubator with 5% COTotal RNA was extracted with TRIzol reagent  from glioma tissues and cells. The stability of circRNA was determined by RNase R (3\u2009U/mg) treatment for 15\u2009min at 37\u2009\u00b0C. cDNAs were synthesized by a transcriptor-stranded synthesis kit . The mRNA expression of Mcl-1, ITGA1, hsa_circ_0110757, and hsa-miR-1298-5p was amplified by the ABI 7500 system . The primers used in this study are shown in Table For the construction of hsa_circ_0110757 overexpression plasmids, hsa_circ_0110757 cDNA was inserted into the PcDNA3.1 vector . The PcDNA3.1 vector contains a front circular frame and a back circular frame. Lipofectamine 2000  was used in transfection according to the manufacturer\u2019s instructions. The luciferase reporter containing the hsa_circ_0110757 sequence in the 3\u2032-UTR was constructed by subcloning the hsa_circ_0110757 fragment into the region directly downstream of a cytomegalovirus promoter-driven firefly luciferase cassette in a PcDNA3.1 vector. Mutations of each miRNA-binding site in the hsa_circ_0110757 sequence were created using a site-directed Mutagenesis Kit . The mutations were introduced in both the hsa_circ_0110757-expressing vector and the luciferase reporter containing the hsa_circ_0110757 sequence.4 cells per well were cultured in a 96-well plate for 24\u2009h. Different concentrations of TMZ (0\u2013600\u2009\u03bcmol/l) were incubated with cells for 24\u2009h. Then, the supernatant was removed and 100\u2009\u03bcl MTT reagent (0.5\u2009mg/ml) was added for 4\u2009h. After removing the supernatant, 200\u2009\u03bcl DMSO was added to dissolve purple crystals. A microporous plate detector  was used to detect the absorbance at 570\u2009nm.A total of 1\u2009\u00d7\u200910Cell invasion assay was carried out using 24-well transwells  coated with Matrigel . Overall, 1\u2009\u00d7\u2009105 cells in 500\u2009\u03bcl DMEM (1% FBS) were added to the upper chamber, then 750\u2009\u03bcl DMEM (10% FBS) was added in the lower chamber. After incubation for 48\u2009h, Matrigel and cells in the upper chamber were removed. Cells on the lower surface of the membrane were fixed in 4% paraformaldehyde and stained with 0.5% crystal violet. Cells in five microscopic fields were counted and photographed.The apoptosis rate was detected using the Annexin V-FITC/PI Apoptosis Detection kit . Overall, 5\u2009\u00d7\u2009105 cells were added into six-well plates. After treatment, cells were collected, washed with PBS, and resuspended in 0.5\u2009ml staining buffer. Then, 5\u2009\u03bcl Annexin V-FITC and 5\u2009\u03bcl PI were added to the buffer and incubated at 37\u2009\u00b0C for 15\u2009min in the dark. Cells were analyzed by flow cytometry .31, a biotin-labeled hsa_circ_0110757 probe was obtained from GeneChem (China). In short, 1\u2009\u00d7\u2009107 hsa_circ_0110757-overexpressing U87 cells were lysed and incubated with hsa_circ_0110757 or oligo probe, and the RNA complex bound to the surface of beads were eluted anterior to RT-PCR. The probes sequences are shown in Table As previously mentioned4 cells per well were incubated in 24-well plates for 24\u2009h. hsa-miR-1298-5p or anti-hsa-miR-1298-5p with pMIR-REPORT-hsa_circ_0110757 (Wt) or pMIR-REPORT-hsa_circ_0110757 (Mut) was cotransfected into U87 cells by Lipofectamine 2000  for 48\u2009h. Finally, luciferase activity was detected by the dual-luciferase reporter assay system .The wt 3\u2032 UTR fragment of hsa_circ_0110757 was amplified and cloned into the pMIR-REPORT\u2122 vector . The mutant (mt) hsa_circ_0110757 3\u2032 UTR was induced by using a site-directed Mutagenesis Kit  to in hsa-miR-1298-5p binding sites. First, 6\u2009\u00d7\u200910Glioma tissues or cells were lysed by RIPA buffer  for 20\u2009min. Total protein was collected from the cell lysate supernatant after centrifugation at 12,000\u2009rpm for 10\u2009min. The protein concentration was determined by a BCA Protein Assay Kit . Twenty micrograms of protein per lane was subjected to SDS-PAGE and then transferred to a PVDF membrane. The PVDF membrane was incubated with 5% skim milk for 2\u2009h, followed by incubation with Mcl-1 primary antibody , ITGA1 primary antibody , PI3K primary antibody , p-AKT (phospho S473) primary antibody , and Bcl-2 primary antibody  for overnight. After washing with PBS, an HRP-conjugated secondary antibody  was incubated for 2\u2009h. The signals of the PVDF membrane with chemiluminescence reagent were detected by chemiluminescence system .7 U87/R cells transfected with the si-hsa_circ_0110757 precursor were subcutaneously injected into BALB/c nude mice. When the tumor volume reached 100\u2009mm3, TMZ (60\u2009mg/kg) was applied intraperitoneally and tumor volume was measured every 2 days. After 15 days, the whole mice were systemically anesthetized and tumor tissues were collected for RT-PCR, western blot, and immunofluorescence assays.A total of 10P\u2009<\u20090.05 was considered statistically significant.All data are shown as the mean\u2009\u00b1\u2009standard deviation. SPSS 18.0 software was used to analyze significant differences. ANOVA and subsequent Tukey\u2019s post hoc test were used to analyze the differences between groups. Supplemental Information"}
+{"text": "The development of biometric applications, such as facial recognition (FR), has recently become important in smart cities. Many scientists and engineers around the world have focused on establishing increasingly robust and accurate algorithms and methods for these types of systems and their applications in everyday life. FR is developing technology with multiple real-time applications. The goal of this paper is to develop a complete FR system using transfer learning in fog computing and cloud computing. The developed system uses deep convolutional neural networks (DCNN) because of the dominant representation; there are some conditions including occlusions, expressions, illuminations, and pose, which can affect the deep FR performance. DCNN is used to extract relevant facial features. These features allow us to compare faces between them in an efficient way. The system can be trained to recognize a set of people and to learn via an online method, by integrating the new people it processes and improving its predictions on the ones it already has. The proposed recognition method was tested with different three standard machine learning algorithms (Decision Tree (DT), K Nearest Neighbor(KNN), Support Vector Machine (SVM)). The proposed system has been evaluated using three datasets of face images  via performance metrics of accuracy, precision, sensitivity, specificity, and time. The experimental results show that the proposed method achieves superiority over other algorithms according to all parameters. The suggested algorithm results in higher accuracy (99.06%), higher precision (99.12%), higher recall (99.07%), and higher specificity (99.10%) than the comparison algorithms. The face is considered the most critical part of the human body. Research shows that even a face can speak, and it has different words for different emotions. It plays a crucial role in interacting with people in society. It conveys people's identity and thus can be used as a key for security solutions in many organizations. The facial recognition (FR) system is increasingly trending across the world as an extraordinarily safe and reliable security technology. It is gaining significant importance and attention from thousands of corporate and government organizations because of its high level of security and reliability \u20133.Moreover, the FR system is providing vast benefits compared to other biometric security solutions such as palmprints and fingerprints. The system captures biometric measurements of a person from a specific distance without interacting with the person. In crime deterrent applications, this system can help many organizations identify a person who has any kind of criminal record or other legal issues. Thus, this technology is becoming essential for numerous residential buildings and corporate organizations. This technique is based on the ability to recognize a human face and then compare the different features of the face with previously recorded faces. This feature also increases the importance of the system and enables it to be widely used across the world. It is developed with user-friendly features and operations that include different nodal points of the face. There are approximately 80 to 90 unique nodal points of a face. From these nodal points, the FR system measures significant aspects including the distance between the eyes, length of the jawline, shape of the cheekbones, and depth of the eyes. These points are measured by creating a code called the faceprint, which represents the identity of the face in the computer database. With the introduction of the latest technology, systems based on 2D graphics are now available on 3D graphics, which makes the system more accurate and increases its reliability.Most machine learning algorithms consume a massive amount of resources, so it would be better to perform their tasks on a distributed environment such as cloud computing, fog computing, or edge computing.Biometrics is defined as the science and technology to measure and statistically analyze biological data. They are measurable behavioral and/or physiological characteristics that could be used to verify individual identification. For each individual, a unique biometric could be used for verification. Biometric systems are used in increasingly many fields such as prison security, secured access, and forensics. Biometric systems recognize individuals using authentication by utilizing different biological features such as the face, hand geometry, iris, retina, and fingerprints. The FR system is a more natural biometric information process with better variation than any other method. Thus, FR has become a recent topic in computer science related to biometrics and machine learning , 5. MachCloud computing is based on the shareability of many resources including services, applications, storage, servers, and networks to accomplish economies and consistency and thus provide the best concentration to maximize the efficiency of using the shared resources. Fog computing contains many services that are provided on the network edge, such as data storage, computing, data provision, and application services for end users who can be added to the network edge . These eThe main goals of this paper are to build a deep FR system using transfer learning in fog computing. This system is based on modern techniques of deep convolutional neural networks (DCNN) and machine learning. The proposed methods will be able to capture the biometric measurements of a person from a specific distance for crime deterrent purposes without interacting with the person. Thus, the proposed methods can help many organizations identify a person with any kind of criminal record or other legal issues.The remainder of the paper is organized as follows. Section 2 presents related work in FR techniques and applications. Section 3 presents the components of traditional FR: face processing, deep feature extraction and face matching by in-depth features, machine learning, K-nearest neighbors (KNN), support vector machines (SVM), DCNN, the computing framework, fog computing, and cloud computing. Section 4 explains the proposed FR system using transfer learning in fog computing. Section 5 presents the experimental results. Section 6 provides the conclusion with the outcomes of the proposed system.Due to the significant development of machine learning, the computing environment, and recognition systems, many researchers have worked on pattern recognition and identification via different biometrics using various building mining model strategies. Some common recent works on FR systems are surveyed here in brief.Singh, D et al.  proposedSchiller, D et al.  proposedDeng et al.  proposedWang et al.  proposedTran et al.  proposedMasi et al.  proposedDing and Tao  proposedAl-Waisy, et al.  proposedSivalingam et al.  proposedJonnathann et al.  presenteEthics StatementThe individuals shown in  to publish their image.All participants provided written informed consent and appropriate, photographic release. The whole system comprises three modules, as shown in the face detector is utilized on videos or images to detect faces.In the beginning, feature detector aligns each face to be normalized and recognized with the best match.The prominent Finally, the face images are fed into the FR module with the aligned results.face anti-spoofing, followed by recognition performance.Before inputting an image into the FR module, the image is scanned using Fig 1(C) illustrates the modus operandi of the FR module, where the face is first discovered, and then deep features are evaluated based on their conformity with the face via the following equation:M indicates the face matching algorithm, which is used to calculate the degree of similarity,where F refers to extracting the feature encoded for identity information,P is the face-processing stage of occlusal facial treatment, expressions, highlights, and phenomena; andIi and Ij are two faces in the images.\"one-to-many augmentation\" and \"many-to-one normalization\"  includes an image of a patient / participant in the study.\u00a0http://journals.plos.org/plosone/s/submission-guidelines#loc-human-subjects-research) on papers that include identifying, or potentially identifying, information, the individual(s) or parent(s)/guardian(s) must be informed of the terms of the PLOS open-access (CC-BY) license and provide specific permission for publication of these details under the terms of this license. Please download the Consent Form for Publication in a PLOS Journal . The signed consent form should not be submitted with the manuscript, but should be securely filed in the individual's case notes. Please amend the methods section and ethics statement of the manuscript to explicitly state that the patient/participant has provided consent for publication: \u201cThe individual in this manuscript has given written informed consent (as outlined in PLOS consent form) to publish these case details\u201d.As per the PLOS ONE policy . The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data\u2014e.g. participant privacy or use of data from a third party\u2014those must be specified.The Reviewer #1:\u00a0NoReviewer #2:\u00a0YesReviewer #3:\u00a0NoReviewer #4:\u00a0Yes**********4. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1:\u00a0YesReviewer #2:\u00a0YesReviewer #3:\u00a0NoReviewer #4:\u00a0Yes**********5. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1:\u00a0In this paper authors addressed the deep FR system using TL in fog computing. Problem taken has great significance and technical contribution is also present.Minor changes are recommended:1. The main objective achieved needs some more evidences.2. More details are required about the pre-processing done.3. Very less information is present about the feature extraction.4. Security of the system needs quantitatively parameters support.5. Very few literature reviewed about fog computing, need to incorporate more related and latest work about the problem.6. The related work can be extended by including the following papers:(a) Schiller, D., Huber, T., Dietz, M., & Andr\u00e9, E. (2020). Relevance-based data masking: a model-agnostic transfer learning approach for facial expression recognition.(b) Prakash, R. M., Thenmoezhi, N., & Gayathri, M. . Face Recognition with Convolutional Neural Network and Transfer Learning. In 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT) (pp. 861-864). IEEE.(c)Singh, D., Kumar, V., Vaishali & Kaur, M. (2020). Classification of COVID-19 patients from chest CT images using multi-objective differential evolution\u2013based convolutional neural networks. European Journal of Clinical Microbiology & Infectious Diseases, 1-11.Reviewer #2:\u00a0Main aim of the proposed work is to present face recognition task with the use of transfer function. The evaluation has been done using datasets and better classification results have been achieved. The paper presents the results and analysis very well. A very few grammatical errors may be checked for final presentation.Reviewer #3:\u00a01. The quality of some figures is very poor.2. There are number of grammatical mistakes and Typo errors in the manuscript, such asAs such, face recognition or authenticationarea of research..is still mostly an unexploredenvironmentsTherefore, the main3. The abstract is very poorly written and organized. The number of mistakes in it. It should be concise and clear for better understanding.4. Authors have poorly organized the paper. No sections and subsections are marked properly.5. The paper seems to be review paper than research paper. Authors have added unnecessary details in the manuscript.6. First of all, why authors mentioned Table 2 in related work? Secondly, Description and definitions of parameters and symbols of Table 2 are not mentioned.7. Author should define the parameter settings of each technique including proposed one.8. The current comparisons with competitive models are limited. Consider more effective techniques.9. Significant analyses are completely missing.10. Use either tables or graphs for comparative analysis. Both are creating chaos.Reviewer #4:\u00a0The following suggestions need to be incorporated before submitting the manuscript:1. There are many grammatical and spelling mistakes throughout the manuscript which needs to be modified.2. The abstract should mention the machine learning algorithms used in this work.3. There is no clear mentioning about the contributions of the paper.4. Use of very short sentences such as \"Then, recognition is performed\" must be avoided.5. Discussion of related work on the machine learning approaches should be extended with the following papers, which recently came into my attention because they proved to be successful in various applications:N-semble: neural network based ensemble approachDeep Transfer Learning based Classification Model for COVID-19 DiseaseAn Expert Approach for Data Flow Prediction: Case Study of Wireless Sensor NetworksComputed tomography reconstruction on distributed storage using hybrid regularization approachMachine learning for computer and cyber security: principle, algorithms, and practices6. In Table 2, the parameters such as TP,FN,P, N, TN stands for? It is a much better practice to explain these in paragraph form and then add the formulas.7. Correct he heading \"Materials and Methods\", \"Results and Discussions\". Take care of the typos in the manuscript.8. Instead of our proposed system, it is better practice to use the proposed system. The accuracy of the proposed system in Table 6 for the CNN model comes out to be 100%. In the real-world systems this is impossible, kindly justify the value.9. The conclusion should also include the future perspective of this work.**********what does this mean?). If published, this will include your full peer review and any attached files.6. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool,\u00a0 5 Sep 2020Firstly ;According to the Editor request, the authors confirmed that all images in the paper is created by the authors themselves and have not previously been copyrighted. Also, the authors confirmed that The individual pictured in Fig 1, Fig 5, Fig 6, Fig 11, Fig 12.has provided written informed consent (as outlined in PLOS consent form) to publish their image alongside the manuscript\". Response letter on reviewers PONE-D-20-15335Deep face recognition using computational intelligence algorithms Deep Face Recognition SystemTo: PLOS ONE EditorRe: Response to reviewersDear Editor,Thank you for allowing us to resubmit our manuscript after addressing the reviewers\u2019 comments.We are uploading(a) Our point-by-point responses to the comments (below) (response to reviewers),(b) An updated manuscript with changes highlighted in yellow, and(c) A clean updated manuscript without highlights (PDF main document).Best regards,Sincerely,Dr: Diaa Salama Abd Elminaam Information Systems Department, Faculty of Computers and Informatics, Benha University, Benha City, Egypt,+201019511000Diaa.salama@fci.bu.edu.eg________________________________________First, I would like to thank the Editor for these valuable comments that improve my paper. Second, I replied to every comment, as shown below. First, I would like to thank the Editors for their recommendation of the manuscript.According to Journal Requirements________________________________________Concern # 1: https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf1Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at https://www.aje.com/c/ieee\u201dResponse: I considered this point and improved it. The paper is re-edited and formatted by 3rd party service for language polishing https://www.aje.com/c/ieee) was used for subsequent language editing. The editing certificate is attached in the Supplementary Materials. In addition, the manuscript was revised before the first submission through 3rd party service for language polishing  in your submission contain copyrighted images. 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Please be aware that this license allows unrestricted use and distribution, even commercially, by third parties. Please reply and provide explicit written permission to publish XXX under a CC BY license and complete the attached form.\u201d\u201cI request permission for the open-access journal PLOS ONE to publish XXX under the Creative Commons Attribution License (CCAL) CC BY 4.0  on papers that include identifying, or potentially identifying, information, the individual(s) or parent(s)/guardian(s) must be informed of the terms of the PLOS open-access (CC-BY) license and provide specific permission for publication of these details under the terms of this license. Please download the Consent Form for Publication in a PLOS Journal . The signed consent form should not be submitted with the manuscript, but should be securely filed in the individual's case notes. Please amend the methods section and ethics statement of the manuscript to explicitly state that the patient/participant has provided consent for publication: \u201cThe individual in this manuscript has given written informed consent (as outlined in PLOS consent form) to publish these case details\u201d.As per the PLOS ONE policy First, I would like to thank the reviewer for these valuable comments that improve my paper. Second, I replied to every comment as shown below. Reviewer#1, First, I would like to thank the first reviewer for their recommendation (Minor changes are recommended:) of the manuscript.________________________________________Reviewer#1, Concern # 1: The main objective achieved needs some more evidences.Response: I considered this point and improved it. I revised it and concentrated on the main objective.Author response: This text was revised. An explanation has been provided.________________________________________Reviewer#1, Concern # 2: More details are required about the pre-processing done.Response you are right; I have added more details about the pre-processing step in the material and methods section 4.3  which lists the strategic parameters of each step and the associated values. Author action: The appropriate change was made.________________________________________Reviewer#1, Concern # 3: Very less information is present about the feature extraction.Response: you are right. You are right; I have added more details about the pre-processing step in the material and methods section 4.3  which lists the strategic parameters of each step and the associated values. Author action: The appropriate change was made.________________________________________Reviewer#1, Concern # 4: Security of the system needs quantitatively parameters support.Response : I considered this point and improved it.Author action: An explanation has been provided.________________________________________Reviewer#1, Concern # 5: Very few literature reviewed about fog computing, need to incorporate more related and latest work  about the problem.Response : I revised the literature reviewed about fog computing . I considered this point and improved it that improves the quality of my paper as much as possible. Author action: We updated the manuscript and added an explanation for the literature reviewed ________________________________________Reviewer#1, Concern # 6: The related work can be extended by including the following papers:(a) Schiller, D., Huber, T., Dietz, M., & Andr\u00e9, E. (2020). Relevance-based data masking: a model-agnostic transfer learning approach for facial expression recognition.(b) Prakash, R. M., Thenmoezhi, N., & Gayathri, M. . Face Recognition with Convolutional Neural Network and Transfer Learning. In 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT) (pp. 861-864). IEEE.(c)Singh, D., Kumar, V., Vaishali & Kaur, M. (2020). Classification of COVID-19 patients from chest CT images using multi-objective differential evolution\u2013based convolutional neural networks. European Journal of Clinical Microbiology & Infectious Diseases, 1-11.Response : I considered this point and revised The related work and considered theses good papers that improve the quality of my paper as much as possible . \u201c the reference section reordered\u201dSome references that are recommended are added .Author action: The appropriate change was made.________________________________________Actually, I gained from these comments a lot and worked on them to improve the quality of my paper as much as possible. Thank you. ________________________________________\u2003Reviewer Requirements (2nd reviewer)Reviewer#2, First, I would like to thank the secand reviewer for their recommendation  of the manuscript.________________________________________Reviewer#2, Concern # 1: A very few grammatical errors may be checked for final presentationhttps://www.aje.com/c/ieee\u201dResponse : I considered this point and improved it . Although the paper has been revised 3 times before by 3rd party service for language polishing , We will consider this point and send it again to the 3rd party service for language polishing https://www.aje.com/c/ieee) was used for subsequent language editing. The editing certificate is attached in the Supplementary Materials. In addition, the manuscript was revised before the first submission through 3rd party service for language polishing Reviewer#3, Concern # 1: The quality of some figures is very poor.Response I considered this point and improved the quality of the figures with one more clearly for better understanding Author action: The appropriate change was made.________________________________________Reviewer#3, Concern # 2: There are number of grammatical mistakes and Typo errors in the manuscript, such as As such, face recognition or authentication area of research.. is still mostly an unexplored environmentsTherefore, the mainResponse https://www.aje.com/c/ieee\u201dI considered this point and improved it . Although the paper has been revised 3 times before by 3rd party service for language polishing , We will consider this point and send it again to the 3rd party service for language polishing https://www.aje.com/c/ieee) was used for subsequent language editing. The editing certificate is attached in the Supplementary Materials. In addition, the manuscript was revised before the first submission through 3rd party service for language polishing  .so I considered this point and revised The related work and changed the parameters setting and symbols of table 2 to be in equation form.Author action: The appropriate change was made.________________________________________Reviewer#3, Concern # 7: Author should define the parameter settings of each technique including proposed one.Response the material and methods section  are totally changed which lists the strategic parameters of each step and the associated values. .table 2 shows the Parameters settings used in the experimentsAuthor action: The appropriate change was made.________________________________________Reviewer#3, Concern # 8: The current comparisons with competitive models are limited. Consider more effective techniques.Response I considered this point.Author action: The appropriate change was made.________________________________________Reviewer#3, Concern # 9: Significant analyses are completely missing.Response: you are right. I considered this point and improved it. The and methodology section (section 4.3) are totally updated. Author action: The appropriate change was made.________________________________________Reviewer#3, Concern # 10: Use either tables or graphs for comparative analysis. Both are creating chaos.Response: I considered this point and improved it.Author action: The appropriate change was made.________________________________________Actually, I gained from these comments a lot and worked on them to improve the quality of my paper as much as possible. Thank you. ________________________________________\u2003Reviewer Requirements (4th reviewer)________________________________________Reviewer#4, Concern # 1: There are many grammatical and spelling mistakes throughout the manuscript which needs to be modified.Response https://www.aje.com/c/ieee\u201dI considered this point and improved it . Although the paper has been revised 3 times before by 3rd party service for language polishing , We will consider this point and send it again to the 3rd party service for language polishing https://www.aje.com/c/ieee) was used for subsequent language editing. The editing certificate is attached in the Supplementary Materials. In addition, the manuscript was revised before the first submission through 3rd party service for language polishing  Our point-by-point responses to the comments (below) (response to Editor),Best regards,Sincerely,Dr: Diaa Salama Abd Elminaam Information Systems Department, Faculty of Computers and Informatics, Benha University, Benha City, Egypt,+201019511000Diaa.salama@fci.bu.edu.eg________________________________________First, I would like to thank the editor for these valuable comments that improve my paper. Second, I replied to every comment as shown below. ________________________________________The Editor Concern # 1: We note the following figures contain images of faces: Fig 1, Fig 5, Fig 6, Fig 11, Fig 12.Additionally, we note the following figures may contain copyrighted images: Fig 9 and Fig 10.https://journals.plos.org/plosone/s/file?id=8ce6/plos-consent-form-english.pdf).1) Please disclose whether or not the participants shown in Fig 1, Fig 5, Fig 6, Fig 11, Fig 12 consented to having this image published under the Creative Commons Attribution (CC BY) license and signed the PLOS Consent Form for Publication in a PLOS Journal  to publish their image alongside the manuscript\".Response: I considered this point and improved it. 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+{"text": "Mechanically, hsa_circ_0039053 positively regulated USP21 expression through interacting with miR-637. Moreover, overexpression of USP21 or silencing of miR-637 restored the inhibitory impacts of hsa_circ_0039053 silencing on HCC progression. Collectively, our study confirmed that hsa_circ_0039053 could be regarded as a competing endogenous RNA (ceRNA) to positively modulate the expression of USP21 combining with miR-637, which provided a potential target in HCC treatment.Accumulating evidence indicated that circular RNAs (circRNAs) are crucial regulators in tumorigenesis of hepatocellular carcinoma (HCC), but it is still unclear how hsa_circ_0039053 causes HCC. Herein, hsa_circ_0039053 was upregulated in HCC tissues and cell lines. The upregulation of hsa_circ_0039053 was linked to the advanced clinical characteristics of patients. Downregulation of hsa_circ_0039053 decreased the invasion and proliferative ability of tumors  Hepatocellular carcinoma (HCC) is estimated to be among the major cancers contributing to tumor-associated deaths worldwide Circular RNAs (circRNAs) are a class of newly discovered non-coding RNA (ncRNA), characterized by a 3' polyadenylated tail and a covalently closed loop lacking a 5' cap MicroRNAs (miRNAs) are small ncRNAs that modulate gene expression via binding the 3'UTR of their specific mRNA Here, we discovered a novel circRNA hsa_circ_0039053 in HCC. Next, we determined the biological roles, as well as the molecular pathways of hsa_circ_0039053 on HCC tumorigenesis. Our findings provided the hsa_circ_0039053/miR-637/USP21 regulatory network in HCC, which provided a novel therapeutic target.The expression profile of circRNAs in HCC was downloaded from the GEO database (GSE97332 and GSE94508). A total of 61 paired HCC tissues were obtained from patients at First Affiliated Hospital and College of Clinical Medicine of Henan University of Science and Technology. Only the patients who had not been subjected to radiotherapy or chemotherapy were included. The samples were preserved for subsequent analyses by snap freezing them in liquid nitrogen then storing at -80 \u00b0C. All subjects agreed to participate in the study and assigned informed consent  and human hepatocyte cells (LO2) were procured from ATCC and grown in DMEM Medium  containing 100 \u03bcg/mL streptomycin, 100 IU/mL 10% penicillin, and 10% FBS at 37 \u00b0C and 5% CO2.siRNAs  targeting hsa_circ_0039053  and scrambled negative control (si-NC) were made by GenePharma . MiR-637 inhibitors and mimics, plus their negative controls, were designed by GeneCopoeia . Lipofectamine 2000 reagents  was utilized to transiently transfect oligonucleotides (50 nM) into cells.-\u0394\u0394Ct method.RNA isolation was executed utilizing TRIzol (Invitrogen) in reference to the protocols of manufacturers. After the RNAs were quantified, cDNA was generated with Primer Script\u2122  RT reagent kit or TaqMan  microRNA Assay kit. Then qRT-PCR was manipulated with SYBR Premix Ex Taq II (Takara) and specific primers (GeneCopoeia). Thermal cycler parameters were 95 \u00b0C for 5 s, 45 cycles of 95 \u00b0C for 5 s, 60 \u00b0C for 10 s and 72 \u00b0C for 10 s, and extension at 72 \u00b0C for 5 min. Relative expression was calculated by the 2To prevent gene transcription, HCC cells were subjected to 2 \u03bcg/mL Act D (Sigma) or DMSO (Sigma) treatment at specified time points. Afterward, total RNA was isolated, followed by the determination of hsa_circ_0039053, and linear ITGAL levels.For RNase R treatment, total RNA (5 \u00b5l) was digested with 10 U of RNase R  for 30 min at 37 \u00b0C. Next, the expression of hsa_circ_0039053 and linear ITGAL was estimated through qRT-PCR analysis.Nuclear-cytoplasmic fractionation was conducted using Nuclear and Cytoplasmic Extraction Reagents PARIS\u2122 Kit (Invitrogen) as per the methods described by the manufacturer and previous study We employed the CCK-8 Kit  to determine the proliferative capacity of the cells. Transfected HCC cells (3000 cells/well) were seeded into 96-well plates. In total, 10\u03bcl CCK-8 reagent was added to the wells at various times following incubation. A microplate reader  was utilized to determine the absorbance at 450 nm.Moreover, cells were subjected to colony formation assays to further determine the proliferative capacity of the cells. Briefly, transfected HCC cells were seeded into a 6-well plate and cultured for 10 days. Then, colonies were rinsed thrice with PBS, fixed with methanol and stained with 0.1% crystal violet (Sigma).Cells proliferation and invasion were analyzed using 5-ethynyl-2\u02b9-deoxyuridineassay Kit  and transwell invasion assays according to previous study The sequences of hsa_circ_0039053 (or 3'UTR of USP21), including the binding sequences of mutant or wild type miR-637, were cloned into pmirGLO plasmid (Promega), generating hsa_circ_0039053-WT, hsa_circ_0039053-MUT, USP21 3'UTR WT, and USP21 3'UTR MUT. The constructed luciferase reporter vector was transfected into cells alongside miR-637 mimics or miR-NC. Dual-Luciferase Reporter Assay Kit (Promega) was employed to quantify the luciferase activity.The cell lysates were gathered utilizing RIP lysis buffer. Following, they were subjected to conjugation with anti-Ago2 antibody in magnetic beads. Furthermore, anti-IgG antibody served as control. When immunoprecipitation was accomplished, qRT-PCR was adopted to quantify RNA enrichment.Ten female BALB/c nude mice (4-week-old) were procured from Beijing HFK Bioscience Co., Ltd . Transfect cells (1 \u00d7 106 cells) were intraperitoneally injected into mice (n = 5 per group). Tumor volumes (0.5 \u00d7 length \u00d7 width2) were detected weekly. At the 7th week post-injection, mice were sacrificed by cervical dislocation. Tumors were immediately removed and weighed.P \u2264 0.05 was regarded as statistically significant difference.The SPSS 20.0 software was used to conduct the analyses. Data are displayed as mean \u00b1 SD. The means of 2 groups were compared using Student's t-test. Means of multiple groups were compared with one-way ANOVA. To elucidate the functions of circRNAs in HCC progression, we analyzed two studies on circRNA expression in HCC (GSE94508 and GSE97332). Twenty circRNAs  were found to be aberrantly expressed in both studies , we found that only four miRNAs  existed in circBank and dbDEMC 2.0  subfamily and plays important functions in tumorigenesis In summary, our work identified hsa_circ_0039053 as a cancerogenic molecule in regulating HCC cell proliferation, and invasion on the basis of the regulation of the miR-637/USP21 regulatory axis, providing a biomarker and a possible target for HCC treatment."}
+{"text": "Patient classification based on clinical and genomic data will further the goal of precision medicine. Interpretability is of particular relevance for models based on genomic data, where sample sizes are relatively small (in the hundreds), increasing overfitting risk netDx is a machine learning method to integrate multi-modal patient data and build a patient classifier. Patient data are converted into networks of patient similarity, which is intuitive to clinicians who also use patient similarity for medical diagnosis. Features passing selection are integrated, and new patients are assigned to the class with the greatest profile similarity. netDx has excellent performance, outperforming most machine-learning methods in binary cancer survival prediction. It handles missing data \u2013 a common problem in real-world data \u2013 without requiring imputation. netDx also has excellent interpretability, with native support to group genes into pathways for mechanistic insight into predictive features. The netDx Bioconductor package provides multiple workflows for users to build custom patient classifiers. It provides turnkey functions for one-step predictor generation from multi-modal data, including feature selection over multiple train/test data splits. Workflows offer versatility with custom feature design, choice of similarity metric; speed is improved by parallel execution. Built-in functions and examples allow users to compute model performance metrics such as AUROC, AUPR, and accuracy. netDx uses RCy3 to visualize top-scoring pathways and the final integrated patient network in Cytoscape. Advanced users can build more complex predictor designs with functional building blocks used in the default design. Finally, the netDx Bioconductor package provides a novel workflow for pathway-based patient classification from sparse genetic data. Interpretability is desirable to better understand biological mechanism underlying the phenotype and for rational treatment design. It is also important for genomic applications, where most contemporary datasets have fewer than a thousand samples, increasing the risk of overfit models that do not independently replicate. Separately, most machine learning methods do not handle missing data \u2013 a common feature of real-world datasets \u2013 without prior data imputation or filtering. netDx is a supervised learning algorithm that classifies patients by integrating multimodal patient data2. It is notable among machine learning methods for handling missing data without imputation, and excels at interpretability by enabling users to create biologically-meaningful grouping of features, such as grouping genes into pathway-level features. netDx integrates multi-modal data by converting each layer into a patient similarity network and then integrating these networks . The outer list consists of one entry per data type, with corresponding groupings in the inner list. Assays names must be identical in theassays slot and ingroupList.Running netDx requires a machine with one or more cores running Intel processors (1.7 GHz i7 or later) or equivalent, a minimum of 1Gb RAM per thread, and 1Gb disk space. Feature selection is an embarrassingly parallel problem, and we highly recommend running the software on a multi-core machine to reduce compute time. netDx is currently supported on machines running OS X or Unix-based operating systems. The software requires the Java executable (v1.8 or higher) to be available on the system path, and will not work on recent Windows-based operating systems that lack this type of installation. Windows users can access netDx via a Docker image provided atbuildPredictor. This function runs feature selection and classification over a specified number of train/test splits, and returns all associated feature scores and detailed classification results in alist object. Advanced users can create custom predictor designs by combining the individual steps used inbuildPredictor : Features are defined at the level ofpathways; similarity is defined by pairwise Pearson correlation.Gene expression: Features are defined at the level ofFeature scoring is automatically performed over multiple random splits of the data into train and blind test partitions. Feature selected networks are those that consistently score highly across the multiple splits (e.g. those that score 9 out of 10 in \u226570% of splits).Conceptually, this is what the higher-level logic looks like for building a predictor over multiple random splits of samples into training and test groups. In the example below, the predictor runs for 100 train/test splits. Within a split, features are scored from 0 to 10. Features scoring \u22659 are used to predict labels on the held-out test set (20%). The example shows pseudocode, not actual netDx function calls:featScoreMax <- 10   # max. score for a feature in feature selectionfeatSelCutoff <- 9   # features scoring at least this much are used to                        # classify test patientsnumSplits <- 100     # number of random train/test splits to run feature                        # selection for. Model performance is averaged over\t\t       # these iterations.netScores <- list  # scores from feature selection, one entry per splitperf <- list       # model performance for each splitfor k in 1:numSplits  <- splitData(80:20) # split data using RNG seed  featScores[[k]] <- runFeatureSelection  topFeat[[k]] <- applyFeatCutoff(featScores[[k]]) perf[[k]] <- evalModelPerfendSetupsuppressWarnings(suppressMessages(require(netDx)))Data. In this example, we use curated data from The Cancer Genome Atlas, through the BioconductorcuratedTCGAData package. The goal is to classify a breast tumour into either a Luminal A subtype or otherwise. The predictor integrates clinical variables selected by the user, along with gene expression data.Here we load the required packages and download clinical and gene expression data.suppressWarnings(suppressMessages(library(curatedTCGAData)))List the available data without downloading any:curatedTCGAData##                                         Title DispatchClass## 31                       BRCA_CNASeq-20160128           Rda## 32                       BRCA_CNASNP-20160128           Rda## 33                       BRCA_CNVSNP-20160128           Rda## 35             BRCA_GISTIC_AllByGene-20160128           Rda## 36                 BRCA_GISTIC_Peaks-20160128           Rda## 37     BRCA_GISTIC_ThresholdedByGene-20160128           Rda## 39  BRCA_Methylation_methyl27-20160128_assays        H5File## 40      BRCA_Methylation_methyl27-20160128_se           Rds## 41 BRCA_Methylation_methyl450-20160128_assays        H5File## 42     BRCA_Methylation_methyl450-20160128_se           Rds## 43                 BRCA_miRNASeqGene-20160128           Rda## 44                    BRCA_mRNAArray-20160128           Rda## 45                     BRCA_Mutation-20160128           Rda## 46              BRCA_RNASeq2GeneNorm-20160128           Rda## 47                   BRCA_RNASeqGene-20160128           Rda## 48                    BRCA_RPPAArray-20160128           RdaWe will work only with the gene expression data in this example:suppressMessages,FALSE))brca <- This next code block prepares the TCGA data. In practice you would do this once, and save the data before running netDx, but we run it here in full to see an end-to-end example.staget <- sub$pathology_T_stage))staget <- suppressWarnings(as.integer(staget))colData(brca)$STAGE <- stagetpam50 <- colData(brca)$PAM50.mRNApam50[which] <- \"notLumA\"pam50[which] <- \"LumA\"colData(brca)$pam_mod <- pam50tmp <- colData(brca)$PAM50.mRNAidx <- union),                      which(is.na(staget)))pID <- colData(brca)$patientIDtokeep <- setdiffbrca <- brca# remove duplicate assays mapped to the same samplesmp <- sampleMap(brca)samps <- smpnotdup <- sampsbrca[[1]] <- suppressMessages## harmonizing input:##   removing 44 sampleMap rows with 'colname' not in colnames of experimentsID andSTATUS columns in the sample metadata table. netDx uses these to get the patient identifiers and labels, respectively.The predictor will look for columns namedpID <- colData(brca)$patientIDcolData(brca)$ID <- pIDcolData(brca)$STATUS <- colData(brca)$pam_modDesign custom patient similarity networks (features). netDx provides a set of default functions to compute patient similarity, including Pearson correlation, normalized difference, and scaled Euclidean distance. However, users may choose to define a custom function that takes patient data and variable groupings as input, and returns a set of patient similarity networks (PSN) as output. The user can customize what datatypes are used, how they are grouped, and what defines patient similarity for a given datatype.makeNetFunc parameter when callingbuildPredictor.When running the predictor (next section), the user simply passes this custom function as an input variable; i.e. theNote: While netDx supports flexible experimental design, the user must ensure that the design, i.e. the similarity metric and variable groupings are appropriate for a given application. Domain knowledge is recommended to support good design.makeNetFunc function take some generic parameters as input. These include:netDx requires that thedataList: the patient data, provided as aMultiAssayExperiment object. Refer to onlinetutorials for MultiAssayExperiment to see how to construct those objects from data.groupList: sets of input data that will define individual networks (e.g. genes grouped into pathways)netDir: the directory where the resulting patient similarity networks will be stored.dataListMultiAssayExperiment object:In this example, the breast cancer data is already provided to us as asummary(brca)## Length\t      Class             Mode\t\t\t\t\t\t\t## 1 \t\tMultiAssayExperiment \tS4groupListdataList while each value is itself a list and reflects a potential network.This object tells the predictor how to group units when constructing a network. For example, genes may be grouped into a patient similarity network representing a pathway. This object is a list; the names match those ofgroupList <- list# genes in mRNA data are grouped by pathwayspathList <- readPathways)## ---------------------------------------## Fetching http://download.baderlab.org/EM_Genesets/January_01_2018/Human/symbol/Human_AllPathways_January_01_2018_symbol.gmt## File: 182107f6006ac_Human_AllPathways_January_01_2018_symbol.gmt## Read 3028 pathways in total, internal list has 3009 entries##  FILTER: sets with num genes in ##    => 971 pathways excluded##    => 2038 leftgroupList[[\"BRCA_mRNAArray-20160128\"]] <- pathList[1:3]# clinical data is not grouped; each variable is its own featuregroupList[[\"clinical\"]] <- listgroupList variable has one entry per data layer:So thesummary(groupList)##                         Length Class  Mode## BRCA_mRNAArray-20160128 3      -none- list## clinical                2      -none- listEach entry contains a list, with one entry per feature. Here we have three pathway-level features for mRNA and two variable-level features for clinical data.For example, here are the networks to be created with RNA data. Genes corresponding to pathways are to be grouped into individual network. Such a groupList would create pathway-level networks:groupList[[\"BRCA_mRNAArray-20160128\"]][1:3]## $UREA_CYCLE##  [1] \"SLC25A15\" \"CPS1\"     \"ASL\"      \"ARG2\"     \"SLC25A2\"  \"OTC\"     ##  [7] \"NMRAL1\"   \"NAGS\"     \"ASS1\"     \"ARG1\"    ## ## $`CDP-DIACYLGLYCEROL_BIOSYNTHESIS_I`##  [1] \"AGPAT1\" \"GPD2\"   \"ABHD5\"  \"GPAT2\"  \"CDS1\"   \"LPCAT3\" \"LPCAT4\"##  [8] \"CDS2\"   \"AGPAT6\" \"AGPAT5\" \"MBOAT7\" \"AGPAT9\" \"LCLAT1\" \"MBOAT2\"## [15] \"AGPAT4\" \"GPAM\"   \"AGPAT3\" \"AGPAT2\"## ## $`SUPERPATHWAY_OF_D-_I_MYO__I_-INOSITOL__1,4,5_-TRISPHOSPHATE_METABOLISM`##  [1] \"IPMK\"   \"INPP5B\" \"INPP5F\" \"INPP5D\" \"MINPP1\" \"INPP5A\" \"ITPKA\" ##  [8] \"OCRL\"   \"ITPKC\"  \"ITPKB\"  \"SYNJ2\"  \"INPP5J\" \"INPP5K\" \"PTEN\"  ## [15] \"IMPA2\"  \"INPP1\"  \"SYNJ1\"  \"INPPL1\" \"IMPA1\"  \"IMPAD1\"For clinical data, we will define each variable as its own network:head## $age## [1] \"patient.age_at_initial_pathologic_diagnosis\"## ## $stage## [1] \"STAGE\"Define patient similarity measure for each network. This function is defined by the user and tells the predictor how to create networks from the provided input data.dataList,groupList, andnetDir as input variables. The residual ... parameter is to pass additional variables tomakePSN_NamedMatrix, notablynumCores .This function requiresIn this example, the custom similarity function does the following:pathway-level networks from RNA data using the default Pearson correlation measuremakePSN_NamedMatrix1. Createsvariable-level networks from clinical data using a custom similarity function of normalized difference:makePSN_NamedMatrix.2. CreatesmakeNets <- function {     netList <- c # initialize before is.null check     # make RNA nets (NOTE: the check for is.null is important!)     # (Pearson correlation)     if (!is.null(groupList[[\"BRCA_mRNAArray-20160128\"]])) {      netList <- makePSN_NamedMatrix,                  groupList[[\"BRCA_mRNAArray-20160128\"]],                  netDir,verbose=FALSE,                   writeProfiles=TRUE,...)      }         # make clinical nets      netList2 <- c     if ) {     netList2 <- makePSN_NamedMatrix,          groupList[[\"clinical\"]],netDir,          simMetric=\"custom\",customFunc=normDiff, # custom function          writeProfiles=FALSE,          sparsify=TRUE,verbose=TRUE,...)     }     netList <- c(unlist(netList),unlist(netList2))     return(netList)}Note:dataList andgroupList are generic containers that can contain whatever object the user requires to create a PSN.The custom function supports flexible feature design.Build predictor. Finally, we call the function that runs the netDx predictor. We provide:(numSplits),\u2022 number of train/test splits over which to collect feature scores and average performancefeatScoreMax)\u2022 maximum score for features in one round of feature selection (featSelCutoff); only features scoring this value or higher will be used to classify test patients, and\u2022 threshold to call feature-selected networks for each train/test split (dataList), how variables are to be grouped into networks (groupList) and the custom function to generate features (makeNetFunc).\u2022 the information to create the PSN, including patient data \u2022 scores networks between 0 to 2 (i.e.featSelCutoff) to classify test samples for that split.\u2022 uses networks that score \u22651 out of 2  # make results reproducibleoutDir <- sprintf # location for intermediate work# set keepAllData=TRUE to not delete at the end of the predictor run.# This can be useful for debugging.out <- suppressMessages   )Examine outputbuildPredictor call. This list contains:The results are stored in the list object returned by theinputNets: all input networks that the model started with.\u25cfSplit<i>: a list with results for each train-test split\u25cf\u2013 predictions: real and predicted labels for test patients\u2013 accuracy: percent accuracy of predictions\u2013 featureScores: feature scores for each label . Each entry contains the feature selection scores for the corresponding label.\u2013 featureSelected: vector of features that pass feature selection. List of lengthg, with one entry per label.summary(out)##           Length Class  Mode     ## inputNets 10     -none- character## Split1     4     -none- list     ## Split2     4     -none- listsummary(out$Split1)##                 Length Class     ## featureScores      2   -none-    ## featureSelected    2   -none-    ## predictions     2692   data.frame## accuracy           1   -none-    ##                 Mode   ## featureScores   list   ## featureSelected list   ## predictions     list   ## accuracy        numericReformat results for further analysisThis code collects different components of model output to examine the results.numSplits <- 2st <- unique(colData(brca)$STATUS)acc <- c         # accuracypredList <- list # prediction tablesfeatScores <- list # feature scores per classfor (cur in unique(st)) featScores[[cur]] <- listfor (k in 1:numSplits) {     pred <- out[[sprintf]][[\"predictions\"]];    # predictions table    tmp <- pred     predList[[k]] <- tmp      # accuracy     acc <- c/nrow(tmp))     # feature scores     for (cur in unique(st)) {        tmp <- out[[sprintf]][[\"featureScores\"]][[cur]]        colnames(tmp) <- c        featScores[[cur]][[sprintf]] <- tmp     }}Compute model performanceAfter compiling the data above, plot accuracy for each train/test split:print(acc)## [1] 0.8507463 0.8059701Create a ROC curve, a precision-recall curve, and plot average AUROC and AUPR :predPerf <- plotPerfExamine feature scores and consistently high-scoring features. UsegetNetConsensus to convert the list data structure into a single table, one per patient label. The rows show train/test splits and the columns show features that consistently perform well.callFeatSel to identify features that consistently perform well across the various train/test splits. Because this is a toy example, we set the bar low to get some features. Here we accept a feature if it scores 1 or higher (fsCutoff=1) in even one split (fsPctPass=0.05), setting the latter to a low positive fraction.We then usefeatScores2 <- lapplysummary(featScores2)##         Length Class      Mode## LumA    3      data.frame list## notLumA 3      data.frame listhead(featScores2[[\"LumA\"]])##                                                                     PATHWAY_NAME## 1                                      CDP-DIACYLGLYCEROL_BIOSYNTHESIS_I.profile## 2 SUPERPATHWAY_OF_D-_I_MYO__I_-INOSITOL__1,4,5_-TRISPHOSPHATE_METABOLISM.profile## 3                                                             UREA_CYCLE.profile## 4                                                                   age_cont.txt## 5                                                                 stage_cont.txt##   Split1 Split2## 1      2      2## 2      2      2## 3      2      2## 4     NA      1## 5     NA      1fsCutoff=9 andfsPctPass=0.7. This setting gives us features that score a minimum of 9 in at least 70% of the train/test splits.Where features are scored out of 10, a reasonable setting isfeatSelNet <- lapply {    callFeatSel})print(head(featScores2[[\"LumA\"]]))##                                                                     PATHWAY_NAME## 1                                      CDP-DIACYLGLYCEROL_BIOSYNTHESIS_I.profile## 2 SUPERPATHWAY_OF_D-_I_MYO__I_-INOSITOL__1,4,5_-TRISPHOSPHATE_METABOLISM.profile## 3                                                             UREA_CYCLE.profile## 4                                                                   age_cont.txt## 5                                                                 stage_cont.txt##   Split1 Split2## 1      2      2## 2      2      2## 3      2      2## 4     NA      1## 5     NA      1Visualize pathway features as an enrichment map. An enrichment map is a network-based visualization of pathway connectivity and is used in netDx to visualize themes in predictive pathway-based features5. It is used in conjunction with the AutoAnnotate Cytoscape app to identify clusters, and apply auto-generated labels to these6.getEMapInput_many to create the input that helps generate the enrichment map in Cytoscape.UseEmap_res <- getEMapInput_manyWrite the results to files that Cytoscape can read in:gmtFiles <- listnodeAttrFiles <- listfor (g in names(Emap_res)) {    outFile <- sprintf    write.table    nodeAttrFiles[[g]] <- outFile    outFile <- sprintf    conn <- suppressWarnings))    tmp <- Emap_res[[g]][[\"featureSets\"]]    gmtFiles[[g]] <- outFile    for (cur in names(tmp)) {        curr <- sprintf)        writeLines    }close(conn)}Finally, plot the enrichment map. This step requires Cytoscape to be installed, along with the EnrichmentMap and AutoAnnotate apps. It also requires the Cytoscape application to be open and running on the machine running the code. This block is commented out for automatic builds on Bioconductor, but a screenshot of the intended result is shown below .plotEmapThis example enrichment map isn\u2019t terribly exciting because of the low number of pathway features permitted, the upper bound on feature selection scores and low number of train/test splits in the demonstration example.Here is an example of an enrichment map generated by running the above predictor with more real-world parameter values, and all available pathways :Visualize integrated patient similarity network based on top features. We apply a threshold to define the most predictive features, and integrate these into a single patient similarity network. Such a network is useful for downstream operations such as ascertaining whether or not classes are significantly separated, and for visualization of results.Here we define predictive features as those scoring 2 out of 2 in all train/test splits.featScores2 <- lapplyfeatSelNet <- lapply {     callFeatSel})We next examine the features:print(featSelNet)## $LumA## [1] \"CDP-DIACYLGLYCEROL_BIOSYNTHESIS_I.profile\"                                     ## [2] \"SUPERPATHWAY_OF_D-_I_MYO__I_-INOSITOL__1,4,5_-TRISPHOSPHATE_METABOLISM.profile\"## [3] \"UREA_CYCLE.profile\"                                                            ## ## $notLumA## [1] \"SUPERPATHWAY_OF_D-_I_MYO__I_-INOSITOL__1,4,5_-TRISPHOSPHATE_METABOLISM.profile\"## [2] \"UREA_CYCLE.profile\"                                                            ## [3] \"stage_cont.txt\"groupList limited to top features:Create a newtopPath <- gsub))topPath <- gsub# create groupList limited to top featuresg2 <- list;for (nm in names(groupList)) {    cur <- groupList[[nm]]    idx <- which(names(cur) %in% topPath)    message))    if (length(idx)>0) g2[[nm]] <- cur[idx]}## BRCA_mRNAArray-20160128: 3 pathways## clinical: 1 pathwaysWe plot the integrated patient network based on the features selected above.aggFun=\"MEAN\"). For plotting we retain only the top 5% strongest edges (topX=0.05).In the example below, the networks are integrated by taking the mean of the edge weights )The integrated PSN can also be visualized as a tSNE plot .tsne <- plot_tSNE)summary(tsne)##                     Length Class  Mode   ## N                     1    -none- numeric## Y                   662    -none- numeric## costs               331    -none- numeric## itercosts            20    -none- numeric## origD                 1    -none- numeric## perplexity            1    -none- numeric## theta                 1    -none- numeric## max_iter              1    -none- numeric## stop_lying_iter       1    -none- numeric## mom_switch_iter       1    -none- numeric## momentum              1    -none- numeric## final_momentum        1    -none- numeric## eta                   1    -none- numeric## exaggeration_factor   1    -none- numericIntroduction. In this example, we will use clinical data and three types of \u2019omic data - gene expression, DNA methylation and proteomic data - to classify breast tumours as being one of three types: Luminal A, Luminal B, or Basal. This example is an extension of the one used to build a binary classifier (seeUse Case 1).We also use several strategies and definitions of similarity to create features:variable (e.g. age) is its own feature; similarity is defined asnormalized difference.\u2022 Clinical variables: Eachpathways; i.e. a feature groups genes within the pathway. Similarity is defined as pairwisePearson correlation.\u2022 Gene expression: Features are defined at the level ofdata layer; a single feature is created for all of proteomic data, and the same for methylation. Similarity is defined as pairwisePearson correlation.\u2022 Proteomic and methylation data: Features are defined at the level of the entireSetup. Load thenetDx package.suppressWarnings(suppressMessages(require(netDx)))Data. For this example, we download data from The Cancer Genome Atlas through the BioconductorcuratedTCGAData package. The fetch command automatically creates aMultiAssayExperiment object containing the data.suppressMessages(library(curatedTCGAData))curatedTCGAData command to explore available data types in the breast cancer dataset.We use thecuratedTCGAData##                                         Title DispatchClass## 31                       BRCA_CNASeq-20160128           Rda## 32                       BRCA_CNASNP-20160128           Rda## 33                       BRCA_CNVSNP-20160128           Rda## 35             BRCA_GISTIC_AllByGene-20160128           Rda## 36                 BRCA_GISTIC_Peaks-20160128           Rda## 37     BRCA_GISTIC_ThresholdedByGene-20160128           Rda## 39  BRCA_Methylation_methyl27-20160128_assays        H5File## 40      BRCA_Methylation_methyl27-20160128_se           Rds## 41 BRCA_Methylation_methyl450-20160128_assays        H5File## 42     BRCA_Methylation_methyl450-20160128_se           Rds## 43                 BRCA_miRNASeqGene-20160128           Rda## 44                    BRCA_mRNAArray-20160128           Rda## 45                     BRCA_Mutation-20160128           Rda## 46              BRCA_RNASeq2GeneNorm-20160128           Rda## 47                   BRCA_RNASeqGene-20160128           Rda## 48                    BRCA_RPPAArray-20160128           Rdadry.run=FALSE initiates the fetching of the data.In this call we fetch only the gene expression, proteomic and methylation data; settingbrca <- suppressWarnings,    dry.run=FALSE)))This next code block prepares the TCGA data. In practice this is performed once, and the resulting data is saved before running netDx, but we run it here to see an end-to-end example.# prepare clinical variable - stagestaget <- sub$pathology_T_stage))staget <- suppressWarnings(as.integer(staget))colData(brca)$STAGE <- staget# exclude normal, HER2 pam50 <- colData(brca)$PAM50.mRNAidx <- union),     which(is.na(staget)))idx <- union))pID <- colData(brca)$patientIDtokeep <- setdiffbrca <- brcapam50 <- colData(brca)$PAM50.mRNAcolData(brca)$pam_mod <- pam50# remove duplicate namessmp <- sampleMap(brca)for (nm in names(brca)) {    samps <- smp    notdup <- samps    brca[[nm]] <- suppressMessages}ID andSTATUS columns in the sample metadata slot. netDx uses these to get the patient identifiers and labels, respectively.The important thing is to createpID <- colData(brca)$patientIDcolData(brca)$ID <- pIDcolData(brca)$STATUS <- gsub$pam_mod)Rules to create features (patient similarity networks). We will group gene expression data by pathways and clinical data by single variables. We will treat methylation and proteomic data each as a single feature, so each of those groups will contain the entire input table for those corresponding data types.http://download.baderlab.org/EM_Genesets). We choose the January 2018 source to be consistent with earlier published work, but normally the latest source would be downloaded. We group gene expression measures by pathways.In the code below, we fetch pathway definitions from January 2018 from a source that auto-compiles these from curated pathway databases )## ---------------------------------------## Fetching http://download.baderlab.org/EM_Genesets/January_01_2018/Human/symbol/Human_AllPathways_January_01_2018_symbol.gmt## File: 182107f6006ac_Human_AllPathways_January_01_2018_symbol.gmt## Read 3028 pathways in total, internal list has 3009 entries##  FILTER: sets with num genes in ##    => 971 pathways excluded##    => 2038 leftgroupList[[\"BRCA_mRNAArray-20160128\"]] <- pathList[1:3]# clinical data is not grouped; each variable is its own featuregroupList[[\"clinical\"]] <- list# for methylation generate one feature containing all probes# same for proteomics datatmp <- list(rownames(experiments(brca)[[2]]));names(tmp) <- names(brca)[2]groupList[[names(brca)[2]]] <- tmptmp <- list(rownames(experiments(brca)[[3]]));names(tmp) <- names(brca)[3]groupList[[names(brca)[3]]] <- tmpDefine patient similarity for each network. We provide netDx with a custom function to generate similarity networks (i.e. features). The first block tells netDx to generate correlation-based networks using everything but the clinical data. This is achieved by the call:makePSN_NamedMatrix`makePSN_NamedMatrix, this time requesting the use of the normalized difference similarity metric. This is achieved by calling:To make features from single measures using clinical data, the second block makes a slightly-modified call tomakePSN_NamedMatrixnormDiff is a function provided in thenetDx package, but the user may define custom similarity functions in this block of code and pass those tomakePSN_NamedMatrix, using thecustomFunc parameter.makeNets <- function {    netList <- c # initialize before is.null check    # correlation-based similarity for mRNA, RPPA and methylation data    # (Pearson correlation)    for (nm in setdiff(names(groupList),\"clinical\")) {       # NOTE: the check for is.null is important!        if (!is.null(groupList[[nm]])) {        netList <- makePSN_NamedMatrix,                    groupList[[nm]],netDir,verbose=FALSE,                      writeProfiles=TRUE,...)         }    }        # make clinical nets     netList2 <- c    if ) {    netList2 <- makePSN_NamedMatrix,         groupList[[\"clinical\"]],netDir,         simMetric=\"custom\",customFunc=normDiff, # custom function         writeProfiles=FALSE,         sparsify=TRUE,verbose=TRUE,...)     }     netList <- c(unlist(netList),unlist(netList2))     return(netList)}Build predictor. Finally, we make the call to build the predictor.set.seed(42) # set a custom seed to make results reproducible# location for intermediate work# set keepAllData to TRUE to not delete at the end of the # predictor run.# This can be useful for debugging.outDir <- paste numSplits <- 2Lout <- suppressMessages)## function {##  netList <- c # initialize before is.null check##  # correlation-based similarity for mRNA, RPPA and methylation data##  # (Pearson correlation)##  for (nm in setdiff(names(groupList),\"clinical\")) {##     # NOTE: the check for is.null is important!##      if (!is.null(groupList[[nm]])) {##      netList <- makePSN_NamedMatrix,##                    groupList[[nm]],netDir,verbose=FALSE,##                   writeProfiles=TRUE,...) ##      }##  }##  ##  # make clinical nets ##  netList2 <- c##  if ) {##  netList2 <- makePSN_NamedMatrix,##      groupList[[\"clinical\"]],netDir,##      simMetric=\"custom\",customFunc=normDiff, # custom function##      writeProfiles=FALSE,##      sparsify=TRUE,verbose=TRUE,...)##  }##  netList <- c(unlist(netList),unlist(netList2))##  return(netList)## }##             IS_TRAIN## STATUS       TRAIN TEST##   Basal-like    77   20##   Luminal_A    184   46##   Luminal_B    101   26## ## Luminal_A   nonpred      <NA> ##       184       178         0 ## ## Basal-like    nonpred       <NA> ##         77        285          0 ## ## Luminal_B   nonpred      <NA> ##       101       261         0 ##             IS_TRAIN## STATUS       TRAIN TEST##   Basal-like    77   20##   Luminal_A    184   46##   Luminal_B    101   26## ## Luminal_A   nonpred      <NA> ##       184       178         0 ## ## Basal-like    nonpred       <NA> ##         77        285          0 ## ## Luminal_B   nonpred      <NA> ##       101       261         0Compute accuracy for three-way classification:# Average accuracyst <- unique(colData(brca)$STATUS) acc <- matrix,nrow=numSplits) colnames(acc) <- st for (k in 1:numSplits) {      pred <- out[[sprintf]][[\"predictions\"]];     tmp <- pred     for (m in 1:length(st)) {        tmp2 <- subset        acc <- sum(tmp2$PRED==tmp2$STATUS)/nrow(tmp2)     }}print)##      Luminal_A Basal-like Luminal_B##      57.14        100     28.57##      58.62        100     45.009.On examining the confusion matrix above, we can see that the model perfectly classifies basal tumours, but performs poorly in distinguishing between the two types of luminal tumours. This performance is unsurprising because luminal and basal tumours have different molecular characteristics, with the latter being ER- tumours; in contrast, both Luminal A and B are both types of ER+ tumoursres <- out$Split1$predictionsprint]))##             PRED_CLASS## STATUS       Basal-like Luminal_A##   Basal-like         14         0##   Luminal_A           4        16##   Luminal_B           4         6##             PRED_CLASS## STATUS       Luminal_B##   Basal-like         0##   Luminal_A          8##   Luminal_B          4netDx natively handles missing data, making it suitable to build predictors with sparse genetic data such as somatic DNA mutations, frequently seen in cancer, and from DNA copy number variations (CNVs). netDx handles missing data at two levels. First, netDx uses patient similarity networks, not input data, as its features. Missing data can be handled by the similarity metric used to make this conversion. e.g. If similarity is defined as the Pearson correlation between gene expression measures at the pathway level, then omitting missing genes from the correlation calculation still allows the correlations, and thus the pathway-level network, to be computed. Where patients are missing a particular feature, the network integration step uses what information it has. For example, in a scenario where the data consist of transcriptomic and proteomic measures, if a patient is missing transcriptomic data, the integration step will use only the proteomic data (network edges) for that patient.et al.10. The design for this predictor is shown inThis example demonstrates how to use netDx to build a predictor from sparse genetic data. Here we build a case/control classifier for autism spectrum disorder (ASD) diagnosis, starting from rare CNVs; for this, we use data from PintoDesign and adapting the algorithm for sparse event data. In this design, we group CNVs by pathways. The logic behind the grouping is prior evidence showing that genetic events in diseases tend to converge on cellular processes of relevance to the pathophysiology of the disease10.Binary similarity and label enrichmentif two patients share a mutation in a pathway, their similarity for that pathway is 1; otherwise it is zero. This binary definition, while conceptually intuitive, increases the false positive rate in thenetDx feature selection step. That is, networks with even a single case sample will get a high feature score, regardless of whether that network is enriched for case samples.In this design, similarity is defined as a binary function, a strategy that has advantages and drawbacks. In plain terms,label-enrichment step in the feature selection. A bias measure is first computed for each network, such that a network with only cases scores +1; one with only controls scores -1; and one with an equal number of both has a score of zero. Label-enrichment compares the bias in each real network, to the bias in that network in label-permuted data. It then assigns an empirical p-value for the proportion of times a label-permuted network has a bias as high as the real network. Only networks with a p-value below a user-assigned threshold (default: 0.07) pass label-enrichment, and feature selection is limited to these networks. InnetDx, label-enrichment is enabled by settingenrichLabels=TRUE in the call tobuildPredictor_sparseGenetic.To counter this problem, we introduce aCumulative feature scoringnumSplits times, each time leaving1/numSplits of the samples out. In each split, features are scored between zero andfeatScoreMax, using the same approach as is used for continuous-valued input. Feature scores are then added across the splits so that a feature can score as high asnumSplits*featScoreMax.The other difference between this design and those with non-sparse data, is the method of scoring features . The useEvaluating model performanceFor a given cutoff for features, a patient is called a \u201ccase\u201d if they have a genetic event in pathways that pass feature selection at that cutoff; otherwise, at that cutoff, they are labelled a \u201ccontrol\u201d. These calls are used to generate the false positive and true positive rates across the various cutoffs, which ultimately generates a ROC curve.SetupsuppressMessages(require(netDx))suppressMessages(require(GenomicRanges))Data. CNV coordinates are read in, and converted into aGRanges object. As always, the sample metadata table, here thepheno object, must haveID andSTATUS columns.outDir <- sprintfif (file.exists(outDir)) unlink; dir.create(outDir)cat(\"* Setting up sample metadata\\n\")## * Setting up sample metadataphenoFile <- sprintf)pheno   <- read.delimcolnames(pheno)[1] <- \"ID\"head(pheno)##        ID seqnames    start      end    Gene_symbols Pathogenic STATUS## 3  1020_4     chr3  4110452  4145874                         no   case## 4  1030_3    chr10 56265896 56361311                         no   case## 5  1030_3     chr7 64316996 64593616 ZNF92,LOC441242         no   case## 7  1045_3     chr3 83206919 83239473                         no   case## 11 1050_3     chr6 57021412 57062509        KIAA1586         no   case## 16 1116_4     chr1 30334653 30951250                         no   casecnv_GR    <- GRanges,                           ID=pheno$ID,LOCUS_NAMES=pheno$Gene_symbols)pheno <- phenoGroup CNVs by pathways. ThefetchPathwayDefinitions function downloads pathway definitions frombaderlab.org but users may provide custom.gmt files as well. We use theBiocFileCache package to download gene definitions for the hg18 genome build, and convert these aGRanges object. The functionmapNamedRangesToSets is used to group thisGRanges object into pathway-level sets.pathFile <- fetchPathwayDefinitions## Fetching http://download.baderlab.org/EM_Genesets/February_01_2018/Human/symbol/Human_AllPathways_February_01_2018_symbol.gmtpathwayList <- readPathways(pathFile)## ---------------------------------------## File: f72c2f3fae_Human_AllPathways_February_01_2018_symbol.gmt## Read 3199 pathways in total, internal list has 3163 entries##  FILTER: sets with num genes in ##    => 1044 pathways excluded##    => 2119 leftsuppress(Messagesrequire(BiocFileCache))geneURL <- pastecache <- rappdirs::user_cache_dir(appname = \"netDx\")bfc <- BiocFileCache::BiocFileCachegeneFile <- bfcrpathgenes <- read.delimgenes <- genesgene_GR     <- GRanges,   name=genes)mapNamedRangesToSets does this grouping, generating aGRangesList object.Group gene extents into pathway-based sets, which effectively creates grouping rules for netDx. The functionpath_GRList <- mapNamedRangesToSetsRun predictor. Once the phenotype matrix and grouping rules are set up, the predictor is called usingbuildPredictor_sparseGenetic. Note that unlike with non-sparse data, the user does not provide a custom similarity function in this application; currently, the only option available is the binary similarity defined above. As discussed above, settingenrichLabels=TRUE to enable label-enrichment is highly recommended to reduce false positive rate.predictClass    <- \"case\"out <- suppressMessages)##          TT_STATUS## STATUS    TEST TRAIN##   case     188   376##   control  208   418## [1] 794##    user  system elapsed ##   0.681   0.234  11.180 ##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. ## -1.0000 -0.7143  0.2000  0.1505  1.0000  1.0000 ## [1] 363## Time difference of 7.545976 secs##          TT_STATUS## STATUS    TEST TRAIN##   case     188   376##   control  208   418## [1] 794##    user  system elapsed ##   0.583   0.091   9.431 ##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. ## -1.0000 -0.6295  0.1667  0.1269  1.0000  1.0000 ## [1] 392## Time difference of 12.61768 secs##          TT_STATUS## STATUS    TEST TRAIN##   case     188   376##   control  210   416## [1] 792##    user  system elapsed ##   0.972   0.146  12.872 ##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. ## -1.0000 -0.5668  0.2000  0.1523  1.0000  1.0000 ## [1] 484## Time difference of 16.2142 secsPlot results. Feature selection identifies pathways that are consistently enriched for the label of interest; here, \u201ccase\u201d status. From the diagnostic point of view, a patient with a genetic event in a selected feature - here, a CNV in a feature-selected pathway - is labelled a \u201ccase\u201d. \u201cTrue positives\u201d are therefore cases with CNVs in feature-selected pathways, while \u201cfalse positives\u201d are controls with CNVs in feature-selected pathways. These definitions are used to compute the ROC curve below ,ylim=c,    las=1, xlab=\"False Positive Rate (%)\",     ylab=\"True Positive Rate (%)\",    bty='n',cex.axis=1.5,cex.lab=1.3,    main=\"ROC curve - Patients in label-enriched pathways\")pointsWe can also compute the AUROC and AUPR. tmp <- data.frame stats <- netDx::perfCalc(tmp)tmp <- stats$statsmessage)## PRAUC = 0.63message)## ROCAUC = 0.7011; in a real-world scenario this model would need to be validated on an independent dataset. In our experience, using a combination of sparse genetic data and binary similarity makes classifiers prone to overfitting. Measures commonly used to mitigate overfitting include training the model on larger datasets, and larger number of train/test splits are advised.This predictor performs outperforms previous CNV-based classifiersPathway scores are also added across the splits, for a total of 9 across the 3 splits (3 + 3 + 3).# now get pathway scoretmp <- out$cumulativeFeatScoresrownames(tmp) <- NULLprint(head(tmp))##                                                      PATHWAY_NAME SCORE## 1 NEUROTRANSMITTER_RECEPTORS_AND_POSTSYNAPTIC_SIGNAL_TRANSMISSION     8## 2                                              HUNTINGTON_DISEASE     7## 3              NICOTINIC_ACETYLCHOLINE_RECEPTOR_SIGNALING_PATHWAY     6## 4                          BETA-CATENIN_INDEPENDENT_WNT_SIGNALING     6## 5                                                 G2_M_TRANSITION     6## 6                                          MITOTIC_G2-G2_M_PHASES     6numPermsEnrich=200L,featScoreMax=10L,numSplits=3L identifies a much richer set of themes related to synaptic transmission and cell proliferation, consistent with the known biology of ASD as well as those identified in the original publication10.As before, running the predictor with all possible pathway-related features and realistic training parameters, such asThe nodes inThe dynamic range of feature scores is much larger as well, here ranging from 0 to 30. The resulting ROC curve in12. For label propagation, we use an R-based implementation of random walk with restart, a popular strategy in bioinformatic applications15. The result of using this strategy on a patient\u2019s binary somatic mutation profile is a non-sparse profile in which genes are assigned a continuous score between zero and one, that reflects its network proximity to patient mutations. This propagation value is then ranked and binarized, with the top-ranked fraction set to one; this fraction defaults to 3% and is tunable. The binarization serves to limit inferred mutation to genes closest to the known mutations. For instance, genes distant from the patient's mutation would get a low propagation value, and would be thresholded to zero, i.e. not considered to be mutated. The result of this step is a less sparse binary matrix, which serves as input data to the predictor.netDx provides the option of reducing the sparsity of mutation data by inferring \"indirect mutations\" using prior knowledge of gene-gene interaction networks. Conceptually, the logic is that if a patient has a mutation in a given gene, the mutation indirectly impacts interacting genes. Indirect mutation is inferred by label propagating patient mutations over a gene-gene interaction network onto neighbours. The resulting smoothed network is then used for downstream applications. This network-based smoothing improved mutation-based tumour class discovery in four types of cancer16 by binarized pathologic stage. As with the previous use case, we create pathway-level features to reflect that cancer progression occurs by a combination of genes acting in molecular networks corresponding to cancer hallmark processes such as cell proliferation and apoptosis17. As in Use Case 3, similarity used is the binary function. If two patients share a mutation in a pathway, their similarity for that pathway is one; otherwise it is zero.In this example, we use direct and inferred somatic mutations to classify Testicular Germ Cell Tumours (TGCT)Setupset.seed(8)suppressWarnings(suppressMessages(require(netDx)))suppressWarnings(suppressMessages(require(MultiAssayExperiment)))Data. Clinical and genetic data are downloaded using the Bioconductor packagecuratedTCGAData. Mutations are converted to a binary matrix format where rows represent genes, columns represent patients; entry  is set to one if gene i has a somatic mutation, and zero otherwise.genoFile <- paste,         \"TGCT_mutSmooth_geno.txt\",sep=getFileSep)geno <- read.delimphenoFile <- paste,         \"TGCT_mutSmooth_pheno.txt\",sep=getFileSep)                                          pheno <- read.delimrownames(pheno) <- pheno$IDtable(pheno$STATUS)## ## EARLY  LATE ##    66    14Smooth mutations over a gene interaction network. The gene-gene interaction network used in this example contains high-confidence cancer-specific interactions18. This specific network effectively clusters tumour samples of patients, distinguishing them by tumour type and time of survival. This is a binary symmetric network.# download example nets from remote location for vignetterequire(BiocFileCache)## Loading required package: BiocFileCache## Loading required package: dbplyrnetFileURL <- pastecache <- rappdirs::user_cache_dir(appname = \"netDx\")bfc <- BiocFileCache::BiocFileCachenetFile <- bfcrpathcancerNets <- read.delimhead##        HSPA2 RPN1 GK2 HSPA6 PPP3R1## HSPA2      0    1   1     1      1## RPN1       1    0   0     1      0## GK2        1    0   0     1      0## HSPA6      1    1   1     0      1## PPP3R1     1    0   0     1      0## DLG1       1    0   0     1      0smoothMutations_LabelProp is used to smooth the mutations using the provided interaction network, by using label propagation. The output of this method is a continuous-valued network which reflects the network proximity of the non-zero values to the original mutations.require## Loading required package: doParallel## Loading required package: foreach## Loading required package: iterators# Start the node clusters for parallel propagationsmoothedMutations <- smoothMutations_LabelPropFinally, the smoothed matrix is binarized. Genes with a propagation value greater than a specified cutoff are set to one, with the rest set to zero. This step ensures that genes which get a low propagation value are not used. Genes with lower smoothed values reflect those farther from the original mutation, and setting these to zero signifies a lack of confidence that these were impacted.lessSparseMut <- thresholdSmoothedMutations   )Create pathway-level features with binary patient similarity. Smoothed mutations are now grouped at the level of biological pathways. As with other examples, pathways are downloaded from a compilation of curated pathway databases (GMT format). Thereafter, we define pathway-level patient similarity to be binary; i.e. if two patients share a mutation in genes from the same pathway, their mutual similarity is one; else it is zero. Individual steps below use identical functions to those used in the first use case above.#Setup to build the predictorpathwayList <- readPathways   )   ## ---------------------------------------## Fetching http://download.baderlab.org/EM_Genesets/January_01_2018/Human/symbol/Human_AllPathways_January_01_2018_symbol.gmt## File: 1c25416f319_Human_AllPathways_January_01_2018_symbol.gmt## Read 3028 pathways in total, internal list has 3009 entries##  FILTER: sets with num genes in ##    => 971 pathways excluded##    => 2038 leftexprdat <- SummarizedExperimentobjList <- list(genetic=exprdat)Now we define functions for patient similarity:makeNets <- function {  netList <- c; netList2 <- c    # create genetic nets  if (!is.null(groupList[[\"genetic\"]])) {    netList <- makeMutNets  }   return(netList)}# g geno matrix, genes by patients (columns) - binary# pList list of genesets# outDir - dir where nets are to be writtenmakeMutNets <- function {  g <- t(g) # transpose to have genes as columns  cl    <- makeCluster(numC)  registerDoParallel(cl)    numPat <- c  netList <- foreach(k=1:length(pList)) %do% {    idx <- which(colnames(g) %in% pList[[k]])        if (length(idx)>0) {      has_mut <- rowSums      has_mutp <- names(has_mut)[which(has_mut>0)]            if (length(has_mutp)>=6) {        ##cat[k],        ##            length(has_mutp)))        #numPat <- c)        pat_pairs <- t);        pat_pairs <- cbind;        outFile <- sprintf[k])        write.table        basename(outFile)      } else NULL    } else {      NULL    }  }  stopCluster(cl)  unlist(netList)}Build predictor. Finally, we compile all the data into a MultiAssayExperiment object and as before, run the predictor.exprdat <- SummarizedExperimentobjList <- list(genetic=exprdat)groupList <- list(genetic=pathwayList)dataList <- MultiAssayExperimentThe predictor call is essentially the same as with other simpler designs:outDir <- pasteif (!file.exists(outDir)) unlink out <- suppressMessages)Examine output. This code collects different components of model output to examine the results.numSplits <- 2Lst <- unique(colData(dataList)$STATUS)acc <- c          # accuracypredList <- list # prediction tablesfeatScores <- list # feature scores per classfor (cur in unique(st)) featScores[[cur]] <- listfor (k in 1:numSplits) {    pred <- out[[sprintf]][[\"predictions\"]];    # predictions table    tmp <- pred    predList[[k]] <- tmp     # accuracy    acc <- c/nrow(tmp))    # feature scores    for (cur in unique(st)) {       tmp <- out[[sprintf]][[\"featureScores\"]][[cur]]       colnames(tmp) <- c       featScores[[cur]][[sprintf]] <- tmp    }}Plot the AUROC and AUPR curves :predPerf <- plotPerfExamine features with the highest scores. Here, these are pathways with somatic mutations that best predict vital status:featScores2 <- lapplysummary(featScores2)##       Length Class      Mode## EARLY 3      data.frame list## LATE  3      data.frame listfeatSelNet <- lapply {     callFeatSel})print(head(featScores2[[\"LATE\"]]))##                                                                                               PATHWAY_NAME## 1                             1D-_I_MYO__I_-INOSITOL_HEXAKISPHOSPHATE_BIOSYNTHESIS_II__MAMMALIAN__cont.txt## 2                                                                 3-PHOSPHOINOSITIDE_BIOSYNTHESIS_cont.txt## 3                                                                  3-PHOSPHOINOSITIDE_DEGRADATION_cont.txt## 4                                   ABORTIVE_ELONGATION_OF_HIV-1_TRANSCRIPT_IN_THE_ABSENCE_OF_TAT_cont.txt## 5 ACTIVATED_PKN1_STIMULATES_TRANSCRIPTION_OF_AR__ANDROGEN_RECEPTOR__REGULATED_GENES_KLK2_AND_KLK3_cont.txt## 6                                                     ACTIVATED_TAK1_MEDIATES_P38_MAPK_ACTIVATION_cont.txt##   Split1 Split2## 1      1     NA## 2      2      2## 3      2      2## 4      1     NA## 5      2      2## 6      2      12. The new netDx package supports OS X and Unix platforms. It also supports Windows systems, with the exception of those that do not have the Java executable available in the system search path. The companion R packagenetDx-examples, previously used to store example data, is now deprecated. All examples are now either contained within thenetDx package or are fetched from Bioconductor using local file-caching via theFileCache package. Major functions have been renamed to reflect their role rather than implementation, making their usage more intuitive (Use Case 3), using the functionbuildPredictor_sparseGenetic. We also added the functionality to generate an integrated patient similarity network from features passing selection. TheplotIntegratedPatientNetwork function generates this network, computes statistics on pairwise shortest distance measures (Dijkstra distance) within and across labels, and automatically generates a network visualization in Cytoscape.netDx v1.1.4 has several updates relative to the version released with the netDx methods report (v1.0.23)tuitive . The cursink function. Functions computing model performance and plotting no longer assume a directory structure created by the model-building step. Users now set random number generator seeds at the outset, instead of providing a seed as an input parameter to various functions. Automated network visualization in Cytoscape now usesRCy3, for programmatic access of Cytosape from R.A number of software updates were made as part of Bioconductor integration. Unlike the previous version where all user output was written to a specific output directory, all predictor output is now returned to users as R objects, and intermediate work is written to temporary directories by default. The turnkey predictor-building function no longer automatically generates a log file; rather, users are required to create their own log files using the R20, creating a modified version specifically for netDx. netDx incurs a relatively higher memory footprint because each feature in netDx internally generates a similarity network with pairwise similarity measures. Network integration, a step in feature selection, requires keeping all these networks in memory. Certain grouping rules also incur a greater memory footprint than others. Notably, a model with pathway-level features converts one gene expression data matrix into ~2,000 pathway-level patient similarity networks; such a design is less scalable in the number of nodes, than one which creates a single feature based on all gene expression. We optimized netDx memory usage by customizing the underlying GeneMANIA Java application used for network integration. netDx uses a modified version of the GeneMANIA implementation, which bypasses steps not required for the netDx pipeline, such as the identifier conversion and steps involving file input/output. Memory and computational time improvements were benchmarked by building binary classifiers for breast tumours and schizophrenia case-control classification. The CommonMind Consortium21 dataset (downloaded from Synapse:syn5607607) included 279 controls and 258 cases, with a total of 537 patients, with gene expression data from the prefrontal cortex organized into pathway level features . The breast cancer data was part of the TCGA project9, with tumour gene-expression for 348 patients, including 154 Luminal A and 194 tumours of other subtypes, also organized into pathway-level features . In the benchmark, an approximately 70:30 split of samples was used for cross validation. We measured training time for the predictor using the 70% of samples of a single subtype. All tests were performed on an Intel Xeon @ 2.6GHz machine with 126 GB of available RAM and 12 cores. During benchmarking, threads had a fixed amount of RAM available, with discrete steps of 4 GB, 6 GB and 8 GB. Here each predictor was built using only a single core. Benchmarking runs were parallelized using GNU parallel22, where the performance was averaged over four runs of the 10 queries for each datasets. Following improvements, memory use dropped to one-third of the original amount. With the updated software, the CommonMind dataset also required two-thirds of the time to build the predictor, as compared to with the original version  provided to allow replication of the software development and its use by others?PartlyIs sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?YesReviewer Expertise:Computational statistics, multi omics integration, R software development.I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. We still obtained some errors while running the code with Linux/Windows/French locale. However, all four use cases were running correctly using the docker image of the working environment.Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?YesIs the rationale for developing the new software tool clearly explained?YesIs the description of the software tool technically sound?YesAre sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?PartlyIs sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?YesReviewer Expertise:Systems and Network Biology, Bioinformatics, Computational Biology.We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. The paper entitled \u201cnetDx: Software for building interpretable patient classifiers by multi-'omic data integration using patient similarity networks\u201d is a companion paper to the article \u201cnetDx: interpretable patient classification using integrated patient similarity networks\u201c published in Plos Comp Bio in 2019. Its goal is to present an updated version of the R software implementation of netDx as a Bioconductor package. The netDx tool proposes an approach to building a patient classifier from heterogeneous patient data, from clinical to omics. The availability as an R package is of interest to the community. In addition, the manuscript details 4 different uses cases that could help interest readers to apply the tools. We however had difficulties running the code provided in the use cases and obtained different errors and warnings, so\u00a0have been in contact with the authors to try to solve the problems.\u00a0However, debugging code necessitate a lot of exchanges, and hundreds of lines of error outputs cannot go in a peer review. These problems are not solved yet.\u00a0\u00a0Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?YesIs the rationale for developing the new software tool clearly explained?YesIs the description of the software tool technically sound?YesAre sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?PartlyIs sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?YesReviewer Expertise:Systems and Network Biology, Bioinformatics, Computational BiologyWe confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however we have significant reservations, as outlined above. Responses are shown in bold beneath each reviewer comment, the latter shown in italics. ---The paper entitled \u201cnetDx: Software for building interpretable patient classifiers by multi-'omic data integration using patient similarity networks\u201d is a companion paper to the article \u201cnetDx: interpretable patient classification using integrated patient similarity networks\u201c published in Plos Comp Bio in 2019. Its goal is to present an updated version of the R software implementation of netDx as a Bioconductor package. \u00a0 The netDx tool proposes an approach to building a patient classifier from heterogeneous patient data, from clinical to omics. The availability as an R package is of interest to the community. In addition, the manuscript details 4 different uses cases that could help interest readers to apply the tools. We however had difficulties running the code provided in the use cases and obtained different errors and warnings, so have been in contact with the authors to try to solve the problems. However, debugging code necessitate a lot of exchanges, and hundreds of lines of error outputs cannot go in a peer review. These problems are not solved yet.\u00a0Response: We thank the reviewers for taking the time and effort to work with us to resolve these issues. There were three main sources of errors that the reviewers encountered. The resolution for each is described below.https://hub.docker.com/repository/docker/shraddhapai/netdx.\u201d To better support Windows users we now provide Docker images of working environments with the latest version of netDx. The following text has been added to the \u201cOperation\u201d section: \u201cWindows users can access netDx via a Docker image provided atIncompatibility with French locale: The reviewers tested netDx on a system with a French locale, which identified an unforeseen international incompatibility. netDx uses a Java-based network integration software during feature selection. Parts of this software would break when provided with numbers using a comma for a decimal separator; as such they were incompatible with several non-English locales. We have now fixed the issue in netDx v1.3.1 to ensure that all files passed from R to Java are forced to use a period as decimal separator.https://hub.docker.com/repository/docker/shraddhapai/netdx/general (Tag: v1.3.1_french). Note that French locale users do not have to download this specific version of netDx; the Docker image is provided as a contained environment with a French locale, where we have demonstrated that netDx now works. We are now able to successfully run all vignettes in a French locale; please seeWindows incompatibility: netDx is currently not supported on some versions of the Windows operating system because of variation in how Java is invoked by different Java versions.\u00a0 In particular, newer Windows systems do not have a java executable available on the search path by default. We have noted this in the current version of the manuscript. Therefore for now we will continue to support netDx on OS X and Unix systems in BioConductor, and will provide a Docker container for Windows users. A working Docker image, supporting Windows and other operating systems that support Docker, is available on Docker hub:https://hub.docker.com/repository/docker/shraddhapai/netdx/general (Tag: v1.3.1)Use case 4 had outdated function calls. That has been amended in the current version of the manuscript. We also supplied the reviewers with the updated vignette when we learnt about the error. We hope that the resolution of the above three issues is satisfactory. We are working with the reviewers to ensure that they are able to run the software given the changes above. The authors illustrate full R workflows to apply netDx using four case studies with various ranges of classification difficulty and data analysis settings. The netDx package proposes various ways of grouping features, for example using known biological pathways, and several graphical outputs. I appreciated that netDx builds on MultiAssayExperiment for easier handling of multi omics data sets.\u00a0The manuscript will be useful for readers eager to get started with netDx. Below are some suggestions for improvement of the manuscript.I acknowledge that the original algorithm has been detailed in\u00a0reference [2], however, the present manuscript gives some emphasis on the ability of netDx to handle missing values. A short statement describing how this is done would be helpful.I suggest rewriting the sentence 'The final model is created by choosing features that consistently score highly.' in the introduction. On a first read, it appeared as if there was selection bias during the process.I have some reservations regarding the representation of SEM in the AUROC figures, why not using SD? Implementation aspects:While much effort and improvements have been done in the netDx package v1.1.4, I believe that additional functions could be created to be user friendly. For example, many customs functions  could be recoded into more generic functions. I would encourage the authors to revisit the code they propose in these workflows and improve when possible.\u00a0I am not sure Table 1 column 3 (function name in v1.0.23) and part of the software update paragraph is useful here. Presumably this could appear on the GitHub page and the NEWS files, unless the objective of this manuscript is also to update the users of the latest changes. Minor typos:'published' in the Introduction. Methodological aspects:Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?YesIs the rationale for developing the new software tool clearly explained?YesIs the description of the software tool technically sound?YesAre sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?PartlyIs sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?YesReviewer Expertise:Computational statistics, multi omics integration, R software development.I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Responses are shown in bold under corresponding reviewer comments shown in italics. ---The authors illustrate full R workflows to apply netDx using four case studies with various ranges of classification difficulty and data analysis settings. The netDx package proposes various ways of grouping features, for example using known biological pathways, and several graphical outputs. I appreciated that netDx builds on MultiAssayExperiment for easier handling of multi omics data sets. The manuscript will be useful for readers eager to get started with netDx.We thank the reviewer for their time, appreciative comments, and feedback. We also hope the manuscript helps new users get started with netDx for classification and data integration.Below are some suggestions for improvement of the manuscript. Methodological aspects:I acknowledge that the original algorithm has been detailed in reference [2], however, the present manuscript gives some emphasis on the ability of netDx to handle missing values. A short statement describing how this is done would be helpful.Response: Text added in introduction of use case 3: \u201cnetDx handles missing data at two levels. First, netDx uses patient similarity networks, not input data, as its features. Missing data can be handled by the similarity metric used to make this conversion. e.g. If similarity is defined as the Pearson correlation between gene expression measures at the pathway level, then omitting missing genes from the correlation calculation still allows the correlations, and thus the pathway-level network,, to be computed. Where patients are missing a particular feature, the network integration step uses what information it has. For example, in a scenario where the data consist of transcriptomic and proteomic measures, if a patient is missing transcriptomic data, the integration step will use only the proteomic data (edges) for that patient (network edges) for that patient.\u201dI suggest rewriting the sentence 'The final model is created by choosing features that consistently score highly.' in the introduction. On a first read, it appeared as if there was selection bias during the process.\u00a0Response: We altered the sentence in the manuscript to: \u201cThe final model is created from features that scored highly in feature selection, a step that uses only training samples\u201d.\u00a0I have some reservations regarding the representation of SEM in the AUROC figures, why not using SD?Response: We have now changed the function that plots the AUROC curve to use standard deviation as default, and have provided the user the option of using SEM. The corresponding figures have been updated in the manuscript to show the change .\u00a0\u00a0Implementation aspects:While much effort and improvements have been done in the netDx package v1.1.4, I believe that additional functions could be created to be user friendly. For example, many customs functions  could be recoded into more generic functions. I would encourage the authors to revisit the code they propose in these workflows and improve when possible.\u00a0\u00a0Response: We agree and will continue to create useful utility functions to make it easier for new users to use in future releases of netDx. In order to avoid potentially over complicating or overengineering our API, we are waiting for user feedback before making these additions.I am not sure Table 1 column 3 (function name in v1.0.23) and part of the software update paragraph is useful here. Presumably this could appear on the GitHub page and the NEWS files, unless the objective of this manuscript is also to update the users of the latest changes.Response: We agree and have removed this column from Table 1.Minor typos:'published' in the Introduction.Response: Corrected."}
+{"text": "Colorectal cancer (CRC) is a common malignant tumor with unsatisfactory overall prognosis. CircRNAs could be promising prognostic biomarkers in cancers, and play important role in the process of tumorigenesis and progression. Here, we explored the role of hsa_circ_0004831 in blood extracellular vesicles and its prognostic value in CRC.The circRNA and mRNA expression level matrix in extracellular vesicles of CRC and normal samples were obtained from the exoRBase database. The corresponding miRNA expression level matrix in extracellular vesicles was downloaded from the BBCancer database. Differentially expressed circRNAs, miRNAs and mRNAs were identified using the limma package of R software at the cut-off criteria of fold change (FC)\u2009>\u20092 and adj. p\u2009<\u20090.05. RT-qPCR assay was conducted to measure hsa_circ_0004831 expression level in CRC blood samples. A circRNA-miRNA-mRNA regulatory network of hsa_circ_0004831 was constructed based on competitive endogenous RNA mechanism and differentially expressed genes. The mRNAs co-expressed with hsa_circ_0004831 were screened at the cut-off criteria of pearson |r| > 0.3 and p\u2009<\u20090.05. Gene set enrichment analysis (GSEA) based on co-expressed mRNAs was used to explore the potential molecular function of hsa_circ_0004831.Differentially expressed circRNAs, miRNAs and mRNAs were identified and hsa_circ_0004831 had a FC value of 3.92 in CRC blood extracellular vesicles. The RT-qPCR assay showed that the hsa_circ_0004831 was up-regulated in CRC blood samples. The overall survival analysis found that high expression of hsa_circ_0004831 was linked with poorer prognosis. Finally, a circRNA-miRNA-mRNA regulatory network of hsa_circ_0004831 was constructed based on down-regulated miR-4326 and 12 up-regulated mRNAs. GSEA indicated that mRNAs co-expressed with hsa_circ_0004831 were involved in EMT, WNT and p53 signaling pathways.The study confirmed the up-regulation of hsa_circ_0004831 in CRC, and it may act as a vital prognostic biomarker. The circRNA-miRNA-mRNA regulatory network of hsa_circ_0004831 could be used to uncover the tumorigenesis and progression of CRC. The incidence of colorectal cancer (CRC) ranks the third among all human cancers in 2018 and is a common malignant tumor in the clinic . AlthougAs a series of novel non-coding RNAs, circular RNAs (circRNAs) are produced from precursor mRNAs backsplicing and have attracted lots of attention in science study . CircRNAIn our study, we found that hsa_circ_0004831 was up-regulated in the blood of CRC patients. The Kaplan\u2013Meier analysis with log-rank test found that high expression of hsa_circ_0004831 was linked with poorer prognosis. A circRNA-miRNA-mRNA regulatory network of hsa_circ_0004831 was constructed based on competitive endogenous RNA mechanism and differentially expressed genes in CRC. Moreover, GSEA showed that mRNAs co-expressed with hsa_circ_0004831 were involved in EMT, WNT and p53 signaling pathways. These findings indicated that hsa_circ_0004831 participated in important biological process and may be a potent prognostic biomarker for CRC.A total of 81 patients diagnosed with CRC at Shengjing Hospital of the China Medical University and 50 healthy volunteers were enrolled in present study. All the blood samples used in this research were collected with complete informed consent from the participants and ethics approval was obtained from the ethics review committee of the Shengjing Hospital of China Medical University before this study. Serum samples were extracted from blood samples after being centrifuged at 3000\u00a0rpm, 4\u00a0\u00b0C for 10\u00a0min and were stored at \u2212\u200980\u00a0\u00b0C until RNA isolation.Blood samples in containers without coagulant were preserved at 4\u00a0\u00b0C for 4\u00a0h to ensure serum separation. Then, serum was stored at \u2212\u200980\u00a0\u00b0C until for use after centrifuged for 10\u00a0min at 5000\u00a0rpm and 3000\u00a0rpm, respectively. Total RNA was isolated from serum with TRIzol reagent  according to the manufacturer\u2019s protocol. The concentration and quality of total RNA in all samples were detected using NanoDrop ND-1000 spectrophotometer . The synthesis of cDNA and RT-qPCR reactions were conducted using a reverse transcription kit and the SYBR Green kit . Relative gene expression was determined using the ABI 7500 System  with the following qPCR cycling program: 45 cycles including denaturation at 95\u00a0\u00b0C for 5 s, annealing at 60 \u00b0C\u00a0for 30 s and extension at 72\u00a0\u00b0C for 30 s. GAPDH was selected as the reference gene. The primers used for qRT-PCR were shown in Table\u00a0The circRNA and mRNA expression profiles in extracellular vesicles of CRC and normal samples were obtained from the exoRBase database . The corhttps://www.circbank.cn). The overlaps of predicted results and down-regulated miRNAs in CRC were regarded as miRNAs which could be regulated by hsa_circ_0004831 through competitive endogenous RNA mechanism. The target genes of miRNAs were predicted by TargetScan v7.1 tool [The miRNAs which include miRNA binding sites on hsa_circ_0004831 genome sequence were predicted with Circbank  database7.1 tool . Similar7.1 tool .The mRNAs co-expressed with hsa_circ_0004831 in CRC and normal samples were identified using pearson correlation analysis. Pearson |r| > 0.3 and p value\u2009<\u20090.05 were considered statistically significant. The expression matrix of co-expressed mRNAs was performed for GSEA to explore the biological differences between CRC and normal samples. The hallmark and KEGG  subsets Statistical analysis used in present study was performed with GraphPad Prism v 7.00 for Windows . The differences of genes expression levels between two groups were analyzed by two-tailed Student\u2019s t-test. The overall survival analysis was performed using Kaplan\u2013Meier curves and log-rank test. The optimal cut-off threshold of low or high hsa_circ_0004831 expression was calculated by X-tile . A p valThe flowchart for this study is shown in Fig.\u00a0From circRNA expression profile, we noticed that hsa_circ_0004831 expression level increased 3.92 FC on average in CRC and was significantly up-regulated compared with that in the normal samples  gene located on chromosome 11. The potential function of hsa_circ_0004831 in the tumorigenesis and progression of CRC attracted our interest. The Kaplan\u2013Meier analysis with log-rank test found that high expression of hsa_circ_0004831 was linked with poorer prognosis. Due to the stable existence of circRNAs in plasma and favorable patient compliance, hsa_circ_0004831 may be a potent prognostic biomarker for CRC.Present evidences showed that circRNAs can act as competitive endogenous RNAs to reduce the miRNAs expression levels and release their targeted inhibition to mRNAs, thereby mediating the expression of protein-coding genes \u201327. Lin To further investigate the biological pathways which hsa_circ_0004831 may participate in, we performed GSEA using the expression matrix of mRNAs co-expressed with hsa_circ_0004831 in CRC and normal samples. The findings of GSEA showed that hsa_circ_0004831 can participate in EMT, WNT and p53 signaling pathways. EMT is a common biological pathway that is necessary for cancer progression since it converts immobile epithelial cells into active mesenchymal cells and thereby promotes metastasis . Ma et aHowever, there were also several drawbacks in present study. The study design only confirmed genes expression level and was short of findings from in vivo/vitro experiments. Even so, the subject matter of present study can be of interest to researchers in the field, and we will conduct in-depth study in further explorations. Besides, we selected TRIZOL reagent to extract RNA from serum samples, which may be not an optimal protocol. At least, the concentration and quality of total RNA in all samples were guaranteed, and we will also improve the relevant protocols in future study.This study identified differentially expressed circRNAs, miRNAs and mRNAs in CRC, and RT-qPCR confirmed the up-regulation of hsa_circ_0004831 in CRC. The Kaplan\u2013Meier analysis with log-rank test found that high expression of hsa_circ_0004831 was linked with poorer prognosis. The correlation analysis between hsa_circ_0004831 expression and clinicopathological characteristics suggested that high hsa_circ_0004831 expression was significantly correlated with distant metastasis (p\u2009=\u20090.018) and differentiation grade (p\u2009=\u20090.027). A circRNA-miRNA-mRNA regulatory network of hsa_circ_0004831 was constructed based on competitive endogenous RNA mechanism and differentially expressed genes in CRC. Moreover, GSEA showed that mRNAs co-expressed with hsa_circ_0004831 were involved in EMT, WNT and p53 signaling pathways. These findings indicated that hsa_circ_0004831 participated in important biological process and may be a potent prognostic biomarker for CRC."}
+{"text": "A series of functional experiments were conducted to assess the function of hsa_circ_0107593 in CC development. The Receiver Operating Characteristic (ROC) curve was plotted to estimate the diagnostic value of hsa_circ_0107593 in CC. The dual-luciferase reporter assay was used to explore the interaction between hsa_circ_0107593 and hsa-miR-20a-5p/93-5p/106b-5p. Bioinformatic analysis was conducted to predict the target mRNAs, pathways, and functional enrichment. The results revealed that hsa_circ_0107593 has low expression in CC tissues and CC cell lines. Moreover, negative correlations of hsa_circ_0107593 expression were found against tumor diameter, FIGO stage, and myometrial invasion. Also, hsa_circ_0107593 impedes CC cell proliferation, migration, and invasion. Based on ROC curve analysis, hsa_circ_0107593 could serve as a diagnostic biomarker. Its low expression may indicate increased patient\u2019s risk to developing cervical cancer. Mechanistically, hsa_circ_0107593 serves as a sponge of hsa-miR-20a-5p, hsa-miR-93-5p, and hsa-miR-106b-5p. Collectively, our study implies that hsa_circ_0107593 has tumor-suppressing activity in CC by physically binding with hsa-miR-20a-5p, hsa-miR-93-5p, and hsa-miR-106b-5p.Circular RNAs (circRNAs) are a new class of single-stranded RNAs that form a continuous loop with crucial role in regulation of gene expression. Because their circular conformation conforms numerous properties, circRNAs have been investigated recently to demonstrate their important role in the development and progression of various cancers. However, the function of circRNAs and their regulatory outcomes in cervical cancer (CC) have rarely been explored. In this study, the role and molecular mechanism of hsa_circ_0107593 in cervical cancer are demonstrated. Quantitative polymerase chain reaction (qRT-PCR) was used to determine the expression of hsa_circ_0107593 and three miRNAs  in paired CC tissues (tumor tissue  Cervical cancer (CC) is one of most common gynecological cancers worldwide affecting women . In receWith the emergence of microRNA and long non-coding RNA in the recent years, circRNAs have become a hotspot in disease research . CircRNAABCA5, is located in chr17:67270083-67280213 (http://www.circbase.org/).Previously, Liu et\u00a0al. used Human circRNA Microarray V2.0 to screen CC tissues and three pair-matched adjacent nontumorous tissues. This global profiling in CC yielded 591 differentially expressed circRNAs between CC tissues and pair-matched adjacent nontumorous tissues . Hsa_cirNonetheless, the relationship of hsa_circ_0107593 with cervical cancer has not been experimentally validated. In this paper, we hereby presented the association between hsa_circ_0107593 expression and its clinical significance in CC patients. We further investigated the underlying molecular mechanism of hsa_circ_0107593 in CC development and progression.The study was approved by the Medical Ethics Committee of The First Affiliated Hospital of University of South China. All subjects provided written informed consent in accordance with the Declaration of Helsinki principles. Tissue samples were collected from 52 patients who underwent surgery at The First Affiliated Hospital of University of South China from October 2018 to October 2019. None of them received preoperative chemotherapy or radiotherapy. The tissue specimens were immediately preserved in liquid nitrogen after removal from the body and then stored at \u221280\u00b0C until use. Pair-matched normal adjacent tissues were taken 2\u00a0cm from the edge of the visible cancerous tumor with no obvious tumor cells. Samples were also sent to three independent pathologists for histopathological analysis for confirmation. The clinicopathological data of all the patients including age, lymph node metastasis, pathologic type, tumor differentiation, tumor diameter, FIGO stage, HPV infection, and myometrial invasion were also collected.2.The human cervical cancer cell lines  and HEK-293T were gifted by Doctor Fengbo Tan in Xiangya Hospital of Central South University . The human normal cervical epithelial immortalized cell line H8 was purchased from BeNa Culture Collection . HeLa, SiHa, CaSki, C4-1, and ME-180 cells were cultured in RPMI 1640 medium  supplemented with 10% fetal bovine serum . H8 cells were cultured in MEM (Gibco) medium containing 10% FBS, while C-33A and HEK-293T cells were cultured in DMEM (Gibco) medium containing 10% FBS. Then 100 units/ml penicillin and 100 \u03bcg/ml streptomycin were added to the culture medium. Cells were cultured in a humidified atmosphere containing 5% COTotal RNA was extracted from human tissues and cultured cells by Trizol reagent. Two \u00b5g RNA was utilized to reverse transcribed into single-stranded cDNA. Reverse transcription of circRNA was performed using Reverted First Strand cDNA Synthesis Kit  according to the manufacturer\u2019s instructions. The reverse transcription reaction of miRNA was carried out with TaqMan MicroRNA Reverse Transcription Kit (Thermo Fisher Scientific). The purity and concentration of RNA samples were spectrophotometrically quantified by absorbance measurements at 260 and 280 nm using Nanodrop Spectrophotometer.\u2212\u0394\u0394Ct method.qRT-PCR was done with the MonAmp\u2122 ChemoHS qPCR Mix  in StepOnePlus\u2122 Real-Time PCR System\u2002. The primers listed in in vitro overexpression studies, HeLa and SiHa cells were selected for they showed the lowest expression of hsa_circ_0107593. For knockdown experiments, CaSki and ME180 were selected for they showed the highest expression of hsa_circ_0107593. A plasmid overexpressing hsa_circ_0107593 was transfected into HeLa and SiHa cells. siRNAs targeting hsa_circ_0107593 were transfected into CaSki and ME180 cells. Three siRNAs  and corresponding negative control siRNA (si-NC) were purchased from RiboBio . The siRNA with highest knockdown efficiency was selected for all subsequent experiments. The overexpressing plasmid and its corresponding negative control plasmid (OE-NC) were purchased from TSINGKE Biotech Ltd. . Cells were harvested 48\u00a0h post-transfection, then transfection efficiency was checked by qRT-PCR. For dual-luciferase reporter assay, hsa-miR-20a/93/106b-5p mimics, as well as their respective negative control (NC mimics) were purchased from Shanghai Sangon Biological Engineering Technology & Services Co., Ltd. Cell transfection was performed with Lipofectamine 3000  following the manufacturer\u2019s instructions.For 3 each well with serum-free RPMI 1640 medium. It was replicated with three independent wells. Cell proliferation was measured at 24, 48, and 72\u00a0h after seeding cells. Before observation, each well was treated with 10 \u03bcl CCK-8 reagent and then cells were incubated at 37\u2103 for another 3\u00a0h before measuring absorbance at 450 nm using a microplate reader . All experiments were repeated thrice.After 48\u00a0h post-transfection with overexpressing plasmids and siRNAs, the cell proliferation assay was performed using Cell Counting Kit-8 . Briefly, a total of 100 \u00b5l cells were plated into 96-well plates at a density of 2 \u00d7 102 at 37\u00b0C for 12 days. The cells were washed by phosphate buffer saline (PBS) three times, then fixed with methanol for 30\u00a0min and stained with 0.1% crystal violet. Colonies were counted using a light microscope.Colony-forming ability was evaluated using a six-well tissue culture plates. Transfected cells were resuspended with culture medium and inoculated on a six-well plate with a density of 500 cells/well. The cells were cultured at 5% COTransfected cells were seeded onto 24-well plates and were cultured until 90% confluency. After serum starvation for 6\u00a0h, an artificial wound was made in the cell monolayer with a sterile 10 \u03bcl pipette tip. After incubation for 24\u00a0h, the percentage of wound closure was determined from three independent experiments.4 cells were placed into chambers of transwell inserts without basement membrane coating. For the invasion assay, 5 \u00d7 104 cells were seeded in chambers of transwell inserts with a basement membrane coating. While 450 \u03bcl medium containing 20% FBS were supplied at the lower chamber as chemoattractant. After incubation at 5% CO2/37\u00b0C for 24\u00a0h, residual cells on the upper surface of the membrane were removed with a cotton swab, while cells that had traversed through the membrane were fixed with methanol for 30\u00a0min and then stained with 0.1% crystal violet for 30\u00a0min. Cells were counted using a microscope and the relative migration rate or invasion rate were calculated.Transfected cells were resuspended in 250 \u03bcl serum-free medium and seeded into the upper chamber of 24-well plates of the transwell system  with or without Matrigel-coated membranes  for cell invasion and migration assay. For migration assay, 2.5 \u00d7 10We used an online bioinformatic program miRanda to predict the binding sites to construct the recombinant luciferase vectors carrying wild type (WT) and mutant (Mut) hsa_circ_0107593. The WT and Mut sequences were synthesized by TSINGKE Biotech Ltd. Then WT and Mut hsa_circ_0107593 were inserted into pMIR-REPORT vector . HEK-293T cells were cotransfected with hsa-miR-20a/93/106b-5p mimics or NC-mimics, and the recombinant luciferase vectors using Lipofectamine 3000. After a 48\u00a0h incubation, luciferase activity was detected using a dual-luciferase reporter assay system (Promega Corporation) according to the manufacturer\u2019s protocol. Renilla luciferase activity was detected and used as the internal control.http://www.miranda.org/) and RNAplex (http://www.bioinf.uni-leipzig.de/~htafer/RNAplex/RNAplex.html), and target mRNAs for those miRNAs were predicted by the online software miRanda. Network map was drawn using Cytoscape Software (http://www.cytoscape.org). Gene ontology (GO) enrichment analysis and KEGG pathway analysis were carried out based on goatool (https://github.com/tanghaibao/goatools) and KOBAS (http://kobas.cbi.pku.edu.cn/kobas3/?t=1). While GO and KEGG analysis were performed for targeted mRNAs. The complete datasets were uploaded to www.mendeley.com (https://dx.doi.org/10.17632/s78nsw6v32.1).The target miRNAs of hsa_circ_0107593 were predicted by online software miRanda (All quantitative data were presented as mean \u00b1 standard deviation (SD) as indicated from at least three independent experiments. Statistical analyses were performed using GraphPad Prism 7.0 Software for Windows  and Service Solutions SPSS Software 19.0 . Between-group differences were tested for significance using one-way analysis of variance and Student\u2019s t-test. The associations between circRNA expression and the clinicopathological parameters of CC were assessed by the Chi-square test. The receiver operating characteristic (ROC) curve was plotted to evaluate diagnostic values. Correlation analysis was performed using Spearman\u2019s rank correlation coefficient. P < 0.05 was considered statistically significant.To explore the differential expression of hsa_circ_0107593 in cervical cancer, we performed qRT-PCR analysis on 52 patient specimens. The overall expression level of hsa_circ_0107593 was significantly downregulated in CC tissues compared with their adjacent normal tissues. , FIGO  stage (P = 0.023), and myometrial invasion (P = 0.025). On the other hand, there were no significant associations between hsa_circ_0107593 expression and the other clinicopathological parameters including age (p = 0.577), lymph node metastasis (p = 0.465), pathologic type (p = 0.324), HPV infection (p = 0.174), and tumor differentiation (p\u00a0=\u00a00.075) Table 2.in vitro functional analyses. The transient overexpression of hsa_circ_0107593 significantly decreased cell proliferation  curve was plotted using hsa_circ_0107593 expression level in CC tissues against its pair-matched adjacent normal tissues to estimate the diagnostic value of hsa_circ_0107593. The area under the ROC curve (AUC) was 0.869, suggesting that hsa_circ_0107593 might serve as a diagnostic biomarker for CC Figure 5Because our tissue-based and cell-based analyses provided clues that the low expression of hsa_circ_0107593 promotes tumor formation, we performed further experiments to elucidate how hsa_circ_0107593 perform this role. Using the online bioinformatic tools miRanda and RNAplex, three microRNAs were predicted to be potential targets of hsa_circ_0107593: hsa-miR-20a-5p, hsa-miR-93-5p, hsa-miR-106b-5p. The bioinformatics prediction also showed that hsa_circ_0107593 could harbor those three miRNAs by miRNA seed sequence matching , Scientific Research Fund Project of Hunan Provincial Health Commission .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest."}
+{"text": "The angiosperm family Bromeliaceae comprises over 3.500 species characterized by exceptionally high morphological and ecological diversity, but a very low genetic variation. In many genera, plants are vegetatively very similar which makes determination of non flowering bromeliads difficult. This is particularly problematic with living collections where plants are often cultivated over decades without flowering. DNA barcoding is therefore a very promising approach to provide reliable and convenient assistance in species determination. However, the observed low genetic variation of canonical barcoding markers in bromeliads causes problems.Agt1 is identified as a novel DNA barcoding marker suitable for molecular identification of closely related bromeliad species. Combining a comparatively slowly evolving exon sequence with an adjacent, genetically highly variable intron, correctly matching MegaBLAST based species identification rate was found to be approximately double the highest rate yet reported for bromeliads using other barcode markers.In this study the low-copy nuclear gene Agt1 as a novel plant DNA barcoding marker to be used for barcoding of bromeliads, a plant group with low genetic variation. Moreover, we provide a comprehensive marker sequence dataset for further use in the bromeliad research community.In the present work, we characterize  The rapidly radiating monocot plant family Bromeliaceae is currently considered to comprise 3597 species and 76 genera  with a gWithin the scope of an initiative to improve access to and usability of living plant collections of Cactaceae and Bromeliaceae in Botanical Gardens in Germany (EvoBoGa) , we aim matK and rbcL as well as nuclear markers such as ITS1/ITS2 and their combinations have been suggested for plant barcoding, but the applicability is not universal thus different groups require different barcoding markers  72\u2009\u00b0C 5\u2009min. Before designing bromeliad specific primers, we also used the canonical Agt1 primers that were used in previous studies [matK was performed using the primer combination MatK 5F: 5\u2032-ATACCCTGTTCTGACCATATTG-3\u2032 and trnK2r 5\u2032-AACTAGTCGGATGGAGTAG-3\u2032. Internal sequencing of matK was done using the primer TOmatK 480F 5\u2032-CATCTKGAAATCTTGGTTC-3\u2032 [DNA extraction from fresh or dried leaf material was carried out using the Qiagen  DNeasy Plant Mini Kit, according to the manufacturers instructions. PCR amplification of the  studies , 23. PCRGGTTC-3\u2032 .Prior to sequencing, PCR reactions were cleaned-up by combined treatment with Exonuclease I  and Shrimp Alkaline Phosphatase  according to the supplier recommendations.Sanger Sequencing was performed using an ABI 3730 platform  device at the BiK-F Sequencing core facility  using standard sequencing primers (SP6_Fw 5\u2032-ATTTAGGTGACACTATAG-3\u2032 and M13_Rev 5\u2032-AACAGCTATGACCATG-3\u2032).Agt1 PCR fragment cloning was done using the CloneJET PCR Cloning Kit  according to the supplier recommendations. Four to five plasmids per cloning assay were then sequenced at Eurofins Genomics  using universal plasmid sequencing primers M13 forward and SP6 reverse.matK sequences were downloaded from GenBank. All sequences were aligned and trimmed to a length of 782\u2009bp. A list with the GenBank accession numbers of all species included can be found in Additional file 482 Geneious software  [Geneious implemented MAFFT sequence alignment tool (Version 7.388) [MEGA7 software package [MEGA7, statistical significance was tested using the Wilcoxon rank sum test.Editing and analysis of the sequence data were done using the  11.0.5) . Sequencn 7.388) . Sequenc package . Kimura Agt1 sequence database cluster analysis was performed using the software CD-HIT-EST suite [ST suite , 61. TheGeneious implemented custom BLAST function [Agt1 exon IV were trimmed to a length of 264\u2009bp and the intron IV portions were cut-off at the exon V boundary. Database search was done by pairwise comparison using the MegaBLAST algorithm with the use of the following settings: Scoring (Match Mismatch): 1\u20132; Gap cost (Open Extend): linear; Max E-value: 10; Word Size: 28. The output was ordered by increasing Bit-Score for each hit. We considered identification to be successful only in those cases where the highest Bit-Score corresponded to the same species as the Query and where all other species did have a disparate lower Bit-Score (Additional file BLAST mediated species identification was performed using the function . All FASThe maximum likelihood tree was generated using PAUP* . Total lAdditional file 1. Supplementary_Table_S1_Plant_Material.Additional file 2. Supplementary_Table_S2_CD_Hit_MegaBLAST.Additional file 3. Supplementary_Material_S3_Agt1_Alignment_Tillandsia_ML_Tree.Additional file 4. Supplementary_Material_S4_Agt1_Cloning.Additional file 5. Supplementary_Material_S5_Agt1_exonIV_alignment.Additional file 6. Supplementary_Material_S6_matK_alignment.Additional file 7. Supplementary_Material_S7_Agt1_genmodels_Acomosus_Athaliana."}
+{"text": "Enterobacter cloacae is an opportunistic pathogen that causes hospital-acquired infections in immunocompromised patients. Here, we describe vB_EclM_CIP9, a novel Enterobacter phage that infects a multidrug-resistant isolate of E. cloacae. Phage vB_EclM_CIP9 is a myovirus that has a 174,924-bp genome, with 296 predicted open reading frames. Enterobacter cloacae is an opportunistic pathogen that causes hospital-acquired infections in immunocompromised patients. Here, we describe vB_EclM_CIP9, a novel Enterobacter phage that infects a multidrug-resistant isolate of E. cloacae. Phage vB_EclM_CIP9 is a myovirus that has a 174,924-bp genome, with 296 predicted open reading frames. Enterobacter cloacae isolates are resistant to select \u03b2-lactam antibiotics, including ampicillin and amoxicillin. Antibiotic resistance limits treatment options to control E. cloacae infections in immunocompromised patients and can lead to severe health problems such as bacteremia, endocarditis, and/or death  at 37\u00b0C with aeration. The wastewater sample was centrifuged and filtered. Ten milliliters of the clarified wastewater was mixed with 200\u2009\u03bcl of E. cloacae grown to an optical density (at 600\u2009nm) of 0.7, and the mixture was incubated overnight to enrich for E. cloacae-specific phages (8 PFU/ml) was purified with 20% sucrose  to yield a contig of 174,924 bp (53-fold coverage), with a GC content of 39.9%. The genome was annotated with Rapid Annotations using Subsystems Technology (RAST) v2.0 , independently treated with DNase I (1\u2009\u03bcg/ml) and RNase (1\u2009\u03bcg/ml) , and analyzed by agarose gel electrophoresis to determine the identity of the nucleic acids in the sample. The genomic DNA was prepared for sequencing  with the MiSeq 2000 platform  from an average fragment length of 500 bp . After quality control with FastQC v0.11.8 (ST) v2.0 . All preEdwardsiella phage PEi20 (GenBank accession number NC_028683). Similarly, the genome of vB_EclM_CIP9 exhibited a nucleotide alignment of only 73% and a nucleotide identity of 81.13%, compared with the genome of the Edwardsiella phage PEi26 (GenBank accession number AP014715.1).The vB_EclM_CIP9 genome is circularly permuted and terminally redundant (PhageTerm) . There aMN882610, BioProject accession number PRJNA608533, SRA accession number SRR11178671, and BioSample accession number SAMN14177620.The genome sequence and associated data for phage vB_EclM_CIP9 were deposited under GenBank accession number"}
+{"text": "Our ability to perceive meaningful action events involving objects, people, and other animate agents is characterized in part by an interplay of visual and auditory sensory processing and their cross-modal interactions. However, this multisensory ability can be altered or dysfunctional in some hearing and sighted individuals, and in some clinical populations. The present meta-analysis sought to test current hypotheses regarding neurobiological architectures that may mediate audio-visual multisensory processing. Reported coordinates from 82 neuroimaging studies (137 experiments) that revealed some form of audio-visual interaction in discrete brain regions were compiled, converted to a common coordinate space, and then organized along specific categorical dimensions to generate activation likelihood estimate (ALE) brain maps and various contrasts of those derived maps. The results revealed brain regions  preferentially involved in multisensory processing along different stimulus category dimensions, including 1) living versus nonliving audio-visual events, 2) audio-visual events involving vocalizations versus actions by living sources, 3) emotionally valent events, and 4) dynamic-visual versus static-visual audio-visual stimuli. These meta-analysis results are discussed in the context of neurocomputational theories of semantic knowledge representations and perception, and the brain volumes of interest are available for download to facilitate data interpretation for future neuroimaging studies. The perception of different categories of visual (unisensory) object and action forms are known to differentially engage distinct brain regions or networks in neurotypical individuals, such as when observing or identifying faces, body parts, living things, houses, fruits and vegetables, and outdoor scenes, among other proposed categories . DistincThe ability to organize information to attain a sense of global coherence, meaningfulness, and possible intention behind every-day observable events may fail to fully or properly develop, as for some individuals with autism spectrum disorder (ASD)  and possAt some processing stages or levels, the central nervous system is presumably \u201cprewired\u201d to readily develop an organized architecture that can rapidly and efficiently extract meaningfulness from multisensory events. This includes audio-visual event encoding and decoding that enables a deeper understanding of one\u2019s environment, thereby conferring a survival advantage through improvements in perceived threat detection and in social communication . An undeMore recent neurocomputational theories of semantic knowledge learning entails a sensory-motor framework wherein action perception circuits (APCs) are formed through sensory experiences, which manifest as specific distributions across cortical areas . In thisHere, we addressed the issue of global neuronal organizations that mediate different aspects of audio-visual categorical perception by using activation likelihood estimate (ALE) meta-analyses of a diverse range of published studies to date that reported audio-visual interactions of some sort in the human brain. We defined the term \u201cinteraction\u201d to include measures of neuronal sensitivity to temporal and/or spatial correspondence, response facilitation or suppression, inverse effectiveness, an explicit comparison of information from different modalities that pertained to a distinct object, and cross-modal priming . These iThe resulting descriptive compilations and analytic contrasts of audio-visual interaction sites across different categories of audio-visual stimuli were intended to meet three main goals: The first goal was to reveal a global set of brain regions  with significantly high probability of cross-sensory interaction processing regardless of variations in methods, stimuli, tasks, and experimental paradigms. The second goal was to validate and refine earlier multisensory processing concepts borne out of image-based meta-analyses of audio-visual interaction sites  that useThe third goal, as a special focus, was to test recent hypotheses regarding putative brain architectures mediating multisensory categorical perception that were derived from unisensory auditory object perception literature , which eAfter compiling the numerous multisensory human neuroimaging studies that employed different types of audio-visual stimuli, tasks, and imaging modalities, we sought to test three hypotheses relating to the above mentioned tenets and neurobiological model. The first two hypotheses effectively tested for support of the major taxonomic boundaries depicted in In the course of compiling neuroimaging literature, there was a clear divide between studies using static visual images (iconic representations) versus video with dynamic motion stimuli that corresponded with aspects of the auditory stimuli. The production of sound necessarily implies dynamic motion of some sort, which in many of the studies\u2019 experimental paradigms also correlated with viewable object or agent movements. Thus, temporal and/or spatial intermodal invariant cues that physically correlate visual motion  with changes in acoustic energy are typically uniquely present in experimental paradigms using video . ConversThis work was performed in accordance with the PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions . Depictehttp://brainmap.org). This software was also used to create probability maps, where probabilities were modeled by 3D Gaussian density distributions that took into account sample size variability by adjusting the full-width half-max (FWHM) for each study  rate correction for multiple comparisons . To avoid (or minimize) the potential for errors  an intermediate stage of data entry involved logging all the coordinates and their transformations into one spreadsheet (Appendix A) where they were coded by Table/Figure and number of subjects , facilitparisons  using 10n\u2009=\u200913 in n\u2009=\u20099 in http://brainmap.wustl.edu) for illustration of the main findings  revealed several regions of interest (ROIs)  revealed significant involvement of the left and right posterior temporal gyri (pSTG) and pSTS regions. Conversely, a contrast map of brain regions showing significant preferential involvement in processing incongruent > congruent audio-visual stimuli (P\u2009<\u20090.05) included bilateral IFC, which extended along inferior portions of the middle frontal gyri in locations immediately anterior to those resulting from the congruent\u2009>\u2009incongruent contrast. Because both contrast ALE maps revealed functionally dissociated ROIs, these results are herein regarded as providing evidence for a \u201cdouble-dissociation\u201d of processing along this dimension.A contrast meta-analysis of congruent\u2009>\u2009incongruent audio-visual stimuli (P\u2009<\u20090.05). The nonliving visual and sound-source stimuli .A major categorical distinction in the neurobiological organization mediating auditory perception is that for sounds produced by living versus nonliving sources . This po stimuli  predominP\u2009<\u20090.05). The contrast meta-analysis of nonliving > living congruent audio-visual events revealed the right anterior insula as a common hub of activation (P\u2009<\u20090.05). A main contributing study to this right anterior insula ROI . The action event category was initially restricted to using only nonvocalizations (by living things) as auditory stimuli. This initially yielded nine studies that showed audio-visual interaction foci, and no clusters survived the single study ALE meta-analysis map voxel-wise thresholding. Adjusting the study restrictions to include studies that reported using a mix of action events together with some nonliving visual stimuli and some vocalizations as auditory  event stimuli yielded 13 studies (P\u2009<\u20090.05).Another stimulus category boundary derived from auditory categorical perception literature was that for processing vocalizations versus action sounds . To be c studies . A singlP\u2009<\u20090.05). Conversely, the contrast meta-analysis of action > vocalization audio-visual interactions revealed the left fusiform gyrus ROIs (P\u2009<\u20090.05). This left fusiform ROI had a volume of 8\u00a0mm3, both in the single study and contrast ALE meta-analysis maps. This cluster size fell below some criteria for rigor depending on theoretical interpretation when group differences are diffuse , but also as a contrast meta-analysis with nonemotionally valent paradigms involving living things .A subset of the paradigms involving living things and/or vocalizations included emotionally valent stimuli . This prP\u2009<\u20090.05) and static-visual stimuli  were constructed. A contrast ALE meta-analysis of dynamic-visual > static-visual revealed significantly greater activation of the right pSTS region (P\u2009<\u20090.05). Conversely, the contrast ALE meta-analysis of static-visual > dynamic-visual paradigms preferentially activated the bilateral planum temporale and STG regions (P\u2009<\u20090.05).We next sought to determine if the use of dynamic visual motion versus static visual images in audio-visual interaction paradigms might reveal differences in processing organizations in the brain. Studies using dynamic-visual stimuli , includen\u2009=\u200937 of the 43 in n\u2009=\u200912) meta-analysis revealed clusters that overlapped with the outcomes using the respective full complement of studies, while the natural dynamic-visual (n\u2009=\u200937) meta-analysis similarly revealed clusters that overlapped with the respective full complement of studies. Thus, audio-visual events involving dynamic visual motion  generally recruited association cortices situated roughly between auditory and visual cortices, while audio-visual interactions involving static (iconic) visual images  generally recruited regions located closer to auditory cortex proper along the pSTG and planum temporale bilaterally.Analyses of the dynamic-visual versus static-visual were further conducted separately for those experimental paradigms using artificial versus natural stimuli. With the exception of natural dynamic-visual studies  audio-visual interaction events at a category level  versus static-visual (iconic images) as visual stimuli 11, and are avaiUpon inspection of One of the tenets regarding the taxonomic category model of real-world hearing perception was that \u201cnatural sounds are embodied when possible\u201d , and thiThe bilateral pSTS complexes were significantly more involved with processing audio-visual interactions associated with events by living things, by stimuli involving vocalizations, and by dynamic-visual  audio-visual events cf. : More spFrom a \u201ctop-down\u201d cognitive perspective, however, words and phrases that depict human actions, and even imagining complex audio and/or visual actions, are reported to lead to activation of the pSTS regions . FurtherAnother interpretation regarding the functions of the bilateral pSTS complexes is that they are more heavily recruited by living and dynamic audio-visual events simply because of their greater life-long familiarity in adult observers. They may reflect an individual\u2019s experiences and habits of extracting subtle nuances from day-to-day real-world interactions, including other orally communicating people as prevalent sources of multisensory events. Ostensibly, this experiential multisensory process would start from the time of birth when there becomes a critical need to interact with human caretakers. Consistent with this interpretation is that the pSTS complexes have prominent roles in social cognition, wherein reading subtleties of human expressions and body language is often highly relevant for conveying information that guides effective social interactions .Embodied cognition models  posit that perception of natural events  is at least in part dependent on modal simulations, bodily states, and situated actions . The disThe right anterior insula emerged as a cortical hub that was preferentially involved in processing nonliving and largely artificial audio-visual sources, which are typically deemed as being nonembodiable. Moreover, unlike the pSTS complexes, the right anterior insula did not show significant sensitivity to the dynamic-visual versus static-visual image stimulus dimension, suggesting that intermodal invariant cues were not a major driving factor in its recruitment. Interestingly, the mirror opposite left anterior insula showed preferential activation for incongruent versus congruent audio-visual stimuli cf. B,C.On a technical note, portions of the claustrum are located very close to the anterior insulae, and activation of the claustrum may have contributed to the anterior insula foci in several neuroimaging studies, and thus also in this meta-analysis. The enigmatic claustrum is reported to have a role in integrative processes that require the analysis of the \u201ccontent\u201d of the stimuli, and in coordinating the rapid integration of object attributes across different modalities that lead to coherent conscious percepts .Embodiment encoding functions have been ascribed to the anterior insula in representing \u201cself\u201d versus \u201cnonself.\u201d For instance, the anterior insulae, which receive input from numerous cortical areas, have reported roles in multimodal integrative functions, rerepresentation of interoceptive awareness of bodily states, cognitive functions, and metacognitive functions , and in Although the anterior insula territories are commonly associated with affective states, visceral responses, and the processing of feelings , the emoThe double-dissociation of cortical hubs for processing dynamic-visual versus static-visual audio-visual interactions was consistent with notion of parallel processing hierarchies. The experimental paradigms using video typically included dynamic intermodal invariant cross-modal cues (mostly by living things), where the audio and visual stimuli were either perceived to be coming from roughly the same region of space, moving along similar spatial trajectories, and/or had common temporal synchrony and modulations in stimulus intensity or change. These correlated physical changes in photic and acoustic energy attributes are likely to serve to naturally bind audio-visual interactions, consistent with bottom-up Hebbian-like learning mechanisms. Such stimuli preferentially recruited circuitry of the bilateral pSTS complexes  can have symbolic congruence with sound that must be learned to be associated with, and having few or no cross-modal invariant cues, thereby placing greater emphasis on declarative memory and related semantic-level matching mechanisms. The dynamic versus static visual stimulus dimension was further assessed using a subset of natural-only versus artificial stimuli. While there were insufficient numbers of studies in three of the subgroups for definitive meta-analysis results (data not shown), the outcomes suggested a bias for the dynamic-visual stimuli clusters being driven by natural stimuli while the static-visual stimuli clusters may have been driven more by images involving relatively artificial stimuli . Regardless, a double-dissociation was evident.Another consideration regarding the dynamic/natural versus static/artificial processing was depth-of-encoding. The greater depth required for encoding for subordinate versus basic level information is reported to recruit greater expanses of cortices along the anterior temporal lobes . For insCorrelating static-visual images with sound could be argued to require a more cognitive learning process than perceptually observing dynamic-visual events as they unfold and provide more intermodal-invariant information correlated with ongoing acoustic information. Thus, it was somewhat surprisingly that the static-visual stimuli preferentially recruited of the bilateral planum temporale  images referring to their matching source. Overall, this interpretation supports the tenet from unisensory systems \u201cthat parallel hierarchical pathways process increasing information content,\u201d but here including two parallel multisensory processing pathways mediating the perception of audio-visual interaction information as events that are physically matched from a bottom-up perspective versus learned to be semantically congruent.Several mechanistic models regarding how and why semantic knowledge formation might develop in the brain includes the concept of hubs (and connector hubs) in brain networks , which aWith regard to double-dissociations of cortical function, the right anterior insula and left fusiform ROIs had relatively small volumes, and thus may be considered less robust by some meta-analysis standards . Nonetheless, these preliminary findings provide at least moderate support for a taxonomic neurobiological model for processing different categories of real-world audio-visual events , which iP-values, a statistical correction process that to date remains somewhat contentious in the field of meta-analyses. The biases in stimuli commonly used also led to the limitation that there would be greater heterogeneity of, for instance, nonliving audio-visual sources and action events devoid of any vocalizations. This precluded examination of subcategories such as environmental sources, mechanical (human-made) audio-visual sources, versus \u201cartificial\u201d events (being computer-derived or involving illusory sources), which limited a more thorough testing of the taxonomic model  that typically have greater rationale for being studied (and funded). In particular, the categories of living things (humans) and/or vocalizations (speech) are simply more thoroughly studied as socially- and health-relevant topics relative to the categories of nonliving and nonvocal audio-visual stimuli, as evident in the numbers of studies listed in the provided tables. Because there were fewer numbers of studies in some semantic categories, double-dissociation differences could only be observed in some contrast meta-analyses when using uncorrected ic model  being inAt a more technical level, other potential limitations included methodological differences across study designs, such as 1) differences in alignment methods, 2) imaging large swaths of brain rather than truly \u201cwhole brain\u201d imaging, and 3) potential inclusion of participants in more than one published study (which was not accessible information). Together, these limitations may constitute violations of assumptions by the ALE meta-analysis processes. Nonetheless, the modest support for our first two hypotheses and strong support for our third hypothesis should merit future study to validate and/or refine these basic cortical organization tenets and neurobiological taxonomic model.E that are amenable to future neurocomputational modeling and testing of semantic knowledge representation mechanisms. Exploration of these and other potential multisensory hubs will be important for future studies addressing why specific brain regions may typically develop to process different aspects of audio-visual information, and thereby establish and maintain the \u201cmultisensory brain,\u201d which ultimately subserves many of the complexities of human communication and social behavior.This study summarized evidence derived from meta-analyses across 137 experimental paradigms to test for brain organizations for representing putative taxonomic boundaries related to perception of audio-visual events at a category-level. The semantic categories tested were derived from an ethologically and evolutionarily inspired taxonomic neurobiological model of real-world auditory event perception. The outcomes provided novel, though tentative support for the existence of double-dissociations mediating processing and perception around semantic categories, including 1) living versus nonliving  audio-visual events, and 2) vocalization versus action audio-visual events. The outcomes further provided strong support for a double-dissociation for processing 3) dynamic-visual  versus static-visual  audio-visual interactions. Together, these findings were suggestive of parallel hierarchical pathways for processing and representing different semantic categories of multisensory event types, with embodiment strategies as potential underlying neuronal mechanisms. Overall, the present findings highlighted where and how auditory and visual perceptual representations interact in the brain, including the identification of a handful of cortical hubs in AppendixA_201226_tgab002Click here for additional data file.AVmeta2020_Fig3A_1a_Combine_CongrIncong_Uncorr_p999-Copy_tgab002Click here for additional data file.AVmeta2020_Fig3B_2a_CongruentOnly_FWE05_10k_tgab002Click here for additional data file.AVmeta2020_Fig3B_2b_IncongruentOnly_FWE05_10k_tgab002Click here for additional data file.AVmeta2020_Fig3B_2c_ContrastCongruentVsIncongruent_10k_Puncorr_Z_p05_tgab002Click here for additional data file.AVmeta2020_Fig3B_2d_Contrast_IncongruentVsCongruent_10k_Puncorr_Z_p05_tgab002Click here for additional data file.AVmeta2020_Fig3C_3a_LivingOnly_FWE05_10k_tgab002Click here for additional data file.AVmeta2020_Fig3C_3b_NonLivingOnly_FWE05_10k_tgab002Click here for additional data file.AVmeta2020_Fig3C_3c_Contrast_LivingVsNonLiving_10k_uncorr_Z_p05_tgab002Click here for additional data file.AVmeta2020_Fig3C_3d_Contrast_NonLivingVsLiving_10k_uncorr_Z_p05_tgab002Click here for additional data file.AVmeta2020_Fig3D_4a_Vocalizations_FWE05_10k_tgab002Click here for additional data file.AVmeta2020_Fig3D_4b_ActionEvents_FWE05_10k_tgab002Click here for additional data file.AVmeta2020_Fig3D_4c_Contrast_VocalizationsVsActions_10k_uncorr_Z_p05_tgab002Click here for additional data file.AVmeta2020_Fig3D_4d_Contrast_ActionsVsVocalizations_10k_uncorr_Z_p05_tgab002Click here for additional data file.AVmeta2020_Fig3D_4e_EmotionalLiving_FWE05_10k_tgab002Click here for additional data file.AVmeta2020_Fig3E_9a_DynamicVisual_FWE05_10k_tgab002Click here for additional data file.AVmeta2020_Fig3E_9b_StaticVisual_FWE05_10k_tgab002Click here for additional data file.AVmeta2020_Fig3E_9c_Contrast_DynamicVsStaticVisual_10k_uncorr_Z_p05_tgab002Click here for additional data file.AVmeta2020_Fig3E_9d_Contrast_StaticVsDynamicVisual_10k_uncorr_Z_p05_tgab002Click here for additional data file.LRV_MPRAGE+tlrc_BRIK_tgab002Click here for additional data file.LRV_MPRAGE+tlrc_tgab002Click here for additional data file."}
+{"text": "The low cost of 16S rRNA gene sequencing facilitates population-scale molecular epidemiological studies. Existing computational algorithms can resolve 16S rRNA gene sequences into high-resolution amplicon sequence variants (ASVs), which represent consistent labels comparable across studies. Assigning these ASVs to species-level taxonomy strengthens the ecological and/or clinical relevance of 16S rRNA gene-based microbiota studies and further facilitates data comparison across studies.eHOMD). We also overcame technical limitations to successfully use Illumina sequences for the 16S rRNA gene V1\u2013V3 region, the most informative segment for classifying bacteria native to the human aerodigestive tract. Finally, we generated a full-length eHOMD 16S rRNA gene training set, which we used in conjunction with an independent PacBio single molecule, real-time (SMRT)-sequenced sinonasal dataset to validate the representation of species in our training set. This also established the effectiveness of a full-length training set for assigning taxonomy of long-read 16S rRNA gene datasets.To achieve this, we developed a broadly applicable method for constructing high-resolution training sets based on the phylogenic relationships among microbes found in a habitat of interest. When used with the na\u00efve Bayesian Ribosomal Database Project (RDP) Classifier, this training set achieved species/supraspecies-level taxonomic assignment of 16S rRNA gene-derived ASVs. The key steps for generating such a training set are (1) constructing an accurate and comprehensive phylogenetic-based, habitat-specific database; (2) compiling multiple 16S rRNA gene sequences to represent the natural sequence variability of each taxon in the database; (3) trimming the training set to match the sequenced regions, if necessary; and (4) placing species sharing closely related sequences into a training-set-specific supraspecies taxonomic level to preserve subgenus-level resolution. As proof of principle, we developed a V1\u2013V3 region training set for the bacterial microbiota of the human aerodigestive tract using the full-length 16S rRNA gene reference sequences compiled in our expanded Human Oral Microbiome Database (Here, we present a systematic approach for constructing a phylogeny-based, high-resolution, habitat-specific training set that permits species/supraspecies-level taxonomic assignment to short- and long-read 16S rRNA gene-derived ASVs. This advancement enhances the ecological and/or clinical relevance of 16S rRNA gene-based microbiota studies.Video Abstract In microbiota studies of most ecosystems and/or habitats, achieving species- or strain-level identification of constituents improves the ecological and/or clinical relevance of the results compared with genus-level identification. For example, species-level identification is often critically important for host-associated microbial communities because these communities frequently include commensal and pathogenic species of the same genus, e.g., , 2, withMicrobial databases encompassing broad phylogenetic diversity, such as SILVA , 22, RDPThe na\u00efve Bayesian RDP Classifier  is one oWe hypothesized that we could develop a method to rapidly generate a habitat-specific training set to leverage the strength of the na\u00efve Bayesian RDP Classifier to consistently achieve species- or supraspecies -level taxonomic assignment of ASVs starting from a locally comprehensive, phylogeny-based, high-resolution  set of curated reference sequences with distinct taxonomic names. Here, we show that the use of the na\u00efve Bayesian RDP Classifier with a training set in which each taxon is represented by a collection of highly similar sequences that captures the natural variability of each species resulted in accurate species-level taxonomic assignment of short-read and long-read sequences of the 16S rRNA gene. This represents a methodological advancement. Our systematic approach for generating training sets is applicable to any ecosystem/habitat of interest and is summarized in Fig. eHOMD). This database was originally created and later expanded to serve as a resource for the community of investigators generating datasets to study habitats within the human aerodigestive tract [http://ehomd.org/index.php?name=HOMD&show_tree=_), as-yet unnamed or uncultivated species are defined based on sequence identity and added to the phylogeny using a provisional naming scheme that permits taxonomic assignment for cross-study comparison [To test our hypothesis, we developed and validated short- and long-read training sets for the microbiota of the human aerodigestive tract  using our expanded Human Oral Microbiome Database  The lave tract . A strenmparison . Anothermparison .Genus-level taxonomic assignment is not an inherent limitation of the na\u00efve Bayesian RDP Classifier. Rather, taxonomic assignment to 16S rRNA gene short reads is limited by both the resolution to which sequences in datasets are distinguished and by the nature of the training set used. The former is overcome by using approaches such as oligotyping/MED , 9, DADAT), the higher the occurrence of a given distinguishing \u201ck-mer\u201d (word or wi) in the training set, the greater the confidence with which assignment of that taxon is made, i.e., more sequences can be classified unambiguously. Thus, as the number of sequences (M) for each taxon in the training set increases  that reflects the currently known 16S rRNA gene sequence variability for each taxon, both natural and sequencing-error derived  participants [Peptoniphilus lacydonensis was published in 2018 and is formally recognized [eHOMD. Of the remaining 17 ASVs, eight were mismatches between closely related Streptococcus species. Only six ASVs were nonassigned (NA) using eHOMD; thus, eHOMD included the vast majority (99.7%) of taxa present in the Earl-Mell sinonasal dataset. We note that based on recent additions, both eHOMD and NCBI 16S Microbial allowed correct assignment of ASVs to Lawsonella clevelandensis, which previously might be misidentified by blastn best match as a Dietzia species, and to Neisseriaceae G-1 bacterium HMT-327, which before receiving a stable taxonomic designation in eHOMD might be misidentified by blastn best match as a Snodgrassella species [First, we compared the best match by blastn, as a proxy for taxonomic assignment for these ASVs, using eHOMD compared to using NCBI 16S Microbial to identify any taxa in the dataset that might be missing in eHOMD, and thus the training set Table A. A detaicipants . The newcognized ; therefoSecond, we compared species-level assignment of the dataset FL_sinonasal_SMRT_ASV using blastn against eHOMD versus the na\u00efve Bayesian RDP Classifier with the full-length eHOMD training set FL_Compilation_TS. Less than 2% 1.91%) of the total reads in the dataset FL_sinonasal_SMRT_ASV, which are represented by 25 ASVs, were differentially assigned species-level taxonomy by these approaches . The eHOMD training set resulted in a larger percentage of reads assigned to a specific genus for each dataset; however, all three training sets resulted in genus-level assignment of >\u200990% of the sequences  the thehttpsStaphylococcus aureus and Staphylococcus epidermidis. Another implication is that for species-level analysis of the microbiota of habitats lacking a high-resolution, accurate 16S rRNA gene database, PacBio SMRT sequencing coupled with the newly available DADA2 PacBio pipeline [A key implication of these data is that our overall method yields comparable species-level results for 16S rRNA gene sequencing of the human aerodigestive tract using V1\u2013V3 short-read sequences, which is very cost effective, compared to using close-to-full-length PacBio SMRT sequences. For example, it readily distinguished sequences of pipeline  to generHere, we present a systematic approach for generating and validating habitat-specific 16S rRNA gene training sets to achieve species/supraspecies-level taxonomic assignment for short- or long-read 16S rRNA gene sequences. We used the na\u00efve Bayesian RDP classifier with our training set; however, such a training set can be used with other taxonomic classifiers , 50\u201357. The successful implementation of a training set for species/supraspecies-level assignment of 16S rRNA gene short-read  datasets involved a collaborative give-and-take to iteratively optimize the sequencing protocol and the analysis workflow in conjunction with each other. As a result, here, we also provided three specific protocol recommendations for sequencing of the 16S rRNA gene V1\u2013V3 region with the Illumina MiSeq. As detailed in Additional file We note three\u00a0limitations to the presented method for constructing a habitat-specific training set. First, capturing the natural sequence variation for the 16S rRNA gene(s) for each taxon is limited by the number of such sequences currently available in public repositories. For example, 140 of the ~\u2009770 taxa in eHOMD are represented by four or fewer distinct close-to-full-length sequences in the training set FL_Compilation_TS, whereas ~\u2009630 taxa are represented by five or greater. Second, the comprehensiveness of a training set depends on the comprehensiveness of the phylogeny-based, habitat-specific database from which it is constructed. Therefore, if an ASV is from a species that is absent from the database, then misclassification of that ASV to the most closely related species present in the database is possible. This can also occur during validation and benchmarking if using a simulated dataset with the same origin as the training set , e.g., oWithin these limitations, we note several advantages. First, when coupled with a training set built with our method, the k-mer-based na\u00efve Bayesian approach accommodates the natural variability of 16S rRNA gene sequences that exists within many bacterial species enabling high rates of accurate taxonomic assignment. In contrast, this natural variability limits the utility of any exact match algorithm to assigning species-level taxonomy for only those sequences already existing in a training set Table . Second,Finally, there are several additional recommendations to improve taxonomic assignment achieved with our method. First, it is critical to incorporate a stable provisional naming scheme into any habitat-specific database, e.g., the HMT numbers in eHOMD , 26. Sechttps://benjjneb.github.io/dada2/index.html) [Training datasets were constructed in FASTA format as described on the DADA2 official website (ex.html) , with taAll of the full-length 16S eHOMDrefs, together with their respective taxonomic assignment, were formatted as a training set for the na\u00efve Bayesian RDP Classifier, as above.eHOMD.org as eHOMDv15.1_FL_Compilation_TS.fa.gz. First, to more precisely calculate the percent identity for recruiting sequences for the training set, we trimmed each of the eHOMDrefs to nucleotides 28 and 1373. This is necessary for two reason: (1) several eHOMDrefs have hanging 5\u2032 or 3\u2032 ends that, if left in place, would affect the calculation of percent identity, and (2) this trimming permitted capture of sequences from the extensive datasets that use the reverse primer at ~\u20091390. Second, each of the 998 trimmed eHOMDrefs was queried against the NCBI nonredundant nucleotide (nr/nt) database using blastn (NCBI BLAST 2.6.0+ package) (https://www.ncbi.nlm.nih.gov/books/NBK279690/) with the parameters -db nr -remote -perc_identity 97. Nucleotide sequences of GenBank IDs with \u2265\u200997% sequence identity to any of the eHOMDrefs were downloaded in FASTA format using the efetch.fcgi command of NCBI\u2019s Entrez Programming Utilities . The blastn hits were downloaded in the same orientation as the eHOMDrefs in two batches: (1) for subject sequence length between 1000 and 2000 nt, the entire subject sequence was downloaded; and (2) for subject sequence length >\u20092000 bp , only the aligned portion of the sequence was downloaded. Sequences <\u20091000 bp were not downloaded. Third, the 301,794 downloaded sequences, which matched to human microbial taxa (HMTs) at \u2265\u200997%, were parsed based on their highest sequence percent identity (\u2265\u200999%) and alignment coverage  to any of the eHOMDrefs in a given HMT. The choice of \u2265\u200999% identity was designed to obtain the centrally conserved set of sequences for each eHOMDref. The choice of \u2265\u200998% coverage was to ensure that the majority of the close-to-full-length sequence was present. Sequences that matched to multiple HMTs at equal percent identity and coverage were randomly assigned to only one HMT.  The FL_Compilation_TS training set is comprised of a total of 223,144 sequences parsed to their corresponding HMT, with a range of 1 to 4004 sequences per HMT. Additional file This is the final version of the full-length 16S rRNA gene eHOMD training set, available as Additional file We generated this training set version in two steps. First, each eHOMDref was individually aligned with the compilation of downloaded sequences that were matched to it at \u2265\u200999% identity and \u2265\u200998% coverage (see immediately above). Second, the sequences in the V1\u2013V3 region, defined as positions 40-880 in the gapped eHOMDrefs alignment, were captured and then the alignment gaps removed. These steps were performed using a custom script  and HMT-597 (Bradyrhizobium elkanii) and was supported by 29 sequences. The genus of the concatenated taxa was assigned as Afipia:Bradyrhizobium and species as broomeae:elkanii. Of note, although these two genera are almost identical on the V1\u2013V3 region, they are 97% identical across the full length of the 16S rRNA gene.Sequences that were identical across V1\u2013V3 in V1V3_Raw_TS were collapsed into a single sequence with the names of all taxa involved concatenated with a \u201c:\u201d separator. The majority of such concatenations occurred among either the same species, resulting in no name change, or different species of the same genus, resulting in assignment of a concatenated species name. However, there were a number of cases where species from two different genera were involved. These intergenus concatenations were carefully examined case by case. In all but one case, the concatenation was only supported by two to three sequences and, therefore, was deemed unreliable and rejected. After manual examination, only one intergenus concatenation remained. This was between HMT-559 (eHOMD.org as eHOMDv15.1_V1V3_Supraspecies_TS.fa.gz (http://www.homd.org/ftp/publication_data/20190709/). Additional file This is the final version of the V1\u2013V3 16S rRNA gene eHOMD training set, available as Additional file Simulated reads were generated from each unique sequence in the V1V3_Curated_TS training set. During read generation, 1% of the bases in each unique initial sequence were randomly selected and changed into a different base to simulate a 1% error rate. The resulting dataset contains 19,480 simulated sequences, each of them labeled with the ID of the parent eHOMDref sequence from which they were derived. Then, each error-simulated read was trimmed into two fragments to simulate Illumina pair-end reads (R1 starting from the V3 primer and R2 starting from the V1 primer). We generated multiple length configurations of both the R1 and R2 fragments. First, fixing R2 at 350 bp, we generated R1 fragments from the V3 primer ranging from 20 to 200 bp  and reads were further trimmed into two fragments to simulate Illumina pair-end reads using the configuration R2(250 bp)-10N-R1(100 bp), as described above  already demultiplexed, labeled by sample, pooled together and, then, filtered based on length distribution, terminal matches to the primer sequences, not aligning to a provided host or background genome sequence and with a cumulative expected error (EE <\u20091) . We thenEscherichia coli. position 28-1373 and used to query this compiled dataset via blastn. We retained 27,816 sequences that hit with 100% coverage and \u2265\u200999.5% identity to 401 HMTs as the full-length human aerodigestivetract clone library dataset .We previously used the close-to-full-length 16S rRNA gene sequences from clone library-based microbiota studies of the human aerodigestive tract, as described in Supplemental Text S1 of : Segre-Krdp_train_set_16.fa.gz) and SILVA132 (silva_nr_v132_train_set.fa.gz) genus-level training sets available at https://benjjneb.github.io/dada2/training.html. Since the eHOMD training set V1V3_Supraspecies_TS generates an output with eight taxonomic levels that might not be compatible with common downstream applications that accept only seven levels, we provide a custom-written R function  with the specified training set and with outputBootstraps=TRUE and the indicated minBoot value . In addirdp_species_assignment_16.fa.gz) and SILVA132 (silva_species_assignment_v132.fa.gz) training set files downloaded from https://benjjneb.github.io/dada2/training.html using the dada2::assignSpecies function in R with allowMultiple=TRUE [ASVs and CL sequences were assigned species-level taxonomy with the RDP16 (ple=TRUE .https://www.ncbi.nlm.nih.gov/books/NBK279690/) was installed and the \u201cblastn\u201d command used with max_target_seqs 1, hits with <\u200998.5% identity were considered nonassigned. ASVs  derived from the sinonasal PacBio SMRT-sequenced dataset were also assigned taxonomy using blastn against two databases: the NCBI 16S Microbial database was downloaded from ftp://ftp.ncbi.nlm.nih.gov/blast/db/ on January 2019 [The NCBI BLAST 2.6.0+ package .Additional file 3. V1V3_eHOMDSim_250N100.fa. The simulated eHOMD-derived V1V3_eHOMDSim_250N100 dataset.Additional file 4. A method for achieving highly informative 16S rRNA gene V1-V3 region sequencing data using Illumina MiSeq.Additional file 5 eHOMDv15.1_V1V3_Supraspecies_TS.fa. The 16S rRNA gene V1-V3 eHOMD training set (V1V3_Supraspecies_TS).Additional file 6. FL_sinonasal_SMRT_ASV.fa. Sequences of the resulting 204 ASVs in the Earl-Mell sinonasal 16S rRNA gene dataset .Additional file 7. Earl-Mell_sinonasal_analysis.xlsx with legend and tabs A-D. Species-level taxonomic assignment of the Earl-Mell sinonasal 16S rRNA gene dataset.Additional file 8. V1V3_sinonasal_SMRT_ASV.fa. Sequences of the resulting 204 ASVs in the FL_sinonasal_SMRT_ASV dataset trimmed to the V1-V3 region .Additional file 9 V1V3_hADT_CL.fa. V1-V3 trimmed 16S rRNA gene human aerodigestive tract clone library dataset (V1V3_hADT_CL dataset).Additional file 10. V1V3_HMPnares_ASV.fa. ASVs derived from the HMP nares 16S rRNA gene V1-V3 dataset (V1V3_HMPnares_ASV dataset).Additional file 11. FL_Compilation_TS training set with sequences labeled as unique IDs.Additional file 12. Original GenBank accession numbers for all the sequences compiled in the FL_Compilation_TS training set linked to their unique IDs in\u00a0Additional file Additional file 13. retrieveSegment.py. Custom script developed to generate a trimmed version of a training set  from a compilation of 16S rRNA gene full-length sequences.Additional file 14. V1V3_Supraspecies_TS training set with sequences labelled as unique IDs.Additional file 15. Original GenBank accession numbers for all the sequences compiled in the V1V3_Supraspecies_TS training set linked to their unique IDs in\u00a0Additional file Additional file 16. Simulated_R2_350_R1_20-200.7z. Simulated eHOMD-derived dataset versions with R2 fixed at 350 bp and R1 fragments from the V3 primer ranging from 20 to 200 bp.Additional file 17. Simulated_R2_140-350_R1_200.7z. Simulated eHOMD-derived dataset versions with R1 fixed at 200 bp and R2 fragments from the V1 primer ranging from 140 to 350 bp.Additional file 18. FL_hADT_CL.fa. Full-length 16S rRNA gene human aerodigestive tract clone library dataset (FL_hADT_CL dataset).Additional file 19. eight2seven.R. Custom-written R function that converts the dada2::assignTaxonomy output from eight to seven taxonomic levels for compatibility with downstream applications."}
+{"text": "A growing number of circular RNAs (circRNAs) have been identified and verified in several cancers. However, highly efficient therapeutic methods based on circRNAs in lung cancer remain largely unexplored. In the present study, we identified a novel circular RNA, hsa_circ_103820, based on Gene Expression Omnibus (GEO) data. Functionally, overexpression of hsa_circ_103820 showed significant inhibitory effects on the proliferation, migration and invasion of lung cancer cells, and knockdown of hsa_circ_103820 played promoting roles. Regarding the mechanism, we revealed that miR-200b-3p was a direct target of hsa_circ_103820 and that LATS2 and SOCS6 were the downstream target genes of miR-200b-3p. Therefore, we identified a novel potential tumor suppressive function of hsa_circ_103820 in lung cancer. In the course of lung cancer, metastasis, genetic mutations, multidrug resistance, and other changes can occur3. Although surgical resection, chemotherapy, radiotherapy, targeted drug therapy, immunotherapy and other therapeutic measures are used in clinical practice, the long-term survival rate of lung cancer patients is still unsatisfactory5. Therefore, it is of great significance to detect lung cancer in a timely manner and reveal the vital molecular mechanisms involved in the processes of invasion, metastasis, and recurrence of lung cancer.Lung cancer is estimated to account for approximately one-fifth of all deaths due to malignant tumors worldwide6. Due to the special stable structure, circRNAs are not degraded by RNA enzymes, which are evolutionarily conserved7. Based on these characteristics, circRNAs, as biomarkers, might provide new ideas for drug development and new directions for disease research8. With the development of high-throughput sequencing technology, a large number of differentially expressed circRNAs have been screened in various cancers, revealing the crucial roles of circRNAs in the development of cancers for further studies10. We aimed to select the expression profiles of differentially expressed circRNAs between lung cancer and adjacent normal lung tissues by adopting the data in the Gene Expression Omnibus (GEO) datasets. After comprehensive analysis, we identified a novel circular RNA, hsa_circ_103820 (hsa_circ_0072309), which is located at chr5:38523520\u201338530768 (human GRCh37/hg19) and is encoded by leukemia inhibitory factor receptor (LIFR) gene exons that was significantly underexpressed in lung cancer compared to normal controls. To our knowledge, the functions and mechanisms of hsa_circ_103820 in the tumorigenesis of lung cancer have not been reported.Circular RNAs (circRNAs) are single-stranded, closed circular noncoding RNAs (ncRNAs) that have neither a 5\u2032 cap nor a 3\u2032 poly A tailIn the present study, we selected hsa_circ_103820 for further study and found that it was expressed at low levels, while miR-200b-3p was expressed at high levels in lung cancer tissue samples. In addition, knockdown and overexpression of hsa_circ_103820 were conducted to determine the functions of hsa_circ_103820 in lung cancer cell lines, and we found that hsa_circ_103820 suppressed the proliferation, invasion, and migration of lung cancer cells by sponging miR-200b-3p and releasing its target genes large tumor suppressor kinase 2 (LATS2) and suppressor of cytokine signaling 6 (SOCS6).(fold change) values. The results showed that among the 37 circRNAs, the mean level of hsa_circ_103820 was lowest in the tumor group relative to the normal group ; hsa_circ_103820 expression was connected with the invasion of lung cancer . Moreover, the results of the correlation analysis revealed that the expression of LIFR mRNA was positively correlated with the expression of hsa_circ_103820 in lung cancer . The putative interacting site was predicted between miR-200b-3p in hsa_circ_103820, and WT-hsa_circ_103820 and Mut-hsa_circ_103820 plasmids were constructed . Moreover, the results of AGO2-IP also confirmed the association of hsa_circ_103820 and miR-200b-3p . In addition, biotin-miR-200b-3p or biotin-hsa_circ_103820 was transfected into A549 cells, and the enrichment of miR-200b-3p and hsa_circ_103820 was analyzed by RNA pull-down assay. As exhibited in Fig. P\u2009<\u20090.001). Moreover, our results uncovered that miR-200b-3p expression was observably upregulated in lung cancer tissues with respect to paracarcinoma tissues ; further, we found that miR-200b-3p expression related to the invasion of lung cancer . By statistical analysis, we discovered that miR-200b-3p and hsa_circ_103820 were inversely correlated in lung cancer . qRT-PCR assays showed that overexpression of circ_103820 markedly downregulated miR-200b-3p expression in A549 cells . Further functional studies certified that hsa_circ_103820 was able to suppress the proliferation of A549 and SPCA1 cells, while the addition of miR-200b-3p noticeably abolished hsa_circ_103820-mediated suppression of proliferation, as indicated by CCK-8 and colony formation assays  and actinomycin, respectively. Hsa_circ_103820-overexpressing plasmid and control (vector) were synthesized by GENESEED . Hsa_circ_103820 siRNAs, scramble and miR-200b-3p mimics, and negative control (NC) were obtained from GenePharma . SPCA1 and A549 cells (1\u2009\u00d7\u2009105 cells/well) were inoculated in 6-well plates and transfected with hsa_circ_103820-overexpressing plasmid, hsa_circ_103820 siRNAs, miR-200b-3p mimics and respective controls using Lipofectamine 3000 reagent (Invitrogen) based on the experimental instructions. The detailed procedures of cell functional assays, including CCK-8 assay, colony formation assay, EdU staining, Transwell assay, and wound healing assay are described in the supplementary materials.SPCA1, A549, and HEK-293T cells were obtained from ATCC. All three types of cells were cultured in Dulbecco\u2019s modified Eagle\u2019s medium (DMEM) with 10% fetal bovine serum  at 37\u2009\u00b0C in 5% COhttp://www.ncbi.nlm.nih.gov/geo/) using \u201clung cancer\u201d as the keyword. After analysis, the data in the GSE101586, GSE101684 and GSE112214 datasets were selected and downloaded. The top 250 differentially expressed circRNAs were obtained by GEO2R. The R language package was applied for hierarchical clustering analysis.The circRNA data were obtained from the GEO website (available: http://gepia.cancer-pku.cn/).Survival curves according to LIFR and hsa_circ_103820 expression were obtained based on GEPIA , and the primer sequences are displayed in Table Total RNA was extracted with TRIzol reagent based on the instructions. cDNAs were then synthesized by reverse transcription using an All-in-OneTM First-Strand cDNA Synthesis kit . Gene expression (cDNA and gDNA) was assessed by PCR using 2% agarose gel electrophoresis. Relative gene expression was confirmed using the QuantiFast SYBR Green PCR Kit (Qiagen). The nuclear and cytoplasmic fractions were isolated using NE-PER Nuclear and Cytoplasmic Extraction Reagents (Thermo Scientific). Total RNA from the nuclear and cytoplasmic fractions was isolated with TRIzol. Relative quantitative analysis performed via the 2WT-pmirGLO\u2043hsa_circ_103820, Mut-pmirGLO\u2043hsa_circ_103820, WT-pmirGLO\u2043LATS2, Mut-pmirGLO\u2043LATS2, WT-pmirGLO\u2043SOCS6, and Mut-pmirGLO\u2043SOCS6 were purchased from Wuhan Hualian Biotechnology Co. . HEK293T cells were cotransfected with the corresponding plasmids and scramble or miR-200b-3p using Lipofectamine 3000 reagent (Invitrogen). After 48\u2009h, the harvested cells were examined using the Dual-Luciferase Reporter Assay System (Promega).After enzyme digestion of the constructed vector, transcription and biotin labeling were performed using an in vitro transcription kit (Roche). After transcription, DNase I was added to digest the transcription template, and TRIzol was utilized to extract the transcription products in vitro. After quantification, 3\u2009\u03bcg RNA was mixed with the extracts of SPCA1 and A549 cells. The reaction system of the RNA pull-down experiment was 25\u2009mM Tris-Cl [pH 7.4], 150\u2009mM Na Cl, 0.5% NP-40, 0.5\u2009mM DTT and 1\u00d7 complete protease inhibitors (Roche). After incubation for 3\u2009h, streptavidin-coupled T1 beads (Roche) were added for 30\u2009min. The extracted RNAs were used to perform qRT-PCR detection.HEK293T cells were transfected with hsa_circ_103820-Ago2 or miR-200b-3p-Ago2 for 48\u2009h. After washing, proteins were extracted and treated with 2\u2009\u03bcg antibody for 4\u2009h at 4\u2009\u00b0C. Magnetic beads were washed three times, and the qRT-PCR assay was carried out.After washing, RIPA lysate containing protease inhibitor was added to the treated SPCA1 and A549 cells. The cells were lysed on ice for 30\u2009min, and the supernatant was collected after centrifugation  for 15\u2009min. After the protein concentration was determined by the BCA method, 30\u2009\u03bcg protein was isolated by 10% SDS-PAGE by electrophoresis, and the separated proteins were then transferred to PVDF membranes. After sealing for 2\u2009h, the membranes were incubated with primary antibody overnight at 4\u2009\u00b0C. After washing, the membranes were incubated with secondary antibody for 2\u2009h. The results were examined using the ECL substrate kit (Thermo Scientific) based on the instructions.P\u2009<\u20090.05 was considered statistically significant.All experimental data are presented as the mean\u2009\u00b1\u2009SD. The data were calculated by the application of SPSS software . Statistical analysis was performed using GraphPad Prism 7.0. Supplementary methods.Supplementary figure legends.Supplementary Figure S1.Supplementary Figure S2.Supplementary Figure S3."}
+{"text": "Dryopteris fragrans, which is densely covered with glandular trichomes, is considered to be one of the ferns with the most medicinal potential. The transcriptomes from selected tissues of D. fragrans were collected and analyzed for functional and comparative genomic studies. The aim of this study was to determine the transcriptomic characteristics of wild D. fragrans sporangium in tissues from the SR (root), SL (sporophyll), and TRL (sporophyll with glandular trichomes removed). Results: Cluster analysis identified genes that were highly expressed in an organ-specific manner according to read mapping, feature counting, and normalization. The functional map identified gene clusters that can uniquely describe the function of each tissue. We identified a group of three tissue-specific transcription factors targeting the SL, SR, and TRL. In addition, highly expressed transcription factors (TFs) were found in each tissue-specific gene cluster, where ERF and bHLH transcription factors were the two types showing the most distinct expression patterns between the three different tissues. The specific expression of transcription factor genes varied between the different types of tissues. The numbers of transcription factors specifically expressed in the roots and sporophylls were 60 and 30, respectively, while only seven were found for the sporophylls with glandular trichomes removed. The expression of genes known to be associated with the development of glandular trichomes in flowering plants, including MIXTA, ATML1, and MYB106, were also validated and are discussed. In particular, a unigene encoding MIXTA was identified and exhibited the highest expression level in SL in D. fragrans. Conclusions: This study is the first report of global transcriptomic analysis in different tissues of D. fragrans, and the first to discuss these findings in the context of the development of homologous glandular trichomes. These results set the stage for further research on the development, stress resistance, and secondary metabolism of D. fragrans glandular trichomes. Background:  The regulation of gene expression is one of the most significant and complicated of the various biological processes, especially in eukaryotic species, due to their large genomes and complexity in tissue organization ,2. Some Dryopteris fragrans is a member of the Dryopteridaceae family that is mainly located in the temperate regions of North America, Europe, and Asia [D. fragrans is a valuable medicinal plant resource with extensive biological activities, including anticancer, antioxidative, insect-repellent, antimicrobial, and anti-inflammatory activities. Phytochemical investigations of the components of this plant have led to the identification of terpenoids, phloroglucinols, glucosides, and other phenolic derivatives, such as coumarin. It is characterized by multicellular, glandular trichomes with spherical head cells. Although the entire plant is densely covered with glandular trichomes, there have so far been no reports on TF genes involved in regulating their formation in D. fragrans.and Asia ,4. D. frTrichomes can be either non-glandular or glandular. Non-glandular trichomes are unable to synthesize and store secondary metabolites, while the glandular trichomes can synthesize, secrete, or store a variety of secondary metabolites, such as arteannuin, the main component in the treatment of malaria . A trichFormate Dehydrogenase (FDH), LACERATA, AtSHN1, AmMIXTA, SlMIXTA-like, and SlCD2 exhibit abnormal cuticles and reduced trichome density, in addition to mutations in GL1 and GL3, key transcription factors found in the MBW complex. There are also numerous genes involved in the regulation of trichome development, the expression of which changes the profile of metabolites in the glandular trichome; the overexpression of AaMIXTAl, AaHDl, or AaHD8 significantly increased the secretion of artemisinin in the epidermis [Previous studies have shown that the trichome initiation MYB\u2013bHLH\u2013WD40 (MBW) complex (GL3/EGL3\u2013GL1\u2013TTG1) plays a key role in determining the cellular fate of trichome cells. This complex triggers expression of the GLABRA2 (GL2) gene, which encodes a TF that induces the transition and differentiation of trichome cells ,12,13; ipidermis .D. fragrans is covered. D. fragrans grows on talcum slopes, gravel slopes, and magmatic fissures around volcanoes at an altitude of 700\u20132400 m, and can survive at \u221220 \u00b0C. The study of glandular trichome development in D. fragrans is thus of great interest. We sought to determine the genes involved in trichome development in D. fragrans in our study by examining the regulation of trichome development genes in model higher plants as a reference. We then focused on known genes that had been characterized in model plants, such as Arabidopsis thaliana, in order to determine whether the trichome developmental pathway involved homologues/orthologues in ferns.There are around 12,000 species of fern, which are considered to be a natural family of higher plants because of their bona fide vascular system, despite propagating and spreading via spores. Ferns adapted to a land environment during the process of evolution, which has uniquely determined their special trichome structure. There are few ferns with glandular trichomes, with which D. fragrans and the deciphering of tissue-specific genes. Transcription factors are of particular interest among the diverse types of genetic regulatory proteins, because they represent relatively direct regulatory interactions between proteins and chromosomes, which may lead to direct alterations in transcriptional activity [D. fragrans, we collected three different tissue types of D. fragrans grown at 25 \u00b0C with a 16/8 h photoperiod over two years. We focused on the genes and transcription factors that were specific to the different tissues. Illumina sequencing is the most effective high-throughput platform for next-generation RNA-seq transcriptome analysis in non-model tissues lacking available genomic data [There is limited research on activity ,18,19. Tmic data . The objWe analyzed the RNA-seq libraries for various tissues to obtain a global picture of the diversity across the tested tissues and their biological replicates . After qTo identify the differences between each stage of the SR, SL, and TRL tissue samples, as well as differences according to tissue, a cluster map was drawn showing the correlations between biological replicates . A high p \u2264 0.01). In this database, the matching unigenes are divided into several layers, and the lower the level of the node, the more specific the represented function. In SL vs. SR, the \u201ccell part\u201d , \u201cmembrane\u201d , and \u201ccell\u201d  had the highest numbers of matches in the cellular component. For molecular function, \u201ccatalytic activity\u201d  and \u201cbinding\u201d  were significantly higher than the other categories. For biological processes, \u201cmetabolic process\u201d , \u201ccellular process\u201d , and \u201csingle-organism process\u201d  were the most enriched. In SL vs. TRL, the \u201cmembrane\u201d , \u201cmembrane part\u201d , \u201ccell part\u201d , and \u201ccell\u201d  had the highest numbers of matches in the cellular component. In terms of molecular function, \u201ccatalytic activity\u201d  and \u201cbinding\u201d  were remarkably higher than other categories. As for biological processes, \"metabolic processes\" , \u201ccellular process\u201d , and \u201csingle-organism process\u201d  exhibited the greatest enrichment  relating to molecular function, cellular components, or biological processes for the SL, TRL, and SR gene ontology classes , followed by SL (9231) and then TRL (1245) A. Apart BIM2 (NODE_1423_length_4236), which encodes BES1-INTERACTING MYC-LIKE 2, a PAR1 (PHYTOCHROME RAPIDLY REGULATED 1)-interacting protein that positively modulates shade-avoidance syndrome. Specifically, bHLH63 (NODE_22006_length_1227) can trigger flowering in response to blue light. FAMA-like (NODE_32588_length_881), together with MYB88 and MYB124, ensures that stomata contain only two guard cells (GCs) by enforcing a single, symmetric precursor cell division before stomatal maturity. With SPCH and MUTE, FAMA-like (NODE_32588_length_881) regulates stoma formation. The ERF transcription factors mainly include ethylene-responsive transcription factor 7-like (NODE_13763_length_1722). Others found included the ethylene-responsive transcription factor ABR1-like isoform X2 (NODE_5162_length_2827), an ABI1-mediated abscisic acid (ABA) response gene, and ethylene-responsive transcription factor ERF113-like (NODE_5424_length_2767), which regulates stomatal closure and antioxidant enzyme activity through the ABI1-mediated abscisic acid (ABA) signaling pathway. Other members act as transcriptional activators to regulate the components involved in stress signal transduction pathways and plant development.After identifying the transcription factors exhibiting significant differential expression , MADS-domain transcription factor (NODE_22852_length_1190), LOB domain-containing protein 41-like (NODE_25987_length_1067), NAC domain-containing protein 86-like (NODE_37861_length_765), transcription factor DUO POLLEN 1 (NODE_64244_length_437), AIG1 (NODE_64255_length_437), and homeobox protein knotted-1-like 13 isoform X1 (NODE_7223_length_2439), which may participate in sporangial development; in addition, AP2/ERF (NODE_17285_length_1472) may function as a negative regulator of D. fragrans growth and development. ARFF_ARATH auxin response factor 6 (NODE_38417_length_754) and ARF-L1 protein (NODE_34396_length_838) are related to auxin response, and the ethylene-responsive transcription factor RAP2-12-like (NODE_37093_length_779) is related to ethylene. The ERF78_ARATH ethylene-responsive transcription factor 4 (NODE_53904_length_532) is involved in the regulation of gene expression by stress factors and components of stress signal transduction pathways. It also includes squamosa promoter-binding-like protein 7 (NODE_82760_length_331), acting coordinately with HY5 to regulate miR-408 and its target genes in response to changes in light and copper conditions. The dehydration-responsive-element-binding protein 2D (NODE_34268_length_841) can mediate high-salinity-induced transcription, transcription factor Y subunit C-2-like (NODE_30657_length_928) can adjust Ca2+ transport, and DNAJ-class molecular chaperone with C-terminal Zinc finger (NODE_27104_length_1031) is related to metal binding. In the SR, 60 transcription factors are specifically expressed; 15 of these are hybrid proteins, while the others are related to plant morphological development, ubiquitination, and the cell cycle. For example, the PLET2_ARATH AP2-like ethylene-responsive transcription factor PLT2 (NODE_74565_length_371) is essential for root quiescent center (QC) and columella specification, stem cell activity, and the establishment of the stem cell niche during embryogenesis. The class III homeodomain-leucine zipper protein (NODE_64330_length_437), B3 domain-containing transcription factor NGA1-like (NODE_79315_length_347), B3 domain-containing protein At3g19184 (NODE_11730_length_1890), BEL1-like homeodomain protein 1 (NODE_3776_length_3170), and ethylene-responsive transcription factor 2-like (NODE_68637_length_406) can regulate lateral root formation and the meristem. Trihelix transcription factor ASIL2-like (NODE_11049_length_1955) is related to embryonic development; calmodulin-binding transcription activator 3-like isoform X2 (NODE_22697_length_1196) may have similar functions; and baby boom (NODE_42351_length_686) promotes cell proliferation, differentiation, and morphogenesis, especially during embryogenesis. E3 ubiquitin-protein ligase makorin-2 (NODE_14800_length_1638) and putative transcription factor pbx (NODE_23838_length_1151) are related to ubiquitination. The class III HD-Zip protein HDZ2 (NODE_87491_length_312) is related to lipid binding. Cell division cycle 5-like (NODE_71830_length_386) is a component of the MAC complex that probably regulates defense responses through transcriptional control, and would thereby be essential for D. fragrans\u2019 innate immunity. Calmodulin-binding transcription activator 3-like isoform X2 (NODE_22697_length_1196) is possibly related to embryonic organ development. In the TRL, the homeobox protein knotted-1-like 1 (NODE_53201_length_540) interacts with auxin and AS1, which results in the promotion of leaf fate, and Dof2 (NODE_8258_length_2282) may transactivate seed storage protein genes in developing seeds; however, in D. fragrans, Dof2 may instead be related to spore development. WRKY transcription factor 12 (NODE_12261_length_1843) is involved in aging, as well as biological and abiotic stress responses by regulating various plant hormone signaling pathways. Zinc finger protein 36 (NODE_17837_length_1440) can regulate ABA-induced hydrogen peroxide production and antioxidant defenses. Zinc finger CCCH domain-containing protein 35 (NODE_20185_length_1313) and GATA domain-containing protein (NODE_46526_length_622) play roles in metal ion binding and zinc ion binding, respectively.Of the 953 TF genes annotated, 12.6% were expressed in at least one tissue and 81.5% in all three tissues . The SR D. fragrans does contain dense trichomes. Current studies have shown that the development of trichomes in different plants is regulated by transcription factors that are similar between different plants. We compared the sequence fragments in the transcriptome with those found to be related to trichogenesis in other species, as shown in Despite some TF genes being detected in all three tissues, the results imply that these TF genes and other genes still show a differential expression profile A,B, whicMYC4 (NODE_12035_length_1864), which is involved in the regulation of the jasmonic acid (JA) gene. Zinc finger protein 8 (NODE_43095_length_674) regulates the initiation of trichome growth in Arabidopsis thaliana [MYB80 (NODE_10729_length_1989) is related to the development of trichomes, and can negatively regulate the overlap and branching of trichomes. The R2R3\u2013MYB transcription factor MYB2 (NODE_9201_length_2161) regulates the initiation of cotton fiber growth in Gossypium hirsutum [EPF-type Cis2-His2 zinc finger transcription factor (NODE_36794_length_786) and WRKY transcription factor 46 (NODE_41105_length_707). The R2R3-MYB transcription factor MYB9 (NODE_88329_length_309), related to trichome development, is expressed only in SR, so it may not be related to the development of trichomes in D. fragrans. Among the 10 transcription factors shared by SL and TRL, the transcription factor bHLH49 (NODE_35465_length_814) is a transcriptional activator involved in cell elongation. The other genes, such as BEL1-like homeodomain protein 1 (NODE_48395_length_597) and BEL1-like homeodomain protein 2 (NODE_64557_length_435), can establish leaf shape. KNAT4_ARATH Homeobox protein knotted-1-like 4 is a homeodomain protein of the KNOX class I family, which has been shown to play a role in shoot apical meristem development. Genes related to flower development were also identified, such as transcription factor PIF4 (NODE_2431_length_3644), light-regulated zinc finger protein 1 (NODE_83844_length_327), and NFYB6_ARATH Nuclear transcription factor Y subunit B-6 (NODE_23238_length_1174), which play vital roles in the regulation of seed maturation in Arabidopsis [Apart from the 777 transcription factors that were shared among the three tissues, the highest number of shared transcription factors was 43 between SR and SL, followed by 26 between SR and TRL, and 10 between SL and TRL. It is worth noting that the 26 transcription factors shared by SR and TRL are related to trichome development\u2014for example, thaliana . MYB80 (hirsutum . Some trBLH1_ARATH BEL1-like homeodomain protein 1 (NODE_3776_length_3170), MAD50_ORYSJ MADS-box transcription factor 5 (NODE_26719_length_1043), and ARFB_ARATH Auxin response factor 2 (NODE_7811_length_2347). In addition, MYB98 (NODE_17251_length_1474) controls the development of specific features within the synergid cell during female gametophyte development, and is also expressed in trichomes and endosperm.Most genes expressed only in leaves are related to flower and seed development. These include MIXTA of MYB transcription factors, which is related to trichome formation in Anthurium japonicum [MIXTA was relatively high in SL, moderate in roots, and very low in TRL, indicating that MIXTA might be related to the trichomes of D. fragrans. In addition, its homologous gene MYB106 (NODE_11206_LENGTH_1939) is also associated with the occurrence of trichomes, and the similarity in expression patterns with those of MIXTA consistently indicates that they may have the same function. Moreover, MYB106 is involved in the negative regulation of trichome branching. The MIXTA-like MYBs regulate the biosynthesis of cutin nanoridges and wax accumulation by promoting the expression of related genes [homeodomain leucine zipper class I (HD-Zip I) HOX21 (NODE_7286_length_2427), HOX5 (NODE_8227_length_2288), and HOX4 (NODE_30917_length_922) transcriptional activators, which are involved in leaf development, have the same expression pattern, which is relatively high in the SL.We further analyzed the differentially expressed genes and transcription factor gene expression trends. The sequence NODE_11206_length_1939 is the aponicum . In thised genes . Notablytranscriptional activator LAC17 likely contributes to lignin biosynthesis, and hence, cell wall biosynthesis. Third, GATA5 is involved in regulating carbon and nitrogen metabolism. Overall, these genes may regulate the functions of spore and leaf cell development. The fourth cluster includes proteins involved in photoresponse, chloroplasts, and cell development, as well as cell-silencing clusters associated with GLU1, FUG1, EMB25, and GTL1 gene interactions. Gene clusters interacting with the transcription factor Col1 are related to sporocyst development.We found several genes and transcription factor genes to be highly expressed in the SL and thus, conducted transcription factor genes differentially expressed analyses . The 36 ARATH Homeobox-leucine zipper protein MERISTEM L1 (NODE_908_length_4702) was highly expressed in the SL, and encodes a homeobox protein similar to the homeobox\u2013leucine zipper protein GLABRA 2 in Arabidopsis thaliana. Gene HD-ZIP (NODE_33044_length_869) of the homeodomain GLABROUS 2 encodes a homeobox\u2013leucine zipper family protein belonging to the HD-ZIP IV family, similar to ATML1, Mutants have trichomes that appear glass-like under a dissecting microscope compared to the wild-type trichomes, although interaction may occur in both types. The homeobox\u2013leucine zipper protein anthocyaninless 2 (NODE_25007_LENGTH_1104) encodes a homeodomain protein of the HD\u2013GLABRA2 group, involved in the accumulation of anthocyanins, root development, and regulating tissue polarity in A. thaliana. KAN (NODE_56983_length_501) is required for abaxial identity in both leaves and carpels, and appears to be involved in the development of the carpel and outer integument of the ovule. Interaction may also occur between ANTHOCYANINLESS and KAN. Eight genes were selected for validation based on the results acquired from the KEGG pathway enrichment analysis. As shown in D. fragrans spores were collected from lava rock near Bagua Lake, Wudalianchi, Heilongjiang, China , within a 30 m radius during July 1\u20136, 2016, with permission from the government. To ensure the uniformity of samples, we used D. fragrans that was grown at 25 \u00b0C with a 16/8 h photoperiod over 2 years. We subsequently selected three tissues of this D. fragrans to investigate gene regulation and glandular trichome development. We selected the roots, sporophylls, and sporophylls with the glandular trichomes of D. fragrans removed; for their removal, the trichomes were wiped with a brush dipped in liquid nitrogen. A transcriptome database was established using Illumina sequencing to further elucidate the genes of the fern .To characterize the transcriptome, three biological replicates were obtained for each tissue . For real-time quantitative PCR (qPCR) analysis, the same samples originally used for RNA-seq were used with three biological replicates and three technical replicates for each. All experimental materials were transplanted from the tissue culture, and the optical microscope pictures were taken in the laboratory.Total RNA was isolated from the samples using a TIANGEN RNAprep Pure Plant Kit, according to the manufacturer\u2019s instructions. A NanoDrop 2.0 Spectrophotometer and Agilent 2100 Bioanalyzer were used to characterize the RNA purity and concentration prior to transcriptomic sequencing. Transcriptomic data were obtained from nine RNA-seq libraries of three tissue samples with three biological replicates.\u00ae Ultra\u2122 RNA Library Prep Kit for Illumina\u00ae . The mRNA was enriched using oligo(dT) magnetic beads. Fragmentation buffer was then added to randomly fragment the mRNA. Firstly, cDNA was prepared from 1 microgram total RNA with random hexamers, according to the manufacturer\u2019s instructions. Then, the double-stranded cDNA was end-repaired and purified using AMPure XP beads, and tails and sequencing linkers were attached. AMPure XP beads were also used to select for the appropriate fragment sizes, and PCR enrichment was performed to obtain the cDNA libraries. A Qubit 2.0 Fluorometer and Agilent 2100 Bioanalyzer were used to determine the concentrations of the libraries and inserted fragment size. The effective library concentrations were determined by qPCR. Finally, the libraries were subjected to high-throughput Illumina sequencing .Sequencing libraries were generated using NEBNextIllumina reads were checked for quality using FastQC software. Trinity  was emplp-value adjustment for multiple testing, based on the Holm\u2013Bonferroni method.Gene ontology (GO) enrichment was performed using the Singular Enrichment Analysis (SEA) tool of agriGO with the default settings. The significance of the differences in annotation frequencies was tested for each gene ontology term, at level 4, for biological process, molecular function, and cellular component, using Fisher\u2019s exact test followed by a http://www.genome.jp/kegg/). We used the KOBAS software to test the statistical enrichment of differentially expressed genes (DEGs) in the KEGG pathways . Pathway was significantly enriched in the differentially expressed Unigene.Metabolic pathway analysis was performed using the Kyoto Encyclopedia of Genes and Genomes Pathway database  was performed. Trend analysis was used to cluster the gene expression patterns of three groups of tissue samples: SR (root), SL (sporophyll), and TRL (sporophyll with glandular trichomes removed). The gene sets were then selected from the clustering results according to certain biological characteristics . We used the STEM Short Time-series Expression Miner (http://www.cs.cmu.edu/~jernst/stem) and set the maximum number of model profiles to eight to analyze trends.The average FPKM values of the all differentially expressed genes and transcription factors in the three tissues were used as the starting data for expression pattern analysis. Specifically- or highly-expressed genes and transcription factors were only identified in leaves. The log2 standardization of the FPKM values (Ten transcription factor unigenes were chosen for validation by qPCR. The reference gene selected for normalization in this experiment was 18S rRNA . The priD. fragrans in the root and sporophyll. A total of 90,977 unigenes were identified, including 25,875 transcription factors, among which 50.65% were aligned to sequences in the Nr database. Although no D. fragrans reference genome sequence is available, the unigenes were annotated in the database. Eight unigenes were chosen for validation by qPCR, and the trends were largely in accordance with the transcriptomic data. A total of 1794 differentially expressed unigenes were found in the SL, SR, and TRL, including key trichome development genes, such as DfMIXTA and DfAML1. In summary, our results provide important insight into the complex transcriptional regulation and potential mechanisms underlying glandular trichome development, stress resistance, and secondary metabolism in D. fragrans. Some basic studies on the glandular trichomes of ferns were also discussed.This work provides the first comprehensive transcriptomic analysis of D. fragrans was studied using high-throughput sequencing. Through transcript profiling and comparative transcriptomic analysis, the gene expression patterns in diverse tissues were determined. The data included 90,977 unigenes, of which 25,875 were transcription factors. These may include be transcripts unique to each of the three examined tissues of D. fragrans. The differentially expressed genes shown in the heat mapping allow us to understand what is occurring in individual tissues. It is worth noting that some of the poorly repeated data should be studied further, rather than simply discounted.In this study, global gene transcription in three different tissues of Identifying genes expressed in different tissues provides baseline information for a broader understanding of tissue function and physiology. The study classified the collection of genes expressed in these three tissues and recorded crucial changes according to tissue. Moreover, we found that for the most abundant transcripts and transcription factor genes that exhibited tissue-specific expression, the results are consistent with expectations regarding their possible specific roles in different tissues. In particular, tissue specificity should be considered in the context of the tested tissue, and additional measurements in diverse tissues could not necessarily be used to confirm the observations. However, the GO enrichment profile captured from the transcriptome is in line with our expectations, which implies that this high-quality dataset is suitable for further analysis.MADS domain transcription factor, lob domain containing protein 41 like, NAC domain containing protein 86 like, transcription factor duo pollen 1, AIG1, homeobox protein known-1-like 13 isoform x1, and AP2/ERF\u2014were also found in the sporophyll, and may be expressed in the sporangium. The other transcription factors were mainly related to plant development, secondary metabolism, hormone response, and inorganic salt transport. In the SR, except for hybrid proteins, the other TF genes were related to plant morphological development, ubiquitination, and the cell cycle. In the TRL, the transcription factor genes were mainly associated with the promotion of leaf fate and spore development, and involved in aging, biological-, and abiotic-stress responses, hydrogen peroxide production, antioxidant defenses, and ion binding. The transcription factors specifically expressed in the glandular trichomes of D. fragrans may also be related to plant development, secondary metabolism, hormone response, and inorganic-salt transport.The transcription factors were differentially expressed in the different tissues. The numbers of transcription factors specifically expressed in the roots and sporophylls were 60 and 30, respectively, but only 7 in the sporophylls with glandular trichomes removed. This suggests that the regulation of development and metabolism by transcription factors is more prominent in the roots, while more transcription factors may be specifically expressed in glandular trichomes. Transcription factors related to sporangium development\u2014such as putative constans-like protein, MIXTA determines the occurrence of trichomes in Antirrhinum majus. In this study, DfMIXTA expression in the SL was significantly higher than that in the TRL. The trend of DfMIXTA expression was high in the SL and SR but low in the TRL, suggesting that this gene might be related to the development of trichomes in D. fragrans. MYB106, a gene homologous to MIXTA, plays the same role in angiosperms, such as Arabidopsis thaliana, and coexists in D. fragrans; thus, it may perform the same function in both plants. Zinc finger protein 8 is one of the key transcription factors regulating trichome formation in angiosperms, while zinc finger protein 1-like regulates the initiation of trichome growth in A. thaliana [D. fragrans, suggesting that the regulation of glandular trichomes in ferns may be different from that in angiosperms. This further indicates that these transcription factors regulate root hairs or possibly the development of trichomes in D. fragrans. Although these genes are not expressed in sporophylls, they may play an important role in the early development of trichomes. Elucidating the details of the interaction of these genes with each other and how they function requires further research.We hypothesized that the expression of certain genes and transcription factor genes were affected by external stimuli, which may further influence multiple downstream genes and some unknown metabolic pathways . We expethaliana ,33. BothARATH homeobox-leucine zipper protein MERISTEM L1 possibly interacts with the HD-ZIP. Similar to those in Arabidopsis, these complexes may initiate the expression of DfAML1, a gene encoding transcription factors, and the transformation of cells into trichomes, which is worthy of future study.In addition, some genes are related to auxin, spore maturation, and photosynthesis. We predict that the D. fragrans in Root and Sporophyll. A total of 90,977 UniGenes were identified, including 25,875 transcription factors, among which 50.65% were aligned to sequences in the Nr database. Although no D. fragrans reference genome sequence is available, the UniGenes were annotated in the database. Eight UniGenes were chosen for validation by qPCR, and the trends were largely in accordance with the transcriptomic data. A total of 1794 differentially expressed UniGenes were found in SL, SR, and TRL, including key trichome development genes, such as DfMIXTA and DfAML1. In summary, our results provide important insight into the complex transcriptional regulation and potential mechanisms underlying glandular trichome development, stress resistance, and secondary metabolism in D. fragrans. Some basic studies on the glandular trichomes of ferns were also discussed.This work provides the first comprehensive transcriptomic analysis of"}
+{"text": "Colorectal cancer (CRC) poses a heavy threat to human health owing to its high incidence and mortality. Circular RNAs (circRNAs) were investigated to participate in the progression of CRC, whereas there was no revenant data on the CRC process regulated by hsa_circ_0000231. This study aimed to explore the effects of hsa_circ_0000231 on CRC progression and underneath regulatory mechanism.The expression levels of hsa_circ_0000231, miR-502-5p, and Myosin VI (MYO6) mRNA were detected by quantitative real time polymerase chain reaction (qRT-PCR). Western blot was employed to determine the protein expression levels of MYO6 and proliferating cell nuclear antigen (PCNA). The effects of hsa_circ_0000231 on cell proliferation, apoptosis, migration, and invasive in CRC were determined by cell counting kit-8 proliferation (CCK-8) and colony formation assays, flow cytometry analysis, wound-healing assay, and transwell invasion assay, respectively. Glucose uptake and lactate production were severally illustrated by glucose assay kit and lactate assay kit. The relationship between miR-502-5p and hsa_circ_0000231 or MYO6 was predicted by circular RNA interactome or targetScan online databases, and identified by dual-luciferase reporter and RNA immunoprecipitation (RIP) assays. In vivo tumor formation assay was carried out to determine the effects of hsa_circ_0000231 knockdown on tumor growth in vivo.Hsa_circ_0000231 expression was dramatically upregulated while miR-502-5p was obviously downregulated in CRC tissues and cells compared with control groups. Hsa_circ_0000231 knockdown repressed the expression levels of MYO6 and PCNA protein. Functionally, hsa_circ_0000231 knockdown repressed cell glycolysis, proliferation, migration and invasion, and induced cell apoptosis, whereas these effects were decreased by miR-502-5p inhibitor. Mechanistically, hsa_circ_0000231 acted as a sponge of miR-502-5p and miR-502-5p bound to MYO6. Furthermore, hsa_circ_0000231 knockdown decreased tumor volume and weight of CRC in vivo.Hsa_circ_0000231 knockdown inhibited CRC progression and glycolysis by downregulating MYO6 expression through sponging miR-502-5p, which might provide a theoretical basis in further studying circ_0000231-directed therapy in CRC. Colorectal cancer (CRC) is one of the main reasons of cancer-caused mortality worldwide . The surCircular RNAs (circRNAs) are a kind of non-coding RNA and are more stable than their linear form with closed loop . CircRNAMicroRNAs (miRNAs) are a kind of transcripts of 18-22 nucleotides and work via targeting mRNA 3\u2032-untranslated regions (3\u2032UTR) \u201311. For Myosin VI (MYO6) is a member of the actin-related myosin family and takes part in multiply vital biologic processes . It was In this study, hsa_circ_0000231 expression and MYO6 protein expression were dramatically increased while miR-502-5p expression was obviously decreased in CRC tissues and cells. Hsa_circ_0000231 regulated cell proliferation, migration, invasion, apoptosis, and glycolysis by controlling MYO6 expression through binding to miR-502-5p in CRC. Furthermore, hsa_circ_0000231 knockdown repressed tumor formation in vivo.The Ethics Committee of the Second Affiliated Hospital of Hainan Medical University approved this experiment. Forty pairs of CRC tissues and paracancerous healthy tissues were collected from the Second Affiliated Hospital of Hainan Medical University. Tissues were kept in liquid nitrogen. All patients signed the written informed consents.2.The Otwo Biotech  supplied human normal colonic epithelial cells NCM460 and human CRC cell lines HCT116 and LoVo. Cells were cultivated in Dulbecco\u2019s modified Eagle\u2019s medium  or Roswell Park Memorial Institute-1640 (RPMI-1640) with 10% fetal bovine serum (FBS) and 1% streptomycin/penicillin (Invitrogen) at 37\u2009\u00b0C with 5% COSmall interfering RNAs targeting hsa_circ_0000231 (si-hsa_circ_0000231#1 and si-hsa_circ_0000231#2), short hairpin RNA (shRNA) against hsa_circ_0000231 (sh-hsa_circ_0000231), miR-502-5p mimic (miR-502-5p), hsa_circ_0000231 overexpression vector (hsa_circ_0000231), miR-502-5p inhibitor (anti-miR-502-5p), MYO6 overexpression vector (MYO6), and their control groups  were amplified by Ribobio Co., Ltd. . Cells were transfected with various treatments with Lipofectamine 2000 in agreement with literature methods  and coll\u2212\u0394\u0394Ct method. The sense and anti-sense primers were presented in Supplementary Table The sample was lysed with TransZol , and RNA concentration was measured by the NanoDrop-1000 instrument . Then cDNA was synthesized with a reverse transcription kit . After that, qRT-PCR detection kit (Takara) was employed to quantify the expression of circRNA, miRNA, or mRNA. GAPDH and U6 were chosen as references. Data was calculated by the 2The RNA of HCT116 and LoVo cells was extracted and incubated with RNase R . Then, the samples were purified and results were detected with RT-PCR.3 per well) and cultured for 24\u2009h. Then si-hsa_circ_0000231#1, si-hsa_circ_0000231#2, anti-miR-502-5p, miR-502-5p, or MYO6 was transfected into cells with their controls, and cells were continued to cultivate for 24, 48, and 72\u2009h. Following 10\u2009\u03bcL CCK-8 regent (Beyotime) was added into plate, and cells were continued to culture another 4\u2009h. Results were analyzed by detecting the absorbance at a wavelength of 450\u2009nm using a microplate reader (Thermo Fisher).Cell viability was detected with a CCK-8 kit . Briefly, HCT116 and LoVo cells were calculated and seeded in a 96-well plate . Then cells were suspended with binding buffer, and were incubated with Annexin V-FITC for 5\u2009min and propidium iodide  for 30\u2009min. Cell apoptosis was determined by a FACSort flow cytometer .C-caspase-3 activity was detected with Caspase 3 Activity Assay Kit  according to the manufacturer\u2019s protocol. In short, HCT116 and LoVo cells were collected, washed with PBS, and lysed. The lysates were incubated with C-caspase-3 reaction reagent. The results were visualized by a plate-reading luminometer (Thermo Fisher).5) were cultivated in 12-well upper chamber  with Matrigel  with serum-free medium to examine cell invasion. Medium containing 20% FBS was added into the lower chamber. Chambers were taken out from the plate after 24\u2009h and cells were washed using PBS. Then, cells were incubated with methanol and crystal violet. Invaded cells were counted with a microscope at a 100 magnification.HCT116 and LoVo cells  and Lactate Assay Kit (Abcam) according to the protocols, respectively. Briefly, HCT116 and LoVo cells were cultivated in a 6-well plate for 48\u2009h. Then, supernatants were collected. And glucose uptake and lactate production were analyzed by detecting absorbance at 450\u2009nm or 570\u2009nm using a microplate reader (Thermo Fisher).HCT116 and LoVo cells were lysed. The sample was loaded into 12% SDS-PAGE. After that, proteins bands were transferred onto nitrocellulose membranes. Following bands were blocked in 5% skim milk. Then, membranes were incubated with primary antibodies. Following, a secondary antibody labeled with horseradish peroxidase was utilized to incubate membranes. Results were visualized by enhanced chemiluminescence . GAPDH was chosen as a control. The primary antibodies were anti-hexokinase 2 (anti-HK2) , anti-MYO6 , anti-proliferating cell nuclear antigen (anti-PCNA) , and anti-GAPDH .Renilla luciferase activity was chosen as a reference.The binding sequences between miR-502-5p and hsa_circ_0000231 or MYO6 were predicted by circular RNA interactome or targetScan online database. The wide-type (wt) sequences of hsa_circ_0000231 and MYO6 3\u2032UTR contained the target sequences of miR-502-5p were synthesized and inserted into the pmirGLO vector . The sequences bound to miR-502-5p in hsa_circ_0000231 and MYO6 3\u2032UTR were mutated, and the mutant (mut) hsa_circ_0000231 and MYO6 3\u2032UTR were synthesized and sub-cloned into pmirGLO vector (Promega). Then plasmids were co-transfected into cells with miR-502-5p or NC and cells were cultured for 48\u2009h. Luciferase activities were detected by dual-luciferase reporter assay kit (Promega). HCT116 and LoVo cells were collected and lysed. Cell lysates were incubated with magnetic beads coated with anti-Ago2  or anti-IgG (Abcam) for 24\u2009h. RNA was purified, and the enrichment of hsa_circ_0000231, miR-502-5p, and MYO6 was detected by qRT-PCR.6) cells transfected with sh-hsa_circ_0000231 or sh-NC were injected into the flank of mice. Tumors volume was measured every 5\u2009days. All mice were euthanized after 30\u2009days. The volume and weight of tumors were analyzed.All protocols were approved by the Animal Care Committee of the Second Affiliated Hospital of Hainan Medical University. Charles River  provided nude mice. HCT116  Hsa_circ_0000231 knockdown suppressed the invasion and migration of HCT116 and LoVo cells. *P<0.05.Additional file 3. Supplementary Figure 2 Hsa_circ_0000231 knockdown represses cell migration and invasion via binding to miR-502-5p in CRC. (A and B) MiR-502-5p inhibitor attenuated the inhibition effects of hsa_circ_0000231 knockdown on the invasion and migration of HCT116 and LoVo cells. *P<0.05.Additional file 4. Supplementary Figure 3 MiR-502-5p suppresses cell migration and invasion via associating with MYO6 in CRC. (A and B) MYO6 attenuated the inhibition effects of miR-502-5p on the invasion and migration of HCT116 and LoVo cells. *P<0.05."}
+{"text": "In contrast, overexpression of hsa_circ_0085576 had the opposite effects. Moreover, hsa_circ_0085576 silencing significantly suppressed tumor growth and metastasis, whereas overexpression of hsa_circ_0085576 had the opposite effects, in vivo, Our results further showed that hsa_circ_0085576 acted as a competitive endogenous RNAs to interact with microRNA-498, to attenuate its repressive effect on target gene Yes-associated protein 1 (YAP1). Finally, functional studies revealed that inhibition of hsa_circ_0085576 suppressed cell growth and metastasis by regulating miR-498/YAP1 signaling, in ccRCC cells. Based on these findings, hsa_circ_0085576 may represent a valuable prognostic biomarker and a potential therapeutic target to curb the tumorigenesis and metastasis of ccRCC.There is emerging evidence that circular RNAs (circRNAs) act as important regulators in various cancers. It is less clear, however, what role circRNA plays in the tumorigenesis and metastasis of clear cell renal cell carcinoma (ccRCC). In this study, using bioinformatics analysis and a series of experimental analysis, we characterized a novel circRNA, hsa_circ_0085576 was up-regulated in ccRCC tissues and cell lines. High hsa_circ_0085576 expression was significantly correlated with tumor size, clinical stage, and metastasis status and poorer survival. Knockdown of hsa_circ_0085576 notably inhibited cell proliferation, migration, invasion, whereas enhanced cell apoptosis of ccRCC cells,  Renal cell carcinoma (RCC) is one of the most common tumors originated from renal tubular epithelial cells with a high mortality rate worldwide . Clear cCircular RNAs (circRNAs) are a newly appreciated class of RNAs found across phyla that are generated most commonly from back-splicing of protein-coding exons. It is more stable and not easily degraded by exonuclease when comparable to linear RNA . CircRNAIn this study, we focused on the upregulated circRNAs based on the data from circRNAs microarray of ccRCC tissues, and identified that hsa_circ_0085576, which has not been reported before, was significantly upregulated in ccRCC tissues and cell lines. The present study demonstrated that elevated hsa_circ_0085576 was positively associated with the clinical pathological stage and may serve as a candidate prognostic biomarker. Mechanically, our data revealed that hsa_circ_0085576 could directly sponge miR-498 to upregulate YAP1 expression and consequently promote the growth and metastasis of ccRCC. Hsa_circ_0085576 may serve as an oncogene to promote ccRCC metastasis and be applied to a novel therapeutic target.To determine the circRNAs profiling in ccRCC tissues, we used the microarray gene profiling data of ccRCC GSE100186 and GSE137836. GSE100186 consists of 3 normal, 3 RCC samples and GSE137836 consists of 3 primary tumor tissues and 3 humans metastatic RCC samples. As shown in We then focused on the most significantly upregulated circRNA has-hsa_circ_0085576, which is located on chromosome 8, and consists of 3 exons (exons 10\u201312) from its host gene ASAP1  genome. Sanger sequencing then confirmed the head-to-tail splicing of hsa_circ_0085576 with the expected size . SubsequWe further explore the association between the hsa_circ_0085576 expression level and clinical significance. The fold change of hsa_circ_0085576 in the tumor tissues and adjacent normal ones were shown in To investigate the biological functions of hsa_circ_0085576 in ccRCC, LV-sh-hsa_circ_0085576 and pLVX-hsa_circ_0085576 vectors were constructed, and the efficiency of infection was verified by RT-qPCR . CCK-8 ain vivo. The tumor growth model showed that hsa_circ_0085576 knockdown notably inhibited tumor growth whereas overexpression of hsa_circ_0085576 facilitated tumor growth  tail. Multiple factors could regulate the biogenesis of circRNAs from the precursor-mRNA, such as alternative splicing factors , 23. PreRecent researches have pointed to an important role for YAP1 signaling in tumor progression . As the Our study has certain limitations. First, we identified a novel regulatory role of hsa_circ_0085576/miR-498/YAP1 in ccRCC, However, why hsa_circ_0085576 expressed at high levels in ccRCC still unknown. More studies on alternative splicing during the transcription of hsa_circ_0085576 or its coding gene ASAP1, might be carried out to illustrate the regulatory mechanism. In addition, the diagnostic performance of circRNA has been reported in gastric cancer , cervicaIn summary, hsa_circ_0085576 could serve as a predictor for clinical outcomes in patients with ccRCC. Hsa_circ_0085576 plays a critical role in promoting cell growth and metastasis of ccRCC by regulating YAP1 expression via miR-498 sponging. Our findings might provide valuable insights into the development of potential therapeutic targets for ccRCC.The study was approved by the Ethics Committee of Peking Union Medical College Hospital and each participant signed informed consents before sample collection. Animal experiments were performed in line with the protocols approved by the Institutional Animal Research Committee of Peking Union Medical College Hospital. Seventy-six paired ccRCC tumor tissues and adjacent normal tissues were collected from patients in the Department of Urology , with a definite pathological diagnosis by two pathologists independently. Adjacent normal tissues were acquired at least 3cm away from the tumor site. The tissues were stored in liquid nitrogen. After histopathological vetting, 45 samples were assigned to the non-metastasis group and the rest were assigned to the metastasis group, according to the Kidney Cancer, NCCN Clinical Practice Guidelines in Oncology . ClinicaNormal kidney epithelial cells (HK2) and renal cancer cell lines  and were preserved in our lab, as previously described [Hsa_circ_0085576 overexpression and silencing recombinant lentiviral vectors were constructed in the pLVX-hsa_circ_0085576 and LV-sh-hsa_circ_ 00855767 vectors by inserting the hsa_circ_0085576 PCR fragment and shRNA, were constructed by Shanghai Genechem Co, Ltd. . Control lentivirus particles LV-shRNA-negative control (LV-sh-Ctrl) and pLVX-Ctrl were served as the control. The interference sequence was listed in Approximately 5,000 A498 and 786O cells were plated into 96-well plates and cultured for 0, 1d, 2d, 3d, 4d, and 5d, followed by treatment with 10 \u03bcL CCK-8 solution  for 1 h at 37 \u00b0C. The absorbance was measured at 450 nm using a microplate reader.Cells were seeded in 6-well plates and wounds were created by 200-\u03bcL pipette tip when cell confluence reached 80%. After washing with PBS, the cells were treated as indicated for 48 h. The healing wounds were photographed twice at 0 h and 48 h after scratching with a digital camera (Leica DFC300FX).The migratory and invasive abilities of A498 and 786O cells were assessed through transwell chambers (8.0-\u03bcm pore size polycarbonate filter). In terms of the invasion assay, the upper chamber pre-coated with Matrigel was supplemented with 1\u00d7105 cells, while the lower chamber was replenished with RP1640 medium (500 \u03bcl) containing 10% FBS. After 24 h, cells of the upper chamber were wiped by a cotton swab and paraformaldehyde was used to fix the invasive cells that were manually counted under a microscope. As for the migration experiment, cells were put into the top chamber without Matrigel. The experiment was conducted in triplicate with the mean value calculated.The procedures of cell cycle analysis were carried out following the manufacturer\u2019s instructions of Cell Cycle Assay Kit . Attached cells were harvested cells and fixed in 70% ice-cold ethanol overnight at 4 \u00b0C. Finally, cells were stained with RNase A (10 mg/mL) and Propidium Iodide (50 mg/mL) before analyzed by a flow cytometer . Data were analyzed using CELL Quest 3.0 software.6/ml. The cells were stained with Annexin V (10 \u03bcl) and Propidium Iodide (5 \u03bcl) per 1ml cell suspension and incubated for 15min at room temperature in dark. Then 400 \u03bcl binding buffer was added. At last, the apoptosis rate was assayed by flow cytometry.Apoptosis assay was performed with an Annexin V-FITC/PI apoptosis detection kit . The cells were trypsinized and washed twice with PBS after centrifugation. Subsequently, cells were resuspended in binding buffer followed by adjusting to the concentration of 1 \u00d7 10A probe for the has-hsa_circ_0085576 (5\u2019- CAAA GGCGCAGTTCCTTGGGAT-3\u2019) containing a biotin label was used for RNA FISH analysis. The methods were as described previously by Chen et al. .Total RNA was extracted from pulmonary tissues or cells by Trizol reagent (Invitrogen). RT-qPCR was performed using the SuperScript VILO cDNA Synthesis Kit (Invitrogen) and Sybr Green PCR mastermix . Actin was an internal parameter of circRNA and mRNA, while U6 was as an internal parameter of miRNA. Each assay was performed in triplicate and the 2-\u0394\u0394Ct method was used to determine the relative expression of genes. Primers were listed in RIP assay was carried out using the Magna RIP Kit  in accordance with the manufacturer\u2019s instructions. Antibodies against argonaute 2 (anti-AGO2)  and immunoglobulin G were used for the RIP assays. Purified RNAs were extracted and the enrichment of circ-008576 and miR-498 was analyzed by RT-qPCR, as described above.4 cells/well) were cultured in 24-well plates and co-transfected with wild-type (WT) or mutant (Mut) hsa_circ_0085576 and miR-NC or miR-498 using Lipofectamine 2000 (Invitrogen). The cells were also transfected with a Renilla plasmid . The luciferase activities were measured with a Dual-Luciferase reporter assay system (Promega) according to the manufacturer\u2019s protocol.The mutation of hsa_circ_0085576 and the YAP1 3\u2019UTR was performed by changing the conserved binding sites of miR-498 using a Gene Mutation Kit (Takara). A498 and 786O cells . Protein was separated by electrophoresis in 12% SDS-PAGE and transferred to PVDF membranes, blocked with 5% milk in Tris-buffered saline with Tween 20, and probed with the primary antibodies against YAP1 (ab52771), LATS1 (ab70562), LATS2 (ab110780), TEAD2 (ab92279), TEAD3 (ab75192) and \u03b2-actin antibody . After incubation with peroxidase-conjugated secondary antibodies, bands were visualized with enhanced chemiluminescence (Millipore). The relative band intensities were quantified using a ChemiDoc XRS imaging system.http://www.r-project.org) using DESeq Bioconductor library using a negative binomial distribution [Total RNA was isolated from cells using Trizol, as described above. The quality of RNA was analyzed using a 2100 Bioanalyzer system (Agilent Technologies). In brief, the purified RNAs were subjected to cDNA synthesis followed by adaptor ligation and enrichment with a low-cycle according to instructions of TruSeq\u00ae RNA LT/HT Sample Prep Kit . The purified library products were evaluated using the Agilent 2200 TapeStation and Qubit2.0  and then subjected to sequencing on HiSeq3000. RNA-seq data were normalized in R v3.2.3 statistical environment (ribution .http://www.geneontology.org) and Kyoto Encyclopedia of Genes and Genomes  pathway analysis. Based on the GO categories, the aberrant mRNAs were classified under different GO terms in terms of their characteristics and the enrichment of the GO terms was calculated. The KEGG database was employed to analyze the aberrant mRNAs and the enrichment of different pathways was calculated. The false discovery rate (FDR) was used to evaluate the significance of the P-value and an FDR<0.05 was recommended.The differentially expressed mRNAs were selected for Gene Ontology  on the right back of BALB/C athymic nude mice. The two perpendicular diameters of xenografted tumors were measured each time before compound administration, and the tumor volume was calculated by the formula (a \u00d7 b2)/2 . After 30 days following implantation, mice were all sacrificed, the tumors were removed, weighed and photographed. The lungs were harvested for H&E staining and the area of metastatic foci was quantified by ImageJ and normalized to the total area of lung tissues.A total of 20 female BALB/c athymic nude mice  were housed under pathogen-free conditions and randomly divided into the LV-shCtrl group, the LV-sh-circ0085576 group, the pLVX-Ctrl group and pLVX-circ0085576 group (5 per group). The xenograft model was established by subcutaneous injection of A498/LV-shCtrl, A498/LV-sh-circ0085567 or 786O/pLVX-Ctrl, 786O/pLVX-circ0085567 cells  and disease-free survival (DFS) were estimated by the Kaplan-Meier method. Receiver operating characteristic (ROC) curves were used to determine the cut-off threshold value of hsa_circ_0085576. The experimental data were shown as the mean \u00b1 standard deviation (SD). Analysis of variance (ANOVA), Student t-test, Wilcoxon matched-pairs signed rank test, Chi-square test and Spearman\u2019s correlation were responsible for p-values. If P < 0.05, the difference was considered statistically significant.Supplementary Figure 1Supplementary Tables"}
+{"text": "Single-cell RNA sequencing (scRNA-seq) unfolds complex transcriptomic datasets into detailed cellular maps. Despite recent success, there is a pressing need for specialized methods tailored towards the functional interpretation of these cellular maps.Here, we present DrivAER, a machine learning approach for the identification of driving transcriptional programs using autoencoder-based relevance scores. DrivAER scores annotated gene sets on the basis of their relevance to user-specified outcomes such as pseudotemporal ordering or disease status. DrivAER iteratively evaluates the information content of each gene set with respect to the outcome variable using autoencoders. We benchmark our method using extensive simulation analysis as well as comparison to existing methods for functional interpretation of scRNA-seq data. Furthermore, we demonstrate that DrivAER extracts key pathways and transcription factors that regulate complex biological processes from scRNA-seq data.By quantifying the relevance of annotated gene sets with respect to specified outcome variables, DrivAER greatly enhances our ability to understand the underlying molecular mechanisms. Single-cell RNA sequencing (scRNA-seq) experiments dissect biological processes or complex tissues at the cellular and molecular levels , 2. OwinBiological meaning can be extracted from the data manifold following in-depth analysis. After cells are stratified into separate groups or along a continuum, differential expression analysis is performed. Gene set enrichment analysis represents one of the most popular approaches to interpreting lists of differentially expressed (DE) genes and has been frequently used on bulk RNA-seq data . More reHowever, choosing the best parameters to identify DE genes across diverse scRNA-seq datasets is still an open challenge . MoreoveHere, we present DrivAER, a method for the identification of driving transcriptional programs based on autoencoder-derived relevance scores. Transcriptional programs (TPs) are sets of genes sharing biological properties  such as We evaluated DrivAER by application to 2 publicly available scRNA-seq datasets and comparison to 2 competing methods called VISION  and PAGODrivAER is based on 1 assumption: the data manifold of relevant TPs shares information with the outcome of interest. Irrelevant TPs, on the other hand, will generate data manifolds where the cells fall randomly with respect to the outcome of interest. DrivAER builds upon our Deep Count Autoencoder (DCA) method , which hTo demonstrate the ability of DrivAER to perform correct manifold interpretation, we reanalyzed 2 publicly available scRNA-seq datasets. The first dataset by Kang et al.  describeIFIT2 in the \u201cINTERFERON_GAMMA_RESPONSE\u201d , Uniform Manifold Approximation and Projection (UMAP), and t-distributed stochastic neighbor embedding (tSNE). Across the gene sets that vary in the fraction of truly DE genes, DCA overall achieved the highest relevance scores Fig.\u00a0. At low t-test, P <\u00a00.05, Fig.\u00a0Next, we compared random forest and support vector machines (SVM) for the classification task. We did not observe any significant differences in performance between these 2 methods, indicating that random forest models represent an appropriate choice for this task Fig.\u00a0. MoreoveAdditionally, we evaluated the different bottleneck configurations in a more complex simulation scenario consisting of 4 unbalanced groups of cells. All 3 configurations successfully recovered the varying degree of signal in the gene sets. The 4D and 8D bottleneck layers outperformed the 2D bottleneck layer slightly . VisualiNext, we compared DrivAER to VISION  and PAGOWe applied VISION in directed and undirected mode, as well as PAGODA, to the simulated gene sets. As expected, for all 3 methods, the respective scores increased with the fraction of truly DE genes Fig.\u00a0\u2013J. HowevFor additional comparison, we applied VISION and PAGODA to the interferon stimulation and blood development datasets . All 3 mWhile autoencoders have been applied for unsupervised dimension reduction in bulk  and scRNUnlike VISION, DrivAER does not require a predefined distinction between the sign of regulation (repression or activation) of genes in a given gene set. The unsupervised nature of the DCA embedding captures any form of non-random, coordinated expression pattern. Therefore, DrivAER captures complex, non-linear expression patterns commonly observed in scRNA-seq data. An additional benefit of DrivAER is its ability to visualize the gene set\u2013specific data manifold. These visualizations promote discovery of transcriptional regulation that may otherwise be hidden in the summary statistics generated by other methods including gene set enrichment analysis or VISION and PAGODA. Moreover, as demonstrated in the simulation analysis, DrivAER's relevance score is readily interpretable.As illustrated in the blood development example, we divided the manifold into independent trajectories for interpretation. However, DrivAER provides the flexibility to be applied to the entire manifold or any subset of it. The user can make this choice and arbitrarily define regions of the manifold, which are expected to be regulated by a TP.Additionally, as demonstrated in the blood development example, DrivAER enables users to make inferences about regulators that were not measured or where measurements are noisy. We envision that users will apply DrivAER to infer activity of regulators not generally detected in scRNA-seq data such as microRNAs and long noncoding RNAs.In the present approach DCA needs to be retrained for each gene set because the input genes and thus the network architecture changes between gene sets. Therefore, the running time of DrivAER depends on the number of gene sets included in the analysis. In the interferon stimulation analysis, the running time per gene set averages between 20 and 30 seconds depending on the number of genes and convergence of the model. To improve speed, we plan to extend DrivAER by developing a \u201chot-start\u201d approach in future work.In summary, specialized methods facilitating the functional interpretation of scRNA-seq data are needed to fuel the rapid progress in the field. DrivAER is a novel machine learning approach that is effective for manifold interpretation in scRNA-seq data. Our results demonstrate that relevance scores represent a useful measure to extract driving transcriptional regulators from complex scRNA-seq datasets. DrivAER, including interactive use tutorial, is freely available from Github  and we acis-regulatory motifs from known TF binding sites in the TRANSFAC (v7.4) , Cancer Prevention and Research Institute of Texas [CPRIT RP180734], and The Chair Professorship for Precision Medicine Funds from the University of Texas Health Science Center at Houston. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.L.M.S. conceived the idea and designed the project. L.M.S. and F.Y. analyzed the data. Z.Z. participated and supervised the project. All authors wrote the manuscript and read and approved the final manuscript.giaa122_GIGA-D-20-00038_Original_SubmissionClick here for additional data file.giaa122_GIGA-D-20-00038_Revision_1Click here for additional data file.giaa122_GIGA-D-20-00038_Revision_2Click here for additional data file.giaa122_GIGA-D-20-00038_Revision_3Click here for additional data file.giaa122_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giaa122_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giaa122_Response_to_Reviewer_Comments_Revision_2Click here for additional data file.giaa122_Reviewer_1_Report_Original_SubmissionChristoph Ziegenhain -- 2/23/2020 ReviewedClick here for additional data file.giaa122_Reviewer_1_Report_Revision_1Christoph Ziegenhain -- 6/4/2020 ReviewedClick here for additional data file.giaa122_Reviewer_2_Report_Original_SubmissionCasey S. Greene -- 2/25/2020 ReviewedClick here for additional data file.giaa122_Reviewer_3_Report_Original_SubmissionVladimir V. Galatenko -- 2/28/2020 ReviewedClick here for additional data file.giaa122_Reviewer_3_Report_Revision_1Vladimir V. Galatenko -- 6/8/2020 ReviewedClick here for additional data file.giaa122_Reviewer_3_Report_Revision_2Vladimir V. Galatenko -- 9/21/2020 ReviewedClick here for additional data file.giaa122_Reviewer_4_Report_Original_SubmissionAndrew McDavid -- 3/5/2020 ReviewedClick here for additional data file.giaa122_Reviewer_4_Report_Revision_1Andrew McDavid -- 6/5/2020 ReviewedClick here for additional data file.giaa122_Reviewer_4_Report_Revision_2Andrew McDavid -- 9/12/2020 ReviewedClick here for additional data file.giaa122_Supplemental_FileClick here for additional data file."}
+{"text": "Digitaria exilis, white fonio, is a minor but vital crop of West Africa that is valued for its resilience in hot, dry, and low-fertility environments and for the exceptional quality of its grain for human nutrition. Its success is hindered, however, by a low degree of plant breeding and improvement.Gypsy to Copia long terminal repeat retrotransposons (\u223c6.7) was found to be exceptionally high. Several genes related to future improvement of the crop were identified including shattering, plant height, and grain size. Analysis of fonio population genetics, primarily in Mali, indicated that the crop has extensive genetic diversity that is largely partitioned across a north-south gradient coinciding with the Sahel and Sudan grassland domains.We sequenced the fonio genome with long-read SMRT-cell technology, yielding a \u223c761 Mb assembly in 3,329 contigs . The assembly approaches a high level of completion, with a BUSCO score of >99%. The fonio genome was found to be a tetraploid, with most of the genome retained as homoeologous duplications that differ overall by \u223c4.3%, neglecting indels. The 2 genomes within fonio were found to have begun their independent divergence \u223c3.1 million years ago. The repeat content (>49%) is fairly standard for a grass genome of this size, but the ratio of We provide a high-quality assembly, annotation, and diversity analysis for a vital African crop. The availability of this information should empower future research into further domestication and improvement of fonio. Digitaria exilis, NCBI:txid1010633) is a vital cereal crop of West Africa, where it is commonly known as fonio or acha. A related Digitaria species, black fonio (Digitaria iburura), is a very minor crop, mostly of Nigeria, Benin, and Togo. Fonio (D. exilis) has an exceptionally small but very nutritious grain, with both high protein and high dietary fiber content [White fonio  and other West African Digitaria have been harvested by farmers in times of famine throughout recorded history [Setaria italica), pearl millet (Cenchrus americanus), and proso millet (Panicum miliaceum) [Wild  history , but ver history , as is s history . Applyin history , 7. The  history . As an o history , 10. In  history  publishe history . Beyond liaceum) . With thliaceum) , it shou2O and centrifuged for 10\u00a0min. Then, 5\u00a0mL of 1.5\u00a0M NaCl and 6 \u03bcL of 10\u00a0mg/mL RNaseA was added to the pellet and incubated at 37\u00b0C until completely resuspended. A chloroform extraction was performed as above to remove RNaseA and any additional contaminants. The aqueous phase was collected and DNA was precipitated and washed with ethanol. The pellet was then resuspended in 100 \u03bcL ddH2O.Fonio millet (cv. Niatia) seed was obtained from Dr. Sara Patterson , which was collected in Mali at GPS coordinates 3.9861\u00a0W, 17.5739\u00a0N.\u00a0Niatia is a popular local variety in Mali . BrieflyRRID:SCR_015880) [DNA samples were used to construct a PacBio  SMRT sequencing library according to manufacturer recommendations at the University of California at Davis Genome Center. Fragments >10\u00a0kb\u00a0were selected for sequencing via BluePippen . A total of 88 Gb of raw PacBio reads from 76 SMRT cells were passed through the secondary analysis pipeline in SMRT Link (v6.0 ) and fil_015880)  with theRRID:SCR_017642) was used to polish the original assembly for 2 rounds with the Canu-corrected PacBio reads. Sequentially, Arrow  and Pilon v1.23  were used to further polish the assembly with 36 Gb of Illumina paired-end reads obtained on the HiSeq 4000  at the Georgia Genomics and Bioinformatics Core at the University of Georgia.Racon  has a total length of 760.66 Mb and 3,329 contigs, with N50 of 1.73 Mb (L50 of 126) and N90 of 75.85\u00a0kb (L90 of 889). The longest contig is 10.17 Mb and the shortest contig is 1,013\u00a0bp, with a mean of 228.5\u00a0kb. We compare the quality of our genome with that of CM05836 , which w.0 has a k-mers in Illumina raw reads and to compare the results with the k-mers counted from the genome assembly at several different k-mer sizes, from 17 to 30. These all yielded similar results but with a somewhat larger fonio genome predicted at smaller k-mer lengths. The distribution of k-mer counts was modeled and the heterozygosity level was estimated using GenomeScope2.0 [Kmer Analysis Toolkits  was usedScope2.0 .k-mer distribution. We interpret the peaks at \u223c50 and \u223c100 counts/coverage as the 2 subgenomes in fonio  were identified and masked with GMATA [de novo using the bioinformatic tools LTR_FINDER  [RRID:SCR_018970) [RRID:SCR_012954) [RRID:SCR_017623) [Repeated sequences were mined and annotated with a combination of th GMATA . Long te_015247)  and LTRh_018970) , which u_018970)  to minim_018970)  was used_018970) . Small D_018970) , and Hel_018970)  was used_018970) . The fon_018970)  multispe_012954)  in the g_012954) . The pre_017623) . The SSRD. exilis were downloaded from the NCBI SRA (accession No. SRX1967865 [RRID:SCR_014583) [RRID:SCR_011848) [RRID:SCR_015530) [Illumina RNA sequencing data (paired-end 100\u00a0bp) of X1967865 ) from RN_014583)  was used_011848) . The rem_015530) . The spl_015530)  and asse_015530) , was useRRID:SCR_008417) [RRID:SCR_011930) [RRID:SCR_015008) [ab initio gene predictions. Detailed settings for each round of Maker can be found in the S. bicolor and S. italica [Digitaria (described above), and the soft-masked genome assembly. A second round of Maker used as input the genome file, the annotation produced by the previous round, and a SNAP species parameter/hmm file based on the prior annotation. Finally, the third round of Maker was run using the following input: the genome assembly, the annotation produced by round 2, and the GeneMark models. Functional annotation was done using the accessory scripts of Maker as described by Campbell and coworkers [RRID:SCR_005829) [For gene prediction and genome annotation, we used the Maker-P pipeline , in comb_008417) , SNAP [4_008417) , and Gen_011930) . Augustu_015008)  dataset  italica  as well oworkers . Brieflyoworkers  search a_005829)  was usedRRID:SCR_017647). A total of 58,305 candidate transcripts were obtained, of which 50,389 had predicted ORFs.Following mapping of RNAseq data with HISAT2, 88% of the RNAseq reads could be well aligned to the genome. Transcripts were assembled with Stringtie and ORFs were predicted with TransDecoder . After the second and third round, where Augustus, SNAP, and Genemark-ES models were included, the number of predicted protein-coding genes increased to 67,921 and finally to 68,302. We removed 447 candidate genes that were judged to be spurious because they were fragments of otherwise fully assembled genes in the annotation, so the final number of genes annotated as protein-coding genes is 67,855. The statistics for the gene annotation can be found in Our first round of Maker predicted 60,300 protein-coding genes  with default settings [D. exilis, and where the D. exilis duplicates were anchor pairs . For these gene families, we performed pre-alignment homology filtering using PREQUAL [RRID:SCR_011811) [RRID:SCR_012067) [Oryza\u2014Hordeum divergence of 34 My based on the review of Iles et al. [We obtained gene families for a set of 9 species in the Poaceae family using OrthoFinder  . For eac_012067)  under th_012067) , 63 usin_012067) . We use s et al. . Next, as et al.  databasede novo discovery analysis that was used. This underestimation is routine in other plant genome annotations as well [Digitaria species to see whether this Gypsy/Copia ratio trait is shared by other close relatives and thus a possible outcome of common ancestral properties.The \u223c42.6% TE content of the fonio genome is a minimal estimate, given that degraded TE fragments are often missed by the  as well , so it i as well  or \u223c36%  as well ), the \u223c6 as well . This fo as well , so one Analysis of LTR-RT insertion dates demonstrated that most of the elements had been inserted within the past 2 My. This high level of recent activity is a standard observation in the grasses, at least partly caused by the fact that the rapid DNA removal by accumulated small deletions quickly excises and otherwise obscures any DNA that is not under positive selection , 71.Digitaria exilis.Ks distributions present a clear signature of WGD in the recent evolutionary past of D. exilis, with this event not shared with the closest relative in our analyses   mya, with these estimates associated with a posterior mean substitution rate across the 3 codon positions of 2.5 \u00d7 10\u22129  substitutions per year per site. This is consistent with CM05836 [P. miliaceum, S. italica, and C. americanus. The diploid ancestor to D. exilis is not clear [We inferred whole-paranome and 1-vs-1 ortholog Ks distributions and performed syntenic analyses to further assess the clear signature of a relatively recent whole-genome duplication (WGD) in ca) Fig.\u00a0. We noteria Fig.\u00a0. Analysiate Fig.\u00a0. Phylogeate Fig.\u00a0 estimate CM05836 . The cloot clear .It is interesting that Fig.\u00a0In the \u223c3.1 My since the latest WGD, most of the duplicated genes have had both copies retained. For instance, the BUSCO gene set yielded 86.5% of the genes still in a duplicated state. Our genome assemblies did not yield complete chromosomes, so we could not investigate the details of major chromosomal rearrangements, preferential gene loss , or parent-specific gene expression differences that might differentiate the 2 ancestral genomes in this tetraploid . The larPanicum miliaceum L.) was added in the phylogenetic analysis because it experienced a recent tetraploidization estimated at \u223c5.8 mya that is similar to fonio.To see the expansions and contractions of gene families, broomcorn millet  was also observed in the broomcorn millet genome, also an allotetraploid crop. Of the fonio gene families, 57.4% contain 2 copies (the most abundant category in these 10 species) and 30.4% contain >2 copies Fig.\u00a0.O. sativa were performed. The analysis identifies negative regulators and recognition factors for biotic and abiotic stresses, as well as pollen/fertility recognition, as single-copy genes. In contrast, there is general expansion of gene families encoding positive regulators of multiple-copy genes. These results suggest that further analysis of these genes may reveal their roles in heat and drought stress tolerance, and in understanding of crossing barriers in fonio.In addition to the majority of multi-copy genes, there are many  that are single-copy genes and thus a likely outcome of at least some deletion after polyploidy. Gene Ontology enrichment analyses of contracted genes 1 copy;  and expaImprovement of fonio will require further domestication, particularly to solve the issues of shattering and lodging. This process should be greatly assisted by the provision of a comprehensive genome sequence.SSH1 (SUPPRESSION of SEED SHATTERING-1) are associated with panicle retention of the grain after seed maturation (the \u201cnon-shattering\u201d trait) in domesticated accessions [In rice, sorghum, and maize, mutations in the gene cessions . Nine secessions .SSH1, but the phylogenetic tree indicated that 2 are more closely related to the rice SSH1 gene associated with shattering than to the other SSH1-like gene in rice  genes of sorghum is responsible for the semi-dwarf trait that diminishes lodging and thereby greatly improves yield and input response in this important crop of arid and semi-arid agriculture [dw3 also should be targets for inactivation-mutation and molecular breeding in fonio. Once again, fonio has more copies of this gene than do any of the other grasses screened, all of which are diploids  gene controls seed weight in wheat and rice, with inactivation of the gene leading to larger grain [D. exilis only differ from each other by 3 amino acid residue substitutions. The fonio genes were found to be nearly identical to the unmutated GW2 version that yields smaller grain in rice and wheat (data not shown). Although increased seed weight does not always increase yield , it is a particularly important trait in fonio to enable sowing for uniform stands and mechanical threshing.The er grain , 82. OrtFonio genetic diversity was assessed using 184 samples from \u223c130 accessions collected from Mali and Niger, signatories to the Cartagena Protocol on Biosafety . ConsistSeedlings of each sample were grown at the respective institutions in West Africa, and DNA was extracted from young leaves with a QIAGEN DNeasy Plant Mini Kit . Lyophilized DNA was then sent to Data2Bio  for tunable genotyping-by-sequencing using 2-bp selection and 5 runs on an Ion Torrent Ion Proton Instrument . The resulting raw sequences were quality-trimmed by Data2Bio, which removed bases with PHRED quality scores <15.\u00a0These trimmed sequences were then aligned to the genome assembly with GSNAP v2020\u201304-08  using deD. exilis) genome indicates its recent tetraploid origin and the retention of most of the genes duplicated in this process. This retention of duplicated genes likely explains why recessive mutations for important agronomic traits like shattering, seed size, semi-dwarfism, and others like day-length dependence have not yet been detected in fonio. However, it is now possible to identify such mutations by using modern mutation detection schemes, like those used for the tetraploid cereal Eragrostis tef [Genome analysis of any polyploid is challenging, especially when no diploid ancestors are known. Our sequence of the white fonio  or even the selection of larger grain yield from the panicles because greater weight on the top of the plant can cause more lodging. The same will almost certainly be true for fonio, hence providing a partial explanation for its tiny seed size in cultivated landraces. With domestication traits fully penetrant into fonio cultivars, one can expect dramatic increases in fonio performance, with expectations of a 2-fold or greater yield enhancement easily within the short-term range of possibilities.The absence of an outcrossing protocol for fonio is another technical deficiency that severely limits this crop's potential for improvement. Our diversity analysis on cultivar Niatia indicates <0.01%\u00a0heterozygosity, showing that crosses occur very rarely by natural processes. Hence, generating controlled crosses will probably require a serious dedication to this pursuit. Our results indicate a great deal of genetic variability within fonio landraces, so we have no doubt that hybridization could be used in breeding projects to optimize fonio germplasm quality for future West African and other farmers.https://bioinformatics.psb.ugent.be/orcae/aocc/overview/Digex. The GenBank project number for the assembly is PRJNA640067. All scripts for diversity analysis and data tables are available at [PRJNA644458. All supporting data and materials are available at the GigaScience GigaDB database [The genome and annotation underlying this article are available in the African Orphan Crops Consortium\u2013specific branch of the ORCAE platform , 93 athtlable at  includindatabase .Supplementary Methods.Supplementary Figure S1. A. Comparison of the contiguity of the Niatia Genome and CM05836 [ CM05836  genome.  CM05836  genome.Supplementary Figure S2. The k-mer distribution of raw Illumina reads at k-mer value 33 bp.Supplementary\u00a0Figure\u00a0S3. A. Marginal posterior distributions for 2 independent chains (green and orange) and induced marginal prior distributions (blue) for internal node ages , overall mean substitution rate (mu), mean substitution rate for different codon positions , and variance parameter of the uncorrelated relaxed clock  for the 3 codon positions. B. Trace plots for the MCMC chains associated with panel (A).Supplementary Figure S4. There are 10,075 families that have 2 copies in fonio and 1 copy in Setaria italica, and 90% of 2-copy families are located in synteny blocks. The above 4 examples indicate the high degree of collinearity and synteny between S. italica and fonio.Supplementary Figure S5. GO of single-copy, contracted genes in fonio.Supplementary Figure S6. GO enrichment for expanded genes in D. exilisand relative to O. sativa.Supplementary Figure S7. Phylogenetic tree of the SSH-like genes from fonio and related species. The genes shaded in light blue are the family members most closely related to SSH-1 in O. sativa and D. exilis. Genes are named according to their PLAZA identifiers. Abbreviations for species names are as follows: Bradi (Brachypodium distachyon), pgl_GLEAN (Cenchrus amercianus), Digex (Digitaria exilis), Oropetium (Oropetium thomaeum), OsR (Oryza sativa), Seita , Sobic (Sorghum bicolor), and Zm (Zea mays).Supplementary Figure S8. Phylogenetic tree of thedw3 gene family of fonio and related species.Supplementary Figure S9. Gene family tree for GW2-A-like genes in fonio and related species. This figure also includes the genes from 2 additional Pooid species, barley (Hordeum vulgare) (HORV) and wheat (Triticum turgidum) (TRITD).Supplementary Table S1. Comparison of genome assembly statistics of fonio.Supplementary Table S2. Statistics for the gene annotation.Supplementary Table S3. Annotated non-coding RNA genes.Supplementary Table S4. Orthologs for suppression of Shattering1 genes.Supplementary Table S5. Orthologs of Dwarf Gene-3.Supplementary Table S6. Orthologs of Grain Weight-2 genes.Supplementary Table S7. Passport data for accessions and samples used for diversity study (see Supplementary Tables Excel file).Supplementary Table S8. Single-nucleotide polymorphism database used for diversity study (see Supplementary Tables Excel file).AED: annotation edit distance; BLAST: Basic Local Alignment Search Tool; BUSCO: Benchmarking Universal Single-Copy Orthologs; CTAB: cetyl trimethylammonium bromide; Dw3: dwarf3; EDTA: ethylenediaminetetraacetic acid; Gb: gigabase pairs; GO: Gene Ontogeny; GW2: grain weight2; kb: kilobase pairs; LINE: long interspersed nuclear element; LTR: long terminal repeat; LTR-RT: long terminal repeat retrotransposon; MAFFT: Multiple Alignment with Fast Fourier Transform; Mb: megabase pairs; MITE: miniature inverted repeat transposable element; My: million years; mya: million years ago; NCBI: National Center for Biotechnology Information; ORF: open reading frame; PacBio: Pacific Biosciences; PAML: Phylogenetic Analysis by Maximum Likelihood; SINE: small interspersed nuclear element; SMRT: single-molecule, real-time sequencing; SRA: Sequence Read Archive; SSH1: suppression of shattering1; SSR: simple sequence repeat; TE: transposable element; TIR: terminal inverted repeat transposable element; WGD: whole-genome duplication.The authors declare that they have no competing interests.J.L.B. acknowledges the Giles Fellowship from the University of Georgia as a source of funding for this project. Y.V.d.P. acknowledges funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program . A.V. acknowledges funding from the Seed Biotechnology Center, University of California. J.G.W. acknowledges funding from the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) and the University of Georgia. M.D.S. acknowledges funding from the McKnight foundation.J.L.B., J.W., Y.V.d.P., and A.V.D. conceived, designed, and interpreted the study; S.C., X.M., X.W., A.E.J.Y., S.R.C., M.S.J., P.G., F.H., M.D.S., and A.Z. prepared the materials, conducted the experiments, and analyzed all data; J.L.B. and A.V. led on manuscript preparation, while all other authors revised the manuscript and approved the final version.giab013_GIGA-D-20-00197_Original_SubmissionClick here for additional data file.giab013_GIGA-D-20-00197_Revision_1Click here for additional data file.giab013_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giab013_Reviewer_1_Report_Original_SubmissionXupo Ding -- 8/24/2020 ReviewedClick here for additional data file.giab013_Reviewer_1_Report_Revision_1Xupo Ding -- 12/28/2020 ReviewedClick here for additional data file.giab013_Reviewer_2_Report_Original_SubmissionFanna Maina -- 9/3/2020 ReviewedClick here for additional data file.giab013_Reviewer_2_Report_Revision_1Fanna Maina -- 12/31/2020 ReviewedClick here for additional data file.giab013_Reviewer_3_Report_Original_SubmissionGincy P. Thottathil -- 9/13/2020 ReviewedClick here for additional data file.giab013_Supplemental_FilesClick here for additional data file."}
+{"text": "Non-small cell lung cancer (NSCLC) is a common type of lung cancer, characterized by a poor prognosis. In the last several years, more and more studies have demonstrated the significant roles played by circular RNAs (circRNAs) in different human tumors progression including NSCLC. The present study was to explore the mechanism of hsa_circ_101237 in regulating non-small cell lung cancer (NSCLC). Totally 303 NSCLC cases were enrolled. A549 and H1299 cells were transfected. Cells viability, migration and invasion were determined by CCK-8 assay and transwell experiment, respectively. Luciferase reporter gene assay and RNA immunoprecipitation (RIP) assay were performed. hsa_circ_101237, miR-490-3p and MAPK1 expression in tissues/cells were detected by qRT-PCR. The study found an elevation in the expression of Hsa_circRNA_101237 in both NSCLC tissues and cell line. High Hsa_circRNA_101237 expression predicted poor survival in NSCLC. Meanwhile, we found that hsa_circRNA_101237 expression sponged miR-490-3p to enhance MAPK1 expression, thus significantly promoting NSCLC cell lines proliferation, migration, and invasion. MAPK1 restoration prevented NSCLC cells proliferation, migration, and invasion to be repressed due to hsa_circRNA_101237 knockdown. To sum up, as revealed by the study, hsa_circRNA_101237 promoted the expression of MAPK1 via miRNA-490-3p sponge, thus affecting the NSCLC as an important onco-circRNA. As is well known, smoking is the main cause of NSCLC2. As NSCLC does not show obvious clinical symptoms and the screening programs are not effective, most patients with NSCLC, once they are diagnosed, are at advanced stages with a poor prognosis3. More and more clinical studies found that metastasis greatly hinders the treatment of NSCLC cancer; therefore, in-depth understanding the metastasis mechanisms is beneficial for effectively treating NSCLC.Lung cancer, characterized by a poor prognosis but a high morbidity, is a common malignant tumor, non-small cell lung cancer (NSCLC) are identified as the most common type of lung cancer (occupying about 85%)4. Due to the feature, circRNAs exhibit a lot of properties, and many properties were found not long ago. circRNAs have a special closed loop structure, making them able to resist the degradation medicated by exonuclease. On that account, they are more stable compared with most other linear RNAs, thus they can be used as a biomarker to effectively diagnose and treat cancers5. circRNAs can interact with RNA-binding proteins, so as to regulate target gene expression6. Besides, it has been indicated that circRNAs can be sinks for miRNAs, to control the function processed by miRNAs7. Many human cancers see altered circRNA expression, and many researches have revealed the key role played by circRNAs in the tumorigenesis. circRNA_101237, a new circRNA identified recently, whose encoding gene is located at chromosome (chr) 13:26974589-26975761 and which is produced by backsplicing of exons 10, 11 and 12 of cyclin-dependent kinase (CDK) 8, has been reported to be implicated in Cisplatin resistance-associated of HCC8. Nevertheless, the function and mechanism of hsa_circ_101237 in regulating NSCLS remain unknown.Circular RNAs (circRNAs) are a kind of non-coding RNAs with endogenous and conserve, forming a covalently closed continuous loop via back-splice without 3\u02b9-end or 5\u02b9-endin vitro. In terms of the function, hsa_circRNA_101237 facilitated the development, the migration and the invasion of NSCLC cell. In terms of the mechanism, hsa_circRNA_101237 regulated the miR-490-3p/MAPK1 axis, and contributed to NSCLC progression.The study focused on illuminating how hsa_circRNA_101237 affects the pathogenesis of NSCLC, as well as the regulating mechanism We obtained 303 snap-frozen NSCLC tissues together with paired nearby non-tumorous tissues in total from NSCLC patients. The study has gained the informed consent of these patients prior to study, and been approved by the ethics committee of the First Affiliated Hospital of Henan University of Science and Technology. The performance of the study followed the guidelines of the committee and the Declaration of Helsinki. Table\u00a0\u2212\u0394\u0394CT method helped to assess the relative expression exhibited by MAPK1, miR-490-3p and hsa_circ_101237.Total RNA was isolated from cells and tissues by virtue of the Trizol reagent (Invitrogen). TaqMan MicroRNA reverse transcription kit  was employed to perform cDNA synthesis for miR-490-3p. One Step PrimeScript cDNA kit  was employed to perform cDNA synthesis for MAPK1 and hsa_circ_101237. The GeneAmp 7500 system (Applied Biosystems) was adopted to perform qRT-PCR in triplicate for determining the expression of MAPK1 and hsa_circ_101237. miR-490-3p expression was also evaluated via the TaqMan MicroRNA assay. GAPDH and U6 were regarded as the endogenous reference gene for the MAPK1 and hsa_circ_101237 and the loading control for the miR-490-3p, respectively. The 2The total RNAs were treated via the RNase R (Epicentre Technologies). In brief, experimenters divided the RNAs aliquots extracted from A549 cells and H1299 cells into 2 parts: one was for the digestion of RNase R and another was only for the control group with digestion buffer. As for the first part, we mixed the total RNA (2\u2009\u03bcg) with 2\u2009\u03bcl 10\u2009\u00d7\u2009RNase R Reaction Buffer and 2\u2009\u03bcl RNase R (20\u2009U/\u03bcl); as for the second part, DEPC-treated water was used to replace the RNase R. Subsequently, RNA samples received 30\u2009minutes of incubation at 37\u2009\u00b0C water. qRT-PCR helped to analyze the detected GAPDH mRNA and hsa_circ_101237. RNA treated with RNase R helped to detect the resistance exhibited by hsa_circ_101237 to the RNase R exonuclease digestion.The human embryonic lung fibroblasts (MRC-5) cells and the NSCLC cell lines (A549 cells and H1299 cells) were provided by the American Type Culture Collection . Cells received incubation treatment in RPMI-1640  and dulbecco\u2019s modified eagle medium  composed of 1% penicillin/streptomycin (Solarbio) and 10% fetal bovine serum  under 5% CO2 at 37\u2009\u00b0C. Plasmids and viruses involved in this paper were all purchased from GENEWIZ . Lipofectamine 2000  was employed to transfect abovementioned plasmids into A549 cells and H1299 cells. 48\u2009hours after the transfection, we harvested cells for later study.3 per well) was seeded into 10\u2009cm culture dish, and cultured for 2 weeks. Cells underwent culture treatment under 5% CO2 at 37\u2009\u00b0C in RPMI-1640 composed of 80 U/ml penicillin and 20% FBS and 100\u2009\u00b5g/ml streptomycin . Finally, 1% crystal violet was used to stain these colonies, followed by number counting.Each group of treated cells  were coated on the transwell inserts . After cells were incubated for 24\u2009hours, a cotton swab was used to remove cells on transwell member\u2019s upper surface gently and methanol helped to fix cells on transwell member\u2019s lower surface, then 0.5% crystal violet (Solarbio) was used to stain these cells, followed by being counted from 5 microscopic fields under random selection.We plated H1299 cells and A549 cells in 96-well plates at 2\u2009\u00d7\u2009103 cells/well and let them grow in 10% FBS medium for 24\u2009hours. Following the transfection, each well was added with cell count kit-8 (CCK-8) solution  and cells underwent 2\u2009hours of incubation at 37\u2009\u00b0C in a incubator with 5% CO2. A spectrophotometer  helped to read each well\u2019s absorbance at 450\u2009nm.We first amplified the wild-type (WT) sequence of the has-circ_101237 together with the WT 3\u02b9UTR sequence of the MAPK1 that contained predicted binding site of the miR-490-3p and then subcloned them into the pGL3 Basic reporter vectors  by virtue of Nhe I and Xho I restriction sites, aiming at generating pGL3-circ_101237-WT vector and pGL3-MAPK1 3\u02b9UTR vector. Quick Change Lightning kit  was adopted to conduct site-directed mutagenesis, for constructing mutant-type (MUT) sequence of the has-circ_101237. A549 cells and H1299 cells were under transfection of the pGL3-circ_101237-MUT, pGL3-circ_101237-WT, or pGL3-MAPK1 3\u02b9UTR, together with the miR-490-3p or the matched controls. Cell collection was performed 48\u2009hours after the transfection and Dual-Luciferase Assay System (Promega) was adopted to analyze these collected cells, following the instruction of manufacturer.To localize circRNA, Cytoplasmic & Nuclear RNA Purification Kit (Amyjet) helped to isolate and extract the nuclear and cytoplasmic RNA of A549 cells, and qRT-PCR helped to measure the expression of circ_101237 in above two types of RNA. GAPDH was the cytoplasm control and U1 was the nuclear control.SPSS 17.0  helped to analyze above results, which were expressed as means \u00b1\u2009SD. Student\u2019s t-tests together with proper one-way ANOVAs were conducted to compare data. The chi-squared test assisted in analyzing how hsa_circ_101237 expression affects the clinical findings of patients. Kaplan-Meier curves together with log-rank tests were employed to compare the survival outcomes. Factors related to patient\u2019s outcomes were identified via the univariate regression analyses, with significant ones being included into the multivariate analysis. All these experiments were performed repeatedly in triplicate, and P\u2009<\u20090.05 was considered significant.Firstly, qRT-PCR assay was conducted aiming at characterizing the hsa_circ_101237 in NSCLC tissues, which demonstrated that NSCLC tissues held an obviously higher hsa_circ_101237 expression compared with nearby non-tumor tissues Fig.\u00a0. In addiAlso, to assess the clinical significance of hsa_circ_101237, we evaluated the association between its expression and clinic-pathological parameters. Then patients were divided into two groups, group with high hsa_circ_101237 expression (n\u2009=\u2009153) and group with low hsa_circ_101237 expression (n\u2009=\u2009150) considering median circRNA expression level, for further examining the way that hsa_circ_101237 expression affects the NSCLC progression. Subsequently, these patients\u2019 clinical findings were compared, which demonstrated that hsa_circ_101237 expression could greatly increase under the impact of and lymph node metastasis (p\u2009=\u20090.006), large size of tumor (p\u2009=\u20090.024), and advantaged TNM stage (p\u2009=\u20090.025) Table\u00a0. SimilarConsidering the up-regulation of hsa_circ_101237 in NSCLC, hsa_circ_101237 was knocked down for investigating the biological function owned by hsa_circ_101237 in NSCLC by using sh-circ_101237 to transfect A549 cells and H1299 cells. In comparison with the control, transfecting sh-circ_101237 could greatly lower hsa_circ_101237 expression in above two cells Fig.\u00a0. The CCK10. For figuring out if hsa_circ_101237 could be a ceRNA, its subcellular localization was firstly detected. Nuclear/cytoplasmic fraction assay results showed that, most hsa_circ_101237 presented a preferential localization in cytoplasm  13:26974589-26975761 and which is produced by backsplicing of exons 10, 11 and 12 of cyclin-dependent kinase (CDK) 8, has been reported to be implicated in Cisplatin resistance-associated of HCC8. Nevertheless, we still do not well understand how circRNA_101237 affects NSCLC as well as the potential mechanisms it holds. The study found an obvious up-regulation of circRNA_101237, a kind of circular RNA in NSCLC tissues. High expression of circRNA_101237 positively correlated with lymph node metastasis, large size of tumor, and advantaged TNM stage as well as predicted poor prognosis specific to patients suffering NSCLC.circRNAs first appeared in the 1970s26. Here, miRNAs that bind with circRNA_101237 were screened by bioinformatic analyses together with the pull-down assays of circRNA_101237. At the same time, circRNA_101237 luciferase reporter was designed for identifying how circRNA_101237 directly interacted with miRNA. Accordingly, in the pull-down assay of circRNA_101237, miR-490-3p exhibited the largest enrichment and miR-490-3p was capable of reducing the luciferase activity possessed by circRNA_101237 luciferase reporter. Based on this, circRNA_101237 could be a ceRNA to bind to miR-490-3p in terms of the mechanism.As proposed by the ceRNA hypothesis, miRNA response elements for RNA transcripts are the same, which compete for binding to the miRNAs, thus the expression is modulated mutually28. The regulatory effect of hsa_circ_101237 on MAPK1 was confirmed via many experiments including RNA pull-down assay, western blot assay, luciferase assay, as well as qRT-PCR assay. In line with the functional test, hsa_circ_101237 over-expression posed an obvious impact on reversing how hsa_circ_101237 kncok down inhibited the proliferation, the migration, and the invasion abilities owned by NSCLC cells. Above data helped to confirm that hsa_circ_101237/miR-490-3p/MAPK1 regulatory network existed in NSCLC. Meanwhile, there are some limitations in our research, as hsa_circ_101237-miR-490-3p axis might also plays key role in enhancing NSCLC progression by targeting other target genes, which is worthy of further exploration in the future.Specific to circRNA mechanism acting as ceRNA, the circRNA-miRNA-mRNA regulatory network about hsa_circ_101237 was explored, finding the ability of hsa_circ_101237 to facilitate MAPK1 expression through miR-940 sponge. It has been validated that MAPK1 can mediate the proliferation as well as the metastasis, so as to regulate the progression of tumorTo sum up, hsa_circ_101237 can facilitate the progression of NSCLC. The study stressed on a mechanism through which hsa_circ_101237 posed a positive effect on the growth and the metastasis of NSCLC cell. It is necessary to target hsa_circ_101237 as a possible method to treat NSCLC.Supplementary information."}
+{"text": "Elsholtzia ciliata ethanolic extract by freeze-drying method using skim milk, sodium caseinate, gum Arabic, maltodextrin, beta-maltodextrin, and resistant-maltodextrin alone or in mixtures of two or four encapsulants. The encapsulation ability of the final mixtures was evaluated based on their microencapsulating efficiency (EE) of total phenolic compounds (TPC) and the physicochemical properties of freeze-dried powders. Results showed that the freeze-dried powders produced using two encapsulants have a lower moisture content, but higher solubility, Carr index, and Hausner ratio than freeze-dried powders produced using only one encapsulant in the formulation. The microencapsulating efficiency of TPC also varied depending on encapsulants used. The lowest EE% of TPC was determined with maltodextrin (21.17%), and the highest with sodium caseinate (83.02%). Scanning electron microscopy revealed that freeze-drying resulted in the formation of different size, irregular shape glassy particles. This study demonstrated good mucoadhesive properties of freeze-dried powders, which could be incorporated in buccal or oral delivery dosage forms. In conclusion, the microencapsulation of E. ciliata ethanolic extract by freeze-drying is an effective method to produce new value-added pharmaceutical or food formulations with polyphenols.The present study reports on the encapsulation of  Natural substances, polyphenols, have attracted attention of many investigators and from the wider society, due to their health benefits to humans, as they are known for their antioxidant , antibacFreeze-drying is the most commonly used method of encapsulation  based onElsholtzia ciliata herb by the freeze-drying method. Elsholtzia ciliata is an annual plant used as a spice and medicine in traditional China medicine. E. ciliata is native to Asia, but it grows in Europe, Africa, North America, South America, and India [E. ciliata also naturally grow in Lithuania. In Lithuania E. ciliata mostly used as a spice for cuisine or decoration, but it does not have a wide range of uses for health benefits. E. ciliata belongs to the Lamiaceae family, the most widely distributed family of plants. The enlarged Lamiaceae contains about 236 genera and 6900 to 7200 species [Lamiaceae family of plants are a rich source of biologically active compounds\u2014their therapeutic effect is attributed to the presence of a wide range of secondary metabolites or phytochemicals, such as flavonoids, glycosides, alkaloids, saponins, terpenoids, and phenols, which have various pharmacological activities [E. ciliata is a rich source of various biologically active compounds. The main compounds obtained in this plant are phenylpropanoids, terpenoids, phytosterols, polyphenols, ketones [E. ciliata essential oil from different plant parts. Elsholtzia ketone, caryophyllene and 3-octanol were predominant compounds of essential oil produced from stem, leaf, and flower [E. ciliata herbal samples. The main compounds of essential oil produced from dried herb were dehydroelsholtzia ketone (78.28%) and elsholtzia ketone (14.58%) [The coating or carrier materials have an important role in the encapsulation process, since they may influence the efficiency of encapsulation and physicochemical properties, which impact the stability of freeze-dried powders ,10. Wallnd India ,24. It i species . The Lamtivities ,27. Acco ketones  had idend flower . PudziuvE. ciliata herb. Guo et al. [E. ciliata extracts. Pudziuvelyte et al. [E. ciliata ethanolic extracts, .Poplyphenols are other major group of active compounds determined in o et al.  and Kim o et al.  had detee et al.  for the E. ciliata herb could possess various beneficial effects for health. The scientific studies report that E. ciliata have antiviral, antibacterial [E. ciliata\u2019s chemical composition and potential useful health effects. Also, it will be beneficial to extend the consumption of E. ciliata as a medicine in Lithuania and other countries.According to a rich source of active compounds, acterial , anti-inacterial ,31, antiacterial , anticanacterial , and vasacterial  activitiE. ciliata ethanolic extract and essential oil were microencapsulated using a spray-drying technique [E. ciliata extract or essential oil. Some advantages of microencapsulation using freeze-drying techniques are known and an expectation of this study is that the microencapsulation of E. ciliata biologically active compounds by freeze-drying techniques would make it possible to increase their bioavailability, producing new value-added pharmaceutical or food formulations with polyphenols, and protect them from environmental factors during storage.Previously, echnique . In thisechnique . The sciE. ciliata ethanolic extract and essential oil. Further, the moisture content, solubility, bulk and tapped volumes, morphology, and mucoadhesive properties were evaluated during experiments.The present study aimed for the selection of optimal encapsulants for freeze-dried powders to stabilize the concentration of polyphenols, and to reach suitable physicochemical parameters of powders. Sodium caseinate, skim milk, maltodextrin, beta-maltodextrin, resistant-maltodextrin, and gum Arabic were used as carriers for Plants secondary metabolites polyphenols are useful biologically active substances for human health. Polyphenols possess various biological effects such as antioxidant, anti-inflammatory, antiviral, antibacterial, anticancer, and others. For this reason, herbs are used for prevention and treatment. However, herbal material and products produced from herbs are not always are effective enough because of low amounts and inactive compounds, which are very sensitive for environmental conditions, such as oxidation, pH, temperature, enzymes, and others. The negative effect of these conditions may increase degradation, reduce total amounts of active compounds in herbal preparations. To protect active compounds and to increase their potential positive effects for health, it is suitable to apply microencapsulation methods. A freeze-drying method for the microencapsulation of E. ciliata will be analyzed in this study.At the early stage of the experiments, the most suitable encapsulant agent for core material microencapsulation using freeze-drying technique was selected. Six substances\u2014skim milk, sodium caseinate, maltodextrin, resistant-maltodextrin, beta-cyclodextrin, and gum Arabic\u2014were used as potential encapsulants. These coating materials were chosen according to their good properties, such as good solubility in water, low viscosity, ability to form films, resistance to gastrointestinal tract, solid content, biodegradability, safety, and low price. Also, maltodextrin and gum Arabic are the most commonly used encapsulants for microencapsulation. After freeze-drying, the physicochemical properties of freeze-dried powders  were anap < 0.05) and the highest for these samples: SKIM_E, SKIM_MALTO_E, GUM_BETA_E, and RES_BETA_SOD_SKIM_E.The yield of freeze-dried powders ranged from 75% to 100% . StatistAs Using different wall materials impact the moisture content of freeze-dried powders. Data obtained by Ezhilarasi et al.  correspoAn increased moisture content could negatively affect the freeze-dried powders during storage. Higher moisture content in the freeze-dried powders could reduce the quality of the powders, such as lower flowability, change the color, flavor, reduce amounts of predominant compounds, and their activity. Also, freeze-dried powders with high moisture content could be the perfect environment for microorganisms .The solubility of the freeze-dried powders ranged from 42.50% to 92.50% . B_CYCL_The effects of different encapsulating agents on the Carr index and Hausner ratio of freeze-dried powders are shown in The lowest Carr index and Hausner ratio were obtained for the MALTO_E and B_CYCL_E samples and the highest for the GUM_RES_E sample. The data shows that using mixtures of two or four wall materials increased Carr index and Hausner ratio values, which indicates that freeze-dried powders were characterized by poor flowability. As our study shows, using maltodextrin and beta-cyclodextrin alone is better for Carr index and Hausner ratio values than using maltodextrin and beta-cyclodextrin in mixtures with other substances  . The flop < 0.05). The same effect was obtained using resistant-maltodextrin in composition with sodium caseinate and skim milk. RES_E sample which contains only resistant-maltodextrin determined 29.85% EE% of TPC, when samples SKIM_RES_E and SOD_RES_E possessed 2 and 2.5 times higher values EE% of TPC (61.79% and 77.13%), respectively (p < 0.05). According to the data, there was no statistically significant differences using dextrins and gum Arabic in the same compositions (p > 0.05). For example, using gum Arabic alone on the GUM_E sample the EE% of TPC was 32.73%, when using gum Arabic in composition with maltodextrin (GUM_MALTO_E), resistant-maltodextrin (GUM_RES_E), and beta-cyclodextrin (GUM_BETA_E) the values of EE% of TPC were 26.83%, 39.79%, and 29.62%, respectively.The EE% TPC of freeze-dried powders are shown in The freeze-dried product microencapsulated with sodium caseinate (SOD_CAS_E) demonstrated an exceptional conservation of phenols and had the highest EE% of all the freeze-dried samples. Good microencapsulation of polyphenols using sodium caseinate could be because of perfect emulsifier, gelation properties. Caseins according to various studies have been shown to protect their contents against cold (storage and freeze-drying), oxidation, heat, UV radiation ,15,16. SUsing different wall materials or mixtures impacts EE% of TPC. According to Papoutsis et al. , encapsuStructural analysis of the freeze-dried powders was conducted by scanning electron microscope (SEM). A comparison of the images showed a notable variation in terms of particle structure and size allotment amongst different microencapsulated products. All images of freeze-dried powders presented an irregular shape like broken glass, with some pores on surface . The strp < 0.05) influenced by the type of adhesive layer and composition of freeze-dried powders. Using a gelatin disc and porcine buccal mucosa, values of detachment force (Fmax) varied from 0.147 N to 0.390 N and from 0.085 N to 0.444 N. In the case of porcine buccal mucosa, the highest work of mucoadhesion (Wad) value 0.086 was observed for the RES_BETA_SOD_SKIM_E sample of freeze-dried powders. The lowest Wad value using porcine buccal mucosa was determined for the SOD_BETA_E sample. A type of material used for the preparation of freeze-dried powders affected the adhesion, which was the highest for the RES_BETA_SOD_SKIM_E sample. SOD_CAS_E used alone in the composition of freeze-dried powders presented higher adhesion than used in a mixture with beta-cyclodextrin. Porcine stomach and buccal mucosa are valuable models of the adhesive membrane, due to their similarity to human mucosa in terms of histology, ultrastructure, and composition; they can be used to mimic the behavior of dosage forms in vivo [ad for all three samples using porcine buccal mucosa model (p < 0.05). The highest value of Wad was determined for RES_BETA_SOD_SKIM_E sample (0.086 \u00b1 0.003 \u00b5J). When porcine stomach mucosa was used, Fmax values ranged from 0.173 N to 0.444 N. Statistically significant differences for the values of Wad were obtained between all three samples (p < 0.05). According to the results, the RES_BETA_SOD_SKIM_E sample obtained the highest value of Wad (0.075 \u00b1 0.007 \u00b5J) compared to the SOD_CAS_E and SOD_BETA_E samples. In an acidic environment, freeze-dried powders with SOD_CAS_E presented lower adhesion to the mucous membrane than freeze-dried powders with SOD_BETA_E . This might be caused by the poor swelling of powders in acidic or/and neutral environment. The best mucoadhesive properties were noted when a mixture of four encapsulants (RES_BETA_SOD_SKIM_E) was utilized in the freeze-drying process.Mucoadhesive analysis of freeze-dried powders was performed to evaluate the potential use of the powders in buccal or oral delivery dosage forms. Two samples with the highest EE of TPC and sample, which contains four encapsulants, were chosen for the mucoadhesive test. Mucoadhesive properties of freeze-dried powders are presented in  in vivo . StatistE. ciliata (Thunb.) Hyl were obtained from \u201cZolynu namai\u201d, Vilnius, Lithuania. Dried herb was ground using Ultra Centrifugal Mill ZM 200 . Grinding was performed at 6000 rpm using 0.25 mm trapezoid holes sieve.Dried Resistant-maltodextrin (Promitor 85\u2122) was purchased from Bang & Bonsomer, , gum Arabic, skim milk, maltodextrin, sodium caseinate, beta-cyclodextrin were purchased from Sigma-Aldrich, . Ethanol (96%) used for extraction was purchased from Vilniaus degtine . All the chemicals used were of analytical grade.E. ciliata extract was prepared by ultrassound-assisted extraction method, and essential oil by hydrodistillation, as described in a previous study [E. ciliata herb was extracted 1:20 with 70% (w/v) ethanol in a conical flask using an ultrasound bath  at 25 \u00b0C for 30 min. Essential oil was obtained using Clevenger distillation apparatus. A dried grounded herb (30 g) was mixed with 500 mL purified water and submitted to extraction for 4 h at 120 \u00b0C.Ethanolic us study . Dried pE. ciliata were encapsulated using six different wall materials and their combinations: gum Arabic (GUM_E), maltodextrin (MALTO_E), resistant-maltodextrin (RES_E), skim milk (SKIM_E), sodium caseinate (SOD_CAS_E), beta-cyclodextrin (B_CYCL_E), gum Arabic and maltodextrin (GUM_MALTO_E); sodium caseinate with resistant-maltodextrin (SOD_RES_E), beta-cyclodextrin with skim milk (BETA_SKIM_E), skim milk with maltodextrin (SKIM_MALTO_E), sodium caseinate with beta-cyclodextrin (SOD_BETA_E), gum Arabic with resistant-maltodextrin (GUM_RES_E), skim milk with resistant-maltodextrin (SKIM_RES_E), sodium caseinate with maltodextrin (SOD_MALTO_E), gum Arabic with beta-cyclodextrin (GUM_BETA_E); and resistant-maltodextrin with beta-cyclodextrin, sodium caseinate, and skim milk (RES_BETA_SOD_SKIM_E). A sum of 20% (w/v) of each single encapsulant was mixed with purified water  at 22\u201325 \u00b0C and left for 12 h. After that, all the mixtures were stirred using magnetic stirrer  for 30 min at 25 \u00b0C. The solutions with dissolved encapsulants were mixed with E. ciliata ethanolic extract (50 mL) and essential oil (10 \u00b5L) mixture. All the prepared mixtures were homogenized for 5 min at 4000 rpm using IKA T18 digital Ultra-Turrax homogenizer . The mixtures were frozen in the laboratory freezer FORMA\u2122 88,000 Series  at \u221280 \u00b0C for 24 h before the freeze-drying process. Finally, frozen samples were freeze-dried using laboratory freeze-dryer  at \u221250 \u00b0C 0.05 mbar for 24 h. The freeze-dried powders were collected, packed in foil bags and stored in a dessicator prior to other analysis.Further, ethanolic extract and essential oil of The moisture content of the freeze-dried powders was measured by estimating the powder\u2019s weight loss after oven drying at 105 \u00b0C, until a constant weight was obtained .g for 10 min at 25 \u00b0C, using centrifuge SIGMA3-18KS . A total of 20 mL of supernatant was transferred to a pre-weighed Petri dish and dried overnight in an oven at 105 \u00b0C. The solubility (%) of freeze-dried powder was calculated as the percentage of dried supernatant in relation to the amount of microcapsules by the equations:According to Antonio et al.  method w0 and Vtapped) of freeze-dried powders were investigated using the density tester , according to the Caliskan and Dirim [The bulk and tapped density . The mixture was stirred using a magnetic stirrer for 1 min and ultrasonic bath for 20 min at 25 \u00b0C. After that, the mixture was filtered through a micro filter (0.45 \u00b5m). A sum of 100 \u00b5L of the sample and 2.5 mL of Folin-Ciocalteau reagent were mixed in a tube and left in the dark place for 5 min. Then, 2 mL of 7.5% sodium carbonate solution was added into the tube, mixed and left in the dark place for 1 h at 25 \u00b0C. TPC was expressed as mg equivalent of gallic acid per gram of freeze-dried powders. The absorbance was measured at 760 nm using a UV/VIS 1800 Shimadzu spectrophotometer . For the determination of SPC of the freeze-dried powders, a 100 mg of sample was mixed with 10 mL of ethanol:methanol solution , and then filtered through a micro filter (0.45 \u00b5m). The SPC was obtained using the same method described for TPC determination. The SPC and TPC encapsulation efficiency (EE) were calculated according to Equations (5) and (6), respectively.The total phenolic and surface phenolic contents were determined according to the methods of Tolun, Altintas, and Artik  with somThe morphological characteristics of the freeze-dried powders were examined using scanning electron microscopy . A small amount of freeze-dried powder sample was placed on the specimen holder. Images with magnifications of 50\u00d7 and 300\u00d7 were recorded at 3 kV.w/w) aqueous solution. Adhesive layers were adhered to an upper probe and moisturized with 0.1 M HCl (pH = 1.2) (stomach mucosa) and salive (pH = 6.8) . The tests were performed at 37 \u00b1 1 \u00b0C. The mucoadhesive characteristics were obtained as the maximum detachment force (Fmax) and the work of mucoadhesion (Wad), calculated from the area under the force versus distance curve, expressed in \u00b5J.Evaluation of the mucoadhesive properties was performed using TA.XT.Plus Texture Analyser , according to the Szekalska et al. method . Porcinep < 0.05) differences between samples.One-way analysis of variance (ANOVA) followed by Tukey\u2019s multiple comparison tests were performed using the software SPSS Statistics 20.0  to determine the significant  presented stronger adhesion to buccal and stomach mucosa, as compared to the SOD_CAZ_E and SOD_BETA_E samples.In this study, an ethanolic E. ciliata ethanolic extract and that the obtained freeze-dried powders contain high levels of polyphenols. The method and formulations of freeze-dried powders are appropriate for use in the pharmaceutical, cosmetics, or food industries. Freeze-dried powders could be incorporated in solid pharmaceutical form like hard capsules or tablets.This data showed that freeze-drying is a suitable method for encapsulation of"}
+{"text": "Capitulum mitella (Crustacea: Cirripedia) is an important stalked barnacle. The first mitochondrial genome of C. mitella from China was presented, which is a circular molecule of 15,930\u2009bp in length and AT content is 64.4%. It encodes 37 genes, including 13 PCGs, 22 tRNAs, and two rRNAs, which is consistent with most barnacles species reported. There are 15 genes encoded on the light strand and 22 genes encoded on the heavy strand. Identical to most barnacles species reported, srRNA and lrRNA genes are adjacent and separated only by trnV gene. Phylogenetic trees showed that C. mitella clustered with Pollicipes polymerus, indicating Pollicipedidae is monophyletic. However, Scalpelliformes was not monophyletic from the phylogenetic tree. From the level of order, the Lepadiformes was located at the base of the phylogenetic tree, indicating that its divergence time was earlier than Scalpelliformes. The results provided more insights into phylogenetic consideration at the genomic level within superorder Thoracica. Capitulum mitella (Crustacea: Cirripedia), distributing on the rocks of intertidal zone, is an important stalked barnacle , Zhejiang Province, China. The total DNA was extracted from muscle tissue, using TIANamp Marine Animal DNA Kit (TIANGEN), which was stored at Marine Museum of Jiangsu Ocean University (Accession number: Cmi-002). Sixty pairs of specific primers were designed with reference to the mitogenome of Pollicipes mitella  and encoded a set of 37 typical metazoan mitochondrial genes, including 13 PCG, two rRNA, 22 tRNA genes, and one control region , and the remaining 22 genes were transcribed on the heavy strand. The base composition of C. mitella is 33.85% A, 22.06% C, 12.62% G, and 31.42% T. AT and GC skews of the whole genome are \u22120.186 and \u22120.028, respectively.The mitochondrial genome of cox1 and nad1 genes started with \u2018CGA\u2019 and \u2018TAT\u2019 respectively, the remaining 11 genes started with \u2018ATN\u2019 . In addition, for stop codons, three genes  end with incomplete \u2018T\u2013\u2013\u2019, the other 10 PCGs terminate with complete stop codons \u2018TAA\u2019 or \u2018TAG\u2019. The srRNA  and lrRNA  of C. mitella were arranged continuously and separated only by the trnV gene, which is consistent with most barnacle species reported  with PhyloSuite software ; NC_006293 ; NC_026466 ; NC_039849 ; NC_023945 (Nobia grandis); NC_029169 (Chelonibia testudinaria); NC_008974 (Tetraclita japonica); NC_029154 (Tetraclita serrata); MH791045 (Catomerus polymerus); NC_026730 ; KJ754820 (Octomeris sp. BKKC_2014); NC_036957 (Eochionelasmus ohtai); NC_005936 (Pollicipes polymerus); MH119184 (Capitulum mitella); NC_037244 (Altiverruca navicula); MN061491 (Vulcanolepas fijiensis); MH791047 ; MH891848 ; NC_026576 (Lepas anserifera); NC_025295 ; NC_023943 (Perinereis aibuhitensis); NC_020609 (Perinereis nuntia).The accession numbers of the genomes used for comparison were NC_029168 ("}
+{"text": "Background: Osteosarcoma (OS) is a type of malignant bone tumor with a growing incidence. Increasing studies indicate circular RNA (circRNA) has a vital function in tumorigenesis. Yet, how circRNA regulates OS development is not clear. In the present work, we aimed to investigate the roles of hsa_circ_0136666 in OS progression.Results: hsa_circ_0136666 was shown to be upregulated in OS and correlated with advanced stage and poor prognosis. Functional investigation using CCK8, colony formation assay and Transwell assay demonstrated that hsa_circ_0136666 promoted OS proliferation, migration and invasion, but inhibited cell death. Additionally, we identified hsa_circ_0136666 was a molecular sponge for miR-593-3p to facilitate ZEB2 expression. MiR-593-3p and ZEB2 were inversely expressed in OS tissues. And hsa_circ_0136666 exerts oncogenic roles in OS relying on miR-593-3p and ZEB2.Conclusion: Our results demonstrate the involvement of hsa_circ_0136666 in regulating OS tumorigenesis and it may be a therapeutic target.in vivo.Methods: The expression of hsa_circ_0136666 was analyzed by qRT-PCR in OS tissues and cell lines. Proliferation was measured via CCK8 and colony formation assays. Migration and invasion were determined through Transwell assay. Luciferase reporter assay was utilized to determine the interaction between hsa_circ_0136666 and miR-593-3p or between miR-593-3p and ZEB2. Animal experiment was performed to investigate the role of hsa_circ_0136666  Osteosarcoma (OS) is the most frequent and aggressive cancer in bone among young people . OS is cAs a group of noncoding RNA, circular RNAs (circRNAs) are featured by a covalently closed loop and no protein-coding ability , 5. As aHsa_circ_0136666 has been reported to promote colon cancer growth and invasiveness . HoweverTo check the role of hsa_circ_0136666 in OS, we first measured the expression patterns of hsa_circ_0136666. Via qRT-PCR, hsa_circ_0136666 was upregulated in OS samples . And hsaTo further explore the effects of hsa_circ_0136666 on OS progression, we selected U2OS and Saos2 cells for investigation. The expression of hsa_circ_0136666 was then knocked down in U2OS and Saos2 cells by using two independent siRNAs . Based oCircRNAs have been reported to be the sponge for miRNAs . We obseWe found that miR-593-3p expression was downregulated in OS tissues . And ZEBin vivo. Moreover, we found that hsa_circ_0136666 silencing promoted apoptosis while inhibiting proliferation and invasion  and written informed consent was achieved from involved patients. Experiments involving human tissues were conducted in accordance with the Declaration of Helsinki.hFOB1.19 cell line and human OS cell lines were obtained from Shanghai Academy of Sciences. Cell lines were maintained in DMEM medium  supplemented with 10% fetal bovine serum .Si-hsa_circ_0136666 (5\u2019-ACAGUCUCUUUGUUGGGCAAT-3\u2019), miR-593-3p mimics (5\u2019-UGUCUCUGCUGGGGUUUCU-3\u2019), miR-593-3p inhibitors (5\u2019-AGAAACCCCAGCAGAGACA-3\u2019) and negative controls were purchased from GenePharma . Cell transfection was carried out using Lipofectamine 3000 reagent (Invitrogen) according to the protocols of manufacturer.Total RNAs were extracted using Trizol reagent (Invitrogen) and qPCR was performed as reported before . Primer CCK8  was used to test the ability of OS cells. In brief, 2000 cells were seeded into the 96-well plates and cultured for indicated times. Then CCK8 solution was added and incubated for 2 hours. Absorbance at 450 nm was determined using a microplate reader.1000 cells were seeded into the 6-well plates and cultured for 14 days. The clones were then fixed and stained. Colony numbers were eventually counted.Animal experiments were approved by the ethic committee of the First Affiliated Hospital of Soochow University (20180602082). U2OS cells were injected into the flank of recipient nude mice (n=5 for each group). At indicated time points, the tumor volumes were measured. And 5 weeks later, tumor weights were determined.4 cells per well were seeded into the upper chamber  with serum-free medium. The lower chamber was filled with 600 \u03bcl complete medium. After culture for 24 h, the migrated or invaded cells in the lower chamber was fixed and stained with crystal violet. Cell number was then counted.Migration and invasion were measured using Transwell assay as previously described . In brieThis assay was conducted as previously described . In brieThe Caspase-3/7 activities were detected with Apo-ONE homogenous caspase 3/7 activity assay kit  according to the manufacturer\u2019s instruction.The sequences containing miR-593-3p binding site in hsa_circ_0136666 (nucleotides: 200~400) or ZEB2 (nucleotides: 0~200 of the 3\u2019-UTR) were inserted into the pGL3 luciferase vector (Promega). For luciferase reporter assay, the wild-type or mutant luciferase reporter was transfected into the U2OS cells as well as miR-593-3p mimics or negative control. 24 h later, the luciferase activity was determined using the Dual-Luciferase Reporter Assay . And the relative firefly luciferase activity was normalized to Renilla activity.P < 0.05 was considered significant.Data were analyzed by SPSS 22.0  and presented as mean \u00b1 standard deviation. Statistical differences were calculated using Student\u2019s t-test or One-way ANOVA. Clinical data were analyzed using Kaplan-Meier curve and the log-rank test. All data were included in this manuscript."}
+{"text": "Osteoporosis has increased and developed into a serious public health concern worldwide. Despite the high prevalence, osteoporosis is silent before major fragility fracture and the osteoporosis screening rate is low. Abdomen-pelvic CT (APCT) is one of the most widely conducted medical tests. Artificial intelligence and radiomics analysis have recently been spotlighted. This is the first study to evaluate the prediction performance of femoral osteoporosis using machine-learning analysis with radiomics features and APCT.500 patients  underwent both dual-energy X-ray absorptiometry and APCT within 1 month. The volume of interest of the left proximal femur was extracted and 41 radiomics features were calculated using 3D volume of interest analysis. Top 10 importance radiomic features were selected by the intraclass correlation coefficient and random forest feature selection. Study cohort was randomly divided into 70% of the samples as the training cohort and the remaining 30% of the sample as the validation cohort. Prediction performance of machine-learning analysis was calculated using diagnostic test and comparison of area under the curve (AUC) of receiver operating characteristic curve analysis was performed between training and validation cohorts.The osteoporosis prevalence of this study cohort was 20.8%. The prediction performance of the machine-learning analysis to diagnose osteoporosis in the training and validation cohorts were as follows; accuracy, 92.9% vs. 92.7%; sensitivity, 86.6% vs. 80.0%; specificity, 94.5% vs. 95.8%; positive predictive value, 78.4% vs. 82.8%; and negative predictive value, 96.7% vs. 95.0%. The AUC to predict osteoporosis in the training and validation cohorts were 95.9%  and 96.0% , respectively, without significant differences (P = 0.962).Prediction performance of femoral osteoporosis using machine-learning analysis with radiomics features and APCT showed high validity with more than 93% accuracy, specificity, and negative predictive value. As the elderly population has rapidly grown, osteoporosis has increased and developed into a serious public health concern . ApproxiFat, percentage ratio of HU range \u2264 0HU) or thick cortical bone content  Reviewers' comments:Reviewer's Responses to QuestionsComments to the Author1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the \u201cComments to the Author\u201d section, enter your conflict of interest statement in the \u201cConfidential to Editor\u201d section, and submit your \"Accept\" recommendation.Reviewer #1:\u00a0All comments have been addressedReviewer #2:\u00a0All comments have been addressed**********2. Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1:\u00a0(No Response)Reviewer #2:\u00a0Yes**********3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1:\u00a0NoReviewer #2:\u00a0Yes**********4. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data\u2014e.g. participant privacy or use of data from a third party\u2014those must be specified.The Reviewer #1:\u00a0YesReviewer #2:\u00a0Yes**********5. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1:\u00a0YesReviewer #2:\u00a0Yes**********6. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1:\u00a0Thank you for giving me this opportunity to re-review your revised manuscript.I am happy that almost all of the suggested corrections have been made.Please refer only to the minor comments below.Thank you for spending so much time for revised manuscript.1. Did you evaluate the validity of the model by cross-validation? If not, please add it. If you have done so, please add it to the supplementary materials.The authors explained that they performed cross-validation to fine-tune the hyperparameters.Cross-validation is generally an analysis performed to ensure generalization.Is the result of this analysis of the gini coefficient etc. the holdout method?Please show the results of cross-validation.In addition, please show me how to split data for cross-validation. Results may vary depending on the proportion of test data.Additionally, did you change the hyperparameter adjustment from the default value? Also show the changes from the initial values of hyperparameters.Reviewer #2:\u00a0It is an interesting study on the bone radiomics analysis.**********what does this mean?). If published, this will include your full peer review and any attached files.7. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool,\u00a0 2 Feb 2021Response to ReviewersReviewer #1: Thank you for giving me this opportunity to re-review your revised manuscript.I am happy that almost all of the suggested corrections have been made.Please refer only to the minor comments below.Thank you for spending so much time for revised manuscript.1. Did you evaluate the validity of the model by cross-validation? If not, please add it. If you have done so, please add it to the supplementary materials.The authors explained that they performed cross-validation to fine-tune the hyperparameters.Cross-validation is generally an analysis performed to ensure generalization.Is the result of this analysis of the gini coefficient etc. the holdout method?Please show the results of cross-validation.In addition, please show me how to split data for cross-validation. Results may vary depending on the proportion of test data.Additionally, did you change the hyperparameter adjustment from the default value? Also show the changes from the initial values of hyperparameters\ufffd We attach the original results of 5-folds cross validation confusion matrix. Supplementary file named \u201cS1. Table\u201d is summary of this results.  We divided the total 500 cases into 70% of the train set and 30% of the validation set. And hyperparameter tuning was performed while performing 5-fold cross validation on the train set. The test set result was the validation result of RF model using a tuned hyperparameter by 5-fold cross validation. In addition, we attach the our R-statistical code. This code contains all information of our random forest model about data split, train and test set composition and hyperparameter searching algorithm, etc. This is critical information of this research because we have 2nd phase study related this research. So, we can't publish this \"R-code data\". However, we already submit our full data set so anyone can test and validate our research. A. Five-fold cross validation confusion matrix results 1) Fold 1.2) Fold 23) Fold 34) fold 45) Fold 5B. R- code for random forest model library(randomForest)library(MASS)library(caret)library(dplyr)library(caTools)library(e1071)library(tidyverse)library(tictoc)library(janitor)library(doSNOW)library(ranger)library(BradleyTerry2)library(randomForestExplainer)setwdgetwddata <- read.csvdata[\"Dx\"] <- as.factor(data$Dx)levels(data$Dx)=cset.seed(123)trainIndex <- createDataPartitiontrain = datatest = dataprint(table(train$Dx) / nrow(train))print(table(test$Dx) / nrow(test))# K-folds set.seed(123)cv_folds_lst <- createFoldsset.seed(123)cv_folds <- createFoldsranger_tune_grid <- expand.grid( .mtry = c(2:32), .splitrule = c, .min.node.size = c(2:30))fit_ctrl <-trainControlset.seed(123)gc_grid_ranger_model <- train, tuneGrid = ranger_tune_grid, #tuneLength = 15, trControl = fit_ctrl)gc_grid_ranger_model# The final values used for the model were mtry = 10, splitrule = extratrees and min.node.size = 3.fit_ctrl <- trainControl, summaryFunction = twoClassSummary, classProbs = TRUE,  verboseIter = TRUE)gc_ranger_model <- train, trControl = fit_ctrl, tuneLength = 7)gc_ranger_modelfor ){ print(ntrees) acc_vec <- c for ){ num <- numeric(idx) #print(idx) cv_train <- train cv_validation <- train X_validation <- cv_validation y_validation <- cv_validationformula.init <- \"Dx ~ .\"formula.init <- as.formula(formula.init) set.seed(123)formula.init, data=cv_train, proximity=T, ntree=ntrees,mtry = 3 rf.model <- randomForest rf.predictions <- predict #print(rf.model) #print) acc_vec <- append) } print(acc_vec) print(mean(acc_vec))}for ){ num <- numeric(idx) print(idx) cv_train <- train cv_validation <- train X_validation <- cv_validation y_validation <- cv_validationformula.init <- \"Dx ~ .\"formula.init <- as.formula(formula.init)formula.init, data=cv_train, proximity=T,ntree=500,mtry = 3,  rf.model <- randomForest rf.predictions <- predict print)}X_test <- testy_test <- testformula.init <- \"Dx ~ .\"formula.init <- as.formula(formula.init)set.seed(123)formula.init, data=train, proximity=T,ntree=500,mtry =3, splitrule=\"extratrees\", min.node.size=11)rf.model <- randomForestconfusion <- confusionMatrixrf.model$importancevarImpPlot(rf.model)X_test <- testy_test <- testtest_predict <- predictlevels(y_test)=clevels(test_predict)=cy_test <- as.numeric(levels(y_test))[y_test]test_predict <- as.numeric(levels(test_predict))[test_predict]train_model <- rf.model$predictedtrain_model <- as.factor(as.numeric(train_model))levels(train_model)=clibrary(pROC)par(pty=\"s\")trainROC <- roc(y_train ~ as.numeric(levels(train_model))[train_model],plot=TRUE,print.auc=TRUE,col=\"blue\", lwd =4,legacy.axes=TRUE,main=\"ROC Curves\", percent=TRUE) #ylab=\"False Positive Percentage\", xlab = \"True Positive Percentage\"## Setting levels: control = 0, case = 1## Setting direction: controls < casestestROC <- roc## Setting levels: control = 0, case = 1## Setting direction: controls < caseslegend,col=c,lwd=4)library(ROCR)library(pROC)library(randomForest)#data dependent variable setset.seed(123)train$Dx = as.factor(train$Dx)data1.rf <- randomForesttest$Dx = as.factor(test$Dx)data2.rf <- randomForestpar(pty=\"s\")set.seed(123)require(pROC)rf.roc1 <-rocrf.roc2 <-rocci(rf.roc1)ci(rf.roc2)roc.test, sensitivity = NULL, specificity = NULL, alternative = c, paired=NULL, reuse.auc=TRUE, boot.n=2000, boot.stratified=TRUE, ties.method=\"first\", progress=getOption(\"pROCProgress\")$name, parallel=FALSE)library(randomForestExplainer)library(randomForest)library(tidyverse)set.seed(123)forest <- randomForest::randomForestsuppressPackageStartupMessages)))Attachmentpoint by point response of Reviewer comment2 Plos one.docxSubmitted filename: Click here for additional data file. 5 Feb 2021Prediction of femoral osteoporosis using machine-learning analysis with radiomics features and abdomen-pelvic CT: A retrospective single center preliminary studyPONE-D-20-33039R2Dear Dr. Ha,We\u2019re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you\u2019ll receive an e-mail detailing the required amendments. When these have been addressed, you\u2019ll receive a formal acceptance letter and your manuscript will be scheduled for publication.http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at onepress@plos.org.If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they\u2019ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact Kind regards,Alfredo VellidoAcademic EditorPLOS ONEAdditional Editor Comments :Reviewers' comments:Reviewer's Responses to QuestionsComments to the Author1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the \u201cComments to the Author\u201d section, enter your conflict of interest statement in the \u201cConfidential to Editor\u201d section, and submit your \"Accept\" recommendation.Reviewer #1:\u00a0All comments have been addressed**********2. Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1:\u00a0Yes**********3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1:\u00a0Yes**********4. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data\u2014e.g. participant privacy or use of data from a third party\u2014those must be specified.The Reviewer #1:\u00a0Yes**********5. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1:\u00a0Yes**********6. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1:\u00a0Thank you for giving me this opportunity to re-review your revised manuscript.I am happy that all of the suggested corrections have been made.Thank you for spending so much effort.**********what does this mean?). If published, this will include your full peer review and any attached files.7. PLOS authors have the option to publish the peer review history of their article (If you choose \u201cno\u201d, your identity will remain anonymous but your review may still be made public.Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.Reviewer #1:\u00a0No 10 Feb 2021PONE-D-20-33039R2 Prediction of femoral osteoporosis using machine-learning analysis with radiomics features and abdomen-pelvic CT: A retrospective single center preliminary study Dear Dr. Ha:I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. onepress@plos.org.If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact plosone@plos.org. If we can help with anything else, please email us at Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staffon behalf ofDr. Alfredo Vellido Academic EditorPLOS ONE"}
+{"text": "Accumulating evidences have shown that circular RNAs (circRNAs) play important roles in regulating the pathogenesis of cancer. However, the role of circRNAs in gastric cancer (GC) remains largely unclear.In this study, we identified a novel upregulated circRNA, hsa_circ_0001829, in chemically induced malignant transformed human gastric epithelial cells using RNA-seq. Subsequent qRT-PCR and ISH assays were performed to detect the expression level of hsa_circ_0001829 in GC cell lines and tissues. Functional roles of hsa_circ_0001829 in GC were then explored by loss- and gain-of- function assays. Bioinformatic prediction and luciferase assay were used to investigate potential mechanisms of hsa_circ_0001829. Finally, the mice xenograft and metastasis models were constructed to assess the function of hsa_circ_0001829 in vivo.We found that hsa_circ_0001829 was significantly upregulated in GC tissues and cell lines. Loss- and gain-of- function assays showed that hsa_circ_0001829 promotes GC cells proliferation, migration and invasion, and the affected cell cycle progression and apoptosis rates may account for the effect of hsa_circ_0001829 on GC proliferation. In addition, bioinformatic prediction and luciferase assay showed that hsa_circ_0001829 acts as a molecular sponge for miR-155-5p and that SMAD2 was a target gene of miR-155-5p; moreover, hsa_circ_0001829 sponges miR-155-5p to regulate SMAD2 expression and hsa_circ_0001829 promotes GC progression through the miR-155-5p\u2013SMAD2 pathway. Finally, suppression of hsa_circ_0001829 expression inhibited tumor growth and aggressiveness in vivo.Taken together, our findings firstly demonstrated a novel oncogenic role of hsa_circ_0001829 in GC progression through miR-155-5p\u2013SMAD2 axis, and our study may offer novel biomarkers and therapeutic targets for GC.The online version contains supplementary material available at 10.1186/s13046-020-01790-w. As the fifth most common malignancies, gastric cancer (GC) is the third leading cause of cancer-associated deaths worldwide and continues to be a major threat to human health . Despitetransformed human gastric epithelial cells (GES-1-T cells) induced by MNNG were established to explore the molecular mechanism of MNNG-induced gastric carcinogenic processes in humans [In recent years, with the advances in molecular biology, noncoding RNAs (ncRNAs) have emerged as key molecular players in many pathological conditions, especially cancers , 8. Incrn humans ; moreoven humans , 12.As a new type of endogenous ncRNAs, circular RNAs (circRNAs) are characterized by a covalently closed loop without 5\u2032 caps and 3\u2032 poly (A) tails \u201315. Due non-malignant control GES-1-N cells using high-throughput RNA sequencing (RNA-seq), and identified a novel circRNA hsa_circ_0001829, which was significantly upregulated in GC cell lines and tissues. Loss- and gain-of- function assays showed that hsa_circ_0001829 promotes GC cells proliferation, migration and invasion. Mechanistically, hsa_circ_0001829 functions as a miR-155-5p sponge to upregulate SMAD2 expression and consequently facilitates GC progression. Our findings provide a further insight into mechanism of carcinogen-related GC tumorigenesis and reveal a novel candidate for GC diagnosis/prognosis and therapy.In present study, we analyzed the expression profile of circRNAs in GES-1-T and GES-1-T cells and control GES-1-N cells were constructed in the previous study , and werA human tissue microarray of 98 cases of GC patients which contained 83 paired GC samples (Cat No. HStmA180Su15) was purchased from Shanghai Outdo Biotech Co., Ltd. . 20 paired of GC and adjacent samples were obtained from patients underwent surgery at the Fifth Affiliated Hospital of Guangzhou Medical University. All samples and clinical data were collected with patients\u2019 consents. All experiments were approved by the Ethics Committee of the Fifth Affiliated Hospital of Guangzhou Medical University.High-throughput sequencing was performed and data was analyzed as we previously described , 20, 21.-\u0394\u0394Ct method was used to analyze relative expression levels. The specific primers were listed in Supplementary Table\u00a01.RNA extraction, nuclear-cytoplasmic fractionation and qRT-PCR assay were performed as previously described .GAPDH orAfter dewaxing and hybridization, the tissue microarray was dealt with Proteinase K, fix in 4% paraformaldehyde and hybridized with digoxigenin-labeled hsa_circ_0001829 probes at 55\u2009\u00b0C overnight and subsequently incubated overnight at 4\u2009\u00b0C with anti-digoxin monoclonal antibody . After staining with Strept-Avidin-Biotin-Peroxidase Complex, the tissue microarray was observed and images were captured under a microscope. The analysis software Image-pro 6.0  was applied to acquire the Integrated Optical Density (IOD) for evaluating the expression level of has_circ_0001829 in GC tissues.Actinomycin D (10\u2009\u03bcg/ml) or DMSO was added to the culture medium to evaluate the stability of circRNA and its linear isoform. For RNase R treatment, total RNA was incubated for 30\u2009min at 37\u2009\u00b0C with or without 3\u2009U/\u03bcg RNase R . After treatment with actinomycin D and RNase R, the expression levels of SLC45A4 and hsa_circ_0001829 were measured by qRT-PCR.The overexpression vector was constructed with PCR product of hsa_circ_0001829 based on pcDNA3.1. MiRNA mimics/inhibitors and siRNAs were synthesized by GenePharma . The siRNAs were designed base on the circRNA backspliced region. For transient transfection, GC cells were cultured and transfected with these reagents using Lipofectamine\u2122 2000  according to the manufacturer\u2019s instructions. To stably knockdown hsa_circ_0001829, sh-circRNA-1# and sh-circRNA-2# lentivirus vectors were constructed and the lentiviruses were packaged and purified by GenePharma . Stable transfection procedures with lentivirus vectors were performed according to manufacturer\u2019s instructions.Cy3-labeled hsa_circ_0001829 specific probe was designed for RNA FISH, and FAM-labeled hsa_circ_0001829 specific probe and CY5-labeled miR-155-5p probe were designed and used for co-localized RNA FISH. Cells attached to slides were immobilized and then digested with protease K. After dehydration with 70, 85 and 100% alcohol, hybridization was performed at 42\u2009\u00b0C overnight in dark. The slides were then washed with 50% formamide/2\u2009\u00d7\u2009SSC preheated to 43\u2009\u00b0C, 0.1% NP- 40/2\u2009\u00d7\u2009SSC preheated to 37\u2009\u00b0C, and DAPI staining solution at room temperature. Images were captured under a laser confocal microscope .Cell proliferation was assayed by CCK-8  according to the manufacturer\u2019s instructions. A total of 3000 cells were plated in each well of a 96-well plate. Then, on the indicated day, 10\u2009\u03bcl of CCK-8 solution was added to each well. Following 1\u2009h of incubation at 37\u2009\u00b0C, the absorbance of each well at 450\u2009nM was measured by Synergy 2 microplate reader .4) were seeded in each well of a 96-well plate. After incubation with 50\u2009\u03bcM EdU for 2\u2009h, the cells were fixed in 4% paraformaldehyde and stained with Apollo Dye Solution. The Hoechst 33342 was used to stain the nucleic acids. Then the EdU-positive cells were photographed and IPP software was used to analyze the resultant data.EdU assays were carried out using a Cell-light EdU DNA Cell Proliferation Kit  according to the manufacturer\u2019s protocol. Cells , which was coated with Matrigel . A total of 500\u2009\u03bcL medium supplemented with 20% FBS was added into the lower chamber. After incubation for 24\u2009h, the non-invaded cells on the upper side of the chamber were removed with a cotton swab, while invaded cells were fixed in 4% paraformaldehyde and stained with 0.1% crystal violet solution. The stained cells were analyzed.Transfected cells were seeded into 6-well plates and cultured overnight at 37\u2009\u00b0C. When the cells were fully confluent, a uniform straight scratch was made in the center of the well using a sterile 200\u2009\u03bcl pipet tip. Images were obtained as a baseline after washing three times with PBS. Subsequently, fresh medium contained 2% FBS was added. After 24\u2009h, images of the same location were acquired.Flow cytometric analysis was performed as previously described . Cell cy293-T cells were seeded into 24-well plates. Cells at 60\u201370% confluence were co-transfected with wild-type or mutated hsa_circ_0001829 3\u2032 UTR or SMAD2 3\u2032 UTR reporter plasmids, and with miR-155-5p mimics or negative controls using the Lipofectamine\u21222000 as transfection reagent. After 24\u2009h incubation, luciferase assays were conducted using the Dual Luciferase Reporter Assay System  to detect firefly and Renilla luciferase activities. The ratio of firefly fluorescence and Renilla fluorescence was calculated as the relative luciferase activity.The GES-1-T and MKN-28 cells were lysed with RIPA lysis buffer , and protein was harvested and quantified by bicinchoninic acid (BCA) analysis . Protein extractions were separated by 10% SDS-PAGE and transferred onto polyvinylidene fluoride (PVDF) membranes . After 2\u2009h blocking with 5% bovine serum albumin, the membranes were incubated with primary anti-GAPDH antibody (1:2000), anti SMAD2 antibody (1:1000)  overnight at 4\u2009\u00b0C. The membranes were then incubated with a secondary antibody. After washes, signals were detected and analyzed using an Odyssey Imaging System .6) stably transfected with vectors were injected into the flanks of NOG mice (n\u2009=\u20095 per group). The tumor volumes and weights were measured at the indicated time points and the tumor tissues were then harvested for immunostaining analysis. For metastasis studies, GES-1-T cells (5\u2009\u00d7\u2009106) stably transfected with vectors were injected from tail vein of NOG mice (n\u2009=\u20095 per group). The mice lung and liver were carefully examined for tumor metastasis 4\u2009weeks later.Four-week-old female NOG mice were used for animal experiments. For tumor growth studies, GES-1-T cells  were prepared. Then, immunohistochemistry was performed using the primary antibodies against MMP2, PCNA and SMAD2 from Abcam. The complex was visualized with DAB complex, and the nuclei were counterstained with haematoxylin. All sections were scored by the semi-quantitative H-score approach.p\u2009<\u20090.05.All experiments were repeated at least three times. Statistical analysis was performed using SPSS 13.0 or the Prism statistical software package. Differences between the different groups were evaluated using the Student\u2019s t-test or analysis of variance (ANOVA). Pearson\u2019s correlation coefficient analysis was used to analyze correlations. All experimental data were presented as the mean\u2009\u00b1\u2009S.D.. The differences were considered to be significant at hsa_circ_0001829 is upregulated in GC cell lines and tissues, and is a stable circular RNAP\u2009<\u20090.05, of which 1509 circRNAs were upregulated and 3141 circRNAs were downregulated in GES-1-T cells  more than 2.0 or less than 0.5 and With a backspliced length of 641\u2009bp, hsa_circ_0001829 is derived from exon 1 within solute carrier family 45 member 4 (SLC45A4) gene locus  targeting the junction sites of hsa_circ_0001829 were designed to silence hsa_circ_0001829 expressions. These siRNAs significantly decreased hsa_circ_0002819 expression level with no effect on SLC45A4 linear isoform in both GES-1-T and MKN-28 cell lines  was constructed. The increase of hsa_circ_0001829 expressions induced by pcDNA-circ_0001829 was confirmed by qRT-PCR analysis, which showed approximately tenfold and fifteenfold overexpression in GES-1-T and MKN-28 cells, respectively; meanwhile, no significant change in SLC45A4 mRNA level was observed Fig.\u00a0a. SubseqNext, the effect of hsa_circ_0001829 overexpression on cell cycle and apoptosis rates of GES-1-T and MKN-28 cells were also analyzed. Contrary to the effect induced by hsa_circ_0001829 knockdown, a significant reduction of cells in the G2/M phase and an increase of cells in the G0/G1 phase were observed upon hsa_circ_0001829 overexpression Fig. c. Furthe4.Hsa_circ_0001829 acts as a molecular sponge for miR-155-5pTaken together, these data indicated that overexpression of hsa_circ_0001829 promotes GC cells proliferation, migration and invasion, and the promoting cell cycle progression and decreased apoptosis may account for the promotional effect of hsa_circ_0001829 overexpression on GC proliferation.To explore the regulatory mechanism of hsa_circ_0001829 in GES-1-T cells, we first evaluated its subcellular distribution. The subcellular localization was first evaluated via qRT\u2013PCR in nuclear or cytoplasmic fractions; as shown in Fig.\u00a0Given that circRNAs in the cytoplasm have been widely reported to serve as miRNA sponges, we subsequently explored whether hsa_circ_0001829 could act as a miRNA sponge. TargetScan  and miRa5.SMAD2 was validated as a target gene of miR-155-5pThese results indicated that hsa_circ_0001829 directly binds to miR-155-5p, acting as a sponge for miR-155-5p.Having investigated the interaction between hsa_circ_0001829 and miR-155-5p, we next used TargetScan to predict the target genes of miR-155-5p and 556 target genes were obtained. Then KEGG pathway analysis by David , an expeWe then overexpressed or silenced miR-155-5p expression and measured the expression levels of SMAD2. Results showed that transfection of miR-155-5p mimics decreased the expression of SMAD2 mRNA, while transfection of miR-155-5p inhibitors increased SMAD2 mRNA expression in both GES-1-T and MKN-28 cells  were constructed and subcutaneously injected into the flank of NOG mice. Consistent with the observations in vitro, stably transfection of sh-circRNA-1# resulted in a significant decrease in the size and weight compared to those in the sh-NC group Fig.\u00a0a-c. IHC These results indicated that hsa_circ_0001829 knockdown inhibits tumor growth and aggressiveness of GC in vivo.Despite environmental carcinogen exposure is one of the most important causes of GC, the roles of circRNAs in environmental carcinogen-induced malignant transformation and their underlying mechanisms remain largely unknown. In this study, we applied RNA-seq analysis to screen differentially expressed circRNAs in MNNG induced malignant-transformed gastric epithelial cells, and then, we focused on the top 10 upregulated circRNAs and hsa_circ_0001829 was identified as the most highly upregulated circRNA through qRT-PCR assay, which was subsequently confirmed to be also upregulated in other four GC cell lines and GC tissues. Furthermore, results of loss- and gain-of-function assays showed that hsa_circ_0001829 promotes GC cells proliferation, migration and invasion, and the affected cell cycle progression and apoptosis rates may account for the effect of hsa_circ_0001829 on GC proliferation. Moreover, animal experiments revealed that hsa_circ_0001829 knockdown repressed tumor growth in vivo. These findings collectively indicated that hsa_circ_0001829 is upregulated in GC and promotes GC progression.With the deepening of research on the role of circRNAs in tumors, numerous aberrantly expressed circRNAs have been unveiled in tumor tissues and cell lines, and they were considered as promising biomarkers for tumors in terms of their structural stability, specificity and high abundance , 35. ForAlthough our understanding of the functions of circRNAs is still nascent, increasing studies have shown that circRNAs can sequester microRNAs or proteins, modulate transcription, interfere with splicing, and even translate to produce polypeptides or proteins . CircRNAMiR-155-5p is a multifaceted regulator of cell proliferation, cell cycle, development, immunity and inflammation that plays pivotal roles in numerous cancers . MiR-155SMAD2, as a receptor-regulated SMAD member, is the primary intracellular signaling mediator and transcription factor for transforming growth factor-\u03b2 (TGF\u03b2) family signaling. Zhang et al. reported that both the mRNA and protein levels of SMAD2 were elevated in MNNG-initiated GC rats , and BruTaken together, we identified a novel circRNA, hsa_circ_0001829 that is upregulated in GC cell lines and tissues. Furthermore, we demonstrated that hsa_circ_0001829 promotes the malignant behaviors of GC cells by sponging miR-155-5p to regulate SMAD2 expression. Our findings firstly identify the role of hsa_circ_0001829 in GC, which may offer an effective biomarker for diagnosis/prognosis and a promising target for therapy in GC.Additional file 1."}
+{"text": "Colorectal cancer (CRC) is one of the most common cancers worldwide. Circular RNAs (circRNAs), a novel class of non-coding RNAs, have been confirmed to be key regulators of many diseases. With many scholars devoted to studying the biological function and mechanism of circRNAs, their mysterious veil is gradually being revealed. In our research, we explored a new circRNA, hsa_circ_0026416, which was identified as upregulated in CRC with the largest fold change (logFC\u2009=\u20093.70) of the evaluated circRNAs via analysing expression profiling data by high throughput sequencing of members of the GEO dataset (GSE77661) to explore the molecular mechanisms of CRC.qRT-PCR and western blot analysis were utilized to assess the expression of hsa_circ_0026416, miR-346 and Nuclear Factor I/B (NFIB). CCK-8 and transwell assays were utilized to examine cell proliferation, migration and invasion in vitro, respectively. A luciferase reporter assay was used to verify the combination of hsa_circ_0026416, miR-346 and NFIB. A nude mouse xenograft model was also utilized to determine the role of hsa_circ_0026416 in CRC cell growth in vivo.Hsa_circ_0026416 was markedly upregulated in CRC patient tissues and plasma and was a poor prognosis in CRC patients. In addition, the area under the curve (AUC) of hsa_circ_0026416 (0.767) was greater than the AUC of CEA (0.670), CA19-9 (0.592) and CA72-4 (0.575). Functionally, hsa_circ_0026416 promotes cell proliferation, migration and invasion both in vitro and in vivo. Mechanistically, hsa_circ_0026416 may function as a ceRNA via competitively absorbing miR-346 to upregulate the expression of NFIB.In summary, our findings demonstrate that hsa_circ_0026416 is an oncogene in CRC. Hsa_circ_0026416 promotes the progression of CRC via the miR-346/NFIB axis and may represent a potential biomarker for diagnosis and therapy in CRC. Colorectal cancer (CRC) is one of the most common cancers worldwide, with an incidence of 1.8 million cases and 896,000 deaths in 2017 . AlthougCircRNAs are novel stars of the non-coding RNA world that are abundant in mammalian cells, featuring relative stability and high tissue and cell specific expression \u201319. In cIn our research, we conducted a comprehensive exploration of a new circRNA (hsa_circ_0026416). We found that hsa_circ_0026416 significantly upregulated in CRC tissues and high expression of hsa_circ_0026416 leads to poor prognosis. In addition, hsa_circ_0026416 acts as a ceRNA to regulate NFIB via competition for miR-346, facilitating CRC proliferation and metastasis. In summary, this study extends our knowledge on the biological roles and mechanisms of hsa_circ_0026416 in CRC and provides a novel biomarker for the diagnosis and treatment of CRC.P-value\u2009<\u20090.05 and |log2FC|>\u20091.A circRNA expression profile (GSE77661) was downloaded from GEO. Analysis of colorectal-related differentially expressed circRNAs was performed using the Bioconductor Limma package in R software. The criteria for selection of differentially expressed circRNAs (DEcircRNAs) were From January 2017 to November 2018, 169 pairs of CRC tissues and adjacent normal mucosa were collected from Qilu Hospital of Shandong University, China. At the same time, 212 cases of preoperative plasma samples and 64 cases of postoperative plasma samples were collected. All patients met the following inclusion criteria: received enterectomy and were confirmed by pathological diagnosis; complete clinicopathological data, including gender, age, tumour location, tumour diameter, tumour differentiations, pT stage, pN stage, distant metastasis, pTNM, lymphovascular invasion, and perineural invasion; complete follow-up information; and written informed consent. Patients were excluded if they received radiotherapy or chemotherapy before surgery, presented with other malignant disease within the past 5\u00a0years, were lost to follow-up, or exhibited incomplete clinicopathological data. After surgery, all patients were followed-up for at least 2\u00a0years, and patients with advanced CRC received chemotherapy and/or radiotherapy according to the National Comprehensive Cancer Network (NCCN) Guidelines. In addition, fresh normal plasma samples were obtained from 183 healthy people at Qilu Hospital of Shandong University in February 2017. All tissue and plasma samples were quickly frozen in liquid nitrogen after removal and stored at \u221280\u00a0\u00b0C until further experiments. This study was approved by the Ethics Committee of Qilu Hospital. Each study participant provided informed consent.2. After 2\u20133 stable generations, cells were used for subsequent experiments and analyses. Short tandem repeat (STR) DNA fingerprinting was utilized to identify all cell lines. Mycoplasma contamination was checked at least once a month using the MycoAlert Mycoplasma Detection Kit . No mycoplasma was detected in any of the cell lines.The normal intestinal epithelial cell line (HCO), HEK297T and four human CRC cell lines  were purchased from the Culture Collection of Chinese Academy of Sciences . DLD-1 and HCT-8 cell lines were maintained in RPMI-1640 media  with 10% foetal bovine serum . HCO, HEK293T, HCT-116 and SW480 cell lines were maintained in DMEM  supplemented with 10% FBS  and cultured in a 37\u00a0\u00b0C incubator containing 5% COTotal RNA was extracted using TRIzol Reagent , and the concentration and purity of total RNA were determined by an ultraviolet spectrophotometer . Nuclear and cytoplasmic RNA isolation was performed using a PARIS Kit  following the manufacturer\u2019s instructions. PrimeScript\u2122 RT Reagent Kit with gDNA Eraser  was used to reverse transcribe RNA to cDNA. Real-time PCR was implemented using SYBR Premix Ex Taq  on a LightCycler\u00ae 2.0 Real-time PCR System . GAPDH was used as an internal reference gene. The results were analysed by the 2-\u0394\u0394Ct method. All primer sequences are listed in Additional file To overexpress hsa_circ_0026416, hsa_circ_0026416 cDNA (704\u00a0bp) was inserted into the PLCDH-ciR vector . Amplified from the cDNA of HCT-8 cells, full length NFIB was cloned into the pcDNA3.1 (\u2009+) vector, full length of hsa_circ_0026416 and 3\u2032UTR of NFIB was inserted into the pmirGLO vector. PmirGLO-mut (hsa_circ_0026416), pmirGLO-mut (NFIB 3\u2032UTR), all miRNA mimics, the miR-346 inhibitor, NFIB si-RNA and 2\u2032-O-Me-Modified-si-circ_0026416 were synthesized by Genepharma . Hsa_circ_0026416 si-RNA was obtained from Suzhou Ribo Life Science Co., Ltd .For transient transfection of plasmids, HCT-8, SW480 and HEK293T cells were transfected with Lipofectamine 3000 reagent . Si-RNAs, miRNA mimics and the miRNA inhibitor were transfected into cells using the X-tremeGENE transfection reagent . All cell transfections were based on the manufacturer\u2019s instructions. The sequences of these nucleic acids are listed in Additional file 5 (loss of function) HCT-8 cells and 2\u2009\u00d7\u2009105 (loss of function) SW480 cells were suspended in 200\u00a0\u00b5l serum-free medium and placed into the upper chambers of a Transwell . When we performed gain of function and rescue experiments, 7\u2009\u00d7\u2009104 HCT-8 cells and 1.3\u2009\u00d7\u2009105 SW480 cells were seeded into the upper chambers of each Transwell. Then, we added 600\u00a0\u00b5l media containing 20% FBS into the lower well of each chamber. After incubation for 48\u00a0h, cells in the upper cavity were removed with cotton swabs, and cells that had invaded the lower surface were fixed, stained using crystal violet and imaged under a microscope at 100\u2009\u00d7\u2009magnification . When we assessed cell migration, all steps were the same as above, except that we did not add Matrigel.Matrigel  was added into the Transwell chamber, and the 24-well plates containing the chamber were placed into the cell culture incubator to accelerate the solidification of Matrigel. Twenty-four hours after transfection, 1\u2009\u00d7\u200910For CCK-8 assay, 3000 HCT-8 or SW480 cells per well were cultured in 96-well plates, and each treatment featured three repeats. At 0, 24, 48, 72 and 96\u00a0h after transfection, 10\u00a0\u03bcl CCK-8 reagent  was added to each well with the cell culture medium, and samples were placed back into the incubator for another 2\u00a0h. Absorbance was measured at 450\u00a0nm by a microplate reader .Cell cycle distribution was analysed by flow cytometry. Cells were harvested, washed twice with PBS and fixed in 75% ethanol at \u221220\u00a0\u00b0C for one hour. RNA was removed by incubating cells with RNase A  at 37\u00a0\u00b0C for 30\u00a0min. Cells were then stained with propidium iodide (PI) solution  at 4\u00a0\u00b0C for 30\u00a0min and analysed by flow cytometry .The HEK293T cells were seeded in 24-well plates overnight and then co-transfected with pmirGLO-circ_0026416 WT or MUT and miR-346 mimics or NC. For HCT-8 and SW480 cells, they were co-transfected with pmirGLO-NFIB 3\u2032UTR WT or MUT and miR-346 mimics or NC. After a 48-h culture, Firefly and Renilla luciferase activities were measured with the Dual-Luciferase Reporter Assay System  according to the manufacturer\u2019s instructions, and Firefly luciferase activity was standardized by Renilla luciferase activity.To assess protein expression, both the immunoreactive percentage and intensity were scored. The percentage of positive cells was graded as 0,\u2009<\u20095%;\u2009+\u20091, 5\u201325%;\u2009+\u20092, 26\u201350%;\u2009+\u20093, 51\u201375%; and\u2009+\u20094, 76\u2013100% positive cells, and the intensity of cellular staining was scored as: 0, negative;\u2009+\u20091, weak;\u2009+\u20092, moderate; and\u2009+\u20093, strong. The final staining score was obtained by multiplying the two scores.Protein extraction and western blot were performed as previously described . Antibod6 cells in 0.1\u00a0ml PBS) were subcutaneously inoculated into the right axillary fossa of mice (n\u2009=\u20096 per group). 2\u2032-O-Me-Modified-si-circ_0026416 was injected into the tumours every 3\u00a0days after tumours formed. Tumour growth was measured every 3\u00a0days for 22\u00a0days, at which time the mice were sacrificed. Tumour volume (V) was calculated as follows: V\u2009=\u2009(length diameter)\u2009\u00d7\u2009(width diameter)2 /2. The longest diameter did not exceed 2.0\u00a0cm, and the general condition of all mice was well throughout the experiment. All animal experiments were approved by the Animal Care and Use Committee of Shandong University, as well as the animal welfare settings [BALB/c athymic male nude mice (4\u00a0weeks old) were obtained from Charles River Biotechnology . SW480 cell suspensions . The efficiency of hsa_circ_0026416 in the diagnosis of CRC was evaluated by receiver operating characteristic (ROC) curve. The two groups were compared using two-tailed Student\u2019s t-test. The relationship between the expression of hsa_circ_0026416 and clinicopathological characteristics was assessed by Chi-squared test. Univariate and multivariate Cox proportional hazard regression models were performed to analyse the effects of different clinicopathological factors on overall survival. Kaplan\u2013Meier survival curve and log-rank test were used to determine the OS rate of CRC patients with different hsa_circ_0026416 expression levels. Data are displayed as means\u2009\u00b1\u2009SD of three independent experiments. P\u2009<\u20090.05). Finally, we selected the upregulated circRNA (hsa_circ_0026416) with the largest fold change (logFC\u2009=\u20093.70) for additional research , we determined that hsa_circ_0026416 is derived from regions of exons 3, 4, 5, 6, 7 and 8 of the KRT6C gene (GenBank: NM_173086)  . Furthermore, compared to hsa_circ_0026416 expression in healthy individuals\u2019 plasma, its expression in CRC patients was significantly increased (P\u2009<\u20090.001). Next, ROC curve analysis was used to assess the diagnostic ability of hsa_circ_0026416 , distant metastasis (P\u2009<\u20090.001), pTNM stage (P\u2009=\u20090.004), lymphovascular invasion (P\u2009=\u20090.002) and perineural invasion (P\u2009=\u20090.004) (Table P\u2009<\u20090.05) and has value as a biomarker for the diagnosis of CRC.First, we compared hsa_circ_0026416 expression in the plasma of 212 patients with CRC before surgery with that of hsa_circ_0026416 in 63 patients after surgery  targeting hsa_circ_0026416 and an hsa_circ_0026416 overexpression plasmid, respectively Fig.\u00a0a, b. TraNext, taking into consideration that hsa_circ_0026416 was present primarily in the cytoplasm and many circRNAs have been proven to act as miRNA sponges, we next explored the capability of hsa_circ_0026416 to bind miRNAs. RegRNA2.0 and CircInteractome were used to predict miRNA response elements (MREs) harboured by hsa_circ_0026416. Among all predicted miRNAs, we selected the top 12 as candidate miRNAs  proteomic data to verify the expression of NFIB in human CRC tissue compared to normal colorectal tissue  targeting NFIB. qRT-PCR revealed that NFIB and miR-346 were upregulated in HCT-8 and SW480 cells in response to pcDNA3.1-NFIB and miR-346 mimics co-transfection, while si-NFIB and miR-346 inhibitor co-transfection downregulated expression of NFIB and miR-346 in HCT-8 and SW480 cells Fig.\u00a0a\u2013d. As aTo further explore the tumour-promoting effect of hsa_circ_0026416 in vivo, we purchased stable small interfering RNA (2\u2032-O-Me-Modified-si-circ_0026416) for intratumoral injection. Compared to the NS  group, we discovered that mean tumour volume and weight in the 2\u2032-O-Me-Modified-si-circ_0026416 group was markedly reduced  circRNAs can interact with RNA binding proteins, such as circ-Foxo3, of which there are many. By interacting with CDK2 and p21, circRNA inhibits the cell cycle and block the transition from G1 to S phase . (ii) CiIn our research, hsa_circ_0026416 was identified as an upregulated circRNA with the largest fold change (logFC\u2009=\u20093.70) via analysing expression profiling by high throughput sequencing from the GEO dataset (GSE77661). Hsa_circ_0026416 was upregulated in CRC tissues, plasma, and HCT-8 and SW480 cells, conveying a poor prognosis in CRC patients. In addition, our data revealed compared to CEA, CA19-9 and CA724 levels, hsa_circ_0026416 levels in plasma may represent a more promising diagnostic marker for CRC. Hsa_circ_0026416 promotes CRC cell proliferation, migration and invasion both in vitro and in vivo by competitively absorbing miR-346 and upregulating its target gene, NFIB. As previous studies have shown, the majority of circRNAs act as miRNA sponges or competitive endogenous RNAs (ceRNAs) to regulate the expression of target genes [MiR-346, which functions as a tumour suppressor , was conOur data indicate that hsa_circ_0026416 can be considered a potential biomarker for the diagnosis of CRC. Its expression was tremendously upregulated in CRC tissues and plasma, and its AUC value of 0.767 was higher than that of CEA (0.670), CA19-9 (0.592) and CA72-4 (0.575). In vivo experiments further revealed that targeted inhibition of hsa_circ_0026416 slowed tumour growth. Taken together, these factors all indicate that hsa_circ_0026416 is an excellent biomarker for the diagnosis of CRC and a novel target for the treatment of CRC.In summary, our study demonstrated that hsa_circ_0026416 is upregulated in CRC tissues and CRC patient plasma. Univariate and multivariate Cox regression analysis both revealed that.high hsa_circ_0026416 expression was the indicative of poor prognosis. Functionally, hsa_circ_0026416 promotes CRC cell proliferation, migration and invasion both in vitro and in vivo. Mechanistically, hsa_circ_0026416 acts as a ceRNA for miR-346 to upregulate NFIB and promote tumour progression (Fig.\u00a0Our research proves that hsa_circ_0026416 is upregulated in CRC patient tissues and plasma and high expression of hsa_circ_0026416 lead to poor prognosis in CRC patients. Hsa_circ_0026416 promotes CRC cell proliferation, migration and invasion via the miR-346/NFIB axis in vitro and in vivo. Therefore, hsa_circ_0026416 may represent a potential biomarker for diagnosis and therapeutic targeting.Additional file 1: Table S1. Primers and Oligonucleotides sequences."}
+{"text": "The gut microbiota diversity of eight panda cubs was assessed during a dietary switch.Gut microbiota diversity of panda cubs significantly decreased after bamboo consumption.Carnivorous species living on a plant-based diet possess low microbial diversity.Mice were fed a bamboo diet but did not display low gut microbiota diversity.Giant pandas have an exclusive diet of bamboo; however, their gut microbiotas are more similar to carnivores than herbivores in terms of bacterial composition and their functional potential. This is inconsistent with observations that typical herbivores possess highly diverse gut microbiotas. It is unclear why the gut bacterial diversity of giant pandas is so low. Herein, the dynamic variations in the gut microbiota of eight giant panda cubs were measured using 16S rRNA gene paired-end sequencing during a dietary switch. Similar data from red panda (an herbivorous carnivore) and carnivorous species were compared with that of giant pandas. In addition, mice were fed a high-bamboo diet (80% bamboo and 20% rat feed) to determine whether a bamboo diet could lower the gut bacterial diversity in a non-carnivorous digestive tract. The diversity of giant panda gut microbiotas decreased significantly after switching from milk and complementary food to bamboo diet. Carnivorous species living on a plant-based diet, including giant and red pandas, possess a lower microbial diversity than other carnivore species. Mouse gut microbiota diversity significantly increased after adding high-fibre bamboo to their diet. Findings suggest that a very restricted diet (bamboo) within a carnivorous digestive system might be critical for shaping a low gut bacterial diversity in giant pandas. The gut microbiota diversity of eight panda cubs was assessed during a dietary switch. Gut microbiota diversity of panda cubs significantly decreased after bamboo consumption. Carnivorous species living on a plant-based diet possess low microbial diversity. Mice were fed a bamboo diet but did not display low gut microbiota diversity. Despite living on a bamboo-dominated diet, the giant panda lacks genes for bamboo digestion (During growth and development (0~1.5 year old), giant pandas in captivity change from a diet of breast and formula milk and supplementary food  to bamboo. Herein, to investigate why a low-diversity bacterial community exists in giant pandas, a 16S rRNA gene deep-sequencing study of their gut microbiota was performed when switching them from a milk-based to bamboo-based diet. This could determine whether all bamboo specialists that have evolved from a carnivorous diet display a lower gut bacterial diversity than other carnivores. To test this hypothesis, the gut microbiotas of giant pandas, red pandas and other species of selected carnivores were compared. In addition, an experiment was performed on mice to test if a bamboo diet in a non-carnivorous digestive system leads to a gut bacterial community with low diversity.This study was approved by the Institutional Animal Care and Use Committee of Sichuan Agricultural University under permit number DKY-B20130302. Eight captive giant panda cubs, including four males and four females born within the same week from eight different mother pandas, were selected to survey the giant panda gut microbiome dynamics in early life. All giant panda cubs lived together with their mother from birth to 8\u00a0mos; they were subsequently housed in a separate house with large yard. A series of faecal samples were collected from the eight giant panda cubs (samples were taken once a month from 4 to 17\u00a0mos) and 31 adult giant pandas (> 5\u00a0years) from the China Conservation and Research Center for the Giant Panda . The giant panda enclosure was broad and complex and panda cubs often entered bushes for a week, so there may have been some impact on regular sample collection times. Diet and antibiotic usage were recorded. All samples were stored at \u221280\u00b0C until use.A frozen aliquot (200\u00a0mg) of each sample was processed using an MO BIO Power Faecal TM DNA Isolation Kit  according to the manufacturer\u2019s protocol. The DNA concentration was measured using a NanoDrop Spectrophotometer (Thermo Scientific), and the overall DNA quality was assessed by agarose gel electrophoresis. Only samples that met the following criteria were used for sequencing: (i) DNA concentration\u2009>\u200910\u00a0ng/ul and (ii) DNA total quantity >\u2009100\u00a0ng. Polymerase chain reaction (PCR) amplification and paired-end sequencing of the 16S V4 region (250\u00a0bp length) were performed by the Beijing Genomics Institute  using an Illumina MiSeq platform. Briefly, the V4 hypervariable region of the bacterial 16S rRNA gene was amplified from extracted DNA using standard barcoded primers  . Chimeric sequences were excluded using the VSEARCH algorithm  of the Miseq platform .Sequencing data were obtained for eight giant panda cubs (4\u201317 mos) and 31 adult pandas (>5\u00a0years). Giant panda cubs were categorized as S1, S2 and S3 based on their diet . S1 (4\u20137n\u2009=\u20091953), followed by S1 (n\u2009=\u2009476) and S3 (n\u2009=\u2009326). Unexpectedly, adult giant pandas had the fewest unique OTUs (n\u2009=\u2009276) and only 359 OTUs were present in all growth stages .After basic data processing using Mothur (v1.39), 4\u2009936\u2009942 high-quality reads were assigned to 6443 OTUs with a threshold of 97% similarity. A Venn diagram  shows thP\u2009<\u20090.05). The dynamic variation of observed OTUs , with diet being the most influential (F\u2009=\u200912.9142).To determine which factors significantly affect the development of gut microbial communities in giant panda cubs, permutational multivariate analysis of variance (PERMANOVA) was used on diet, individual, genetics, sex, season and age data (see P\u2009<\u20090.05) compared with specific carnivorous and omnivorous representatives. Unsurprisingly, there was no significant difference between the bamboo specialists\u2014the giant panda (Ailuropoda melanoleuca) and red panda (Ailurus fulgens) .To verify whether bamboo specialists that have evolved from a carnivorous diet harbour a lower gut bacterial diversity than carnivores, we compared the gut microbiotas of giant pandas which mainly live on bamboo  with carnivores. Significantly, lower numbers of OTUs  and Shan, P\u2009<\u20090.05), which subsequently returned to the original level when rat feed was reintroduced (P\u2009<\u20090.05)  . Not sur\u2009<\u20090.05) . The lowet al. divided giant panda cubs into four groups: S1 (<2 mos); S2 (between 3 and 12 mos and no bamboo in faeces); S3 (>6\u00a0mos and bamboo stems or leaves in faeces); and S4 (>6 mos and bamboo shoots in faeces). We found that their partial sample collection age of S2, S3 and S4 overlapped; for example, the age range of group S2 was 3\u201312 mos, and the age range of groups S3/S4 was 6\u201324 mos. However, in our study, cub faeces were divided into different groups according to diet and age (completely different ages)  had the higher Shannon diversity indices than group S3 (>6 mos and bamboo stems or leaves in faeces) and group S4 (>6 mos and bamboo shoots in faeces)  . This ma faeces) .Lactobacillus and Bifidobacterium decreased significantly after weaning in group S3. It has been reported that Lactobacillus and Bifidobacterium positively correlate with breast milk consumption  adults also demonstrate a lower alpha diversity of gut bacteria  compared with cubs during weaning (bamboo introduced) and post-weaning (early stage of bamboo diet)  . This reHapalemur griseus), a primate bamboo specialist with an omnivorous digestive tract, showed significantly greater gut microbiota diversity compared with the two bamboo specialists (the giant and red pandas) evolved from carnivores  harbour a lower gut bacterial diversity than other carnivores. However, a high-bamboo diet in a non-carnivorous digestive tract (mice and bamboo lemur) does not lead to the development of low gut bacterial diversity. This suggests that a very specialized diet with a carnivorous digestive system establishes a low-diversity bacterial community in giant and red pandas.This study was approved by the Institutional Animal Care and Use Committee of the Sichuan Agricultural University under permit number DKY-B20130302.All authors read and approved the submission of this article.The dataset used in this study was deposited into the National Centre for Biotechnology Information\u2019s Sequence Read Archive under accession bioproject number: PRJNA524253.This work was supported by the National Natural Science Foundation of China (31900307) to W.G and School-level fund of chengdu medical college (18Z171) to W.G.W.G. and Y.L. designed the study. W.G., R.N, J.T, C.W., H.Z., C.L. and D.L. collected the samples. W.G., R.N., Y.C and B.Z. performed the laboratory work. M.Z., Y.L., Q.N. and X.N. contributed the experimental design. W.G and J.Z. analysed the data. W.G. and Y.L. wrote the article.figure_s1_coz104Click here for additional data file.figure_s2_coz104Click here for additional data file.figure_s3_coz104Click here for additional data file.figure_s4_coz104Click here for additional data file.figure_s5_coz104Click here for additional data file.table_s1_coz104Click here for additional data file.table_s2_coz104Click here for additional data file.table_s3_coz104Click here for additional data file.table_s4_coz104Click here for additional data file.table_s5_coz104Click here for additional data file.table_s6_coz104Click here for additional data file.supplemental_file_legends_coz104Click here for additional data file."}
+{"text": "Although the situation is evolving, current methods have various difficulties with the accurate mapping of loops even in mammalian Hi-C data, and most of them fail to identify chromatin loops in animal species with substantially different genome architecture. This paper presents the loop and significant contact annotation (LASCA) pipeline, which uses Weibull distribution-based modeling to effectively identify loops and enhancer\u2013promoter interactions in Hi-C data from evolutionarily distant species: from yeast and worms to mammals. Available at:  These megabase-scale compartments are partitioned into self-interacting structures, termed topologically associating domains (TADs). The distinguishing feature of TADs is that spatial contacts of remote genomic elements are more frequent within TADs than between individual TADs4. Boundaries between individual TADs are enriched with cohesin complex and CCCTC-binding factor (CTCF). Recent data show that TADs are formed via dynamic DNA loop extrusion and may harbor smaller contact domains, some of which are chromatin loops6. Along with chromatin compartments and TADs, chromatin loops represent one of the major levels of hierarchical folding of the genome. Although most of the loops in mammals are anchored by CTCF and cohesin, several other proteins can mediate long-distance genomic interactions: Yin Yang 1 (YY1), zinc finger protein 143 (ZNF143), LIM domain-binding factor 1 (LDB1)7. All the three are involved in establishing enhancer-promoter interactions either by direct binding and bridging specific genomic sites (YY1 and ZNF143)9, or by binding to a subset of transcription factors (LDB1)10. Species that lack CTCF-dependent loops can however utilize DNA extruding complexes such as condensin and cohesin to organize their genomes or genome parts into consecutive similar-sized chromatin loops. Specifically, Caenorhabditis elegans X chromosome contains dozens of loops associated with a condensin-like dosage compensation complex (DCC)12. In budding yeast, S-phase chromatin forms consecutive loops which base points often colocalize with binding sites of cohesin protein Scc113.Techniques exploiting the proximity ligation procedure  have significantly improved our understanding of the spatial (3D) genome organization. C-methods have confirmed the existence of chromosomal territories that are spatially compartmentalized into active and repressed chromatin domains, referred to as A and B compartmentscis-regulatory elements14. Hence, the identification of loops is an essential part of most studies that involve Hi-C-based 3D genome analyses. Methods that have been developed to detect chromatin loops and/or statistically significant genomic interactions from Hi-C contact maps comprise two groups: (1) statistical/probabilistic model-based methods , and (2) peak-calling methods . All of them are primarily focused on mapping the loops in Hi-C datasets from mammals, and experience some (in most cases insurmountable) challenges working with Hi-C datasets from animal species with substantially different genome architecture. Here, we present the LASCA pipeline that uses Weibull distribution-based modeling to effectively identify chromatin loops, including enhancer-promoter interactions, in Hi-C data from different animal species . Our results demonstrate that LASCA-detected loops are (1) reproducible, (2) highly supported by aggregate peak analyses and genomic/epigenomic correlates of loop formation, (3) validated by protein-centric chromatin conformation methods (ChIA-PET and HiChIP). We have compared LASCA with the most commonly used methods from each of the abovementioned groups (HiCCUPS and Fit-Hi-C) and with a very recent approach MUSTACHE. Working with mammalian Hi-C data, LASCA showed very similar results to HiCCUPS and MUSTACHE, and even outperformed HiCCUPS in detecting CTCF-independent loops. In contrast to methods compared, LASCA could also detect chromatin loops in C. elegans and S. cerevisiae, which makes it an omni-purpose approach.In contrast to chromatin compartments and TADs, chromatin loops have clear biological roles, such as bringing gene promoters to their cognate 22 to identify these significant interactions. Comparison of several statistical distributions  performed in Sanyal et al.20 clearly showed that the Weibull distribution fits Hi-C interaction frequency the best. We further analyzed the quality of the Weibull distribution fitting of Hi-C interaction frequency at different distance ranges and found that its good performance is distance-independent . Adjacent significant pixels are grouped into clusters; for each cluster, the center is defined, and its coordinates are retrieved and considered as the loop coordinates. Identified loops may be filtered according to their aggregate peak analysis (APA)17 or peak analysis (PA)11 scores, signal intensity, and signal enrichment over random signals located at the same distance from the central diagonal. The loops should also display a signal decay from the central loop pixel. These filters are optional and should be used depending on the animal species under study. Specifically, using these filters with mammalian Hi-C data will enhance the accuracy of loop annotation, whereas utilizing the filters with the yeast genome appears to be less useful.In Hi-C heatmaps, loops appear as bright dots located at different distances from a central diagonal. Here, we applied Weibull distribution-based modelingrix Fig.\u00a0. The p-vH. sapiens (GM12878 cells17), M. musculus (CH12.LX cells17), C. elegans24, and S. cerevisiae (S-phase cells13). We managed to annotate a significant number of loops in each case  and HiChIP (68%) loops . Nevertheless, we found that most of the LASCA-identified chromatin loops belonged to significant genomic contacts detected by Fit-Hi-C . LASCA identified approximately the same number of loops as MUSTACHE and two times more loops than HiCCUPS in Hi-C datasets from both human and mouse  was not mapped by HiCCUPS Fig.\u00a0c. To finThe LASCA pipeline is suitable for the identification of enhancer-promoter interactions Fig.\u00a0a. In thiC. elegans and S. cerevisiae distinguishes LASCA from most other chromatin loop callers. LASCA, the protocol, and suite of scripts, are publicly available at https://github.com/ArtemLuzhin/LASCA_pipeline.LASCA allows mapping of chromatin loops as well as enhancer\u2013promoter interaction in Hi-C datasets obtained from animal species with different genome size and genome organization complexity, such as human, mouse, worm, and yeast. To annotate representative loops/contacts, LASCA requires minimal adjustments and filtering, particularly when analyzing worm or yeast data. High-quality performance with Hi-C data from p-values are calculated as the probability of finding a model pixel with the same or higher intensity, and FDR correction of the p-values is performed to obtain corresponding q-values (default argument: 0.1). Additionally, q-values may be corrected in accordance with the scaling of a particular chromosome (default argument: turned off). Briefly, for the selected range of diagonals of the Hi-C matrix, the average value of the contact frequency for each diagonal is calculated. Then, all values in the obtained set of average values of the contact frequencies are divided by the average value of the contact frequency in the first diagonal, thus forming a set of normalization coefficients with a value\u2009=\u20091 in the first diagonal. The q-values in each diagonal are divided by the corresponding normalization coefficient determined for this particular diagonal. The q-value threshold is defined by a user (default argument: 0.1) to determine significant pixels (contacts).The LASCA pipeline consists of three main steps Fig.\u00a0. In the On the second step, adjacent significant pixels are grouped into clusters using a density-based spatial clustering of applications with noise (DBSCAN) algorithm (default argument: minimum cluster size is three pixels). Pixels are assigned as neighbors related to a particular cluster if the maximum Euclidean distance between these two pixels\u2009=\u20091. The size of the cluster (in pixels) may be specified by a user. For each cluster, the center is defined (default argument: the brightest pixel), and its coordinates are retrieved and considered as the loop coordinates. The cluster center is defined as either the arithmetic mean of the x and y coordinates of pixels in a cluster or a pixel in a cluster possessing maximum intensity (default option).On the third step, identified loops may be subjected to various filters, such as the enrichment of signal over background , signal intensity, signal decay from the center of a cluster, and signal enrichment over random signals at the same distance. All steps and parameters in the LASCA pipeline can be turned on/off and adjusted by the user (default argument: turned off).17 was calculated as the ratio of the intensity of the central pixel of the loop to the average intensity of the right corner of the 11\u2009\u00d7\u200911 pixel-circle around the center of the loop. PA-score11 was calculated as the average ratio between the nearest pixels to the center of the loop and the average intensity of the right corner of the 11\u2009\u00d7\u200911 pixel-circle around the center of the loop.APA-scoreC. elegans, loops were annotated at 5 and 10\u00a0kb resolution with a subsequent merging of overlapped loops. The following parameters were used: q\u2009=\u20090.95, adjust_by_scale\u2009=\u2009False, q_value_trhd\u2009=\u20090.05, min_cluster\u2009=\u20092, filter_zeros\u2009=\u20092, filter_PA\u2009=\u20091, filter_APA\u2009=\u20091.7, filter_intensity\u2009=\u20090.1. Finally, for S. cerevisiae, loops were mapped with following settings: q\u2009=\u20090.95, adjust_by_scale\u2009=\u2009False, q_value_trhd\u2009=\u20090.01, min_cluster\u2009=\u20093, filter_zeros\u2009=\u20092, filter_intensity\u2009=\u20090.1.Using the LASCA pipeline, we identified loops in several organisms. For human cell line GM12878 Hi-C maps, loops were annotated at 5 and 10\u00a0kb resolution followed by merging of overlapped loops and filtering them by signal enrichment over random signals located at the same distance. The following parameters were used for 5 and 10\u00a0kb Hi-C maps: q\u2009=\u20090.95, adjust_by_scale\u2009=\u2009True, q_value_trhd\u2009=\u20090.2, scaling_q_value_trhd\u2009=\u20090.2, min_cluster\u2009=\u20092, filter_zeros\u2009=\u20092, filter_PA\u2009=\u20091, filter_APA\u2009=\u20091.9, filter_intensity\u2009=\u20090.3. For mouse cell line CH-12LX, loops were identified at 10\u00a0kb resolution with the following parameters: q\u2009=\u20090.95, adjust_by_scale\u2009=\u2009True, q_value_trhd\u2009=\u20090.1, scaling_q_value_trhd\u2009=\u20090.05, min_cluster\u2009=\u20093, filter_zeros\u2009=\u20092, filter_PA\u2009=\u20091, filter_APA\u2009=\u20091.8, filter_intensity\u2009=\u20090.1. For 17 (GSE63525). To identify loops in GM12878 cells using MUSTACHE, we annotated the loops separately on the 5 and 10\u00a0kb maps with the following parameters: -pt 0.05, -st 0.88, -sz 1.6, -oc 2, -i 10. Then we merged the loop lists found at different resolutions. Significant contacts in GM12878 cells and S. cerevisiae were also annotated using FitHiC2 with default settings, except for the -U option . We cut off significant contacts by q\u2009<\u2009\u2009=\u200910\u22129.HiCCUPS loops for GM12878 (primary) and CH12-LX cells were obtained from Rao et al.We used Bedtools to count the overlapping of the loops. We considered the loops overlapped if at least 70% of the reciprocal intersection of regions between loop base points was observed.25 with default settings, except \u2013pad parameter, which was 10 for S. cerevisiae and 50 for C. elegans.We constructed metaplots for each of the selected organisms using Coolpup.py27 with\u2014reference Point TSS (upstream loop anchor), and -a and -b parameters were selected as half of the mean loop size for each Hi-C dataset. In the case of enhancer-promoter interaction analysis, we set -a and -b parameters to 2\u00a0Mb.To analyze the enrichment of proteins involved in looping, we applied deepTools222 (GSM1872886). Following the methods of the original paper, we removed from the consideration CTCF-PET clusters that had less than four interactions. Then we binned the resulting CTCF-PET cluster interaction map using a 10\u00a0kb window. The resulting interaction coordinates of 10\u00a0kb windows, we considered as CTCF-mediated loops. The data for CTCF-HiChIP in .hic format was obtained from Mumbach et al.26 (GSE115524). In accordance with the original paper, we annotated CTCF-mediated interactions using HiCCUPS with the following parameters: -m 500 -r 5000, 10,000 -f 0.1,0.1 -p 4,2 -i 7,5 -d 20,000, 20,000.A table containing the contacts of the CTCF-PET clusters was obtained from Tang et al.https://genome.ucsc.edu/cgi-bin/hgLiftOver). The base of the loop was considered to contain a peak if it contained at least one peak from the corresponding mark.Data for GM12878 cells were obtained from the ENCODE . Coordinates of ATAC-seq peaks in hg38 have been translated to hg19 using the LiftOver utility  to 1\u00a0kb upstream of that TSS. We intersected these promoters and significant contacts and left only those contacts for which one of the anchors fell inside the promoter region; another anchor, therefore, was assigned as an enhancer.Supplementary Information."}
+{"text": "Circular RNAs (circRNAs) have been reported to be involved in many diseases. But there is no report on circRNAs in non-obstructive azoospermia (NOA). The purpose of this paper is to explore the circular RNA expression profile and potential functions of circRNAs in NOA patients. We first preformed circRNA expression profiling analysis using a circRNA microarray in testicular samples from NOA and obstructive azoospermia (OA) patients. CircRNAs were validated by qRT-PCR. Bioinformatics analysis were used to construct the ceRNA network. GO and KEGG enrichment analysis were performed by using DAVID. Microarray analysis identified 82 differentially expressed circRNAs in NOA specimens. The differential expression of hsa_circRNA_402130, hsa_circRNA_072697, hsa_circRNA_030050, hsa_circRNA_100812 and hsa_circRNA_406168 was confirmed by qRT-PCR. Enrichment analysis revealed the association of hsa_circRNA_402130 and hsa_circRNA_072697 with multiple signaling pathways. The data indicated that circRNAs were significantly dysregulated in NOA specimens and might involve in the pathogenesis of NOA. Circular RNAs (circRNAs) are a class of closed circular RNA molecules that rarely encode proteins and have no 5 \u2018caps and 3\u2019 poly(A) tails. Their unique characteristics, such as better stability and resistance to nucleases make circRNAs potential candidates for clinical diagnostic/prognostic biomarkers . The molNOA is considered a major cause of male infertility , 13, whiHence, the purpose of this study was to elucidate the expression profile of circRNAs and unravel the possible functions and interactions of circRNAs in NOA.We performed microarray-based profiling analyses to screen the differentially expressed circRNAs in 6 NOA and 3 OA tissues. The box plot depicts similar distributions of the intensities from all the normalized datasets . Raw varTo further confirm the results of circRNA microarray, we chose six dysregulated circRNAs at random, including three down-regulated  and three up-regulated . qRT-PCR in NOA and OA samples showed that the expression of hsa_circRNA_072697, hsa_circRNA_402130, hsa_ circRNA_100812, hsa_circRNA_030050 and hsa_circ RNA_406168 was consistent with the microarray data. And the difference in expression of hsa_circ RNA_072697, hsa_circRNA_030050, hsa_circRNA_ 100812, hsa_circRNA_402130 and hsa_circRNA_406168 was statistically significant  included 3 miRNAs and 212 mRNAs in the ceRNA network of hsa_circRNA_402130 , and 2 mThe functions of all the target genes were analyzed by online biological classification tool DAVID. The top 10 GO terms and KEGG terms are displayed as bubble diagram. The GO biological process (BP) analysis  showed tResults from the Human Genome Project and the DNA Elements Encyclopedia Project indicated that protein-coding gene sequences account for only 1-3% of the human genome sequence. Also, most of the transcribed sequences in the human genome are non-coding RNA sequences . StudiesHere, we constructed the expression profile of circRNAs in NOA by high-throughput circRNA microarray for the first time. 16 up-regulated and 66 down-regulated circRNAs in NOA were identified. As most of these circRNAs originate from exons or introns, these results imply that dysregulated expression of circRNAs may be involved in the process of NOA. Subsequently, differential expression of circRNAs was confirmed by qRT-PCR. As circRNAs usually function as RNA sponge to regulate gene expression , 20, 21,Moreover, based on the above two circRNAs, we constructed \u201ccircRNA-miRNA-mRNA\u201d ceRNA regulatory networks. From these regulatory networks, we found many transcription factors that regulate spermatogenesis. Studies have shown that SP3 presents a unique expression pattern during spermatogenesis in mice and other mammalian , 34. It in vitro and in vivo experiments.Additionally, we also performed GO and KEGG enrichment analysis of miRNA target genes for the first time. GO enrichment analysis revealed miRNA target genes that positively regulate transcription from RNA polymerase II promoter, transcription, cell migration, protein phosphorylation, palate development, pathway-restricted SMAD protein phosphorylation, circadian regulation of gene expression and so on. Given that phosphorylation of SMAD promotes differentiation of mouse SSC , and proOur study also has two limitations. First, more testicular tissue samples could be included in this study. This might reduce the error caused by individual differences of patients. In addition, due to the difficulties in acquiring testicular samples, we were unable to use the testicular samples of volunteers who had already been known fertility and normal spermatogenesis as normal controls.In summary, this is the first report that reveals the expression profile of differentially expressed circRNAs and, in conjunction with bioinformatics analysis, provides the first assessment of ceRNA networks and circRNAs associated pathways in NOA. Results indicated that circRNAs may play important functions and have potential to become therapeutic targets for NOA.The study was approved by the Ethics Committee of the Institute of Human Reproduction and Stem Cell Engineering of Central South University. Testicular biopsies were collected from 6 NOA and 3 OA patients. All participants signed informed consent, and routine semen analysis based on the World Health Organization criteria showed no sperm. Most patients have been tested for follicle-stimulating hormone (FSH), luteinizing hormone (LH) and testosterone (T). All the patients underwent testicular fine needle aspiration for histological examination at Reproductive and Genetic Hospital of CITIC-Xiangya. Those patients without testicular sperm were defined as NOA patients. OA patients had normal spermatogenic function accompanied by blockage of the vas deferens, but no other congenital diseases. The results of the pathological examinations are in the . None ofNine RNA samples of testicular biopsies from NOA and OA patients were extracted using TRIzol  following the manufacturer\u2019s protocol. The quantity and purity of total RNA was measured on the NanoDrop\u00ae ND-1000 spectrophotometer . RNA integrity was measured by denaturing agarose gel electrophoresis.All RNA samples were analyzed by Arraystar circRNA Microarray at Kangchen Bio-tech . Sample labeling and array hybridization were performed according to the manufacturer\u2019s protocol (Arraystar Inc.). The hybridized arrays were washed, fixed and scanned on the Agilent Scanner G2505C. Agilent Feature Extraction software was used for raw data extraction. The data was then processed using the limma package of R software. Low intensity was filtered after quantile normalization of the raw data. Student\u2019s t-test was used to estimate the statistical significance between groups. CircRNAs with a fold change \u2265 1.5 and p < 0.05 were considered significant.The cDNA was prepared from 1ug of total RNA using Super Script TMIII Reverse Transcriptase kit (Invitrogen) according to the manufacturer\u2019s instructions. Quantitative real-time reverse transcription PCR (qRT-PCR) was performed using a 2\u00d7PCR master mix (Arraystar) on the ViiA 7 Real-time PCR System (Applied Biosystems) to detect the relative expression levels of target circRNAs. The primer sequences for the target circRNAs are as follows: hsa_ circRNA_402130 forward: 5\u2032-GTGGCCGAGGACTTTGATTG-3\u2032, reverse: 5\u2032-CCTGTAACAACGCATCTCATATT-3\u2032; hsa_circRNA_100812 forward: 5\u2032-TATTCTCAAGCTGTCACAGGACATT-3\u2032, reverse: 5\u2032-TGAGGGTAGCAGCAGAACGAG-3\u2032; hsa_circRNA_072697 forward: 5\u2032-TGATAGAAAAGTTAGAATTTTCAGA-3\u2032, reverse: 5\u2032-ACTCTTTCAAACTCTAAGAGCTTAG-3\u2032; hsa_circRNA_104078 forward: 5\u2032-GCTTATGGCTATAAAATTACAGAGA-3\u2032, reverse: 5\u2032-CGGGACAACATCCTTTCTTAC-3\u2032; hsa_circRNA_030050 forward: 5\u2032-GGGAGAAGCAGCTAGAACCA-3\u2032; reverse: 5\u2032-TT TGCCAGAATACCCCTTTG-3\u2032; hsa_circRNA_406168 forward: 5\u2032-ATTGGGTTCTTTGCCTGTTG-3\u2032; reverse: 5\u2032-GGGGCAGACAGATGAGAAAG-3\u2032; \u03b2-actin forward: 5\u2032-GTGGCCGAGGACTTTGATTG-3\u2032, reverse: 5\u2032-CCTGTAACAACGCATCTCATATT-3\u2032. Transcript level of the housekeeping gene \u03b2-actin was used to normalize the relative expression of circRNAs. Data is represented as mean \u00b1 SE of three independent experiments.http://starbase.sysu.edu.cn/browser.php) [http://www.mirdb.org) [https://david.ncifcrf.gov/. Then OmicShare tools (http://www.omicshare.com/tools) was used to create potential maps of the circRNA/miRNA/mRNA interaction networks of hsa_circ_402130 and hsa_circ_072697.The circRNA-microRNA interactions of hsa_circRNA_402130 and hsa_circRNA_072697 were predicted with Arraystar\u2019s home-made microRNA target prediction software based on TargetScan  and miRaser.php) , 47 and rdb.org) , 49. GO Student\u2019s t-test (two-tailed) was used to estimate statistical significance between groups. Data analysis was performed using GraphPad Prism 5.0 . A p-value < 0.05 was considered significant.Supplementary Figure 1Supplementary Table 1Supplementary Table 2"}
+{"text": "Buffalo milk is rich in various nutritional components and bioactive substances that provide more essential health benefits to human body. Recently, exosome identified in the breast milk has been reported as a neotype nutrient and can mediate intercellular communication with exosomal miRNAs. In the present study, we therefore hypothesized that exosome-derived miRNAs from buffalo milk would play the potential physiological importance of consumption of buffalo milk.We isolated exosomes from buffalo and cow milk samples that were obtained at mid-lactation period, and the exosomal miRNA profiles were then generated using miRNA-seq. In addition, miRNAomes of pig, human and panda milk exosomes were downloaded from GEO database. Finally, a total of 27 milk exosomal miRNA profiles that included 4 buffalo, 4 cow, 8 pig, 4 human and 7 panda were analyzed using the miRDeep2 program. A total of 558 unique miRNA candidates existed across all species, and the top 10 highly expressed miRNA were evolutionarily conserved across multiple species. Functional analysis revealed that these milk enriched miRNAs targeted 400 putative sites to modulate disease resistance, immune responsiveness and basic metabolism events. In addition, a total of 32 miRNAs in buffalo milk were significantly up-regulated compared with non-buffalo milks, while 16 were significantly down-regulated. Of interest, functional analysis showed that up-regulated miRNAs were mainly related to host metabolism processes, while the predicted functions of down-regulated miRNAs were enriched in immune response.In this study, we explored the exosomal miRNAome differences between milks of different animals, expanding the theoretical basis for potential applications of the miRNA-containing vesicles. Water buffaloes are predominant dairy animals, contributing the most important source of 13% to the milk production worldwide , proteinIn 27 sequencing libraries, there were 558 unique miRNA candidates sequenced across all species, and only 395 miRNAs which were identified at least in four libraries were considered further , suggesting these miRNAs could be important nutritional components of milk . In detaMilk exosomal miRNA profiles that included 4 buffalo, 4 cow, 8 pig, 4 human and 7 panda were analyzed, and milk enriched miRNA candidates across all species were annotated to modulate disease resistance, immune responsiveness and basic metabolism events. This study provided an important mechanistic framework for future studies of dietary extracellular vesicles and the roles of milk miRNAs in human health and disease.Murrah breed) and cow (Friesian breed) milk samples were obtained respectively from 4 healthy mid-lactating animals in a local farm , and crude exosomes were purified following our previous method [Buffalo (s method . In briehttps://www.ncbi.nlm.nih.gov/gds/), respectively. Using the miRDeep2 program [http://www.mirbase.org/) to identify conserved candidates between different species. The read count of each identified miRNA was firstly normalized with TMM-normalized algorithm, and the R Bioconductor package EdgeR analysis (http://bioconductor.org/packages/edgeR/) was applied to identify differentially expressed (DE) miRNAs with P value <\u20090.05 and fold change \u22652 between different groups.In addition to the indexed sequencing data of buffalos and cows, we downloaded the exosomal small RNA data of pig , human  and pand program , a totalhttp://genome.ucsc.edu/cgi-bin/hgTables). The targets of highly and differentially expressed miRNAs were successfully predicted using miRanda software (http://34.236.212.39/microrna/home.do), and the biological KEGG pathway analysis of the predicted targets were further performed by an online version of the DAVID program (https://david.ncifcrf.gov/).The 3\u2032UTR sequences of the bovine RefSeq genes were downloaded from the University of California Santa Cruz (UCSC) table browser (Additional file 1. Exosomal miRNA expression between buffalo and non-buffalo milks. Buffalo_9, buffalo_10, buffalo_11 and buffalo_12 represented for 4 buffalo milk samples; cow_13, cow_14, cow_15 and cow_16 represented for 4 cow milk samples; pig_0_rep1, pig_0_rep2, pig_0_rep3, pig_3d, pig_7d, pig_14d, pig_21d and pig_28d represented for 8 pig milk samples; human_rep1, human_rep2, human_rep3 and human_rep4 represented for 4 human milk samples; panda_0d, panda_3d_rep1, panda_3d_rep2, panda_3d_rep3, panda_7d, panda_15d and panda_30d represented for 7 panda milk samples; logFC, log fold change; logCPM, log counts per million; FDR, false discovery rate.Additional file 2. Predicted targets of top 10 expressed miRNAs in buffalo milk exosome. Query represented for miRNA candidates; Ref represented for reference genes.Additional file 3. The KEGG analysis of top 10 expressed miRNAs\u2019 targetsAdditional file 4. Predicted targets of significantly up-regulated miRNAs in buffalo milk exosomeAdditional file 5. Predicted targets of significantly down-regulated miRNAs in buffalo milk exosomeAdditional file 6. The KEGG analysis of the up-regulated miRNAs\u2019 targetsAdditional file 7. The KEGG analysis of the down-regulated miRNAs\u2019 targets"}
+{"text": "ERG11) of Candida albicans have been frequently reported in fluconazole-resistant clinical isolates. Exploring the mutations and their effect could provide new insights into the underlying mechanism of fluconazole resistance. Erg11p_Threonine285Alanine (Erg11p_THR285ALA), Erg11p_Leucine321Phenylalanine (Erg11p_LEU321PHE) and Erg11p_Serine457Proline (Erg11p_SER457PRO) are three fluconazole-resistant suspected mutations reported in clinical isolates of C. albicans. Therefore, our study aims to investigate the role of these suspected mutations in fluconazole resistance using in-silico methods. Molecular dynamics simulation (MDS) analysis of apo-protein for 25ns (nanosecond) showed that suspected mutant proteins underwent slight conformational changes in the tertiary structure. Molecular docking with fluconazole followed by binding free energy analysis showed reduced non-bonded interactions with loss of heme interaction and the least binding affinity for Erg11p_SER457PRO mutation. MDS of suspected mutant proteins-fluconazole complexes for 50ns revealed that Erg11p_SER457PRO and Erg11p_LEU321PHE have clear differences in the interaction pattern and loss or reduced heme interaction compared to wild type Erg11p-fluconazole complex. MDS and binding free energy analysis of Erg11p_SER457PRO-fluconazole complex showed the least binding similar to verified mutation Erg11p_TYR447HIS-fluconazole complex. Taken together, our study concludes that suspected mutation Erg11p_THR285ALA may not have any role whereas Erg11p_LEU321PHE could have a moderate role. However, Erg11p_SER457PRO mutation has a strong possibility to play an active role in fluconazole resistance of C. albicans. Mutations in the ergosterol biosynthesis gene 11 ( Candida albicans is often observed in clinical isolates. Invasive infections in humans are widely caused by the drug-resistant C. albicans. This infection leads to high morbidity and mortality rate and reported to be the fourth most common nosocomial bloodstream infection . The study of residue mutations in Erg11p will help us in identifying the crucial amino acids that may involve in drug resistance of C. albicans. In the modern era, in-silico techniques play a very important role in the biological system worldwide. In-silico mutation techniques are used to develop mutations in protein crystal structure to have insights into the conformational changes and stability of the proteins . CurrentAs mentioned above, in-silico techniques would be of significant importance in exploring residue mutations involved in drug resistance before proceeding for rigorous wet lab experiments. Crystal structures available in the Protein Data Bank (PDB) have been very useful in this regard. Herein, we aim to study three suspected Erg11p mutations such as Erg11pTHR285ALA, Erg11p_LEU321PHE, and Erg11p_SER457PRO for their involvement in fluconazole resistance using in-silico methods , 10.Selection of Erg11p residue mutations: The point mutations within the ERG11 gene that caused amino acid changes in Erg11 protein were selected based on previous reports as shown in Workflow and computational components: The workflow is shown as a flow chart  were retrieved from the Protein Data Bank (PDB) [nk (PDB) . 3D confnk (PDB) .Protein alignment, preparation, validation and ligand preparation: Fasta format of the crystal structure sequences was downloaded and Clustal Omega multiple sequence alignment was used to align the structures with Erg11p sequence obtained from the Candida Genome Database (CGD) [se (CGD) -17. The se (CGD) . Thereafse (CGD) , 20. Prese (CGD) , 22.Generation of apo-protein, development of in-silico mutations and apo-protein mutation analysis: Prepared protein structure (5V5Z) was used as the starting template for apo-protein (protein structure without ligand) generation by removing protoporphyrin IX. Incorporation of suspected mutations Erg11p_THE285ALA_apo, Erg11p_LEU321PHE_apo, Erg11p_SER457PRO_apo and a verified mutation Erg11p_TYR132HIS_apo were done using mutation tool [HIS, Erg11p_THE285ALA, Erg11p_LEU321PHE, Erg11p_SER405PHE, Erg11p_TYR447HIS, Erg11p_GLY464SER, Erg11p_ARG467LYS and Erg11p_SER457PRO . The side-chain conformations were selected according to the best available conformation using the rotamer tool in the Schr\u00f6dinger suite. All the mutant structures were subjected to a short minimization to fix the minor changes which occurred due to mutations  [ion tool . Also, s 2017-1) . The gen Molecular docking: The crystal structure 4WMZ of yeast Saccharomyces cerevisiae CYP51 protein contains fluconazole in its active site. The selected structure 5V5Z has a similar active site as that of 4WMZ; however, it contains itraconazole instead of fluconazole. The structures containing protoporphyrin IX with developed mutations were used in the docking study. Azole binding site of 5V5Z was used as a grid center for docking fluconazole with reference to 4WMZ. Water molecules were retained around 5\u00c5 near the hetero group due to their key role in Erg11p-fluconazole interactions [ractions . Moleculractions -27.Molecular dynamics simulations: Molecular dynamics simulations were carried out using an explicit TIP4P water model to perform high-speed extensive simulation on biological systems using the Desmond package. Erg11p is a membrane protein, therefore, membrane placement was done using positions retrieved from the PDB of transmembrane proteins. The membrane setup using a POPC membrane model was built with the system building panel by assigning the periodic boundary condition of orthorhombic box size with a distance of 10 \u00c5 unit buffer. The system charge was balanced by adding sodium or chloride ions to neutralize the system and subsequently, apo-proteins and protein-ligand complexes were fixed appropriately in the solvated system. Then it was allowed to relax before simulation with a short minimization by default setting using OPLS_2005 force-field present in the Desmond suite. Further explicit MDS was carried out using the NPT ensemble at 300 K temperature and 1 atmospheric pressure using Nose-Hoover thermostat and Martyna-Tobias-Klein barostat scaling controls for a specified time of 50ns. Erg11p\u2013fluconazole interaction percentage and fractions at the binding cavity were analyzed throughout the 50ns time. Visualization was done using the Maestro interface (version 11.0.014). Images for MD simulations were generated using simulation interaction diagram, event analysis and simulation quality analysis panel in the Desmond suite  [ 2016-4) -31.Molecular mechanics energies with generalized born surface area (MM-GBSA): Binding free energy for Erg11p\u2013fluconazole docked complexes were calculated using the Prime MM-GBSA tool in the Schr\u00f6dinger suite which uses a novel energy generation model VSGB 2.0  for calculating the binding free energy  [ 2017-1) , 33.The equation to calculate binding free energy: \u0394G (binding) = \u0394G (complex) - (\u0394G (free receptor) - \u0394G (free ligand))Where \u0394G (binding) - binding free energy, \u0394G (complex) - free energy of protein-ligand complex, \u0394G (free receptor) - free energy of protein, \u0394G (free ligand) - free energy of ligand.Sequence alignment of 5FSA, 5TZ1 and 5V5Z with Erg11 protein sequence revealed that all the three structures obtained from the PDB lacked few residues due to crystallization error. 5FSA and 5TZ1 were similar to each other and lacked first 44 residues at N-terminus and had six mutations compared to wild type sequence. However, 5V5Z lacked the first 24 residues at N-terminus, the last 4 residues at C-terminus and 12 residues in the middle of the structure. N-terminal segments with missing residues are unusually long and cannot be filled. On the other hand, only 4 C-terminal residues are missing in 5V5Z and it has been considered as negligible. Internal missing residues in 5V5Z were filled by the \u201cPrime\u201d tool (an accurate protein structure prediction tool) at Schr\u00f6dinger suite. The sequence present in 5V5Z is found to be more similar to wild type Erg11p than that of 5FSA and 5TZ1. Molecular dynamics simulations were performed for 50ns (50000ps) to assess the quality of crystal structures and revealed that internal energy was stable for all the three structures. However, the RMSD value of 5V5Z was found to be more stable whereas RMSF value for 5V5Z showed the least amino acid fluctuations compared to 5FSA and 5TZ1 .Schematic of eight wild type residue locations in the Erg11p crystal structure and its respective residue mutations are shown in . Individual mutations developed using the selected Erg11p crystal structure (5V5Z) containing heme is shown in Supplement .The RMSD and RMSF analyses for wild type apo-protein, suspected mutations  and verified mutation Erg11p_TYR132HIS_apo are shown in Apo-protein analysis revealed that RMSD values of suspected mutations were similar to that of wild type Erg11p and lesser than that of Erg11p_TYR132HIS_apo. However, there is no much variation in the RMSF values among the mutations that were analyzed.The developed Erg11p mutant structures along with wild type Erg11p were subjected to molecular docking against antifungal drug fluconazole. The superimposition of the top eight fluconazole poses and RMSD calculations concerning the first frame showed that fluconazole had a stable pose with less deviation. This confirmed the consistency of the docking pose (Supplement  and Supplement ). Thereafter, the ranking was done based on non-bonded interaction, Glide Emodel score and Gibbs binding free energy ( and ). The Erg11p-fluconazole docking interaction results for (A) Erg11p_WT (B) Erg11p_TYR132HIS (C) Erg11p_TYR447HIS (D)Erg11p_THR285ALA (E) Erg11p_LEU321PHE and (F) Erg11p_SER457PRO are shown in Figure 4. All other Erg11p mutant-fluconazole interactions are shown in supplement Figure 3.y  and . The ErGlide Emodel score for the best pose selection showed a greater binding affinity for Erg11p_WT compared to other mutants. Erg11p_WT showed the least value of -65.436 kcal/mol and the verified mutation Erg11p_TYR132HIS showed a higher value of -58.676 kcal/mol. MM-GBSA binding free energy analysis also showed the least value for Erg11p_WT compared to other mutants except for Erg11p_THR285ALA. The binding free energy value for Erg11_WT was -54.33 kcal/mol  whereas -35.04 kcal/mol for the mutant Erg11p_TYR447HIS is the highest.Docking and MM-GBSA results suggested that Erg11_LEU321PHE and Erg11p_SER457PRO might involve in fluconazole resistance. Therefore, Erg11p_SER457PRO-fluconazole and Erg11_LEU321PHE-fluconazole docked complexes were subjected to explicit molecular dynamics simulation for an extended 50ns. Similar analyses were done for wild type Erg11p and a verified mutation Erg11p_TYR447HIS that gave the least binding free energy and reduced interactions with fluconazole. After the molecular dynamics simulations Erg11p\u2013fluconazole interactions were analyzed.After 50ns molecular dynamics simulations, it has been found that wild type Erg11p-fluconazole interactions had the highest number of contacts  and higher percentage of interactions as well. However, the verified mutation Erg11p_TYR447HIS showed reduced residue contacts  and lower percentage of interactions. On the other hand, suspected mutation Erg11p_LEU321PHE-fluconazole interaction had shown contacts with TYR_118, PHE_126, TYR_132, ILE_131, THR_311, ILE_304, LEU_376, CYS_470 & HEME whereas suspected mutation Erg11p_SER457PRO-fluconazole interaction had contacts with TYR_118, PHE_126, TYR_132, GLY_307, GLY_308, HIS_310, HIS_311, LEU_376, MET_508, VAL_509 & HEME. This showed that the number of residue contacts of fluconazole with wild type and suspected mutations is more or less the same but the percentage of interaction was less. Moreover, for suspected mutations, fluconazole had interacted with few different residues than that of wild type Erg11p .-TYR_118, PHE_126, TYR_132, MET_508; water bridges\u2013TYR_118, TYR_132, SER_378, MET_508; hydrogen bond\u2013TYR_132; Pi-pi stacking\u2013TYR_118, TYR_132; metal coordination\u2013CYS_470, HEME); Erg11p_TYR447HIS ; Erg11p_LEU321PHE ; Erg11p_SER457PRO  , Erg11p_TYR447HIS , Erg11p_LEU321PHE  and Erg11p_SER457PRO . There are at least five different mode of interactions through which Erg11p and fluconazole complexes are stabilized. These are for Erg11p_WT( hydrophobic interactionTYR_118) . MDS data of 50ns after docking were used to calculate MM-GBSA binding free energy. Subsequently, it was used to comprehend the structural decomposition of Erg11p-fluconazole complexes such as Erg11p_WT, Erg11p_TYR447HIS, Erg11p_LEU321PHE and Erg11p_SER457PRO .The MM-GBSA binding free energy values calculated throughout 50ns MDS produced a wide range of values. Erg11p_WT showed the least binding free energy value of -50.886 kcal/mol and the highest value of -22.648 kcal/mol. On the other hand, mutants showed lower and higher values of -38.171 kcal/mol and -3.486 kcal/mol for Erg11p_TYR447HIS, -40.853 and -4.1796 for Erg11p_LEU321PHE and -26.785 kcal/mol and 1.208 kcal/mol for Erg11p_SER457PRO.C. albicans. The first and foremost step for the in-silico study is to select the best crystal structure. The appropriate template selection was done by aligning all the three available crystal structure sequences of C. albicans with wild type sequence of Erg11p  to assess the binding of fluconazole with mutated Erg11 proteins . SuperimS. cerevisiae play an important role in fluconazole resistance [C. albicans Erg11p, TYR_118, TYR_132 and SER_378 are the corresponding positions of those amino acids. To analyze the mode of Erg11p-fluconazole interactions and stability, 50ns MDS was performed for two suspected mutations (Erg11p_LEU321PHE and Erg11p_SER457PRO) complexed with fluconazole whereas verified mutation Erg11p_TYR447HIS and wild type Erg11p serve as positive and negative controls, respectively.Previous studies reported that water-mediated interactions of Erg11p residues TYR_126, TYR_140 and SER_382 of sistance . In C. aC. albicans had similar water-mediated interactions with fluconazole involving TYR_132 and SER_378 residues. Furthermore, TYR_118 had shown higher interaction with fluconazole, however, it lacks hydrogen or water-mediated hydrogen bonds. This implies that TYR_132 and SER_378 might have a critical role in drug resistance of C. albicans as reported in S.cerevisiae [MDS results revealed that Erg11p of MM-GBSA analysis was performed followed by MDS to understand the binding efficiency of fluconazole to Erg11p. These analyses showed the least binding free energy for wild type Erg11p throughout the simulation and suspected mutation Erg11p_LEU321PHE comes next to wild type. However, Erg11p_SER457PRO showed higher binding free energy similar to that of a verified mutation Erg11p_TYR447HIS . These rC. albicans. Our study strongly suggests that Erg11p_SER457PRO mutation may play an active role in fluconazole resistance. However, suspected mutation Erg11p_LEU321PHE could have a moderate role whereas Erg11p_THR285ALA may not have any significant role in fluconazole resistance. Further validation using the experimental methods will confirm the involvement of these mutations in fluconazole resistance. Moreover, this study will help in exploring other suspected drug-resistant mutations of Erg11 protein. This could pave the way for developing new versions of antifungals to overcome the growing drug resistance in fungal pathogen"}
+{"text": "We investigate the genes encoding the presumed two component system MSMEG_0244/MSMEG_0246, the neighboring gene msmeg_0243 and their involvement in this process. We show that purified MSMEG_0243 indeed is a heme binding protein. Deletion of msmeg_0243/msmeg_0244/msmeg_0246 in Mycobacterium smegmatis leads to a defect in biofilm formation and colony growth on solid agar, however, this phenotype is independent of the supplied iron source. Further, analysis of the corresponding mutant and its lipids reveals that changes in morphology and biofilm formation correlate with altered acylation patterns of phosphatidylinositol mannosides (PIMs). We provide the first evidence that msmeg_0244/msmeg_0246 work in concert in cellular lipid homeostasis, especially in the maintenance of PIMs, with the heme-binding protein MSMEG_0243 as potential partner.Ferric and ferrous iron is an essential transition metal for growth of many bacterial species including mycobacteria. The genomic region  Two intensively studied proteins in lipid metabolism are the close homologs membrane proteins MmpL11 , MmpL11 (Rv0202c) and the periplasmic protein Rv0203  are MSMEG_0250 and MSMEG_0241 (termed MmpL3SM/MmpL11SM in the following), respectively  are present only in the M.sm genome. The corresponding gene products are a putative heme-binding protein , a two component system (MSMEG_0244/MSMEG_0246), a transmembrane protein (MSMEG_0245) and a secreted peptidase (MSMEG_0247). Upon analyzing mycobacterial genomes using Kyoto Encyclopedia of Genes and Genomes , we noticed that several other fast-growing species from the phylum such as Mycobacterium goodie, Mycobacterium fortuitum, or Mycobacterium phlei possess homologs of these genes within the heme uptake region. Especially the presence of a two component system including a putative extracellular heme-binding protein within the same locus fueled our interest in deeper investigation of these genes.The homologs of MmpL3 and MmpL11 in the fast growing non-pathogenic model organism ectively . Interesvia gene expression , a two component system shown to be essential for intracellular growth of tubercle bacilli  with the accessory protein PrrC is a well-studied global regulatory system in piration . It is npiration . The clo bacilli , 2004. Inditions , howevernditions . PrrA/Prnditions .M.sm possesses a paralog of MSMEG_0244/MSMEG_0246, encoded by msmeg_5662/msmeg_5663 genes, which is a closer homolog of PrrAtb/PrrBtb. MSMEG_5662/MSMEG_5663 was initially described as being essential in M.sm  sources. We found that the knockout mutant \u0394msmeg_0243/msmeg_0244/msmeg_0246 shows reduced biofilm formation and abrogated colony morphology independent of the supplied iron source. Further investigations on the lipid composition of this mutant identified an altered acylation-state of cell envelope phosphatidylinositol mannosides (PIMs). Our data provide insights into a putative role of MSMEG_0244/MSMEG_0246 and the potential accessory and heme-binding protein MSMEG_0243 in regulation of PIMs acylation.In this study, we have delineated the above described genomic region including the genes encoding dual histidine kinase M. smegmatis mc2155 or derived strains were cultured in 7H9 broth  supplemented with 10% (v/v) ADC  and 0.05% (v/v) Tween 80. To obtain growth curves in the absence of iron or at low iron (FeCl3 or hemin) concentrations, cells were pre-cultured in 7H9 medium (lacking ADC) and subsequently transferred into Fe(III)-free modified Sauton\u2019s medium . Cells were sub-cultured five times to deplete intracellular iron pools (Escherichia coli Xl1-Blue was grown in Luria Bertani (LB) broth and LB-agar. For selection purposes kanamycin and/or hygromycin B were used at final concentrations of 50 \u03bcg/mL.Strains, plasmids and primers used in this study are listed in on pools . For cloMycobacterium smegmatis mc2155 has been handled in a Biosafety 2 Laboratory in accordance to German Federal Law.2 and Microbes online.Homologs/Paralogs were identified using blastp suite . Secretion signal peptide predictions were performed using SignalP 5.0 Server . Operon analysis was performed using OperonDB, DoorM.sm mc2155 using the primer pair MSMEG_0243_fw/rev as indicated in NcoI and HindIII and ligated using T4 DNA ligase. After transformation, colonies were selected on LB- agar plates with Ampicillin and sequenced to validate correct sequence incorporation.The open reading frame encoding truncated MSMEG_0243 was amplified from genomic DNA of StrepMSMEG_0243 was transformed into chemically competent E. coli Bl21 Rosetta cells and transformants were selected on LB plates containing Ampicillin. Single colonies were isolated and validated by PCR analysis. One colony was transferred into LB-medium (containing Ampicillin) and grown over night at 37\u00b0C. One milliliter pre-culture was transferred into 200 mL auto-inducing medium SO4, 0.68 g/L KH2PO4, 0.71 g/L Na2HPO4, 1 mL of 1 M MgSO4, 1 mL of 1000x trace elements mix) and grown for 2 days at 28\u00b0C. Cells were harvested by centrifugation and cells were lysed in cell lysis buffer  by sonication. The obtained crude cell lysate was centrifuged and the supernatant was applied on Streptavidin beads (250 \u03bcL 50% slurry/mL lysate). Subsequently, Streptavidin beads were washed with wash buffer  to remove any non-specifically bound proteins. Proteins were eluted from the beads using elution buffer . The obtained fractions were analyzed by SDS-PAGE, pooled, concentrated and dialyzed against 20 mM Tris-HCl, 100 mM NaCl, 0.05% glycerol, pH 7.4 overnight at 4\u00b0C. Further purification was achieved by size exclusion chromatography on a HiLoad Superdex 200 PG column . Protein was eluted from the column in 20 mM Tris-HCl, 100 mM NaCl, pH 7.4. Fractions were analyzed by SDS-PAGE, pooled, concentrated and stored at \u221280\u00b0C until further use.Plasmid pET-28(b) + -StrepMSMEG_0243 (3 mg/mL) was transferred into a hypoxic chamber and diluted in assay buffer  in a cuvette. Spectra were recorded on a JASCO V-650 Spectrophotometer . Subsequently, increasing equivalents of hemin (reconstituted freshly in DMSO under oxygen limiting conditions at 10 mM concentration and further diluted in assay buffer) were added and incubated for 5 min, until, after recording of the spectra, an additional equivalent of hemin was introduced. Samples were reduced by the addition of 0.2 mM dithionite. Heme:protein ratio was determined by recording spectra for hemin only at the indicated concentrations and apo-protein (10 \u03bcM), which was subsequently saturated with increasing hemin concentrations. Delta absorbance values at 420 nm were calculated using Spectra ManagerTM II Software and Excel 2016.Concentrated purified protein M. smegmatis mc2155 using the primer pairs as indicated in PacI and KpnI restrictions sites of p1NIL for the upstream fragment and KpnI and HindIII restrictions sites for the downstream fragment. Transformants were selected on LB plates containing kanamycin, screened by PCR and further validated by Sanger sequencing. Subsequently, the resistance marker cassette from pGOAL19 was cloned into the PacI site of p1NIL-\u03940243 (or \u03940244/0246 or \u03940243/0244/0246) to give the final suicide vector p1NIL-\u03940243 (or \u03940244/0246 or \u03940243/0244/0246)-RM. Vector DNA was pretreated with 100 mJ UV light cm\u20132 and used to transform electro-competent M.sm mc2155 cells. Single cross-over events were selected on 7H9 agar containing kanamycin and hygromycin B. Single colonies were re-streaked on X-GAL plates and blue colonies were isolated and analyzed by PCR. One representative single cross-over mutant was selected and incubated in liquid 7H9 medium containing 10% sucrose and plated in serial dilutions on 7H9 X-GAL plates to screen for second-crossover events. White colonies were analyzed by PCR to distinguish between wild-type and knockout clones. The obtained double cross-over mutant was further validated by qRT-PCR analysis using the primer pair as indicated in Knockout mutants were constructed using the pGOAL19/p1NIL system as published previously by msmeg_0243-0246 locus comprising an additional 800-bp upstream fragment containing the putative native promoter region was amplified from genomic DNA by PCR using the oligonucleotide pair as indicated in via Gateway Technology (Invitrogen), following the instructions by the manufacturer. The plasmid was transformed into the mutant by electroporation. Transformants were selected on 7H9 agar plates containing kanamycin, screened by PCR and further validated by Sanger sequencing.The 600 of 0.01. Medium was supplemented with either 1 \u03bcM FeCl3 or 1 \u03bcM hemin. Recording of OD750 at the indicated time points was used to monitor growth. To disperse cells, suspensions were passed through a syringe (26-gauge needle) several times prior to the measurements. In order to determine the dry weight of cells pellets, iron depleted cells were diluted into modified Sauton\u2019s medium at a starting OD600 of 0.01. Medium was supplemented with 1 \u03bcM hemin. After 65 h, cells were spun down and the supernatant was discarded. Cell pellets were subsequently dried in an oven at 65\u00b0C for 1 day and pellet weight was determined using an analytical balance.Iron depleted cells were diluted into modified Sauton\u2019s medium at a starting OD600. Cells were diluted to OD600 0.05 and incubated with different concentrations of inhibitor in a 96-well plate in a final volume of 200 \u03bcL. After 24 h, 20 \u03bcL resazurin (0.15 mg/mL) were added and color change was observed after 24 h (ex/em: 560 nm/590 nm). As a positive control ciprofloxacin was used. Fluorescence relative units were converted into % viability by normalizing to control well reads containing cells only and DMSO. OriginLab Origin 2019b software was used to generate viability curves and fitting of curves to determine IC50 values was achieved by applying the sigmoidal fitting function \u201cDoseResp.\u201dMIC was determined using the resazurin reduction method as described elsewhere . Briefly3 or 10 \u03bcM hemin or no supplement at a starting OD600 of 0.006. In the absence of Tween 80, standing cultures were grown in 12-well dishes to promote biofilm formation in a humidified incubator at 30\u00b0C for 4 days. For lipid analysis the cultures were diluted into standard Sauton\u2019s medium or M63 salts (600 of 0.001 and grew 3 days at 37\u00b0C (Sauton) or 5 days at 30\u00b0C (M63).Cultures were diluted into modified Sauton\u2019s medium containing 10 \u03bcM FeCl63 salts  at a sta600 of 0.1, 0.01 and 0.001. 1 \u03bcL starting culture was pipetted in the center of the plate on 7H9 agar (no Tween 80) and grown in an incubator at 37\u00b0C for 6 days. For lipid analysis the cultures were diluted to OD600 of 0.01, 0.001 and 0.0001 and 4 \u03bcL of each culture was pipetted on 7H9 agar supplemented with 10% (v/v) ADC or 7H11 agar supplemented with 10% Middlebrook oleic acid-albumin-dextrose-catalase (OADC) enrichment. The plates were cultivated 3 days at 37\u00b0C. For the cultures carrying pJAK1.A vector kanamycin was used at final concentration of 20 \u03bcg/mL.Cultures were diluted into 7H9 liquid medium without Tween 80 supplement at a starting ODCultures were grown to mid-log phase in 7H9 medium. Cells were collected by centrifugation. Cell pellets were flash-frozen in liquid nitrogen and stored at \u221280\u00b0C until further use. Total RNA was isolated using the RNeasy Mini-Kit (Qiagen) following the manufacturer\u2019s instructions. Amount and quality of the RNA were assessed spectrophotometrically. Integrity of each RNA sample was assessed by agarose gel electrophoresis. RNA was stored at \u221280\u00b0C in nuclease free water.\u00ae Green PCR Master Mix (Thermo Fisher Scientific) according to the protocol provided by the supplier. SigA primers were used as a control.qRT-PCR analysis was performed with the primer pairs described in \u00ae first strand cDNA synthesis kit  according to the protocol provided by the manufacturer. SigA primers were used as a control. Control reactions were performed in the absence of reverse transcriptase. Subsequently, 1 \u03bcg of cDNA was used in each reaction, supplemented with the Phusion DNA Polymerase, reaction buffer and intergenic region specific primers  or 0.5% (w/v) \u03b1-naphtol in 5% (v/v) sulphuric acid in ethanol and heating. The intensity of the spots on the TLC was quantified using ImageJ software (The cell pellets were resuspended in 6 mL of chloroform/methanol (1:2) and extracted for 2 h at 56\u00b0C with shaking. After centrifugation at 2200 \u00d7 g, the supernatants were stored and subsequently the pellets were extracted twice with chloroform/methanol (2:1), each extraction at 56\u00b0C for 2 h. Supernatants of all three extractions were pooled, dried under the stream of nitrogen and washed in chloroform/methanol/water (4/2/1) as described by Folch . The orgsoftware .n-hexane: ethyl acetate ) and detected as described above.Methyl esters of fatty acids (FAME) and mycolic acids (MAME) were prepared as previously described . SamplesmmpL3 and mmpL11 genes in M.tb as input sequence identified the presence of orthologs regions throughout the mycobacterium genus , a two component system (msmeg_0244/msmeg_0246), a transmembrane protein (msmeg_0245) and a secreted peptidase (msmeg_0247) (msmeg_0244/msmeg_0246 is transcribed from one operon together with msmeg_0243 and we subsequently confirmed the in silico prediction by RT-PCR (via the Sec pathway (M.tb (42% sequence similarity) and has six paralogs in M.sm. Interestingly, one paralog MSMEG_0242, is located directly upstream of MSMEG_0243, and is a heme-binding protein potentially located in the periplasm. KEGG analysis determined one paralog of MSMEG_0244/MSMEG_0246 in M.sm (MSMEG_5662/MSMEG_5663) and homologs throughout the mycobacterium genus including M.tb. Pairwise alignment between the M.sm and M.tb proteins revealed a 54% similarity between PrrBtb (Rv0902c) and MSMEG_0246 and a 70% similarity between PrrAtb (Rv0903c) and MSMEG_0244.KEGG analysis using the predicted gene cluster of the putative \u201cheme-uptake region\u201d surrounding um genus . Close aeg_0247) . Operon y RT-PCR . MSMEG_0 pathway . The proin vitro.In order to investigate the function of the two component system MSMEG_0244/MSMEG_0246 and its potential accessory protein MSMEG_0243, at first, we decided to validate the predicted heme binding of MSMEG_0243 Escherichia coli (E. coli) cell lysates. Purification was achieved via Strep-tag of a truncated version of MSMEG_0243 lacking the predicted signal peptide , followed by size exclusion column chromatography . Anaerobic reduction by addition of dithionite produces a characteristic red-shifted Soret peak. The observed peak is broad with two maxima at 388 and 424 nm, potentially indicating that heme has partially dissociated from the protein, as it has been observed in similar experiments for Rv0203, the MSMEG_0243 homolog from M.tb. Upon subsequent titration of 3 equivalents apo-MSMEG_0243, the peak at 424 nm reaches a maximum, while the shoulder at 388 nm becomes less apparent , no significant differences in growth could be observed between the strains indicating that the investigated genes are non-essential under the studied conditions  free medium complemented with iron(III)chloride or protoporphyrin IX iron(III). While in the presence of iron chloride all strains grew to a similar extent, in the presence of hemin we observed a slight but significant growth retardation of \u0394msmeg_0243/msmeg_0244/msmeg_0246 during logarithmic growth phase . Complementation of the knockout mutant strain \u0394msmeg_0243/msmeg_0244/msmeg_0246 partially restored growth under the applied conditions -porphyrin (Ga-PPIX), a toxic heme analog. Hence, we subsequently tested the minimal inhibitory concentration (MIC) of Ga-PPIX in M.sm wild-type and our knockout mutants. We rationalized that loss of the putative heme sensing mechanism might affect the sensitivity to this toxic heme substitute. We conducted the assay with non-starved bacteria under three conditions: 7H9 medium (+0.05% Tween 80), modified Sauton\u2019s medium (complemented with 10 \u03bcM ironIII + 0.05% Tween 80) and modified Sauton\u2019s medium (iron free + 0.05% Tween 80). Results are summarized in M.sm.Interestingly, 2, CO, NO or radical species  and incubated the cultures in standing 24-well plates at 30\u00b0C in a humidified incubator for 4 days. Biofilm formation was quantified by crystal violet stain  when investigating the three described mutants. However much more striking is the overall appearance of the biofilm and colonies when comparing the wild-type and knockout mutant \u0394spension . A recenmsmeg_0243/msmeg_0244/msmeg_0246 mutant cells, while the other mutants grew similarly to the wild-type strain. Lipids were isolated using chloroform: methanol extraction followed by Folch wash  and diacyl phosphatidylinositol hexamannosides (Ac2PIM6).To that end, we isolated and analyzed total lipids and fatty/mycolic acids from wild-type and all studied strains grown on 7H11 or 7H9 agar at 37\u00b0C, as well as in the form of biofilms in Sauton\u2019s medium at 37\u00b0C for 3 days and in M63 salts at 30\u00b0C for 5 days. In all of these conditions we observed different colony morphology or disturbed biofilm formation of \u0394lch wash , and sublch wash . Mycolicmsmeg_0243/msmeg_0244/msmeg_0246 from M.sm in heme/iron sensing. We report the expression, purification and in vitro heme binding properties of MSMEG_0243, an annotated periplasmic heme binding protein. Our presented follow-up experiments using the knockout mutant M.sm\u0394msmeg_0243 do not provide evidence of the observed in vitro heme-binding being linked to cellular heme uptake mechanisms, as it has been proposed for the homologous protein Rv0203 from M.tb. Hence, we hypothesized that heme binding of MSMEG_0243 might be linked to a sensing mechanism involving heme as chemical ligand  in M.sm, providing reason for the absence of observed phenotypes in the single mutant. Furthermore, it is worth mentioning that the double mutant \u0394msmeg_0244/msmeg_0246, displays upregulated expression of msmeg_0243 (msmeg_0244/msmeg_0246. Additionally, in the single mutant \u0394msmeg_0243 we observed a high upregulation of the prospective sensor-type kinase gene msmeg_0246, further pointing toward an interplay of the studied genes and indicating an attempt of the bacteria to re-optimize the system under the given conditions.In our study, we do not find a direct link between heme/iron supplementation and growth characteristics of the described mutants, as we would expect from a potential role in heme/iron sensing. Yet, we observed a significant influence on biofilm formation, colony morphology and clumping phenotype in the mutant \u0394meg_0243 . Increasmsmeg_0243/msmeg_0244/msmeg_0246 mutant strain exhibiting changes in colony morphology or impaired biofilm formation. PIMs are one of the most abundant and bioactive glycolipid families in the mycobacterial cell wall. Acylated PIMs are found in the cell wall and cytoplasmic membrane of mycobacteria, they exhibit a wide spectrum of immune-regulatory effects and are known to play major roles in inducing granuloma and recruitment of natural killer cells (M.tb, which results in reduced lung burden and inflammation of infected mice. They also showed that the deletion of mpbR in M.sm affects the formation of biofilm. Furthermore, a knockout mutant of the universal stress protein Rv2623 in M.tb shows a smoother, less ruffled appearance of colonies as compared to wild-type cells, a phenomenon, which was also linked to a distinct PIMs profile with increased amount of mono- and diacylated forms of PIM2 and PIM6 in the mutant strain (Our further investigations of cell envelope lipid profiles revealed that acylation of PIMs is significantly altered in the cells of \u0394er cells . These mer cells . Furtherer cells . Just reer cells . Li et at strain .M.sm\u0394SMmmpL11 grown in the same conditions (mmpl1LSM was associated with lower levels of mycolic acid wax ester and long-chain triacylglycerols and did not lead to alteration of PIMs production further confirming that our observation is not evolving from any polar effect on SMmmpL11 gene. These considerations indicate that the two component system msmeg_0244/msmeg_0246 and the potential accessory protein MSMEG_0243 might be involved in the regulation of genes encoding proteins carrying out important steps in PIMs acylation, a process which is required for cell envelope homeostasis. Biosynthesis of mycobacterial PIMs is crucial, as exemplified by essentiality of the mannosyltransferase PimA involved in the first mannosylation of PI in M.sm, as well as M.tb (M.tb H37Rv and results in severe growth defects in M.sm (Affected biofilm formation was observed also in the case of knockout mutant nditions , p. 11.  as M.tb . Also di in M.sm . EnzymesM.sm. There is significant literature precedence that bacterial heme-binding proteins (via the Sec translocation pathway. Proteins are exported in an unfolded state, hence heme binding to MSMEG_0243 is most possibly occurring in the extracellular space. MSMEG_0243 might bind ferric heme and interact with the sensor kinase MSMEG_0246, which results in signal transduction to MSMEG_0244 and subsequent target gene regulation. Control of heme binding and as we postulate MSMEG_0246 interaction with MSMEG_0243 might occur by a mechanism that is involving reduction of ferric to ferrous heme. Upon dissociation of ferrous heme from MSMEG_0243, interaction with MSMEG_0246 is potentially altered, with downstream effects on enzymes involved in the synthesis of cell wall lipids. Future investigations might involve in vitro protein-protein interaction studies in the presence of ferric/ferrous heme molecules or pulldown-experiments using MSMEG_0243 as bait.Taken together from our presented data we can postulate a preliminary working model for the action of MSMEG_0244/MSMEG_0246 and MSMEG_0243 in proteins  are ofteproteins , and ourM.sm. Similar systems have been described in Streptomyces, Bacillus subtilis, or Rhodococcus, but to our knowledge have, to date, not been described in Mycobacteria. Disturbance in these genes results in the altered processing of essential components of the mycobacterial cell envelope, PIMs, in certain growth conditions, which are associated with severe defects in biofilm and colony formation.Our results underline the importance of two component systems and related proteins in mycobacteria. Our study points toward an interplay of a heme-binding accessory protein and a two component system in All datasets presented in this study are included in the article/JK and CJ-T design of the study and manuscript preparation. ML, SC, and XW: mutant construction and characterization. ML and SC: cloning, protein purification and characterization. HG: lipid analysis. All authors read and approved the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest."}
+{"text": "Bassiana duperreyi which has heteromorphic XY sex chromosomes.Homologous sex chromosomes can differentiate over time because recombination is suppressed in the region of the sex determining locus, leading to the accumulation of repeats, progressive loss of genes that lack differential influence on the sexes and sequence divergence on the hemizygous homolog. Divergence in the non-recombining regions leads to the accumulation of Y or W specific sequence useful for developing sex-linked markers. Here we use in silico whole-genome subtraction to identify putative sex-linked sequences in the scincid lizard 9 150\u2009bp paired-end genomic sequence reads from a XY male and 81.4\u2009\u00d7\u2009109 paired-end reads from an XX female for in silico whole genome subtraction to yield Y enriched contigs. We identified 7 reliable markers which were validated as Y chromosome specific by polymerase chain reaction (PCR) against a panel of 20 males and 20 females.We generated 96.7\u2009\u00d7\u200910B. duperreyi can be reversed by low temperatures . We have developed sex-specific markers to identify the underlying genotypic sex and its concordance or discordance with phenotypic sex in wild populations of B. duperreyi. Our pipeline can be applied to isolate Y or W chromosome-specific sequences of any organism and is not restricted to sequence residing within single-copy genes. This study greatly improves our knowledge of the Y chromosome in B. duperreyi and will enhance future studies of reptile sex determination and sex chromosome evolution.The sex of  Most vertebrates reproduce sexually with distinct male and female phenotypes that arise from the complement of chromosomes that are inherited from their parents. These species are said to have their sex determined genotypically (GSD), and the influential genes reside on sex chromosomes that typically assort randomly during meiosis. In the absence of differential investment by the parents in male and female offspring, this system yields an evolutionarily stable 1:1 primary offspring sex ratio .Sex chromosomes are thought to evolve from autosomes when genes they carry assume the role of determining sex . What foAmhr2 in the pufferfish Takifugu rubripes [Unlike mammals, squamates show a remarkable diversity in sex chromosome structure, representing various degrees of differentiation in sex homologs \u201313. Suchrubripes , 23. Forrubripes \u201326.Various approaches have been used to identify sex-linked markers in non-classical model organisms. Random amplified polymorphic DNA fingerprinting (RAPD) \u201329 and aWith the development of next-generation sequencing technologies, new methods have been developed for screening sex linked DNA. For example, assaying for sex-specific expressed genes by RNA-seq  or wholeBassiana duperreyi). The species has heteromorphic XY sex chromosomes [B. duperreyi, however, the fragments are short and difficult to amplify reliably. Here, we use low depth whole genome sequencing of a male and a female B. duperreyi to apply an in silico whole genome subtraction approach, and develop new practical markers, useful in ongoing studies of this species in the laboratory and the wild.Here, we report an in silico approach to isolate sex specific markers based on sequence unique to the Y or W chromosome, analogous to genomic representational difference analysis (gRDA) . Subtracomosomes . Identifomosomes , 59. Quiomosomes  develope9 150\u2009bp PE reads from the male and 81.4\u2009\u00d7\u2009109 PE reads from the female sequencing libraries for the in silico whole genome subtraction pipeline. This equates to approximately 8x coverage of the genome estimated from the k-mer analysis. We decomposed these reads into 14,310,783,435 and 36,695,139,446 27-mers for the male and female respectively  individual and reassemble the k-mers to yield Y (or W) enriched contigs that can be validated using PCR on a panel of individuals whose sex is known. In this way, we were able to isolate seven Y chromosome markers. There are several advantages to our in silico whole genome subtraction approach for identifying sex specific sequence when compared to AFLP, microsatellite or RAD-seq approaches. Specifically, our in silico subtraction method surveys the entire available genome, assuming adequate read depth, to identify sex specific differences and does not rely on a highly reduced representation of the genome as with RAD and ddRAD approaches, that may miss many putative markers. This is particularly important for species with small sex chromosomes or relatively small differences between the X and the Y (or Z and W) chromosomes. Our method is cost-effective because as demonstrated here, low coverage sequencing (~8x) for a single individual of each sex is sufficient to obtain informative and robust Y-chromosome (or W chromosome) markers.UBE2H (Ubiquitin Conjugating Enzyme E2 H) is present on the Y chromosome in both B. duperreyi (this study) and the skink E. heatwolei [UBC4-testis in the rat, [UBE2H plays a role in sex determination in these skinks, merely that it is a gene on the sex chromosomes.We have shown that the gene eatwolei . This steatwolei . Althougthe rat,  and an athe rat, ) we makeB. duperreyi subject to sex reversal [B. duperreyi because it reduces the risk of a recombination event being misinterpreted as evidence of sex reversal. Investigating the occurrence of temperature sex reversal will increase our understanding of sex reversal as a driver of sex-chromosome turn-over in the wild [B. duperreyi exhibits the asynchronous gonadal and genital development observed in other species with sex reversal [Our study paves the way for future work that relies upon successful identification of chromosomal sex in wild populations of reversal , 75. Isothe wild . Also, oreversal . In addiIn this study, we identified a modest number of Y-chromosome markers, numbering 7 of 92 screened (8%). The success rate of future Y-marker discovery via genome subtraction could be improved by implementing efforts to reduce false positives caused by autosomal insertion/deletion polymorphisms in the focal sequenced individuals. This could be achieved through several complementary strategies: 1. subtracting multiple XX individuals from the XY focal individual/s; 2. selecting individuals for sequencing from populations with lower rates of heterozygosity ; 3. sequencing siblings or related individuals. These improvements would increase the efficiency of sex chromosome sequence identification using whole genome subtraction.ca 40 million years of evolution.Here we describe an effective tool for characterising sex chromosomes in non-model organisms. Our approach targets sex-specific insertions and highly differentiated sex chromosome regions that are suitable for developing diagnostic sex-markers. This approach complements existing methods for identifying sex chromosome homologues and aids the classification of sex determination systems in a wide range of species. The ability of our method to provide insights about the evolutionary origins of sex chromosomes is demonstrated here by the discovery of a scincid Y-chromosome gene, common to species separated by B. duperreyi, is a medium-sized (80\u2009mm snout\u2013vent length) lizard widely distributed through south eastern Australia, from the coast to montane cool-climate habitats [n\u00a0=\u200976) were captured by hand at Piccadilly Circus  in Namadgi National Park, 40\u2009km west of Canberra in the Australian Capital Territory, and from Anglesea  in Victoria  with 2.5\u2009\u03bcg/ml of Antibiotic Antimycotic Solution  and incubated at room temperature for 24\u2009h  before tDNA was extracted from fresh liver samples of the two focal animals and from the tail snips of the 60 validation animals using the Gentra Puregene Tissue Kit  following manufacturer protocols. DNA suspensions were assessed for purity using a NanoDrop 1000 spectrophotometer  and quantified using Qubit 2.0 fluorometer . Library preparation and sequencing were performed at the Biomolecular Resource Facility at the Australian National University  using the Illumina HiSeq 2000 platform yielding 150\u2009bp paired end reads.Reads from the focal male and the focal female were analysed independently as follows Fig.\u00a0. First, The remaining Y enriched k-mers were then reassembled into contigs using an inchworm assembler  with stringent extension criteria. Briefly, the assembler initially took a focal k-mer at random and searched for other k-mers that matched exactly k-1\u2009bp of the focal k-mer. If this second k-mer was unique, then the focal k-mer was extended by one bp, and the process was repeated. If the k-mer was not unique, then the extension process was terminated. The extension occurred to both the left and the right, yielding relatively short contigs (up to ca 1400\u2009bp) that contain sequence unique to the male individual.To validate the sex specificity of each of the contigs and remove false positives derived from autosomal and X chromosome polymorphisms, we designed primers for each contig using Primer 3  implemen24, maximal for autosomal or X allele frequency\u2009=\u20090.5) to eliminate false positives, despite the error rate compounding over multiple markers. Thus, male specific markers that survive the validation process are Y-specific markers.The PCR screening process was conducted in three stages. To confirm that the subtraction pipeline had successfully identified a presence/absence polymorphism in the two focal individuals, we first screened those two individuals to confirm presence of an amplified fragment in the male and the absence of an amplified fragment in the female. We then screened a panel of an additional 4 male and 4 female individuals for putative sex-linked markers showing a male-specific positive pattern. In a third step, we screened those putative markers on a further 20 males and 20 females from Piccadilly Circus. At each of the stages, the loci that did not appear as sex specific were eliminated as candidate sex markers. The probability of an autosomal or X chromosome polymorphism being present in the focal male, 4 males and 20 additional males, and absent in the focal female, 4 females and 20 additional females, is sufficiently low  and 4 male individuals from Anglesea (Victoria).The phenotypic sex of each of the karyotyped animals was confirmed by gross examination of gonads followed by histological examination. Dissected gonads were dehydrated through graduations of ethanol  and two changes of xylene for 45\u2009min each, before being embedded in paraffin wax, and sectioned 5 to 6\u2009\u03bcm using a Leica Rotary Microtome . Slides were stained with haematoxylin and eosin, with a staining time of 2\u20133\u2009min in haematoxylin, and 10 dips in 0.25% eosin in 80% ethanol, before being mounted in Depex medium . Gonads were characterized according to standard cellular structures , 92.2. At approximately 80% confluency, cells were split into three T25 flasks for a further 3 to 4 passages before they were harvested by adding colcemid (0.05\u2009\u03bcg/mL) for 3.5\u2009h and treated with hypotonic solution . Slides were fixed with an ice-cold (ca 4\u2009\u00b0C) 3:1 mixture of methanol and acetic acid. The cell suspension was dropped on to slides, air dried and frozen at \u2212\u200980\u2009\u00b0C until use. For DAPI  staining, each slide was mounted with anti-fade medium Vectashield  containing 1.5\u2009mg/ml DAPI.Karyotyping was carried out by examining metaphase chromosomes prepared from fibroblast cell lines of tail tissues as outlined by Ezaz et al.  with minAnolis carolinensis, Crocodylus porosus, Gallus gallus, Pelodiscus sinensis, Podarcis muralis, Pogona vitticeps, Pseudonaja textilis, Notechis scutatus, Varanus komodoensis, Sphenodon punctatus) with a minimum E-value of 0.000001 for reported alignments and a filter for low complexity regions. We used the same cut-off and filter to search the non-redundant database at the NCBI (https://blast.ncbi.nlm.nih.gov). The Dfam database [To discover homologies of the male-specific contigs and identify any partial gene sequences that may exist, we used BLASTN to search each contig against representative reptilian and avian genomes available in Ensembl, Release 99  for the 92 subtraction contigs selected for PCR-based screening. Figure S5. Sequencing coverage for the 92 subtraction contigs selected for PCR-based screening. Figure S6. External and histological views of a) ovary b) testis in adult individuals of B. duperreyi. Figure S7. Karyotype of a male scincid lizard Bassiana duperreyi. Figure S8. Sequence alignment (a) and phylogeny (b) of bdM27_23_X5_798 contigs (top blue color highlight) with amplified 4 males (Piccadilly Circus_ACT) and 4 males (Anglesea _VIC). Figure S9. Sequence alignment (a) and phylogeny (b) of bdM27_10_X7_874 contigs (top blue color highlight) with amplified 4 males (Piccadilly Circus_ACT) and 4 males (Anglesea _VIC). Figure S10. Sequence alignment of bdM27_74_X11_649 contigs (top blue color highlight) with amplified 4 males (Piccadilly Circus_ACT) and 4 males (Anglesea _VIC). Figure S11. Sequence alignment (a) and phylogeny (b) of bdM27_82_X5_636 contigs (top blue color highlight) with amplified 2 males (Piccadilly Circus_ACT) and 4 males (Anglesea _VIC). Figure S12. Sequence alignment (a) and phylogeny (b) of bdM27_79_X5_643 contigs (top blue color highlight) with amplified 4 males (Piccadilly Circus_ACT) and 4 males (Anglesea _VIC). Figure S13. Sequence alignment (a) and phylogeny of bdM27_69_X9_658 contigs (top blue color highlight) with amplified 4 males (Piccadilly Circus_ACT) and 4 males (Anglesea _VIC). Figure S14. Sequence alignment of bdM27_87_X6_628 contigs (top blue color highlight) with amplified 4 males (Piccadilly Circus_ACT) and 4 males (Anglesea _VIC). Table S1. Estimates of evolutionary divergence between Piccadilly Circus and Anglesea individuals of Bassiana duperryi. Table S2. BLAST results for Y-specific contigs queried against representative reptile genomes. Table S3. Hits to known repeats in the Dfam database. Table S4. Specimen data, sex, locality and measurements for the Bassiana duperreyi specimens used in this study.Additional file 2: Original gel images to accompany Figure 1 of the manuscript. Figure S15. Raw gel images for primer sets A. bdM27_10_X7_874, B. bdM27_87_X6_628, C. bdM27_23_X5_798, D. bdM27_69_X9_658, E. bdM27_74_X11_649, F. bdM27_79_X5_643, G. bdM27_82_X5_636. Each row represents alternating Male (band) and Female (no band) individuals (n\u00a0=\u200920) spanned by ladders. Individuals from left to right in each gel are; 1st row \u2013 DDBD_8, DDBD_23, DDBD_9, DDBD_24, DDBD_12, DDBD_25, DDBD_13, DDBD_27, DDBD_14, DDBD_30, DDBD_16, DDBD_35, DDBD_18, DDBD_36, DDBD_19, DDBD_39, DDBD_21, DDBD_40, DDBD_22, DDBD_41; 2nd row DDBD_26, DDBD_47, DDBD_28, DDBD_56, DDBD_29, DDBD_57, DDBD_31, DDBD_59, DDBD_32, DDBD_60, DDBD_33, DDBD_62, DDBD_42, DDBD_100, DDBD_43, DDBD_287, DDBD_44, DDBD_288, DDBD_45, DDBD_289. Detailed specimen list available in the Additional file"}
+{"text": "Accumulating lines of evidence indicate that circular RNAs (circRNAs) are involved in the pathogenesis of human cancers, including nasopharyngeal carcinoma (NPC). However, the influences of hsa_circ_0081534 upon the pathogenesis and dynamics of NPC are undescribed. In this study, we identified a circRNA hsa_circ_0081534 was significantly upregulated in NPC tissues and cell lines. Inhibition of hsa_circ_0081534 induced a decrease in NPC cells proliferation and invasion in vitro, and repressed tumor growth in vivo. In mechanism, hsa_circ_0081534 promoted NPC progression by sponging miR-508-5p. Fibronectin 1 (FN1) is a target gene of miR-508-5p. In addition, rescue assays showed that FN1 overexpression (or miR-508-5p inhibitors) abolished the roles of hsa_circ_0081534 inhibition on NPC cells proliferation and invasion. Therefore, hsa_circ_0081534 promoted the proliferation, and invasion of NPC cells via regulating the miR-508-5p/FN1 axis. Our findings suggested that hsa_circ_0081534 could be a novel therapeutic target for the treatment of NPC patients. Nasopharyngeal carcinoma (NPC) is a common malignant head and neck cancer that affects humans on a global scale, and disproportionately affects a relatively high percentage of Chinese , 2. DespCircular RNAs (circRNAs) are a new class of endogenous functional non-coding RNAs and characterized by covalently closed and continuous loop structures without 3\u2019 or 5\u2019 end . In receIt is well documented that microRNAs (miRNAs) exert tumor-suppressive activity in different human malignancies . NotablyFibronectin 1 (FN1) is a member of the FN family, is widely expressed by multiple types of cells, and may facilitate the development of cancers , 17. ForTherefore, in the present study, we sought to assess the influence of hsa_circ_0081534 in NPC progression. We also aimed to characterize crosstalk among hsa_circ_0081534, miR-508-5p, and FN1. Ultimately, we hoped our novel assessments and findings might lead to better treatment approaches and outcomes for patients afflicted with NPC.p < 0.05) . Next, we selected the top 5-upregulated circRNAs from dataset GSE143797 and sought to verify the predictions in small samples (N = 5) of NPC tissues by use of qRT-PCR. Results indicated that hsa_circ_0081534 (circEPHB4) was the most significantly upregulated type of circRNAs of those we assessed in the NPC tissues  comparing circRNA levels between NPC tissues and normal tissues. Results indicated that expression of 296 circRNAs were significantly altered in NPC tissues compared to adjacent normal tissues (fold change > 2.0 and  tissues .Hsa_circ_0081534 (circEPHB4) was derived from exons 5 and 6 of the EPHB4 gene . To ruleNext, we examined hsa_circ_0081534 expression in NPC tissues and cell lines. Results indicated that hsa_circ_0081534 expression increased in NPC tissues compared with adjacent normal tissues (NT) , 2B. SimTo assess mechanisms underlying the influence of hsa_circ_0081534 in NPC, we determined the location of hsa_circ_00815343 in NPC afflicted cells. Results indicated that subcellular localizations of hsa_circ_0081534 mainly occurred in cytoplasm , 3B. BioWe also detected miR-508-5p expression in NPC tissues and cells by using qRT-PCR. As shown in in We also sought to elucidate the mechanisms of miR-508-5p in the progression of NPC. Bioinformatics databases were utilized to determine potential targets of miR-508-5p. As shown in We next sought to assess FN1 expression in NPC samples. QRT-PCR results indicated that FN1 expression was relatively highly expressed in NPC tissues compared with adjacent normal tissues . In addiNext, we explored if hsa_circ_0081534 influenced the progression of NPC through regulation of the miR-508-5p/FN1 axis. Findings from western blot indicated that silencing hsa_circ_0081534 subsequently decreased FN1 expression in NPC cells, and the effects could be reversed through inducing miR-508-5p inhibition , and theTo assess roles of hsa_circ_0081534 in vivo, we established a murine xenograft using S18 cells expressing sh-circ_0081534 or sh-NC. As shown in Figures 8A, hsa_circ_0081534 depletion subsequently inhibited NPC tumor growth in vivo. Furthermore, both dimensions and weights of tumors were smaller in the sh-circ_0081534 group compared with sh-NC group , 8C. AbuRecently, numerous of evidence has indicated that circRNAs play important roles in NPC pathogenesis. For example, Hong et al. showed that circular RNA CRIM1 functioned as a ceRNA to promote NPC metastasis and docetaxel chemoresistance through FOXQ1 regulation . Li et aRecently, accumulating evidence have indicated that circRNAs mediate tumorigenesis by acting as sponges, or by competing for endogenous RNAs (ceRNAs) of miRNAs . In the FN1 acts as direct transcriptional target of several miRNAs and involves in the tumorigenesis of multiply tumors, such as breast cancer, cervical cancer, and gastric cancer, et al. \u201328. In tIn summary, these findings demonstrated that hsa_circ_0081534 potentiated the proliferative and invasive capabilities of NPC cells through up-regulating FN1 via sponging of miR-508-5p. Therefore, our findings provided a novel therapeutic target for the treatment of NPC patients.Patients diagnosed with NPC (N = 41) were recruited from The First Affiliated Hospital of Zhengzhou University. NPC tissues and adjacent normal tissues (NT) were collected during surgery. Prior to surgery, patients had not received any treatments. I Informed consent form was acquired from every patient. All procedures in our study were reviewed approved by the Ethics Committee of our hospital.2.7 NPC cell lines  and a human bronchial epithelial cell line (NP69) were bought from the Chinese Academy of Science . Cell lines were cultured in Dulbecco\u2019s Modified Eagle\u2019s Medium  and supplemented with 10 % fetal bovine serum  at a constant temperature of 37 \u00b0C and in an atmosphere containing a constant level of 5 % COSmall interfering RNA against hsa_circ_0081534 (si-circ_0081534), miR-508-5p mimics, and miR-508-5p inhibitors and respectively matched controls were obtained from RiboBio . The overexpression vector for FN1 and the negative control (pcDNA) were bought from GenePharma . Cell transfection procedures were completed by the application of Lipofectamine 3000 (Invitrogen) following all manufacturer protocols.In RNase R assay, we used 2 \u03bcg of RNA, which was incubated either with, or without RNase R  for 30 min at 37 \u00b0C. After the treatment above, RNA was transcribed into cDNA, and the expression was determined by qRT-PCR assay.\u2212\u0394\u0394Ct method [RNA was extracted from tissues and cells using TRIzol reagent . Following all manufacturer protocols, reverse transcription of miR-508-5p was conducted using the TaqMan MicroRNA Reverse Transcription Kit . The cDNA of hsa_circ_0081534 and of FN1 was synthesized using the PrimeScript Reverse Transcriptase Kit . Amplification reactions were conducted using SYBR-Green Master Mix (Takara). Expression levels were analyzed using the 2t method . U6 and Total protein from tissue samples was extracted using lysis buffer (Beyotime Biotechnology). Next, we subjected proteins to 10 % SDS-PAGE, and then, proteins were electro-transferred onto a PVDF membrane (Millipore). Subsequently, membranes were blocked using 5 % non-fat milk, incubated with primary antibodies, and then were incubated with corresponding secondary antibodies. Finally, signals corresponding to levels of protein in samples were determined using the ECL Detection System . Blots were analyzed using ImageJ software  Both nuclear and cytoplasmic fractions for NPC cells were isolated using the PARIS kit (Invitrogen) and following methods detailed in a previous study .Transfected NPC cells were plated in 96-well plates and incubated for 24, 48, or 72 h. Next, cells were mixed with 20 \u03bcL of MTT  for a further 4 h period of incubation. Formazan products were dissolved using 150 \u03bcL of DMSO. We recorded absorbance values at 490 nm and detections were facilitated by use of a microplate reader .Transfected NPC cells were suspended and inoculated into 6-well plates (200 cells/well). After 14 days, cells were immobilized by using 4 % paraformaldehyde and were dyed with 0.1 % crystal violet. The colonies were photographed by a camera and counted using a microscope .Transfected NPC cells were inoculated into the upper chamber of transwell chamber , which was covered by 50 \u03bcL of Matrigel. Cells were maintained in serum-free medium. The complete medium with added 10 % FBS was mixed into the lower transwell chamber. After 24 h of cultivation, cells were immobilized by use of 4 % paraformaldehyde and dyed with 0.1 % crystal violet. Measures of invasion of cells were determined under microscopy .According to bioinformatics tool derived predictions, miR-508-5p was expected to have interacted with hsa_circ_0081534 or FN1 3\u2019 UTR. To confirm this interaction, wild- and mutant-types of hsa_circ_0081534 or FN1 3\u2019 UTR (WT/MUT-circ_0081534 or WT/MUT-FN13\u2019UTR) were cloned into the pGL3 vector (Promega). Next, every constructed sample and miR-508-5p or miR-NC were co-transfected into NPC cells using Lipofectamine 3000. 24 h post-transfection, luciferase activities were evaluated by use of dual-luciferase assay kits  following all manufacturer protocols.Measures of combinations between miR-508-5p and hsa_circ_0081534 were assessed using Magna RIP Assay Kits (Millipore) following methods detailed in previous research .Biotin-labeled miR-NC or miR-508-5p were respectively named as Bio-miR-NC and Bio-miR-508-5p. Biotin-coupled complexes were immunoprecipitated, and qRT-PCR facilitated determinations of levels of enrichment of miRNA.6 cells) were injected subcutaneously into mice. Tumor volume (length \u00d7 width2 \u00d7 0.5) was assessed and recorded every seven days through 6 weeks post-injection. At 6-weeks post-injection, mice were anesthetized and sacrificed by cervical dislocation whereafter tumors were immediately removed and weighed. Animal experiments were conducted following the National Animal Care and Ethics Institution guidelines and were authorized by the Animal Research Committee of The First Affiliated Hospital of Zhengzhou University.A total of N = 10 male BALB/c nude mice (4 weeks old) were obtained from Beijing HFK Bioscience Co., Ltd  and used for conducting xenograft assays. Briefly, stably expressing sh-NC, sh-circ_0081534 (sh-circRNA)  transfec"}
+{"text": "Chitosan fibers blended with polyethylene oxide (CHIT_PEO) and crosslinked with genipin were fabricated by electrospinning technique. Subsequently, CHIT_PEO bioactive glass composite electrospun mats were fabricated with the aim to achieve flexible structures with adequate mechanical properties and improved biological performance respect to CHIT_PEO fibers, for potential applications in wound healing. Three different compositions of bioactive glasses (BG) were selected and investigated: 45S5 BG, a Sr and Mg containing bioactive glass (BGMS10) and a Zn-containing bioactive glass (BGMS_2Zn). Particulate BGs (particles size < 20 \u03bcm) were separately added to the starting CHIT_PEO solution before electrospinning. The two recently developed bioactive glasses (BGMS10 and BGMS_2Zn) showed very promising biological properties in terms of bioactivity and cellular viability; thus, such compositions were added for the first time to CHIT_PEO solution to fabricate composite electrospun mats. The incorporation of bioactive glass particles and their distribution into CHIT_PEO fibers were assessed by SEM and FTIR analyses. Furthermore, CHIT_PEO composite electrospun mats showed improved mechanical properties in terms of Young\u2019s Modulus compared to neat CHIT_PEO fibers; on the contrary, the values of tensile strain at break (%) were comparable. Biological performance in terms of cellular viability was investigated by means of WST-8 assay and CHIT_PEO composite electrospun mats showed cytocompatibility and the desired cellular viability. Electrospinning is a widely used technique to produce nano- and microfibers for various applications ranging from agriculture, food packaging to the biomedical field. This technique creates fiber mats with small fiber size, small pores, high porosity and large specific surface area . EspeciaGardenia jasminoides Ellis [Chitosan solubility in aqueous acidic media is attributed to amino groups; however, such groups make chitosan solution highly viscous, complicating its electrospinnability . Moreovees Ellis . Genipines Ellis . The croes Ellis . The croes Ellis ; under aes Ellis .In this work, chitosan/polyethylene oxide (CHIT_PEO) were mixed with acetic acid to fabricate fibers by electrospinning technique, adding genipin as crosslinker before electrospinning. The purpose was to use natural polymers to fabricate fibers suitable for tissue engineering applications and in particular wound healing because of their interactions with the host tissues . Previou2:CaO:Na2O:P2O5) have been already fabricated by electrospinning, as reported in literature [Additionally, since the electrospinning technique permits to incorporate various bioactive inorganic materials into polymer fibers ,33, bioaterature ,35.To the best of our knowledge, CHIT_PEO composite electrospun mats with Sr and Mg containing bioactive glasses and Zn-containing bioactive glasses have not been fabricated yet. To this purpose, we decided to incorporate in CHIT_PEO fibers two novel bioactive glasses: BGMS10 ,37,38,39Commercial raw powders  were separately mixed for 3 h in a jar and then melted in a platinum crucible in air. The bioactive glasses were melted at 1450 \u00b0C for 45 min, by a classic melt-quenching route as reported in ,44,45. Tw/v in aqueous acetic acid  solution and polyethylene oxide  3% w/v in aqueous acetic acid  solution were mixed at ratio 90/10 and 95/5 and stirred for 48 h before the electrospinning process.Chitosan  3% For the fabrication of CHIT_PEO_45S5, CHIT_PEO_BG10 and CHIT_PEO_BGZn composites, 20% wt of bioactive glasses  with respect to the total polymeric amount was added and stirred 10 min before electrospinning.w/v genipin solution was prepared by dissolving genipin powders in ethanol  and it was stored at +4 \u00b0C. The CH/genipin weight ratio in the solutions was 3%wt of genipin respect to chitosan amount; the volume of genipin solution was added at room temperature to CHIT_PEO acetic acid solution and stirred for 5 min before electrospinning [Genipin (\u226598% (HPLC), powder, Sigma Aldrich, Taufkirchen, Germany) was used as crosslinker . To obtaspinning . For comspinning .The same electrospinning parameters were used to fabricate both neat CHIT_PEO fibers and CHIT_PEO composite electrospun mats . Briefly, 20 kV as voltage, 10 cm as distance between the needle tip (diameter 21G) and the target, and a flow rate of 3 mL/h were used during the process. A commercially available setup  was used for electrospinning at temperature (T) in the range 25\u201328 \u00b0C and relative humidity (RH%) in the range 23\u201335%.The morphology of samples was investigated using a SEM microscope  after sputtering samples\u2019 surface with gold . Subsequently, ImageJ  was employed to measure the diameter of 50 fibers and 50 joints of each sample to calculate the average of fibers and joints diameters .\u22121, wave number range 1800\u2013500 cm\u22121) using a spectrometer .To investigate samples before immersion in SBF (Simulated Body Fluid), FTIR spectroscopy was performed  was performed after soaking samples in Simulated Body Fluid solution see  to obser\u00ae, Darmstadt, Germany), equipped with a load cell of 100N and a cross-head speed of 5 mm/min.Furthermore, CHIT_PEO fibers and CHIT_PEO composite electrospun mats were fixed in a paper frame for investigating their mechanical properties at room temperature using uniaxial tensile test  were fixed on round scaffold supports  before immersion in SBF (4 mL). Before SEM analysis, the samples were rinsed and dried 1, 7 and 14 days after incubation at 37 \u00b0C.The procedure developed by Kokubo et al.  was foll5 cells/mL with a drop of 50 \u00b5l per sample. 1 mL of RPMI medium was added to each well 15 min after incubation [2) in RPMI 1640 medium  with the addition of 10% fetal bovine serum (Lonza) and 1% penicillin/streptomycin (Lonza), before the seeding.Bone murine stromal cells ST-2 cell line  were seeded and kept in contact with samples for 1 and 7 days before performing WST-8 assay . All samples were fixed on sample holders designed and printed with a 3D Printer  to fit inside a 48-multiwell plate. Before the cell seeding, the samples were disinfected by exposure to UV light for 1 h. Drop seeding was performed by using an inoculum ratio of 2.0 \u00d7 10p-value < 0.05 was considered statistically significant.ANOVA one-way analysis was performed to evaluate the results of cell viability. A Initially, CHIT_PEO was produced using CHIT_PEO ratio of 90/10 and 95/5. SEM analyses of both CHIT_PEO 90/10 a and CHIThus, the ratio 95/5 was used to fabricate CHIT_PEO and CHIT_PEO composite electrospun mats . CHIT_PEO, CHIT_PEO_45S5, CHIT_PEO_BG10 and CHIT_PEO_BGZn showed homogeneous fibers diameter distribution and CHIT_PEO composite electrospun mats showed some embedded bioactive glass particles a\u2013h. FurtThese interconnections between the main fibers and among the pre-formed joints are generated by electric poles which produce the main fibers. The electric poles do not only generate the main fibers but also the pre-formed joints creating other connection between these new joints  . FurtherThe results demonstrate that BGs particles did not influence the morphology of fibers in terms of both average fiber diameter and joints diameters. CHIT_PEO fibers and CHIT_PEO composites fibers are thinner compared to poly (\u03b5-caprolactone) (PCL)/CH fibers previously obtained by using benign solvents ,55; the \u22121 could be ascribed to O-P-O bending [\u22121 could be ascribed to Si-O stretching [FTIR analysis performed before soaking samples in SBF solution revealed the characteristic bands of chitosan and some corresponding to the bioactive glasses. The bands at 666 cm bending  and at 9retching  of bioac\u22121 correspond to amide I and amide II, respectively [\u22121 were ascribed to CH2 bending, CH2 wagging and CH2 symmetric twisting, respectively [\u22121 can be ascribed to oxygen stretching band [\u22121 can be attributed to C-O stretching [The characteristic bands of chitosan are marked by red dash lines in ectively . The banectively . The baning band . Finallyretching ,54.Mechanical properties of CHIT_PEO, CHIT_PEO_45S5, CHIT_PEO_BG10 and CHIT_PEO_BGZn were evaluated by uniaxial tensile test. The values of tensile at break (%) and Young\u2019s Modulus are detailed in p > 0.05) with respect to that of CHIT_PEO fibers, the findings suggest that the incorporation of bioactive glass particles could slightly improve the Young\u2019s Modulus.Values of tensile strain at break (%) of CHIT_PEO composite electrospun mats are quite comparable to neat CHIT_PEO fibers, used as control. Although the Young\u2019s Modulus of CHIT_PEO composite electrospun mats  is not sGenerally, the incorporation of particles  into polymers causes a decrease in the strain at break  because In vitro bioactivity of CHIT_PEO fibers and CHIT_PEO composite electrospun mats was investigated by immersion in SBF; this test assesses the capability of biomaterials to bond to bone by estimating the nucleation ability of hydroxycarbonate apatite (HCA) on samples\u2019 surface. Such in vitro bioactivity test is generally performed for bone tissue applications and its role and relevance for soft tissues applications is still a matter of debate in the literature . Some stp < 0.05) as shown by WST-8 assay  of CHIT_PEO composite electrospun mats was comparable to that of CHIT_PEO fibers.Additionally, CHIT_PEO and CHIT_PEO composite mats showed limited bioactivity . However, as discussed, this result does not necessarily represent a drawback for composites developed for wound healing applications.On the other hand, WST-8 assay showed that CHIT_PEO and CHIT_PEO composite mats are non-cytotoxic. In particular, 1 day after seeding, CHIT_PEO_2Zn showed higher OD value (p < 0.05) compared to CHIT_PEO fibers. Further studies incorporating different and higher concentration of BGs should be explored to identify the ad hoc concentration of these particular compositions of BGs to achieve enhanced biological performance.Furthermore, in vivo animal tests should be performed in the future to corroborate the preliminary results obtained in this in vitro study."}
+{"text": "P < 0.050) and that hsa_circ_0000467 expression levels were correlated with gastric cancer histological grade (P < 0.050). In addition, hsa_circ_0000467 was remarkably upregulated in gastric cancer cell lines (P < 0.001). Cell function experiments indicated that hsa_circ_0000467 downregulation decreased the proliferation and invasion ability of BGC-823 and SGC-7901 cells and the number of cells entering the G2/M phase. A direct binding interaction was detected between hsa_circ_0000467 and miR-326-3p by dual-luciferase reporter assays. In addition, the results showed that inhibition of miR-326-3p reversed the decreases in the proliferation and invasion of BGC-823 and SGC-7901 cells caused by hsa_circ_0000647 downregulation. Inhibition of miR-326-3p also decreased the number of cells entering the G2/M phase and the expression of cyclin D1. In conclusion, hsa_circ_0000467 plays a regulatory role in the development and progression of gastric cancer by regulating miR-326-3p, and this circRNA may be a potential diagnostic marker and therapeutic target of gastric cancer.Circular RNAs are a class of endogenous noncoding RNAs that play an important role in gene regulation. These RNAs are involved in the development and progression of various cancers, but their roles in gastric cancer have not yet been thoroughly elucidated. This study showed that hsa_circ_0000467 expression was higher in gastric cancer tissues than in corresponding adjacent tissues ( Gastric cancer (GC) is a common digestive system tumor that occurs worldwide and is the second most common cause of cancer morbidity and mortality in China . In 2015Circular RNAs (circRNAs) exist widely in humans. These RNAs are covalently closed-loop structures that do not have 5\u2032 to 3\u2032 polarity or a polyadenylation tail . CircRNAMicroRNAs (miRNAs) are a common class of noncoding, single-stranded RNA molecules with a length of 19\u201325 nucleotides that can regulate the expression of corresponding mRNAs by targeting their three-prime untranslated region (3\u2032-UTR) . Some stIn this study, we examined the expression level of hsa_circ_0000467 in GC tissues and corresponding adjacent tissues by qRT-PCR; we also detected its expression in GC cells and normal gastric mucosal cells. We confirmed a higher expression level of hsa_circ_0000467 in GC tissues, as well as in cell lines. At the same time, we evaluated the clinical significance of our findings. The effects of hsa_circ_0000467 downregulation on the proliferation, invasion, and cell cycle of GC cells were verified by CCK8 assays, Transwell assays, and flow cytometry. In addition, we explored the possible molecular mechanisms of hsa_circ_0000467 in promoting GC development by competitively binding to miR-326-3p through dual-luciferase reporter assays and rescue assays. Finally, we provided new ideas for potential new diagnostic and therapeutic targets of GC.Cancerous tissues and corresponding adjacent tissues were collected from 30 patients with GC from October 2017 to January 2018 at the Third Affiliated Hospital of Soochow University. No patients received preoperative radiotherapy or chemotherapy, and all patients were confirmed by pathology as having gastric adenocarcinoma and classified according to TNM staging. GC specimens and corresponding normal stomach mucosa tissues were chopped and stored in liquid nitrogen until further use. This study was approved by the Ethics Review Committee of Soochow University (No. SUERC-GC-2017-048). Before the experiment, all patients signed written informed consent.2 at 37\u00b0C. The cells were passaged every 2 to 3 days and were used for the experiments within 6 months.The GES-1, BGC-823, and SGC-7901 cell lines were used in this study. All of the cells were obtained from the Chinese Academy of Sciences and Shanghai Institutes for Biological Sciences. The cells were cultured in RPMI 1640 medium  containing 10% fetal bovine serum . All cells in this medium were placed in 5% CO\u2212\u0394\u0394CT). Each qRT-PCR analysis was repeated three times. All of the primers were synthesized by Ribobio, and the primer sequences are shown in Total RNA was extracted using Trizol reagent . RNA concentrations were measured by Beckman Coulter, and each of the paired RNA samples was adjusted to the same concentration. A One-Step SYBR PrimeScript RT-PCR Kit II  was used to conduct qRT-PCR assays for hsa_circ_0000467. A TaqMan MicroRNA Assays Kit  was used to assay miR-326-3p. We used GAPDH and U6 as internal controls, and qRT-PCR assays were performed on an ABI 7500 Real-Time PCR System . The relative gene expression of hsa_circ_0000467 and miR-326-3p in tissue specimens and cells was shown as the fold change . The knockdown efficiency was examined by qRT-PCR using RNA extracted 48\u2009h after transfection. GenePharma  synthesized the si-NC, si-hsa_circ_0000467, miR-326-3p inhibitor, and si-hsa_circ_0000467\u2009+\u2009miR-326-3p inhibitor.The GC cell lines BGC-823 and SGC-7901 were separately plated into four 60 mm culture dishes with 7.0\u2009\u00d7\u200910\u03bcl CCK8 solution  was added to the wells. After 1\u2009h of incubation, the absorbance was measured at 450\u2009nm by a microtiter plate reader . The final results were calculated as the average values measured three times under the same conditions. The GraphPad Prism5.0 software  was used to plot the histograms.In this experiment, cells from different transfection groups were inoculated on 96-well plates. Based on a comprehensive evaluation of various factors, the number of cells used in each well was 2,000. Then, the cells were incubated at 37\u00b0C. At 0\u2009h, 24\u2009h, 48\u2009h, and 72\u2009h, and 10\u2009\u03bcl of DMEM  was added to the lower chamber . These cells were cultured in a 37\u00b0C, 5% CO2 incubator for 24\u2009h. Finally, the Transwell chamber was rinsed, fixed in methanol, and stained with a 0.1% crystal violet solution , and the cells were observed under a microscope and photographed.Different groups of BGC-823 and SGC-7901 cells were plated onto a Matrigel-coated membrane in the upper well of a 24-well Transwell insert and 600\u2009BGC-823 and SGC-7901 cells were collected by centrifugation at low speed for five minutes, followed by adequate washing with phosphate buffer, and then, the cells were fixed with 70% ethanol, incubated for 12\u2009h at 4\u00b0C in an ice bath, and stained with Ribonuclease A  and PI  staining buffer. The cells were suspended in staining solution , and the cell cycle was analyzed by flow cytometry . The Modfit software  provided the estimation of the percentage of cells in G0/G1, S, and G2/M phases of the cycle.https://circinteractome.nia.nih.gov) was used to predict potential downstream miRNAs of hsa_circ_0000467. Then, qRT-PCR assays were used to detect the expression of these miRNAs when hsa_circ_0000467 was downregulated.The online target predicting database Circular RNA Interactome  and were named psiCHECK-hsa_circ_0000467-wild type (WT) and psiCHECK-hsa_circ_0000467-mutant (Mut). BGC-823 cells were cotransfected with miR-326-3p mimics and psiCHECK-hsa_circ_0000467-WT or psiCHECK-hsa_circ_0000467-Mut. Next, a dual-luciferase reporter assay system  was used to detect luciferase activity after transfection for 24\u2009h. Finally, the results were recorded.\u03bcg of protein for each sample was then transferred to polyvinylidene fluoride membranes . The membranes were blocked with skim milk powder and incubated with primary antibody for 12\u2009h at 4\u00b0C. The primary antibodies used for western blotting were rabbit anti-cyclin D1 , rabbit anti-c-MYC , and rabbit anti-HRP-GAPDH . Then, the blots were immunostained with a secondary antibody for 1\u2009h to 2\u2009h. The second antibody was goat anti-rabbit . The cells were then washed three times with TBST, subjected to chemiluminescence, and finally imaged with a gel imaging system. The relative expression of the protein was analyzed by densitometry analysis using Quantity One Analysis Software .BGC-823 and SGC-7901 cells were lysed with RIPA lysis buffer  containing the protease inhibitor PMSF . Protein quantification was performed using a Bradford protein assay kit . Next, polyacrylamide gel electrophoresis was used to separate equal amounts of protein, and 30\u2009t-test method was selected, and a variance test was carried out. The difference level was P < 0.05.The collected data were analyzed and processed using SPSS 22.0 . We used GraphPad Prism 5.0 software  to generate the figures. The results are described as the mean\u2009\u00b1\u2009standard deviation (SD), and the count data are expressed as percentages. For analyzing differences in results, an independent sample P=0.0224). However, there was no correlation between its expression level and other clinicopathological features (We performed a circRNA microarray assay using paired samples from 10 patients (H1710082 AS-CR-005 Human Circular RNA Microarray v2) and detected >10,000 circRNAs differentially expressed between the GC tissues and their corresponding adjacent tissues. These circRNAs were ranked according to the fold changes in expression between the groups, and the top 15 upregulated circRNAs are presented in features .In an attempt to study the biological function of the target circRNA in GC, we designed a small interfering RNA (siRNA) to silence hsa_circ_0000467 in BGC-823 and SGC-7901 cells. qRT-PCR was used to verify the transfection efficiency . We thenTo explore the mechanism of hsa_circ_0000467 in the process of tumorigenesis, we explored the target miRNAs of hsa_circ_0000467. We determined the sequence of hsa_circ_0000467 through an online target prediction database (Circular RNA Interactome) and identified the miRNAs that were most likely to bind to hsa_circ_0000467; then, we verified the targets by qRT-PCR assays. The results showed that under decreased hsa_circ_0000467 conditions, the expression level of miR-326-3p in GC was the highest .  FigureTo verify whether the target circRNA regulated the proliferation, invasion, and cell cycle of GC cells through miR-326-3p, we performed rescue experiments with hsa_circ_0000467 and miR-326-3p. The GC cell lines BGC-823 and SGC-7901 were used to create four different transfection groups: si-NC, si-hsa_circ_0000467, miR-326-3p inhibitor, and si-hsa_circ_0000467\u2009+\u2009miR-326-3p inhibitor. The transfection efficiency was verified by qRT-PCR . We founGC is a common digestive tract tumor. The incidence and mortality of GC are second to only lung cancer in China . AlthougWe selected hsa_circ_0000467 as a research object due to the results of a microarray assay analysis. Subsequent qRT-PCR results showed that this target circRNA was highly expressed in GC tissues and cells. When we knocked down it, the proliferation and invasion ability of BGC-823 and SGC-7901 cells decreased, and the number of cells entering the G2/M stage was reduced significantly. These results indicated that hsa_circ_0000467 could affect the biological function of GC.In recent years, there have been many studies on the role of miRNAs in cancer. It has been found that various abnormally expressed miRNAs are associated with GC; for example, aberrant expression of microRNA-31 may inhibit the proliferation of GC cells and induce apoptosis at an early stage . MiR-204MiR-326 is a newly discovered miRNA. This RNA affects the expression of various cytokines and transcription factor activity by regulating the corresponding target genes. For this reason, miR-326-3p could participate in the development of GC. Ji et al. identified that miR-326 inhibited GC cell growth by downregulating NOB1 . Li et aCyclin D1 and c-MYC are cyclins and proto-oncogenes involved in the development of GC. A variety of molecules can affect the biological function of GC by regulating cyclin D1 and c-MYC. For example, lycorine inhibits SGC-7901 cell proliferation mainly via reducing the expression level of cyclin D1, and the experimental results confirm this . SimilarDue to the short duration of the study, we did not compare hsa_circ_0000467 expression levels and patient survival rates. Further research will result in a better understanding of hsa_circ_0000467 and may provide more ideas for the diagnosis and treatment of GC.In conclusion, the results of this paper show that hsa_circ_0000467 is highly expressed in GC and plays a regulatory role in promoting the development of GC. In addition, hsa_circ_0000467 affects the proliferation, invasion, and cell cycle of GC by sponging miR-326-3p. In the future, hsa_circ_0000467 might be used as a marker for the diagnosis and therapy of GC."}
+{"text": "Sequencing technologies have advanced to the point where it is possible to generate high-accuracy, haplotype-resolved, chromosome-scale assemblies. Several long-read sequencing technologies are available, and a growing number of algorithms have been developed to assemble the reads generated by those technologies. When starting a new genome project, it is therefore challenging to select the most cost-effective sequencing technology, as well as the most appropriate software for assembly and polishing. It is thus important to benchmark different approaches applied to the same sample.de novo assembly of a plant genome, Macadamia jansenii. We have generated sequencing data using Pacific Biosciences (Sequel I), Oxford Nanopore Technologies (PromethION), and BGI (single-tube Long Fragment Read) technologies for the same sample. Several assemblers were benchmarked in the assembly of Pacific Biosciences and Nanopore reads. Results obtained from combining long-read technologies or short-read and long-read technologies are also presented. The assemblies were compared for contiguity, base accuracy, and completeness, as well as sequencing costs and DNA material requirements.Here, we report a comparison of 3 long-read sequencing technologies applied to the M. jansenii. At the time of sequencing, the cost associated with each method was significantly different, but continuous improvements in technologies have resulted in greater accuracy, increased throughput, and reduced costs. We propose updating this comparison regularly with reports on significant iterations of the sequencing technologies.The 3 long-read technologies produced highly contiguous and complete genome assemblies of  Advances in DNA sequencing enable the rapid analysis of genomes, driving biological discovery. Sequencing of complex genomes, which are very large and have a high content of repetitive sequences or many copies of similar sequences, remains challenging. Many plant genomes are complex, and the quality of published sequences remains relatively poor. However, improvements in long-read sequencing are making it easier to generate high-quality sequences for complex genomes.de novo sequencing of a plant, Macadamia jansenii. This is a rare species that is a close relative of the macadamia nut recently domesticated in Hawaii and Australia. In the wild, it grows as a multi-stemmed, evergreen tree reaching 6\u20139\u00a0m height with leaves having entire margins and generally in whorls of 3. The nuts are small (11\u201316\u00a0mm diameter) and have a smooth, hard, brown shell that encloses a cream, globulose kernel that is bitter and inedible [We now report a comparison of 3 long-read sequencing methods applied to the inedible . This isinedible . Knowledinedible  but are Macadamia integrifolia, Macadamia tetraphylla, Macadamia ternifolia, and Macadamia jansenii. Macadamia cultivars are diploid (2n = 28), with k-mer\u2013based genome size estimates ranging from 758 Mb for M. tetraphylla [M. integrifolia [M. integrifolia cultivar HAES 741 was constructed from short-read Illumina sequence data and was highly fragmented  [M. tetraphylla was also recently produced using a combination of long-read Oxford Nanopore Technologies (ONT) and short-read Illumina sequence data  [The macadamia genus contains 4 species: id 2n = 2, with k-id 2n = 2, with k-de novo genome assembly. ONT enables direct and real-time sequencing of long DNA or RNA fragments by analysing the electrical current disruption caused by the molecules as they move through a protein nanopore. More recently, BGI has introduced the single-tube Long Fragment Read (stLFR) [de novo assemblies. Here we compare Sequel I (PacBio), PromethION (ONT), and stLFR (BGI) data for the same DNA sample and evaluate the quality of the assemblies that can be generated directly from these datasets.Long-read sequencing provides data that facilitate easier assembly of the genome than is possible with short reads . The len (stLFR)  technolo (stLFR) ,16, i.e.M. jansenii were sourced from a tree with accession No. 1005 and located at the Maroochy Research Facility, Department of Agriculture and Fisheries, Nambour 4560, Queensland, Australia. The specimen of M. jansenii used in these experiments was a clonally propagated ex situ tree planted in the arboretum at Maroochy Research Facility. None of the leaves used in these experiments were collected from wild in situ trees. Young leaves were harvested, placed in on ice in bags, and within 3\u00a0h snap-frozen under liquid nitrogen and stored at \u221220\u00b0C until further processed for tissue pulverization using either a mortar and pestle or the Mixer Mill as outlined below.Young leaves (40\u00a0g) of g for 5\u00a0min in a swing-out bucket rotor. The supernatant was transferred into fresh 50-mL tubes and the chloroform extraction repeated twice. The supernatant was transferred to fresh 50-mL tubes and the DNA precipitated using isopropanol. For every 1\u00a0mL of the supernatant, 0.6\u00a0mL of isopropanol was added, and the content gently mixed by inverting the tubes 20\u201325\u00a0times. The tubes were incubated at room temperature for 15\u00a0min and then centrifuged at 3,500g for 5\u00a0min in a swing-out bucket rotor. The supernatant was discarded and the DNA pellet was washed of any co-precipitated salts by adding 10\u00a0mL of 70% ethanol and incubating the tubes at room temperature for 30\u00a0min. The tubes were centrifuged at 3,500g for 5\u00a0min in a swing-out bucket rotor, the supernatant discarded, and the DNA pellet semi-dried to remove any residual 70% ethanol by incubating the tubes for 10\u00a0min upside down over filter paper. The DNA was dissolved by adding 100 \u03bcL of TE buffer and then adding incremental 50 \u03bcL of TE buffer where required. The DNA solution was transferred to 2-mL nuclease-free tubes and then centrifuged at 14,000g for 45\u00a0min in a tabletop centrifuge. The supernatant was carefully transferred to fresh 2-mL tubes and the quality checked on a spectrophotometer, and the DNA was resolved on a 0.7% agarose gel. The DNA was then stored at \u221220\u00b0C until used for sequencing.Leaf tissue (10\u00a0g) was first coarsely ground under liquid nitrogen using a mortar and pestle. The mortar and pestle with the coarsely ground tissue with residual liquid nitrogen was then placed on dry ice. This step ensured that the temperature of the coarsely ground tissue was maintained close to \u221280\u00b0C while allowing the liquid nitrogen to evaporate off completely, an essential requirement for the pulverization step. The coarsely ground leaf tissue was pulverized into fine powder in 50-mL steel jars using the Mixer Mill MM400 . The pulverized leaf tissue was stored at \u221220\u00b0C until further required for DNA extraction. Genomic DNA (gDNA) was isolated from pulverized leaf tissue according to , with soRRID:SCR_017989) (software/chemistry v6.0.0). The library was prepared for sequencing according to the SMRT Link sample set-up calculator, following the standard protocol for Diffusion loading with AMPure PB bead purification, using Sequencing Primer v3, Sequel Binding Kit v3.0, and the Sequel DNA Internal Control v3. The polymerase-bound library was sequenced on 8 SMRT Cells with a 10\u00a0h movie time using the Sequel Sequencing Kit 3.0  and a Sequel SMRT Cell 1M v3 . Library preparation and sequencing was performed at the Institute for Molecular Bioscience Sequencing Facility (University of Queensland).DNA sequencing libraries were prepared using the Template Prep Kit 1.0-SPv3  according to the protocol for >30\u00a0kb SMRTbell Libraries . Genomic DNA (15 \u03bcg) was not fragmented and was instead just purified with AMPure PB beads. The purified gDNA (10 \u03bcg) was treated with Exonuclease VII, followed by a DNA damage repair reaction, an end-repair reaction, and purification with AMPure PB beads. Adapters were ligated to the purified, blunt-ended DNA fragments in an overnight incubation. The adapter-ligated sample was digested with Exonuclease III and Exonuclease VII to remove failed ligation products, followed by purification with AMPure PB beads. The purified sample was size selected using the Blue Pippin with a dye-free, 0.75% agarose cassette and U1 marker  and the 0.75% DF Marker U1 high-pass 30\u201340\u00a0kb vs3 run protocol, with a BPstart cut-off of 35,000 bp. After size selection, the samples were purified with AMPure PB beads, followed by another DNA damage repair reaction, and a final purification with AMPure PB beads. The final purified, size-selected library was quantified on the Qubit fluorometer using the Qubit dsDNA HS assay kit  to assess the concentration, and a 0.4% Megabase agarose gel  to assess the fragment size. Sequencing was performed using the PacBio Sequel I  and PromethION . The MinION library was prepared from 1,500\u00a0ng input DNA using the ligation sequencing kit  according to the manufacturer\u2019s protocol except the end-repair and end-prep reaction and ligation period were increased to 30\u00a0min. Third-party reagents NEBNext end repair/dA-tailing Module (E7546), NEBNext formalin-fixed paraffin-embedded DNA Repair Mix (M6630), and NEB Quick Ligation Module (E6056) were used during library preparation. The adapter-ligated DNA sample was quantified using QubitTM dsDNA HS Assay Kit . The MinION flow cell R9.4.1  was primed according to the manufacturer\u2019s guidelines before loading a library mix (75 \u03bcL) containing 438\u00a0ng of adapter-ligated DNA, 25.5 \u03bcL LB , and 37.5 \u03bcL SQB . The MinION sequencing was performed using MinKNOW (v1.15.4), and a standard 48-h run script. Before preparing the PromethION library, short DNA fragments (<10\u00a0kb) were first depleted from DNA sample (9 \u03bcg) as described in the manufacturer\u2019s instructions for the Short Read Eliminator (SRE) kit . The PromethION library was prepared from 1,200\u00a0ng SRE-treated DNA using the ligation sequencing kit . All steps in the library preparation were the same as the MinION library preparation except that the adapter-ligated DNA was eluted in 25 \u03bcL of Elution Buffer. The PromethION flow cell  was primed according to the manufacturer\u2019s guidelines before loading a library mix (150 \u03bcL) containing 390\u00a0ng of adapter-ligated DNA (24 \u03bcL), 75 \u03bcL of SQB, and 51 \u03bcL of LB . Sequencing was performed using MinKNOW (v3.1.23) and a standard 64-h run script. The sequencing run was stopped at 21\u00a0h and nuclease flush was performed to recover clogged pores. The Nuclease flushing mix was prepared by mixing 380 \u03bcL of Nuclease flush buffer  and 20 \u03bcL of DNase I . The Nuclease flushing mix was loaded into the flow cell and incubated for 30\u00a0min. The flow cell was then primed as mentioned above and loaded with the fresh library mix (150 \u03bcL) containing 390\u00a0ng of adapter-ligated DNA and the standard 64-h run script was rerun using MinKNOW. Refuelling of the sequencing run was performed at each 24\u00a0h by adding 150 \u03bcL of diluted SQB  to keep the stable translocation speed of sequencing. ONT fast5 reads were base-called using Guppy v3.0.3 with the config file dna_r9.4.1_450bps_hac_prom.cfg (PromethION) or dna_r9.4.1_450bps_hac.cfg (MinION) and parameters --qscore_filtering -q 0 --recursive --device \u201ccuda:0 cuda:1 cuda:2 cuda:3\".The quality of the DNA sample was assessed in NanoDrop, Qubit, and the Agilent 4200 TapeStation system. The DNA sample was sequenced on the ONT MinION  and the QubitTM dsDNA HS Assay Kit  for a more accurate quantification result. Approximately 1.5\u00a0ng of original genomic DNA molecules were used for library preparation. In the first step, transposons composed of a capture sequence and a transposase recognition sequence were inserted at a regular interval along the gDNA molecules. Next, these transposon-inserted DNA molecules were hybridized with barcode-labelled 3-\u03bcm diameter magnetic beads containing oligonucleotide sequences with a PCR primer annealing site, an stLFR barcode, and a sequence complementary to the capture sequence on the transposon. After hybridization, the barcode was transferred to the transposon-inserted DNA subfragments through a ligation step. The excess oligonucleotides and transposons were then digested with exonuclease and the transposase enzyme was denatured with sodium dodecyl sulfate. Next, the second adapter was introduced by a previously described 3\u2032-branch ligation using T4 ligase [TM dsDNA HS Assay Kit . The PCR product fragment sizes were assessed using an Agilent High Sensitivity DNA Kit  on a Agilent 2100 Bioanalyzer. The average fragment size of the prepared stLFR library was 1,003\u00a0bp. A quantity of 20\u00a0ng of PCR product from the stLFR library was used to prepare DNA Nanoballs (DNBs) using the DNBSEQ-G400RS High Throughput stLFR Sequencing Set  following the manufacturer\u2019s protocol. The prepared DNB library was loaded onto 2 lanes of a DNBSEQ-G400RS flow cell  and then sequenced on a DNBSEQ-G400RS  using the DNBSEQ-G400RS stLFR sequencing set . Library preparation and sequencing were performed at the BGI Australia Sequencing Facility  and BGI-Shenzhen .The stLFR sequencing libraries were prepared using the MGIEasy stLFR Library Prep Kit  following the manufacturer\u2019s protocol. Briefly, genomic DNA samples were serially diluted and then quantified using the Qubit4 ligase . FinallyThe Illumina library was prepared using the Nextera Flex DNA kit. The library was sequenced on an SP flow cell (14%) of the Illumina Nova Seq 6000 sequencing platform  using the paired-end protocol to produce 112 million 150-bp reads in pairs, an estimated 43\u00d7 genome coverage. The median insert size was 713\u00a0bp.ONT read length and quality were calculated with NanoPlot v1.22 . Long reAll: no filtering of readsRRID:SCR_016967) [Filtered: ONT long reads were adapter-trimmed using Porechop v0.2.4  . ONT and_016967)  by removPass (ONT only): only the passed reads were used .RRID:SCR_011848) [RRID:SCR_016962) [The PacBio subreads were randomly subsampled down to a 32\u00d7 genome coverage using Rasusa v0.1.0 . Raw Ill_011848)  (LEADING_016962) .k-mer counting using the trimmed Illumina and BGI reads was performed using Jellyfish v2.210  [k-mer frequency distributions of 21-, 23-, and 25-mers. The histograms of the k-mer occurrences were processed by GenomeScope  [The _005491) , generat_017014) , which eDe novo assembly of ONT and PacBio reads was performed using Redbean v2.5  [RRID:SCR_017016) [RRID:SCR_015880) [RRID:SCR_017642) [RRID:SCR_014731) [RRID:SCR_010910) [RRID:SCR_010691) [de novo genome assembly of PacBio reads was performed with FALCON v1.3.0  [RRID:SCR_000131) [_017225) , Flye v2_017016) , Canu v1_015880) , and Rav_015880)  with def_015880) . For ONT_017642)  with rec_017642)  overlaps_017642)  using th_014731)  using th_010910)  and with_010910)  with def_010691)  using th_016089)  using a _016089) , polishi_016089) . A read _016089)  to obtai_000131) .RRID:SCR_011841) [De novo assembly was performed by Supernova v2.1.1  [RRID:SCR_017633) [Two lanes of stLFR reads for the same sample were demultiplexed using a subfunction of SuperPlus v1.0  and comb_011841)  with the_016756)  using th_017633) ,46 was uThe technical specifications of the computing clusters used in this study are provided in RRID:SCR_001228) [M. integrifolia v2 (Genbank accession: GCA_900631585.1) [RRID:SCR_015008) [RRID:SCR_016741) [k-mer assembly completeness by reference to the Illumina or stLFR short reads.Assembly statistics were computed using QUAST v5.0.2   with a m_015008)  with the_016741)  comp andIllumina sequencing generated 112.5 million 150-bp paired-end reads, which correspond to \u223c41\u00d7 coverage of the genome. After adapter and polyG tail trimming, short reads were assembled using the SPAdes software. The resulting assembly consisted of 1,631,183 contigs totaling 864 Mb in length and contained 15,583 contigs >10\u00a0kb with a total length of 338 Mb . The assFor the ONT sequencing, we combined the results of 1 PromethION and 1 MinION flow cell, generating a total of 24.9 Gb of data with a read length N50 of 27.8\u00a0kb , 70% (Canu), or 79%  to 85% (Redbean) or 89%  after long-read polishing and 92% (Redbean) or 95%  after long-read and short-read polishing . As an eThe base accuracy metrics suggest that NextPolish performed slightly better than Pilon. In particular, the number of indels was greatly reduced after polishing with NextPolish as compared to Pilon  followed by Canu (97.9%) and Raven (97.4%) and finally Redbean (92.3%). The trends were similar when the k-mer analysis was performed using the stLFR short reads.Assembly completeness was also estimated by comparing the rt reads . The k-mAs an alternative method to long-read\u2013only assembly followed by polishing with short reads, a hybrid assembly was generated using MaSuRCA. The ONT + Illumina assembly showed a similar size (797 Mb), contiguity (contig N50 = 1.18 Mb), completeness (94.8% complete BUSCOs including 15.5% duplicated BUSCOs), and a slighlty lower accuracy  as the Flye and Raven assemblies with subsequent polishing with Illumina reads Figs\u00a0. Short-rk-mer spectra  as compared to the other assemblies . The PacBio + Illumina hybrid assembly  contained 94.9% of complete BUSCOs including 16% of duplicated genes  led to a similar (Flye and Raven) or slightly higher (Redbean) assembly contiguity without affecting the genome completeness  . FinallystLFR generated 738 million 100-bp paired-end reads. To meet the requirements of the assembler, the barcodes with < 10 reads were removed, which resulted in 373 million reads representing 74.6 Gb of data and corresponding to \u223c96\u00d7 coverage of the genome Table\u00a0. When cok-mer assembly completeness increased in the gap-filled assemblies from 95.8% to 96.7% (ONT) and 97.4% (PacBio)  . Further(PacBio)  and a de(PacBio) .M. jansenii was selected for this study because of its significance in conservation and breeding. All 4 species of Macadamia are listed as threatened under Australian legislation, but M. jansenii is particularly vulnerable because it has been recorded at only 1 location. M. jansenii has not been domesticated, and its small and bitter nuts are obstacles that restrict simple introgression in breeding. However, the characteristic small tree size, being 50% smaller than commercial cultivars, is of interest for use in high-density orchard design and it is being trialled as a rootstock for this purpose [Macadamia species and may be a source of genes for adaptation to warmer climates [M. integrifolia and M. jansenii have been produced.We report a comparison of 3 long-read sequencing datasets generated from the same plant DNA sample.  purpose . It is tclimates . HybridsThe 3 long-read sequencing technologies significantly improved the assembly completeness as compared to the assembly produced using the Illumina reads only (65% of complete BUSCOs). The cost of generating 1 Gb of sequencing data (including the library preparation) was 193 USD for PacBio Sequel I, 97 USD for ONT PromethION, and 12 USD for BGI stLFR (raw reads subsequently used in assembly). Virtual long reads were generated using the stLFR protocol. This technology benefits from the accuracy and the low cost of a short-read sequencing platform while providing long-range information. stLFR was the cheapest approach, and it generated an assembly with the fewest single base and indel errors. Furthermore, the assembly generated by Supernova was phased. That said, the stLFR assembly was more fragmented than the other long-read technologies. We also demonstrated that stLFR could be used as a complementary technology to ONT. Indeed, the inclusion of Nanopore reads significantly increased the stlFR assembly contiguity, with N50 reaching 1 Mb, and improved the genome completeness. Interestingly, the gap-filling step only used 1.7% of the ONT reads, suggesting that a real-time selective sequencing approach could be used to select specific molecules that would be informative for filling the gaps .When all the reads were incorporated, the assemblies generated using the PacBio and ONT data were comparable in terms of assembly contiguity (contig N50 of \u223c1.5 Mb) and genome completeness (95% of complete BUSCOs). However, when we utilized the same amount of data for each platform (32\u00d7 coverage), the contiguity of the PacBio assembly produced by Falcon was halved and became only half the size of the ones from the ONT Flye or Canu assemblies. The Flye and Raven assemblers proved to be more robust to the PacBio coverage drop as the assembly contig N50 only decreased from 1.47 to 1.26 Mb (Flye) and from 919 to 894\u00a0kb (Raven). Additionally, we found that polishing the ONT assembly with the Illumina short reads was required to reach a similar genome completeness to that of the PacBio assembly. For both ONT and PacBio data, the highest contiguity was obtained with a long-read polished assembly as compared to a hybrid assembly incorporating both the short and long reads.Since the sequence data were generated, the PacBio SMRT platform has transitioned from the Sequel I to the Sequel II instrument, with an 8-fold increase in the data yield. The latest platform produces high-fidelity reads that are more accurate than the continuous long reads assembled in this study. Consequently the cost to generate a similar PacBio assembly on the Sequel II system will be dramatically reduced and the assembly quality is likely to improve while requiring fewer computational resources.The DNA material requirements to prepare the sequencing library are another important parameter to consider when choosing a sequencing technology. For ONT sequencing, it is recommended to obtain \u22651\u20132 \u03bcg of high molecular weight DNA. The stLFR library construction requires \u226510\u00a0ng of high molecular weight DNA. PacBio SMRT sequencing has a high genomic DNA input requirement of 5\u201320 \u03bcg of high molecular weight DNA for standard library protocol depending on the genome size but the PacBio low DNA input protocol has reduced this requirement to as low as 100\u00a0ng per 1 Gb genome size . FurtherThe computational requirements and associated cost should be considered and will largely depend on the genome size of the species of interest. There were important differences in the assembly run time and memory usage depending on the tool used. For instance, short-read polishing using NextPolish used less memory than Pilon while providing similar results. GPU-accelerated computing greatly reduced the computing time for some tools such as Racon, Medaka, or Raven. There are also challenges associated with the rapid evolution of technologies and software. For example we observed a significant improvement in the ONT assembly contiguity depending on the basecaller or assembler version used. The newest releases of assemblers such as Canu v2.1, Flye v2.8, or Raven v1.1.10 will likely generate improved assemblies.The 3 long-read technologies produced highly contiguous and complete genome assemblies. Next, long-range scaffolding approaches such as chromosome conformation capture  or physical maps technologies  are required to order and orient the assembled contigs into chromosome-length scaffolds .GigaScience GigaDB repository [BGI, PacBio, ONT, and Illumina sequencing data generated in this study have been deposited in the SRA under BioProject PRJNA609013 and BioSample SAMN14217788. Accession numbers are as follows: BGI (SRR11191908), PacBio (SRR11191909), ONT PromethION (SRR11191910), ONT MinION (SRR11191911), and Illumina (SRR11191912). Assemblies and other supporting data are available from the pository .Table S1: Technical specifications of computing clustersTable S2: Estimation of computational costs based on Amazon EC2 on-demand pricing as of 19 September 2020Table S3: Illumina genome assembly statistics using SPAdes assemblerTable S4: ONT genome assembly statistics using Redbean, Flye, Canu, Raven, and MaSuRCA assemblersTable S5: BUSCO genome completeness assessment of ONT long-read assemblies  and hybrid assembly (MaSuRCA)Table S6: QUAST assembly statistics using the Illumina short-read assembly as the reference genomeTable S7: \u00a0k-mer completeness of ONT, PacBio, and stLFR assembliesTable S8: PacBio genome assembly statistics using Redbean, Flye, Falcon, Canu, Raven, and MaSuRCA assemblersTable S9: PacBio genome assembly statistics and genome completeness assessment before and after Purge HaplotigsTable S10: BUSCO genome completeness assessment of PacBio long-read assemblies  and hybrid assembly (MaSuRCA)Table S11: BGI stLFR genome assembly statistics using Supernova assembler and TGS-GapCloser gap-closing softwareFigure S1: Genome assembly statistics. The total assembly length is plotted against the contig N50 for each assembler and sequencing coverage. (A) ONT assemblies, (B) PacBio assemblies.Figure S2: BUSCO genome completeness assessment. (A) ONT assemblies before and after Illumina short-read polishing using 1 iteration of NextPolish  and MaSuRCA hybrid assembly, (B) PacBio assemblies using 32\u00d7 or 84\u00d7 sequencing coverage, (C) BGI stLFR assemblies before and after gap filling using ONT or PacBio data and after polishing using stLFR reads and 1 iteration of NextPolish.Figure S3: Number of mismatches and indels identified in the long-read assemblies as compared to the Illumina short-read assembly generated by SPAdes. (A) ONT assemblies before and after Illumina short-read polishing using 1 iteration of NextPolish  and MaSuRCA hybrid assembly; (B) PacBio assemblies before and after Illumina short-read polishing using 1 iteration of NextPolish  and MaSuRCA hybrid assembly; (C) BGI stLFR assemblies before and after gap filling using ONT or PacBio data and after polishing with stLFR reads using 1 iteration of NextPolish.Figure S4: \u00a0k-mer spectra plots from the k-mer Analysis Toolkit comparing the k-mers found in Illumina reads to the k-mers found in ONT, PacBio, stLFR, and Illumina assemblies.AUD: Australian dollars; bp: base pairs; BGI: Beijing Genomics Institute; BUSCO: Benchmarking Universal Single-Copy Orthologs; BWA: Burrows-Wheeler Aligner; DNB: DNA Nanoballs; dsDNA: double-stranded DNA; Gb: gigabase pairs; gDNA: genomic DNA; GPU: graphics processing unit; kb: kilobase pairs; Mb: megabase pairs; ONT: Oxford Nanopore Technologies; PacBio: Pacific Biosciences; QUAST: QUality ASsessment Tool; SMRT: single-molecule real-time; SPAdes: St. Petersburg genome assembler; SRA: Sequence Read Archive; SRE: Short Read Eliminator; stLFR: single-tube long fragment reads; SQB: sequencing buffer; USD: United States Dollar.Employees of BGI , MGI (H.W.), and Complete Genomics  have stock holdings in BGI. The authors declare that they have no other competing interests.This work was funded by the Genome Innovation Hub, Office of Research Infrastructure, The University of Queensland. This work was supported in part by the Shenzhen Peacock Plan (NO.KQTD20150330171505310). L.J.M.C. was supported by a Discovery Project with grant number DP170102626 awarded by the Australian Research Council.A.F. prepared the sample. B.T. supervised plant collection. S.K.R. performed ONT library preparation and sequencing. T.J.C.B. performed PacBio library preparation and sequencing. V.M. performed Illumina, ONT and PacBio assemblies and assembly evaluation. Q.Y. and H.W. performed stLFR library preparation and sequencing. I.H. supervised and reviewed stLFR library preparation and sequencing. W.T. performed stLFR assembly, gap filling, and statistics for stLFR. E.A., Q.M., R.D., O.W., and B.A.P. designed stLFR experiments and performed stLFR analyses. M.X. and P.W. supported stLFR analyses. B.Y. reviewed the manuscript. V.M. wrote the manuscript with input from all authors. R.J.H. and L.J.M.C. designed and supervised the project.giaa146_GIGA-D-20-00077_Original_SubmissionClick here for additional data file.giaa146_GIGA-D-20-00077_Revision_1Click here for additional data file.giaa146_GIGA-D-20-00077_Revision_2Click here for additional data file.giaa146_GIGA-D-20-00077_Revision_3Click here for additional data file.giaa146_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giaa146_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giaa146_Response_to_Reviewer_Comments_Revision_2Click here for additional data file.giaa146_Reviewer_1_Report_Original_SubmissionC\u00c3\u00a9cile Monat, Ph.D. -- 4/20/2020 ReviewedClick here for additional data file.giaa146_Reviewer_1_Report_Revision_1C\u00c3\u00a9cile Monat, Ph.D. -- 7/23/2020 ReviewedClick here for additional data file.giaa146_Reviewer_2_Report_Original_SubmissionMile \u00c5 iki\u00c4\u2021 -- 5/2/2020 ReviewedClick here for additional data file.giaa146_Reviewer_2_Report_Revision_1Mile \u00c5 iki\u00c4\u2021 -- 7/26/2020 ReviewedClick here for additional data file.giaa146_Reviewer_2_Report_Revision_2Mile \u00c5 iki\u00c4\u2021 -- 10/1/2020 ReviewedClick here for additional data file.giaa146_Supplemental_FilesClick here for additional data file."}
+{"text": "Spodoptera litura female moths. Results show a divergent change in the differentially expressed genes (DEGs) between reproduction and immunity: the immune response was largely downregulated shortly after mating (~6 h postmating), which has some recovery at 24 h postmating; reproductive response is trivial shortly after mating (~6 h postmating), but it largely upregulated at 24 h postmating . Considering the fact that most of the total DEGs downregulated from 0 to 6 h postmating (from 51/68 to 214/260) but most of the total DEGs upregulated at 24 h postmating (816/928), it is possible that trade-offs between reproduction and immunity occurred in mated females. For example, they may shut down immunity to favor sperm storage and save limited resources to support the increased energy required in reproduction . Mating-induced infections should be trivial due to low polyandry in S. litura. A reduced immune defense may have no threat to S. litura survival but may benefit reproduction significantly. Furthermore, obvious expression changes were detected in genes related to hormone production, suggesting that endocrine changes could play important roles in postmating responses.Mating promotes reproductive activity, which may impact immune performance. Paradoxically, mating frequently challenges females\u2019 immunity . Therefore, studies of postmating resource allocation between reproduction and survival are likely to shed new light on life-history trade-off and sexual selection. Here, we used RNAseq to test whether and how mating affected mRNA expression in genes related to reproduction and immunity in  Studies have investigated the effects of mating on female gene expression in a number of insect species and found mating-induced expression changes in many (dozens to hundreds) genes, such as transcription factors, metabolic enzymes, and genes related to hormones synthesis, immune defense , egg maturation  the postmating behavioral and physiological changes in females is related to gene expression, 2) mating will positively affect the activity of reproductive-related genes but negatively affect the activity of immunity-related genes in females. To test these hypotheses, we performed transcriptome differential analysis between virgin and mated females at three time points . The previous substantial study on reproductive behavior and physiology in S. litura (see references above) and the reliable sequencing data and in-depth gene mining in this study allow us to discuss the evolutionary significance of the postmating gene expression regulation between reproduction and immunity.Based on the above findings in S. litura larvae were reared on an artificial diet  and the concentration and purity of RNA were measured by using Qubit RNA Assay Kit  and the NanoPhotemeter spectrophotometer . The integrity of RNA was detected by the RNA Nano 6000 Assay Kit . A total of 3 \u03bcg RNA per sample was used for the preparation of the sequencing libraries by using NEBnext Ultra RNA Library Prep Kit for Illumina  following the manufacturer\u2019s instructions and index codes were added to attribute sequences to each sample.The clustering of the index-coded samples was performed on a cBot Cluster Generation System using TruSeq PE Cluster Kit v3-cBot-HS  according to the manufacturer\u2019s protocol. After cluster generation, the library preparations were sequenced on an Illumina HiseqTM 4000 platform and 125 bp/150 bp paired end reads were generated.S. litura (https://www.ncbi.nlm.nih.gov/genome/?term=spodoptera+litura) using Hisat2 v2.0.5 software.The original data was filtered to ensure the quality and reliability for further analysis, which mainly includes removing the reads of the adapter, reads containing N (N means that the base information cannot be determined) and low-quality reads  from the raw data. The values of Q20, Q30, and GC content of the clean data were calculated. These clean reads were then mapped to the reference genome sequence of P-value was adjusted using q-value (q < 0.05 and |log2(foldchange)|>1 was set as the threshold for significantly differential expression.Gene expression levels were analyzed by using the expected number of Fragments Per Kilobase of transcript sequence per Millions base pairs sequenced (FPKM) method. The differential expression analysis between samples was performed using the edgeR R package (3.0.8).  q-value . q < 0.0q < 0.05 were significantly enriched in DEGs.Using the BLAST  softwareActin (GeneBank ID: 111359844) was used as a reference gene , and the cDNA synthesis was performed using PrimeScript RT reagent Kit with gDNA Eraser  . The qRT-PCR primers were designed by Prime Premier 6.0 . Real-tince gene . The 2-\u0394T method  was usedBy RNAseq using Illumina HiSeq4000 platform, ~60,000,000 clean reads were obtained from each of the 12 sequenced libraries . The perS. litura. There were eight genes shared between Mated-0h versus Virgin-0h DEGs and Mated-6h versus Virgin-6h DEGs, 21 genes shared between Mated-0h versus Virgin-0h DEGs and Mated-24h versus Virgin-24h DEGs, and 33 genes shared between Mated-6h versus Virgin-6h DEGs and Mated-24h versus Virgin-24h DEGs, while only one gene  was shared by the three DEGs groups    , 111355246 (Odorant receptor), and 111350604 (Yolk protein), which may function in egg development and oviposition and/or host location. However, all three genes were downregulated in mated females at the time of 0 h postmating compared to virgin females.Only three reproductive-related genes were found within these DEGs . They arSimilarly, only five immunity-related genes were found within these DEGs . They alAt 6 h after mating, 260 DEGs were identified between mated and virgin groups, in which 214 genes were downregulated and 46 genes were upregulated in mated females at the time of 6 h postmating compared to virgin females . The LFC111361107 (Insulin-degrading enzyme), 111364657 . The upregulation of 111364657 may promote oogenesis, while the upregulation of 111361107 may inhibit oogenesis. The three downregulated genes include 111348993 , 111352474 (Odorant receptor), and 111364510 .Still fewer (five) reproductive related genes were found within these DEGs with two upregulated and three downregulated (LFC ranged from \u22122.36 to 1.43) . The twoLysozyme  and others were antimicrobial peptides (2 upregulated and 14 downregulated) (Relative more (17) immunity-related genes were found within these DEGs. One is gulated) . Among tThere were 928 DEGs between Mated-24h and Virgin-24h groups, of which 112 genes were downregulated and 816 genes were upregulated in mated females at the time of 24 h postmating compared to virgin females . The LFCEcdysteroid UDP-glucosyltransferases , one Octopamine receptor (upregulated) and one Dopamine receptor-interacting protein (upregulated); 2) egg development related, including Yolk protein, Vitellogenin and its receptor, Chorion peroxidase and Sex combs reduced, all upregulated with LFC ranged from 2.22 to 12.72; 3) olfactory activity related, including five Pheromone-binding proteins  and nine Odorant receptors (four upregulated and five downregulated); 4) pheromone production related, all of the seven genes were Alcohol-forming fatty acyl-CoA reductase .A total of 54 reproductive related genes were found within the 928 DEGs with 10 of them downregulated and 44 upregulated . These gLysozyme (LFC: 2.32 to 5.97), two Phenoloxidase (LFC: 1.92 to 3.48), one Fungal protease inhibitor (LFC: 2.66) and 14 Antimicrobial peptides .Relatively fewer (19) immunity-related genes were found within these DEGs , includi111350604, 111350990, 111360733 and 111363907) and one immunity-related gene (Novel00495) , which includes four reproduction-related genes (el00495) . The expq < 0.05) was small (68) immediately after mating, which then increased to 260 at 6 h postmating and then soared to 928 at 24 h postmating  and showed high levels of oviposition-site searching activity  (D. melanogaster (the no. of DEGs increased from 237 to 545 during 0\u201372 h postmating) (D. melanogaster (the no. of DEGs decreased from 64 to 10 during 1\u201324 h postmating)   and the tmating) , while atmating) . In the stmating . In femastmating . Althoug. litura  and othe. litura .Insect immune defenses are carried out through humoral and hemocyte responses. The humoral response occurs by synthesis of antimicrobial peptides, while hemocytes play defensive roles through encapsulation and phagocytosis . AntimicYolk protein, Vitellogenin and its receptor, which play vital roles in oocyte and embryo development in insects  pheromone production-related DEGs, such as Alcohol-forming fatty acyl-CoA reductase that function in proportion regulation of each component in the pheromone blend.Compared to immunity-related DEGs, the categories of the reproductive related DEGs are more abundant . These i insects ; 3) olfaSpodoptera litura females usually mate only one or two times in their lifetime  is transferred to females , Mated-6h vs Virgin-6h group (B), and Mated-24h vs Virgin-24h group (C). The function of DEGs was divided into three parts: BP , CC (cell composition), and MF (molecular function). The red bars indicate upregulated DEGs and blue bars indicate downregulated DEGs.q-value ranges.Fig. S2. KEGG pathway enrichment of DEGs in Mated-0h vs Virgin-0h group , Mated-6h vs Virgin-6h group , and Mated-24h vs Virgin-24h group . The size of the dot indicates the number of DEGs in this pathway, and the color of the dot corresponds to different ieaa003_suppl_Supplementary_Figure_S1Click here for additional data file.ieaa003_suppl_Supplementary_Figure_S2Click here for additional data file.ieaa003_suppl_Supplementary_Table_S1Click here for additional data file.ieaa003_suppl_Supplementary_Table_S2Click here for additional data file.ieaa003_suppl_Supplementary_Table_S3Click here for additional data file.ieaa003_suppl_Supplementary_Table_S4Click here for additional data file.ieaa003_suppl_Supplementary_Table_S5Click here for additional data file.ieaa003_suppl_Supplementary_Table_S6Click here for additional data file.ieaa003_suppl_Supplementary_Table_S7Click here for additional data file.ieaa003_suppl_Supplementary_Table_S8Click here for additional data file.ieaa003_suppl_Supplementary_Table_S9Click here for additional data file.ieaa003_suppl_Supplementary_Table_S10Click here for additional data file.ieaa003_suppl_Supplementary_Table_S11Click here for additional data file.ieaa003_suppl_Supplementary_Table_S12Click here for additional data file.ieaa003_suppl_Supplementary_Table_S13Click here for additional data file.ieaa003_suppl_Supplementary_Table_S14Click here for additional data file."}
+{"text": "Whereas the OCMLGSI takes into consideration the index correlation values among stages, the DCMLGSI imposes the restriction that the index correlation values among stages be zero. Using real and simulated datasets, we compared the efficiency of both indices in a two-stage context. The criteria we applied to compare the efficiency of both indices were that the total selection response of each index must be lower than or equal to the single-stage combined linear genomic selection index (CLGSI) response and that the correlation of each index with the net genetic merit should be maximum. Using four different total proportions for the real dataset, the estimated total OCMLGSI and DCMLGSI responses explained 97.5% and 90%, respectively, of the estimated single-stage CLGSI selection response. In addition, at stage two, the estimated correlations of the OCMLGSI and the DCMLGSI with the net genetic merit were 0.84 and 0.63, respectively. We found similar results for the simulated datasets. Thus, we recommend using the OCMLGSI when performing multistage selection.A combined multistage linear genomic selection index (CMLGSI) is a linear combination of phenotypic and genomic estimated breeding values useful for predicting the individual net genetic merit, which in turn is a linear combination of the true unobservable breeding values of the traits weighted by their respective economic values. The CMLGSI is a cost-saving strategy for improving multiple traits because the breeder does not need to measure all traits at each stage. The  The linear selection index can be a linear combination of phenotypic values , genomicThe selection response and the correlation between the index and the net genetic merit are the main index parameters; they are also the criteria used to compare the efficiency of any linear index to predict the net genetic merit. When the mean of the original population is zero, the selection response is the expected net genetic merit of the selected individuals .Some problems associated with the OMLPSI are as follow. First, after the first selection stage, the OMLPSI values could be non-normally distributed. Second, for more than two stages, the OMLPSI requires multiple integration techniques to derive selection intensities. Third, there are problems of convergence when the traits and the index values of successive stages are highly correlated, and finally, the computational time could be unacceptable if the number of selection stages becomes too high . For thedecorrelated multistage index (In a similar manner as ge index . Under tge index .In the marker-assisted selection (MAS) context, In this work, we adapted the optimum combined multistage linear genomic selection index (OCMLGSI), while the decorrelated index was called decorrelated combined multistage linear genomic selection index (DCMLGSI) because, at stage two, both indices use GEBV and phenotypic information jointly to predict the net genetic merit. While the OCMLGSI was based on the We validated the results of the proposed index using the optimum and decorrelated selection index theory in a two-stage breeding selection scheme (this approach can be extended to any number of stages). The optimum index was named We compared the relative efficiency of OCMLGSI and DCMLGSI using real and simulated datasets. The criteria used to compare the relative efficiency of both indices were that the total selection response of each index must be lower than, or equal to, the single-stage CLGSI  selectiohttp://hdl.handle.net/11529/10548356.We completed this section with three supplementary materials  that are located at Two objectives of the OCMLGSI and DCMLGSI are to maximize the selection response and predict the net genetic merit , we extend the OCMLGSI theory to the multistage context. Here we present only the main results for the two stages. Since the OCMLGSI theory is based on LPSI theory, the OCMLGSI vector of coefficients (http://hdl.handle.net/11529/10548356).In Supplementary material 1 (see http://hdl.handle.net/11529/10548356), we extended the DCMLGSI theory to the multistage context, and we showed that the DCMLGSI vector of coefficients at stage 2 ishttp://hdl.handle.net/11529/10548356) that makes the covariances of the DCMLGSI values among stages null. Matrix In Supplementary material 2 (see The maximized DCMLGSI selection response for stage two isThe maximized correlation between Note that the only difference between Equations (10) and (7), and Equations (11) and (8) is the vector of coefficients of each index.The OCMLGSI selection intensities for stages 1 and 2  and analytical test procedures . The corroboration procedure was as follows. In a two-stage context, let vs. DCMLGSI efficiency were that the total OCMLGSI and DCMLGSI selection responses (The criteria to compare OCMLGSI efficiency Zea mays L.) F2 population with 247 genotypes, 195 markers and 4 phenotypic traits: grain yield , plant height , ear height , and anthesis days  to compare OCMLGSI efficiency vs. DCMLGSI efficiency to predict the net genetic merit. We used a real maize . At stage 2 we used all three matrices .The estimated matrices of Zea mays L.) populations. A different number of QTL affected each of the four traits: 300, 100, 60, and 40, respectively. The common QTL affecting the traits generated genotypic correlations of -0.5, 0.4, 0.3, -0.3, -0.2, and 0.1 between The datasets were simulated with QU-GENE software  by CeronApplication of a Genomics Selection Index to Real and Simulated Data repository, at http://hdl.handle.net/11529/10199. The real dataset used in this work is the folder named \u201cFile Real_Data_Sets_GSI\u201d which contains four folders named \u201cDATA_SET-3, 4, 5, and 6\u201d. Each of the four folders contains four Excel data files. The four Excel data files within the folder DATA_SET-3 are as follows: DATA_SET-3_Markers_Cycle-0, 1, 2, and DATA_SET-3_Phenotypic_Cycle-0. The first three Excel files contain the marker-coded values for cycles 0, 1, and 2, while the Excel file DATA_SET-3_Phenotypic_Cycle-0 contains the phenotypic information of cycle 0 (training population). The Excel data files of the other folders were described in a similar manner as for folder 3. In this work, we used dataset 3 for cycle 0 to make selections and to estimate the selection response and the correlation of the OCMLGSI and DCMLGSI with the net genetic merit. The results are presented in The real and simulated datasets are available in the Folder Simulated_Data_GSI contains two folders: Data_Phenotypes_April-26-15 and Haplotypes_GSI_April-26-15. In turn, folder Data_Phenotypes_April-26-15 also contains two folders: GSI_Phenotypes-05 and PSI_Phenotypes-05. Within folder GSI_Phenotypes-05, there are six Excel data files, each denoted as C2_GSI_05_Pheno, C3_GSI_05_Pheno, C4_GSI_05_Pheno, C5_GSI_05_Pheno and C6_GSI_05_Pheno, corresponding to the phenotypic simulated information for the genomic selection index for cycles 2-7. In addition, folder GSI_Phenotypes-05 contains eight Excel datasets denoted as C0_Pheno_05, C1_PSI_05_Pheno, C2_PSI_05_Pheno, C3_PSI_05_Pheno, C4_PSI_05_Pheno, C5_PSI_05_Pheno, C6_PSI_05_Pheno, and C7_PSI_05_Pheno corresponding to the phenotypic simulated information for the phenotypic selection index for cycles 0-7. File Haplotypes_GSI_April-26-15 contains the haplotypes of the markers for cycles 0-7 of GSI. We present the results of the simulated datasets in DATA_SET-3_Phenotypic_Cycle-0\u201d (which contains the raw phenotypic data) and (2) the \u201cDATA_SET-3_Markers_Cycle-0\u201d (which contains the coded molecular markers). Both datasets are in the folder named \u201cDATA_SET-3\u201d.To estimate the OCMLGSI and DCMLGSI parameters and make selections, we use the following two Excel files: (1) \u201cSimulated_Data_GSI\u201d. To estimate the OCMLGSI and DCMLGSI parameters, the foregoing files were matched as follows. For selection cycle 1, we matched the Excel file C1_PSI_05_Pheno with the text files C1_PSI_S2_05_Haplo.pop; for selection cycle 2, we matched the Excel file C2_PSI_05_Pheno with the text files C2_PSI_S2_05_Haplo.pop, etc. Finally, in cycle 6, we matched the Excel file C6_PSI_05_Pheno with the text files C6_PSI_S2_05_Haplo.pop.To estimate the OCMLGSI and DCMLGSI parameters and to make selections, we used the data of two folders. The first one is called \u201cPSI_Phenotypes-05\u201d (which contains the raw phenotypic data of six Excel files named: C1_PSI_05_Pheno to C6_PSI_05_Pheno) and a second one named \u201cHaplotypes_GSI_April-26-15\u201d(which contains the raw marker data of six text files named: C1_PSI_S2_05_Haplo.pop to C6_PSI_S2_05_Haplo.pop). Both datasets are in the folder named \u201cThe estimated OCMLGSI values at stages 1 and 2 were n R-code  where, bnse R^Ot . Thus, f18 value .In the OCMLGSI and DCMLGSI context, ribution , 1992. Ty-axis, whereas the x-axis presents the possible values of the combinations of x-axis takes values from 1 to 4676. For x-axis number 1, we should have a possible combination of x-axis number 4676, we should have an additional realization denoted as y-axis. The In the single-stage context, when ^Ot=8.18 . In thisFor ectively . Thus, tAppendix 3, Equation A14) and because matrices The estimated maximized OCMLGSI correlations with the net genetic merit (Equation 8) at stages 1 and 2 were The estimated DCMLGSI values for both stages were ^b^=7.71  and wereFor ctively) . Thus, fThe estimated DCMLGSI correlations with the net genetic merit at stages 1 and 2 were For For The foregoing results indicate that while the average of the total OCMLGSI selection responses  to the GS context based on the The OCMLGSI is an application of the OMLPSI . The method proposed here was good for obtaining the selection intensity values of OCMLGSI in a two-stage context and did not overestimate the OCMLGSI selection intensity. Thus, breeders should use the proposed method when they perform multistage selection.The method used in this work to obtain the OCMLGSI selection intensities in a two-stage context is simple and can be programmed in a computer using an R code. p) increased, e.g., from 0.05 to 0.30, the estimated total OCMLGSI and DCMLGSI selection response should tend to be more similar to the estimated single-stage CLGSI selection response. This was true for the estimated total OCMLGSI selection response but not true for the estimated total DCMLGSI selection response for the real and simulated datasets. Thus, for the real dataset, when The estimated total OCMLGSI and DCMLGSI selection response should be lower, or equal to, the single-stage CLGSI. This implies that when the total proportion selected (We evaluated the relative efficiency of two combined multistage linear genomic selection indices. We determined the efficiency of both indices based on the estimated total selection response and correlation of each index with the net genetic merit using real and simulated datasets. In both datasets, we found that the OCMLGSI was a better predictor of the net genetic merit than the DCMLGSI. Therefore, breeders should use the OCMLGSI when performing multistage selection."}
+{"text": "Emerging evidences indicated that exosomal circular RNAs (circRNAs) could serve as diagnostic biomarkers for cancers. However, the expression profiles and clinical significance of circRNAs in lung squamous cell carcinoma (LUSC) remain largely unknown. Herein, we analyzed circRNAs expression profile in six pairs of plasma exosome samples of LUSC patients using high-throughput sequencing. A total of 252 differentially expressed exosomal circRNAs were identified, including 133 up-regulated circRNAs and 119 down-regulated circRNAs. Subsequently, the circRNAs\u2013miRNAs\u2013mRNAs interaction network was built to investigate potential function of circRNAs. Three up-regulated circRNAs  were implied to participate in cancer-related pathways. QRT-PCR experiment confirmed the up-regulation of hsa_circ_0014235 and hsa_circ_0025580. Finally, clinical studies indicated that hsa_circ_0014235 and hsa_circ_0025580 could serve as novel diagnostic biomarkers for LUSC. Taken together, our study revealed exosomal circRNAs expression profile in LUSC for the first time and showed the important diagnostic potential for circRNAs in LUSC. Lung cancer is one of the most widely spread cancers world-wide, accounts for the highest rate of cancer-related mortality . Lung sqExosomes are small membranous vesicles, serve as important carriers among cells and transmit proteins, RNAs and lipids from cell into the extracellular space ,7. RecenIn the present study, we performed high-throughput sequencing and bioinformatics program to analyze the alteration of circRNAs expression in plasma exosome samples from LUSC patients for the first time. The circRNAs\u2013miRNAs\u2013mRNAs interaction network was then constructed for top 10 differentially expressed circRNAs. Three up-regulated circRNAs that involved in cancer-related pathways were validated by qRT-PCR experiment. Further medical studies showed that certain circRNAs may serve as novel diagnostic biomarkers for LUSC.A total of 30 pairs of LUSC patient samples and normal control samples were collected from Qilu Hospital with participant\u2019s consent. None of the participants had received chemotherapy treatment before. The study was approved by Ethics Committee of Qilu Hospital and was in accordance with the Helsinki Declaration. For exosome isolation, the process was performed according to the instruction of Hieff exosome isolation kit . The plasma was separated in a centrifuge tube and centrifuge at 3000 rcf for 10 min (4\u00b0C). The clear supernatant was then transferred to another labeled tube and the pelleted exosomes were re-suspended in 1\u00d7 PBS, stored at \u221280\u00b0C. The Bradford assay was used to measure the concentration of exosomes.Total RNA was generated from six pairs of exosome samples (LUSC and control) using Trizol  following the instructions. The integrity and concentration of RNAs were then examined and the results were met the standards for subsequent experiments: (RIN)\u2009\u2265\u20097.0; 28S:18S ratio\u2009\u2265\u20091.5. After removal of rRNA, the total RNA was digested with RNase R to remove linear RNAs. The cDNA library was prepared according to Illumina TruSeq library preparation instruction. CircRNA sequencing was conducted on an Illumina HiSeq sequencer .P-value < 0.01. The pathway enrichment analysis was performed by KOBAS software [The raw sequencing reads were evaluated by FastQC software. After filtering out low-quality reads, the remaining reads were aligned to GRCH38 genome using TopHat2 software . Reads tsoftware .We predicted the target miRNAs of top 10 differentially expressed circRNAs using miRanda and TargetScan algorithms . The miRTo validate the reliability of sequencing data and explore the circRNAs expression trend in LUSC, qRT-PCR experiment was conducted. Firstly, total RNAs were digested by RNase R to remove linear RNAs. Primers for circRNAs were designed by crossing back-spliced junction sites and were synthesized by Biotech company . The real-time PCR analysis was conducted using SYBR kit  on Bio-Rad CFX96 detection system, and glyceraldehyde 3-phosphate dehydrogenase (GAPDH) was used as internal control.P-value < 0.05. Data were presented as the means \u00b1 standard error of the mean and determinations were performed at least three times. P-value <\u20090.05 was considered as statistically significant.All statistical analyses in the present study were performed using R software. The categorical data were analyzed by Chi-square test and setting the significance threshold as P-value < 0.01 . The distribution pattern of these exosomal circRNAs were investigated. As shown in e < 0.01 B, among e < 0.01 C. Besidee < 0.01 D.By screening expression level and fold change of exosomal circRNAs, we listed top 10 differentially expressed circRNAs and their detailed information in P < 0.05), consistent with previous sequencing data . However, hsa_circ_0026403 expression did not show a significant increase in LUSC group .To further detect the abundance of hsa_circ_0014235, hsa_circ_0025580 and hsa_circ_0026403 in plasma, we performed qRT-PCR validation in 30 pairs of plasma exosome samples of LUSC patients and normal controls. As shown in P-value: 0.028 and 0.019, respectively) and larger tumor size . To further assess the diagnostic potential of two circRNAs in LUSC, ROC curve analyses were conducted. The results showed that there were acceptable diagnostic values for hsa_circ_0014235  and hsa_circ_0025580  in LUSC samples ; while no significant change was observed in hsa_circ_0026403 expression, it was likely to be driven by sequencing inaccuracy. The Chi-square test indicated the positive correlation between circRNAs expression and TNM stage. Further ROC analyses indicated that there were acceptable diagnostic values for hsa_circ_0014235 and hsa_circ_0025580.The abundance of three circRNAs was validated by qRT-PCR and the results showed that expression of hsa_circ_0014235 and hsa_circ_0025580 were notably increased in LUSC exosome samples (In conclusion, our study explored the exosomal circRNAs expression pattern in LUSC patients for the first time. Moreover, hsa_circ_0014235 and hsa_circ_0025580 could serve as novel diagnostic biomarkers for LUSC.Click here for additional data file."}
+{"text": "Vibrio cholerae, the bacterium underlying the disease, infects humans utilizing proteins encoded on horizontally acquired genetic material. Here, we provide evidence that TsrA, a Vibrionaceae-specific protein, plays a critical role in regulating these genetic elements and is essential for V. cholerae virulence in a mouse intestinal model.Cholera is a potentially lethal disease that is endemic in much of the developing world.  Vibrio cholerae require careful regulation of horizontally acquired virulence factors that are largely located on horizontally acquired genomic islands (HAIs). While TsrA, a Vibrionaceae-specific protein, is known to regulate the critical HAI virulence genes toxT and ctxA, its broader function throughout the genome is unknown. Here, we find that deletion of tsrA results in genomewide expression patterns that heavily correlate with those seen upon deletion of hns, a widely conserved bacterial protein that regulates V. cholerae virulence. This correlation is particularly strong for loci on HAIs, where all differentially expressed loci in the \u0394tsrA mutant are also differentially expressed in the \u0394hns mutant. Correlation between TsrA and H-NS function extends to in vivo virulence phenotypes where deletion of tsrA compensates for the loss of ToxR activity in V. cholerae and promotes wild-type levels of mouse intestinal colonization. All in all, we find that TsrA broadly controls V. cholerae infectivity via repression of key HAI virulence genes and many other targets in the H-NS regulon.Pathogenic strains of IMPORTANCE Cholera is a potentially lethal disease that is endemic in much of the developing world. Vibrio cholerae, the bacterium underlying the disease, infects humans utilizing proteins encoded on horizontally acquired genetic material. Here, we provide evidence that TsrA, a Vibrionaceae-specific protein, plays a critical role in regulating these genetic elements and is essential for V. cholerae virulence in a mouse intestinal model. Vibrio cholerae is the causative agent of the potentially lethal disease cholera. Several factors on the progenitor genome and horizontally acquired genetic islands (HAIs) (\u2013V. cholerae virulence gene expression. While multiple HAIs play some role in virulence (5\u2013V. cholerae pathogenicity island 1 (VPI-1) and the cholera toxin (CTX) prophage are most involved with the major virulence pathway, the ToxR regulon (4\u201313\u201316\u2013\u2013V. cholerae.s (HAIs) \u20134 act inirulence , 5\u201311, g regulon . ToxR anegulon 4\u2013, 13\u201315. lon 4\u201313\u2013, 16\u201326,  4\u201313\u201316\u2013\u201332. To dVibrionaceae-specific protein that is by far most common in the genomes of organisms within the Vibrio genus, as determined via BLAST-based  than their progenitor genome counterparts (adjusted R2 = 0.582)  exhibited similar behavior in the \u0394hns strain  . With res strain . These 1V. cholerae chromosomes as well  was used at 40\u2009\u03bcg/ml.Strains and plasmids used in this study are listed in sacB-mediated allelic exchange . After transfer, membranes were blotted with monoclonal anti-V5 antibody (Sigma-Aldrich) or anti-RpoB antibody (BioLegend). RpoB was blotted as a loading control. Pierce ECL Western blotting substrate (Thermo Scientific) was added before exposing the X-ray film. Experiments were carried out in at least biological triplicates.RNA sequencing (RNA-seq) was performed essentially as previously described . Total RRNA-seq data were aligned to a transcriptome  derived P values were calculated using Tukey\u2019s honest significant difference test following one-way analysis of variance.Assays were performed as previously described . At leasThe mouse experiment was reviewed and approved by the UT Austin IACUC .https://www.ncbi.nlm.nih.gov/sra) under accession number SRP242320. Processed RNA-seq results are provided in Raw sequence reads for the RNA-seq data were uploaded to the Sequence Read Archive (SRA) (10.1128/mSphere.01014-20.2TABLE\u00a0S2Table\u00a0S2, XLSX file, 0.01 MB.Strains and plasmids used in this study. Download Copyright \u00a9 2020 DuPai et al.2020DuPai et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the"}
+{"text": "All acquired demographic and neuropsychological data are included.Here, we present unprocessed and preprocessed Attention Network Test data from 25 adults with Parkinson\u2019s disease and 21 healthy adults, along with the associated defaced structural scans. The preprocessed data has been processed with a provided Analysis of Functional NeuroImages Attention dysfunction is a common symptom of Parkinson\u2019s disease (PD) and has a significant impact on quality of life. Approximately half of all people with PD suffer from attention and/or memory symptoms  and subjects provided written informed consent.The sample of subjects includes 25 participants with PD and 21 healthy controls (HC) who participated in two scanning sessions, which were one to three weeks apart. PD participants were recruited from a larger parent study where they underwent extensive clinical examination and neuropsychological assessment (Demographic information is provided inOne subject (RC4206) had an acquisition error during their second session structural scan. Correspondingly, their structural scan from their first session has been copied for their second session to create a valid Brain Imaging Data Structure (BIDS) directory.At each of the two sessions, we acquired six repetitions of the task and T1-weighted structural images from each subject. Data were acquired using a Philips 3.0T X-Series Achieva MR System  with a 32-channel SENSE head coil. Each session included functional and structural scans. For task scans, whole-brain axial echo-planar images  were collected parallel to the AC-PC line. Each functional scan was 149 volumes (5.96 min). A sagittal T1-weighted 3D MPRAGE  with 1 mm isotropic voxels was also acquired for registration and tissue analyses.In total, 45 subjects completed all six task scans in both sessions. One subject did not complete the second session; and one subject is missing task data for the first four task scans (out of six) at the second session.dcm2niix_afni. Subjects with missing DICOMs had Philips format PAR/RECs available and were also converted to NIfTI format using AFNIdcm2niix_afni  file format; and were converted to the Neuroimaging Informatics Technology Initiative (NIfTI) file format using the Analysis of Functional NeuroImages (AFNI) programWe used the ANT , treating each repetition of the ANT task as a single scan (i.e. no concatenation).fMRI data were preprocessed usingAFNI , versionafni_proc.py callFirst four parameters are set on a per-subject basis and represented here with asterisks (*).afni_proc.py \\      -subj_id\t\t\t*\t\t\t\t\t\t\\      -dsets                    *                                       \\      -outdir\t\t\t*\t\t\t\t\t\t\\      -script\t\t\t*\t\t\t\t\t\t\\      -copy_anat                T1.nii.gz\t\t\t\t\t\\      -blocks despike tshift align tlrc volreg blur mask regress \t\\      -align_opts_aea\t\t-cost\tlpc+ZZ\t\t\t\t\\      -tlrc_base\t\tMNI152_T1_2009c+tlrc\t\t\t     \t\\      -tlrc_NL_warp\t\t\t\t\t\t\t\\      -volreg_warp_dxyz         2                                       \\      -volreg_align_e2a\t\t\t\t\t\t\t\\      -volreg_tlrc_warp\t\t\t\t\t\t\t\\      -volreg_align_to\t\tMIN_OUTLIER\t\t\t\t\\      -regress_anaticor\t\t\t\t\t\t\t\\      -regress_est_blur_epits\t\t\t\t\t\t\\      -regress_est_blur_errts\t\t\t\tlpc+ZZ cost function. Following structural alignment, we aligned the data to the Montreal Neurological Institute (MNI) 152 standard space (2009c) template, and the data was blurred with a 4 mm full-width half-max filter and masked using3dAutomask algorithms. Frames were registered to the minimum outlier and then aligned to standard space. We usedanaticor , align, tlrc, volreg (default), blur (default), regress (default). Frames were despiked and slice-timing corrected (tshift). During the align stage, we aligned the functional to the structural using thepydeface before organizing in BIDS format. Skull-stripping and registration were performed on the undefaced anatomical scans.The anatomical scans were defaced usingGitHub (All code is available onGitHub .func/ andanat/ directories.Data are organized according to the Brain Imaging Data Structure (BIDS)  that supply the \u201cTaskName\u201d and \u201cSliceTiming\u201d parameters. Slice timing information is required by the BIDS format, and as the pre-processed (\u201cderivatives\u201d) data has been slice-timing corrected, an array of zeros is provided for this field.Finally, individual scans have matching JSON files in both datasets, created byTask timing data are included on the scan level. The \u201conset\u201d and \u201cduration\u201d columns are in seconds, and the \u201ctrial_type\u201d column includes cue events , target events , and cue/target errors . Only correct-response trials are included. Errors are also generated when the subject responded too early or not at all.afniscript.sh) and demographic information (demographics.csv) are included at the top level.The processing script These folders each contain the following underlying data:-ses-1/anat (T1w.json and defaced T1w.nii.gz files for session 1)-ses-1/func -ses-2/anat (T1w.json and defaced T1w.nii.gz files for session 2)-ses-2/func https://doi.org/10.18112/openneuro.ds001907.v2.0.3 -afniscript.sh (processing script)-dataset_description.json (BIDS dataset parameters)-demographics.csv (demographic information for participants)-README -task-ANT_bold.json (acquisition parameters for task scan)-derivatives/ Creative Commons Zero \"No rights reserved\" data waiver (CC0 1.0 Public domain dedication).Data are available under the terms of the-https://github.com/IBIC/UdallANTSource code available from:-https://doi.org/10.5281/zenodo.2847832  clearly described?YesAre the datasets clearly presented in a useable and accessible format?YesAre the protocols appropriate and is the work technically sound?YesReviewer Expertise:Neuroimaging in neurological diseasesI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Please state reason for repeating scanning sessions. If time between two sessions may be relevant for analysis, then please provide timing log for each subject.Would include sex difference comparison between groups. They look like they may be different.BIDS in Materials section could be referenced as is done later under \u201cOrganization\u201d.Would use \u201cMDS-UPDRS\u201d in Table 1 to distinguish values from the \u201cUPDRS\u201d scale.Tables should note if parentheses represent standard deviations. (Parentheses are incorrectly used to note units.)There were 149 volumes resulting in 357.6 sec of scanning time. Were the scans actually 6 min long? Were there any dummy scans at begging for T1 equilibration and if so have these been removed?Would explicitly state matrix size and thickness for EPI scans. Was there any gap?Under \u201cOrganization\u201d do not need to define BIDS again. Also the sentence about \u201cskull-stripped anatomical images\u201d being included is confusing as all images provided are the defaced images.The authors have done a nice job presenting their imaging data that are being made available for public download. The article is well written and concise and provides background information necessary to enable the utilization of these data by other investigators. A sampling of imaging data provided online was looked at and appears to be appropriate and of good quality. The accompanying demographics file was reviewed and contains pertinent data. One issue I noticed on quick review is that there are MoCA and MMSE scores that exceed 30. Otherwise I have largely minor suggestions in order to improve the accessibility and utilization of these data by others:Are sufficient details of methods and materials provided to allow replication by others?YesIs the rationale for creating the dataset(s) clearly described?YesAre the datasets clearly presented in a useable and accessible format?YesAre the protocols appropriate and is the work technically sound?YesReviewer Expertise:Parkinson's disease, neuroimagingI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard."}
+{"text": "Recent work by  Convergent adaptation occurs when natural selection independently orchestrates the evolution of the same set of trait or traits in multiple populations . EffortsTo facilitate use of the convergent adaptation models of rdmc requires two kinds of allele frequency data. The first is allele frequencies from unlinked neutral sites across all populations. The second is allele frequencies from at least three populations that have putatively undergone convergent adaptation at a specific locus, and three or more populations that did not. Sample allele frequencies can be estimated with a number of existing software resources including VCFtools  library(rdmc)devtools, rdmc depends on several other packages. Namely, MASS data(selected_freqs)data(positions)rdmc as a matrix, where each row is a population and each column is a locus. Users should note that the simulated positions here take on values between zero and one, but that base pair positions along the chromosomes of empirical data should not be altered prior to fitting the models.The example data consists of 10,000 simulated base pairs from six populations, three of which  independently mutated to the selected allele at position 0, along with three other populations that evolved neutrally. Allele frequencies must be be passed to parameter_barge that can be used when fitting any of the possible models. The list of quantities is generated using:When fitting possible convergent adaptation models, several quantities are reused regardless of which modes of convergent adaptation are of interest. In efforts to minimize computation, all parameters and quantities that are common across models are stored in a single named list generated with the function #specify parameters and input data.param_list parameter_barge,positions = positions,n_sites = 10,sample_sizes = rep,num_bins = 1000,sels = c,seq,seq),times = c,gs = c(1 / (2 * 10000), 10 -^(4:1)),migs = c), 0.5, 1),sources = selected_pops,locus_name = \u201dtest_locus\u201d,cholesky = TRUE)sels, times, gs, migs, sources, and n_sites or positions) that will be used in the likelihood calculations. Depending on which modes of convergent adaptation are being studied, some of these grid parameters may not be used for inferences. Users must still input values for all of the grid parameters.where all the arguments are fully described in Naturally, features of the input data , will impact the model results, and will determine the resolution we have to infer the model parameters. The number and density of points along the grid of parameters also affect the resolution one has to make inferences. However, computation time and memory usage may become infeasible if these grids are made too large.mode_cle, passing the desired mode as an argument to the function. The neutral, independent mutations, migration, and standing variation with a source population modes can be fit, respectively with:Once the parameter barge is constructed, all models can be fit using this list of quantities as the only data input. All of the mode types  are implemented using the same function, #fit composite likelihood modelsneut_cle ind_cle mig_cle sv_cle sets and modes arguments, which groups the population indices according the vector of modes, and specifies which modes are to be used. For example, to fit a model where populations with indices 1 and 3 adapted via standing variation, and population 5 gained the same mutation independently, and another mixed-mode model where populations 1 and 3 adapted via migration, and population 5 mutated independently:Models of mixed modes, where specified populations are modeled under different modes, can be also implemented by modifying the parameter list object in-place. Specifically, mixed modes are constructed by adding the #update barge to fit a mixed-mode modelparam_list <-update_mode, 5),modes = c)#fit mixed-mode modelmulti_svind <- mode_cle#update to another mixed-modeparam_list <-update_mode, 5),modes = c)#fit mixed-mode modelmulti_migind <- mode_clemode_cle, the data frame returned will always contain the same 10 features: The 6 grid parameters generated by parameter_barge (NA) values are added when variables are not used for a given mode type. As will be shown below, this design facilitates combining results from multiple models for downstream analyses.Regardless of which mode is used when calling _barge , the comrdmc increases with the complexity of the model and size of the input data used. Compared to the original code implemented by rdmc is slightly faster computationally, and requires substantially less memory. However, the reduced time and memory allocation for rdmc only occurs when Cholesky factorization is used to obtain the inverse of the neutral and selected covariance matrices , hence, a denser grid will also have a considerable impact on time and memory usage. The size of the example data provided gives a realistic sense of memory and time usage for potential empirical data. While most modern laptops are capable of handling the required memory, many users will be interested in genome-wide analysis, where the mode of convergence for many separate regions are of interest. In these instances, cloud or high performance computing environments will be more appropriate. Making nakemake  or NextfOnce the models of interest have finished, the common format of the returned data frames allows all of the inferences to be combined into a single data frame, which simplifies creation of statistical and graphical summaries, and storage:#rdmc loads dplyr::bind_rowsall_mods <-bind_rows#save results to filereadr::write_csvreadr::write_csvWith a single data frame containing output from all tested models, there are many visualization and summary methods available in the R ecosystem . For exa#rdmc loads dplyr::group_by and magrittr::%>%all_mods %>%group_by(model) %>%filter(cle == max(cle)) %>%select#returns (model names edited here for space)# A tibble: 4 \u00d7 3# Groups: model [4]selected_sites sels model<dbl> <dbl> <chr>1 0.0017 0.03 independent2 0.0017 0.03 migration3 0.0017 0.03 stdvar-stdvar-ind.4 0.0017 0.03 mig.-mig.-ind.Visualizing the composite likelihood values by genomic position   can be dlibrary(ggplot2)library(cowplot)theme_set(theme_cowplot(font_size = 18))neut <- unique(neut_cle$cle)all_mods %>%group_by %>%summarize(mcle = max(cle) - neut) %>%ggplot) +geom_line +geom_point +xlab(\u201dPosition\u201d) +ylab(\u201cComposite likelihood\u201d) +theme(legend.position = \u201dn\u201d) +scale_color_brewerLastly, one can visualize the likelihood surface with respect to specific parameter, such as selection :#visualize likelihood surface wrt selectionall_mods %>%group_by %>%summarize(mcle = max(cle) - neut) %>%ggplot) +geom_line +geom_point +ylab(\u201dComposite likelihood\u201d) +xlab(\u201dSelection coefficient\u201d) +scale_color_brewerrdmc was made to facilitate the use of convergent adaptation models of rdmc an R package, the code is highly portable and has relatively few, highly maintained dependencies, making it simpler to adopt to different computing systems. Because of its portability and ease of use, rmdc also simplifies downstream tasks which facilitates usage at large scales, such as modeling thousand of genomic regions simultaneously on high performance computing resources.rdmc package could be addded. Since the methods developed in rdmc, future development of the package would include functions to perform bootstrapping. However, for the same reasons mentioned above, rdmc should facilitate creation and computation of bootstrap replicates in parallel.Several elaborations to the currently available utilities in the https://github.com/silastittes/rdmc. The package is released under GNU General Public License (v3.0). All of the presented analyses were computed on a personal laptop  using R version 4.0.0 2020-04-24).The source of the package and workflow outlined above are available at https://github.com/kristinmlee/dmc.The original code associated with"}
+{"text": "Bacillus thuringiensis CH_48 exhibits extremely high levels of Vero cell cytotoxicity and sphingomyelinase activity. Bacillus thuringiensis CH_48 exhibits extremely high levels of Vero cell cytotoxicity and sphingomyelinase activity. Bacillus cereussensu stricto and Bacillus thuringiensis are genetically intermingled, with B. thuringiensis exhibiting additional toxin genes that encode crystal toxins with insecticidal properties  agar at 30\u00b0C using a DNA blood and tissue kit . A transposome-based library was prepared for Illumina MiniSeq paired-end sequencing using the Nextera DNA Flex sample preparation kit . Two data sets were obtained, each containing 1,446,327 reads of 150\u2009bp, corresponding to an estimated coverage of 110-fold. The reads were assembled using Shovill version 1.0.9, a software tool that uses SPAdes for genome assembly (B. thuringiensis strain CH_48 were deposited in GenBank under the accession number JABERC000000000. Reads were deposited to the Sequence Read Archive under the BioProject accession number PRJNA629343.The sequence and annotation data of"}
+{"text": "Shigella is a major diarrheal pathogen for which there is presently no vaccine. Whole genome sequencing provides the ability to predict and derive novel antigens for use as vaccines. Here, we aimed to identify novel immunogenic Shigella antigens that could serve as Shigella vaccine candidates, either alone, or when conjugated to Shigella O-antigen.Shigella immunome via an antigen microarray, we aimed to identify novel immunogenic Shigella antigens. A core genome analysis of Shigella species, pathogenic and non-pathogenic Escherichia coli, led to the selection of 234 predicted immunogenic Shigella antigens. These antigens were expressed and probed with acute and convalescent serum from microbiologically confirmed Shigella infections.Using a reverse vaccinology approach, where genomic analysis informed the Shigella antigens displayed IgG and IgA seroconversion, with no difference in sero-reactivity across by sex or age. IgG sero-reactivity to key Shigella antigens was observed at birth, indicating transplacental antibody transfer. Six antigens  were identified in in vivo testing as capable of producing binding IgG and complement-mediated bactericidal antibody.Several Shigella proteins that could serve as candidate vaccine antigens, species-specific carrier proteins, or targeted adjuvants.These findings provide six novel immunogenic The online version contains supplementary material available at 10.1186/s13073-020-00824-4. Shigella is the causative agent of shigellosis, a severe acute gastrointestinal infection that frequently presents as bloody diarrhea, fever, and severe abdominal pain [Shigella was estimated to cause >\u2009250 million cases and >\u2009200,000 deaths globally [Shigella infections among travelers, aging populations, deployed military personnel, and men who have sex with men (MSM) [Shigella disease burden is in children aged under 5\u2009years residing in low-middle income countries (LMICs). Infection in this vulnerable group can also result in significant long-term consequences such as severe stunting and impaired cognitive development [Shigella is worsened by the emergence and spread of multi- and extensively drug resistant (MDR and XDR) variants, making infections increasingly difficult to treat [Shigella prevention has been improvements in water, sanitation, and hygiene (WASH) [Shigella infections found a severe underestimation of the global Shigella burden [nal pain . In 2016en (MSM) . Howeverelopment . The gloe (WASH) . Furthera burden , 9, highShigella [Shigella species. Long-term homologous protection has been attributed to serotype-specific systemic (serum IgG) and mucosal (IgA) antibody responses [Shigella IgG and IgA response is the O-antigen component of lipopolysaccharide (LPS) [Shigella O-antigen are highly specific for the infecting species only [Shigella species. Since the Shigella genus consists of four species and >\u200950 serotypes, a lack of cross-protection against heterologous species and serotypes poses a major challenge for vaccine development [Shigella species  [There is currently no licensed vaccine against Shigella . HoweverShigella , 12. Natesponses . The mosde (LPS) , 15. LPSde (LPS) . Howeveries only , 13, andelopment . This ch sonnei) .Shigella vaccine has been to elicit antibody responses targeting Shigella O-antigen [Shigella [Shigella O-antigen on live-attenuated vectors [Shigella O-antigens and have shown protection in early clinical development [Shigella O-antigen have also been pursued as vaccine candidates [Shigella O-antigen conjugated to a carrier protein [Shigella have not been evaluated as a carrier protein for Shigella O-antigen.The primary strategy of developing an efficacious -antigen . Live-atShigella \u201320, or t vectors , have beelopment , 23. Recndidates , with a  protein \u201326, whic protein . Various protein , 29. HowShigella antigens that could serve as Shigella vaccine candidates, either alone, or when conjugated to Shigella O-antigen. Therefore, we conducted immunogen prediction using bioinformatic analysis, then created a protein microarray of predicted immunogenic Shigella antigens. These expressed antigens were screened for immunogenicity using polyclonal antibodies from patients who recovered from confirmed Shigella infections, to identify a novel set of proteins which may facilitate the development of novel Shigella vaccines.Whole genome sequencing provided the ability to predict and derive novel antigens for use as vaccines, and this approach ultimately gave rise to the meningococcal B vaccine \u201332. FurtHuman serum samples for the purposes of this investigation were collected from an observational study of children with diarrheal disease and a cohort study of healthy infants, both conducted in Ho Chi Minh City (HCMC), Vietnam , 37. BotShigella (n\u00a0=\u200933) or Salmonella (n\u00a0=\u200924) cases when they first presented at hospital with acute diarrheal disease (patients bled prior to diagnostic testing) (Table\u00a0S. sonnei O-antigen IgG [n\u00a0=\u200945) and low (n\u00a0=\u200940) S. sonnei O-antigen IgG titers  Table\u00a0. Convaleigen IgG ; the corrs Table\u00a0.Table 1Shigella and 47 Escherichia coli (E. coli) were retrieved from GenBank  and non-pathogenic variants (n\u00a0=\u200915). A complete list of E. coli and Shigella genome sequences utilized in the present study is indicated in Additional\u00a0File\u00a0The complete chromosomal sequences of 10 S. sonnei and absent from all E. coli, (2) sequences present in all S. flexneri and absent from all E. coli, and (3) sequences present in all S. sonnei and S. flexneri and absent from all E. coli. The output identified from each subset was manually curated by performing a BLASTN search of their sequences against the NCBI database. Since Shigella is phylogenetically nested within the E. coli species, they show a very low level of divergence in chromosomal genetic makeup. Hence, the number of proteins that fulfilled the above criteria  was limited  sequences present in all oli, and  sequenceS. sonnei and S. flexneri genomes, notwithstanding their presence in the examined E. coli genomes. In brief, the annotation and protein sequences of each orthologous group were retrieved from the input Shigella genomes  E. coli system . Controls lacking DNA were included to account for background reactivity with E. coli, where IVTT was conducted without plasmid DNA. Expressed Shigella antigens from IVTT reactions were printed onto nitrocellulose-coated glass GraceBio slides using an Omni Grid 100 microarray printer (Genomic Solutions). LPS from Shigella (Sigma) was also printed on the microarray slides to act as positive control. Slides (with E. coli lysate (McLab) at a final concentration of 1\u2009mg/ml) were probed with human serum (diluted 1:200), followed by biotin-conjugated secondary antibodies specific for human IgM, IgG, and IgA (Jackson ImmunoResearch). Binding antibody was detected using streptavidin-conjugated SureLightH P-3 (Columbia Biosciences), measured using Perkin Elmer ScanArray Express HT microarray scanner. Spot intensities were quantified using the ScanArray software.Proteins selected through the bioinformatics pipeline are shown in Additional file t test adapted from Cyber-T for protein arrays [p values were subjected to Benjamini and Hochberg (BH) correction to control for false discovery rate [http://www.r-project.org) and packages \u201cSuperheat,\u201d \u201cggplot2,\u201d \u201crgl,\u201d and \u201cfmsb.\u201dFold-over-control (FOC) normalizations were conducted to reduce assay to assay variation by dividing the mean spot intensities for each antigen by the intensity for the no DNA control IVTT. Positive serum reactivity to an antigen was defined as a FOC >\u20092 . Log2-transformed FOC values from paired acute and convalescent samples were compared using a Bayes regularized n arrays \u201349. p vaery rate . Data weE. coli protein expression system and purified using nickel affinity chromatography . Four months old, male New Zealand rabbits (n\u00a0=\u20092 per protein) were immunized with 0.2\u2009mg of the successfully expressed and purified protein, and serum drawn at 1-week post-immunization of the third dose. The immunogenicity of each protein was assayed by testing the pre-immune and post-immune rabbit sera for sero-positivity using indirect enzyme-linked immunosorbent assay (ELISA) and immunoblot. For ELISA, plates were coated with protein at 4\u00a0\u03bcg/ml, blocked, incubated with sera (at 1\u2009mg/ml IgG concentration), and detected using anti-rabbit IgG Fc-HRP secondary antibody. For western blots, 50\u2009ng/well of purified proteins was run on SDS-PAGE, transferred to nitrocellulose membrane, blocked, probed with pre-immune and post-immune rabbit sera, and detected with goat anti-rabbit IgG-IRDye800cw secondary antibody.His-tagged variants of selected proteins  , S. sonnei , and S. Typhimurium (strain ATCC 14028) using a previously described SBA protocol [Shigella-infected patient was used as a positive control [S. sonnei DE 1404 were performed with and without the supplementation of 10\u2009\u03bcg/ml chloramphenicol. S. sonnei has the propensity to lose the virulence plasmid and O-antigen culture during culture, and the addition of 10\u2009\u03bcg/ml chloramphenicol was to ensure the maintenance of plasmid and O-antigen during the SBA assay via the added cat gene. These data were compared to assess potential killing differences between plasmid+ and plasmid\u2212 organisms.Purified serum antibody from immunized rabbits was tested for serum bactericidal activity (SBA) against protocol , 52. Hea control . All serShigella and E. coli core genomes was conducted with the aim of selecting both species-specific and species cross-reactive Shigella proteins common to the most globally dominant species, S. flexneri and S. sonnei. Protein sequences were extracted from the annotated chromosomes of various Shigella species (n\u00a0=\u200910), pathogenic (n\u00a0=\u200932) and non-pathogenic (n\u00a0=\u200915) E. coli . Another 100 potentially immunogenic orthologs, from both S. sonnei and S. flexneri, were further included to expand the downstream immunogenic assays. Shigella LPS (O-antigen) was included as a positive control. This resulted in a total of 235 proteins that were expressed in vitro, and successfully printed on an antigen microarray for downstream analysis  sera from microbiologically confirmed Shigella-infected diarrheal patients (n\u00a0=\u200934)  and S. sonnei (n\u00a0=\u200932).The 4) Table\u00a0. The preShigella infection led to widespread seroconversion in all measured antibody isotypes  among individuals and across multiple tested antigens, as observed by the increase in sero-reactivity  from acute to early convalescence   from diarrheal patients infected with an alternative genus of diarrheal pathogen, Salmonella. Notably, there were no significant increases in IgG, IgA, or IgM responses between the acute and convalescence in Salmonella-infected diarrheal cases, indicating that the antibody reactivity observed with the serum from the Shigella-infected patients was specific to Shigella  as compared to non-inflammatory  disease  from comparison between acute and convalescent antibody responses, and the highest mean antibody responses  values) at early convalescence, etc. The twelve selected antigens were NmpC (SF_nmpC) and FepA (SF_fepA) from S. flexneri, and HtrB (SSON_htrB), EmrK (SSON_emrK), NlpB (SSON_nlpB), FhuA (SSON_fhuA), CjrA (SSON_cjrA), MdtA (SSON_mdtA), SbmA (SSON_sbmA), MviN (SSON_mviN), PldA (SSON_pldA), and 3803 (SSON_3803) from S. sonnei. Sero-reactivity between acute and early convalescence was compared using the Benjamini-Hochberg corrected Cyber-T test. For all 12 antigens, we observed a statistically significant (p <\u20090.05) increase in mean sero-reactivity between acute to early convalescence in all three antibody isotypes  had positive IgG, IgA, and IgM  responses to all selected antigens, with the exception of SSON_mdtA and SSON_fhuA  in comparison to those aged >\u20092\u2009years (n\u2009=\u200926). At early convalescence, there were no significant differences in both the mean antibody (IgG and IgA) sero-reactivity  and male (n\u00a0=\u200914) patients with diarrhea  and low (n\u00a0=\u200940) antibody titers (to Shigella O-antigen)  in infants born to mothers with high antibody titers, than those with low antibody titers  Fig.\u00a0. In geneers Fig.\u00a0. Specifiers Fig.\u00a0c. These E. coli protein expression system was used to express SF_nmpC, SF_fepA, and SSON_htrB, SSON_emrK, SSON_nlpB, SSON_fhuA, SSON_cjrA, SSON_mdtA, SSON_sbmA, SSON_mviN, SSON_pldA, and SSON_3803. We were unsuccessful in expressing SSON_sbmA, SSON_mviN, SSON_3803, and SSON_pldA using this system  induced by the immunized antigens was Shigella specific. The SBA assay was unable to detect bactericidal activity below 50\u2009\u03bcg/ml against S. sonnei  induced antibody responses with strong serum bactericidal activity against S. flexneri   Table\u00a0; the lowShigella proteins. Genomic comparison and bioinformatic analysis of 57 Shigella and E. coli genomes allowed us to narrow down to 235 predicted immunogenic antigens. The predicted antigens were then expressed and printed onto a microarray, probed with a panel of sera from Shigella-infected individuals, to narrow the selection to 12 highly sero-reactive antigens. We confirmed that antibody responses to these 12 antigens were similar across sex  and two age groups . Using cord blood samples, we additionally observed that IgG responses to 11 of these 12 antigens could be transmitted transplacentally, hence suggesting the possible application of these antigens as prenatal vaccine candidates. Among the 12 antigens, 8 were successfully expressed as recombinant proteins. Six of these antigens were both immunogenic in animal models and generated functionally protective antibody responses against Shigella. These were SF_fepA, SSON_cjrA, SSON_emrK, SSON_fhuA, SSON_mdtA, and SSON_nlpB, with SSON_cjrA being the most immunogenic in terms of eliciting antibody-mediated bactericidal responses. Such bactericidal effects against S. sonnei were not observed in vitro, probably due to the protection against complement-mediated killing afforded by its high molecular weight capsule [S. sonnei infections, the capsule can be modulated to expose these functional proteins in vivo. This highlights the complex pathogenesis of S. sonnei and the difficulty in developing a suitable vaccine candidate.In the current study, we exploited reverse vaccinology 2.0 to integrate both comparative genomics and human immuno-proteome analysis to identify novel immunogenic chromosomal  capsule . HoweverShigella immunogenic proteins have been characterized previously. FepA and FhuA serve as outer membrane proteins which bind and transport siderophores  [Pseudomonas aeruginosa PhuW and potentially acts to sequester iron from heme, the most common iron source in mammals [E. coli. In addition, EmrKY has been shown to confer Shigella survival in infected macrophages, facilitating its invasive pathogenesis in the human host [Shigella infections, concurring with the survival strategies of pathogenic bacteria. Particularly, within-host iron is key to bacterial replication, and the ability to sequester and transport host iron is pivotal to the pathogenesis of Klebsiella pneumoniae [Staphylococcus aureus [The biological functions of these six ctively) , 57. Cjr mammals . EmrK an mammals , 60, whiman host . NlpB foman host , 62. Theeumoniae  and Staps aureus .Shigella antigens  identified as immunogenic in the present study has been previously characterized for immunogenicity either in the context of natural Shigella infections or vaccination. Five of these antigens  are conserved in pathogenic E. coli, but they have not been tested for immunogenicity following pathogenic E. coli infection either. However, the presence of the genetic cluster cjrABC-senB has been previously linked to uropathogenic E. coli [Shigella  have historically been thought to be highly virulent and immunomodulatory and are currently being developed as a vaccine immunogen [Shigella antigens, such as those identified in our current study, are may be partly responsible for the immunogenic properties of GMMA.None of the  E. coli . The immmmunogen . GMMA-bammunogen . Immunogmmunogen . Based oShigella faces many challenges, including the ability to protect against multiple Shigella species and to raise sustained mucosal immunity [S. flexneri and S. sonnei. Therefore, these antigens could be used to create a vaccine that protects against the two globally dominant Shigella species, which accounted for almost 90% of all Shigella cases in the Global Enteric Multicenter study (GEMS) [Shigella infection of host cells and downregulating inflammation and intestinal tissue pathology at infected sites [Shigella-specific serum IgA positively correlates with mucosal IgA in the stool [Shigella antigens were capable of transplacental transfer, indicating that the antigens could additionally serve as prenatal vaccine candidates to protect neonates.Development of a vaccine against immunity . Fortunay (GEMS) . Furthered sites , 70. Althe stool . AdditioShigella infections cause over a quarter of a billion gastrointestinal infection cases globally per annum [Shigella diseases. At present, LPS is a key antigen for the development of a vaccine against Shigella [Shigella proteins that could serve as additional vaccine candidates or could be conjugated to O-antigens to provide some cross-protection. Future Shigella challenge studies in animal models or human controlled infection models are needed to test the potency of these identified six antigens as vaccine candidates alone or as new generation glycoconjugates.er annum . DespiteShigella , 71. HerAdditional file 1: Supplementary file 1. In-house script to retrieve complete Shigella and Escherichia coli chromosomal sequences from GenBank (accessed July 2014).Additional file 2: Table S1. List of Shigella and E. coli strains used for the bioinformatics genome comparison.Additional file 3: Table S2. Identified protein within subsets of interests during chromosomal genome comparison between S. sonnei, S. flexneri, pathogenic and non-pathogenic E. coli. Additional file 4: Supplementary file 2. Script used to retrieve annotation and protein sequences from the input Shigella genomes.Additional file 5: Table S3. List of Shigella proteins and antigens that were successfully tested on the Immunoassay protein microarray.Additional file 6: Table S4. The recombinant expression of highly reactive Shigella proteins for in vivo immunogen testing.Additional file 7: Figure S1. No statistically significant seroconversion to Shigella antigens detected in diarrheal patients during Salmonella infections.Additional file 8: Figure S2. Antibody responses to natural Shigella infections are influenced by severity of disease.Additional file 9: Figure S3. Convalescent antibody responses to top reactive Shigella antigens by age and sex.Additional file 10: Table S5. Comparison of SBA titre from Rabbits immunized with Shigella candidate proteins using Shigella strain DE1404 grown with and without chloramphenicol (50mg/L)."}
+{"text": "Circular RNA (circRNA) is a type of non-coding RNA known to affect cancer-related micro RNAs and various transcription factors. circRNA has promise as a cancer-related biomarker because its circular structure affords high stability. We found using high-throughput sequencing that seven candidate circRNAs  were downregulated in HCC. The expression of these circRNAs was examined by quantitative PCR in 233 sets of HCC and matched background normal liver tissues, and correlations between candidate circRNA expression and prognosis were evaluated. The results of quantitative PCR showed that expression of hsa_circ_0041150, hsa_circ_0001020 and hsa_circ_0008558 was significantly lower in HCC than in background normal liver tissues. Kaplan\u2013Meier analysis revealed that low expression of hsa_circ_0001020, hsa_circ_0036683, and hsa_circ_0058087 was associated with poor recurrence-free (RFS) and overall survival (OS) in HCC. Additionally, multivariate analysis revealed that low hsa_circ_0036683 expression was a significant prognostic factor, independent from other clinicopathological features, for inferior RFS and OS. There was no significant association between the expression of these circRNAs and hepatitis B/C status or cirrhosis. This study therefore identified circRNAs as potential prognostic markers for patients who undergo curative surgery for HCC and highlighted hsa_circ_0036683 as the most useful biomarker. Thus, there is an abundance of non-coding RNA (ncRNA) in the eukaryotic cell. Numerous studies have uncovered important functional roles for ncRNA in regulating the interplay between DNA, RNA and protein expression2. Many different ncRNAs have been identified to date and they are predominantly categorized according to length3. Long non-coding RNAs (lncRNAs) are ncRNAs that exceed 200 base pairs and they represent a relatively abundant component of the mammalian transcriptome4. While lncRNAs form the biggest group of mammalian ncRNAs, circular RNA (circRNA) is a recently identified subtype of lncRNA that demonstrates greater stability than linear RNA owing to its covalently closed loop structure5. It is believed that circRNA regulates gene expression through various interactions with other RNA types  as well as through interactions with RNA-binding proteins. circRNAs may also regulate gene expression directly by influencing transcription and splicing. More recently, some circRNAs have been shown to encode proteins6. There is growing interest in the possible roles that circRNAs may play in the development of human diseases including malignant neoplasms. Recent studies have demonstrated abnormal circRNA expression in several types of malignancies6. However, the relationship between circRNA expression and cancer prognosis requires further study.Recent advances in genome-wide analytical techniques have revealed that while almost all genomic lesions are transferred into RNA, as little as 2% of these encode proteins7. Hepatocellular carcinoma (HCC) accounts for 75\u201385% of primary liver cancers and is the second leading cause of cancer-related death in East Asia and sub-Saharan Africa, and the sixth most common cause in Western countries8. The main risk factors for HCC include lifestyle factors such as heavy alcohol intake, obesity, smoking, type 2 diabetes, and aflatoxin-contaminated foodstuffs, as well as background liver status including chronic hepatitis B (HBV) or hepatitis C (HCV) viral infection and associated cirrhosis10. Although there are several recommended treatment options for HCC, surgical resection remains the most effective therapy for prolonging patient survival11. However, because of complexities related to background liver status, the possibility of postoperative recurrence is higher in HCC, when compared with other gastrointestinal cancers. Consequently, the 5-year survival rate for HCC following surgery is approximately 30\u201357%18. Therefore, there is an urgent need to understand not only the molecular mechanisms of HCC itself, but also the molecular relationship between this disease and underlying background liver status.Liver cancer is predicted to be the sixth most commonly diagnosed cancer and the fourth leading cause of cancer-related death worldwideIn this study, we assessed the expression of circRNA in resected HCC and corresponding background normal liver tissues using a genome-wide approach. We also determined the level of circRNA in resected HCC tissues and paired normal liver tissues from patients who underwent curative HCC surgery. Expression data and clinical data were subsequently analysed to determine whether circRNA has utility as a prognostic marker in patients with HCC.19. The IDs for the seven circRNAs were hsa_circ_0041150, hsa_circ_0025624, hsa_circ_0001020, hsa_circ_0028129, hsa_circ_0008558, hsa_circ_0036683, and hsa_circ_0058087. According to the HTS data, these circRNAs were all expressed at a lower level in HCC tissues, when compared with paired background normal liver tissues .To develop a circRNA signature specific to HCC tumour and background normal liver tissue, we first interrogated high throughput sequencing (HTS)-based circRNA expression profiles for four cases of resected HCC. This genome-wide circRNA analysis identified 15 circRNAs that were differentially expressed between HCC and background normal liver tissue, omitting circRNAs with null expression in any of the four samples. A heatmap of the 15-circRNA signature is shown in Fig.\u00a0HCC and background normal liver tissues taken from 233 HCC patients who underwent liver resection, were analyzed by qPCR to determine the expression of the seven candidate circRNAs identified in the HTS analysis . Expression profiles for these circRNAs in the collected paired samples are shown in Fig.\u00a0Expression of the seven candidate circRNAs according to hepatitis virus or liver cirrhosis status is shown in Supplementary Fig. hsa_circ_0001020, hsa_circ_0008558 and hsa_circ_0036683 expression in background normal tissues was lower in patients with liver cirrhosis than in patients without cirrhosis , while expression in HCC tissues was independent of cirrhosis status. hsa_circ_0041150, hsa_circ_0025624, hsa_circ_0001020, hsa_circ_0028129, hsa_circ_0008558, and hsa_circ_0058087 were all lower in HCC tissues than in background normal tissues in patients with liver cirrhosis . hsa_circ_0041150 hsa_circ_0001020, hsa_circ_0028129, and hsa_circ_0008558 were lower in HCC tissues than in background normal tissues in patients without liver cirrhosis .Analysis of recurrence free-survival (RFS) and overall survival (OS) indicated that low expression of hsa_circ_0001020, hsa_circ_0036683, and hsa_circ_0058087 in HCC tissues was associated with a significantly inferior RFS , hsa_circ_0001020 expression (low), hsa_circ_0036683 expression (low), and hsa_circ_0058087 expression (low) were all significant predictors of inferior RFS. When multivariate analysis was performed hsa_circ_0036683 expression (low) and hsa_circ_0058087 expression (low) were identified as independent predictive factors for inferior RFS , pathological stage (\u2265\u2009III), hsa_circ_0001020 expression (low), hsa_circ_0036683 expression (low), and hsa_circ_0058087 expression (low) were all significant predictors of worse OS. When multivariate analysis was performed on these predictors, AFP (\u2265\u200920\u00a0ng/dl), and hsa_circ_0036683 expression (low) were identified as independent predictive factors for worse OS  and normal hepatic tissues, with 99 dysregulated circRNAs identified in total29. Ji et al., reported the involvement of circ_0070963 in liver fibrosis30. Unexpectedly, this study did not reveal any significant association between circRNA expression and HBV, HCV, or cirrhosis status.Previous studies have examined the relationship between circRNA expression and background liver status35. Low circ_0000567 and circ-ITCH expression was predictive of poor prognosis in a study of 134\u2013288 HCC patients, as determined using Kaplan\u2013Meier analysis37. In our study of 233 HCC patients we now demonstrate, using both Kaplan\u2013Meier and multivariate analysis, that decreased expression of hsa_circ_0001020, hsa_circ_0036683, and hsa_circ_0058087 is predictive of poor disease prognosis.In addition to our study, others have also examined the relationship between circRNA expression and patient prognosis in HCC using qPCR. In a study of 70\u2013200 HCC patients, high expression of circ_0021093, circ_0008450, circ_0128298, circ_0003998 and hsa_circ_0006916 was associated with unfavorable prognosis, as determined using Kaplan\u2013Meier and multivariate analysisAlthough we have identified several circRNAs as useful biomarkers and prognostic predictors for HCC, there are some limitations to this study. Firstly, the study cohort consisted of individuals from a single institution. Secondly, we did not investigate the underlying mechanisms of altered circRNA expression and how these changes may impact on prognosis by identifying potential gene or microRNA targets. This study simply evaluated circRNA expression in HCC and normal background liver tissues to examine its utility as a biomarker and prognostic factor in clinical practice.In conclusion, the circRNAs hsa_circ_0041150, hsa_circ_0025624, hsa_circ_0001020, hsa_circ_0028129, hsa_circ_0008558, hsa_circ_0036683, and hsa_circ_0058087 were all associated with prognosis in HCC. In particular, hsa_circ_0036683, which was found to be an independent and significant prognostic factor for HCC, has potential utility as a candidate biomarker for this disease. Moreover, we believe that further functional analysis of these circRNAs may identify novel therapeutic targets for the treatment of this disease.38.A total of 233 frozen tumour specimens and paired para-tumour normal background liver tissues were collected from patients with HCC who underwent surgery at Nagoya University Hospital between January 1998 and January 2012. All fresh tissues were immediately frozen in liquid nitrogen and stored at \u2212\u00a080\u00a0\u00b0C until required. Patient characteristics are summarized in Table Total RNA was extracted from tissue samples using the Qiagen miRNeasy mini-kit . Approximately 10\u00a0\u03bcg total RNA was then subject to ribosomal RNA depletion using the Ribo-Zero Gold Kit, as per the manufacturer\u2019s instructions . The RNA fragments were then reverse-transcribed to create the final cDNA library using the ncRNA-Seq sample preparation kit (Illumina) according to the manufacturer\u2019s recommended protocol. The prepared libraries were then sequenced on an Illumina Hiseq X ten platform (Illumina) and 2\u2009\u00d7\u2009100-bp paired-end reads (PE100) were generated according to the standard Illumina protocol. All procedures for circRNA sequencing were performed by BGI Genomics Services . Sequencing reads containing low-quality, adaptor-polluted and a high content of unknown base (N) reads were removed using QCleaner v4.0.1  before downstream analysis.39 was used to predict circRNA in this project and to integrate results based on the start and end position of circRNA. Burrows-Wheeler Aligner software 40 was used to align discovered reads to the hg38 reference genome. circRNAs that had the same start and end position within 10 bases were assigned to the same class. If a circRNA was recorded in the circBase (http://www.circbase.org/cgi-bin/downloads.cgi), the corresponding ID code was provided. circRNA was considered novel if it did not overlap with any registered circRNA in circBase.CIRIhttps://bioconductor.org/packages/release/bioc/html/limma.html)41. The read count for circRNA was logarithmically transformed (log2 [count\u2009+\u20091]) and established linear models were assessed using the empirical Bayes method. The acquired p-value was adjusted by the Benjamini\u2013Hochberg method.circRNAs detected in only HCC samples were considered to be tumour-specific. Tumour-specific circRNAs with a number of reads\u2009\u2265\u20096 were used in down-stream analyses. Differential expression analysis of circRNAs between HCC and background normal liver tissues was conducted using the limma package 19 and divergent primers were designed with primer3 42. All qPCR experiments were performed in duplicate, including the template-omitted negative controls.Total RNA was extracted from tissue samples using the Qiagen miRNeasy mini-kit and then converted to complementary DNA using M-MLV Reverse Transcriptase  for subsequent use in qPCR assays. PCR was performed using SYBR Premix Ex Taq II  under the following conditions: denaturing at 95\u00a0\u00b0C for 10\u00a0s, 40 cycles of denaturing at 95\u00a0\u00b0C for 5\u00a0s, and annealing/extension at 60\u00a0\u00b0C for 30\u00a0s. The SYBR Green signal was detected in real-time using a StepOne Plus real-time PCR System . The relative quantification method was used where the expression level of each gene in a sample was determined after normalization to the housekeeping control glyceraldehyde-3-phosphate dehydrogenase (GAPDH). Relative gene expression levels were determined using the comparative threshold cycle (2-\u0394\u0394CT) method. To design PCR primers for circRNA targets, templates were generated using CircPrimer  43. Statistical significance was set at p\u2009<\u20090.05, using two-tailed tests.Continuous variables were expressed as the median and range, and gene expression comparisons were performed using the Mann\u2013Whitney U, Wilcoxon singed rank, Kruskal\u2013Wallis or Steel\u2013Dwass tests. Categorical variables were compared using Fisher\u2019s exact tests. The OS and RFS rate at each follow-up time point was estimated using the Kaplan\u2013Meier method and comparisons were made using a log-rank test. The Cox proportional hazard model was used to perform univariate and multivariate analysis for RFS and OS. All statistical analyses were performed using R software (version 3.5.3, Supplementary Table 1.Supplementary Figure 1.Supplementary Figure 2a.Supplementary Figure 2b.Supplementary Figure 2c.Supplementary Figure 2d.Supplementary Figure 2e.Supplementary Figure 2f.Supplementary Figure 2g.Supplementary Figure 3."}
+{"text": "Circular RNAs (circRNAs) are a novel class of noncoding RNAs that regulate gene expression at the transcriptional or posttranscriptional level. According to recent studies, circRNAs are involved in the pathogenesis of cancer, but the roles of circRNAs in lung adenocarcinoma are largely unknown.In this study, we identified a novel upregulated circRNA, hsa_circ_0000326, in human lung adenocarcinoma tissues using microarray analysis and qRT-PCR. We then explored the biological role of hsa_circ_0000326 using gain- and loss-of-function assays in adenocarcinoma cells. Bioinformatics databases were used to screen for potential target miRNAs and the luciferase reporter assays and RNA-FISH further validated the interaction. Downstream protein was detected by western blot. Finally, we established xenografts in nude mice to assess the function of hsa_circ_0000326 in vivo.We found that high expression of hsa_circ_0000326 was correlated with tumor size, regional lymph node status and differentiation in human lung adenocarcinoma. Additionally, we conducted gain- and loss-of-function assays and found that hsa_circ_0000326 acted as a positive regulator of cell proliferation and migration and a negative regulator of apoptosis. Mechanistic studies showed that hsa_circ_0000326 acted as a miR-338-3p sponge and altered the function of miR-338-3p, which in turn upregulated the expression of the downstream target RAB14 and affected the proliferation, migration and apoptosis of lung adenocarcinoma cells.Collectively, our study results reveal crucial roles for hsa_circ_0000326 in the proliferation, migration and apoptosis of lung adenocarcinoma cells and suggest that hsa_circ_0000326 may represent a potential therapeutic target in patients with lung adenocarcinoma. Lung cancer is one of the most common causes of cancer-related death , and theCircular RNAs (circRNAs) are produced by the circularization of exons, exons and introns, or intron sequences alone and are widely expressed in various cell types , 5. SincHsa_circ_0000326 is located on chromosome 11:65272490\u201365,272,586, and its associated gene symbol is MALAT1. Interestingly, in our early screening experiment, hsa_circ_0000326 was found to be markedly upregulated in a cohort of lung adenocarcinoma tissues and adjacent tissues by microarray analysis. Herein, we provide evidence that aberrant hsa_circ_0000326 expression could promote proliferation and migration and inhibit apoptosis in lung adenocarcinoma cells. Further study showed that hsa_circ_0000326 acted as a miR-338-3p sponge to inhibit its activity and thus upregulate its target RAB14, which in turn affected proliferation, migration and apoptosis in lung adenocarcinoma cells. Based on these results from the present study, targeting hsa_circ_0000326 may be a viable treatment strategy for lung adenocarcinoma.N\u00a0=\u2009100) were obtained from patients who received surgical treatment at Tongji Hospital  from June 2014 to February 2015. Fresh tissues were immediately snap-frozen and stored at \u2212\u200980\u2009\u00b0C. All of the patients gave written informed consent, and none of the patients had previously undergone radiotherapy or chemotherapy. This study was approved by the Human Assurance Committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology.Human lung adenocarcinoma tissues and corresponding adjacent tissues (>\u20095\u2009cm from the tumor edge) . Sample preparation and microarray hybridization were performed according to standard protocols from Arraystar . Briefly, total RNA from each sample was amplified and transcribed into fluorescent cRNA using random primers according to the Arraystar Super RNA Labeling Kit protocol. Labeled cRNAs were hybridized onto an Arraystar Human circRNA Array . After the slides were washed, the arrays were scanned using an Axon GenePix 4000B microarray scanner. The scanned images were then imported into GenePix Pro 6.0 software for grid alignment and data extraction. Quantile normalization and subsequent data processing were performed using the R software package. All of the circRNAs array data were listed in Supplement Table\u00a02 .Human lung adenocarcinoma cell lines  were purchased from the Type Culture Collection of the Chinese Academy of Sciences . And the cell lines was authenticated by short tandem repeat method at Suzhou Genetic Testing Biotechnology Co.,Led.  in Feb., 2018. The cells were cultured in Roswell Park Memorial Institute (RPMI) 1640 medium supplemented with 10% fetal bovine serum (FBS), 100\u2009mg/mL streptomycin and 100\u2009IU/mL penicillin in a 5% COThree siRNAs targeting hsa_circ_0000326 and a nontargeting control , miRNA mimics, a miRNA inhibitor and a negative control miRNA (miRNA-NC) were obtained from RiboBio . The sequence for siRNAs were as follows: si-circ0000326\u20131: 5\u2032-GTAACTGGCATGTGAACAA-3\u2032; si-circ0000326\u20132: 5\u2032-ACTGGCATGTGAACAAGCT-3\u2032; si-circ0000326\u20133:5\u2032-TGGCATGTGAACAAGCTTT-3\u2032. The sequence of mature hsa_circ_0000326 was synthesized and inserted into a circRNA vector plasmid (named pAV-circ0000326) from Vigene Biosciences . The transfection of siRNAs and plasmids was optimized using Lipofectamine 3000 , according to the manufacturer\u2019s instructions.The si-circ0000326 or si-NC sequence was inserted into the lentiviral expression vector piLenti-siRNA-GFP and packaged into viral particles . H1299 cells were infected with either LV-si-circ0000326 or LV-NC particles. Three days after infection, the cells were selected in medium containing 5\u2009\u03bcg/mL puromycin for 7\u2009days and then maintained in medium containing 1\u2009\u03bcg/mL puromycin.-\u0394\u0394Ct method and normalized to \u03b2-actin expression levels. The primers for human hsa_circ_0000326 and \u03b2-actin were as follows: hsa_circ_0000326, forward: 5\u2032-TTG AAT AGA TTT CAG CTT TAT GC-3\u2032 and reverse: 5\u2032-CCC ATA ACT GAT CTG ACT TTG T-3\u2032; \u03b2-actin, forward: 5\u2032-CCT GGC ACC CAG CAC AAT-3\u2032 and reverse: 5\u2032-GGG CCG GAC TCG TCA TAC-3\u2032.Total RNA was isolated using TRIzol reagent  as previously described , 18. ForTo detect the miRNA expression level, a total of 1\u2009\u03bcg miRNAs were reverse transcripted with Bulge-LoopTM miRNA qRT-PCR Primer Set  and the miR-338-3p expression was detected with SYBR\u00ae Premix Ex Taq\u2122 . Primers for miR-338-3p and U6 snRNA were purchased from Ribobio.6 cells/mL and fixed with 70% ice-cold ethanol. Next, the cells were stained with 400\u2009\u03bcL of a propidium iodide solution  for 30\u2009min and then subjected to cell cycle analysis using flow cytometry .For cell cycle assays, transfected cells were harvested, diluted to a density of 1\u2009\u00d7\u2009102O2 was applied to induce apoptosis. Cells were collected 48\u2009h after transfection and resuspended in binding buffer. The cells were then incubated with annexin V and propidium iodide  for 15\u2009min in the dark and then analyzed by flow cytometry on a FACSCalibur flow cytometer.For apoptosis analysis, 300\u2009nM HTransfected cells were seeded in a 96-well plate at a density of 4000 cells per well and cultured for 24\u2009h, 48\u2009h, 72\u2009h or 96\u2009h. Cell Counting Kit 8 (CCK-8) solution  was added to each well, and the cells were then incubated at 37\u2009\u00b0C for 60\u2009min. The absorbance was measured at 450\u2009nm with a spectrophotometer. The data are representative of three individual experiments performed in triplicate.For colony formation assays, 450 transfected cells were seeded in each well of a 6-well plate and maintained for 10\u2009days, with replacement of the medium every 3\u2009days. The colonies were then fixed with methanol, stained with 0.5% crystal violet, and counted under a microscope.4 transfected cells were suspended in 200\u2009\u03bcL of serum-free medium and then seeded in the upper chamber of each transwell insert ; 600\u2009\u03bcL of culture medium containing 15% FBS was added to the lower chamber.For the migration assay, 2\u2009\u00d7\u200910After 24\u2009h of incubation, the cells that had migrated were fixed with 4% paraformaldehyde and stained with 0.5% crystal violet, and the cells remaining on the upper surface of the filter membrane were removed with a cotton swab. Images of cells on the lower surface of the membrane were captured using a microscope equipped with a camera.Total proteins were extracted using RIPA lysis buffer supplemented with a protease inhibitor cocktail, as previously described , 20. Prohttp://www.microrna.org) and TargetScan (www.targetscan.org). The target genes of miRNAs were acquired from TargetScan, PicTar and miRanda. The putative binding site of hsa_circ_0000326 or putative binding site mutants were inserted into the MCS of the pmirGLO vector for use in a dual luciferase reporter assay . HEK293T cells were cotransfected with wild-type or mutant pmirGLO-hsa_circ_0000326  along with 50\u2009nM miR-NC, miR-338-3p, miR-9-3p, miR-16-2-3p, miR-320a or miR-320b mimic using Lipofectamine 2000 . Firefly and Renilla luciferase activity was measured with a Dual-Luciferase Reporter System  48\u2009h after transfection. The effect of each miRNA on the activity of the luciferase reporter containing the hsa_circ_0000326 sequence was normalized to the activity of the luciferase reporter cotransfected with miRNA-NC.The relationships between circRNAs and miRNAs were predicted with custom Arraystar miRNA target prediction software based on miRanda  for 30\u2009min at 37\u2009\u00b0C. FAM-labeled hsa_circ_0000326 probes (5\u2032-AAG CTT GTT CAC ATG CCA GTT ACT-3\u2032) and Cy3-labeled miR-338-3p probes (5\u2032-CAA CAA AAT CAC TGA TGC TGG A-3\u2032)  were denatured at 73\u2009\u00b0C for 8\u2009min and hybridized to the slides for 24\u2009h at 42\u2009\u00b0C. Subsequently, blocking was performed, and 4,6-diamidino-2-phenyl-indole (DAPI) was used to stain the cell nuclei. Images were obtained with a confocal microscope .RNA pulldown assay was conducted by Shanghai Ruantuo Biological Technology Co.  using established techniques .6 cells in 100\u2009\u03bcL of PBS) were subcutaneously injected into the right armpits of nude mice. The tumor size was measured every 7\u2009days. Three weeks later, the mice were sacrificed, and the weight of each tumor was measured.Male BALB/c nude mice (4\u20135\u2009weeks of age) were purchased from Hunan SJA Laboratory Animal Company  and maintained under specific pathogen-free conditions. Cells stably infected with LV-si-circ0000326 or LV-NC H1299 . Paired t tests, independent t tests, chi-square tests and one-way analysis of variance (ANOVA) were used in this study as appropriate. GraphPad Prism 5.0 software  and SPSS 22.0 software  were used for statistical analysis of the data. A two-tailed P\u00a0<\u20090.05 and FDR\u2009<\u20090.05). The top ten upregulated and downregulated circRNAs are listed in Supplement Table\u00a0N\u00a0=\u2009100) by RT-PCR , sex (P\u00a0=\u20090.258), smoking status (P\u00a0=\u20090.181) or distant metastasis  in normal lung tissue samples compared to lung cancer samples . Yao et In the present study, we used microarray technology to show that hsa_circ_0000326 expression was significantly upregulated in lung adenocarcinoma tissues, and we validated this result with a larger sample size using RT-PCR. Moreover, we found that high hsa_circ_0000326 expression correlated with T stage, N stage and tumor differentiation level. We then explored the biological role of hsa_circ_0000326 using gain- and loss-of-function assays. Hsa_circ_0000326 promoted cell proliferation by inhibiting apoptosis rather than by affecting the cell cycle and increased the migration of lung adenocarcinoma cells.Based on emerging data, circRNAs exert key regulatory effects by acting as competing endogenous RNAs. According to bioinformatics analysis, miR-338-3p was predicted to interact with hsa_circ_0000326, and luciferase reporter assays further validated this interaction. We detected the subcellular localization of hsa_circ_0000326 and found that hsa_circ_0000326 was mainly located in the cytoplasm, the same subcellular location as miR-338-3p. Functional assays showed that miR-338-3p mimics reversed the effects of hsa_circ_0000326 overexpression and that miR-338-3p inhibition reversed the effects of hsa_circ_0000326 depletion. However, hsa_circ_0000326 did not affect miR-338-3p expression. In our opinion, hsa_circ_0000326 may competitively bind and inhibit the activity of miR-338-3p, rather than effect the expression. Just as previous study, CircNT5E  or CircuAs shown in previous studies, miR-338-3p expression exhibits marked changes in various tumors, such as hepatocellular carcinoma , gastricUsing three bioinformatic algorithms , we identified RAB14, SOX4 and ZEB2 as potential targets of miR-338-3p. RAB14 is a member of the RAB family of low-molecular-weight GTPases and is expressed in a wide range of cell lines . As showFinally, we established xenografts in nude mice to assess the function of hsa_circ_0000326 in vivo. Interestingly, hsa_circ_0000326 affected lung adenocarcinoma progression in vivo, as evidenced by the significantly lower weights and volumes LV-si-circ0000326 xenograft tumors than control xenograft tumors.Our study had some limitations. First, due to the poor tumorigenesis of A549 cells in nude mice, we did not create stable knockdown hsa_circ_0000326 cell lines or subcutaneously inject mice with these cells. Second, the role of hsa_circ_0000326 on the tumor cell invasion was unknown, although we have conducted invasion assay and optimized the experimental conditions for many times. Third, A549 and H1299 were used as the experimental cells because of the significantly high expression of hsa_circ_0000326. Although the different expression pattern of P53 in A549 and H1299 cells and P53 acted as a tumor suppressor, we observed that hsa_circ_0000326 acted as a negative regulator of apoptosis, along with RAB14 overexpression in A549 and H1299 cells, which indicated P53 may not be the target for hsa_circ_0000326/miR-338-3p/RAB14 signal pathway. A recently study found RAB14 overexpression upregulated Bcl-2, an important anti-apoptotic protein thus inhibited apoptosis [Aberrant hsa_circ_0000326 expression in lung adenocarcinoma promotes tumor proliferation and metastasis. We have constructed a model of the mechanism of hsa_circ_0000326  Effect of hsa_circ_0000326 knockdown on cell cycle. (b) Effect of hsa_circ_0000326 overexpression on cell cycle, as determined by colony formation assay.Additional file 2: Supplement fig. 2. RNAase H digestion results for hsa_circ_0000326.Additional file 3: Table\u00a0S1. The results of circRNAs array for lung adenocarcinoma tissues and adjacent normal tissues.Additional file 4: Table\u00a0S2. Dysregulated circRNAs in lung adenocarcinoma tissues compared with adjacent normal tissues.Additional file 5: Table S3. Correlation between Circ_0000326 expression and clinical pathological characteristics."}
+{"text": "The dataset is organized into six directories: Training_fake, Training_original, Validation_fake, Validation_original, External_test1, and External_test2. The training directories include 2088 histograms of fake voice recordings and 2020 histograms of original voice recordings. Each validation directory has 864 histograms obtained from fake voice recordings and original voice recordings. Finally, External_test1 has 760 histograms , and External_test2 has 76 histograms . With this dataset, the researchers can train, cross-validate and test classification models using machine learning techniques to identify fake voice recordings.This paper presents H-Voice, a dataset of 6672 histograms of original and"}
+{"text": "Carthamus tinctorius L.) is an important cash crop, of which the dried tube flower is not only an important raw material for dyes and cosmetics but also an important herb widely used in traditional Chinese medicine. The pigment and bioactive compounds are composed of flavonoids , and studies have reported that MeJA can promote the biosynthesis of quinone chalcones, but the mechanism underlying the effect of MeJA in safflower remains unclear. Here, we attempt to use metabolomics and transcriptome technologies to analyse the molecular mechanism of flavonoid biosynthesis under MeJA treatment in safflower.Safflower  and downregulate the expression of downstream genes , thus promoting the biosynthesis of quinone chalcones, such as HSYA. The transcription expressions of these genes were validated by real-time PCR. In addition, the promoters of two genes (CtCHI and CtHCT) that were significantly upregulated under MeJA treatment were cloned and analysed. 7 and 3 MeJA response elements were found in the promoters, respectively.Based on a UHPLC-ESI-MS/MS detection platform and a self-built database , a total of 209 flavonoid metabolites were detected, and 35 metabolites were significantly different after treatment with MeJA. Among them, 24 metabolites were upregulated upon MeJA treatment, especially HSYA. Eleven metabolites were downregulated after MeJA treatment. Integrated metabolomics and transcriptome analysis showed that MeJA might upregulate the expression of upstream genes in the flavonoid biosynthesis pathway (such as MeJA might upregulate the expression of the upstream genes in the flavonoid biosynthesis pathway and downregulate the expression of the downstream genes, thus promoting the biosynthesis of quinone chalcones. Our results provide insights and basic data for the molecular mechanism analysis of flavonoid synthesis in safflower under MeJA treatment. Carthamus tinctorius L., is a member of the Asteraceae family and is an important economic plant worldwide. Its dried tubular flowers are an important raw material for dyes and cosmetics and are also an important herb widely used in traditional Chinese medicine. As a traditional Chinese medicine, the dried tubular flowers of safflower have been widely used to improve cerebral blood flow and to treat coronary heart disease, hypertension, and cerebrovascular diseases [Safflower, diseases , 2. Flavdiseases \u20135.Arabidopsis [CHIs) [CHSs) [F3Hs) [UGTs) [HCTs) [The flavonoid biosynthesis pathway is well understood, especially in some model plants, such as bidopsis , 7. Chalbidopsis . With ans [CHIs) , chalcon) [CHSs) , 11, fla) [F3Hs) , UDP-glu) [UGTs) , and shi) [HCTs) .As a well-known exogenous inducing factor, methyl jasmonate (MeJA) participates in many plant processes, ranging from plant defence to growth and development . MeJA isRecently, the application of metabolomics to medicinal plants has significantly facilitated the identification of the metabolic pathways of active medicinal compounds in plants. The UHPLC-ESI-MS/MS-based, widely targeted metabolomics method has become very popular in the field of analysis and identification of plant metabolites due to the advantages of high throughput, fast separation, high sensitivity, and wide coverage. Our methodology was based on a multiple reaction monitoring (MRM) approach , with a Here, metabolic profiling and differential flavonoid metabolites were screened based on a UHPLC-ESI-MS/MS detection platform and a self-built database (including HSYA). In addition, transcriptome sequencing and differential transcripts were analysed. Integrated metabolomics and transcriptome sequencing was analysed based on the KEGG pathway, and the expression of different flavonoid biosynthesis genes with or without MeJA treatment were analysed by real-time PCR. The promoters of genes that were significantly upregulated under MeJA treatment were cloned and analysed. Our results provide insights and basic data for the regulation mechanism analysis of flavonoid synthesis under MeJA treatment.The flavonoid metabolites in safflower with and without MeJA treatment were investigated based on UHPLC-ESI-MS/MS and a self-built database (including HSYA). A total of 209 flavonoid metabolites were detected, including 62 flavones, 42 flavone C-glycosides, 40 flavonols, 20 flavanones, 18 anthocyanins, 11 isoflavones, 12 flavanols, 2 flavonolignans, 1 quinone chalcone, and 1 alkaloid.  was used for variables with less correlation. As the experiment had biological duplication, the fold change and VIP value of the OPLS-DA model were combined to screen differential metabolites. There were 35 significantly different flavonoid metabolites between MeJA-treated and untreated materials. Among them, 24 metabolites were upregulated upon MeJA treatment, especially hydroxysafflor yellow A (HSYA). Eleven metabolites were downregulated after MeJA treatment. Fig.\u00a0a,b The dThe transcriptomes of the mixed safflower samples were sequenced. In the untreated samples,a 6.50\u2009G clean base was obtained , and Q30 was 92.40%. In the MeJA-treated samples, a 6.17G clean base was obtained , and Q30 was 91.89%. The different expressed genes were analysed with DESeq2. The total number of differentially expressed genes was 31,822 . , anthocyanin biosynthesis (ko00942), isoflavonoid biosynthesis ko00943), and flavone and flavonol biosynthesis (ko00944). The results showed that there were 61 significantly different flavonoid biosynthesis transcripts between MeJA-treated and untreated materials . Most of transcripts were involved in flavonoid biosynthesis (ko00941). The details could be viewed in Supplementary Table S, and flaCHSs, CHIs, and HCTs) and might downregulate the expression of downstream genes , thus promoting the biosynthesis of quinone chalcones, such as HSYA.The metabolic components were mapped onto the pathway of flavonoid metabolism  by combining metabolic components that had been detected by UHPLC-ESI-MS/MS, thus constructing the metabolic pathway map of integrative flavonoid biosynthesis in safflower. , TRINITY_DN36537_c1_g1 (annotated as HCT), TRINITY_DN37574_c1_g2 (annotated as HCT), TRINITY_DN39063_c0_g1 (annotated as FLS), TRINITY_DN43344_c1_g1 (annotated as CHI), TRINITY_DN41734_c0_g5 (annotated as CHS), TRINITY_DN42700_c3_g2 (annotated as F3H), TRINITY_DN43120_c5_g1 (annotated as ANR), TRINITY_DN44094_c0_g1 (annotated as ANS), TRINITY_DN44565_c1_g1 (annotated as F3M). The primers used for this experiment could be found in Supplementary Table\u00a0+, and O2 into 3\u2032-hydroxyflavonoid and NADP+, and H2O, which functions like F3H. FLS catalyzes the formation of flavonols from dihydroflavonols. CHS catalyses the reaction of one molecule of 4-coumaroyl-CoA and three molecules of malonyl-CoA to form tetrahydroxychalcone. Chalcone isomerase converts tetrahydroxychalcone into naringenin. ANR and ANS are all involved in the synthesis of anthocyanins. The results showed that the expressions of 6 genes, including TRINITY_DN28401_c0_g1 (CHI), TRINITY_DN36537_c1_g1 (HCT), TRINITY_DN37574_c1_g2 (HCT), TRINITY_DN39063_c0_g1 (FLS), TRINITY_DN43344_c1_g1 (CHI) and TRINITY_DN41734_c0_g5 (CHS), were significantly upregulated, and 4 genes, including TRINITY_DN42700_c3_g2 (F3H), TRINITY_DN43120_c5_g1 (ANR), TRINITY_DN44094_c0_g1 (ANS) and TRINITY_DN44565_c1_g1 (F3M), were significantly downregulated.  were successfully cloned, and the fragments were cloned into the T vector and transformed into DH5a bacteria. A positive bacterial solution was selected for sequencing. The sequences were shown in Fig.\u00a0To analyse how MeJA regulates gene expression, the promoters of differentially expressed flavonoid biosynthesis genes were cloned and analysed. Because there were no reference genome sequences from safflower, single oligonucleotide nested PCR (SON-PCR) was used to clone the promoters . In our http://bioinformatics.psb.ugent.be/webtools/plantCARE) [pCtCHI, while three MeJA response elements could be found in the promoters of pCtHCT.  . The seqCT. Fig.\u00a0 The resuErythrina lysistemon cell suspension culture in response to MeJA elicitation, and results revealed that triterpene i.e. oleanolic acid and fatty acid i.e. hydroxy-octadecadienoic acid were elicited in response to MeJA, whereas pterocarpans i.e. isoneorautenol showed a decline in response to MeJA elicitation [Many secondary metabolites can be induced by MeJA, such as volatile, stilbene, carotenoids, unsaturated fatty acids, flavonoids, lycopene, among others \u201331. Flavcitation . Our resIt have been reported that 104 compounds from safflower were isolated and detected . In prevArabidopsis, MeJA similarly induced expression of almost all anthocyanin biosynthetic genes , but PAL, CHS, CHI and F3H were at only low levels. Further analysis show that the late anthocyanin biosynthesis genes (such as DFR and UFGT), were found to be up-regulated strongly by MeJA [Gynura bicolor DC., the expression of flavonoid biosynthesis genes, GbCHS, GbCHI, GbDFR and GbANS, was markedly up-regulated. Compared with that in Arabidopsis, the genes, which are classified as up flavonoid biosynthesis genes, were found to be up-regulated strongly by MeJA [CHSs, CHIs, and HCTs) and downregulate the expression of downstream genes , thus promoting the biosynthesis of quinone chalcones, such as HSYA. It is probably that MeJA could upregulate the expression of TRINITY_DN28401_c0_g1 (CHI), TRINITY_DN36537_c1_g1 (HCT), TRINITY_DN37574_c1_g2 (HCT), TRINITY_DN43344_c1_g1 (CHI), and TRINITY_DN41734_c0_g5 (CHS), and downregulate the expression of TRINITY_DN42700_c3_g2 (F3H), TRINITY_DN43120_c5_g1 (ANR), TRINITY_DN44094_c0_g1 (ANS), and TRINITY_DN44565_c1_g1 (F3M), thus promoting the biosynthesis of quinone chalcones  in our experiment. The sequence analysis results showed that there were MeJA response elements on the promoters, which further proved the RT-PCR results. Athough our results showed that MeJA upregulate the expression of upstream genes in the flavonoid biosynthesis pathway and downregulate the expression of downstream genes, thus promoting the biosynthesis of quinone chalcones, such as HSYA, what genes MeJA regulated to promote the production of HSYA is still unknown, as the genes involved in the biosynthesis of HSYA have not been fully identified. Future researches can be strengthened in this domain.Since there is no reference genome in safflower, it is still difficult to clone the fragment. However, we fortunately cloned two promoters of up-regulated gene  and downregulate the expression of downstream genes , thus promoting the biosynthesis of quinone chalcones, such as HSYA. The transcription expressions of these genes were validated by real-time PCR. In addition, the promoters of two genes that were significantly upregulated under MeJA treatment were cloned and analysed. Ten MeJA response elements were found in the promoters. Our results provide insights and basic data for the molecular mechanism analysis of flavonoid synthesis in safflower under MeJA treatment.Here, we used metabolomics and transcriptome technologies to analyse the molecular mechanism of flavonoid biosynthesis under MeJA treatment in safflower. Based on a UHPLC-ESI-MS/MS detection platform and a self-built database (including HSYA), a total of 209 flavonoid metabolites were detected, and 35 metabolites were significantly different. Among them, 24 metabolites were upregulated upon MeJA treatment, especially HSYA. Eleven metabolites were downregulated after MeJA treatment. Integrated metabolomics and transcriptome analysis showed that MeJA might upregulate the expression of upstream genes in the flavonoid biosynthesis pathway (such as Carthamus tinctorius L.) by professor Pei Jin. It was cultivated at the medicinal botanical garden on the Wenjiang Campus of Chengdu University of Traditional Chinese Medicine. The treatment was primarily applied according to the previous report with some modifications [Safflower used in this experiment was named as \u201cChuanhonghua No.1\u201d, which is cultivated by Industrial Crop Research Institute, Sichuan Academy of Agricultural Sciences. It was presented by Renchuan Yao and identified as safflower  and filtered  with a zirconia bead for 1.5\u2009min at 30\u2009Hz. One hundred milligrams of powder was weighed and extracted overnight at 4\u2009\u00b0C with 1.0\u2009mL 70% aqueous methanol. Following centrifugation at 10000\u2009The sample extracts were analysed using an LC-ESI-MS/MS system . The analytical conditions were as follows: UHPLC column, Waters ACQUITY UHPLC HSS T3 C18 ; solvent system, A water (0.04% acetic acid): B acetonitrile (0.04% acetic acid); gradient program, 0% B at 0\u2009min, 95% B at 11.0\u2009min, 95% B at 12.0\u2009min,5% B at 12.1\u2009min, and 5% B at 15.0\u2009min; flow rate, 0.40\u2009mL/min; temperature, 40\u2009\u00b0C; and injection volume, 2\u2009\u03bcL. The UHPLC effluent was connected to an ESI-triple quadrupole-linear ion trap (Q TRAP)-MS.Mass spectrometry followed the method of Chen et al. . LIT andThe flavonoid identification and quantification in our study was made according to a method of scheduled multiple reaction monitoring (MRM), which has been previously described . With thwww.r-project.org/). The parameter: scale\u2009=\u2009True. After conversion of the original data by log2, the data was centralized (Mean Centering) and analyzed by OPLSR. Anal of Metabo Analyst in R software. The main steps of our study can be referred to a previouly research [Principal component analysis (PCA) can effectively extract main variance information and was used in many other research , 38. In research . ScreeniRNA isolation and purification and cDNA library construction and sequencing were performed as previously described . All tisP value) with the Benjamini-Hochberg procedure to obtain the false discovery rate (FDR) when |log2fold change|\u2009\u2265\u20091and FDR\u2009<\u20090.05.DESeq2 , 42 was CHS, CHI, HCT, etc.) by real-time PCR were designed by Primer 5.0, and parts of the safflower 28S coding region were used as an internal reference gene. The primer details are listed in Supplementary Table\u00a0Real-time PCR analysis was performed as previously described . Total RpMD19-T vector (Takara) and sequenced. The flavonoid gene promoter sequence was analysed with the PLACE Web SignalScan program  were performed with 200\u2009\u03bcmol of each dNTP, 2\u2009\u03bcmol of primer and 2\u2009units of LA Taq DNA polymerase (Takara). The primer details are listed in Supplementary Table\u00a0lantCARE ;.Additional file 1: Table\u00a0S1. List of the 209 metabolites detected in safflower samples and OPLS-DA results. Two hundred nine flavonoid metabolites details were listed in the table. CK represent treatment without MeJA, M-MeJA represent treatment with MeJA. Three biological repeats were made in the experiment. Table\u00a0S2. Extract of the 35 metabolites significantly different between MeJA and non-treated materials. CK represents treatment without MeJA. Three biological replicates were used in the experiment. Table\u00a0S3. Details of the transcripts belonging to the flavonoid biosynthesis which were significantly different after MeJa treatment. M-CK represent treatment without MeJA, M-MeJA represent treatment with MeJA. Table\u00a0S4. List of primers used for RT-PCR and promoter cloning experiments."}
+{"text": "Brassica rapa var. parachinensis, Brassica oleracea var. alboglabra and Amaranthus spp. \u2013 grown in a commercial, soil-based urban farm. Of these, 128 are near-complete , 540 are substantially complete , while the rest have a completeness \u226550% and redundancy <10%. The draft genomes together span 292 bacterial and 3 archaeal species, a subset\u00a0of which are from underrepresented genus-level lineages in public databases. We expect our dataset to facilitate a wide range of comparative studies that seek to understand the different functional aspects of vegetable crop phytobiomes and for devising new strategies for microbial cultivation in the future.The genome sequences of many microbial species from the phytobiomes of several leafy Asian greens remain unknown. Here, we address this gap by reconstructing 910 prokaryotic draft genomes from 24 leaf, 65 root, 12 soil, and 6 compost metagenomes from the seedling and adult developmental stages of three leafy Asian greens \u2013  Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12472847 Previous studies have greatly expanded our understanding of the diversity and composition of specific phytobiome-associated microbiomes9, but only a few have investigated their genetic underpinnings in a systematic manner11. Knowledge of the latter is especially critical for improving our ability to manipulate phytobiome-associated microbiomes with a view to enhance crop productivity and agricultural sustainability. Metagenomic strategies used to gain such insights rely on curated and well-referenced catalogs of microbial reference genomes that have been specifically recovered from phytobiome-associated microbiomes. Using a catalog of 3,837 bacterial reference genomes, 1,160 of which were from a limited number of phytobiomes, Levy et al.10 identified genetic traits associated with bacterial adaptation to the phytobiome. Deeper insights into other aspects such as identifying the functional roles of different microbial species within the phytobiomes of specific crops will, however, require access to an expanded catalog of microbial reference genomes recovered from crop phytobiomes of interest.Microbiomes within the phytobiome12. They are well suited for cultivation in urban farms13, where microbiome-based solutions can be readily test-bedded in comparison to trials in large, conventional agricultural farms. Although leafy Brassicas represent the nearest commercial crops to the model plant Arabidopsis, their microbiomes remain poorly understood in comparison to the latter. Similarly, the microbiomes of low-cost leafy vegetables such as Amaranthus, also remain poorly understood.Leafy Asian greens which include a range of Brassicas and Amaranthus are widely consumed in Asia and are rich in phytochemicals with known health benefitsBrassica rapa var. parachinensis (commonly known as Choy sum or Cai xin), Brassica oleracea var. alboglabra (commonly known as Kai lan) and Amaranthus spp.\u00a0(commonly known as Bayam) \u2013 across their seedling and adult developmental stages. Sampled niches span the above-ground (phyllosphere) and below-ground  compartments of the crop-specific phytobiomes. Nearly two-thirds of the genomes are substantially complete with a completeness \u226570% and redundancy <10%, while the rest have a completeness \u226550% and redundancy <10%. However, several MAGs lack the full complement of rRNA genes owing to well-known challenges in assembling them from metagenomes15. The draft genomes cluster into 292 bacterial and 3 archaeal species-level groups, operationally defined based on 95% average nucleotide identity. A vast majority of them belong to the phyla Proteobacteria, Actinobacteria and Bacteroidetes  release 89\u00a0(r89)  reconstructed from 107 metagenomes, each of which represents a snapshot of the microbial communities sampled from different niches in the phytobiomes of three leafy Asian greens \u2013 We expect our collection of MAGs, together with the metagenomes from which they were recovered, to be useful for addressing a wide range of both fundamental and applied research questions concerning the various functional aspects of leafy vegetable phytobiomes. They are also likely to be useful for a range of comparative studies seeking to understand, among others, the genomic basis of\u00a0microbe-plant relationships and the evolutionary context of individual genes, especially those related to the provisioning of plant-beneficial services. Finally, they may also offer clues for improving cultivation strategies for certain microbial species that lack cultured representatives.\u22122) whereas Kai lan seeds were germinated in a nursery within the same farm. The nursery comprised of vertically stacked polypropylene seedling trays in which one seed was sown in each compost-filled cavity. Kai lan seedlings were on an average 15\u201320 days old when they were transplanted into the greenhouse soil bed. All three crops were on an average 30\u201345 days old with individual plants weighing 70\u201380\u2009mg at the time of harvest.Plant, soil and compost samples were collected from an intensively managed, soil-based commercial farm located in Lim Chu Kang, Singapore. A variety of horticultural crops, including leafy Asian greens, have been cultivated in this farm for nearly three decades. The farm produces on an average 300 tons per year of leafy Asian greens. Bayam and Kai lan were grown in the same greenhouse whereas Choy sum was grown in a different greenhouse. Bayam and Choy sum seeds were directly sown in soil (39 plants m\u22122), approximately 1.5\u20132 weeks before harvest. Micronutrients were only applied when plants showed signs of deficiency. Microbial products were not used at any stage. Post-harvest, plants that did not meet the quality requirements for consumption were used for producing compost by allowing them to decompose in large pits. This compost was applied to the greenhouse bed before planting the next batch of crops.Plants grown in the greenhouses were exposed only to sunlight and were irrigated using overhead water sprinklers on a daily basis. They were supplied with macronutrients through a one-time application of NPK  fertilizers  were collected in this manner. Plants were manually extricated in a gentle manner so that roots with any attached soil remained intact as much as possible. They were then stored in sterile, air-tight plastic bags and placed in an ice box. A total of 12 seedlings were sampled in a similar manner on 2 October 2018 and were on an average 15\u201320 days old at the time of collection. Kai lan seedlings were collected from four\u00a0randomly chosen seedling trays. On both occasions, approximately 250\u2009g of bulk\u00a0soil from each greenhouse bed and compost samples from the seedling trays were collected using sterile plastic shovels and stored in a manner similar to the plant samples. Microbial cells from the phyllosphere and the rhizocompartments  were isolated from each plant sample within 12\u2009h from sample collection, using previously described protocolsGenomic DNA was extracted from all the samples using the ZymoBIOMICS DNA miniprep kit . Sequencing libraries were prepared and sequenced at the Singapore Center for Environmental Life Sciences Engineering genomics facility. Paired-end libraries (2\u2009\u00d7\u2009250\u2009bp) were prepared using the TruSeq DNA library preparation kit  and sequenced on the HiSeq 2500 platform .17 with parameters:\u00a0--error-rate 0.2, --minimum-length 75, --no-indels to remove sequencing adapters and BBDuk v38.56 (sourceforge.net/projects/bbmap/) with parameters: trimq=20, qtrim=rl, minlen=75 to trim low-quality regions.Raw demultiplexed reads were processed using Cutadapt v2.318 with parameters:\u00a0--k-min 27, --k-max 197, --k-step 10. Assembled contigs <1 kbp were discarded. Read containment was estimated by mapping the quality trimmed reads used for each assembly to the assembled contigs using Bowtie2 v2.3.519 with parameters:\u00a0--no-unal, -X 1000 and SAMtools20. Summaries of individual samples, assemblies including sample groupings for the co-assemblies, contigs >1 kbp from the individual and co-assemblies are available on figshare21 and are contained in the files \u201cglv_sample_data.tsv\u201d, \u201cglv_asm_summary.tsv\u201d, \u201cglv_single_sample_asm.tar.gz\u201d and \u201cglv_co_asm.tar.gz\u201d respectively.Samples were de novo assembled both individually and by co-assembling those from the same niche or plant organ in a plant-type and growth stage specific manner using MEGAHIT v1.2.822 with parameters: --minS 80, CONCOCT v1.1.023 with parameters: -l 2500 and MaxBin2 v2.2.724 with parameters: -min_contig_length 2500, all of which use a combination of sequence composition and differential coverage information. The latter was generated by mapping quality trimmed reads from individual samples to the contigs from each assembly using Bowtie2 v2.3.519 with parameters:\u00a0--no-unal, -X 1000 and SAMtools20, the results of which were processed using the jgi_summarize_bam_contig_depths script from MetaBAT2 v2.12.122. Samples used for mapping comprised those that were used to generate a particular assembly as well as those expected to have similar microbial populations albeit at varying abundances. Multiple bins recovered from the same microbial population contained within a particular assembly were then aggregated and dereplicated using DAS Tool v1.1.025 with parameters:\u00a0--score_threshold 0.Contigs were clustered into metagenomic bins using MetaBAT2 v2.12.115, and then using marker and reference-based approaches implemented in MAGPurify v1.026. Contigs in each bin were removed if either their GC content or tetranucleotide distance fell outside the 98th percentile of their expected distributions derived empirically from a highly curated set of genomes. Contigs were also removed if the absolute percentage difference between their mean coverage and the mean coverage of the bin was \u2265 50%. MAGpurify v1.026 was then used to identify and remove taxonomically discordant contigs using the phylo-markers and clade-markers modules as well as contigs that aligned poorly to conspecific genomes from the IGGdb database26, when available, using the conspecific module. Finally, contigs that mapped to the nearest plant genomes from the Phytozome database v12.127 , to those plant species included in this study, were removed using the known-contam module. A summary of the quality of the 910 MAGs before and after decontamination is available on figshare21 as \u201cglv_mags_decontam_summary.tsv\u201d. Decontaminated MAGs were then dereplicated using dRep v2.2.328 with parameters: -comp 50, -con 10, -sani 0.95/0.99, --S_algorithm gANI.Bins were refined by removing contigs with divergent genomic properties using RefineM v0.0.2529 and are summarized in Fig.\u00a030 using the domain-specific models. MAGs were designated as near-complete drafts if they had a completeness >90%, redundancy <5% and transfer RNA gene sequences for at least 18 unique amino acids or as medium-quality drafts if they had a completeness \u226550% and a redundancy <10%. A summary of the assembly statistics for the 910 MAGs is available on figshare21 as \u201cglv_mags_qual_tax_summary.tsv\u201d.Assembly statistics for the 910 MAGs such as completeness, redundancy, size, number of contigs, contig N50, length of the longest contig, average GC content and the\u00a0number of predicted genes were computed using the lineage workflow from CheckM v1.0.1819 with parameters:--no-unal, -X 1000 and SAMtools20. The sample-specific mean coverage of each MAG was then computed using CoverM v0.4.0 (https://github.com/wwood/CoverM) with parameters:\u00a0--min-read-percent-identity 0.95,\u00a0--min-read-aligned-percent 0.75,\u00a0--proper-pairs-only,\u00a0--methods trimmed_mean. Coverage profiles were converted to global presence or absence across different vegetable crop types using R v.4.0.031.MAGs were detected across samples by mapping quality trimmed reads from all the samples to each MAG using Bowtie2 v2.3.538 with the GTDB r8940. This was cross-referenced with that inferred using 16S rRNA gene sequences, which were identified and extracted using the 16SfromHMM.py script (https://github.com/christophertbrown/bioscripts) from the ctbBio python package with parameters: -l 250 -m. Insertions \u226510\u2009bp were removed using the strip_masked.py script from the same package with parameters: -l 10. Sequences were classified up to the genus level using the assignTaxonomy function with parameters: tryRC=TRUE, outputBootstraps=TRUE, while species labels were inferred using the addSpecies function from the DADA2 R package v1.14.141. The reference database used for classification comprised of 20,486 bacterial and 1,073 archaeal full-length 16S rRNA gene sequences extracted from the set of representative species-level genomes in the GTDB r8942. Taxonomy inferred using both approaches and the full set of 16S rRNA gene sequences extracted from the MAGs are available on figshare21 and are contained in the files \u201cglv_mags_qual_tax_summary.tsv\u201d, \u201cglv_mags_16SrDNA_tax.tsv\u201d and \u201cglv_mags_16SrDNA_seq.fa\u201d respectively.The taxonomy of the 910 MAGs were inferred using GTDB-Tk v1.0.238 with parameters:\u00a0--bacteria, --skip_gtdb_refs and\u00a0--outgroup_taxon p_Patescibacteria. The tree was rooted on the branch leading to the MAGs from the phylum Patescibacteria. The rooted tree was visualized and annotated with data corresponding to MAG completeness, redundancy, size and detection across plant types using iTOL v5.5.143. The unrooted version of this tree with bootstrap support values is available on figshare21 as \u201cglv_mag_de_novo_unrooted.tree\u201d. Domain-specific trees incorporating the 295 MAGs with species-level reference genomes from the GTDB r8940 were constructed using the de novo workflow in GTDB-Tk v1.2.038 with parameters:\u00a0--bacteria, --outgroup_taxon p_Patescibacteria and\u00a0--archaea, --outgroup_taxon pAltiarchaeota for the bacterial and archaeal trees respectively. These trees were used to calculate the phylogenetic gain at different taxonomic levels using the pd_clade routine in genometreetk v0.1.6 (https://github.com/dparks1134/GenomeTreeTk). The unrooted, bootstrapped versions of the bacterial and archaeal trees are available on figshare21 and are contained in the files \u201cglv_bac_de_novo_gtdb_unrooted.tree\u201d and \u201cglv_arc_de_novo_gtdb_unrooted.tree\u201d respectively.Phylogenetic relationships among the 292 bacterial species-level MAGs were inferred by constructing a maximum-likelihood tree using the de novo workflow in GTDB-Tk v1.2.044. Datasets and data products generated from the raw sequence data are available in figshare21. They have been appropriately specified in the text where required.The raw sequence data is available on the NCBI Sequence Read ArchiveThis catalog comprises of only those genomes that met specific quality thresholds as described in the manuscript. Additionally, the taxonomy of MAGs inferred using whole-genome based methods were cross-referenced with those inferred using the 16S rRNA gene sequences, when available."}
+{"text": "Neuroendocrine prostate cancer (NEPC) is a lethal subtype of prostate cancer. It develops mainly via NE transdifferentiation of prostate adenocarcinoma in response to androgen receptor (AR)-inhibition therapy. The study of NEPC development has been hampered by a lack of clinically relevant models. We previously established a unique and first-in-field patient-derived xenograft (PDX) model of adenocarcinoma (LTL331)-to-NEPC (LTL331R) transdifferentiation. In this study, we applied conditional reprogramming (CR) culture to establish a LTL331 PDX-derived cancer cell line named LTL331_CR_Cell. These cells retain the same genomic mutations as the LTL331 parental tumor. They can be continuously propagated in vitro and can be genetically manipulated. Androgen deprivation treatment on LTL331_CR_Cells had no effect on cell proliferation. Transcriptomic analyses comparing the LTL331_CR_Cell to its parental tumor revealed a profound downregulation of the androgen response pathway and an upregulation of stem and basal cell marker genes. The transcriptome of LTL331_CR_Cells partially resembles that of post-castrated LTL331 xenografts in mice. Notably, when grafted under the renal capsules of male NOD/SCID mice, LTL331_CR_Cells spontaneously gave rise to NEPC tumors. This is evidenced by the histological expression of the NE marker CD56 and the loss of adenocarcinoma markers such as PSA. Transcriptomic analyses of the newly developed NEPC tumors further demonstrate marked enrichment of NEPC signature genes and loss of AR signaling genes. This study provides a novel research tool derived from a unique PDX model. It allows for the investigation of mechanisms underlying NEPC development by enabling gene manipulations ex vivo and subsequent functional evaluations in vivo. Prostate cancer (PCa) is the second most commonly diagnosed cancer in men worldwide, with 1.3 million new cases and 360,000 deaths reported in 2018 . Ge. GehttpsThe NE and AR scores were calculated as previously reported ,12. BrieLentiviruses expressing mCherry were produced in 293T cells following established protocols . Culture2O2, tissue sections were blocked with 5% normal goat serum in Tris-buffered saline with 0.1% Tween-20 for 1 h at room temperature. They were then incubated with primary antibodies at 4 \u00b0C overnight. The next day, biotinylated secondary antibodies  were added for 30 min at room temperature. The slides were then incubated with avidin\u2013biotin complex  for another 30\u2009minutes at room temperature. Finally, after the application of DAB chromogen, tissue sections were stained with hematoxylin, dehydrated, and mounted [Formalin-fixed paraffin-embedded (FFPE) tissue sections were stained manually using our established protocols. Briefly, tissue sections on glass slides were deparaffinized in xylene, rehydrated in graded ethanol solutions, and washed in tap water. Antigen retrieval was performed by boiling the slides in a citrate buffer . After a 10-min treatment with 3% HFor cultured cells, total RNA was extracted using TRIzol . For tumor tissues, total RNA was extracted using the miRNeasy kit . First-strand cDNA was synthesized from 1 \u03bcg of total RNA using the QuantitectTM reverse transcription kit . RT-PCR was performed using SYBR Green reagent and the Applied Biosystems ViiA-7 Real Time PCR system . The qRT-PCR primers used in this study are as follows: AR_forward: 5\u2032- TCTTGTCGTCTTCGGAAATGT; AR_reverse: 5\u2032- AAGCCTCTCCTTCCTCCTGTA; KLK3_forward: 5\u2032- CACCTGCTCGGGTGATTCTG; KLK3_reverse: 5\u2032- CCACTTCCGGTAATGCACCA. GAPDH forward: 5\u2032- CACCAGGGCTGCTTTTAACTC; GAPDH reverse: 5\u2032- GACAAGCTTCCCGTTCTCAG. Relative gene expression was calculated using the 2-\u2206\u2206ct method with GAPDH as an internal reference gene.p-values lower than 0.05 is regarded as statistically significant, indicated by * for p < 0.05, ** for p < 0.01, and *** for p < 0.001. In GSEA, the nominal p-value estimates the statistical significance of the enrichment score for a single gene set. The FDR q-value is the estimated probability that a gene set with a given NES represents a false-positive finding. A nominal p-value less than 0.05 and a FDR q-value less than 0.25 is considered statistically significant [The Student t-test was used to determine statistical significance between groups with discrete measurements. Any differences with nificant .We applied the CR culture method to establish LTL331 primary cells with the hope of providing an ex vivo interface to study NEPC development. In brief, freshly collected LTL331 tumor was enzymatically disassociated. The cells were then plated onto irradiated 3T3-J2 feeder cells and cultured using CR culture F medium A. The esAR and its critical target gene KLK3 using qRT-PCR . Interestingly, we found that LTL331_CR_Cells in CSS-medium behaved similarly to those in complete medium with a nearly identical population doubling curve A. We the qRT-PCR . Because qRT-PCR C. This s qRT-PCR . PreviouPrevious studies have reported that CR cells, when implanted back into immunodeficient mice in vivo, can form tumors representing the original histopathology of the parental tumor and not the in vitro dedifferentiated state ,21,27,40www.livingtumorlab.com) has established over 50 PCa PDX models covering hormone-na\u00efve, castration-resistant, and neuroendocrine PCa subtypes [In this study, we successfully established CR cells from our LTL331 PCa PDX model. As a proof-of-principle, this method can establish ex vivo models from existing PDX collections, thus expanding their applications. To the best of our knowledge, this is the first report of CR cultures being successfully used for PCa PDX models. Cell-based models are simple and cost- and time-efficient research tools . Howeversubtypes . These Psubtypes ,46,47. Osubtypes . CR techsubtypes ,27,28,48subtypes . Howeversubtypes ,19,50. Wsubtypes ,51. We eLTL331_CR_Cells show unexpected cellular plasticity, developing into NEPC in vivo. One feature of the CR technology is that it temporarily maintains cells in a stem-like state in vitro. Once regrafted in vivo, the CR cells re-differentiate into parental tissue morphology ,27,40. PWe established CR cells from the LTL331 PDX model. They can be efficiently propagated and genetically manipulated in vitro. LTL331_CR_Cells spontaneously give rise to NEPC tumors in vivo, providing a novel model for studying the mechanisms underlying NEPC development and offering a novel platform for screening drug candidates in a preclinical setting."}
+{"text": "Two datasets of reconstructed and segmented images were produced in typical regions, namely in the thin and thick epoxy regions where the resin thickness between the adjacent carbon fibers was small and large, respectively. This novel study presents the first non-destructive three-dimensional (3D) visualization of resin deformation behavior around crack tips, and provides a valuable and unique insight for the future design of CFRPs.Crack initiation and propagation in carbon fiber-reinforced plastic (CFRP) was observed  Specifications TableThis study presents the first non-destructive three-dimensional (3D) evaluation of resin deformation behavior around crack tips. Nano-scale observations were achieved, where spatial resolution has previously been limited to the micro-scale (\u03bcm) using other conventional techniques.e.g. carbon fiber-reinforced plastic (CFRP) and other composite materials and plastic) and theoretical aspects  for further research and engineering applications).The acquired data provides valuable and fundamental insight regarding the related materials  Thin_raw.tif and (b) Thick_raw.tifReconstructed 3D images of typical regions, namely the thin and thick epoxy regions in which the resin thickness between the adjacent carbon fibers is small and large, respectively.Segmented data:(a-1) Thin_seg_fiber.tif and (a-2) Thin_seg_crack.tif(b-1) Thick_seg_fiber.tif and (a-2) Thick_seg_crack.tifThe reconstructed data were segmented into carbon fibers, cracks, and resin. Each dataset includes segmented images of carbon fibers and cracks in the thin and thick epoxy regions. The remaining voxels correspond to resin. Segmentation was performed automatically only. In the full paper Movie data:(A) MovieS1_CFRP_thin.avi and (B) MovieS2_CFRP_thick.aviMovieS1_CFRP_thin.avi and MovieS2_CFRP_thick.avi present the 3D automatically segmented data which is composed of carbon fibers (black), resins (dark yellow), and cracks (red) for \u201cthin\u201d and \u201cthick\u201d epoxy regions, respectively. The size of FOV is 20\u202f\u03bcm in diameter and 40\u202f\u03bcm in length.2 and resin content of 35\u202fwt.% at a fiber volume fraction of ca. 60%. A polyacrylonitrile (PAN)-based carbon fiber with an elastic modulus of 294\u202fGPa and tensile strength of 5880\u202fMPa  was used as the reinforcing fiber, while a 453\u202fK cure-type epoxy resin compound was used as the matrix resin. Very thin unidirectional laminates [02] were autoclave-cured in a standard cure cycle at a ramp rate of 1.5\u202fK/min and held for 2\u202fh at 450\u202fK and 6 atm. The autoclave-cured CFRP was cut along the fiber direction  using a razor blade to produce columnar samples  for the nanoscopic SR X-CT measurements.Carbon fiber/epoxy prepreg samples were used with a fiber areal weight of 190\u202fg/mThe nanoscopic SR X-CT setup  includedThe radiographs were reconstructed to 3D volume images using a filtered back projection method. The 3D volume images were segmented, where the Watershed Segmentation feature of the Morphological Segmentation plug-in the Fiji software The authors have read and understood the journal's ethical requirements and we believe that neither the article nor the study violates any of these.The authors declare that they have no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article."}
+{"text": "Generation of oligodendrocytes is a sophisticated multistep process, the mechanistic underpinnings of which are not fully understood and demand further investigation. To systematically profile proteome dynamics during human embryonic stem cell differentiation into oligodendrocytes, we applied in-depth quantitative proteomics at different developmental stages and monitored changes in protein abundance using a multiplexed tandem mass tag-based proteomics approach.Our proteome data provided a comprehensive protein expression profile that highlighted specific expression clusters based on the protein abundances over the course of human oligodendrocyte lineage differentiation. We identified the eminence of the planar cell polarity signalling and autophagy (particularly macroautophagy) in the progression of oligodendrocyte lineage differentiation\u2014the cooperation of which is assisted by 106 and 77 proteins, respectively, that showed significant expression changes in this differentiation process. Furthermore, differentially expressed protein analysis of the proteome profile of oligodendrocyte lineage cells revealed 378 proteins that were specifically upregulated only in 1 differentiation stage. In addition, comparative pairwise analysis of differentiation stages demonstrated that abundances of 352 proteins differentially changed between consecutive differentiation time points.Our study provides a comprehensive systematic proteomics profile of oligodendrocyte lineage cells that can serve as a resource for identifying novel biomarkers from these cells and for indicating numerous proteins that may contribute to regulating the development of myelinating oligodendrocytes and other cells of oligodendrocyte lineage. We showed the importance of planar cell polarity signalling in oligodendrocyte lineage differentiation and revealed the autophagy-related proteins that participate in oligodendrocyte lineage differentiation. Oligodendrocytes  improve NSC, NPC, and OPC migration into the required site; (ii) enhance the aforementioned cells' survival especially during this process; and (iii) boost their differentiation into myelinating OLs in the demyelination niche. To fulfill these prospects, we conducted an in-depth quantitative proteomic analysis that spanned the entire course of OL lineage cell generation in an attempt to survey the order, timing, and magnitude of proteome changes during human embryonic stem cell (hESC) differentiation into OL lineage cells. This versatile differentiation model system provides tremendous insight into human OL development, in addition to the information needed for targeted/specific cell-based medical therapies and overall disease modelling , 5.Therefore, we studied the global proteome signature of developing OL by conducting a stepwise differentiation process to differentiate the hESC RH6 line into an OL lineage. This process provided us with cell samples from each of the distinct stages of OL differentiation: hESCs, NSCs, NPCs, pre-OPCs, OPCs, and OLs . We atte+ NSCs   was diffNSCs d8, . Further 12 d12, , which t 80 d80, . FinallyLs d120, .Following recapitulation of OL lineage development, the cells were harvested at 6 distinct time points , which corresponded to the hESC (d0), NSC (d8), NPC d12), pre-OPC (d20), OPC (d80), and OL (d120) differentiation stages , demonstrated a slight decreasing expression profile (d0\u2013d120). Based on the functional enrichment analysis, this cluster mostly contained proteins that contribute to gene expression and translation Fig.\u00a0. The 2 oin vivo OL differentiation of various neural precursors in patients with myelination defects. It also may provide us with stage-specific profiles that correlate with predominant biological functions associated with this differentiation process.To confirm the authenticity of our clusters, we checked the expression patterns of several marker proteins related to the OL differentiation stages. The depicted heat map showed that the time-dependent changes of these markers were consistent with their fitted clusters and aligned with the progression of the differentiation process Fig.\u00a0. Notablyleft, and +  pathway, and PCP  pathway are fundamental mechanisms associated with various levels of vertebrate developmental procedures , pH 2.72) and were injected onto a PolySULFOETHYL A\u2122 column , which was also equilibrated with buffer A. The adsorbed peptides were eluted with a linear gradient of 10\u201345% buffer B  for 70\u00a0min, which was then rapidly increased to 100% buffer B for 10\u00a0min at a flow rate of 300 \u03bcL/min. The collected samples were then desalted using SDB-RPS Stage Tips, dried by vacuum centrifuge, and reconstituted in 40 \u03bcL of 0.1% FA in preparation for nanoLC-ESI-MS/MS as reported by Mirzaei et\u00a0al. . To. ToP \u2264 0P \u2264 0.05  for PearP \u2264 0.05 , we perfP \u2264 0.05 . The BioP \u2264 0.05  181]. T. TP \u2264 0.P \u2264 0.05 . Then, t results , 42  Schematic representative of OL differentiation protocol . First NANOG+ hESCs (B) were induced to NSCs by dual SMAD inhibition. Then, neural progenitor patterning and OPC commitment were achieved by the application of 2 morphogens, RA and SAG. Subsequently, PDGF medium was used to promote OPC formation. From day 80 onward, Glial medium was utilized for OL derivation. SOX1+ NSCs were detected on day 8 (C) and OLIG2+ NPCs appeared on day 12 (D) and participated in aggregate formation after being detached. Consequently, NKX2.2+ pre-OPCs (E), day 20, differentiated into PDGFRA+ OPCs on day 80 (F); their further differentiation resulted in MBP+ OLs (G) on day 120. The attained OLs demonstrate a typical OL morphology that consisted of a round, central soma with multiple branching processes that expanded symmetrically outward and gave the OL a spider-in-a-web\u2013like appearance [pearance . hESC: hFig. S2: Overall validation of the collected proteome data. (A) Proportional Venn diagram compares the depth of protein coverage in 3 replicates. A total of 3,527 proteins were identified in the 3 TMT mixtures that were analysed, while 1,056 proteins were only identified in 2 replicates. (B) The doughnut chart represents the diversity of the quantified proteins based on PANTHER protein class annotation.Fig. S3: Enrichment plot of Wnt signalling components (from GSEA data set) between the last 3 time points  compared to the others using MSigDB set for Wnt signalling components .Fig. S4: The contribution of macroautophagy in generation of the OL lineage. (A) Cluster enrichment analysis (see Supplementary Table S6) featured the prominent participation of macroautophagy and autophagy pathways in OL lineage differentiation of hESCs. \u201cin C2 and C3\u201d reveal the clusters in which these GO terms were enriched. (B) Schematic illustration of the process and main regulatory machinery of macroautophagy. AMP-activated kinase (AMPK) signalling is depicted as the activator of the macroautophagy process (initiation) that targets the ULK1 (Unc-51-like kinase 1) initiation complex. The initiation complex then triggers membrane nucleation and phagophore formation. Hence, the cup-shaped double membrane phagophore begins to engulf the autophagic cargo and expands into the double-membrane vesicle (autophagosome) that sequesters the cargoes completely (phagophore expansion). Subsequently, the autophagosome fuses with acidic lysosomes (fusion with the lysosome) and forms autolysosomes, where the cargo will be degraded (degradation). The coloured ovals encompass the proteins quantified in our study of OL lineage differentiation. This figure is adapted from Hansen et al. [n et al. .Fig. S5: Illustration of the abundances of differentially expressed proteins (DEPs) at each stage of oligodendrocyte (OL) lineage differentiation. Heat map shows the standardized relative protein expression changes of DEPs at each step of OL lineage differentiation. The 7 expression profile clusters (left colour-coded bar) describe stage-specific patterns of the dynamics of 378 DEPs. The clusters are indicated by different colours, each of which demonstrates 1 specific differentiation step.Fig. S6: Supporting figure for Supplementary Fig. S2. (A) Cells at the NSC stage show an almost uniform expression of CDH2.  Immunofluorescent staining of the control NSC line, RSCB0181, by CDH2 and SOX1 antibodies. (D\u2013L) Nearly 100% of the generated cells at OPC stage (d80) expressed PDGFRA, and 97% of them were also SOX10+. (M) Immunofluorescent staining of the OLIG2+ NPCs at the d12 stage with an antibody from a different provider shows the same result.  A total of 90% of the cells at pre-OPC stage express NKX2.2. We counted 5 different fields of these 2 figures as well as Supplementary Fig. S1E. (P\u2013W) A total of 22% of the cells at the OL stage were mature oligodendrocyte. (X\u2013Z) Phase contrast photos of the cells at the OL stage.Table S1: The neXtProt entry and the full name of the proteins mentioned in the text of this article.Table S2: Protein identification, TMT reporter ion ratios, protein quantitation, and study design. The tables numbered 171102, 171108, and 171105 represent the data acquired by the first, second, and third TMT experiments. The \u201cAggregated Data\" table includes all of the identified proteins of the 3 replicates in 1 table. The \u201cStudy Design\" table shows the TMT experimental design and the \u201cReplicates Correlation\" table illustrates the Pearson correlation coefficients of the replicates. The analysis of variance  represents 3,132 proteins  that showed significant changes (adjusted P \u2264 0.05) through the OL lineage differentiation of hESCs. On the basis of the Pearson correlation coefficient analysis, \u201cd0_r1\" was left out of the study. In addition, we decided to present the most correlated triplicates for further analysis.Table S3: The first table (Quantified Proteins) shows the relative expression of all of the identified and quantified proteins in every biological replicate of each time point during OL lineage differentiation. The second table (Variable Loadings Matrix) includes the details of the PCA of the proteome profile of each differentiation stage in Fig.\u00a0Table S4: Cluster 1, 2, and 3 tables present each cluster's members, their relative expression, and their membership score. The highlighted BPs table involves the highlighted biological processes of each protein cluster.Table S5: Wnt signalling\u2013related GOs of the demonstrated Wnt signalling\u2013associated proteins in Fig.\u00a0Table S6: The relative expression of the autophagy-associated proteins, which have been illustrated by heatmap in Fig.\u00a0Table S7: The first 6 sheets , 425 intra/extracellular trafficking and signalling proteins, 203 cytoskeletal and extracellular matrix (ECM) proteins, and 57 structural and adhesive proteins.Asn: asparagine; Asp: aspartate; BCA: bicinchoninic acid; bioDBnet: biological database network; BP: biological process; BSA: bovine serum albumin; CaM: calmodulin; cAMP: cyclic adenosine monophosphate; CNS: central nervous system; d0: day 0 of differentiation; d12: day 12 of differentiation; d120: day 120 of differentiation; d20: day 20 of differentiation; d8: day 8 of differentiation; d80: day 80 of differentiation; DAVID: Database for Annotation, Visualization and Integrated Discovery; DDA: data-dependent acquisition; DEP: differentially expressed protein; ECM: extracellular matrix; Eph: ephrin; EpiSC: epiblast stem cell; FA: formic acid; FDR: false discovery rate; Gln: glutamine; Glu: glutamate; GO: gene ontology; GSEA: gene set enrichment analysis; HCD: higher energy collisional dissociation; hESC: human embryonic stem cell; ictrl: inner control; IGF1: insulin-like growth factor 1; iPSC: induced pluripotent stem cell; LDN: LDN193189; mhDEP: most highly differentially expressed protein; MIN6: mouse insulinoma 6; MS: mass spectroscopy; MSigDB: Molecular Signatures Database; mTOR signalling pathway: mammalian target of rapamycin signalling pathway; mTORC1: mTOR complex 1; NADP: nicotinamide adenine dinucleotide phosphate; nanoLC/ESI-MS/MS: high-resolution nanoflow liquid chromatography positive ion electrospray ionization tandem mass spectrometry; NCE: normalized collision energy; NPC: neural progenitor cell; NSC: neural stem cell; NSPCs: neural stem and progenitor cells; NTF3: neurotrophin 3; OL: oligodendrocyte; OPC: oligodendrocyte progenitor cell; PANTHER: protein analysis through evolutionary relationships; PCA: principal component analysis; PCC: Pearson correlation coefficient; PCP: non-canonical planar cell polarity; PDGF-AA: platelet-derived growth factor AA; PKCe signalling pathway: protein kinase C epsilon signalling pathway; pO/L: poly-L-ornithine/laminin; pre-OPC: pre-oligodendrocyte progenitor cell; PRIDE: Proteomics Identifications Database; PS: protein set; RA: all trans-retinoic acid; RH6: Royan human embryonic stem cell line 6; SAG: smoothened agonist of sonic hedgehog; SB: SB431542; STSP: stage transition\u2013specific protein; T3: 3,3,5-triiodo-l-thyronine; TF: transcription factor; TGF-\u03b2 signalling pathway: transforming growth factor \u03b2 signalling pathway; TMT: tandem mass tag; UniProt: Universal Protein Resource.The authors declare that they have no competing interests.G.H.S., P.P., and M.J. conceived the project, designed the study, and interpreted results with the efforts of R.K., M.M., and H.B.; P.P. performed the experiments, except for the TMT experiment, which was designed, performed, and, in part, analysed by M.M., Y.W., A.A., and V.G.; A.M. contributed in RH6 preparation; P.P. and R.K. performed computational analyses and prepared figures and tables; P.P. wrote the manuscript with input from the co-authors; and G.H.S., M.M., and H.B. oversaw all aspects of the study. All authors proofread the manuscript and approved the final version.giaa116_GIGA-D-20-00058_Original_SubmissionClick here for additional data file.giaa116_GIGA-D-20-00058_Revision_1Click here for additional data file.giaa116_GIGA-D-20-00058_Revision_2Click here for additional data file.giaa116_GIGA-D-20-00058_Revision_3Click here for additional data file.giaa116_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giaa116_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giaa116_Response_to_Reviewer_Comments_Revision_2Click here for additional data file.giaa116_Reviewer_1_Report_Original_SubmissionAmit Yadav -- 4/17/2020 ReviewedClick here for additional data file.giaa116_Reviewer_1_Report_Revision_1Amit Yadav -- 8/9/2020 ReviewedClick here for additional data file.giaa116_Reviewer_2_Report_Original_SubmissionTim Ahfeldt -- 4/29/2020 ReviewedClick here for additional data file.giaa116_Supplemental_Figures_and_TablesClick here for additional data file."}
+{"text": "Osteosarcoma (OS) is a primary malignant bone tumor with a high fatality rate. Circular RNAs (circRNAs) are a type of endogenous noncoding RNA that have been verified to participate in cancer pathophysiological processes. We aim to investigate the roles of circRNAs in osteosarcoma tumorigenesis. In the present study, we showed that hsa_circ_0003732 was up-regulated in OS tissues and elevated level of hsa_circ_0003732 was linked to poor prognosis of OS patients. Functional investigation indicated that hsa_circ_0003732 promoted proliferation of OS cells. Furthermore, we identified miR-545 as the hsa_circ_0003732-associated microRNA and CCNA2 was a direct target of miR-545. In addition, hsa_circ_0003732 could elevate CCNA2 expression via miR-545, resulting in the promotion of OS cells proliferation. Altogether, our findings demonstrate that hsa_circ_0003732 promotes OS cells proliferation via miR-545/CCNA2 axis and imply hsa_circ_0003732 may be a potential prognosis biomarker and therapeutic target for OS. Osteosarcoma (OS) is the most common bone malignancy that usually occurs in young people . DespiteCircular RNAs (circRNAs) are a class of noncoding RNA that is characterized with a covalently closed loop structure . CircRNAin vitro and discussed the probability to be a therapeutic target for osteosarcoma.In this study, we found the expression of hsa_circ_0003732 that derived from back-splicing of HUWE1 transcript was increased in OS tissues using microarray analysis. Hsa_circ_0003732 rank the first among the upregulated circRNAs and its roles in osteosarcoma remained unclear, so it was chosen for further validation. Then, we verified the expression of hsa_circ_0003732 in OS tissues and cell lines by quantitative real-time PCR (qRT-PCR) as well as explored its value in acting as a clinical biomarker. We further investigated the functions and mechanisms of hsa_circ_0003732 in OS This study was approved by the Ethics Committee of the First Affiliated Hospital of Anhui Medical University and conformed to the Ethical Guidelines of the Declaration of Helsinki. Forty-six pairs of OS tissues and adjacent noncancerous tissues (ANTs) from primary osteosarcoma patients were collected from patients who underwent complete resection surgery at the Department of Orthopedics of the First Affiliated Hospital of Anhui Medical University. Written informed consent was obtained from all the participants.Total RNA was extracted from three pairs of OS tissues and ANTs and digested with Rnase R  to increase the enrichment of circRNA. circRNA microArray was performed using circRNA chip provided by Arraystar containing the specific probes for human circRNA.Total RNA was isolated using TRIzol reagent  and reverse-transcribed by PrimeScript RT reagent Kit (Takara). qRT-PCR was performed using SYBR Green qPCR Master Mix (Thermo Fisher Scientific). The expressions of hsa_circ_0003732, c-Myc and CCND1 were normalized to GAPDH expression. miR-545 expression was assessed using Hairpin-it\u2122 miRNAs qPCR Quantitation Kit  and normalized to U6 expression. The primers were: hsa_circ_0003732, F: 3\u2032- CAGCAATGGCTGCCAGAATTA-5\u2032, R: 3\u2032- TTCAATGGGGCGGTGTAAGG-5\u2032; GAPDH, F: 5\u2032-AATGGGCAGCCGTTAGGAAA-3\u2032, R: 5\u2032- TGAAGGGGTCATTGATGGCA-3\u2032.The human osteosarcoma cell lines  and human osteoblastic cell line (hFOB1.19) were cultured in DMEM Medium  containing 10% fetal bovine serum. Oligoribonucleotides were transfected into cells using Lipofectamine 2000 (Invitrogen). The siRNA sequences for hsa_circ_0003732 siRNA were, sense: 5\u2032- AGUGUCUGAUAACAAAGGCUUTT-3\u2032, antisense: 5\u2032- AAGCCUUUGUUAUCAGACACUTT-3\u2032.Cell Counting Kit-8 (CCK-8) assay was used to study cell proliferation. Cells were seeded in 96-well plates (2000 cells/well) and transfected with oligoribonucleotides. CCK-8 kit  was used at 24, 48 and 72 h after transfection. The optical density was measured by a microplate reader at 450 nm.Cells were seeded in six-well plates and transfected with oligoribonucleotides. After 48 h, cells were harvested and cell cycle was analyzed by DNA Content Quantitation Assay (Cell Cycle)  and Flow cytometry.EdU assay was applied for detection of DNA replication activity. Cells were seeded in six-well plates and transfected with oligoribonucleotides for 48 h. A Cell-Light EdU Apollo567 In Vitro Kit  was employed to conduct the EdU assays. Hoechst staining was used for labeling living cells. Images were acquired with a fluorescence microscope .Cells were lysed with RIPA  supplemented with protease inhibitor cocktail to extract proteins. Protein concentrations were measured with BCA protein assay Kit . Equal amounts of proteins were resolved on SDS-denaturing polyacrylamide gels, and transferred onto PVDF membranes . The primary antibodies used for Western blot were: CCNA2 antibody . Protein levels were normalized to \u03b2-actin .The reporter plasmids were obtained by inserting wild-type or mutant hsa_circ_0003732 or CCNA2 3\u2032UTR sequences that contained predicted miR-545 binding sites into the pmirGLO vector . miR-545 mimics and reporter plasmids were co-transfected into cells and cultured for 48 h. Luciferase activity was detected using Dual-luciferase reporter assay system (Promega) and normalized to the Renilla luciferase internal control.t-test and one-way analysis of variance were applied to evaluate differences among groups. Chi-squared test was used to analyze the correlation of hsa_circ_0003732 expression with clinicopathological factors. Kaplan\u2013Meier method and log-rank test was used to conduct survival analysis. P<0.05 was considered statistically significant. The independent experiments were performed for at least three replicates.Statistical analyses were performed with the SPSS 19.0 software . Data are presented as the mean \u00b1 SD. Student\u2019s Through microarray analysis, we found that hsa_circ_0003732 expression was increased in OS tissues compared with ANTs A. In furBecause the expression of hsa_circ_0003732 was significantly higher in OS cells than that of hFOB1.19 cells C. SpecifThen, we sought to explore the molecular mechanism of hsa_circ_0003732 in promoting proliferation of OS cells. Because circRNAs could act as a sponge to repress miRNA availability, so, we performed bioinformatics analysis to uncover the potential miRNAs that might binding to hsa_circ_0003732. We found hsa_circ_0003732 possessed a complementary sequence to miR-545 seed region A. SubseqNext, the role of miR-545 in OS was explored. Functional experiments showed that overexpression of miR-545 significantly inhibited the proliferation of OS cells A\u2013C. In aThrough TargetScan and microRNA.org, we identified CCNA2 as a potential target of miR-545 A. To proCircRNAs has been reported to be exert very important roles in human cancers, including OS . Our stuIn the present study, we first validated that hsa_circ_0003732 expression was significantly increased in OS tissues and cells. Further study showed that hsa_circ_0003732 expression was associated with tumor size and Enneking stage. High hsa_circ_0003732 expression was also correlated with low overall survival rates of OS patients. So, we assumed that hsa_circ_0003732 might function as an oncogene in OS and serve as a prognostic marker indicating poor outcome.in vitro. We found that knockdown of hsa_circ_0003732 inhibited proliferation of OS cells. Mechanism research showed that hsa_circ_0003732 acted as a sponge for miR-545. MiR-545 was proved to be down-regulated and act as a tumor suppressor gene in many cancers, including gastric cancer [Then, we investigated the biological function of hsa_circ_0003732 in OS c cancer , colorecc cancer , epithelc cancer , lung cac cancer  and oralc cancer . We furtc cancer  and promIn conclusion, our study reveals that hsa_circ_0003732 is up-regulated and may serve as a potential prognostic marker in OS. Hsa_circ_0003732/miR-545/CCNA2 axis plays important roles in promoting OS cells proliferation and may be a potential therapeutic target for OS intervention."}
+{"text": "FecBBB (MM) and FecB++ (ww) STH sheep, respectively, and differentially expressed (DE) lncRNAs and mRNAs associated with reproduction were identified.Long noncoding RNA (lncRNA) has been identified as important regulator in hypothalamic-pituitary-ovarian axis associated with sheep prolificacy. However, little is known of their expression pattern and potential roles in the pineal gland of sheep. Herein, RNA-Seq was used to detect transcriptome expression pattern in pineal gland between follicular phase (FP) and luteal phase (LP) in XLOC_466330, XLOC_532771, XLOC_028449 targeting RRM2B and GSTK1, XLOC_391199 targeting STMN1, XLOC_503926 targeting RAG2, XLOC_187711 targeting DLG4 were included.Overall, 135 DE lncRNAs and 1360 DE mRNAs in pineal gland between MM and ww sheep were screened. Wherein, 39 DE lncRNAs and 764 DE mRNAs were identified (FP vs LP) in MM sheep, 96 DE lncRNAs and 596 DE mRNAs were identified (FP vs LP) in ww sheep. Moreover, GO and KEGG enrichment analysis indicated that the targets of DE lncRNAs and DE mRNAs were annotated to multiple biological processes such as phototransduction, circadian rhythm, melanogenesis, GSH metabolism and steroid biosynthesis, which directly or indirectly participate in hormone activities to affect sheep reproductive performance. Additionally, co-expression of lncRNAs-mRNAs and the network construction were performed based on correlation analysis, DE lncRNAs can modulate target genes involved in related pathways to affect sheep fecundity. Specifically, All of these differential lncRNAs and mRNAs expression profiles in pineal gland provide a novel resource for elucidating regulatory mechanism underlying STH sheep prolificacy.The online version contains supplementary material available at 10.1186/s12863-020-00957-w. BMPRIB, BMP15 [GDF9 [FecB is a mutation in BMPRIB occurring in base 746 from A to G, one copy of this mutation significantly increases ovulation rate in sheep about 1.5 and two copies by 3.0 [FecB mutation in STH sheep, namely FecBBB (with two-copy FecB mutation), FecBB+ (with one-copy FecB mutation) and FecB++ (with no FecB mutation), which is closely correlated with litter size of ewes [FecB gene regulation of reproductive traits in sheep.Reproduction, one of the major factors significantly affecting profitability of sheep production, is a complicated physiological process and determined by the integrated hypothalamic-pituitary-ovarian axis in breeding season . ReproduB, BMP15  and GDF915 [GDF9  are majo of ewes . TherefoXLOC-2222497 and its target AKR1C1 could interact with progesterone in porcine endometrium for controlling pregnancy maintenance [Long noncoding RNA (lncRNA) is polymerase II transcript with length longer than 200 nucleotides that lacks the protein coding ability, its expression has high tissue specificity and distributes in cytoplasm or nucleus . LncRNA ntenance . These sFecBBB (MM) and FecB++ (ww) genotypes, to determine the DE lncRNAs and genes, and predict their potential function that related to reproduction. Which is essential for better understanding the molecular mechanisms by lncRNAs regulate sheep reproduction with different genotypes, also providing insight for other female mammals.In light of this, the study presented herein was focused on analyzing transcriptomics of pineal gland in STH sheep with Ovis aries reference genome  and mRNAs (P\u2009<\u20090.05) were statistically significant.A total of 21,282 lncRNAs  and 43,674 mRNAs were identified from four groups  , GSH metabolism and tight junction pathway Fig.\u00a0D. DE mRNHence, we acquired DE mRNAs closely related to reproductive signal pathways on the whole from above four comparison groups Table S.To better understand the relationship between lncRNA and mRNA, we constructed network of co-expression of DE lncRNAs and DE target mRNAs, after screening the overlaps between target mRNAs and DE mRNAs in each comparison group, which indicated regulation of lncRNA and mRNA in reproduction (|Pearson correlation| >0.95). Between MM_FP and MM_LP, a total of 5 DE lncRNAs and 9 DE mRNAs were involved in the network, and it consists of 9 edges  and 4 DE targets   and 7 DE targets   and 9 DE targets  in two groups of sheep (MM vs ww) at follicular phase  and 11 DE targets  in two groups of sheep (MM vs ww) at luteal phase  DE lncRNAs and 1360 (764\u2009+\u2009596) DE mRNAs in pineal gland at follicular and luteal phases between high and low prolificacy STH sheep (WW vs ww). GO annotation and KEGG enrichment analysis of top 20 terms indicated that DE mRNAs were enriched in reproduction-related pathways such as GnRH, cGMP-PKG, thyroid hormone, MAPK, phototransduction, circadian rhythm, steroid biosynthesis, hippo, mTOR and melanogenesis. It is well known that productive cycle of mammals is regulated through association or acting alone of hypothalamic-pituitary-thyroid (HPT) axis and hypothalamic-pituitary-gonadal (HPG) axis , 27. In  by GnRH , 30. In  by GnRH . MAPK pa by GnRH , 33. PI3 by GnRH . AdditioFecBBB genotype sheep, XLOC_466330 and the targets  up regulated at follicular phase, which related to GSH metabolism. Whereas XLOC_391199 and the target (STMN1), XLOC_503926, XLOC_517836 and the target (RAG2) up regulated at luteal phase, which mainly enriched in MAPK, FoxO signaling pathways, respectively. In FecB++ genotype sheep, XLOC_347557 and the target (GPX2), XLOC_532771 and the targets , XLOC_339502 and the target (GPX1), XLOC_028449 and the target (GSTK1) up regulated at follicular phase, which also related to GSH metabolism. Meanwhile, 105,604,037 and the target (MGST1), XLOC_187711 and the target (DLG4) down regulated at the same phase that related to GSH metabolism and hippo signaling. Wherein GSTK1 and RRM2B involved in GSH metabolism at follicular phase, but their targeted regulators lncRNAs were markedly different among two FecB genotypes. RRM2B gene encodes p53R2, and p53R2 is expressed at all phases of cell cycle to ensure ample supply of mitochondrial DNA [GSTK1 gene encodes a member of GSTK superfamily of enzymes that function in cellular mitochondria and peroxisomes detoxification during GSH metabolism [Co-expression analysis of differential lncRNA-mRNA and functional prediction of target genes revealed that lncRNA affects sheep fecundity by modulating genes associated with above signaling pathways and biological processes. In tabolism , a crititabolism , and so STMN1 is a highly conserved gene that codes for cytoplasmic phosphoproteins, acting role in cell cycle progression, signal transduction and cell migration through diverse intracellular signaling pathways. Studies have found the potential role of STMN1 in regulation of hormone secretion in rodent pituitary and insulinoma cell lines [Star and Cyp11a1 in mouse granulosa cells [RAG2 is indispensable for generation of antigen receptor diversity in immune cells [STMN1, RAG2 were down regulated at follicular phase in FecBBB sheep, and mainly related to MAPK, FoxO signaling pathways, respectively. DLG4 was down regulated at follicular phase in FecB++ sheep and enriched in hippo signaling term. As known that DLG4 encodes a member of MAGUK family, is widely expressed and playing an essential role in regulation of cellular signal transduction, circadian entrainment [Furthermore, DE target genes like ll lines . Over-exsa cells . Besidesne cells . We founrainment . Taken tFecB genotyping. We screened several sets of target genes of DE lncRNAs and DE genes under reproductive cycle and genotypes, they were annotated to multiple biological processes such as phototransduction, circadian rhythm, melanogenesis, GSH metabolism and steroid biosynthesis, which directly or indirectly participate in hormone activities to affect sheep reproductive performance. Additionally, we predicted potential role of these DE lncRNAs and constructed network of lncRNAs-mRNAs to expand our understanding. All of these differential lncRNAs and mRNAs expression profiles provide a novel resource for elucidating regulatory mechanism underlying STH sheep prolificacy.In summary, the pineal gland transcriptomic study reveals differential regulation of lncRNAs and mRNAs related to prolificacy in sheep with different Experimental animals in this study were authorized by the Science Research Department  of the Institute of Animal Science, Chinese Academy of Agricultural Sciences . Additionally, ethical approval of animal survival and the sample collection was given by the animal ethics committee of IAS-CAAS (No. IAS2019\u201349).n\u2009=\u2009890), to identify the FecB genotypes using TaqMan probe [Animals were from a core population of STH sheep in Luxi district of Shandong province, China. We collected jugular vein blood of healthy non-pregnant sheep aged 2\u20134\u2009years  for 12\u2009days. 3 MM and 3 ww ewes were euthanized  on the 50th hour after CIDR removal, pineal tissues were collected . The other 6 sheep were euthanized  on the 7th day after CIDR removal, and pineal tissues were collected  . ObtaineTotal RNA was extracted from 12 samples using TRIzol reagent  according to manufacturer\u2019s instruction. 1% agarose gel was used to monitor whether isolated RNA was degraded or contaminated. Quality, integrity and concentration of RNA were assessed by NanoPhotometer\u00ae spectrophotometer , RNA Nano 6000 Assay Kit of the Bioanalyzer 2100 system  and Qubit\u00ae RNA Assay Kit in Qubit\u00ae 2.0 Flurometer , respectively. Among them, the ratio of intact RNA with RIN\u2009\u2265\u20097, 28S: 18S\u2009\u2265\u20091.5:1.A total amount of 3\u2009\u03bcg RNA per sample was used as input material for the RNA sample preparation. Firstly, rRNA was removed by Epicentre Ribo-zero\u2122 rRNA Removal Kit  and rRNA free residue was cleaned up by ethanol precipitation. Subsequently, libraries were generated using the rRNA-depleted RNA by NEBNext\u00ae Ultra\u2122 Directional RNA Library Prep Kit for Illumina\u00ae  following manufacturer\u2019s recommendation. After the clustering of the index-coded samples was performed on a cBot Cluster Generation System using TruSeq PE Cluster Kit v3-cBot-HS (Illumia), libraries were then sequenced through an Illumina Hiseq 4000 platform and 150\u2009bp paired-end reads were generated.Oar v. 4.0 [Raw reads of fast-q format were firstly processed through in-house perl scripts to obtain clean reads. At the same time, Q20, Q30 and GC content of the clean data were calculated. All downstream analyses were based on the high quality clean reads. HISAT2 v. 2.0.4 was used to align paired-end clean reads of each sample to sheep reference genome r v. 4.0 . The mapr v. 4.0 .Potential lncRNA candidates were identified using the following workflow. (1) Transcripts with uncertain chain direction were removed by Cuffmerge. (2) Transcripts length\u2009>\u2009200\u2009nt with exon number\u2009\u2265\u20092 were selected. (3) Cuffcompare v. 2.1.1. was used to compare different classes of class_code annotated by \u201ci\u201d, \u201cu\u201d and \u201cx\u201d that were retained, which corresponded to intronic, intergenic, and anti-sense transcripts, respectively. (4) Transcripts with FPKM \u22650.5 were obtained after calculating the expression level of each transcript by Cuffdiff v. 2.1.1. (5) Three tools of CNCI v.2.0 , CPC v. P-value <\u20090.05 and a fold change (FC) >\u20092.0 were considered as differentially expression between two groups of data.The fragments per kilobase of transcript per million reads mapped (FPKM) value was used to estimate the expression levels of transcripts (lncRNAs and mRNAs) . For expP-value \u22640.05 defined as the significant threshold, significance of the term enrichment analysis was corrected by FDR and corrected P-value  was obtained [LncRNA targets could be predicted by the correlation or co-expression of lncRNA and mRNA expression levels between samples. Among them, Pearson correlation coefficient (PCC) was used to analyze the correlation between lncRNA and mRNA among samples, mRNAs with |PCC| \u22650.95 for functional enrichment to predict lncRNAs . Statistobtained .To predict function of DE lncRNAs and their target genes in sheep reproduction, a network based on lncRNAs and mRNAs was bulit using Cytoscape software (v. 3.8.0) .t-test was performed and P\u2009<\u20090.05 was considered statistically significant.All data were assessed as the \u201cmeans \u00b1 SD\u201d. Student\u2019s Additional file 1: Figure S1. Distribution of DE lncRNAs on chromosomes in MM_FP vs MM_LP.Additional file 2: Figure S2. Distribution of DE lncRNAs on chromosomes in MM_FP vs ww_FP.Additional file 3: Figure S3. Distribution of DE lncRNAs on chromosomes in MM_LP vs ww_LP.Additional file 4: Figure S4. Distribution of DE lncRNAs on chromosomes in ww_FP vs ww_LP.Additional file 5: Figure S5. Distribution of DE mRNAs on chromosomes in MM_FP vs MM_LP.Additional file 6: Figure S6. Distribution of DE mRNAs on chromosomes in MM_FP vs ww_FP.Additional file 7: Figure S7. Distribution of DE mRNAs on chromosomes in MM_LP vs ww_LP.Additional file 8: Figure S8. Distribution of DE mRNAs on chromosomes in ww_FP vs ww_LP.Additional file 9: Figure S9. Density distribution of candidate transcripts.Additional file 10: Table S1. Overview of DE mRNAs closely related to reproductive signal pathways.Additional file 11: Supplementary material 1A. Total set of known lncRNAs were identified from four groups. Supplementary material 1B. Total set of novel lncRNAs were identified from four groups.Additional file 12: Supplementary material 2. Total set of mRNAs were identified from four groups.Additional file 13: Supplementary material 3A. Total set of lncRNAs were up- and down- regulated in MM_FP vs MM_LP. Supplementary material 3B. Total set of lncRNAs were up- and down- regulated in MM_FP vs ww_FP. Supplementary material 3C. Total set of lncRNAs were up- and down- regulated in MM_LP vs ww_LP. Supplementary material 3D. Total set of lncRNAs were up- and down- regulated in ww_FP vs ww_LP.Additional file 14: Supplementary material 4A. Total set of mRNAs were up- and down- regulated in MM_FP vs MM_LP. Supplementary material 4B. Total set of mRNAs were up- and down- regulated in MM_FP vs ww_FP. Supplementary material 4C. Total set of mRNAs were up- and down- regulated in MM_LP vs ww_LP. Supplementary material 4D. Total set of mRNAs were up- and down- regulated in ww_FP vs ww_LP.Additional file 15: Supplementary material 5A. GO enrichment of differentially expressed lncRNA targets in MM_FP vs MM_LP. Supplementary material 5B. GO enrichment of differentially expressed lncRNA targets in MM_FP vs ww_FP. Supplementary material 5C. GO enrichment of differentially expressed lncRNA targets in MM_LP vs ww_LP. Supplementary material 5D. GO enrichment of differentially expressed lncRNA targets in ww_FP vs ww_LP.Additional file 16: Supplementary material 6A. GO enrichment of differentially expressed mRNAs in MM_FP vs MM_LP. Supplementary material 6B. GO enrichment of differentially expressed mRNAs in MM_FP vs ww_FP. Supplementary material 6C. GO enrichment of differentially expressed mRNAs in MM_LP vs ww_LP. Supplementary material 6D. GO enrichment of differentially expressed mRNAs in ww_FP vs ww_LP.Additional file 17: Supplementary material 7A. Total set of the top 20 KEGG enrichment pathways for differentially expressed lncRNA targets in MM_FP vs MM_LP. Supplementary material 7B. Total set of the top 20 KEGG enrichment pathways for differentially expressed lncRNA targets in MM_FP vs ww_FP. Supplementary material 7C. Total set of the top 20 KEGG enrichment pathways for differentially expressed lncRNA targets in MM_LP vs ww_LP. Supplementary material 7D. Total set of the top 20 KEGG enrichment pathways for differentially expressed lncRNA targets in ww_FP vs ww_LP.Additional file 18: Supplementary material 8A. Total set of the top 20 KEGG enrichment pathways for differentially expressed mRNAs in MM_FP vs MM_LP. Supplementary material 8B. Total set of the top 20 KEGG enrichment pathways for differentially expressed mRNAs in MM_FP vs ww_FP. Supplementary material 8C. Total set of the top 20 KEGG enrichment pathways for differentially expressed mRNAs in MM_LP vs ww_LP. Supplementary material 8D. Total set of the top 20 KEGG enrichment pathways for differentially expressed mRNAs in ww_FP vs ww_LP.Additional file 19: Supplementary material 9A. Co-expression details of DE lncRNA-mRNA after lncRNA targets coincided with DE mRNAs in MM_FP vs MM_LP. Supplementary material 9B. Co-expression details of DE lncRNA-mRNA after lncRNA targets coincided with DE mRNAs in MM_FP vs ww_FP. Supplementary material 9C. Co-expression details of DE lncRNA-mRNA after lncRNA targets coincided with DE mRNAs in MM_LP vs ww_LP. Supplementary material 9D. Co-expression details of DE lncRNA-mRNA after lncRNA targets coincided with DE mRNAs in ww_FP vs ww_LP."}
+{"text": "Hepatocellular carcinoma (HCC) is a common tumor characterized by high morbidity and mortality rates. The importance of circRNA in cancer diagnosis has been established. The study aimed to identify differentially-expressed circRNAs (DECs) in human blood exosomes from patients with HCC and to investigate their diagnostic value.The circRNA expression profiles of HCC and normal human blood samples were downloaded and processed from the exoRBase database. At the cutoff criteria of a fold change (FC)\u2009>\u20092.0 and P\u2009<\u20090.05, DECs were screened utilizing the limma package in the R software. A receiver operator characteristic curve (ROC) was used to study its diagnostic value. Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) analysis was performed to confirm the three-circRNAs expression in the blood samples with HCC. Various bioinformatics tools were used to characterize the potential biological pathways induced by circRNAs.Compared with the normal samples, seven up-regulated and five down-regulated circRNAs were determined in the HCC samples. ROC analyses demonstrated that hsa_circ_0004001, hsa_circ_0004123, hsa_circ_0075792, and a combination of the three biomarkers exhibited higher sensitivity and specificity. The qRT-PCR confirmed that the three circRNAs were upregulated in the blood samples with HCC. Chi squared tests implied that the expression of three circRNAs was positively correlated with the TNM stage and tumor size. The circRNAs participated in VEGF/VEGFR, PI3K/Akt, mTOR, and Wnt signaling pathways by targeting miRNAs.The study established the existence of seven up-regulated and five down-regulated circRNAs in HCC. Additionally, hsa_circ_0004001, hsa_circ_0004123, hsa_circ_0075792, and a combination of the three were utilized as valuable diagnostic biomarkers in HCC. Hepatocellular carcinoma (HCC) is one of the most common malignant gastrointestinal tumors worldwide and is characterized by high morbidity and mortality rates . In ChinThe circRNA is a type of non-coding RNA, which is widely present in many organisms, and exhibits a covalent closed loop structure . StudiesIn the present study, we identified a number of differentially expressed circRNAs (DECs) in blood with HCC. The increased expression levels of hsa_circ_0004001, hsa_circ_0004123, and hsa_circ_0075792 were verified utilizing quantitative reverse transcription-polymerase chain reaction (qRT-PCR) analysis. The expression of the three circRNAs was positively correlated with the TNM stage and tumor size. In addition, the combination of hsa_circ_0004001, hsa_circ_0004123, and hsa_circ_0075792 can be used as a new biomarker for the diagnosis of patients with HCC.http://www.exorbase.org/exoRBase/toIndex) database [The circRNA expression profiles, which included 21 HCC and 32 normal blood samples, were obtained from the exoRBase \u2009>\u20092.0, the DECs in 21 HCC blood samples were identified utilizing the limma package in the R software.g for 10\u00a0min at 4\u00a0\u00b0C. Following high-speed centrifugation at 12,000\u00d7g for 10\u00a0min, the plasma was separated and stored at \u2013\u200980\u00a0\u00b0C until needed. The present study was conducted with the approval of the ethics review committee of the Shengjing Hospital of China Medical University (No.: 2018PS362K), and all of the research participants signed informed consent form.The study enrolled 71 patients diagnosed with HCC at Shengjing Hospital of the China Medical University. Forty healthy individuals were enrolled as controls. Fasting venous blood samples were collected, and none of the patients received any treatment prior to collection. Ethylenediamine tetraacetic acid was added for anticoagulation, and all samples were centrifuged at 1600\u00d7\u00ae reagent  was added to each 200 \u03bcL blood sample, and the extraction procedure described in the manufacturer\u2019s instructions was followed. The nucleic acid OD260/OD280 ratio was determined using a NanoDrop spectrophotometer . The values obtained for the samples were in the range of 1.8\u20132.0. The synthesis of cDNA and the qRT-PCR reactions were conducted using a reverse transcription kit, as well as the SYBR fluorescence quantitative kit . The reaction system was 20 \u03bcL in volume and consisted of 10 \u03bcL of SYBR Premix Ex Taq, 0.5 \u03bcL of cDNA, 0.5 \u03bcL of upstream and downstream primers, 0.5 \u03bcL of ROX, and 8 \u03bcL of ddH2O. The primers for target circRNAs and the internal reference GAPDH are listed in Table\u00a0\u2212\u2206\u2206ct method.Eight hundred \u03bcL of the TRIzolhttp://funrich.org) [To further explore the molecular mechanism of circRNAs, the online Circular RNA Interactome  tool wasich.org) , which iThe paired and unpaired t-tests were conducted for group comparisons. The comparisons of gene expression and patient clinical features were conducted by Chi square test. The receiver operating characteristic (ROC) curve and the corresponding area under the curve (AUC) were determined for individual and combined circRNAs. All statistical analyses were performed using SPSS version 20.0 , and the data were considered statistically significant when P\u2009<\u20090.05.Compared with normal samples, seven up-regulated and five down-regulated circRNAs were detected in the HCC blood samples. The DECs and the corresponding FC and p-values are listed in Table\u00a0ROC curves were used to explore the diagnostic value of the differentially expressed circRNAs. It was found that hsa_circ_0004001, hsa_circ_0004123, and hsa_circ_0075792 exhibited adequate sensitivity and specificity in discriminating HCC patients from the healthy subjects. Furthermore, the combined three-circRNA  considerably improved the sensitivity, specificity, and AUC values for diagnosis (Fig.\u00a0The expression levels of hsa_circ_0004001, hsa_circ_0004123, and hsa_circ_0075792 in the serum samples of HCC patients and healthy volunteers were analyzed by qRT-PCR. The results suggested that the expression levels of hsa_circ_0004001, hsa_circ_0004123, and hsa_circ_0075792 in the HCC patient blood samples were considerably higher than those in the samples of healthy individuals (Fig.\u00a0The results obtained from the Circular RNA Interactome evaluation showed that hsa_circ_0004001, hsa_circ_0004123, and hsa_circ_0075792 interacted with 10, 19, and six miRNAs, respectively, by targeting regulation (Fig.\u00a0HCC is the most important pathological type of primary liver cancer. It is characterized by strong invasiveness, high malignancy, and poor prognosis. In China, the mortality rate associated with HCC ranks second among malignant tumors of the digestive system, and is second only to gastric cancer . At presCircRNA was first found in plant-like viruses in 1976, when it was considered to be a product of miss-splicing and did not receive any significant attention . With thIn the present study, we downloaded the RNA-Seq data for circRNA from HCC blood samples in the exoRBase database. The exoRBase database stores RNA-Seq of circRNA, lncRNA, and mRNA in blood exosomes from various human diseases, as well as published experimental data . The datTo further explore the diagnostic role of up-regulated circRNA in HCC, we conducted ROC curve analysis. The results demonstrated that hsa_circ_0004001, hsa_circ_0004123, and hsa_circ_0075792 exhibited higher diagnostic sensitivity and specificity, and the AUC values of all three were greater than 0.7. The qRT-PCR verification experiments also showed that hsa_circ_0004001, hsa_circ_0004123, and hsa_circ_0075792 were significantly higher in the plasma of liver cancer patients, when compared with those of the healthy group. At present, serum AFP is the preferred diagnostic marker for primary liver cancer. However, various studies have shown that there is a possibility of false negative diagnosis using serum AFP; thus, this technique is characterized by poor diagnostic sensitivity and specificity . PreviouFurther exploration of the downstream regulatory mechanism of circRNA revealed that hsa_circ_0004001, hsa_circ_0004123, and hsa_circ_0075792 regulated 35 target miRNAs, and participated in key biological signaling pathways, such as VEGF/VEGFR, PI3K/Akt, mTOR, and Wnt. Zhang et al. found that miRNA-146a inhibits distant metastasis of hepatocellular carcinoma by downregulating VEGF expression through multiple pathways . FurtherThe present study identified a number of DECs in the HCC blood samples, which provided a valuable platform for further exploration of its molecular mechanism of action. In addition, the combination of hsa_circ_0004001, hsa_circ_0004123, and hsa_circ_0075792 is a promising biomarker for the diagnosis of patients with HCC."}
+{"text": "To explore potential biomarkers to accurately diagnose patients with acute pancreatitis (AP) at early stage and to auxiliary clinicians implement the best treatment options.  We selected 3 patients with AP and 3 healthy controls for microarray analysis to obtain differentially expressed circular RNAs (circRNAs). To further verify the results of the microarray analysis, the six differentially expressed circRNAs were confirmed by quantitative polymerase chain reaction (qPCR). The diagnostic accuracy and sensitivity of differentially expressed circRNAs were assessed using the receiver operating characteristic (ROC) curve. A ceRNA network was constructed based on the 6 differentially expressed circRNAs.  There were 25 upregulated circRNAs and 26 downregulated circRNAs in the blood of patients with AP. Next, the qPCR verification results further confirmed three downregulated circRNAs, including hsa_circRNA_002532, has_circRNA_059665, and hsa_circRNA_104156, and three upregulated circRNAs including hsa_circRNA_101015, hsa_circRNA_101211, and hsa_circRNA_103470. Among them, hsa_circRNA_101015, hsa_circRNA_101211, and hsa_circRNA_103470 increased with the severity of the disease. ROC analysis showed that the three circRNA models show promise to diagnose AP. And a ceRNA network revealed that above six circRNAs could participate in complex regulated network.  Elevated hsa_circRNA_101015, hsa_circRNA_101211, and hsa_circRNA_103470 could be used as novel biomarkers to diagnose AP patients. AP is one of the most common gastrointestinal diseases, and patients need hospitalization . AlthougAs a pancreatic inflammatory disease, AP has become one of the leading causes of gastrointestinal disease admission in the United States and many other countries. And the incidence rate demonstrates an increasing trend. There are many factors that cause the disease, such as gallstones, smoking, and alcohol abuse , 9. Of cTherefore, it is critical to explore early diagnostic biomarkers of AP . More anWe reviewed 60 patients who were diagnosed with AP from April 2018 to September 2018 at the First Affiliated Hospital of China Medical University . In addition, we recruited 30 subjects who underwent routine health checks at the First Affiliated Hospital of China Medical University and showed no signs of disease as a control group. According to the \u201cGuidelines for the diagnosis and treatment of acute pancreatitis (2014),\u201d clinically meets 2 of the following 3 characteristics to diagnose AP: (1) abdominal pain consistent with AP; (2) serum amylase and/or lipase activity is at least 3 times higher than the upper limit of normal; (3) abdominal imaging examination is consistent with AP imaging changes.In clinical treatment, mild acute pancreatitis (MAP) patients receive only relatively simple treatment, while severe acute pancreatitis (SAP) patients usually require intensive care. Therefore, we selected 30 MAP and 30 SAP patients to diagnose AP patients with early stage. Currently, common scoring standards are the APPACHE II scoring standard, the MCTSI scoring standard, and BISAP scoring standard. MAP diagnostic criteria were good response to fluid supplementation, without organ failure, and local or systemic complications, recovery within 1-2 weeks. And APACHE-II score <8 points or the MCTSI score <4 points or BISAP <2 are MAP. Diagnostic criteria for SAP were with persistent organ failure (48\u2009h or more). And APACHE-II score \u22658 points or MCTSI score \u22654 points or BISAP \u22652 are SAP.The exclusion criteria were as follows: <18 years of age, pregnant and lactating women, taking anticoagulant drugs, blood system diseases, tumors, liver disease, and gastrointestinal bleeding patients. The patients' information is shown in Then we selected 3 patients with MAP and 3 healthy participants for microarray analysis. The study was approved by the Ethics Committee of the First Affiliated Hospital of China Medical University, and informed consent was obtained from all subjects. \u03bcgcRNA) were measured by NanoDrop ND-1000 . 1\u2009\u03bcg of each labeled cRNA was fragmented by adding 5\u2009\u03bcl of 10\u00d7 blocking agent and 1\u2009\u03bcl of 25\u00d7 fragmentation buffer; then the mixture was heated at 60\u00b0C for 30 minutes, and finally 25\u2009\u03bcl of 2\u00d7 hybridization buffer was added to dilute the labeled cRNA. 50\u2009\u03bcl of the hybridization solution was dispensed into a spacer slide and assembled onto a circRNA expression microarray slide. Slides were incubated for 17 hours at 65\u00b0C in Agilent Hybridization Oven. The hybridization array was fixed and scanned using an Agilent Scanner G2505C wash.We collected whole blood from 3 MAP patients and 3 healthy participants. Total RNA from each sample was quantified using a NanoDrop ND-1000 . Sample preparation and microarray hybridization were performed based on the standard protocol of Arraystar. Briefly, total RNA was digested with Rnase R  to remove linear RNA and enrich for circular RNA. Then, the enriched circular RNA was amplified by a random priming method  and transcribed into fluorescent cRNA. The labeled cRNA was hybridized to Arraystar Human circRNA Array V2 . The labeled cRNA was purified by RNeasy Mini Kit (Qiagen). The concentration and specific activity of the labeled cRNA (pmol Cy3/Agilent Feature Extraction software (version 11.0.1.1) was utilized to analyze the acquired array images. Quantile normalization and subsequent data processing were performed using the R software limma package (R version 3.1.2).P values \u22640.05) were identified utilizing fold change cutoffs and volcano plots, respectively. Among them, P value was calculated utilizing the unpaired t-test. Differentially expressed circRNAs between the two samples were identified by fold change filtration. Hierarchical clustering was performed to show the distinguishable circRNA expression patterns in the samples. Shanghai Kangcheng Biological Engineering Co., Ltd. of the People's Republic of China conducted microarray work.A scatter plot is a visualization method used to assess circRNA expression variation. Differentially expressed circRNAs with statistical significance  using random primers and Transcriptor First Strand cDNA Synthesis according to the manufacturer's instructions. 6 differentially expressed circRNAs were measured by qPCR using a ViiA 7 Real-time PCR System (Applied Biosystems). The reaction conditions were as follows: 95\u00b0C for 10 minutes and 40 cycles of 95\u00b0C for 10 seconds, 60\u00b0C for 60 seconds. RNA levels were normalized to human  primers .  Table \u2212\u0394\u0394Ct. The expression difference in circRNAs among SAP, MAP patients, and healthy individuals and between MAP patients and posttreatment serum samples was assessed using the t-test. To assess the diagnostic value, ROC curve was established. The cut-off value of each circRNA was analyzed by using SPSS software. Area under the ROC curves (AUCs) were calculated to evaluate the ability of the differentially expressed circRNAs. Due to the relative small sample size for ROC analysis, the statistical power was calculated using PASS (version 15.0), under the following conditions: \u03b1\u2009=\u20090.05, AUC0\u2009=\u20090.5, and n\u2009=\u200930. P value\u2009<\u20090.05 was considered statistically significant.Statistical analyses were performed using SPSS  and GraghPad Prism . The relative expression level of each circRNA was expressed by fold change and converted into 2N is the total number of miRNAs used to predict the target; (ii) K is the The number of miRNAs interacting with the selected gene; (iii) n is the number of miRNAs that interact with the candidate ceRNA of the selected gene; (iv) the miRNA number common between the two genes [P value:By combining cotargeted miRNAs, we constructed a ceRNA network by cytoscape package of R language , 18. In wo genes . This teX and Y axes are the average normalized signal values  of the sample set. And the green line is the fold line, and the circRNA above the top green line and below the bottom green line indicates that the circRNA change between the two samples is more than 1.5 times. Next, volcano maps were used to identify differentially expressed circRNAs that were statistically significant between the two groups and are shown in P=0.05. The red dot indicates upregulated circRNAs while blue represents downregulated circRNAs. We found 51 differentially expressed circRNAs in patients with MAP  compared with normal subjects. Among them, To investigate the expression profile of circRNAs in AP, we used microarray analysis to perform circRNA expression profiling in the blood of patients with MAP and matched normal humans. The box plot visualized the dataset distribution of circRNAs. After normalization, P values are 0.008 (hsa_circRNA_002532), <0.0001 (hsa_circRNA_059665), and <0.0001(hsa_circRNA_104156), respectively.To verify differentially expressed circRNAs in AP, we made qPCR. And we selected three upregulated circRNAs including hsa_circRNA_101015, hsa_circRNA_101211, and hsa_circRNA_103470 and three downregulated circRNAs including hsa_circRNA_002532, hsa_circRNA_059665, and hsa_circRNA_104156. We validated the expression of three downregulated circRNAs in MAP and normal humans. And the results are shown in P value was 0.0003, <0.0001, and <0.0001, respectively), hsa_circRNA_101211 , and hsa_circRNA_103470  also increased significantly. Then we validated the expression levels of three upregulated circRNAs in MAP patients and MAP after treatment. The results are demonstrated in P value <0.0001).To further explore the relationship between the three upregulated circRNAs and disease severity, we compared the expression levels in healthy individuals, MAP, and SAP patients, respectively. As shown in P < 0.001, power\u2009=\u20090.97119), 0.731 , 0.770 , respectively. RUC of the three circRNAs combination was 0.838 . The above results show that hsa_circRNA_101015, hsa_circRNA_101211, and hsa_circRNA_103470 can be used as biomarkers to diagnose AP at early stage.The ROC curve was constructed to assess the diagnostic significance of the three elevated circRNAs . RUCs ofRecent evidence suggests that circular RNA plays a crucial role in the regulation of miRNA-mediated gene expression regulation by isolating miRNAs. Their interaction with disease-associated miRNAs suggests that circular RNA is important for disease regulation . To asseAP is an inflammatory process of the pancreas and has become an increasingly common clinical disease. In the second or third week of the disease, 40\u201370% of patients develop infectious necrosis and are the leading cause of late death . In the Accurate diagnosis of AP may allow for effective treatment to begin earlier. At present, the accuracy of different scoring systems is not high, and a unique model is needed . There aIn recent years, circRNA has received wide attention as a new class of endogenous and regulatory noncoding RNAs. At the same time, with the widespread use of RNA sequencing (RNA-seq) technology and bioinformatics prediction, a large number of circRNAs have been identified. So far, no study has examined the role of circRNA in AP. Considering that circRNA is involved in a variety of diseases, it is necessary to explore differences in the expression of circRNA in patients with AP. Microarrays are an effective tool for analyzing circRNA. Therefore, in our study, we made full use of microarray technology and selected three patients with MAP and three healthy individuals. Our aim was to explore the differential expression of circRNA in the patients' blood with AP to diagnose AP patients as soon as possible. As a result, we found 25 upregulated circRNAs and 26 downregulated circRNAs. The vast majority of circRNAs have not been studied and require more in-depth exploration.To further validate the six circRNAs in AP, we performed qPCR analysis. The results of qPCR studies provide novel biomarkers for molecular diagnosis and evaluation in AP. Three circRNAs including hsa_circRNA_002532 hsa_circRNA_059665, and hsa_circRNA_104156 were downregulated in MAP compared with healthy individuals. Three upregulated circRNAs including hsa_circRNA_101015, hsa_circRNA_101211, and hsa_circRNA_103470 significantly increased expression levels as the condition worsens. Furthermore, the expression levels of hsa_circRNA_101015, hsa_circRNA_101211, and hsa_circRNA_103470 were significantly reduced after treatment. This result suggests that the above three circRNAs may be involved in the development of AP and show promise as potential biomarkers for AP. To evaluate the specificity and sensitivity of the hsa_circRNA_101015, hsa_circRNA_101211, and hsa_circRNA_103470, we performed a ROC curve for further validation. The RUC was 0.768, 0.731, and 0.770, respectively, and the combined RUC values of the three circRNAs were 0.838. Therefore, the three circRNAs can be used as diagnostic biomarkers for AP with high sensitivity.However, the pathogenesis of AP remains unknown. Therefore, the 6 differentially expressed circRNAs were selected to construct a ceRNA network including three upregulated hsa_circRNA_101015, hsa_circRNA_101211, and hsa_circRNA_103470 and three downregulated hsa_circRNA_002532 hsa_circRNA_059665, and hsa_circRNA_104156. In the ceRNA network, we found a complex regulatory network for the above six circRNAs. However, the above six differentially expressed circRNAs excluding hsa_circRNA_104156 have not been reported. Considering that circRNAs can act as sponges of miRNAs, it is of importance to explore miRNAs in AP. More importantly, miRNAs play a critical role in the progression of AP. For example, a previous study found that miR-92b, miR-10a, and miR-7 are downregulated in the blood of patients with AP and can be used to distinguish between patients with AP and healthy cases. In addition, the expression level of miR-551b-5p distinguishes between MAP and SAP . HoweverTherefore, we inferred that the six circRNAs could participate in pathogenic process of AP. However, specific research mechanisms need to be explored. In the future, we would continue to research the molecular mechanisms of hsa_circRNA_101015, hsa_circRNA_101211, and hsa_circRNA_103470 in AP.After microarray analysis and qPCR, we found three downregulated circRNAs including hsa_circRNA_002532, hsa_circRNA_059665, and hsa_circRNA_104156 and three upregulated circRNAs including hsa_circRNA_101015, hsa_circRNA_101211, and hsa_circRNA_103470 in the blood of AP patients. The ceRNA network revealed that the six circRNAs could play a critical role in AP. Therefore, elevated hsa_circRNA_101015, hsa_circRNA_101211, and hsa_circRNA_103470 can be used as biomarkers in the blood to diagnose AP at early stage. The molecular mechanisms of elevated hsa_circRNA_101015, hsa_circRNA_101211, and hsa_circRNA_103470 in the pathological processes of AP would be explored in our further experiments."}
+{"text": "Tandem repeat sequences are widespread in the human genome, and their expansions cause multiple repeat-mediated disorders. Genome-wide discovery approaches are needed to fully elucidate their roles in health and disease, but resolving tandem repeat variation accurately remains a challenging task. While traditional mapping-based approaches using short-read data have severe limitations in the size and type of tandem repeats they can resolve, recent third-generation sequencing technologies exhibit substantially higher sequencing error rates, which complicates repeat resolution.We developed TRiCoLOR, a freely available tool for tandem repeat profiling using error-prone long reads from third-generation sequencing technologies. The method can identify repetitive regions in sequencing data without a prior knowledge of their motifs or locations and resolve repeat multiplicity and period size in a haplotype-specific manner. The tool includes methods to interactively visualize the identified repeats and to trace their Mendelian consistency in pedigrees.TRiCoLOR demonstrates excellent performance and improved sensitivity and specificity compared with alternative tools on synthetic data. For real human whole-genome sequencing data, TRiCoLOR achieves high validation rates, suggesting its suitability to identify tandem repeat variation in personal genomes. Almost half of the human genome is estimated to be covered by repetitive sequences . Among tde novo  be a region from the BED file, ranging from a start coordinate S to an end coordinate E for a given chromosome. Each sequencing read entirely spanning R is fetched and trimmed so that the actual sequence REFER stores is that included between S and E, which significantly improves the runtime of the subsequent POA algorithm to generate a consensus sequence.For each region in the BED file, REFER first fetches from the haplotype-specific BAM files the sequencing reads spanning the selected region and trims them, so that the length of each read is approximately the size of the region. Let Once the sequencing reads of interest have been collected and trimmed, TRiCoLOR uses SPOA , a singlWith the haplotype-specific consensus sequences at hand, REFER aligns these to the reference genome using minimap2 , which cTogether with the haplotype-specific consensus sequences, the corresponding reference is screened in a similar manner, with few differences being noteworthy: (i) the algorithm assumes the reference does not contain errors and does not look for approximate repetitions of the motifs identified; and (ii) among overlapping repetitions, the longest repeat is taken.TRs  varying between the haplotypes or the reference are eventually stored in BCF-compliant format. TRiCoLOR REFER also stores in the output folder several BED files describing the TRs identified (for both the reference and each haplotype) and haplotype-specific BAM files containing the aligned consensus sequences.The TRs profiled using TRiCoLOR REFER can be interactively visualized through the ApP (Alignment Plotter) module. This module takes as inputs the BED and the BAM files generated by TRiCoLOR REFER, together with an additional BED file describing 1 or more regions to plot.TRiCoLOR ApP produces a static HTML file illustrating the alignment between the reference and the individual\u2019s haplotypes at single-base resolution, highlighting the TRs detected .In pedigree studies, assigned genotypes can be either Mendelian consistent or inconsistent. TRiCoLOR enables genotype consistency checks for TRs identified in the index child when haplotype-resolved long-read alignments for both parents are available. This is achieved through the SAGE (SAmple GEnotyper) module with special emphasis on the common situation that parents have been sequenced at low depth.Using the same aforementioned TRiCoLOR REFER approach, SAGE computes haplotype-specific consensus alignments for each child TR in each parent. Next the module checks whether the parental TRs are more similar  to the reference or to the TR identified in the child and assigns them the most likely genotype. Knowing the genotype of both parents, the module eventually flags each TR as Mendelian consistent or inconsistent with the \u201c\u2013mendel\" parameter enabled. The output of TRiCoLOR SAGE is a multi-sample BCF file that contains the genotypes for the index child and both parents.We benchmarked TRiCoLOR using both synthetic data generated with VISOR  and realWe used the TR simulator VISOR to generate synthetic ONT and PB alignments containing TR contractions and expansions. First, we simulated haplotype-resolved ONT and PB BAM files , HG00733 (Puerto Rican), and NA19240 (Yoruban Nigerian) and the PB sequencing data for HG00731 , HG00732 , and HG00733 (son).We applied TRiCoLOR to call TRs We aligned the ONT FASTQ files to the human GRCh38 reference genome using minimap2, and we merged the chromosome-specific PB alignments using samtools . We thenWe then ran TRiCoLOR SENSoR using the default parameter settings on the HG00733 (ONT and PB), HG00514, and NA19240 individuals. Using an Ubuntu 16.04.6 LTS desktop with Intel\u00ae Xeon\u00ae processors X5460 (clock rate 2.93\u00a0GHz), the module took \u223c4\u00a0hours to scan the ONT samples and \u223c8\u00a0hours to scan the PB sample, which reflects the higher coverage available for PB. For the HG00733, HG00514, and NA19240 ONT individuals the module identified \u223c160,000, \u223c190,000, and \u223c260,000 low-entropy regions (mean length of the regions \u223c900 bp), which were reduced to \u223c70,000, \u223c100,000, and \u223c160,000, respectively, after filtering for regions with mean coverage >8. For the HG00733 PB individual the module identified \u223c380,000 low-entropy regions (mean length of the regions \u223c850 bp), which were reduced to \u223c150,000 after filtering for regions with mean coverage >10\u00d7. For HG00733, \u223c97% of the low-entropy regions originally identified in the ONT individual overlapped those in the PB one; owing to the different coverage distributions, this percentage was reduced to \u223c31% after filtering.We ran TRiCoLOR REFER on the samples processed by TRiCoLOR SENSoR using the default parameter settings. With 7 processors on our Ubuntu desktop, the module took \u223c10\u201312\u00a0hours to profile TRs on the ONT individuals and \u223c14\u00a0hours to profile TRs on the PB individual.We calculated the number of TRs properly called by TRiCoLOR using an alignment-free validation approach. Current benchmarks for TR calling in human genomes are mainly based on short-read sequencing and are biased towards regions of the genome that are easy to call with such a technology . It has We eventually ran TRiCoLOR SAGE on the Puerto Rican PB trio HG00731, HG00732, and HG00733, with the default parameter settings and the \u201c\u2013mendel\" parameter enabled to check the Mendelian consistency of the TRs identified in HG00733. With 7 processors on our Ubuntu desktop, the module took \u223c2\u00a0hours to complete the analysis. Filtering for variants differing from the reference for \u226510 bp and for multi-allelic variants differing from each other by the same distance, we identified \u223c80% of Mendelian consistent TRs, which is low compared to trio-based single-nucleotide variant and InDel Mendelian consistency rates but above reported genotype agreement rates for structural variants in repetitive regions .Among the Mendelian consistent TRs called by TRiCoLOR on the HG00733 PB individual, we identified 32 long TRs \u2265150 bp) that were absent in the HGSVC ground truth for the same individual. To identify the cause of these apparent discrepancies, we aligned the HG00733 phased contigs from HGSVC to the GRCh38 human reference genome with minimap2, using the assembly-to-reference alignment mode and the parameters suggested by QUAST-LG , and we 50 bp thade novo identification of TRs in whole-genome sequencing data. TRiCoLOR profiles TRs through an efficient POA algorithm combined with a RegEx-based string-matching search, facilitating a robust and accurate discovery of the full spectrum of expanded and contracted TRs in personal genomes.TRiCoLOR is a comprehensive TR caller for long reads that supports the de novo and does not require a priori knowledge of annotated TR regions. The unique combination of features for genome-wide, de novo discovery and genotyping of TRs in ONT and PB data is to the best of our knowledge unmet by any other TR caller for long-read data. Besides the detection of TRs, TRiCoLOR visualizes TRs in their haplotype context and it can infer parental genotypes using low-coverage parental sequencing data.In comparison with previous tools, TRiCoLOR works with ONT and PB data seamlessly. TRiCoLOR also identifies TRs de novo identification of repetitive stretches that we empirically estimated is well suited for short repeated motifs (2\u20133 bp) but may need adjustments for long motifs of higher nucleotide complexity. Last, by default TRiCoLOR profiles TRs with motif lengths \u22646 bp , excluding those with motif lengths \u22657 bp , which are less abundant in diploid organisms [TRiCoLOR has been designed for diploid organisms , and futrganisms . The RegGiven these limitations, future work will focus on extending TRiCoLOR to other ploidies, broadening the size spectrum of detectable repeat motif lengths and taking advantage of improved sequencing read accuracy . The latter directly improves the RegEx-based identification of repeats used by TRiCoLOR, and we thus believe that TRiCoLOR is well suited to characterize the TR landscape in present and future long-read data sets, making it an instrumental tool for robustly deciphering the multiplicity of TRs in repeat-mediated clinical disorders.Project name: TRiCoLORhttps://github.com/davidebolo1993/TRiCoLOR. A dockerized version of TRiCoLOR is available at https://hub.docker.com/r/davidebolo1993/tricolor. On-line documentation is available at https://davidebolo1993.github.io/tricolordoc.Project home page: Operating system: UnixProgramming languages: Python, Bash, C++Other requirements: Python 3.6 or higher, GCC 4.8 or higher, and CMake 3.2 or higherLicense: GNU Lesser General Public License 3.0SCR_018801RRID:biotools ID: tricolorhttps://www.internationalgenome.org/human-genome-structural-variation-consortium). Specifically:HGSVC whole-genome long-read sequencing data are available on the HGSVC website .The GRCh38 human reference genome used for alignments is available at Arabidopsis thaliana KBS-Mac-74 is available at ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR217/003/ERR2173373/ERR2173373.fastq.gz. The TAIR10 reference genome for A. thaliana can be downloaded through the Arabidopsis Information Resource database (https://www.arabidopsis.org/index.jsp). Several scripts used to perform the analyses described in this article and the TR calls generated by TRiCoLOR for the HGSVC individuals and the A. thaliana KBS-Mac-74 are available through the GitHub code repository of TRiCoLOR (https://github.com/davidebolo1993/TRiCoLOR). More in detail:A whole-genome ONT FASTQ file of the https://github.com/davidebolo1993/TRiCoLOR/tree/master/paper/data folder contains the BED file with annotated TRs from the GRCh38 human reference genome (GRCh38.TRs.bed), a bash script that illustrates how to haplotype-resolve a long-read alignment using phased single-nucleotide variants (prepare.sh), a python script used for the Shannon entropy simulations (entropy.py), a python script used to calculate precision, recall, and F1 scores of TRiCoLOR on synthetic data (pr.py), and a couple of C++ source code files  for validating TRiCoLOR calls on real human data.The https://github.com/davidebolo1993/TRiCoLOR/tree/master/paper/samples folder contains TRiCoLOR calls for the HGSVC individuals and the A. thaliana KBS-Mac-74 in standard BCF format.The GigaScience GigaDB database [A snapshot of the archival code is available in the database .Supplementary Figure S1.Supplementary Figure S2.Supplementary Figure S3.Supplementary Figure S4.Supplementary Figure S5.Supplementary Figure S6.Supplementary Figure S7.Supplementary Note S1.Supplementary Note S2.Supplementary Note S3.Supplementary Note S4.Supplementary Note S5.Supplementary Note S6.Supplementary Note S7.Supplementary Note S8.giaa101_GIGA-D-20-00168_Original_SubmissionClick here for additional data file.giaa101_GIGA-D-20-00168_Revision_1Click here for additional data file.giaa101_GIGA-D-20-00168_Revision_2Click here for additional data file.giaa101_GIGA-D-20-00168_Revision_3Click here for additional data file.giaa101_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giaa101_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giaa101_Response_to_Reviewer_Comments_Revision_2Click here for additional data file.giaa101_Reviewer_1_Report_Original_SubmissionWouter De Coster -- 6/23/2020 ReviewedClick here for additional data file.giaa101_Reviewer_1_Report_Revision_1Wouter De Coster -- 8/14/2020 ReviewedClick here for additional data file.giaa101_Reviewer_1_Report_Revision_2Wouter De Coster -- 8/26/2020 ReviewedClick here for additional data file.giaa101_Reviewer_2_Report_Original_SubmissionRobert S Harris -- 6/28/2020 ReviewedClick here for additional data file.giaa101_Reviewer_2_Report_Revision_1Robert S Harris -- 8/16/2020 ReviewedClick here for additional data file.giaa101_Reviewer_3_Report_Original_SubmissionNansheng (Jack) Chen -- 7/5/2020 ReviewedClick here for additional data file.giaa101_Supplemental_FileClick here for additional data file.bp: base pairs; F1: F1 score; HGSVC: Human Genome Structural Variation Consortium; ONT: Oxford Nanopore Technologies; P: precision; PB: Pacific Biosciences; POA: partial order alignment; R: recall; RegEx: regular expression; SENSoR: Shannon ENtropy ScanneR; TR: tandem repeat; UCSC: University of California Santa Cruz.The authors declare that they have no competing interests.J.O.K. is supported by GraphGenomes grant 031L0184C. A.M. is supported by AIRC grant 20307. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.D.B. and T.R. designed and benchmarked the software. D.B. wrote the code. T.R. supervised the work. D.B. and T.R. co-wrote the manuscript draft. A.M., V.B., and J.O.K. contributed to the interpretation of the results, provided critical feedback, and helped to write the manuscript. All the authors read and approved the manuscript."}
+{"text": "Correction to: Mol Cancer (2019) 18:16https://doi.org/10.1186/s12943-018-0936-4Following the publication of , we receHsa_circ_0001946 as a prognostic biomarker in both frozen and FFPE tissuesAs Additional file 6: Table S2 showed, hsa_circ_0001946 was the only one associated with the recurrence rate of ESCC patients. As for disease-free survival (DFS) and overall survival (OS) prediction, patients in the high hsa_circ_0001946 group (according to the median level) had a much longer DFS and OS , whose hazard ratio (HR) and 95% confidence interval (CI) were 0.357(0.164\u20130.781) and 0.209(0.076\u20130.579) respectively.Furthermore, we used FFPE tissues to verify this conclusion. The results showed that high hsa_circ_0001946 was associated with longer DFS and OS in K-M curves  while univariate Cox proportional hazard models showed that expression of hsa_circ_0001946 in FFPE tissues was an independent prognostic indicator of OS but not DFS. The multivariate Cox proportional hazard models shown that combination of these two group supports the result that hsa_circ_0001946 was a promising and independent prognostic biomarker for ESCC patients in both frozen and FFPE tissues.In Figure 3E, the legend of tumor growth curve (left panel) was reversedly marked between Eca109-V and Eca109-N group. The modified plot with corrected legend were presented below.Figure 3EIn Figure S7, the image of TE-1-V in migration assay  and the image of wound-healing assay of K50-N  were arranged in wrong placed. These image errors were caused by repeatedly selecting, below is the corrected images.Figure S7EFigure S7IThese errors were caused by our carelessness and misoperation in image combination. After carefully re-checked the manuscripts, raw data, and lab records. We assure that the correction of these will not alter the conclusion of the results. We sincerely apologize for the errors and any confusion it may have caused."}
+{"text": "Circular RNAs (circRNAs) have been shown to play a crucial role in tumorigenesis. In this study, we investigated the function of hsa_circ_0137008 and its underlying molecular mechanism in colorectal cancer (CRC).Gene expression was conducted by quantitative real-time PCR or western blot. Functional experiments were performed by cell count kit-8, colony formation assay, wound healing, and transwell assays. Luciferase reporter assay and RNA pull-down assay were performed to investigate the molecular mechanism of hsa_circ_0137008 in CRC. In addition, the xenograft tumor model was applied to determine the role of hsa_circ_0137008 in vivo.Downregulation of hsa_circ_0137008 was observed in CRC tissues and cell lines. Functionally, overexpression of hsa_circ_0137008 inhibited the proliferation of CRC cells, as indicated by the inhibition of proliferative protein expression (Ki67 and PCNA), reduced cell viability and colony formation ability. Upregulation of hsa_circ_0137008 suppressed the migration, invasion, and epithelial to mesenchymal transition (EMT) of CRC cells. Mechanically, hsa_circ_0137008 negatively regulated the expression of microRNA-338-5p (miR-338-5p). Furthermore, hsa_circ_0137008 abated the miR-338-5p mediated promotion on CRC cell progression. Tumor suppressive function of hsa_circ_0137008 was validated in vivo.These findings highlighted the fact that overexpression of hsa_circ_0137008 inhibited the progression of CRC via sponging miR-338-5p, suggesting that hsa_circ_0137008/miR-338-5p axis is a principal regulator of CRC tumorigenesis. Colorectal cancer (CRC), a malignant tumor of the colon or rectum, is one of the main reasons of cancer-related mortality in the world \u20133. AlthoCircular RNAs (circRNAs) are a new subtype of endogenous non-coding RNAs, which is characterized by a covalently closed continuous loop \u20139. CircRMicroRNAs (miRNAs), a class of small non-coding RNAs with approximately 20 nucleotides, able to directly bind to the 3\u02b9 untranslated region of mRNAs, thereby regulating mRNA degradation and translational inhibition , 16. MiRIn the current study, we investigated the function and molecular mechanism of hsa_circ_0137008 in CRC cells. We found that hsa_circ_0137008 was significantly downregulated in CRC tissues and cell lines. Mechanistically, hsa_circ_0137008 acted as miR-338-5p sponge to regulate CRC progression, indicating that hsa_circ_0137008 may be a potential therapeutic strategy for CRC patients.N\u2009=\u200930) and adjacent normal tissue samples (N\u2009=\u200930) were collected from CRC patients at The Second Affiliated Hospital of Guangzhou University of Chinese Medicine. The patients had not received any treatment before surgery. After excision, the tissues were immediately plunged into liquid nitrogen and stored at \u2212\u00a080\u00a0\u00b0C. This study was approved by The Second Affiliated Hospital of Guangzhou University of Chinese Medicine and in accordance with the ethical standards formulated in the Helsinki Declaration, and written informed consents were obtained from all the participants.Human CRC tissues  and CRC cell lines  were obtained from the American Tissue Culture Collection . The cells were cultured in Roswell Park Memorial Institute-1640 medium  containing 10% fetal bovine serum  at 37\u00a0\u00b0C in a humidified atmosphere with 5% (v/v) COEmpty pcDNA3.1 vector (Vector) and hsa_circ_0137008-overexpressing plasmid (circ_0137008) were synthesized by GeneCopoecia . While miR-338-5p mimic and miRNA negative control (miR-NC) were obtained from RiboBio . SW480 and HCT116 cells were transfected with these molecules by using Lipofectamine 2000 , according to the manufacturer\u2019s instructions. At 48\u00a0h later, the cells were collected for the following experiments.Transfection was performed using Lipofectamine 2000 reagent (Invitrogen) according to the manufacturer\u2019s protocol. The transfected cells were selected by screening of G418  for 6\u00a0weeks. Surviving cells were subjected to identification of overexpression efficiency. The cells stably overexpressing hsa_circ_0137008 could be used for further experiments.\u2212\u0394\u0394Ct method, using \u03b2-actin and U6 as internal reference genes, respectively.Total RNA was isolated from tissues and cells using TRIzol reagent (Invitrogen) according to the manufacturer\u2019s instructions. The quality of isolated RNA was determined using NanoDrop ND-1000 spectrophotometer. Then the RNA was reverse transcribed into cDNA using PrimeScript RT Reagent Kit , following by qRT-PCR analysis on Bio-Rad CFX96 system . TaqMan miRNA assay was performed to examine the expression of miR-338-5p. The relative expression levels of hsa_circ_0137008 and miR-338-5p were calculated by the 2After transfection, SW480 and HCT116 cells were collected, followed by protein extraction using RIPA lysis buffer. After 10% sodium dodecyl sulphate\u2013polyacrylamide gel electrophoresis, isolated proteins were electro-transferred to polyvinylidene fluoride membranes and then blocked for 60\u00a0min with 5% skim milk. Subsequently, the membranes were incubated with primary antibodies from Boster  at 4\u00a0\u00b0C overnight, followed by incubation with horseradish peroxidase-conjugated secondary goat antibody  from Boster for 1\u00a0h at room temperature. The primary antibodies used in this experiment were list as follows: mouse anti-Ki-67 antibody , mouse anti-proliferating cell nuclear antigen antibody , rabbit anti-\u03b2-actin antibody , rabbit anti-E-cadherin antibody , rabbit anti-Vimentin antibody , mouse anti-N-cadherin antibody .4 cells/well) and cultured for indicated times at 37\u00a0\u00b0C with 5% (v/v) CO2. Then, the cells were treated with 10\u00a0\u00b5l CCK-8 solution at 37\u00a0\u00b0C for 2\u00a0h. Subsequently, the absorbance of each well at 450\u00a0nm was assessed by mean of a spectrophotometer.The proliferation of SW480 and HCT116 cells was tested by using Cell Counting Kit-8 . After transfection, SW480 and HCT116 cells were seeded in 96-well plates .Colony formation assay was used to evaluate the proliferation of SW480 and HCT116 cells. After transfection, SW480 and HCT116 cells were plated into 6-well plates. The cells were incubated at 37\u00a0\u00b0C in a humidified incubator with 5% (v/v) CO5 cells/well) were seeded in 6-well plates and incubated for 24\u00a0h. A straight scratch was introduced by dragging the tip of the sterile pipette across the monolayer of SW480 and HCT116 cells. After washing with sterilized phosphate buffer saline (PBS), the cells were incubated in serum-free medium. The width of wound was pictured at 0\u00a0h and 48\u00a0h under a microscope.The migration of SW480 and HCT116 cells was measured by wound-healing assay. After transfection, SW480 and HCT116 cells (1\u2009\u00d7\u2009104 cells/well) were suspended in serum-free medium and plated into the upper chamber pre-coated with Matrigel . The lower chamber was filled with RPMI-1640 medium plus 10% FBS. After 48\u00a0h of incubation, a cotton swab was used to remove the cells on the upper surface of the chamber. The invaded cells on the lower surface of the chamber were fixed with 4% paraformaldehyde, and then stained with 0.1% crystal violet. The stained cells were counted with an inverted microscope .Transwell invasion assay was performed to evaluate the invasion of SW480 and HCT116 cells. After transfection, SW480 and HCT116 cells  following the manufacturer\u2019s instructions, and then tested for hsa_circ_0137008 expression by qRT-PCR.https://circinteractome.nia.nih.gov) was used to predict the potential miRNA downstream of hsa_circ_0137008. The wild type (WT) and mutated (MUT) sequence of hsa_circ_0137008 were cloned into pcDNA3.1 vector. Then, SW480 and HCT116 cells were plated into 24-well plates and co-transfected with circ_0137008-WT or circ_0137008-MUT and miR-NC or miR-338-5p using Lipofectamine 2000 (Invitrogen). At 48\u00a0h after transfection, dual luciferase reporter assay kit  was used to measure the luciferase activity, according to the manufacturer\u2019s manual.The online database CircInteractome  for 3\u00a0h at 4\u00a0\u00b0C and washed five times by ice-cold lysis buffer. The bound RNAs were subjected to qRT-PCR analysis.SW480 and HCT116 cells were transfected with biotinylated miR-NC (Bio-NC) or biotinylated miR-338-WT (Bio-miR-338-WT) or biotinylated miR-338-MUT (Bio-miR-338-MUT), which were commercially synthesized by RiboBio . At 48\u00a0h post-transfection, SW480 and HCT116 cells were collected and lysed in lysis buffer containing 20\u00a0mM pH 7.5TRIS-HCl, 5\u00a0mM MgClN\u2009=\u200910) were acquired from Vital River Laboratories  and handled in strict with the procedures approved by the Ethics Committee of The Second Affiliated Hospital of Guangzhou University of Chinese Medicine. SW480 cells stably expressing circ_0137008 were inoculated into nude mice. Tumor size was monitored every 7\u00a0days and the volume of xenograft tumors was calculated according to the following formula: Volume\u2009=\u20090.52\u2009\u00d7\u2009length\u2009\u00d7\u2009width2. At the 35th days after inoculation, mice were killed and the tumors were resected, photographed and weighed.The effect of hsa_circ_0137008 on the growth of CRC in vivo was measured by the xenograft experiments. Five-week-old BALB/c nude mice  20.0  software. The data represent the mean\u2009\u00b1\u2009standard deviation (SD) of at least three independent experiments. The difference between groups was analyzed by using one-way ANOVA or Student\u2019s First, we detected the expression of hsa_circ_0137008 in CRC tissues and adjacent normal tissues, using qRT-PCR. The results showed that hsa_circ_0137008 expression was remarkably down-regulated in CRC tissues compared with that in their adjacent normal tissues (Fig.\u00a0Since hsa_circ_0137008 was downregulated in CRC, we upregulated the expression of hsa_circ_0137008 to assess its biological functions in CRC. qRT-PCR analysis indicated that the expression of hsa_circ_0137008 was evidently increased in SW480 and HCT116 cells transfected with pcDNA3.1-circ_0137008 compared with that in SW480 and HCT116 cells transfected with Vector Fig.\u00a0a. The reNext, we explored the effect of hsa_circ_0137008 on CRC cell migration and invasion using wound-healing assay and transwell invasion assay. The results showed that upregulation of hsa_circ_0137008 led to a marked decrease in cell migration and invasion in SW480 and HCT116 cells (Fig.\u00a0To evaluate the cellular location of hsa_circ_0137008, the nuclear and cytoplasmic RNA was extracted from SW480 and HCT116 cells and then assayed for hsa_circ_0137008 expression using qRT-PCR. We found that hsa_circ_0137008 mainly expressed in the cytoplasm of SW480 and HCT116 cells (Fig.\u00a0The above results indicated that hsa_circ_0137008 acted as a sponge of miR-338-5p in CRC cells. We speculated that hsa_circ_0137008 might modulate CRC progression via sponging miR-338-5p. To test this, SW480 and HCT116 cells were transfected with miR-338-5p mimic alone or with pcDNA3.1-circ_0137008. The results of qRT-PCR assays discovered that the expression of miR-338-5p was up-regulated in SW480 and HCT116 cells transfected with miR-338-5p mimic, but co-transfection of SW480 and HCT116 cells with pcDNA3.1-circ_0137008 and miR-338-5p mimic abated this effect (Fig.\u00a0To explore whether hsa_circ_0137008 regulates CRC cell migration, invasion, and EMT by sponging miR-338-5p, SW480 and HCT116 cells were transfected with miR-338-5p mimic alone or with pcDNA3.1-circ_0137008. Upregulation of miR-338-5p promoted the migration and invasion of SW480 and HCT116 cells, and this action was mitigated following pcDNA3.1-circ_0137008 transfection (Fig.\u00a0Given the functional role of hsa_circ_0137008 in vitro, we assessed the effect of hsa_circ_0137008 on CRC growth in vivo. The expression level of hsa_circ_0137008 in SW480 cells stably expressing hsa_circ_0137008 was higher than that in SW480 cells stably expressing empty Vector (Fig.\u00a0Recently, circRNAs have been widely studied because of their characteristics, including stable, abundant, conserved, and diverse \u201327. DereUp to now, accumulating evidence suggested that circRNAs exert their biological functions via serving as competing endogenous RNA during the tumorigenesis of human cancers , 30, 31.Several researches have proved that miR-338-5p exerts distinct functional roles in different types of cancers. For instance, in esophageal squamous cell carcinoma, miR-338-5p repressed the proliferation, migration, and invasion of CE-81T cells, as well as sensitized CE-81T cells to cisplatin through inhibiting the expression of fermitin family homolog 2, suggesting the anti-oncogenic role of miR-338-5p . In glioIn conclusion, our findings implied that upregulation of hsa_circ_0137008 inhibited the progression of CRC through inhibiton of miR-338-5p. Our study therefore identified a novol regulatory mechanism of tumorigenesis and metastasis in CRC, contributing to a better understanding of the role of circRNAs in CRC progression. Hsa_circ_0137008 may be developed as a prognosis predictor and a promising therapeutic target for CRC."}
+{"text": "Circular RNAs represent a new type of non-coding RNA molecules that influence the occurrence and development of various human diseases by sponging microRNAs, although their roles in heart failure have not been clarified. In this study, peripheral blood samples from 5 patients with heart failure and 4 healthy volunteers were analyzed by next-generation sequencing (NGS) to screen for differentially expressed Circular RNAs. Fifty-six differentially expressed Circular RNAs were identified, of which 29 were up-regulated and 27 were down-regulated. Dysregulated expression of 6 Circular RNAs was verified by quantitative polymerase chain reaction (PCR) analysis, and hsa_circ_0097435 expression was confirmed to be significantly up-regulated in 40 patients with heart failure. Further study with extracted exosomes showed that hsa_circ_0097435 expression was significantly higher in patients with heart failure. In cardiomyocytes, hsa_circ_0097435 was up-regulated after doxorubicin treatment, promoting cardiomyocyte apoptosis. Hsa_circ_0097435 overexpression promoted cardiomyocyte apoptosis, and silencing hsa_circ_0097435 inhibited apoptosis. Moreover, RNA-pulldown experiments and AGO2-immunoprecipitation experiments revealed that hsa_circ_0097435 potentially served a role in heart failure by sponging multiple microRNAs. Collectively, these results suggest that hsa_circ_0097435 can be used as a biological blood marker and revealed a new pathway involved in regulating myocardial cell injury. Our findings may provide a rational basis for developing new treatments for heart failure. Almost all cardiovascular diseases eventually lead to heart failure (HF). Despite advances in clinical and drug interventions that have increased the survival time of patients with HF, mortality rates remain high . Recent Circular RNA (circRNA) molecules have a stable, closed-ring structure and are widely present in whole blood, plasma, and extracellular vesicles . CircRNAIn this study, next-generation sequencing (NGS) was performed to identify differentially expressed circRNAs between HF and healthy control subjects. We aimed to identify circRNA profiles in peripheral blood cells from patients with HF and to explore the roles of circRNAs in HF pathogenesis. We found that hsa_circ_0097435 expression was markedly increased in patients with HF. Further study showed that the level of hsa_circ_0097435 in exosomes from patients with HF was significantly increased. In cardiomyocytes, hsa_circ_0097435 was up-regulated after doxorubicin (DOX) treatment, promoting apoptosis in cardiomyocytes. The expression level of hsa_circ_0097435 participates in regulating apoptosis. Hsa_circ_0097435 overexpression promoted apoptosis in cardiomyocytes, and silencing hsa_circ_0097435 expression inhibited apoptosis in cardiomyocytes. Hsa_circ_0097435 overexpression resulted in significant increases in the recovery of 5 miRNAs in RNA-pulldown assays, and the AGO2 protein was significantly pulled down by hsa_circ_0097435. Immunoprecipitation of AGO2 from AC16 cells showed that AGO2 protein directly bound to 4 miRNAs. Therefore, we predicted that hsa_circ_0097435 could bind to miRNAs through the AGO2 protein and act as a sponge for multiple miRNAs.Forty-five patients with HF and 44 healthy volunteers who underwent physical examinations in the Affiliated Hospital of Qingdao University were included in this study, and informed consent forms were signed by all participants. Among the patient samples, we selected 5 from patients with HF and 4 from volunteers for sequencing. The inclusion criteria were clinical symptoms, b-mode ultrasound results, and laboratory examinations. Routine blood-examination results from all participants were within the normal range. Six milliliters of Trizol reagent  was added to each 2 mL peripheral blood sample immediately after collection, and the samples were preserved at \u201380\u00b0C for subsequent RNA extraction.RNA degradation and contamination, especially DNA contamination, was monitored on 1.5% agarose gels. RNA concentrations and purities were measured using a NanoDrop 2000 Spectrophotometer . RNA integrity was assessed using the RNA Nano 6000 Assay Kit and the Agilent Bioanalyzer 2100 System . The optical density (OD) ratio (absorbance at 260 nm divided by that at 280 nm) of pure RNA ranged between 1.8 and 2.1. All quality standards set by the manufacturer were met.\u00ae UltraTM Directional RNA Library Prep Kit for Illumina\u00ae  following the manufacturer\u2019s recommendations, and index codes were added to attribute the sequences to each sample. Briefly, fragmentation was performed using divalent cations at an elevated temperature in NEBNext First Strand Synthesis Reaction Buffer (5\u00d7). First-strand complementary DNA (cDNA) was synthesized using random hexamer primers and reverse transcriptase. Second-strand cDNA synthesis was subsequently performed using DNA Polymerase I and RNase H. The remaining overhangs were converted into blunt ends via exonuclease/polymerase activities. After adenylation of the 3\u2032 ends of the DNA fragments, NEBNext Adaptors with a hairpin loop structure were ligated to prepare for hybridization. To select for insertion fragments, preferentially 150\u2013200 base pairs in length, the library fragments were purified using AMPure XP Beads . Then, the cDNA fragments were treated with 3 \u03bcL USER Enzyme  at 37\u00b0C for 15 min before amplification by polymerase chain reaction (PCR). PCR was performed with Phusion High-Fidelity DNA polymerase, Universal PCR primers, and the Index(X) Primer. Finally, the PCR products were purified (AMPure XP system) and the quality of each library was assessed on an Agilent Bioanalyzer 2100 and by performing quantitative PCR (qPCR).For each sample, 1.5 \u03bcg RNA was used as input material for rRNA removal using the Ribo-Zero rRNA Removal Kit , followed by digestion of linear RNA using RNase R. Sequencing libraries were generated using the NEBNextClustering of the index-coded samples was performed on a cBot Cluster Generation System using TruSeq PE Cluster Kit v3-cBot-HS (Illumina), according to the manufacturer\u2019s instructions. After cluster generation, the prepared libraries were sequenced using an Illumina platform, and reads were generated.P-values were adjusted using the Benjamini\u2013Hochberg approach for controlling the false-discovery rate. Genes with an adjusted P < 0.01 and an absolute log2 (fold-change) > 1 found using DESeq were assigned as being differentially expressed.CircRNAs were predicted based on the number of junction reads identified using the CircRNA Identifier (CIRI) tool and find_circ software. Differential-expression analysis of two conditions/groups was performed using the DESeq R package. DESeq provides statistical routines for determining differential expression in digital gene-expression data using a model based on the negative binomial distribution. The resulting TM Quick Exosome Isolation Kit . Blood samples (1 mL each) were transferred to individual centrifuge tubes and centrifuged for 10 min at 3000 \u00d7 g and 4\u00b0C, after which the particles were discarded and each supernatant was transferred to a new centrifuge tube. After pretreatment, 4 volumes of 1\u00d7 phosphate-buffered saline were added to each tube, and the samples were mixed evenly.All exosome-extraction steps were performed following the instructions of the Hieff\u2013\u0394\u0394CT method for each circRNA. The relative expression of miRNAs was calculated using the same real-time qPCR method as verified with circRNAs. U6 RNA was detected as an endogenous control gene for miRNAs.Six upregulated circRNAs found in blood cells from 40 HF patients and 40 healthy controls were verified by real-time qPCR. Total RNA was used for synthesizing cDNAs with the PrimeScript RT Reagent Kit . First-strand cDNA (2 mL) was used for PCR experiments (performed in triplicate), with TB Green Premix Ex Taq II . Beta-actin was detected as an internal reference for circRNAs to avoid potential aberrances in concentrations and transcription efficiencies. Relative circRNA-expression levels were measured using the 2EcoRI and BamHI restriction enzyme sites. The chromatograms were normal, without any hybrid peaks or superimposed bands, and the sequence comparison was consistent, indicating that hsa_circ_0097435 and hsa_circ_0097435-MS2 were successfully inserted into pLC5-ciR, and the overexpression vectors were successfully constructed. The overexpressed plasmids and the parental vector were separately transfected into ac16 cells using the LipofectamineTM 3000 transfection reagent , and overexpression of the target gene was measured by qPCR.Hsa_circ_0097435 and hsa_circ_0097435-MS2 were synthesized by total gene synthesis, and cloned individually into the pLC5-ciR vector using the TM 3000 transfection reagent (Thermo Fisher Scientific), according to the manufacturer\u2019s instructions.Small-interfering RNA (siRNA) oligonucleotides specific for hsa_circ_0097435 were designed using Ambion\u2019s siRNA design tool and purchased from GenePharma Co., Ltd. . The siRNA sequence for hsa_circ_0097435 was 5\u2032-ACUUGUGAUGCUGACUUGGTTCCAAGUCAGCAUCA CAAGUTT-3\u2032. The specificity of the oligonucleotides was confirmed through comparing with all other sequences in GenBank using Nucleotide BLAST. Transfection of siRNAs was performed using the LipofectamineApoptosis was determined by performing terminal deoxynucleotidyl transferase dUTP nick-end labeling (TUNEL) assay using the TUNEL Apoptosis Detection Kit , per the manufacturer\u2019s instructions. The samples were stained with anti-fluorescence attenuation sealant containing DAPI  and detected with a Zeiss LSM510 META microscope. The percentage of apoptotic nuclei was calculated by dividing the total number of TUNEL-positive nuclei by the total number of DAPI-positive nuclei.http://circinteractome.nia.nih.gov. Select the \u201cmiRNA Target Sites\u201d tab. CircInteractome (http://www.bioinf.com.cn/) that uses miRanda  and an hsa_circ_0097435-MS2-overexpression plasmid (C97435-MS2). The expression vectors C97435-MS2 and MS2-CP were transfected in the cells to induce transient MS2-CP expression. MS2-CP and MS2-labeled circRNAs could specifically bind to form an MS2-CP-MS2\u2013circRNA complex. The MS2-CP-MS2\u2013circRNA complex was pulled down, and the capture products were detected to identify proteins or miRNA molecules that may interact with hsa_circ_0097435.An AGO2-specific antibody was used for AGO2 immunoprecipitation, and an IgG antibody was selected as the negative control. Mouse monoclonal anti-AGO2  or mouse normal IgG antibody  were preincubated with Magna Bind goat anti-mouse IgG Magnetic Bead slurry  and used for immunoprecipitation. In brief, cells were lysed in 150 mM KCl, 25 mM Tris-HCl, pH 7.4, 5 mM EDTA, 0.5% Triton X-100, and 5 mM DTT supplemented with RNase inhibitor  and proteinase inhibitor cocktail . The lysate was mixed with antibody-coupled Sepharose beads and left under rotation for 4 h at 4\u00b0C. Beads were subsequently washed six times in lysis buffer and the RNA was extracted using Trizol reagent.1. The clusterProfiler R packages were used to identify which KEGG pathways the target genes were significantly enriched in.The Gene Ontology (GO) database is a structured standard biological annotation system established by the GO Consortium in 2000, which is aimed at establishing a standard vocabulary of knowledge regarding genes and their products that is applicable to various species. GO function classification statistics of the mRNAs targeted by the miRNAs associated with hsa_circ_0097435 was implemented using the clusterProfiler R packages. Kyoto Encyclopedia of Genes and Genomes (KEGG) is a database resource for understanding the high-level functions and utilities of biological systems, such as a cell, an organism, or an ecosystem, based on molecular-level information, especially large-scale molecular datasets generated by genome sequencing and other high-throughput experimental technologiest-test was used for comparisons of two groups. A p < 0.05 was considered to reflect a statistically significant difference . GraphPad Prism software, version 8  was applied for data management and analysis.Data are expressed as mean \u00b1 standard error of the mean of at least three independent experiments. One-way analysis of variance was used for multiple comparisons. Student\u2019s unpaired PRJNA574863). After obtaining clean reads, the sequences were aligned with the reference genome to determine their locations within the reference genome or gene, as well as the sequence characteristics unique to the sequencing samples. Alignments were performed using the Burrows\u2013Wheeler Aligner  > 1 found by DESeq were considered to be differentially expressed. Finally, 56 circRNAs with significant differences in expression were identified  groups was performed using the DESeq R package. DESeq provides statistical routines for determining differential expression in digital gene-expression data using a model based on the negative binomial distribution. The resulting entified . Compareentified  and clusentified .p < 0.01), hsa_circ_0099476 (p < 0.01), hsa_circ_0001312 (p < 0.01), hsa_circ_0005158 (p < 0.01), hsa_circ_0029696 (p < 0.01), and hsa_circ_0040414 (p < 0.01) were significantly higher in the HF group than in the NC group, and the expression difference of hsa_circ_0097435 was most obvious  were detected in exosomes from patients with HF than in those from normal volunteers  experiments on the pulled-down products, which showed that AGO2 protein was significantly pulled down by hsa_circ_0097435 . FurtherBased on the sequence information of known miRNAs, miRanda  and TargHF is becoming an increasingly prevalent epidemic. Due to the aging of the population and medical advances, increasing number of people are diagnosed with HF and their survival time has been extended, although the mortality rate remains high. As a blood biomarker for HF-related, high-frequency diagnosis and prognosis, BNP is not applicable to all types of HF patients. Another blood biomarker or a group of blood biomarkers is needed to better diagnose and treat HF and predict prognosis. Mounting evidence suggests that HF is mainly caused by genetic variations, that HF has a complex genetic basis , and thap < 0.01) was significantly more abundant in the HF group than in the NC group. The circular structure of hsa_circ_0097435 was verified by Sanger sequencing and a divergent-primer strategy. Further study showed that the hsa_circ_0097435 levels in exosomes from patients with HF was significantly higher (p < 0.05) than those from normal volunteers. Based on these experiments, we postulate that most hsa_circ_0097435 is encapsulated in exosomes, which otherwise could be decomposed by the large amount of RNase present in plasmas. This postulate is the same as previous studies found that circRNAs may be encapsulated in exosomes , hsa_miR_5000_5P (p < 0.01), hsa_miR_609 (p < 0.01), and hsa_miR_1294 (p < 0.01) were significantly higher in the AGO2 group than in the IgG group. Therefore, we predicted that hsa_circ_0097435 could bind to miRNAs through the AGO2 protein and act as a sponge for multiple miRNAs.To understand the role of hsa_circ_0097435 in cardiomyocyte apoptosis, we further examined its expression in DOX-treated cardiomyocytes. Hsa_circ_0097435 was the highest at 12 h after DOX treatment. To verify whether this increase was related to myocardial apoptosis, we conducted a series of validation analyses using loss-of-function and gain-of-function experiments. Hsa_circ_0097435 overexpression promoted cardiomyocyte apoptosis, and silencing hsa_circ_0097435 expression inhibited cardiomyocyte apoptosis. CircRNAs play important regulatory roles in diseases by interacting with disease-related miRNAs. To investigate the mechanism of action between hsa_circ_0097435 and miRNAs, further studies were carried out. In the RNA-pulldown assays, we identified 5 miRNAs that were significantly pulled down following hsa_circ_0097435. In addition, the AGO2 protein was significantly pulled down by hsa_circ_0097435. Immunoprecipitation of AGO2 from AC16 cells showed that the AGO2 protein could directly bind to miRNAs. Among the 4 selected miRNAs, the levels of hsa_miR_6799_5P .RNA sequencing data was deposited in the NCBI Sequence Read Archive  for the publication of any potentially identifiable images or data included in this article.JH and LZ analyzed the sequencing results of circRNA, completed cell experiment, and wrote this manuscript. LH, HY, FX, BY, RZ, and YZ completed clinical sample collection. YA conceived and designed the research, and revised the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest."}
+{"text": "During evolutionary history, molecular mechanisms have emerged to cope with deleterious mutations. Frameshift insertions in protein-coding sequences are extremely rare because they disrupt the reading frame. There are a few known examples of their correction through translational frameshifting, a process that enables ribosomes to skip nucleotides during translation to regain proper reading frame. Corrective frameshifting has been proposed to act on the single base pair insertion at position 174 of the mitochondrial NADH dehydrogenase subunit 3 gene (ND3) that has been observed in several turtles and birds. However, the relatively sparse taxonomic representation has hampered our understanding of the evolution of this insertion in vertebrates.Here, we analyzed 87,707 ND3 sequences from 10,309 vertebrate taxa to reveal the evolutionary history of this insertion and its common genomic characteristics. We confirmed that the insertion only appears in turtles and birds and reconstructed that it evolved independently in both groups with complex patterns of gains and losses. The insertion was observed in almost all bird orders but was absent in all members of the diverse Passeriformes. We found strong conservation in the nucleotides surrounding the insertion in both turtles and birds, which implies that the insertion enforces structural constraints that could be involved in its correction.Our study demonstrates that frameshifts can be widespread and can be retained for millions of years if they are embedded in a conserved sequence theme. Comparative analysis of molecular sequences across the diversity of life lets us discover which molecular mechanisms have been conserved and which have been modified throughout evolution. Insertions or deletions in protein-coding genes are usually selected against because they result in frameshifts that disrupt the amino acid coding frame and result in dysfunctional proteins . Albeit Ty1 and Ty3 genes in yeast, antizyme gene in mammals, prfB in Escherichia coli): a transfer RNA (tRNA) that enables the ribosome to \u201cslip\u201d on the ribosome P-site, a rarely used codon in the A-site promoting the stall, and a commonly used codon in the +1 frame . Ne. Ne18]. To confirm that all sequences of the same unique taxon had the same pattern at position 174, we used the filtered alignment as a framework for aligning all other intraspecific records, by using the \u2013add function of MAFFT and applied the same filters as above .The frameshift insertion ND3\u2013174+1 was only observed in certain species of turtles and birds, and we therefore restricted analyses to Diapsida, i.e., birds, crocodiles, turtles, and Lepidosauria . This left 4,233 of 10,397 vertebrate taxa. We recorded the state of position 174 in each sequence, either being a gap in the alignment  or being a nucleotide .We used the R package rotl (v3.0.10)  to obtaiML ancestral states were reconstructed using the function hsp_mk_model in the R package castor (v1.5.5) . The funMP reconstruction of ancestral states was performed with the function MPR in the R package ape v5.3 . MP analTo count the number of nodes of the phylogeny where transitions from one state to another likely occurred, we related the likelihood from the ancestral state reconstruction of each descendant node to its parent node using the R package phangorn . In the H is the Shannon entropy in position i of the DNA sequence and freqN is the frequency of nucleotide N of state {A, T, G, C}. Shannon entropy was transformed into information content per nucleotide position:R is the information content at position i of the DNA sequence [P < 0.05. Weblogo [Nucleotide frequencies per position across the entire ND3 sequence were obtained separately for diapsids without the insertion and with the insertion. We calculated Shannon entropy as a measure of nucleotide diversity :\\documesequence . The inf Weblogo  was usedCodon frequencies were calculated for both the shifted reading frame, the 0 reading frame, and for the corrected reading frame, the +1 reading frame. Codons containing unknown nucleotides (N) were removed. Codon frequencies were calculated for the 6 codons surrounding the insertion (position 163\u2013181). The calculation of codon frequencies in the +1 reading frame excluded the adenosine at position 175 (A-175) following the insertion .The sequences for 3 tRNAs involved in translation of codons surrounding the insertion were extracted from the compiled set of full mitochondrial genomes of birds and turtles. We extracted the tRNA for leucine, which translates the codon upstream of the insertion; serine, which translates the codon following the insertion in the shifted reading frame; and valine, which translates the codon following the insertion in the corrected reading frame. We split the sequences in a group of birds and a group of turtles, each with and without the insertion. Short sequences were removed if they were between 2 times the standard deviation of the mean nucleotide length of the group. An initial alignment was done using the mlocarna tRNA aligner (v2.0.0RC8) , which sde novo assembled from shotgun genomic reads [To extend the study to other potential insertions in the ND3 gene in Diapsida, we compiled a dataset of 1,050 complete mitochondrial genomes from the Refseq database. We excluded records without annotations or with undetermined nucleotides (N) in the ND3 gene, resulting in 1,044 ND3 gene sequences . We also investigated a second dataset focused on birds of the B10K including 328 mitochondrial genomes that were ic reads .Gallus gallus ND3 amino acid sequence (NC_040902.1) to each of the diapsid ND3 nucleotide gene sequences. Exonerate designates potential frameshifts with the symbol \u201c#.\u201dBoth ND3 sequence sets were aligned using the vertebrate mitochondrial genetic codon table (\u201c-gc_def 2\u201d) in the \u201calignSequence\u201d module of MACSE (v2.01) , which rhttps://github.com/sergioSEa/ND3_174_vertebrates2020Scripts used for data generation and analysis can be found at: Operating system(s): Bash scripts should be run in Linux OS/Mac OS. Python and R scripts are platform independent.Programming language: Bash, R, PythonOther requirements: Python 3 or higher, MAFFT v7.4, pxclsq v0.1. Python packages: biopython. R packages: rotl, castor, ape, phytools, ggtree, ggimage, phangorn, ggstance, Biostrings, ggrepel, and tidyverse.License: GNUGigaScience database GigaDB [The datasets supporting the results of this article are available in the e GigaDB .Supplementary Table S1: Diapsida MAFFT alignment. Alignment of diapsid ND3 sequences after removal of positions not present in \u22655% of taxa.Supplementary Table S2: Status at ND3\u2013174+1 across Diapsida. Table of diapsid taxa and the corresponding status at ND3\u2013174+1 used for ancestral state reconstruction extracted from Addtional_file_1.Supplementary File S1: Diapsida phylogeny. Open Tree of Life synthetic phylogenetic tree matched to the Diapsida taxa included in the study.Supplementary File S2: Diapsida MACSE alignment. Alignment of 1,044 Refseq Diapsida sequences used for identification of other frameshifts in the mitochondrial ND3 gene.Supplementary Figure S1: Diapsida phylogeny with tip labels. Diapsida tree as presented in Fig.\u00a0Supplementary Figure S2: Consensus tRNA structure. Consensus predicted tRNA structure of birds and turtles with and without gap for the codons valine, leucine, and serine. Nucleotides presented are mock nucleotides that do not represent the actual consensus sequence.Pelusios castaneus by mapping of short read sequences. Genomic (SRR9091461) and transcriptomic (SRR629649) short reads were mapped to the P. castaneus mitochondrial genome (NC_026049.1) to check whether the predicted frameshift site was present in short reads as well.Supplementary Figure S3: Confirmation of insertion in the mitochondrial genome of A: adenosine; B10K: Bird 10,000 Genomes Project; bp: base pairs; C: cytosine; G: guanine; MACSE: Multiple Alignment of Coding Sequences; MAFFT: Multiple Alignment using Fast Fourier Transform; ML: maximum likelihood; MP: maximum parsimony; N: undetermined nucleotide; NCBI: National Center for Biotechnology Information; ND3: NADH dehydrogenase 3 complex gene; T: thymine; tRNA: transfer RNA.The authors declare that they have no competing interests.This project was supported by the Carlsberg Foundation (CF16-0663). It was partially supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB31020000). G.Z. is also supported by the Villum Foundation (No. 25900).S.A.-S.: Formal analysis, writing, visualization, conceptualization; W.C.: data curation, formal analysis, visualization; J.S.: conceptualization, supervision, visualization, writing; G.Z.: conceptualization, supervision, resources, writing.giaa161_GIGA-D-20-00122_Original_SubmissionClick here for additional data file.giaa161_GIGA-D-20-00122_Revision_1Click here for additional data file.giaa161_GIGA-D-20-00122_Revision_2Click here for additional data file.giaa161_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giaa161_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giaa161_Reviewer_1_Report_Original_SubmissionEdward Louis Braun, Ph.D. -- 5/24/2020 ReviewedClick here for additional data file.giaa161_Reviewer_1_Report_Revision_1Edward Louis Braun, Ph.D. -- 11/16/2020 ReviewedClick here for additional data file.giaa161_Reviewer_2_Report_Original_SubmissionFidel Botero-Castro -- 6/23/2020 ReviewedClick here for additional data file.giaa161_Supplemental_FilesClick here for additional data file."}
+{"text": "Lung neuroendocrine neoplasms (LNENs) are rare solid cancers, with most genomic studies including a limited number of samples. Recently, generating the first multi-omic dataset for atypical pulmonary carcinoids and the first methylation dataset for large-cell neuroendocrine carcinomas led us to the discovery of clinically relevant molecular groups, as well as a new entity of pulmonary carcinoids (supra-carcinoids).https://tumormap.ucsc.edu/?p=RCG_lungNENomics/LNEN). The data, source code, and compute environments used to generate and evaluate the map as well as the raw data are available, respectively, in a Nextjournal interactive notebook  and at the EMBL-EBI European Genome-phenome Archive and Gene Expression Omnibus data repositories.To promote the integration of LNENs molecular data, we provide here detailed information on data generation and quality control for whole-genome/exome sequencing, RNA sequencing, and EPIC 850K methylation arrays for a total of 84 patients with LNENs. We integrate the transcriptomic data with other previously published data and generate the first comprehensive molecular map of LNENs using the Uniform Manifold Approximation and Projection (UMAP) dimension reduction technique. We show that this map captures the main biological findings of previous studies and can be used as reference to integrate datasets for which RNA sequencing is available. The generated map can be interactively explored and interrogated on the UCSC TumorMap portal (We provide data and all resources needed to integrate them with future LNENs transcriptomic studies, allowing meaningful conclusions to be drawn that will eventually lead to a better understanding of this rare understudied disease. Lung neuroendocrine neoplasms (LNENs) are rare understudied diseases with limited therapeutic opportunities. LNENs include poorly differentiated and highly aggressive lung neuroendocrine carcinomas (NECs)\u2014i.e., small-cell lung cancer (SCLC) and large-cell neuroendocrine carcinoma (LCNEC)\u2014as well as well-differentiated and less aggressive lung neuroendocrine tumors (NETs), i.e., typical and atypical carcinoids  have been characterized by Fernandez-Cuesta et\u00a0al.  and LaddFig.\u00a0RRID:SCR_010910) [RRID:SCR_000468) [RRID:SCR_003277) [WES and WGS were performed, respectively, on 16 and 3 fresh-frozen atypical carcinoids in the Cologne Centre for Genomics and the Centre National de Recherche en G\u00e9nomique Humaine. For WES, the SeqCap EZ v2 Library capture kit from NimbleGen (44 Mb) and the Illumina HiSeq 2000 machine  were used for the sequencing. For WGS, the Illumina TruSeq DNA PCR-Free Library Preparation Kit was used for library preparation and the HiSeqX5 platform from Illumina for the sequencing as described in . The seq_010910) , samblas_000468) , and sam_000468) , respectRRID:SCR_014583) [RRID:SCR_001209) [RRID:SCR_014982) [The QCs of the WES and WGS data were performed using FastQC v0.11.8   and Qual_001209)  using th_001209)  workflow_001209)  and IARC_001209)  reposito_014982)   [RRID:SCR_015899) [RRID:SCR_003277) [RRID:SCR_001876) [RRID:SCR_016323) [RRID:SCR_014583) [RRID:SCR_005275) [RRID:SCR_005514) [RRID:SCR_014982) [RNA-Seq was performed on 20 fresh-frozen atypical samples. The Illumina TruSeq RNA sample preparation Kit was used for library preparation and the Illumina TruSeq PE Cluster Kit v3 and the Illumina TruSeq SBS Kit v3-HS kits were used on an Illumina HiSeq 2000 sequencer. The data generated were processed in 5 steps  , (ii) re_011847) ) using S_015899) , (iii) r_003277) , (iv) ba_001876) , 31, and_014583) , RSeQC v_005275) , and HTS_005514)  were use_014982) . These s_014982)  release _014982)  release _014982)  release _014982)  release Fig.\u00a0RRID:SCR_015687) [Finally, to apply dimensionality reduction methods to the RNA-Seq data (see below), the DESeq2 package v1.26.0   was usedRRID:SCR_012830) [The methylation analyses were performed on the basis of the EPIC 850K methylation arrays and the Infinium EPIC DNA methylation beadchip platform (Illumina) for 33 typical carcinoids, 23 atypical carcinoids, 20 LCNECs, and 19 technical replicates in total. These arrays interrogate >850,000 CpGs and contain internal control probes that can be used to assess the overall efficiency of the sample preparation steps. The raw intensity data (IDAT files) were processed using the R package minfi v.1.24.0  . Fig.\u00a01 _012830)  GitHub r2 of the methylated and unmethylated intensities, indicates that all samples cluster together with a log median intensity >11 for both channels, which supports the absence of failed samples; (ii)in the right panel, the multidimensional scaling plot shows that the samples cluster together by histological groups. We used the depectionP function (minfi package), which compares the DNA signal to the background signal based on the negative control probes to provide a detection P-value per probe, lower P-value indicating reliable CpGs. Fig.\u00a0P-values per sample and shows that all samples' mean detection P-values were <0.01. To correct for the variability identified in the control probes, a normalization step was applied to the raw intensities using the preprocessFunnorm function from minfi.Fig.\u00a0P-value >0.01 in \u22651 sample. After between-array normalization, different sets of probes that could generate artifacts were removed successively from the methylation dataset: (i) 19,634 probes on the sex chromosomes, in order to identify differences related to tumors but unrelated to sex chromosomes; (ii) 41,818 cross-reactive probes, which are probes co-hybridizing with multiple CpGs on the genome and not only to the one for which it has been designed ; (iii) 1Here we have generated a pan-LNEN molecular map with the whole-transcriptomic (RNA-Seq) data available from individual studies of each LNEN tumor type ,4\u20138. ThiThe pan-LNEN map was obtained using the Uniform Manifold Approximation and Projection (UMAP) method  on the gFig.\u00a0P = 6.2 \u00d7 10\u22124). Similarly, the LCNEC/SCLC-like samples [P = 3.3 \u00d7 10\u22123). In 2018, George et\u00a0al. showed also that LCNEC samples can be subdivided into Type I and Type II molecular groups [P = 9.9 \u00d7 10\u221214) and that SCLC/LCNEC-like samples were closer to Type II than to Type I LCNECs [P = 3.9 \u00d7 10\u22123). Like the LCNECs, pulmonary carcinoids have been subdivided into molecular groups. Alcala et\u00a0al. [P = 5 \u00d7 10\u22122). We also note that 1 sample from Laddha et\u00a0al. [First, the SCLC/LCNEC-like samples , which aa et\u00a0al.  identifia et\u00a0al. . In the a et\u00a0al. , 2 carcia et\u00a0al. . These oIn any dimensional reduction technique, there is a trade-off between preserving the global structure of the data and the fine-scale details, and UMAP has been designed to reach a better balance compared with previous methods.On the basis of the previously published analyses of LNEN data , 4\u20138, wek; k values and global for large k values. We calculated X we scaled the values of We used the sequence difference view (SD) metric (eq.\u00a03 from ) to evalX preserves k neighborhoods as well as PCA-5D, whereas values close to 1 indicate that X preserves k neighborhoods worse than PCA-5D but as well as PCA-2D, and values >1 indicate that X preserves k neighborhoods worse than PCA-2D and PCA-5D. Note that X preserves k neighborhoods better than By definition, k > 30 . With n_neighbors = 238, the UMAP projection provides a clear improvement over PCA-2D for k \u223c 135  the original space, (ii) the PCA-5D projection, and (iii) the UMAP projection (with n_neighbors = 238). We used the implementation of MI from the Moran.I function of R package ape (v. 5.3) [Under the hypothesis that close points on projections share a similar molecular profile, spatial auto-correlations were measured according to the Moran index (MI) metric . MI valu(v. 5.3) .N genes based on the mean MI values for these 3 cases and calculated the overlap between the lists . For example, for N = 1,000 , the French National Cancer Institute , the Ligue Nationale contre le Cancer (LNCC 2016 to L.F.C.), France Genomique (to J.D.M.), and the Neuroendocrine Tumor Research Foundation . L.M. has a fellowship from the LNCC.M.F. and L.F.C. conceived and designed the study. A.A.G.G., E.M., N.A., L.M., and C.V. performed the analyses. V.C. and A.G. gave scientific input for the methylation part. J.D.M. helped with logistics and gave scientific input. A.A.G.G., E.M., N.A., M.F., and L.F.C. wrote the manuscript. All the authors read and commented the manuscript.giaa112_GIGA-D-20-00021_Original_SubmissionClick here for additional data file.giaa112_GIGA-D-20-00021_Revision_1Click here for additional data file.giaa112_GIGA-D-20-00021_Revision_2Click here for additional data file.giaa112_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giaa112_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giaa112_Reviewer_1_Report_Original_SubmissionFuqiang Li -- 2/25/2020 ReviewedClick here for additional data file.giaa112_Reviewer_2_Report_Original_SubmissionSaurabh V Laddha -- 3/11/2020 ReviewedClick here for additional data file.giaa112_Supplemental_TablesClick here for additional data file."}
+{"text": "Prostate cancer (PCa) is one of the most common malignant tumors worldwide. Accumulating evidence has suggested that circular RNAs (circRNAs) are involved in the development and progression of various cancers, and they show great potential as novel biomarkers. However, the underlying mechanisms and specific functions of most circRNAs in PCa remain unknown. Here, we aimed to identify circRNAs with potential roles in PCa from the PCa expression profile.We used data downloaded from the Gene Expression Omnibus to identify circRNAs that were differentially expressed between PCa samples and adjacent non-tumor samples. Relative expression levels of identified circRNAs were validated by quantitative real-time PCR. Micro (mi)RNA response elements were predicted by the CircInteractome database, and miRNA target genes were predicted by miRDB, miRTarBase, and TargetScan databases. Gene ontology (GO) enrichment analysis and pathway analysis revealed the potential biological and functional roles of these target genes. A circRNA\u2013miRNA\u2013mRNA interaction network was constructed by Cytoscape. The interaction between circRNAs and miRNAs in PCa was thoroughly reviewed in the PubMed. Finally, the mRNA expression of these genes was validated by the Cancer Genome Atlas (TCGA) and Gene Expression Profiling Interactive Analysis (GEPIA) databases. The expression of proteins encoded by these genes was further validated by the Human protein Atlas (HPA) database.PDE7B, DMRT2, and TGFBR3 were identified as potentially playing a role in PCa progression. Finally, three circRNA\u2013miRNA\u2013mRNA interaction axes were predicted by bioinformatics: hsa_circ_0024353\u2013hsa-miR-940\u2013PDE7B, hsa_circ_0024353\u2013hsa-miR-1253\u2013DMRT2, and hsa_circ_0085494\u2013hsa-miR-330-3p\u2013TGFBR3.A total of 60 circRNAs that were differentially expressed between PCa and healthy samples were screened, of which 15 were annotated. Three circRNAs  certified the criteria were studied. The results of quantitative real-time PCR demonstrated that the expression of hsa_circ_0024353 was significantly downregulated in PC-3 cells when compared with RWPE-1 cells, while the expression of hsa_circ_0031408 and hsa_circ_0085494 was significantly upregulated in PC-3 cells when compared with RWPE-1 cells. GO and Kyoto Encyclopedia of Genes and Genomes analyses found that target genes were mainly enriched in metabolic processes and pathways involving phosphoinositide 3-kinase-Akt signaling, hypoxia-inducible factor-1 signaling, p53 signaling, and the cell cycle. A total of 11 miRNA target genes showing differential expression between PCa and healthy samples were selected, and their mRNA and protein expression were validated by GEPIA and HPA databases, respectively. Of these, This study identified three circRNA\u2013miRNA\u2013mRNA interaction axes that might provide novel insights into the potential mechanisms underlying PCa development. Prostate cancer (PCa) is one of the most common cancers worldwide, and its incidence has gradually increased in many counties . For exaCircular RNAs (circRNAs) are a class of endogenous non-coding RNAs that have covalent closed-loop structures with no 5\u2032 cap or 3\u2032 poly(A) tail. Their ring structure makes them more stable than mRNAs. They have many roles, including acting as micro (mi)RNA sponges , regulatIn this study, we aimed to identify circRNAs that might be involved in PCa carcinogenesis by analyzing the expression profile of PCa. Data downloaded from the National Center of Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) were used to identify novel circRNAs that were differentially expressed in PCa. Gene ontology (GO) enrichment analysis and pathway analysis revealed the potential function of miRNA target genes. Finally, mRNA expression levels of these genes were validated by the Cancer Genome Atlas (TCGA) and Gene Expression Profiling Interactive Analysis (GEPIA) databases. The protein expression of these genes was further validated by the Human Protein Atlas (HPA) database.P < 0.05 were set as hub circRNA screened criteria.Differentially expressed circRNAs were screened and identified using the GEO2R online tool from the NCBI GEO database GSE140927. |LogFC| \u2265 1 and We used circBase  and circPC-3 and the human prostatic epithelial cell line RWPE-1 were purchased from the American Type Culture Collection . PC-3 cells were cultured in F-12K medium. The medium was supplemented with 10% fetal bovine serum (Invitrogen) and 5% penicillin\u2013streptomycin (Invitrogen). RWPE-1 cells were cultured in keratinocyte serum-free medium supplemented with 50 mg/ml bovine pituitary extract, 5 ng/ml human epidermal growth factor, and 1% penicillin\u2013streptomycin.TM RT reagent kit with gDNA Eraser (Takara) to detect the expression of hsa_circ_0024353, hsa_circ_0085494, and hsa_circ_0031408. Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) was amplified as an internal control. The DNA extraction kit  was used to extract genomic DNA from cells and tissues. PCR reactions were performed using Thermo Scientific DreamTaq PCR Master Mix (2\u00d7) . The amount of circRNAs and mRNA was quantified after the RT step using SYBR Green qPCR Master Mix (Takara) according to the manufacturer\u2019s instructions. The primers used in this study were: 5\u2032-TCCATCCTGCGAGCTCCTTG-3\u2032 (F) and 5\u2032-GCTGCATGGCACCTCTGTTC-3\u2032 (R) for hsa_circ_0024353; 5\u2032- GACTTGCAGGCTACGTTGAAGC-3\u2032 (F) and 5\u2032- CAAGTCCAGCTAGATCTGACACAAGAT-3\u2032 (R) for hsa_circ_0085494; 5\u2032- TCCTTTCTTGGCAACTGGAGGT-3\u2032 (F), 5\u2032-ACAAGTGAGGACAGCACGCA-3\u2032 (R) for hsa_circ_0031408; and 5\u2032-AGCCTCAAGATCATCAGCAATG-3\u2032 (F) and 5\u2032-ATGGACTGTGGTCATGAGTCCTT-3\u2032 (R) for GAPDH.Total RNA was isolated from cells and tissues using RNAiso Plus reagent . Reverse transcription (RT) was performed with random primers using the PrimeScript1, miRTarBase  and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses including gene set analyses were performed using the ConsensusPathDB-human 5 databasThe original mRNA expression profile used in the present study was downloaded from the TCGA database, and included 499 PCa tissues and 52 healthy prostatic tissues. Genes that were differentially expressed between tissues were screened for their interaction with predicted miRNA target genes.6 to validate the mRNA expression of target genes. Protein expression was validated by the HPA  were annotated by circPrimer. We next used circBase and circPrimer software to annotate these circRNAs. Corresponding gene symbols are _0024353 , TMEM65 _0085494 , and STX_0031408 . hsa_cirWe used circPrimer (version 1.2) and Primer3 (version 4.1) software to design divergent primers for hub circRNAs. Primer diagrams are shown in The CircInteractome database predicted a total of 26, 10, and eight MREs for hsa_circ_0024353, hsa_circ_0031408, and hsa_circ_0085494, respectively .Target genes of predicted miRNAs were predicted by miRDB, miRTarBase, and TargetScan databases, and interactions among these three databases were obtained. The selected genes were used in the construction of the circRNA\u2013miRNA\u2013mRNA interaction network.Hub circRNAs and mRNAs contain corresponding miRNA binding sites. Interactions between circRNAs and miRNAs were predicted by the Circular RNA Interactome online tool and combined with interactions identified between miRNAs and corresponding target genes. The circRNA\u2013microRNA\u2013mRNA interaction network was constructed using Cytoscape (version 3.6.1) software, and the roles of hub circRNAs are shown in GO and KEGG analyses of potential target genes of hsa_circ_0024353, hsa_circ_0031408, and hsa_circ_0085494 were performed by ConsensusPathDB.A total of 48 GO terms were shown to be enriched by ConsensusPathDB. These potential target genes were mainly enriched in the primary metabolic process, cellular metabolic process, organic substance metabolic process, and nitrogen compound metabolic process . A totalA total of 15 GO terms were shown to be enriched by ConsensusPathDB. These potential target genes were mainly enriched in the primary metabolic process, cellular metabolic process, organic substance metabolic process, and the intracellular process . A totalA total of 40 GO terms were shown to be enriched by ConsensusPathDB. These potential target genes were mainly enriched in cell communication, cellular metabolic process, and primary metabolic process . A totalP < 0.05). A total of 413 predicted differentially expressed mRNAs were also identified. Venn diagrams were used to select 11 overlapping genes, including SIAH3, EPHB1, CASKIN1, PDE7B, KIAA0408, HOXC4, SLC24A4, DMRT2, TGFBR3, PPP1R1C, and ONECUT2 . The roles of these circRNAs in cancer, especially PCa, have not been explored.CircRNAs have previously been reported to regulate gene expression by MREs . We therPDE7B, DMRT2, and TGFBR3 were identified as novel genes regulated by the identified circRNAs and with potential roles in PCa. PDE7B is the target gene of hsa-miR-940 which can be regulated by hsa_circ_0024353; DMRT2 is the target gene of hsa-miR-1253 which can also be regulated by hsa_circ_0024353; and TGFBR3 is the target gene of hsa-miR-330-3p which can be regulated by hsa_circ_0085494.We used Cytoscape to construct a circRNA\u2013miRNA\u2013mRNA interaction network which revealed that hsa_circ_0024353 interacts with the most dominant miRNAs such as hsa-miR-646, hsa-miR-940, hsa-miR-665, and hsa-miR-1253. Following GEPIA and HPA database validation of mRNA and protein expression of the 11 genes, PDE7B, DMRT2, and TGFBR3 are involved in several types of tumor such as lung cancer  can be found in the article/supplementary material.Y-PW: conceptualization. X-DL, Z-BK, and S-HC: data curation. FL: formal analysis. X-YX and D-NC: investigation. Y-PW, NX, and YW: methodology. YW, Q-SZ, and X-YX: project administration. Q-SZ: visualization. Y-PW, Z-BK, X-DL, and S-HC: writing \u2013 original draft. Y-AW and NX: writing \u2013 review and editing. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest."}
+{"text": "Deinococcus geothermalis has a total of 73 insertion sequences (ISs) in genomes, and some of them are actively transposed to other loci with replicative mode due to oxidative stress of hydrogen peroxide treatment. Here, we detected two transposition events in wild-type (WT) strain and LysR family member gene disrupted strain (\u0394dgeo_2840). Similar to our previous report (dgeo_0524), a key enzyme of carotenoid biosynthesis, was disrupted by the integration of IS element, thereby detected a single phenotypically non-pigmented colony in each WT and \u0394dgeo_2840 strain. Two separate types of IS element have been integrated into non-pigmented clones: ISDge11 for WT and ISDge6 for \u0394dgeo_2840 strain. Surprisingly, \u0394dgeo_2840 mutant strain revealed higher resistance to oxidative stress than WT strain at late exponential growth phase. From the qRT-PCR analysis, OxyR (dgeo_1888) was highly up-regulated to 30-fold by oxidative stress through hydrogen peroxide treatment in both WT and \u0394dgeo_2840 mutant strains. However, the oxidative stress response enzyme, catalase or superoxide dismutase, was not significantly induced by overexpressed OxyR. Thus, a putative LysR family regulator Dgeo_2840 controlled the expression of ISDge6 type transposase and the induction of OxyR under oxidative condition. There is LysR family DNA-binding protein dependent active transposition of specific type IS and the up-regulated OxyR has not positively controlled ROS scavenger enzymes in D. geothermalis.Radiation-resistant bacterium s report , phytoen Deinococcus species can generally survive extreme and/or harmful conditions such as high stress of radiation, oxidative stress, desiccation, toxic substances, and starvation  DNA repair systems such as RDR regulon Rec, Ssb, Uvr, and Ddr proteins; (2) high efficiency of enzymatic reactions, such as catalase, peroxidase, and superoxide dismutase; (3) unique protective deinococcal proteins, such as Irr and Ppr, and DNA protection protein in stress condition such as Dps; (4) protective small molecules: pigment compound, such as carotenoids, metal ions, such as manganese, and other antioxidant systems including redox potential control by bacillithiol and cysteine residues; (5) stressors response regulators such OxyR, SoxRS, and RpoR , a non-specific DNA-binding protein (dps), alkyl hydroperoxide reductase (ahpCF), glutathione reductase (gorA), glutaredoxin 1 (grxA), and a small regulatory RNA (oxyS). In D. radiodurans, however, it represses two dps genes . During the oxidative stress resistance assay of \u0394dgeo_2840 mutant strain comparing to the response of wild-type strain, we identified two colorless mutants from wild-type and \u0394dgeo_2840 mutant strain. We hypothesized that IS transposition occurred in carotenoid biosynthesis pathway.The LysR-type transcriptional regulator (LTTR) family is the most abundant group of transcriptional regulators that are highly preserved in prokaryotes. LTTRs may act as the most common type of positive regulators and/or also as some diverse negatively regulated genes and functions. Some LTTRs control regulons that form complex regulatory network by other genes. On the other hand, others regulate themselves . The famps genes . For exarmitilis . However to H2O2 . The gensruption . Other LD. geothermalis genome, there are 19 IS types in a total of 73 IS elements. The maximum number of copies of IS type is 15 copies of ISDge2 (ISfinder)1. In general, IS element is transposed to other sites in genomic DNA by the oxidative stress via reactive oxygen species (ROS) which is produced from H2O2 and \u03b3-irradiation, high-temperature, and other DNA-damage toxic substances  scavengers in many non-phototrophic bacteria for cellular protection  is a simple transposable element consisting of a length less than 3,000 bp, with a single or two transposases and typical repeat sequences, such as terminal inverted repeat (TIR), and direct repeat (DR) sequences and is classified 30 family members followed criteria . Followibstances . Howeverbstances . We havereatment . ISDge7 t mutant . When caotection . Here, wD. geothermalis DSM 11300T was obtained from the Korean Agricultural Culture Collection (KACC 12208). D. geothermalis was cultured on TGY medium containing 1% tryptone, 0.5% yeast extract, and 0.1% glucose at 48\u00b0C. Escherichia coli DH5\u03b1 was used as a competent cell for the transformation of recombinant DNA, and grown on Luria-Bertani medium at 37\u00b0C. The dgeo_2840 gene locus disrupted mutant strain was constructed by integration of a kanamycin-resistance cassette into a target gene through homologous recombination, following previous study , was amplified from genomic DNA using target-region primers, and purified using a PCR purification kit . Firstly, the purified right border DNA fragments and plasmid pKatAPH3 were cleaved by XbaI-PstI, and ligated into a plasmid (named pKR2840). Then to yield pKRL2840 as a left border DNA fragment ligation, the purified DNA fragments and plasmid pKR2840 were digested with KpnI-SalI, ligated, and propagated in E. coli. The final recombinant plasmid pKRL2840 was purified from E. coli, and transformed into D. geothermalis competent cell using a CaCl2-dependent technique described previously , WTw, \u0394dgeo_2840 mutant, and \u0394dgeo_2840w were grown overnight in TGY broth at 48\u00b0C. Then the strains were inoculated to OD600 0.06 in TGY broth, and continuous growth of the strains was monitored hourly.To evaluate the growth curve of the wild-type and \u0394dgeo_2840 mutant of D. geothermalis were grown to an OD600 2.0 for early exponential phase or OD600 4.0 for late exponential phase in TGY broth at 48\u00b0C. The cells of identical OD600 2.0 from each culture were exposed to hydrogen peroxide with 50, 80, and 100 mM of final concentration, and incubated continuously for 1 h. The stressed cells were serially diluted 10-fold in buffered saline from 100 to 10\u20135. A 5 \u03bcl volume from each diluted suspension was spotted on the TGY agar plates, incubated at 48\u00b0C for 1 or 2 days, and analyzed by photography and colony counting.To evaluate viability test on hydrogen peroxide treatment, the WT and \u0394dgeo_2840 mutant D. geothermalis strains were harvested at OD600 4.0 in TGY broth at 48\u00b0C for RNA-Seq analysis. The total RNA was extracted by the RIDOEx reagent . The extracted total RNA was purified using an RNeasy Mini Purification Kit  and RNase-Free DNase I Set . We commissioned D. geothermalis bacterial RNA-Seq, and data analysis was performed using the ExDEGA analysis tool of e-biogen Co. (South Korea). The data discussed in this study have been deposited in NCBI\u2019s Gene Expression Omnibus , and the synthesized cDNAs were quantified by DeNovix (United States), normalized, and stored at \u221270\u00b0C, until real time PCR performance. Quantitative PCR was conducted using the RT-PCR machine . Relative gene expression levels were calculated using the comparative threshold cycle (\u0394\u0394CT) method and normalized to the expressed level of the gene encoding glyceraldehyde-3-phosphate dehydrogenase (GAPDH), such as a control for stable expression level on H2O2 treatment (2. The two-way ANOVA was used to test difference between the samples which were represented the means and standard deviations (SD) of three replicate experiments and it was considered to be significant at p < 0.05.To determine the expression levels of target transposase genes and oxidative stress related genes, we performed qRT-PCR, as previously reported . After creatment 2. The twdgeo_2840 gene was transformed into wild-type and \u0394dgeo_2840 mutant strain. The empty vector and recombinant DNA were well-transformed into wild-type strain and selected on chloramphenicol contained medium . Surprisingly, \u0394dgeo_2840 mutants showed better viability than WT under oxidative stress with variable H2O2 concentration in OD600 4.0 growth cells  containing typical DDE motif, terminal inverted repeat (TIR) sequences, such as \u201cAGACCtGCTGCGAAAcaAGGGGC,\u201d and direct repeat (DR) sequence such as \u201cTCA\u201d in \u0394dgeo_2840w mutant strain. This IS type has 335 aa-long Tnp with 5\u2032 extended 46 nt length and 3\u2032 extended 63 nt length at the end of ORF. This IS was integrated on the 66th nucleotides with counter transcriptional direction , pillin (Dgeo_2111), RpiR family regulator (Dgeo_2619), and cytochrome C (Dgeo_0015-0019 and Dgeo_1247-1251) or cytochrome D complexes-thiol reductant ABC transporter subunit CydD (Dgeo_2704-2706)  . Then a ion loci . In partion loci .Dge2, ISDge6, and ISDge11 are mainly up-regulated under oxidative stress condition .One of the implicational reasons for the slight slower growth of the LysR family member disrupted mutant was found from the RNA-Seq results. According to those results, the expression of the three related genes of cytochrome C or D complex, problems . The mutdgeo_2840 mutant strains were shown to be more resistant to oxidative stress than wild-type strain, but catalase (Dgeo_2728), such as a major oxidative stress protector enzyme, did not affect a gene expression level under H2O2 present condition. Because the general positive activator OxyR was known to respond immediately to oxidative stress or redox change, over 30-fold up-regulated OxyR (Dgeo_1888) was expected to be a global activator for the positive control of catalase expression in D. geothermalis. However, the up-regulated OxyR was not positive controlled. This phenomenon was also reported for Corynebacterium glutaricum. Despite oxyR gene being deleted, mutant strain showed higher resistance against H2O2 stress, as compared to WT strain  is an enzyme that converts phytoene to lycopene. Phytoene and other carotenoids, lycopene and phytofluene, were used for skin whitening, UV blocker, and also medical needs . In accoiodurans . Thus, thttps://www.ncbi. nlm.nih.gov/bioproject/PRJNA637617.The original contributions presented in the study are publicly available. This data can be found here: CL, KC, and S-JL designed the experiments, analyzed the data, and wrote the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest."}
+{"text": "Emerging evidence has shown that circular RNAs (circRNAs) play a crucial regulatory role in the occurrence and development of cancer. Exploring the roles and mechanisms of circRNAs in tumorigenesis and progression may help to identify new diagnostic markers and therapeutic targets. In the present study, we investigated the role and regulatory mechanism of hsa_circ_0004872 in gastric cancer (GC).qRT-PCR was used to determine the expression of hsa_circ_0004872 in GC tissues and cells. EdU, CCK-8, transwell and scratch wound healing assays were used to assess the role of hsa_circ_0004872 in GC cell proliferation, invasion and migration, respectively. Subcutaneous and tail vein tumor injections in nude mice were used to assess the role of hsa_circ_0004872 in vivo. RIP assay, biotin-coupled probe pull-down assay, FISH and luciferase reporter assay were performed to confirm the relationship between hsa_circ_0004872 and the identified miRNA. ChIP assay, luciferase reporter assay and western blot were used to determine the direct binding of Smad4 to the promoter of the ADAR1 gene.In this study, we found that hsa_circ_0004872 was dramatically downregulated in GC tissues compared with adjacent noncancerous tissues. The expression level of hsa_circ_0004872 was associated with tumor size and local lymph node metastasis. Enforced expression of hsa_circ_0004872 inhibited the proliferation, invasion and migration of GC cells, whereas knockdown of hsa_circ_0004872 had the opposite effects. Nude mice experiments showed that ectopic expression of hsa_circ_0004872 dramatically inhibited tumor growth and metastasis in vivo. Moreover, we demonstrated that hsa_circ_0004872 acted as a \u201cmolecular sponge\u201d for miR-224 to upregulate the expression of the miR-224 downstream targets p21 and Smad4. Importantly, we found that the RNA-editing enzyme ADAR1 inhibited hsa_circ_0004872 expression and further led to the upregulation of miR-224. Smad4, the downstream target of miR-224, could further affect hsa_circ_0004872 levels by directly binding to the promoter region of ADAR1 to inhibit ADAR1 expression.Our findings showed that hsa_circ_0004872 acted as a tumor suppressor in GC by forming a negative regulatory loop consisting of hsa_circ_0004872/miR-224/Smad4/ADAR1. Thus, hsa_circ_0004872 may serve as a potential biomarker and therapeutic target for GC.Supplementary information accompanies this paper at 10.1186/s12943-020-01268-5. Gastric cancer (GC) is one of the most common malignancies in the world and ranks third in terms of cancer-related deaths . DespiteCircular RNA (circRNA) is a class of single-stranded RNAs with development/ tissue-specific expression patterns in eukaryotic cells . CircRNAIn this study, we used next-generation sequencing of circRNAs to screen the differentially expressed circRNAs in GC and adjacent nontumor tissues and identified the downregulated hsa_circ_0004872 , whWe collected 76 pairs of GC tissues and corresponding adjacent noncancerous tissues from Shandong Cancer Hospital in 2012\u20132013 and Taian City Central Hospital in 2016\u20132017. None of the patients enrolled in the study received chemotherapy or radiotherapy before surgery, and there was no evidence of any other malignancies. For each tumor, age, sex, tumor size, invasiveness and regional and distant metastases were evaluated and described in supplementary Table The total RNA was isolated from the GC and adjacent nontumor tissue using TRIzol reagent  according to the manufacturer\u2019s instructions. The RNA purity and concentration were determined with NanoDrop ND-1000 (NanoDrop Thermo). The RNA integrity of the samples was assesed by denaturing agarose gel electrophoresis.The Ribo-Zero rRNA Removal Kit  was used to remove the rRNA. The high-throughput whole transcriptome sequencing and subsequent bioinformatics analysis were performed by Cloud-Seq Biotech  as previously reported . Paired-2.The human GC cell lines AGS, BGC-823, SGC-7901 and gastric epithelial immortalized GES-1 cells were purchased from the Cell Resource Center, Institute of Biochemistry and Cell Biology at the Chinese Academy of Science . All cells were cultured in RPMI-1640 medium  with 10% fetal bovine serum  and 100\u2009U/mL penicillin-streptomycin . All cells were cultured at 37\u2009\u00b0C with 5% COPlasmids were transfected into GC cells using X-tremeGENE HP Transfection Reagent . SiRNAs, miRNA mimics or inhibitors were transfected into cells using Lipofectamine 2000 . The experiment was performed according to the manufacturer\u2019s instructions. To construct the hsa_circ_0004872 stably overexpressing cell lines, we transfected the control vector pLCDH-circ or the hsa_circ_0004872 overexpression vector pLCDH-hsa_circ_0004872 into BGC-823 cells and then selected them with puromycin  for 2\u20133\u2009weeks until hsa_circ_0004872 was stably overexpressed in the cells.Xho I and Sal I sites. The pMIR-p21 and pMIR-Smad4 luciferase reporter plasmids were constructed by inserting the 3\u2019UTR fragment of p21 or Smad4 into the pMIR reporter vector  between the Spe I and Hind III sites. The miR-224 complementary sequence \u201cGTGACTT\u201d in hsa_circ_0004872 and the 3\u2032UTRs of p21 and Smad4 were mutated to remove the complementarity. The pGL3-ADAR1 luciferase reporter plasmid was constructed by inserting the promoter sequence of ADAR1 (+\u200912 to \u2212\u20091999) into the pGL3-basic vector  between the Mlu I and Hind III sites. All of the primer sequences are listed in Table The hsa_circ_0004872 overexpression vector pLCDH-hsa_circ_0004872 was constructed by BioSune  by inserting the sequence of hsa_circ_0004872 into the pLCDH-circ expression vector. The mutated vector was constructed by changing the binding site of miR-224 in hsa_circ_0004872\u201cGTGACTT\u201d into \u201cACAGTCC\u201d and inserting the mutated sequence into the pLCDH-circ expression vector. SiRNAs targeting hsa_circ_0004872 and the control siRNA were synthesized by Genepharma . All the siRNA sequences are listed in Table -\u25b3\u25b3Ct method. \u03b22-M and U6 were used as the internal control genes for mRNA and miRNA, respectively. For the RNase R treatment, 2\u2009\u03bcg total RNA was digested at 37\u2009\u00b0C for 20\u2009min and 70\u2009\u00b0C for 5\u2009min with 3\u2009U/\u03bcg RNase R . Then, the expression levels of linear mRNA and circular RNA were determined by qRT-PCR, RT-PCR\u00a0and Northern blot. For actinomycin D (ActD) treatment, 2\u2009\u03bcg/mLActD  for BGC-823 cells and 1\u2009\u03bcg/mL ActD for SGC-7901 cells were used. Cells were collected in a series of time intervals, and the expression of linear mRNA and circRNA was detected by qRT-PCR.The total RNA was isolated from tissues and cells using TRIzol reagent  according to the manufacturer\u2019s protocol. cDNA was synthesized with random primers or miRNA-specific stem-loop primers using a Revert Aid First Strand cDNA Synthesis kit . qRT-PCR was performed on the Bio-Rad CFX96TM Real-Time PCR System (Bio-Rad). Primers for mRNAs were synthesized by BioSune , and miRNA primers were synthesized by RiboBio . The relative RNA expression levels were analyzed using the 2Total proteins from GC cells were extracted with RIPA lysis buffer containing proteinase inhibitor. The protein concentrations were measured by the BCA reagent kit . The proteins were separated by SDS-PAGE and transferred to polyvinylidene difluoride membranes , which were blocked in 5% nonfat milk and then incubated with primary antibodies against p21 , Smad4 , HOXD10 , PTEN , ADAR1 , myc , \u03b2-actin  and GAPDH  at 4\u2009\u00b0C overnight. Then, the membranes were washed in Tris-buffered saline with Tween, incubated with anti-mouse or anti-rabbit horseradish peroxidase-conjugated secondary antibody for 1\u2009h at room temperature, and developed with the enhanced chemiluminescence method . \u03b2-Actin or GAPDH served as a loading control.IHC was performed by using the kit PV-9000  according to the manufacturer\u2019s protocol. Paraffin sections were incubated with primary antibodies against p21  or Smad4  at 4\u2009\u00b0C overnight. After washing in PBS, the sections were incubated with anti-mouse or rabbit horseradish peroxidase-conjugated secondary antibodies for 1\u2009h at room temperature. Then paraffin sections were stained with DAB and hematoxylin. Finally, the paraffin sections were covered with coverslips for microscopic  observation.The Cy3-labeled hsa_circ_0004872 probes and FITC-labeled hsa-miR-224 probes were designed and synthesized by Geneseed Biotechnology Co., Ltd. .The sequences of the probes are listed in Table The RIP assay was performed using the EZ-Magna RIP RNA-binding protein immunoprecipitation kit 17-701  according to the manufacturer\u2019s instructions. Briefly, BGC-823 cells were collected and lysed in RIP lysis buffer with protease and RNase inhibitors. The cell lysates were incubated with magnetic beads conjugated with Ago2 antibodies or IgG (Millipore) at 4\u2009\u00b0C overnight. Then, the beads were washed and incubated with proteinase K to remove proteins. Finally, RNA was extracted and subjected to RT-PCR and agarose gel electrophoresis analysis.The biotin-labeled hsa_circ_0004872 probe and the negative control probe were synthesized by RiboBio Biotech , and the sequences are listed in Table + nylon membrane (Millipore).The membranes were crosslinked, pre-hybridized and hybridized with DIG-labeled DNA probes overnight. After washing, the membranes was incubated with alkaline phosphatase (AP)-conjugated anti-DIG antibodies (Roche). Immunoreactive bands were visualized after adding the chemiluminescent substrate CSPD (Roche) followed by exposure in the exposure apparatus .The probe which spanned the back-splice junction of hsa_circ_0004872 was labeled with Digoxin. The probe sequences were listed in Table 4 cells/well). Negative control mimics or miR-224 mimics were cotransfected with the reporter plasmid into GC cells using Lipofectamine 2000. After 48\u2009h, the cells were collected and lysed with passive lysis buffer. The luciferase activities were assessed using the Dual Luciferase Assay Kit (Promega) according to the manufacturer\u2019s protocol. Firefly luciferase activity was normalized to Renilla luciferase activity.BGC-823 and SGC-7901 cells were seeded in 24-well plates (3\u2009\u00d7\u200910Treated GC cells were cross-linked in 1% (vol/vol) formaldehyde-containing medium for 10\u2009min at 37\u2009\u00b0C and then lysed and sonicated to disrupt chromatin DNA into fragments between 200 and 1000\u2009bp. ChIP was performed using the ChIP Assay 17\u2013295 kit  according to the manufacturer\u2019s protocol. An antibody against Flag  was used to immunoprecipitate the DNA fragments. The protein-DNA complexes were collected with protein A Sepharose beads, eluted and reverse cross-linked. The samples were extracted with Dr.GenTLE\u2122 Precipitation Carrier . The recovered DNA was resuspended in DDW and used as the template to amplify the ADAR1 promoter. The primer sequences are listed in Table Cell proliferation was detected with EdU and CCK-8 assays. The EdU assay was performed according to the protocol of the Cell-Light\u2122 EdU Apollo\u00ae567 In Vitro Imaging Kit . Briefly, the treated cells were seeded in 96-well plates and incubated with 50\u2009\u03bcM EdU for 2\u2009h at 37\u2009\u00b0C. After being fixed with 4% paraformaldehyde, the cells were exposed to 100\u2009\u03bcL of 1\u2009\u00d7\u2009Apollo\u00ae reaction cocktail and then incubated with 5\u2009\u03bcg/mL Hoechst 33342 to stain cell nuclei. Images were captured using a fluorescence microscope . The percentage of EdU-positive cells was defined as the proliferation rate. For the CCK-8 assay, the treated cells were seeded in 96-well plates and incubated with 100\u2009\u03bcL 10% CCK-8 solution for 4\u2009h at 37\u2009\u00b0C. The absorbance was measured at 450\u2009nm with an Infinite M200 spectrophotometer (Tecan). All of the experiments were repeated three times in triplicate.GC cells with different transfections were cultured in six-well plates at 37\u2009\u00b0C. Scratch wounds were created by using the fine end of 10-\u03bcL pipette tips. Images of migrated cells were captured under phase-contrast microscopy at different times.4 cells for the migration assay or 1\u2009\u00d7\u2009105 cells for the invasion assay were seeded into the upper compartment of a 24-well chamber with an 8.0\u2009\u03bcm pore . RPMI-1640 medium containing 20% FBS was added to the lower chambers as a chemoattractant. The cells were incubated for 48\u2009h for the invasion assay or 24\u2009h for the migration assay. Then, the cells on the upper surface of the polycarbonate membrane were removed with cotton swabs, while the cells on the lower side were fixed with 100% methanol, stained with 0.05% crystal violet and imaged under a microscope. The number of migrating cells was counted from three independent experiments.The invasion and migration assays were performed with a Transwell chamber coated with Matrigel  (for the invasion assay) or without Matrigel (for the migration assay). The transfected GC cells were collected and resuspended in serum-free RPMI-1640 medium. A total of 5\u2009\u00d7\u2009105) that were stably transfected with the hsa_circ_0004872 overexpression vector (pLCDH-hsa_circ_0004872) or control vector (pLCDH_circ) were subcutaneously injected into either side of the back of each mouse. Tumor size was monitored by measuring the length (L) and width (W) of the tumor every 3\u2009days with a caliper, and the tumor volume (V) was calculated with the formula V\u2009=\u20091/2\u2009\u00d7\u2009L\u2009\u00d7\u2009W2. Twenty days after injection, the mice were euthanized, and the tumors were weighed.Five-week-old female BALB/c nude mice were purchased from the Nanjing Biomedical Research Institute of Nanjing University . For the tumor formation experiment, BGC-823 cells (4\u2009\u00d7\u2009105) stably transfected with pLCDH-hsa_circ_0004872 or pLCDH_circ were injected into the tail veins of nude mice. After six weeks, the mice were sacrificed. The lungs of the nude mice were collected and stained with hematoxylin-eosin staining. All mice experiments were approved by the ethics committee of the School of Basic Medical Sciences, Shandong University and were performed according to the guidance of animal experiments in the Laboratory Animal Center of Shandong University.To investigate the effect of hsa_circ_0004872 on the metastatic ability of GC cells in nude mice, GC cells (6\u2009\u00d7\u200910http://www.circbase.org/). The target miRNAs of hsa_circ_0004872 were predicted with circular RNA interactome (https://circinteractome.nia.nih.gov), BIOINF (http://www.bioinf.com.cn/), and starBase (http://starbase.sysu.edu.cn/index.php). The binding sites of Smad4 in the ADAR1 promoter region were predicted with Jaspar (http://jaspar.genereg.net/).hsa_circ_0004872 sequence data were obtained from circBase  using a paired or unpaired t-test. Linear regression analysis was used to analyze the correlation between hsa_circ_0004872 and miR-224 expression and between Smad4 and ADAR1 expression in GC samples. Overall survival comparison between the high ADAR1 group and the low Smad4 group was conducted by log-rank (Mantel\u2013Cox) test in the Kaplan\u2013Meier plots. p\u2009<\u20090.0001) in GC tissues .The differentially expressed circRNAs were listed in Table ues Fig. . Therefop\u2009<\u20090.0001)   into the GC cells BGC-823 and SGC-7901. The transfection efficiency was validated by Northern blot and qRT-PCR Fig.\u00a0A. Then, p\u2009<\u20090.01)  Fig. C and D. 01) Fig.  E-H.Since the functions of circRNAs are usually related to their different localization in cells , we deteThen, we used different databases  to predict the potential miRNAs that bind with hsa_circ_0004872 Table  and founp\u2009=\u20090.0098)   Fig. A. Statis44) Fig. k. Taken Having confirmed the interaction of hsa_circ_0004872 and miR-224, we next sought to explore the biological role of miR-224 in gastric carcinogenesis and progression. We transfected the miR-224 mimics or inhibitor into GC cells. The transfection efficiency was verified by qRT-PCR Fig.\u00a0A. Then, Smad4 , 31, p21To determine whether miR-224 could directly target the 3\u2019UTRs of Smad4 and p21 in GC cells, we cloned the wild-type 3\u2032-UTR sequences and the mutant sequences of p21 and Smad4 to construct wild-type luciferase reporter vectors (pMIR-Smad4(p21)-3\u2019UTR-WT) and mutant vectors Fig. B. GC celWe further determined the effects of hsa_circ_0004872 on Smad4 and p21 expression and found that hsa_circ_0004872 knockdown reduced the expression of Smad4 and p21 in GC cells  and miR-224 mimics into GC cells to determine whether the tumor-suppressing effect of hsa_circ_0004872 could be blocked by miR-224 mimics. The results showed that miR-224 mimics could partly attenuate the inhibition of cell proliferation Fig.\u00a0a-c, invaWe next investigated whether hsa_circ_0004872-mediated upregulation of p21 and Smad4 can be abolished by miR-224 mimics. As shown in Fig. To investigate whether hsa_circ_0004872 suppressed the tumorigenesis and metastasis of GC cells in vivo, we performed subcutaneous injection and tail vein injection in nude mice. The subcutaneous injection results showed that the mean tumor volume in the hsa_circ_0004872 overexpression group was much smaller than that in the control group Fig.\u00a0a, b, andp\u2009<\u20090.0001)  Fig. A-F. Our 01) Fig. B, which Given that hsa_circ_0004872 acts as a \u201cmolecular sponge\u201d of miR-224 to upregulate the miR-224 target Smad4, we asked whether Smad4, as a transcription factor, can regulate ADAR1 to further lead to the dysregulation of hsa_circ_0004872. We used software to analyze the promoter of ADAR1 and found 5 potential binding sites of Smad4 at the region from nucleotide \u2212\u20091993 to \u2212\u20091981 (Site A), \u2212\u20091959 to \u2212\u20091947 (Site B), \u2212\u20091590 to \u2212\u20091578 (Site C), \u2212\u20091280 to \u2212\u20091268 (Site D) and\u2009\u2212\u2009760 to \u2212\u2009748 (Site E). Then, we performed a ChIP assay to determine the binding of Smad4 on the promoter of ADAR1. We transfected BGC-823 cells with the overexpression vector of Smad4 (p-3\u2009\u00d7\u2009flag-CMV-Smad4) or the control vector (p-3\u2009\u00d7\u2009flag-CMV) and used the flag antibody to precipitate the promoter fragment spanning these five sites. The results illustrated in Fig.\u00a0To further determine whether Smad4 regulates ADAR1 and hsa_circ_0004872 expression, we transfected p-3\u2009\u00d7\u2009flag-CMV-Smad4 or p-3\u2009\u00d7\u2009flag-CMV-14 into GC cells. Western blot and qRT-PCR results showed that Smad4 overexpression led to a significant downregulation of the protein and mRNA levels of ADAR1 Fig. C. BGC-82p\u2009<\u20090.0001)   and found that the expression of Smad4 was lower in GC tissues than in adjacent\u00a0nontumor tissues (01) Fig. f. Statis01) Fig. g. Kaplan01) Fig. h\u00a0and sur01) Fig. i indicatCircular RNA (circRNA) is a kind of single-stranded RNA that is widely found in eukaryotic cells . In contIt is well known that exonic circRNAs are usually located in the cytoplasm. Our FISH experiment also showed that hsa_circ_0004872 mainly existed in the cytoplasm. The most frequently reported function of exonic circRNAs is their role as an \u201cmiRNA sponge\u201d to bind with miRNAs and inhibit the function of the miRNAs, thus protecting the target genes from miRNA-mediated degradation . For exaWe next sought to elucidate the biological role and mechanism of miR-224 in GC cells and found that miR-224 exerted critical functions in GC progression and metastasis by targeting p21 and Smad4. p21 is an inhibitor of the cell cycle and affects the progression of the cell cycle, leading to the inhibition of cell proliferation . Smad4 iIt has been reported that the formation of circRNAs is regulated by the regulatory proteins MBl , QKI 3636, 37, aWe further investigated whether Smad4, as a transcription factor, was able to regulate ADAR1 expression, thereby forming a regulatory feedback loop of Smad4/ADAR1/hsa_circ_0004872/miR-224/Smad4. We used the Jaspar database to predict the binding sites of Smad4 in the promoter region of ADAR1 and found five potential sites. As expected, our results showed that Smad4 overexpression decreased the expression level of ADAR1 and increased the level of hsa_circ_0004872 in GC cells. ChIP and dual luciferase reporter experiments verified that Smad4 could bind to the promoter region of ADAR1 to regulate its expression in GC cells. Furthermore, according to the public database NCBI GEO (GSE66229), the expression level of Smad4 was significantly low in GC tissues. Statistical analysis showed that there was a remarkable negative correlation between ADAR1 and Smad4 in GC tissues. The Kaplan-Meier Plotter Database indicated that higher expression of ADAR1 or lower expression of Smad4 in GC tissues resulted in a shorter survival period.In summary, in this study, we investigated the expression, role and regulatory mechanism of the tumor-suppressor circular RNA hsa_circ_0004872 in GC. We found that hsa_circ_0004872 was dramatically downregulated in GC tissues, which was due at least partially to the overexpression of ADAR1. hsa_circ_0004872 inhibited the proliferation, invasion and migration of GC cells by acting as a \u201cmiRNA sponge\u201d to bind with miR-224 and increase the expression of the endogenous miR-224 targets p21 and Smad4. Moreover, Smad4, as a transcription factor, could also regulate hsa_circ_0004872 expression by directly binding to the promoter region of ADAR1 and decreasing its expression. Therefore, we revealed a novel regulatory feedback loop formed by hsa_circ_0004872/miR-224/Smad4/ADAR1 in GC. Our findings may provide new ideas and targets for the diagnosis and treatment of GC.Additional file 1: Figure S1. qRT-PCR analysis of the expression of hsa_circ_0004872, hsa_circ_0002483,hsa_circ_0000847, hsa_circ_0001566 in 42 paired GC tissues and corresponding nontumor tissues.Additional file 2: Figure S2. qRT-PCR analysis of the expression of hsa_circ_0004872 in BGC-823 and SGC-7901 cells transfected with hsa_circ_0004872 overexpression vector (pLCDH-circ_4872) (A) or the miR-224 binding site mutated hsa_circ_0004872 overexpression vector ((pLCDH-circ_4872-Mut) (B).Additional file 3: Figure S3. hsa_circ_0004872 siRNAs promote the proliferation, invasion and migration of GC cells. (A) Schematic representation of the siRNA sequences specifically target the junction site of hsa_circ_0004872. (B) qRT-PCR analysis of hsa_circ_0004872 and MAPK1 mRNA level in BGC-823 cells transfected with hsa_circ_0004872 siRNAs (si-circ_4872) or the control siRNA. (C) EdU analysis of the cell proliferation ability in BGC-823 cells transfected with the si-circ_4872 or the control siRNA.Representative images are shown. Scale bar: 20\u2009\u03bcm. (D) Statistical analysis of the EdU-positive cell ratio in the cells transfected with si-circ_4872 or the control siRNA. (E) The scratch wound healing assays in BGC-823 cells transfected with the si-circ_4872 or the control siRNA. Scale bar: 500\u2009\u03bcm. (F) Statistical analysis of the cell migration in the scratch wound healing assays.The data are expressed as the means\u00b1SD from three experiments. (G) Transwell invasion and migration assay in BGC-823 cells transfected with the si-hsa_circ_0004872 or the control siRNA. Scale bar: 100\u2009\u03bcm. (H) Statistical analysis of the cell numbers passing through the transwell chamber in the transfected BGC-823 cells. The data are expressed as the means\u00b1SD from three experiments.Additional file 4: Figure S4. qRT-PCR analysis of the expression of hsa_circ_0004872 in different GC cells.Additional file 5: Figure S5. Schematic diagam of dual luciferase vector. (A) Schematic diagam of dual luciferase vector pMIRGLO-circ_4872-WT/Mut. Upper: diagram of the luciferase reporter construct containing the sequences of hsa_circ_0004872. The mutations were generated at the predicted miR-224 binding sites in the hsa_circ_0004872 sequences. Lower: the predicted complementary sequences of miR-224 in the sequences of hsa_circ_0004872. (B) Schematic diagam of dual luciferase vector pMIR-Smad4(p21)-WT/Mut. Upper: diagram of the luciferase reporter construct containing 3\u2019UTR sequences of Smad4 (p21). The mutations were generated at the predicted miR-224 binding sites located in the 3\u2019UTR of Smad4(p21). Lower: the predicted complementary sequences of miR-224 in the 3\u2019UTR of Smad4 (p21). (C) Schematic diagram of dual luciferase vector pGL3-ADAR1-WT/Mut. Upper: diagram of the luciferase reporter construct containing promoter sequence of ADAR1. The mutations were generated at the predicted Smad4 binding sites located in promoter sequence of ADAR1. Lower: the predicted complementary sequences of Smad4 in promoter sequence of ADAR1.Additional file 6: Figure S6. qRT-PCR analysis of the expression of miR-224 (A) and ADAR1 (B) in 39 paired GC tissues and corresponding nontumor tissues.Additional file 7: Figure S7. miR-224 inhibitor inhibited the proliferation, invasion and migration in GC cells (A) The expression level of miR-224 was analyzed with qRT-PCR in BGC-823 and SGC-7901 cells transfected with miR-224 inhibitor or the control inhibitor. (B) EdU analysis of the cell proliferation ability in BGC-823 and SGC-7901 cells transfected with miR-224 inhibitor or the control inhibitor. Scale bar: 20\u2009\u03bcm. (C) Statistical analysis of the EdU-positive cell ratio in the transfected cells. (D) CCK-8 analysis of the cell proliferation ability in BGC-823 and SGC-7901 cells transfected with miR-224 inhibitor or the control inhibitor. (E) The scratch wound healing assays of the migration ability in transfected BGC-823 and SGC-7901 cells. Scale bar: 500\u2009\u03bcm. (F) Statistical analysis of the scratch wound healing assays. (G) Transwell assay of the migration (without matrigel) and invasion ability (with matrigel) in BGC-823 and SGC-7901 cells transfected with miR-224 inhibitor or the control inhibitor. Scale bar: 100\u2009\u03bcm. (H) Statistical analysis of the cell numbers passing through the transwell chamber in the transfected BGC-823 and SGC-7901 cells. All datas were the means \u00b1 SD.Additional file 8: Figure S8. The expression of ADAR1, MBl and QKI were analyzed in NCBI GEO database GSE27342 and GSE66229. (A) The expression level of ADAR1 was analyzed with paired t-tests  in GEO database GSE27342. (B) The expression level of ADAR1 was analyzed with unpaired t-tests  in GSE66229. (C) The expression level of MBL was analyzed with paired t-tests  in GSE27342. (D) The expression level of MBL was analyzed with unpaired t-tests  in GSE66229. (E) The expression level of QKI was analyzed with paired t-tests  in GSE27342. (F) The expression level of QKI was analyzed with unpaired t-tests  in GSE66229.Additional file 9: Table S1. Patients, tumor characteristics and\u00a0hsa_circ_0004872 expression in GC samples.Additional file 10: Table S2. siRNA sequences in the research.Additional file 11: Table S3. Primer sequences in this study.Additional file 12: Table S4. Probe sequences of hsa_circ_0004872 and miR-224 in this study.Additional file 13: Table S5. CircRNA-seq analysis of the differentially expressed circRNAs in GC tissue and corresponding nontumor tissue.Additional file 14: Table S6. Predicted miRNAs with potential binding ability with hsa_circ_0004872 in different databases."}
+{"text": "We present an image dataset related to automated segmentation and counting of macrophages in diffuse large B-cell lymphoma (DLBCL) tissue sections. For the classification of DLBCL subtypes, as well as for providing a prognosis of the clinical outcome, the analysis of the tumor microenvironment and, particularly, of the different types and functions of tumor-associated macrophages is indispensable. Until now, however, most information about macrophages has been obtained either in a completely indirect way by gene expression profiling or by manual counts in immunohistochemically (IHC) fluorescence-stained tissue samples while automated recognition of single IHC stained macrophages remains a difficult task. In an accompanying publication, a reliable approach to this problem has been established, and a large set of related images has been generated and analyzed.Provided image data comprise (i) fluorescence microscopy images of 44 multiple immunohistostained DLBCL tumor subregions, captured at 4 channels corresponding to CD14, CD163, Pax5, and DAPI; (ii) \u201dcartoon-like\u201d total variation\u2013filtered versions of these images, generated by Rudin-Osher-Fatemi denoising; (iii) an automatically generated mask of the evaluation subregion, based on information from the DAPI channel; and (iv) automatically generated segmentation masks for macrophages (using information from CD14 and CD163 channels), B-cells (using information from Pax5 channel), and all cell nuclei (using information from DAPI channel).A large set of IHC stained DLBCL specimens is provided together with segmentation masks for different cell populations generated by a reference method for automated image analysis, thus featuring considerable reuse potential. We present an image dataset generated as a part of an accompanying publication, which is concerned with method development and comparison for automated segmentation and counting of macrophages in diffuse large B-cell lymphoma (DLBCL) tissue sections . DLBCL iUntil now, most information about macrophages has been obtained either by gene expression profiling  or by maOur dataset contains monochrome fluorescence microscopy images of 44 DLBCL tissue samples wherein different macrophage populations (using antibodies against CD14 and CD163) and B-cells (using antibody against Pax5) as well as all cell nuclei  have been stained and imaged at different wavelengths. Furthermore, we supply processed images, comprising \u201dcartoon-like\u201d total variation-filtered images (generated by Rudin-Osher-Fatemi [ROF] filtering), as well as results of the automated macrophage segmentation. For this publication, we completed these data by automated segmentation of B-cells and the cell nuclei.avoid headline numbering at all.RRID:AB_2827391; 1:10), CD163 , and Pax5  labeled with donkey anti-rabbit Alexa 488, donkey anti-mouse Alexa 555, and donkey anti-goat Alexa 647  as secondary antibodies. Subsequently, the slices were incubated with DAPI  and cover-slipped with mounting medium. Use of tissue was in accordance with the guidelines of the internal review board of the Medical Faculty of the Christian-Albrechts-University Kiel, Germany (No. 447/10).From the files of the Lymph Node Registry Kiel, 44 DLBCL biopsy specimens have been selected. For every specimen, from formalin-fixed paraffin-embedded tissue a slice of 2 \u00b5m thickness has been obtained. To detect specific macrophages and their relation to B-cells, a triple IHC staining was performed, using primary antibodies against CD14  staining in a neighboring reference slice. Subsequently, within the IHC stained slice, a rectangular subregion of the tumor area was selected, taking care for acceptable tissue and staining quality. Maximum size of tumor subregions is 10 mm2 in all images.Images of tumor subregions within the IHC stained slides were captured by Hamamatsu Nanozoomer 2.0 RS slide scanner  with 20\u00d7 magnification at 4 wavelengths, resulting in single images for the CD14, CD163, Pax5, and DAPI channels, respectively, which were saved in .ndpi output format with default settings as used in clinical trial routine. Note that, at this point, moderate built-in compression by imaging device was accepted. Single-channel raw images were converted into .tif format without further compression and sliced into tiles of 1,000 \u00d7 1,000 pixel format , using the software package ImageJ with the extension ndpitools _type__mode_.png. The size of losslessly compressed .png image files has been minimized by application of the OptiPNG routine [specimen_xx_tile_yy_zz__logfile.txt is provided, containing detailed information about procedures, parameters, and results of automated segmentation.Image data are organized by tissue specimens (top-level folders) and tiles (second-level folders), the latter ordered by position. Top-level folders are named Although there is a vast number of publications concerned with the composition of the tumor microenvironment in various types of lymphoma disease, image datasets of IHC stained cancer tissue are rarely publicly accessible if at all . Most dWe outline the most important options for further use of the dataset. First, it allows for a detailed morphometrical investigation of the imaged macrophages and B-cells with respect to the distribution of geometrical parameters such as size, diameter, and perimeter, as well as to overall shape patterns. Second, the data may be used for validation, calibration, and comparison of cell segmentation methods  and related software packages, making available a large reference dataset together with the output of a reference method as described in . Note thTo illustrate the described reuse potential, we include a set of composite figures, each combining information from several separate images. Fig.\u00a0To improve reusability, BLC2 scores for the biopsy specimens are provided..zip file and bears a separate identifier, e.g.,\u00a0https://health-atlas.de/lha/7YXMMFNPDG-0 within the repository  repository  and can  as well .DAPI: 4\u00b4,6-diamidino-2-phenylindole; DLBCL: diffuse large B-cell lymphoma; H&E: hematoxylin-eosin; IHC: immunohistochemical(ly); LHA: Leipzig Health Atlas; ROF: Rudin-Osher-Fatemi.Tissue usage is covered by statement No.\u00a0447/10 of the internal review board of the Medical Faculty of the Christian-Albrechts-University Kiel, Germany.The authors declare that they have no competing interests.giaa016_GIGA-D-19-00311_Original_SubmissionClick here for additional data file.giaa016_GIGA-D-19-00311_Revision_1Click here for additional data file.giaa016_GIGA-D-19-00311_Revision_2Click here for additional data file.giaa016_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giaa016_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giaa016_Reviewer_1_Report_Original_SubmissionChris Armit -- 9/23/2019 ReviewedClick here for additional data file.giaa016_Reviewer_1_Report_Revision_1Chris Armit -- 1/16/2020 ReviewedClick here for additional data file.giaa016_Reviewer_2_Report_Original_SubmissionGuy M Hagen, PHD -- 9/26/2019 ReviewedClick here for additional data file.giaa016_Reviewer_2_Report_Revision_1Guy M Hagen, PHD -- 1/13/2020 ReviewedClick here for additional data file."}
+{"text": "Paralimna  concors was sequenced. The mitogenome was 16,155\u2009bp totally, consisting of 13 protein-coding genes (PCGs), two rRNAs, and 22 transfer RNAs. The nucleotide composition biases toward A and T is 78.6\uff05 of the entirety. All PCGs start with ATN codons except COI and ND1, and end with TAA or incomplete stop codon. Phylogenetic analyses based on 11 dipteran species supported the monophyly of Ephydroidea and the relationship of Opomyzoidea + (Ephydroidea + (Lauxanioidea + (Sphaeroceroidea + (Sciomyzoidea\u2009+\u2009Tephritoidea)))).The mitogenome of  Ephydridae are often dull and dark colored, but unusually diverse in body structure, vestiture, and ornamentation  concors (Voucher number: CAU-YDEPHY-Para-1) used for this study were collected from 2\u2009W Hown District , Pak Beng, Laos, on 25 Jun 2015. The specimens were identified by Liang Wang and deposited in the Entomological Museum of China Agricultural University (CAU).The adult specimens of de novo assembler IDBA_UD  concors (MT938921) was 16,154\u2009bp in length and consisted of 13 typical invertebrate PCGs, 22 transfer RNA genes, two rRNA genes (12S and 16S), and part control region, which were similar to other Diptera flies reported before .The genomic DNA was extracted from adult\u2019s whole body using the DNeasy DNA Extraction kit (TIANGEN) and stored at \u221220\u2009\u00b0C refrigerator. DNA samples were pooled for next-generation sequencing library construction following the method of Gillett et\u00a0al. . All quaAnopheles oryzalimnetes NC_030715, Bactrocera cucurbitae NC_016056.1, Ceratitis capitata NC_000857, Drosophila melanogaster NC_024511, Drosophila yakuba NC_001322, Ilythea japonica MT_527723, Liriomyza trifolii NC_014283, Nemopoda mamaevi NC_026866, *Paralimna  concors MT938921, Simosyrphus grandicornis NC 008754.1, Suillia sp. MN026917. Thirteen PCGs were used to reconstruct phylogenetic relationship with the maximum likelihood method using IQTREE Web Server (http://iqtree.cibiv.univie.ac.at/) ))) was supported.There are 10 species retrieved from NCBI and one new sequenced datum used in phylogenetic analysis. The genbank accession numbers are listed as follows: Paralimna  concors provides valuable information for future genetic and evolutionary studies of family Ephydridae and superfamily Ephydroidea.The complete mitochondrial genome of"}
+{"text": "The Funding statement is incorrect. The correct Funding statement is as follows: A.A: This project was funded by the Armed Forces Health Surveillance Division, Global Emerging Infections Surveillance (GEIS) Branch, ProMIS ID P0072_19_NS."}
+{"text": "Radiomic features, extracted from positron emission tomography, aim to characterize tumour biology based on tracer intensity, tumour geometry and/or tracer uptake heterogeneity. Currently, radiomic features are derived from static images. However, temporal changes in tracer uptake might reveal new aspects of tumour biology. This study aims to explore additional information of these novel dynamic radiomic features compared to those derived from static or metabolic rate images.18F]FDG PET/CT scans. Spatial intensity, shape and texture radiomic features were derived from volumes of interest delineated on static PET and parametric metabolic rate PET. Dynamic grey level cooccurrence matrix (GLCM) and grey level run length matrix (GLRLM) features, assessing the temporal domain unidirectionally, were calculated on eight and sixteen time frames of equal length. Spearman\u2019s rank correlations of parametric and dynamic features with static features were calculated to identify features with potential additional information. Survival analysis was performed for the non-redundant temporal features and a selection of static features using Kaplan-Meier analysis.Thirty-five patients with non-small cell lung carcinoma underwent dynamic [parametric features showed moderate correlations with corresponding static features (\u03c1\u22650.61), all other features showed high correlations (\u03c1>0.7). Dynamic features are robust independent of frame duration. Five out of 22 dynamic GLCM features showed a negligible to moderate correlation with any static feature, suggesting additional information. All sixteen dynamic GLRLM features showed high correlations with static features, implying redundancy. Log-rank analyses of Kaplan-Meier survival curves for all features dichotomised at the median were insignificant.Three out of 90 static features, some dynamic GLCM radiomic features show different information, whereas parametric features provide minimal additional information. Future studies should be conducted in larger populations to assess whether there is a clinical benefit of radiomics using the temporal domain over traditional radiomics.This study suggests that, compared to  In the field of radiomics, researchers aim to find stable and clinically relevant image-derived biomarkers, so-called radiomic features, that provide a non-invasive way of quantifying and monitoring tumour characteristics in clinical practice , 2. For spatial distribution of radiotracer uptake, but do not take into account tracer uptake heterogeneity over time, while this might contain additional information concerning tumour biology.Traditional radiomic features describe heterogeneity along the Research into these so-called temporal radiomics is limited. There are some studies that apply texture feature analysis on parametric images in magnetic resonance imaging (MRI)  and PET et al. have investigated the use of 4D texture analysis in dynamic contrast-enhanced MRI fluoro-2-deoxy-D-glucose ([18F]FDG) positron emission tomography scans combined with X-ray computed tomography (PET/CT) of a previously published prospective cohort FDG PET/CT with additional histologic staging of the mediastinum or other sites suspicious for cancer when necessary. Only tumours that were considered resectable, with a diameter larger than 30 mm were included to minimize the partial volume effect and be able to quantify heterogeneity [Dynamic 2-[e cohort  were anaand 2014 . All patogeneity . Clinica18F]FDG PET/CT scan with the primary tumour located centrally in the field of view using either the Biograph Duo (n = 21) or Biograph 40 mCT with TrueV z-axis gantry extension (n = 17) . Details on patient preparation, data acquisition and image reconstruction can be found in the original publication [18F]FDG.Within 7 days of surgery, patients underwent a dynamic [lication  and was lication . This re18F]FDG PET image. The resulting voxel sizes were 2.56\u00d72.56\u00d73.38 and 1.59\u00d71.59\u00d72.03 mm3 for the Biograph Duo PET/CT and Biograph 40 mCT PET/CT, respectively. Parametric glucose metabolic rate (glcMR) images were computed based on image-derived tissue and blood time-activity concentration curves using Patlak method, with data acquired between 15 and 60 min normalized Patlak-time, using the Patlak slope , assuming a lumped constant of 1 and considering the plasma glucose concentration measured prior to [18F]FDG-injection FDG is reached after 10\u201315 min of Patlak time, corresponding to approximately 10\u201315 min in real time FDG-avid non-tumour tissue by drawing an oversized container around the tumour and surrounding tissue by a radiation oncologist under supervision of an experienced nuclear medicine physician FDG PET/CT to death of any cause, censoring all patient that were alive at the closeout date (July 30th 2018).Survival data are presented using Kaplan-Meier estimators. Overall survival is defined from date of the FDG PET/CT scans in patients with non-small cell lung carcinoma, certain dynamic GLCM radiomic features show different information than traditional radiomic features. These novel dynamic features are robust to an alternation in the frame duration. Features from parametric images only demonstrated minimal additional information. Future studies should assess whether there is a clinical benefit of radiomic features from dynamic images compared to traditional features derived from static images.In dynamic Reviewers' comments:Reviewer's Responses to QuestionsComments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: YesReviewer #2: YesReviewer #3: YesReviewer #4: Yes**********2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: YesReviewer #2: NoReviewer #3: YesReviewer #4: Yes**********3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data\u2014e.g. participant privacy or use of data from a third party\u2014those must be specified.The Reviewer #1: YesReviewer #2: YesReviewer #3: NoReviewer #4: No**********4. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: YesReviewer #3: YesReviewer #4: Yes**********5. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: The authors presented the possibility of adding PET radiomics features by additional information of temporal dynamic PET images. Currently, static information was used in the texture analysis for the radiomics analysis. However, kinetic information from the PET study may have significant data as a phenotyping information.The authors describe the possibility of the use in PET radiomics using dynamic and kinetic data on PET.They included only a 35-patients with NSCLC and dynamic FDG PET/CT, many kinds of additional parameters may have a additional information compared to the static PET images.This is an preliminary report, but well-designed, prospective study with a meaningful suggestion for the use of total body PET with real dynamic PET study. Thus, this paper is very helpful for the development of imaging biomarker as well as PET radiomics phenotyping study.Reviewer #2: This manuscript focuses on the development of a new method to evaluate the tumor heterogeneity based on dynamic 18F-FDG PET/CT images. Authors showed that new \u201cdynamic\u201d features can be extracted with a moderate correlation with \u201cstatic\u201d features.Even if this new approach is promising, authors failed to demonstrate any added value. This may be because the cohort is too small or because the features do not carry information related to the patient survival. Worse, perhaps these new features do not reflect relevant biological information. Did the authors test the link between the \u201cdynamic\u201d features and the tumor histology or the differentiation for instance?For the survival analysis, the authors do not seem to have taken into account the pleural invasion or the treatment (chemotherapy and/or radiation therapy), event though it is known that this strongly influences the prognosis.This study may be too preliminary and requires further investigations to demonstrate a clinical benefit.Reviewer #3: The paper addressed radiomics analysis on dynamic PET studies and include the temporal domain (either by using dynamic frames or using patlak Ki images). The paper is of interest as it generates some hypotheses and new ideas on the use of dynamic information for radiomics studies. As there is not much clinical benefit I would recommend to also describe the difference in RF values when analysed on Ki and SUV images Main comments:VOIs \u2013 it seems that previously defined vois were reused or were these newly drawn?. Any differences in voi when defined on static and parametric images?. Did the authors redraw voi in the dynamic frames? If not, which one was used for dynamic analysis?Bin width \u2013 I understand that for parametric images and for dynamic frames you cannot use the SUV=0.5 bin width, but you can estimate the slope between SUV and KI and then estimate how to convert SUV=0.5 bins into Ki bins. I recommend to do such an analysis to see if RF values become more comparable when you try to match the bin width (taking into account the different units of SUV and Ki).Likewise, you can derive how many bins you got per static SUV image and use that number to process the dynamic frames or parametric images (for that patient).Apart from assessing spearman correlations, it would be nice to demonstrate the difference in RF values, eg by using a distance metric or else, but it will likely require the above suggested bin width adaptions as well.You have mainly stage 2 subjects. There is no difference in KM plots. Maybe add some case control evaluations by looking at the feature values for stage 1 versus stage 3 and see if there are any features that are significantly different between these 2 more extreme cases\u2026I realize it is only about 7 subjects per group (in this case), but it would give an hypothesis if some features might have prognostic value?Likewise, you can take the 25% short survivors and 25% long survivors and see if there is any difference between these groups.Minor comments:Abstract,results: 3 out of 90 show moderate correlation, so the others were highly correlated. please state so.Two scanners are used. Was there any cross-calibration between the systems. They do not find significant differences .Patlak images \u2013 which software was used?2 software packages were used for RF. Any chance of different implementation issues? Did the authors compare results from the 2 packages.Reviewer #4: In the manuscript\u201eAdding the temporal domain to PET radiomic features\u201d,the authors analyze the effect of introducing the temporal domain in the assessment of radiomics features in PET data. This is a new and innovative idea in radiomics analysis of PET data and of high interest for the community. The manuscript is very well written and good to understand. I just have some minor issues that should be changes:- How were the blood time-activity concentration curves estimated, by blood-sampling or image-based; if so, was the left ventricle used?- The resolution of figure 4 is way to low, it is hardly possible to see anything. Pleas also include the risk tables below the Kaplan-Meier curves.**********what does this mean?). If published, this will include your full peer review and any attached files.6. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool,\u00a0 23 Jul 2020Article ID: PONE-D-20-05431Title: Adding the temporal domain to PET radiomic featuresWe would like to thank the reviewers for their helpful comments which have improved the quality of the paper. The specific comments of the reviewers are in blue/italic below together with their responses. Unfortunately, we did not manage to get the text in blue/italic in this editor, but this version can be found in the attached files. We have changed the manuscript accordingly (changes are marked in red).Response to Journal requirements:1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found athttps://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf andhttps://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdfWe apologize for missing these files when submitting the manuscript. The manuscript is now updated according to the style requirements.2. In the ethics statement in the manuscript and in the online submission form, please provide additional information about the patient records used in your retrospective study, including:a) whether all data were fully anonymized before you accessed them andb) the date range (month and year) during which patients' medical records were accessed.After informed consent, relevant patient data were included in the case report forms by the treating physicians. Follow-up data were updated in July 2018. Clinical data were pseudonymized before radiomic analysis. Imaging data (DICOM files) were left unchanged, as advanced quantitative analysis was part of the original study for which the patients provided written informed consent and as DICOM anonymization might remove DICOM tags that are crucial for absolute [18F]FDG quantification. After data extraction all further analyses were performed pseudonymized. Imaging data were accessed between January and October 2018. Pseudonymized clinical data and radiomic features were assessed between January 2018 and February 2020.3. Please confirm in your methods section and ethics statement that the 'Commission on Medical Research Involving Human Subjects Region Arnhem-Nijmegen' consists of a committee of experts that reviewed and approved your study.In addition, please clarify whether the present retrospective study was also granted ethical approval, in addition to the original study, and whether the previous prospective cohort were recruited by the same authors.In the present study, we performed an additional analysis of this previously published cohort. For the current study ethical approval was granted within the approval of the original prospective study. The application included quantitative image analysis, as implemented in this study. The prospective cohort was recruited by the same authors .4. Thank you for stating in your Funding Statement:'Dennis Vriens was supported in part by the Netherlands Organisation for Health Research and Development (ZonMw) stipends for Qualified Doctor Training to become a Clinical Researcher (AGIKO) (project no. 92003552) for design and data collection of the original clinical study.'http://journals.plos.org/plosone/s/submit-now a. Please provide an amended statement that declares *all* the funding or sources of support  received during this study, as detailed online in our guide for authors at Please also include the statement \u201cThere was no additional external funding received for this study.\u201d in your updated Funding Statement. b. Please include your amended Funding Statement within your cover letter. We will change the online submission form on your behalf. Thank you, the funding statement was changed.http://journals.plos.org/plosone/s/supporting-information5. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: Thank you for pointing this out, the supporting information files were added as captions at the end of the manuscript, in line with the Supporting Information guidelines.\u2003Response to comments to the author:Comments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: YesReviewer #2: YesReviewer #3: YesReviewer #4: Yes________________________________________2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: YesReviewer #2: NoReviewer #3: YesReviewer #4: Yes________________________________________3. Have the authors made all data underlying the findings in their manuscript fully available?The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data\u2014e.g. participant privacy or use of data from a third party\u2014those must be specified.Reviewer #1: YesReviewer #2: YesReviewer #3: NoReviewer #4: No ________________________________________4. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: YesReviewer #3: YesReviewer #4: Yes________________________________________\u20035. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: The authors presented the possibility of adding PET radiomics features by additional information of temporal dynamic PET images. Currently, static information was used in the texture analysis for the radiomics analysis. However, kinetic information from the PET study may have significant data as a phenotyping information.The authors describe the possibility of the use in PET radiomics using dynamic and kinetic data on PET.They included only a 35-patients with NSCLC and dynamic FDG PET/CT, many kinds of additional parameters may have a additional information compared to the static PET images.This is an preliminary report, but well-designed, prospective study with a meaningful suggestion for the use of total body PET with real dynamic PET study. Thus, this paper is very helpful for the development of imaging biomarker as well as PET radiomics phenotyping study.We would like to thank the reviewer for the compliments and endorsement of the potential benefit of these novel dynamic radiomics features in future total body PET studies.\u2003Reviewer #2: This manuscript focuses on the development of a new method to evaluate the tumor heterogeneity based on dynamic 18F-FDG PET/CT images. Authors showed that new \u201cdynamic\u201d features can be extracted with a moderate correlation with \u201cstatic\u201d features.Even if this new approach is promising, authors failed to demonstrate any added value. This may be because the cohort is too small or because the features do not carry information related to the patient survival. Worse, perhaps these new features do not reflect relevant biological information. Did the authors test the link between the \u201cdynamic\u201d features and the tumor histology or the differentiation for instance?We would like to thank the reviewer for this suggestion. Unfortunately, homogeneous and large cohorts of patients who underwent dynamic PET scans are scarce. We investigated the association of the parametric, dynamic and traditional quantitative PET features with clinical parameters. We added supporting file 3 to the manuscript, where these findings are presented. Table 1 of S3 presents the association with clinical parameters. Two parametric features and the SUVmax showed significant differences in mean between adenocarcinoma and squamous cell carcinoma, but the ranges of the values are overlapping, indicating that these parameters cannot be used to discriminate between both histopathological subtypes. These findings were added to the manuscript .For the survival analysis, the authors do not seem to have taken into account the pleural invasion or the treatment (chemotherapy and/or radiation therapy), event though it is known that this strongly influences the prognosis.The reviewer is correct. We did not take into account these factors, because we only performed Kaplan-Meier analysis. In table 1 we added the univariate and multivariate Cox regression analysis of the clinical characteristics. In our population, only one variable, the age of diagnosis, is significantly associated with survival. Table 1: Univariate and multivariate Cox regression analysis of clinical characteristics, traditional quantitative PET features and selected radiomic features for overall survival (OS), disease-free survival (DFS) and disease-specific survival (DSS). Characteristics and features with a p-value < 0.20 in univariate analysis, were selected for forward and backward multivariate analysis based on the likelihood ratio. Pleural invasion and adjuvant chemotherapy were removed from the model in both forward and backward multivariate Cox regression. Hazard ratio  p-valueUnivariate Cox regression analysisGender  1.937 (0.545 - 6.889) 0.298Age of diagnosis (years) 1.086 (1.014 - 1.164) 0.015Stadium  IB 1.000 0.697 IIA 1.514 (0.252 - 9.100)  IIB 1.081 (0.198 - 5.922)  IIIA 2.233 (0.449 - 11.112) Pleural invasion 0.486 (1.175 - 1.349) 0.157Negative resection margin 0.594 (0.074 - 4.756) 0.620Adjuvant chemotherapy 2.390 (0.796 - 7.181) 0.109Post-operative radiotherapy 0.849 (0.189 - 3.809) 0.830Traditional quantitative PET featuresSUVmax (g/mL) 1.029 (0.957 - 1.106) 0.432MTV (mL) 1.000 (1.000 - 1.000) 0.606TLG (g) 1.000 (1.000 - 1.000) 0.611Parametric features GLRLM SRLGLE 5.645 (0.000 - 94206.982) 0.727GLSZM SAE 17.494 (0.099 - 3083.885) 0.279GLSZM SALGLE 1.615 (0.016 - 160.357) 0.838Dynamic GLCM Correlation 0.253 (0.000 - 1287.290) 0.253IMC1 18.304 (0.002 - 149105.596) 0.526IMC2 1.925 (0.000 - 182838.748) 0.911IDMN 0.000 (0.000 - 4.23E+73) 0.714IDN 0.000 (0.000 - 2.339E+17) 0.663Multivariate Cox regression analysisIterative forward selection Age of diagnosis (years) 1.083 (1.009 - 1.163) 0.027Pleural invasion Adjuvant chemotherapy Iterative backward selectionAge of diagnosis (years) 1.083 (1.009 - 1.163) 0.027Pleural invasion Adjuvant chemotherapy This study may be too preliminary and requires further investigations to demonstrate a clinical benefit.We would like to thank the reviewer for the helpful suggestions and hope that by adding these additional analyses to the manuscript we have improved its quality. We agree that the study is too preliminary to show clinical benefit and that this study only shows that some dynamic GLCM radiomic features show different information. We further agree that future studies with a larger cohort should be conducted to show the additional clinical benefit of these dynamic features. This is acknowledged in the discussion.\u2003Reviewer #3: The paper addressed radiomics analysis on dynamic PET studies and include the temporal domain (either by using dynamic frames or using patlak Ki images). The paper is of interest as it generates some hypotheses and new ideas on the use of dynamic information for radiomics studies. As there is not much clinical benefit I would recommend to also describe the difference in RF values when analysed on Ki and SUV images We would like to thank the reviewer for the helpful recommendations. We hope to have sufficiently addressed all of the reviewers concerns below.Main comments:VOIs \u2013 it seems that previously defined vois were reused or were these newly drawn?. Any differences in voi when defined on static and parametric images?. Did the authors redraw voi in the dynamic frames? If not, which one was used for dynamic analysis?The reviewer is correct, we reused the VOIs that were previously defined in the original study. These were defined on the static images and parametric images separately. For the current analysis, we copied them unchanged to the static and parametric images. To avoid that the delineation would change between different frames and thereby impact feature extraction, we reused the static VOIs as VOIs for the dynamic frames. So these VOIs were also not redrawn. This can be found in lines 152-156 of the manuscript.Bin width \u2013 I understand that for parametric images and for dynamic frames you cannot use the SUV=0.5 bin width, but you can estimate the slope between SUV and KI and then estimate how to convert SUV=0.5 bins into Ki bins. I recommend to do such an analysis to see if RF values become more comparable when you try to match the bin width (taking into account the different units of SUV and Ki).Likewise, you can derive how many bins you got per static SUV image and use that number to process the dynamic frames or parametric images (for that patient).Thank you for the interesting suggestion, we did not consider this. The graph in figure 1 (left) shows how the SUVmean translates to the mean glucose metabolic rate of the parametric images for all patients in our study. We used the slope of this graph to calculate the parametric bin width, which would be 0.0296*0.5 = 0.016 \u00b5mol/mL/min. We used the same approach for the dynamic frames, as an example, figure 1 (right) shows how the static SUVmean translates to the SUVmean in frame 62, which would result in a bin width of 0.7038*0.5 = 0.35 g/mL. We did this for all dynamic frames. It turns out that these bin widths are very similar to the bin widths calculated with the Freedman-Diaconis rule, especially, when we calculate the bin widths using the slope and a static bin width of 0.55 g/mL, as used in our study. Table 2 shows the calculated slopes and bin widths and the bin widths calculated with the Freedman-Diaconis rule, for all used images. Since these values are quite similar, we did not change the bin widths, but we did mention this in our manuscript (lines: 354-363).Figure 1. Right: Calculation of the slope of the static SUVmean and the mean glucose metabolic rate for all subjects. Left: Calculation of the slope of the static SUVmean and the SUVmean of dynamic frame 62. The slopes can be used to translate the bin width of the static images to parametric and dynamic bin widths, respectively.Table 2: Calculated slopes and slope bin widths and bin widths calculated using the Freedman-Diaconis rule.Image Static MRglu F46 F48 F50 F52 F54 F56 F58 F60Slope 0.0296 0.4317 0.4666 0.5017 0.5442 0.5723 0.606 0.6434 0.6755Bin width slope 0.55 0.016 0.24 0.26 0.28 0.30 0.32 0.33 0.35 0.37Bin width Freedman-Diaconis 0.55 0.018 0.26 0.29 0.31 0.34 0.36 0.36 0.37 0.4Image F62 F63 F64 F65 F66 F67 F68 F69Slope 0.7038 0.7368 0.7542 0.7794 0.8025 0.8189 0.856 0.8814Bin width slope 0.39 0.40 0.42 0.43 0.44 0.45 0.47 0.49Bin width Freedman-Diaconis 0.42 0.44 0.48 0.49 0.5 0.51 0.53 0.54Apart from assessing spearman correlations, it would be nice to demonstrate the difference in RF values, eg by using a distance metric or else, but it will likely require the above suggested bin width adaptions as well.In the past we have investigated comparing features using distance metrics, but it is difficult to interpret these results for the different radiomic features, since mathematical definitions vary largely from feature to feature. You have mainly stage 2 subjects. There is no difference in KM plots. Maybe add some case control evaluations by looking at the feature values for stage 1 versus stage 3 and see if there are any features that are significantly different between these 2 more extreme cases\u2026I realize it is only about 7 subjects per group (in this case), but it would give an hypothesis if some features might have prognostic value?Likewise, you can take the 25% short survivors and 25% long survivors and see if there is any difference between these groups.We would like to thank the reviewer for this suggestion. We added supporting file 3 to the manuscript. Table 1 of S3 presents the association with clinical parameters. Unfortunately, no significant differences were found.Minor comments:Abstract,results: 3 out of 90 show moderate correlation, so the others were highly correlated. please state so.We added this for clarity (line 43). Two scanners are used. Was there any cross-calibration between the systems. They do not find significant differences .The scanners were not used at the same time, so cross-calibration between the scanners has not been performed. However, both scanners were EARL accredited and were cross-calibrated with the dose calibrator. The same dose calibrator was used throughout the whole study. Patlak images \u2013 which software was used?The Patlak analysis was performed in Inveon Research Workplace . This was added to Appendix S1, which contains more information on image acquisition and reconstructions.2 software packages were used for RF. Any chance of different implementation issues? Did the authors compare results from the 2 packages.The main difference between PyRadiomics 1.3 and 2.0 is the calculation of the matrices from which the features are extracted. In PyRadiomics 1.3, these matrices are calculated in Python, while in PyRadiomics 2.0, the matrices are calculated in C. We adjusted PyRadiomics 1.3 for the extraction of the dynamic features. For comparison, table 3 presents radiomic features calculated for the PyRadiomics example data, for version 1.3 and 2.0. No differences were found between both implementations. This was added in line 200 of the manuscript.Table 3. Feature values for PyRadiomics version 1.3 and 2.0 and differences between implementations.Feature Version 1.3 Version 2.0 DifferenceImage lung1_image.nrrd lung1_image.nrrd Mask lung1_label.nrrd lung1_label.nrrd general_info_BoundingBox   general_info_EnabledImageTypes {'Original': {}} {'Original': {}} general_info_GeneralSettings {'minimumROIDimensions': 1, 'minimumROISize': None, 'normalize': False, 'normalizeScale': 1, 'removeOutliers': None, 'resampledPixelSpacing': None, 'interpolator': 'sitkBSpline', 'preCrop': False, 'padDistance': 5, 'distances': [1], 'force2D': False, 'force2Ddimension': 0, 'resegmentRange': None, 'label': 1, 'additionalInfo': True, 'voxelBased': False} {'minimumROIDimensions': 1, 'minimumROISize': None, 'normalize': False, 'normalizeScale': 1, 'removeOutliers': None, 'resampledPixelSpacing': None, 'interpolator': 'sitkBSpline', 'preCrop': False, 'padDistance': 5, 'distances': general_info_ImageHash 34dca4200809a5e76c702d6b9503d958093057a3 34dca4200809a5e76c702d6b9503d958093057a3 general_info_ImageSpacing   general_info_MaskHash 054d887740012177bd1f9031ddac2b67170af0f3 054d887740012177bd1f9031ddac2b67170af0f3 general_info_NumpyVersion 1.19.0 1.19.0 general_info_PyWaveletVersion 1.1.1 1.1.1 general_info_SimpleITKVersion 1.2.4 1.2.4 general_info_Version 1.3.0 2.0.0 general_info_VolumeNum 1 1 general_info_VoxelNum 837 837 original_shape_Elongation 0.718791031 0.718791031 0original_shape_Flatness 0.514335768 0.514335768 0original_shape_LeastAxis 8.936318224 8.936318224 0original_shape_MajorAxis 17.37448332 17.37448332 0original_shape_Maximum2DDiameterColumn 16.04444054 16.04444054 0original_shape_Maximum2DDiameterRow 13.53756348 13.53756348 0original_shape_Maximum2DDiameterSlice 15.97893091 15.97893091 0original_shape_Maximum3DDiameter 18.18259471 18.18259471 0original_shape_MinorAxis 12.48862278 12.48862278 0original_shape_Sphericity 0.75931875 0.75931875 0original_shape_SurfaceArea 782.241458 782.241458 0original_shape_SurfaceVolumeRatio 0.574671403 0.574671403 0original_shape_Volume 1361.197815 1361.197815 0original_firstorder_10Percentile -245.4 -245.4 0original_firstorder_90Percentile 71 71 0original_firstorder_Energy 16291991 16291991 0original_firstorder_Entropy 4.020834927 4.020834927 0original_firstorder_InterquartileRange 198 198 0original_firstorder_Kurtosis 2.695927096 2.695927096 0original_firstorder_Maximum 106 106 0original_firstorder_MeanAbsoluteDeviation 105.0944475 105.0944475 0original_firstorder_Mean -63.9080048 -63.9080048 0original_firstorder_Median -31 -31 0original_firstorder_Minimum -506 -506 0original_firstorder_Range 612 612 0original_firstorder_RobustMeanAbsoluteDeviation 81.58090535 81.58090535 0original_firstorder_RootMeanSquared 139.5161078 139.5161078 0original_firstorder_Skewness -0.73366595 -0.73366595 0original_firstorder_TotalEnergy 26495367.44 26495367.44 0original_firstorder_Uniformity 0.074426645 0.074426645 0original_firstorder_Variance 15380.51125 15380.51125 0original_glcm_Autocorrelation 411.4164748 411.4164748 0original_glcm_ClusterProminence 9732.694396 9732.694396 0original_glcm_ClusterShade -345.713367 -345.713367 0original_glcm_ClusterTendency 58.74756668 58.74756668 0original_glcm_Contrast 20.7134493 20.7134493 0original_glcm_Correlation 0.470613617 0.470613617 0original_glcm_DifferenceAverage 3.216603092 3.216603092 0original_glcm_DifferenceEntropy 3.187524502 3.187524502 0original_glcm_DifferenceVariance 9.381995813 9.381995813 0original_glcm_Id 0.417361964 0.417361964 0original_glcm_Idm 0.344350177 0.344350177 0original_glcm_Idmn 0.972582394 0.972582394 0original_glcm_Idn 0.899630707 0.899630707 0original_glcm_Imc1 -0.17331187 -0.17331187 0original_glcm_Imc2 0.818766382 0.818766382 0original_glcm_InverseVariance 0.278697167 0.278697167 0original_glcm_JointAverage 20.04512484 20.04512484 0original_glcm_JointEnergy 0.017918271 0.017918271 0original_glcm_JointEntropy 6.932828996 6.932828996 0original_glcm_MaximumProbability 0.089125606 0.089125606 0original_glcm_SumAverage 40.09024968 40.09024968 0original_glcm_SumEntropy 4.635501946 4.635501946 0original_glcm_SumSquares 19.865254 19.865254 0original_gldm_DependenceEntropy 6.550399892 6.550399892 0original_gldm_DependenceNonUniformity 120.9761051 120.9761051 0original_gldm_DependenceNonUniformityNormalized 0.144535371 0.144535371 0original_gldm_DependenceVariance 18.00291477 18.00291477 0original_gldm_GrayLevelNonUniformity 62.29510155 62.29510155 0original_gldm_GrayLevelVariance 24.73367791 24.73367791 0original_gldm_HighGrayLevelEmphasis 383.9199522 383.9199522 0original_gldm_LargeDependenceEmphasis 37.44922342 37.44922342 0original_gldm_LargeDependenceHighGrayLevelEmphasis 20425.03584 20425.03584 0original_gldm_LargeDependenceLowGrayLevelEmphasis 0.075562484 0.075562484 0original_gldm_LowGrayLevelEmphasis 0.005605862 0.005605862 0original_gldm_SmallDependenceEmphasis 0.318186003 0.318186003 0original_gldm_SmallDependenceHighGrayLevelEmphasis 84.05116859 84.05116859 0original_gldm_SmallDependenceLowGrayLevelEmphasis 0.003630175 0.003630175 0original_glrlm_GrayLevelNonUniformity 48.265238 48.265238 0original_glrlm_GrayLevelNonUniformityNormalized 0.066018368 0.066018368 0original_glrlm_GrayLevelVariance 24.66124095 24.66124095 0original_glrlm_HighGrayLevelRunEmphasis 362.3993952 362.3993952 0original_glrlm_LongRunEmphasis 1.756790194 1.756790194 0original_glrlm_LongRunHighGrayLevelEmphasis 758.781125 758.781125 0original_glrlm_LongRunLowGrayLevelEmphasis 0.007773425 0.007773425 0original_glrlm_LowGrayLevelRunEmphasis 0.006164929 0.006164929 0original_glrlm_RunEntropy 4.555631762 4.555631762 0original_glrlm_RunLengthNonUniformity 602.3643647 602.3643647 0original_glrlm_RunLengthNonUniformityNormalized 0.819694212 0.819694212 0original_glrlm_RunPercentage 0.868853966 0.868853966 0original_glrlm_RunVariance 0.382061813 0.382061813 0original_glrlm_ShortRunEmphasis 0.920028572 0.920028572 0original_glrlm_ShortRunHighGrayLevelEmphasis 322.2128305 322.2128305 0original_glrlm_ShortRunLowGrayLevelEmphasis 0.005979334 0.005979334 0original_glszm_GrayLevelNonUniformity 18.29530201 18.29530201 0original_glszm_GrayLevelNonUniformityNormalized 0.061393631 0.061393631 0original_glszm_GrayLevelVariance 21.55781271 21.55781271 0original_glszm_HighGrayLevelZoneEmphasis 262.7449664 262.7449664 0original_glszm_LargeAreaEmphasis 93.21812081 93.21812081 0original_glszm_LargeAreaHighGrayLevelEmphasis 51136.9698 51136.9698 0original_glszm_LargeAreaLowGrayLevelEmphasis 0.184875307 0.184875307 0original_glszm_LowGrayLevelZoneEmphasis 0.010725736 0.010725736 0original_glszm_SizeZoneNonUniformity 138.7248322 138.7248322 0original_glszm_SizeZoneNonUniformityNormalized 0.465519571 0.465519571 0original_glszm_SmallAreaEmphasis 0.709478222 0.709478222 0original_glszm_SmallAreaHighGrayLevelEmphasis 170.3079618 170.3079618 0original_glszm_SmallAreaLowGrayLevelEmphasis 0.009509532 0.009509532 0original_glszm_ZoneEntropy 5.514483642 5.514483642 0original_glszm_ZonePercentage 0.356033453 0.356033453 0original_glszm_ZoneVariance 85.32918562 85.32918562 0original_ngtdm_Busyness 0.19930534 0.19930534 0original_ngtdm_Coarseness 0.008985148 0.008985148 0original_ngtdm_Complexity 617.8974921 617.8974921 0original_ngtdm_Contrast 0.183769662 0.183769662 0original_ngtdm_Strength 2.786428904 2.786428904 0\u2003Reviewer #4: In the manuscript\u201eAdding the temporal domain to PET radiomic features\u201d,the authors analyze the effect of introducing the temporal domain in the assessment of radiomics features in PET data. This is a new and innovative idea in radiomics analysis of PET data and of high interest for the community. The manuscript is very well written and good to understand. I just have some minor issues that should be changes:We would like to thank the reviewer for this positive feedback.- How were the blood time-activity concentration curves estimated, by blood-sampling or image-based; if so, was the left ventricle used?We would like to thank the reviewer for this suggestion, we indeed forgot to mention this in the manuscript. The image-derived input function was based on a 10 mL VOI of the descending aorta on which endothelial wall and calcifications were excluded to identify only blood, drawn on the images obtained during the first 60 seconds. We added this to S1, which contains more information on image acquisition and reconstruction.- The resolution of figure 4 is way to low, it is hardly possible to see anything. Pleas also include the risk tables below the Kaplan-Meier curves.We thank the reviewer for pointing this out, we have resolved this issue by using a larger font size and larger images. Also, the survival curves have been moved to an additional supporting information file (S3), combined with the association of radiomic features with other clinical parameters.6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.If you choose \u201cno\u201d, your identity will remain anonymous but your review may still be made public.Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.Reviewer #1: NoReviewer #2: NoReviewer #3: NoReviewer #4: NoAttachment20200720 point by point response temporal PET radiomics.docxSubmitted filename: Click here for additional data file. 7 Sep 2020Adding the temporal domain to PET radiomic featuresPONE-D-20-05431R1Dear Dr. Noortman,We\u2019re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you\u2019ll receive an e-mail detailing the required amendments. When these have been addressed, you\u2019ll receive a formal acceptance letter and your manuscript will be scheduled for publication.http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. 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+{"text": "Dynamic somatosensory evoked potentials (DSSEP) can be used to disclose abnormalities of ascending sensory pathways at dynamic positions and diagnose cervical spondylotic myelopathy (CSM). However, radiographic tests including magnetic resonance imaging (MRI) and dynamic X-ray are used much more widely in the management of CSM. Our study aims to clarify the correlations between several radiographic parameters and the DSSEP results, and further determine their reliability with clinical data.We retrospectively enrolled 38 CSM patients with surgical intervention. DSSEP tests were performed before surgery. Amplitude ratios of DSSEP N13 and N20 waves at extension and flexion were calculated and recorded as N13_E, N20_E, N13_F, N20_F, respectively. Baseline severity was evaluated with the modified Japanese Orthopedic Association (mJOA) score and the Nurick grades. Prognosis was evaluated based on the 2-year recovery rate. Sagittal diameter and transverse areas of the cord and canal were measured and the the compressive ratios at the compressed site (Compression_Ratio), central , and 1/4-lateral points , and spinal cord/Canal Area Ratio were calculated. The intramedullary T2 hyperintensity patterns (Ax-CCM types) were also collected from MRI axial images. Dynamic X-rays were used to test for segmental instability of the cervical spine. The correlations between radiologic findings, DSSEP data, and clinical assessments were investigated.p\u2009<\u20090.05) and Ax-CCM types  in MRI axial images and cervical segmental instability in dynamic X-ray . Apart from the 1/4-Lateral_Compression_Ratio, these radiographic parameters above also correlated with the baseline clinical assessments  and postoperative recovery rate .We found that DSSEP N13_E and N13_F correlated with the Compression_Ratio, Central_Ratio, 1/4-Lateral_Compression_Ratio (Pearson, We found that the preoperative Compression_Ratio, Central_Ratio and 1/4-Lateral_Compression_Ratio in MRI and cervical segmental instability in dynamic X-ray could reflect the dynamic neural dysfunction of the spinal cord. Different Ax-CCM types corresponded to different DSSEP results at extension and flexion, suggesting divergent pathophysiology. These radiographic parameters could help evaluate disease severity and predict postoperative prognosis. In cervical spondylotic myelopathy (CSM) patients, cervical myelopathy is caused by both static compressions as well as dynamic compression during cervical motion (flexion/extension) . At a neMagnetic resonance imaging (MRI) is widely used in the management of CSM. There have been some studies on the associations between MRI factors, including relating cord compression and signal changes of the spinal cord on T1- and T2-weighted imaging and clinical symptoms and recovery after surgery . From thSomatosensory evoked potentials (SSEP) have been utilized as useful neurophysiological indicators to detect objective functional abnormalities of the spinal cord , 12. It The Human Ethics Committee of the First Affiliated Hospital of Sun Yat-sen University approved the trial, and informed consent was acquired before the DSSEP tests. The single-center retrospective study included 38 CSM patients with preoperative DSSEP, MRI, and dynamic X-ray tests and later had surgery at the Spine Surgery Department, First Affiliated Hospital of Sun Yat-sen University  between 2015 to 2017. All participants had at least two years follow-up. Patients with congenital spinal deformity, a history of stroke, surgical treatment, peripheral neurological disease, ulnar or carpal tunnel syndrome, or diabetes were excluded. Demographic data collected included sex, age, and critical comorbidities. Measures of neurological disability included the modified Japanese Orthopaedic Association (mJOA) score  and NuriAn electrophysiological monitoring system (Nicolet Endeavor CR) was used to elicit and record the SSEPs. Median and ulnar nerve SEPs were examined using established methods . The ulnThe amplitudes for each recording position labeled Erb\u2019s, C2s-Fz, and C4\u2019-C3\u2019 were recorded as N9, N13, and N20, respectively. We also simultaneously recorded the response latencies. Both the ulnar and median nerve stimuli were recorded. According to our previous study , only thAll MR examinations were performed with a 3.0-T MR imager (Siemens Trio) with the patients lying in a supine position on a spine-array coil. The authors evaluated compressed spinal cords using standard imaging sequences. T1 and T2-weighted spin-echo sagittal sequences were performed using the following parameters: T1: TR 650, TE 10, Slice thickness 3\u2009mm, dist 0.3\u2009mm gap, FA 150\u00b0, TA 1\u203236\u2033, Matrix 320\u2009\u00d7\u2009224; T2: TR 2800, TE 97, Slice thickness 3\u2009mm, dist 0.3\u2009mm gap, FA 160\u00b0, TA 2\u203210\u2033, Matrix 384\u2009\u00d7\u2009308. T1 and T2-weighted spin-echo axial sequences were performed using the following parameters: T1: TR 466, TE 11, Slice thickness 4\u2009mm, dist 0.4\u2009mm gap, FA 120\u00b0, TA 2\u203226\u2033, Matrix 256\u2009\u00d7\u2009205; T2: TR 3260, TE 90, Slice thickness 4\u2009mm, dist 0.4\u2009mm gap, FA 150\u00b0, TA 1\u203252\u2033, Matrix 320\u2009\u00d7\u2009256.The measurements of the cervical spinal cord in MRI T2WI axial images were performed with Photoshop CC . The transverse area of the spinal canal and spinal cord were respectively measured. The Cord/Canal_Area_Ratio was defined as follows: Cord/Canal_Area_Ratio\u2009=\u2009Area of the spinal cord / Area of the canal. The linear parameters including transverse diameter (TD), central sagittal diameter (CSD) and sagittal diameter (SD) were measured, and the Central_Ratio, Compression_Ratio and 1/4-Lateral_Compression_Ratio were calculated  X-ray studies of all 38 patients were analyzed. Cervical segmental instability was determined according to the White-Panjabi standard : (1) traThe MRI and dynamic X-ray films of the cervical spines were studied three times by two spine surgeons and a radiologist, and the mean values were used. The measurements were performed in a blind fashion, so that patients\u2019 names, clinical characteristics, and the results of the electrophysiological studies were unknown to the observers.Amplitudes of left and right sides of the DSSEP N13 or N20 waves were averaged first. Then the N13 or N20 amplitude ratios at extension and flexion were calculated and recorded as N13_E, N20_E, N13_F, N20_F, respectively. We defined the DSSEP amplitude ratio as the following: amplitude ratio\u2009=\u2009\u2009\u00d7\u2009100%. For absent N13 or N20 waves, their latencies were excluded and their amplitudes were set as the baseline value (0\u2009mV) for statistical analysis . For CSMP-value <\u20090.05 was considered significant. The statistical software R (R version 3.6.0) was used for statistical analysis.Student t-test, Pearson or Spearman correlation method and one-way analysis of variance analysis (ANOVA) were applied in this study. All data were presented as mean\u2009\u00b1\u2009SD, and a p\u2009<\u20090.001) and flexion  positions , negative correlation with Nurick grades  and positive correlation with recovery rates . However, only the N13_E in ataxic patients was significantly lower than that in patients without ataxia . The N13_F did not show significant difference between the ataxia and non-ataxia groups .We compared the DSSEP results with our clinical assessments. We found that the N13 DSSEP amplitude ratios at both extension and flexion positions positively correlated with the baseline clinical symptoms and postoperative outcomes: results having positive correlation with mJOA scores , Central_Ratio  and 1/4-Lateral_Compression_Ratio  measured in MRI axial images. There was no correlation between DSSEP N13 amplitude ratios and spinal cord area, Cord/Canal Area Ratio or Sagittal Diameter  A post hoc test showed that the N13_E for the type 1 pattern was significantly lower than that for the type 2 pattern (p\u2009<\u20090.05) , but not for N13_E   Fig. b.p\u2009<\u20090.05), Nurick grades  and recovery rates . The 1/4-Lateral_Compression_Ratio was significantly correlated with the recovery rates , but not with baseline mJOA scores and Nurick grades. Patients\u2019 mJOA scores, Nurick grades and recovery rates were all significantly different among different Ax-CCM groups . The mJOA scores, Nurick grades and recovery rates in patients with or without cervical segmental instability were also significantly different   had their electro-neurophysiology most severely impacted at extension. While patients with focal intramedullary T2 hyperintensity and faint borders (Ax-CCM type 2) had their DSSEP changed most greatly at flexion. Our findings suggested that the compressive degree evaluated by the three mentioned MRI measurements and segmental instability evaluated by dynamic X-rays were associated with the extent of the dynamic neural deficit of the chronic compressive spinal cord. Patients\u2019 different MRI intramedullary T2 hyperintensity patterns may suggest different modes of transient dynamic spinal cord injuries and distinct pathophysiology. These radiographic findings might have good potentials in aiding the diagnosis and prognostication of CSM clinically.Our previous study has already proven the diagnostic effect of DSSEP N13 amplitude ratios . TransieCompression of the spinal cord does not always cause clinical symptoms and it is difficult to infer the degree of dysfunction of the spinal cord from MRI findings. In this study, we used N13 DSSEP amplitude ratios as a tool for assessing the dynamic neurological deficit of CSM, and compared them with MRI measurements. We found both the N13_E and N13_F positively correlated with the Compression_Ratio, Central_Ratio and 1/4-Lateral_Compression_Ratio measured in MR axial images. Our results suggested that the extent of dynamic neural dysfunction of the cord was associated with the degree of spinal cord deformity caused by central canal stenosis as well as lateral compression. The Compression_Ratio and Central_Ratio are sensitive markers for myelopathy , which hp\u2009<\u20090.05) among different Ax-CCM groups. The N13_E and N13_F in the Ax-CCM Type 1 and 2 groups were lower than that in the other two groups, suggested spinal cords in these groups were more vulnerable during motion. Interestingly, we found patients in Ax-CCM Type 1 group were most severely affected at extension, while the patients in Ax-CCM Type 2 group were at flexion. According to You et al., the type 1 pattern seems to indicate an acute, transient, and recuperative cord injury with relatively good circulation [Intramedullary T2WI hyperintensity has been proved to be a sensitive marker for severe myelopathy and poor prognosis by many studies \u201334, and culation , making culation . The vasculation . In the culation . This caculation , 42. If We also found the N13_F were significantly lower among patients with cervical segmental instability, while the N13_E did not show a significant difference. We assume it was because patients with cervical segmental instability suffered greater subluxation and (or) rotation instability during flexion movements. During extension, the relative movements between the adjacent vertebrae of the unstable segment are generally milder, because the superior and inferior articular processes from adjacent vertebrae can help stabilize the cervical segment. Therefore, for CSM patients with segmental instability, cervical canal stenosis and neurological deteriorations mainly occurred at the flexion position.Finally, we compared the radiographic data with baseline mJOA scores, Nurick grades and 2-year postoperative recovery rates. Compression_Ratio, Central_Ratio, 1/4-Lateral_Compression_Ratio, Ax-CCM types and cervical segmental instability in radiographs were statistically related to recovery rates. Apart from the 1/4-Lateral_Compression_Ratio, all these parameters also correlated with preoperative clinical assessments. These findings further testified the clinical utility of these radiographic parameters.Our study had several limitations. Firstly, the reference electrode for the N13 should be located beyond the scalp, such as the anterior neck. We would modify our DSSEP method in our future studies. Secondly, this is a single-center retrospective study, which is limited in depth. A prospectively designed study with dynamic SSEPs is warranted to address this problem. Thirdly, not all patients received uniformed treatment, therefore we could not rule out other factors that different surgical methods may have on patient outcomes. Lastly, the sample size of the study was relatively small, limiting statistical power. In the future, we plan to expand the sample size to confirm the efficacy of our method.The spinal cord compression degree parameters including the Compression_Ratio, Central_Ratio and 1/4-Lateral_Compression_Ratio and the Ax-CCM classification system in MRI axial images as well as cervical segmental instability in dynamic X-ray corresponded to the dynamic neurological deficit of CSM. They could serve as substitutes to evaluate neurological damage in the absence of DSSEP to some extent, and reflect symptom severity and predict prognosis. Different intramedullary T2 hyperintensity patterns were associated with different DSSEP characteristics at extension and flexion, suggesting their differing pathophysiology.Additional file 1."}
+{"text": "However, the cross-talk and potential roles of these \u201cwriters\u201d in the tumor microenvironment (TME), drug sensitivity, and immunotherapy remain unknown.The four major RNA\u00a0adenosine modifications, i.e., mWe systematically characterized mRNA expression and genetic alterations of 26 RNA modification \u201cwriters\u201d in colorectal cancer (CRC), and evaluated their expression pattern in 1697 CRC samples from 8 datasets. We used an unsupervised clustering method to assign the samples into two patterns of expression of RNA modification \u201cwriters\u201d. Subsequently, we constructed the RNA modification \u201cwriter\u201d Score (WM_Score) model based on differentially expressed genes (DEGs) responsible for the RNA modification patterns to quantify the RNA modification-related subtypes of individual tumors. Furthermore, we performed association analysis for WM_Score and characteristics of TME, consensus molecular subtypes (CMSs), clinical features, transcriptional and post-transcriptional regulation, drug response, and the efficacy of immunotherapy.We demonstrated that multi-layer alterations of RNA modification \u201cwriter\u201d are associated with patient survival and TME cell-infiltrating characteristics. We identified two distinct RNA modification patterns, characterized by a high and a low WM_Score. The WM_Score-high group was associated with worse patient overall survival and with the infiltration of inhibitory immune cells, such as M2 macrophages, EMT activation, and metastasis, while the WM_Score-low group was associated with a survival advantage, apoptosis, and cell cycle signaling pathways. WM_Score correlated highly with the regulation of transcription and post-transcriptional events contributing to the development of CRC. \ufeffIn response to anti-cancer drugs, WM_Score highly negatively correlated (drug sensitive) with drugs which targeted oncogenic related pathways, such as MAPK, EGFR, and mTOR signaling pathways, positively correlated (drug resistance) with drugs which targeted in apoptosis and cell cycle. Importantly, the WM_Score was associated with the therapeutic efficacy of PD-L1 blockade, suggesting that the development of potential drugs targeting these \u201cwriters\u201d to aid the clinical benefits of immunotherapy.Our study is the first to \ufeffprovide a comprehensive analysis of four RNA modifications in CRC. We revealed the potential function of these writers in TME, transcriptional and post-transcriptional events, and identified their therapeutic liability in targeted therapy and immunotherapy. This work highlights the cross-talk and potential clinical utility of RNA modification \u201cwriters\u201d in cancer therapy.The online version contains supplementary material available at 10.1186/s12943-021-01322-w. Colorectal cancer (CRC) is the third most prevalent cancer and the second most frequent cause of cancer-related deaths worldwide . Previou5C, m3C, m7G, Pseudouracil(\u03c8), Nm modification [1A, carries a positive charge under physiological conditions [6A methylation, m1A methylation, APA, and A-to-I RNA editing. These modifications are mainly produced by the activity of enzymes known as \u201cwriters\u201d.In nature, RNA modification is widespread on all nucleotides: A, U, C, and G . There afication \u201310. It inditions , it is lnditions . Therefo6A is methylation at the sixth nitrogen atom of RNA base A. It is the most abundant form of internal RNA modifications, affecting RNA stability and translational efficiency. This modification is written by m6A-methyltransferases, such as METTL3, METTL14, WTAP, RBM15, RBM15B, ZC3H13, and KIAA1429 [6A can cause profound changes in cellular processes and plays a key role in pathological conditions, including the development of cancer [mf cancer .1A affects the first nitrogen atom of the adenine base and carries a positive charge under physiological conditions [1A modification \u201cwriters\u201d include TRMT61A, TRMT61B, TRMT10C, and TRMT6 [1A modification affects the tertiary structure of ribosomes and the translation of genes. It has an essential function in regulating gene expression and controlling cell fate, thus affecting the occurrence and progression of diseases [The modification of mnditions . Known mnd TRMT6 , 20. m1Adiseases , 22.APA is an RNA-processing mechanism that cleaves mRNA at different sites and adds poly(A) tails to generate transcripts containing different lengths of 3\u2032-untranslated region (UTR) or coding regions , 24. CPSRNA editing is a well-documented post-transcriptional mechanism altering nucleotide in selected transcripts . The comTo fully understand the significance of post-transcriptional modifications, the investigation of cross-talk between different patterns of these alterations is urgently needed. The four types of RNA modification \u201cwriters\u201d may form an important and complex cellular regulatory network in CRC, and the understanding of this network may provide important insights into the mechanisms underlying CRC tumorigenesis.6A modification promotes the activation and maturation of dendritic cells (DCs). Specific depletion of Mettl3 in DCs resulted in an impaired phenotypic and functional maturation of DCs and reduced their ability to stimulate T cell responses [The immune checkpoint blockade (ICB) therapy has been applied for cancer treatment and delivered promising clinical outcomes; however, it generally shows a low response rate. To improve the efficacy of immunotherapy, dissecting the tumor microenvironment (TME) and identifying the mechanism underlying the low rate of response rate to ICB are urgently needed . Recent esponses . Distincesponses . HoweverIn this study, we explored genomic alterations in 1697 CRC samples from Gene Expression Omnibus (GEO) \u201340 and T6A modification \u201cwriters\u201d, 4 m1A modification \u201cwriters\u201d, and 12 APA modification \u201cwriters\u201d were included in the current study [Based on the published data, a total of 26 RNA modification \u201cwriters\u201d Table , includint study , 24, 28.p\u2009=\u20090.021), suggested that genetic alteration of \u201cwriter\u201d may play functional role in CRC. Next we performed Gene Set Variation Analysis (GSVA) of enrichment analysis [To determine the genetic alterations in RNA modification writers in cancer, we assessed the prevalence of non-silent somatic mutations in 26 writers. The mutation frequency of individual writers was relatively low across LAML, PCPG, and UVM cohorts in TCGA, while the COAD cohort showed relatively high mutation frequency of \u201cwriters\u201d Figure . Of the analysis  using thanalysis , 44. ThiWe then examined \ufeff\ufeffsomatic copy number alterations of these \u201cwriters\u201d and found that CSTF1, CPSF1/4, ZC3H13, and KIAA1429 had a widespread frequency of copy number variation (CNV) gain Fig. . Indeed,This analysis demonstrated a high heterogeneity of genetic landscape and expression of RNA modification \u201cwriters\u201d between normal and CRC samples, indicating that the expression imbalance of RNA modification \u201cwriters\u201d has potential roles in the onset and development of CRC.To gain a comprehensive understanding of the expression pattern of the \u201cwriters\u201d involved in tumorigenesis, 1695 CRC samples from eight datasets that contained clinical information  were selected for further analysis Table . UnivariTo explore the relationship among writers, we calculated pairwise correlations among the expression of 26 writers in CRC and found that positive correlations were more frequent than negative correlations . To identify the biological significance of these distinct RNA modification patterns, we performed GSVA enrichment analysis , T regulatory cells (Tregs) (p\u2009=\u20092.2\u2009\u00d7\u200910\u2212\u200912), T follicular helper cells (Thf cells) (p\u2009=\u20090.0016), and T gamma delta cells (p\u2009=\u20090.0062) was higher in Cluster_1. The infiltration of activated DCs (p\u2009=\u20099.7\u00d7\u200910\u2212\u20095), natural killer cells (p\u2009=\u20090.015), and M1 macrophages (p\u2009=\u20090.0068) were higher in Cluster_2 , similarly to patients in Cluster_1 . In consistence, gene.cluster_A had significantly higher WM_Score than gene.cluster_B . To assess the effect of the WM_Score on TME, we compared the infiltration of immune cells between the WM_Score-low and -high groups. We found that the infiltration of M2 macrophages, Tregs, Tfh cells, and T gamma delta cells was higher in the WM_Score-high group, and the infiltration of activated DCs and M1 macrophages was higher in the WM_Score-low group . The AUCs of the time-dependent ROC curves for the WM_Score were 0.64, 066, and 0.64 at, respectively, 3, 6, and 12-months overall survival . The reliability of the WM_Score was validated using 562 samples of CRC patients from the TCGA  and multivariate  Cox regression analysis. These results imply that the WM_Score can reflect the RNA modification patterns and predict the prognosis of CRC patients.To further assess the clinical relevance of the WM_Score, we divided patients into WM_Score-low and -high group with the cutoff value determined by the survminer package. Patients with low WM_Score demonstrated a prominent survival benefit . Moreover, the EMT score was significantly higher in the WM_Score-high group than in the WM_Score-low group in the TCGA-COAD/READ cohort .CRC can be divided into four consensus molecular subtypes (CMSs), CMS1\u20134, with distinct molecular features . They inTo examine the association between the WM_Score and CMS subtypes, we compared the WM_Scores of different CMS subtypes in four GEO datasets  and the TCGA Cohort, respectively Table \u20134. We fop\u2009=\u20090.01). We further documented that the WM_Score is different among tumor stages and is higher in more advanced CRC . Given that transcripts processed by APA have a short 3\u2019UTR, thus tolerating the regulation of miRNAs , we hypop\u2009=\u20097.61\u00a0\u00d7\u200910\u2212\u20097) and YBX2  transcripts exhibited statistically significant shortening, which was associated with worse survival of CRC patients . We raised the possibility that in the WM_Score-high group, due to the shortening of HEATR3 and YBX2, which, in turn, shorten the 3\u2019UTR, miRNA may not be able to target the corresponding gene, resulting in the activation of gene expression and contributing to the initiation and development of CRC.To explore the functional role of RNA modification writers, we analyzed the APA and A-I editing events of each gene in the TCGA-COAD/READ cohort to observe the post-transcription characteristics. We identified the genes with the differences in APA between different RNA modification patterns and compared the survival associated with these genes to determine whether the length of 3\u2019UTR affects the survival of CRC patients Figure \u201313. Mostp\u2009=\u20098.0\u00a0\u00d7\u200910\u2212\u20096) and KCNE3  is associated with shorter survival time of CRC patients . The difference in the rate of A-to-I editing of these genes between WM_Score-high and -low groups may be regulated by miRNA via the editing of the 3\u2019UTR regions, thus affecting the occurrence and development of CRC.We identified the genes with differences in A-to-I editing between WM_Score-high and -low groups in 159 TCGA-READ/COAD samples Table \u201315. The Rs\u2009=\u2009\u2212\u20090.29, p\u2009=\u20091.31\u2009\u00d7\u200910\u2212\u200915), mTOR inhibitor LJI308 , and EGFR inhibitor AZD3759 . Eighteen pairs exhibited drug resistance correlated with the WM_Score, including cell cycle checkpoint kinase inhibitor AZZD7662  and Bcl-2 inhibitor AZD5991 . Further, we analyzed the signaling pathways of the genes targeted by these drugs. We found that drugs whose sensitivity was associated with WM_Score-high were mostly targeting MAPK, mTOR, and VEGF signaling pathways. In contrast, the drug whose sensitivity was associated with WM_Score-low were targeting apoptosis and cell cycle signaling pathway  database  Fig.\u00a06a. Among tp\u2009=\u20090.022) and the bladder cancer cohort [p\u2009=\u20090.0065). The 348 patients of the IMvigor210 cohort exhibited different degrees of response to anti-PD-L1 blocker, including complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD). The CR patients showed the lowest WM_Score than patients with other types of responses . We analyzed the WM_Score of the three immune subtypes of IMvigor210, including \u201cimmune inflamed\u201d, \u201cimmune excluded\u201d, and \u201cimmune desert\u201d [A major effort has been made to identify biomarkers to predict the response to immunotherapy, including tumor mutation burden (TMB) and the expression of PD-L1 protein \u201367. Consr cohort  . Eight GEO colorectal cancer cohorts  and TCGA-COAD/READ cohort were included for further analysis. The data information is summarized in Table The workflow of our study was shown in Figure http://research-pub.gene.com/IMvigor210CoreBiologies. Expression and clinical information of bladder cancer with anti-PD-L1 cohort downloaded from 10.5281/zenodo. 546,110 [We collected datasets with immunotherapy. The IMvigor210 cohort and bladder cancer with anti-PD-L1 cohort were included in the study of the relationship between WM_Score and immunotherapy prognosis. The IMvigor210 cohort : Interve 546,110 .6A modification enzymes , 4 m1A modification enzymes ,12 APA modification enzymes  and 3 A-I modification enzymes . Unsupervised clustering was applied to detect the robust clustering of colorectal cancer. We used the Consensus-Clusterplus package for the above steps, and conduct 1000 repetitions to ensure the stability of the classification [Unsupervised clustering algorithm was applied to cluster analysis of RNA-modified \u201cwriters\u201d in 1695 colorectal cancer samples. RNA modification \u201cwriter\u201d consists of 7 mfication , 87.In order to study the differences of RNA modification patterns in biological processes, we used \u201cGSVA\u201d R package to conduct GSVA enrichment analysis . The genhttps://cibersort.stanford.edu/) to quantify the relative abundance of 22 types of immune cells in colorectal cancer with parameters as follows: the input mixture matrix is our gene expression matrix, the input of gene signature reference for 22 immune cell types from Newman et al. [We use CIBERSORT algorithm , determines the weight of each observed value through the voom function, then applies the data to linear modeling, and uses empirical Bayesian statistics to analyze the DEGs between the RNA modification patterns. We used a univariate Cox regression model to calculate the risk ratio (HR) of DEGs. Then DEGs related to survival were extracted to construct a scoring system.org.hs.eg.db\u201d was used as annotation to carry out enrichment analysis of GO and KEGG in gene set. DEGs was used as input gene set, p value was calculated by ORA  [Enrichment analysis of DEGs. Enrichment analysis and functional annotation of DEGs were performed using the clusterProfiler R package . \u201corg.hsnalysis) , and useWMScore\u2009=\u2009(betai\u2009\u00d7\u2009Expi\u00a0),where i means the RNA modification phenotype-related genes.Construction of scoring system. After obtaining the prognostic value of each gene signature score, we applied a method similar to GGI  to definM and E represent the expression of the mesenchymal gene and epithelial gene, respectively, and N and n respectively represent the number of mesenchymal genes and epithelial genes.We obtained epithelial-to-mesenchymal transition gene signatures from Mak et al , includius study , where MThe expression of miRNA in CRC were obtained from TCGA. Analyzing the differentially expressed miRNAs between the WM_Score-high and low groups, the targeted signaling pathways of differentially expressed miRNAs were enriched by KEGG enrichment analysis. To compare the expression of miRNA between WM_Score-high and low-groups, we used the Wilcoxon test and used Benjamini and Hochberg adjustment for FDR and considered an FDR <\u20090.05 as statistical significance.http://tc3a.org) [https://github.com/ZhengXia/DaPars) to identify the alternative proximal polyA site and calculate the Percentage of Distal polyA site Usage Index (PDUI) for each transcript. The alteration of APA usage in each tumor can be quantified as a change in PDUI, which is capable of identifying 3\u2019UTR lengthening (positive index) or shortening (negative index). To compare PDUI between WM_Score-high and -low groups, we used the t test and used Benjamini and Hochberg adjustment for FDR and considered an FDR\u2009<\u20090.05 and PDUI difference\u2009>\u20090.1 as statistical significance.APA in CRC were obtained from The Cancer 3\u2032 UTR Atlas  , 92, whiA-to-I RNA editing profile in CRC were obtained from Han et al. . Those ep\u00a0<\u20090.05 as statistical significance. We also divided patients into two groups based on the PDUI of APA events or the A-to-I editing rate, and used the Kaplan-Meier curve and the log-rank test to determine the significance of the differences.To characterize the clinical relevance of APA and RNA editing sites affected by WM_score, we performed the univariate Cox regression analysis to identify the RNA editing level or PDUI that was significantly correlated with patient survival, and considered http://www.cancerrxgene.org/downloads) [Rs|\u2009>\u20090.2 and used Benjamini and Hochberg adjustment for FDR and considered an FDR\u2009<\u20090.05 as significant correlation.The transcription profiles for about 1000 cancer cell lines, drug response measurements as AUC for antitumor drugs in cancer cell lines, and targets/pathways of drugs are downloaded from Genomics of Drug Sensitivity in Cancer  . We perfSpearman and distance correlation were used to calculate the correlation coefficient of RNA modification \u201cwriters\u201d expression. Wilcoxon test was used to compare the differences. Receiver operating characteristic (ROC) curve was used to verify the validity of the model.p\u2009<\u20090.05 as statistical significance.Based on the correlation between WM_Score and patient survival, survminer package was used to determine the cutoff point of survival information for each dataset. The \u201csurv-cutpoint\u201d function was used to dichotomy WM_Score, and all potential cutting points were repeatedly tested to find the maximum rank statistic, and then the patients were divided into the WM_Score-high group and the WM_Score-low group according to the maximum selected log-rank statistics, so as to reduce the calculated batch effect. Survival curves for prognostic analysis were generated using the Kaplan-Meier method, and the log-rank test was used to determine the significance of the differences. Univariate Cox regression model was used to calculate the hazard ratio (HR) between differentially expressed genes and \u201cwriters\u201d. To assess whether WM_Score is an independent predictor, we consider age, gender, and stage as variables to perform multivariate Cox regression model analysis. All statistical analysis was two-side and considered Additional file 1 : Figure S1. Overview of study design. (A) Flowchart of the steps in the performed analyses. Figure S2. Analysis of mutation frequency and CNV in TCGA- COAD/READ. (A) The mutation frequency of RNA modification \u201cwriters\u201d among 33 cancer types in the TCGA cohort. The horizontal axis represents cancer types, and the number of samples is given in the parentheses. The vertical axis lists the names of the genes. (B) Comparison of GSEA enrichment analysis between \u201cwriters\u201d mutation samples and non-mutation samples. NES, Normalized enrichment score. (C) The distribution of correlation coefficient between \u201cwriters\u201d expression and CNV in CRC. |Rs|\u2009>\u20090.3 and p value <\u20090.05 indicates that \u201cwriters\u201d expression is related to CNV. (C) The expression of \u201cwriters\u201d among CNV groups in CRC. The sample size for each group based on the CNV alteration . Wilcoxon test was used to assess the difference. The boxes indicate the median\u2009\u00b1\u20091 quartile, with the whiskers extending from the hinge to the smallest or largest value within 1.5\u00d7 IQR from the box boundaries. Figure S3. Biological characteristics of RNA modification \u201cwriters\u201d. (A) Association of gene expression for 26 RNA modification \u201cwriters\u201d with patient overall survival times based on Univariate Cox regression analysis in GSE39582 cohort. (B) Heatmap shows the positive (red) and the negative (blue) correlation between TME infiltration and WM_Score in CRC. *p\u2009<\u00a00.05, **p\u2009<\u00a00.01, and ***p\u2009<\u00a00.001, as determined by the Spearman correlation analysis. Figure S4. Enrichment analysis of differentially expressed genes and the relationship between survival and the WM_Score. (A-B) GO (A) and KEGG (B) enrichment analysis of the 463 DEGs. The x-axis indicates gene counts within each GO term. The brightness of the column color represents the statistical significance of enrichment. (C) Kaplan-Meier curves comparing overall survival between two DEG clusters, gene.cluster_A (red) and gene.cluster_B (blue), in the GSE39582 cohort. The grouping of CRC samples is shown under the Kaplan-Meier plot. p\u2009<\u20090.05 in the two-sided log-rank test was considered statistically significant. (D) Heatmap shows the differences in TME infiltration between WM_Score-high and -low groups in the GEO-CRC cohort. Red, high enrichment score; blue, low enrichment score. (E-F). Overlap (E) and frequency (F) of classifiers of WM_Score-high/\u2212low and Cluster_1/2 in CRC. (G-H). Overlap (G) and frequency (H) of classifiers of WM_Score-high/\u2212low and gene.cluster_A/B in CRC. The Fisher\u00a0test was used to determine the statistical significance of the difference. Figure S5. Relationship between the WM_Score and the molecular subtype of CRC. (A) Correlation between the EMT score and WM_Score in the TCGA-COAD/READ cohort by Spearman analysis. (B) The difference of EMT scores in WM_Score-high (red) and -low (blue) groups in the TCGA-COAD/READ cohort. (C-D) Distribution of CMS subtypes within WM_Score-high and -low groups in four GEO-CRC datasets (C) and the TCGA-COAD/READ cohort (D). (E-F) Enrichment in signaling pathways in CMS subtypes in four GEO-CRC datasets (E) and the TCGA-COAD/READ cohort (F). (G) Distribution of TNM stage within CMS subtypes in four GEO-CRC datasets. Statistical significance (p\u2009<\u20090.05) was calculated using the fisher.test. (H) Kaplan-Meier curves show the difference in overall survival between two RNA modification patterns, WM_Score-high (red) and -low (blue), in the GSE39582 cohort. The grouping of CRC samples is shown below. Figure S6. The length of APA PDUI gene affects the survival prognosis of CRC. (A-B) The bar graphs show the difference between WM_Score-high and -low groups in PDUI (A) and A-I editing (B). The forest plots show univariate Cox regression analyses for PDUI differential genes (A) and A-I editing differential genes (B) between WM_Score-high and -low group.\u00a0 Figure S7. Graphic summary. (A) m6A, m1A, APA, and A-to-I RNA editing enzymes. (B) Genetic alterations (left panel), transcriptional alterations (intermediate panel), and mutual correlation (right panel) of four types of RNA modification \"writers\" in CRC. (C) Two distinct RNA modification patterns based on 26 RNA modification enzymes, and constructed a scoring model\u00a0WM_Score. (D) Clinical relevance of WM Score. (E) Distinct patterns of WM_Score associated with immune infiltration. (F) Molecular subtypes associated with WM_Score in CRC. (G) WM_Score involved in transcriptional and post-transcriptional regulation. (H) Therapeutic ability of the WM_Score.Additional file 2 Supplementary Table\u00a01. Summary of RNA Modification Writers. The annotation of 26 RNA modification \u201cwriters\u201d based on the published data. Supplementary Table\u00a02. Clinical information of CRC cohorts from GEO/TCGA. The accession number, platform of microarray, the number of tumor and normal samples, clinical characteristics , citation of CRC cohort. Supplementary Table\u00a03. Samples clustering in eight GEO-CRC cohorts. The detailed information of eight GEO-CRC Cohorts with different group methods, including clustering, CMS subtypes, and WM_Score. Supplementary Table\u00a04. Group information of samples in TCGA-COAD/READ cohorts. The WM_Score and the classification of WM_Score types and CMS subtypes in TCGA-COAD/READ cohorts. Supplementary Table\u00a05. Enrichment score of KEGG pathways in eight GEO-CRC cohorts. The table shows enrichment score of KEGG pathways performed by Gene Set Variation Analysis (GSVA) in eight GEO-CRC cohorts. Supplementary Table\u00a06. TME infiltration characteristics of samples in eight GEO-CRC cohorts. Table detailly lists the infiltration of 22 immune types of each sample which performed by CIBERSORT method in eight GEO-CRC cohorts. Supplementary Table\u00a07. Difference of TME infiltration characteristics between Cluster_1 and Cluster_2 in Eight GEO-CRC Cohorts. The difference of TME infiltration characteristics between Cluster_1 and Cluster_2 in eight GEO-CRC cohorts. Supplementary Table\u00a08. Difference of TME infiltration characteristics between WM_Score-high and WM_Score-low in eight GEO-CRC cohorts. The difference of TME infiltration characteristics between WM_Score-high and WM_Score-low in eight GEO-CRC cohorts. Supplementary Table\u00a09. The correlation of writers and TME infiltration Characteristics. The Spearman correlation of gene expression of 26 writers and the infiltration of 22 immune cells in eight GEO-CRC cohorts. Supplementary Table\u00a010. Differentially expressed genes between Cluster_1 and Cluters_2 associated with survival. The association of hypoxia status differentially expressed genes between Cluster_1 and Cluters_2, with patient overall survival times based on both univariate multivariate Cox proportional hazards models. Supplementary Table\u00a011. Difference of miRNAs and miRNAs-DEGs target pathways between WM_Score-high and WM_Score-low in TCGA COAD/READ cohorts. The differentially expressed miRNA between WM_Score-high and WM_Score-low groups, and miRNA targeted mRNAs and mRNA enriched pathways. Supplementary Table\u00a012. Difference of PDUI between WM_Score-high and WM_Score-low in TCGA COAD/READ cohorts. To compare PDUI between WM_Score-high and -low groups in TCGA COAD/READ cohorts, we used the t test and used Benjamini and Hochberg adjustment for FDR and considered an FDR\u2009<\u20090.05 and PDUI difference\u2009>\u20090.1 as statistical significance. Supplementary Table\u00a013. The Univariate Cox regression analysis of PDUI and overall survival in TCGA COAD/READ cohorts. The association of PDUI for APA events with patient overall survival times Univariate Cox regression analysis in TCGA COAD/READ cohorts. Supplementary Table\u00a014. Difference of A-to-I editing rate between WM_Score-high and WM_Score-low in TCGA COAD/READ cohorts. We used the t test to detect RNA editing sites with differential editing between WM_Score-high and -low groups in TCGA COAD/READ cohorts, and defined significantly differential editing sites as p\u2009<\u20090.05 and difference\u2009>\u20095%. Supplementary Table\u00a015. The Univariate Cox regression analysis of A-to-I editing rate and overall survival in TCGA COAD/READ cohorts. The association of A-to-I editing rate with patient overall survival times Univariate Cox regression analysis in TCGA COAD/READ cohorts. Supplementary Table\u00a016. The association of WM_Score and drug sensitivity in GDSC database. The Spearman correlation of WM_Score and drug sensitivity which quantified by AUC in GDSC database."}
+{"text": "Circular RNAs (circRNAs) are thought to be involved in the development of various malignancies. The expression and function of hsa_circ_0006916, a newly identified circRNA, in hepatocellular carcinoma remain unclear.Quantitative RT-PCR was used to detect hsa_circ_0006916 in hepatocellular carcinoma. In vitro function assays were conducted to explore growth and invasion of hepatocellular carcinoma cells. Next, the mechanism of hsa_circ_0006916 function in hepatocellular carcinoma was determined by luciferase reporter and RIP assays.Hsa_circ_0006916 was substantially overexpressed in hepatocellular carcinoma tissues and cells. High levels of hsa_circ_0006916 in hepatocellular carcinoma patients were associated with advanced clinical characteristics. Down-regulation of hsa_circ_0006916 decreased the growth and invasion of hepatocellular carcinoma cells in vitro. The results suggested that hsa_circ_0006916 acted as a sponge of miR-337-3p and had an important functional use in the regulation of STAT3 levels in hepatocellular carcinoma cells. Moreover, miR-337-3p inhibition or STAT3 overexpression abolished the effect of hsa_circ_0006916 suppression on the progression of hepatocellular carcinoma cells.Our data suggest a novel hsa_circ_0006916/miR-337-3p/STAT3 axis in hepatocellular carcinoma, and provide a new target for treatment. Hepatocellular carcinoma is a common malignant disease worldwide with a high mortality rate , 2. AlthIncreasing data have shown that non-coding RNAs (ncRNAs) have a critical function in post-transcriptional gene regulation of different diseases , 6. CircHere, we identified a new circular RNA (hsa_circ_0006916) in hepatocellular carcinoma. Our results are the first to reveal that the growth and invasion of hepatocellular carcinoma cells were improved by highly expressed hsa_circ_0006916. Regarding the mechanism, the data suggested that hsa_circ_0006916 may act as a sponge of miR-337-3p and play a vital role in upregulating STAT3 levels. Our data may provide experimental evidence for potential hepatocellular carcinoma therapy.Overall, 59 cases of hepatocellular carcinoma and matched adjacent non-tumor tissues  were acquired from the Henan Provincial People\u2019s Hospital from June 2017 to January 2018. The experimental protocols were granted approval by the ethics committee of our hospital.2.Five human hepatocellular carcinoma  and one healthy human liver (HL-7702) cell lines were acquired from the Chinese Academy of Sciences , and sustained in DMEM  supplied with 10% FBS , 1% penicillin  or streptomycin  at 37\u00a0\u00b0C, 5% COsiRNAs targeting hsa_circ_0006916 (si-circ_0006916#1/2), STAT3 overexpression plasmids, miR-337-3p mimics and inhibitors, and scramble controls were obtained from Genechem  and transfected into hepatocellular carcinoma cells according to the instructions of Lipofectamine 3000 .Cytoplasmic and nuclear RNA were isolated through the Cytoplasmic and Nuclear RNA Purification Kit  in accordance with the product\u2019s guidelines. Relative changes in gene expression were measured by quantitative RT-PCR (RT-qPCR), which was conducted using previously published methods .3 cells/well) were incubated with EdU solution, supplemented with 4% paraformaldehyde for half an hour, Triton X-100 was added to each well, and then the cells were stained with Apollo solution. Finally, cell nuclei were stained with DAPI .The growth of hepatocellular carcinoma cell was evaluated using an EdU kit . Cells . Next, cells were stained with 0.1% crystal violet after 1\u00a0day. The quantity of hepatocellular carcinoma cells that had invaded through the Matrigel were measured under a microscope.A total of 1\u2009\u00d7\u200910The wild-type (Wt) and mutant (Mut) sites of miR-337-3p binding on hsa_circ_0006916 or STAT3 3\u2032UTR were sub-cloned into a dual-luciferase vector (pmirGLO). According to the instructions of the detection kit, the plasmids and miR-337-3p mimics were co-transfected into HEK293 cells and co-incubated for 48\u00a0h. Next, the luciferase activity was evaluated using a luciferase detection kit .The RIP assay was carried out by the method detailed in a previous report .SPSS 20.0 was applied for analyzing data. The measurement data were represented as means\u2009\u00b1\u2009SD. Student\u2019s t-test or one-way ANOVA was carried out for comparison between different groups. p\u2009<\u20090.05 represents statistical significance.. demonstrated that hsa_circ_0006916 is one of the highest expressed circRNAs in hepatocellular carcinoma [Recently, Xu et alarcinoma . NeverthTo elucidate the effect of hsa_circ_0006916 on hepatocellular carcinoma, we first identified the genomic location of hsa_circ_0006916 through UCSC, which was at chr5q14.1 Fig.\u00a0a. Then, Increasing studies have revealed that circRNAs in the cytoplasm regulate gene expression by sponging miRNAs , 15. In Next, we evaluated the downstream miR-337-3p targets, and bioinformatics analysis  indicated that STAT3 might be a putative target Fig.\u00a0a, b. TheNext, we explored the roles of STAT3 in hepatocellular carcinoma. Immunohistochemistry (IHC) demonstrated that STAT3 was substantially overexpressed in hepatocellular carcinoma tissues Fig.\u00a0a. The reThen, we determined whether hsa_circ_0006916 regulated hepatocellular carcinoma progression through the miR-337-3p/STAT3 axis using qRT-PCR and western blotting. The results demonstrated that hsa_circ_0006916 suppression reduced STAT3 levels in Hep3B and Huh-7 cells, whereas miR-337-3p inhibitors resolved the effects (Fig.\u00a0. demonstrated that circRNA_104075 promotes YAP-dependent tumorigenesis in hepatocellular carcinoma through regulating HNF4a expression [. reported that circRHOT1 encouraged hepatocellular carcinoma progression by initiating NR2F6 [. showed that the circRNA cSMARCA5 decreased hepatocellular carcinoma cell growth and metastasis [Recently, a number of circRNAs have been revealed to have a critical role in HCC progression. For example, Zhang et alpression . Wang etng NR2F6 . Yu et atastasis . These fAccumulating evidence has shown that circRNA serves as a competing endogenous miRNA and restores miRNA gene function . In hepa. demonstrated that miR-218 suppressed lung cancer progression by targeting the IL-6/STAT3 axis [. reported that HOTAIR suppression in gastric cancer increased miR-454-3p to reduce tumor growth by the STAT3/Cyclin D1 axis [Next, we used bioinformatics to identify miR-337-3p\u2019s possible target genes, and STAT3 was chosen as the candidate one. Previous studies have shown that STAT3 serves as an important regulator of tumorigenesis. For example, Yang et alAT3 axis . Jang et D1 axis . Herein,In conclusion, our findings indicated a hsa_circ_0006916/miR-337-3p/STAT3 axis in hepatocellular carcinoma progression for the first time. We identified hsa_circ_0006916 as an oncogenic circRNA in hepatocellular carcinoma development, and provide a novel target for tumor treatment."}
+{"text": "Background: Inadequate endometrial receptivity contributes to recurrent implantation failure (RIF) during IVF\u2013embryo transfer. Though multiple circRNAs have been confirmed differentially expression in RIF, the potential function of novel circRNAs needed to be detected.Results: The top ten DEcircRNAs were selected as initial candidates. A ceRNA network was conducted on the basis of circRNA\u2013miRNA\u2013mRNA potential interaction, consisting of 10 DEcircRNAs, 28 DEmiRNAs and 59 DEmRNAs. Three down-regulation circRNAs with high degree of connectivity were verified by RT-qPCR, and results suggested that only hsa_circ_0038383 was significantly downregulation in RIF compared with control group. Subsequently, three hub genes  were identified as hub genes. Ultimately, a subnetwork was determined based on one DEcircRNA (hsa_circ_0038383), two DEmiRNAs (has-miR-196b-5p and has-miR-424-5p), and three DEmRNAs . Following verification, hsa_circ_0038383/miR-196b-5p/HOXA9 axis may be a key pathway in affecting RIF.Conclusion: In summary, a hsa_circ_0038383-mediated ceRNA network related to RIF was proposed. This network provided new insight into exploring potential biomarkers for diagnosis and clinical treatment of RIF.Methods: We retrieved the expression profiles of RIF from GEO databases  and constructed a competing endogenous RNAs (ceRNA) network based on predicted circRNA\u2013miRNA and miRNA\u2013mRNA pairs. The expression levels of three hub DEcircRNAs identified by cytoscape were validated by RT-qPCR. Howegy (ART) , 4. The gy (ART) , 6. CurrCircular RNAs (circRNAs), as novel class of endogenous non-coding RNAs (ncRNAs), are covalently closed loop structures generated by a process named back-splicing with high stability, abundance and tissue specificity \u20139. The lThe competing endogenous RNA (ceRNA) hypothesis was described as ncRNAs regulating mRNA expression through competitively binding to shared miRNAs and forming a regulatory RNA network . In receThe basic information of three GEO datasets  used in this study was shown in After batch effect normalization and further analyses using a limma (versions 3.30.0) R package, we screened out 143 upregulation miRNAs and 58 downregulation miRNAs with p<0.05 from GSE71332 , 3B. AccUsing the same data processing method, 678 upregulation and 988 upregulation mRNAs in GSE58144 were selected , 4B. On To better comprehend the role of circRNAs in miRNAs mediated mRNAs, a circRNA\u2013miRNA\u2013mRNA regulatory network was generated employing a combination of circRNA\u2013miRNA pairs and miRNA\u2013mRNA pairs after multiple screening steps. The ceRNA contained 33 circRNA\u2013miRNA pairs and 63 miRNA\u2013mRNA pairs, including 10 DEcircRNAs, 28 DEmiRNAs and 59 DEmRNAs. The subnetwork was presented using cytoscape software .To find out the biological functions and pathway of DEmRNAs in ceRNA network, GO annotation and KEGG pathway analysis was performed. GO enrichment analysis was constituted with biological processes, molecular functions and cellular components. In biological process terms, the DEmRNAs were primarily involved in \u2018regulation of transcription\u2019 and \u2018gene expression\u2019. For cellular component terms, the DEmRNAs were mainly enrichment in \u2018nucleus\u2019, \u2018nucleoplasm\u2019, \u2018transcription regulator complex\u2019 and \u2018cell-cell junction\u2019. While \u2018transcription factor activity\u2019, \u2018BMP receptor activity\u2019, \u2018transmembrane receptor protein serine/threonine kinase activity\u2019 and \u2018SMAD binding\u2019 were mainly related to molecular function . As showCircRNAs act as hub node in biological networks. Three down-regulation circRNAs  with high degree of connectivity calculated by the cytoHubba plugin of cytoscape were filtered as hub-circRNAs. Their basic characteristics were expressed in The PPI network was established based on protein interaction provided by STRING database. 30 nodes and 134 edges were included in this PPI network . Then thRIF is a major cause of female infertility and a challenging problem in assisted reproduction field. In addition, the pregnancy rate after the therapy of IVF-ET still remains approximate 25% . Good enNowadays, more and more attention was attracted in investigating the underlying functions and mechanisms of circRNAs through establishing ceRNA network. For instance, in the research of lung adenocarcinoma, Liang et al.  appraiseTo further understand the potential molecular mechanism and functions of circRNAs on RIF, we constructed a circRNA\u2013miRNA\u2013mRNA regulatory network by differential expression analysis, intersection analysis and correlation analysis. The DEmRNAs involving in biological progress and pathway were performed by GO and KEGG analysis. Although there were few reports on the GO terms directly related to RIF, the GO terms related to endometrial receptivity have been gradually discovered. The GO analysis showed BMP signaling pathway and SMAD binding are two important terms. For example, BMP mediated endometrial receptivity and decidualization . AdditioHsa_circ_0038383/miR-196b-5p/HOXA9 and hsa_circ_0038383/miR-196b-5p/PBX1 were two essential axes from the subnetwork. The verification results revealed that only the hsa_circ_0038383/miR-196b-5p/HOXA9 axis may be a key pathway leading to RIF. HOXA9 (Homeobox A9), a homeodomain transcription factor, was a regulator of embryonic development similar to HOX members . RecentlHsa_circ_0038383/hsa-miR-424-5p/HOXA3 was also included in the subnetwork. The expression of miRNAs and target genes between menstrual endometria and early pregnancy were compared, of which miR-424-5p were significantly downregulated during early pregnancy deciduas [In this study, a novel circRNA\u2013miRNA\u2013mRNA network related with RIF was proposed and hsa_circ_0038383 imbalance was verified in RIF. This network would contribute to explore the initiation and progression of RIF and further develop potential treatment strategies for this disease. However, in view of the results are mainly based on computational biology and RT-qPCR, further biological and molecular experiments are indispensable to verify our hypothesis.In summary, we constructed a potential hsa_circ_0038383-mediated ceRNA subnetwork which provided new insight into exploring molecular mechanism and offering a candidate biomarker for RIF. Especially, hsa_circ_0038383/miR-196b-5p/HOXA9 axis may be a key pathway in affecting uterine receptivity and embryo implantation, and further in-depth molecular biology experiments on this axis are necessary to verify the circRNA role in RIF. This study contributed to seeking new ideas for diagnostic biomarkers and therapeutic targets for patients with RIF.http://www.ncbi.nlm.nih.gov/geo/) dataset, which is an international public repository and recording platform employed for searching any appropriate datasets. GSE147442 is a circRNAs database of RIF and based on the GPL21825 platform, containing 8 RIF endometrial and 8 normal endometrium specimens. GSE71332 is a miRNAs database of RIF and based on the GPL18402 platform, consisting of 7 RIF endometrial and 5 normal endometrium specimens. GSE58144 is a mRNAs database of RIF and based on the GPL15789 platform, involving in 43 RIF endometrial and 42 normal endometrium samples. The fundamental information of this gene chip was shown in The raw data of circRNA, microRNA and mRNA were obtained from the GEO http://www.circbase.org/) was utilized to observing the basic information of circRNAs [https://circinteractome.nia.nih.gov/) [http://starbase.sysu.edu.cn/index.php) [The circBase website (circRNAs . The tarih.gov/)  and the dex.php) . The ovehttp://www.targetscan.org/) [http://www.mirdb.org/) [The miRNA\u2013mRNA interaction were predicted with three online websites respectively, including TargetScan (an.org/) , miRDB (db.org/)  and miRTdb.org/) . Only tahttp://cytoscape.org/; version 3.7.1) software [To better uncover the correspondence among circRNAs, miRNAs and mRNAs, we constructed a circRNA\u2013miRNA\u2013mRNA regulatory network utilizing a combination of circRNA-miRNA pairs and miRNA\u2013mRNA pairs. And the ceRNA network was visualized using cytoscape  [The PPI network was established for indicating the interaction among the determined DEmRNAs more intuitively, according to the Search Tool for the Tetrieval of Interacting Genes (STRING) database (on 11.0) . And the34 women (aged 24-40) treated at Reproductive Medicine Center of Yantai Yuhuangding Hospital were enrolled in our study. All of them signed informed consent forms approved by the Institutional Review Board of Yantai Yuhuangding Hospital (reference number 2019-121). The endometrial tissue samples in mid-secretory phase were obtained from RIF patients (n = 18) and control groups (n = 16), respectively. The women still did not get pregnancy after at least three IVF\u2013ET failure cycles were assigned as RIF patients. The control women who experienced IVF\u2013ET cycle due to tubal obstruction without hydrosalpinx achieved a clinical pregnancy after their first or second embryo transfer. Endometrium-related diseases, hydrosalpinx, polycystic ovarian syndrome (PCOS), etc. were excluded. All participants hold normal hormone level, normal endometrial thickness and morphology and a regular menstrual cycle (28-31 days).-\u0394\u0394CT method.Total RNA was isolated from the tissue samples using Trizol reagent following manufacturer\u2019s instructions . Then 1 \u03bcg RNA of each sample was reverse-transcribed to obtain cDNA using SPARKscript II RT Plus Kit (With gDNA Eraser) . The expression of circRNAs and mRNAs in these individual samples was performed by qRT-PCR reaction using SYBR Green qPCR Mix kit (With ROX)  following: 94\u00b0 C for 2 min, followed by 40 cycles of 95\u00b0 C for 10 s and 60\u00b0 C for 30 s. The extraction and amplification of miRNAs was performed according to the manufacturer\u2019s instructions . All RT-qPCR were repeated three times. The relative expression of all genes was calculated using the 2The continue variables were expressed with means \u00b1 standard deviation. The statistical analysis of circRNAs was performed using GraphPad Prism . The significance between groups was tested by Student\u2019s t test or Fisher\u2019s exact test. When p<0.05 were considered as statistically significant."}
+{"text": "GSE78520, GSE94508, and GSE97332. Moreover, hsa_circ_0001955 expression in HCC cells and tissues was significantly higher than that in corresponding normal controls. Functional experiments revealed that knockdown of hsa_circ_0001955 markedly inhibited proliferation, migration, and invasion of HCC, and its overexpression led to the opposite effects. hsa_circ_0001955 was mainly located in the cytoplasm, in which hsa_circ_0001955 could directly bind to miR-145-5p. miR-145-5p was downregulated in HCC, and its expression was negatively linked to hsa_circ_0001955 expression. Furthermore, we identified that\u00a0NRAS was a downstream direct target of the hsa_circ_0001955/miR-145-5p axis in HCC. Collectively, our findings demonstrate the oncogenic roles of the hsa_circ_0001955/miR-145-5p/NRAS axis in HCC, which may represent a potential therapeutic target for HCC.Increasing circular RNAs (circRNAs) have been reported to act as key players in human malignancies. However, the expression, role, and mechanism of circRNAs in HCC are not well elucidated. In this study, some differentially expressed circRNAs (DECs) between hepatocellular carcinoma (HCC) and normal tissues were identified using three circRNA microarrays . Twenty-one DECs were found to be commonly upregulated in all the three datasets. Among the 21 DECs, hsa_circ_0001955 ranked as the top three most upregulated DECs in GEO:   The findings demonstrate the oncogenic roles of the hsa_circ_0001955/miR-145-5p/NRAS axis in HCC, which may represent a potential therapeutic target for HCC. Liver cancer is one of the leading causes of cancer-associated mortality all over the world. Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, accounting for approximately 80% of all cases.Circular RNAs (circRNAs) are a class of endogenous non-coding RNAs, with covalently closed continuous loop structures.GSE78520, GSE94508, and GSE97332. Among all DECs, we noticed that hsa_circ_0001955 ranked in the top three most upregulated DEC sets of all three datasets. We also confirmed that hsa_circ_0001955 expression was dramatically upregulated in HCC cell lines and tissues compared with corresponding normal controls, and found that its high expression was significantly positively correlated with large tumor size and advanced TNM stage. Moreover, functional experiments demonstrated that hsa_circ_0001955 facilitated proliferation, migration, and invasion of HCC cells. Mechanistically, our results showed that hsa_circ_0001955 increased NRAS expression by sponging miR-145-5p, thereby leading to growth and\u00a0metastasis of HCC. Taken together, we illustrated that hsa_circ_0001955 functioned as an oncogenic circRNA by affecting the miR-145-5p/NRAS axis in HCC, which may provide a promising target for HCC therapy in the future.To further explore the expression, function, and mechanism of circRNAs in HCC, we obtained differentially expressed circRNAs (DECs) between HCC and normal tissues by analyzing circRNA microarray data from three Gene Expression Omnibus [GEO] datasets, including GEO: GSE78520, GSE94508, and GSE97332 were listed in GSE78520, GSE94508, and GSE97332 to further obtain some DECs with more interest. As shown in GSE78520, GSE94508, and GSE97332. However, none of these significant DECs were commonly downregulated in all three datasets  A\u20131C, anddatasets E. For be tissues I\u20131K. Add tissues . The resThe above results together demonstrated that hsa_circ_0001955 might act as an oncogenic circRNA in HCC. Thus, we intended to explore its functions in HCC. Considering the high expression level of hsa_circ_0001955 in HCC, we first used the small interfering RNA (siRNA) knockdown method. Three siRNAs were designed to target the unique back-splicing junction of hsa_circ_0001955. SMMC7721 with the lowest expression of hsa_circ_0001955 and HCCLM3 with the highest expression of hsa_circ_0001955 were employed as two represented cell lines in this study. As shown in http://www.csbio.sjtu.edu.cn/cgi-bin/lncLocator.py). As presented in As reported, cytoplasmic circRNAs may act as miRNA sponges to exert their roles. To explore the possibility and ability of hsa_circ_0001955 to bind to miRNAs in HCC, the potential subcellular location of hsa_circ_0001955 was first predicted by an online tool named lncLocator , we identified 21 circRNAs that were commonly upregulated in HCC in all three datasets. Some of them have been found to act as key players in cancer initiation and progression. For example, Bian et\u00a0al.GSE78520, GSE94508, and GSE97332, and we noticed that only hsa_circ_0001955 commonly appeared in all three DEC sets from GEO: GSE78520, GSE94508, and GSE97332. hsa_circ_0001955 has been reported to be differentially expressed between triple-negative and luminal A subtypes of breast cancer and may be involved in the carcinogenesis of breast cancer.circRNAs, ubiquitous endogenous RNAs, are found in most organisms. Despite circRNAs having been widely investigated during recent years, the function and mechanism of them in human cancers are still not well understood. In this study, we first focused on the expression levels of circRNAs in HCC. By intersection of DECs of three HCC circRNA microarray profiles from the GEO database  is one of the most widely used public databases all over the world. Only the datasets about circRNA expression in human HCC tissue level\u00a0were included. Finally, three datasets, containing GEO: GSE78520, GSE94508, and GSE97332, were selected for differential expression analysis. All three datasets were based on the platform of GPL19978 Agilent-069978 Arraystar Human CircRNA microarray V1. GEO: GSE78520 included three HCC cancer and three adjacent normal samples, GEO: GSE94508 included five HCC cancer and five adjacent normal samples, and GEO: GSE97332 included seven HCC cancer and seven adjacent normal samples. GEO2R, an online tool provided by the GEO database, was employed to perform differential expression analysis for the three datasets. |FC| \u2265 2 and p\u00a0< 0.05 were set as the criteria to identify significant DECs.In this study, we wanted to find the potential functional circRNAs in HCC. GEO . The fresh tissues were quickly frozen and stored in liquid nitrogen until usage. This study was approved by the Ethics Committee of the First\u00a0Affiliated Hospital of Zhejiang University School of Medicine. The written informed consent was obtained from each individual patient.All cell lines used in this study were purchased from the cell bank of the Chinese Scientific Academy. QSG7701, HL7702, Bel7402, HCCLNB, Huh7, HepG2, and HCCLM3 were maintained in DMEM medium , and SMMC7721 was cultured in RPMI 1640 medium  supplemented with 10% fetal bovine serum  at 37\u2009\u00b0C and 5% CO\u2212ddCt.Total RNAs from cells and tissues were first extracted using RNAiso plus Reagent . RNAs were reverse transcribed into\u00a0cDNA, and qRT-PCR analysis was conducted as we previously described.The siRNAs targeting hsa_circ_0001955 and its negative control, hsa_circ_0001955 overexpression plasmid and its negative control,\u00a0and the mimic and inhibitor of miR-145-5p and their corresponding negative control were purchased from Ribobio . These reagents mentioned above were transfected into HCC cells\u00a0using Lipofectamine 3000 in accordance with the manufacturer\u2019s instructions. After 12\u00a0h post-transfection, culture medium was replaced with fresh medium. The target sequences of siRNAs were hsa_circ_0001955#1 (5\u2032-TTCGAAATCAGGTGAAGGT-3\u2032), hsa_circ_0001955#2 (5\u2032-GAAATCAGGTGAAGGTCTC-3\u2032), and hsa_circ_0001955#3 (5\u2032-CTCTTCGAAATCAGGTGAA-3\u2032).4 cells were re-seeded into six-well plates and cultured for 3\u00a0days. Cells in each well were counted.Cell proliferation was evaluated by cell counting assay. First, cells were transfected as described. At 12\u00a0h post-transfection, 10\u00a0\u00d7 104 pre-transfected cells were re-plated into six-well plates. When cells grew to 100% confluence, wound heal was made by a micropipette tip. Photographs were taken using microscopy at 0, 24, and 48 h.A total of 50\u00a0\u00d7 104 pre-transfected cells was added into the pre-coated upper inserts of 24-well transwell chambers . After incubation for 48 h, cells on the upper surface of the membrane were removed, and cells on\u00a0the lower surface were fixed with 100% methanol for 15\u00a0min and stained with 0.1% crystal violet for 20\u00a0min. Finally, five random fields of each insert were obtained using a microscope .Cell invasion was assessed through transwell invasion assay. The inserts were first coated with Matrigel . As a chemoattractant, 0.6\u00a0mL medium with 15% FBS was added into the lower compartment.\u00a0Then, 0.2\u00a0mL serum-free medium containing 10\u00a0\u00d7 10http://starbase.sysu.edu.cn/) and CRI (https://circinteractome.nia.nih.gov/). The intersected miRNAs from the two databases were identified using VENNY 2.1 (https://bioinfogp.cnb.csic.es/tools/venny/index.html) and were chosen for subsequent analysis.miRNAs that potentially bind to hsa_circ_0001955 were predicted using two databases, starBase , which is capable of accessing the survival effects of gene and miRNA from 20 various types of cancer. Genes and miRNAs were first entered into the Kaplan-Meier plotter. Then, survival plots were automatically generated, and log rank p value, hazard ratio, and 95% confidence interval were calculated and displayed on the webpage. Log rank p\u00a0< 0.05 was considered as statistically significant.The prognostic values of potential miRNAs of hsa_circ_0001955 and target genes of miR-145-5p in HCC were assessed by Kaplan-Meier plotter (https://www.mirnet.ca/), a comprehensive database and analytic platform to dissect miRNA-target interactions and functional associations through network-based visual analysis, was employed to predict potential target genes of miR-145-5p. The miR-145-5p-target gene interactions were directly downloaded from miRNet.miRNet  was introduced to measure the luciferase activity by a Varioskan Flash Spectral Scanning Multimode Reader . Firefly luciferase activity was normalized to Renilla luciferase.The results were presented as mean\u00a0\u00b1 standard deviation from at least\u00a0three independent experiments. Differences between two groups were analyzed using Student\u2019s t test. Chi-square test was utilized to estimate the correlation between circRNA expression and clinicopathological features. A p value\u00a0< 0.05 was considered statistically significant.W.L. and B.D. designed this work, performed experiments, analyzed data, and drafted the manuscript. W.F. revised the manuscript. All authors have read and approved the final version of the manuscript.The authors declare no competing interests."}
+{"text": "Many circular RNAs (circRNAs) are reportedly in regulating the progression of NSCLC. To identify potential therapeutic targets for NSCLC, we conducted a bioinformatics analysis of circRNAs differentially expressed between NSCLC tissues and adjacent normal tissues. Hsa_circ_0007580 was upregulated in NSCLC tumor tissues, and the expression of its host gene (protein kinase Ca) correlated negatively with overall survival. Short-hairpin RNAs were used to knock down hsa_circ_0007580 in NSCLC cells, and gene and protein levels were measured with qRT-PCR and Western blotting, respectively. NSCLC cell proliferation, migration and apoptosis were evaluated with CCK-8 assays, Ki-67 staining, Transwell assays and flow cytometry, respectively. Knocking down hsa_circ_0007580 inhibited proliferation and invasion by NSCLC cells and induced their apoptosis. Dual luciferase reporter assays indicated that miR-545-3p can bind to hsa_circ_0007580 (suggesting that hsa_circ_0007580 sponges miR-545-3p) and to protein kinase Ca (suggesting that miR-545-3p directly inhibits this gene). In a xenograft tumor model, downregulating hsa_circ_0007580 inhibited NSCLC tumorigenesis by inactivating p38/mitogen-activated protein kinase signaling. Thus, silencing hsa_circ_0007580 notably inhibited NSCLC progression  Lung cancer is the most commonly diagnosed cancer and the leading cause of cancer death globally . Lung caCircular RNAs (circRNAs) are endogenous RNAs characterized by a covalently closed cyclic structure . IntracePrevious reports have indicated that circRNAs can alter gene expression in cancer-associated signaling pathways , 8. MoreTo analyze the differentially expressed circRNAs in NSCLC, we performed a bioinformatics analysis of NSCLC tissues and adjacent normal tissues in the GSE101586 and GSE112214 data sets. The results were evaluated using principal component analysis and a volcano plot  and 1B. PRKCA (encoding protein kinase C\u03b1), was associated with the prognosis of NSCLC  and pathway analyses were performed based on the host genes of the circRNAs. As shown in of NSCLC . Based oNext, we used short-hairpin RNAs (shRNAs) to knock down hsa_circ_0007580 in NSCLC cells. The transfection efficiency was assessed using quantitative real-time PCR (qRT-PCR), which indicated that shRNA1 and shRNA2 each significantly downregulated hsa_circ_0007580 expression in NSCLC cells  and 3B. Next, a Cell Counting Kit 8 (CCK-8) assay was performed to detect cell viability. The results demonstrated that silencing of hsa_circ_0007580 notably inhibited NSCLC cell viability  and 3D. Next, flow cytometry was used to investigate the effects of hsa_circ_0007580 shRNA on cell apoptosis. As shown in https://circinteractome.nia.nih.gov/). As indicated in To investigate the mechanism by which hsa_circ_0007580 induced the progression of NSCLC, we analyzed the CircInteractome  hsa_circ_0007580 reporter, but not a mutated (MT) reporter sequence . Fluoreshttp://www.targetscan.org/vert_71/) and the miRDB (http://www.mirdb.org/) to search for miR-545-3p target genes, and we verified the results using a dual luciferase reporter assay. As demonstrated in PRKCA was identified as a direct target of miR-545-3p. We then used qRT-PCR to assess PRKCA expression in NSCLC cells transfected with miR-545-3p mimics. As shown in PRKCA expression was notably downregulated in NSCLC cells overexpressing miR-545-3p. These results indicated that miR-545-3p directly inhibited PRKCA expression.We next used TargetScan -p38 and PRKCA were significantly downregulated in NSCLC cells treated with hsa_circ_0007580 shRNA, while these results were partially reversed in the presence of miR-545-3p inhibitors \u20137C. Thesin vivo. The tumor sizes  and cultured in Dulbecco\u2019s Modified Eagle\u2019s Medium  with 10% fetal bovine serum (Thermo Fischer Scientific), 1% penicillin and streptomycin (Thermo Fisher Scientific) at 37\u00b0C and 5% COhttps://www.ncbi.nlm.nih.gov/geo/). Principal component analysis and volcano plot were performed to asses the expressions of cirRNAs in NSCLC and adjacent normal tissues. GO analysis was performed to explore the functions of circRNA host genes in terms of biological processes, cellular components and molecular functions. Biological pathways were assessed in the Kyoto Encyclopedia of Genes and Genomes. Survival curves were generated using The Cancer Genome Atlas .Two datasets (GSE101586 and GSE112214) containing gene expression data for NSCLC and adjacent normal tissues (controls) were obtained from the Gene Expression Omnibus database (-\u0394\u0394Ct method using the formula: 2-(sample \u0394Ct \u2013 control \u0394Ct), where \u0394Ct is the difference between the fluorescent amplification thresholds of the gene of interest and the internal reference gene used for normalization (U6 or \u03b2-actin).Total RNA from NSCLC cell lines was extracted with TRIzol reagent  according to the manufacturer\u2019s protocol. Then, cDNA was synthesized with a reverse transcription kit  according to the manufacturer\u2019s protocol. The following protocol was used to perform qRT-PCR in triplicate: 2 minutes at 94\u00b0C, followed by 35 cycles of 30 seconds at 94\u00b0C and 45 seconds at 55\u00b0C. The following primers were obtained from GenePharma : Hsa_circ_0018818: forward 5\u2019-CAGGACCTTCTGTGGGACTC-3\u2019 and reverse 5\u2019-TCCAAAACTCCCCTTTCCCA-3\u2019. MiR-545-3p: forward 5\u2019- TGCGCTCAGCAAACATTTATTG-3\u2019 and reverse 5\u2019- CCAGTGCAGGGTCCGAGGTATT-3\u2019. \u03b2-actin: forward 5\u2019-AGCGAGCATCCCCCAAAGTT-3\u2019 and reverse 5\u2019-GGGCACGAAGGCTCATCATT-3\u2019. U6: forward 5\u2019-CGCTTCGGCAGCACATATAC-3\u2019 and reverse 5\u2019- AAATATGGAACGCTTCACGA-3\u2019. The relative fold changes were calculated with the 2Two shRNAs directly targeting hsa_circ_0007580 (shRNA1 and shRNA2) and one shRNA with a nontargeting sequence (negative control) were obtained from Hanbio Biotechnology Co., Ltd  and packaged into lentiviruses. The lentiviral vector DNAs were then transfected into 293T cells, and the cells were incubated at 32\u00b0C. Then, the supernatants were collected and filtered for the retrieval of lentiviral particles. Finally, NSCLC cells were infected with the lentiviral particles according to the manufacturer\u2019s protocol. After 48 h of incubation, stably transfected NSCLC cells were selected with puromycin , and qRT-PCR was used to verify the efficiency of transfection.For miR-545-3p transfection, Lipofectamine 2000 was used to transfect A549 or NCI-H520 cells with miR-545-3p mimics, miR-545-3p inhibitors or negative controls, as described previously . The mim3 cells per well) overnight. Then, the cells were treated with hsa_circ_0007580 shRNA2 or the negative control for 0, 24, 48 or 72 h. The cells in each well were then treated with 10 \u03bcL of CCK-8 reagent and further incubated for 2 h at 37\u00b0C. Finally, the absorbance of the NSCLC cells was measured at 450 nm on a microplate reader (Thermo Fisher Scientific).A549 or NCI-H520 cells were seeded in 96-well plates  at 4\u00b0C overnight, After that, cells were incubated with goat anti-rabbit IgG  at 37\u00b0C for 1 h. The nuclei were stained with DAPI  for 5 min. Finally, cells were observed under a fluorescence microscope .A549 or NCI-H520 cells were trypsinized, washed with phosphate-buffered saline and resuspended in Annexin V Binding Buffer. The cells were then stained with 5 \u03bcL of fluorescein isothiocyanate and 5 \u03bcL propidium iodide (PI) for 15 minutes. A flow cytometer  was used to determine the cell apoptosis rate.PRKCA, respectively, the partial sequences of hsa_circ_0007580 and the 3\u2032-untranslated region (UTR) of PRKCA containing the putative binding sites for miR-545-3p were synthesized by Sangon Biotech  and cloned into pmirGLO Dual-Luciferase miRNA Target Expression Vectors . Lipofectamine 2000 (Thermo Fisher Scientific) was used to transfect 293T cells with the hsa_circ_0007580/PRKCA (WT) or hsa_circ_0007580/PRKCA (MT) vectors, together with the control, vector-control or miR-545-3p mimics, according to the manufacturer\u2019s instructions. The relative luciferase activity was analyzed on a Dual-Glo Luciferase Assay System (Promega).For the construction of the WT/MT reporter vectors for hsa_circ_0007580 and in vitro. An RNA structure buffer (Thermo Fisher Scientific) was used to induce secondary structure formation from the biotin-labeled RNAs. Streptavidin beads (Thermo Fisher Scientific) were washed three times with 500 \u03bcL of RNA immunoprecipitation wash buffer (Thermo Fisher Scientific) and then added to the biotinylated RNAs at 4\u00b0C overnight. The overnight mixture was separated by a magnetic field so that streptavidin bead-RNA complexes could be obtained. Then, lysates of NSCLC cells were added to the complexes and incubated on a rotator at room temperature for one hour. The incubated mixture was again separated with a magnetic field so that streptavidin bead-RNA-protein complexes could be obtained.For RNA pull-down assay, the Biotin RNA Labeling Mix  was used to transcribe and label probe-control or probe-hsa_circ_0007580 from hsa_circ_0007580 shRNA2 lenti vector The co-localization of miR-545-3p and hsa_circ_0007580 in the cytoplasm was investigated using FISH detection as described previously .Total protein was isolated from cell lysates or tumor tissues with radio-immunoprecipitation assay buffer and quantified with a bicinchoninic acid protein assay kit . Proteins were resolved on 10% sodium dodecyl sulfate polyacrylamide gels and then transferred to polyvinylidene difluoride membranes (Bio-Rad). After being blocked, the membranes were incubated with primary antibodies at 4\u00b0C overnight and then incubated with an anti-rabbit secondary antibody  at room temperature for 1 h. The membranes were scanned on an Odyssey Imaging System and analyzed with Odyssey v2.0 software . The primary antibodies used in this study were: anti-p38 , anti-PRKCA  and anti-\u03b2-actin . \u03b2-actin was used as an internal control.in vivo experiments were performed in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals, following a protocol approved by the Ethics Committees of The First Affiliated Hospital, Zhejiang University.Eight BALB/c nude mice (six to eight weeks old) were purchased from Vital River . The mice were housed in a dedicated specific-pathogen-free facility. A549 cells (control or stably expressing hsa_circ_0007580 shRNA2) were transplanted subcutaneously into each mouse as described previously . The tumFor each analysis, at least three independent experiments were performed. All data are expressed as the mean \u00b1 standard deviation. Differences were analyzed with Student\u2019s t-test (for two groups) or one-way analysis of variance followed by Tukey\u2019s test (for three or more groups) in GraphPad Prism 7. P<0.05 was considered to indicate a statistically significant difference."}
+{"text": "It was manifested loss of circ_0014717 induced HCC progression, which was reversed by BTG2 in Hep3B cells. In conclusion, our findings illustrated a novel circ_0014717/miR-668-3p/BTG2 regulatory signaling pathway in HCC.Recent studies have reported a close association between circRNAs and cancer development. CircRNAs have been recognized to be involved in various biological processes. Up to now, the function of circRNAs in hepatocellular carcinoma (HCC) is still poorly known. qRT-PCR was used to test circ_0014717 expression in HCC tissue samples and cells was determined. It was shown that circ_0014717 was significantly decreased in HCC. Then, we observed overexpression of circ_0014717 obviously repressed HCC cell growth, migration and invasion. Next, we predicted circ_0014717 acted as a sponge of miR-668-3p. miR-668-3p has been reported to participate in several diseases. In our work, it was shown miR-668-3p was greatly increased in HCC and the direct binding sites between circ_0014717 and miR-668-3p were validated. In addition, B-cell translocation gene 2 (BTG2) is closely involved in cellular carcinogenic processes. BTG2 was predicted as a target for miR-668-3p. By performing rescue assays, we demonstrated that circ_0014717 repressed HCC progression  Recently, hepatocellular carcinoma (HCC) is becoming a prevalent cancer across the world , 2. Meanvia inducing FOXM1 . DMEM medium  was used to incubate the cells. The medium was added with 10% FBS  and antibiotics  in a humidified incubator with 5% COLentivector-mediated shRNA of circ_0014717 (LV-shcirc_0014717) and non-targeting sh-control were synthesized by GeneChem . The full-length of circ_0014717 were sub-cloned into the lentivirus vector (LV-circ_0014717) by GeneChem . Lentivirus infection was carried out under 8 ng/ml Polybrene. pcDNA3.1-BTG2 and the empty plasmid pcDNA3.1 were obtained from GeneChem . miR-668-3p mimics, inhibitors and negative controls were obtained from RiboBio . Lipofectamine 3000  was employed to do cell transfection.To carry out CCK8 assay, the transfected cells were seeded into 96-well plates with 3,000 cells in each well. Ten microliters of CCK 8 solution  was added to the cells and they were maintained at 37\u00b0C for 2\u00a0h. The OD values were tested at 450 nm using\u00a0a SpectraMax microtiter plate reader.EdU assay was carried out using EdU kit . Results were obtained using Zeiss fluorescence photomicroscope .To carry out clone formation experiment, the transfected cells were seeded in six-well plates. Then, after cells were cultured for 2 weeks, cells were fixed using 30% formaldehyde for 15\u00a0min and stained using 0.1% crystal violet .6 cells were resuspended using 500 \u03bcl PI/RNase Staining Buffer. Then, the cells were incubated for 15min with no light. A FACSCanto II flow cytometer  was utilized to analyze cell cycle. To perform cell apoptosis assay, the PE Annexin V Apoptosis Detection Kit I was used. After cells were washed using pre-cold PBS buffer, 5 \u03bcl PE Annexin V and 5 \u03bcl FITC solution were added to the cells.To carry out cell cycle analysis, cells were fixed by 70% ethanol. 1 \u00d710To perform the migration assay, cells were trypsinized and then grown in the upper chamber of each insert  with non-coated membrane with 1% FBS (600\u00a0\u03bcl). After 24\u00a0h, the upper surface of the membrane was removed by using a cotton tip. Cells on the lower surface were stained for half an hour with 0.1% crystal violet. To carry out the invasion assay, matrigel chambers  were performed. Briefly, cells were collected, re-suspended in medium without serum, and shifted to the hydrated matrigel chambers. The bottom chambers were incubated in 500\u00a0\u03bcl culture medium with 10% FBS. Then, we scraped the cells on the upper surface, whereas the invasive cells on the lower surface were fixed and colored using 0.1% crystal violet for half an hour.Cell lysates were extracted by RIPA buffer added with a protease inhibitor cocktail. Equal amounts of protein samples were separated on 10% SDS-polyacrylamide gel electrophoresis and then transferred onto PVDF membranes . After blocked in 5% non-fat dry milk, the PVDF membranes were incubated with the anti-human BTG2 antibody  and GAPDH antibody . The protein bonds were visualized by a chemiluminescent detection system . A FluroChem E Imager  was carried out to visualize the western blots.-\u0394\u0394Ct. The sequences of primers were demonstrated in Total RNA of HCC cells and tissues was isolated using TRIzol. 1\u00a0\u00b5g DNase-treated RNA was reverse transcribed to cDNA using MMLV reverse transcriptase . Then, relative quantitative real-time PCR was conducted using SYBR Premix Ex Taq II  on LightCycler 96 . Then, the expression of target genes was detected using the formula 2^The biotinylated probe was constructed to bind to the junction area of circ_0014717. The circ_0014717 probe  was incubated with streptavidin magnetic beads . The bound miRNAs in the pull-down materials were extracted using Trizol reagent and qRT-PCR assay was carried out to detect miRNA expression.The sequence of 3\u2019-UTR of BTG2 or circ_0014717 were subcloned into pGL3 luciferase reporter vector . The WT/MUT 3\u2019-UTR of BTG2 vector or WT/MUT circ_0014717 vector and control mimics or miR-668-3p mimics were co-transfected. The luciferase activity was normalized using a dual luciferase reporter assay system .6 per injection) that were transfected with LV-circ_0014717 and LV-NC, respectively, were implanted into the right flank of the mice. After six weeks, mice were sacrificed under anesthesia. Tumor tissues were subjected to HE and Ki-67 staining. The animal experiments were approved by the Animal Care and Use Committee of The Affiliated Hospital of Youjiang Medical University for Nationalities.BALB/c nude mice  were purchased from SLAC  and divided into two groups (n = 6 in each group). Hep3B cells . The Student\u2019s t-test was carried out to analyze differences between two groups. Two-way ANOVA was carried out when more than two groups were compared. Differences were statistically significant if Firstly, we displayed that circ_0014717 in 30 pair of HCC tissues was greatly lower than that in matched tumor-adjacent tissues confirmed using qRT-PCR analysis in Next, we studied the roles of circ_0014717 in HCC cell growth. Circ_0014717 was stably increased in Hep3B and SMMC7721 cells as shown in in vivo, Hep3B cells with increased circ_0014717 were implanted into nude mice. The findings of tumor growth curves and tumor weight demonstrated circ_0014717 obviously suppressed tumor growth in mice . miR-668-3p overexpression depressed the expression of BTG2 in HCC cells most significantly. Then, it was indicated that BTG2 expression in Hep3B and SMMC7721 cells was reduced by miR-668-3p mimics in Then, we demonstrated the mRNA levels of top six predicted genes after the up-regulation of miR-668-3p in Hep3B and SMMC7721 cells in To study whether BTG2 was critical for HCC cell proliferation, apoptosis and invasion upon loss of circ_0014717, Hep3B cells with circ_0014717 knockdown were transfected with BTG2 expression vectors. In CircRNAs exist in a wide range among various organisms, such as human cells . ImportaIn our current work, we first reported that circ_0014717 was greatly decreased in HCC tissues and cells. Low circ_0014717 expression levels were associated with significantly reduced overall survival and an increased risk of tumor recurrence. Circ_0014717 might represent an independent prognostic biomarker in HCC patients. Then, overexpression of circ_0014717 was induced in Hep3B and SMMC-7721 cells. We found that circ_0014717 repressed the progression of HCC significantly. Previous studies have confirmed that circ_0014717 is reduced in gastric and colorectal cancer . Then, wvia regulating AKT1/mTOR signaling , the Young and middle-aged teachers\u2019 basic scientific research ability improvement project in Guangxi colleges and universities (No. 2020KY13020), and the Innovation Project of GuangXi Graduate Education (JGY2020166).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest."}
+{"text": "Using liquid biopsy-based RT-ddPCR, we discovered the correlation between increased hsa_circ_0000190 plasma level (p < 0.0001) and higher programmed death-ligand 1 (PD-L1) level in tumor (p = 0.0283). Notably, long-term follow-up of the immunotherapy treated cases showed that upregulated plasma hsa_circ_0000190 level correlated with poor response to systemic therapy and immunotherapy . Secretory circRNAs are detectable in blood by LB-based RT-ddPCR and may serve as blood-based biomarkers to monitor disease progression and treatment efficacy.Lung cancer (LC) causes the majority of cancer-related deaths. Circular RNAs (circRNAs) were reported to play roles in cancers by targeting pro- and anti-oncogenic miRNAs. However, the mechanisms of circRNAs in LC progression and their prognostic value of treatment response remain unclear. By using next generation sequencing (NGS) of LC cell lines\u2019 transcriptomes, we identified highly overexpressed hsa_circ_0000190 and hsa_circ_000164 as potential biomarkers. By using the highly sensitive RT-ddPCR method, these circRNAs were shown to be secreted by cell lines and were detected in human blood. Clinical validation by RT-ddPCR was carried out on 272 (231 LC patients and 41 controls) blood samples. Higher hsa_circ_0000190 levels were associated with larger tumor size ( Lung cancer (LC) is the leading cause of cancer-related deaths throughout the world, and more than 85% of LC cases belong to non-small-cell lung carcinoma (NSCLC) . Most LCEGFR), rapidly accelerated fibrosarcoma 1 (RAF1), phosphoinositide 3-kinase catalytic subunit delta (PIK3CD), mammalian target of rapamycin (mTOR), and insulin receptor substrate-1 (IRS-1) [Circular RNAs (circRNAs) are non-coding RNAs that consist of a circular loop with multiple microRNA (miRNA) binding sites known as miRNA response elements (MREs); hence they function by competing with endogenous miRNAs to regulate gene expression ,10. Due  (IRS-1) ,14,15. T (IRS-1) ,17,18,19Tissue biopsies are used by clinicians for histological diagnosis and, more recently, for the genetic profiling of the tumor to predict tumor progression and response to treatment. However, the limitations of tissue biopsies such as procedure considerations, tumor heterogeneity, and dynamic genomic change of the tumor after treatment impede practicable repeated tissue biopsy assessment ,21. ThisTherefore, the main objective of this study was to identify potential circRNA biomarkers of LC, and apply the LB-based RT-ddPCR for cancer detection, diagnosis, and prediction of treatment efficacy in advanced LC. Next generation sequencing (NGS) was used to identify circRNAs overexpressed in LC cell lines with different EGFR mutation status, and two circRNAs, hsa_circ_0000190 and hsa_circ_0001649, were selected as potential biomarkers. These two circRNAs were shown to be secreted by LC cells in vitro, and were also detected in blood by RT-ddPCR. The expression of these two circRNAs was monitored in blood plasma of a cohort of LC patients, and was found to correlate with a number of pathological parameters and response to immunotherapy, thus confirming their usability as secreted biomarkers.Although increasing evidence has revealed that specific circRNAs, such as circRNA 100876 and hsa_circ_0013958, are upregulated in LC tissue comparing to adjacent normal tissue, little is known about the secreted circRNAs in patients\u2019 peripheral blood ,28. TherEGFR gene, one of the major pathological parameters in LC. Therefore, we selected 65 upregulated circRNAs with higher expression in HCC827 than in A549 (HCC827 > A549) and 121 downregulated circRNAs with lower expression in HCC827 than in A549 (HCC827 < A549)  of the transcriptomes of two distinct lung adenocarcinoma cell lines, HCC827 and A549, and a normal bronchial epithelial cell line BEAS-2B. The differential expression of candidate circRNAs was then examined among three distinct cell lines, including A549 (wild type EGFR lung adenocarcinoma cells), HCC827 (lung adenocarcinoma cells with an activating mutation in the EGFR tyrosine kinase domain), and the normal bronchial epithelial cells BEAS-2B according to the pipeline shown on  < A549) C. By thi < A549) C. These  < A549) C,D. Thes < A549) E. Collechttp://www.circbase.org/)  C. As wasse.org/) D. In addse.org/) E. Comparg intact E. To sumAccumulating data have revealed the advantages of using ddPCR in detecting cell-free RNA and DNA in a variety of human body fluids . Indeed,p < 0.0001) (p < 0.0001), and hsa_circ_0001649 was upregulated to a lesser extent  (p < 0.0001) (p < 0.0001).As shown above, hsa_circ_0000190 demonstrated potential to predict the efficacy of lung cancer treatment. As human blood is the most commonly accessible specimen for diagnostic purposes, we performed serial blood analysis of the differential expression of hsa_circ_0000190 and hsa_circ_0001649 in a cohort of 231 LC patients and 41 healthy donors . Interse 0.0001) A. qRT-PCr extent B. Considanalyzed D,E, and analyzed E was higanalyzed D. Altoge 0.0001) F, and th 0.0001) G. In add 0.0001) H and hsa 0.0001) I expressp = 0.0039) (p = 0.0028) than those with a non-micropapillary/solid-predominant pattern  (p = 0.0004). LC with positive PD-L1 expression (\u22651%) had a higher level of hsa_circ_0000190 (p = 0.0283); however, in patients with negative PD-L1 expression, hsa_circ_0000190 could still be detected. The correlation coefficients between the expression level of PD-L1 and the level of hsa_circ_0000190 and hsa_circ_0001649 were 0.037 and 0.167, respectively. Patients with partial regression (PR) after treatment had lower levels of hsa_circ_0000190 compared with patients with stable disease (SD), progressive disease (PD), or SD/PD  (p = 0.0058) (p = 0.047)  A. Lung aatterns) B. The leatterns) C and PD-atterns) D. LC patctively) E. Forty  0.0058) F. Seven = 0.047) G, but th= 0.047) H. CollecThe serial blood tests from lung cancer patients were also performed for evaluating the association between treatment response and the serial changes in the levels of hsa_circ_0000190 and hsa_circ_0001649 during the treatment period. The changes of plasma circRNA levels during immunotherapy treatment were monitored in six lung cancer patients . Three ohttp://circinteractome.nia.nih.gov): the former contained the binding sites of 12 miRNAs and the latter of 31 miRNAs (CircRNAs are known to work as competing endogenous RNAs (ceRNAs) that sequester miRNAs like a sponge, and hence regulate the stability or translation of target mRNAs by alleviating them from the miRNA-mediated inhibition . To anal1 miRNAs . We perf1 miRNAs B. The ta1 miRNAs C. Given 1 miRNAs  and were1 miRNAs . InteresAdvanced LC is often characterized by drug resistance-related treatment failure. The role of miRNAs, a type of small non-coding RNAs (ncRNAs), as prognosis predictors for LC and other cancer types has been explored by previous studies, but the developed miRNA panels use relative miRNA ratio measurement ,38,39. MIn this study, in order to detect circRNAs secreted into blood plasma, we applied highly sensitive and specific RT-ddPCR method with absolute quantification. By using it, we discovered that the levels of hsa_circ_0000190 and hsa_circ_0001649 were increased in LC cell lines and patients\u2019 plasma. These two circRNAs were shown to be secreted by LC cells, as their presence was detected in the conditioned media of LC cell lines. Furthermore, we propose that RT-ddPCR can be used to analyze the plasma levels of hsa_circ_0000190 and hsa_circ_0001649 in patients\u2019 blood. Our result showed that the plasma level of hsa_circ_0000190 was related to clinicopathological features, treatment response, and overall survival in LC patients. Accordingly, hsa_circ_0000190 was demonstrated to be a potentially valuable blood-based biomarker to assess the prognosis of LC and the treatment efficacy of immunotherapy by ddPCR-based LB.p = 0.0028)  B. This r 0.0028) D\u2013F. In o 0.0028) . The hsa 0.0028) E,F. ThesImmunotherapy through immune checkpoint inhibition by blocking the PD-1/PD-L1 signaling pathway that enhances anti-cancer T cell immunity has shown promising and significant efficacy in a variety of malignancies, including LC ,45,46,47p = 0.0283) (p = 0.0058)  D. A prev 0.0058) F. Seven Summarily, in this study, we used LB-based ddPCR platform to demonstrate that the plasma level of hsa_circ_0000190 can be monitored and may serve as a useful blood-based biomarker to monitor the disease status and the treatment efficacy. In terms of cost, dye-based ddPCR is more economical than its probe-based counterpart, making it more adaptable to an average molecular laboratory setting.EGFR mutation  (Lung cancer (LC) patients who had received diagnosis, staging, and treatment were recruited into the present study. Patients were excluded if they had been diagnosed with other cancers; had incomplete medical records or had received less than 3 months of follow-up. This study enrolled 272 cases, including 231 LC patients and 41 healthy controls. Of 231 patients, 156 (67.5%) were men and 75 (32.5%) were women . The his1 L858R) . The chag, no later than 30 min after the collection of specimens for harvesting plasma under room temperature. Plasma was stored at \u221280 \u00b0C in 1 mL aliquots until use and analyzed within 2 days. All procedures of tissues acquirements have followed the tenets of the Declaration of Helsinki and are reviewed by Institutional Review Committee at Taipei Veterans General Hospital.Serial blood specimens were collected from patients before and after treatment. All specimens were collected in Vacutainer EDTA tubes  and centrifuged for 10 min at 1000\u00d7 2). Subculturing was performed using trypsin-EDTA. The medium was refreshed every two days. All cells lines were tested negative for mycoplasma contamination.Human lung cancer cell lines  were obtGAPDH) were 5\u2032-GCATTGCCCTCAACGAC-3\u2032 and 5\u2032-GTCTCTCTCTTCCTCTTGTGC--3\u2032. These primers were synthesized by Invitrogen. The gradient dilution was performed to determine their suitable concentration.Total RNA was isolated from human lung cancer cells using the RNeasy Mini Kit . Oligonucleotides were designed using the computer software package Primer Express 2.0 . All oligonucleotides were synthesized by Invitrogen . Oligonucleotide specificity was computationally tested by homology search with the human genome using BLAST  and later confirmed by dissociation curve analysis. The real-time quantitative polymerase chain reaction (qRT-PCR) was performed using the SYBR Green method in an ABI 7000 sequence detection system (Applied Biosystems) per the manufacturer\u2019s guidelines. The relative quantification of circRNA expression levels in qRT-PCR was evaluated by the \u0394Cq method. The sequences of divergent primers to amplify the spice junction of hsa_circ_0000190 were 5\u2032-TTGCTCCTTGGGCGCTATAC-3\u2032 and 5\u2032-AGAGTCCAGCGGCAAAACTA-3\u2032 . The seqBlood specimens were collected in Vacutainer EDTA tubes (Becton Dickinson) and processed as per the above-described protocol. Total RNA was isolated from 1 mL of plasma by using the QIAamp Circulating Nucleic Acid Kit (QIAGEN). The RNA was eluted from spin columns in 15 \u03bcL of nuclease-free water. Following the manufacturer\u2019s instruction for plasma specimens, the cDNA Synthesis Kit  was used to synthesize complementary DNA (cDNA) in a 20 \u03bcL reaction, starting from 3 \u03bcL of RNA. Then, 1 \u03bcL of synthesized cDNA was assayed in a 20 \u03bcL PCR reaction volume according to the manufacturer\u2019s protocol. The ddPCR was processed on the QX200 Droplet System with ddPCR EvaGreen Supermix  and 1 \u03bcL of synthesized cDNA. The droplets for the PCR reactions were generated according to the manufacturer\u2019s protocol (Bio-Rad). Then, the samples were transferred into a 96-well PCR plate and the ddPCR was conducted. The above-mentioned procedure was performed for all test specimens and negative controls. At the end of the PCR reaction, PCR-positive and PCR-negative droplets were counted by the QX200 Droplet Reader (Bio-Rad) and the data were analyzed by QuantaSoft software (Bio-Rad).2 fold change of the FPKM value of each gene between samples was used to identify up- or down-regulated genes over the control. The PCA analysis was applied to several samples with all the FPKM values as features and three top principal components were extracted and used as the new basis to further discover the similarity between samples. The RNA-Seq data were deposited to the Gene Expression Omnibus (GEO) database with the accession number GSE152434.For NGS data analysis, we first used PEAT algorithm to remove adapter contamination and then aligned the clean reads to the mm10 genome using RNA-Star (version 2.5.3a) to retain junction reads before sending into Cufflinks (version 2.2.1) with gene annotation by GENCODE (version 21) for normalized expression level estimation . We only considered circRNAs, miRNAs, and protein-coding mRNAs in this work. The LogThe baseline evaluations of the LC characteristics for each patient were performed within the 3 weeks prior to treatment. This included a chest computed tomography (CT) scan, which was repeated every 3 months thereafter or when confirmation of treatment response or disease progression was needed. Treatment response assessment was performed according to the Response Evaluation Criteria in Solid Tumors (RECIST) group criteria (version 1.1). Progression-free survival (PFS) was calculated from the date of treatment initiation to the earliest sign of disease progression, as determined by the RECIST criteria, or death from any cause. If disease progression had not occurred at the time of the last follow-up visit, the data on PFS was censored at that time. Overall survival was measured from the date of treatment initiation until the date of death or last follow-up.t-test. The Kaplan\u2013Meier method with a log-rank test was used for survival analysis. When comparing the treatment response and biomarkers, the Mann\u2013Whitney test was used for non-parametric data and Pearson\u2019s \u03c72 test was used for parametric data. Statistical analysis was performed using the Statistical Package for the Social Sciences (SPSS) 18.0 Software  and PRISM . All p-values were 2-sided, and a value of <0.05 was considered statistically significant.Quantifiable data are expressed as mean \u00b1 standard error of the mean (SEM). Differences between the groups were analyzed using one-way ANOVA followed by Student\u2019s In conclusion, our research is the first to determine the absolute cell-free circRNA profile in the plasma of patients with LC. Furthermore, we discovered that hsa_circ_0000190 and hsa_circ_0001649 levels increased in lung cancer cell lines and patients\u2019 plasma. In addition, the level of hsa_circ_0000190 was associated with clinicopathological features and the treatment response of LC patients. As a result, hsa_circ_0000190 may be a valuable blood-based biomarker to estimate the prognosis of LC and the treatment efficacy of immunotherapy by ddPCR-based LB. We demonstrated that LB-based circRNA-ddPCR systems serve as a platform of personal precision medicine\u2014prospectively validating the efficacy of chemotherapy, targeted remedy, and immune therapeutics in advanced LC."}
+{"text": "The availability of reference genomes has revolutionized the study of biology. Multiple competing technologies have been developed to improve the quality and robustness of genome assemblies during the past decade. The 2 widely used long-read sequencing providers\u2014Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT)\u2014have recently updated their platforms: PacBio enables high-throughput HiFi reads with base-level resolution of >99%, and ONT generated reads as long as 2 Mb. We applied the 2 up-to-date platforms to a single rice individual and then compared the 2 assemblies to investigate the advantages and limitations of each.The results showed that ONT ultralong reads delivered higher contiguity, producing a total of 18 contigs of which 10 were assembled into a single chromosome compared to 394 contigs and 3 chromosome-level contigs for the PacBio assembly. The ONT ultralong reads also prevented assembly errors caused by long repetitive regions, for which we observed a total of 44 genes of false redundancies and 10 genes of false losses in the PacBio assembly, leading to over- or underestimation of the gene families in those long repetitive regions. We also noted that the PacBio HiFi reads generated assemblies with considerably fewer errors at the level of single nucleotides and small insertions and deletions than those of the ONT assembly, which generated an average 1.06 errors per kb and finally engendered 1,475 incorrect gene annotations via altered or truncated protein predictions.It shows that both PacBio HiFi reads and ONT ultralong reads had their own merits. Further genome reference constructions could leverage both techniques to lessen the impact of assembly errors and subsequent annotation mistakes rooted in each. Homo sapiens [Oryza sativa ssp. indica, 2n = 2x = 24, variety 9311) [The availability of reference genomes has revolutionized the study of biology. The high-quality human reference genome enabled the identification of disease causative alleles , 2; the  sapiens . The 2 c sapiens , 18, 19.ty 9311) , 21 thatFollowing DNA extraction from the rice sample, we sequenced the 2 extracts using the ONT PromethION and PacBio Sequel II platforms, respectively. The PromethION generated a total of 92 Gb data 230\u00d7) with an N50 of 41,473\u00a0bp, and the Sequel II produced a total of 253 Gb data (632\u00d7) with each molecular fragment being sequenced 14.72 times on average and produced \u223c20 Gb HiFi reads (50\u00d7) with an average length of 13,363\u00a0bp. We applied multiple software packages, including Canu1.9 , NextDen0\u00d7 with aWe then randomly took 1 chromosome (Chr. 6) where ONT's 1 single contig  corresponded to 9 contigs  of the PacBio assembly to investigate and visualize the incongruencies between them. For the 9 PacBio contigs assembled for Chr. 6, 4 reached a length \u22656 Mb and 5 had a length of merely 10\u201370\u00a0kb. We investigated the 3 gaps where the top 4 PacBio contigs  failed to connect Fig.\u00a0. We mappIn addition to those gaps that PacBio failed to connect, we noticed that there were a bunch of small-scale mismatches (<85\u00a0bp) between the 2 assemblies. First, we extracted the reciprocal matches \u22651\u00a0Mb between the 2 assemblies for comparison using QUAST . Then, wInstead of using the assemblies generated by 2 different methods (Canu vs NextDenovo), a further examination for the 2 sequencing techniques using the same assembly methods  achievedIn conclusion, our study investigated genome assembly qualities between the 2 up-to-date competing long-read sequencing techniques\u2014PacBio HiFi reads and ONT ultralong reads. It showed both techniques had their own merits: (i) ONT ultralong reads delivered higher contiguity and prevented false redundancies caused by long repeats, which, in our case of the rice genome, assembled 10 of the 12 autosomes into 1 single contig; and (ii) PacBio HiFi reads produced fewer errors at the level of single nucleotides and small InDels and obtained >1,400 genes that were incorrectly annotated in the ONT assembly owing to its error-prone reads. However, the present study has several limitations, including, among others, (i) NextDenovo, which generated the most contiguous assembly for the ONT reads, is a newly developed assembler whose performance has not been validated on other species; (ii) rice, which has a relatively small and simple genome, cannot characterize the full spectrum of the strengths and weaknesses of the 2 sequencing technologies. Genome studies, especially for large and complex genomes, will shed more light on this matter. Therefore, we suggest that further genome reference constructions leverage both techniques to lessen the impact of assembly errors and subsequent annotation mistakes rooted in each. There is also an urgent demand for improved assembly and error correction algorithms to fulfill this task.The DNA samples used for ONT and PacBio Sequel II platform sequencing were isolated from leaf tissues using the sodium dodecyl sulfate method and Q13323kit , respectively . The ONTRRID:SCR_017642) [RRID:SCR_015880) were selected out on the basis of N50 value   through _017642)  was appl50 value  and usedRRID:SCR_010910) [We identified centromere- and telomere-related sequences using the RCS2 family repeats and 5\u2032-AAACCCT-3\u2032 repeats, respectively , 39. For_010910) , and reg_010910) .Collinearity: We aligned both assemblies to a high-quality rice genome  using minimap2 [minimap2  with a pminimap2   with default parameters [rameters . QUAST cIdentification of errors in forms of single nucleotides and small Indels: We aligned PacBio HiFi reads onto the ONT assembly and then identified single-nucleotide polymorphisms (SNPs) and InDels using GATK4  [_001876)  with filGene loss and redundancies: In the case that multiple PacBio assembly contigs mapped onto the same regions of the ONT assembly, we defined the relatively shorter ones as redundancies conditional on the following 2 criteria: (i) similarity score \u226597% between them; (2) total depth <60 and both have depths <40 , we aligned amino acid sequences of the PacBio assembly onto corresponding ONT regions using exonerate [xonerate  (\u2013model RRID:SCR_016157) with called_sites \u2265 10. The methylation profiles and GC content were recorded throughout the genome with a window size of 1,000\u00a0bp and a step length of 500\u00a0bp. Windows that contained \u22655 ONT errors were defined as ONT error-enriched regions and were used to compare for the methylation and GC content with other genomic regions.We calculated the genome-wide methylation level for the ONT assembly using Nanopolish v0.11.1  and ONT (assembled using NextDenvo) are deposited on NCBI under project IDs PRJNA600693, PRJNA644721, and PRJNA644720, respectively. Supporting data, including annotation files, assemblies, and BUSCO results, are also available via the , GigaDB .Supplementary Methods.Supplementary Figure S1. Collinearity between genome assembly of rice R498 and that of PacBio (left) and ONT (right). Note: The figure only shows alignments \u226530\u00a0kb and query sequences \u22651 Mb.Supplementary Figure S2. IGV plots of the 3 PacBio gaps on Chr. 6. Gray shadows represent gap regions in the PacBio assembly. Red rectangles represent the repeat elements.Supplementary Figure S3. Details of PacBio Gap 1. The 2 repetitive regions matched to another PacBio assembly contig corresponding to Chr5 (PB_Chr5) with high identities. IDY means similarity identities between each other. The bottom panel highlights local IDY values of 100% between each other with an alignment length of 10\u00a0kb (PB-L1 vs PB-S1), 12\u00a0kb (PB-L1 vs PB_Chr5), and 13\u00a0kb (PB-S1 vs PB_Chr5).Supplementary Figure S4. Assembly statistics for the subsampling test. Contig N50 value (upper) and raw read coverage (under) were demonstrated for each assembly. Assemblies applied the same parameters in Supplementary Table S1 for Canu and NextDenovo.Supplementary Figure S5. The length distribution of the ONT InDel errors. Note that InDels of length >20\u00a0bp had a total count of 216 and are not shown here.Supplementary Figure S6. Distances between adjacent ONT errors. Those errors tended to cluster in the same region rather than distribute randomly and evenly on the genome, because the distances should have a peak at \u223c1,000\u00a0bp for an average error rate of 1.06 per kb in the case of random distribution. The yellow curve represents a theoretical error distribution with a mean (SD) distance of 1,000 (200).Supplementary Figure S7. Depth of (a) shotgun reads, (b) ONT raw reads, and (c) PacBio HiFi reads for those ONT error sites. Note that Illumina shotgun read depth >30 had a total count of 10,294  and is not shown here.Supplementary Figure S8. Comparison of GC content and methylation level between the ONT error-enriched regions and other regions for the ONT assembly.Supplementary Figure S9. The paralogous copy number distribution of the genes affected by ONT errors. Paralogs were searched using BLAST with e-value cutoff of 1e\u22125 for each gene.Supplementary Figure S10. Two examples (1 SNP and 1 InDel) that show the mismatches between the ONT and PacBio assemblies, which were well covered by shotgun reads and thus could be errors on HiFi reads generated during the CCS process.Supplementary Figure S11. Examples of the mismatches >85\u00a0bp and their corresponding IGV plots for the genome alignments for the PacBio (upper) and ONT (bottom) assemblies. (a) A 1,432-bp InDel where reads mapped onto PacBio's assembly with soft-clips. (b) A 231-bp mismatch on which ONT's assembly displayed a cluster of small-scale errors . (c) A 204-bp InDel (at the end of contig tig00004207) on which no PacBio HiFi reads showed in the alignments . We also noted that this InDel was introduced during the genome-polishing step by Racon, which may corrupt the correctly assembled sequence within repetitive regions.Supplementary Figure S12. Contig alignments of Chr. 6. Red represents contigs that contain InDel mismatches of length \u226585\u00a0bp, and green, those that do not. The percentage values represent the coverage ratios .Supplementary Table S1. Assembly parameters and computational resource statistics.Supplementary Table S2. Assembly quality evaluation.Supplementary Table S3. The centromeres and telomeres for each chromosome-level contig of ONT and PacBio assemblies.Supplementary Table S4. Results of genome completeness assessment using BUSCO.Supplementary Table S5. Gene loss and redundancies of the PacBio assembly.Supplementary Table S6. Read summary of the subsampling test.bp: base pairs; BUSCO: Benchmarking Universal Single-Copy Orthologs; BWA: Burrows-Wheeler Aligner; CCS: circular consensus sequencing; GATK: Genome Analysis Toolkit; Gb: gigabase pairs; GC: guanine-cytosine; IGV: Integrative Genomics Viewer; kb: kilobase pairs; Mb: megabase pairs; ONT: Oxford Nanopore Technologies; NCBI: National Center for Biotechnology Information; PacBio: Pacific Biosciences; SMRT: single-molecule real-time; SNP: single-nucleotide polymorphism; SNV: single-nucleotide variant; T2T: telomere to telomere; Tb: terabase pairs.D.L., P.R., F.L., Z.S,, G.M., Y.T., X.L., Q.L, L.H., D.W. and S.L. are employees of Grandomics Biosciences, a company that provides bioinformatics and genomics services.SL.L., D.W. and W.W. concieved the idea and coordinated the project. S.Z. and W.W. contributed the rice samples. D.L. led the analysis with helps from S.L., P.R., F.L., Z.S,, G.M., Y.T.. X.L., Q.L. and L.H. led the benchwork. S.L. and D.L. formulated the first draft, and all authors contributed to the final version. All authors read and approved the final manuscript. S.L. was supported by Chinese Postdoctoral Science Foundation (2019M660051) and Wuhan Technology Innovation Programme (2020020602012107).giaa123_GIGA-D-20-00061_Original_SubmissionClick here for additional data file.giaa123_GIGA-D-20-00061_Revision_1Click here for additional data file.giaa123_GIGA-D-20-00061_Revision_2Click here for additional data file.giaa123_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giaa123_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giaa123_Reviewer_1_Report_Original_SubmissionJason Chin -- 3/22/2020 ReviewedClick here for additional data file.giaa123_Reviewer_1_Report_Revision_1Jason Chin -- 7/14/2020 ReviewedClick here for additional data file.giaa123_Reviewer_2_Report_Original_SubmissionTodd Michael -- 4/3/2020 ReviewedClick here for additional data file.giaa123_Reviewer_2_Report_Revision_1Todd Michael -- 7/17/2020 ReviewedClick here for additional data file.giaa123_Reviewer_3_Report_Original_SubmissionSergey Nurk -- 4/20/2020 ReviewedClick here for additional data file.giaa123_Supplemental_FilesClick here for additional data file."}
+{"text": "Here, we report the coding-complete genome sequences of nine clinical severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants and their mutations. The samples were collected from nine Bangladeshi coronavirus disease 2019 (COVID-19) patients. We have identified the E484K escape mutation and the S359T mutation within the spike protein coding region of the sequenced genomes. Here, we report the coding-complete genome sequences of nine clinical severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants and their mutations. The samples were collected from nine Bangladeshi coronavirus disease 2019 (COVID-19) patients. We have identified the E484K escape mutation and the S359T mutation within the spike protein coding region of the sequenced genomes. Betacoronavirus genus in the Coronaviridae family, has been responsible for more than 2 million deaths globally (TC) values ranging from 27 to 30, which, according to the kit\u2019s information, implies high viral load.Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a member of the globally . In thisglobally . The samMN996528)  suspected of being infected with SARS-CoV-2, using the ReliaPrep viral TNA miniprep system (Promega) according to the manufacturer\u2019s instructions. This was followed by preparation of libraries using Illumina RNA preparation with enrichment in combination with the Illumina respiratory virus oligonucleotide panel v2 according to the manufacturer\u2019s instructions . The sequencing was carried out in an Illumina MiniSeq instrument implementing a paired-end protocol . The Fastq sequences were trimmed, quality controlled, and mapped and a consensus sequence was generated using DRAGEN v3.5.1.15 (Illumina) (996528) 36. The noEight strains  belong to the emerging clade 20B, whereas one strain (GRBL_S11) is affiliated with the 20F clade . In thisGATCAT and its subsequent replacement by G at nucleotide position 108 of ORF7b resulted in the introduction of a stop codon, ultimately resulting in a frameshift mutation and D36E substitution, potentially without any loss of function  and GRBL_S3 (68 evidence), the deletion of MW532093, MW532094, MW532095, MW532096, MW532097, MW532098, MW532099, MW532100, and MW532101 (PRJNA692653 and SRA accession number SRP302071.The sequences of nine SARS-CoV-2 genomes were submitted to the GISAID database under the identifiers EPI_ISL_774976, EPI_ISL_775019, EPI_ISL_775020, EPI_ISL_890189, EPI_ISL_775215, EPI_ISL_775218, EPI_ISL_890192, EPI_ISL_890193, and EPI_ISL_890194 and to the NCBI GenBank under the accession numbers MW532101 . The raw"}
+{"text": "Ochrobactrum genus consists of an extensive repertoire of biotechnologically valuable bacterial strains but also opportunistic pathogens. In our previous study, a novel strain, Ochrobactrum sp. POC9, which enhances biogas production in wastewater treatment plants (WWTPs) was identified and thoroughly characterized. Despite an insightful analysis of that bacterium, its susceptibility to bacteriophages present in WWTPs has not been evaluated. Using raw sewage sample from WWTP and applying the enrichment method, two virulent phages, vB_OspM_OC and vB_OspP_OH, which infect the POC9 strain, were isolated. These are the first virulent phages infecting Ochrobactrum spp. identified so far. Both phages were subjected to thorough functional and genomic analyses, which allowed classification of the vB_OspM_OC virus as a novel jumbo phage, with a genome size of over 227 kb. This phage encodes DNA methyltransferase, which mimics the specificity of cell cycle regulated CcrM methylase, a component of the epigenetic regulatory circuits in Alphaproteobacteria. In this study, an analysis of the overall diversity of Ochrobactrum-specific (pro)phages retrieved from databases and extracted in silico from bacterial genomes was also performed. Complex genome mining allowed us to build similarity networks to compare 281 Ochrobactrum-specific viruses. Analyses of the obtained networks revealed a high diversity of Ochrobactrum phages and their dissimilarity to the viruses infecting other bacteria.The  Ochrobactrum spp. are non-fermenting, aerobic, Gram-negative bacteria of the Alphaproteobacteria class, which are frequently isolated from a variety of environmental  and clinical  samples [Ochrobactrum spp.  are recognized as opportunistic pathogens. In these cases, bacteria are isolated mostly from immunocompromised patients, as pathogens colonizing respiratory tract and wounds. These are mostly opportunistic infections, including central venous catheter-associated bloodstream infections, prosthetic valve endocarditis, septic arthritis, osteomyelitis, and peritonitis [Ochrobactrum spp. in bioremediation or industry ought to be preceded by a careful and insightful analysis of their metabolic properties and preferentially their genomic sequences. samples ,5,6,7,8. samples ,10,11. T samples ,13,14,15itonitis ,18,19,20Ochrobactrum spp. were described: a myovirus POA1180 and a podovirus POI1126 [Ochrobactrum isolates [Caudovirales representatives. This diversity was also reflected in the host range of the induced prophages. A detailed analysis of the PAO1180 phage (originating from O. anthropii POA1180) showed high similarity to the prophage previously identified in the Brucella strain 10RB9215 isolated from an African bullfrog [O. intermedium POI1126, exhibited similarities to the Sinorhizobium meliloti phage PCB5, the Erwinia pyrifoliae phage PEp14, and the Burkholderia cenocepacia phages DC1, Bcep22, and BcepIL02. Interestingly, the POI1126 phage genome was nearly identical to the unnamed plasmid of the O. intermedium strain LMG 3301, which suggests that this phage appears in a plasmid-like form in both strains [In 2017, the first two active lysogenic phages of  POI1126 . These pisolates . The perbullfrog . The annOchrobactrum spp. For their identification, the previously characterized strain Ochrobactrum sp. POC9  was used [To our best knowledge, we are the first to identify two virulent phages of was used ,23. Thiswas used . This stOchrobactrum spp., and then a complex comparative genomic analysis of all known Ochrobactrum viruses was performed. This offered new insights into the overall diversity of Ochrobactrum phages.Both phages identified in this study, vB_OspP_OH and vB_OspM_OC, were isolated from a waste sample originating from WWTP \u201cWolomin\u201d (Poland) and were subjected to further genomic and functional analyses. Moreover, a set of lysogenic phages was in silico identified in genomes of sequenced Ochrobactrum sp. POC9 were isolated from a wastewater sample via the standard enrichment method [The phages that are able to infect t method . After pMyoviridae and Podoviridae families, respectively . The TEM analysis of purified OC and OH phages revealed that they belong to the ectively . Hence, Enterobacteria T4 phages, with 35.3% GC content, while the E. coli (its host) %GC content is around 50% [The vB_OspM_OC phage has a dsDNA circularly permuted genome of 227,654 bp and has a %GC content of 37.7%, which is 18% lower than the %GC content of the host POC9 genome. Such low %GC content is typical for T-even phages, particularly ound 50% . The annAs mentioned above, the vB_OspM_OC phage carries 24 tRNA genes which cover all amino acids except of aspartic acid (Asp), phenylalanine (Phe), selenocysteine (Sec) and tyrosine (Tyr). The analysis of the vB_OspM_OC codon usage shows that, when considering all proteins being expressed, the composition of tRNAs encoded by vB_OspM_OC could cover nearly 40% of codons used during the translation. Moreover, this set of tRNAs does not seem to be correlated with low %GC content of the vB_OspM_OC genome. Interestingly, it was found, that there are only 42 (20%) or 94 (44%) of jumbo phages carrying at least 10 or only one tRNA gene, respectively. Therefore, the presence of the numerous tRNA genes is not common in jumbo phages and, as we speculate, in the case of vB_OspM_OC it may have an impact on the efficacy of its infection and propagation within the host cell.The vB_OspM_OC phage has a large genome; based on the NCBI Genome database as of October 19th 2019, there were 214 phages with genomes over 200 kb, and 137 phages had genomes bigger than vB_OspM_OC. The size of a phage genome above 200 kb is an informal threshold for classifying phages as giant  phages. Moreover, the phages within that group are supposed to encode the proteins responsible for DNA replication, nucleotide metabolism, several proteins for host-cell lysis, and paralogous genes encoded across different gene clusters ,28. SincIt was also indicated that vB_OspM_OC encodes proteins that show homology with the proteins encoded by the genomes of T4-like phages. Comparative analyses revealed that vB_OspM_OC shares its core genome with this group of viruses and can thus be conceived as a novel member of the group of T4-like phages .Shewanella amazonensis  and the DNA polymerase III subunit beta of Eschericia coli O157:H7 . Within this protein, AAA+-type ATPase containing the peptidase M41 domain  was identified based on HHpred searches. However, the exact sites involved in the priming of vB_OspM_OC DNA synthesis were not determined. The latter type of replication may be conducted by a set of DNA recombination repair proteins, such as UvsWXY homologs , like in the T4 phage [phiOC_p016-17 genes, which are homologs of the T4 genes, gp46 and gp47.An annotation of the vB_OspM_OC genome revealed the presence of at least 21 genes encoding proteins potentially involved in replication. For 15 of them, direct counterparts of the T4 phage were identified ,29. AmonT4 phage ,29. The i). Moreover, genes encoding two polynucleotide kinases (PhiOC_p107 and PhiOC_p110) and a putative nucleotidyltransferase (PhiOC_p140) were also identified, which together take part in the nucleotide salvage pathway [The vB_OspM_OC also encodes at least seven genes that are potentially involved in pyrimidine nucleotide metabolism. It is possible that the predicted deoxycytidylate deaminase  converts deoxycytidylic acid (dCMP) to deoxyuridylic acid (dUMP), which can be further converted into thymidylic acid (dTMP) via flavin-dependent thymidylate synthase . Moreover, the presence of thymidine kinase  possibly helps to catalyze the conversion of (deoxy)thymidine into dTMP. Moreover, a gene encoding the nucleoside triphosphate pyrophosphohydrolase-like protein was also identified . This protein may convert nucleoside triphosphate (dNTP) into a nucleotide (dNMP) and diphosphate  metabolism. The vB_OspM_OC phage encodes at least seven proteins that are potentially involved in that process. The presence of bifunctional NMN adenylyltransferase (NAmPRTase)/Nudix hydrolase  and nicotinate phosphoribosyltransferase  possibly allows the vB_OspM_OC phage to convert nicotamide to nicotamide D-ribonucleotide and nicotinate into nicotinate D-ribonucleotide, respectively. Both of these ribonucleotides can be further transformed into NAD+ or deamino-NAD+, respectively, via nicotinamide\u2013nicleotide adenylyltransferase . The Nudix hydrolase domain of NatV may also help reverse the last reaction and transform deamino-NAD+ into a nicotamide D-ribonucleotide. The presence and activity of NatV and NatD encoded by the Vibrio phage KVP40 were recently shown to be sufficient for the NAD+ salvage pathway [nadV gene in their genomes [+ biosynthesis pathway. Furthermore, three other genes encode ribonucleotide-diphosphate reductase  and ribonucleotide reductase , which possibly catalyze the formation of deoxyribonucleotides from ribonucleotides and can further be used in the synthesis of DNA [Another feature typical for jumbo phages is nicotinamide adenine dinucleotide (NAD genomes . Amongsts of DNA .N-acetylneuraminidase in a tail fiber protein (PhiOC_p358). The repertoire of lysing enzymes of vB_OspM_OC is extended by the putative N-acetylmuramoyl-L-alanine amidase (PhiOC_p356), lytic transglycosylase (PhiOC_p145), and chitinase (PhiOC_p147), of which the latter two were not located within structural gene clusters.The vB_OspM_OC virion is relatively large, and 33 out of 113 proteins with recognized functions are possibly involved in its formation. Eleven of these proteins were identified as baseplate and/or wedge proteins that may correspond to the firm and complex structure of the baseplate shown on the micrographs a. WithinAlphaproteobacteria often have DNA methyltransferases (MTases) that exhibit GANTC (methylated nucleotide is underlined) specificity, just as the host-encoded cell cycle regulated MTase CcrM [E. coli cultures with the HinfI restriction enzyme . The DNA isolated from the induced cultures was resistant to cleavage by HinfI but sensitive to cleavage with the various enzymes  used as controls. The pET_PhiOC_p213 DNA isolated from the non-induced cultures was susceptible to all restriction enzymes used, including HinfI.Interestingly, within the vB_OspM_OC genome, a predicted N6-adenine (m6A) DNA methyltransferase gene (PhiOC_p213) was found. As shown previously, viruses infecting ase CcrM ,34,35. TSilicibacter phage DSS3-P1 (GenBank acc. no. AET42337.1) and the Loktanella phage pCB2051-A (GenBank acc. no. AGH31487.1), with a ~45% protein sequence identity. It is worth mentioning that, in contrast to previously characterized phage-encoded MTases with GANTC specificity (such as PhiLM21_p027 [Ochrobactrum sp. POC9 (GenBank acc. no. PWU77214.1) and shows 29% identity with for example CcrM of O. pseudogrignonense K8 (GenBank acc. no. ANG95423.1), for which methylation specificity was shown by a PacBio analysis (as found in REBASE: http://rebase.neb.com/cgi-bin/pacbioget?19606). This sequence conservation is limited to the N-terminal methyltransferase domain of CcrM, since a C-terminal nonspecific DNA-binding domain (~80 residue segment) is present only in bacterial CcrM MTases  of phage MTases has been shown for, e.g., P1 [E. coli in this case) Dam enzyme [Ochrobactrum sp. POC9. The potential functions of the maintenance of both, the specificity of the T4-like phage and the bacterial CcrM (in Alphaproteobacteria) and Dam MTases (in Gammaproteobacteria), as well as the conservation of the amino acid sequences of these proteins in virus\u2013host biology, remain to be elucidated.The co-occurrence of phage- and host-encoded enzymes with the same sequence specificity mimicking the host strategies in DNA methylation by viruses is a known phenomenon discovered not only in bacteria  but alsoe.g., P1 , VT-2 [3e.g., P1  and the e.g., P1 , but onlm enzyme , like thThe second isolated phage, vB_OspP_OH, has a dsDNA genome, with a size of 41,227 bp. In contrast to vB_OspM_OC, its %GC content is 55.2%, which is nearly the same as the content of the host strain. Within its genome, 65 potential protein encoding genes were identified, of which 23 (35.4%) had their functions predicted. The vB_OspP_OH phage seems to be unique, showing only limited similarities with the viruses recovered from the environmental samples collected by Mizuno and coworkers, .Amongst the predicted proteins of vB_OspP_OH, there are three involved in phage genome replication, including a potential bifunctional DNA primase-polymerase protein (PhiOH_p22), DNA polymerase I (PhiOH_p28), and DNA helicase (PhiOH_p36) b. The seWithin the genome of vB_OspP_OH, three genes encoding proteins potentially involved in nucleotide metabolism were found b. These The virion structure of vB_OspP_OH allows its classification as a podovirus. Within the vB_OspP_OH genome, 10 genes encoding structural proteins were identified b. These Ochrobactrum phage POI1126 secretion activator protein . An analysis of the domains of PhiOH_p52 and APU92960.1 revealed the presence of the glycosyl hydrolase 108 domain , which acts as a lysozyme [The assumed endolysin of vB_OspP_OH (PhiOH_p52) shows the highest similarity to the lysozyme .Ochrobactrum phages, the PhiSpy v3.4 tool  was applied. This enabled us to significantly extend the knowledge in this field since, in the previous works by Jackel et al. [Ochrobactrum (pro)phages were indicated, while in our analysis, 277 prophages were identified within 104 out of the 113 analyzed genomes . This resulted in the identification of 294 phages similar to Ochrobactrum spp. (pro)phages. A comparison of all those (pro)phages is presented in the form of a similarity network composed of 575 (pro)phages in total  or 59% for vB_OspP_OH (PhiOH_p53), when at least a 75% sequence coverage was considered. To perform a complex analysis of the diversity of l et al. ,42,43 a  genomes . Moreovein total .Ochrobactrum (pro)phages diversity. It was shown that 198 (70.5%) Ochrobactrum (pro)phages shared similarity to the phages infecting other Proteobacteria (223 (pro)phages in the network), which is representatives of the Terrabacteria group (54) or of phages recovered from the metagenomic samples for which hosts were not assigned (17). Amongst the Proteobacteria (pro)phages grouped together with Ochrobactrum phages, 89 viruses infected Alphaproteobacteria, 15 Betaproteobacteria, and 115 Gammaproteobacteria. As for the Terrabacteria group, these phages were recognized as infecting agents only for cyanobacteria, representing the Synechoccoccus (42 viruses) and Prochlorococcus (12) genera. It was also shown that 75 (26.7%) Ochrobactrum viruses shared homologous proteins exclusively with other Ochrobactrum prophages, and 8 viruses formed orphan nodes, which exemplifies the uniqueness of these (pro)phages.The distribution of (pro)phages (represented by nodes) in the network allows for more precise insight into Sinorhizobium phages phiN3, phiM7, phiM12, and phiM19), Stenotrophomonas phage vB_SmaS_DLP_6, and Caulobacter phage Cr30  [Terrabacteria group of bacteria. This may suggest their common origin.This global analysis also allowed us to determine the relatives of the vB_OspM_OC and vB_OspP_OH phages. vB_OspM_OC is related to large, mostly T4-like, phages. Its highest similarity was observed to be with myoviruses  . InteresSinorhizobium meliloti phage phiM5 [Vibrio podoviruses  and the Alteromonas phage ZP6, as well as two siphoviruses the Salmonella phage PMBT28 and the Acinetobacter phage SH-Ab 15497  [vB_OspP_OH created a separate cluster with another podovirus, the genomically highly mosaic ge phiM5 . Direct ctively) ,48.Agrobacterium tumefaciens LBA288, Escherichia coli C600, E. coli DH5\u03b1, E. coli K-12, E. coli BR825, Paracoccus alcaliphilus JCM 7364, Paracoccus aminophilus JCM 7686, Pseudomonas aeruginosa PAO1161, and Variovorax paradoxus EPS), the isolates from wastewater treatment plants , and other environmental isolates , were used in the host range assay analysis. The host range and lytic activity of the vB_OspM_OC and vB_OspP_OH phages were assessed using spot test assays. Even though various bacteria representing the 18 genera were used in the assay, we observed clear zones only on the plates with Ochrobactrum sp. POC9 and Ochrobactrum sp. LM19. The comparison of plaques obtained for LM19 and POC9 showed no differences in the number of plaques (when performing a spot test assay using a series of various concentrations of phages) and in their morphology. Both analyzed phages produced plaques on both Ochrobactrum species. This suggests that both phages are specific to Ochrobactrum spp.Various bacterial strains , including the reference ones . Moreover, around 90% of the vB_OspM_OC phages were adsorbed into the host cells in 15 min, but only when a bacterial culture of OD600 = 0.4 was used. Otherwise, the attachment efficiency of vB_OspM_OC was stable, and the amount was around 65% during the whole experiment.The efficiency of phage development depends on two main steps: the phage\u2019s adsorption to the bacteria and its development inside the host cell. Adsorption assays of vB_OspM_OC and vB_OspP_OH were performed to analyze the kinetics of their adsorption to periment a,b reveaperiment c,d. We oThe phage growth parameters were determined with the use of a one-step growth curve assay. The latent period of vB_OspP_OH is 135 \u00b1 5 min, and the burst size of one lytic cycle is approximately 95 \u00b1 10 pfu per infected cell . The vB_Ochrobactrum sp. POC9. The manual annotation of the POC9 prophage regions identified an abortive infection protein (GenBank acc. no. WP_109988358.1) encoded within vB_OspX_pp134   or pseudpX_pp134 . This kiOchrobactrum sp. POC9, bacterial growth was monitored by measuring the optical densities of OD600 after phage infection. To evaluate the efficiency of phage development, various concentrations of phages were used for infection  (To assess the killing activity of vB_OspM_OC and vB_OspP_OH on 1 to 10) .8\u20131010 pfu/mL) were used (7 pfu/mL), a decrease in optical density was observed (except of 102 and 103 pfu/mL for vB_OspM_OC), albeit at a longer time after infection , Bacillus sp. LPSUB4 (laboratory collection), Brevundimonas sp. LM18 [Brevundimonas sp. LPMIX5 [Brevundimonas sp. POC21 (laboratory collection), Ensifer sp. M14 [E. coli C600 (laboratory collection), E. coli DH5\u03b1 [E. coli K-12 (laboratory collection), Janthinobacterium sp. M1_6 (laboratory collection), Janthinobacterium sp. M1_18 (laboratory collection), Janthinobacterium sp. M2_12 (laboratory collection), Janthinobacterium sp. W1_1 (laboratory collection), Klebsiella sp. POC16 (laboratory collection), Lysinibacillus sp. LPSUB15 (laboratory collection), Ochrobactrum sp. LM19 [Ochrobactrum sp. POC9 [Paracoccus alcaliphilus JCM 7364 [Paracoccus aminophilus JCM 7686 [Pseudomonas aeruginosa PAO1161 [Pseudomonas sp. LM7 [Psychrobacter sp. DAB_AL32B [Rummelibacillus sp. POC4 [Sinorhizobium sp. LM21 [Sphingomonas sp. WLOD2_3 (laboratory collection), Stenotrophomonas sp. POC10 (laboratory collection), and Variovorax paradoxus EPS [E. coli) and 30 \u00b0C . The medium was solidified by the addition of 1.5% (w/v) agar. Where necessary, the medium was supplemented with X-gal, IPTG, and kanamycin (50 \u03bcg/mL). Plasmid pET30a was used for cloning of the DNA methyltransferase gene.The following bacterial strains were used: p. LM16R , Agrobacs LBA288 , Bacillusp. LM18 , Brevund. LPMIX5 , Brevund sp. M14 , E. colioli DH5\u03b1 , E. colisp. LM19 , Ochrobasp. POC9 , ParacocJCM 7364 , ParacocJCM 7686 , Pseudom PAO1161 , Pseudomsp. POC4 , Sinorhisp. LM21 , Sphingooxus EPS . These sE. coli ER2566 by chemical transformation [Standard DNA manipulations were performed as described by Sambrook and Russell (2001) . Plasmidormation .E. coli ER2566. The protein expression and restriction enzyme digestion protection assays for revealing the sequence specificity of the tested MTase were performed as previously described [The predicted DNA MTase gene identified within the vB_OspM_OC phage was amplified by PCR using a Thermo Scientific\u2122 Phusion\u2122 High-Fidelity DNA Polymerase  and following the primer pairs OcFnde (5\u2032-GTTGTTCATATGGAAAATTTGACACTTTTTAATGGTAAC-3\u2032) and OcRxho (5\u2032-GTTGTTAATAAAAATAGGCGTTAGCTCACC-3\u2032). DNA amplification was performed using a Mastercycler . Each thermocycle started with an initial denaturation at 95 \u00b0C for 1 min followed by 30 cycles of denaturation at 98 \u00b0C for 10 s, annealing at 66 \u00b0C for 20 s, extension at 72 \u00b0C for 30 s, and finished with a final extension at 72 \u00b0C for 2 min. The PCR product (after purification) was digested with NdeI and XhoI and ligated with a pET30a vector cut with the same enzymes. The recombinant proteins were expressed in the escribed ,65.Both phages, vB_OspP_OH and vB_OspM_OC, were isolated from a wastewater sample collected from a sewage treatment plant in Wo\u0142omin  on 25 August 2018. Phage isolation was performed according to the previously described procedure . After tTransmission electron microscopic images of the phage virions were obtained using a TEM LIBRA 120  microscope . Samples were prepared by applying a 10 \u03bcL droplet of phage suspension to thin, carbon-coated copper grids (400 mesh), followed by immersing the grids in 1% uranyl acetate for contrasting. Then, the samples were left to dry at room temperature. The visualization of the phages was performed at the Laboratory of Theory and Applications of Electrodes, Faculty of Chemistry, University of Warsaw, Poland.To determine the host range of the vB_OspM_OC and vB_OspP_OH phages, 2.5 \u03bcL of each phage lysate was spotted on double layer agar plates with various tested bacteria in the upper layer. The appearance of the plaques was observed after an overnight incubation of the plates at 30 \u00b0C.Ochrobactrum sp. POC9 culture was spiked with 10 \u00b5L of the phage suspension (around 109 pfu/mL). The mixture was incubated without shaking at 37 \u00b0C. After 1, 2.5, 5, 10, and 15 min, 100 \u00b5L aliquots were withdrawn and centrifuged  to deposit the phage-adsorbed cells as sediment. The titer of the remaining free phages was determined by supernatant titration on double-layer agar plates. The initial number of phages (100% of the phages used) was determined by adding an appropriate volume of the vB_OspM_OC or vB_OspP_OH phage lysate to a medium without bacteria, followed by titration. The number of adsorbed phages was determined via the decrease in PFUs in the supernatant relative to the initial number of phages. The data were obtained from three independent experiments.The adsorption assay was conducted as described previously . BrieflyOchrobactrum sp. POC9 cultures were infected with vB_OspM_OC or vB_OspP_OH phage lysates. Briefly, an overnight culture of bacteria was refreshed in an LB medium and allowed to grow until reaching an OD600 of 0.25. Then, the culture was infected with the vB_OspM_OC or vB_OspP_OH phage lysate at various concentrations ranging from 102 to 1010 pfu/mL. The OD600 measurement was made every 15 min for 24 h. Measurements were carried out with the use of an Infinite 200 PRO multimode plate reader and microplate reader software v 1.8 2010 . The data were obtained from three independent experiments.For the host culture collapse studies, liquid Ochrobactrum sp. POC9 . The culture was grown at 37 \u00b0C with shaking (150 rpm) until OD600 = 0.2 and then infected with the vB_OspP_OH phage at MOI of 0.1 and incubated without shaking at 37 \u00b0C for 5 min. Next, 1 mL of the infected bacteria was centrifuged at 5000 rpm for 1 min at 37 \u00b0C. The resulting pellet was washed and re-suspended in 1 mL of fresh LB broth. Then, 25 \u00b5L of infected cells was transferred to a fresh LB broth and incubated at 37 \u00b0C with shaking (150 rpm). At appropriate time points, the PFUs (300 \u00b5L of samples treated with 300 \u00b5L of chloroform) were calculated. The samples for estimation of the number of infection centers (ICs) and the burst size presented as pfu/IC were analyzed as described previously [For a one-step growth analysis, 25 mL of LB medium was inoculated with an overnight culture of eviously . The dat9 pfu/mL) was mixed with 990 \u00b5L LB broth in a series of tubes, each with a different pH (adjusted using NaOH or HCl), and incubated for 10, 30, or 60 min at room temperature. For temperature stability testing, 10 \u00b5L of the phage lysate (1 \u00d7 109 pfu/mL) was mixed with 990 \u00b5L LB broth in a series of tubes, and the samples were incubated at 43, 60, or 80 \u00b0C for 15, 30, or 60 min. At each of these time-points, the number of phages  was calculated. The control phage samples were incubated at pH 7 and room temperature (22 \u00b0C), respectively. The data were obtained from three independent experiments.For pH stability testing, 10 \u00b5L of the vB_OspM_OC or vB_OspP_OH phage lysate (1 \u00d7 109 pfu/mL) was mixed with 990 \u00b5L of LB broth. The mixture was then spotted onto a polystyrene Petri dish and exposed to UV-C light  from a distance of 50 cm for 1, 2.5, 5, 10, 15, 25, and 30 min. A control sample was incubated on a laboratory bench at room temperature. The data were obtained from three independent experiments.For UV resistance testing, 10 \u00b5L of the vB_OspM_OC or vB_OspP_OH phage lysate  in the paired-end mode using a v3 chemistry kit (Illumina). The obtained sequence reads were filtered for quality using fastp v0.19.5 with a window size of 10 bps moving from 5\u2032 to 3\u2032 and removing bps with a quality lower than 15, polyX at the 3\u2032 end, and the reads of lengths lower than 150 bps . AfterwaOchrobactrum spp. were retrieved from the National Center for Biotechnology Information (NCBI) genome browser  and a draft genome of Ochrobactrum sp. POC9 were manually inspected for the presence of the prophage regions phages was conducted based on the results of vConTACT v2 with the use of the ProcaryoticViralRefSeq85-Merged database applying DIAMOND v0.9.24 for protein clustering and ClusterOne v1.0 for protein and viral cluster analyses ,74,75.The manual annotation of the analyzed phage genomes was conducted using Artemis software . First, Ochrobactrum spp. genomes and the phage genomes from NCBI genome browser (accessed on 10 July 2019) using protein-based similarity network constructed with vConTACT v2. During the program run, ProkaryoticViralRefSeq85-Merged was used as the reference database. The phages present in that database were removed from the set of genomes downloaded from the NCBI to avoid redundancy. In total, 10629 phage genomes were analyzed using the same settings for vConTACT as described in Ochrobactrum spp. and edges reflecting the similarity between the two phages connecting those nodes were retained. The resulting network was visualized in Gephi v0.92 using ForceAtlas 2 and Noverlap layouts to arrange the nodes in a two-dimensional space [Genomes of the isolated phages were compared with prophage regions indicated in the al space ,85.The nucleotide sequences of the vB_OspM_OC and vB_OspP_OH phages have been deposited in the GenBank (NCBI) database with the accession numbers MT028491 and MT028492, respectively.Ochrobactrum sp. POC9, the strain enhancing the reduction of organic biomass and biogas production in WWTPs, was characterized. The identified phages, vB_OspM_OC (myovirus) and vB_OspP_OH (podovirus), are the first (to the best of our knowledge) virulent phages observed to infect Ochrobactrum spp. so far. The vB_OspM_OC virus was recognized as a representative of giant (jumbo) phages. Moreover, vB_OspM_OC encodes a CcrM-like DNA methyltransferase, which constitutes another example of the molecular mimicry between the phage-encoded DNA methylases and host-specific regulatory MTase in Alphaproteobacteria.In this study, the biological and genomic properties of two novel phages infecting Ochrobactrum spp. genomes combined with a complex comparative genomic analysis was also performed. This analysis discovered 277 prophages within 104 out of 113 analyzed genomes of Ochrobactrum spp. Moreover, by applying similarity networking, it was shown that that nearly 30% of Ochrobactrum viruses shared homologies exclusively with other Ochrobactrum prophages or formed orphan nodes, which exemplifies the uniqueness of this (pro)phages. This global analysis also revealed the relatives of the vB_OspM_OC and vB_OspP_OH phages. It was shown that vB_OspM_OC is related to giant T4-like phages, while vB_OspP_OH created a separate sub-cluster with another podovirus (Sinorhizobium meliloti phage phiM5) and showed some similarities to the Vibrio and Alteromonas podoviruses, as well as the Salmonella and Acinetobacter siphoviruses.In this study, a thorough identification of the prophages in Moreover, in this study, bacteriophages were recognized as another factor besides chemical  and physical  agents that influence bacterial cultures applied to environmental biotechnologies. The presence of phages may be a critical, or at least important, factor interfering in the overall effectiveness of bioaugmentation and thus the success of the whole biotechnological process."}
+{"text": "Breedbase is an open-source, web-database designed to manage all of a breeder\u2019s informatics needs: management of field experiments, phenotypic and genotypic data collection and storage, and statistical analyses. The genotyping data is stored in a PostgreSQL data-type known as binary JavaScript Object Notation (JSONb), where the JSON structures closely follow the Variant Call Format (VCF) data model. The Breedbase genotyping data model can handle different ploidy levels, structural variants, and any genotype encoded in VCF. JSONb is both compressed and indexed, resulting in a space and time efficient system. Furthermore, file caching maximizes data retrieval performance. Integration of all breeding data within the Chado database schema retains referential integrity that may be lost when genotyping and phenotyping data are stored in separate systems. Benchmarking demonstrates that the system is fast enough for computation of a genomic relationship matrix (GRM) and genome wide association study (GWAS) for datasets involving 1,325 diploid Zea mays, 314 triploid Musa acuminata, and 924 diploid Manihot esculenta samples genotyped with 955,690, 142,119, and 287,952 genotype-by-sequencing (GBS) markers, respectively.Modern breeding programs routinely use genome-wide information for selecting individuals to advance. The large volumes of genotypic information required present a challenge for data storage and query efficiency. Major use cases require genotyping data to be linked with trait phenotyping data. In contrast to phenotyping data that are often stored in relational database schemas, next-generation genotyping data are traditionally stored in non-relational storage systems due to their extremely large scope. This study presents a novel data model implemented in Breedbase ( Routine genotyping is now possible with the advent of low-cost, high-throughput genotyping platforms, giving rise to enormous amounts of data but presenting challenges for data management and queriability . Plant bhttps://cassavabase.org and https://solgenomics.net ;To construct a query with the following criteria: (1) all triploid stocks were genotyped for a marker named \u2018S8_0880\u2019 in a genotyping protocol named \u20182019_GT_MAP\u2019 with genotyping depth of coverage (\u2018DP\u2019) greater than 10 and genotyping quality (\u2018GQ\u2019) greater than 90 and (2) have the \u2018T\u2019 allele on all chromosomes in an unphased call where the \u2018T\u2019 allele is the reference allele, and (3) were phenotyped for a trait called \u2018plant height in cm\u2019 at a value greater than 5, an SQL query could be written as:SELECT stock.uniquename FROM stockJOIN nd_experiment_stock USING (stock_id)JOIN nd_experiment_phenotype USING (nd_experiment_id)JOIN nd_experiment_protocol USING (nd_experiment_id)JOIN nd_experiment_genotype USING (nd_experiment_id)JOIN phenotype USING (phenotype_id)JOIN nd_protocol USING (nd_protocol_id)JOIN nd_protocolprop USING (nd_protocol_id)JOIN genotypeprop USING (genotype_id)JOIN cvterm ON WHERE cvterm.name = \u2018plant height in cm\u2019AND phenotype.value::int > 5AND nd_protocol.name = \u20182019_GT_MAP\u2019AND nd_protocolprop.value->\u2018markers\u2019->\u2018S10_0880\u2019->\u2018DP\u2019::int > 10AND nd_protocolprop.value->\u2018markers\u2019->\u2018S10_0880\u2019->\u2018GQ\u2019::int > 90AND genotypeprop.value->\u2018S8_0880\u2019->\u2018NT\u2019 = \u2018T,T,T\u2019AND genotypeprop.value->\u2018S8_0880\u2019->\u2018GT\u2019 = \u20180/0/0\u2019Perl Moose objects named CXGN::Genotype::Search, CXGN::Genotype::GRM, and CXGN::Genotype::GWAS are available in Breedbase to facilitate query and analyses construction, and to provide an interface to the file cache system.The CXGN::Genotype::Search object allows genotypes to be queried for specific accessions, tissue samples, field trials, genotyping protocols, markers, chromosomes, and base pair positions, using the \u2018accession_list\u2019, \u2018tissue_sample_list\u2019, \u2018trial_list\u2019, \u2018protocol_id_list\u2019, \u2018marker_name_list\u2019, \u2018chromosome_list\u2019, and \u2018start_position\u2019 and \u2018end_position\u2019 parameters, respectively. Minimally, a list of accessions and a genotyping protocol should be supplied. The required configuration fields for instantiation are \u2018bcs_schema\u2019 and \u2018cache_root\u2019 for the Bio::Chado::Schema database schema connection and the directory of the cache file system, respectively; all other fields are query parameters.For convenience and performance reasons, the CXGN::Genotype::Search object provides three entry-points for retrieving results from the file cache, formatted as either VCF, dosage matrix, or internal JSON. There are two additional entry-points for retrieving genotypes in VCF and dosage matrix formats computed from genotyped parents; the progeny\u2019s genotypes are calculated for each marker as an average of the parental dosage genotypes, simulating the inbreeding coefficient of each marker genotype of the hybrid as one-half of each of the two parents . An exammy $genotypes_search = CXGN::Genotype::Search->new;my @required_config = ;# Retrieving VCF using cache file systemmy $result_filehandle_VCF = $genotypes_search->get_cached_file_VCF(@required_config);# Retrieving Dosage Matrix using cache file systemmy $result_filehandle_dosage_matrix = $genotypes_search->get_cached_file_dosage_matrix(@required_config);# Retrieving Internal JSON using cache file system. There is an option to retrieve metadata only without the genotype scoresmy $result_filehandle_markerprofile_JSON = $genotypes_search->get_cached_file_search_json;# Retrieving VCF using cache file system for genotypes computed from genotyped parentsmy $result_filehandle_VCF = $genotypes_search->get_cached_file_VCF_compute_from_parents(@required_config);# Retrieving Dosage Matrix using cache file system for genotypes computed from genotyped parentsmy $result_filehandle_dosage_matrix = $genotypes_search->get_cached_file_dosage_matrix_compute_from_parents(@required_config);A Perl Moose object named CXGN::Genotype::GRM provides a standardized interface for retrieving a genomic relationship matrix (GRM) by minimally specifying a list of accessions and a genotyping protocol. The required configuration fields are \u2018bcs_schema\u2019, \u2018people_schema\u2019, \u2018cache_root\u2019, and \u2018grm_temp_file\u2019 for the Bio::Chado::Schema database schema connection, the CXGN::Metadata::Schema database schema connection, the directory of the cache file system, and a temporary file to save the GRM result, respectively; all other fields are query parameters and parameters for calculating the GRM. The GRM is computed using the R rrBLUP package and imputes missing genotypes using an \u2018Expectation Maximization\u2019 (EM) algorithm . The genmy $geno = CXGN::Genotype::GRM->new;My $result_filehandle_grm = $geno->download_grm(@required_config);A genome-wide association study (GWAS) can be computed using the CXGN::Genotype::GWAS Perl Moose object by minimally specifying a list of accessions, a list of phenotypic traits, and a genotyping protocol. The required configuration parameters are \u2018bcs_schema\u2019, \u2018people_schema\u2019, \u2018cache_root\u2019, \u2018grm_temp_file\u2019, \u2018gwas_temp_file\u2019, and \u2018pheno_temp_file\u2019 for the Bio::Chado::Schema database schema connection, the CXGN::Metadata::Schema database schema connection, the directory of the cache file system, and temporary files to process the GWAS result, respectively; all other fields are query parameters and parameters for performing the GWAS. The R rrBLUP package is used to perform imputation using the \u2018EM\u2019 algorithm ; rrBLUP my $geno = CXGN::Genotype::GWAS->new;My $result_filehandle_gwas = $geno->download_gwas(@required_config);Breedbase provides a web-interface compatible with all modern internet browsers on any device. A suite of web-pages are available for management of germplasm resources, pedigrees, seed inventories, field trials, experimental locations, phenotypic records, crossing blocks, genotyping storage, and other plant breeding program aspects. Once the information is entered into Breedbase, the primary means of searching and retrieving information is through the Search Wizard .https://breedbase.org/breeders/search) enables construction of queries spanning accessions, field trials, genotyping protocols, locations, years, and phenotypic traits, and also provides an interface for downloading phenotypic and genotypic results as data files in several formats. Genotypic data can be filtered by chromosome, start position, and end position and can be downloaded in VCF or dosage matrix formats. More precise filtering is possible by selecting a marker set; a marker set is a user defined list of markers or a range of physical positions. Once accessions are selected the GRM can be downloaded, and if phenotypic traits are selected then a GWAS can also be downloaded. The Search Wizard internally uses the entry-points previously described in the \u201cPackaged Queries\u201d section. Phenotypic records can be filtered by minimum and maximum values prior to downloading as CSV or Excel files.The Search Wizard  markers; this data is available in the Breedbase instance https://musabase.org/breeders_toolbox/protocol/1 and contains triploid genotypes [Manihot esculenta samples genotyped with 287,952 GBS markers and is available in the Breedbase instance https://cassavabase.org/breeders_toolbox/protocol/6 [Zea mays samples genotyped with 955,690 GBS markers and is available in the Breedbase instance https://imagebreed.org/breeders_toolbox/protocol/5 [To test the Breedbase JSON genotype storage system, three datasets were loaded into a test Breedbase instance running on a HP Z820 workstation with 256 GB RAM and 2x Intel Xeon E5-2660v2 CPUs. The first dataset loaded is a VCF containing 314 enotypes . The secotocol/5 .https://musabase.org of 75 phenotypic traits evaluated across 3 field trial experiments of Musa acuminata accessions. The second dataset is from https://cassavabase.org of 18 phenotypic traits evaluated across 3 field trial experiments of Manihot esculenta accessions. The third dataset is from https://imagebreed.org of 14 phenotypic traits evaluated across 3 field trial experiments of Zea mays accessions. To test computing genotypes from genotyped parents, pedigrees between hybrid Zea mays progeny accessions and parent accessions are uploaded into Breedbase; a fourth dataset was uploaded of 14 phenotypic traits evaluated across 3 field trial experiments for the hybrid Zea mays accessions.Additionally, four phenotypic datasets were loaded into the Breedbase instance; the phenotypic records include accessions evaluated in field trials for which genotypic records in the aforementioned VCF datasets exist. The first dataset is from The Perl test script, the three genotypic data VCF files, the four phenotypic data CSV files, and an SQL dump of the data loaded into the test Breedbase instance are included with this publication in the \u201cSupplemental Information\u201d. The phenotypic data files include the field experiment metadata and pedigree information. Note that there exist significant typographical errors between the accession names listed in the genotype VCF files and the accessions listed in the phenotypic information files, both for the tested accessions and for the pedigree accessions; however, Breedbase consolidates these names through a curation interface during upload of new accession names. Typographical errors such as \u2018Tx303\u2019 vs \u2018TX-303\u2019 are flagged by a text similarity score and the interface allows for correctly storing the relationships between identifiers.Musa acuminata samples genotyped with 142,119 GBS markers required a maximum of 2.36 GB RAM and 93 minutes to complete. Uploading the VCF containing 924 Manihot esculenta samples genotyped with 287,952 GBS markers required a maximum of 10.8 GB RAM and 237 minutes to complete. Uploading the VCF containing 1,325 Zea mays samples genotyped with 955,690 GBS markers required a maximum of 8.49 GB RAM and 535 minutes to complete. Future development to improve the upload process can parallelize genotype loading and can provide email responses to the user.For the benchmark test, all VCF files were uploaded consecutively through the Breedbase web-interface. Uploading the VCF containing 314 Zea mays in which the genotypes were computed from genotyped parents in the pedigree. For each of the three species in the test data, a random set of 25 accessions were chosen 10 different times with replacement. Those accessions were then (1) queried for 500 random markers and genotypic data was returned in VCF and dosage matrix formats, (2) the GRM was computed using all genotypes in the genotyping protocol after filtering for 1% MAF, 60% missing marker genotypes, and 80% missing sample genotypes, (3) GWAS was performed for two phenotypic traits using all genotypes in the genotyping protocol after filtering for 1% MAF, 60% missing marker genotypes, and 80% missing sample genotypes, and (4) the accessions were queried for all phenotypic traits evaluated. An additional scenario was tested for https://cassavabase.org) is the Breedbase instance currently with the largest genotypic data, nearly 100,000 samples with dense GBS genotypes from genotyping protocols of up to 287,952 markers, as well as thousands of samples with low density genotypes from genotyping protocols of around 20 markers. The system is used routinely to perform genomic selection analysis by affiliated breeding programs using the built-in solGS tool [Cassavabase Click here for additional data file.S1 File(PL)Click here for additional data file.S2 File(CSV)Click here for additional data file.S3 File(CSV)Click here for additional data file.S4 File(CSV)Click here for additional data file.S5 File(CSV)Click here for additional data file."}
+{"text": "This data can be interpreted as energy use per month by certain HVAC components. The ground source heat pump (GSHP) home energy use by city was generated from EnergyPlus\u2122 and the respective city weather file. The GSHP model was created by the authors to model the alternate closed loop, GSHP system. Reuse potential for heat pump coefficients and home energy use analysis is strong.This data captures climate information and HVAC energy use for a baseline prototype home and for a replacement alternative energy home. The baseline home is a traditional DX cooling/gas furnace system, and the alternate system is a geothermal heat pump. Cooling degree days (CDD), heating degree days (HDD) and relative humidity were gathered from historical weather data for 12 cities across the contiguous United States  The cities chosen represent 12 diverse climate zones across the United States. The GSHP model built in EnergyPlus\u2122 is a template that can be applied to any location. The heat pump coefficient data are particularly useful because they represent an overall performance curve across several high-efficiency geothermal heat pump manufacturers. Individual manufacturer's data and heat pump coefficients resulted in enough variability to desire a general, or normalized, heat pump performance curve for this simulation. Because the study performed was not comparing one manufacturer to another, the heat pump performance needed to represent the market-available equipment. The data presented will save much time and effort for future researchers looking for general heat pump performance coefficients for EnergyPlus\u2122 input. Capacities represented are 2, 3 and 4-ton systems.\u2022The data presented benefits researchers studying geothermal heat pump performance, particularly for residential applications. The EnergyPlus\u2122 heat pump coefficients benefit those seeking performance curves for an overall market-available geothermal heat pump, not tied to a specific manufacturer. The energy use data is useful to researchers seeking to quantify usage and financial savings for alternative energy HVAC systems. The original, or baseline, system is a DX cooling / natural gas heating traditional system. The replacement system is a ground source heat pump that has been sized accounting for soil characteristics local to specific regions of the United States.\u2022How can these data be used for further insights and development of experiments? Further insights and development of experiments can benefit from the data presented. The heat pump performance coefficients apply for any analysis of a residential geothermal heat pump that is simulated with EnergyPlus\u2122. The data can be used as direct inputs into the Water-To-Air Heat Pump cooling coil and heating coil objects. The home energy use data is useful for further insights to compare or contrast with other cities across the country or the globe. This comparison is possible with the baseline prototype home data or the geothermal heat pump system data.1Dataset contains four folders grouping the data appropriately and described below.\u2022The file \u201cAll Cities System Comparison.xlsx\u201d contains month by month energy use for the HVAC system operation for all 12 cities in the study.The folder titled \u201cBaseline Prototype Home Energy Use by City\u201d contains 1 file.\u2022The file \u201c12 Cities Temperature Humidity.xlsx\u201d contains a summation by month of the heating degree days, cooling degree days, and average relative humidity for all 12 cities in the study.The folder titled \u201cCDD, HDD and RH by City\u201d contains 1 file \u2022\u201cWaterAir_PE_Cooling 024 - Combined\u201d contains the heat pump performance data for five manufacturers and the resulting EnergyPlus\u2122 heat pump coefficients for a 2-ton heat pump cooling coil.\u2022\u201cWaterAir_PE_Cooling 036 - Combined\u201d contains the heat pump performance data for five manufacturers and the resulting EnergyPlus\u2122 heat pump coefficients for a 3-ton heat pump cooling coil.\u2022\u201cWaterAir_PE_Cooling 048 - Combined\u201d contains the heat pump performance data for five manufacturers and the resulting EnergyPlus\u2122 heat pump coefficients for a 4-ton heat pump cooling coil.\u2022\u201cWaterAir_PE_Heating 024 - Combined\u201d contains the heat pump performance data for five manufacturers and the resulting EnergyPlus\u2122 heat pump coefficients for a 2-ton heat pump heating coil.\u2022\u201cWaterAir_PE_Heating 036 - Combined\u201d contains the heat pump performance data for five manufacturers and the resulting EnergyPlus\u2122 heat pump coefficients for a 3-ton heat pump heating coil.\u2022\u201cWaterAir_PE_Heating 048 - Combined\u201d contains the heat pump performance data for five manufacturers and the resulting EnergyPlus\u2122 heat pump coefficients for a 4-ton heat pump heating coil.The folder titled \u201cGeothermal Heat Pump Coefficient Generator\u201d contains 6 files.\u2022The file \u201cSF_Arizona_Phoenix-Sky.Harbor.Intl.AP.722780_Composite HP036Meter.csv\u201d contains the electric meter readings by month for the 3-ton system broken down into the components that make up the HVAC system for Phoenix, AZ.\u2022The file \u201cSF_Arizona_Phoenix-Sky.Harbor.Intl.AP.722780_Composite HP036Table.csv\u201d contains the comprehensive tabular view of climactic and end use component details for Phoenix, AZ.\u2022The file \u201cSF_California_Los.Angeles.Intl.AP.722950 Geothermal Composite HP024Meter.csv\u201d contains the electric meter readings by month for the 2-ton system broken down into the components that make up the HVAC system for Los Angeles, CA.\u2022The file \u201cSF_California_Los.Angeles.Intl.AP.722950 Geothermal Composite HP024Table.csv\u201d contains the comprehensive tabular view of climactic and end use component details for Los Angeles, CA.\u2022The file \u201cSF_Colorado_Gunnison.County.AWOS.724677_Geothermal HP024Meter.csv\u201d contains the electric meter readings by month for the 2-ton system broken down into the components that make up the HVAC system for Gunnison, CO.\u2022The file \u201cSF_Colorado_Gunnison.County.AWOS.724677_Geothermal HP024Table.csv\u201d contains the comprehensive tabular view of climactic and end use component details for Gunnison, CO.\u2022The file \u201cSF_Florida_Miami.Intl.AP.722020_Geothermal Composite HP036Meter.csv\u201d contains the electric meter readings by month for the 3-ton system broken down into the components that make up the HVAC system for Miami, FL.\u2022The file \u201cSF_Florida_Miami.Intl.AP.722020_Geothermal Composite HP036Table.csv\u201d contains the comprehensive tabular view of climactic and end use component details for Miami, FL.\u2022The file \u201cSF_Iowa_Des.Moines.Intl.AP.725460_gasfurnace_crawlspace_Geothermal Composite HP036Meter.csv\u201d contains the electric meter readings by month for the 3-ton system broken down into the components that make up the HVAC system for Des Moines, IA.\u2022The file \u201cSF_Iowa_Des.Moines.Intl.AP.725460_gasfurnace_crawlspace_Geothermal Composite HP036Table.csv\u201d contains the comprehensive tabular view of climactic and end use component details for Des Moines, IA.\u2022The file \u201cSF_Maryland_Baltimore-Washington.Intl.AP.724060 Geothermal Composite HP036Meter.csv\u201d contains the electric meter readings by month for the 3-ton system broken down into the components that make up the HVAC system for Baltimore, MD.\u2022The file \u201cSF_Maryland_Baltimore-Washington.Intl.AP.724060 Geothermal Composite HP036Table.csv\u201d contains the comprehensive tabular view of climactic and end use component details for Baltimore, MD.\u2022The file \u201cSF_Minnesota_Duluth.Intl.AP.727450 Geothermal Composite HP036Meter.csv\u201d contains the electric meter readings by month for the 3-ton system broken down into the components that make up the HVAC system for Duluth, MN.\u2022The file \u201cSF_Minnesota_Duluth.Intl.AP.727450 Geothermal Composite HP036Table.csv\u201d contains the comprehensive tabular view of climactic and end use component details for Duluth, MN.\u2022The file \u201cSF_Montana_Helena.Rgnl.AP.727720_gasfurnace_crawlspace_Geothermal Composite HP036Meter.csv\u201d contains the electric meter readings by month for the 3-ton system broken down into the components that make up the HVAC system for Helena, MT.\u2022The file \u201cSF_Montana_Helena.Rgnl.AP.727720_gasfurnace_crawlspace_Geothermal Composite HP036Table.csv\u201d contains the comprehensive tabular view of climactic and end use component details for Helena, MT.\u2022The file \u201cSF_Nevada_Las.Vegas-McCarran.Intl.AP.723860 Geothermal Composite HP036Meter.csv\u201d contains the electric meter readings by month for the 3-ton system broken down into the components that make up the HVAC system for Las Vegas, NV.\u2022The file \u201cSF_Nevada_Las.Vegas-McCarran.Intl.AP.723860 Geothermal Composite HP036Table.csv\u201d contains the comprehensive tabular view of climactic and end use component details for Las Vegas, NV.\u2022The file \u201cSF_Nevada_Reno-Tahoe.Intl.AP.724880_gasfurnace_crawlspace_Geothermal Composite HP036Meter.csv\u201d contains the electric meter readings by month for the 3-ton system broken down into the components that make up the HVAC system for Reno, NV.\u2022The file \u201cSF_Nevada_Reno-Tahoe.Intl.AP.724880_gasfurnace_crawlspace_Geothermal Composite HP036Table.csv\u201d contains the comprehensive tabular view of climactic and end use component details for Reno, NV.\u2022The file \u201cSF_Oregon_Portland.Intl.AO.726980_Geothermal Composite HP036Meter.csv\u201d contains the electric meter readings by month for the 3-ton system broken down into the components that make up the HVAC system for Portland, OR.\u2022The file \u201cSF_Oregon_Portland.Intl.AO.726980_Geothermal Composite HP036Table.csv\u201d contains the comprehensive tabular view of climactic and end use component details for Portland, OR.\u2022The file \u201cSF_Tennesse_Memphis.Intl.AP.723340 Geothermal Crawlspace Composite HP036Meter.csv\u201d contains the electric meter readings by month for the 3-ton system broken down into the components that make up the HVAC system for Memphis, TN.\u2022The file \u201cSF_Tennesse_Memphis.Intl.AP.723340 Geothermal Crawlspace Composite HP036Table.csv\u201d contains the comprehensive tabular view of climactic and end use component details for Memphis, TN.The folder titled \u201cGSHP Home Energy Use by City\u201d contains 24 files.2Experimental design was performed by modelling a geothermal heat pump HVAC system for a prototype home file. Materials included EnergyPlus\u2122 simulation tool and Microsoft Excel. Baseline energy use data was acquired through simulation of the prototype home file for each city. The baseline home consisted of a traditional DX cooling system and natural gas furnace heating system. The retrofit geothermal system required several inputs including the heat pump cooling and heating coil coefficients, ground heat exchanger parameters, soil conductivity and specific heat capacity, and borefield type. For the heat pump coefficient data, an extensive amount of data entry was required from the engineering specifications published by various heat pump manufacturers. Once reported, the annual energy use from the baseline system is compared to the annual energy use from the retrofit geothermal heat pump system. This comparison allowed for a city-specific financial analysis to determine the viability of geothermal system implementation. Methods were simulation executions. Detailed information on the methods is provided in Ref. Rebecca Neves: Conceptualization, Methodology, Software, Formal Analysis, Writing \u2013 Original Draft, Visualization Heejin Cho: Writing \u2013 Review & Editing, Supervision. Jian Zhang: Writing \u2013 Review & Editing.The authors declare that they have no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article."}
+{"text": "Vibrio parahaemolyticus and discovered that a vp0980 mutant (vp0980 encodes a predicted transmembrane protein) could not be lysed by phage OWB. Complementation of this mutant with wild-type vp0980 in trans restored phage-mediated lysis. Phage adsorption and confocal microscopy assays demonstrated that phage OWB had dramatically reduced adsorption to the vp0980 mutant compared to that to the wild type. Pulldown assays showed that phage tail tubular proteins A and B (TTPA and TTPB) interact with Vp0980, suggesting that Vp0980 is a TTPA and TTPB receptor. Vp0980 lacking the outer membrane region (aa 114\u2013127) could not bind to TTPA and TTPB, resulting in reduced phage adsorption. These results strongly indicated that TTPA and TTPB binding with their receptor Vp0980 mediates phage adsorption and subsequent bacterial lysis. To the best of our knowledge, this study is the first report of a bacterial receptor for phage tail tubular proteins.The adsorption of phages to hosts is the first step of phage infection. Studies have shown that tailed phages use tail fibres or spikes to recognize bacterial receptors and mediate adsorption. However, whether other phage tail components can also recognize host receptors is unknown. To identify potential receptors, we screened a transposon mutagenesis library of the marine pathogen  Phages are viruses that infect and replicate within bacteria . The DNABacillus subtilis is YueB [Escherichia coli [Salmonella sp. phage P22, respectively [The first step of phage infection is adsorption to the bacterial cells ,8. Durin is YueB . The outhia coli . Bindingectively ,16. The ectively ,17\u201320. IVibrio parahaemolyticus is a halophilic gram-negative bacterium that can cause seafood-associated bacterial gastroenteritis in humans through contaminated raw or undercooked seafood consumption [V. parahaemolyticus lytic phage vB_VpaS_OWB (abbreviated as phage OWB in this study) [V. parahaemolyticus surface and cause cell lysis [V. parahaemolyticus and causes bacterial lysis are unknown. In particular, phage ligands and bacterial receptors that are required for adsorption need to be elucidated. In this study, transposon mutagenesis library screening revealed that the predicted V. parahaemolyticus transmembrane protein Vp0980 is required for phage OWB adsorption. Further pulldown assays demonstrated that Vp0980 could bind the phage OWB tail tubular proteins A and B (TTPA and TTPB). Lack of such binding lead to reduced phage adsorption and bacterial cell lysis, demonstrating that Vp0980 is the receptor of podophage tail tubular proteins A and B.sumption . In our s study) . Morpholll lysis . HoweverE. coli strains and V. parahaemolyticus strains were cultured at 37\u00b0C in Luria\u2013Bertani (LB) medium supplemented with 1% NaCl. Complementation was conducted by cloning the respective genes into the low-copy vector pMMB207 as described previously [V. parahaemolyticus cultures were centrifuged , and the supernatants containing phage OWB were used in this study after filtration with a 0.22 \u03bcm filter [vp0190-vp0214) for lipopolysaccharide biosynthesis as described previously [All eviously . The strm filter . Expressm filter . Expresseviously . BrieflyDNA of phage OWB was extracted as previously described . BrieflyA transposon mutant library of ATCC17802 was constructed with the conjugal helper plasmid pEVS104 and Mini-Tn5 delivery plasmid pEVS170 as described previously . The mutBamH I and EcoR I and inserted into the plasmid pGEX that was predigested with BamH I and EcoR I, resulting in the plasmids pGEX-OWB027, pGEX-OWB028, pGEX-OWB030, pGEX-OWB031 and pGEX-OWB035, respectively (Table S1). These plasmids were used to express GST-tagged OWB027, OWB028, OWB030, OWB031 and OWB035. The vp0980 gene was amplified using the primer pair pmmbvp0980_1F/pmmbvp0980_2R. A 6xHis tag was added at the C-terminus of the encoded protein. The PCR product was inserted into Hind III/Xba I double-digested pMMB207 [vp0879 was amplified with pmmbvp0879_1F/pmmbvp0879_2R and inserted into pMMB207, resulting in the plasmid pMMB207-vp0879 (Table S1). To express vp0980 lacking its transmembrane or outer regions, the up- and downstream regions flanking amino acids 91\u2013113, 114\u2013127 and 128\u2013150 of Vp0980 were amplified from V. parahaemolyticus using the primer pairs pmmbvp0980_1F/pmmbvp0980_91_1R and pmmbvp0980_91_2F/pmmbvp0980_2R, pmmbvp0980_1F/pmmbvp0980_114_1R and pmmbvp0980_114_2F/pmmbvp0980_2R, and pmmbvp0980_1F/pmmbvp0980_128_1R and pmmbvp0980_128_2F/pmmbvp0980_2R (Table S2), respectively. The resulting upstream and downstream products were inserted into Hind III/Xba I double-digested pMMB207, resulting in the plasmids pMMB207-vp0980\u039491-113, pMMB207-vp0980\u0394114-127 and pMMB207-vp0980\u0394128-150 (Table S1), respectively. These plasmids were used to complement \u0394vp0980. To express vp0879 with a point mutation, the primers pmmbvp0879_1F/pmmbvp0879_K54A_1R and pmmbvp0879_K54A_2F/pmmbvp0879_2R (Table S2) were used to amplify two PCR products that were cloned into pMMB207, resulting in the plasmid pMMB207-vp0879K54A (Table S1).The coding sequences for OWB027, OWB028, OWB030, OWB031 and OWB035 were PCR amplified from phage OWB using the primer pairs OWB027_FwBamH I/OWB027_ReEcoR I, OWB028_FwBamH I/OWB028_ReEcoR I, OWB030_FwBamH I/OWB030_ReEcoR I, OWB031_FwBamH I/OWB031_ReEcoR I and OWB035_FwBamH I/OWB035_ReEcoR I, respectively. The resulting PCR products were digested with  pMMB207 , resultiV. parahaemolyticus strains were dropped on LB plates (approximately 104 CFU/drop). After the bacterial culture dried, phage OWB was dropped on top of the dried bacterial lawn. After 6 h of incubation at 37\u00b0C, clear zones were recorded to reflect the bacterial cell lysis. Each experiment was repeated three times, and representative images are shown.A phage drop assay was performed as previously described . Brieflyg at 4\u00b0C for 20\u2005min, the pellet containing phages was resuspended in SM buffer. All V. parahaemolyticus strains were transformed with the plasmid pVSV208, which constitutively expresses red fluorescent protein (RFP) [V. parahaemolyticus strains (red) were infected with the SYBR Green-labelled phages (green) at an MOI of 10 for 30\u2005min. Subsequently, infected bacteria were centrifuged, and the bacteria in the pellet were resuspended in phosphate-buffered saline (PBS) and visualized using a confocal microscope. Representative images of at least three experiments are shown. To determine if GST-TTPA and GST-TTPB bind the whole cells of V. parahaemolyticus, a bacterial culture was resuspended in 50\u2005\u00b5l PBS to reach a concentration of 106 CFU/ml and incubated with 10 \u03bcl of the purified recombinant protein GST-TTPA, GST-TTPB or GST (0.5\u2005mg/ml) for 1 h. After extensive washing with PBS, bacterial cells were incubated sequentially with a mouse primary anti-GST antibody and Alexa Fluor 594-conjugated secondary anti-mouse IgG before visualization with a confocal microscope.For visualization of phage attachment, phages were stained with the fluorescent dye SYBR Green as previously described . Brieflyin (RFP) . ExponenV. parahaemolyticus culture to reach an MOI of 0.01. After incubation at 37\u2009\u00b0C for 5, 10, 20, 30\u2005min or 60\u2009min, the phage-bacteria mixture was centrifuged at 12,000\u2009rpm for 10\u2009min. The free phage titre (pfu) in the supernatant was determined. The percent adsorption was determined as follows: percent adsorption\u2009=\u2009(pfuadded-pfusupernatant)/pfuadded, and the average data of at least three experiments are shown for each time point. To determine if TTPA and TTPB would block phage adsorption, we incubated wild type V. parahaemolyticus with GST-TTPA or GST-TTPB or GST at the concentration of 0.1\u2005mg/ml for 1 h, and subsequently phage adsorption assay was performed as described above.Adsorption was analyzed as previously described . Briefly\u0394114\u2013127. To determine if GST-TTPA and GST-TTPB bind whole cells of V. parahaemolyticus, a bacterial culture was resuspended in 50 \u03bcl of PBS to reach a concentration of 106 CFU/ml and incubated with 10 \u03bcl of the purified recombinant protein GST-TTPA, GST-TTPB or GST (0.5\u2005mg/ml) for 1 h. After extensive washing with PBS, bacterial cells were lysed, and the cell lysate was blotted with an anti-GST antibody. An anti-RNA polymerase (RNAP) antibody was used to indicate that equal bacterial protein was loaded across different samples. To determine whether phage fibre protein (OWB035) binds to whole cells, we incubated wild-type (WT) or LPS mutant with GST-OWB035 or GST. A western blot using anti-GST and anti-RNAP antibodies was performed similarly as described above.The GST-fusion proteins OWB027, OWB028, OWB030, OWB031 and OWB035 from cellular lysates were bound on glutathione agarose beads. After washing with PBS, the membrane protein 6xHis-Vp0980 (solubilized in PBS containing 1% Triton X-100) was then added to the preloaded beads. After additional washing with PBS, the bound proteins were eluted using a buffer containing reduced glutathione. The elution was used for western blotting with anti-GST and anti-His monoclonal antibodies. A similar pull down experiment was also carried out using 6xHis-Vp09804 CFU/ml (with or without preincubation with GST-TTPA or GST-TTPB at the concentration of 0.1\u2005mg/ml for 1 h) was mixed with phage OWB at the MOI of 10. CFU were determined at different time points. To determine the effect of LPS on phage OWB infection, a bacterial culture of both wild type and LPS mutant (1\u2005ml) at the concentration of approximately 104 CFU/ml was mixed with phage OWB at the MOI of 10. CFU were determined at different time points.A bacterial culture (1\u2005ml) at a concentration of approximately 10V. parahaemolyticus strain RIMD2210633 when the polar flagellum is knocked out [V. parahaemolyticus strain 17802 is naturally susceptible to phage OWB infection and can be lysed by phage OWB. Therefore, in this study, we used the strain 17802 as the WT host to identify potential receptors for phage OWB. We first screened a V. parahaemolyticus 17802 transposon mutagenesis library to identify mutants that could not be lysed by phage OWB. A clear lysis zone was present at the centre of the WT strain in the phage drop assay, in which phage OWB was placed at the centre of the bacterial lawn . The phage drop assay showed phage-mediated lysis in \u0394vp0879:pvp0879 but not in \u0394vp0879:pvp0879K54A . Comparative analysis showed that 88.3% (38/43) of the phage OWB ORFs share homology with the of Chile   and mediate adsorption to the host are tail fibres or tail spikes ,33\u201339. W\u0394114\u2013127 (Vp0980 lacking aa 114\u2013127) was not present in the elution of glutathione agarose preloaded with GST-TTPA , two regions that are inside of the membrane (aa 85\u201390 and aa 151\u2013169) and two regions that are outside of the membrane (aa 59\u201361 and aa 114\u2013127) B. We wermembrane D, indicavp0980:pvp0980 or \u0394vp0980:pvp0980\u0394114\u2013127 with recombinant GST-tagged TTPA or TTPB, followed by incubation with a primary mouse anti-GST antibody and secondary Alexa Fluor 594-conjugated anti-mouse IgG. The results showed that both GST-TTPA and GST-TTPB bound the whole cells of \u0394vp0980:pvp0980  antibodies. RNAP was used to indicate that equal amount of bacterial cells was used across different samples. The results showed that GST-TTPA and GST-TTPB bound the whole cells of \u0394vp0980:pvp0980  or vp0980 lacking aa 114\u2013127 (\u0394vp0980:pvp0980\u0394114\u2013127) and performed phage adoption and phage drop assays. The results showed that the phage adsorption rate for \u0394vp0980:pvp0980\u0394114\u2013127 was only \u223c4%, which is comparable to that for \u0394vp0980  . However, Vp0980 homologs are not observed in these bacterial species, and it is possible that Vp0980 functional orthologs are used as receptors for TTPA and TTPB to mediate phage adsorption to these bacterial species. It remains to be determined whether the binding of Vp0980 with TTPA and TTPB is responsible for reversible or irreversible adsorption.The most common structures used by tailed phages to recognize bacterial receptors are tail spikes, tail fibres and tail membrane-penetrating proteins . Tail splow TTPA . TTPA incharides ,43, but (OWB035) A. We reanfection C and D. Vibrio transmembrane protein, Vp0980, that mediates phage adsorption by binding the phage ligand proteins TTPA and TTPB. Our findings highlighted the importance of this unprecedented receptor/ligand interaction in podophage infection of Vibrio species and possibly other bacterial species.In summary, we identified a conserved"}
+{"text": "Many disease causing genes have been identified through different methods, but there have been no uniform annotations of biomedical named entity (bio-NE) of the disease phenotypes of these genes yet. Furthermore, semantic similarity comparison between two bio-NE annotations has become important for data integration or system genetics analysis.>\u20090.94, precision >\u20090.56, and F1\u00a0>\u00a00.70, demonstrating better performance than the state-of-the-art tools DNorm and TaggerOne in recognizing MeSH terms from short biomedical phrases. The semantic similarity in MeSH terms recognized by pyMeSHSim and the previous manual work was calculated by pyMeSHSim and another semantic analysis tool meshes, respectively. The result indicated that the correlation of semantic similarity analysed by two tools reached as high as 0.89\u20130.99.The package pyMeSHSim recognizes bio-NEs by using MetaMap which produces Unified Medical Language System (UMLS) concepts in natural language process. To map the UMLS concepts to Medical Subject Headings (MeSH), pyMeSHSim is embedded with a house-made dataset containing the main headings (MHs), supplementary concept records (SCRs), and their relations in MeSH. Based on the dataset, pyMeSHSim implemented four information content (IC)-based algorithms and one graph-based algorithm to measure the semantic similarity between two MeSH terms. To evaluate its performance, we used pyMeSHSim to parse OMIM and GWAS phenotypes. The pyMeSHSim introduced SCRs and the curation strategy of non-MeSH-synonymous UMLS concepts, which improved the performance of pyMeSHSim in the recognition of OMIM phenotypes. In the curation of 461 GWAS phenotypes, pyMeSHSim showed recall The integrative MeSH tool pyMeSHSim embedded with the MeSH MHs and SCRs realized the bio-NE recognition, normalization, and comparison in biomedical text-mining. Biomedical named entity (bio-NE) recognition, normalization, and comparison are fundamental tasks for extracting and utilizing valuable biomedical information from textual data. They are important to disease diagnosis , drug reMedical Subject Heading (MeSH) is a controlled vocabulary that can be used in bio-NE recognition, normalization and comparison . It conshttps://meshb.nlm.nih.gov/search) to parse MeSH terms from the input phrases. However, the browser is neither tolerant to even subtle difference of input phrases from MeSH terms, nor applicable to batch processing. Although some Bio-NE tools based on machine learning method have come out with good performance on specific corporas, they were designed for recognizing certain categories, like diseases and chemicals, of MeSH terms from literature abstracts, and have unknown performance for other categories of MeSH terms or from short biomedical phrases. As MeSH tools for bio-NE comparison, meshes  range used to measure the relation between two terms. In this study, we tuned \u03c9 from 0 to 1 with a step of 0.1 to test the robustness of our results  the data subpackage normalizing UMLS concepts into MeSH terms by the embedded MeSH dataset, and (3) the Sim subpackage comparing semantics of MeSH terms by measuring the distance between MeSH terms .The metamapWrap subpackageThe pyMeSHSim consists of three subpackages 1) the  the metametamapWrap subpackage which was a wrapper for MetaMap [metamapWrap curated MeSH-synonymous UMLS concepts from free texts including non-MeSH-synonymous UMLS concepts, and then converted the curated MeSH-synonymous UMLS concepts into corresponding MeSH terms via the data subpackage. We set parameters \u201c-N -J semantic_type _list -R MSH -I -z -conj -Q 4 -silent --sldi\u201d, where semantic_type list was the list of disease-related semantic types  as the default of pyMeSHSim. Users can customize the parameters to suit their needs.2)The data subpackageThe bio-NE recognition and normalization of pyMeSHSim were realized by the  MetaMap . The subdata subpackage in bcolz format with a corresponding data interface (Supplementary Table\u00a03)The Sim subpackageThe MeSH dataset was embedded into the Sim subpackage by measuring the distance between MeSH terms. Each narrower record of the SCR was converted into one or more broader terms of MHs before the measurement. Like the tool meshes, pyMeSHSim offered five representative semantic similarity measurements, including four information content (IC) based  and one graph-based (Wang\u2019s) algorithms.The bio-NE comparison of pyMeSHSim was conducted with the To test whether the introduction of SCRs and our curation strategy of non-MeSH-synonymous UMLS concepts contributes to improving the performance of pyMeSHSim in bio-NE recognition, we compared the genes annotated with MeSH MHs and SCRs from OMIM  phenotypp value (6.57E-35 vs 8.87E-19) of enrichment in the disease Osteochondrodysplasias  showed higher recall than DNorm (> 0.32) and TaggerOne (> 0.49) across all the similarity thresholds used to determine matches with Nelson\u2019s manual work as true positives (Supplementary Table\u00a0>\u20090.56) than DNorm (>\u20090.62) and TaggerOne (>\u20090.64), the differences in precision were subtle when consider only perfect match (Table > 0.70) was always higher than DNorm (> 0.42) and TaggerOne (> 0.55)  GWAS phenotypes, while DNorm and TaggerOne only identified 129 (28%) and 192 (42%)  and 17 missed by pyMeSHSim. The manual work preferred mapping the phenotypes to disease category (C). For example, phenotypes like \u201cVitamin E levels\u201d, \u201cHematology traits\u201d and \u201cPulmonary function\u201d were parsed as \u201cVitamin E Deficiency\u201d (D014811), \u201cHematologic Diseases\u201d (D033461) and \u201cLung Diseases\u201d (D008171) by Nelson\u2019s group, while identified as \u201cVitamin E\u201d (D014810), \u201cHematology\u201d D006405) and \u201cLung\u201d (D008168) by pyMeSHSim. However, such preference of the manual work could lead to bias. For example, \u201cEye color\u201d, \u201cHair color\u201d and \u201cSerum urate\u201d were parsed as \u201ccolor vision defects\u201d, \u201chair diseases\u201d and \u201curinary calculi\u201d by Nelson\u2019s group, while as \u201cColor, Eye\u201d, \u201cColor, Hair\u201d and \u201cAcid, Uric\u201d by pyMeSHSim -0.97 (Res\u2019)  We compared the performance of pyMeSHSim in bio-NE recognition and normalization with manual work in parsing GWAS phenotypes, and found high consistency between them, indicating the great potential of pyMeSHSim for aiding professional manual curation of bio-NEs; (ii) We compared the performance of pyMeSHSim in bio-NE recognition and normalization with another two tools base on machine learning methods, and showed higher sensitivity and accuracy of pyMeSHSim in parsing short biomedical phases like GWAS phenotypes; (iii) We converted the OMIM phenotypes to MeSH terms using pyMeSHSim, and demonstrated improved effectiveness in bio-NE recognition and normalization by including SCRs in its embedded dataset; (iv) We compared the similarity measurement between pyMeSHSim and www.orpha.net). However, whether general concepts such as MHs or specific concepts such as SCRs are preferable will depend on the end use. Users should be cautious to select the according right terms in using pyMeSHSim.Considering that MeSH is one of the most widely used biomedical vocabulary, pyMeSHSim will further contribute to data integration. In addition, the introduction of SCRs to the implemented dataset enables pyMeSHSim to handle rare diseases in public databases like OMIM and Orphanet , Lin\u2019s (lin), Resnik\u2019s (res), Schlicker\u2019s (rel), and) and Wang\u2019s (wang) algorithms. The effect of weight \u03c9 for Wang\u2019s algorithm was test by tuning it from 0 to 1 (weight_0.0 to weight_1.0). B. The Pearson\u2019s correlation between the results of pyMeSHSim (Y axis) and meshes (X axis). The 40 MeSH pairs with semantic similarity between 0\u2009~\u20091 were shown in Supplementary Table\u00a0Additional file 2.Additional file 3 Supplementary Table\u00a01. MeSH terms and UMLS concepts correspond to MeSH MH D000544.Additional file 4 Supplementary Table\u00a02. GWAS phenotypes parsed by Nelson\u2019s group and pyMeSHSim, and the semantic similarity between them calculated by pyMeSHSim and meshes.Additional file 5 Supplementary Table\u00a03. Number of MHs and SCRs in each MeSH category.Additional file 6 Supplementary Table\u00a04. The OMIM MH-gene pairs from MH, SCR, and Non-MesH.groups.Additional file 7 Supplementary Table\u00a05. GWAS phenotypes parsed by Nelson\u2019s group and pyMeSHSim, TaggerOne and DNorm. the semantic similarity between them calculated by pyMeSHSim. pyMeSHSim_Score is semantic similarity between Nelson_MeSH _ID and pyMeSHSim_MeSH_ID, taggerOne_score is semantic similarity between Nelson_MeSH _ID and TaggerOne_MeSH_ID, DNorm_score is semantic similarity between Nelson_MeSH _ID and Dnorm_MeSH_ID.Additional file 8 Supplementary Table\u00a06. MeSH term number in each category correctly identified by pyMeSHSim, Dnorm, TaggerOne and Nelson\u2019s manual work.Additional file 9 Supplementary Table\u00a07. pyMeSHSim perfectly recognized MeSH term, but DNorm and TaggerOne failed. The semantic similarity between them calculated by pyMeSHSim. pyMeSHSim_Score is semantic similarity between Nelson_MeSH _ID and pyMeSHSim_MeSH_ID, taggerOne_score is semantic similarity between Nelson_MeSH _ID and TaggerOne_MeSH_ID, DNorm_score is semantic similarity between Nelson_MeSH _ID and Dnorm_MeSH_ID.Additional file 10 : Supplementary Table\u00a08. DNorm or TaggerOne perfectly recognized MeSH terms, but pyMeSHSim failed. The semantic similarity between them calculated by pyMeSHSim. pyMeSHSim_Score is semantic similarity between Nelson_MeSH _ID and pyMeSHSim_MeSH_ID, taggerOne_score is semantic similarity between Nelson_MeSH _ID and TaggerOne_MeSH_ID, DNorm_score is semantic similarity between Nelson_MeSH _ID and Dnorm_MeSH_ID."}
+{"text": "Clostridiales and Bacteroidales , which may promoted FE through protecting gut barrier function, compared with those of the HRFI pigs. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis found that the LRFI pigs were likely have microbiota with higher levels of amino acid metabolism. Moreover, redundancy analysis (RDA) showed that litter size, parity, and date of birth had significant effects on the bacterial community structure. These results improved our knowledge of the porcine early-life fecal microbiota and its potential link underlying RFI, which would be useful for future development of microbial biomarkers for predicting and improving porcine FE as well as investigation of targets for dietary strategies.Improving the predication efficiency of porcine production performance at early stage will contribute to reducing the breeding and production costs. The intestinal microbiota had received plenty of attention in recent years due to their influence on host health and performance. The purpose of this study was to investigate the relationship between the fecal microbiota at early growth period and porcine feed efficiency (FE) under a commercial feeding environment. Ninety-one pigs were reordered according to the residual feed intake (RFI) values between day 90 on test and day 160 off test, 9 lowest RFI pigs and 9 highest RFI pigs were selected as the LRFI group and the HRFI group, respectively. Fecal samples from pigs in the early grower phase (day 80) were performed for microbial diversity, composition, and predicted functionality by using 16S rRNA sequencing. The results showed that no significant differences in microbial alpha diversity were observed between two RFI groups, whereas, some RFI-associated compositional differences were revealed. In particular, the microbiota of the LRFI group (more feed-efficient) had significantly higher levels of some members of  Feed accounts for more than 60% of total production costs in growing pigs. Therefore, improving FE has been an important part of the breeding goal in commercial pig production for both economic and environmental reasons. A variety of factors that can influence porcine FE, such as genetics . The piglets were weaned at the same age of 28\u00a0days and raised under the same nursery conditions. All experimental pigs were moved to an environmentally controlled fattening house (ten pigs in each pen) at the age of day 70, and were fed with the same standard diets without antibiotics or medicines. Daily feed intake (DFI) and individual body weights (BW) data were recorded by Electronic Feed Intake Recording Equipment  and used to calculate performance indicators, such as average daily feed intake (ADFI), average daily gain (ADG), and feed conversion ratio (FCR). ADFI, individual BW, and FCR data were collected from day 90 to 160. The backfat thickness (BF) was measured using ultrasound measurements . The equation used to predict RFI has been previously described . The V3\u2013V4 region of the 16S rRNA gene was amplified by polymerase chain reaction (PCR) with universal bacterial 16S rRNA gene PCR amplicon primers (341F-806R)  (version 1.9.1) platform  Chao , ACE ab, Sobs t, SimpsonThe average RFI value of LRFI and HRFI was \u2212\u20090.047\u2009\u00b1\u20090.11 (mean\u2009\u00b1\u2009SD) . After subsampling each sample to an equal sequencing depth  and clustering, 1011 OTUs at 97% identity were obtained. From a taxonomic perspective, 14 phyla, 24 class, 33 order, 60 families, and 114 genera were identified across all pig fecal samples.Sobs, Shannon index, Simpson index, ACE, Chao 1, Coverage, Shen, PD index values were used as parameters of the alpha diversity of fecal microbiota in our study Table\u00a0. The SobFusobacteria uniquely identified in the LRFI group. The three dominant phyla detected in both groups were Firmicutes (70.41% in the LRFI group and 75.34% in the HRFI group), Bacteroidetes (25.02% in the LRFI group and 21.04% in the HRFI group), and Actinobacteria (1.43% in the LRFI group and 1.13% in the HRFI group) , norank_f_Ruminococcaceae (10.25%), Megasphaera (6.34%), and norank_f_Lachnospiraceae (5.31%) were found in the LRFI group, whereas the four dominant genera in the HRFI group were Prevotella (12.48%), Lactobacillus (11.57%), norank_f_Ruminococcaceae (11.18%), and norank_f_S24-7 (4.93%) , with 7 (6.14%) and 1 genus (0.0088%) uniquely identified in the LRFI group and the HRFI group, respectively  , norank_f__Erysipelotrichaceae (P\u2009=\u20090.016), g_Kitasatospora (P\u2009=\u20090.0034) were significantly higher in the HRFI group. In contrast, the levels of norank_f_p_2534_18B5 (P\u2009=\u20090.034), g_1_68 (P\u2009=\u20090.034) were significantly higher in the LRFI group than those in the HRFI group  Fig.\u00a0c. At thep_Chlamydiae, c_Chlamydiia, g_Chlamydia, f_Chlamydiaceae, o_Chlamydiales, f_Streptomycetaceae, g_Kitasatospora and norank_f__Erysipelotrichaceae in the HRFI group. We also found that, o_Burkholderiales, f_Tissierellaceae_, g_1_68, p_2534_18B5, and norank_f_p_2534_18B5 were higher relative abundances in the LRFI group compared to those in the HRFI group  genera were potential biomarkers for distinguishing between high and low RFI groups  were chosen for RDA. As a result, litter size was found to have significant (P\u2009=\u20090.007) effect on the fecal bacterial community structure at phylum level Fig.\u00a0a, which Actinobacteria showed a significant positive correlation with date of birth . Fibrobacteres demonstrated a significant negative correlation with litter size . The factor of pen was significant positive correlated with Proteobacteria , whereas significant negative correlated with WPS-2 . The birth weight revealed a significant negative correlation with Firmicutes , whereas significant positive correlated with Proteobacteria  and Bacteroidetes . Similarly, result of heatmap showed the relationship between environmental factors and bacterial genera at the genus level were different , Catenibacterium , Collinsella  and g_Oscillospira , whereas significant negatively correlated with Clostridium , g_norank_o_Bacteroidales , g_norank_o_Clostridiales . The birth weight revealed a significant positive correlation with [Prevotella] , Enterobacteriaceae  and Phascolarctobacterium , nevertheless, significant negatively correlated with g_norank_f_Coriobacteriaceae . The factor of parity was significant positive correlated with [Mogibacteriaceae]  and g_norank_f_S24-7 , however, significant negatively correlated with Roseburia , Anaerovibrio , Faecalibacterium . Megamonas  and Megasphaera . The factor of pen was significant positive correlated with Clostridium .The correlation heatmap showed that the relationship between bacterial phyla and environmental factors were different Fig.\u00a0c. ActinoOscilibacter, Christensenellaceae, and Cellulosilyticum were more abundant in high FE pigs -l-cysteinyl-d-valine and convert this tripeptide into the final penicillin or cephalosporin molecules  and regulating the metabolism of acids Holzer . We obseFirmicutes showed a significant negative correlation with birth weight, whereas Bacteroidetes has a significant positive correlation with birth weight. Ding et al. reported that the abundances of Firmicutes showed a significant negative correlation with pre-weaned weight gain and Bacteroidetes showed a significant positive correlation with pre-weaned weight gain in the colon  and some environment factors. For instance, at the phylum and genus levels, the date of number, litter size and parity have a significant influence on intestinal microbial structure composition. Yang et al. reported that significant effects of sex and kinship on fecal microbial community structure (Yang et al. Clostridiales and Bacteroidales, such as g_1_68, g_norank_f_p_2534_18B5, were found to be potential early life predictive biomarkers for high FE. Predictive functional analysis also indicated that fecal microbes of the high FE pigs may have a high level of utilize dietary protein. Besides, our results indicated that the composition of fecal bacterial community was related to some host factors, especially litter size and parity. Although, as of now, more studies are required to clarify the relationship between the intestinal microbiota at a growing stage and FE at a mature stage pig, these results may provide insights into understanding the host-microbe interactions occurring in the early-life pig intestine and will be helpful for the assisted early selection of porcine FE.In conclusion, the present results provided novel information of RFI-associated fecal bacterial profiles in Duroc pigs at early growth period, suggesting that the microbiota has a possible link with porcine FE. Importantly, the Additional file 1. Characteristics of the pigs used in this study."}
+{"text": "Tumor tissues of all patients were tested for EGFR mutation status. A PET/CT radiomics prediction model was established through multi-step feature selection. The predictive performances of radiomics model, clinical features and conventional PET-derived semi-quantitative parameters were compared using receiver operating curves (ROCs) analysis.We enrolled total 173 patients with histologically proven NSCLC who underwent preoperative max) and total lesion glycolysis (TLG), the PET/CT radiomics model showed better performance to discriminate between EGFR positive and negative mutations with the AUC of 0.769 and the accuracy of 67.06% after 10-fold cross-validation. The combined model, based on the PET/CT radiomics and clinical feature (gender) further improved the AUC to 0.827 and the accuracy to 75.29%. Only one PET radiomics feature demonstrated significant but low predictive ability (AUC = 0.661) for differentiating 19 Del from 21 L858R mutation subtypes.Four CT and two PET radiomics features were finally selected to build the PET/CT radiomics model. Compared with area under the ROC curve (AUC) equal to 0.664, 0.683 and 0.662 for clinical features, maximum standardized uptake values (SUV18F-FDG PET/CT radiomics and clinical feature, providing an alternative useful method for the selection of targeted therapy.EGFR mutations status in patients with NSCLC could be well predicted by the combined model based on  Current molecular testing for identifying EGFR mutation status is mainly based on tumor tissue from biopsies and surgical resection  PET/CT, as a noninvasive molecular imaging tool, has been widely used in the evaluation of glucose metabolic phenotype of tumor  of NSCLCs with EGFR mutations than those with wild type  with histologically proven NSCLC, who had undergone pre-treatment 18F-FDG PET/CT scan using GE Discovery VCT64 system, and their serum glucose levels were maintained to < 7.8 mmol/L. Whole-body imaging was performed approximately 60\u00a0min after the intravenous administration of 5.55 MBq of 18F-FDG per kilogram of body weight. Emission images were acquired for 3\u00a0min per bed position using 128 \u00d7 128 matrix size, 28 subsets, 2 iterations and full-width half-maximum post-filtering. CT images were acquired using 140 kV tube voltage, 220 mA tube current, and 3.75\u00a0mm section thickness. PET images were reconstructed based on an ordered-subset expectation maximization algorithm with photon attenuation correction from CT data.Patients were required to fast for at least 6\u00a0h before Tissue samples from lung tumors were obtained through biopsy or surgical resection followed by 10% formalin fixation, paraffin embedding, and sectioning. After extracting DNA from sample sections, the nucleotide sequence encoding the kinase domain (exons 18-21) of EGFR was tested using an amplification refractory mutation system polymerase chain reaction  or targehttps://www.itksnap.org) to manually outline the contour of the volume of interest on CT images, and automatically delineated on PET images using a fixed SUVmax threshold of 2.5 as previously reported , metabolic tumor volume (MTV) and total lesion glycolysis (TLG) (illustrated in max and TLG were included.The Spearman correlation coefficient (r) was used to assess the correlation between 100 PET/CT radiomics features and four conventional PET-derived semi-quantitative parameters including SUVThe univariate and multivariate  or traditional statistics of LR were performed to test the generalization ability of the models. ROC curves were analyzed to evaluate the performance of PET/CT radiomics model for predicting EGFR mutation status. Statistical significance was set at p < 0.05.Data were analyzed using SPSS version 19.0 . Spearman correlation analysis was performed to remove redundant radiomics features. Continuous data were compared using the independent samples t test. The \u03c7As shown in Eventually, four CT and two PET radiomics features were selected to build the radiomics model based on the 173 patients, including ct_original_glszm_High Gray Level Zone Emphasis (GLSZM_HGLZE), ct_wavelet_HLL_glszm_Gray Level Non-Uniformity Normalized (GLSZM_GLNN), ct_wavelet_HLL_glszm_Zone Entropy (GLSZM_ZE), ct_exponential_gldm_Dependence Variance (GLDM_DV), pet_wavelet_LHH_firstorder_Skewness (First-order_Skewness (LHH)), pet_wavelet_LLL_firstorder_Skewness (First-order_Skewness (LLL)). The definitions of these selected radiomics features were shown in max and TLG) was shown in max and TLG between the EGFR+ and EGFR- groups. Meanwhile, the tumors with EGFR+ had higher radiomics model score than those with EGFR- . The PET/CT radiomics model prediction score for each patient was displayed in The median and the interquartile range for selected PET/CT radiomics features and conventional PET parameters (SUVmax (AUC=0.683), TLG (AUC=0.662) and gender (AUC=0.664). The AUC of PET/CT radiomics model further reached 0.868 with sensitivity of 92.8%, specificity of 66.3% and accuracy of 77.1%. Gender was only significant clinical predictor of EGFR mutation status (AUC=0.664), and used in the combined model in our study, whereas other clinical characteristics were excluded from the diagnostic model after multivariate regression analysis. The combined model, based on the PET/CT radiomics features and gender showed a comparable AUC (0.866) to PET/CT radiomics model. The sensitivity, specificity, and accuracy of different models and individual parameter in the training set were shown in The performance of PET/CT radiomics model was evaluated and compared with conventional PET-derived semi-quantitative parameters and clinical features for distinguishing EGFR+ from EGFR-. Both CT (AUC=0.792) and PET alone (AUC=0.738) radiomics model had better predictive performance than SUVmax or TLG between the 19 del and the 21 L858R mutation group. In all radiomics features, only one PET radiomics feature  was significantly predictive (AUC=0.661) for differentiating these two mutation subtypes. However, it had low accuracy (43.1%) for the prediction of EGFR mutation subtypes.In addition, we tried to investigate the possibility of radiomics features for discriminating two main mutation subtypes  as a volumetric measurement of tumor glucose metabolism showed no higher predictive performance either. Therefore, our present study established a model based on 18FDG PET/CT radiomics to improve the predictive performance for EGFR mutation status in patients with NSCLC.Although a significant correlation between the tumor glucose metabolism level captured on PET images and EGFR mutation status has been found in multiple previous studies \u201314, namemax and TLG. Among these selected radiomics features, GLSZM_HGLZE from CT images measures the distribution of the higher gray-level values with a higher value indicating larger high-density areas proportion in tumor, which suggested that the tumors with EGFR+ had lower density than the EGFR- group in our study. In agreement with our finding, more ground-glass opacity and less solid components were observed in lung cancers with EGFR mutation  or CT  radiomiClinical features in patients with NSCLC are also non-negligible variables in the evaluation of EGFR mutations, which are more likely to occur in Asians, adenocarcinomas, females, and nonsmokers . In our max or TLG had no ability to classify the 19 del and the 21 L858R mutation. We tried to investigate the possibility of PET/CT radiomics features for distinguishing these two subtypes. Only GLCM_DV from PET images, which measures the heterogeneity of different intensity-level matrix, showed significant but unsatisfactory predictive performance in our study (AUC=0.661). Liu Q, et al. recent study , National Natural Science Foundation of China (81974276), Shanghai Jiao Tong University Med-X Interdisciplinary Research Funding (YG2017MS61), Shanghai Pujiang Program (18PJD030) and Shanghai Municipal Key Clinical Specialty (shslczdzk03403).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest."}
+{"text": "Copy number variants (CNVs) are known to play an important role in the development and progression of several diseases. However, detection of CNVs with whole-exome sequencing (WES) experiments is challenging. Usually, additional experiments have to be performed.n = 100) we observed superior performance of CopyDetective compared with ExomeCNV, VarScan2, ControlFREEC, ExomeDepth, and CNV-seq.We developed a novel algorithm for somatic CNV calling in matched WES data called \u201cCopyDetective\". Different from other approaches, CNV calling with CopyDetective consists of a 2-step procedure: first, quality analysis is performed, determining individual detection thresholds for every sample. Second, actual CNV calling on the basis of the previously determined thresholds is performed. Our algorithm evaluates the change in variant allele frequency of polymorphisms and reports the fraction of affected cells for every CNV. Analyzing 4 WES data sets (Individual detection thresholds reveal that not every WES data set is equally apt for CNV calling. Initial quality analyses, determining individual detection thresholds\u2014as realized by CopyDetective\u2014can and should be performed prior to actual variant calling. In recent years, next-generation sequencing (NGS) has found its way to clinical routine . With thDespite continuously decreasing costs for experiments, it is desirable to keep the number of necessary genetic experiments to a minimum\u2014not least because of limited tumor material ,6. Thus,Although there still remain challenges to be addressed, relatively short mutations, such as SNVs and indels, can already be determined reliably , 8. In cNumerous algorithms, all following different approaches, exist for calling CNVs in WES data. While some concentrate on normalizing coverage, e.g., VarScan , others Considering SNV and indel calling in NGS data, it is obvious that every data set\u2019s characteristics define its individual detection thresholds. An essential characteristic is coverage. If data are sequenced with only 10\u00d7 coverage, they are not apt to detect mutations at allelic frequencies of 5% because only 0.5 reads are expected to carry the mutation. When calling CNVs in NGS data, it is only consistent to assume that comparable detection thresholds exist.We present a novel algorithm, performing detection threshold\u2013aware CNV calling in WES data: CopyDetective . Prior tSubsequently, CopyDetective analyzes data according to these thresholds. Comparing a case sample to its matching control sample, coverage and SNP information are evaluated to identify regions of significant difference. CopyDetective reports merged and filtered CNVs along with additional information on the calls, e.g., quality values and information on the estimated CF.n = 100) the performance of our novel approach is evaluated and compared with that of 5 established approaches for CNV calling in WES data: ExomeCNV  and cnvPartition v3.2.0 , minimum 100 probes for 1 call, for analysis) [The second set covers 15 samples from 10 patients with Burkitt lymphoma . Data from 5 of 10 patients have been sequenced twice\u2014at the point of primary and relapse. The remaining 5 patients did not experience relapse and only 1 tumor sample (primary) is available. CNV-calling results  are based on SNP arrays  . AdditioRRID:SCR_010973] and cnvPartition v3.2.0 , minimum 100 probes for 1 call ), and the calls are overlapping. If two CNV calls are not overlapping but no significant \u201cno CNV\" region is located in between and the regions are close (<20\u00a0Mb), they are likewise merged.Note that for estimating the CFs of the merged regions, all significant SNPs are re-evaluated. This can, in some rare cases, lead to a merged region with an overall estimated CF below the actual detection thresholds. However, these regions always contain \u22652 raw CNV calls with CFs above the detection thresholds.Optionally, the merged results can be filtered on the basis of the CNV call quality. Depending on the data being analyzed, it can be useful to consider the merged calls directly. However, we recommend filtration of low-quality calls.RRID:SCR_010815) [RRID:SCR_006849) [RRID:SCR_002663) [RRID:SCR_010822) [RRID:SCR_013357) [Over recent years, several review articles have been published, considering tools available for CNV calling in NGS data ,23,24. T_010815) , VarScan_002663) , Control_010822) , and CNVExomeCNV uses read depth and B-allele frequencies (BAF) from matched WES data to detect deletions, duplications, and LOH. It is frequently used for benchmarking . We analVarScan2 analyzes normalized read depth in matched WES samples to detect deletions and duplications. For every region, num.mark and seg.mean are reported. We exclude all variants with num.mark <10. If seg.mean \u22650.25, the variant is considered a duplication. If seg.mean \u2264\u22120.25, the variant is considered a deletion. All variants with \u22120.25 < seg.mean < 0.25 are discarded.P value >0.05 are excluded. We consider both WilcoxonRankSumPvalue (WR) and KolmogorovSmirnovPvalue (KS). The copynumber, considering deletions, duplications, and LOH, reported in column \u201ccopy number\" is evaluated.Control-FREEC analyzes copy number and BAF profiles. Matched control samples are evaluated to distinguish germline variants from somatic ones. Information on subclonal gains and losses is reported and additionally evaluated if biological truth contains information on clonal composition of the samples. In addition to the standard Control-FREEC pipeline, we applied the additional script \u201cassess_significance.R\u201d . CNV calExomeDepth applies a \u03b2-binomial model to a set of exons. Normally, the tool requires multiple samples as input. The idea is that each exome is automatically compared to the exome featuring best correlation. However, because for all samples in our data sets matched controls are available, we assume that the matching control is always the best exome to be used for comparison. The copy number, considering deletions and duplications, reported in column \u201ctype\" is evaluated.Additionally, we consider CNV-seq. The tool has not been specifically designed for WES data. However, the general approach is similar to our novel approach CopyDetective: a sliding window is evaluated. The window size is defined by data quality, i.e., in this case coverage. Copy number ratios, as well as confidence values, are determined. However, different from CopyDetective, CNV calling with CNV-seq is solely based on coverage and not on BAFs. To process the raw output, we exclude all calls with missing values in columns \u201clog2\" and/or \u201ccnv.size.\" Regions belonging to the same CNV (identifier in column \u201ccnv\") are merged. All merged calls with cnv.p.value >0.05 are excluded. The remaining calls are categorized as deletions if cnv.log2 <\u22120.25 and as duplications if cnv.log2 >0.25. All the other calls are categorized as LOH.RRID:SCR_001860) [Details on the precise commands for executing CNV calling with the common approaches are provided in _001860)  or iCNV _001860) . InformaWe apply CopyDetective (simulation approach) on 4 sets of real data. Performance is compared to 5 established tools for CNV calling in WES data: ExomeCNV, VarScan2, ExomeDepth, Control-FREEC (WR and KS), and CNV-seq. Two samples from Data Set 2 (BL_03: P3 and R3) were excluded from analysis. Detailed analyses have shown that almost all validated CNVs appear to have been already present in the control sample, being either contamination or germline calls , it is reported in parentheses, as true-positive call with false type. We evaluate sensitivity (sens), the positive predictive value (PPV), and the F1 score only considering the true-positive calls with correct CNV type . Considering our novel approach CopyDetective without filtration (configuration \u201craw\"), PPV ranges between 0.04 and 0.26. Over all data sets, performance is comparable to the best common approach ExomeDepth (PPV 0.11\u00a0vs 0.12). If we apply filtration with our default threshold, values between 0.12 and 0.86 can be observed .It can be observed that a majority of common variant-calling tools are characterized by low PPV. For ExomeCNV, ControlFREEC , and CNV-seq PPV ranges between <0.01 and 0.09 for all data sets. Only in the cases of VarScan and ExomeDepth\u2014both tools that are unable to detect LOH\u2014can higher PPVs partly be observed. However, performance is highly data dependent , much higher values can be observed for ExomeCNV . However, owing to low PPV, the overall performance considering the F1 score is, over all data sets, relatively poor. Similar to PPV, ExomeDepth features highly data-dependent performance with respect to sensitivity (ranging between 0.27 and 0.68). In contrast to this, CopyDetective is characterized by stable sensitivity. For both the raw and the filtered results, sensitivity ranges between 0.92 and 1.00. On average, sens = 0.96, which slightly exceeds our user-defined sensitivity of 0.95 when determining the detection thresholds.Table\u00a0For all data sets it can be observed that true CNV calls are characterized by higher quality values compared with false-positive calls. Combining all data sets, no true-positive call with a quality value <10.76 can be observed see . Three oCopyDetective is a novel tool for calling somatic CNVs in matched WES data. It has been developed for but is not limited to the analysis of cancer samples. Different from any other approach, CopyDetective performs initial quality analysis of every sample to estimate the individual detection thresholds, covering the minimum CNV length and the minimum cell fraction. These detection thresholds allow subsequent CNV calling with user-defined sensitivity (default: 0.95).in situ hybridization; see Considering the performance of our new approach, we observe high sensitivity regarding high- as well as low-coverage data. Over all data sets, CopyDetective outperforms all the other tools we considered, even without optional filtration of low-quality calls. Application of the quality filter results in further improvement of performance, especially with respect to PPV. Data indicate that a threshold of 10.76 can be used safely to exclude false-positive calls. Detailed additional analyses show that the coordinates of the CNVs, determined by CopyDetective, match the coordinates based on validation experiments see . FurtherYet, the fact that CopyDetective is able to estimate CFs is an important characteristic, especially with respect to clonal evolution. While allele frequencies of pathogenic mutations can easily be analyzed to determine the subclonal composition of a tumor, this should also be done when analyzing CNVs. However, most tools report only a copy number variant and its CNV value but not the fraction of cells affected by the mutation. To our knowledge, only 2 additional tools are able to estimate tumor purity in NGS data: CNAnorm  and THetIt may seem astonishing that CNVs reported by CopyDetective match the validated CNVs, spread all over the genome, with respect to coordinates and CFs so well while just analyzing WES data. However, the main advantage of our approach lies in the analysis of a sliding window and the subsequent merging of windows located in close vicinity. This approach allows us to explore between 99.5% and 99.8% of the human genome see .However, CopyDetective certainly has some limitations. We need a specific scenario\u2014matching control samples\u2014to evaluate changes in VAF for every polymorphism. CopyDetective's performance is dependent on the accuracy of polymorphism calling in the control sample. However, analyses of robustness have shown that CopyDetective is especially tolerant towards false-negative polymorphism calls see . Our appCurrently, gonosomes are not evaluated by CopyDetective. CNVs on the Y chromosome cannot be detected because all polymorphisms are hemizygous . However, our approach is expected to work for women's X chromosomes.Depending on the quality of the input data provided, CopyDetective may not detect and report any small CNVs like focal CNVs, which are known to play an important role in cancer . HoweverCopyDetective unites an established idea\u2014evaluating the change in VAF of polymorphisms to detect CNVs\u2014with a completely new aspect\u2014determining individual detection thresholds for every sample. Thereby, CopyDetective shines a new light on CNV calling in WES data: individual detection thresholds reveal that not every data set is equally apt for CNV calling. The general idea of our algorithm\u2014applying a 2-step procedure\u2014can be combined with any other CNV-calling approach. Initial quality analyses, determining individual detection thresholds, can and should be performed prior to actual variant calling.Project name: CopyDetective https://github.com/sandmanns/CopyDetectiveProject home page: \u00a0Operating system: Platform independentProgramming language: ROther requirements: NoneLicense: AGPL-3.0bio.tools ID: biotools:copydetectiveRRID:SCR_018909GigaScience GigaDB database [Sequencing data are available at the NCBI SRA , the EMBL-EBI European Nucleotide Archive (PRJEB36436), Array Express (E-MTAB-8763), and the Gene Expression Omnibus (GSE68078). All supporting data and materials are available in the Supplementary Figure S1. Exemplary coverage distribution.Supplementary Figure S2. Exemplary relation between window size and cell fraction.Supplementary Figure S3. General idea of CopyDetective.Supplementary Figure S4. Expected change in VAF of polymorphisms in the presence of CNVs.Supplementary Figure S5. Beta allele frequency.Supplementary Figure S6. Relation bewteen the distance between two polymorphisms and the corresponding percentile.Supplementary Figure S7. Exemplary variant calling output.Supplementary Figure S8. Relation between detection thresholds (simulation vs exact approach).Supplementary Figure S9. Relation between sensitivity and PPV.Supplementary Figure S10. Overlap of CNVs reported by CopyDetective with validated CNVs.Supplementary Figure S11. Relative deviation of the true start and end position from the called ones.Supplementary Figure S12. Relation between estimated CF and true CF.Supplementary Figure S13. Relation between detection thresholds (FN polymorphisms).Supplementary Figure S14. Relation between detection thresholds (FP polymorphisms).Supplementary Figure S15. Coverage indicator for true CNVs.Supplementary Table S1. Sequencing data characteristics of data set 1.Supplementary Table S2. Sequencing data characteristics of data set 2.Supplementary Table S3. Sequencing data characteristics of data set 3.Supplementary Table S4. Sequencing data characteristics of data set 4.Supplementary Table S5. CNV calling results in data set 4, including LOH.Supplementary Table S6. Detection thresholds of data set 1.Supplementary Table S7. Detection thresholds of data set 2.Supplementary Table S8. Detection thresholds of data set 3.Supplementary Table S9. Detection thresholds of data set 4.Supplementary Table S10. Detailed CNV calling results for data set 1.Supplementary Table S11. Detailed CNV calling results for data set 2.Supplementary Table S12. Detailed CNV calling results for data set 3.Supplementary Table S13. Detailed CNV calling results for data set 4.Supplementary Table S14. Performance of CopyDetective using the exact approach.Supplementary Table S15. Lowest and highest quality values for TP vs FP CNV calls.Supplementary Table S16. Performance of CopyDetective simulating FN polymorphisms.Supplementary Table S17. Performance of CopyDetective simulating FP polymorphisms.Supplementary Table S18. Performance of CopyDetective changing detection thresholds.Supplementary Data S1. CNV calling output from CopyDetective, including raw and filtered calls for Data Sets 1\u20134.BAF: B-allele frequencies; BL: Burkitt lymphoma; bp: base pairs; CF: cell fraction; CNV: copy number variant; EMBL-EBI: European Molecular Biology Laboratory European Bioinformatics Institute; FP: false positive; indel: insertion and deletion; KS: KolmogorovSmirnovPvalue; LOH: loss of heterozygosity; Mb: megabase pairs; MDS: myelodysplastic syndromes; NCBI: National Center for Biotechnology Information; NGS: next-generation sequencing; NMZL: nodal marginal zone lymphoma; PPV: positive predictive value; sens: sensitivity; sd: standard deviations: SNV: single-nucleotide variant; SNP: single-nucleotide polymorphism; SRA: Sequence Read Archive; SV: structural variant; T-LBL: T-lymphoblastic lymphoma; TP: true positive; VAF: variant allele frequency; WES: whole-exome sequencing; WGS: whole-genome sequencing; WR: WilcoxonRankSumPvalue.All patient material was collected and analyzed in accordance with the relevant ethical guidelines and regulations. Informed consent was obtained from all subjects.The authors declare that they have no competing interests.This work has been supported by the EU grant Horizon2020 MDS-RIGHT (grant No. 634789), the DFG grant TU 298/5-1 , a grant from Deutsche Krebshilfe DKH (grant No. 111347), by L\u00f6wenkinder\u2013Verein zur Unterst\u00fctzung krebskranker Kinder e.V., and by Deutsche Kinderkrebsstiftung .S.S. developed the algorithm, performed data analyses, and wrote the manuscript. S.S. and M.W. performed analysis of validation data. A.O.d.G. and J.H.J. collected patient samples and coordinated targeted mutational and WES on the MDS cases. B.B. collected patient samples and coordinated WES on the T-LBL cases. M.D. reviewed development of the algorithm and reviewed the manuscript. All authors read, revised, and approved the final version of the manuscript.giaa118_GIGA-D-20-00138_Original_SubmissionClick here for additional data file.giaa118_GIGA-D-20-00138_Revision_1Click here for additional data file.giaa118_GIGA-D-20-00138_Revision_2Click here for additional data file.giaa118_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giaa118_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giaa118_Reviewer_1_Report_Original_SubmissionSheida Nabavi -- 7/3/2020 ReviewedClick here for additional data file.giaa118_Reviewer_1_Report_Revision_1Sheida Nabavi -- 9/9/2020 ReviewedClick here for additional data file.giaa118_Reviewer_2_Report_Original_SubmissionRobert Nowak -- 7/5/2020 ReviewedClick here for additional data file.giaa118_Reviewer_2_Report_Revision_1Robert Nowak -- 8/18/2020 ReviewedClick here for additional data file.giaa118_Supplemental_FilesClick here for additional data file."}
+{"text": "As a results, increased CWM_Hmax, CWM_LN and CWM_SLA positively influenced grassland productivity. In contrast, functional divergence decreased with increasing N and P and showed negative relationships with grassland productivity. Our results emphasized that CWM traits and functional diversity contrastingly drive changes in grassland productivity under N and P addition.Fertilization could influence ecosystem structure and functioning through species turnover (ST) and intraspecific trait variation (ITV), especially in nutrient limited ecosystems. To quantify the relative importance of ITV and ST in driving community functional structure and productivity changes under nitrogen (N) and phosphorous (P) addition in semiarid grasslands. In this regard, we conducted a four-year fertilizer addition experiment in a semiarid grassland on the Loess Plateau, China. We examined how fertilization affects species-level leaf and root trait plasticity to evaluate the ability of plants to manifest different levels of traits in response to different N and P addition. Also, we assessed how ITV or ST dominated community-weighted mean (CWM) traits and functional diversity variations and evaluated their effects on grassland productivity. The results showed that the patterns of plasticity varied greatly among different plant species, and leaf and root traits showed coordinated variations following fertilization. Increasing the level of N and P increased CWM_specific leaf area (CWM_SLA), CWM_leaf N concentration (CWM_LN) and CWM_maximum plant height (CWM_H GrasslaBoth N and P, play critical roles in improving community functions and accelerating the grassland restoration process . In the At species level, plants can improve their adaptability to environmental changes through trait plasticity . Trait pAt community level, environmental changes such as nutrient addition can influence community functional structure through intraspecific trait variation (ITV), species turnover (ST), or both depending on the intensity of changes. ITV represents contribution to the overall functional trait response to environmental changes. ST represents the change of species composition . Under lSeveral studies have shown that species functional traits predominantly regulate ecosystem functioning . CommuniNutrient addition caused species loss and trait divergence, resulting in variation in community functional structure, further affecting grassland productivity . Hence, 22.1-1, respectively. For this research, a 600 m2 area of the grassland was fenced to avoid grazing disturbance (Bothriochloa ischaemum (L.) Keng (27.30%), Artemisia sacrorum Ledeb. (11.70%), Lespedeza davurica (Laxmann) Schindler (11.70%), Stipa bungeana Trin. (12.67%), Potentilla tanacetifolia Willd. ex D.F.K.Schltdl. (16.36%), and Artemisia scoparia  Pamp (5.39%). The coverage and aboveground biomass of the grassland community were 41% and 118.7\u00a0g m-2 before N and P additions in 2017 . Moreover, four subplots per main plot were randomly assigned to four levels of P, namely 0, 20, 40, and 80\u00a0kg P2O5 ha\u20101 yr-1  (-1 yr-1) on the Loess Plateau  and four subplots (2 \u00d7 2\u00a0m). Each main plot was laid out in a randomized block design with three replicates. A 1.5-m buffer zone was allocated between adjacent blocks. Four main plots were randomly assigned to four levels of N, namely 0, 25, 50, and 100\u00a0kg N ha Plateau . N50 and2.3max), abundance, and coverage were measured in August 21, 2020 in each 1\u00d71 m fixed quadrat. The aboveground material of all plant species within each quadrat was cut, oven-dried at 80\u00b0C, and weighed. The community productivity was estimated as the sum of the harvested aboveground biomass. Relative biomass of each species was the aboveground biomass of each species/community aboveground biomass.Plant maximum plant height (Hhttp://imagej.nih.gov/ij/). Each fine root image was processed using the WINRHIZO software  to determine the root length and root surface area. All samples were oven-dried for 48\u00a0h (65\u00b0C) to determine the leaf and root dry mass. We then calculated the specific leaf area  as leaf area/leaf dry mass and the specific root length  as root length/root dry mass. Leaf tissue density  was calculated as leaf dry mass/leaf volume, and leaf volume was calculated as leaf area \u00d7 leaf thickness. The specific root surface area  was calculated as root surface area/root dry mass. Finally, the leaves and fine roots of each species were ground and passed through a 0.25-mm sieve. The sieved samples were used to measure the leaf and root N and P concentrations . LN and RN were measured with an auto-Kjeldahl instrument . LP and RP were determined using the molybdenum\u2013antimony colorimetric method.Plant functional traits were evaluated using 2\u20133 fully mature individuals for each dominant species  in each fixed quadrat. 10-15 mature and fully expanded sun-exposed leaves per individual were chosen for measuring leaf traits, and 10-15 fine roots (diameter< 2\u00a0mm) of the same individuals from which we collected leaves were chosen for measuring fine root traits. Functional traits of leaves and roots were measured according to standard protocols . The lea2.4Plant trait plasticity was evaluated using the plasticity index (PI). Under fertilization addition, a positive PI indicates that trait values are higher than that of the control treatment . The PI where \u2018Mean (fertilization)\u2019 and \u2018Mean (N0P0)\u2019 denote the mean functional traits of species under nutrient addition and N0P0 treatments, respectively, \u2018Max \u2019 represents the maximum mean values of the assessed traits in each treatment.FD package was used to calculate the CWM traits and FD in R 4.1.3(R Development Core Team). The contribution of ST and ITV in explaining variations in functional diversity and CWM traits was determined based on the method proposed by To determine the community functional structure, The \u2018dbFD\u2019 function in the post hoc multiple comparisons of significant differences between different N and P treatments. The ANOVA was performed using GenStat version 18.0 .The effects of N and P addition on CWM traits and FD were analyzed using an analysis of variance (ANOVA). The least significant difference (LSD) criterion was used for 2) test with the associated probability were adopted to evaluate model fitness. The model fit was deemed acceptable when 0 \u2264 \u03c72/df \u2264 2, 0 \u2264 RMSEA \u2264 0.1 and \u03c72 and RMSEA values were nonsignificant (p>0.05).A principal component analysis (PCA) was conducted to correlate functional traits and relative biomass using CANOCO 5.0 . In orde33.1p< 0.05; p<0.05; It was noted that N addition resulted in significant impacts on all functional traits except for SLA, SRA, RN and RP p< 0.05; Table S4B. ischaemum (except for N0P40), P. tanacetifolia, and A. scoparia. The PI of LP for A. scoparia was greater than zero in all treatments, while the PI of LP was negative for the other five species when they were treated with N addition only. The PI of LN: LP was only higher than zero for all species treated only with N. The PI of SLA was greater than zero for all six species in all treatments, except for A. sacrorum treated with P-only . In contrast, a combination of N and P resulted in significantly greater relative biomass in A. sacrorum compared to those treatments involving only one of these nutrient elements (p<0.05). The relative biomass of A. scoparia was significantly higher in treatments involving N100 with various P levels than in the P-only treatment , while P addition significantly impacted all leaf traits (p<0.05). The interaction between N and P addition significantly impacted CWM_LN: LP, CWM_LTD and CWM_Hmax (p< 0.05). CWM LN increased significantly after treatment with N or P alone than N0P0. Increasing level of P addition significantly promoted CWM_LP. CWM_SLA promoted significantly under all P levels as compared with P0. However, the impact of P addition on CWM_SLA was more pronounced at the highest level of N. CWM_Hmax increased significantly with increasing the level of N, with P addition synergizing the positive impact of N . P addition significantly impacted CWM_RN, CWM_RP and CWM_RN: RP (p< 0.05). However, N addition only significantly decreased CWM_RN . N and P interactions greatly affected FDiv (p<0.05). The greatest and lowest values for RaoQ and FDis at all P levels were recorded at N50 and N100, respectively. FDiv was generally lower in all N levels compared to the N0P0, with P addition exacerbating the reduction, especially at N100 . positive covariance effects between ST and ITV were found for CWM_SLA, CWM_LTD and CWM_Hmax. Under P addition, ST explained more variations in CWM_LN (15%), while the contribution of ITV to the variations in CWM_LP (60%), CWM_LN: LP (75%), CWM_SLA (9%) and CWM_LTD (6%) was more significant. Covariance effects between ITV and ST were only negative for CWM_LN and CWM_LN: LP under P addition. Under N and P interactive effects. Covariance effects between ST and ITV were positive for CWM_Hmax and all CWM leaf trait values (except for CWM_LP) under the interactive effects of N and P addition  and CWM_LTD (12%). Both ST and ITV significantly contributed to variations of CWM_LN, CWM_LP, CWM_LN: LP, and CWM_Haddition Figure\u00a08ITV and ST had significant impacts on all CWM values for root traits except for CWM_RP and CWM_SRA under N addition. Under P addition, ITV contributed significantly to changes in CWM_RP (49%) and CWM_RN: RP (80%). Positive covariance effects between ITV and ST were only found for CWM_RP Figure\u00a083.5max , followed by CWM_SLA , FDiv  and CWM_LN . The model explained 74% of total variations in grassland productivity , implying that this species was conservative species. Conservative perennial plant species have high resource utilization efficiency with low requirements; however, at higher levels of fertilization, they are at competitive disadvantages with acquisitive species , leading to excluding species with low competitive abilities  and belowground  traits exhibit coordinated variations under N and P addition, with ITV predominantly driving the variations in community functional structures in response to nutrient addition. We also found that variations in CWM traits and functional diversity jointly affect the response of grassland productivity to nutrient addition. Future studies should involve further long-term studies and additional traits data under different environments in order to extend the finding of this research to broader regions.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.YY, ZC, BX and ZW planned and designed the research and guided the entire process of the study; CD, WL and RZ conducted the experiments and finished the material collection, YY, HG and ZW analyzed data, wrote and edited the manuscript. All authors contributed to the article and approved the submitted version."}
+{"text": "Bubalus bubalis carabanesis) is an economically important livestock supplying milk, meat, leather, and draft power. Several female buffalo genomes have been available, but the lack of high-quality male genomes hinders studies on chromosome evolution, especially Y, as well as meiotic recombination.The swamp buffalo  and structural variants (SVs) may contribute to buffalo evolution by influencing adjacent gene expression. We further found that the pseudoautosomal region (PAR) of the Y chromosome is subject to stronger purification selection. The meiotic recombination map showed that there were 2 obvious recombination hotspots on chromosome 8, and the genes around them were mainly related to tooth development, which may have helped to enhance the adaption of buffalo to inferior feed. Among several genomic features, TE density has the strongest correlation with recombination rates. Moreover, the TE subfamily, SINE/tRNA, is likely to play a role in driving recombination into SVs.The male genome and sperm sequencing will facilitate the understanding of the buffalo genomic evolution and functional research. Recombination events tend to be unevenly distributed in many species and frequently occur in small genomic regions termed hotspots , 2. Genohotspots . Hotspothotspots . TherefoBubalus bubalis carabanesis; NCBI:txid346063) and river buffalo , are classified. Swamp buffaloes are mainly distributed in China and Southeast Asian countries, serving as the primary draft animals for rice growing over thousands of years [The domestic water buffalo is an importantly economic animal resource. The global population size of the buffalo is about 200 million, and they supply milk, meat, leather, and draft power in agricultural production for more than 2 billion people , 8. Wateof years . Their sof years , which mof years , 12. Aloof years , 14. Bufof years .Although several of female buffalo genomes have been finished , 14, theMany mammalian genome projects prioritize sequencing female individuals (XX) over males (XY), as the haploid nature of the Y chromosome results in half its sequencing depth. This can decrease the assembled contiguity and length of the Y chromosome . AdditioWe further sequenced \u223c60\u00d7 Hi-C data to scaffold these contigs. Interestingly, a contig with a length of 7.6 Mb showed a strong interaction signal with both X- and Y- contigs , which i\u22126 per base pair based on genomic short-read alignment. Besides, about 92% of the annotated Y genes in the bull genome (Btau_5.0.1) could be explicitly (>90% identity and >95% coverage) mapped to the Y chromosome. To perform genome annotation, we combined 3 methods, including de novo, homology-based, and transcriptome-based prediction. In total, we predicted 22,608 protein-coding genes in the male buffalo genome   offer an alternative approach for genome evolution by influencing gene expression and phenotypes . We mapp05) Fig.\u00a0. We inve05) Fig.\u00a0. Our anaThe genome construction of the Y chromosome provides an opportunity to study the evolution of the sex chromosome in buffalo. It has been reported that mammals' Y chromosome undergoes abundant gene conversion , which lTo investigate the landscape of recombination events in buffalo, we sequenced 78 sperms from the same male buffalo with an average depth of \u223c5\u00d7, in total achieving 99.8% genome coverage. By employing a set of stringent filtering measurements and the donor's heterozygous SNP information, we identified a total of 1,934,008 high-confidence SNP loci. Using Hapi  softwareP = 5.6E-4), which included 3 tooth-related genes   . MEPE, iTo determine which factor(s) have the greatest impact on recombination rates, several such as PRDM9 binding, TEs, and GC content have been investigated. We performed a correlation analysis between these genomic features and the recombination rates. The effects of density and length were analyzed separately for genes and TEs. We found that gene density and length had almost equal correlations with recombination rates, but for TEs, the density was significantly more correlated than the length Fig.\u00a0. UltimatPrevious studies have reported that TEs are also the main source of SVs . TherefoWe present here the chromosome-scale genome of male buffalo, which exhibits better contiguity than published buffalo genomes , 14. In The assembly of the Y chromosome presents a significant challenge due to abundant and lengthy repeats, reduced sequencing depth, and high homology with some regions of the X chromosome . In thisMeiotic recombination is well studied in model species , 42, 52 Several factors, such as PRDM9 binding, TEs, and GC content, can influence recombination rates. We found that TE density had the strongest correlation with the recombination rate of swamp-type buffalo. Furthermore, SINE/tRNA, a TE subfamily, was found to have a significant effect on both recombination rate and SVs. We speculate that this SINE/tRNA subfamily may contribute to intraspecies or interspecies genetic variation by promoting recombination. Several studies have shown that the ZnF domain of PRDM9 recognizes specific DNA motifs and is responsible for the formation of recombination hotspots , 53\u201355. In the future, the genome and recombination map of male river buffalo could be constructed, providing insights into the divergent domestication features between the 2 subspecies of water buffalo and facilitating modern breeding for meat and milk production, as well as identifying genetic variation related to traits of interest. Additionally, further functional assays need to be performed to characterize the binding motif of swamp buffalo PRDM9, which may lead to a better understanding of the factors affecting recombination rates. We plan to continue investigating the genetic basis of important traits in swamp buffalo and to explore ways to use this information to improve breeding programs and animal welfare. We also hope to develop new technologies and methodologies for studying the genetics of nonmodel organisms.RRID:SCR_016387) with PE100 reads. We generated about 466.1 Gb (174\u00d7) Illumina short reads, 271.9 Gb (102\u00d7) nanopore long reads, 561.4 Gb (210\u00d7) Bionano molecules, and 291.8 Gb (109\u00d7) Hi-C data (RRID:SCR_020132) [We sampled blood DNA from a local male buffalo in the Guangxi Zhuang Autonomous Region. To construct a high-quality genome of the male swamp buffalo, several platforms, including Illumina, nanopore, Bionano, and Hi-C, were used to generate a bulk of datasets. Bionano Saphyr technology was applied and DLE1 restriction enzyme was used for digestion. Illumina Hi-C technology was used in this study. For the construction of Hi-C libraries, the buffalo DNA was digested with the restriction enzyme MboI and then was sequenced on an Illumina Novoseq 6000 platform (i-C data . The Hi-_020132) . The det_020132) .k-mers and separated long reads belonging to the Y chromosome.We selected short-read datasets from 59 male swamp buffaloes and 62 female swamp buffaloes from our previous buffalo population study  to identRRID:SCR_015008) v5.4.3 [RRID:SCR_022964) v1.3 [The long reads of the Y chromosome and other chromosomes of the male swamp buffalo were assembled with nextdenovo (v2.4.0) , respect) v5.4.3 , Merqury64) v1.3 , and shode novo and homology-based approaches to identify repetitive elements in the male buffalo genome. For the de novo approach, we used RepeatModeler (RRID:SCR_015027) v1.0.11 [de novo repeat library with default parameters. Then, RepeatMasker (RRID:SCR_012954) (v4.0.9) [de novo library. RepeatMasker was also run against RepBase (RRID:SCR_021169) (v20181026) [RRID:SCR_000764) (v5.3.3) [We combined  v1.0.11  to const(v4.0.9)  was run 0181026)  for homo0181026)  with par0181026)  were alide novo, homolog-based, and transcriptome-based approaches, were used to predict protein-coding genes of male buffalo. To perform de novo predictions, we used Augustus (RRID:SCR_008417) [RRID:SCR_012902) [RRID:SCR_002654) [RRID:SCR_007936) [RRID:SCR_016323) (v2.0.6) [RRID:SCR_014659) (r2012-06-25) [Three methods, including _008417) , Genscan_012902) , Glimmer_002654) , and SNA_007936)  in the r_007936)  was then_007936)  was used_007936) , and Str(v2.0.6)  was subs2-06-25)  and filtRRID:SCR_001010) (ncbi-blast-2.9.0+) . The best hits were used to assign homology-based gene functions. We used DAVID (RRID:SCR_001881) (v6.8) [Homo sapiens) with Fisher's exact test.To obtain gene functional annotation, the SwissProt protein database  was sear) (v6.8)  to perfoRRID:SCR_018171) (v4.0.0beta2) [We utilized the nucmer program in the Mummer package (.0beta2)  to perfoRRID:SCR_014932) (v4.9) [To compute the dN/dS value of genes on the Y chromosome, we used blastp (ncbi-blast-2.9.0+) with e-value<1-E05 to generated protein alignments for genes in the PARs of the X and Y chromosomes as well as self-to-self alignments for genes in SDR. Optimal alignments other than to themselves were considered as homologous gene pairs. The yn00 in the PAML package  (v4.9)  was furtRRID:SCR_002105) (v1.9) [Sequencing short reads for each sperm were mapped onto the male buffalo genome using BWA (v0.7.17-r1188) . Bam fil) (v1.9) . Duplica) (v1.9)  filter cn = 10) were selected for constructing the high\u2010quality framework by the \u201chapiFrameSelection\u201d function, separately. Imputation of missing data was performed by the \u201chapiImupte\u201d function with settings . Third, we inferred and proofread draft haplotypes by \u201chapiPhase\u201d and \u201chapiCVCluster\u201d functions. Multiple crossovers (cv\u2010links \u22652) within 1 Mb were filtered. We further adopted a maximum parsimony of recombination (MPR) strategy to eliminate incorrect crossovers by the \u201chapiBlockMPR\u201d function. Fourth, chromosome-level haplotype assembly was achieved by the \u201chapiAssemble\u201d function, and the haplotypes located at the end of the chromosome were polished using the \u201chapiAssembleEnd\u201d function with default parameters. Finally, we identified crossovers in sperm by the \u201chapiIdentifyCV\u201d function based on haplotypes for each sperm. Notably, some recombination events may not be accurately identified despite strict conditions for the process of sperm genotyping and recombination event identification.To detect recombination events in sperm, the Hapi package  in R wasgiad063_GIGA-D-22-00319_Original_SubmissionClick here for additional data file.giad063_GIGA-D-22-00319_Revision_1Click here for additional data file.giad063_GIGA-D-22-00319_Revision_2Click here for additional data file.giad063_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giad063_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giad063_Reviewer_1_Report_Original_SubmissionJames Prendergast -- 1/4/2023 ReviewedClick here for additional data file.giad063_Reviewer_1_Report_Revision_1James Prendergast -- 4/26/2023 ReviewedClick here for additional data file.giad063_Reviewer_2_Report_Original_SubmissionGiovanni Chillemi -- 1/5/2023 ReviewedClick here for additional data file.giad063_Reviewer_3_Report_Original_SubmissionDina El-Khishin -- 1/26/2023 ReviewedClick here for additional data file.giad063_Supplemental_FileClick here for additional data file."}
+{"text": "Underwater endoscopic mucosal resection (EMR) has been reported as an effective treatment for superficial nonampullary duodenal adenomasA 63-year-old-man presented with a duodenal adenoma with a white opaque substance that was 20\u200amm in diameter and located in the superior duodenal angulus . SalineVideo\u20061\u2002Tip-in underwater endoscopic mucosal resection for superficial nonampullary duodenal adenoma.During tip-in EMR, making a pre-cut on the distal side of the lesion with prior submucosal injections and fixing the snare tip can make the snare less slipperyEndoscopy_UCTN_Code_TTT_1AO_2AG"}
+{"text": "Meleagris gallopavo) is a species of significant agricultural importance and is the second largest contributor, behind broiler chickens, to world poultry meat production. The previous genome is of draft quality and partly based on the chicken  genome. A high-quality reference genome of M. gallopavo is essential for turkey genomics and genetics research and the breeding industry.The domesticated turkey . From a total of 40 chromosomes (2n = 80), we captured 35 chromosomes in a single scaffold, showing much improved genome completeness and continuity compared to the old assembly build. The 3 assemblies are of higher quality than the previous draft quality assembly and comparable to the chicken assemblies (GRCg7) shown by the largest contig N50 (26.6 Mb) and comparable BUSCO gene set completeness scores (96\u201397%). Comparative analyses confirm a previously identified large inversion of around 19 Mbp on the Z chromosome not found in other Galliformes. Structural variation between the parent haplotypes was identified, which poses potential new target genes for breeding.We contribute a new high-quality turkey genome at the chromosome level, benefiting turkey genetics and other avian genomics research as well as the turkey breeding industry. Meleagris gallopavo, NCBI:txid9103) is an important agricultural species and the second largest contributor to world poultry production [The domesticated turkey  genome. However, that version of the chicken genome had many microchromosomes missing altogether or only partially characterized. Avian microchromosomes have proved to be difficult to assemble even today [The first turkey genome assembly (UMD2), published in 2010 , was amoen today . ReliancThe problems in characterizing microchromosomes are partly due to sequence characteristics  and partly due to their extremely small size and lack of genetic linkage group markers to differentiate the microchromosomes from other chromosomes . Hence, High-quality genome sequences are an essential resource for research and applications in the life sciences. In domestic animal breeding, genome-wide marker panels are routinely used to support genomic selection, and this significantly accelerates genetic progress . An imprCurrently, more species in the Galliformes have high-quality long-read\u2013based assemblies, including the chicken , JapanesThird-generation sequencing techniques have made it possible to produce high-quality chromosome-based assemblies. The chicken individual broiler (GRCg7b) and layer (GRCg7w) assemblies and, more recently, the complete chicken genome assembly  have beeIn this study, our aims were to use the trio-binning approach to produce a chromosome-level turkey assembly (F1) and 2 parental haplotype assemblies. We further aim to compare the 2 parental haplotypes to identify structural differences. A good reference genome is essential for many research and commercial applications. In this study, we highlight how our new turkey genome can benefit both research and the breeding industry.We used a trio-sequencing  approachTelomeres and centromeres are generally enriched for simple repeats. Telomeric repeats (TTAGGG) were identified on the tail(s) of 18 chromosomes, supporting further completeness of the genome assembly . A 41-bpAs part of the trio-binning approach, both parental haplotypes were assembled with TrioCanu . We wereThe completeness and accuracy of the assemblies were assessed using BUSCO  and wholThe turkey genome is highly congruent with the chicken genome Fig.\u00a0, indicatWe annotated the repeats using a custom repeat library built using RepeatModeler . RepeatsP < 0.00001, Fig.\u00a0The Ensembl annotation pipeline was used to annotate Mgal_WU_HG_1.0 . The preWe identified chicken and Turkey_5.1 homologs of the Mgal_WU_HG_1.0 genes . Most ofCoturnix japonica) [Numida meleagris) [Taeniopygia guttata) [MANBAL and POL3) (OrthoFinder  was usedaponica) , helmeteleagris) , and zebguttata) . From thnd POL3) .While most orthogroups studied showed no change in the copy number of protein-coding genes, 71 groups showed expansions or contractions of gene families predicted using CAFE5 software  61 expa. ExpandeThe F1 and the paternal haplotypes are completely colinear . There aBLB2 gene, which is duplicated within the MHC-B region in chicken and plays a crucial role in disease resistance or susceptibility [TRIM36, GRIA2, and MAN2B2 genes. Specifically, the parent 2 haplotype exhibits a 20-kb duplication of the 3\u2032 end of MAN2B2  complex. Moreover, a 53-Kbp duplication was found affecting the 3\u2032 end of the RIMKLB gene . The distribution of copy gains and copy losses is in tibility , was fouf MAN2B2 , a gene entified . The GEMKLB gene , resultiA full overview of structural variation between the parental haplotypes is provided in RYR2 gene, which is affected by 4 LoF variants in parent 2, likely leading to an impaired RYR2 protein. Mutations in the RYR2 gene are associated with stress in broiler chickens [LRRC41, which, in the parent 2 haplotype, contains a stop-gained variant. Knockouts of this gene lead to increased lean body mass in mice, and hence this gene poses an interesting candidate for selection for body weight in turkey [The most common effect of selection is to alter gene expression, leading to phenotypic changes. However, a small proportion of phenotypic variation is due to impaired gene functioning . We assechickens . A seconn turkey .Single-nucleotide polymorphism (SNP) chips are useful to study variation between individuals and are widely applied in genomic selection. We mapped SNP chip markers from a 65,000\u00a0SNP array  to Mgal_WU_HG_1.0  using a P < 0.01) and LINE elements (P = 0.0281) compared to the intermediate and microchromosomes  in chromosome types, we analyzed RNA sequencing (RNA-seq) datasets from 16 tissues  enables the capture of chromosome arms in a single contig, resulting in a highly continuous and contiguous chromosome-level assembly. Furthermore, long reads can span long repetitive regions, including DNA transposons and LINE elements, as well as large structural variants. We observe an enrichment of telomeric and TM repeats at the tails of chromosomes, likely indicating telomeric and centromeric regions, as most of the turkey chromosomes are likely telocentric . We showImproving genome assemblies improves all analyses that depend on them. One of the reasons to improve the turkey assembly was to better map SNP chip markers to the genome. SNP chips are widely used in genomic selection, and a better genome representation and gene annotation directly affect its use for breeding. Specifically, the new turkey genome build overcomes the lack of SNPs mapped to gene-dense microchromosomes, as 85.3% of the SNP markers previously mapped to unplaced scaffolds on Turkey_5.1 are now mapped to chromosomes on Mgal_WU_HG_1.0, especially improving the representation of microchromosomes 31 to 35.Turkey breeding is done on pure elite lines, which can be selected for different purposes. In our study, 1 parent was from a female breeding line, with more focus on egg production and conformation, whereas the other parent was from a male breeding line focusing on growth and production traits. In producing a commercial product, lines are crossed to produce hybrid offspring that show the benefit of the breeding goals of both parental lines. In addition, the hybrid offspring benefit from hybrid vigor, resulting from 2 relatively differentiated lines. For the trio-binning method, having parents that are genetically distinct helps in resolving the haplotypes. Nevertheless, in this study, we present 2 high-quality parental haplotype assemblies where the low heterozygosity of the parents presented no obstacle to resolving the parental haplotypes.BLB2 (inversion); TRIM36, GRIA2, and MAN2B2 ; and a loss-of-function variant in the LRRC41 gene in the parental haplotype from the male line. An additional duplication of the GEMIN8 gene was identified in the parental haplotype from the female line. The BLB2 gene plays an important role in the presentation of extracellular antigen and initiation of an immune response [TRIM36 gene is associated with the spermatozoa acrosome reaction in mice, and knockouts are incapable of in vitro fertilization [GRIA2 gene is a excitatory neurotransmitter associated with various neurodevelopmental disorders in humans [MAN2B2 gene is associated with ovulation rate in pigs [GEMIN8 gene encodes a protein that is part of the SMN complex, which is necessary for spliceosomal small nuclear ribonucleoproteins (snRNP) assembly in the cytoplasm and pre-mRNA splicing in the nucleus [LRRC41 gene is likely knocked out in the male parental haplotype. Knockout mice of the LRRC41 gene show increased circulating calcium and glucose levels and increased lean body mass [Interestingly, we found specific structural variation in response . Howeverlization . Hence,  in pigs , while i in pigs . However nucleus . The LRRody mass . TherefoAmong the remaining challenges in variation analysis is the characterization of structural variants. The challenge is 2-fold. First, these large-scale variants are often not robustly detected using short-read sequencing. Second, individuals usually have a sequence that is population specific and may not be present in a reference assembly. This can make such large insertions hard to characterize, even by resequencing. In the process of assembling Mgal_WU_HG_1.0, we now have reference assemblies for 2 distinct breeding lines, which should greatly aid in variation analysis. Even though such large structural variants appear to be uncommon between breeding lines, we demonstrate how genes potentially important in breeding may be affected. These genes can be further prioritized in routine genomic breeding practice.PHF7 acts during spermiogenesis for histone-to-histone protamine exchange and is a determinant of male fertility in Drosophila and mouse [As more genomes are characterized with high accuracy and at a chromosome level, comparative genomics is increasingly used to study the function of genes and variants, including copy number variants. The new Mgal_WU_HG_1.0 genome assembly was applied to identify orthogroups that have expanded or contracted in turkey compared to other avian species. Expanded orthogroups included various distinct keratin families, encoding major structural proteins of feathers and claws . One gennd mouse , and it nd mouse . This gend mouse . In addiA characteristic of avian genomes is that they comprise a huge range of chromosome sizes. Interestingly, bird genome organization may be ancestral to all vertebrates . Among tBird genomes have very high retained synteny . This paThe Z chromosome presents a moderate yet striking deviation from the observed evolutionary stability. This chromosome exhibits a few rearrangements within the Galliformes, and in line with the findings of Zhang et al. , we obseIn conclusion, the new turkey genome presented here   represents a substantial improvement over the previous assembly and is an important resource with many applications in research and in the turkey breeding industry.M. gallopavo, 3 individuals were sequenced using the trio-binning approach\u20142 parents and 1 F1. The 2 parents come from 2 distinct commercial lines from Hendrix Genetics, 1 male line (parent 1) and 1 female line (parent 2). The F1 turkey was sequenced by Dovetail Genomics using PacBio SMRT sequencing technology  with a total depth of 270\u00d7. We generated short-read sequencing data from the F1 (90.4\u00d7 coverage) and both parents (35.4\u00d7 and 39.7\u00d7 coverage) on an Illumina HiSeq 4000 . In addition, Hi-C data were generated with a coverage of 32\u00d7. An initial assembly was created by Dovetail Genomics using wtdgb2  [De Novo Assembly Process, which uses Chicago and Dovetail Hi-C proximation ligation methods and the HiRise scaffolder as described in [To create a high-quality chromosome-level genome assembly of _017225) , polisheRRID:SCR_014731) [Pilon v1.23 (_014731)  was usedRRID:SCR_010910) [RRID:SCR_006525) [RRID:SCR_022013) [RRID:SCR_022964) [We scaffolded the F1 assembly received by Dovetail Genomics using the Hi-C reads and the PacBio long reads, both from the F1. The Hi-C reads were mapped to the polished assembly based on the Arima Mapping pipeline , using B_010910)  with def_006525) \u2014AddOrRep_022013) , which i_022013)  was usedTo validate our F1 assembly and look for misassemblies, we used Hi-C contact maps.RRID:SCR_017226) [de novo assembly pipeline , to scaffold our assembly. Juicebox v1.11.08 (RRID:SCR_021172) [RRID:SCR_001004) [Juicer v1.6 (_017226)  was used_021172)  was used_001004)  to visuaRRID:SCR_015880) [TrioCanu    was usedRRID:SCR_018550) [The corrected reads from TrioCanu were mapped to the Triocanu assembly with Minimap2 v2.17-r941 (_018550) , options_018550)  was used_018550)  was usedRRID:SCR_015008) [BUSCO v4.1.2 (_015008)  was run RRID:SCR_018967) [Genome assembly alignments were generated using D-GENIES v1.3.0 (_018967) , using mStructural variation between the 2 parental haplotypes was discovered using SyRI v1.5.4 . First, RRID:SCR_010910) [RRID:SCR_000468) [RRID:SCR_002105) [RRID:SCR_010761) [RRID:SCR_002105) [RRID:SCR_005227) [RRID:SCR_001209) [The short Illumina reads from the F1 individual were mapped back to the assembly using BWA-MEM v0.7.17 (_010910) . Samblas_000468)  was used_002105)  to sort _010761)  was used_010761)  was used_002105) , was use_005227)  was used_001209) .RRID:SCR_018171) [RRID:SCR_001414) [RRID:SCR_001173) [RRID:SCR_001598) [In order to map SNP markers from the 65,000 SNP array  to the new genome build, we first aligned the 2 genome builds  using nucmer v4.0.0rc1  . Next we_001414) . We used_001173)  to ident_001598)  to identRRID:SCR_002344) [RRID:SCR_015027) [de novo repeat library from our assembly using the Recon and RepeatScout tools. RepeatMasker v4.0.7 (RRID:SCR_012954) [Tandem repeats were identified using the TRF tool , and tel_002344) . The tra_015027)  was used_012954)  was usedRRID:SCR_017118) [RRID:SCR_001010) [The proteomes of 5 bird species were used to infer orthogroups (option -og) using OrthoFinder v2.5.4 (_017118) . The pro_001010)  against Expansions and contractions of protein-coding gene families were assessed by CAFE5 . The phyt-test was used to test for difference of repeat content and families between macrochromosomes, intermediate chromosomes, and microchromosomes.To better understand the differences between macrochromosomes (>40 Mbp), intermediate chromosomes , and microchromosomes (<20 Mbp), we investigated repeat content, gene structure, and gene expression. A Welch RRID:SCR_001905) [A custom repeat library created with RepeatModeler and custom R scripts was used to investigate the differences in repeat content between macrochromosomes, intermediate chromosomes, and microchromosomes. Each chromosome was split into bins (each bin corresponding to 2% of the chromosome length), allowing us to compare the chromosomes by relative length. We calculated the average repeat content in each bin. An ideogram of the density of each repeat feature was created for macrochromosomes, intermediate chromosomes, and microchromosomes with the R v4.0.2   package _001905) . RIdeogrRRID:SCR_015530) [RRID:SCR_016323) [Expression data for 16 turkey tissues  from a male individual at 3 developmental stages  were downloaded from Bioproject PRJNA259229. Not all tissues were available at all stages: testis was not available at day 21 and cecal tonsil at day 28. HISAT2 v2.2.1 (_015530)  was used_016323)  was usedt-test was used to test for difference of gene densities between macrochromosomes, intermediate chromosomes, and microchromosomes.We used RIdeogram v0.2.2  and R (vRRID:SCR_017650) [M. gallopavo), chicken , Japanese quail (C. japonica), helmeted guineafowl (N. meleagris), great tit (P. major), zebra finch (T. guttata), and emu (D. novaehollandiae).The MCScan Python pipeline from the JCVI utility libraries v1.1.11   was usedThe genome  and annotation files for these species were obtained from Ensembl release 106. The files for Mgal_WU_HG_1.0 and GRCg7b were obtained from the Ensembl rapid release (April 2022). The annotation file for the emu assembly ZJU1.0 was shared with us from . This anWe started by trimming the accession IDs in the FASTA file and converting the GFF3 annotation file to BED format. The jcvi.compara.catalog ortholog and jcvi.compara.synteny screen (with parameters \u2013simple) were used to create the necessary input files for plotting. The synteny plots were created with jcvi.graphics.karyotype using parameter \u2013basepair. To validate the chromosome Z inversion, first we manually checked the inversion breakpoints (reads spanning) using JBrowse 1.16.9.giad051_GIGA-D-22-00193_Original_SubmissionClick here for additional data file.giad051_GIGA-D-22-00193_Revision_1Click here for additional data file.giad051_GIGA-D-22-00193_Revision_2Click here for additional data file.giad051_GIGA-D-22-00193_Revision_3Click here for additional data file.giad051_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giad051_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giad051_Reviewer_1_Report_Original_SubmissionYunyun Lv -- 8/22/2022 ReviewedClick here for additional data file.giad051_Reviewer_1_Report_Revision_1Yunyun Lv -- 12/27/2022 ReviewedClick here for additional data file.giad051_Reviewer_2_Report_Original_SubmissionLuohao Xu -- 9/8/2022 ReviewedClick here for additional data file.giad051_Reviewer_2_Report_Revision_1Luohao Xu -- 12/24/2022 ReviewedClick here for additional data file.giad051_Reviewer_2_Report_Revision_2Luohao Xu -- 4/29/2023 ReviewedClick here for additional data file.giad051_Supplemental_FilesClick here for additional data file."}
+{"text": "Gymnosporangium asiaticum and G. yamadae can share Juniperus chinensis as the telial host, but the symptoms are completely different. The infection of G. yamadae causes the enlargement of the phloem and cortex of young branches as a gall, but not for G. asiaticum, suggesting that different molecular interaction mechanisms exist the two Gymnosporangium species with junipers.G. asiaticum and G. yamadae at different stages. Functional enrichment analysis showed that genes related to transport, catabolism and transcription pathways were up-regulated, while genes related to energy metabolism and photosynthesis were down-regulated in juniper branch tissues after infection with G. asiaticum and G. yamadae. The transcript profiling of G. yamadae-induced gall tissues revealed that more genes involved in photosynthesis, sugar metabolism, plant hormones and defense-related pathways were up-regulated in the vigorous development stage of gall compared to the initial stage, and were eventually repressed overall. Furthermore, the concentration of cytokinins (CKs) in the galls tissue and the telia of G. yamadae was significantly higher than in healthy branch tissues of juniper. As well, tRNA-isopentenyltransferase (tRNA-IPT) was identified in G. yamadae with highly expression levels during the gall development stages.Comparative transcriptome analysis was performed to investigate genes regulation of juniper in responses to the infections of G. asiaticum and G. yamadae differentially utilize CKs and specific adaptations on juniper during their co-evolution.In general, our study provided new insights into the host-specific mechanisms by which The online version contains supplementary material available at 10.1186/s12864-023-09276-7. Gymnosporangium asiaticum and G. yamadae are obligate biotrophic pathogens that cause pear-rust disease and apple-rust disease, respectively, hindering the development of orchard industry [Juniperus chinensis), threatening the cultivation of juniper [G. asiaticum and G. yamadae are demicyclic and heteroecious rust fungi, because they parasite on gymnosperms as telial host, while selecting dicotyledons as aecial hosts [G. asiaticum produces tongue-shapes or wedge-shaped telia directly at the base of needle leaves with slightly swelling -type cytokinins synthesis are synthesized de novo by the activity of adenylate isopentenyltransferases (IPTs); on the other hand, a large fraction of the cis-zeatin (cZ)-type cytokinins is derived from tRNA degradation, which depends on the catalysis of tRNA-isopentenyltransferase (tRNA-IPTs) [Claviceps purpurea and was proved essential for cZ biosynthesis and contributing to the formation of iP and tZ in the fungus [Magnaporthe oryzae [U. maydis [Puccinia recondita f. sp. tritici [G. juniper-virginianae [The mechanism of cytokinins biosynthesis is conserved in plants . On the NA-IPTs) , 36. HowNA-IPTs) . In recee fungus . Researce oryzae  and U. m. maydis , 37 also tritici  and G. jginianae  are ableGymnosporangium spp. infections at different stages and revealed similar and different genetic responses of juniper in the interaction between G. asiaticum and G. yamadae. The temporal transcriptional analysis of the gall showed an extensive and dynamic gene regulation pattern that is G. yamadae dominated. Furthermore, we obtained evidence that G. yamadae-derived CKs depended on the catalysis of tRNA-isopentenyltransferase (tRNA-IPT) identified in G. yamadae are most likely contribute to galls formation and development, which suggested the distinct infection strategies between G. asiaticum and G. yamadae. Taken together, the study provides new insights into the host-specificity mechanism of G. asiaticum and G. yamadae.In this study, we executed RNA-seq analysis of juniper branch tissues after the two G. asiaticum and G. yamadae, respectively  unigenes of juniper and 25,623 (94.30%) unigenes of rust were annotated in at least one database, and 9,065 (22.34%) and 6,975 (25.67%) unigenes, respectively, were annotated in all of the above databases  were downregulated in III_GA_J sample while upregulated in III_GY_J sample, 226 DEGs (Type_b) were upregulated in III_GA_J sample while downregulated in III_GY_J sample  and PR-proteins (64 DEGs), compared to G. asiaticum DEGs   Fig.\u00a0c.The functional enrichment analyses in GO databases showed that mainly up-regulated genes were mostly classified into cellular component and biological process categories. The common GO terms, including \u2018nucleus\u2019 and \u2018kinase activity\u2019 between I_GY_J and II_GY_J; \u2018cytoplasm\u2019 and \u2018intracellular membrane-bounded organelle\u2019 between II_GY_J and III_GY_J; \u2018cellular process\u2019 and \u2018ion binding\u2019 between I_GY_J and III_GY_J Fig.\u00a0a. KEGG pThe heatmaps showed that during the middle stage (II_GY_J) of gall development, genes associated with photosynthesis, sugar metabolism, plant hormones and defense-related pathways were predominantly up-regulated at Fig.\u00a0c. In conG. yamadae contained substantially higher concentrations of CKs than healthy young branches, while concentrations of CKs in G. asiaticum telia were slightly higher than healthy young branches -isopentenyl pyrophosphate transferase\u201d. However, tRNA-IPT identified in this study was obtained from only one candidate, TRINITY_DN3125_c0_g1_i1, in juniper transcriptome database , as well as G. asiaticum candidate tRNA-IPT (c25847_g1) and two juniper candidate tRNA-IPTs (TRINITY_DN46394_c0_g1_i2 and TRINITY_DN16719_c0_g1_i1) were identified in this study to examine the relatedness to other tRNA-IPTs from a phylogenetically distinct group of organisms  relative to in the early stage (I_GY_J) and the late stage (III_GY_J) of gall development  and numbers of energy metabolism-related genes were up-regulated in the late stage (III_GY_J), which suggested a general function of galls as a carbohydrate source for the growth of G. yamadae teliospores. Correspondingly, rapid changes in the water and nutrient content in the gall lead to an increase in osmotic pressure, which induces the expression of ABA synthesis-related genes and an increase in ABA content, especially during the formation of large numbers of teliospores (II_GY_J) [G. yamadae induced gall tissues and indicated that the gall probably provide nutrients and water for teliospores of G. yamadae during the long-time development [Genes involved in \u2018glycine, serine and threonine metabolism\u2019 pathway at the early stage (I_GY_J) of the gall development may suggest the nutrient reserves in gall tissues. More importantly, the increase of soluble sugar content might contribute to the elevated osmotic, thus increasing the water content in gall tissues . SubsequII_GY_J) , 52. BesII_GY_J) , 53, 54.elopment .G. yamadae and juniper along with the gall development and the teliospores maturation. Ultimately, the delayed activation of juniper defense responses at the late stage (III_GY_J) reflected a common infection strategy of biotrophic and hemibiotrophic plant pathogens [Many studies demonstrated that insects may have to suppress plant resistance systems during gall formation and development for better survival , 29. Howathogens , 55\u201357.G. yamadae and juniper [The main distinguishing features between other parasite-gall systems are the observation of gene expression profiles and the capture of dynamical changes in multiple developmental stages of gall, consistent with the phenotypic development of gall and the interaction between  juniper , 57, 58. juniper \u201361. Gymn juniper . Consequ juniper .G. juniper-virginianae induced gall tissues as well as the wet telia, but not in control cedar branchlets [G. yamadae derived CKs on the induction of galls, we firstly examined the CKs content in juniper and G. yamadae, and found that galls and the telia contained significantly higher concentrations of CKs than the control sample  from our published data which contains all the typical characteristics of eukaryotic tRNA-IPTs [C. purpurea tRNA-IPT and U. maydis tRNA-IPTs whose functions have been well understood, which suggested their similar functions [Austropuccinia psidii, Puccinia striiformis f. sp. tritici and Melampsora larici-populina were clustered in eukaryotic tRNA-IPTs clade . To illustrate the morphology and anatomy of the gall tissues, infected branches of G. yamadae at the middle stage of gall development, with full-grown gall and obvious teliospores, were utilized. Healthy juniper branches and the gall tissues were collected simultaneously from a single juniper tree. The branches or galls were cut into longitudinal sections by hand or into 0.2\u00a0mm thick sections using a freezing microtome (Leica CM1950) for cross-sections. Longitudinal sections of branch were visualized under a stereomicroscope ; cross-sections of healthy branch and gall tissues were photographed by light microscopes .The upper surface and longitudinal sections of G. asiaticum and G. yamadae, including the early stage of gall development without the teliospores formation (I_GY_J), the middle stage with the teliospores break through the gall (II_GY_J) and the late stage of gall development with the matured teliospores , and healthy leaves and young branches (0_GY_J and 0_GA_J) as control were collected from Northwest A&F University, Yangling, Shaanxi and Haidian park, Beijing, China, between April and May 2021. All plant material were identified by Prof. Yingmei Liang. All samples were deposited in the Mycological Herbarium, Museum of Beijing Forestry University Mycologicum, Academiae (BJFC). Deposition number were: BJFC-R01700 (0_GA_J), BJFC-R02162 (III_GA_J), BJFC-R01692 (0_GY_J), BJFC-R02376 (I_GY_J)), BJFC-R02382 (II_GY_J), BJFC-R03656 (III_GY_J). Figure\u00a0Juniper leaves and young branches infected by Total RNA was isolated from 100\u00a0mg of infected leaf and branch tissues using the RNA Easy Fast Plant Tissue Kit  according to the manufacturer\u2019s instructions. RNA quality and quantity were checked with a Qubit\u00ae RNA Assay Kit with a Qubit\u00ae 2.0 Fluorometer . As shown in Additional file\u00a0J. chinensis to the genomes of Pinus lambertiana (GCA_001447015.2_Sugar_pine_JHU_assembly_genomic.fna), Pinus taeda (GCA_000404065.3_Ptaeda2.0_genomic.fna), Picea abies (GCA_900067695.1_Pabies01_genomic.fna), Picea glauca (GCA_000411955.6_PG29_v5_genomic.fna) and Gnetum montanum  through BLASTn and BLASTp [G. asiaticum and G. yamadae were annotated as described in the previous study [J. chinensis, G. asiaticum and G. yamadae  was used to predict the coding sequences (CDS) for all unigenes. Gene function was annotated in KO (KEGG Ortholog database), GO (Gene Ontology), Pfam (Protein family), and NR databases by EggNOG-emapper [TransDecoder  and more than a twofold change in transcript level were deemed as differentially expressed. Volcano plots of DEGs and heatmaps of gene expression profiles were generated using TBtools software [p-value\u2009<\u20090.05.The FPKM expression levels and counts for all unigenes were estimated in each replicate by RSEM . Principsoftware . To derip-value\u2009<\u20090.05, and the TBtools software [The annotated DEGs were used in functional enrichment analysis. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis  and GO csoftware  was usedsoftware  with defG. yamadae infected young branches  and the telial of G. asiaticum and G. yamadae. Measured teliospores were artificially germinated by immersing them in distilled water [Total cytokinins were extracted from 100\u00a0mg of healthy branches, ed water . Then thG. asiaticum and G. yamadae. TBtools Reciprocal BLAST was then conducted to search for homologous genes in the transcriptome of this study. Combined the results from previous studies [Cholletotrichum gloeosporioides (hemitropic fungi) and 3 tRNA-IPTs from rust (biotrophic fungi) through a BLASTp search [G. asiaticum and G. yamadae were aligned using MAGA 6.0 using Neighbor-joining algorithm. Bootstrap values were determined using 1000 replications. Protein sequences were aligned by multiple alignment with SnapGene 6.1 and visualized using GeneDoc [G. yamadae tRNA-IPT protein were predicted by using LOCALIZER [Based on the previous published data , potenti studies , 66, we p search  in NCBI. GeneDoc . LocalizOCALIZER .ubiquitin-conjugated enzyme E3 [RT-qPCR analysis was conducted to validate genes expression profiles between infected leaf samples and healthy leaf samples. Primers were designed by Primer 3  and syntnzyme E3 . Data loAdditional file 1:\u00a0Figure S1. The pipeline of RNA-seq and bioinformatic analysis used in the study. The plant unigenes were mapped to five publicly available databases of Pinus lambertiana, P.s taeda, Picea abies, P. gluca, Gnetum montanum in National Center for Biotechnology Information. The rust fungi unigenes were annotated as described in the methods. Figure S2. The proportional distribution by species of Juniperus chinensis (a) and Gymnosporangium species\u00a0(b) unigenes with homology in NR database. Figure S3. Assessment of RNA-seq data reproducibility. (a), Principal component analysis based on gene expression level of healthy leaves and branches (0_GA_J and 0_GY_J), Gymnosporangium asiaticum infected leaves with the the matured teliospores (III_GA_J), G. yamadae infected branch tissues at the early (I_GY_J), middle (II_GY_J) and late stage (III_GY_J) of the gall development, showing the clear separation of the six tested samples and the proximity of biological replicates. (b), Sample correlation matrix of replicates of healthy leaves and branches (0_GA_J and 0_GY_J), G. asiaticum infected leaves with the the matured teliospores (III_GA_J), G. yamadae infected branch tissues at the early (I_GY_J), middle (II_GY_J) and late stage (III_GY_J) of the gall development based on gene expression. Figure S4. Statistics of annotated different expression genes. Venn diagram showing the number of up- and down-regulated genes  in III_GA _J and III_GY_J samples compared with the control, respectively. Type_a and Type_b refer to juniper unigenes regulated reversely in III_GA _J and III_GY_J samples. Figure S5. Amino acid sequence alignment of tRNA-IPT homologs. The tRNA-IPTs from Saccharomyces cerevisiae (NP_014917.3), Claviceps purpurea (CCE29200.1), Sporisorium reilianum (CBQ67583.1), Ustilago maydis (XP_011386632.1), Gymnosporangium asiaticum (c25847_g1) and Gymnosporangium yamadae (TRINITY_DN3125 c0_gI_i1). The dark to light colors indicate amino acids conservation from high to low. Black lines indicate ATG/GTP binding site, DMAPP binding site and zinc-finger motif; the two putative nuclear localization signals of GytRNA-IPT (TRINITY_DN3125 c0_gI_i1) and GatRNA-IPT (c25847_g1) are highlighted by green and red boxes; black box indicate chloroplast localization signal of GytRNA-IPT.Additional file 2:\u00a0Table S1. The statistics of unigene classification. Unigenes assigned with \u201cjuniper\u201d species were determined as juniper unigenes, whie assigned with \u201cfungi\u201d species were determined as the two Gymnosporangium spp. unigenes. Table S2. Functional annotation of Juniperus chinensis and the Gymnosporangium spp. unigenes in selected six public databases . Table S3. Differentially expressed genes in III_GA, I_GY, II_GY and III_GY. Table S4. Swissport annotations of Type_a and Type_b DEGs in the Venn diagram. Table S5. MapMan annotations of different expression genes  in III_GA and III_GY samples. Table S6. FPKM and KO number of photosynthesis, sugar metabolism, plant hormone and defense-related genes in I_GY, II_GY and III_GY samples. Table S7. The tRNA-isopentenyltransferase proteins used for sequence alignment and phylogenetic tree creation."}
+{"text": "Endoscopic submucosal dissection (ESD) can facilitate complete removal of residual tumors even after a failed endoscopic resection and even when endoclips are left in placeA 72-year-old woman was referred for a suspected residual tumor after conventional EMR in the distal rectum; intramucosal cancer with a positive horizontal margin was identified on histopathological assessment. Outpatient colonoscopy revealed a 10-mm residual lesion with four endoclips remaining from the previous EMR. Magnifying narrow-band light examination suggested a low-grade adenoma . EndoscVideo\u20061\u2002Complete resection of a rectal post-endoscopic-resection residual tumor including four endoclips using underwater endoscopic mucosal resection.This case demonstrates that a residual tumor with four endoclips still in place after EMR can be safely and completely resected using UEMR.Endoscopy_UCTN_Code_TTT_1AQ_2AC"}
+{"text": "A 65-year-old Japanese woman was admitted to our hospital with complaints of abdominal pain and a fever of 39.0\u200a\u00b0C. Computed tomography (CT) revealed an infected lymphocele after surgery for ovarian cancer. Endoscopic ultrasound-guided lymphocele drainage (EUS-LD) was planned while avoiding free space between the colon and lymphocele using a convex EUS scope .During the EUS procedure, VISCOCLEAR gel  was injected through the accessory channel to secure the visual field without gas insufflationVideo\u20061\u2002An effective procedure for treating lymphocele using endoscopic ultrasound-guided lymphocele drainage.A lymphocele is a cystic mass that may occur in the retroperitoneum following a systematic pelvic and/or para-aortic lymphadenectomy. The most severe complication of a lymphocele is infection. Generally, minimally invasive methods involving catheter drainage and sclerotization tend to be popular; however, surgery remains the first option in recurring, poorly accessible or inflammatory lymphocelesEndoscopy_UCTN_Code_TTT_1AS_2AG"}
+{"text": "A 48-year-old woman was admitted for severe upper abdominal pain, with impressive dilation and multiple high-density stones in the main pancreatic duct (PD). The patient was diagnosed with \u201cchronic pancreatitis\u201d and a therapeutic endoscopic retrograde cholangiopancreatography (ERCP) was planned.A predominant stricture, 2\u200acm long, was found at the head of the PD, with significant upstream dilation. Purulent juice and dozens of multiple movable stones up to 9\u200amm in diameter were noted . A 10\u200a\u00d7One month after her discharge, the patient was readmitted due to recurrent epigastric pain. During the third ERCP, peroral pancreatoscopy  was performed. Many villous and fish-egg-like lesions were found at the stricture segment, with a fragile and ulcerated surface . No notVideo\u20061\u2002A rare pancreatic tumor mimicking chronic calcified pancreatitis.IPMN generally has a long and hidden pathogenesis, and rarely includes pancreatic stone formation. This tumor usually produces much mucus, and sepsis infections are uncommon. The typical manifestations under Spyglass play an essential role in establishing the diagnosisEndoscopy_UCTN_Code_CCL_1AZ_2AB"}
+{"text": "Endoscopic biliary drainage with a self-expandable metal stent (SEMS) is the standard treatment for malignant biliary obstructions. Duodenobiliary reflux, which is an unavoidable concern after SEMS placement, results in stent dysfunctionA 78-year-old woman who had undergone SEMS placement for ampullary carcinoma was admitted to our hospital with acute cholangitis caused by RBO . The plVideo\u20061\u2002One-step balloon-assisted direct peroral cholangioscopy procedure and placement of a duckbill-type anti-reflux self-expandable metal stent.Endoscopy_UCTN_Code_TTT_1AR_2AH"}
+{"text": "Compared with group L and H, FMD in M group has higher average daily gain, feed efficiency and neutral detergent fiber digestibility. For the fecal bacterial community, the percentage of Firmicutes was increased, Bacteroidetes was decreased and the diversity of microbiota significantly reduced (p\u2009<\u20090.05) with the increasing of dietary protein. The proportion of Ruminococcaceae_005, Ruminococcaceae_UCG-014 and uncultured_bacterium_f_Lachnospiraceae were significantly increased wtih rising CP, the proportions of Bacteroides and Rikenellaceae_RC9_gut_group were significantly decrease nevertheless at the genus level. The higher abundance of f_Prevotellaceae and g_Prevotellaceae_UCG_004 were found at M group by LEfSe analysis. The relative abundance of uncultured_bacterium_f_Ruminococcaceae was positively correlated with the average daily gain and feed conversion ratio (p\u2009<\u20090.05), whereas Family_XIII_AD3011_group was negatively correlated with feed conversion ratio (p\u2009<\u20090.05). The UPGMA tree showed L and M groups were closer in clustering relationship, while H group was clustered separately into a branch, which indicated that the bacterial structure had changed greatly with protein level increased from 13.37 to 15.48%. Overall, our results indicated that the optimum dietary CP for the growing FMD was 13.37%.It is necessary to assess the appropriate dietary protein level of the forest musk deer (FMD), as nutritional needs are unclear. The microbiome in gastrointestinal tracts plays an important role in regulating nutrient utilization, absorption and host growth or development. Thus, we aimed to evaluate growth performance, nutrient digestibility and fecal microbiome of growing FMD supplied with different protein levels of diets. Eighteen 6-month-old male FMD with an initial weight 5.0\u2009\u00b1\u20090.2\u2009kg were used in a 62-day trial. The animals were randomly distributed to three groups, the dietary crude protein (CP) level was 11.51% (L), 13.37% (M), and 15.48% (H). The results showed that the CP digestibility decreased as dietary CP level increased ( Moschus berezovskii), one of the small ruminants, belongs to a special economic animal in China. Musk, which secreted by male forest musk deer (FMD), is a valuable resource in the traditional Asian medicine and the international perfume industry  levels of TMR pellet feed on the growth performance, nutrient digestibility and fecal microorganisms of FMD during the growing phase, and provide reference data for the standardization of the nutritional needs for the captive FMD.ad libitum. The health condition of each animal was closely monitored. The composition of the diets is presented in Eighteen 6-month-old FMD with a similar initial weight (5.0\u2009\u00b1\u20090.2\u2009kg) were randomly divided into 3 groups consisting of 3 replicates with 3 FMD per replicate. The FMDs were fed with low CP diet , middle CP diet  and high CP diet , respectively. The experiment last for 62\u2009days and all FMDs were fed at 9: 00\u2009a.m. and 16: 00\u2009p.m. All animals were housed individually and provided feed and water 2SO4 solution was evenly added to 0.1\u2009kg of feces to fix fecal nitrogen. Other samples were snap-frozen in liquid nitrogen, and then stored at \u221280\u00b0C for further analysis.At 1 and 62 d of the trial period, body length gain (BLG), chest girth gain (CGG), body oblique length gain (BOLG) and weight gain were measured individually after overnight fasting (12\u2009h), meanwhile, average daily feed intake (ADFI), average daily gain (ADG) and feed to gain (F: G) were calculated. Fresh fecal samples were collected on days 56\u201362 of the trial. Feces for apparent nutrient digestibility analysis were collected into valve bags, and 10\u2009ml of 10% HAll feed and fecal samples were assessed in triplicate with a 1-mm screen after finely ground. Samples were analyzed for dry matter (DM), CP, neutral detergent fiber (NDF) and acid detergent fiber (ADF), and apparent total tract digestibility of DM, CP, NDF and ADF were accumulated as previously described by 2O. The following thermal cycling conditions was used: initial denaturation of 5\u2009min at 95\u00b0C, 25\u2009cycles of denaturation at 95\u00b0C for 30\u2009s, annealing at 50\u00b0C for 30\u2009s, extension at 72\u00b0C for 40\u2009s, and a final extension at 72\u00b0C for 7\u2009min. Fresh PCR products of almost-full-length 16S rRNA gene were purified by agarose gel and ligated into TA cloning kit of TOP 10 . Library construction was undertaken, then transformants were picked up randomly and the 16S rRNA gene was sequenced on the PacBio Sequel sequencing platform.The total genomic DNA of fecal bacteria was extracted using TIANGEN DNA Kit  according to the manufacturer\u2019s instructions. The genomic DNA was used as a template for PCR amplification. Full-length (V1-V9) 16S rRNA was amplified by using the 27F (5\u2032-AGR GTT YGA TYM TGG CTC AG-3\u2032) and 1492R (5\u2032-RGY TAC CTT GTT ACG ACT T-3\u2032) primers and sample-specific barcodes. 10\u2009\u03bcl mixture containing 40\u2009ng of template DNA, primers, buffer, Taq polymerase and ddHCircular consensus sequences (CCSs) were obtained by correcting the original subreads  according to minPasses\u2009of \u22655 and minPredictedAccuracy\u2009of \u22650.9. Lima v1.7.0 software was used to identify different samples according to the barcode. Cutadapt V2.7 (error rate 0.2) was used to identify the forward and reverse primers, and CCSs without a primer were discarded. Finally, CCS length was filtered, and sequences that did not meet the length threshold  were discarded. Usearch v 10.0.240  was usedVenn diagram was drawn with VennDiagram version 1.6.20 . Conductp\u2009<\u20090.05, and greatly significantly different at p\u2009<\u20090.01.An individual FMD was considered as an experimental unit in all statistical analysis. All data were analyzed using SPSS statistical package . After checking the normality of the data distribution, the growth performance data were analyzed by one-way analysis of variance (ANOVA). All data were expressed as the mean with standard error (SEM), differences were considered as statistically significant at p\u2009<\u20090.05) and lower F:G (p\u2009<\u20090.05) than that receiving dietary CP at 11.51%. Meanwhile, BLG significantly increased (p\u2009<\u20090.05) in M group compared with the L group.The effects of different dietary protein level on the growth performance and morphometric traits are shown in p\u2009<\u20090.05) than other groups. With the increase of CP in diet of FMD, the digestibility of CP significantly decreased (p\u2009<\u20090.01). FMD received 13.37% CP diet have the highest NDF digestibility (p\u2009<\u20090.01), but no difference was observed among groups on ADF digestibility (p\u2009>\u20090.05).As presented in p\u2009<\u20090.05, p\u2009<\u20090.01), but the species richness values (Chao1 and ACE indexes) exhibited a similar trend.As shown in The composition and abundance of fecal microbiota for FMD fed with different CP diets were analyzed. As shown in With the increase of crude protein level in dietary, Ruminococcaceae and Christensenellaceae increased, but Rikenellaceae reduced at the family level .Ruminococcaceae_005, Bacteroides, Ruminococcaceae_014, Lachnospiraceae, Mollicutes_RF39 and Rikenellaceae_RC9 were the major bacterial genera. Among these, the proportion of Ruminococcaceae_005, Ruminococcaceae_014 and Lachnospiraceae increased with the increase of dietary protein, the proportions of Bacteroides and Rikenellaceae_RC9 were decreased.As presented in g_uncultured_bacterium_o_Choroplast, f_uncultured_bacterium_o_Choroplast, g_Streptococcus, f_Streptococcaceae, g_Shuttleworthia, g_uncultured_bacterium_f_Ruminococcaceae, f_Ruminococcaceae and o_Clostridiales were found in H group; the higher abundance of g_Prevotellaceae_UCG_004 and f_Prevotellaceae were determinde in M group; and the higher abundance of g_Alistipes, g_Rikenellaceae_RC9_gut_group, f_Rikenellaceae and o_Bacteroidales were determined in group L.Based on LEfSe analysis in uncultured_bacterium_f_Ruminococcaceae was positively correlated with the ADG and feed conversion ratio of FMD (p\u2009<\u20090.05), only Family_XIII_AD3011_group was positively correlated with feed conversion ratio (p\u2009<\u20090.05). In contrast, Ruminococcaceae_UCG-010 was negatively correlated with feed conversion ratio (p\u2009<\u20090.05). The UPGMA tree showed significant differences in the structure of fecal microbiota among three dietary treatments, L and M groups were closer in clustering relationship. This indicated that the high protein played an important role in the change of fecal microbial communities , there was a trend of increase in the feed conversion ratio compared with H, which indicated that 15.48% CP level may be nutritional imbalance. Excessive dietary protein would reduce the growth performance and feed conversion rate. Daily intake of food is required for metabolism in animals, and the feed intake is based on energy requirement. g behind . Therefop\u2009<\u20090.01). p\u2009>\u20090.05).Rumen microbes play an important role in uptake and digestion of the feed energy and nutrients, which help to convert the food into more valuable metabolites for the host animal. The growth and reproduction of microorganisms not only need to provide sufficient nitrogen, but also a sufficient carbon, carbon mainly comes from the decomposition of glucose, starch and cellulose. NDF is one of the main sources of carbon for rumen microorganisms . In thisThe rumen and its microbiota play a particularly important role in the degradation of feedstuffs for FMD. A nutritionally balanced diet is important as it provides an environment that maximizes the growth and activity of these microbes. Rumen microorganism produces end products that are either utilized directly by the host or by other microorganisms as energy . FurtherThe Venn analysis showed that the L, M and H groups had 31, 13 and 34 unique OTU, respectively, and shared 931 OTU. PCoA and NMDS showed that the structure of microflora changed regularly with the improvement of CP levels. This indicated that the microflora structure of M group is between L and H. Ruminococcaceae_UCG-005, Ruminococcaceae_ucg-014, uncultured_bacterium_f_Ruminococcaceae, Ruminococcaceae_NK4A214_group and Ruminococcaceae_UCG-013 increased at the genus level. Combined with ADFI, ADG, F:R and morphometric traits of growth FMD, it can be concluded that except for Ruminococcaceae_UCG-013, most genera in Ruminococcaceae were positively correlated with growth performance, and uncultured_bacterium_f_Ruminococcaceae being the most representative. In addition, Family_XIII_AD3011_group was also positively correlated with the growth performance of FMD during the growing phase (p\u2009<\u20090.05). At the family level, the relative abundances of Ruminococcaceae and Christensenellaceae increased, while the relative abundances of Rikenellaceae and Barnesiellaceae decreased. In the current study, with the increase of CP levels, the relative abundance of fecal f_Prevotellaceae and g_prevotellaceae_UCG_004 were found at M group as shown in taxonomic cladogram. It indicated that Prevotellaceae might plays an important role in promoting growth performance in FMD. According to the results of this experiment, it is speculated that the bacterium needs a suitable C:N ratio.As the main precursor for glucose synthesis, higher propionate levels are generally play beneficial effect on ruminant production . Poudel In present experiment, the growth performance of FMD in medium protein group is better than that H and L group. The feed conversion ratio and growth performance were improved linearly with an increased level of dietary CP , but theuncultured_bacterium_f_Ruminococcaceae, Family_XIII_AD3011_group, f_Prevotellaceae and g_prevotellaceae_UCG_004 could be used as a positive correlation indicators with growth performance, and Ruminococcaceae_UCG-013, might be a negative correlation discriminant index. Collectively, these findings also suggest optimal dietary CP levels of 13.37% for growing forest musk deer.In conclusion, based on growth performance and microbiome, the relative abundance of The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.The animal study was reviewed and approved by Northwest Agriculture & Forest University Animal Care and Use Committee.RG: conceptualization, methodology, data curation, and writing\u2014original draft preparation. SS: investigation and data curation. YA: data curation. SW: sample collection and software. XD: visualization, investigation, and writing\u2014review and editing. ZR: supervision and writing\u2014reviewing, editing, project administration, and funding acquisition. HX, BJ, and LZ: resources. All authors contributed to the article and approved the submitted version.This work was supported by Key Research and Development Program of Shaanxi Province (K3180220029), and the Shaanxi Science and Technology Promotion Project (K3330218004 and K3130220004).LZ is employed by Shaanxi Shenglinyuan Biotechnology Co., Ltd.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher."}
+{"text": "Via bioinformatics algorithm and GEO datasets, differential miRNA expression was evaluated using the Limma package. A miRNA-mRNA regulatory network was predicted by cyTargetLinker. ClusterProfiler was employed to perform functional enrichment analysis of the circRNA network to investigate its role in CHD pathogenesis.p\u2009<\u20090.05). By bioinformatics analysis, 405 gene ontology terms were identified. The Kyoto Encyclopedia of Genes and Genomes terms focused principally on the PI3K-Akt signaling pathway. hsa_circ_0001445 was associated with the expression of three miRNAs that may regulate 18 genes involved in KEGG processes: hsa-miR-507, hsa-miR-375\u20133p, and hsa-miR-942\u20135p.The expression of hsa_circ_0001445 in peripheral blood leukocytes of CHD patients was downregulated compared with that of healthy controls. Positive correlations were evident between hsa_circ_0001445 expression level and the levels of hemoglobin, triglycerides, high- and low-density lipoprotein cholesterol. A significant negative correlation was also found between hsa_circ_0001445 expression level and age and the neutrophil level. Low expression of hsa_circ_0001445 exhibited a discriminatory ability between CHD patients and healthy controls with a sensitivity of 67.5% and a specificity of 76.6% (The hsa_circ_0001445 level in peripheral blood leukocytes may serve as a biomarker for CHD diagnosis. Our work on circRNA-miRNA-mRNA networks suggests a potential role for hsa_circ_0001445 in CHD development. Coronary heart disease (CHD) is the most common heart condition worldwide and the leading cause of death of elderly men and women . DespiteCircRNAs are a kind of non-coding RNA that consists of continuous covalently closed loops without the 3\u2032- and 5\u2032 end like linear RNA, which enables it to resist degradation, and thus has relative conservation and stability . Recentl.  admitted to the Department of Cardiology of the People's Autonomous Hospital of Guangxi Zhuang from January 1 2019 to December 31 2020. All patients underwent coronary angiography (CAG), and those with stenoses \u226550% in at least one of the three main coronary arteries or their major branches (diameter \u22652\u2005mm) were diagnosed with CHD. The exclusion criteria were diabetes mellitus, any other clinically acute or chronic inflammatory systemic disease, uncontrolled hypertension, liver or kidney dysfunction, endocrine disease, autoimmune disease, a malignancy, prior percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG), and a history of CHD. The control group included 126 healthy subjects aged 60.75\u2009\u00b1\u20098.82 years (61 men and 65 women) recruited in the same period from the Second Affiliated Hospital of Guangxi Medical University. They were confirmed healthy after physical check-ups; none had a history of coronary atherosclerosis or microvascular disease. This study was conducted in accordance with the Declaration of Helsinki (1975) and was approved by the ethics committee of Guangxi Medical University . All patients and controls gave written informed consent.Total RNAs were extracted from PBLs of CHD patients and healthy controls using the SanPrep column microRNA extraction kits ; all samples were stored at \u221280\u00b0C.RNA reverse transcription into cDNA was performed using 5\u00d7 HiScript III qRT SuperMix kits  according to the manufacturer's instructions. One microgram of RNA and 4x gDNA wiper mix were incubated at 42\u00b0C for 2\u2005min, then 5\u00d7 HiScript III qRT SuperMix was added, followed by incubation at 37\u00b0C for 15\u2005min and 85\u00b0C for 5\u2005s. The products served as qRT-PCR templates.via one cycle at 95\u00b0C for 5\u2005s, 60\u00b0C for 1\u2005min, and 97\u00b0C for 1\u2005s. The expression level of hsa_circ_0001445 was calculated using the 2\u2212\u0394Ct method relative to hGAPDH.The expression level of hsa_circ_0001445 was detected by qRT-PCR with Light Cycler 96 platform , and Glyceraldehyde-3-phosphate dehydrogenase (hGAPDH) served as the internal standard for normalization. The specific primers were listed as follows: (hsa_circ_0001445) forward primer: 5\u2032-TGGGCGAAAGTTCACTTAGAA-3\u2032, reverse primer: 5\u2032- CACATGTGTTGCTCCATGTCT-3\u2032; (hGAPDH) forward primer: 5\u2032- TGTTGCCATCAATGACCCCTT-3\u2032, reverse primer: 5\u2032-CTCCACGACGTACTCAGCG-3\u2032 . Each sat-test  to determine statistically significant difference between the means of two groups. Spearman's rho coefficient was used to assess the correlation between continuous variables. Logistic regression was used to assess relationships between various factors and the PBL levels of hsa_circ_0001445. A p-value\u2009<\u20090.05 was considered significant. Analysis of receiver operating characteristic (ROC) curves was performed to calculate the optimal area under the ROC curve (AUC) for evaluating the CHD diagnostic ability of hsa_circ_0001445.Data were statistically analyzed using SPSS 22.0  and GraphPad Prism 8 . Continuous data were presented as mean\u2009\u00b1\u2009standard deviation (means\u2009\u00b1\u2009SD) if normally distributed, and otherwise as median (interquartile range). The circRNA expression levels between CHD and controls were compared using the Student's https://www.ncbi.nlm.nih.gov/geo/) using the following criteria: peripheral blood cells from humans, \u22653 samples of patients and normal controls, and miRNAs expression in CHD patients. The relevant datasets were GSE105449 (GPL22949) (miRNA expression levels of CHD patients were collected from the public Gene Expression Omnibus database (GEO) (PL22949)  and GSE6PL22949) . There whttps://www.r-project.org/), the Bioconductor software package (https://bioconductor.org/packages)  correction toolkit. A p-value <0.05 and with an FDR\u2009<\u20090.05 for all GSE files (fold change >1) were considered significant. Then, Venn diagrams were framed to identify overlapping miRNAs among the predicted datas in order to determine which potential miRNAs were associated with hsa_circ_0001445 in CHD. miRNAs identified as significant were entered into Cytoscape ver. 3.6.1 (http://cytoscape.org/) and were used to establish a network. A regulatory network was predicted by cyTargetLinker (https://cytargetlinker.github.io/) using the targetscan-hsa-7 and miRTarBase-hsa-7 databases  (ackages) , and the package  were useatabases . Finallyp-value <0.05 served as a cut-off when determining significant enrichments of GO terms and KEGG pathways. That is, the more likely that the gene associated with the listed entry/pathway influences cellular life activities and warrants further research (via intersection) (https://bioinfogp.cnb.csic.es/tools/venny/). Finally, the interaction network of miRNAs with genes of the KEGG pathway was established.ClusterProfiler ver. 3.14.3 package in R software was used to analyze the gene ontology and signaling pathways. Genome-wide annotation for humans was based on mapping employing the Entrez gene identifiers; we used several methods to visualize and interpret the functional enrichment results. A research . Venny 2p\u2009<\u20090.001)  (<\u20090.001) .p\u2009<\u20090.05).We compared the age, sex, blood parameters, fasting blood glucose levels (GLU), three renal function items , three liver enzymes  and cholesterol levels of the two groups. As shown in p\u2009<\u20090.05).Clinical parameters associated with hsa_circ_0001445 expression level in CHD patients were shown in \u0394Ct\u2212 values for hsa_circ_0001445, the CHD group was subdivided into those with low and high circRNA expression. In addition, based on the normal ranges and median values of NEU, HGB, T-Cho, and LDL-C, patients were subdivided into high and normal subgroups. The HDL-C values were used to define normal- and low-expression subgroups.We used multivariate logistic regression to explore whether hsa_circ_0001445 independently predicted CHD. Model 1 included NEU and HGB; model 2 included T-Cho, HDL-C, and LDL-C; and model 3 included NEU, HGB, T-Cho, HDL-C, and LDL-C. Based on the median 2Low hsa_circ_0001445 expression level was an independent risk factor for CHD in all three models .p\u2009<\u20090.001; J index [(Se\u2009+\u2009Sp\u2009\u2212\u20091)\u2009=\u20090.441]; a 2\u0394Ct\u2212 value of 0.814 was thus chosen as the cut-off. The sensitivity was 67.5%, the specificity was 76.6%, and the likelihood ratio [Se/(1-Sp)] was 2.88.ROC curve analyses of the healthy and CHD groups showed that the AUC for hsa_circ_0001445 was 0.816\u2009\u00b1\u20090.028  >1 and p\u2009<\u20090.05   .The GO terms indicated biological process pathways such as dephosphorylation, negative regulation of phosphorylation, and venous blood vessel development .p-values <0.05)  . The topTo enhance accuracy, the results afforded by the mirtarbase and targetscan databases were intersected with those described above . This yip\u2009<\u20090.001). In addition, logistic regression revealed that hsa_circ_0001445 was an independent predictor of CHD. Further studies on hsa_circ_0001445 as well as other circRNAs involved in CHD is required to investigate their potential in CHD diagnosis.Identifying factors involved in CHD pathogenesis can not only improves our understanding of its development but also suggests new approaches to the diagnosis, prognosis, and management of CHD. Numerous biomarkers associated with CHD have been applied, however, to identify its primary stage by regular examinations, such as cardiac ultrasound and electrocardiography, remains challenging. CHD diagnosis in its early stage with a sensitive biomarker are crucial for treatment and prognosis. Recent large-scale studies have suggested that circRNAs play an essential role in the pathogenesis and progression of CHD , 12, 23..  promotes circRNA formation . QKI was.  found thnditions . Cai et tions (.  found th. (. (BCL6, FBXL18, MMP9, and FCGR3B, as confirmed in other studies describing associations of MMP9, BCL6 (JAK2 (FZD4 (PDGFC (YWHAZ (SP1 (LRP5 (The circRNA-miRNA-mRNA axis has been recently researched in terms of how circRNAs regulate CHD development. Lin et al.  construc. (.  hypothesP9, BCL6 , 33, hsaP9, BCL6 , and hsaP9, BCL6  with CHDP9, BCL6 , and AKTP9, BCL6 . In thisL6 (JAK2 , 38, FZDK2 (FZD4 , PDGFC (4 (PDGFC , YWHAZ (C (YWHAZ , SP1 (42P1 (LRP5 \u201345 whichP1 (LRP5 , 47. TheP1 (LRP5 \u201352. Phosin vivo and in vitro research.This study had certain limitations. First, it was a cross-sectional study with a modest sample size. Our findings require confirmation in larger studies to obtain higher reliability. Second, we did not perform luciferase assay, WB assay; but only database-derived links. Those binding assays would reinforce our suggestion that hsa_circ_0001445 is a good candidate biomarker of CHD. The mechanism by which the circRNA-miRNA-mRNA axis regulates CHD pathogenesis requires further We identified hsa_circ_0001445 as a potential biomarker for CHD diagnosis. The predicted genes involved in CHD participate in many signaling pathways, of which PI3K-Akt signaling may be particularly relevant to CHD. Our results provide a basis for further research on the molecular mechanism of hsa_circ_0001445 in CHD pathogenesis."}
+{"text": "WAC, gene is one example. Dysfunction of this gene underlies a rare syndrome, DeSanto\u2013Shinawi syndrome (DESSH), with those diagnosed having symptoms including cranio-facial changes, autism, and attention deficit hyperactivity disorder. We sought to understand how the WAC protein functions in brain cells implicated in DESSH in two ways. First, we used available technologies to predict important conserved regions in the protein that may underlie cellular function, including how it localizes to distinct areas of a cell, and further correlated these findings with reported human genetic variants in these regions. These efforts uncovered novel regions in the protein necessary and sufficient for it to localize to the nucleus. Second, we deleted/used key regions of the WAC protein to test whether they were necessary/sufficient to localize WAC to distinct cell regions in brain neurons, and we found that the amino-terminus of the protein fulfilled this function. Moreover, other regions contribute to distinct biological functions of WAC, and this study first highlights these aspects of this unique neurodevelopmental protein. There are several rare, disrupted genes that underlie neurological dysfunction. Many have not been characterized for their role in brain function or how they work in individual brain cells. The WW domain-containing adaptor with coiled-coil, WAC, gene underlies a rare autosomal dominant disorder, DeSanto\u2013Shinawi syndrome (DESSH). DESSH is associated with facial dysmorphia, hypotonia, and cognitive alterations, including attention deficit hyperactivity disorder and autism. How the WAC protein localizes and functions in neural cells is critical to understanding its role during development. To understand the genotype\u2013phenotype role of WAC, we developed a knowledgebase of WAC expression, evolution, human genomics, and structural/motif analysis combined with human protein domain deletions to assess how conserved domains guide cellular distribution. Then, we assessed localization in a cell type implicated in DESSH, cortical GABAergic neurons. WAC contains conserved charged amino acids, phosphorylation signals, and enriched nuclear motifs, suggesting a role in cellular signaling and gene transcription. Human DESSH variants are found within these regions. We also discovered and tested a nuclear localization domain that impacts the cellular distribution of the protein. These data provide new insights into the potential roles of this critical developmental gene, establishing a platform to assess further translational studies, including the screening of missense genetic variants in WAC. Moreover, these studies are essential for understanding the role of human WAC variants in more diverse neurological phenotypes, including autism spectrum disorder.Dysfunction of the WW domain-containing adaptor with coiled-coil,  Rare disease syndrome genes have increasingly been identified as an underlying cause of neuropsychiatric and neurological symptoms, with more than one thousand genes suggested to contribute to neurodevelopment . The proWAC) gene is associated with DeSanto\u2013Shinawi syndrome (DESSH). Those diagnosed with DESSH have symptoms of cranial dysmorphia and hypotonia with comorbidities including attention deficit hyperactivity disorder (ADHD), autism, and seizure susceptibility [WAC variants rank it as a high-confidence autism spectrum disorder risk gene [The WW domain containing adaptor with coiled-coil  variants were extracted during November 2022. The WAC protein structure came from AlphaFold prediction [WAC gene expression was annotated from GTEx human broad tissues, the Allen Brain Atlas BrainSpan [https://doi.org/10.6084/m9.figshare.22263538.v1 (accessed on October 2022).The bioinformatics of WAC were assessed using a combination of previously published tools ,19. In sediction . The humediction . WAC genrainSpan  of differainSpan . A combiA colony of wild type (WT) CD-1 mice were bred to generate the embryos used for primary cultures. Experimenters were blinded to the parameters. All mouse procedures were performed in accordance with NIH Guidelines for the Care and Use of Laboratory Animals and were approved by the Michigan State University Institutional Animal Care and Use Committee.CMV-GFP-hWAC DNA vector was generated by amplifying human WAC cDNA from a DNASU plasmid (hsCD00442491) and cloning in frame to the 3\u2032 end of GFP in a previously described GFP expressing plasmid [WAC was cloned into 5\u2032 BsrGI and 3\u2032 BamHI restriction enzymes sites. We subsequently edited a 3\u2032 deletion of two adenosines in the WAC coding domain to produce the correct full-length clone. The following primers were used to generate the full length and mutants: WAC forward 5\u2032-GAGATGTACAAGATGGTAATGTATGCGAGG-3\u2032, and WAC reverse 5\u2032-GAGAGGATCCTCACACCATGAAGGAATTC-3\u2032. Forward primers contained a BsrGI restriction site and reverse primers a BamHI restriction site (underlined); numbers denote amino acids of WAC.The  plasmid ; WAC wasTGTACATTGATGGTGGGACCAGTTAC-3\u2032W90 forward 5\u2032-GAGATGTACATTGAACAGAGACAAAAAGAAGC-3\u2032W166 forward 5\u2032-GAGATGTACATTGATCATGCAGAGAAGCAG\u20193\u2032W576 forward 5\u2032-GAGAGGATCCTCACCTCTCTCTAACTCTGTG-3\u2032W89 reverse 5\u2032-GAGAGGATCCTCATCTTTCAAGCCACTCTTTTGG-3\u2032W165 reverse 5\u2032-GAGAGGATCCTCATGCAGGCCATCCTTGAAC-3\u2032W575 reverse 5\u2032-GAGAThe WW domain deletion construct was generated by obtaining a synthesized gene block from integrated DNA technologies (IDTs) of a partial WAC coding domain that lacked the WW DNA sequence (encoding amino acids 133\u2013165) with flanking 5\u2032 BsrGI and internal 3\u2032 NsiI restriction sites that were used to ligate in the GFP-WAC vector described above.Primary neurons were washed in phospho-buffered saline containing 0.3% Triton-X100, blocked in the same solution containing 5% bovine serum albumin and then incubated in primary antibodies for 1\u20132 h. They were washed three times and then incubated with secondary antibodies containing fluorophores for 1 h before three final washes. A Leica DM2000 microscope (DFC3000G camera) captured primary cell images for quantification, and a Zeiss LSM800 confocal microscope was used to attain representative images for figures. A blinded individual scored the nuclei/cytoplasm and obtained punctate measurements by counting GFP+ cells merged with DAPI; to be nuclear or cytoplasmic, the majority of GFP signal had to reside in that compartment, and punctate status was determined if any punctate spots were observed.TM 2000 (ThermoFisher 11668027) on day one and, after four hours, replaced with neurobasal media, supplemented with B27, glucose, glutamax, and penicillin/streptomycin. Cells grew in this medium for five days in vitro and then w2ere fixed in 4% paraformaldehyde and assessed via immuno-fluorescence labeling.We generated primary cultures from embryonic day (E) 13.5 MGE tissue as described in ,31. BrieTM 2000. After 48 h, cells were collected and lysed in standard RIPA buffer with protease and phosphatase inhibitors and combined with Laemmli buffer (BioRad 1610737EDU) containing 2-mercaptoethanol and incubated at 95 \u00b0C for 5 min. Equal amounts of protein lysates were separated on 10% SDS-PAGE gels and then transferred to nitrocellulose membranes. The membranes were washed in Tris-buffered saline containing Triton X-100 (TBST) and blocked for 1 h in TBST containing 5% non-fat dry milk . Membranes were incubated with primary antibodies overnight at 4 \u00b0C, washed three times with TBST, incubated with secondary antibodies for 1 h at room temperature, and then washed three more times with TBST. Membranes were incubated in ECL solution (BioRad Clarity substrate 1705061) for 5 min, and chemiluminescent images were obtained with a BioRad Chemidoc\u2122 MP imaging system. Antibodies : rabbit anti-GFP (ThermoFisher A6455), rabbit anti-WAC (abCam ab109486), and rabbit anti-GAPDH , as well as goat anti-rabbit HRP (BioRad 170-6515).HEK293T cells, cultured in DMEM with 10% FBS, were transfected with DNA plasmids using Lipofectaminep value of <0.05 was considered significant. For non-parametric data sets (proportions), we used the chi-square test to determine significance. GFP distribution and punctate analyses sampled 20\u201325 cells per primary culture, from three independent biological replicates.Graphpad Prism software, version 7, was used to calculate statistical significance; a The WAC protein contains an internal WW domain, known for protein\u2013protein interactions with proline rich regions . At the https://www.proteinatlas.org/ENSG00000095787-WAC/subcellular, accessed on October 2022), WAC is localized within the nucleus of cells but lacks an annotated nuclear localization domain. We screened the WAC sequence with multiple NLS bioinformatic screening tools, without a single tool returning a potential NLS. Surprisingly, a large portion of the linear motif predictions of WAC are also identified to be within the nuclear cell compartment, suggesting sequence convergence to nuclear function. These nuclear motifs include conserved sites for DOC_MAPK_gen_1 , LIG_FHA_2 , DEG_SPOP_SBC_1 (322-326), DOC_CYCLIN_yClb5_NLxxxL_5 (357-366), DEG_APCC_DBOX_1 (363-371), DOC_CYCLIN_yCln2_LP_2 (366-372), DOC_PP2A_B56_1 , LIG_PCNA_PIPBox_1 (400-409), LIG_EH1_1 (413-421), LIG_CtBP_PxDLS_1 (437-441), LIG_14-3-3_CanoR_1 , DOC_CKS1_1 , DEG_SCF_FBW7_1 (468-475), DOC_CKS1_1 (469-474), DOC_PP1_RVXF_1 (623-629), and LIG_UBA3_1 (626-634). Each of the above motifs has an included hyperlink to descriptions within the ELM database. It should be noted that several of these regulation motifs are linked to similar neurodevelopmental disorders including MAPK dysfunction and DEG_SCF_FBW7_1, which was observed as modified in multiple individuals with MED13-related neurodevelopmental disorder [According to the Human Protein Atlas (3_1 626-6. Each ofKMLRRSDSPENKYSDSTGHSKNVHTHRKAVRERDGGTSYSPQENSHNHSALHSS). Near this region is an ELM annotated MOD_SUMO_rev_2 site (amino acids 62-70), a SUMOylation modification site found within nuclear proteins. Additionally, a TRG_NES_CRM1_1 nuclear export signal (NES) is found within amino acids 604-616, flanked by a MOD_SUMO_rev_2 site (amino acids 599-608). This finding suggests that nuclear localization is potentially regulated by both the N- and C-terminal regions through nonclassical motifs.Polar basic amino acids are often critical for nuclear localization, with WAC having the highest density of conserved polar basic residues from amino acids 77-85. Polar basic amino acids 57, 60, 61, 68, 75, 79, 82, 84, 85, 87, and 89 are conserved in greater than 90% of sequences analyzed  contains multiple phosphorylation sites and a 14-3-3 interaction motif, a site that requires phosphorylation for interactions to occur. The critical amino acid R517 for 14-3-3 interaction has two variants within Geno2MP connected to neurological phenotypes. The R517C variant was found heterozygous in two affected individuals with epileptic encephalopathy, and R517H was found heterozygous in an individual with neurodevelopmental abnormality. 14-3-3 signaling has been suggested to have a neuroprotective function with elevation in seizure model systems .The most surprising motif with functional variants connected to neurological phenotypes in Geno2MP is the C-terminal region with the nuclear export signal (WAC 596\u2013636). This region, in addition to the nuclear export signal, has a SUMOylation motif, several phosphorylation sites, and multiple protein interaction sites. None of the Geno2MP variants are found in the nuclear export signal; rather, they flank this site. E600K is found within the SUMOylation motif and in two individuals with nervous system abnormalities. The other variants fall within a DOC_PP1_RVXF_1 motif, which is a dephosphorylation site. R625S was found homozygous in three affected individuals, including one with seizures and one with abnormality of the nervous system. Four affected individuals were heterozygous for R625S, with two having abnormality of brain morphology and one with intellectual disability. I626L is the most common homozygous Geno2MP variant, with 16 affected individuals annotated with abnormal brain morphology, four with spastic paraplegia, and three with intellectual disability. The variant is absent in the gnomAD and TOPmed databases, suggesting significant enrichment in neurological disease. L627I was found heterozygous in two affected individuals with spastic paraplegia, and Q632K was found heterozygous in four affected individuals with abnormality of brain morphology. Overall, this outcome suggests that N and C-terminal regions with nuclear localization and export signals are critical for neurological development and associated with human variants of neurological phenotypes.WAC expression is relatively the same throughout post-coital weeks (pcw), with higher variability later in life  antibody raised against the WAC protein on the deletion mutants to detect where the epitope may reside in WAC (We expressed the GFP-WAC full-length gene and mutant versions in brain-derived MGE cells. We collected and cultured embryonic day (E)13.5 MGE cells. The cells were transfected with the GFP fusions at 24 h and allowed to develop for five days, a time when their morphology would allow us to decipher cytoplasmic vs. nuclear localization of the proteins. To this end, we found multiple phenotypes related to the necessity or sufficiency of these protein domains related to nuclear/cytoplasmic localization, as well as distinct sequestration/puncta-like distribution in neurons expressing each mutant.Consistent with a previous study , full leIn addition to nuclear sequestration of WAC, we also noted the puncta appearance of the protein when certain domains were present. For example, the full-length protein and WW deletion mutant both have a distinct punctate pattern J, the reWAC underlies.Dysfunction of the WAC gene underlies DESSH, as well as several comorbid neurological symptoms, including autism spectrum disorder, ADHD, and seizures ,5,7,7,8.The WAC protein has several conserved domains and unexplored regions. The WW and CC domains have been reported in previous reports ,14, but Wac loss of function [Wac expression in brain cell types and found a preferential enrichment for both GABAergic and glutamatergic neurons. Unfortunately, Wac deletion is embryonically lethal, but conditional mutants may be able to uncover further functions of this gene in both GABAergic and glutamatergic neurons in future studies. The role of WAC\u2019s conserved regions reported here is a strong starting point in understanding the various mechanisms by which this protein regulates each neuron subtype. We predict that, while the localization of this protein may be similar between cell types, the functional partners may differ; thus, the phenotypes may be distinct within each brain cell type. To this end, we found that the same WAC mutants used in MGE primary neurons had the same distribution and punctate patterns in HEK293T cells .Our assessments herein focused on GABAergic neurons due to our previous data, suggesting that some of these neurons were susceptible to function . We alsoWac is an important developmental gene with relatively few known functions. Herein, we developed the first assessment of this important protein\u2019s conserved regions in cellular localization. These studies will help to interpret the growing number of genetic variants in Wac associated with neurodevelopmental disorders, including autism [g autism ,10. In a"}
+{"text": "Placement of multiple metal stents by endoscopic retrograde cholangiopancreatography for malignant hilar biliary obstruction (MHBO) contributes to longer stent patencyA 58-year-old woman with gallbladder carcinoma and a history of multiple endoscopic treatments for MHBO, including placement of five stents, was admitted with cholangitis . ComputVideo\u20061\u2002We successfully performed biliary drainage of the posterior intrahepatic bile duct using a bridging method from the endoscopic ultrasound-guided transgastric approach in a patient after placing multiple metal stents.Following the puncture of B2 under EUS guidance, a guidewire was advanced beyond the MHBO and into the duodenum. Subsequently, a double-lumen cannula  was inserted, followed by a 0.025-inch hydrophilic guidewire into the RPD through the mesh of the previously placed metal stent . After Recovery was uneventful, and cholangitis subsided within a few days. Although this patient had multiple metal stents placed, EUS-HGS with the bridging method was a feasible treatment option.Endoscopy_UCTN_Code_TTT_1AS_2AD"}
+{"text": "Streptomyces lincolnensis is well known for producing the clinically important antimicrobial agent lincomycin. The synthetic and regulatory mechanisms on lincomycin biosynthesis have been deeply explored in recent years. However, the regulation involved in primary metabolism have not been fully addressed.Streptomyces. The inactivation of the SLCG_7083 gene indicated that SLCG_7083 promotes glucose utilization, slows mycelial growth and affects sporulation in S. lincolnensis. Comparative transcriptomic analysis further revealed that SLCG_7083 represses eight genes involved in sporulation, cell division and lipid metabolism, and activates two genes involved in carbon metabolism.SLCG_7083 protein contains a Per-Arnt-Sim (PAS) domain at the N-terminus, whose homologous proteins are highly distributed in S. lincolnensis. Our results first revealed the regulatory function of SLCG_7083, and shed new light on the transcriptional effects of SLCG_7083-like family proteins in Streptomyces.SLCG_7083 is a PAS domain-containing regulator on morphological development and glucose utilization in The online version contains supplementary material available at 10.1186/s12934-023-02263-3. Streptomycetes are well known for their abundant secondary metabolites that are significant resource of candidate drug, such as antibacterial, antifungal, antiparasitic, anticancer, and immunosuppress agents . Their cCampylobacter jejuni, which modulated the flagella-flagella interactions [Streptomyces avermitilis [Streptomyces natalensis [The Per-Arnt-Sim (PAS) domain was first discovered in eukaryotes and named after three proteins: period circadian protein (Per), aryl hydrocarbon receptor nuclear transporter protein (Arnt) and single-minded protein (Sim). In various organisms, the PAS domain of proteins plays an important role in signal transmission and cellular regulation. PAS domain exists in many proteins and can bind with a variety of ligands, leading to the domain triggering specific cellular reactions or making proteins containing this domain sensitive to additional physical or chemical signals. Different PAS protein has the ability to sense redox potential, light, oxygen, energy level, carboxylic acid, fatty acid and other stimuli, and participates in cell development, virulence, sporulation, hypoxia adaptation, circadian rhythm, metabolism, and gene regulation and expression . The PASractions . The PASrmitilis . Anothertalensis .mmy) BGC located in a giant linear plasmid (SCP1) in Streptomyces coelicolor A3(2) [S. coelicolor A3(2), thus initiating the synthesis of methylenomycin [SGR_ 6891 in Streptomyces griseus is adjacent to A factor synthetase gene asfA, and is induced by A factor (\u03b3-butyrolactone) regulating antibiotic synthesis and cell differentiation [The PAS domain-containing protein MmyB is a positive regulator on methylenomycin biosynthesis, and encoded by methylenomycin (or A3(2) . MmyB isenomycin . Besidesenomycin . The Mmyenomycin . The actntiation .Streptomyces lincolnensis is well known for producing the clinically important antimicrobial agent lincomycin. The genomes of S. lincolnensis had been sequenced in three strains, S. lincolnensis NRRL 2936 [S. lincolnensis LC-G [S. lincolnensis B48 [lmb cluster), was reported as a significant pleiotropic transcriptional regulator in lincomycin biosynthesis by entirely activating the lmb cluster and regulating nineteen non-lmb genes involved in multi-drug flux to self-resistance, nitrate and sugar transmembrane transport and utilization, and redox metabolisms [lmb cluster, in addition to its own resistance function, also participated in the regulation of lincomycin biosynthesis by antibiotic-driven signaling cascade transduction [lmb genes were all identified to participate in regulation of the lmb cluster, that are the nitrogen regulator GlnR [S. lincolnensis: GlnR up-regulates the transcription of nitrate-specific ABC transporter and nitrate assimilation genes [SLCG_2920 encoding a lincomycin resistance protein [bldA gene for the cascade regulation of lincomycin biosynthesis [SLCG_3127 encoding LysE protein; RamR affects cell growth in S. lincolnensis [lnR and ramC, to indirectly regulate lincomycin biosynthesis and morphology, respectively [RRL 2936 , S. lincsis LC-G  and S. lnsis B48 , which fabolisms . The ATPsduction . Six regtor GlnR , the Tettor GlnR , the devtor GlnR , the A-ftor GlnR , the leutor GlnR  and the tor GlnR . In addion genes ; SLCG_29 protein ; BldD po protein ; AdpA acolnensis . Besidesectively .S. lincolnensis, we conducted the bioinformatic analysis and found the SLCG_7083 protein, whose homologous proteins widely spread in Streptomyces and belong to a new regulator family. We constructed the SLCG_7083 deletion strain to comprehensively understand the regulatory effects of SLCG_7083. And then we performed the comparative transcriptomic analysis andwhich further proved the regulatory effects of SLCG_7083 on morphological development and glucose utilization.In this study, to investigate PAS domain containing proteins in S. lincolnensis. The secondary structure predicted by PSIPRED software (http://bioinf.cs.ucl.ac.uk/psipred/) showed that the SLCG_7083 protein contains multiple \u03b1 helices and \u03b2 folds at the C-terminator. As shown in Fig.\u00a0For searching the PAS domain, a protein (SLCG_7083) with 223 amino acids was found in Streptomyces  per sample were obtained after cleaning and checking the reads quality. Approximately 95.21%~98.91% of clean reads were aligned uniquely to the S. lincolnensis genome. The correlation clustering among the two biological replicates of each sample was conducted based on the expression level of all genes. All biological replicates (three RNA samples for each strain) showed correlation coefficients over 0.8, indicating good reproducibility between biological replicates. To investigate transcriptional regulation effects of SLCG_7083, the whole transcriptomes were compared.To obtain insight into regulatory mechanism of SLCG_7083 protein on glucose utilization and mycelial growth and differentiation, comparative transcriptional analysis was conducted between the SLCG_7083 deletion strain ST708  involved in sporulation and cell division increased significantly, which were 5.60 and 3.17 times higher than that in original strain SyBE2901, respectively  were continuously arranged in the genome.Other six up-regulated genes in SLCG_7083 gene, the expression of the above six lipid metabolism genes increased, which promoted the lipid utilization, resulting in the increase of the biomass. Therefore, SLCG_7083 regulates the growth of S. lincolnensis by negatively regulating the lipid metabolism.After deleting SLCG_7083 gene being deleted, the transcription level of SLCG_5038 and SLCG_5041 respectively decreased to 25.20% and 35.22% of that in the original strain, demonstrating SLCG_7083 positively regulates transcription of SLCG_5038 gene and SLCG_5041 gene.In contrast, after Lactococcus lactis, the SLCG_5038 homologous protein shows mutase activity, catalyzing the mutual transfer of glucose 1-phosphate and glucose 6-phosphate. Therefore, SLCG_5038 may participate in the intracellular glycolysis pathway, tricarboxylic acid cycle, pentose phosphate pathway and other basic glucose metabolism through hexose phosphate translocation and hydrolysis of phosphate sugar.Bioinformatics analysis showed that SLCG_5038 protein predicted to be phosphohexomutase/phosphatase with haloacid dehalogenase domain. In SLCG_7083 deletion strain ST708, another down-regulated gene SLCG_5041 encodes protein similar to SalM [In  to SalM  from the to SalM  from aca to SalM , indicatSLCG_5038 gene and SLCG_5041 gene are all located in the predicted C7-cyclitol BGC, which involved in C7-cyclitol derivatives synthesis from sedoheptulose 7-phosphate that comes from pentose phosphate pathway. It was reported that four C7-cyclitol derived carbasugars were isolated from S. lincolnensis DSM 40,355 [7-cyclitol biosynthesis in S. lincolnensis. Deleting SLCG_7083, resulted in down-regulated expression of SLCG_5038 and SLCG_5041 in C7-cyclitol BGC, which reduced use of sedoheptulose 7-phosphate as substrate, following by slowing down the consumption of glucose. Therefore, the SLCG_7083 regulator positively regulated SLCG_5038 and SLCG_5041 gene to affect glucose utilization, which is consistent with fermentation results above.Interesting, the M 40,355 , suggestS. lincolnensis, by negatively regulating two sporulation/cell division related genes and six lipid metabolism related genes, and positively regulating two carbon metabolism related genes. However, the detailed regulation mechanism of SLCG_7083 were not be addressed in this study, and await further research. Our results provided evidence to elucidate the regulatory functions of SLCG_7083-like PAS domain-containing proteins in Streptomyces.The results of this study demonstrated that the PAS domain containing protein SLCG_7083 is a regulator not only on mycelial growth and morphological development, but also on glucose utilization in PSIPRED software was used to predicted the secondary structure of the SLCG_7083 protein. The SLCG_7083-like proteins were collected by BLASTp search against NCBI database, and then used to construct phylogenetic tree. Alignments and phylogenetic analysis were performed using MEGA7  by the nS. lincolnensis strains were carried out as previously described [S. lincolnensis were routinely cultivated on the modified Gauze\u2019s Medium No.1 for 7 days at 30\u00a0\u00b0C. For fermentation, the spores inoculated into 25\u00a0ml seed medium 2SO4, 0.4% CaCO3, pH 7.1) and grown for 2 days at 30\u00a0\u00b0C, 250\u00a0rpm. Then 2\u00a0ml seed culture was added into 25\u00a0ml fermentation medium 2SO4, 0.03% K2HPO4, 0.8% CaCO3, pH 7.1) and cultivated for 7 days at 30\u00a0\u00b0C, 250\u00a0rpm. Appropriate antibiotics were added in the medium when necessary.The bacterial strains and plasmids used in this study are described in Table\u00a0escribed . BrieflySLCG_7083 in-frame deletion. As shown in Fig. SLCG_7083 gene was 21-nt sequence \u201cTgccgatgctggcgcacttcg\u201d before the protospacer adjacent motif (PAM) \u201cCGG\u201d, which was added to the 5\u2019-end of primer 7083gRNA-F. Using plasmid pKCcas9do as template, the 112\u00a0bp sgRNA fragment was amplified by primers 7083gRNA-F and gTEMdn  [Mycelia of the \u0394 pH 7.0)  at 30 \u2103 S. lincolnensis. After cultivation, the precipitate was collected from fermentation broth with centrifuge for 12,000 r/min 5\u00a0min, washed twice with water, dried in an 80 \u2103 oven, and then weighed by electronic analytical balance. Meanwhile, supernatant collected from fermentation broth was used to detect the residual glucose by biosensor .The dry weight of mycelia was used to measure the biomass of \u2212\u20091, adjust to pH 9.0 with ammonia) and 50% methanol.The analysis of lincomycin was performed as previously described . BrieflyS. lincolnensis as the procedures described previously [S. lincolnensis LC-G  by Bowtie2-2.2.3 [2(FoldChange)\u2223>1 and q value\u2009<\u20090.05 found by DEGSeq were assigned as differentially expressed.Strand specific RNA sequencing (ssRNA-Seq) was used to investigate transcription of eviously . The mRNe2-2.2.3 . Genes wS. lincolnensis mycelia in fermentation medium, using RNAprep pure Cell/Bacteria Kit  according to the manufacturer\u2019s protocol. Reverse transcription was conducted using HiScript II 1st Strand cDNA Synthesis Kit (+\u2009gDNA wiper) , with the conditions set as following: 55\u2103 for 30\u00a0min, 85\u2103 for 2\u00a0min. Using gene specific primers and 500 ng of total RNA as template, the first strand was generated. For analyzing transcription, the 1st cDNA reaction mixture was diluted five times, and then used as the template to amplify ds-cDNA in the following semi-quantitative PCR. All experiments were conducted in triplicate in each case. The semi-quantitative PCR products were detected by agarose gel electrophoresis, and then exposed under UV to analyze relative intensities using densitometric analysis software.Primers used in semi-quantitative PCR experiments were listed in Table Below is the link to the electronic supplementary material.Supplementary Material 1"}
+{"text": "Artificial intelligence (AI) programs that train on large datasets require powerful compute infrastructure consisting of several CPU cores and GPUs. JupyterLab provides an excellent framework for developing AI programs, but it needs to be hosted on such an infrastructure to enable faster training of AI programs using parallel computing.An open-source, docker-based, and GPU-enabled JupyterLab infrastructure is developed that runs on the public compute infrastructure of Galaxy Europe consisting of thousands of CPU cores, many GPUs, and several petabytes of storage to rapidly prototype and develop end-to-end AI projects. Using a JupyterLab notebook, long-running AI model training programs can also be executed remotely to create trained models, represented in open neural network exchange (ONNX) format, and other output datasets in Galaxy. Other features include Git integration for version control, the option of creating and executing pipelines of notebooks, and multiple dashboards and packages for monitoring compute resources and visualization, respectively.https://github.com/usegalaxy-eu/gpu-jupyterlab-docker.These features make JupyterLab in Galaxy Europe highly suitable for creating and managing AI projects. A recent scientific publication that predicts infected regions in COVID-19 computed tomography scan images is reproduced using various features of JupyterLab on Galaxy Europe. In addition, ColabFold, a faster implementation of AlphaFold2, is accessed in JupyterLab to predict the 3-dimensional structure of protein sequences. JupyterLab is accessible in 2 ways\u2014one as an interactive Galaxy tool and the other by running the underlying Docker container. In both ways, long-running training can be executed on Galaxy\u2019s compute infrastructure. Scripts to create the Docker container are available under MIT license at  Bioinformatics comprises many subfields, such as single cell, medical imaging, sequencing, proteomics, and many more, that produce a huge amount of biological data in myriad formats. For example, the single-cell field creates gene expression patterns for each cell that are represented as matrices of real numbers. The medical imaging field generates images of cells and tissues, radiography images such as chest x-rays, and computed tomography (CT) scans. Next-generation sequencing generates DNA sequences that are stored as FASTA and FASTQ  files. MDocker  containeJupyterLab is a web-based, robust editor used for varied purposes such as data science, scientific computing, and ML. It is a program editor that supports more than 40 programming languages, including Python, R, Julia, and Scala. Python is one of the most popular languages used by researchers for performing numerous scientific and predictive analyses. Therefore, it is used as the programming language in Galaxy\u2019s JupyterLab because many popular packages such as Scikit-learn and TensorFlow for ML, data manipulation packages such as Pandas, and visualization packages such as Seaborn , MatplotMany features such as easy accessibility, support of a wide variety of programming languages on JupyterLab, and extensibility to install useful plugins make it a desirable editor for researchers for creating project prototypes rapidly. Many such features have been integrated into our JupyterLab infrastructure that is served online on Galaxy Europe, enabling researchers to create prototypes and end-to-end artificial intelligence (AI) projects Fig.\u00a0. A few iThere are a few other infrastructures available, free and commercial, that offer JupyterLab or similar environments for developing data science and AI projects. A few popular ones are Google Colab , Kaggle JupyterLab infrastructure is developed in 2 stages. First, a Docker container is created containing all the necessary packages such as JupyterLab itself, CUDA from the base Docker image , TensorFJupyterLab infrastructure in Galaxy Europe is used to reproduce the results of 2 recent scientific publications. They demonstrate its robustness and usefulness to develop ML models using COVID-19 CT scan images  and predIn , COVID-1AlphaFold2 has made a breakthrough in predicting the 3D structures of proteins with outstanding accuracy. However, due to their large database size (a few TBs), it is not easily accessible to researchers. Therefore, a few approaches have been developed to replace the time-consuming steps of AlphaFold2 with slightly different steps. They predict 3D structures of proteins with similar accuracy while consuming less memory and time. One such approach is ColabFold, which replaces the large database search in AlphaFold2 for finding homologous sequences by a significantly faster (40\u201360\u00a0times) MMseqs2 API  call to For large datasets, ML model training may need several hours or even days. In such cases, it would be cumbersome to keep JupyterLab open in a browser\u2019s tab until the training finishes. Therefore, another Galaxy tool  is develThe customized Docker container developed as shown in Fig.\u00a0Notebooks created in Galaxy\u2019s JupyterLab infrastructure can instantly be shared with other researchers and collaborators only by sharing the public URL of a notebook. Researchers and users who share a notebook can collaborate on the same notebook without having to store it anywhere as it is directly served by Galaxy Europe.Resembling many tools in Galaxy, JupyterLab can also be used in any Galaxy workflow where it can accept datasets from different tools and then executes an IPython notebook to process the input datasets. It outputs a collection of datasets, which can further be used by other Galaxy tools . In addiJupyterLab is integrated as an interactive tool in Galaxy Europe running on a public and powerful compute infrastructure comprising several CPU cores and GPUs having large memory and disk space. A Docker container is created that wraps JupyterLab along with packages such as TensorFlow-GPU, Scikit-learn, Pandas, and many others to provide a robust architecture for the development and management of projects in ML and data science. Remote model training makes it convenient to run multiple analyses in parallel in different Galaxy jobs by executing the same Galaxy tool. The resulting datasets of each job become available in different Galaxy histories. Features such as Git integration are useful for managing entire code repositories on GitHub and Elyra AI for creating pipelines of notebooks working as one software unit. All notebooks created by a user run on the same session of JupyterLab in different tabs. The entire infrastructure of JupyterLab is readily accessible through Galaxy Europe. In contrast to commercial infrastructures that host editors similar to JupyterLab and offer powerful and reliable compute only through paid subscriptions, this infrastructure provides large compute resources free of cost that are invariant to usage and has an unlimited usage time while ensuring a constant amount of compute resources across successive usages. Sustaining and improving such an openly accessible infrastructure would highly benefit ML practitioners and researchers from various fields of science.Project name: GPU-enabled Docker container with JupyterLab for artificial intelligencehttps://github.com/usegalaxy-eu/gpu-jupyterlab-dockerProject home page: https://github.com/usegalaxy-eu/galaxy/blob/release_22.05_europe/tools/interactive/interactivetool_ml_jupyter_notebook.xmlGalaxy interactive tool: Operating system: LinuxProgramming languages: Python, XML, Docker, BashLicense: MIT LicenseRRID: SCR_022695Biotools ID: gpu-enabled_docker_container_with_jupyterlab_for_aigiad028_GIGA-D-22-00220_Original_SubmissionClick here for additional data file.giad028_GIGA-D-22-00220_Revision_1Click here for additional data file.giad028_GIGA-D-22-00220_Revision_2Click here for additional data file.giad028_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giad028_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giad028_Reviewer_1_Report_Original_SubmissionPhilippe Boileau -- 9/8/2022 ReviewedClick here for additional data file.giad028_Reviewer_1_Report_Revision_1Philippe Boileau -- 2/2/2023 ReviewedClick here for additional data file.giad028_Reviewer_2_Report_Original_SubmissionMilot Mirdita -- 10/26/2022 ReviewedClick here for additional data file.giad028_Reviewer_2_Report_Revision_1Milot Mirdita -- 2/16/2023 ReviewedClick here for additional data file.giad028_Supplemental_FileClick here for additional data file."}
+{"text": "Legionella spp. can survive and replicate inside host cells such as protozoa and macrophages. After enough growth, Legionella is released from the host cells as free legionellae or Legionella-filled vesicles. The vesicles support Legionella to survive for a long time in the environment and transmit to a new host. In this study, we identified the differentially expressed genes of Acanthamoeba infected by Legionella  and examined their roles in the formation of the excreted vesicles and escape of Legionella from the Acanthamoeba.Escherichia coli and Legionella pneumophila, expression levels of target genes in Acanthamoeba were measured by real-time polymerase chain reaction (PCR) analysis. The roles of target genes were investigated by transfection of small interfering RNA (siRNA). The formation of Legionella-containing excreted vesicles and the vesicular co-localization with the lysosomes were examined by Giemsa stain and LysoTracker stain.After ingestion of Legionella in Acanthamoeba. ACA1_114460- and ACA1_091500-silenced Acanthamoeba failed to form the Legionella-containing excreted vesicles. Legionella was released as free legionellae from the Acanthamoeba. When the ACA1_362260 of Acanthamoeba was silenced, Legionella-containing excreted vesicles were fused with the lysosome.ACA1_114460, ACA1_091500, and ACA1_362260 were upregulated after ingestion of Acanthamoeba played important roles in the formation of Legionella-containing excreted vesicles and inhibition of the lysosomal co-localization with the phagosome.These results indicated that ACA1_114460, ACA1_091500, and ACA1_362260 of  Acanthamoeba, humans are prone to contacting this protozoan organism, which consequently leads to various amoeba-borne ocular diseases [Acanthamoeba ingests various microorganisms in the surrounding environment to promote its growth in the trophozoite stage, these trophozoites are transformed into highly drug-resistant cysts under unfavorable conditions [Legionella pneumophila [Legionella pneumophila is a gram-negative bacillus prevalent in the aquatic environment and its infection causes severe respiratory diseases including pneumonia and Legionnaires\u2019 disease in humans [L. pneumophila can thrive within eukaryotic host cells including Acanthamoeba, irrespective of trophozoites or cyst stages, it serves as a suitable host organism for L. pneumophila. Furthermore, identifying factors that regulate intracellular bacterial growth in Acanthamoeba will contribute to developing a novel treatment option for diseases inflicted by L. pneumophila.Given the ubiquitous nature of diseases \u20133. Whilenditions , 5. The umophila , 7. Legin humans . BecauseL. pneumophila forms a Legionella-containing vacuole (LCV) upon successful host cell infiltration to prevent its digestion via lysosomal fusion [Legionella [L. pneumophila infection requires a functional intracellular multiplication/defective in organelle trafficking (icm/dot) type-IV secretion system [Legionella pneumophila is capable of replicating in host cells, which subsequently release free legionellae or Legionella-filled vesicles that aid in bacterial transmission [Legionella-containing vesicles to the surrounding environment may be necessary for surviving for a long time and efficient transmission to other host cells. As such, investigating how L. pneumophila forms these vesicles within Acanthamoeba spp. or their egress from hosts would be beneficial to understanding their replication and transmission.Unlike bacteria-containing phagosomes, l fusion , 10. Thegionella . Inhibitn system \u201314. Legismission . The relL. pneumophila infection in Acanthamoeba and several other amoebic organisms are similar to those of macrophages, and as such, these protozoans were used as good models to investigate Legionella\u2013macrophage interactions [L. pneumophila grown in human monocytes and Acanthamoeba had different gene-expression profiles, thus suggesting that survival strategies employed by L. pneumophila may vary across hosts. For example, when L. pneumophila was cultured in THP-1 cells, expression of the pyroptotic protein flaA was downregulated and the pyroptosis-inhibiting sdhA protein expression which contributes to LCV stabilization was upregulated. Contrary to this finding, when L. pneumophila was permitted to grow within Acanthamoeba castellanii, sdhA gene expression was downregulated [L. pneumophila vesicle formation or their release from Acanthamoeba. After Legionella has replicated enough in specialized vesicles, it is released via an exocytic pathway in protozoa that is either absent or unused in mammalian cells [Legionella transmission activator (LetA) was necessary for intracellular multiplication in A. castellanii, and letA mutants exhibited reduced infectivity [Legionella isoprenylcysteine carboxyl methyltransferase (IcmT) is essential for pore formation involved in bacterial egress, whereas its amino terminus is essential for the export of specific effectors [Legionella effector proteins, LepA and LepB, played a role in the non-lytic release of L. pneumophila from A. castellanii [icm/dot type-IV secretion system of Legionella required for LCV formation and multiplication in the host cells are well known. Nonetheless, as demonstrated above, gene expression-related studies involving L. pneumophila and Acanthamoeba spp. were predominantly focused on the former of the two. Specifically, Acanthamoeba-related factors contributing to the growth and release of Legionella remain largely elusive.The molecular pathogenesis of ractions , 14, 15.egulated . Numerouan cells . It has ectivity . The carffectors , 20. Legtellanii . In addiA. castellanii after ingesting either Escherichia coli or L. pneumophila [Acanthamoeba infected by Legionella. In the aforementioned study, we identified 22 genes involved in vesicle trafficking, membrane fusion, and phagocytosis. Interestingly, several of the 22 genes described above were upregulated in Legionella-infected Acanthamoeba but not when E. coli was taken up by the Acanthamoeba. Therefore, we hypothesized that these genes could be involved in vesicle formation which promotes L. pneumophila survival. To address this, in this study, we selected these upregulated genes in Legionella-infected Acanthamoeba and investigated their function in vesicle formation as well as their release from Acanthamoeba. Also, to account for L. pneumophila and host relationship issues, we used both environmental and clinical isolates of Acanthamoeba.To address these limitations, in our previous study, we conducted a comparative analysis of differentially expressed genes (DEGs) in umophila . Among tAcanthamoeba castellanii Castellani  and Acanthamoeba castellanii Neff  were obtained from the American Type Culture Collection and cultured axenically in peptone-yeast-glucose (PYG) medium at 25\u00a0\u00b0C. Legionella pneumophila Philadelphia-1 (ATCC 33152) was cultured on a buffered charcoal yeast extract (BCYE) agar plate at 37\u00a0\u00b0C with 5% CO2. Escherichia coli DH5\u03b1  was cultured in tryptone-yeast-NaCl lysogeny broth (LB) media at 37\u00a0\u00b0C using a shaking incubator. Acanthamoeba castellanii was infected by L. pneumophila at multiplicity of infection (MOI) of 100 as previously described [L. pneumophila was diluted in phosphate-buffered saline (PBS) until the optical density (OD)600 absorbance reading reached 1, which corresponds to 109\u00a0colony-forming units (CFU)/ml [7 of Acanthamoeba were incubated with 1\u00a0ml of Legionella suspension at 37\u00a0\u00b0C with 5% CO2 for 1\u00a0h in PYG medium. After incubation, Acanthamoeba was washed with Page\u2019s Amoeba Saline (PAS) and incubated with new PYG media containing 100\u00a0\u03bcg/ml of gentamicin for 2\u00a0h to kill extracellular Legionella. Acanthamoeba infected with Legionella (A+L) was washed with PAS twice and incubated with new PYG media for 12\u00a0h in a 25\u00a0\u00b0C incubator. Acanthamoeba infection of E. coli (A+E) was conducted in the same way.escribed . Briefly(CFU)/ml . Next, 1The expression of target genes Table  was deteAcanthamoeba were synthesized by Bioneer, Inc. , based on their cDNA sequences  following the manufacturer\u2019s protocol. The transfection efficiency of siRNA was determined by fluorescing cells under a fluorescent microscope .Small interfering RNAs (siRNAs) targeting ACA1_114460, ACA1_091500, and ACA1_362260 of A. castellanii containing L. pneumophila were observed with Giemsa and LysoTracker staining. Acanthamoeba was transfected with siRNA (siRNA-A) and infected by Legionella (siRNA-A+L) as mentioned above. For the Giemsa stain, cells were fixed with methanol for 5\u00a0min and stained with Giemsa solution  for 10\u00a0min. For the LysoTracker stain, cells were stained with 50\u00a0\u03bcM LysoTracker Red DND-99  for 1\u00a0h. Stained cells were observed under a fluorescent microscope. Vesicles or phagolysosomes were counted from a total of three randomly selected fields of view under the microscope at 1000\u00d7 magnification.Excreted vesicles of t-tests were performed using GraphPad Prism version 8 . Statistical significance between the means of groups was denoted using an asterisk. P-values less than 0.05 was considered statistically significant .Data are presented as mean\u2009\u00b1\u2009standard deviation (SD) from three independent experiments. Student\u2019s A. castellanii that phagocytosed either E. coli or L. pneumophila [A. castellanii Castellani post-L. pneumophila ingestion (A+L/A), which were associated with phagosomal maturation (Table A. castellanii Castellani and A. castellanii Neff which ingested either E. coli (A+E) or L. pneumophila (A+L) were acquired and real-time PCR was performed using the gene-specific primers listed below  produced excreted vesicles containing Legionella, as indicated by the black arrows in the A+L panel . Excreted vesicles from all groups were quantified under the microscope .To investigate the effect of ACA1_114460, ACA1_091500, and ACA1_362260 gene knockdown in Acanthamoeba ingesting Legionella formed the excreted vesicles containing Legionella, as denoted by the black arrow  attachment protein receptors (SNAREs) have been suggested to be involved in the mechanisms of vesicle trafficking, budding, and fusion [L. pneumophila vesicle formation in A. castellanii. In line with this notion, our results confirmed that ACA1_114460 and ACA1_091500 of A. castellanii are involved in the formation of membrane-bound organelles  in"}
+{"text": "Pathway-level survival analysis offers the opportunity to examine molecular pathways and immune signatures that influence patient outcomes. However, available survival analysis algorithms are limited in pathway-level function and lack a streamlined analytical process. Here we present a comprehensive pathway-level survival analysis suite, PATH-SURVEYOR, which includes a Shiny user interface with extensive features for systematic exploration of pathways and covariates in a Cox proportional-hazard model. Moreover, our framework offers an integrative strategy for performing Hazard Ratio ranked Gene Set Enrichment Analysis and pathway clustering. As an example, we applied our tool in a combined cohort of melanoma patients treated with checkpoint inhibition (ICI) and identified several immune populations and biomarkers predictive of ICI efficacy. We also analyzed gene expression data of pediatric acute myeloid leukemia (AML) and performed an inverse association of drug targets with the patient\u2019s clinical endpoint. Our analysis derived several drug targets in high-risk KMT2A-fusion-positive patients, which were then validated in AML cell lines in the Genomics of Drug Sensitivity database. Altogether, the tool offers a comprehensive suite for pathway-level survival analysis and a user interface for exploring drug targets, molecular features, and immune populations at different resolutions.The online version contains supplementary material available at 10.1186/s12859-023-05393-y. Organizing biological knowledge into pathways facilitates the integrative analysis of gene expression data derived from RNA sequencing and proteomics profiling. Common pathway-level analysis tools, such as ENRICHR  and GSEAA one-stop tool for expression-based survival analyses.The ability to include multiple covariates inside the Cox-proportion hazard pathway model.The ability to summarize prioritized gene signatures into relevant clusters and pathway modules.The ability to perform hazard ratio ranked gene set enrichment analysis.With several drug screening databases now available with perturbed gene sets after drug treatment in cancer cell lines, survival analysis can also be utilized to perform drug screening by identifying drugs that can reverse expression associated with highly refractory diseases . For exaAltogether, survival analysis is a critical branch of statistics for analyzing the time-to-event, and our tool facilitates a comprehensive survival analysis of pathway-level scores.. Additional installation and usage instructions is available in the Additional file PATH-SURVEYOR is implemented in the R environment, and packages can be automatically installed during runtime. There are four major components of the PATH-SURVEYOR Fig.\u00a0, which iPATH-SURVEYOR interactive mode provides the ability to analyze and visualize \u201con-the-fly\u201d associations of immune signatures and pathways scores with a clinical endpoint Fig.\u00a0A. The apP value is calculated on the likelihood ratio, wald test. An adjusted P value can be calculated based on Benjamini\u2013Hochberg correction method. In the output table, genes and pathways are ranked by the likelihood ratio P value.To facilitate the identification of top high-risk pathways and genes, we have developed a pipeline to systematically assess pathways associated with hazard by a Cox proportional hazard function Fig.\u00a0C. The usThe Connectivity Mode offers the user the ability to analyze the similarity between pathways associated with survival Fig.\u00a0D. The haThe Jaccard score function J for two pathways A and B is defined asThe Jaccard matrix can be visualized as a heatmap.Next, the pathways can be clustered using the hclust function from R (v4.1.0) into k-groups (user-specified). Clusters can be visualized as a dendrogram. To overlap survival-associated gene expression, genes within the pathway can be displayed as a table with a flexible sorting feature and added annotation information.From the pipeline mode, we can derive a hazard ratio ranked gene list, which can then be applied as input to the Pre-Ranked-Hazard-Ratio GSEA R Shiny app Fig.\u00a0E. This aTo demonstrate the functionalities of the PATH-SURVEYOR framework, we have included use-case examples of biomarker discovery in a cohort of immunotherapy-treated melanoma patients. We have also provided an example use-case strategy for drug repurposing in pediatric acute myeloid leukemia patients.To identify predictive biomarkers that facilitate patient selection of patients suitable for immune checkpoint inhibitor (ICI) treatment, we integrated 313 melanoma patients treated with ICI from Riaz et al. n\u2009=\u200951) , Hugo et1 , Hugo To leverage our framework for therapeutic discovery, we obtained the gene expression data and clinical annotation of 220 patients with the KMT2A fusion event from the National Cancer Institute TARGET pediatric acute myeloid leukemia (AML) 1031 cohort (0\u201322\u00a0years of age). The translocation event of the gene KMT2A, also known as mixed lineage leukemia (MLL), is frequently identified in pediatric AML. Through its multiple fusion partners arises a diverse patient population with a need for advanced risk stratification . ThroughPATH-SURVEYOR is designed to visualize and perform systematic survival analysis based on gene and pathway information. The application is designed for users with limited experience in programming as well as for advanced users to perform systematic high-throughput pathway screening. In the interactive mode, the Shiny application will ensure reproducibility and can be easily set up and applied in any cohort. In the pipeline mode, the user can apply univariate and multivariate analysis of pathway and patient covariates associated with patient survival outcomes. Our current application can also perform GSEA based on hazard ratio ranking as well as pathway clustering to examine shared gene and pathway features associated with survival, which is unique to PATH-SURVEYOR when compared with other tools, including TRGAted , UALCAN As more RNA sequencing and proteomics data are being captured in large clinical trials, generating user- interfaces will facilitate access to these data sets. Thus, we present PATH-SURVEYOR, a program for inferring survival through pathways, immune components, and drug-induced targets. We anticipate PATH-SURVEYOR will enable a collaborative environment for exploring pathway-level, drug targets, and immune features that are predictive of treatment efficacy, especially for high-risk malignancies.https://github.com/shawlab-moffitt/PATH-SURVEYOR-Suite. URL Links to the Input File Prep App: https://shawlab-moffitt.shinyapps.io/path_surveyor_fileprep/. URL Links to the PATH-SURVEYOR App: https://shawlab-moffitt.shinyapps.io/path_surveyor/. Preloaded Examples: https://shawlab-moffitt.shinyapps.io/path_surveyor_preloaded_example_aml/. https://shawlab-moffitt.shinyapps.io/path_surveyor_preloaded_example_melanomaici/. URL Links to the Connectivity Analysis App: https://shawlab-moffitt.shinyapps.io/pathway_connectivity/. Preloaded Example: https://shawlab-moffitt.shinyapps.io/pathway_connectivity_preloaded_example_melanomaici/. URL Links to the Hazard Ratio GSEA App: https://shawlab-moffitt.shinyapps.io/preranked_hazardratio_gsea/. Preloaded examples: https://shawlab-moffitt.shinyapps.io/preranked_hazardratio_gsea_preloaded_example_melanomaici/. Data and Code to the Example Use Cases: http://shawlab.science/shiny/PATH_SURVEYOR_ExampleUseCases/. Github Repository of the Supplementary Examples: https://github.com/shawlab-moffitt/PATH-SURVEYOR_Manuscript_Supplementary. Downloadable Instructions for setting up the Docker Images are available here: https://github.com/shawlab-moffitt/PATH-SURVEYOR-Suite/tree/main/7-PATH-SURVEYOR-Docker. Operating system: Platform independent. Programming language: R version 4.1 or higher. License: BSD License.Project name: PATH-SURVEYOR. Project home page: Additional file 1.\u00a0Supplementary Figure S1.Additional file 2.\u00a0Supplementary Figure S2.Additional file 3.\u00a0Supplementary Figure S3.Additional file 4.\u00a0Supplementary Figure S4.Additional file 5.\u00a0Supplementary Figure S5.Additional file 6.\u00a0Supplementary Figure S6.Additional file 7.\u00a0Supplementary Figure S7.Additional file 8. Supplementary Table S1\u2013S4.Additional file 9. Supplementary Method.Additional file 10. PATH-SURVEYOR-Suite-main.zip."}
+{"text": "A 43-year-old woman was referred for endoscopic resection after detection of a giant subepithelial lesion (SEL) during routine physical assessment. The lesion was approximately 50 \u00d7 25 mm in size and was located in the posterior wall of the gastric fundus . A compApplication of a new-generation dual-frequency ultrasonic miniprobe in diagnosing giant gastric subepithelial lesions.Video 1Initially, the 20 MHz setting of the probe elucidated the lesion origin at the muscularis propria, revealing hypoechoic alterations a. In puFor SELs, radial endoscopic ultrasound or miniprobe have become widely adopted across the globe to ascertain the depth of the lesion and to predict its natureEndoscopy_UCTN_Code_TTT_1AS_2AB"}
+{"text": "A gallbladder intraductal papillary neoplasm, similar to a bile duct and pancreas intraductal papillary mucinous neoplasm (IPMN), is a rare premalignant lesion characterized by super\ufb01cial spread, dilated gallbladder and bile ducts, and multifocal distributionA 91-year-old woman presented to the emergency department with jaundice. An abdominal computed tomography scan revealed a markedly dilated cystic duct and intra- and extrahepatic bile ducts, without visible stones or masses . On endVideo\u20061\u2002Argon-plasma coagulation was performed at papillary lesions to reduce tumor burden for reducing mucin production during 2nd session of direct peroral cholangioscopy. The lesions were successfully ablated.Although surgery is the treatment of choice for gallbladder IPMN, surgically unfit patients bene\ufb01t from minimally invasive endoscopic therapies, including APCEndoscopy_UCTN_Code_TTT_1AR_2AF"}
+{"text": "Sci., 2023, 14, 8458\u20138465, https://doi.org/10.1039/D3SC03090C.Correction for \u2018A visible-light-driven molecular motor based on barbituric acid\u2019 by Kim Kuntze  The authors regret that the link included in the data availability statement was incorrect in the original article. The corrected data availability statement is shown below:https://figshare.com/articles/dataset/A_Visible-Light-Driven_Molecular_Motor_Based_on_Barbituric_Acid/23276999.The datasets supporting this article have been uploaded as part of the ESI. All cartesian coordinates for all the compounds considered are provided as a separate additional file in a fig-share repository with the following DOI: The Royal Society of Chemistry apologises for these errors and any consequent inconvenience to authors and readers."}
+{"text": "Bothriochloa decipiens belongs to the BCD clade, a group with a complex history of hybridization and polyploid. This is the first genome assembly and annotation of a species that belongs to this fascinating yet complex group.The adaptive significance of polyploidy has been extensively debated, and chromosome-level genome assemblies of polyploids can provide insight into this. The Australian grass B. decipiens, with a total length of 1,218.22 Mb and scaffold N50 of 42.637 Mb. Comparative analysis revealed that the species experienced a relatively recent whole-genome duplication. We clustered the 20 major scaffolds, representing the 20 chromosomes, into the 2 subgenomes of the parental species using unique repeat signatures. We found evidence of biased fractionation and differences in the activity of transposable elements between the subgenomes prior to hybridization. Duplicates were enriched for genes involved in transcription and response to external stimuli, supporting a biased retention of duplicated genes following whole-genome duplication.Using Illumina short reads, 10X Genomics linked reads, and Hi-C sequencing data, we assembled a highly contiguous genome of B. decipiens is a widespread species with the ability to establish across many soil types, making it a prime candidate for climate change\u2013 resilient ecological restoration of Australian grasslands. This reference genome is a valuable resource for future population genomic research on Australian grasses.Our results support the hypotheses of a biased retention of duplicated genes following polyploidy and point to differences in repeat activity associated with subgenome dominance.  Whole-genome duplication (WGD), or polyploidy, occurs via the doubling of chromosomal material either involving 1 species (autopolyploidy) or via hybridization of 2 species . The polyploid origin of many plant species has long been recognized , 4, whilAfter a WGD event, various molecular changes occur to restore the diploid state , belongs to the tribe Andropogoneae (subfamily Panicoideae). This tribe contains many ecologically and economically important species, and independent allopolyploidization events have been exceptionally frequent in this group [B. decipiens is part of a cosmopolitan grass genus [Capillipedium and Dichanthium (together referred to as BCD). These 3 genera have the ability to interbreed despite their morphological differences; the term compilospecies was coined to describe this type of hybrid species complex [B. decipiens may be a donor species to this compilospecies complex [Poaceae (grasses) is the most successful plant family in terms of occurrence, ecological dominance, and species richness , and appis group . B. deciss genus  closely  complex , and B.  complex .B. decipiens. This is the first genome assembly and annotation of a species that belongs to this fascinating yet complex group. Our highly contiguous B. decipiens genome assembly showed clear evidence of recent paleo-polyploidy. Using repeat signatures diverged between putative homoeologous chromosomes, we were able to organize chromosomes into subgenomes, allowing estimation of the timing of the speciation event prior to the most recent allopolyploidization event in this species. We further describe signatures of biased fractionation between subgenomes, as well as biases in the functions of genes retained as duplicated or single copy. This genome reference will act as an important resource for population genomic analysis of the BCD clade and will aid our understanding of the rich history of allopolyploidy in this group and its evolutionary significance.Here we report a chromosome-level genome assembly, annotation, and comparative analysis of a species from the BCD clade, B. decipiens is known to have a haploid chromosome number n = 20 [B. decipiens.Using flow cytometry (FCM) (see Methods), the haploid genome size of the accession COB1-7 was estimated to be 1.25 Gb. The genome was assembled combining assemblies from linked read sequencing (10X) with HiRise scaffolding using Chicago and Hi-C libraries r n = 20 , 36 and B. decipiens using Trinity v.2.8.5 (RRID:SCR_013048) [RNA sequencing (RNA-seq) data from 2 tissues (leaf and stem) were used to assemble the transcriptome of _013048) . The finRRID:SCR_005309) [RRID:SCR_008417) [RRID:SCR_022063) [B. decipiens repeat and gene density across each chromosome reveals that gene density was low toward the center of each scaffold, where repeat density was high   and trai_008417)  and SNAP_008417) . FunctioB. decipiens had undergone a WGD, a reciprocal BLASTP (RRID:SCR_001010) [B. decipiens protein sequences as the query against themselves, and homoeologous scaffolds were identified using the collinear blocks obtained via MCScanX (RRID:SCR_022067) [RRID:SCR_018550) [B. decipiens and Sorghum bicolor  by conducting a reciprocal BLASTP [B. decipiens genes that were orthologous to 19,611 S. bicolor genes across syntenic blocks. A relatively recent paleo-polyploidization event was evident as each chromosome from S. bicolor almost completely aligned to a pair of B. decipiens scaffolds as seen in the dot plot in Fig.\u00a0B. decipiens scaffolds show large syntenic blocks of duplicated genes, as seen in the dot plot in Fig.\u00a0B. decipiens genome alignment against itself .In order to determine if _001010)  was cond_022067) . This wa_018550)  .The difference in the distribution of repetitive elements between pairs of homoeologous chromosomes can provide evidence of subgenome ancestry . Diagnosribed in   identified in subgenome A and only 1 (Copia-9_SB/Copia) identified in subgenome B. Their genomic locations are shown in We examined subgenome-enriched LTRs, as differences in LTR activity in parental species can help differentiate subgenomes and can be used to assess the timing of allopolyploidy. We identified LTR repeats that belonged to 9 LTR families that were at least 3 times more common in 1 subgenome. A- or B-preferred RRID:SCR_017118) [Panicum hallii and Setaria italica as outgroups to identify the likely timing of the subgenome divergence in B. decipiens . Our tree also dated the divergence of the progenitors of M. sinensis to around 6 MYA.We used 1:1 orthologs identified using OrthoFinder v.2.3.8 (_017118)  across mThe greater abundance of LTR subfamilies specific to 1 subgenome is a signal of mobile element activity unique to one of the diploid ancestors of the allopolyploid . TherefoS. bicolor and B. decipiens to assess differences in retention of duplicated genes between subgenomes. Subgenome-specific retention was inferred as the number of genes retained in a given subgenome divided by the number of inferred ancestral  gene numbers. Collinear blocks obtained from the 2 independent scanning methods McScanX [P = 2.2 \u00d7 10\u221216 and OrthoFinder, Fisher's exact test, P = 1.63 \u00d7 10\u221210  [Differences in the function of duplicated genes retained after the WGD compared to those returning to single copy were tested through gene ontology enrichment analysis using the R/topGo package (_014798) . All sinU test, P = 3.3 \u00d7 10\u22121)     Table\u00a0 and also1) Table\u00a0. The TE 1) Table\u00a0 or downs1) Table\u00a0.B. decipiens, a native Australian grass species important in grassland rehabilitation. Our comparative analysis revealed that this species is a paleo-polyploid, consistent with previous phylogenetic analysis of the group [B. decipiens speciated approximately 5.8 MYA. Additionally, we showed evidence of biased fractionation with significantly higher gene retention in one of the subgenomes. This subgenome also appeared to have had more active LTRs just prior to the allopolyploidy event. Consistent with expectations, genes that were retained as duplicates following the WGD event were enriched for functions involving transcription and stress response.Here we report the chromosome-level genome assembly of he group . Our assArabidopsis [Brassica [k-mers and LTR families associated with subgenome A  is a warm-season, perennial, tufted grass that can grow up to a meter in height [Bothriochloa macra, which is a widespread native grass species in southeastern Australia. The sporophytic chromosome number of B. decipiens is reported to be 2n = 40 [n height . Due to n height . It is a 2n = 40 , and it B. decipiens accession COB1-7 used in this study were collected from Cobbitty, NSW . Using these seeds, a plant was grown and maintained at Monash University, Clayton to obtain leaf and inflorescence tissue samples for DNA and RNA extractions for the study.The seeds used to grow the diploid B. macra and B. decipiens collected from different locations in states Victoria and NSW and also sought evidence for within-population variation in ploidy. Estimating ploidy from different populations was necessary as B. decipiens and polyploid B. macra are extremely similar morphologically, and ploidy is the best method to reliably distinguish the 2 species [Solanum lycopersicum (2C = 1.96) and Pisum sativum (2C = 9.09), and grown from seed. Approximately 40\u00a0mg of fresh leaf material was used for each sample and placed into a 2.0-ml tube with a single 3-mm tungsten carbide bead and 436 \u00b5L of an ice-cold nuclei suspension buffer modified from de Laats buffer (1984): 15\u00a0mM HEPES, 1\u00a0mM EDTA, 0.2% (v/v) Triton X-100, 80\u00a0mM KCl, 20\u00a0mM NaCl, 300\u00a0mM sucrose, 0.5\u00a0mM spermine, 15\u00a0mM \u03b2-mercaptoethanol, and 0.25\u00a0mM PVP, adjusted to pH 7. Samples were placed in a Qiagen Tissuelyser II and ground for 24 seconds at 25 hertz, and then the sample rack was reversed and ground again. The homogenate was filtered through 2 layers of Millipore Miracloth (22\u201325\u00a0\u00b5m pore size) suspended in a 3-piece nozzle. Then, 1 \u00b5L of 10\u00a0\u00b5g/\u00b5L RNAse was added for every 100 \u00b5L of filtrate and incubated at 37\u00b0C for 20 minutes. Next, 15 \u00b5L of 0.1\u00a0\u00b5g/444 \u00b5L propidium iodide station solution was added to the filtrate, and samples were run on the BD Accuri C6 Cytometer using the settings outlined in [We used FCM to estimate the genome size and predict the relative ploidy of 24 populations of  species . We esti species . Leaf salined in . InternaBothriochloa samples by comparing the FL2-A value of the sample to the internal standards, Solanum and Pisum, which have a known 2C value of 1.96 and 9.09 pg, respectively [B. macra.A total of 38 samples produced an observable signal in the FCM run. All samples, excluding standards, were run in a blind fashion so that prior knowledge of expected ploidy did not bias the identification of nuclei peaks. The 2C values were determined for all ectively . The aveFor DNA extraction, fresh leaf tissue was collected from diploid individual COB1\u20137, flash frozen in liquid nitrogen, and stored at \u221280\u00b0C. The tissue was then shipped to Dovetail Genomics for the completion of DNA extractions by using the following steps. To obtain high molecular weight DNA for 10X Genomics linked read sequencing, 1.8\u00a0g of leaf material was ground with mortar and pestle to a fine powder, to which 200\u00a0ml prewarmed CTAB and 100 \u00b5L BME was added. This was incubated at 68\u00b0C for 15 minutes. Once incubated, a mixture of 2\u00d7 phenol chloroform, 1\u00d7 isoamyl, and 0.7\u00d7 isopropanol was added and centrifuged to form a pellet. The pellet was combined with 9.5\u00a0ml G2 DNA enhancer buffer solution, 200 \u00b5L protease, and 19 \u00b5L RNase. Again, the mixture was incubated at 50\u00b0C for 1 hour. The precipitated genomic DNA was used in library construction.RRID:SCR_020150) (Illumina) with paired-end 150-bp reads.Genomic DNA (gDNA) with an adjusted concentration between 1.0 and 1.25\u00a0ng/\u00b5L was used to prepare the whole-genome sequencing libraries using the Chromium Genome Library and Gel Bead Kit v.2, Chromium Genome Chip Kit v.2, Chromium i7 Multiplex Kit, and Chromium controller according to the manufacturer's instructions (10X Genomics). Genomic DNA was combined with Master Mix, a library of Genome Gel Beads, and partitioning oil to create Gel Bead-in-Emulsions (GEMs) on a Chromium Genome Chip. The GEMs were isothermally amplified with primers containing an Illumina Read 1 sequencing primer, a unique 16-bp 10X barcode, and a 6-bp random primer sequence. Barcoded DNA fragments were recovered for Illumina library construction. The amount and fragment size of post-GEM DNA were quantified prior using a Bioanalyzer 2100 with an Agilent high-sensitivity DNA kit. Prior to Illumina library construction, the GEM amplification product was sheared on an E220 Focused Ultrasonicator (Covaris) to approximately 350\u00a0bp. Then, the sheared GEMs were converted to a sequencing library following the 10X standard operating procedure. The library was quantified by quantitative polymerase chain reaction (qPCR) with a Kapa Library Quant kit (Kapa Biosystems\u2013Roche) and sequenced on a NovaSeq6000 sequencer (RRID:SCR_014941) library was prepared as described in [RRID:SCR_016385) to produce 467 million 2 \u00d7 150-bp paired-end reads.A Chicago (ribed in . BrieflyA Dovetail Hi-C library was prepared as described in . BrieflyRRID:SCR_016756) [RRID:SCR_023037), a proprietary software designed specifically for using proximity ligation data to scaffold genome assemblies [de novo assembly from Supernova using SNAP (RRID:SCR_007936) [RRID:SCR_021172) [The 10X sequence data were assembled de novo with Supernova (_016756) . This desemblies . An iter_007936) . The sep_021172)  to make RNA was extracted separately from young (a few weeks) and old (a year) tissue (leaf and stem) from 1 individual using the Qiagen RNeasy kit. RNA was pooled and a library was synthesized and sequenced by Genewiz on an Illumina Novaseq 6000 platform in 2 \u00d7 150-bp mode, resulting in 64,756,621 reads.RRID:SCR_011848) with the parameter \u201cILLUMINACLIP:TruSeq3- PE.fa:2:30:10:2:keepBothReads LEADING:3 TRAILING:3 MINLEN:36\u201d [RRID:SCR_013048) [Raw RNA-seq reads were first cleaned by trimming the adapters using Trimmomatic v. 0.38 (NLEN:36\u201d . The tri_013048)  using deRRID:SCR_020946) [RRID:SCR_018970) and LTR-digest [RRID:SCR_015027) [RRID:SCR_012954) [RRID:SCR_001653) [RRID:SCR_005305) [A custom repeat library was constructed following recommendations of the MAKERP pipeline for advanced repeat construction . Both st_020946)  using alR-digest , 97. TheR-digest  to reduc_015027)  by provi_015027)  with tra_015027) . To iden_015027) . This da_012954)  and from_012954) , 103, an_001653) . The sec_001653) , which w_005305)  implemen_005305) . The seq_005305)  was usedRRID:SCR_005309) [JALGXP000000000), the reference transcriptome assembly fasta file (obtained from Trinity), and the protein homology evidence from a plant protein database [RRID:SCR_015008) [MAKER v.3.01.03 (_005309)  genome adatabase  that comdatabase . Iteratidatabase  were unddatabase  and AUGUdatabase  as recomdatabase . The firdatabase  to obtaidatabase  using BU_015008) . First, _015008)  to calcu_015008)  with theB. decipiens obtained after the final annotation round of MAKER above, and a BED file with the locations of genes in the genome as predicted by the MAKER annotation pipeline above.Apart from constructing a repeat library to be used in the MAKER genome annotation pipeline above, EDTA v.2.0.0  was usedRRID:SCR_018550) [k-mer distributions. We partitioned the B. decipiens genome into subgenomes A and B by modifying the methods described in [RRID:SCR_005491) [k-mers at high abundance in the assembly (100\u00d7 or above). For each pair of scaffolds, we compared the counts of these 13-mers, identifying those that differed in abundance by 3-fold or more between scaffolds. To control for any differences in scaffold length impacting this assessment, we further reduced the set of diagnostic 13-mers to those that retained a 3-fold difference after standardizing k-mer count for the scaffold length, while keeping only those diverging in the same direction as the absolute k-mer count. Hierarchical clustering of scaffolds based on difference in 13-mer counts was used to identify putative subgenomes as implemented in the R/ComplexHeatmaps package (RRID:SCR_017270) [First, we identified the 20 largest scaffolds . These scaffolds were then aligned against themselves using Minimap2 v.2.1.8 (_018550)  to ident_018550) . Of thesribed in . Specifi_005491)  and reta_017270) .k-mer type (A or B) in 1-Mbp windows as the observed states and the subgenome type for each of the 1,121 windows. The initial HMM used equal starting probabilities and transition probabilities of 0.01. We trained the HMM emission probabilities (viterbiTraining) using scaffold 5 and scaffold 15 as they appeared not to be subject to any subgenome exchange based on the A and B k-mer density plots (We tested for homoeologous exchange among the subgenomes using an HMM implemented in the R/HMM package . We usedty plots .RRID:SCR_004870) [k-mers to confirm that k-mers were representing longer repetitive sequences and to confirm that these k-mers were marking repeat expansion that occurred just before the allopolyploidy event.We examined subgenome-enriched LTRs, as differences in LTR activity in parental species can help differentiate subgenomes and can be used to assess the timing of allopolyploidy. LTRs were used for this because they are rapidly evolving, making it easy to differentiate between related subfamilies. The timing of insertions can be calculated by examining the substitution rates for members of the same subfamily using the 5\u2032 and 3\u2032 regions . Specifi_004870)  on the L_004870)  to clustRRID:SCR_001010) [B. decipiens query. InterProScan v. 5.51\u201385.0 (RRID:SCR_005829) [B. decipiens query sequences. The query protein sequences were BLAST searched against the KEGG database [The predicted protein sequences obtained from the final run of MAKER were aligned to the UniProtKB/Swiss-Prot  and TAIR_001010)  with an _005829)  was used_005829)  protein database  using thdatabase  with an B. decipiens genome had undergone WGD, a reciprocal BLASTP was conducted using B. decipiens protein sequences as the query against themselves with a minimum e-value greater than 1.0e-5. Then, MCScanX [B. decipiens were visualized using Synvisio [B. decipiens and S. bicolor by conducting a reciprocal BLASTP comparing protein sequences from each species using MCScanX and plotted the results using Synvisio.To determine if the  MCScanX  was usedSynvisio . SimilarP. hallii and S. italica as outgroups. The reference gene sets for S. bicolor v3.1.1, P. hallii v2.2, S. italica v2.1, M. sinensis v7.1, and Zea mays (B73 RefGen_v4) were downloaded from Phytozome v12.1 (RRID:SCR_006507) [Saccharum spontaneum reference gene set [M. sinensis  using OrthoFinder v.2.3.8 (RRID:SCR_017118) [RRID:SCR_011811) [RRID:SCR_015945) [We estimated the timing of speciation events in the Andropogoneae using _006507) . The Sacgene set  was alsoified in ), as wel_017118) . A core _017118)  and Maff_011811) . Poorly _015945) , and theRRID:SCR_006086) [S. italica and P. hallii were designated as outgroups.The best model of evolution was inferred using jModelTest2 , 130. A _006086)  with theRRID:SCR_010228) [Setaria\u2013Panicum (12.8\u201320 MYA) and the Andropogoneae (13\u201321.2 MYA) nodes, using a uniform distribution between the minimum age and maximum ages of divergence times obtained from the TimeTree database [RRID:SCR_017307) [Divergence among lineages in the phylogeny were estimated from the concatenated alignment using BEAST v.2.5 (_010228) , 133 aft_010228)  to inferdatabase . BEAST2 _017307)  analysis_017307) . We usedRRID:SCR_011811) [\u22128 as the substitution rate per site per year [We aligned the LTRs of each LTR family cluster using Mafft (_011811) . We comp_011811) . We esti_011811) . We usedper year .S. bicolor and B. decipiens to assess differences in retention of duplicated genes between subgenomes. Subgenome-specific retention was inferred as the number of genes retained in a given subgenome divided by the number of inferred ancestral  gene numbers. Consequently, we calculated the number of ancestral (preduplication) genes as those orthologous genes present in S. bicolor and in 1 or both of the 2 subgenomes. We then compared this number to the total number of genes present only in subgenome A, only in subgenome B, or in both subgenomes. We then used a 2-sided Fisher's exact test to determine if there was a significant difference in retention of genes between the subgenomes under the null hypothesis that gene loss between subgenomes was random. We also used the results from OrthoFinder (RRID:SCR_017118) to confirm this pattern. Specifically, we identified 1:1 and 1:2 orthologs between S. bicolor and B. decipiens, only retaining 1:2 orthologs that were mapped to chromosomes on both B. decipiens subgenomes.We analyzed the collinear blocks between RRID:SCR_014798) [Differences in the function of duplicated genes retained after the WGD compared to those returning to single copy were tested through gene enrichment analysis using R/topGo (_014798)  where a U test to determine if there was a significant difference between the average TE density in both upstream and downstream of genes found in the 2 subgenomes. We then investigated if there were any differences in TE density near genes identified as single copy or duplicated in the above analysis of biases in gene retention (McScanX scanning method). To do this, we used a Mann\u2013Whitney U test to determine if there was a significant difference between the average TE density in both upstream and downstream of genes found retained as single copy or as duplicated in the genome.For this, we used the software pipeline TE density . Here, TR scripts used in this study can be found in the public GitHub repository .Bothriochloa, Capillipedium, and Dichanthium; BLAST: Basic Local Alignment Search Tool; BLASTP: Basic Local Alignment Search Tool Program; bp: base pairs; BUSCO: Benchmarking Universal Single-Copy Orthologs; FCM: flow cytometry; Gb: Giga bases; gDNA: genomic DNA; GO: Gene Ontology; HMM: hidden Markov model; KEGG: Kyoto Encyclopedia of Genes and Genomes; LTR: long terminal repeat; Mb: mega base pairs; MITE: miniature inverted repeat transposable element; MYA: million years ago; NCBI: National Center for Biotechnology Information; NSW: New South Wales; PCR: polymerase chain reaction; RNA-seq: RNA sequencing; TE: transposable element; UTR: untranslated region; WGD: whole-genome duplication.BCD: This study was supported by the Hermon Slade Foundation (grant number HSF1703), Monash Graduate Scholarship, Monash University, Monash International Tuition Scholarship, Monash University and Denis and Maisie Carr Award and Travel grant 2020, and School of Biological Sciences, Monash University.giad034_GIGA-D-22-00164_Original_SubmissionClick here for additional data file.giad034_GIGA-D-22-00164_Revision_1Click here for additional data file.giad034_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giad034_Reviewer_1_Report_Original_SubmissionDhanushya Ramachandran -- 8/27/2022 ReviewedClick here for additional data file.giad034_Reviewer_2_Report_Original_SubmissionMichael McKain -- 9/19/2022 ReviewedClick here for additional data file.giad034_Reviewer_2_Report_Revision_1Michael McKain -- 3/15/2023 ReviewedClick here for additional data file.giad034_Reviewer_3_Report_Original_SubmissionKehua Wang -- 10/5/2022 ReviewedClick here for additional data file.giad034_Supplemental_FilesClick here for additional data file."}
+{"text": "Machine learning (ML) approaches are a crucial component of modern data analysis in many fields, including epidemiology and medicine. Nonlinear ML methods often achieve accurate predictions, for instance, in personalized medicine, as they are capable of modeling complex relationships between features and the target. Problematically, ML models and their predictions can be biased by confounding information present in the features. To remove this spurious signal, researchers often employ featurewise linear confound regression (CR). While this is considered a standard approach for dealing with confounding, possible pitfalls of using CR in ML pipelines are not fully understood.We provide new evidence that, contrary to general expectations, linear confound regression can increase the risk of confounding when combined with nonlinear ML approaches. Using a simple framework that uses the target as a confound, we show that information leaked via CR can increase null or moderate effects to near-perfect prediction. By shuffling the features, we provide evidence that this increase is indeed due to confound-leakage and not due to revealing of information. We then demonstrate the danger of confound-leakage in a real-world clinical application where the accuracy of predicting attention-deficit/hyperactivity disorder is overestimated using speech-derived features when using depression as a confound.Mishandling or even amplifying confounding effects when building ML models due to confound-leakage, as shown, can lead to untrustworthy, biased, and unfair predictions. Our expose of the confound-leakage pitfall and provided guidelines for dealing with it can help create more robust and trustworthy ML models. Key Points:Confound removal is essential for building insightful and trustworthy machine learning (ML) models.Confound removal can increase performance when combined with nonlinear ML.This can be due to confound information leaking into the features.Possible reasons are skewed feature distributions and the feature of limited precision.Confound removal should be applied with utmost care in combination with nonlinear ML.Machine learning (ML) approaches have revolutionized biomedical data analysis by providing powerful tools, especially nonlinear models, that can model complex feature\u2013target relationships , 2. HoweImagine building a diagnostic classifier for attention-deficit/hyperactivity disorder (ADHD) based on speech patterns. This will be a useful clinical tool aiding objective diagnosis . HoweverWhen confounding masks the true feature\u2013target relationship, its removal can clean the signal of interest, leading to higher generalizability  . On the information-reveal: CR reveals information that was masked by confounding or (ii) confound-leakage: leakage of confounding information into the features. In the case of information-reveal, CR could suppress linear confounding or noise, in turn enhancing the underlying (non)linear signal and making learning easier for a suitable ML algorithm . This data includes 126 individuals with 6,016 speech-related features, a binary target describing ADHD status (ADHD or control) and contains 4 confounds: gender, education level, age, and depression score measured using the BDI. For more information on the datasets, see Confound removal was performed following the standard way of using linear regression models. Following the common practice, we applied CR to all the features. Specifically, for each feature, a linear regression model was fit with the feature as the dependent variable and the confounds as independent variables. The residuals of these models, that is, original feature minus the fitted values were used as confound-free features (t folds) , 22.The TaCo framework allows systematic analysis of confound removal effects. Confounding is a 3-way relationship between features, confounds, and the target. This means that a confound needs to share variance with both the feature and the target. Measuring or simulating such relationships can be hard, especially if linear univariate relationships cannot be assumed. Furthermore, effects of confound removal should increase with the actual strength of the confound. The target itself explains all the shared variance and thus is the strongest possible confound. Therefore, using the target as a confound  measures the most possible extent of confounding. In addition, using the TaCo simplifies the analysis to a 2-way relationship. Lastly, the TaCo approach is applicable to any dataset and can help to measure the strongest possible extent of confound-leakage even without knowing the confounds.To study the effect of CR on both linear and nonlinear ML algorithms, we employed a variety of algorithms: linear/LR, linear kernel SVM, RBF kernel SVM, DT, RF, and MLP with a single hidden layer (relu). Additionally, we used dummy models to evaluate chance-level performance.In the preprocessing steps, we normalized the continuous features and continuous confounds to have a mean of zero and unit variance, again in a CV-consistent fashion. Any categorical features were one-hot encoded following standard practice.R2 from scikit-learn [We compared the performance of ML pipelines with and without CR. To this end, we computed the out-of-sample AUCROC for classification and predictive it-learn  for regrit-learn  to deterIn this study, we used the Bayesian ROPE  approachShuffling the features while keeping the confounds and target intact destroys the feature\u2013target and feature\u2013confound relationships while preserving the confound\u2013target relationship. Therefore, after feature shuffling, any confound adjustment method cannot reveal the feature\u2013target relationship, but it can still leak information. In other words, any performance above the chance level after CR on shuffled features is an indication of confound-leakage. Feature shuffling is also used in other approaches such as permutation testing (see section\u00a0\u201cThe Bayesian ROPE for model comparison\u201d) to test effectiveness of confound adjustment methods . PermutaProject name: Confound-leakagehttps://github.com/juaml/ConfoundLeakageProject homepage: Operating system(s): GNU/LinuxProgramming language Python 3.10.8 Other requirements: scikit-learn 0.24.2, baycomp 1.0.2, matplotlib 3.5.1, seaborn 0.11.2, dtreeviz 1.3.5, numpy 1.22.3, pandas 1.2.5License: GNU Affero General Public License v3.0giad071_GIGA-D-23-00004_Original_SubmissionClick here for additional data file.giad071_GIGA-D-23-00004_Revision_1Click here for additional data file.giad071_GIGA-D-23-00004_Revision_2Click here for additional data file.giad071_GIGA-D-23-00004_Revision_3Click here for additional data file.giad071_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giad071_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giad071_Response_to_Reviewer_Comments_Revision_2Click here for additional data file.giad071_Reviewer_1_Report_Original_SubmissionRichard Dinga -- 2/8/2023 ReviewedClick here for additional data file.giad071_Reviewer_2_Report_Original_SubmissionQingyu Zhao -- 2/15/2023 ReviewedClick here for additional data file.giad071_Reviewer_2_Report_Revision_1Qingyu Zhao -- 6/12/2023 ReviewedClick here for additional data file.giad071_Supplemental_FileClick here for additional data file."}
+{"text": "Retropharyngeal abscess, a rare complication of foreign body ingestion that is usually associated with trauma to the retropharyngeal wallA 72-year-old man presented with fever and pain on swallowing for 2 days; the patient had swallowed a fish bone 20 days earlier. Computed tomography showed a retropharyngeal abscess . GastroEffective management of a retropharyngeal abscess by endoscopic complete-layer resection and drainage.Video 1Computed tomography on postintervention Day 2 revealed an empty abscess cavity a. CompuEndoscopy_UCTN_Code_TTT_1AS_2AG"}
+{"text": "This study aims to identify potential myopia biomarkers using machine learning algorithms, enhancing myopia diagnosis and prognosis prediction.GSE112155 and GSE15163 datasets from the GEO database were analyzed. We used \u201climma\u201d for differential expression analysis and \u201cGO plot\u201d and \u201cclusterProfiler\u201d for functional and pathway enrichment analyses. The LASSO and SVM-RFE algorithms were employed to screen myopia-related biomarkers, followed by ROC curve analysis for diagnostic performance evaluation. Single-gene GSEA enrichment analysis was executed using GSEA 4.1.0.The functional analysis of differentially expressed genes indicated their role in carbohydrate generation and polysaccharide synthesis. We identified 23 differentially expressed genes associated with myopia, four of which were highly effective diagnostic biomarkers. Single gene GSEA results showed these genes control the ubiquitin-mediated protein hydrolysis pathway.Our study identifies four key myopia biomarkers, providing a foundation for future clinical and experimental validation studies.The online version contains supplementary material available at 10.1186/s12886-023-03119-5. Myopia, or nearsightedness, is a common vision condition where close objects appear clear, but distant ones are blurred. It increases the risk of several eye-related complications such as retinal detachment, dry eye, cataracts, and glaucoma. Additionally, symptoms like headaches and eye strain can occur \u20133. With Several studies have examined the relationship between mutations in disease-causing genes and myopia. A study collected data from 593 individuals with high myopia for gene-set analysis (GSA) of new genome-wide association study (GWAS) data and identified by whole-genome sequencing 45 triplet families with high myopia, screening 196 genes with ab initio mutations for over-representation analysis (ORA), and 284 previously reported myopia risk genes for ORA for human genetic analysis. At last, it implicated the HIF-1\u03b1 signaling pathway in promoting human myopia through mediating interactions between genetic and environmental factors . PAX6 haAdditional data from a study support the hypothesis that the PAX6 SNP rs644242 is linked to severe myopia. The gene may contribute to the emergence or progression of severe myopia . Loss ofThe increasing prevalence of myopia has accelerated our research on the pathogenesis of myopia. To further investigate the mutated genes in the corneas of myopic samples, we explored the differences in gene expression between myopic and normal corneas to discover the molecular biological mechanism of myopia pathogenesis and precisely target myopia treatment to provide a reference for clinical treatment of myopia.https://www.ncbi.nlm.nih.gov/geo/) database in the GSE112155 and GSE151631 datasets were used for analysis in this study. Gene expression levels were normalized using transcripts per kilobase million (TPM) values; the following equation was used: TPM\u2009=\u2009Read count \u00d7 1,000,000/Mapped Reads [Data from the Gene Expression Omnibus (GEO)  and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed for differentially expressed genes using the \u201cGO plot\u201d and \u201cclusterProfiler\u201d software packages. The GO enrichment analysis includes cell composition (CC), biological process (BP), and molecular function (MF).The software packages \u201cglmnet\u201d and \u201ce1071\u201d were used to perform Support Vector Machine Recursive Feature Elimination (SVM-RFE) analysis and Least Absolute Shrinkage and Selection Operator (LASSO) Logistic Regression. Two machine learning algorithms were used to screen the biomarkers, and the genes they identified were then shown in a Venn diagram with the myopia-related biomarkers occupying the overlapped areas. The diagnostic effectiveness of the biomarkers was then tested by plotting the receiver operating characteristic (ROC) and measuring the AUC.P-value\u2009<\u20095%.To predict biomarker-related pathways software GSEA_4.1.0 was used to perform single-gene GSEA enrichment analysis. The GSE112155 and GSE151631 data were first merged and platform effects were eliminated, and the four biomarkers were divided into high and low-expression groups based on their expression, respectively. The significance criterion was a nominal The datasets GSE112155 and GSE151631 were transformed into TPM for differentially expressed gene analysis. 308 differentially expressed genes were identified in GSE112155  using GSE151631 and validated them in GSE112155. NR1D1 (AUC\u2009=\u20090.986) in GSE151631, PGBD2 (AUC\u2009=\u20091.000), PPP1R3D (AUC\u2009=\u20091.000), PPP1R18 (AUC\u2009=\u20091.000) , KEGG_UBIQUITIN_MEDIATED_PROTEOLYSIS , KEGG_PANCREATIC_CANCER , KEGG_CELL_CYCLE , KEGG_RENAL_CELL_CARCINOMA , KEGG_ERBB_SIGNALING_PATHWAY , KEGG_AMINO_SUGAR_AND_NUCLEOTIDE_ SUGAR_METABOLISM , and KEGG_LONG_TERM_POTENTIATION . This result suggests that NR1D1 may play a negative regulatory role in these pathways.To predict potentially relevant pathways for the biomarkers, we combined GSE151631 and GSE112155 and performed GSEA based on the expression of each of the four biomarkers. eight pathways were enriched in the NR1D1 low expression group   Fig.\u00a0. This suP\u2009=\u20090.000), KEGG_RNA_DEGRADATION , KEGG_ PROPANOATE_METABOLISM , and KEGG_LONG_TERM_POTENTIATION . These results imply that PPP1R3D has a positive regulatory effect on these pathways.The PPP1R3D high expression group was enriched to 4 pathways Fig.\u00a0: KEGG_UBP\u2009=\u20090.015), KEGG_RNA_DEGRADATION , KEGG_ REGULATION_OF_AUTOPHAGY , and KEGG_PROPANOATE_METABOLISM . These results suggest that PPP1R3D may inhibit the activation of these pathways.The PPP1R18 low expression group was enriched to 4 pathways Fig.\u00a0: KEGG_UBThere is growing evidence confirming that myopia is not simply a refractive error, but is influenced by many factors . In thisNR1D1 is involved in metabolism, autophagy, cell proliferation, inflammation and other processes and regulates a variety of diseases \u201322. It iIn the current study, we compared patients with different degrees of myopia to normal cornea patients, searching for differentially expressed genes, investigating the functions of these genes, identifying key myopia biomarkers, studying the diagnostic efficacy of these key biomarkers, and based on GSEA analysis, identifying several key pathways that may be involved in myopia progression. These findings have contributed to our understanding of the pathophysiology of myopia. However, due to the limited sample size in this study, the strength of the evidence is reduced. We will use this research as a stepping stone for more clinical and basic experimental studies to further validate our findings, as the exact mechanisms of myopia are still largely unknown.To further understand the potential roles of these genes in high myopia, future research should consider using larger sample populations and including more patients with high myopia. We will also explore whether these genes are associated with high myopia. Additionally, we plan to further investigate how these genes influence cellular functions and how they may interact with environmental factors to affect the severity of myopia. Through such efforts, we hope to gain a better understanding of the genetic basis of high myopia and potentially guide future treatment strategies.Our study shows that NR1D1, PPP1R18, PGBD2, and PPP1R3D are effective as biomarkers in the diagnosis of myopia and that NR1D1, PPP1R18, PGBD2, and PPP1R3D may be potential therapeutic targets.Additional file 1."}
+{"text": "Tumor residue after concurrent chemoradiotherapy (CCRT) in nasopharyngeal carcinoma (NPC) patients often predicts poor prognosis. Thus, the objective of this retrospective study is to develop a nomogram that combines magnetic resonance (MRI) radiomics features and clinical features to predict the early response of locally advanced nasopharyngeal carcinoma (LA-NPC).A total of 91 patients with LA-NPC were included in this study. Patients were randomly divided into training and validation cohorts at a ratio of 3:1. Univariate and multivariate analyses were performed on the clinical parameters of the patients to select clinical features to build a clinical model. In the training cohort, the Least Absolute Shrinkage and Selection Operator (LASSO) regression model was used to select radiomics features for construction of a radiomics model. The logistic regression algorithm was then used to combine the clinical features with the radiomics features to construct the clinical radiomics nomogram. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were drawn to compare and verify the predictive performances of the clinical model, radiomics model, and clinical radiomics nomogram.Platelet lymphocyte ratio (PLR) and nasopharyngeal tumor volume were identified as independent predictors of early response in patients with locally advanced nasopharyngeal carcinoma. A total of 5502 radiomics features were extracted, from which 25 radiomics features were selected to construct the radiomics model. The clinical radiomics nomogram demonstrated the highest AUC in both the training and validation cohorts . The calibration curve and DCA indicated good predictive performance for the nomogram.A clinical radiomics nomogram, which combines clinical features with radiomics features based on MRI, can predict early tumor regression in patients with LA-NPC. The performance of the nomogram is superior to that of either the clinical model or radiomics model alone. Therefore, it can be used to identify patients without CR at an early stage and provide guidance for personalized therapy. The Currently, concurrent chemoradiotherapy (CCRT) has been the standard treatment for locally advanced nasopharyngeal carcinoma (LA-NPC) , 10. WitThe rapid advancements in modern medical imaging have made it possible to extract features from tomographic images through high-throughput computing, thereby converting medical images into analyzable data. This process is commonly known as radiomics . The mai22.1The data of 145 NPC patients with pathologically confirmed were reviewed and collected in The First Affiliated Hospital of Nanjing Medical University  from January 2020 to January 2023.The tumor node metastasis (TNM) staging system, as outlined in the 8th edition of the American Joint Committee on Cancer (AJCC), was used to classify the stage of the disease.This retrospective research enrolled patients who met the following inclusion criteria: 1) pathological confirmation of nasopharyngeal squamous cell carcinoma with III to IVA stage; 2) complete pre-treatment and post-treatment MRI images of the nasopharyngeal neck, including axial T1-weighted images (T1-WI), contrast-enhanced T1-weighted images (T1-C) and T2-weighted images (T2-WI); 3) completion of radical CCRT; 4) available clinical data; 5) adequate bone marrow, liver and renal function. Exclusion criteria included: 1) MRI images with motion artifacts, blurring, or discontinuity; 2) history of prior malignancy or previous treatment for nasopharyngeal carcinoma (NPC); 3) coexistence of immune system diseases or long-term use of hormone drugs.The patient selection process is shown in 2.22 or paclitaxel 135-175 mg/m2 on day 1 and cisplatin or nedaplatin at a dose of 80 mg per square meter on day 1 were administered intravenously once every 3 weeks for 2-3 cycles. CCRT was recommended to be performed within 21 to 28 days after the first day of the last cycle of IC. Radiation therapy (RT) was performed in intensity-modulated radiotherapy mode with 6 MV photon irradiation. The prescribed doses were 66-70 Gy, 64-70 Gy, 60-62 Gy, and 54-56 Gy, in 30-33 fractions, for the PTVs derived from GTVnx, GTVnd, CTV1, and CTV2, respectively. Cisplatin or nedaplatin that was concurrent with radiotherapy was then administered intravenously at a dose of 80 mg per square meter every 3 weeks on days 1, 22, and 43. The quantity of chemotherapy cycles was modified in concordance with the patient\u2019s physical status. It is recommended to undergo an MRI examination within 1-3 months after completing CCRT.Induction chemotherapy (IC) regimen was taxane and cisplatin (TP): docetaxel 75 mg/m2.3Tumor response was evaluated by two radiologists, based on MRI images taken before treatment and 1-3 months after completion of CCRT. Using the Response Evaluation Criteria in Solid Tumors 1.1 (RECIST 1.1) , patient2.4The clinical characteristics before treatment were collected through the health information system (HIS) of Jiangsu Province Hospital. Characteristics included age, sex, height, weight, smoking, drinking, family history, EBV DNA, white blood cell count (WBC), platelet count (PLT), neutrophil count, lymphocyte count, monocyte count, platelet to lymphocyte ratio (PLR), neutrophil to lymphocyte ratio (NLR), lymphocyte to monocyte ratio (LMR), lactate dehydrogenase (LDH), alkaline phosphatase (ALP), serum albumin (Alb), and D-dimer. The volume of nasopharyngeal tumor, maximum coronal length of positive lymph node (N-cor-length), maximal axial diameter of positive lymph node  and the total volume of lymph node  were obtained after delineation of the region of interest (ROI) on ITK-SNAP software.2.5www.itk-snap.org) by one radiation oncologists with 3 years of experience in radiotherapy for NPC. The final validation was performed by a senior radiation oncologist with 10 years of experience. They settled their differences by discussion. All radiomics features are extracted with an in-house feature analysis program implemented in Pyradiomics (http://pyradiomics.readthedocs.io). All patients completed image acquisition on 3.0TMR Machines. Magnetic resonance acquisition parameters are in the 2.6p <0.05 were selected to construct a clinical model.Clinical factors were analyzed using T-test, Mann-Whitney U tests, or \u03c7\u00b2 tests. Univariate and multivariate analyses were performed to compare the clinical characteristics between the CR group and the non-CR group. Clinical parameters with p <0.05 of radiomic features were kept. Pearson\u2019s rank correlation coefficient was also used to calculate the correlation between features and one of the features with correlation coefficient greater than 0.9 between any two features is retained. We use greedy recursive deletion strategy for feature filtering, that is, the feature with the greatest redundancy in the current set is deleted each time. The least absolute shrinkage and selection operator (LASSO) regression model was used on the discovery data set for signature construction and 10-fold cross-validation was performed. After this, 25 features were finally kept. These 25 Nonzero coefficients and features were selected to establish the Rad-score with LASSO logistic regression model.We conducted T-test statistical test and feature screening for all radiomic features. Only the 2.7The final retained clinical features were input into classifiers such as univariate logistic regression (LR), support vector machine (SVM), K-nearest neighbor (KNN), random forest (RF), extra tree (ET), extreme gradient enhancer (XGBoost), light gradient enhancer (LightGBM) and multilayer perception (MLP) to develop the clinical model for predicting early response in LA-NPC patients. The radiomics model was constructed in the same way.We used the logistic regression algorithm to combine clinical and radiomics features, resulting in an optimal clinical radiomic model. Next, we developed a clinical radiomics nomogram for clinical use. To evaluate the predictive ability of the three models, ROC curves were drawn for the training and validation cohorts, and the average area under the ROC curve (AUC), accuracy, sensitivity, and specificity were calculated. The clinical practicability of the models was evaluated using calibration curve and decision curve analysis (DCA).2.8X\u00af\u00b1 SD), and count data were expressed as count and percentage. Independent sample t-test, Mann-Whitney U test and \u03c72 test were used for comparison. Significance was set at two-sided p < 0.05 .The analysis was performed using various software tools, including SPSS 26  and custom code written in Python v.3.7.12. Onekey v.2.2.3 platform python packages used in the analysis include scikit-learn v.1.0.2 for making machine learning algorithms, PyRadiomics v.3.0 for extracting features and statsmodels v0.13.2 is used for statistical analysis. Measurement data were expressed as mean \u00b1 standard deviation (33.1p=0.004), nasopharyngeal tumor volume (p=0.008), N-cor-length (p=0.043), and N-axial-length (p=0.04) showed significant differences between the CR group and non-CR group.Between January 2020 and January 2023, a total of 91 patients with LA-NPC were enrolled in this study. Among them, 56 patients were assigned to the CR group and the remaining 35 patients to the non-CR group based on tumor regression after treatment. The rate of CR was 61.5%, and the overall response rate (ORR) was 96.7%. 3.2p of these radiomics features. Finally, the LASSO classifier selected 25 features. A total of 5502 radiomics features were extracted, which included 1080 first-order features, 42 shape features, and 4380 texture features. The texture features were further divided into 1320 gray-level co-occurrence matrix (GLCM), 960 gray-level run length matrix (GLRLM), 840 gray-level dependence matrix (GLDM), 960 gray-level size zone matrix (GLSZM), and 300 neighborhood gray-tone difference matrix (NGTDM) methods. 3.3The 25 features and coefficients are fitted linearly, and the formula of Rad score is as follows:Rad-score= 0.37486358311575735+0.075939 * gradient_firstorder_Skewness_T1-0.040816 * lbp_3D_m2_gldm_SmallDependenceLowGrayLevelEmphasis_T1+0.037343 * lbp_3D_m2_glszm_SmallAreaHighGrayLevelEmphasis_T1-0.002198 * logarithm_ngtdm_Strength_T1-0.073031 * wavelet_LLH_firstorder_Median_T1-0.008931 * wavelet_LLL_glcm_Imc2_T1+0.035514 * exponential_glszm_LowGrayLevelZoneEmphasis_T1C+0.080297 * lbp_3D_k_firstorder_Variance_T1C+0.006255 * lbp_3D_m1_firstorder_10Percentile_T1C+0.069564 * lbp_3D_m1_gldm_DependenceVariance_T1C+0.037281 * lbp_3D_m1_glszm_LowGrayLevelZoneEmphasis_T1C-0.052401 * lbp_3D_m2_gldm_SmallDependenceLowGrayLevelEmphasis_T1C-0.099613 * log_sigma_3_0_mm_3D_glszm_ZoneVariance_T1C-0.022222 * wavelet_HHH_glcm_ClusterShade_T1C+0.015730 * wavelet_HHL_firstorder_Maximum_T1C+0.103909 * wavelet_HLH_glcm_Correlation_T1C+0.035693 * wavelet_LLH_glszm_SmallAreaHighGrayLevelEmphasis_T1C+0.038661 * exponential_gldm_DependenceVariance_T2+0.012215 * gradient_glcm_Correlation_T2+0.040093 * lbp_3D_k_glcm_ClusterProminence_T2+0.005076 * log_sigma_1_0_mm_3D_glcm_Correlation_T2+0.023038 * log_sigma_2_0_mm_3D_glcm_Correlation_T2+0.061645 * original_shape_Flatness_T2+0.003769 * squareroot_glcm_Correlation_T2+0.002816 * wavelet_LHL_glcm_Correlation_T2We utilized the 25 selected radiomics features to construct a radiomics model for each classifier, and subsequently evaluated the performance of each model in both the training and validation cohorts. 3.4p < 0.05). Subsequently, multivariate analysis of these four clinical parameters in the training cohort showed that PLR and nasopharyngeal tumor volume were independent predictors of early response of LA-NPC . Considering the small sample size of this study, we performed a univariate analysis of clinical parameters for all patients. The results showed that there were significant differences in PLR, N-cor-length, N-axial-length and the volume of nasopharyngeal tumor between CR group and non-CR group . It suggests that the prediction of nasopharyngeal tumor volume may not be evident over a short duration. Nasopharyngeal tumor regression is more pronounced with the treatment of CCRT after IC and its prediction of response may be realized at this time. Furthermore, EBV DNA has historically been an essential indicator of NPC but its significance was not observed in this study. This might be due to the fact that our study participants were from non-endemic areas with inconsistent detection methods and low infection rates of EBV.In-depth analysis was conducted in this research where multiple models were created, including the clinical model, radiomics model, and clinical radiomics nomogram. The objective was to evaluate the predictive performance of these models in determining early response post completion of CCRT in LA-NPC patients. In addition to the routinely considered clinical factors, the volume of nasopharyngeal tumor, N-cor-length, N-axial-length, and N-total volume were also taken into account as crucial parameters. The study by Zhao et\u00a0al. failed to demonstrate the potential of pre-treatment nasopharyngeal tumor volume, lymph node volume and diameter in predicting IC response in NPC patients . Howeverp <0.05) (p=0.0005) (p<0.0001) (p=0.001). In addition, the simple radiomics model also provided a better AUC than the clinical model   . Piao et=0.0005) . The afo=0.0005) . AUC in <0.0001) . Our fin<0.0001) , 34. The p=0.03) . Accordi p=0.03) . Our papp < 0.0001)  or positron emission tomography with computed tomography (PET/CT) can be used to predict treatment response or prognosis of NPC. Hao et\u00a0al. build a radiomics nomogram with 18 radiomics signatures. Patients in the high-risk group defined by this nomogram had lower 5-year disease-free survival (DFS) rate than low-risk patients  . The stu 0.0001) . Our stuIn the present investigation, we developed a nomogram that incorporates clinical and radiomics features that effectively forecast the initial response to CCRT in patients diagnosed with LA-NPC. Nevertheless, it is pertinent to underscore that there exist certain limitations within this study. Firstly, this research was a retrospective study conducted in a single center which failed to undergo external validation and the sample size was limited. Secondly, since long-term survival follow-up data was not available, we could not undertake a thorough analysis of PFS and OS of LA-NPC patients. In the future, we will intend to expand the data and further focus on patient survival rates and overall prognosis.5The utilization of an MRI-based clinical radiomics nomogram has demonstrated superior ability to predict early response for LA-NPC patients as compared to simplistic clinical or radiomics models. This nomogram has the capacity to identify patients who fail to attain CR following CCRT at earlier stages, thereby facilitating timely intervention treatment or personalized therapy can be carried out to improve the patient\u2019s survival and prognosis.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The study was approved by the local scientific research ethics committee, and informed consent was waived because it was a retrospective study. The study process was in accordance with the Declaration of Helsinki Ethics statement.MW, WX and YF contributed equally to this article and share first authorship. MW, WX and YF collected the clinical data. YL, JY and LQ collected MRI images. YZ, GC and YCh completed the data sorting and induction. MW finished segmentation of the MRI images. MW, WX and YF analyzed the data and wrote the manuscript. YCa validated ROI. XS and SZ provided study supervision, article revision and project funding. All authors contributed to the article and approved the submitted version."}
+{"text": "Alternative splicing is an important mechanism that enhances protein functional diversity. To date, our understanding of alternative splicing variants has been based on mRNA transcript data, but due to the difficulty in predicting protein structures, protein tertiary structures have been largely unexplored. However, with the release of AlphaFold, which predicts three-dimensional models of proteins, this challenge is rapidly being overcome. Here, we present a dataset of 315 predicted structures of abnormal isoforms in 18 uveal melanoma patients based on second- and third-generation transcriptome-sequencing data. This information comprises a high-quality set of structural data on recurrent aberrant isoforms that can be used in multiple types of studies, from those aimed at revealing potential therapeutic targets to those aimed at recognizing of cancer neoantigens at the atomic level. Therefore, it is not surprising that the gene isoforms play important roles in many biological processes, such as processes related to development, pluripotency and apoptosis5. Aberrant isoforms have been implicated in multiple human tumors, including uveal melanoma (UM), showing extensive changes via alternative splicing and the expression of critical gene isoforms8. Specific splicing isoforms are important for the initiation, metastasis and drug resistance of cancer, and some AS events have been shown to be significantly related to patient survival11. Although the role of a few splicing isoforms in cancer has been studied, 3D protein structure prediction on a scale that covers the transcriptome and can be used for evaluating biological functionality remains unexplored.Alternative splicing (AS) can influence transcriptome and proteome diversity, as evidence shows that approximately 95% of genes with multiple exons produce multiple isoforms12. Long-read mRNA sequencing (long-read RNA-seq) shows advantages over short-read RNA-seq in isoform detection because long reads directly cover the entire transcript without the need of reconstruction, which is needed for short reads15. However, because of the high sequencing error rate (~15%) of raw long-read RNA-seq data, it is still challenging to determine the precise splicing sites with only long-read RNA-seq data16. Hybrid sequencing, combining long-read RNA-seq reads with high quality short-read RNA-seq reads and taking advantage of both platforms, improves the identification of AS events and gene isoforms18. Although great efforts have been made to study the alternative splicing mechanisms and functions of different isoforms, our knowledge of the 3D structure of splicing isoforms is very limited20. This lack of information means that for a large majority of spliced isoforms, no documented structures have been deposited in the Protein Data Bank (PDB), causing a large knowledge gap; hence, accurate prediction of protein structure is one of the most challenging goals in biology22. As structures carry vital information about how different isoforms with a certain degree of sequence homology perform different functions, it is necessary to investigate the 3D structure of abnormal isoforms to explore their functions23. The most recent achievement in related technology, AlphaFold, a deep-learning-based approach, has been proven to be highly successful in predicting the 3D structures of proteins based on their amino acid sequences24. This is a significant advance that might have a profound impact on the study of protein dysfunction and the discovery of new polypeptides with potential medical applications25.The suitability of short-read mRNA sequencing (short-read RNA-seq) in the discovery of AS events is limited because of the mapping uncertainty of short read lengths or assembly problemsIn this study, we provide an information resource based on the predicted structures of 315 novel isoforms obtained by long-read RNA-seq and short-read RNA-seq of transcriptome data from 18 UM patients. To better understand the structural differences of abnormal isoforms and the potential effects, we compared the structural differences between 295 abnormal-gene-encoded isoforms and their normal gene-encoded protein counterparts. We also identified 13 potential AS-derived neoantigens in 10 abnormal isoforms with altered amino acid sequences. These data constitute particularly valuable information on aberrant isoform structures that intersects with that on abnormal isoforms in other datasets, which can be used for an investigation into the roles of these isoforms in multiple cancer types. This study also offers new insights into the structure-based prediction of neoantigens and potential drug targets.A total of 20 patients with primary UM who visited Shanghai Ninth People\u2019s Hospital between 2018 and 2021 were selected for sampling. The detail information of the 20 UM patients is listed in Table\u00a0Total RNA was isolated using Trizol Reagent (Invitrogen Life Technologies), then the concentration, quality and integrity of RNA were determined by NanoDrop spectrophotometer (Thermo Scientific). Three micrograms of RNA were used as input material for the RNA sample preparations. Sequencing libraries were generated which was then sequenced on Illumina NovaSeq. 6000 platform by Shanghai Personal Biotechnology Cp. Ltd.Total RNA was isolated using the Trizol Reagent (Invitrogen Life Technologies), and the concentration, quality and integrity were determined by NanoDrop spectrophotometer (Thermo Scientific). A total of 1ug RNA was prepared for cDNA libraries using the cDNA-PCR Sequencing Kit (SQK-PCS109) according to the instructions of Nanopore Technologies (ONT). Defined PCR adaptors were directly added to both ends of the first-strand cDNA by reverse transcriptase. After 14-cycle of PCR by LongAmp Tag (NEB), the PCR products were subjected to ONT adaptor ligation using T4 DNA ligase (NEB). Agencourt XP beads were used for DNA purification according to ONT protocol. The final cDNA libraries were added to FLO-MIN109 flow cells and were sequenced on the PromethION platform. GUPPY (version 3.2.6) was used for basecalling to convert the fast5 format data to fastq format.17. LoRDEC is a new and efficient hybrid correction algorithm based on De Bruijn Graphs (DBG) of short reads. Achieving a comparable accuracy, LoRDEC runs six times faster and requires 93% less memory than PacBioToCA and LSC. LoRDEC first reads the short reads, builds their DBG of order k and then corrects each long read one after the other independently.Hybrid error correction, a simple and cost-effective approach involved with high quality short-read RNA-seq data, was used to improve the quality of long reads Fig.\u00a0. Here we26. Spliced_bam2gff was used to convert sorted BAM files with spliced alignments (from minimap2) into GFF2 format. With sorted GFF2 file as input, based on the median of exon boundaries from all transcripts in the cluster, cluster_gff clusters reads with similar exon/intron structures into a rough consensus set of clusters. Then, by mapping all reads to the median length of read within each cluster generated by cluster_gff, polish_clusters creates an error corrected read and polishes it using racon27. Finally, taking polished and consistent transcripts as input, collapse_partials filters transcripts which are likely caused by 5\u2032 end degradation and collapses input transcripts into a polished and collapsed transcripts set of each UM case  after the cuffcompare process. Isoform labeled by \u201c\u2009=\u2009\u201d and \u201cj\u201d tags in the output \u201c.tracking\u201d file was considered as an annotated and unannotated (novel) isoform, respectively. We got a median of 8,989 annotated and a median of 9,150 novel isoform candidates based on pinfish pipeline (Table\u00a0We filtered out transcripts supported by less than two FL reads. With the cuffcompare tool in the Cufflinks package30. The tool of ORFfinder (https://www.ncbi.nlm.nih.gov/orffinder/) was subsequently employed to search for open reading frames (ORFs)31. The 3D structure was predicted using AlphaFold-Multimer version 2.2.0 using Shanghai Jiao Tong University\u2019s supercomputing resources  memory)24. The version and parameters of AlphaFold-Multimer databases used were outlined as below:For novel isoforms, we first extracted corresponding DNA sequences from the human reference genome (hg38) using \u201csamtools faidx\u201d based on the coordinates and orders of all their exons, as well as the strand\u201cpython run_alphafold.py--use_gpu_relax--data_dir\u2009=\u2009$DIR--uniref90_database_path\u2009=\u2009$DIR/uniref90/uniref90.fasta--mgnify_database_path\u2009=\u2009$DIR/mgnify/mgy_clusters_2018_12.fa--bfd_database_path\u2009=\u2009$DIR/bfd/bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt--uniclust30_database_path\u2009=\u2009$DIR/uniclust30/uniclust30_2020_06/UniRef30_2020_06--pdb_seqres_database_path\u2009=\u2009$DIR/pdb_seqres/pdb_seqres.txt--template_mmcif_dir\u2009=\u2009$DIR/pdb_mmcif/mmcif_files--obsolete_pdbs_path\u2009=\u2009$DIR/pdb_mmcif/obsolete.dat--uniprot_database_path\u2009=\u2009$DIR/uniprot/uniprot.fasta--model_preset\u2009=\u2009multimer--max_template_date\u2009=\u20092022-1-1--db_preset\u2009=\u2009full_dbs--output_dir\u2009=\u2009output--fasta_paths\u2009=\u2009input.fasta\u201dhttps://www.uniprot.org) according to the priority order of EM, NMR, X-ray and alphafold predicted sources of structures. We used TM-score (https://zhanggroup.org/TM-score) to compare the predictive results with typical structures32. Protein pairs with a TM-score\u2009>\u20090.5 are mostly in the same fold while those with a TM-score\u2009<\u20090.5 are mainly not in the same fold, and some of them with a TM-score\u2009<\u20090.17 just have random structural similarity33. We then check the distribution of comparison scores of novel isoforms based on the gene ontology enrichment results above , and define the peptide as a weak binder if the % Rank is above the threshold of the strong binders but below the specified threshold (2%)ens Fig.\u00a0. Finally36. Our cohort includes a total of 20 cases, of which 20 cases have short-read RNA-seq data and 18 cases have long-read RNA-seq data. This study is based on the 18 cases with both short-read and long-read RNA-seq data. AlphaFold-Multimer predicted structures files are accessible at figshare37. Each folder, whose name consists of gene name and isoform ID, corresponds to each isoform structure files . Each folder contains multiple format text files which represent the predicted structures information. Among all predicted structures files, \u201cranked_0.pdb\u201d file has the highest confidence.The datasets presented here have been stored at GEO under GSE206464https://github.com/nanoporetech/pychopper) was used to identify, orient and rescue FL cDNA reads. The number of total long reads ranged from 4,788,440 to 14,048,314 in which the FL reads were between 4,112,595 and 12,828,344 (Table\u00a0Pychopper package ("}
+{"text": "Gallstone-related disease and complications are common in pregnancy. Complications of gallstone disease are associated with increased mortality for both the patient and the fetusAn 18-year-old woman  at 30 weeks\u2019 gestation presented with acute gallstone pancreatitis. Magnetic resonance cholangiopancreatography (MRCP) demonstrated cholelithiasis and numerous bile duct stones including an impacted stone at the ampulla . As theVideo\u20061\u2002Cholangioscopy-guided basket retrieval of bile duct stones in a pregnant patient.Endoscopy_UCTN_Code_TTT_1AR_2AH"}
+{"text": "Magnetic compression anastomosis (MCA) has been used for the treatment of severe colorectal stenosis and atresiaA 15-year-old boy had severe stenosis of the descending colon due to repeated pancreatitis. Colonoscopy and colonography showed long, severe, and eccentric colonic stenosis . After Video\u20061\u2002Self-shaping ring for magnetic compression anastomosis (MCA), and use for the treatment of colonic stenosis.Under X-ray surveillance, a stiff guidewire was inserted through the narrow segment of the colon into the proximal colon using a colonoscope. The 10 units of the novel self-shaping magnetic ring were inserted along the guidewire in a linear fashion see. Next, Endoscopy_UCTN_Code_TTT_1AQ_2AF"}
+{"text": "Avena sativa) experienced a more severe bottleneck than naked oat (Avena sativa var. nuda). Combined with the divergence time of \u223c51,200 years ago, the previous speculation that naked oat was a variant of hulled oat was rejected. It was found that the common segments that hulled oat introgressed to naked oat cultivars contained 444 genes, mainly enriched in photosynthetic efficiency-related pathways. Selective sweeps during environmental adaptation and breeding improvement were identified in the naked oat genome. Candidate genes associated with smut resistance and the days to maturity phenotype were also identified. Our study provides genomic resources and new insights into naked oat domestication and breeding.As an important cereal crop, common oat, has attracted more and more attention due to its healthy nutritional components and bioactive compounds. Here, high-depth resequencing of 115 oat accessions and closely related hexaploid species worldwide was performed. Based on genetic diversity and linkage disequilibrium analysis, it was found that hulled oat ( Avena sativa, NCBI:txid4498) ranks seventh in production among global cereal crops [Avena sativa var. nuda) landraces are distributed from the warm and humid Yunnan\u2013Guizhou region to the cold and dry Shanxi\u2013Gansu region.Common oat  . Current nuda L. ; it has  nuda L. , 14. How nuda L. . HoweverGenome sequencing can significantly accelerate functional genomic studies of crops . Owing iBased on 455 previously defined accessions comparing the oat core collection of the NGBC , geneticAvena sativa) [Avena fatua L., Avena occidentalis Dur., Avena sterilis L,. and Avena byzantina Koch.).There are 3,255 oat accessions in the NGBC . From am sativa) , and sinThe average Q20 and Q30 ratios of sequenced data were 97.92% and 93.29%, respectively , indicat\u22123), followed by ONC (\u03c0 = 1.12e\u22123) and then OH (\u03c0 = 1.03e\u22123). The genetic distance between ONL and OH is the farthest (Fst = 0.116), and the genetic distance between ONC and ONL/OH is similar (Fst = 0.085\u00a0vs. Fst = 0.082). This may be related to the breeding history of hulled\u2013naked hybridization in ONC [Calculating the nucleic acid diversity (\u03c0) and genetic distance (Fst) of the ONL, ONC, and OH sets of accessions Fig.\u00a0,B showedn in ONC .r2 of LD decay was greater than 0.4 for windows larger than 1 Mb in all populations. This level of LD was much higher than in other crops such as rice [The runs of homozygosity (ROH) analysis was conducted, which indicated the following order for the degree of inbreeding (highest first): ONC, OH, ONL, and the closely related hexaploid species (OG) Fig.\u00a0. We dete as rice , maize [ as rice , 21, and as rice , 23. Bec as rice , the LD A. nuda L.), researchers now generally regard naked oat as a variant of hulled oat, potentially resulting from a mutation that occurred after hulled oat was introduced into China [n = 19) and ONLc (n = 57), respectively. Some accessions did not cluster according to the hulled\u2013naked phenotype, reflecting cross-breeding. ADMIXTURE analysis  of ONLc were performed to calculate genetic diversity. We found that the genetic diversity of ONLc was still higher than that of OHc (1.08e\u22123\u00a0vs. 0.90e\u22123). Although the mapping rates of ONL and OH were high and not significantly different  based on the closely related hexaploid species ago Fig.\u00a0. We havez-score = \u221215.23). Patterson's D statistical analyses even indicated that ONC and OHc shared more derived alleles than ONLc  analysis was performed , 29. ThiAmong these 444 introgressed genes, many predicted gene products could be possibly related to differential yield, including, for example, a VIN3-like protein (Pepsico2_Contig10032), ABC transporters , a polypyrimidine tract-binding protein homolog 1-like (Pepsico2_Contig20359), a BTB/POZ domain-containing protein (Pepsico2_Contig17276), cytochrome P450 enzymes , a GDSL Oat is highly adaptable to various climates, including arid and cold regions, and is an excellent species for studying crop abiotic stress tolerance , 31. Stut-test: the annual rainfall . In particular, spikelet number \u2013like family plays an essential role in inflorescence and spikelet development [Seeking to identify genes that were selected during the improvement of ONC, we performed a selective sweep analysis using ONLc as a reference Fig.\u00a0. A totalelopment . These cOat smut is a major oat disease caused by fungal pathogens of the family Ustilaginaceae ; it affeAn agronomic trait known as days to maturity (DTM) describes the average number of days from planting until harvest. For a given species, in general, the longer the DTM, the higher the yield . To obta\u22123\u00a0vs. 1.12e\u22123). This is contrary to previous reports [\u22123 and 2.45e\u22123 , 22 hulled oats, and 4 closely related hexaploid species, were collected. The genetic diversity of each oat population was calculated based on high-depth sequencing data for these 115 accessions. It was found that the genetic diversity of naked oat was higher than that of hulled oat  can enhance rice's drought and salt stress tolerance [UGT79B2/B3 significantly enhanced plant tolerance to low temperature as well as drought and salt stress, whereas ugt79b2/b3 double mutants generated by RNA interference and CRISPR-Cas9 were more susceptible to adverse environmental conditions [There is a large amount of arid and semiarid land worldwide . Researcolerance . In Arabnditions . These cIn summary, our study provides valuable genomic resources for oat genomic and genetic research. We have overturned proposals about the origin of the naked oat and raised the idea that the naked oat was independently domesticated. Through introgression, selective sweep, and GWAS analyses, we provide a genomic framework and valuable information for facilitating marker-assisted selection for oat breeding.The seeds of the 189 selected accessions mentioned above were sowed in a seedling tray at a depth of 2\u00a0cm, keeping the soil moist. When the oats grew to the 3-leaf stage, we collected leaves from 3 plants for each accession, mixed the samples, placed them in 2-mL centrifuge tubes, and immediately stored them in liquid nitrogen. The sample source map was visualized using the Python package folium. The background map used is from Stamen Design .RRID:SCR_017981).Genomic DNA was extracted from young leaves using DNeasy Plant Mini Kits (Qiagen GmbH). The quality and concentration of DNA were assessed by 1.0% agarose gel electrophoresis and using a nanodrop spectrophotometer (Thermo Fisher Scientific). Sequencing library construction was performed using the MGIEasy FS PCR-Free DNA Library Prep Set (MGI Tech), following the manufacturer's instructions. DNA sequencing was performed using the 2 \u00d7 150-bp paired-end mode of the DNBSEQ-T7 platform  [RRID:SCR_010910) [RRID:SCR_002105) [RRID:SCR_005227) [RRID:SCR_005191) [The quality of the generated sequencing data was assessed using fastp _016962) , and hig , and hi_010910)  with the_002105)  was used_005227)  was used_005191)  softwareRRID:SCR_017343) [RRID:SCR_001757) [RRID:SCR_001263) [RRID:SCR_004965) [An identity by state (IBS) distance-based neighbor-joining tree was built using the bionj function of the R package ape (_017343) . The pai_001757) . A model_001263)  with K =_004965)  package.RRID:SCR_001235) [RRID:SCR_001757) [Nucleotide diversity  and fixation index (Fst) across the whole genome were calculated using VCFtools (_001235)  with a s_001757)  with theRRID:SCR_022509) [r2) values of all SNP pairs within 1 Mb. A bin size of 500\u00a0bp was used to generate the LD decay plot.To estimate and compare LD decay patterns, we used PopLDdecay (_022509)  to calcuA. fatua) were then genotyped using BCFtools (RRID:SCR_005227) [RRID:SCR_010228) [A. fatua and A. sativa acquired from TIMETREE (RRID:SCR_021162) [RRID:SCR_019121) [To estimate the divergence time between hulled oat and naked oat, we identified all 6,814,874 fourfold degenerate loci according to the gene models of the oat reference genome gene annotations. These loci from 3 high-depth sequencing accessions  . To obta_010228) . The div_019121)  was used\u20139 per site per generation. The atmospheric surface air temperature relative to the present (\u00b0C) and the ice volume contribution to the marine isotope signal (relative to the present) were downloaded from the national climatic data center (NCDC) database [To infer demographic history, MSMC2  was useddatabase .RRID:SCR_018495) [RRID:SCR_001789) [OH \u2212 nIBDONL. rIBD values of all windows were calculated and then normalized following a standard normal distribution. Windows with z-scores greater than 2 were considered putative introgression segments.To detect introgression between naked oat cultivars and hulled oat, we used Patterson's D statistic , which w_018495) , to test_018495)  analysis_001789)  and then_001789)  to detecRRID:SCR_016884) [P value less than 0.05 were considered significantly enriched.GO and KEGG enrichment analyses of selected genes were performed using the R package ClusterProfiler (Selective sweep analysis was performed using a reimplementation of the Python version  of XPCLRA linear mixed model implemented in the GEMMA  softwaregiad061_GIGA-D-22-00306_Original_SubmissionClick here for additional data file.giad061_GIGA-D-22-00306_Revision_1Click here for additional data file.giad061_GIGA-D-22-00306_Revision_2Click here for additional data file.giad061_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giad061_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giad061_Reviewer_1_Report_Original_SubmissionMona Schreiber -- 2/23/2023 ReviewedClick here for additional data file.giad061_Reviewer_1_Report_Revision_1Mona Schreiber -- 5/21/2023 ReviewedClick here for additional data file.giad061_Reviewer_2_Report_Original_SubmissionXupo Ding -- 2/6/2023 ReviewedClick here for additional data file.giad061_Reviewer_2_Report_Revision_1Xupo Ding -- 5/28/2023 ReviewedClick here for additional data file.giad061_Reviewer_2_Report_Revision_2Xupo Ding -- 6/13/2023 ReviewedClick here for additional data file.giad061_Supplemental_TableClick here for additional data file."}
+{"text": "Bats harbor various viruses without severe symptoms and act as their natural reservoirs. The tolerance of bats against viral infections is assumed to originate from the uniqueness of their immune system. However, how immune responses vary between primates and bats remains unclear. Here, we characterized differences in the immune responses by peripheral blood mononuclear cells to various pathogenic stimuli between primates  and bats (Egyptian fruit bats) using single-cell RNA sequencing.We show that the induction patterns of key cytosolic DNA/RNA sensors and antiviral genes differed between primates and bats. A novel subset of monocytes induced by pathogenic stimuli specifically in bats was identified. Furthermore, bats robustly respond to DNA virus infection even though major DNA sensors are dampened in bats.Overall, our data suggest that immune responses are substantially different between primates and bats, presumably underlying the difference in viral pathogenicity among the mammalian species tested. Macaca mulatta), are naturally infected with Cercopithecine herpesvirus 1  without any observable disorders, while humans (Homo sapiens) exhibit severe disorders after infection [Rousettus aegyptiacus), a putative natural host of this virus [CCL8, FAS, and IL6, which are related to disease severity in humans, upon Marburg virus infection, suggesting that the lack of cytokine induction is one of the reasons why Egyptian fruit bats exhibit asymptomatic infection with Marburg virus [Although a virus can infect various animal species, the pathogenicity of the infection can differ among host species. For example, Old World monkeys, including rhesus macaques (nfection . For exais virus . One posrg virus .Mus musculus), double-stranded RNAs (dsRNAs), a PAMP for RNA viruses, are recognized by RNA sensors, such as RIG-I, MDA5, LGP2, TLR3, and TLR7/8 [Pathogen sensing is the initial step in triggering innate immune signaling. In a broad range of animals, including vertebrates, pathogen-associated molecular patterns (PAMPs) are recognized by pattern recognition receptors (PRRs) to induce subsequent immune responses . In humad TLR7/8 . Extrachd TLR7/8 , 9. Lipod TLR7/8 , 10. Oncd TLR7/8 .Pteropus alecto) are constitutively expressed in unstimulated tissues, leading to the constitutive expression of ISGs [In contrast to the similarities in the immune system between humans and mice, the immune system of bats is assumed to be quite different from that of humans in various aspects . Genome  of ISGs . These o of ISGs , 14, 16. of ISGs . These dPrevious works have highlighted the uniqueness of the bat immune system using genomic analysis , 15, 17,H. sapiens, Hs), chimpanzees , rhesus macaques , and Egyptian fruit bats   Fig.\u00a0. In thisTo analyze immune responses to stimuli at single-cell resolution, we performed scRNA-seq analysis of 16 types of PBMC samples: 4 mammalian species  versus 4 conditions  using the 10X Genomics Chromium platform at 1 day postinfection. Next, quality control (QC) was performed to exclude both cells with lower data quality and cells not targeted in this study  see Met. Althoug+ T cells, nonnaive CD4+ T cells , naive CD8+ T cells, nonnaive CD8+ T cells , natural killer (NK) cells, mucosal-associated invariant T cells (MAITs), monocytes (Monos), conventional dendritic cells (cDCs), and plasmacytoid DCs (pDCs) were identified   Fig.\u00a0. This redetected . To estaThe ratio of the 6 cell types exhibited different changes upon exposure to the stimuli in the different species Fig.\u00a0. The freTo describe the differences in immune responses to various stimuli in specific cell types among animal species, we first calculated the average expression levels of appropriate genes in each condition . Using this \u201cpseudobulk\u201d transcriptome dataset, we first investigated which axis  was the most impactful element in shaping the expression patterns of immune cells. Thereby, we calculated the fold-change (FC) values of gene expression levels between unstimulated and corresponding stimulated conditions and performed principal component analysis (PCA) on the FC values. Hierarchical clustering analysis was subsequently performed according to principal components (PCs) 1\u201330. The transcriptome data branch according to the animal species and then branch according to the cell type followed finally by the stimulus Fig.\u00a0. This suWe next characterized the differences in the immune responses to pathogenic stimuli among animal species. The FC values of our pseudobulk transcriptome dataset were represented by a 4-mode tensor . To characterize this extraordinary high-dimensionality transcriptome dataset, we utilized Tucker decomposition, a method of tensor decomposition Fig.\u00a0. In thisTo characterize species-specific immune responses, we developed a gene classification system according to the pattern of the species-associated latent factor in the tensor decomposition framework. First, we calculated the product of a core tensor and the 3-factor matrices A2 (for stimulus), A3 (for cell type), and A4 (for gene) Fig.\u00a0. ConsequTo highlight the uniqueness of immunity in bats compared to that in primates, we focused on the expression pattern represented by the bat-specific factor (L1_2) and performed Gene Ontology (GO) analysis on the 10 gene categories Fig.\u00a0. In the To dissect the \u201cALL_high\u201d genes in the bat-specific factor, we further extracted the genes that belonged not only to the \u201cALL_high\u201d category in the bat-specific factor but also to that in the common factor (L1_1). This fraction represented genes that were upregulated by stimuli in all species but whose induction levels were highest in bats. These genes included various PPRs, such as RLRs  and cGAS, a DNA sensor, suggesting that these genes were upregulated to higher levels in bats than in the other species across the cell types and stimuli Fig.\u00a0. There amamm ISGs\u201d\u2014a set of genes that are commonly induced by type I IFNs across mammals that were defined in a previous study [mamm ISGs were upregulated upon HSV-1 infection in most cell types in bats  in clusters 5 and 7 compared to the other clusters of bat monocytes. According to GO analysis, cluster 5 is characterized by lower expression of ISGs Fig.\u00a0, F. AddiDifferences in viral pathogenicity among host species are thought to be attributed to differences in immune responses against viral infections among the species . HoweverEptesicus fuscus) suggested that the TLR9-mediated DNA-sensing pathway is also weakened in bats [It is known that 2 DNA-sensing pathways mediated by the cGAS-STING pathway  and PYHI in bats . Based o in bats , 13. How in bats , 35. AddTo characterize the bat-specific innate immune responses based on ultrahigh-dimensionality transcriptome data , we established an analytical framework utilizing tensor deconvolution Fig.\u00a0. This frAnother factor that can explain the differences in immune responses among host species is the presence of species-specific cellular subsets. In bat monocytes, we identified 2 subsets that were specifically induced by stimuli  Fig.\u00a0. ClusterIn the present study, we elucidated differences in innate immune responses among host species from various aspects. However, we did not address differences in the outcomes of the innate immune responses, such as differences in viral pathogenicity. Another limitation is that the bioinformatic resources we used, such as gene annotation, gene ontology, and cellular annotation, have been developed in a human-centric way. Therefore, there is the possibility that immune responses induced by species-specific genes and cell types were overlooked. Moreover, because the results of this study rely on an analysis using a single bat species, the Egyptian fruit bat, it is unclear whether the observed bat-specific characteristics are conserved across bat species. Furthermore, we did not perform biological replicates of scRNA-seq in this study. Despite these limitations, we present valuable resources to illuminate differences in immune responses among host species, including Egyptian fruit bats, and clues to elucidate differences in viral pathogenicity among species. Further study to elucidate the functional consequences of these differences is needed to reveal the mechanisms by which bats can tolerate infections with various viruses.Vero cells .LLC-MK2 cells  .Human peripheral blood was obtained from the arm vein. To obtain chimpanzee peripheral blood, a chimpanzee was anesthetized for a regular health examination. Anesthesia was induced with intramuscular administration of the combination of 0.012\u00a0mg/kg medetomidine , 0.12\u00a0mg/kg midazolam , and 3.5\u00a0mg/kg ketamine (Fujita Pharm) and maintained with constant rate infusion (4\u201310\u00a0mg/kg/h) of propofol . Peripheral blood was obtained from the femoral vein. To obtain rhesus macaque peripheral blood, a rhesus macaque was anesthetized. Anesthesia was induced with intramuscular administration of 8\u00a0mg/kg ketamine followed by deep anesthetization using an intravenous injection of sodium pentobarbital (30\u00a0mg/kg) (Kyoritsu Seiyaku). Peripheral blood was obtained by cardiac puncture before exsanguination and perfusion. Bat peripheral blood was obtained from the cephalic vein in the patagium. PBMCs were isolated from peripheral blood by density gradient centrifugation using Ficoll-Paque Plus .GU734771) [HSV-1   was prepU734771)  and kindAB855654) was prepared as previously described [SeV  and infected with HSV-1 or SeV at a multiplicity of infection of 0.1. To mimic microbial infection, LPS  was added at a final concentration of 200\u00a0ng/mL. At 1 day postinfection, all types of infected/stimulated PBMCs were centrifuged, resuspended in PBS, and used for bulk quantitative reverse transcription polymeraes chain reaction (RT-qPCR) and scRNA-seq (see below).RT-qPCR was performed as previously described . BrieflyRRID:SCR_016387).scRNA-seq libraries were constructed using the Chromium Next GEM Single Cell 3\u2032 Kit according to the manufacturer\u2019s instructions (10X Genomics). Briefly, cells, gel beads, and oil were loaded onto the Chromium platform to generate single-cell gel beads-in-emulsion (GEMs). Before loading, cell numbers and viability were confirmed. To acquire 5,000 cells recovery, 8,000 cells were loaded. Barcoded cDNAs were pooled for amplification, and adaptors and indices for sequencing were added. The evaluation was conducted using a BioAnalyzer (Agilent Technologies). The libraries were sequenced with paired-end reads using the Illumina NovaSeq6000 platform , chimpanzees , rhesus macaques , and Egyptian fruit bats , were obtained from NCBI RefSeq . From thGene annotations of humans , chimpanzees , rhesus macaques , and Egyptian fruit bats  were obtained from NCBI RefSeq. From the gene annotations, only the records for protein_coding, transcribed_pseudogene, lncRNA, pseudogene, antisense_RNA, ncRNA_pseudogene, V_segment, V_segment_pseudogene, C_region, C_region_pseudogene, J_segment, J_segment_pseudogene, and D_segment were extracted according to the CellRanger tutorial . In addiA list of orthologous genes between humans and the other animal species  was obtained from NCBI on 26 July 2021 . From thThe ortholog list from NCBI lacked information on some critical immune-related genes of Egyptian fruit bats, such as CD4 and IRF1. Therefore, we retrieved information from the Bat1K gene annotation , 46 downAs a result of the integration of gene annotations, the number of orthologous genes in the custom gene annotation of bats increased from 16,374 to 16,903. Importantly, immune-related genes that were not defined in the RefSeq gene annotation, such as TLR1, IRF1, and CD4, were added to the custom gene annotation.Considering the orthologous relationships, we prepared 3 types of gene sets for each animal species: (i) \u201call genes,\u201d including all genes in the animal species; (ii) \u201cgenes shared with humans,\u201d including genes with orthologs in humans; and (iii) \u201ccommon genes,\u201d genes shared among the 4 analyzed animal species. Unless otherwise noted, \u201call genes\u201d were used up to cell annotation, and \u201ccommon genes\u201d were used after cell annotation.RRID:SCR_023221) (v6.0.1) (10X Genomics) [Gene expression count matrices for scRNA-seq data were generated using CellRanger (enomics) , 50. FirRRID:SCR_016341) (v4.0.4) [z score| were excluded as outliers.First, we removed cells with abnormal genes per cell (genes/cell) and counts per cell (counts/cell) values using the Seurat package ((v4.0.4) , 24: cel(v4.0.4) , a referData integration, visualization, and cell clustering for each animal species were performed using the Seurat package. In these processes, the expression levels of HSV-1 and SeV were not used.RRID:SCR_022146)  was performed using the SCTransform function for each count matrix. Next, to extract 2,000 genes with higher variance and thus greater information for integration, the 4 count matrices were processed using the SelectIntegrationFeatures function. Next, we used the PrepSCTIntegration function to transform normalized counts into counts per 10,000 counts in the cell (CP10k). After that, we used the FindIntegrationAnchors function with the setting Mock as a reference to find \u201cIntegration anchors.\u201d Finally, we integrated the 4 normalized count matrices using the IntegrateData function with the option normalization.method=\u201cSCT\u201d.Data integration is a method merging the gene expression count matrices obtained from different experimental conditions while removing batch effects. We integrated the count matrices from the 4 different conditions for each animal species. In the data integration, SCTransform (RRID:SCR_018217) [For visualization, we first performed PCA using the RunPCA function. Then, UMAP (_018217)  was perfTo define cell clusters in each animal species, we performed graph-based unsupervised clustering . First, Regarding each cluster identified by graph-based unsupervised clustering in the section \u201cData integration, visualization, and cell clustering\u201d , 11 cellAfter categorizing cells into 11 cell types, the 11 cell types were coarse-grained into 6 cell types based on the results of hierarchical clustering analysis . The 6 cell types were used in the subsequent analysis.To examine the similarities in expression patterns among the conditions , hierarchical clustering analysis was performed. In this analysis, the 5,000 genes with the highest median absolute deviation (mad) values were used . First, To determine which factor  was the most impactful on the gene expression in immune cells, hierarchical clustering was performed using induction patterns upon stimulation Fig.\u00a0. Unlike To extract species-specific/common induction patterns upon stimulation from transcriptome data with complex structures , we used tensor decomposition Fig.\u00a0. As the A schematic of the gene classification using tensor decomposition is shown in Fig.\u00a0Initially, the product of the core tensor and the 3 factor-matrices, A2 (for stimulus), A3 (for cell type), and A4 (for gene), was calculated to obtain 3 cubic data with 3 axes, stimulus, cell type, and gene, using the ttl function of rTensor (v1.4.8) . Each cuThen, in each cubic data, genes were classified into 11 categories Fig.\u00a0 through In the first step , the valIn the second step , a \u201csimiIn the third step , the genRRID:SCR_022870) (v7.3) [P values were calculated using the Benjamini\u2012Hochberg (BH) method.GO analysis was performed with Fisher\u2019s exact test. This analysis used the GO canonical pathways and GO biological processes defined by MSigDB  (v7.3) . AdjusteRRID:SCR_021058) (v1.38.2) [The gene set-wise expression scores used in Fig.\u00a0v1.38.2) , 55 withIn bat monocytes, DEGs were identified in cluster 5 or cluster 7 compared to the other clusters using the FindMarkers function of Seurat packages. A gene that met the following 3 criteria was considered a DEG: (i) the false discovery rate (FDR) calculated using the BH method was less than 0.05, (ii) the average log2FC was greater than 1 or less than \u22121, and (iii) the proportion of expressing cells was greater than 0.2.The marker genes of cluster 5 and cluster 7 of bat monocytes  were defined as upregulated DEGs in cluster 5 Fig.\u00a0 and clusProject name: scRNA-seq_PBMC_Animals_Aso_et_alhttps://github.com/TheSatoLab/scRNA-seq_PBMC_Animals_Aso_et_al [Project homepage: so_et_al Operating system: LinuxProgramming languages: bash, R, PythonLicense: CC0-1.0giad086_GIGA-D-23-00007_Original_SubmissionClick here for additional data file.giad086_GIGA-D-23-00007_Revision_1Click here for additional data file.giad086_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giad086_Reviewer_1_Report_Original_SubmissionUrs Greber -- 3/14/2023 ReviewedClick here for additional data file.giad086_Reviewer_1_Report_Revision_1Urs Greber -- 8/24/2023 ReviewedClick here for additional data file.giad086_Reviewer_2_Report_Original_SubmissionDoreen Ikhuva Lugano -- 5/23/2023 ReviewedClick here for additional data file.giad086_Supplemental_FilesClick here for additional data file."}
+{"text": "Moreover, congregated, digitized and quality-improved Vogt-Vogt legacy histology data is made available. Finally, to allow for cross-modality correlations, maps of quantitative myelin estimates and corresponding von Economo-Koskinas\u2019 cytoarchitectonic features are also included. We share all necessary surface and volume-based registration files as well as shell scripts to facilitate applications of MYATLAS to future in vivo MRI studies.Obtaining precise and detailed parcellations of the human brain has been a major focus of neuroscience research. Here, we present a multimodal dataset, MYATLAS, based on histology-derived myeloarchitectonic parcellations for use with contemporary neuroimaging analyses software. The core of MYATLAS is a novel 3D neocortical, surface-based atlas derived from legacy myeloarchitectonic histology studies. Additionally, we provide digitized quantitative laminar profiles of intracortical myelin content derived from postmortem photometric data, cross-correlated with  Specifications Table\u2022This 3D myeloarchitectonic atlas builds on both meta-analyses-derived and ground-truth histological data, providing access to qualitative and quantitative information on cortical myeloarchitectonics.\u2022Since MRI surrogate markers of myelin demonstrate close overlap with histological cortical parcellations, this atlas can be utilized to further explore biological validity and underpinnings of novel, non-invasive metrics or sequences in pathological conditions\u2022in vivo cortical myelin in health and disease. To facilitate its use, the atlas has been expanded to several commonly used brain templates.Due to its seamless integration with widely used neuroimaging analysis software such as FreeSurfer, MYATLAS can inform researchers and clinicians studying 1Myeloarchitectonics, i.e. the parcellation of the cortex into distinct areas according to layering, arrangement, packing and density of myelinated fibers and bundles, has been the focus of Oskar Vogt and associates at the beginning of the 20th century Here we share a) ready-to-use myeloarchitectonic parcellations in common space aligned with several brain templates in both volumetric and surface formats, b) raw and processed histology slices used for constructing the atlas and their original publications, c) intracortical laminar profiles of myelin content derived from photometric data of Vogt and Hopf, and, finally, d) Bash shell and MATLAB scripts to apply myeloarchitectonic parcellations to new projects.22.1\u201cScene files\u201d for the connectome workbench \u201cwb_view\u201d facilitate data handling and visualization Colin27.scene illustrates the newly developed myeloarchitectural atlas, superimposed onto the MNI-Colin27 brain template in MNI space, as well as accompanying maps of cortical myelin density (MGL). Index2 of this scene file contains maps of fiber bundle intrusion and fiber orientation types per cortical field as well as general myeloarchitectonic categories introduced by Vogt.in vivo T1q data myelin proxy data cross-correlated to histology-derived MGL. Index 2 and 3 contain cytoarchitectonic feature maps , correlated with MGL of MYATLAS. conte69_10k.scene and conte69_32k.scene contain MYATLAS cortical segmentations for use with FreeSurfer Conte69 brain templates, matched to the respective vertex resolution, i.e. 10 and 32\u00a0k vertices. For Conte69, inflated surface maps are also included (black and white). fsaverage.scene and fsaverage5_scene contains MYATLAS cortical segmentation for use with FreeSurfer fsaverage brain templates in their native space, for both FreeSurfer 5 and 6 releases.MRI-Histology.scene contains in-vivo and histological validation maps. Index1 illustrates 2.2These files contain individual myeloarchitectonic cortical areas created by manual cortical delineation directly on the pial surface of MNI-Colin27 in MNI space (GitHub directory /maps/Surface/Freesurfer_COLIN27/annot/). All cortical areas in *.label format are given as single annotation files for the left (lh.*) and right (rh.*) hemisphere. For reference purposes, all individual Vogt areas in *.label file format are also available. rh.colin27.vogt_vogt.annot contains Vogt-Vogt parcellations for the right hemisphere based on the MNI-colin27 brain template in MNI space lh.colin27.vogt_vogt.annot contains Vogt-Vogt parcellations for the right hemisphere based on the MNI-colin27 brain template in MNI space rh.colin27.Baillarger_type.annot contains a map of fiber bundle types, i.e. myeloarchitectonic categories according to the behavior of the bands of Baillarger per each Vogt cortical area, superimposed on to the MNI-colin27 brain template in MNI space rh.colin27.Intrusion_type.annot complements the myeloarchitectonic parcellations by describing Vogt areas according to their bundle intrusion type, i.e. penetration patterns of tangential and radial fiber bundles2.3Individual cortical areas in FreeSurfer label format were converted into volumetric *.nifti files and combined into a single hemispherical volume atlas, aligned with the MNI-Colin27 brain template in common space. Separate files for each hemisphere (_rh for the right and _lh for the left hemisphere) as well as a whole-brain atlas with a corresponding reference table are provided. For reference purposes, all individual cortical areas in volumetric nifti file format are also available in a *.tar archive. Both hemispheric, whole brain and individual cortical files are available from /main/maps/Volume/ on GitHub. vogt_multilabel_rh.nii and vogt_multilabel_lh.nii These volumetric atlas files in MNI space contain all Vogt areas of the individual right and left hemisphere, aligned with the MNI ICBM152 brain template, while vogt_bilateral_myatlas.nii contains labels for both hemispheres. These files are best used together with the accompanying look-up table label-descriptions_vogt-labels.txt for referencing and label color assignment. This file can be used in volumetric viewers such as ITK-Snap or mricron label-descriptions_vogt-labels.txt The reference table for volumetric atlas files contains label numbers and names as well as color codes per each cortical area vogt-labels_nifti.tar contains individual Vogt labels for both hemispheres as a reference. These areas were used to create the individual and bilateral atlas map as described above2.4These Excel files contain information on fiber bundle penetration patterns, myelin density and light absorption profiles . The txt reference tables contain further myeloarchitectonic information per each cortical field, i.e. Baillarger band type, bundle intrusion type and original Vogt label names with RGB colorization information for use with volumetric visualization software (.txt) and FreeSurfer (.ctab). All lookup-tables and other files mentioned here are located in the /main/ GitHub directory, titled accordingly and include: myeloarchitectectonic_table.xlsx This file contains label descriptions, area numbers, myeloarchitectonic characteristics such as Baillarger bands and bundle intrusion types and their respective source publication. It is intended for use as a machine-readable source of data, e.g. for reference purposes as a Matlab variable. myeloarchitectonic_table_clear.xlsx represents the human-readable version and is intended as a quick-reference for myeloarchitectural properties of a given brain area label_list_Baillarger_type.txt, label_list_Intrusion_type.txt and label_list_vogt_vogt.txt: These files contain information on the Baillarger band and bundle intrusion types per each cortical field and are intended for use with volumetric visualization toolkitsIntrusion_type.ctab, vogt_vogt.ctab, vogt_vogt_new.ctab. These ctab files are intended as reference files for FreeSurfer. Intrusion_type.ctab contains the same myeloarchitectural categories and -properties per each individual field like the txt files. Both vogt_vogt.ctab and vogt_vogt_new.ctab are color maps and contain color references for the surface-based atlas files. The \u201cnew.ctab\u201d file represents the colorization of the scene files, with colors assigned to areas according to their myeloarchitectural properties.2.5in vivo subject data for use with FreeSurfer are provided. mapping_colin27_labels_onto_individuals.sh and mapping_colin27_labels_onto_individuals_batch.sh are used for newly acquired data. The file depth_profiling_generic.py ca, a Python script for newly acquired histology data is available for image normalization and depth-profiling as described in the methods section of the original publication. These shell scripts are available from the /main directory.Two shell scripts for applying MYATLAS to newly acquired 2.6This file contains raw and normalized Vogt myeloarchitectonic histology slices together with their corresponding cortical depth profile. All files are labelled according to their cortical field. The zip archive is located in the /main/ directory.33.1tksurfer\u201d (surfer.nmr.mgh.harvard.edu/fswiki/TkSurfer). To facilitate cross-referencing, prominent sulci and gyri on the original 2D Colin27 map such as the frontoparietal sulcus were used as systematic landmarks, since these are easily recognizable in a 3D representation. In order to reach optimal anatomical matching of area boundaries, we further relied on the main gyro-sulcal patterns surrounding each region. Accuracy of each cortical label was then verified by a second rater. For area mismatch across view planes or between 2D illustrations and 3D cortical surface, an inter-rater consensus was reached to minimize discrepancies and manual corrections were applied when necessary. All manually segmented labels were numbered according to Vogt numeric conventions and merged with color tables to create a single annotation file, containing 214 parcellations . Finally, all parcellations and gray-level intensity maps are available in gifti and nifti [\u2018dscalar for mean gray levels (MGL) and \u2018dlabel for parcellation] format, aligned with three brain spaces .All original 2D illustrations by Nieuwenhuys 3.2First, histologically-stained microphotographs available from the original publications 3.3in vivo T1q-weighted MRI, serving as a myelin surrogate marker, against the legacy ground-truth histology data. For this, we randomly selected quantitative T1w data of 202 healthy individuals from the Leipzig Study for Mind-Body-Emotion Interactions (LEMON) dataset equivolumetric_surfaces.py (https://github.com/kwagstyl/surface_tools.git). These surfaces systematically sampled an axis perpendicular to the cortical ribbon, with interpolation at each vertex in vivo data with depth profiles acquired from the digitized histology slices.As a second step, we examined reliability of 3.4To further explore the neurobiological validity of MYATLAS, we investigated relationships of myelin density, as represented by MGL, with cytoarchitectonic features, including gyral dome thickness, cellular density and cell size Institutional Review Board approval was waived due to the publicly available character of data. Information on ethics board review approval of the LEMON dataset has been previously published Niels Alexander Foit: Data curation, Methodology, Formal analysis, Conceptualization, Writing \u2013 review & editing. Seles Yung: Software, Formal analysis. Hyo Min Lee: Writing \u2013 review & editing. Andrea Bernasconi: Supervision, Conceptualization, Writing \u2013 review & editing. Neda Bernasconi: Supervision, Conceptualization, Writing \u2013 review & editing. Seok-Jun Hong: Data curation, Supervision, Methodology, Formal analysis, Writing \u2013 review & editing.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper."}
+{"text": "Circular RNAs (circRNAs), which are involved in various human malignancies, have emerged as promising biomarkers. The present study aimed to investigate unique expression profiles of circRNAs in hepatocellular carcinoma (HCC) and identify novel biomarkers associated with HCC development and progression.CircRNA expression profiles of HCC tissues were jointly analyzed to identify differentially expressed circRNAs. Overexpression plasmid and siRNA targeting candidate circRNAs were used in functional assays in vitro. CircRNA-miRNA interactions were predicted using miRNAs expressed in the miRNA-seq dataset GSE76903. To further screen downstream genes targeted by the miRNAs, survival analysis and qRT-PCR were conducted to evaluate their prognostic role in HCC and construct a ceRNA regulatory network.DTYMK, DAP3, and STMN1, which were targeted by hsa-miR-1343-3p, were significantly downregulated in HCC cells when hsa_circ_0002003 was silenced and were significantly correlated with poor prognosis in patients with HCC.Three significantly upregulated circRNAs, hsa_circ_0002003, hsa_circ_0002454, and hsa_circ_0001394, and one significantly downregulated circRNA, hsa_circ_0003239, were identified and validated by qRT-PCR. Our in vitro data indicated that upregulation of hsa_circ_0002003 accelerated cell growth and metastasis. Mechanistically, Hsa_circ_0002003 may play critical roles in HCC pathogenesis and serve as a potential prognostic biomarker for HCC. Targeting the hsa_circ_0002003/hsa-miR-1343-3p/STMN1 regulatory axis could be an effective therapeutic strategy in patients with HCC.The online version contains supplementary material available at 10.1186/s12885-023-11086-9. Primary liver cancer is a commonly diagnosed malignancy worldwide . HepatocA form of alternative splicing called back-splicing could join a 3\u2032-splice donor to an upstream 5\u2032-splice acceptor to generate a covalently-closed circular RNA (circRNA) . In receIn this study, we performed an integrative analysis of circRNA microarray datasets  of tumor tissues from patients with HCC and RNA-seq data from Yu et al.  Fig.\u00a01)1). We foP\u2009<\u20090.05. RNA-sequencing (RNA-seq) data in RPM from an HCC tissue-related dataset was also obtained from Yu et al. [P\u2009<\u20090.05. Then, the DECs in HCC tissues were intersected using an online tool (http://bioinformatics.psb.ugent.be/webtools/Venn/).Three publicly available HCC tissues-related circRNA microarray datasets  were downloaded from the Gene Expression Omnibus (GEO) database. The \u2018limma\u2019 R package was used to perform differential analysis of the pooled expression data , with thu et al. . All raw2.The liver cancer cell lines HepG2, Hep3B, Huh7, and SNU-387 were obtained from the Cell Bank, Type Culture Collection, Chinese Academy of Science. MHCC97H cells were purchased from Beyotime Biotechnology. Cells were cultured in Dulbecco\u2019s modified eagle medium (DMEM) supplemented with 10% fetal bovine serum  and were incubated at 37\u00a0\u00b0C with 5% COPlasmid overexpressing hsa_circ_0003239 and small interfering RNAs (siRNAs) targeting hsa_circ_0001394 were purchased from GenePharma Biotechnology. siRNAs against hsa_circ_0002003 were designed and synthesized by Guangzhou Geneseed Biotech Co., Ltd. siRNA sequences are shown in Table Total RNA was extracted from cells using the Total RNA Kit I (Omega Bio-Tek) according to the manufacturer\u2019s instructions. cDNA was synthesized from total RNA using the RevertAid First Strand cDNA Synthesis Kit (Thermo Scientific) following the manufacturer\u2019s instructions. Gene expression was quantified on a CFX96 Touch Real-Time PCR Detection System (Bio-Rad) with gene-specific primers. Primer sequences are shown in Table 450) using the Varioskan LUX Multimode Microplate Reader (Thermo Scientific).Cells were plated in clear-bottom 96-well plates. Cell proliferation was measured using a Cell Counting Kit-8  according to the manufacturer\u2019s instructions, and the absorbance was quantified at 450\u00a0nm (ODCell migration assay was performed in 24-well plates with 8.0-\u00b5m-pore polycarbonate membrane inserts (Corning). Huh7 or SNU-387 cells were seeded in the upper chamber of a Transwell in serum-free DMEM. The lower chamber was filled with complete DMEM (10% FBS). Huh7 and SNU-387 cells were allowed to migrate for 48\u00a0h. Non-migrated cells on the upper surface of the inserts were detached using a cotton swab. Filters were fixed with 4% formaldehyde for 15\u00a0min at 4\u00a0\u00b0C, and the cells located on the lower surface of the inserts were stained with 0.1% crystal violet for 20\u00a0min and counted under a light microscope in three random fields. The cell invasion assay was essentially the same as the migration assay, except that the membrane insert was coated with Matrigel (BD Biosciences).4 cells were inoculated in 48-well plates, fixed by 4% paraformaldehyde, and treated with Triton X-100. Subsequently, the probe mix was added, denatured for 30\u00a0min, and incubated overnight. For tissue sections, dewaxing was first performed using xylene and alcohol, followed by proteinase K digestion, denaturation, and probes hybridization. Images were acquired using a fluorescent microscope (OLYMPUS). The probe sequences are listed in Table S3.RNA fluorescence in situ hybridization (FISH) was performed according to the FISH kit instructions (GenePharma). For cellular samples, 1\u2009\u00d7\u200910http://www.circbank.cn/) database [The miRNA-binding sites, also known as miRNA-recognition elements (MREs), of the identified DECs  were predicted with two web tools, Cancer-Specific CircRNA (CSCD)  and Circdatabase . Based oP\u2009<\u20090.01. With the same filtering criteria, DEGs from an expression profiling array  containing 225 HBV-related HCC and 220 non-tumor tissues were also determined.The miRNA\u2013mRNA interactions were predicted using miRWalk , with thhttps://xenabrowser.net/). Patients were divided into two groups based on the median expression values for TCGA-LIHC. Kaplan-Meier (KM) survival analysis for the target genes was performed using the survminer R package (version: 0.4.9).Survival data of patients from the TCGA-LIHC dataset was obtained from UCSC Xena , DAP3 , STMN1 , and Tubulin ; this was followed by incubation with secondary antibody and final exposure in an automated exposure machine (Clinx ChemiScope).P\u2009<\u20090.05.Statistical analysis was performed using GraphPad Prism (version: 8.0.2) and R software. Survival analysis was performed using the KM method, and a log-rank test assessed the differences. Statistical significance was set at P\u2009<\u20090.05, a total of 687 DECs (421 upregulated and 266 downregulated DECs) were identified in the pooled 15 pairs of samples. We visualized the top 15 significantly DECs in the paired samples  were identified in Yu et al. A volcano plot depicting the expression of the four candidate circRNAs  in the Yu et al. dataset was plotted  in HCC tissues, three public microarray datasets  and RNA-seq data from Yu et al. (PMID: 29378234), which contained circRNA expression profiles, were obtained. First, we performed normalization and combined the three HCC tissues-related datasets (GSE78520&GSE94508&GSE97332), resulting in a meta-cohort of 15 pairs of HCC and matched non-tumor tissues. According to the screening criteria of |log2(foldchange)| \u2265 0.585 and CircRNAs are evolutionally conserved and relatively stable, accounting for their potential as prognostic biomarkers and therapeutic targets for personalized medicine . We desiTo verify whether the three circRNAs have biological functions, Cell Counting Kit 8 (CCK8), scratch wound healing, and Transwell assays were performed. In the CCK8 assay, hsa_circ_0002003 knockdown significantly inhibited the proliferation of Huh7 cells compared with controls Fig.\u00a0C. In theThe cyclization site sequence of hsa_circ_0002003 is GATCTTCCCTCCCTGAGGTGGACTAGCAGA, where the cyclization site is between G and A Fig.\u00a0A. qRT-PCDAP3, DTYMK, STMN1, TUBA1C, E2F3, and ASNS, out of the 17 candidate target genes, had significant differences in overall survival  and one significantly downregulated circRNA (hsa_circ_0003239) by intersecting HCC-related tissue profiling data. Hu et al. found that hsa_circ_0002003 was upregulated in Crohn\u2019s disease compared with healthy controls . A studyThe relative abundance of circRNAs and miRNAs, the stability of circRNAs, and the potential miRNA response elements (MREs) in circRNAs contribute to the \u2018sponging\u2019 crosstalk between circRNAs and miRNAs . CircRNADAP3, DTYMK, STMN1, TUBA1C, E2F3, and ASNS) was significantly correlated with OS. In SNU-387 and Huh7 cells, DTYMK, DAP3, and STMN1 had the same expression trend as hsa_circ_0002003. KM survival curves showed that only STMN1 expression was significantly correlated with DFS, DSS, and PFI simultaneously in patients from TCGA-LIHC. STMN1 is considered an oncogene, and its upregulation is closely associated with malignant behavior and poor prognosis in multiple cancer types [STMN1 gene is a target of thyroid hormone (T3) in the HepG2 hepatoma cell line and found that the oncogene STMN1 is transcriptionally downregulated by T3 in the liver [DTYMK is upregulated in tumors and is correlated with poor prognosis in patients with HCC [In the miRWalk database , we obtaer types . Tseng ehe liver . In addihe liver . Zhou etwith HCC . Inhibitwith HCC . Sato etwith HCC .In summary, hsa_circ_0002003 may play critical roles in HCC pathogenesis and may serve as a potential biomarker of HCC. Targeting the hsa_circ_0002003/hsa-miR-1343-3p/STMN1 regulatory axis could be an effective therapeutic strategy against HCC. The effect of steady hsa_circ_0002003 knockdown in HCC cells on tumor growth and metastasis warrants in vivo study in the future.Below is the link to the electronic supplementary material.Supplementary Material 1Supplementary Material 2"}
+{"text": "The greatest technical challenge in endoscopic ultrasound (EUS)-guided hepaticogastrostomy with antegrade stenting is breaching the bile duct stricture with guidewireA woman in her 70\u200as was admitted to our hospital for obstructive jaundice caused by unresectable pancreatic head cancer. Transpapillary biliary cannulation was unsuccessful due to duodenal invasion of the tumor, and the patient underwent EUS-guided hepaticogastrostomy with antegrade stenting. An echoendoscope  was inserted and the intrahepatic bile duct (B3) was punctured with a 19-gauge needle . After cholangiography, a 0.025-inch guidewire  was inserted, followed by a tapered-tip catheter . Severe stenosis was observed in the distal bile duct. A 0.025-inch hydrophilic guidewire  was used to attempt breaching the stenosis; however, the guidewire could not be visualized due to retention of the contrast medium . A totaVideo\u20061\u2002Bile aspiration technique in endoscopic ultrasound-guided hepaticogastrostomy with antegrade stenting.The advantages of the bile aspiration technique include improved guidewire visibility and reduced bile duct diameter, which facilitate the stenosis breach. It is a simple and effective method in EUS-guided hepaticogastrostomy with antegrade stenting.Endoscopy_UCTN_Code_TTT_1AS_2AD"}
+{"text": "A 57-year-old woman who had never undergone surgery previously was hospitalized due to a 6-month history of intermittent abdominal pain. Computed tomography revealed a communication between a portion of the small intestine and the adjacent segment of the sigmoid colon, with lumen dilatation and localized air and fluid accumulation .The patient underwent gastroenteroscopy. Surprisingly, during colonoscopy, a fistula was observed 22 cm from the anus . After A case of small-bowel and colon malformation identified during endoscopy.Video 1Postoperative pathology indicated tubular adenoma with low grade intraepithelial neoplasia of the small intestine . The paGastrointestinal duplication is a rare congenital malformation that can involve any part of the gastrointestinal tractEndoscopy_UCTN_Code_TTT_1AO_2AB"}
+{"text": "Periplaneta americana is a cosmopolitan pest cockroach endemic to tropical and subtropical climates. It occurs frequently in urban sewer and wastewater system and transit in human proximities, spreading pathogens that causes serious public health concerns such as asthma, allergies, and others. By using the Next-generation Sequencing (NGS) known as Illumina NovaSeq 6000, this article documents for the draft genome data set of P. americana collected in Penang Island, Malaysia. This article displays the pair-end 150 bp genome dataset and results on the sequence quality. This genome dataset presents the information for further understanding of P. americana populations at molecular level and the opportunity to develop effective control and management strategies for the species. This dataset is available under Sequence Read Archive (SRA) databases with the SRR23867103. Specifications Table\u2022Periplaneta americana represents a pest species endemic to tropical and subtropical climates and can be found inhabiting in urban areas, especially in sewers.\u2022Results obtained are particularly useful for urban and medical entomologist in Malaysia.\u2022P. americana genome data could be used for the development of species-specific microsatellite marker.The \u2022P. americana.Further study could potentially contribute to pest control and management approach according to genetic diversity of 1Periplaneta americana (P. americana), is predominantly found indoors in warm, humid regions. In addition, unlike German cockroaches which in general do not reproduce outside human structures, the American cockroaches has the capabilities to maintain their large populations in outdoor niches P. americana can reproduce in locations where there are accumulations of waters and of these, several studies implicated sewer systems as the main source of urban American cockroaches P. americana using Next-generation Sequencing (NGS) via Illumina NovaSeq 6000.The American cockroach, or also known as 2SAMN33762737 which is under the BioProject PRJNA944812. This data is registered under the Sequence Read Archive (SRA) databases with the accession number SRR23867103. The data set comprised of two high throughput sequencing fastq files:\u2022PA1_DKDN220024683-1A_HNVCCDSX5_L4_1.fq\u2022PA1_DKDN220024683-1A_HNVCCDSX5_L4_2.fqThis article described a dataset of whole-genome pair-end sequencing result of BioSample st-150th base position meanwhile the 151th-300th base position composed of from PA1_DKDN220024683-1A_HNVCCDSX5_L4_2.fq of each sequence.PA1_DKDN220024683-1A_HNVCCDSX5_L4_1.fq and PA1_DKDN220024683-1A_HNVCCDSX5_L4_2.fq contained half of the full sequence in a total of 36,359,668 raw reads with 150bp each. PA1_DKDN220024683-1A_HNVCCDSX5_L4_1.fq composed of the 1In this manuscript, characteristics and quality of dataset is presented. Firstly, the dataset's quality score scored between Q30 to Q40, whereby Q30 showed 99.9% of the correct base meanwhile Q40 showed 99.99% of correct base . Secondl33.1Periplaneta americana, was collected using a baited glass jar with beer-soaked bread and was left in a sewer manhole shaft overnight from late evening till the next morning P. americana was then freeze killed and stored in 95% ethanol under \u201320\u00b0C. An individual of P. americana was used for the genomic DNA (gDNA) extraction. The gDNA extraction was extracted solely from the leg tissue in order to reduce the possibility of DNA extraction contamination from endosymbionts TM Genomic DNA Mini Kit (Blood/Tissue/Cultured Cells)  according to the manufacturer's instruction. The leg tissues were vortexed by using MX-S Dragon Lab Single-head Vortex Mixer in lysis buffer with Proteinase K and incubated for 1 hour at 60\u00b0C by using MINIC-100 Mini Dry Bath. Elution was then be carried out twice using 50 \u00b5L elution buffers after the DNA binds to the filter column through an ethanol wash to get a total of 100 \u00b5L gDNA solution TM NanoQ Lite Microvolume Spectrophotometer.The American cockroach, 3.2The general workflow of library construction is as shown in Subsequently after that, the library was checked with Qubit and real-time PCR for quantification and bioanalyzer for size distribution detection. The quantified libraries will be pooled and sequenced on Illumina platforms, according to effective library concentration and data amount required 3.3Periplaneta americana were assembled, and an assembly with a total length of 15,064,661 bp and a contig N50 of 2089 bp was yielded  with a minimum E-value of 0.001 whereby 423 queries were scanned Periplaneta americana.Our work does not involve studies with humans. Our work involves studies with animals, specifically the American Cockroaches, Marcellinus Isaac Stia Dominic: Methodology, Investigation, Data curation, Formal analysis, Writing \u2013 original draft, Writing \u2013 review & editing. Abdul Hafiz Ab Majid: Conceptualization, Methodology, Supervision, Project administration, Resources, Funding acquisition, Writing \u2013 review & editing.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper."}
+{"text": "A Morgagni hernia is an uncommon diaphragmatic hernia. Complications such as obstruction and resulting necrosis can be life-threatening, and surgery is mandatory. However, minimally invasive treatments are preferred for elderly patients with comorbidities. Although treatment of a Morgagni hernia by endoscopic reduction has been reportedAn 84-year-old woman with multiple cardiac comorbidities was hospitalized with epigastric pain and vomiting. A computed tomography scan showed prolapse of the transverse colon into the mediastinum and was consistent with obstruction due to a Morgagni hernia . BearinVideo\u20061\u2002A Morgagni hernia treated by means of endoscopic reduction with low-pressure endoscopy using the gel immersion method.Low-pressure endoscopy using the gel immersion method facilitates endoscopic reduction as a minimally invasive treatment of a Morgagni hernia.Endoscopy_UCTN_Code_CCL_1AD_2AJ"}
+{"text": "A 56-year-old man was admitted to our hospital with obstructive jaundice caused by extrahepatic cholangiocarcinoma that developed from a congenital choledochal cyst . EndoscA linear echoendoscope was advanced to the stomach. The intrahepatic bile duct (B3) was punctured with a 19G aspiration needle . A 0.03We then switched the echoendoscope to a gastroscope . The enVideo\u20061\u2002Following maldeployment of the proximal flange of the stent into the abdominal cavity during endoscopic ultrasound-guided hepaticogastrostomy, transgastric natural orifice transluminal endoscopic surgery (NOTES) was used as a rescue procedure.Stent maldeployment during EUS-guided hepaticogastrostomy is a significant adverse event that needs to be immediately managedEndoscopy_UCTN_Code_CPL_1AL_2AC"}
+{"text": "FecB genotype.MicroRNAs (miRNAs) play an important regulatory role in mammalian reproduction. Currently, most studies are primarily concentrated on ovarian miRNAs, ignoring the influence of endometrial miRNAs on the fecundity of female sheep. To uncover potential regulators of sheep fecundity, RNA-seq was used to comparatively analyze miRNA expression profiles of endometrium between high prolificacy sheep  and low prolificacy sheep  with ESR1) and unconservative_NC_019481.2_1637827 targets to transcription factor 7 (TCF7). Moreover, functional annotation analysis showed that the target genes (NRCAM and NEGR1) of the DE miRNAs were significantly enriched in cell adhesion molecules (CAMs) signaling pathway, which was related to uterine receptivity.Firstly, genomic features of miRNAs from endometrium were analyzed. Furthermore, 58 differentially expressed (DE) miRNAs were found in the endometrium of Hu sheep with different litter size. A co-expression network of DE miRNAs and target genes has been constructed, and hub genes related litter size are included, such as DE miRNA unconservative_NC_019472.2_1229533 and unconservative_NC_019481.2_1637827 target to estrogen receptor \u03b1 (Taken together, this study provides a new valuable resource for understanding the molecular mechanisms underlying Hu sheep prolificacy.The online version contains supplementary material available at 10.1186/s12864-023-09681-y. BMPR1B/FecB), bone morphogenetic protein 15 (BMP15/FecX), and growth differentiation factor9 (GDF9/FecG) [FecB) of high ovulation rate still reveal low prolificacy [Hu sheep, as an excellent local breed in China, are famous for their high prolificacy and year-round estrus . A varieF9/FecG) , 3. In tlificacy . EnvironMicroRNAs (miRNAs) belong to endogenous small non-coding RNAs that perform important gene-regulatory functions in animals by pairing to the mRNAs of protein-coding genes to direct their posttranscriptional repression . During As we all known, ovulation and uterine receptivity are the two main controlling factors for mammalian fertility. Therefore, in addition to ovulation performance, the development and receptivity of uterus are closely related to reproductive efficiency of sheep to maintain large litters . To dateIn this study, RNA sequencing (RNA-seq) was applied to analyze the expression profiling of endometrial miRNAs between different fecundity Hu sheep, and screen the candidate miRNAs involved in high prolificacy. These results could provide useful information for under-standing the molecular basis of miRNAs in the regulation of endometrial functions, as well as the mechanism of Hu sheep prolificacy.This study was carried out according to the Guide for the Care and Use of Laboratory Animals (permit no. DKY2021021) prepared by the Ethics Committee of Qingdao Agricultural University.n\u2009=\u20093, litter size\u2009=\u20093) and low prolificacy sheep group  were selected from the nucleus herds of Hu sheep at Taizhou Sheep Industry according to their littering records (three consecutive lambing records) and polymorphism analysis of FecB [FecB genotype. All sheep were housed under the same conditions with free access to feed and water. Synchronous estrus of sheep was conducted according to previously described [The high prolificacy sheep group  for RNA sequencing (RNA-seq). The purity and concentration of RNA were assessed by NanoDrop 2000 spectrophotometer . The integrity of RNA was assessed using the RNA Nano 6000 Assay Kit of the Agilent Bioanalyzer 2100 system . Sequencing libraries were generated using the small RNA Sample Pre Kit . TruSeq PE Cluster Kit v4-cBot-HS (Illumia) was used to cluster the index-coded samples on a cBot Cluster Generation System according to the manufacturer\u2019s instructions, then library preparations were sequenced on an Illumina Hiseq 2500 platform. Clean data with high quality was generated from Raw data (raw reads) of fastq format by quality control step. Clean data were obtained by removing reads containing ploy-N and low quality reads from raw data, retained 18\u201330 nt sequences. At the same time, the Q30 of the clean data were calculated. All the downstream analyses were based on clean data , transfer RNA (tRNA), small nuclear RNA (snRNA), small nucleolar RNA (snoRNA) and other ncRNA and repeats, Clean Reads respectively performed on Silva database, GtRNAdb database, Rfam database and Repbase database sequence alignment by Bowtie software . Ovis_arAccording to the values of normalized transcripts per kilobase per million reads (TPM), the differentially expressed miRNAs were identified by a p-value threshold of <\u20090.05 and |log2(fold change)| > 1. The data of differently expressed mRNAs was taken from our previous study . Accordihttp://www.genome.jp/kegg) pathway analysis of target genes was performed by the KOBAS (v2.0) software [P-values (t-test)\u2009<\u20090.05 was indicated as significant enrichment.Kyoto Encyclopedia of Genes and Genomes (KEGG)  software. RNA (1\u00a0mg) was reverse transcribed using the miRNA 1st Strand cDNA Synthesis kit , and the SYBR green  method was used for RT-qPCR. The expression levels of genes were evaluated by 2Further analysis of RNA-seq data and graphical representations were performed using the statistical R package . SPSS 19.0 software  was used to analyze the RT-qPCR data, which were presented as the means\u2009\u00b1\u2009standard deviations (SDs). Differential gene expression levels were calculated using a t-test, and Statistical significance was defined as p\u2009<\u20090.05.A total of 1726 unique miRNAs were screened from sheep endometrium, result of length distribution analysis showed that most miRNAs ranged from 21 to 22nt. The percentage of the 22nt miRNAs in the total miRNAs was 36.73% , including 39 up-regulated and 19 down-regulated miRNAs in high prolificacy samples compared with low prolificacy samples Fig.\u00a0A. In theIn the down-regulated miRNAs, 6 specific miRNAs expression were discovered in LP group   was targeted by 2 miRNAs (unconservative_NC_019472.2_1229533 and unconservative_NC_019481.2_1637827). Furthermore, miRNA unconservative_NC_019481.2_1637827 also targeted to TCF7, which might play crucial roles in the molecular mechanism of the sheep prolific trait.The targets of 10 down-regulated miRNAs Fig.\u00a0A and 25 GO analysis of target gene functions showed developmental process and reproductive process were enriched in biological process analysis, cell junction was enriched in cellular component analysis Fig.\u00a0.Results of KEGG pathway analysis revealed that DE miRNA targets were enriched in some pathways, including cell adhesion molecules (CAMs), tight junction, and other pathways involved in reproduction Fig.\u00a0. EspeciaNEGR1; DE miRNAs (unconservative_NC_019462.2_631388 and unconservative_NC_019468.2_996653) in CAMs signaling pathway targeted to NRCAM. Results of RT-qPCR showed the expression levels of miRNAs were negative connected with their predicted target genes (Fig.\u00a0As shown in Fig.\u00a0nes Fig.\u00a0B.The Hu sheep is famous for high prolificacy and year-around estrus, so it has attracted much attention in the mechanism research of sheep reproduction. As reported, uterus were important organs in prolific breeds of ewes that possess an intrinsically high ovulation rate as well as enhanced uterine capacity to maintain large litters . Our preESR1 was identified the target of miRNAs unconservative_NC_019472.2_1229533 and unconservative_NC_019481.2_1637827. Furthermore, we also discovered unconservative_NC_019481.2_1637827 targeted to TCF7, which might play crucial roles in the molecular mechanism of the sheep prolific trait. As reported, the polymorphisms of TCF12 gene was related to litter size in pigs [NRCAM and NEGR1 in CAMs signaling pathway were significantly enriched in high prolificacy sheep as the targets of DE miRNAs. Previous research indicated NRCAM secreted by endometrial stromal cells, which enhanced the progestin sensitivity through epigenetic modulation [In present study, analysis of length distribution of endometrial miRNAs showed intensive enriching effects for 22 nt miRNAs in all samples, which was consistent with previous researches , 37. Due in pigs . These rdulation . Endometdulation . NEGR1, dulation . ResearcIn summary, this study provides the comprehensive analysis of endometrial miRNAs in Hu sheep with different fecundity, and discovered several target genes of miRNAs related to sheep prolificacy by the construction of miRNA-mRNA interaction network. In addition, CAMs signaling pathway was enriched in the endometrium of high prolificacy Hu sheep. Our study has crucial roles in understanding the regulatory mechanism of prolificacy in sheep.Below is the link to the electronic supplementary material.Supplementary Material 1Supplementary Material 2Supplementary Material 3Supplementary Material 4Supplementary Material 5Supplementary Material 6Supplementary Material 7"}
+{"text": "Endoscopic ultrasound-guided gallbladder drainage (EUS-GBD) has emerged as an alternative drainage technique, especially for high-risk surgical patientsEUS-GBD was performed on a 67-year-old Japanese man who presented with cholecystitis and concurrent biliary tract cancer during chemotherapy. The gallbladder was punctured from the duodenum using a 19G fine needle. The puncture tract was dilated using an electrocautery dilator, and a fully covered self-expandable metal stent  was deployed . To preVideo\u20061\u2002Endoscopic ultrasound-guided gallbladder drainage procedure with a modified single-pigtail plastic stent (mSPPS) inserted into a fully covered self-expandable metal stent. The mSPPS is improvised from a commercial 6-Fr endoscopic nasobiliary drainage tube that was shortened to 15\u200acm from the straight section. For mSPPS insertion, the remaining endoscopic nasobiliary drainage tube and a guidewire are used as a pusher catheter.Endoscopy_UCTN_Code_TTT_1AS_2AD"}
+{"text": "Pseudomonas aeruginosa is a nosocomial bacterium responsible for variety of infections. Inappropriate use of antibiotics could lead to emergence of multidrug-resistant (MDR) P. aeruginosa strains. Herein, a virulent phage; vB_PaeM_PS3 was isolated and tested for its application as alternative to antibiotics for controlling P. aeruginosa infections.Phage morphology was observed using transmission electron microscopy (TEM). The phage host range and efficiency of plating (EOP) in addition to phage stability were analyzed. One-step growth curve was performed to detect phage growth kinetics. The impact of isolated phage on planktonic cells and biofilms was assessed. The phage genome was sequenced. Finally, the therapeutic potential of vB_PaeM_PS3 was determined in vivo.Myoviridae. The phage vB_PaeM_PS3 displayed a\u00a0broad host range, strong bacteriolytic ability, and higher environmental stability. Isolated phage showed a\u00a0short latent period and large burst size. Importantly, the phage vB_PaeM_PS3 effectively eradicated bacterial biofilms. The genome of vB_PaeM_PS3 consists of 93,922 bp of dsDNA with 49.39% G\u2009+\u2009C content. It contains 171 predicted open reading frames (ORFs) and 14 genes as tRNA. Interestingly, the phage vB_PaeM_PS3 significantly attenuated P. aeruginosa virulence in host where the survival of bacteria-infected mice was markedly enhanced following phage treatment. Moreover, the colonizing capability of P. aeruginosa was markedly impaired in phage-treated mice as compared to untreated infected mice.Isolated phage has an icosahedral head and a contractile tail and was assigned to the family P. aeruginosa infections.Based on these findings, isolated phage vB_PaeM_PS3 could be potentially considered for treating of The online version contains supplementary material available at 10.1007/s10096-023-04649-y. Pseudomonas aeruginosa is a widely distributed opportunistic pathogen that could survive in water, soil, animals and humans  and mixed with isolated phage at multiplicity of infection (MOI) of 0.1. Then, the mixture was centrifuged for 10 min at 10000\u2009\u00d7\u2009g, pellets were resuspended in fresh TS broth and incubated at 37\u00b0C. Simultaneously, phage titer was determined by collecting samples of 100 \u00b5L at 5 min intervals then plated by double agar layer method.One-step growth curve for isolated phage was carried out to detect phage burst size and latent period as described . Initial600) as mentioned before [600. The antibiofilm activity of isolated phage was determined as exactly as previously described [In vitro bacteriolytic activity of isolated phage was determined by measuring optical density (ODd before . The phaescribed . The phaThe frequency of occurrence of bacteriophage insensitive mutants (BIMs) was determined as previously described . Brieflypseudomonas infections and represent different antibiotic classes, gentamicin (aminoglycoside) and ciprofloxacin (quinolone), was determined using checkerboard microdilution assay by calculating the fractional inhibitory concentration index (FICI) [5 CFU/mL) to each well of 96-well plate. The plates were incubated overnight at 37\u00b0C. The FICI was calculated using the following equation: FICI\u2009=\u2009FIC antibiotic\u2009+\u2009FIC phage; FIC antibiotic\u2009=\u2009Cantibiotic / MICantibiotic; FIC phage\u2009=\u2009Cphage / MICphage, where MICantibiotic and MICphage are the respective minimum inhibitory concentration (MICs) of the antibiotic and the phage alone, and Cantibiotic and Cphage are the respective concentrations of the antibiotic and phage in combination. The results were interpreted as the follow; synergy if FICI was\u2009\u2264\u20090.5; indifferent if 0.5 \u02c2 FICI\u2009\u2264\u20091; additive if 1 \u02c2 FIC\u2009\u2264\u20092 and antagonistic if FICI\u2009>\u20092.The synergetic effect between isolated phage and two antibiotics commonly used for x (FICI) . BrieflyThe genome of isolated phage was extracted using QIAamp1 DNA Mini kit  following the manufacturer guidelines. The Nextera XT DNA Library Preparation Kit  was used to prepare the DNA library. The phage genome was sequenced at Genomics and Epigenomics Program, Cairo, Egypt, using Illumina Miseq next-generation sequencing. The raw sequence within the phage genome was checked for quality with FastQC and reads trimming was performed using Trimmomatic v0.36 . The triPseudomonas infectivity was investigated in vivo using mice [st group, mice were infected intraperitoneally (IP) with P. aeruginosa (2.5\u2009\u00d7\u2009107 CFU/mL), while in the 2nd group, mice were infected with P. aeruginosa and treated IP with phage at MOI of 100 (2.5\u2009\u00d7\u2009109 PFU/mL). The 3rd group represents mice without bacterial infection but inoculated only with phage. As negative controls, mice injected with PBS only and non-injected mice were included in the experiment. Mice survival was monitored and statistically analysed using Log-rank test. Additionally, three mice from each group were subjected to bacterial load and phage titer determination. Mice spleen and liver were collected, then bacterial burden as well as phage titer were quantified and statistically analyzed with P value\u2009<\u20090.05 is considered significant.The impact of phage on ing mice . BrieflyP. aeruginosa PS3 as a host. Isolated phage produced clear circular plaques surrounded by halos with a diameter of 4\u20135 mm P. aeruginosa strains (55.5%) indicating a higher lytic efficiency of isolated phage  was infected with vB_PaeM_PS3 phage at various MOIs . As shown in Fig.\u00a0P. aeruginosa significantly decreased compared to the control P. aeruginosa culture without phage treatment. Remarkably, the cell lysis capacity of the phage was MOI dependent and bacterial inhibition was more obvious at higher MOIs. Furthermore, the ability of vB_PaeM_PS3 to eradicate biofilms of five clinical P. aeruginosa isolates recovered from various sources as well as the reference strains; ATCC 27853 and 9027 was evaluated. The phage vB_PaeM_PS3 showed a remarkable biofilm degrading efficiency both at higher and lower MOIs  , Pseudomonas phage vB_PaM_EPA1 (GenBank Acc. No MN013356.1), Pseudomonas phage PaYy-2 (GenBank Acc. No MH725810.1) and Pseudomonas phage SRT6 (GenBank Acc. No MH370478.1) representing percent identities of 96%, 95.2%, 95%, and 94.9%, respectively  following treatment with vB_PaeM_PS3  compared to those of phage-untreated mice  Below is the link to the electronic supplementary material."}
+{"text": "A 56-year-old man suffered from epigastric pain for 5 days with elevated amylase (2600\u200aIU/L), and computed tomography indicated acute pancreatitis. Magnetic resonance cholangiopancreatography showed the confluence between dilated biliary and pancreatic ducts , and enHowever, owing to the long common channel and the sharp angle, the guidewire could not be inserted into the pancreatic duct during prior attempts . TherefVideo\u20061\u2002Peroral cholangioscopy-assisted pancreatic duct cannulation in a patient with a pancreaticobiliary maljunction.Peroral cholangioscopy has been widely applied in diagnosing pancreatobiliary diseases and shown its vital role in selective cannulation of complex biliary stricturesEndoscopy_UCTN_Code_TTT_1AR_2AI"}
+{"text": "Cardiac aging and ageing-related cardiovascular diseases remain increase medical and social burden. Discovering the molecular mechanisms associated with cardiac aging is expected to provide new perspectives for delaying aging and related disease treatment.The samples in GEO database were divided into older group and younger group based on age. Age-associated differentially expressed genes (DEGs) were identified by limma package. Gene modules significantly associated with age were mined using weighted gene co-expression network analysis (WGCNA). Protein-protein interaction networks (PPI) networks were developed using genes within modules, and topological analysis on the networks was performed to identify hub genes in cardiac aging. Pearson correlation was used to analyze the association among hub genes and immune and immune-related pathways. Molecular docking of hub genes and the anti-aging drug Sirolimus was performed to explore the potential role of hub genes in treating cardiac aging.We found a generally negative correlation between age and immunity, with a significant negative correlation between age and b_cell_receptor_signaling_pathway, fc_gamma_r_mediated_phagocytosis, chemokine signaling pathway, t-cell receptor signaling pathway, toll_like_receptor_signaling_pathway, and jak_stat_signaling_pathway, respectively. Finally, 10 cardiac aging-related hub genes including LCP2, PTPRC, RAC2, CD48, CD68, CCR2, CCL2, IL10, CCL5 and IGF1 were identified. 10-hub genes were closely associated with age and immune-related pathways. There was a strong binding interaction between Sirolimus-CCR2. CCR2 may be a key target for Sirolimus in the treatment of cardiac aging.The 10 hub genes may be potential therapeutic targets for cardiac aging, and our study provided new ideas for the treatment of cardiac aging. The As biotechnology progresses in recent years, research represented by transcriptomics has provided new insight into disease pathogenesis. Bioinformatics has demonstrated significant potential in detecting biomarkers related to disease pathogenesis and progression , 7. CurrBy regulating oxidative stress, inflammation and organelle function, rapamycin  may inhibit cardiac ageing . StudiesIn this study, we identified age-related co-expressed gene modules by weighted gene co-expression network analysis (WGCNA) through analyzing different age cohort samples in Gene Expression Omnibus (GEO) database, and further identified closely related pathways and hub genes through linking genes in the modules to immune status. Further, molecular docking simulations were performed between anti-aging drugs and hub genes to evaluate pathogenesis and therapeutic targets. In particular, the pathogenesis of cardiac aging were comprehensively investigated to provide so as to new evidence for subsequent in-depth studies.2.2.1.https://www.ncbi.nlm.nih.gov/geo/) database. Each data set was processed as follows: (1) Disease samples were removed, and only normal and healthy samples were retained; (2) Samples \u226565 were defined as the elderly, and samples <65 were defined as the young group; (3) Samples with age information and expression value were retained; (4) The expression of samples was transformed into a probe symbol.The RNA-Seq data (FPKM standardized data) and clinical information of chip data sets GSE57338 (136 samples), GSE141910 (166 samples) and GSE173608 (20 samples) as well as the annotation information of chip probes of corresponding platforms were obtained from GEO (2.2.p\u2009<\u20090.05).We employed the estimation of stromal and Immune cells in malignant tumour tissues using expression data (ESTIMATE) algorithm  to calcu2.3.p\u2009<\u20090.05.We carried out a differential expression analysis between the two groups of samples in the GSE57338 cohort using the limma package  under th2.4.p value\u2009<\u20090.05.Enrichment analysis allows to obtain important biological processes associated with DEGs. In this study, we conducted GO and KEGG functional enrichment analysis oncardiac aging-associated DEGs using the WebGestaltR (V0.4.4) package . The enr2.5.\u03b2 for module analysis was determined by analyzing the scale independence and average connectivity of the modules with different weighting factors. After we determined soft threshold, a scale-free topological distribution network was constructed, and the correlation matrix was converted into an adjacency matrix based on the Pearson correlation coefficient among genes and further into a topological overlap matrix (TOM). The similarity between genes (1-TOM) was calculated and genes with similar expression profiles were grouped into the same gene module using hierarchical clustering function and dynamic shear tree and a minimum size of 100. The gene modules most associated with age were determined using Pearson.To identify genes highly correlated with age, we used the WGCNA package  to ident2.6.http://www.string-db.org/)  (The PPI network was constructed here through the STRING database (db.org/)  for co-en 3.7.2) . The MCO2.7.https://www.datanovia.com/en/lessons/heatmap-in-r-static-and-interactive-visualization/).The intersection of MCODE and core genes was defined as the hub genes of cardiac aging. To clarify the immune status of each sample in the GSE35959, GSE141910, and GSE173608 cohorts, StromalScore, ImmuneScore, and ESTIMATEScore were calculated for all the samples using the ESTIMATE algorithm, and the abundance of 28 immune cells was determined by the ssGSEA method. We then calculated Pearson correlations between hub genes and each immune correlation score. Finally, the H.A. v7.4.symbols.gmt pathway of HALLMARK was obtained from GSEA website and its enrichment scores were calculated to evaluate the correlation between hub genes and pathway enrichment scores. The correlation heatmap was generated by the heatmap package (2.8.https://www1.rcsb.org/) and AlphaFold  database. Water molecules and proligands from target were removed by PyMOL 2.3.0. The Sirolimus molecular structures were obtained from the Pubchem database (https://pubchem.ncbi.nlm.nih.gov/). The conformation of Sirolimus was molecularly and mechanically optimized using Chem3D  software to obtain the optimal energy-minimized conformation of Sirolimus. The pretreated target protein molecules were hydrotreated using Auto Dock Tools1.5.6. The optimal conformation of Sirolimus was hydrogenated and the torsional bond was determined. POCASA protein active pocket online prediction tool was used to predict the protein active pocket, the docking range was set in the predicted active pocket and the docking range information was saved for formal docking. Auto Dock Vina v.1.2.0 was employed to conduct molecular simulation docking between target proteins and Sirolimus molecules using Lamarkian genetic algorithm and the semi-flexible. The exhaustiveness was set to 8, the maximum number of conformations output was set to 9. The binding free energy of Sirolimus to each hub gene protein was obtained.Sirolimus is an anti-aging drug , and we 2.9.p\u2009<\u20090.05 was considered statistically significant. Sangerbox provided analytical assistance in this article , PyMOL (version 2.3.0), and Chem3D (version 2020). And  article .3.3.1.R\u2009=\u2009\u22120.259, p\u2009=\u20090.00231), FC_GAMMA_R_MEDIATED_PHAGOCYTOSIS , CHEMOKINE_SIGNALING_PATHWAY , T_CELL_ RECEPTOR_SIGNALING_PATHWAY , TOLL_LIKE_RECEPTOR_SIGNALING_PATHWAY , JAK_STAT_SIGNALING_PATHWAY  were negatively correlated with age , and positively correlated with ImmuneScore , B_CELL_RECEPTOR_SIGNALING_PATHWAY , FC_GAMMA_R_MEDIATED_PHAGOCYTOSIS , CHEMOKINE_SIGNALING_PATHWAY , T_CELL_RECEPTOR_SIGNALING_PATHWAY , TOLL_LIKE_RECEPTOR_SIGNALING_PATHWAY , JAK_STAT_SIGNALING_PATHWAY . The turquoise module was significantly positively correlated with age  and significantly negatively correlated with ImmuneScore , B_CELL_RECEPTOR_SIGNALING_PATHWAY , FC_GAMMA_R_MEDIATED_PHAGOCYTOSIS , CHEMOKINE_SIGNALING_PATHWAY , T_CELL_RECEPTOR_SIGNALING_PATHWAY , TOLL_LIKE_RECEPTOR_SIGNALING_PATHWAY , JAK_STAT_SIGNALING_PATHWAY   . Finally\u2009<\u20090.05) . The tur\u2009<\u20090.05) . Therefo3.4.The intersection of the yellow module, the turquoise module and 606 DEGs had a total of 380 genes, which were mapped in the String database to construct a PPI network. The nodes with fewer edges in the PPI network were eliminated, and 255 nodes were finally retained for subsequent analysis . Nodes i3.5.After the intersection of MCODE module gene and 15 potential core genes, a total of 10 genes including LCP2, PTPRC, RAC2, CD48, CD68, CCR2, CCL2, IL10, CCL5 and IGF1 were obtained. To assess the association between the 10 hub genes and immunity, we first calculated the StromalScore, ImmuneScore, ESTIMATEScore and ssGSEA scores of 28 kinds of immune cells in the GSE35959, GSE141910 and GSE173608 cohorts. Through Pearson correlation analysis, we found that the 10 hub genes showed a positive correlation with most immune scores, and that they were significantly positively correlated with Type 1\u2005T helper cell Immature dendritic cell and immature dendritic cell . Conside3.6.We obtained the h.all.v7.4.symbols.gmt pathway from HALLMARK in the GSEA website and used the ssGSEA method to calculate these pathway scores in the GSE35959, GSE141910, and GSE173608 cohorts. We then calculated Pearson correlation coefficients between the 10 hub genes and pathway scores. The heat map showed that the trend of the 10 hub genes in the GSE35959, GSE141910, and GSE173608 cohorts was generally consistent, and that they were mainly positively correlated with IL6_JAK_STAT3_SIGNALING, P53_PATHWAY, EPITHELIAL_MESENCHYMAL_TRANSITION, ALLOGRAFT_REJECTION but negatively correlated with OXIDATIVE_PHOSPHORYLATION, FATTY_ACID_METABOLISM .3.7.In this study, the binding stability of Sirolimus to 10-hub genes was assessed using molecular docking techniques to identify the optimal cardiac aging genes. Generally speaking, a binding energy less than \u22125\u2005kcal/mol indicates an excellent binding and less than \u22127\u2005kcal/mol indicates a strong binding. The molecular docking results were shown in 3.8.The 10 hub genes were used to construct diagnosis model in GSE57338 dataset using Xgboost package , and validated in GSE141910 dataset and GSE173608 dataset. The Accuracy, sensitivity, specificity and F1 of the diagnosis model in GSE57338 dataset and GSE173608 dataset were both 1, and in GSE141910 dataset were respectively 0.994, 1, 0.978 and 0.996 . In addi4.Given the lack of age-related biomarkers of cardiac aging, it is necessary to identify potential molecular mechanisms and hub genes in cardiac aging through emerging technologies. Here, we identified age-associated hub genes of cardiac aging by analyzing transcriptome sequencing data from different age-stratified populations, and primarily explored the potential mechanisms associated with cardiac aging.+ macrophages in mice correlation with the development of PAH.The 10 identified genes included LCP2, PTPRC, RAC2, CD48, CD68, CCR2, CCL2, IL10, CCL5 and IGF1. We also found that most of the 10-hub genes were positively correlated with immune scores and immune-related pathways. Previous studies have confirmed a close relationship between these genes and immunity. Lymphocyte Cytosolic Protein 2 (LCP2) encodes the bridging protein SLP76, and Siggs et al.  showed tJung et al.  pointed Robbie et al.  noted thin vivo and in vitro assays to explore more in-depth molecular mechanisms is our subsequent key research targets.In summary, this report applied bioinformatics such as WGCNA to mine 10 cardiac aging-related hub genes, which may provide new insights for elucidating the risk of cardiac aging. Apart from our systematic bioinformatics analyses, the present study also has limitations. Firstly, this study was based on bioinformatics approach using a calculator and other related devices as a preliminary data analysis, and the specific biological functions of the 10-hub genes will have to be explored at the cellular, molecular, and animal levels, as well as clinical shape. Secondly, Sirolimus is an anti-aging drug but is not currently used in treating cardiac aging-related diseases, and its potential relationship with CCR2 should be further explored. Therefore, conducting comprehensive and systematic 5.In the present report, we identified 10 hub genes associated with cardiac aging and systematically elucidated the correlation between these genes and immunity. Our study revealed that cardiac aging was correlated with immune system activity, and that CCR2 may be a potential core target in cardiac aging. The Sirolimus-CCR2 interaction relationship provided an important scientific basis for elucidating cardiac aging-related gene functions and may help to elucidate aging-related mechanisms in human life."}
+{"text": "The purpose of this study was to investigate the role of hsa_circRNA_102051 in colorectal cancer (CRC) and its effect on the stemness of tumor cells.CircRNA microarray was under analysis to screen differentially expressed novel circRNAs in the pathology of CRC. Quantitative real-time PCR was used to detect the relative RNA expression in CRC cells and samples. The effects of hsa_circRNA_102051 on biological functions in CRC cells were accessed both in vitro and in vivo. FISH, RIP and luciferase reporter assay were conducted to confirm the regulatory correlations between hsa_circRNA_102051 and miR-203a, as well as miR-203a and BPTF. Xenograft models were applied to further verify the impacts and fluctuations of hsa_circRNA_102051/miR-203a/BPTF. Moreover, the mechanism how hsa_circRNA_102051 affected the Notch signals was also elucidated.Hsa_circRNA_102051 was up-regulated in CRC tissues and cell lines, capable to promote the growth and invasion of CRC. In addition, hsa_circRNA_102051 could enhance stemness of CRC cells. BPTF was identified as downstream factors of hsa_circRNA_102051, and miR-203a was determined directly targeting both hsa_circRNA_102051 and BPTF as an intermediate regulator. Hsa_circRNA_102051 in CRC could block miR-203a expression, and subsequently activated BPTF. Hsa_circRNA_102051/miR-203a/BPTF axis modulated stemness of CRC cells by affecting Notch pathway.Our findings provided new clues that hsa_circRNA_102051 might be a potential predictive or prognostic factor in CRC, which induced the fluctuation of downstream miR-203a/BPTF, and subsequently influenced tumor growth, activities and stemness. Thereinto, the Notch signals were also involved. Hence, the hsa_circRNA_102051/miR-203a/BPTF axis could be further explored as a therapeutic target for anti-metastatic therapy in CRC patients.The online version contains supplementary material available at 10.1186/s12935-023-03026-1. Colorectal cancer (CRC) is a malignancy that poses a significant challenge to the biotechnology field due to its high aggression and poor survival rates. Current data indicate that CRC has become leading cause of cancer deaths worldwide . WhereasPrevious reports have validated that circRNAs play a crucial role in various bioprocesses involved in malignancies by interacting with microRNAs and regulating transcription , 5. Hsa_MiR-203a was proved to negatively affect multiple cancers, including CRC . And bioIn addition, BPTF was the downstream gene affected by hsa_circRNA_102051 and focused on in this paper. Being important in targeting the NURF (nucleosome remodeling factor) remodeling complex, BPTF was considered vital for the cell stemness . NumerouBriefly, this paper discovered the regulatory function of hsa_circRNA_102051 on miR-203a/BPTF expression, as well as downstream biological processes, especially the Notch signaling pathway, which composed a complete axis influencing CRC progression and metastasis.The tumor tissues and matched adjacent tissues of 20 CRC patients admitted to the Affiliated Hospital of Guizhou Medical University from January 2018 to December 2019 were collected. All cases were confirmed by histopathological examination, and none of them received chemotherapy or radiotherapy before surgery. The study followed the Declaration of Helsinki, and all patients signed informed consent. Twenty samples of intestinal cancer tissues and adjacent tissues  were collected in strict accordance with the collection standards during the operation. Some samples were frozen at -80\u2103, and some samples were fixed with 4% paraformaldehyde, dehydrated by automatic dehydrator, and preserved by paraffin embedding.P value (corrected by Bonferroni - Holm)\u2009<\u20090.05, Fold Change\u2009>\u20092. Starbase and Circular RNA Interactome were respectively used to predict the miR-203a binding sites on hsa_circRNA_102051 and BPTF.GSE147597 chip was downloaded from the GEO database and then utilized to analyze circRNAs differentially-expressed in metastatic CRC, involving samples from 20 CRC tissues from patients with or without liver metastasis. Microarray analysis was performed using the software package \u201cLimma\u201d of R software (Ver. 3.6.3). The threshold was set to 2 in air atmosphere, and routinely subcultured every 3 days.Human CRC Cell lines including SW480, HT-29, SW1463 and HCT116, as well as FHCs , were obtained from ATCC. SW480 and SW1463 cells were seeded in ATCC-formulated Leibovitz\u2019s L-15 medium. HT-29 and HCT116 cells were cultured in ATCC-formulated McCoy\u2019s 5a medium modified. FHCs were maintained in DMEM:F12 medium. All the cell lines were incubated under 37\u00a0\u00b0C with 5% COSpecific oligonucleotides and plasmids were designed to regulate the expression of hsa_circRNA_102051, miR- 203a, and BPTF. All the plasmids, siRNA, shRNA or mimics along with their corresponding negative controls, were designed and provided by GeneChem. The above oligonucleotides and plasmids were transfected into cell lines using a Lipofetamine 2000 transfection reagent according to the instructions. The expression efficiency was examined using quantitative PCR.\u2212\u0394\u0394Ct with U6 or GAPDH as internal reference. All primer sequences are listed in Supplementary Table\u00a0Total RNAs were extracted from CRC tissues and cells using Trizol reagent (Invitgen), and cDNA was then synthesized using a reverse transcription kit (Takara). In the ABI7900 system, cDNA amplification was performed using the Power SYBR Green (Takara) reaction mixture. The expression levels of hsa_circRNA_102051, miR-203a and BPTF were calculated by 2CCK-8 assay was performed to detect the viability of CRC cells (SW480 and HT-29 cell lines) with different transfections. Briefly, transfected cells were seeded in 96-well plates and supplemented with 10\u00a0\u00b5l CCK8 solution to each well at 0\u00a0h, 24\u00a0h, 48\u00a0h, and 72\u00a0h. After 2.5\u00a0h incubation, the cell viability was presented by the OD value at 450\u00a0nm under a microplate analyzer.RNA was extracted from CRC cell lines using Trizol reagent . After that, 100\u00a0\u00b5g extracted RNA was incubated with RNase R at 37\u00a0\u00b0C for 20\u00a0min, and then purified using an RNEasy Minelute Cleanup Kit . Finally, the expression of RNA was detected through PCR.All specimens were fixed in 4% formalin, embedded in paraffin and sectioned into 5\u00a0\u03bcm sections. Next, the sections were submerged in citrate buffer for antigen retrieval and incubated with 1% bovine serum albumin (BSA) to block nonspecific binding. Primary antibodies against Ki67 , BPTF  and appropriate secondary antibody  were utilized according to the manufacturer\u2019s protocol. The sections were then incubated with DAB and hematoxylin and then scored independently by two observers. The score was based on both the proportion of positively stained tumor cells and the intensity of staining.The digoxin-labeled probes specific to hsa_circRNA_102051 and biotin-labeled probes against miR-203a were prepared by Servicebio. SW480 and HT-29 cells were maintained on coverslips and fixed with 4% paraformaldehyde in PBS for 15\u00a0min. The probes were diluted in hybridization solution in PCR tubes and heated at 95\u00a0\u00b0C for 2\u00a0min in a PCR block to denature the probe. The probe was immediately chilled on ice to prevent reannealing. The hybridization solution was drained, and 100 \u00b5L of diluted probe per section was added to cover the entire sample. The samples were covered with a coverslip to prevent evaporation and were incubated in the humidified hybridization chamber at 65\u00a0\u00b0C overnight. The signals were detected by Cy3-conjugated anti-digoxin and FITC-conjugated anti-biotin antibodies (Jackson ImmunoResearch Inc.). Cell nuclei were counterstained with 4,6-diamidino-2-phenylindole (DAPI). The final images were obtained under a laser scanning confocal microscope (Nikon).24-well plates were coated with laminin  and incubated at least 4\u00a0h at 37\u00a0\u00b0C, then washed with PBS prior to cell seeding. Then, CRC cells in suspension were seeded manually at 2500 cells/well (5 cells/\u00b5L). Afterward, EdU staining assays were conducted to evaluate cell proliferation. Briefly, 10 \u00b5M 5-ethynyl-2\u2032- deoxyuridine  solution was added to each well and incubated for 3\u00a0h. After being washed with PBS, the cells were fixed in 4% paraformaldehyde and stained with EdU staining (Riobio) based on the manufacturer\u2019s instructions. A fluorescence microscope (Leica DMI6000B) was utilized for eventual visualization and measurement.According to the manufacture\u2019s protocol, cells were firstly fixed with formaldehyde for 15\u00a0min on ice and then washed with PBS. After supplementation with ice-cold 70% ethanol and incubation for 30\u00a0min, cells were resuspended in wash buffer again. Staining solution was then added to the cells and maintained for 60\u00a0min at 37\u00baC. After another washing with rinse buffer, supernatant was discarded. The propidium iodide/RNAse A solution was applied for resuspending cells and 30\u00a0min incubation. And the eventual staining results were observed under a fluorescence microscope.6) with different transfections were injected into the right flank of the nude mice. The tumor volumes were measured every 5 days using the formula: V = (length \u00d7 width2)/2. After 25 days, mice were sacrificed, and tumors were collected, weighed and measured. All the animal experiments were performed under the guidelines of the Institution Animal Care and Use Committee.4-weeks-old female BALB/c nude mice were obtained from the Model Animal Research Center of Guizhou Medical University. For the establishment of CRC xenograft models, SW480 cells (3\u2009\u00d7\u200910Cells were lysed on ice with RIPA buffer (protein lysis buffer containing protease inhibitor and phosphatase inhibitor) for 30\u00a0min. And then the protein extracted from the supernatant was quantified through a BCA kit (Thermo Fisher). Then, the cell lysates were separated on SDS-PAGE and transferred to PVDF membranes. The membranes were blocked with 5% nonfat milk, and then incubated with primary antibodies at 4\u02daC overnight and with the secondary antibodies at room temperature for 2\u00a0h. Afterward, the target proteins were visualized with an enhanced chemiluminescence detection system.Dual luciferase reporter assays were performed to verify the binding sites between hsa_circRNA_102051 and miR-203a, as well as miR-203a and BPTF. Wild type or mutant sequences of above genetic factors were constructed using pmirGLO vectors (Promega). Cells were co-transfected with mimics or inhibitors, or their corresponding negative controls along with the luciferase reporter vector. After 48\u00a0h, luciferase activity of each system was measured using the dual luciferase reporter gene assay system (Promega) according to the manufacturer\u2019s instructions.RIP experiments were carried out utilizing a Magna RIP RNA-Binding Protein Immunoprecipitation Kit (Millipore) following the manufacturer\u2019s instructions. Cell lysates from SW480 and HT-29 were incubated with protein A magnetic beads conjugated to either antiAgo2 or IgG antibody. The coprecipitated RNA was purified and reversely transcribed into cDNA using PrimeScript RT Master Mix (TaKaRa Bio). The enrichment of hsa_circRNA_102051 and miR-203a pulled down by Ago2 or IgG from endogenous complex was then measured through quantitative PCR.2 with saturated humidity for 12\u201314 days, the tumor sphere was defined as >\u20092000 cells. The number of spheres divided by the original number of seeded cells was then counted and analyzed.A mixture culture medium was prepared including serum-free 1640 medium (Invitrogen), 2% B27 Supplement (Invitrogen), 20 ng/ml basal fibroblast growth factor (bFGF) (PeproTech), 20 ng/ml epidermal growth factor (EGF) (PeproTech), 0.4% BSA (Sigma-Aldrich), and 5\u00a0\u00b5g/ml insulin (Sigma-Aldrich). CRC cells were digested and resuspended at a density of 1000 cells/well into prepared medium. After being incubated at 37\u00a0\u00b0C and 5% COP value less than 0.05 was considered statistically significant.All experiments were conducted at least three times, with the data presented as mean\u2009\u00b1\u2009standard deviation. Statistical analysis was performed using GraphPad 7.0 (La Jolla). Differences between two groups were compared using t-test, and differences between multiple groups were compared using one-way analysis of variance (ANOVA), followed by Tukey-Kramer post-hoc analysis. GSE147597 chip downloaded from the GEO database described the expression profiling of circRNAs in human CRC (Fig.\u00a0Therefore, hsa_circ_102051 was chosen for subsequent research and then identified through RNase R, indicating that TADA2A mRNA was digested while hsa_circRNA_102051 tolerated digestion (Fig.\u00a0CRC cell lines, SW480 and HT-29, were cultured in four groups. The first group of SW480 cells was transfected with si-NC, while the second group was transfected with si-hsa_circRNA_102051. The third group of HT-29 cells was transfected with vectors, and the fourth group was transfected with hsa_circRNA_102051. (Fig.\u00a0After transfection of si-hsa_circRNA_102051, the number of gram invasive cells in SW480 cells decreased, while after transfection of hsa_circRNA_102051, the cell infiltration of HT-29 cell line increased (Fig.\u00a0Xenograft mice were divided into two groups and separately treated with either sh-NC or sh-hsa_circRNA_102051 Fig.\u00a0A The micProliferation of malignancies in mice were assessed by Ki-67 and TUNEL staining of tissue samples. Meanwhile, the expression of E-cadherin, N-cadherin and vimentin was also determined. With blockade of hsa_circRNA_102051, cell proliferation and invasion were both repressed, while apoptosis was promoted with blockade of hsa_circRNA_102051 Fig.\u00a0C-E. FurtSOX9, OCT-4 and CD44 are known to confer stemness properties in embryonic cells and several malignancies. Hence, this study measured their expression in SW480 and HT-29 cell lines to explore whether hsa_circRNA_102051 could influence cell stemness in CRC. SW480 cells were treated with si-NC or si-hsa_circRNA_102051, while HT-29 cells were treated with vector or hsa_circRNA_102051. According to outcomes of western blot and qRT-PCR, expressions of SOX9, OCT-4 and CD44 were all downregulated in si-circ-treated SW480 while upregulated in HT-29 cells transfected with hsa_circRNA_102051 (Fig.\u00a0The previous studies have shown that BPTF has been prove to mediate the tumorigenesis, metastasis and relapse of TICs in colon via circRNA signals . TherefoSince BPTF was confirmed to be modulated by hsa_circRNA_102051 level, the mediate factor between hsa_circRNA_102051 and BPTF was predicted by Starbase and Circular RNA Interactome, and miR-203a was screened out (Fig.\u00a0According to the dataset of COAD cancer, patients with high miR-203a expression exhibited better overall survival than those with low level miR-203a (Supplementary Fig.\u00a0To further investigate the regulatory mechanisms of hsa_circRNA_102051, miR-203a and BPTF, CRC cells were divided in to 4 groups, respectively transfected with vector, hsa_circRNA_102051, miR-203a, and hsa_circRNA_102051 plus miR-203a (Supplementary Fig.\u00a0in-vitro sphere formation indicated that hsa_circRNA_102051 promoted cell growth, while miR-203a inhibited it,and the hsa_circRNA_102051 plus miR-203a group generated a similar number of tumor spheres as the negative control (Fig.\u00a0The stemness markers BPTF, SOX9, OCT-4 and CD44 were all detected in the four groups of CRC cells. According to the outcomes of qRT-PCR and western blot, expressions of BPTF, SOX9, OCT-4 and CD44 were all promoted by additional hsa_circRNA_102051 while repressed by miR-203a, which were reversed in the hsa_circRNA_102051 plus miR-203a group Fig.\u00a0A and B. rol Fig.\u00a0C. Moreovrol Fig.\u00a0D. TherefIn this research, hsa_circRNA_102051 is screened out and identified with overexpression in metastatic CRC tissues. Upregulated hsa_circRNA_102051 is capable to suppress miR-203a and mediately trigger BPTF expression, which enhances the proliferation, migration, invasion and stemness of CRC cells, activating Notch signaling pathway and eventually promoting tumor growth and metastasis. Hsa_circRNA_102051/miR-203a/BPTF axis provided a novel angle of the modulation of CRC progression, and could be applied to clinic in future as predictive markers or therapeutic targets.On the contrary to this study, previous researches on breast cancer reported that hsa_circRNA_102051 was downregulated in breast cancer patients, acting as a tumor suppressor and regulating miR-197-5p/CDH19 expression . As descAs for miR-203a, abundant studies have its upregulation in CRC and several other cancers , 10, 22.In this study, stemness markers including BPTF, SOX9, OCT-4 and CD44 were all detected, indicating that overexpressed hsa_circRNA_102051 enhanced the stemness of CRC. Thereinto, SOX9 activation was proved to repress miR-203a transcription by binding to miR-203a promoter , while BOverall, this study identified hsa_circRNA_102051 as a key regulator of CRC progression and metastasis. The study demonstrated that hsa_circRNA_102051 promoted the expression of stemness markers including BPTF, SOX9, OCT-4, and CD44 through its ability to suppress miR-203a. This, in turn, activated the Notch signaling pathway, which promoted CRC growth and metastasis. The study highlights the potential of hsa_circRNA_102051 as a predictive biomarker or therapeutic target for CRC treatment. However, there are still unanswered questions, such as the inner mechanisms of the interaction between stemness factors and miR-203a/BPTF, which may be explored in future research.Below is the link to the electronic supplementary material.Supplementary Material 1Supplementary Material 2Supplementary Material 3"}
+{"text": "Background: Baicalein is an active ingredient extracted from the root of S. baicalensis Georgi, which exhibits cardiovascular protection, anti-inflammatory, and anti-microbial properties. Our previous study showed that chronic treatment of Baicalein ameliorated cognitive dysfunction in a mouse model of Alzheimer's disease (AD). However, it remains unknown whether Baicalein ameliorates cognitive deficits in AD mouse models by altering gut microbiota and its metabolites.Methods: Behavioral tests, metagenomic and untargeted metabolomics analyses were used to evaluate the effects of Baicalein on the APP/PS1 mice.Results: Our research showed that treatment of Baicalein for 2 weeks ameliorated cognition and memory in a dose-dependent manner, as indicated by the significant increases in the Discrimination index and Number of crossings and decrease in latency to the previous platform location in 8-month of age APP/PS1 mice in novel object recognition and water maze tests. The metagenomic analysis showed the abundance of the dominant phyla in all groups, including Bacteroidetes (14.59%\u201367.02%) and Firmicutes (20.19%\u201361.39%). LEfSe analysis of metagenomics identified three species such as s__Roseburia_sp_1XD42_69, s__Muribaculaceae_bacterium_Isolate_104_HZI, s__Muribaculaceae_bacterium_Isolate_110_HZI as Baicalein-treated potential biomarkers. Metabolite analysis revealed the increment of metabolites, including glutamate, thymine and hexanoyl-CoA.Conclusion: The effects of Baicalein on memory and cognition may relate to the metabolism of nucleotides, lipids and glucose. Alzheimer\u2019s disease (AD) is the most common and progressive neurodegenerative disease that seriously affects patients\u2019 thinking ability, cognition, and ability to perform daily activities . Beta-amThe root of Scutellaria baicalensis Georgi, a classic compatible component in the decoction of herbal medicine, may have such anti-AD effects. Baicalein is an active ingredient extracted from the root of S. baicalensis Georgi, which exhibits cardiovascular protection, anti-inflammatory, and anti-microbial properties . Recent Transgenic male APP/PS1 mice  were purchased from Beijing Huafukang Bioscience CO, LTD. , and age-matched male wild-type C57BL/6 mice were obtained from the Animal Center of Hangzhou Medical College . They were acclimatized to the laboratory environment for 5\u20137 days before the experiment. At the animal center, the mice were housed in a barrier system with specific pathogen-free conditions, controlled temperature (25\u00b0C \u00b1 1 \u00b0C), and a 12-h light/dark cycle. The animals had free access to water and food. The experimental procedures were performed as per the National Institutes of Health Guide for Animal experimentation and the European Communities Council Directive of 24 November 1986 (86/609/EEC). All procedures were approved by the Care and Use of Laboratory Animals Committee of Hangzhou Medical College.via gavage  for 14 days according to previous studies  and was dissolved in dimethyl sulfoxide (DMSO). The concentration did not exceed 0.1% of the total volume in the working solution. The chemical structure of Baicalein is shown in  studies .The Morris Water Maze (MWM) test was conducted following the previous study with minor modifications . A waterThe novel object recognition (NOR) test was performed to measure the object memory recognition ability of mice that reflected the degree of scattered memory loss in mice similar to the symptoms of AD . A transThe stool samples were stored at \u221280 \u00b0C after collection. DNA from stool samples was extracted using the Stool DNA Kit  according to the manufacturer\u2019s instructions. The purity and quality of the genomic DNA were assessed on 1% agarose gels and Qubit detection.DNA was sheared to 300-bp fragments by using the Covaris ultrasonic crusher. To prepare the sequencing library, the fragments were subjected to end repair, tailing, and ligation of Illumina-compatible adapters. DNA sequencing libraries were deep sequenced on the Illumina HiSeq platform at Allwegene Company (Beijing). After the run, image analysis, base calling, and error estimation were performed using Illumina Analysis Pipeline Version 2.6. The quality control of the raw data was performed using the Trimmomatic method , which iHigh-quality sequences were compared with the NR database and classified into different taxonomic groups using the diamond . MEGAHITp-value of the Student\u2019s t-test <0.05 and the VIP value >1. The personalized analysis included KEGG analysis of differential metabolites and metabolic pathway analysis. Differential metabolites were mapped using KEGG, PubChem, and other metabolite databases, and pathways involved in all differential metabolites were identified. The differential metabolites were further analyzed for metabolic pathways. Through a comprehensive analysis  of the pathway where the differential metabolites were located, further pathway screening was performed to identify the key pathway with the strongest correlation with differential metabolites.The metabolites were detected using UHPLC-QE-MS (UHPLC-QE-MS) at Allwegene Company . Statistical analyses included data standardization, principal component analysis (PCA), OPLS-DA analysis, Student\u2019s t-test, and screening and enrichment analysis of differential metabolites. Differential metabolites were screed based on the following criteria: post hoc comparisons of Dunnett\u2019s test. Two groups were compared using the t-test. The interpretation of the symbols (* and #) for the different group comparisons are shown in the Figure legends. The Spearman algorithm was used to determine the correlation between metagenomics and metabolomics. R and Python were used to draw graphics. Statistical significance was set at p < 0.05.Statistical analysis was performed using R. All data are expressed as the mean \u00b1 standard deviation (SD). Differences among multiple groups were analyzed by one-way analysis of variance (ANOVA), with p < 0.001). One-way ANOVA revealed that Baicalein improved memory retention and retrieval in a dose-dependent manner, as indicated by the increased RI compared with that of the vehicle-treated APP/PS1 mice [p < 0.001]. As shown in p < 0.01), but not changed in low dose group (25\u00a0mg/kg), which indicated that 50\u00a0mg/kg of Baicalein better improve episodic-like memory of APP/PS1 mice. No significant change was observed among the groups in the total distance traveled  and longer mean latency to the platform (p < 0.001) as compared with the age-matched control group. This impairment was rescued by the administration of Baicalein at 25\u00a0mg/kg (p < 0.05) and 50\u00a0mg/kg (p < 0.001) intraperitoneal in a dose-dependent manner. On Day 5 in MWM, APP/PS1 mice had poorer performance than non-dementia control, as evidenced by a decreased number of platforms crossing (p < 0.001), and reduced time in the target quadrant (p < 0.001). Baicalein at 25\u00a0mg/kg and 50\u00a0mg/kg ameliorated memory retention also in a dose-dependent manner, as shown by the increased number of platforms crossing (p < 0.001) and increased time spent in the target quadrant (p < 0.001). Furthermore, Baicalein exhibited an overall enhancing effect on the entries to the platform at a higher dose of 50\u00a0mg/kg  in the 24\u00a0h probe trial. No significant changes in swimming speed were observed among the groups  group was used to eliminate background differences between the mice. PCA results revealed significant differences between APP/PS1-saline treated  and APP/PS1-Baicalein treated (APP_B) microbial communities. The species composition of wildtype-saline , WT_B, and APP_sal groups was similar . In addiaicalein . In addiaicalein .p < 0.05), s_Ruminococcus_sp_AF18-29 (p < 0.05), and s_Odoribacter_sp_43_10 (p < 0.05) in the APP_sal group was significantly lower than that in the WT_sal group. However, the APP_sal and APP_B groups exhibited no significant difference in the abundance of these species (p < 0.05) and s_Streptococcus_vestibularis (p < 0.05) in the APP_sal group was significantly higher than that in the APP_B groups. The species abundances in the WT_sal and APP_B groups were similar at the species level. These results suggested that Baicalein treatment can reverse the abundance of these species  of effect size (LEfSe) in combination with the statistical analysis was used to screen key biomarkers after treatment with Baicalein. The LDA scores of APP_sal and APP_B (log10 > \u00b12) groups showed greater abundance at the phylum level. The LDA scores of APP_sal and APP_B (log10 > \u00b13) groups showed greater abundance at the genus level. LDA scores of APP_sal and APP_B (log10> \u00b14) groups indicated greater abundance at the species level. Metagenomic results revealed that p_Deferribacteres can be considered as a specific biomarker of the APP_sal group at the phylum level, whereas p_candidatus_saccharibacteria can be considered as a specific biomarker of the WT_B group . At the _B group .To determine the effect of Baicalein on the alterations in gut metabolites associated with AD, we used UHPLC-QE-MS and PCA cluster analysis to conduct metabonomic studies. The metabolic changes of the APP_sal group relative to the WT_sal or APP_B group were further determined. All samples were analyzed with a 95% confidence interval . The uprp values <0.05.To determine the correlation between the metagenome of AD mice and various metabolites, we analyzed the microbial and host metabolites of the intestinal flora of the APP/PS1 mice. The intersections of APP_sal and APP_B, APP_B and WT_sal, and WT_ Sal and WT_B were used to screen 25 metabolites with the following criteria: FC values <0.25 and >4, and Clostridium_ sp_CAG557, s_Mucispirillum_schaedleri, s_Muribaculaceae_bacterium_Isolate_110_HZI, s_Eubacterium_plexicaudatum. The correlation analysis was conducted for these 25 metabolites.Totally 6 biomarkers in group APP_sal and APP_B at the species level were identified using the LEfSe analysis with the following criteria: LDA>4, which include_Roseburia_sp_1XD42_69, s_Muribaculaceae_bacterium_Isolate_104_HZI, s_Chlamydia_abortus and s_ Muribaculaceae_bacterium_Isolate_080_Janvier were screened for mantel test. In the APP_B and APP_sal groups, the correlation analysis revealed that L-histidine trimethylbetaine was positively correlated with methylgingerol. Moreover, both Ssioriside and Falcarindiol exhibited significant positive correlations with multiple metabolites, and the correlation coefficient was greater than 0.9. Methylgingerol was significantly negatively correlated with multiple metabolites. The correlation coefficients were all greater than 0.9. According to the Mantel test results, s_Muribaculaceae_bacterium_isolate-104_HZI with PS (16:0/16:1), PC (14:1/14:1), alpha-Ionol O-[arabinosyl-(1->6)-glucoside] were strongly correlated with each other, and the correlation coefficient was >0.85. A significant positive correlation was observed between s_Mucispirillum_schaedleri and p-Aminobenzoic acid, and the correlation coefficient was >0.8 -4--1,2,4-butanetriol, s_Eubacterium_ plexicaudatum, and isoleucyl-threonine  -3-butylhexahydro-1(3H)-isobenzofuranone, PC (14:1/14:1) were significantly positively correlated with -3-butylhexahydro-1(3H)-isobenzofuranone, succinic acid semialdehyde, and ribothymidine. The correlation coefficients were all greater than 0.9. Furthermore, a significant negative correlation was observed between d-tocotrienol and 20-carboxy-leukotriene B4, with a correlation coefficient greater than 0.9. Mantel test revealed that the s_hreonine .Clostridium_sp_CAG557). Both APP_sal and APP_B biomarkers encoded several enzymes in the succinic acid semiphosphate metabolism pathway (KO00760). However, L-histidine trimethylbetaine and succinic acid semialdehyde were highly correlated with the biomarker of APP_B (s_Roseburia sp. 1XD42-69) , succinic acid semialdehyde, and inosine were correlated with APP_sal biomarkers (s_XD42-69) .etc. Among the biomarkers of APP_B, the genes mainly involving metabolic pathways were hisH, hisD, hisI, and thrC, which mainly encode imidazole glycerol-phosphate synthase subunit HisH, histidinol dehydrogenase, phosphoribosyl-AMP cyclohydrolase, and threonine synthase pathways.Among the biomarkers of APP_sal, the genes mainly involving metabolic pathways were nadD, pncC, plsY, and plsC. These genes particularly encode nicotinate-nucleotide adenylyltransferase, nicotinamide-nucleotide amidase, acyl phosphate: glycerol-3-phosphate acyltransferase, 1-acyl-Sn-glycerol-3-phosphate acyltransferase, via reshaping the gut microbiome and metabolites.In the present study, chronic Baicalein treatment for 14 days significantly reversed cognitive deficits in 8-month-old APP/PS1 mice. The composition and metabolites of gut microbiota were altered in the APP/PS1 mice, and Baicalein changed the gut microbiota and subsequently corrected the metabolism of AD mice to that of controls. Modulating the gut microbiota through various diets and beneficial microbiota interventions may serve as an AD therapy. Alterations in gut microbes led to changes in the metabolites by influencing the amino acid, taurine, hypotaurine, glutathione, and histidine metabolism pathways. Thus, Baicalein administration could be a novel strategy to treat AD Many herbal remedies are now gathering more attention as complementary and alternative interventions and are important sources for developing drug candidates for AD. More and more studies are investigating the use of various medicinal plants and their principal phytochemicals for the treatment of AD. One example of such herbal medicine is Scutellaria baicalensis Georgi. Baicalein is the main bioactive ingredient in this herbal plant, with anti-inflammatory, antioxidant, and cognitive-enhancing properties . Our preThe earliest clinical symptoms of Alzheimer\u2019s disease are changes in episodic memory, compromised judgment, and orientation. These early findings correlate with synapse loss in the hippocampus induced by amyloid-beta (A\u03b2) 1\u201342 plaque burden . The synHelicobacter pylori infection is a significant risk factor for the pathological development of neurological diseases such as AD (Helicobacter pylori can cross the blood-brain barrier (BBB) in the disease state. Some studies suggest that H. pylori are responsible for neuroinflammation and the subsequent amyloid beta accumulation and BBB disruption, which resultantly interacts with gut metabolites and leads to neuronal damage in AD mouse models  were involved in purine metabolism, histidine metabolism, tyrosine metabolism, and nicotinate and nicotinamide metabolism. These results indicated that Baicalein treatment could significantly improve the cognitive ability of mice may via the gut microbiome and metabolite dysfunction. Further experiments are needed to determine how Baicalein improves memory by regulating intestinal microbiota directly or indirectly.The subsequent metagenomic sequencing results showed that Baicalein treatment significantly changed intestinal metabolites in these 8-month APP/PS1 mice. These changes were involved in metabolisms of alanine, aspartic acid, and glutamate, the central carbon in cancer metabolism, protein digestion and absorption, histidine metabolism, and neuroactive ligand-receptor interaction. After LEfSe analysis of metabolic pathways related to these metabolisms, six biomarkers about APP_sal and APP_B were significantly positively correlated with different metabolites, for example, six biomarkers with the largest LDA values associated with APP_sal, APP_B, and WT_sal. These results suggested that s_Roseburia_sp_1XD42-69, s_Muribaculaceae_ bacterium_Isolate-104_HZI, s_via the anti-glutamate pathway. Niacin includes two vitamers, nicotinic acid and nicotinamide, giving rise to the coenzymatic forms nicotinamide adenine dinucleotide (NAD) and nicotinamide adenine dinucleotide phosphate (NADP), which are required for oxidative reactions and crucial for energy production. NAD and NADP regulate biological functions, including gene expression, cell cycle progression, DNA repair and cell death (Glutamate is an essential excitatory neurotransmitter in neurons, and abnormal increases in glutamate may lead to cell death . Glutamall death . Previoull death . Nicotinll death . The abnll death . An incrll death . Lower cll death . Due to Baicalein exhibited an overall enhancing memory consolidation and retrieval in novel object recognition and Morris water maze tests. The present study also demonstrated how Baicalein improved learning and memory by regulating the gut microbiome and metabolite dysfunction in APP/PS1 mice. These results provide a valuable reference for using gut microbiota as a diagnostic biomarker and therapeutic target in clinical studies of AD."}
+{"text": "Endoscopic ultrasound-guided hepaticogastrostomy (EUS-HGS) is now widely performed for patients in whom endoscopic retrograde cholangiopancreatography is unsuccessfulA 77-year-old woman was admitted for treatment of obstructive jaundice due to unresectable hepatic hilar carcinoma. Multiple uncovered self-expandable metal stents (UCSEMSs) were inserted and systemic chemotherapy was attempted; however, recurrent biliary obstruction was observed after 6 months. Reintervention using UCSEMS was then performed for the left, anterior, and posterior bile ducts, but biliary obstruction recurred after 4 months. Further reintervention was successful for the right hepatic bile duct but failed for the left hepatic bile duct. Therefore, EUS-HGS was attempted.The intrahepatic bile duct was punctured using a 19\u200aG needle. After injection of contrast medium, a 0.025-inch guidewire was deployed. Tract dilation was performed using a drill dilator. An 8.5\u200aFr stent delivery system was inserted and the stent was successfully deployed  . HoweveVideo\u20061\u2002Troubleshooting procedure in cases of difficult removal of a stent delivery system due to insufficient stent expansion.In cases of difficult removal of a stent delivery system due to insufficient stent expansion, additional dilation at the site of insufficiency may enable removal of the system.Endoscopy_UCTN_Code_CPL_1AL_2AD"}
+{"text": "Circular RNAs (circRNAs) exert an important role in cancer progression. Meanwhile, considering its widespread regulatory effect and the potential of noninvasive testing, understanding the role of circRNA in cancer is particularly meaningful. Our study identified the circRNA hsa_circ_0008234 in colon cancer based on an open-accessed circRNAs expression profile. Moreover, we found that hsa_circ_0008234 could promote proliferation, invasion, and migration abilities of colon cancers, which was partly dependent on the competitive endogenous RNA mechanism (miR-338-3p/ETS1 axis). Meanwhile, we discovered that PI3K/AKT/mTOR signaling is the downstream pathway of the has_circ_0008234/miR-338-3p/ETS1 axis, which improves the effect network of circRNAs in colon cancer.Circular RNAs (circRNAs) have been shown to play a crucial role in cancer occurrence and progression. This present work investigated the link between hsa_circ_0008234 and colon cancer. Data retrieved from GSE172229 was used to compare the circRNA profiles of colon cancer and surrounding non-tumorous tissues. The amount of RNA and protein in the molecules was determined using quantitative real-time PCR (qRT-PCR) and Western blot analysis, respectively. The cell proliferation ability was assessed using CCK8, EdU, colon formation, and nude mice tumorigenesis tests. Cell invasion and migration abilities were evaluated using transwell wound healing and mice lung metastasis model. Hsa_circ_0008234 piqued our interest because bioinformatics and qRT-PCR analyses revealed that it is upregulated in colon cancer tissue. Cell phenotypic studies suggest that hsa_circ_0008234 may significantly increase colon cancer cell aggressiveness. Mice experiments revealed that inhibiting hsa_circ_0008234 significantly reduced tumor growth and metastasis. Moreover, the fluorescence in situ hybridization experiment demonstrated that hsa_circ_0008234 is primarily found in the cytoplasm, implying that it potentially functions via a competitive endogenous RNA pathway. These findings indicated that hsa_circ_0008234 may act as a \u201cmolecular sponge\u201d for miR-338-3p, increasing the expression of miR-338-target 3p\u2019s ETS1. In addition, the traditional oncogenic pathway PI3K/AKT/mTOR signaling was found to be the potential downstream pathway of the hsa_circ_0008234/miR-338-3p/ETS1 axis. In conclusion, hsa_circ_0008234 increases colon cancer proliferation, infiltration, and migration via the miR-338-3p/ETS1/PI3K/AKT axis; therefore, it could serve as a target and a focus for colon cancer therapy. Colon cancer is one of the most prevalent malignant tumors and one of the leading causes of cancer mortality deaths across the globe . AlthougCircular RNA is an endogenous RNA with a special single-stranded closed structure, including exonic circRNAs, exon\u2013intron circRNA, and circularized intron RNA . Exonic In this present investigation, we assessed the expression sequence and biological activity of hsa_circ_0008234 in colon cancer. Hsa_circ_0008234 was first identified using the circRNA expression profile of GSE172229, revealing a high expression value in colon cancer tissue and cells. The FISH test revealed predominant levels of hsa_circ_0008234 within the cell cytoplasm, indicating its potential link to the ceRNA pathway. In vitro and in vivo studies revealed that hsa_circ_0008234 might promote the growth, infiltration, and migration of colon cancer. In addition, hsa_circ_0008234 was discovered to potentially increase ETS1 expression via miR-338-3p, which may explain its cancer-promoting activity. The PI3K/AKT/mTOR signaling pathway was revealed as the downstream pathway of the hsa_circ_0008234/miR-338-3p/ETS1 axis.p value < 0.05. Heatmap was plotted using the heatmap package. Gene set enrichment analysis (GSEA) was performed to explore the biological effect based on Hallmark gene set [The circRNA expression profile of colon cancer and adjacent non-tumor was retrieved from the GEO database, GSE172229 . Open-accessed transcriptional profiles and clinical information of colon cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed circRNAs analysis was performed in the R program (v. 4.0.0) (limma package) with the threshold of |logFC > 1| and gene set .5 cells were inoculated on a 6-well plate, and 2 mL of complete medium was added. Cells converged to 70\u201390% before transfection; 2. Add 3 \u03bcg plasmid into 100 \u03bcL serum-free medium and shake it gently; 3. Add 4 \u03bcL Lipofectamine 2000 to 100 \u03bcL serum-free medium, mix slowly, and leave it at room temperature for 5 min; 4. Mix the diluted Lipofectamine reagent with the plasmid, shake it slowly, and place it at room temperature for 20 min to form the plasmid\u2013Lipofectamine mixture; 5. According to the instructions, add 200 \u03bcL plasmid\u2013Lipofectamine mixture to the cell hole containing 800 \u03bcL serum-free medium and slowly shake the cell plate; 6. Remove the old medium and add the complete medium after 6 h of culture under conventional conditions (5% CO2 and at 37 \u00b0C).The normal colon epithelial cell NCM460 and the four colon cancer cell lines HCT116, SW480, HCT8, and DLD-1, were all laboratory stocks. Cells were cultivated in RPM1640 culture media and replaced every four days. Jima Com  provided the short hairpin RNAs (shRNAs) of hsa_circ_0008234 and the control vector, as well as the miR-338-3p, mimics, inhibitor, and ETS1 overexpressed plasmids. Lipofectamine 2000 was used to transfect cells following the standard protocol. The process includes: 1. 5 \u00d7 10Total RNA was extracted by following the instructions provided in the RNA simple Total RNA extraction kit that was manufactured and provided by TIANGEN in Beijing, China. The RNA was then reverse transcribed using a High-Capacity cDNA Reverse Transcription Kit (Themofisher). As a result, total RNA was reverse transcribed into cDNA. The circular nature of the PCR result of hsa_circ_0008234 was determined by Sanger sequencing. RiboBio Co., Ltd.  designed and synthesized the PCR primers. The following primers were used were: Hsa_circ_0008234, Divergent primer, forward: 5\u2032-CCTTCCAAGACCTCCTTAATA-3\u2032, reverse: 5\u2032-TTTCCAGCATGTTGTTGTTG-3\u2032; Convergent primer, forward: 5\u2032-CACCTCAAGTTATCACTCCC-3\u2032, reverse: 5\u2032-GGGCTGAATTGTCAGAAGG-3\u2032; hsa-miR-338-3p, RT primer, 5\u2032-GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACCAACAAA-3\u2032, forward, 5\u2032-GGTGGTCCAGCATCAGTGAT-3\u2032, reverse, 5\u2032-CAGTGCAGGGTCCGAGGT-3\u2032; ETS1, forward: 5\u2032-GATAGTTGTGATCGCCTCACC-3\u2032, reverse: 5\u2032-GTCCTCTGAGTCGAAGCTGTC-3\u2032; GAPDH, forward: 5\u2032-ACAACTTTGGTATCGTGGAAGG-3\u2032, reverse: 5\u2032-GCCATCACGCCACAGTTTC-3\u2032.The total cellular protein was extracted from samples using a total protein extraction kit . AKT (1:5000), phospho-AKT (1:3000), mTOR (1:5000), phospho-mTOR (1:5000), PI3K (1:1000), CyclinD1 (1:5000), and \u03b2-actin (1:5000) primary antibodies were purchased from Protentech (1:5000). Phospho-PI3K (1:1000) primary antibody was purchased from cell signaling technology . Western blot analysis was performed as previously described . Detaile5 cells were inoculated into each 6-plate well.The cell proliferation ability was assessed using CCK8, colony formation, and 5-ethyl-2\u2032-deoxyuridine (EdU) assays. A CCK8 assay was performed using a CCK8 instrument . Colony formation and EdU test were performed as previously described . For col4 cells were added into the upper compartment for 12 h. Finally, cells were fixed with 4% formaldehyde and stained with crystal violet.Cell infiltration and migration potential were assessed using the transwell assay, as previously described . A mediuThe wound-healing assay process was performed as previously described . InoculaThe FISH technique was employed to establish where circRNAs are located subcellularly. Briefly, the FISH assay was performed in accordance with the protocol of the FISH Kit (RiboBio). The cells were then repaired, made permeable, and placed in a prehybridization buffer environment for 30 min. The hsa_circ_0008234 FISH probe was incorporated into the cells overnight at room temperature, along with a previously heated hybridization buffer to a higher temperature. DAPI dye was used for nuclear staining.The RIP assay was performed using an RIP kit provided by Millipore, Burlington, MA, USA. Briefly, 100 \u03bcL of RIP wash buffer was prepared for washing, then added and mixed into a 50 \u03bcL suspension of magnetic beads. After that, AGO2 and IgG antibodies were applied and incubated for 30 min. The lysates were placed in a beads\u2013antibody complex, and all the tubes were rotated and incubated at 4 \u00b0C for 1 night. The immunoprecipitated RNA was investigated using qRT-PCR.The hsa_circ_0008234 and ETS1 sequences, in both their wild-type and mutant forms, were cloned into pGL3-control luciferase reporter vectors. The luciferase reporter vectors co-transfection into control and miR-mimics cells employing lipofectamine 2000. The luciferase activities of the luciferase reporter vectors were assessed using a dual-luciferase reporter assay kit .IHC was used to detect Ki67 in the tumor tissues from nude mice. Briefly, tissues of cell xenografts were fixed with 4% formaldehyde for 24\u2009hours and cut into 4 \u03bcm paraffin sections. Anti-Ki67 antibody (1:200 dilution) was incubated overnight at 4 \u00b0C after sections were treated with 10 mmol/L sodium citrate buffer. Cells with homogeneous Ki67 staining were considered positive.6) and treated cells (at a concentration of 5 \u00d7 106) were injected into the backs of the naked mice. All mice were executed 25 days following injection, and tumor weights were measured for IHC. The lung metastasis model was used in in vivo metastasis testing. Cells (1 \u00d7 106) were infused into mice via the tail veins. Mice were euthanized, and all lungs were retrieved for IHC examination after another 25 days.The xenograft model and tumor metastasis experiment were conducted on five-week-old nude male BALB/c mice. Both control cells  were employed for all statistical analyses. All trials were repeated three times, and the results are presented as the mean with the standard deviation. Based on the circRNA throughput data of GSE172229, differentially expressed circRNAs were evaluated between five colon cancer and adjacent normal tissues. To examine the biological function of hsa_circ_0008234 in colon cancer, we knocked it down and performed qRT-PCR to validate the effectiveness of this knockdown in colon cancer cells. The results indicated that sh-circ#2 was the most efficient, so it was chosen for future experiments . The SW4CircRNA subcellular localization can reveal its function mechanism. As such, we employed the FISH test to determine the subcellular localization of hsa_circ_0008234. Hsa_circ_0008234 was found mostly in the cytoplasm, indicating that it may function via a ceRNA mechanism A. Additip = 0.003). We discovered that cells containing miR-338-3p mimics expressed lower ETS1 RNA values at the cellular level  exons 8, 9, 10, and 11. Elevated hsa_circ_0008234 levels were found in colon cancer cells. In vitro and in vivo tests revealed that hsa_circ_0008234 promoted colon cancer aggressiveness. In particular, hsa_circ_0008234 acts as a miRNA-sponge for miR-338-3p to increase ETS1 RNA expression, which potentially influences AKT/mTOR pathway stimulation. Previous research investigated the biological effects of hsa_circ_0008234 on cancer. For instance, Cai et al. discovered that hsa_circ_0008234 promoted the growth of cutaneous squamous cell carcinoma via the miR-127-5p/ADCY7 axis . To the Recent research suggests that the ceRNA is the primary function mechanism for circRNAs found in the cytosol . AccordiETS1 is a member of the ETS transcription factors that can recognize the core consensus DNA sequence of GGAA/T in specific genes (ETS-binding domain) . The ETSResearch evidence suggests that ETS1 may accelerate the development of cancer by influencing the function of the PI3K/AKT/mTOR signaling pathway. Xu et al. discovered that miR-129 could inhibit prostate cancer and metastasis by blocking ETS1 and modulating the PI3K/AKT/mTOR pathway . In addiSome limitations should be noticed. Firstly, we only proved the in vivo cancer-promoting effect of hsa_circ_0008234. However, the in vivo experiments of hsa_circ_0003596/miR-502-5p/IGF1R/PI3K/AKT axis have not been performed. Comprehensive in vivo experiments can enhance the stability of the conclusion. Secondly, the evaluation of the tissue level of hsa_circ_0008234 is limited in our study. In the future, a larger sample size of colon cancer is needed to support our conclusions. Thirdly, since circRNAs have a broad regulatory effect, including protein\u2013protein interaction and protein encoding, other underlying mechanisms have not been identified.In conclusion, this work revealed the tumor-promoting effect and potential molecular mechanisms of the circRNA hsa_circ_0008234 in colon cancer. Significantly elevated circRNA hsa_circ_0008234 levels in colon cancer indicated that it could play a pivotal part in the diagnosis and prognosis prediction. Moreover, results from in vitro and in vivo investigations demonstrate that hsa_circ_0008234 may promote colon cancer aggressiveness. These findings suggest that hsa_circ_0008234 could be a therapeutic target for colon cancer."}
+{"text": "Biliary cannulation of an intradiverticular papilla, classified type 1 as per the Li-Tanaka classification, is sometimes challengingAn 80-year-old woman presented to another hospital with high fever and abdominal pain. She was diagnosed with cholangitis due to common bile duct stones . Three Video\u20061\u2002Biliary cannulation was successfully performed using a unique double guidewire technique with a guidewire that had perforated the diverticulum.The guidewire sometimes perforates the papilla during ERCP; however, most cases improve conservativelyEndoscopy_UCTN_Code_TTT_1AR_2AH"}
+{"text": "Cedrela odorata L.) and known or hypothetical genes related to the response to herbivory were used as reference. The protocol key points are:\u2022Implementation of a transcriptome thinning process to eliminate redundant and non-coding sequences, optimizing the analysis and reducing processing time.\u2022Use of a custom gene database to identify and retain coding sequences with high precision.\u2022Focus on specific genes of interest, allowing a more targeted analysis for specific experimental conditions.The following protocol introduces a targeted methodological approach of differential gene expression analysis, which is particularly beneficial in the context of non-model species. While we acknowledge that biological complexity often involves the interplay of multiple genes in any given biological response our method provides a strategy to streamline this complexity, enabling researchers to focus on a more manageable subset of genes of interest. In this context, red cedar transcriptome (This approach holds particular value for pilot studies, research with limited resources, or when rapid identification and validation of candidate genes are needed in species without a reference genome.  Specifications tableDifferential gene expression analysis constitutes the primary application of data derived from RNAseq sequencing. This methodological approach facilitates the identification of genes or transcripts that display differential expression in samples with significant contrasts. This process is essential for understanding gene regulation and biological responses to various experimental or pathological conditions RNA-seq data frequently encapsulates a multifaceted landscape of gene expression, influenced by a myriad of factors such as biological and technical noise ,3. This (Cedrela odorata L.) were sequenced at the DNA and Genomics Laboratory of the National Center for Genetic Resources of INIFAP. The experimental conditions included six individuals showing signs of attack from the shoot borer (Hypsipyla grandella) and five healthy plants.Eleven paired 75\u00a0bp RNAseq libraries corresponding to red cedar The Linux OS, Ubuntu version 22.10, was used, installed on an Intel Xeon E510 computer with 120 GB of RAM and 64 CPUs. Anaconda 3.11 and Docker 23.0.3 were employed for software package installation.(1) Library cleaning with fastp > fastp -i file1_R1.fastq -I file1_R2.fastq -o clean_file1_R1.fastq -O clean_file1_R2.fastq -l 20 -q 20The ``-l'' flag instructs fastp that the minimum read length is 20\u00a0bp, and the ``-q'' specifies that the minimum quality score is 20.de novo transcriptome assembly, the selection of a suitable software tool is at the discretion of the researcher. However, in this protocol, we provide instructions for conducting it with Trinity RNAseq (1)>cat clean*_R1.fastq > all_R1.fastq>cat clean*_R2.fastq > all_R2.fastq>Trinity \u2013seqType fq \u2013left all_R1.fastq \u2013right all_R2.fastq \u2013full_cleanup \u2013output Trinity_referenceDe novo transcriptome assembly using all RNAseq libraries:(2)aIndex creation for Bowtie2:>bowtie2-build Trinity_reference.fasta out_bowtie2_indexbAlignment of RNAseq libraries with Bowtie2.>bowtie2 -x out_bowtie2_index \u22121 all_R1.fastq \u22122 all_R2.fastq \u2013sensitive \u2013local -S out.sam 2>&1 | tee result_bowtie2.logReads support analysis with Bowtie2 For the purpose of conducting The results from Bowtie2 are displayed upon completion of the program's execution. By using the option \u201c2>&1 | tee result_bowtie2.log\u201d, the results are also saved in a file named \u201cresult_bowtie2.log\u201d, allowing for future reference if necessary.(3)>busco -m transcriptome -l embryophyta -i Trinity_reference.fasta -o output_buscoGeneral assembly quality assessment with BUSCO If the read alignment exceeds 70%, it is considered that an appropriate assembly was performed. However, if it is lower, it might indicate poor quality in the assembly, and it is suggested to consider the possibility of repeating the process or discarding the data, as appropriate The flag ``-m transcriptome'' indicated that BUSCO performed the gene search in transcriptome mode, while the \u201c-l embryophyta\u201d flag corresponds to the lineage of the analyzed organism. This step is essential in de novo assemblies, as it allows for the search of functional genes. Homology values above 80% indicate good assembly quality ,8.Transcriptomes encompass a substantial quantity of redundant and non-coding sequences, thereby impeding downstream analyses by demanding greater processing power and execution time ,10. Ther(1) Redundancy removal from the transcriptome using CD-HIT > cd-hit-est -c 0.9 -i Trinity_reference.fasta -o cd-hit-reference.fastaThe flag ``-c 0.9'' specifies that the similarity value for CD-HIT was set at 90%. This means that the program grouped sequences that share a 90% similarity with each other and selects one representative sequence per group, thus eliminating redundancies.aUsers can choose the genomic database of their preference. However, for the purposes of this protocol, a custom protein database corresponding to embryophytes was constructed, downloaded from UNIPROT bDiamond database.>diamond makedb --in uniprot.fasta -d diamond_db(2) Removal of non-coding sequences using diamond >Transdecoder.LongOrfs -t cd-hit-reference.fastac Translation of the redundancy-free transcriptome to proteins.>diamond blastp \u2013fast \u2013max-target-seqs 5 \u2013outfmt 6 \u2013query transdecoder.pep \u2013db diamond_db \u2013outfmt 6 \u2013out blastp.fmt6d BLASTP with diamond.The flag ``\u2013max-target-seqs 5'' indicates that the top 5 alignments are reported for query sequences. \"\u2013outfmt 6\u2033 means that the diamond result is saved in tabular BLAST format. It's important to note that TransDecoder generates files with various extensions, so to perform the BLASTP, it is necessary to use the file with the ``.pep'' extension.The results from diamond are essential for TransDecoder to more accurately predict coding sequences. The decision to use this program was based on its speed and efficiency in handling large volumes of data e Predicting Coding Sequences with TransDecoder.> TransDecoder.Predict -t cd-hit-reference.fasta \u2013retain_blastp_hits blastp.fmt6.In the context of the TransDecoder framework, the command-line option \"\u2013retain_blastp_hits blastp.fmt6\u2033 is utilized to incorporate the results obtained from the diamond BLASTP algorithm. This action serves to retain sequences that have exhibited significant matches in the BLASTP analysis. This procedure improves the precision of coding sequence predictions. At the conclusion of this process, the resultant nucleotide coding sequences were preserved within the \".cds\" file, while the corresponding amino acid protein sequences were stored in the \".pep\" file.Once the thinning process is completed, a transcriptome will be obtained that can be used as a reference for differential expression analysis. That is, the researcher may choose to conclude the protocol at the previous point. However, if the primary objective is to conduct an analysis focused on a specific group of genes, it is recommended to proceed with the following steps.(1) The researcher can choose to download the group of genes of interest from any of the available genomic databases. However, for the purposes of this protocol, searches for embryophyte protein sequences containing the keyword \"herbivore\" in UNIPROT were conducted, and a custom database was generated using BLAST+ a BLAST gene database.> makeblastdb -dbtype prot -in uniprot_herbivore.fasta -out blast_herbivore.The flag ``-dbtype prot'' specifies that the sequences used correspond to proteins. If the data are nucleotide sequences, the type should be changed to ``-dbtype nucl''.b Searching for homologous genes by BLAST.> blastp \u2013max_target_seqs 5 -db blast_herbivore \u2013query transdecoder-cd-hit-reference.pep \u2013outfmt \u20186 qseqid sseqid salltitles pident qcovs evalue\u2019 -out blastp_herbivore.fmt6.The \u201c-outfmt\u201d flag contains several fields that will produce an output in BLAST tabular format with columns in the following order: Query ID, Subject ID, All subjects titles, Identity percent, Query coverage per subject, and Evalue. This information will be very helpful for subsequent information extraction.TIP: It is recommended to save the BLAST result in ASN.1 archive format (-outfmt 11) and then reformat with blast_formatter to BLAST tabular (-outfmt 6). The ASN.1 format contains all the metadata information related to the BLAST results, which can be useful for future queries.c Extraction of homologous sequences from the reference transcriptome using seqkit > cat blastp_herbivore.fmt6 | awk \u2018{ if ($4 >=30 && $5>=50){print}}\u2019 | cut -f 1 | sort | uniq > transcripts_list.txt> seqkit grep -f transcripts_list.txt transdecoder-cd-hit-reference.cds > transcripts_hervibore.fasta.In Linux, the pipe symbol (|) is used to sequentially execute a series of commands applied to the same dataset. In the first line of code, ``cat'' opens the \u201cblastp_herbivore.fmt6\u201d file, which is the result of the BLAST. Then, \u201cawk\u201d applies a filter to the data to print only those with an identity percentage value  greater than or equal to 30% and a coverage percentage  greater than or equal to 50%. It is important to note that in protein alignments, sequences are considered homologous if the similarity and coverage percentage exceeds 30% and 50% respectively In the second line of code, the ``grep'' option indicates to seqkit to use the sequence search function by ID and using ``-f'' specifies that the IDs are found within the ``transcripts_list.txt'' file. It's important to highlight that the search for the transcripts must be carried out in the TransDecoder results specifically within the file with the ``.cds'' extension.Cedrela odorata L. The first was executed conventionally using the entire transcriptome. The second was conducted following the protocol described in this paper. In both cases, transcript quantification was carried out with Salmon Two differential gene analyses were conducted on 11 RNAseq libraries from Our findings demonstrate a notable contrast between a whole-transcriptome approach and the targeted methodology proposed in this study for differential gene expression analysis. Utilizing a comprehensive transcriptomic analysis, we identified a substantial set of 819 differentially expressed genes . It is iOur findings indicate that while whole-transcriptome analysis offers a comprehensive perspective on gene expression changes, it may also yield a complex dataset influenced by various factors such as biological variability and noise ,3,10. OnThe data used for this protocol were provided by the DNA and Genomics Laboratory of the National Center for Genetic Resources of INIFAP.Arag\u00f3n-Magad\u00e1n Marco Aurelio: Conceptualization, Formal analysis, Investigation, Project administration, Software, Supervision, Writing \u2013 original draft, Writing \u2013 review & editing. Calvillo-Aguilar Francisco Fabi\u00e1n: Conceptualization, Formal analysis, Investigation, Project administration, Software, Supervision, Writing \u2013 original draft, Writing \u2013 review & editing. Cruz-C\u00e1rdenas Carlos Iv\u00e1n: Conceptualization, Formal analysis, Investigation, Project administration, Software, Supervision, Writing \u2013 original draft, Writing \u2013 review & editing. Guzm\u00e1n Luis Felipe: Conceptualization, Formal analysis, Investigation, Project administration, Software, Supervision, Writing \u2013 original draft, Writing \u2013 review & editing."}
+{"text": "Endoscopic retrograde cholangiopancreatography (ERCP) in patients with surgically altered anatomy is intrinsically challengingA 70-year-old man with gastric diffuse large B-cell lymphoma underwent partial gastrectomy with Roux-en-Y gastrojejunostomy followed by chemotherapy 10 years prior to the current admission. He presented this time with a 6-week history of severe upper abdominal pain, jaundice, and pruritus. Evaluation showed acute mild biliary pancreatitis, cholelithiasis with choledocholithiasis, and a polypoidal growth at the right vesico-ureteric junction. Magnetic resonance cholangiopancreatography showed chronic cholecystitis with choledocholithiasis . We proVideo\u20061\u2002Novel motorized spiral enteroscopy-assisted endoscopic retrograde cholangiopancreatography.After identifying the anastomotic and jejunojenostomy sites , the afThe patient underwent cystoscopy 2 days later, with transurethral resection of the bladder tumor and cystodiathermy. Biopsy revealed noninvasive papillary urothelial carcinoma. He then underwent laparoscopic cholecystectomy .After 6 weeks, NMSE-assisted ERCP was repeated and the CBD stent removed. The patient recovered well and was discharged.In surgically altered anatomy, the normal ERCP procedure has limited success. NMSE-assisted ERCP can make the procedure more accessible.Endoscopy_UCTN_Code_TTT_1AR_2AH and Endoscopy_UCTN_Code_TTT_1AP_2AD"}
+{"text": "Phytophthora infestans) is a disease of potatoes with economic importance worldwide. Control is primarily through field monitoring and the application of fungicides. Control of late blight with fungicides and host plant resistance is difficult, with documented cases of such control measures failing with the advent of new pathotypes of P. infestans. To better understand host plant resistance and to develop more durable late blight resistance, Quantitative Trait Locus/Loci (QTL) analysis was conducted on a tetraploid mapping population derived from late blight-resistant potato cultivar Palisade Russet. Additionally, QTL analyses for other traits such as Verticillium wilt and early blight resistance, vine size and maturity were performed to identify a potential relationship between multiple traits and prepare genetic resources for molecular markers useful in breeding programs. For this, one hundred ninety progenies from intercrossing Palisade Russet with a late blight susceptible breeding clone (ND028673B-2Russ) were assessed. Two parents and progenies were evaluated over a two-year period for response to infection by the US-8 genotype of P. infestans in inoculated field screenings in Corvallis, Oregon. In Aberdeen, Idaho, the same mapping population was also evaluated for phenotypic response to early blight and Verticillium wilt, and vine size and maturity in a field over a two-year period. After conducting QTL analyses with those collected phenotype data, it was observed that chromosome 5 has a significant QTL for all five traits. Verticillium wilt and vine maturity QTL were also observed on chromosome 1, and vine size QTL was also found on chromosomes 3 and 10. An early blight QTL was also detected on chromosome 2. The QTL identified in this study have the potential for converting into breeder-friendly molecular markers for marker-assisted selection.Potato late blight (causal agent  Phytophthora infestans (Mont) de Bary, causal agent for potato late blight, has detrimentally impacted potato production worldwide  from Solanum demissum, a wild hexaploid species indigenous to Mexico. Breeders incorporated these resistance genes into cultivated potato (S. demissum appeared as dominant R genes inducing a hypersensitive response. Each R gene was effective against only a specific race(s) of P. infestans indicating vertical resistance  were observed from diverse wild potato species, such as Solanum demissum, S. bulbocastanum, S. polyadenium, S. stoloniferum, S. vernei, and S. verrucosum, (P. infestans (Various genetics studies and quantitative trait locus/loci (QTL) analyses have been conducted with wild potato species to achieve pyramiding genes or stable quantitative resistance performance regardless of rucosum, , variousrucosum, . Chromosnfestans . Similarnfestans . The genAlternaria solani), Verticillium wilt (Verticillium dahliae), and vine size and maturity were characterized during those same years in Idaho. QTL analyses have shown close association of late blight resistance with these four traits  and susceptible breeding clone, A86102-6  with the 4-generation pedigree of Palisade Russet reported by Andigena . The aut4 sporangia per mL) by measuring the spore concentration with a hemocytometer. The adjusted sporangia were stored for two hours between 4 and 12 degree Celsius to promote the release of zoospores before field inoculation. Individuals of family A08241 were evaluated for their response to US-8 in inoculated field trials conducted over a two-year period (2019-2020) at Corvallis, Oregon. The experiment was designed as a randomized complete block with two replications of ten-hill plots. The mapping population was planted on 6/20/19 and spreader rows of Ranger Russet and Russet Burbank were sprayed with US-8 spores on 8/30/19 and 9/6/19. The field was irrigated each morning to maintain humidity favorable to late blight spread. Late blight foliage damage was evaluated on September 13th, 20th, and 27th in 2019. The same procedures were repeated in 2020: planting on 6/24; inoculation on 9/1 and 9/4; foliage damage evaluations on 9/15, 22, and 29; and harvest on 10/22 and 23 of 2020. Late blight field reading scores  indicated severity of late blight symptoms of each plot  to 9 (>90% of the foliage necrotic) measured mapping population response to infection by early blight and Verticillium wilt  represent the number of years and replications, respectively. JMP Pro\u00ae Statistics, Version 12  was used for all statistical analyses and visualization of resulting data  discussed in this study.In equation 3 (eq. 3), \u00ae Statistics, Version 12  was used to conduct all correlation tests. To discriminate the significance of the p-values of correlation coefficients, a p-value< 0.0001 was selected as the standard. Because only one-year data for the early blight damage was available, the correlation test for the three BLUP datasets within the trait was not performed. Instead, the 2019 raw phenotype data were directly used in the correlation test comparing different traits.Multivariate correlation tests were executed to elucidate similarity across the three BLUP datasets for each trait as well as to look into either positive or negative relationships between two different traits  and Illumina iScan system. Initial DNA sample quality check and acquirement of SNP theta scores were executed by GenomeStudio software  as described in filter_missing, filter_segregation, make_seq_mappoly, and elim.redundant functions after the translated SNP marker dataset was loaded onto MAPpoly. MAPpoly assembled and refined 12 linkage groups  constructed overall linkage groups. One strength of MAPpoly is its use in probing polyploids up to octoploid with hidden Markov models (HMM) . Primarye groups .remim function, which carried out a random-effect multiple interval mapping (REMIM) model, was chosen for fitting various random-effect QTL by evaluating a single parameter per QTL. The QTLpoly software then ran linear score statistics tests (fit_model argument (2QTL.\u201d This QTL heritability (h2QTL) should be distinguished from the general heritability , which represents how well a trait was inheritable from two parents to their progeny. The h2QTL with over 10% was considered a major QTL, while another h2QTL \u2264 10% was considered a minor QTL as the software inventor did before , which can run QTL mapping processes of polyploid organisms. The 12 linkage groups and all phenotypic data, converted to BLUP  year datasets, were selected as a late blight resistant panel. Likewise the highest 20% of the most susceptible clones were chosen as a susceptible panel. After collecting all the haplotype images of those selected clones, the place where the significant QTL for the late blight resistance was identified was intensively investigated to confirm the presence of a resistant allele on an appropriate homolog and position in the resistant clones or vice versa . The feature of the resistant haplotype comparison provided helpful information for future MAS.read_geno_csv function, 3315 non-conforming and redundant markers further were eliminated. One hundred fifteen SNPs with 5% or more no-calls were filtered by the filter_missing argument. The filter_segregation function conducted the chi-squared (\u03c72) test, which matches expected genotype frequencies against observed frequencies, resulting in the associated p-value. Informative SNPs were distinguished by the Bonferroni correction . The make_seq_mappoly argument excluded an additional 162 SNP markers, which did not meet expected segregation ratios based on Mendelian inheritance. Finally, 477 markers, which were uninformative, co-segregating, or not belonging to one of 12 linkage groups were removed during two-point and MDS processes in MAPpoly.A total of 8222 tetraploid markers were selected and translated by ClusterCall into readable tetraploid genotypes. Forty-three SNPs having no-call in either of two parents were omitted, as they could not contribute to linkage groups. Since the accurate chromosome numbers for 61 SNPs were not available in the potato reference genome PGSC Version 4.03, those SNPs were omitted to avoid potential errors. Nine SNPs tagged as having incorrect physical map location information were removed to avoid extending the length of a linkage group  were used for the QTL analysis. LB-AUDPC values for each clone were calculated by LB-AUDPC equation 1 (eq. 1), based on raw late blight foliage damage data (No late blight scores of \u201c1\u201d (no symptom of infection) or \u201c2\u201d (more than 0% but less than 10%) were observed in late blight foliage damage records across three reading points, two years, and two replications. This indicates that no individuals in the population displayed an immune or highly resistant response to late blight  were obtained in the same manner. Each BLUP dataset mentioned above was composed of 190 BLUPs equal to the progenies number used for the linkage mapping process. A detailed description of BLUP datasets is summarized in Late blight foliage damage (the third reading point) produced three BLUP datasets and LB-AUDPC data, depending on the combination of BLUP effects of each clone. The first BLUP dataset, \u201cVine maturity evaluation scores of the A08241 population were distributed from \u201c1\u201d to \u201c8\u201d in 2019 and from \u201c2\u201d to \u201c9\u201d in 2020, respectively  and LB-AUDPC (LB-AUDPC) BLUPs above was used, with \u201cVM,\u201d \u201cVS,\u201d and \u201cVW\u201d abbreviating vine maturity, vine size, and Verticillium wilt damage, respectively. See The mixed-effects model (eq. 2) generated three BLUP datasets for each trait except early blight data, which was omitted from the 2020-year data. The same naming system as introduced in late blight foliage damage  were used for a multivariate correlation test with other five traits and QTL analysis. Subsequent interpretation associated with accuracy of QTL analysis relying on raw 2019 early blight damage phenotype data will be further discussed in the Discussion section.Unlike the other five characterized traits above, minimal segregation was observed for early blight in 2020 in the mapping population, with individuals displaying no or very few early blight symptoms of the foliage during the growing season (VS_clo and VS_clo_2019) were adjacent to a normal distribution, but VS_clo_2020 were flat across the whole X-axis except one section from -0.5 to -0.75 cM, which had a significantly higher peak. Three BLUPS of vine maturity were also close to a normal distribution but departed slightly from the normal. Dataset EB_2019_raw_pheno was relatively skewed toward resistance . LB-AUDPC BLUP datasets showed similar patterns  to find potential links between those traits. Because of almost negligible segregation in 2020 early blight data, the EB_2019_raw_pheno dataset was used for the correlation test. As expected, the correlation between LB_clo and LB-AUDPC_clo was 0.96 because the LB-AUDPC_clo BLUP was based on LB raw phenotype data. When LB and LB-AUDPC (late blight resistance associated BLUP data) were compared with resistances against EB and VW, their correlation coefficients ranged from 42% to 59%   and different year BLUP data  within the same trait, we compared pooled phenotypic data across two years , and proximate SNP markers to the mapped QTL  of the six traits  . Other QTL on chromosome 5 were observed near 16.54 cM on chromosome 5   predominantly contributes each of the six traits studied in this research. Investigation of allelic effects indicated that all QTL on chromosome 5 exerted the strongest impacts on significant QTL regardless of trait. Details of allelic effects are further discussed below.LB respectively. Similarly, Palisade Russet and ND028673B-2Russ had 1047.06 (45.13%) and 1273.29 (54.87%) contributions to LB-AUDPC, respectively, which was surprising in that a greater contribution to late blight resistance was anticipated from Palisade Russet, which is late blight resistant. Therefore, unlike the normal pathogen resistance gene , the presence (or absence) of the susceptible allele(s) of the LB resistance-associated gene also seems to be a key factor in contributing to the phenotypic response to late blight infection observed in the segregating population. More details associated with the allele effect values of the two parents\u2019 contribution were provided in When the allele effects of the QTL for LB and LB-AUDPC were inspected together, a total of 24 allele effects (four homologs \u00d7 two parents \u00d7 three QTL) were commonly detected for each trait, respectively, consisting of 10 positive  and 14 negative alleles   and 4.51 (56.98%). ND028673B-2Russ showed a higher impact than Palisade Russet across the six significant QTL associated with VW resistance  persistently showed higher impact than those on chromosome 1  and 5.95 (56.61%). Palisade Russet showed higher effects at VM_clo_ch1 QTL (59.73%), VM_clo_2019_ch1 QTL (58.91%), and VM_clo_2020_ch1 QTL (60.61%). ND028673B-2Russ revealed stronger efficacy at VM_clo_ch5 QTL (61.13%), VM_clo_2019_ch5 QTL (60.94%), and VM_clo_2020_ch5 QTL (61.33%). As observed in VW allelic effects above, allelic effects of QTL on chromosome 5  were consistently higher than those on chromosome 1  and 4.08 (48.11%). At the QTL: VS_clo_ch5, VS_clo_2019_ch5, and VS_clo_2020_ch5, ND028673B-2Russ showed higher effects than Palisade Russet. The rest of the significant QTL showed a higher allele effect in Palisade Russet than ND028673B-2Russ. Like the allele effect analysis results of the previous traits, the allele effects of the QTL on chromosome 5  were consistently higher than those on chromosomes 3 and 10  and 1.43 (54.18%). At EB_2019_pheno_ch2 QTL, the allelic effect of Palisade Russet was slightly higher than ND028673B-2Russ. At EB_2019_pheno_ch5 QTL, ND028673B-2Russ showed a higher impact than Palisade Russet. As before, allelic effects of QTL on chromosome 5 exerted greater impact than those on chromosome 2  were consistently located at 17.09 cM on chromosome 5 (LB & LB-AUDPC QTL (located at 17.09 cM on chromosome 5) had positive (susceptible) effects, but homologs a, c, d, f, and g had negative (resistant) effects  compared to other correlation test results. However, it should be noted that the late blight damage evaluations were conducted in an outdoor potato field, with many variation factors. Besides, the infection rate and propagation of P. infestans were known to be remarkably affected by environmental conditions such as humidity and temperature , QTL analyses of tetraploid mapping populations has not been as commonplace as with the use of diploid potatoes. This is due to the marker phasing process being fully automated in most diploid linkage mapping software, but not being entirely automated in TPMSNP using tetraploid mapping populations, where manual input is commonly required.Newly released R-package MAPpoly  has imprLB, LB-AUDPC, and EB BLUP datasets deviated from a normal distribution; thus, ancillary QTL analyses with transformed data relatively closer to normal were performed to appraise whether the non-normal distributions significantly impacted QTL results or not. For the data transformation, the Ordered Quantile (ORQ) normalization transformation method was utilized  effects on homologs b, e, and h and negative (resistance) allelic effects on homologs a, c, d, f, and g  and BA47f2t7 (cleaved amplified polymorphic sequence: CAPS) markers on chromosome 5, based on tuber observation data of a bi-parental diploid mapping population . Traditionally, auxin has been commonly known as a classical phytohormone, affecting leaf aging, plant and potato tuber development, etc., by interacting with cytokinins and other phytohormones . S. phureja and S. stenotomum background. They explained the VW_ch5_Tai QTL had a major effect and the VW_ch5_Tai QTL\u2019s support interval included the StCDF1 gene, which controls maturity and tuberization earliness. Another QTL, VW_ch9_Tai QTL, co-localized with the known Verticillium wilt resistance gene, Ve2 (Solanum tuberosum CDFs (StCDFs) are a cluster of transcriptional repressors affecting earliness in potatoes (VW QTL identified in this study (http://solanaceae.plantbiology.msu.edu). Further analyses of SNPs near the StCDF1 and the auxin efflux carrier genes on the chromosome linkage map 5 developed in this study are warranted for future MAS for Verticillium wilt resistance.The genetic effect was much larger than the other effects in the variance component estimate . Gossypium hirsutum) Mediator complex and regulates disease resistance. Using Agrobacterium tumefaciens, they developed the transgenic Arabidopsis plants having overexpressed GhCDKE, and then inoculated the transgenic plants with V. dahliae. Interestingly, overexpression of GhCDKE enhanced resistance to V. dahliae , showed improved resistance to V. dahliae. Returning to the potato genetic study, VW resistance with a diploid potato mapping population. Overall, all the experiment results of both current and previous studies commonly pointed out the interconnection between VW resistance mechanism and protein kinase family protein and reported the identification of the VW resistance QTL on chromosome 1, thereby warranting further examination of this region in the development of MAS for VW.On chromosome 1, another  dahliae . Zhang eVM_clo, VM_clo_2019, and VM_clo_2020 BLUP datasets consistently resulted in two significant QTL on chromosomes 1 and 5, respectively. Interestingly, the three QTL, VM_clo_ch5, VM_clo_2019_ch5, and VM_clo_2020_ch5 had similarities with the previously discussed VW QTL. For instance, they were commonly observed at 16.54 cM on chromosome 5 where the three VW resistance QTL were also found, with PotVar0026113 identified as the closest maker. Their LOP scores also reached the software\u2019s maximum LOP limit (>15.65)   . In a gen, etc.)  and the VM was consistently observed at 73.16 cM across the three VM BLUP datasets  were commonly observed on chromosome 5, with >15.65 (maximum) LOP score and 62% h2QTL. Even though the location of VS_clo_2019_ch5 differed from those of the other two QTL, they were only 0.55 cM, and their support intervals commonly shared the zone between 13.90 and 24.79 cM; thus, the three QTL were inferred to represent one gene  and 1.72 cM away from VS_clo_2019_ch5 (PotVar0026113), respectively. Our results and those of QTL controlling vine size were observed on chromosome 3, 5, and 10 across the three BLUP datasets. The three most consequential QTL  showed minor effects with a 4.4 LOP score and 5% h2QTL. The effects of the three QTL on chromosome 10 were also relatively minor, showing between 4.46 and 6.11 LOP scores and an average of 10% h2QTL. The one SNP, PotVar0120301 and another two SNPs, solcap_snp_c2_22594 and solcap_snp_c2_48127 were the most adjacent markers to the QTL on chromosome 3 and 10, respectively.The three QTL on chromosome 3 . Consequently, potato vine size has not been emphasized compared to other more economically attractive agronomic traits. However, the lengths and canopy patterns of the upper parts of the potato can increase the light absorption rate for photosynthesis, affecting yield, plant health, etc. For example, EB resistance were found on chromosomes 2 and 5 while analyzing EB_2019_raw_pheno dataset  for the area near those SNP markers is warranted for the development of MAS for EB.Two different QTL for tant QTL . The Pottant QTL . TherefoEB QTL having relatively lower LOP and h2QTL were detected on chromosomes 2. The EB_2019_pheno_ch2 QTL was located at 19.30 cM. Compared to other QTL, the EB_2019_pheno_ch2 QTL has extremely wider support interval (over 71 cM length), suggesting the potential existence of at least more than one minor EB resistance-associated QTL or more on this chromosome  on chromosome 2, but their results could not be compared with this current study because EB resistances of tubers were not evaluated.Another EB QTL analysis, unlike the other QTL analyses, an additional verification process was executed to evaluate the reliability of the QTL results from the raw phenotype data. Both 2019- and 2020-year raw data (which did not segregate) were first converted to EB BLUP datasets, based on the mixed model (eq. 2), and then the BLUP datasets were loaded on the QTLpoly to run a new QTL analysis. As expected, the 2020 EB BLUP data produced no significant QTL, but the 2019 EB BLUP dataset showed two significant QTL which were located at the exact same positions and chromosomes of the EB_2019_pheno_ch2 and EB_2019_pheno_ch5 QTL, respectively (data not shown). Furthermore, the two QTL derived from the EB BLUP data had the same  LOP scores, QTL heritabilities (h2QTL), and support intervals compared to those of the EB_2019_pheno_ch2 and EB_2019_pheno_ch5 QTL (data not shown). Those same  results between the two different QTL analyses with raw EB damage phenotype and EB BLUP data, respectively, reflect that the direct use of the raw phenotype data did not significantly affect the QTL results. However, further QTL analysis with multiple-year data is necessary to scrutinize the consistency of the EB_2019_pheno_ch2 and EB_2019_pheno_ch5 QTL as well as an interaction between an environmental effect and the two QTL.Since the one-year raw phenotype dataset was only usable and was used for this VW, VM, VS, and EB QTL analyses while comparing the references with our study  is thought to represent either a gene having a pleiotropic effect on several apparently unrelated traits  on chromosome 5 was repeatedly identified as having significant QTL for the three-potato pathogen resistances and two agronomic traits assessed in this study, even though their relationships were not obviously explained (d traits , or the LB, LB-AUDPC, EB, VW, VM, and VS provided useful genetic information, which can be used for future MAS or potato breeding programs of the russet market class. On chromosome 5, the A08241_14-17_hotspot emerged as an essential genetic location for all the five traits evaluated in this study; thus, detailed research on this hotspot is expected to create greater added value in the future russet potato breeding. It was also revealed that chromosome 1 included Verticillium wilt and vine maturity QTL, chromosomes 3 and 10 possessed vine size QTL, and chromosome 2 had an early blight QTL, suggesting additional options for better MAS.The QTL analyses for The original contributions presented in the study are included in the article/All authors contributed to the study conception and design. JP, JW, and RN performed material preparation, field experiments, and data collection in Idaho. In Oregon, VS and SY conducted other field experiments and data collection. JP mainly performed data analysis, statistical analyses, linkage and QTL mapping, and writing the initial draft of the manuscript. All authors commented on previous versions of the manuscript with revisions incorporated. All authors also read and approved the final submitted manuscript."}
+{"text": "A 64-year-old man underwent orthotopic liver transplantation due to hepatocellular carcinoma on liver cirrhosis diagnosed 2 years earlier. Three months after transplantation surgery, he presented with jaundice, and a cholangio magnetic resonance imaging scan revealed a biliary anastomosis stricture with marked kinking and retrograde dilatation. A multidisciplinary committee decided on an endoscopic approach.Initially, retrograde biliary cannulation by endoscopic retrograde cholangiopancreatography was attempted, but advancement of different guidewires through the biliary anastomosis was not possible. The use of peroral digital single-operator cholangioscopy helped us to visualize a complex biliary stricture, which explained why it had not been possible to advance any kind of guidewire into the intrahepatic ducts . A 0.01Video\u20061\u2002Total stricture of liver transplant biliary anastomosis resolved with peroral digital cholangioscopy and the non-flexible end of a 0.018-inch biliary guidewire.Finally, endoscopic maximal stent therapy (MST) was initiated. First, balloon dilation up to 8\u200amm was performed, followed by successful placement of three plastic stents  as the first session of a 1-year MST program .This case of post-liver transplant biliary stricture was resolved using cholangioscopy/fluoroscopy guidance and a guidewire technical variant. The utility of digital single-operator cholangioscopy for the endoscopic management of biliary strictures after liver transplantation has been published previouslyEndoscopy_UCTN_Code_TTT_1AR_2AZ"}
+{"text": "Selective biliary cannulation in patients with Roux-en-Y gastrectomy is considered technically difficultA 78-year-old man with choledocholithiasis was referred to our facility. The patient previously underwent a Roux-en-Y gastrectomy. Computed tomography revealed stones in the common bile duct. Therefore, endoscopic retrograde cholangiopancreatography was performed using a short-type single-balloon enteroscope , with a working length of 152\u200acm and a working channel diameter of 3.2\u200ammVideo\u20061\u2002Successful selective biliary cannulation via transpancreatic biliary sphincterotomy using a novel sphincterotome in a patient with Roux-en-Y gastrectomy.This novel sphincterotome could overcome the difficulty of adjusting the incision direction for sphincterotomy in patients with surgically altered anatomy. Therefore, it facilitates endoscopic sphincterotomy as well as transpancreatic biliary sphincterotomy in difficult biliary cannulation cases. This device could aid in the development of a safe and effective advanced selective biliary cannulation technique.Endoscopy_UCTN_Code_TTT_1AR_2AC"}
+{"text": "Perforation during esophageal endoscopic submucosal dissection (ESD) can lead to severe complications; localized muscle defects can result in large perforations during ESD that require surgeryA 75-year-old man underwent ESD for a 12-mm superficial esophageal tumor in the middle esophagus, diagnosed as T1a (epithelium or lamina propria mucosa) . A largVideo\u20061\u2002Reopenable-clip over-the-line method for closure of large perforation during esophageal endoscopic submucosal dissection.Localized muscle defects may be present in the esophagus and can result in unexpectedly large perforations during ESD. In such cases, ROLM may be a useful endoscopic treatment option to avoid invasive surgery.Endoscopy_UCTN_Code_TTT_1AO_2AI"}
+{"text": "A 47-year-old man suffered from acute appendicitis 4 years ago, relieved by a 3-day course of oral levofloxacin. A follow-up colonoscopy 1 year later revealed a 1.5-cm submucosal bulge near the appendiceal orifice. The patient underwent endoscopic mucosal resection (EMR) for the lesion at that hospital  and wasSince the lesion was recurrent and simple drainage did not solve the problem, endoscopic transcecal appendectomy was suggested. A full-thickness resection was performed using an ITknife2 and a HookKnife, with submucosal injection and circumferential submucosal incision . The exVideo\u20061\u2002Endoscopic transcecal appendectomy for recurrent appendicitis after previous endoscopic mucosal resection.Pathologic diagnosis confirmed chronic appendicitis . The paEndoscopy_UCTN_Code_TTT_1AQ_2AC"}
+{"text": "A 75-year-old man underwent an anterior rectum resection due to an early neoplastic rectal lesion. On surveillance endoscopy 4 years later, the surgical anastomosis was identified at 8\u200acm from the anal margin and was associated with a 35-mm lateral spreading tumor (LST), compatible with neoplastic recurrence. The LST reached the anastomosis as well as an adjacent pseudodiverticular recess . White-The procedure was performed using a Flush Knife BT  . SubmucVideo\u20061\u2002Endoscopic submucosal dissection of rectal lesion recurrence at the anastomosis site.Patients who have undergone colorectal surgery for either cancer or benign lesions continue to be at risk of developing recurrent, residual, or metachronous lesions in the remaining colon, including at the anastomosis siteEndoscopy_UCTN_Code_CPL_1AJ_2AD"}
+{"text": "Prophylactic treatment for bleeding is indicated for esophageal varices if the varices are F2 or greater, or if the red-color sign is positiveA 70-year-old woman with liver cirrhosis had esophageal varices . We therefore decided to perform endoscopic injection sclerotherapy using 5\u200a% EO. First, a fixation balloon and a transparent cap with a slit were attached to an endoscope . The slVideo\u20061\u2002Endoscopic injection sclerotherapy is performed using the transparent cap with a slit, which facilitates puncture and post-puncture fixation of the esophageal varix.The transparent cap with a slit facilitates puncture and post-puncture fixation of varices, even when being used by trainees.Endoscopy_UCTN_Code_TTT_1AO_2AD"}
+{"text": "Limosilactobacillus reuteri strains that each produced a recombinant therapeutic protein with an 11 amino acid tag, which is essential to yield a luminescent signal. Luminescent-based quantification of recombinant protein was more sensitive than commercially available immunoassays. In addition, we demonstrated that the bioluminescent peptide tagging system allows in situ recombinant protein detection in a continuous-culture parallel bioreactor system. This presents an exciting opportunity to determine recombinant protein production dynamics in response to different stimuli. Finally, following oral administration of recombinant microbes, luminescence in intestinal and fecal samples allowed for rapid detection of microbes with equal sensitivity to conventional plate count. Because we demonstrated the functionality of this bioluminescent peptide tagging system in 12 species encompassing nine genera, our approach will create previously unexplored opportunities in lactic acid bacteria research.Biotherapeutic strategies to promote health, including the application of engineered microbes to deliver therapeutic molecules, hold strong promise. However, without precision tools to detect therapeutic microbes and their products, we are hampered in our ability to monitor and fine-tune therapeutic delivery. Here, we adapted a bioluminescent peptide tagging system for use in lactic acid bacteria, a group of organisms whose members are commonly exploited as delivery vehicles of therapeutics and vaccines. As a proof of concept, we developed various in vitro and in situ, while it also can be used to enumerate recombinant bacteria from the mouse gastrointestinal tract with accuracy comparable to that of conventional plate counts. Our work expands the lactic acid bacteria genetic toolbox and will facilitate researchers in industry and academia with opportunities to monitor microbes and proteins under different physiologically relevant conditions.Lactic acid bacteria constitute a genetically diverse group of microorganisms with significant roles in the food industry, biotechnology, agriculture, and medicine. A core understanding of bacterial physiology in diverse environments is crucial to select and develop bacteria for industrial and medical applications. However, there is a lack of versatile tools to track (recombinant) protein production in lactic acid bacteria. In this study, we adapted a peptide-based bioluminescent tagging system that is functional across multiple genera and species. This system enables tracking of tagged proteins both  Apart from some notable exceptions, many LAB are considered nonpathogenic and are naturally occurring in diverse habitats, including the gastrointestinal tract (GIT) of humans and other vertebrates , 2, as wFluorescent methods, such as green fluorescent protein (GFP) or mCherry, have been employed to track recombinant LAB in the GIT \u201311. AlTo track recombinant proteins, bioluminescence-based assays have proven fruitful, including the HiBiT system, which was originally developed for use in eukaryotic cells. The HiBiT system employs an 11 amino acid tag, which generates a luminescent signal upon high-affinity complementation with LgBiT, an 18 kDa subunit . Comparein vitro, in vivo, and in situ applications in multiple LAB species. Key features described in this study are the broad applicability and ability of the bioluminescent peptide tagging system to detect recombinant proteins in situ without cell lysis. In addition, bacteria isolated from culture or intestinal tissues can be quantified in minutes with an accuracy comparable to the conventional plate count method. The bioluminescent peptide tagging system showed superior sensitivity to detect recombinant proteins compared to commercial enzyme-linked immunosorbent assays (ELISAs). Because of the robustness, we envision that the bioluminescent peptide tagging system described in this study will be much welcomed by the LAB communities in academia and industry, and by the microbiome community in general.Here, we developed the bioluminescent peptide (HiBiT) tagging system for Limosilactobacillus reuteri harboring pJP_Leptin or pJP_Leptin_tag, or their respective cell pellets resuspended in phosphate-buffered saline with Tween 20 (PBS-T), were disrupted by mechanical lysis. Lysates derived from the cultures and cell pellets derived from Lm. reuteri harboring pJP_Leptin_tag both yielded a 4-log increase in luminescence, compared to lysates derived from the untagged control . Thus, luminescence is not affected in MRS within the pH range of 4.5\u20136.5.Because the culture pH of etection . The celLm. reuteri harboring pJP_Leptin_tag in 1 mL of MRS or MRS that was diluted up to 1,000-fold, followed by cell disruption and luminescent detection (P < 0.05). Compared to the 2-fold diluted MRS suspension, additional dilutions did not significantly change luminescence levels. Thus, the color of MRS, perhaps combined with inhibitory substance(s) present in MRS, can be recognized as interference factors for luminescence detection, which can be alleviated by diluting the sample 2-fold.To test whether the dark media color, or select ingredients in MRS, interfered with luminescent readings, we resuspended nine 9-log CFU of etection . CompareLm. reuteri harboring pJP_Leptin_tag (0\u201326), we observed a linear correlation between the dilution and the bioluminescence levels (2R = 0.98). To assess a broader range, we resuspended cell pellets of Lm. reuteri harboring pJP_Leptin_tag (9-log CFU/mL) in MRS or PBS-T, followed by 10-fold serial dilution in PBS-T and mechanical cell disruption (0 to 106 (2 =R 0.94), while for MRS the dynamic range spanned from 101 to 106 (2R = 0.92).As an initial step to gain insight into the dynamic range of luminescence, we used a bioluminescent imaging system to visualize twofold dilutions of lysates derived from engineered ptin_tag . Over thsruption . A lineaLm. reuteri-producing murine leptin, we previously engineered Lm. reuteri to produce murine interleukin-22 (IL-22) (Lm. reuteri to produce murine interferon- (IFN-\u03b2). We additionally developed Lm. reuteri harboring pJP_IL-22_tag and Lm. reuteri harboring pJP_IFN-\u03b2_tag. To compare the performance between commercial ELISAs and bioluminescent peptide tagging, lysates derived from each recombinant Lm. reuteri were diluted and subjected to each assay. Although the optical density (OD) of the ELISA correlated with the dilution of the recombinant proteins (P < 0.05). We also found that the presence of the 3\u2032-tag on IFN-\u03b2 interfered with the immune assay, as the level of IFN-\u03b2 detected by ELISA was two times higher than IFN-\u03b2_tag. To test whether moving the tag would overcome this issue, we generated a derivative that had the tag at the 5\u2032 end of IFN-\u03b2. While the luminescent level of 5\u2032-tagged IFN-\u03b2 was comparable to 3\u2032-tagged IFN-\u03b2, the ELISA for the 5\u2032-tagged IFN-\u03b2 failed, suggesting that the 5\u2032-end tag abolished binding of the antibody with the protein (ELISAs are routinely used to quantify (recombinant) proteins, including cytokines. In addition to  (IL-22) . In thisproteins  , the dyproteins ; Fig. S1 protein . Despitein situ detection of recombinant proteins, it is required that the luminescent substrate enters the cell and that the luminescent signal from live cells can be detected. We determined that adding Nano-Glo HiBiT Extracellular Substrate yielded a luminescent signal in cells expressing the luminescent tagged protein and LgBiT, while no signal was obtained in cells lacking LgBiT  = 0.05, which is equivalent to OD600 = 0.3], one vessel was supplemented with induction peptide (2.5 ng/mL) to initiate the expression of leptin_tag. Samples were harvested every hour for up to 12 h (600 = 1.0), approximately 2 h upon induction, the cell density was stably maintained at AU = 0.15 (in situ detection of (recombinant) proteins and opens up exciting opportunities for optimization studies of protein production in microbial cell factories along with addressing fundamental questions on native protein production in different experimental setups.We aimed next to determine  to 12 h . Once ceU = 0.15 . Three hU = 0.15 . ModelinU = 0.15 . Becausein vitro and in situ use of the bioluminescent peptide tagging system in Lm. reuteri, we next explored its applicability to quantify bacteria during and following gastrointestinal transit. First, we investigated to what extent mouse feces, a complex matrix, interferes with luminescence. Lm. reuteri harboring pJP_Leptin_tag_EFTu_LgBiT was inoculated in 1 mL of PBS-T, fecal suspension (100 mg/mL) in PBS-T, or in diluted fecal suspensions at the final concentration of 7-log CFU/mL. Compared to PBS-T, luminescence levels were 10-fold lower in the fecal suspension of 100 mg/mL  . The lumrd curve . The sen7, 108, and 109 CFU of Lm. reuteri harboring pJP_Leptin_tag_EFTu_LgBiT. Twenty-four hours following oral administration, fresh fecal pellets were collected and animals were subsequently sacrificed. We harvested contents from the large intestine, small intestine, and cecum. Using the conventional plate count method, we determined CFU levels. Luminescence levels of the various contents were determined in PBS-T. Using a standard curve we established based on fecal suspensions  Lm. reuteri_pJP_Leptin; (3) L. casei_pNZ8048; (4) Lp. plantarum_pNZ8048; and (5) a random mixture of cultures 1\u20134  ures 1\u20134 . Plates ures 1\u20134 . Colony B. bifidum. Although the 11 LAB strains belonged to a single genus previously, they were recently reclassified into eight different genera due to the high phenotypic, ecological, and genetic diversities  to identify Cre recombinase mutants based on GFP expression level . Attempts to detect luminescence following a boost in recombinant protein release by in vitro mitomycin-C induction, to induce prophages and thus increase therapeutic release, were unsuccessful (data not shown). Possibly, the amount of recombinant protein released was not enough to overcome the inhibitory effects of bile acids and digestive enzymes present in the small intestine  under hypoxic conditions (5% CO2 and 2% O2), and Lactococcus lactis strains were cultured in M17 (BD) containing 0.5% (w/v) of glucose at 30\u00b0C (static). Escherichia coli was grown at 37\u00b0C with gentle agitation (200 rpm) in lysogeny broth . If applicable, erythromycin was used at the concentrations of 300 \u00b5g/mL for E. coli and at 5 \u00b5g/mL for other strains listed in The bacterial strains and plasmids used in this study are listed in Reagents were obtained from Thermo Fisher unless stated otherwise. To fuse DNA fragments, we used ligase cycle reaction (LCR) . For cloLm. reuteri that produces HiBiT-tagged leptin, we first amplified the backbone of pVPL3789\u2014a derivative of pNZ8048 that encodes murine leptin under the control of the EFTu promoter (E. coli EC 1000 by electroporation. By colony PCR (oVPL1447-oVPL329), putative clones were identified. The integrity of the purified plasmid DNA was determined by Sanger sequencing (Genewiz), and the resultant plasmid was named pJP_Leptin_tag (pVPL31599). Next, we transformed by electroporation pVPL31599 into Lm. reuteri VPL1014, resulting in Lm. reuteri harboring pJP_Leptin_tag (VPL31611). The integrity of the transformant was confirmed by amplification of the pJP_Leptin_tag backbone (oVPL2238-oVPL2351) followed by Sanger sequence analyses (oVPL2351).To develop promoter \u2014with oliLm. reuteri harboring pJP_Leptin (VPL3791) and pJP_Leptin_tag were diluted to OD600 = 0.1 and harvested after 8-h growth. After measuring the OD, cells were washed once with PBS-T (PBS with 0.05% (v/v) Tween 20) and resuspended in PBS-T. Subsequently, cells in MRS culture or PBS-T were disrupted by bead-beating. Briefly, approximately 200 \u00b5L of zirconia glass beads (BioSpecP) was added to 1 mL of cell suspension followed by two cycles of 1.5 min bead-beating with a 30-s interval on ice. Cell-free extracts were prepared by centrifugation  and filtered by a 0.2-\u00b5m-pore syringe filter (Argos Technologies). The luminescent signals were determined in a Glomax Discover Microplate Reader (Promega) using the HiBiT extracellular detection kit , following the manufacturer\u2019s instructions. RLUs were normalized as RLU/the initial RLU \u00d75 .To release the intracellularly accumulated (tagged) leptin from bacterial cells, 600 = 4.5) Lm. reuteri harboring pJP_Leptin_tag were washed, as described above, and resuspended to 9-log CFU/mL in the original supernatant  or in the supernatant that was adjusted to pH 6.5 with 5 N NaOH. Subsequently, we prepared lysates and determined luminescence levels as described above.To investigate the inhibitory effect of pH on luminescent signal, cells derived from 1 mL of late log phase (ODLm. reuteri harboring pJP_Leptin_tag was prepared as shown above and resuspended into 1 mL (diluted) MRS to reach 9-log CFU/mL. Subsequently, we prepared lysates and performed luminescent signal analysis as described above.To determine the extent to which MRS inhibits luminescence, we measured the luminescence of leptin_tag in different concentrations of MRS. First, we diluted MRS to 2-, 5-, 10-, 100-, and 1,000-fold with PBS-T (PBS with 0.05% (v/v) Tween 20). Late log phase of Lm. reuteri harboring pJP_Leptin_tag (VPL31611) were centrifuged , and the cell pellets were washed once with PBS-T. Cell suspensions were used to prepare the lysate as described above. The lysate was twofold serially diluted into PBS-T and 100 \u00b5L of each diluted sample was added in a 96-well plate. The luminescent signal of each sample was measured using the HiBiT extracellular detection kit as described above with the Glomax Discover Microplate Reader. The bioluminescence image of the same 96-well plate was obtained with the ChemiDoc imaging system (Bio-Rad), using the chemiluminescent filter. The luminescent intensity was visualized using the ImageLab software .To visualize bioluminescence in bacterial cultures, stationary phase cultures of Lm. reuteri harboring pJP_Leptin_tag. Late log phase culture was harvested, washed, and resuspended into PBS-T or MRS to reach the final concentration of 9-log CFU/mL. Each bacterial suspension was 10-fold serially diluted into PBS-T. After dilution, cells underwent the same procedure to prepare samples for luminescent analysis.To investigate the dynamic range by which CFU can be quantified using luminescent analysis, we measured the luminescent signal of leptin_tag obtained from different concentrations of Il-22 and ifn-\u03b2 genes were amplified with oVPL2239-oVPL2115 and oVPL4493-oVPL4494 from Lm. reuteri harboring pHelp_IL22 (VPL4069) or pJP_IFN-\u03b2_pMut_thyA (pVPL31250), respectively. Each gene was cloned into the pJP_Leptin_HiBiT backbone by LCR, as described previously  using pJP_IFN-\u03b2 (pVPL31994) with oVPL4848-oVPL4849 which contains the HiBiT gene at the 5\u2032-end following the method for pJP_Leptin_tag as described above. After transformation into E. coli, the Sanger sequencing was performed with oVPL1447-oVPL2351 to identify the insertion of HiBiT.To compare luminescent-based quantification of the HiBiT tag with commercial ELISAs, we constructed two additional plasmid constructs including pJP_IL-22_tag (pVPL31970) and pJP_IFN-\u03b2_tag (pVPL31974). First, we amplified a backbone from pJP_Leptin_HiBiT with oligonucleotide pair oVPL1448-oVPL4418 to generate a plasmid backbone lacking the leptin gene. _IL22 VPL69 or pJPLm. reuteri strains harboring pJP_Leptin_tag, pJP_IL-22_tag, or pJP_IFN-\u03b2_tag were inoculated separately into MRS + EM5 to OD600 = 0.1. Once the cell density reached OD600 4\u20135, cells were washed and disrupted in PBS-T as stated above. The lysates were serially diluted in ELISA dilution buffer or PBS-T and quantified by ELISA  and HiBiT extracellular detection kit.Stationary phase in situ, we engineered Lm. reuteri to produce the LgBiT protein that interacts with HiBiT to yield a luminescent signal. Briefly, the LgBiT sequence was codon optimized for expression in L reuteri using the OPTIMIZER web server . Finally, we transformed pVPL31655 into Lm. reuteri VPL1014 to yield Lm. reuteri harboring pJP_Leptin_tag_EFTu_LgBiT (VPL31657).To detect recombinant proteins b server , 43 and b server . The synin situ, stationary phase cultures of Lm. reuteri harboring pJP_Leptin_tag (VPL31611) and Lm. reuteri harboring pJP_Leptin_tag_EFTu_LgBiT (VPL31657) were diluted to OD600 = 0.1 followed by culture until late log (OD600 = 4.5). Each culture was subsequently divided into two groups of which one of the cultures was subjected to bead-beating. The luminescent signal of each sample was measured using the HiBiT extracellular detection kit with or without LgBiT.To detect leptin in situ detection of recombinant protein, we cloned the gene encoding murine leptin with at the 3\u2032-end the sequence encoding HiBiT downstream of the inducible promoter of plasmid pSIP411; downstream we cloned the EFTu promoter fused to the sequence encoding LgBiT to yield pVPL31952. Briefly, the backbone was amplified from pSIP_Leptin_HiBiT (pVPL31948) with oligonucleotide pairs of oVPL4543 and oVPL4544. The fusion of EFTu-LgBiT was amplified from pJP_Leptin_tag_EFTu_LgBiT (pVPL31657) using oVPL1366 and oVPL1221. The amplicons were mixed at a 1:1 molar ratio for blunt-end ligation, followed by electroporation into E. coli EC1000. The insertion of leptin_tag and EFTu_LgBiT was confirmed with the oligonucleotides oVPL659-oVPL4539-oVPL660 and oVPL659-oVPL4337-oVPL660, respectively. The integrity of the DNA sequence was confirmed by Sanger sequencing using oligonucleotides oVPL4339, oVPL4539, oVPL1326, and oVPL1447.To investigate the applicability of a bioluminescent peptide tagging system for Lm. reuteri harboring pSIP_Leptin_tag. We used two continuously operated stirred bioreactor vessels in parallel. Each bioreactor vessel contained 200 mL of MRS supplemented with 5 \u00b5g/mL of erythromycin and was operated with 2% of dissolved oxygen with agitation (50 rpm) at 37\u00b0C. The pH was maintained within a range of 6.3\u20136.7 and was adjusted with 1.5 N NaOH (Sigma-Aldrich). A stationary culture of Lm. reuteri harboring pSIP_Leptin_tag_EFTu_LgBiT was inoculated in each vessel to AU = 0.011 (OD600 = 0.1). At an AU of 0.050 (OD600 = 0.3), induction peptide was added to a final concentration of 2.5 ng/mL. For the continuous bioprocessing, fresh MRS containing 5 \u00b5g/mL of erythromycin was added at a flow rate of 300 mL/h to sustain the AU of 0.15 (OD600 = 1.0). To track in situ recombinant protein production, samples were aseptically collected every hour and luminescence was measured instantly using the above-described procedures.We used the DASbox Mini Bioreactor System  to continuously culture Lm. reuteri harboring pJP_Leptin_tag_EFTu_LgBiT in different fecal dilutions. The fecal suspension (100 mg/mL) was 10-fold serially diluted in PBS-T. Late log phase Lm. reuteri harboring pJP_Leptin_tag_EFTu_LgBiT  was prepared as described above followed by inoculation in each diluted fecal suspension or PBS-T to reach a final concentration of 7-log CFU/mL. The luminescent signal was measured as described above.To examine the inhibitory effect of murine feces on luminescence detection, we measured the luminescent signal from in situ luminescent signal from different concentrations of Lm. reuteri harboring pJP_Leptin_tag_EFTu_LgBiT. The late log phase bacteria were prepared in PBS-T as stated above and inoculated into 100-fold diluted fecal suspension (1 mg/mL) to reach a final concentration of 2- to 7-log CFU/1 mL of fecal suspension. After then, the luminescent signal was measured from each suspension. The standard curve for quantification of bacterial cells was constructed to represent the relationship between bacterial concentration and luminescent signal.The standard curve of luminescent signal vs bacterial concentration in the fecal sample was constructed by measuring the All mouse experiments were performed in accordance with NIH guidelines, Animal Welfare Act, and US federal law and were approved by the Application Review for Research Oversight at Wisconsin (ARROW) committee and overseen by the Institutional Animal Care and Use Committee (IACUC) under protocol ID A006078-R1. Conventional pathogen-free and germ-free mice were housed at the Animal Science and Laboratory of Animal Research Facilities, respectively, at the University of Wisconsin-Madison.ad libitum.Eight-week-old male B6 mice (C57BL/6J) were purchased from Jackson Labs  and adapted for 1 week to the new environment prior to the start of the experiment. Animals were housed at an environmentally controlled facility with a 12-h light and dark cycle. Standard chow diet (LabDiet 5008) and water were provided Lm. reuteri harboring pJP_Leptin_tag_EFTu_LgBiT was cultured in fresh MRS until OD600 = 2. Cells were washed and resuspended in PBS to adjust the concentration at 8-, 9-, and 10-log CFU/mL. Mice were administered oral gavage bacteria (100 \u00b5L of each concentration of cell) or PBS (n = 5/group). At 24 h post-gavage, each mouse was sacrificed, and the contents from the large intestine, small intestine, cecum, and feces were harvested. Each sample was resuspended in PBS to 100 mg/mL, and the luminescent signal was measured from sample suspension as described above. To predict the bacterial concentration, the luminescent signal was subjected to a preconstructed standard curve between the luminescent signal and bacterial concentration in fecal samples. In addition, all samples were plated on MRS agar containing 5 \u00b5g/mL of erythromycin to compare the bacterial concentration determined by RLU.For oral administration, bacteria were prepared as follows. B. bifidum. The plasmid pJP_Leptin_tag_EFTu_LgBiT could only be established in the LAB strains , Lm. reuteri harboring pJP_Leptin_tag_EFTu_LgBiT (VPL31657), L. casei_pNZ8048 (VPL31879), Lp. plantarum_pNZ8048 (VPL31871), or the four-strain mixture, on MRS agar plates (5 mm diameter) supplemented with 5 \u00b5g/mL erythromycin. After incubation at 37\u00b0C for 48 h, 1 mL of Nano-Glo HiBiT Extracellular Buffer containing 10 \u00b5L of Nano-Glo HiBiT Extracellular Substrate was poured on the agar plate. The colonies were visualized by the ChemiDoc imaging system with colorimetric and chemiluminescent filters, and images were analyzed using the ImageLab software.To identify bioluminescent bacteria on agar plates, we plated stationary phase cultures of t-test and one-way analysis of variance (ANOVA) . For Pearson correlation, we performed multivariate pairwise correlations (JMP pro 11.0.0).A minimum of three biological replicates were performed, and the results were expressed as mean \u00b1 SD. All samples were included in the analyses, and experiments were performed without blinding. Graphs were prepared using GraphPad Prism software . All statistical analyses were performed using paired"}
+{"text": "Endoscopic submucosal excavation (ESE) has been successfully applied to the resection of gastric submucosal tumors (SMTs)Video\u20061\u2002Pursestring encirclement before endoscopic submucosal excavation of a cecal submucosal tumor in a 50-year-old man.A 50-year-old man underwent a screening colonoscopy in which an 8-mm SMT was discovered in the cecum . EndoscPSE-ESE can facilitate the closure of a perforation and avoid peritonitis. It is therefore a feasible, effective, and safe treatment for colonic SMTs.Endoscopy_UCTN_Code_TTT_1AQ_2AD"}
+{"text": "Pseudomonas aeruginosa bacteriophage called UF_RH1. This lytic phage has a genome size of 42,567 bp and is classified as a member of the Siphoviridae family and the Septimatrevirus genus. UF_RH1 shares genetic similarities with Stenotrophomonas phage vB_SmaS-DLP_2.Here, we present the genome sequence of a novel  Pseudomonas aeruginosa is an opportunistic pathogen that poses significant challenges for treatment  to 400 \u03bcL of P. aeruginosa (strain DJ06) in logarithmic phase and then performing single-plaque isolation using double-layer agar  genome with a length of 42,567 bp, a GC content of 53.56%, 931,200 total reads, and 23,360\u00d7 average read coverage. PhageTerm predicted a circularly permuted genome for UF_RH1. It includes 57 open reading frames (ORFs), and it shares sequence similarity with members of the genus Septimatrevirus  under BioProject accession number PRJNA936202, SRA accession number SRS16838829, and BioSample accession number SAMN33343989.The complete phage genome sequence was deposited in GenBank under the accession number"}
+{"text": "We present the case of a 52-year-old woman who underwent endoscopic full-thickness resection (EFTR) with one port placement for a 20-mm large gastric submucosal tumor originating from the muscularis propria layer .EFTR was initiated with a mucosal incision around the entire circumference of the lesion using a DualKnife J , followed by a full-thickness resection using an ITknife2  in combination with traction provided by a multi-loop traction device After resection, we employed novel anchor pronged clips  to close the large transmural defect . The MAClosure of a gastric wall defect after endoscopic full-thickness resection using novel anchor pronged clips.Video 1While reports exist on the endoloop-assisted closure methodEndoscopy_UCTN_Code_TTT_1AO_2AI"}
+{"text": "Non-negative tensor factorization (NTF) enables the extraction of a small number of latent components from high-dimensional biomedical data. However, NTF requires many steps, which is a hurdle to implementation. Here, we provide a protocol for TensorLyCV, an easy to run and reproducible NTF analysis pipeline using Snakemake workflow management system and Docker container. Using vaccine adverse reaction data as an example, we describe steps for data processing, tensor decomposition, optimal rank parameter estimation, and visualization of factor matrices.For complete details on the use and execution of this protocol, please refer to Kei Ikeda et\u00a0al.  \u2022TensorLyCV framework to perform NTF\u2022Rank optimization using the masking approach to analyze high-dimensional biomedical data\u2022Snakemake and Docker workflow to analyze vaccine adverse reaction data Publisher\u2019s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Non-negative tensor factorization (NTF) enables the extraction of a small number of latent components from high-dimensional biomedical data. However, NTF requires many steps, which is a hurdle to implementation. Here, we provide a protocol for TensorLyCV, an easy to run and reproducible NTF analysis pipeline using Snakemake workflow management system and Docker container. Using vaccine adverse reaction data as an example, we describe steps for data processing, tensor decomposition, optimal rank parameter estimation, and visualization of factor matrices. This protocol describes a procedure for performing non-negative tensor factorization (NTF) on time-series biomedical data using a workflow called TensorLyCV. NTF requires multiple steps: software installation, data reshaping, rank estimation, decomposition at the optimal rank, and visualization of the tensor components. While the decomposition itself can be performed by existing Python,,,,,,NTF is a topic modeling approach that can decompose the dynamics of biomolecules, biological signals, and clinical symptoms into a small number of time-evolving components. By describing biological phenomena as an ensemble of components, associations with clinical outcomes can be efficiently examined and testable hypotheses about the underlying mechanisms can be generated. NTF has been applied to a variety of biomedical data with non-negative  must be predetermined, but the number of potential components is often not obvious, especially for biomedical data. Traditionally, the elbow methodHere, we present a comprehensive protocol for the NTF analysis using vaccine adverse reaction data from the recent studyhttps://github.com/kokitsuyuzaki/TensorLyCV) to perform TensorLy,To reproduce the analysis in Ikeda K. et\u00a0al.'s study,Timing: 1 hIn this section, we describe how to install required software.https://www.r-project.org).Note: The tools needed will vary depending on the user\u2019s data. Here, we recommend the following R packages.Note: The following code line is in R language, to be inputted into R-console window.https://cloud.r-project.org', dependencies=TRUE)\"> R -e \"install.packages, repos='For preprocessing section later, download and install the latest Rhttps://snakemake.readthedocs.io/en/stable/getting_started/installation.html).TensorLyCV is intended to be run by the snakemake command. Snakemake is a Python package. Therefore, after installing Python first, install Snakemake by using some Python package manager such as pip, conda, or mamba. To download and install Snakemake, follow the instructions on the installation page (https://hub.docker.com/r/koki/tensorlycv_component). Snakemake uses Singularityhttps://docs.sylabs.io/guides/3.0/user-guide/installation.html#).All necessary tools to perform TensorLyCV  have already been pre-installed in a Docker container . To download the appropriate binary file, follow the instructions on each installation page above.Finally, download TensorLyCV and change the working directory. The following code line is in Bash script, to be inputted into terminal window.Timing: 1minIn this section, we describe how to download data for demonstration.https://figshare.com/ndownloader/files/38344040-O data/vaccine_tensor.npy> wget --no-check-certificateIn TensorLyCV, the input data is assumed to be a binary file containing NumPyTiming: 30\u00a0minIn this section, we describe how to preprocess the demonstration data.1.Start R and load the following packages:> library(\"readxl\")> library(\"writexl\")> library(\"tidyverse\")> library(\"einsum\")> library(\"abind\")> library(\"reticulate\")> library(\"rTensor\")2.To set up the subsequent analysis, type as follows:> metadata_name <- c> symptoms <- c> days <- c, paste)> thr <- length(symptoms) \u2217 length(days) \u2217 0.33.Converting 2D data to 3D.First, we show the procedure for constructing tensor data. A tensor can be considered a generalization of a matrix. For example, a third-order tensor is a three-dimensional array that stores values in the depth way in addition to the vertical and horizontal ways. When saving such high-dimensional data  as a 2D Excel spreadsheet, several data types can be considered. Here, we introduce the procedures for constructing tensor data from three data types. In subsequent demonstrations, we will use only a portion of the data taken from Ikeda K. et\u00a0al.Type 1: Wide short data: The first type is wide short matrix data, with rows for subjects and columns for combinations of days and symptoms  -> data1> map{data1 %>% select))}) %>% abind -> vaccine_tensorThen, a set of matrices stratified by days is created with the mapType 2: Tall narrow data: The second type is tall narrow data also known as \"tidy\" datahttps://figshare.com/ndownloader/files/38362235> read_excel(\"type2_long.xlsx\") -> data2> subjects <- as.character(sort(unique(data2$ID)))template <- matrix, ncol=length(symptoms))> dimnames(template) <- list> map{data2 %>% filter %>% pivot_wider -> tmp\u00a0template <- as.matrix\u00a0template\u00a0}) %>% abind -> vaccine_tensorThen, a tidy dataset stratified by days is created with the map function, reshaped as a matrix form by pivot_widerType 3: Multiple sheets data: The third type is one in which Type 1 data is pre-stratified by days and stored as separate sheets , function(x){\u00a0read_excel)}) %>% abind -> vaccine_tensor4.Filter low-quality data.Note: In Ikeda, K. et\u00a0al.'s study,> vaccine_tensor %>% is.na %>% einsum %>% `<`(thr) %>% which -> subjects5.Save tensor data as a NumPy binary file.> np <- import(\"numpy\")> np$save)6.Finally, convert the vaccine_tensor to NumPy\u2019s binary file.Note: This conversion can be performed by reticulateTiming: two daysIn this section, we describe how to perform TensorLyCV.1.Execute the following Snakemake workflow.TensorLyCV consists of 14 rules, and once a rule is successfully executed, the downstream rule is then executed, and this procedure is repeated to ensure that all calculations are properly finished  and 5. T> snakemake -j 5 --config input=data/vaccine_tensor.npy outdir=output rank_min=1 rank_max=10 trials=50 n_iter_max=1000 ratio=30 --resources mem_gb=10 --use-singularityCRITICAL: The above is a code that performs a series of NTF analyses at once, including rank estimation using masking approach, decomposition at the optimal rank, and visualization of the decomposition results. The argument -j is the number of CPU cores to be used in Snakemake. The arguments input and outdir are the input file and output directory, respectively. The arguments associated with rank estimation are rank_min, rank_max, trials, and n_iter_max, meaning a decomposition from rank 1 to 10 with 50 random trials using different initial values for each rank, up to 1,000 iterations before convergence on each random trial, and ratio is an argument related to the masking approach, meaning the percentage of elements to be masked as noise. Previous researchOn a local machine such as a laptop, run TensorLyCV as follows:> snakemake -j 32 --config input=data/vaccine_tensor.npy outdir=output rank_min=1 rank_max=10 trials=50 n_iter_max=1000 ratio=30 --resources mem_gb=10 --use-singularity --cluster \"qsub -l nc=4 -p -50 -r yes\"CRITICAL: Here, the argument --cluster is added to use a distributed environment, and the command \"qsub -l nc\u00a0= 4 -p -50 -r yes\" is given when submitting jobs to GridEngine.On a distributed environment with GridEngine, run TensorLyCV as follows:> snakemake -j 32 --config input=data/vaccine_tensor.npy outdir=output rank_min=1 rank_max=10 trials=50 n_iter_max=1000 ratio=30 --resources mem_gb=10 --use-singularity --cluster \"sbatch -n 4 --nice=50 --requeue\"CRITICAL: The arguments other than --cluster are the same as for GridEngine, but the command enclosed in \"\" after -cluster has been changed for Slurm.On a distributed environment with Slurm, run TensorLyCV as follows:> docker run --rm -v $(pwd):/work ghcr.io/kokitsuyuzaki/tensorlycv:main -i /work/data/vaccine_tensor.npy -o /work/output --cores=5\u00a0\u2013-rank_min=1 --rank_max=10 --trials=50 --n_iter_max=1000 --ratio=30 --memgb=100CRITICAL: In this case, the installation of Snakemake and Singularity can be omitted.Optional: If a user predetermines the optimal rank, either empirically or by using the elbow method, etc., the user can skip the rank estimation step and just decompose and visualize the decomposition results at the specified rank.> snakemake -j 32 --config input=data/vaccine_tensor.npy outdir=output rank_min=4 rank_max=4 trials=50 n_iter_max=1000 ratio=30 --resources mem_gb=10 --use-singularityCRITICAL: By setting rank_min and rank_max to the same value , no rank estimation is performed, and the decomposition is directly performed at the specified rank.Because TensorLyCV itself is also dockerized, if Docker is available, user can perform TensorLyCV by docker command as follows:output, tensorly contains all the results of TensorLy with different ranks and initial values. The directory plot contains only the images of the trials with the smallest reconstruction error in all ranks. The directory benchmarks contains the calculation time and memory usage of all the processes in TensorLyCV and are used to generate the report .In TensorLyCV, there are two types of decomposition: decomposition using mask tensor (tensorly_w_mask) and decomposition without mask tensor . Assuming the same processing time for each TensorLy calculation, the order of computational time is Unable to perform TensorLyCV on M1/M2 Mac .TensorLyCV crashed with Python\u2019s AssertionError (related to The arguments for TensorLyCV may not be properly provided to Snakemake. Check to see if there is any typo in the source-code you have executed.Some steps of TensorLyCV could be performed but it\u2019s going to take an awful lot of time (related to https://snakemake.readthedocs.io/en/stable/executing/cloud.html). Switching the computational environment can be easily accomplished by adding some arguments to the snakemake command as described above.The bottleneck in TensorLyCV workflow is the masking approach  and the order of computational time is The estimated rank by TensorLyCV was too small , it may focus only on global patterns in the data and overlook local patterns, hence the ranks may be underestimated. The same can also happen if the number of iterations is too small . Therefore, setting a smaller ratio and a larger n_iter_max arguments may generate larger rank. Users can also determine the rank of decomposition themselves, empirically or otherwise. It should be noted, however, that there are few cases in biomedicine where the number of latent components is obvious, and there is arbitrariness involved in empirically determining rank.koki.tsuyuzaki@gmail.com).Further information and requests for resources should be directed to and will be fulfilled by the lead contact, Koki Tsuyuzaki (This study did not generate new unique reagents."}
+{"text": "There was some evidence that gut microbiota was closely related to cholelithiasis, but the causal relationship between them remained unclear. In this study, we try to use Two-sample Mendelian randomization (MR) to clarify the potential causal relationship between gut microbiota and cholelithiasis.Summary Genome-Wide Association Studies (GWAS) statistical data for gut microbiota was obtained from MiBioGen, and the data of cholelithiasis was obtained from UK Biobank (UKB). Two-sample MR analyses were performed to assess causalities between gut microbiota and cholelithiasis mainly using the inverse-variance weighted (IVW) method. Sensitivity analyses were used to determine the robustness of the MR results. Reverse MR analyses were performed to examine the reverse causal association.enus Butyrivibrio (p=0.032), Genus Lachnospiraceae_UCG_001 (p=0.015), Genus Ruminococcaceae_NK4A214_group (p=0.003), Genus Ruminococcaceae_UCG_011 (p=0.010) and cholelithiasis, while Order Rhodospirillales (p=0.031), Genus Actinomyces (p=0.010), Genus Phascolarctobacterium (p=0.036), Genus Rikenellaceae_RC9_gutgroup (p=0.023), Genus Ruminococcaceae_UCG_013 (p=0.022) may be associated with a reduced risk of cholelithiasis. We did not find a reverse causal relationship between cholelithiasis and 9 specific gut microbial taxa.Our research results, based primarily on the IVW method, support the existence of a causal relationship between nine gut microbial taxa and cholelithiasis. We observed a positive association between GThis is the first mendelian randomization study to explore the causalities between specific gut microbiota taxa and cholelithiasis, which may provide new ideas and a theoretical basis for the prevention and treatment of cholelithiasis in the future. In addiPhyla Firmicutes, Phyla Bacteroidetes, Phyla Actinobacteria and Phyla Verrucomicrobia (\u03b2-glucuronidase (\u03b2-GD), and \u03b2-glucuronidase plays an important role in the formation mechanism of gallstones  methods use single nucleotide polymorphism (SNP) as an instrumental variable (IVs) to assess the causal relationship between exposure and outcome . In cont22.1MR analysis is a gene-based method using the random allocation of genetic variants at conception to draw conclusions about the causal effects of exposure on the outcome. To obtain reliable results, as shown in the 2.22.2.1https://mibiogen.gcc.rug.nl), which is the largest 16S fecal microbiota data available from 18,340 individuals , containing 211 taxa with 122,110 variant sites , we selected exposure data with p < 10-5 to obtain more correlation results ]/(1-R2), R2 =[2 \u00d7 \u03b22 \u00d7 EAF \u00d7 (1-EAF)]/[2 \u00d7 \u03b22 \u00d7 EAF \u00d7 (1-EAF) + 2 \u00d7 SE2 \u00d7 N \u00d7 EAF \u00d7 (1-EAF)]F = [R ] . Here, Ncrobiota . We removalidity   was used to further assess whether the IVs were potentially associated with confounders or risk factors for cholelithiasis in order to prevent potential pleiotropy. If the IVs had been associated with confounders or risk factors for cholelithiasis, such as body mass index, smoking, or other factors that have been reported, they were excluded from the analysis . The data were adjusted for the first 20 principal components, sex, and age. After obtaining the SNP information for exposure and outcome, we harmonized the data for further analysis.The GWAS summary statistics for cholelithiasis were obtained from the UK Biobank, including 6,986 cases and 330,213 controls (2.2.3-8). Similar to forward mendelian randomization, we also conducted a selection process, which included removing linkage disequilibrium and instrument variables with F less than 10. We will use significant genera from the forward mendelian randomization analysis as the outcome and then perform a two-sample mendelian randomization analysis to determine the causal relationship between cholelithiasis and gut microbiota.The data source for reverse mendelian randomization is the same as for forward mendelian randomization. In this case, we consider cholelithiasis as the exposure and extract SNPs closely related to cholelithiasis as the exposure  as the main MR-analysis method to evaluate the relationships between the human gut microbiome and cholelithiasis . The MR-The MR analysis was performed using the R package \u201cTwoSampleMR\u201d. All statistical analyses and data visualization were performed in R software 4.2.0 .3We utilized the inverse-variance weighted (IVW) method and conducted a sensitivity analysis to find nine gut microbiota taxa with reliable causal relationships with cholelithiasis, as illustrated in Genus Lachnospiraceae_UCG_001 , Genus Butyrivibrio , Genus Ruminococcaceae_NK4A214_group , and Genus Ruminococcaceae_UCG_011 . This suggests that these bacteria may increase the risk of cholelithiasis. Sensitivity analysis did not reveal any evidence of horizontal pleiotropy. Weighted median analysis was performed on four gut microbiota taxa, and the directionality obtained in the forest plot was consistent with IVW , Genus Actinomyces , Genus Phascolarctobacterium , Genus Rikenellaceae_RC9_gutgroup , and Genus Ruminococcaceae_UCG_013 . This suggests that these bacteria may have a protective effect against cholelithiasis. Sensitivity analysis did not reveal any evidence of horizontal pleiotropy. Weighted median analysis was performed on five gut microbiota taxa, and the directionality obtained in the forest plot was consistent with IVW , Genus Actinomyces (p=0.590), Genus Butyrivibrio (p=0.880), Genus Lachnospiraceae_UCG_001 (p=0.422), Genus Phascolarctobacterium (p=0.119), Genus Rikenellaceae_RC9_gutgroup (p=0.806), Genus Ruminococcaceae_NK4A214_group (p=0.681), Genus Ruminococcaceae_UCG_011 (p=0.228), and Genus Ruminococcaceae_UCG_013 (p=0.954). Our MR-Egger regression method and Cochrane\u2019s Q test also confirmed the reliability of our results.As shown in 5To our knowledge, this is the first mendelian randomization study to assess the causal role of gut microbiota on cholelithiasis. Our results suggest that specific gut microbiota is causally associated with cholelithiasis.Firmicutes/Bacteroidetes and the Firmicutes content were reduced as well, indicating the potential impact of gut microbiota on the formation of gallstones  and increasing free bile acids in the intestinal lumen, while hydrophobic free bile acids are not easily reabsorbed by the intestine and are excreted in the feces (Genus Butyrivibrio (OR=1.002), Genus Ruminococcaceae_NK4A214_group (OR=1.005), Genus Ruminococcaceae_UCG_011 (OR=1.003) and Genus Ruminococcaceae_UCG-010 (OR=0.997) had opposite effects on cholelithiasis, which provides a new perspective for future studies.Butyric acid, one of the major members of the short-chain fatty acids, is produced in the intestine mainly by the enzymatic digestion of dietary fiber and is used as the main energy source for the intestinal epithelium . Butyratoccaceae , Butyrivhe feces . To comphe feces . Our stuPhascolarctobacterium and cholelithiasis, but one study showed fecal taurine-conjugated chenodeoxycholic acid correlated with Phascolarctobacterium  can be found in the article/SL and WL designed the study. WL performed the main data analysis and wrote the draft of the manuscript. QQ, AR, and LZ conducted the data acquisition and performed the data analysis and manuscript revision. Both QP and RM contributed to the data analysis and manuscript revision. SL supervised the whole research and is responsible for the integrity of data analysis. All authors contributed to the article and approved the submitted version."}
+{"text": "A 35-year-old pregnant woman presented with upper right abdominal pain and jaundice. Laboratory analysis showed leukocytosis, elevated C-reactive protein, hypertransaminasemia, and cholestasis. Endoscopic ultrasonography (EUS) revealed cholecystolithiasis, acute cholecystitis, and suspected choledocholithiasis . The paVideo\u20061\u2002Peroral cholangioscopy-guided transpapillary gallbladder irrigation and cholecystolithotomy in the treatment of acute cholecystitis and cholelithiasis.First, a cholangioscope fitted with a tapered transparent cap was successfully cannulated into the common bile duct (CBD) under direct vision. Since the opening of the cystic duct was clearly visualized under cholangioscopy , the chAs one of the most effective treatments for acute cholecystitis, endoscopic transpapillary gallbladder drainage (ETGBD) is achieved completely through the natural pathwayEndoscopy_UCTN_Code_TTT_1AR_2AH"}
+{"text": "In this work, we present an intuitive user-friendly platform for gene expression data analysis and visualization called FungiExpresZ. FungiExpresZ aims to help wet-lab scientists with little to no knowledge of computer programming to become self-reliant in bioinformatics analysis and generating publication-ready figures. The platform contains many commonly used data analysis tools and an extensive collection of pre-processed public ribonucleic acid sequencing (RNA-seq) datasets of many fungal species, including important human, plant and insect pathogens. Users may analyse their data alone or in combination with public RNA-seq data for an integrated analysis. The FungiExpresZ platform helps wet-lab scientists to overcome their limitations in genomics data analysis and can be applied to analyse data of any organism. FungiExpresZ is available as an online web-based tool ( Next-generation sequencing (NGS) costs have reduced dramatically in recent years , 2, rendTranscriptome profiles and genomics data are complex, requiring bioinformatics programming skills  for data processing, visualization and downstream analysis. It has been estimated that more than 50% of wet-lab scientists lack these basic bioinformatics skills . VariousFungal Gene Expression Data Analysis and VisualiZations) to facilitate data analysis and generation of publication-ready graphs. This platform can generate 19 different types of commonly used graphs and provide routines for standard bioinformatics tasks such as functional enrichment analysis, principal component analysis (PCA) and clustering analysis. At the time of publication, FungiExpresZ contains about 16\u00a0000 pre-processed SRA gene expression datasets of 23 different fungal species with industrial, agricultural and medical importance. Additional RNA-seq data will be updated periodically and upon request. FungiExpresZ\u2019s intuitive user interface can also be used for the analysis of data from other organisms besides fungi.In this study, we developed an intuitive user-friendly platform called FungiExpresZ (https://cran.r-project.org/web/packages/shiny/). The R packages used in this work are listed in https://cparsania.shinyapps.io/FungiExpresZ/. The source code and detailed local installation instructions are available on GitHub (https://github.com/cparsania/FungiExpresZ).FungiExpresZ was designed and implemented using R (version 3.6) and R-Shiny  were downloaded from NCBI SRA and mapped against the respective reference genomes using HISAT2 [https://github.com/cparsania/FungiExpresZ_reference_genomes. Information regarding the software tools and parameters used in data processing for each species is provided in S. cerevisiae data, normalized gene expression values (expressed in Tags Per Million (TPM)) were calculated by a previous study [https://cparsania.shinyapps.io/FungiExpresZ/).At the time of publication, FungiExpresZ contains 15\u00a0954 processed RNA-seq datasets of 23 different fungal species. Except for g HISAT2  using thg HISAT2  or Cufflg HISAT2  using thus study  and Kallus study  programmP-values are determined using Student\u2019s t-test (t.test) or Wilcoxon signed-rank test (wilcox.test). Unsupervized clustering is performed using the k-means approach with all default parameters.Pair-wise correlation in scatter plots, multi-scatter plots and correlation heat-box plots are determined using Pearson\u2019s correlation using R\u2019s cor function. https://tinyurl.com/52pazteh. Details of how to incorporate sample and gene groups into plots can be found under the tab \u2018About\u2019 and \u2018Step-by-step-tutorial\u2019 on the FungiExpresZ page or from GitHub (https://tinyurl.com/56prkkya).A tutorial video on sample and gene grouping is available at https://tinyurl.com/27bnurcc.PCA analysis is implemented using the R function prcomp with default parameters. Users can define a subset of genes for PCA analysis, select principal components for visualization in a pair-wise scatter plot and colour by sample groups. A tutorial video is available at https://tinyurl.com/26273dw3. Visualization of enriched GO terms, e.g. network diagram (CNET and EMAP plots), heatmap, bar plot, dot plot or UpSet plot, is supported by the enrichplot package [Gene ontology (GO) enrichment analysis is performed using the enricher function from the clusterProfiler . A full  package .To generate a word cloud for selected SRA samples, redundant abstracts and common dictionary words are removed  before tk-means clustering was used to separate the genes into two clusters based on their expression patterns across 151 Aspergillus nidulans RNA-seq data available in FungiExpresZ. Briefly, the ST genes (k-means clustering was performed on the A. nidulans datasets (n\u2009=\u2009151) of different strains and growth conditions and visualized in a heatmap. To identify genes with a similar expression pattern to the ST genes, we assigned all genes other than the ST genes (n\u2009=\u200910\u00a0709) to another gene group. We then performed gene clustering based on an expression pattern similar to the ST genes (i.e. expressed in the same datasets). The number of k-means clusters for rows was empirically determined and set at five. GO enrichment analysis and visualization were performed using the GO enrichment analysis functionality on FungiExpresZ. Statistically significant  GO terms were visualized in a network diagram (CNET plot).Gene co-expression analysis is based on clustering gene expression patterns of all genes or selected groups of genes. This can be done on FungiExpresZ using the clustering function on the public datasets. For the case of the SMURF\u00a0(Secondary Metabolite Unique Regions Finder)-predicted sterigmatocystin (ST) genes , unsuperST genes  were assFungiExpresZ offers an intuitive, user-friendly interface for analysing and visualizing RNA-seq data. Users can use the built-in plotting and analysis functions on their data alone or in combination with public RNA-seq datasets for integrated analyses. The output of each analysis can be directly visualized on the FungiExpresZ web interface or within the standalone tool. Currently, FungiExpresZ contains ~16\u00a0000 pre-processed RNA-seq datasets from 23 different fungal species  that usehttps://shiny.rstudio.com) that can be used online (https://cparsania.shinyapps.io/FungiExpresZ/) or runs as an R package on a local computer or server. A docker for one-click hassle-free standalone installation is available.FungiExpresZ is an R-Shiny application (2 or log10) and select the species to be analysed  on all genes, a given number of genes with the highest or lowest expression or a user-defined gene list.Gene co-expression analysis can easily be performed on FungiExpresZ using the FungiExpresZ also contains a text-mining function  for acquFungi produce many chemical compounds through highly specialized metabolic pathways, generally called secondary metabolism (SM). Genes involved in a given SM pathway are usually arranged in a cluster in fungal genomes . Bioinfok-means clustering on the expression of genes predicted by SMURF [A. nidulans from published RNA-seq datasets (n\u2009=\u2009151). Two clusters with distinct gene expression patterns were observed  have relatively higher expression in those datasets (n\u2009=\u200933) in which ST genes were induced  [C. albicans white and opaque cells [Transcription factors\u2019 activity can be inferred from the expression patterns of their target genes, which can be determined easily from the public data available in FungiExpresZ. We demonstrate this on the conserved heat shock factor Hsf1 of se genes , 32. We ue cells . In addiue cells . This exk-means clustering function on FungiExpresZ to group transcription factor genes with the genes of interest based on their expression patterns in public datasets.Based on the same principles applied in Case 3, the public data of FungiExpresZ can also be used to identify candidate transcription factor(s) for a given set of co-regulated genes, such as those involved in a specific physiological process or metabolic pathway , 41. Co-https://tinyurl.com/mtzhm86s (https://github.com/cparsania/FungiExpresZ/issues).To demonstrate the various utilities in FungiExpresZ, we have included an example analysis of previously published RNA-seq data . The examtzhm86s . Users cThe current version of FungiExpresZ has a few limitations. The online version of FungiExpresZ has a default 30\u00a0min idle-time limit, after which the session would be suspended. Analysis and visualization made in expired sessions are not saved, and users need to initiate a new session and re-upload data to continue analysis. A user login feature may be introduced as an option in a future update to allow the data-saving function. On the other hand, there is no time limit if FungiExpresZ is installed on a local computer or network with the uploaded data saved until the FungiExpresZ webpage is closed. Therefore, the online version is useful for quick and straightforward data exploration and analysis. The locally installed version is ideal for analyses involving large numbers of datasets or datasets of other fungal species and organisms not available in the FungiExpresZ collection. Another limitation of FungiExpresZ is the lack of a tool for differential expression analysis of user-supplied or public data. This function will be added along with other tools (e.g. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, DNA binding motif enrichment analysis and orthologous gene expression analysis) in future updates to expand on the functionality of FungiExpresZ.RNA-seq has become a standard and indispensable approach in molecular biology research . There iThe number of publicly available RNA-seq data has increased exponentially  with larAs genomics and bioinformatics have become a standard approach nowadays for probably all organisms, it is expected that bioinformatics analysis tools like FungiExpresZ will be in increasing demand. The FungiExpresZ platform developed in this work can be easily extended to support any organism of interest, and the development of a similar platform for other organisms is underway.FungiExpresZ is an intuitive and user-friendly bioinformatics tool for data analysis and a database of fungal gene expression profiling data.FungiExpresZ is built with wet-lab scientists in mind to overcome their computational and bioinformatic limitations in data analysis.FungiExpresZ offers 19 different bioinformatics analyses, and their outputs are provided in publication-ready figures.FungiExpresZ is a valuable resource for the research community, particularly the fungal field, as it contains more than 16\u00a0000 pre-processed RNA-seq data of many fungal species, including model fungi and human, plant and insect pathogens.FungiExpresZ_supp_figures_1_bbad051Click here for additional data file.FungiExpresZ_supp_figures_2_bbad051Click here for additional data file.FungiExpresZ_supp_figures_3_bbad051Click here for additional data file.FungiExpresZ_supp_figures_4_bbad051Click here for additional data file.FungiExpresZ_supp_figures_5_bbad051Click here for additional data file.FungiExpresZ_supp_figures_6_bbad051Click here for additional data file.FungiExpresZ_supp_figures_7_bbad051Click here for additional data file.FungiExpresZ_supp_figures_8_bbad051Click here for additional data file.FungiExpresZ_supp_figures_9_bbad051Click here for additional data file.FungiExpresZ_supp_figures_10_bbad051Click here for additional data file.FungiExpresZ_supp_tables_bbad051Click here for additional data file.Supplementary_Figure_legends_bbad051Click here for additional data file."}
+{"text": "Evaluating the impact of amino acid variants has been a critical challenge for studying protein function and interpreting genomic data. High-throughput experimental methods like deep mutational scanning (DMS) can measure the effect of large numbers of variants in a target protein, but because DMS studies have not been performed on all proteins, researchers also model DMS data computationally to estimate variant impacts by predictors.In this study, we extended a linear regression-based predictor to explore whether incorporating data from alanine scanning (AS), a widely used low-throughput mutagenesis method, would improve prediction results. To evaluate our model, we collected 146 AS datasets, mapping to 54 DMS datasets across 22 distinct proteins.We show that improved model performance depends on the compatibility of the DMS and AS assays, and the scale of improvement is closely related to the correlation between DMS and AS results. Deep mutational scanning (DMS) is a functional genomics method that can experimentally measure the impact of many thousands of protein variants by combining high-throughput sequencing with a functional assay . In a tyComputational studies have used DMS data to build predictive models of variant impact. These predictors use supervised or semi-supervised learning models trained on experimental DMS data and various protein features to make predictions . EnvisioLow-throughput mutagenesis experiments that measure tens of variants at a time have also been used extensively to study diverse protein properties, including substrate binding affinity , 27, proIn this study, we explore whether a predictive model can be improved by incorporating low-throughput mutagenesis data Fig.\u00a0. We findDMS data were downloaded from MaveDB , 41, whiThe following process was used to search for candidate AS studies. Papers were identified by searching on PubMed and Google Scholar for the \u201calanine scan\u201d or \u201calanine scanning\u201d together with the name of candidate proteins. While searching in Google Scholar, we included the protein's UniProt ID rather than molecule name as the search term to reduce false positives. Appropriate AS data were collected from the search results. Western blot results were transformed to values by ImageJ if it was the only experimental data available in the study. A total 146 AS experiments were collected from 45 distinct studies , 87\u2013119.Protein features of Shannon entropy and the logarithm of variant amino acid frequency were downloaded from the DeMaSk online toolkit . The subD denote a protein study measuring scores i, DMS and AS datasets were normalized to a common scale using the following approach adapted from previous studies , 120. LeWild-type scores were directly identified from the paper or the median score of synonymous variants. For DMS data, since not all DMS studies report the score of nonsense variants, we defined the nonsense-like scores as the median DMS scores for the 1% missense variants with the strongest loss of function for each dataset. For AS data, nonsense-like scores were defined according to the paper or by using the extreme values .AS data subsets were filtered/matched according to either assay compatibility or score correlation. For assay compatibility filtering, we first categorized each DMS or AS assay by the protein property or function using the following assay types: binding affinity, enzyme activity, protein abundance, cell survival, pathogen infection, drug response, ability to perform a novel function, or other protein-specific activities  . The DMS\u03c1) is calculated between alanine substitution scores in each pair of AS and DMS data. To avoid influence from the size of AS datasets, we estimated the \u03c1 value with the empirical copula, which is related to the standard estimator by a factor of (n \u2013 1)/(n + 1) [For score correlation matching, Spearman's correlation (/(n + 1) , 122:n is the number of alanine substitutions used for correlation calculation. For each DMS dataset, the AS result with the highest where AS data were preprocessed prior to modeling. For variants without available (filtered/matched) AS data, their AS scores were imputed with the mean value of all available AS scores across all studies. Then the AS data were encoded by the wild-type and variant amino acid type with one-hot encoding. For each variant, the AS feature is expanded with 2 one-hot vectors. Each of the vectors has 19 zeros and 1 nonzero value that was the AS score, with the location of the nonzero value indicating the wild-type or variant amino acid type.sklearn.linear_model.LinearRegression from scikit-learn [To build the predictors, we performed linear regression using the function it-learn . TraininV be protein variants assayed by both DMS study D and AS study A. Variant scores are predicted by the previously mentioned predictors either using AS data (\u03c1) was calculated between the DMS scores \u03c1 was used to evaluate the performance change  between the AS scores and DMS scores for the same alanine substitutions. Since each protein may have results from several AS and DMS experiments, we calculated \u03c1 between each possible pair. The median \u03c1 over DMS and AS data (DMS/AS) pairs was 0.2, indicating that the experimental scores were poorly correlated overall  between the experimentally derived DMS scores and the predicted scores for each pair of DMS and AS studies. The performance of our DMS/AS model was compared with a model trained only on DMS data, equivalent to retrained DeMaSk (\u03c1 (see Methods).To test if incorporating AS data into DMS score models would improve prediction accuracy, we decided to build a new model based on DeMaSk . We chosd DeMaSk , by calc\u03c1  by training only with variants that have AS data available , (ii) by\u03c1| < 0.01)  Fig.\u00a0. We also01) Fig.\u00a0.In this study, we integrated AS data into DMS score prediction, leading to modest improvements in the accuracy of variant score prediction. We also explored the impact of the diversity of protein properties measured by DMS and AS. Filtering DMS and AS data based on our manual classification of assay type compatibility led to improved prediction performance.A potential shortcoming of our current approach is that AS data were available for only a small proportion of the DMS data. Although most recent DMS studies can analyze variants of the whole protein, most AS experiments only cover a handful of residues in the target protein, leaving missing AS scores for the vast majority of residues. We explored this here and found that alternative methods for addressing the sparsity of AS data did not improve or degrade performance, but we anticipate further improved prediction accuracy if the low completeness and unevenness of AS data are appropriately handled before modeling.In this study, we identified the importance of DMS/AS assay compatibility as a crucial factor for improving prediction accuracy. An issue with using this concept is that it further shrinks already sparse data. It also fails to take advantage of the fact that even for low-compatible assays, some fundamental information like protein abundance can still be mutually captured. Instead of hard filtering, proper implementation of this underlying information may facilitate variant impact prediction in the future. Nonetheless, filtering on assay compatibility still leads to performance improvement. We also briefly explored whether the consistency of DMS and AS scores can be considered more directly by matching the best-correlated AS data for each DMS dataset. Consistency is partially driven by assay compatibility but also reflects other features of the data, such as bias and noise.The concepts of compatibility and data quality are also relevant to training any DMS-based predictors. DMS assays have been developed to measure variant impacts to distinct protein properties, and a variant can behave similarly to wild-type when measured by one assay yet show altered protein properties in other assay results, which are frequently found in regions with specific biochemical functions , 133\u2013137In summary, we conclude that the careful inclusion of low-throughput mutagenesis data improves the prediction of DMS scores, and the approaches described here can potentially be applied to other prediction methods.Project name: DMS_with_Alanine_scanProject homepage:https://github.com/PapenfussLab/DMS_with_Alanine_scanOperating system: Platform independentProgramming language: PythonOther requirements: Python 3.10 or higherLicense: MIT licenseRRID: SCR_023949giad073_GIGA-D-23-00040_Original_SubmissionClick here for additional data file.giad073_GIGA-D-23-00040_Revision_1Click here for additional data file.giad073_GIGA-D-23-00040_Revision_2Click here for additional data file.giad073_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giad073_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giad073_Reviewer_1_Report_Original_SubmissionJoseph Ng -- 3/21/2023 ReviewedClick here for additional data file.giad073_Reviewer_1_Report_Revision_1Joseph Ng -- 7/12/2023 ReviewedClick here for additional data file.giad073_Reviewer_2_Report_Original_SubmissionLeopold Parts -- 4/7/2023 ReviewedClick here for additional data file.giad073_Reviewer_2_Report_Revision_1Leopold Parts -- 7/21/2023 ReviewedClick here for additional data file.giad073_Supplemental_FilesClick here for additional data file."}
+{"text": "A solid pseudopapillary neoplasm (SPN) is considered a low-grade malignant neoplasm, more often composed of both solid and cystic components with pseudopapillary areas but predominantly solid in 15\u200a% of casesHerein, we report on three women, ages 26, 27, and 63, who had pancreatic head lesions . The case of the 63-year-old woman is described . EUS fiVideo\u20061\u2002Endoscopic ultrasound-guided radiofrequency ablation for solid pseudopapillary neoplasm of the pancreas.For the two other cases, one and two RFA sessions were respectively required to completely destroy the lesions. EUS-RFA procedures were uneventful with no post-procedural adverse events. No recurrence was noted at the 24-month follow-up.\u200aThis treatment option should be considered in patients unfit for pancreatic surgery and could be discussed for small lesions \u2264\u200a2\u200acm.Endoscopy_UCTN_Code_TTT_1AS_2AD"}
+{"text": "The basal ganglia are a complex of interconnected subcortical structures located beneath the mammalian cerebral cortex. The degeneration of dopaminergic neurons in the basal ganglia is the primary pathological feature of Parkinson's disease. Due to a lack of integrated analysis of multiomics datasets across multiple basal ganglia brain regions, very little is known about the regulatory mechanisms of this area.cis-regulatory elements that are specific to medium spiny neurons and associated with schizophrenia.We utilized high-throughput transcriptomic and epigenomic analysis to profile over 270,000 single-nucleus cells to create a cellular atlas of the basal ganglia, characterizing the cellular composition of 4 regions of basal ganglia in adult macaque brain, including the striatum, substantia nigra (SN), globus pallidum, and amygdala. We found a distinct epigenetic regulation on gene expression of neuronal and nonneuronal cells across regions in basal ganglia. We identified a cluster of SN-specific astrocytes associated with neurodegenerative diseases and further explored the conserved and primate-specific transcriptomics in SN cell types across human, macaque, and mouse. Finally, we integrated our epigenetic landscape of basal ganglia cells with human disease heritability and identified a regulatory module consisting of candidate In general, our macaque basal ganglia atlas provides valuable insights into the comprehensive transcriptome and epigenome of the most important and populous cell populations in the macaque basal ganglia. We have identified 49 cell types based on transcriptomic profiles and 47 cell types based on epigenomic profiles, some of which exhibit region specificity, and characterized the molecular relationships underlying these brain regions. We foun07) Fig.\u00a0, G, suggase Fig.\u00a0 57]..CX3CR1, The SN is considered the primary input region of the basal ganglia, and its dysfunction is implicated in a set of neurological disorders, including Parkinson's disease, Huntington's disease, schizophrenia, and obsessive\u2013compulsive disorder . DaNs orn = 289), followed by OLIG (n = 131), AST (n = 125), OPC (n = 56), MIC (n = 37), and ENDO (n = 26)  Fig.\u00a0. We then26) Fig.\u00a0. Our pri26) Fig.\u00a0. We founers Fig.\u00a0 64, 65], 65n = 2ers Fig.\u00a0. By utilers Fig.\u00a0. We obsetes Fig.\u00a0.Disease risk loci identified in genome-wide association studies (GWASs) show different degrees of enrichment in various cell type\u2013specific regulatory elements. However, the lack of epigenetic data in previous basal ganglia datasets has resulted in a dearth of information on disease risk loci enriched in cell type\u2013specific regulatory elements in the basal ganglia. To fill this gap, we mapped all coordinates of DA cREs from each subcluster to the orthologous coordinates in the human hg19 genome, then performed linkage\u2013disequilibrium score regression (LDSC) analysis using GWAS summary statistics for human traits and diseases on the DA cREs  Methods.P = 3.1 \u00d7 10\u22123). PVALB neuron in the amygdala appeared to be particularly susceptible to the effects of chronic stress, which is considered the primary risk factor for MDD M. fascicularis genome and filtered out reads that aligned to mitochondrial or genomic scaffolds , as well as reads with alignment quality less than 10 and PCR duplicates. The fragments obtained from each library in the aforementioned steps were used for downstream analysis.We referred to previous literature and utilized the open-source PISA software workflow to process the snATAC-seq data of DNBelab C4 , 85. We First, we performed initial clustering of the raw data using the ArchR package in R software (version 1.0.2) , retainiRRID:SCR_018217) analysis.First, we extracted the cell gene score and peak matrix from the ArchR object of scATAC-seq. Then, we performed normalization, feature selection, and dimensionality reduction analysis on the scATAC-seq data using the Signac standard pipeline in R. For scRNA-seq data, we performed corresponding dimensionality reduction analysis using the standard pipeline in Seurat. Subsequently, we used the FindTransferAnchors function to calculate anchors between different omics cells using the gene score matrix from scATAC-seq and the gene expression matrix from scRNA-seq, with the top 2,000 VariableFeatures  in common. To improve the accuracy of anchors, we set k.anchor to 20. Subsequently, these anchors were used to assign a predicted ID to each cell in scATAC-seq, and scATAC cells with a score greater than 0.6 were retained and given a predicted gene expression matrix. The data from the 2 datasets were then coembedded into a low-dimensional space with 30 dimensions using standard UMAP  . First, If a cRE or DA cRE associated with a gene contains a binding motif for a certain TF, it is defined that this TF may regulate the gene. If the cRE or DA cRE falls within the promoter, intron, exon, or distal region of the gene, different modes of regulation are named accordingly. Finally, by inputting the TF\u2013gene associations of different cell types into Cytoscape, a regulatory network is constructed. The color of the edges indicates the different modes of regulation of the transcription factors, while the color of the nodes represents the transcription factors and differentially expressed genes in different cell types.Input the DA cRE sets of different cell types into Homer (version 4.11) to calculate the activity of TF binding motifs . Retain We used LDSC to analyze the genetic variation in differentially accessible regions of different cell types and its correlation with GWAS results. First, we retained DA cRE with FDR \u21d0 0.1 and Log2 FC \u2265 0.5, and filtered out cell types with fewer than 100 DA cRE. We then used the liftover software to convert genome data to human hg19 genome data. To prepare for cluster-specific peak analysis for LDSC, we used the make_annotation.py script and then calculated linkage disequilibrium (LD) scores of single-nucleotide polymorphisms in differentially accessible peaks using the ldsc.py script with 1000 Genomes phase 3 data. Then, we downloaded GWAS summary statistics data from the UK Biobank database and publications. Finally, we input HapMap3 single-nucleotide polymorphisms and corresponding 1000G_EUR_Phase3_baseline data and used the standard process to calculate cell type\u2013specific genetic variation.giad095_GIGA-D-23-00121_Original_SubmissionClick here for additional data file.giad095_GIGA-D-23-00121_Revision_1Click here for additional data file.giad095_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giad095_Reviewer_1_Report_Original_SubmissionTrygve Bakken -- 6/16/2023 ReviewedClick here for additional data file.giad095_Reviewer_1_Report_Revision_1Trygve Bakken -- 8/16/2023 ReviewedClick here for additional data file.giad095_Reviewer_2_Report_Original_SubmissionYiqiao Wang -- 7/5/2023 ReviewedClick here for additional data file.giad095_Reviewer_3_Report_Original_SubmissionDijun Chen -- 7/7/2023 ReviewedClick here for additional data file.giad095_Supplemental_FilesClick here for additional data file."}
+{"text": "Limitations remain with respect to cost, scalability, and platform-dependent read accuracy and the tradeoffs between sequence coverage and sensitivity of variant discovery are important experimental considerations for the application of LRS. We compare the genetic variant calling precision and recall of Oxford Nanopore Technologies (ONT) and PacBio HiFi platforms over a range of sequence coverages. For read-based applications, LRS sensitivity begins to plateau around 12-fold coverage with a majority of variants called with reasonable accuracy (F1 score above 0.5), and both platforms perform well for SV detection. Genome assembly increases variant calling precision and recall of SVs and indels in HiFi datasets with HiFi outperforming ONT in quality as measured by the F1 score of assembly-based variant callsets. While both technologies continue to evolve, our work offers guidance to design cost-effective experimental strategies that do not compromise on discovering novel biology.Advances in long-read sequencing (LRS) technology continue to make whole-genome sequencing more complete, affordable, and accurate. LRS provides significant advantages over short-read sequencing approaches, including phased  Over the last five years, long-read sequencing (LRS) technologies have transformed the landscape of genetic variant discovery in two fundamental ways. First, they have increased the sensitivity of structural variant (SV) discovery by approximately threefold by providing access to repetitive regions of genomes typically masked or excluded as part of short-read sequencing analyses , 2019 anAll of Us , variant types , or data input . In this study, we limit our analysis to eight read-based callers: Clair3 [v0.1-r11] , and computational method. For this assessment, we generated downsampled sets of HiFi and both standard and ultra-long ONT (UL-ONT) sequence data at depths of 5, 8, 10, 12, 15, 17, 20, 25, and 30X assuming a 3.1 Gbp haploid genome size. We applied standard practice algorithms and procedures and evaluated precision and recall of each algorithm for single-nucleotide variants (SNVs), small (<50 bp) indels (insertions and deletions), and SVs with respect to the human reference genome GRCh38. We consider two publicly available human genomes that have been sequenced extensively: HG002 (the Genome in a Bottle [GIAB] Ashkenazim child reference genome)  and HG00Read-based SNVs were called with DeepVariant and Clair3 and showed the least variability between callers and technologies out of all three variant categories. At sequence read depth below 15X, recall of PacBio HiFi-tuned algorithms consistently outperformed ONT by an average of 0.03 . In factIndels, defined here as insertions or deletions less than 50 bp, show a similar profile. There is, once again, a characteristic plateau in F1 score around 12X sequence coverage. The greatest difference in recall is demonstrated in this subset between the HiFi and ONT platform (based on the R9 nanopore technology) . While pFor SVs, we consider only insertions and deletions greater than or equal to 50 bp. SVs show the least variability between technologies ). Both sequencing platforms and various coverages converge on a set of ~12,800 SVs with each calling on average 25,634 SVs . DiffereAssembly-based variant calling. Assembly-based callers have the advantage that they call variants from large contiguous haplotype blocks essentially providing access to larger and more complex forms of genetic variation and providing extended phasing for all forms of genetic variation . We geneSNV calling with assembly-based callers generally underperforms read-based discovery especially at lower coverages. Precision in ONT and UL-ONT assembly-based methods shows the greatest difference with an average reduction of 0.33 across all sequencing depths . This isDetecting indels from assembly-based methods is especially challenging , in partSVs follow the trend of assembly-based callsets in general with a steep recall curve, steady precision curve, and early plateau across sequencing depths and technologies. For low (below 8X) HiFi coverages, assembly-based methods underperform their read-based counterparts with respect to recall by an average of 0.03 . While ONT assemblies demonstrate higher recall than their read-based counterparts by 0.09 and 0.10 for standard ONT and UL-ONT, respectively. Above this coverage, all assembly-based methods outperform read-based methods by at least 0.08 for recall. The HG002 assemblies using PacBio HiFi reads at 10X sequencing depth are a clear outlier and may be attributable to a systematic failure to remove false duplications. We did not observe a similar outlier in HG00733. Although the assembly size is larger than expected, metrics such as contiguity (N50) and callable loci are consistent with other assemblies. Similar outliers may be avoidable with deeper coverage to support high-quality assembly-based callsets .Because LRS technologies claim to access more of the genome and more complex classes of genetic variants, we first evaluate genome-wide SV callability. To assess callability across the genome, we first divided GRCh38 into 1 Mbp windows and intersected those windows with the HGSVC SV truth set for HG00733, yielding 2,679 and 2,482 windows for insertions and deletions, respectively. In order for a window to be established as callable, >90% of the calls contained in this window must be accurately recovered . At low A list of clinically relevant SVs was released for the GIAB sample HG002  includinLRS technologies allow for more robust characterization of tandem repeats , the larAccurately calling variants in homopolymer runs is challenging for both PacBio HiFi and ONT technologies . These nLarge (>10 kbp) SVs, especially insertions within or near repeat regions, frequently evade Illumina detection . An advaPacBio and ONT are rapidly developing new sequencing technologies that improve LRS accuracy and throughput. For example, ONT recently released an improved flowcell (R10) as well as a new \u201cduplex\u201d sequencing method . The newUsing 30X of WGS data from HG002 generated by the Revio system .Within the limits of various algorithms and sequencing platforms analyzed here, we make a few general observations and recommendations based on our analysis against current truth sets . With reBy contrast, all LRS platforms currently underperform for indel variant calling and, predictably, they perform the most poorly in regions of homopolymer runs as well as short tandem repeats\u2014precisely the regions that are most mutable for this class of variation . Given tde novo disease  and computational processing. Importantly, the LRS platforms continue to rapidly evolve in terms of accuracy (ONT) and throughput (PacBio). Improved modeling of the platform-dependent errors as well as newer pores, or techniques (duplex sequencing) for ONT show considerable promise with suggestions that sequencing accuracy may in fact rival or surpass that of Illumina . Changes results . Such hyUL-ONT data were generated from the HG00733 lymphoblastoid cell line according to a previously published protocol . BrieflyPacBio HiFi data were generated from the HG00733 lymphoblastoid cell line as previously described  with modftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/HGSVC2/technical/reference/20200513_hg38_NoALT/. Whole-genome analysis was restricted to regions outside centromeres, pericentromeric repeats, and the mitochondrial chromosome where variant calls were previously determined to be less reproducible ```run_clair3.sh --bam_fn={input.merged_bam} --sample_name={sample} --ref_fn={input.ref} --threads={threads} --platform=hifi --model_path=$(dirname $( which run_clair3.sh ) )/models/hifi --output=$(dirname {output.vcf}) --enable_phasing```(ONT|UL-ONT)```run_clair3.sh --bam_fn={input.merged_bam} --sample_name={sample} --ref_fn={input.ref} --threads={threads} --platform=ont --model_path=$(dirname $( which run_clair3.sh ) )/models/ont_guppy5 --output=$(dirname {output.vcf}) --enable_phasing```cuteSV:(PacBio HiFi)```cuteSV -t {threads} -S {sample} --max_cluster_bias_INS 1000 --diff_ratio_merging_INS 0.9 --max_cluster_bias_DEL 1000 --diff_ratio_merging_DEL 0.5 {input.reference} {output.vcf} --genotype-l 50 -s {params.min_supp} {params.outputdir}```(ONT|UL-ONT)```cuteSV -t {threads} -S {sample} --max_cluster_bias_INS 100 --diff_ratio_merging_INS 0.3 --max_cluster_bias_DEL 100 --diff_ratio_merging_DEL 0.3 {input.reference} {output.vcf} --genotype-l 50 -s {params.min_supp} {params.outputdir}```https://github.com/eblerjana/lrs-sv-calling.In addition, we filtered the cuteSV calls based on the minimum read support reported in the output VCF, as it generated unfiltered calls. Similarly, we filtered the SVIM calls based on the reported quality. In both cases, we used value 2 for coverages \u22645; 3 for coverages \u226410; 4 for coverages \u226420; 5 for coverages \u226425; and 10 for coverages >30. These values were selected such that they result in the highest F-scores when comparing the filtered calls to the GIAB medically relevant SVs for HG002. The pipeline used for SV calling with cuteSV, Sniffles2, and SVIM can be found here: DeepVariant:(PacBio HiFi)```run_deepvariant --model_type=PACBIO --ref={ref} --reads={aln} --output_vcf={sample}.vcf.gz --output_gvcf={sample}.gvcf --novcf_stats_report --intermediate_results_dir=/dv_tmp/ --num_shards={threads}```(ONT-duplex)```run_deepvariant --model_type=ONT_R10 --ref={ref} --reads={aln} --output_vcf={sample}.vcf.gz --output_gvcf={sample}.gvcf --novcf_stats_report --intermediate_results_dir=/dv_tmp/ --num_shards={threads}```Delly:(PacBio HiFi)```delly lr -y pb -g {input.ref} -x {input.exc} -o {output.bcf} {input.bam}```(ONT|UL-ONT)```delly lr -y ont -g {input.ref} -x {input.exc} -o {output.bcf} {input.bam}```https://github.com/dellytools/delly/blob/main/excludeTemplates/human.hg38.excl.tsvExcluded regions for Delly can be found here: PBSV:(PacBio HiFi)```pbsv discover --tandem-repeats {input.trf} {input.bam} {output.svsig}pbsv call -j {threads} --ccs --types DEL,INS,INV {input.ref} {input.svsig}{output.vcf}```PEPPER-Margin-DeepVariant:(ONT|UL-ONT)```run_pepper_margin_deepvariant call_variant -b {bam} -f {ref} -o {out_dir} -t {threads} --ont_r9_guppy5_sup```Sniffles:(PacBio HiFi|ONT|UL-ONT)```sniffles -t {threads} -i {input.bam} -v {output} --reference {input.reference} --minsvlen 50```SVIM:(PacBio HiFi|ONT|UL-ONT)```svim alignment {params.outdir} {input.bam} {input.reference} --min_sv_size 50```PAV  was applVariant call comparisons were performed using svpop. SNV-based comparisons were performed using the overlap feature nrid (nonredundant ID match), which requires variants to have the same SNV ID (#CHROM-POS-SNV-REF-ALT) to be called the same. Additionally, indels and SVs were matched using szro-50\u2013200, which first matches variants on ID (#CHROM-POS-SVTYPE-SVLEN), then 50% reciprocal overlap, and then finally variants of the same type that are within 200 bp of each other and have reciprocal size overlap of 50%. This strategy allows for increased accuracy in complex regions of the genome where alignments can be biologically ambiguous.Reference-based annotations for genomic sequence content  are taken directly from the UCSC Genome Browser and the UCSC GoldenPath. This is a built-in functionality of SVPOP for GRCh38.F1 score is defined as the harmonic mean between precision and recall and seeks to represent precision and recall in one metric."}
+{"text": "Brunner\u2019s gland hamartoma is a rare entity and constitutes 10.6\u200a% of all benign duodenal tumorsWe present the case of a heathy 41-year-old woman who presented with melena. Upper gastrointestinal endoscopy and computed tomography scanning revealed a large polyp with ulceration on the anterior wall of the duodenal bulb . EndoscInitially, a standard polypectomy was attempted but the head of the lesion was too large to pass through the pylorus. Therefore, a combined laparoscopic\u2013endoscopic approach was planned . DuringVideo\u20061\u2002Laparoscopic-assisted polypectomy of the giant Brunner\u2019s gland hamartoma.The total operative time was 80 minutes. The patient\u2019s postoperative course was uneventful. Pathology confirmed the lesion was a duodenal Brunner\u2019s gland hamartoma . No recIn the present case, the application of laparoscopy overcame the polyp size-related constraints, allowing endoscopic resection; the combined approach provided a safe and curative therapeutic strategy, avoiding a more invasive surgical treatment.Endoscopy_UCTN_Code_CPL_1AH_2AZ"}
+{"text": "A 72-year-old man underwent a gastroscopy that revealed a 13 \u00d7 11-mm lesion within the left pyriform sinus (0-IIb). The lesion displayed a reddish hue under white light, with well-defined borders a. Its tFollowing this discovery, the patient underwent endoscopic submucosal dissection (ESD) under general anesthesia with endotracheal intubation . MagnifUnderwater endoscopic submucosal dissection with dental floss traction is performed for a pharyngeal superficial squamous cell carcinoma.Video 1ESD has emerged as an effective and safe therapeutic modality for early pharyngeal cancer, preserving patients\u02bc quality of life and physiological functionThe application of dental floss traction during ESD, along with the water immersion technique, which capitalizes on the inherent buoyancy of water, provides enhanced traction and improved visual acuityEndoscopy_UCTN_Code_TTT_1AO_2AC"}
+{"text": "Radon (Rn) and its decay products are the primary sources of natural ionizing radiation exposure for the public, posing significant health risks, including being a leading cause of lung cancer. Porous material-based adsorbents offer a feasible and efficient solution for controlling Rn concentrations in various scenes to achieve safe levels. However, due to competitive adsorption between Rn and water, finding candidates with a higher affinity and capacity for capturing Rn in humid air remains a significant challenge. Here, we conducted high-throughput computational screening of 8641 two-dimensional covalent organic frameworks (2D COFs) in moist air using grand canonical Monte Carlo simulations. We identified the top five candidates and revealed the structure\u2013performance relationship. Our findings suggest that a well-defined cavity with an approximate spherical inner space, with a diameter matching that of Rn, is the structural basis for a proper Rn capturing site. This is because the excellent steric match between the cavity and Rn maximizes their van der Waals dispersion interactions. Additionally, the significant polarization electrostatic potential surface of the cavity can regulate the adsorption energy of water and ultimately impact Rn selectivity. Our study offers a potential route for Rn management using 2D COFs in moist air and provides a scientific basis for further experimentation. The res\u221211 ppb . As a re\u221211 ppb ,5,6. To \u221211 ppb , pluggin\u221211 ppb , and ads\u221211 ppb ,10. AmonCoconut-activated charcoal (AC) is currently the most effective commercialized solid Rn adsorbent, but it also has significant drawbacks. Its ability to maintain a stable and high Rn adsorption level under conditions of high humidity is limited, restricting its widespread use in Rn-rich scenarios with high air humidity, such as water treatment plants, basements, and underground mines. Moreover, due to the lower distribution of Rn size-matching cavities, AC only exhibits moderate Rn capture efficiency and cannot remove Rn thoroughly ,12. InteIn recent years, covalent organic frameworks (COFs) have seen immense popularity as typical porous materials, owing to their unique properties and rapid development ,18,19. TIn this study, we investigated the performance of 8641 2D-layered COFs in the selective adsorption of radon through GCMC simulations. Our results allow us to identified five top-performing candidates with stable and balanced performance in terms of Rn selectivity and adsorption capacity in moist air. Additionally, we reveal the structure\u2013performance relationship of 2D COFs and, importantly, uncover the molecular mechanism by which humidity affects Rn capture performance. These findings not only present a new avenue for Rn capture using 2D COFs but also provide a scientific basis for designing efficient radon trapping materials under high-humidity conditions.Mercado et al.  compiled2/O2/H2O with molar ratios of 0.001/0.776/0.209/0.014, at 298 K and 1 bar. Additionally, we investigated Rn adsorption under various total pressures (10\u2013140 kPa) and humidities , with different Rn concentrations . It is worth noting that the concentration of Rn in the air can differ significantly depending on the location, with values ranging from ~10\u221211 ppb in common places to ~10\u22128 ppb in caves or poorly ventilated basements and houses [2, O2, and H2O. Consequently, higher Rn concentrations were conventionally utilized in the GCMC simulations to ensure the comparability of Rn uptake performance among different materials.To evaluate the Rn capture potential of COFs, we conducted GCMC simulations using a moist air mixture of Rn/Nd houses ,31,32. N2 and O2 adsorbates were modeled with the three-site molecular model [2, H2O, and O2) are listed in The GCMC simulations were performed using the RASPA software (version 2.0), as described in a previous study . The Lenar model ,38,39. Tar model . The Rn ar model , based oar model . The fori with respect to gas j, k and m is defined asx and y are the molar ratios of adsorbed species in the adsorbed and bulk phases, respectively [The adsorption of radon obtained from GCMC simulation is the absolute adsorption amount (ectively . In addiectively  was intr2/O2/H2O) to mimic radon exposure in moist air. To assess the performance of the adsorbent materials, we adopted two indexes, Rn selectivity and Rn uptake capacity, as they are crucial for conducting cost-effective gas separation processes [Moisture is a critical factor that affects the adsorption of noble gas by porous materials . Thus, irocesses .2 and O2, respectively. It is evident from RnAPS, respectively. Based on the adsorption performance results, we screened the top five COFs with the highest RnAPS values (>190 mol/kg), including linker105_C_linker13_C_kgm (~300.85 mol/kg) > linker101_C_linker11_C_kgm (~287.26 mol/kg) > linker107_C_linker13_C_kgm (~215.24 mol/kg) linker99_C_linker11_C_kgm (~198.26 mol/kg) > linker99_C_linker13_C_kgm (~193.60 mol/kg). The IUPAC names and structures of the linker groups in the top five COFs can be found in 3 to 123.56 \u00c53, demonstrating an excellent stereo shape matching the spherically shaped Rn (4.17 \u00c5). This observation is consistent with the principle that cavities with geometries matching those of Rn are more thermodynamically favorable for accommodating Rn atoms, due to the relatively strong vdW dispersion interactions. A similar phenomenon was also observed in the case of ZIF-7, which has a Rn-matching spherical cage with a diameter of ~4.38 \u00c5 and displays remarkable Rn selectivity and uptake capacity.In order to establish a correlation between the structural features of COFs and their performance in capturing Rn, we conducted a detailed analysis of the spatial distributions of Rn in the top five candidates. This allowed us to identify the most preferential binding sites for Rn. Interestingly, our findings, as illustrated in 2/O2 was maintained at 0.001/0.776/0.209, while the RH values were varied from 0 to 1.0.As previously discussed, moisture can significantly impact the Rn capture performance of 2D COFs  . These cAt RH = 0.0, both candidates exhibited two major Rn binding sites  . RemarkaThe structural features of binding site i in linker105_C_linker13_C_kgm enable it to provide greater energy gains from electrostatic interactions with water. As depicted in \u221211 ppb) is often beyond the numerical limit of most GCMC software (single-precision floating-point format). Hence, a relatively high gradient of molar concentration was employed to investigate the impact of gas concentration variations on nanoporous material performance [RnX , where moisture represents 0.5 (waterX = 0.014) at ambient temperature, and the molar ratio of formance ,14. TherRnX was below 0.0005, the Rn uptake capacity of linker105_C_linker13_C_kgm, linker107_C_linker13_C_kgm, and linker99_C_linker13_C_kgm was almost at the same level, while linker101_C_linker11_C_kgm and linker99_C_linker11_C_kgm had a comparable Rn capacity, albeit slightly lower than the former three candidates. At the highest Rn molar fraction (RnX = 0.001), the differences in the performances of the COFs became apparent, with the Rn uptake capacity order being linker105_C_linker13_C_kgm > linker107_C_linker13_C_kgm > linker99_C_linker13_C_kgm = linker99_C_linker11_C_kgm > linker101_C_linker11_C_kgm.As shown in RnX = 0.001, with pressures ranging from 5 to 140 kPa. As shown in We also evaluated the Rn-capture isothermal adsorption curves of screened five COFs under RH = 0.5 at room temperature. 2/O2/H2O of 0.001/0.776/0.209/0.014 at ambient temperature and pressure. After thorough evaluation, we identified five COFs, namely linker105_C_linker13_C_kgm, linker101_C_linker11_C_kgm, linker107_C_linker13_C_kgm, linker99_C_linker11_C_kgm, and linker99_C_linker13_C_kgm, as the top candidates for Rn capture. The analysis of structure\u2013performance relationships suggested that the well-defined cavities in COFs, having a diameter that matches that of Rn, represent a very crucial prerequisite for competitive Rn capturing sites because the excellent steric match between the cavity and Rn could maximize their vdW dispersion interactions for the adsorption energy.In this study, we investigated the potential of 8641 two-dimensional covalent organic frameworks (COFs) to capture radon (Rn) under humid air conditions. To achieve this, high-throughput GCMC simulations were used to screen the COFs based on their Rn capture capability from a quaternary mixed gas with a molar ratio of Rn/NRnX = 0.001), the five screened COFs exhibited slightly lower Rn capacity (ranging from ~0.73 to ~0.96 mol/kg) but much better Rn selectivity (ranging from ~244.1 to ~392.5) compared to ZIF7-Im-1 . To the best of our knowledge, experimentally, ZIF7-Im-1 may remain the record holder for both the highest Rn uptake capacity and selectivity so far [We further evaluated the Rn capture performance of the selected COFs under different humidity conditions . The results showed that both the Rn selectivity and adsorption capacity of all five COFs decreased with increasing RH, with a more pronounced effect observed at lower humidity levels (RH < 0.3). Interestingly, the impact of humidity on the performance of COFs exhibited individual differences. For instance, RH had a greater influence on the performance of linker105_C_linker13_C_kgm than it did on the performance of linker101_C_linker11_C_kgm. The underlying molecular mechanism is attributed to the architecture of polar groups in the binding sites. For instance, in binding site i of linker101_C_linker11_C_kgm, there are six protruding hydroxyl groups that collectively point to the interior space of the cavity, inducing a notable-polarization ESP surface that offers more favorable electrostatic interactions for water. However, in the other three binding sites, the spatial distributions of the polar groups point outward from the cavity  or are arranged in a planar-like manner , which cannot induce the remarkable-polarization ESP surfaces of the cavities and are hence less favorable for electrostatic interactions with water. This is the main molecular mechanism by which RH impacts the Rn capturing performance of COFs. Lastly, we evaluated the influence of Rn molar fractions and pressures on the performance of the COFs. It should be noted that, under equal conditions (at RH = 0.5, 1 bar, 298 K, and y so far . Given t"}
+{"text": "Moreover, the hybrid P(AAm-CO-AAc)-silica aerogel demonstrated improved thermal stability up to 343 \u00b0C, owing to the synergetic effect between the P(AAm-CO-AAc) and the silica aerogel, corresponding to the thermal stability and strong covalent bonding among them. These excellent results illustrate that this new synthetic approach for producing hybrid P(AAm-CO-AAc)-silica aerogels is useful for enhancing the mechanical strength of pristine silica aerogel without impairing its thermal insulating property and shows potential as an industrial heat insulation material.Silica aerogels and their derivatives have outstanding thermal properties with exceptional values in the thermal insulation industry. However, their brittle nature restricts their large-scale commercialization. Thus, enhancing their mechanical strength without affecting their thermal insulating properties is essential. Therefore, for the first time, highly thermally stable poly(acrylamide-co-acrylic acid) partial sodium salt is used as a reinforcing polymer to synthesize hybrid P(AAm-CO-AAc)-silica aerogels via epoxy ring-opening polymerization in the present study. Functional groups in P(AAm-CO-AAc) partial sodium salts, such as CONH Moreoveporosity 0\u201399%, hi44 W/mK) ,12. Howe44 W/mK) ,14,15.Until now, several methods have been explored to overcome this strengthening problem, such as aging, surface modification, co-precursor methods, and radical polymerization, as well as organic polymers and fibers for synthesizing hybrid or composite-reinforced silica aerogels ,19,20,21In this investigation, we reported a one-pot sol\u2013gel synthesis of hybrid poly(acrylamide-co-acrylic acid)-silica aerogel (TGP_X aerogel) via epoxy ring-opening polymerization with enhanced thermal stability and the mechanical properties of pristine silica aerogel. To the best of our knowledge, for the first time, P(AAm-CO-AAc) is used as a crosslinking polymer to enhance the mechanical strength of pristine silica aerogel. In the present study, organic\u2013inorganic hybrid aerogels were prepared by adding P(AAm-CO-AAc) to a tetramethyl orthosilicate (TMOS) silica precursor with (3-glycidyloxypropyl)trimethoxysilane (GPTMS) as a crosslinking agent via epoxy ring-opening polymerization and supercritical alcohol drying. The high thermal stability of P(AAm-CO-AAc) compared to other available organic polymers provides a great advantage for the field of heat insulation . Moreove2 groups of the PACA polymer, where the COOH and CONH2 groups acted as a nucleophile and donated a pair of electrons for the epoxy ring-opening polymerization process [The thermal properties of the hybrid aerogels were investigated by assessing the passage of thermal energy through the material, which occurs via three different mechanisms: solid conductivity, gaseous conductivity, and radiative conductivity ,33. When process ,35, whic\u22121 are attributed to the Si\u2013O\u2013Si asymmetric and symmetric stretching and deformation vibrations, respectively [\u22121 correspond to C\u2013H deformation, C\u2013H symmetric stretching, and asymmetric stretching, respectively, arising from the GPTMS and PACA polymers [\u22121, associated with the C=O and C\u2013N groups, can be seen in the FTIR spectra of all of the samples except for the TGP_0 aerogel sample, which confirms the presence of the PACA polymer. Moreover, the peak intensity of C=O increased with the increasing wt% of PACA in the rest of the hybrid TGP_X samples [\u22121, corresponding to the \u2013OH group, compared to the TGP_0 aerogel, which further confirms the consumption of formed hydroxyl groups due to the epoxy ring-opening of the GPTMS through the reaction with the TMOS precursor. Thus, the FTIR spectrum of the TGP_X aerogels with the varying PACA polymer\u2019s wt% verify the bonding mechanism of PACA in hybrid TGP_X aerogels.To probe the bonding mechanism of the TGP_X aerogels with the varying PACA polymer\u2019s wt%, FTIR spectra of the TGP_X aerogels were carried out and are depicted in ectively ,37,38. Tpolymers . The vib samples ,41. The 2 groups as nucleophiles in the epoxy ring-opening reaction [The XPS spectra showcased in reaction . Therefo3/g due to the addition of the molecularly dense PACA polymer. While the porosity percentage of the TGP_X aerogel, calculated by using Equation (1), decreased from 95.57% to 93.68%:Thermal conductivity and thermal stability are the two most crucial parameters to assess the thermal insulation properties of an aerogel, while the thermal conductivity and stability of porous aerogels are principally subjected to heat transfer through the solid phase, gas phase, and radiation, which are mainly influenced by the density and porosity of the material . Therefo.9 g/cc) . This pr.9 g/cc) . In gene.9 g/cc)  observed.9 g/cc)  analyzed.9 g/cc)  and Hae-.9 g/cc)  reported.9 g/cc) ,53,54,55.9 g/cc)  and the 2/g with the increasing PACA polymer\u2019s wt%. Correspondingly, the pore volume decreased from 1.01 to 0.60 cm3/g, as obtained from the BJH pore size distribution curves shown in Nitrogen adsorption\u2013desorption isotherms were obtained for the TGP_X aerogels; the corresponding Brunauer\u2013Emmett\u2013Teller (BET) and Barrette\u2013Joyner\u2013Halenda (BJH) plots for a specific surface area and pore size distribution are shown at The surface morphologies and reduction in the average particle sizes of the TGP_X aerogels with the PACA polymer\u2019s wt% were observed through FESEM analysis, as shown in The industrial applicability of silica aerogel recommends that it should be mechanically strong enough and hold out against large compressive loads. However, synthesizing hybrid silica aerogel is one of the most effective approaches to obtaining mechanically strong aerogel, which makes it able to survive with a higher compressive load. The mechanical properties of the TGP_X aerogels were measured by using a compressive strength measurement  under the compressive load. The compression stress\u2013strain curves of the TGP_X aerogels with varying PACA concentrations are shown in \u22121 for practical insulating applications. The comparative TGA plotting for the thermal stability of the pristine TGP_0 and hybrid TGP_8 aerogels is shown in The thermal stability of TGP_X aerogels was studied using thermogravimetric analysis (TGA) for pristine TGP_0 and hybrid TGP_8 aerogels from 30 to 1000 \u00b0C in a nitrogen atmosphere with a heating rate of 10 \u00b0C minTo verify the progressive time-dependent thermal insulating performance of the pristine TGP_0 aerogel (a diameter of 20 mm and a thickness of 21 mm) and hybrid TGP_8 aerogel (a diameter of 18 mm and a thickness of 19 mm), samples were subjected to a hot plate heated at ~150 \u00b0C, and time-dependent IR thermal images were captured, as shown in The detailed comparison of the reported hybrid polymer\u2013silica aerogel is summarized in 3/g with the increasing polymer content in the aerogel, while their porosity and density values were maintained between 95.57% to 93.68% and 0.084 to 0.12 cm3/g, respectively. Furthermore, the in situ epoxy crosslinking of pristine silica aerogel with the PACA polymer reveals outstanding enhancement in the compression modulus, from 0.4 to 2.9 MPa of the TGP_X aerogels, while maintaining a thermal conductivity from 22.4 to 30.6 mW/mK. In addition, TGA analysis confirms the increase in thermal stability of the hybrid TGP_X aerogel from 315 to 343 \u00b0C. These results suggest that the epoxy crosslinking between the GPTMS and PACA polymer forms strong covalent bonding that improves the mechanical, thermal, and textural properties of TGP_X aerogels. Conclusively, the facile one-pot synthesis of highly robust and thermally insulating TGP_X aerogel via epoxy ring-opening polymerization using PACA paves a novel way for preparing hybrid aerogels for industrial thermal insulation applications.Thermally stable and structurally reinforced hybrid TGP_X aerogels were successfully prepared via a facile one-pot sol\u2013gel synthesis method by exploiting epoxy ring-opening polymerization using GPTMS and PACA as the crosslinker and reinforcing polymer, respectively. The specific surface area of the TGP_X aerogels decreased from 608.6 to 369.0 cm3)4; 98%) as a silica precursor, GPTMS  as a crosslinker, P(AAm-CO-AAc)) (PACA) as a reinforcing polymer, ammonium fluoride (NH4F) as a base catalyst, and HCl as an acidic catalyst, all of which were purchased from Sigma-Aldrich . Methanol  was used as a solvent, and deionized (DI) water was used in the experiments. All the chemicals were used as received without any further purification.The synthesis of the hybrid TGP_X aerogels was carried out by using TMOS  in Parr autoclave  at 265 \u00b0C and 120 bar pressure for 4 h. The preparation of hybrid TGP_X aerogels was performed by using a TMOS:GPTMS:methanol:DI water molar ratio of 1:0.3:30:2, with varying the wt% of the PACA polymer solution as 0, 2, 4, 6, and 8 wt%, depending upon the solubility of the polymer in the methanol. The influence of the varying PACA polymer (wt%) on the structural, morphological, and chemical properties and their impact on mechanical strength and thermal stability of TGP_X aerogels was investigated using different types of characterization.TGP_X aerogels were prepared by using a facile one-pot sol\u2013gel synthesis method, as schematically represented in \u22121. Chemical crosslinking between GPTMS, PACA, and TMOS was confirmed by using X-ray photoelectron spectroscopy  with a monochromatic Al X-ray source . The surface morphologies of the TGP_X aerogels were investigated via field emission scanning electron microscopy  performed at 5\u201310 kV. Surface area, pore volume, and pore size of TGP_X aerogels were calculated by using the N2 adsorption\u2013desorption isotherm data obtained from Brunauer\u2013Emmett\u2013Teller (BET) and Brunauer\u2013Emmett\u2013Teller (BJH) analyses (Quantachrome Instruments v10.0). According to previous reports, the bulk density (\u03c1b) of TGP_X aerogel samples was calculated using the mass-to-volume ratio. The porosity (%) of the TGP_X aerogels was calculated from the reported Formula (1) by using \u03c1s as the skeletal density (~1.9 g/cc) of TGP_X aerogel [The structural, morphological, chemical, and physical properties of the prepared TGP_X aerogels were investigated by using various characterization techniques. The surface functional group of TGP_X aerogels was examined by using FTIR spectroscopy  over a wavelength range from 400 to 4000 cm aerogel .\u22121. The mechanical properties of TGP_X aerogels were measured using compressive strength measurements .The thermal conductivity of TGP_X aerogels was measured by using an ASTM D7984 . Thermogravimetric analysis  was performed to determine the thermal stability temperature of TGP_X aerogel, where measurements were carried out in the temperature range of 30 \u00b0C to 1000 \u00b0C with a heating rate of 10 \u00b0C min"}
+{"text": "A 60-year-old woman with unresectable pancreatic cancer underwent endoscopic biliary stenting with a covered self-expandable metal stent (SEMS) with an antireflux valve . She waThe patient presented with a third episode of cholangitis 10 months later. Endoscopic retrograde cholangiopancreatography revealed stent obstruction due to biliary stones and debris. When sweeping the lumen of the three stents using stone extraction balloons, all three stents gradually migrated toward the duodenum. We therefore removed all three stents together using an endoscopic snare , which Video\u20061\u2002Successful removal of a biliary metal stent using the stent-in-stent-in-stent technique.Successful removal of both uncoveredEndoscopy_UCTN_Code_CPL_1AK_2AD"}
+{"text": "Our obtained data demonstrated that Eq_ASCs-derived liver progenitor cells (Eq_HPCs) displayed typical flattened polygonal morphology with packed fragmented mitochondrial net, lowered mesenchymal CD105 and CD90 surface markers expression, and significant high expression levels of specific hepatic lineage genes including PECAM-1, ALB, AFP and HNF4A. therewith, generated Eq_HPCs exhibited potentiated stemness and pluripotency markers expression . Hence, in vitro generation of hepatic progenitor-like cells retaining high differentiation capacity represents a promising new approach for the establishment of cell-based targeted therapies for the restoration of proper liver functions in EMS affected horses.Equine metabolic syndrome (EMS) is recognized as one of the leading cause of health threatening in veterinary medicine worldwide. Recently, PTP1B inhibition has been proposed as an interesting strategy for liver insulin resistance reversion in both equines and humans, however as being a multifactorial disease, proper management of EMS horses further necessities additional interventional approaches aiming at repairing and restoring liver functions. In this study, we hypothesized that  Equine Metabolic Syndrome (EMS) is an increasingly recognized endocrine disorder that is diagnosed in horses, ponies and even donkeys worldwide \u20133. In faOne of the potential therapeutic approaches proposed by our group includes inhibition of protein-tyrosine phosphatase PTP1B, which was shown to play a major role in regulating various metabolic and inflammatory mechanisms. In our previous research, we used a low molecular weight inhibitor MSI-1436, that was already shown to improve glucose tolerance and insulin sensitivity in insulin-resistant mice \u201313. We dIn current research, we proposed to develop a cell-based approach to regenerate insulin-resistant liver using experimentally generated hepatic-like progenitor cells in complement to PTP1B inhibitor application. Since we showed that transplantation of ASCs in EMS horses possesses limited clinical value, we considered the establishment of an equine model of liver progenitor cells (Eq_HPCs), as a novel progenitor cell pool for the treatment of equine metabolic syndrome. In this paper, we present a preliminary study of Eq_HPCs obtained from the guided differentiation of adipose-derived stromal cells (ASCs) along with their phenotypic and morphological characteristics, proliferative potential, as well as the expression of key stemness genes.p\u2009<\u20090.05, ** for p\u2009<\u20090.01, and *** for p\u2009<\u20090.001. The differences were considered significant with * p\u2009<\u20090.05.The equine adipose tissue-derived stem cells (Eq_ASCs) were obtained from healthy horses\u2019 adipose tissue from three biopsies . The tisp\u2009<\u20090.001) in Eq_ASCs cells (100% and \u00b1\u200928% respectively ) when compared to Eq_HPCs cells (<\u200950% and <\u20095% respectively)  in the Eq_ASCs cells (11% and <\u20090.15% respectively) compared to Eq_HPCs cells (\u00b1\u20092 and <\u20090.1% respectively)  in EqHPCs cells (0.3% and <\u20093% respectively) compared to Eq_ASCs cells (0.2% and <\u20092% respectively)   Fig.\u00a0. Same rely) Fig.\u00a0. Howeverly) Fig.\u00a0. Also, t01) Fig.\u00a0.Fig. 1Cp\u2009>\u20090.001) absorbances when compared to the Eq_HPCs cells after 24 and 48\u00a0h which correspond to a higher proportion of living Eq_ASCs cells  and thus improves the proliferative potential, by contrast to Eq_HPCs cells  ; after 24\u00a0h, the size of the scratch is more reduced for both cell type, however, it is still more reduced (p\u2009<\u20090.001) for the Eq_ASCs than the Eq_HPCs (<\u2009400 \u00b5M and <300\u00a0\u03bcm respectively)  Fig.\u00a0. Moreove01) Fig.\u00a0. Additioly) Fig.\u00a0.Fig. 2GrHNF4A, AFP, KRT18, ALB, OCT4, NESTIN, SOX2 and NANOG mRNAs  when compared to the Eq_ASCs cells; however, concerning the KRT18, the opposite is observed, the Eq_ASCs present a higher expression of the gene in contrast to the Eq_HPCs (p\u2009<\u20090.01)  of the OCT4, SOX2 and NANOG mRNA. Nevertheless, we note that the genetic expression of the Nestin marker is significantly superior in the Eq_ASCs than in the Eq_HPCs (p\u2009<\u20090.001)  Fig.\u00a0. Likewis01) Fig.\u00a0.Fig. 3Eqin vitro differentiation of various human and mouse MSCs populations toward hepatic-like cells and also hepatic progenitor cells, which showed partial to complete hepatic phenotype within 10 to 14 days differentiation and increased expression of ALB, CPM and EPCAM markers [Liver insulin resistance is an inseparable component of EMS, which is currently one of the most frequent endocrine disorders among horses. One of the potential therapeutic approaches proposed by our group includes inhibition of protein-tyrosine phosphatase PTP1B , 15, 33, markers \u201338. In o markers . The ana markers . Our obs markers , 41\u201343.  markers , who demThe ability of liver progenitor cells to differentiate into various hepatic lineages including hepatocytes and cholangiocytes is a prerequisite for their efficient pro-regenerative potential. In this study, we demonstrated that induced hepatogenic differentiation of equine ASCs resulted in liver progenitor-like cells with substantial stemness capacity. Eq_HPCs exhibited upregulated typical stemness markers, i.e., NANOG, SOX-2 and OCT-4 compared to native Eq_ASCs. Our findings are in accordance to previous reports showing that hepatic progenitor cells are enriched in pluripotent markers such as NANOG, SOX2 and OCT-4, which participate in the regenerative and repair properties of the hepatogenic precursors .in vitro model of generated progenitor cells population and their potential therapeutic role in liver regeneration, fibrosis, inflammation and insulin sensitization [Taken together, these results further uphold the ability of Eq_ASCs to differentiate into functional and potent liver progenitor-like cells, which shed promising light on the use of tization \u201347.This investigation aimed at generating a model of equine liver progenitor-like cells (Eq_HPC) through guided Eq_ASCs hepatogenic differentiation. Obtained data highlighted the high potential of Eq_ASCs to differentiate into hepatogenic precursors characterized by reduced mesenchymal CD105 and CD90 surface markers expression, enriched hepatic lineage PECAM-1, ALB, AFP and HNF4A markers, and enhanced stemness NANOG, SOX-2 and OCT-4 genes. These findings thus provide pledged prospects for the development of new ground-breaking cell-based therapies for the efficient and long-term management of liver failures in the course of equine metabolic syndrome."}
+{"text": "For the simulation process, the finite element method (FEM) was used considering the equation of electric potential and electroneutrality with and without the inclusion of quantum leap. We also provide the code to perform QM simulations in CUDA\u00ae, and COMSOL\u00ae software, the simulation parameters, and data for two metallic arrangements of chromium nanoparticles (CrNPs) electrodeposited on commercial steel substrate. (CrNPs-AISI 1020 steel and CrNPs-A618 steel). Data collection shows the direct relationship between applied potential (DCV), current (A), concentration (ppm), and time (s) for the homogeneous formation of the coating during the electrodeposition process, as estimated by the theoretical model developed. Their potential reuse data is done to establish the precision of the theoretical model in predicting the formation and growth of nanostructured surface coatings with metallic nanoparticles to give surface-mechanical properties.This data article presents a simulation model based on quantum mechanics and energy potentials for obtaining simulation data that allows, from the perspective of materials informatics, the prediction of the electrodeposition mechanism for forming nanostructured metallic coatings. The development of the research is divided into two parts  Specifications Table\u2022These data are useful because allow to understand the electrodeposition mechanism in intermetallic systems and visualize the formation and growth of nanostructured coatings where the input material is nanoparticles.\u2022From these data can benefit professionals and researchers in thin films and coatings areas.\u2022These data and the source codes generated can be used for further insights and development of experiments because a variation of the parameters in the source codes can also be carried out, allowing the analysis of cases of particular interest that involve this electrodeposition mechanism.\u2022That allows researchers to establish the necessary electrodeposition parameters  for the experimental deposition of nanostructured metallic coatings, enabling the development of a sustainable electrodeposition process.\u00a01Provides the predictive part of the electrodeposition mechanism for the fabrication of nanostructured surface coatings with metallic nanoparticles, determines the presence and resulting morphology, and determines the deposition and electrodeposition energies, showing interdiffusion as the form of adhesion of the filler material to the substrate. The data and codes may apply to metallic filler materials and substrate arrangements, opening the experimental application perspective.2r \u2248 40 nm with the respective atoms on the surface of the substrate. In contrast, the electrochemical deposition (absorption) capacity is controlled by electric potential. A subgroup of atoms is identified in a standard zone or interface that shows the presence of interdiffusion during the coating formation process. The data published in this article includes the source codes implemented in the CUDA\u00ae and COMSOL\u00ae programs to analyze the electrodeposition mechanism of nanostructured surface metallic coatings, from the perspective of materials informatics 0 and Cr3+ and, as a substrate, a commercial steel plate, according to ball and stick model obtained from the simulations for the resulting structure at the coating-substrate interface, where the presence of interdiffusion is observed, which enhances the adhesion of the coating to the substrate.In summary, codes source (point 1.1), modify and view files (point 1.2), and the data obtained from simulations (point 1.3) from the Quantum Mechanics (QM) perspective, using a model developed combining the Schr\u00f6dinger- Lenard -Jones-Nernst-Planck equations and discretized by the finite element method (FEM), are presented in detail below .2.1There are different free access programming packages, such is the case of those offered by NVIDIA GPU, which were implemented to solve the quantum chemistry equations. The programming language uses the video cards linked to the systems as a means of processing. computer mechanics through the CUDA language, which is a multiplatform language, the drivers for the execution of the programs with *.cu and *.pdb extension can be obtained from the following link for free.https://developer.nvidia.com/cuda-toolkit and download:-https://github.com/Paco1901/Schrodinger_electrochemistry.git. The extension identifies all executable files *.cu [\u25cbSchrondiger_deposition.cu \u25cbSchrondiger.cu \u25cbSchrondiger_modif.cu (Program that determines deposition energies and probability using equation modified)NVIDIA CUDA-X GPU- Accelerated Libraries for the execution of all the codes located at les *.cu :\u25cbSchrond-\u25cbCristal_00.pdb (AISI-1020 Steel Molecule Bond Lengths)\u25cbCristal_01.pdb (AISI-1020 Steel Molecule Bond Lengths with NNP's)\u25cbCristal_02.pdb (AISI-1020 Steel Molecule Bond Lengths with NNP\u00b4s optimize)\u25cbOxido_cromo.pdb (Chrome oxide Molecule Bond Lengths)CUDA Toolkit Develop, Optimize, and Deploy GPU-Accelerated Apps. Files ending in *. pbd can be run via the CUDA(R) toolkit, without the following files:Go to 2.2Having a license and an installed program is mandatory. The COMSOL\u00ae platform is a simulation platform for electrochemical processes and fluid mechanics.https://github.com/Paco1901/Schrodinger_electrochemistry.git, identified with the *.mph extension:\u25cbElectrodeposition.mph (Dynamic computer simulation of electrodeposition using COMSOL\u00ae program)The files are located at 2.3\u25cbDatos_01.mat \u25cbDatos_02.mat \u25cbDatos_03.mat \u25cbDatos_04.mat \u25cbDatos_05.mat \u25cbDatos_06.mat \u25cbDatos_07.mat \u25cbDatos_08.mat \u25cbDatos_09.mat \u25cbDatos_10.mat The corresponding codes (*.cu) must be executed to obtain the electrodeposition data. The numerical values can be viewed in any numerical and text data management program available to the researchers, obtaining the files with *.mat extension, which can also be viewed with the *.tx format .\u25cbDatos_02.4The source codes request the data to be entered before the simulation process at the beginning of the execution; the data request is textual and corresponds to the parameters shown in The code requests the data only as magnitude; during execution, the corresponding units are considered within the code as base units, for example, voltage (V), thickness (nm), etc.The interpretation of the data and the results are published in the original article published in the Chemical Engineering Science Magazine 3Practical and detailed information on the use of the data and the configuration for the simulations is shown below. The formation and growth of the chromium coating formed on a steel surface were analyzed using the model shown in 3.1The electrodeposition mechanism was analyzed using two coating-substrate arrangements of chromium nanoparticles (CrNPs) and commercial steel substrates, see A computational theoretical model was applied for the analysis that uses a modified Schr\u00f6dinger equation combined with the Lenard -Jones equation and the Nernst-Planck equation. From the quantum mechanics (QM) perspective, the resulting model allows us to predict the electrodeposition capacity in material systems considering electronic stability, interdiffusion, and the minimum potential required for the process. The model used is shown in The description of the related variables in the model, as well as their corresponding units, can be consulted in the reference article The discretization of the model for the simulation process, using the finite element method (FEM) and considering the electric potential and electroneutrality equations with and without the inclusion of quantum leap, is shown in 3.2The following parameters are introduced in the codes in *.cu and *.m, for the endings *.mph is developed in a windowed environment .Fig. 2COwww.github.com/Paco1901/Schrodinger_electrochemistry.git/Schrodinger.cuC:\\\\>star_chrome/unknow/User_LNS_ 1:Schrondiger.cu/*.*.exe;>>V=0.5>>x=0.0001>>y=0>>z=0>>Type: Cr>> Substrate: FeMnOUser_LNS_ 2:Schrondiger.exe/save//Data01.matUser_LNS_3:save 'Data01.mat'/host_UPAEP_Paco1901/*.*.kernelUser_LNS_ 4:dowload'Data 01.mat'/host_UPAEP_Paco1901/save direction as well as in the vector  directions.www.github.com/Paco1901/Schrodinger_electrochemistry.git /matlab/load ('github/Paco1901/Schrodinger_electrochemistry/Data_01\u2032)/waterfall)/plotC:\\>star_chrome/ www.github.com/Paco1901/Schrodinger_electrochemistry.git /matlab/load ('github/Paco1901/Schrodinger_electrochemistry/Data_02\u2032)/probplot) /plotC:\\>star_chrome/ www.github.com/Paco1901/Schrodinger_electrochemistry.git /matlab/load ('github/Paco1901/Schrodinger_electrochemistry/Data_03\u2032)/lineplot) /plotC:\\>star_chrome/ The visualization of the change in the potential charge is determined by means of the commands of the following lines.www.github.com/Paco1901/Schrodinger_electrochemistry.git /matlab/load ('github/Paco1901/Schrodinger_electrochemistry/Data_04\u2032)/probplot) /plotC:\\>star_chrome/ The commands represent the results obtained as a function of time,www.github.com/Paco1901/Schrodinger_electrochemistry.git /matlab/load ('github/Paco1901/Schrodinger_electrochemistry/Data_05 ')/ probplotk) / plot C:\\>star_chrome/ www.github.com/Paco1901/Schrodinger_electrochemistry.git /matlab/load ('github/Paco1901/Schrodinger_electrochemistry/Data_06\u2032)/waterfall) /plotC:\\>star_chrome/ www.github.com/Paco1901/Schrodinger_electrochemistry.git /Matlab/load ('GitHub/Paco1901/Schrodinger_electrochemistry/Data_07\u2032)/probplot) /plotC:\\>star_chrome/ (a)www.github.com/Paco1901/Schrodinger_electrochemistry.git /matlab/load ('github/Paco1901/Schrodinger_electrochemistry/Data_08\u2032)/waterfall) /plot C:\\>star_chrome/ (b)www.github.com/Paco1901/Schrodinger_electrochemistry.git /matlab/load ('github/Paco1901/Schrodinger_electrochemistry/Data_09\u2032)/waterfall) /plot C:\\>star_chrome/ (c)www.github.com/Paco1901/Schrodinger_electrochemistry.git /Matlab/load ('GitHub/Paco1901/Schrodinger_electrochemistry/Data_10\u2032)/waterfall) /plot C:\\>star_chrome/ For the visualization of the electrodeposition energy, it is obtained in a three-dimensional plane (a); for the deposition energy, it is obtained using the command (b) in the same way in a three-dimensional space, for the electrodiffusion in the line in the same three-dimensional space (c).The authors declare that the work does not involve human subjects, animal experiments, or data collected from social media platforms, being exempt from an ethical approval process.G. Rosano-Ortega: Conceptualization, Methodology, Visualization, Formal analysis, Supervision, Writing \u2013 original draft. M. Bedolla-Hern\u00e1ndez: Conceptualization, Methodology, Investigation, Writing \u2013 original draft. F.J. S\u00e1nchez-Ruiz: Conceptualization, Methodology, Investigation, Visualization, Software, Data curation. J. Bedolla-Hern\u00e1ndez: Investigation, Writing \u2013 review & editing. P.S. Schabes-Retchkiman: Investigation, Writing \u2013 review & editing. C.A. Vega-Lebr\u00fan: Project administration, Funding acquisition. E. Vargas-Viveros: Investigation, Resources, Writing \u2013 review & editing.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper."}
+{"text": "Previously attempted and partially resected laterally spreading tumors (PA-LSTs) present a particular challenge for subsequent endoscopic mucosal resection (EMR) owing to the presence of submucosal fibrosis. Techniques such as hot avulsion, or cold avulsion with adjuvant snare tip soft coagulation have been described previously, with local recurrence rates of 15\u200a%We present a case of COSA for a 25-mm nonlifting PA-LST . InitiaVideo\u20061\u2002Cold snare resection of a 25-mm colorectal tubular adenoma with prophylactic thermal ablation of the resection margin and base.Endoscopy_UCTN_Code_TTT_1AQ_2AD"}
+{"text": "Endoscopic ultrasonography-guided hepaticogastrostomy (EUS-HGS) is expected to become widespread in the futureA 75-year-old woman developed cholangitis from perihilar cholangiocarcinoma, which was not controlled with multiple transpapillary stents. EUS-HGS was performed, and after creation of the gastrobiliary fistula, the plastic stent was replaced with a metal stent.For stent replacement, we inserted the guidewire into a 3.5\u200aFr catheter , which was grasped by a snare  . We useVideo\u20061\u2002Easy replacement method for a 7\u200aFr dedicated plastic stent in endoscopic ultrasound-guided hepaticogastrostomy.This method enables the guidewire to be reliably placed in the bile duct, enabling safe exchange of a dedicated plastic stent.Endoscopy_UCTN_Code_TTT_1AS_2AD"}
+{"text": "Early post-implantation development, especially gastrulation in primates, is accompanied by extensive drastic chromatin reorganization, which remains largely elusive.in vitro cultured cynomolgus monkey  embryos to investigate the chromatin status. First, we delineated the cis-regulatory interactions and identified the regulatory networks and critical transcription factors involved in the epiblast (EPI), hypoblast, and trophectoderm/trophoblast (TE) lineage specification. Second, we observed that the chromatin opening of some genome regions preceded the gene expression during EPI and trophoblast specification. Third, we identified the opposing roles of FGF and BMP signaling in pluripotency regulation during EPI specification. Finally, we revealed the similarity between EPI and TE in gene expression profiles and demonstrated that PATZ1 and NR2F2 were involved in EPI and trophoblast specification during monkey post-implantation development.To delineate the global chromatin landscape and understand the molecular dynamics during this period, a single-cell assay for transposase accessible chromatin sequencing (scATAC-seq) was applied to Our findings provide a useful resource and insights into dissecting the transcriptional regulatory machinery during primate post-implantation development. The transition from pre-implantation to gastrulation represents a milestone of early embryogenesis in primates and involves extensive morphogenesis and lineage specification and differentiation. During this stage, a connection between the embryo and the mother is established, while the trophectoderm (TE) differentiates into cytotrophoblasts (CTs), extravillous cytotrophoblasts (EVTs), and syncytiotrophoblasts; the cavitation of the amnion and yolk sac initiates, and the gastrulation of the embryo launches to form 3 germ layers and program the body plan of the fetus , 2. Howein vitro culture systems have enabled us to investigate transcriptional and DNA methylation dynamics during the early embryonic development in humans and monkeys [Recently, advancements in embryo  monkeys . HoweverIn the mouse, chromatin accessibility, histone modifications, and 3-dimensional chromatin structures during post-implantation development have been extensively studied, and epigenetic regulatory networks have been revealed . As signin vitro culture platform to unravel the regulatory chromatin landscape during early embryonic development in monkeys. This study provides a valuable resource for studying chromatin dynamics and chromatin regulation during early embryonic development in primates.Here, we harness the power of a single-cell assay for transposase accessible chromatin sequencing (scATAC-seq) and embryo To determine the regulatory landscape at single-cell resolution during monkey peri- and post-implantation development, we performed scATAC-seq of cultured monkey embryos from day 9 post-fertilization (9 d.p.f.) to 20 d.p.f. as our previously reported and published single-cell RNA sequencing (scRNA-seq) dataset was included for analysis  Fig.\u00a01A. In totaNext, based on these high-quality data, we investigated global gene regulatory activities during monkey early development. First, the resting 978 cells were dimensionally reduced using uniform manifold approximation and projection (UMAP) and clustering analysis. In scRNA-seq analysis, four main cell clusters\u2014namely, epiblast (EPI), TE, visceral endoderm or yolk-sac endoderm (VE/YE), and extra-embryonic mesenchyme cell (EXMC)\u2014were identified Fig.\u00a0 \u00a0. To inteOCT4 locus  in the EPI, TFAP2C in the TE, HNF1B in the VE/YE, and TCF21 in the EXMCs  and NANOG in the EPI, TFAP2C and TEAD4 in the TE, GATA4 in the VE/YE, and TCF21 and FOXF1 in EXMCs  to the differentially expressed genes (DEGs) . FurtherMCs Fig.\u00a0. Gene OnMCs Fig.\u00a0. Taken tFOXH1, indicating their similarities in the regulatory program during early embryogenesis. In contrast, TE and EXMC lineages were distinct from the other lineages  to gastrulating cells (Gast)  .OCT4 , SOX2, and NANOG, belong to cluster 2, in which chromatin opens at pre- and peri-implantation stages , and gene expressions were upregulated until the post-implantation stage (EPI-C)  chromatin became accessible first in EPI-A cells, and then the genes were expressed (pattern 1) Fig.\u00a0 \u00a0, and ii (pattern) Fig.\u00a0 \u00a0, which wIn pattern 2, 5 subpatterns (clusters 1\u20135) were observed. Interestingly, genes involved in mesoderm formation and gastrulation belonged to pattern 2 Fig.\u00a0. To furtOCT4 and NANOG, were upregulated in EPI-C cells, which highly correlated with the expression of FGF signaling members such as FGF2, FGF4, and FGF receptor 1 (FGFR1 )  Fig.\u00a0 \u00a0. Furthered genes   [ GATA3 [ITGA5 [FN1 [MSX2 and CDX2 in TE-A; zinc finger containing proteins (ZNFs ) including ZNF707, ZNF75A and ZNF589 and et al. in TE-B; EGR1 and EGR2 in TE-C; and EST2 and FOSL2 in TE-D  in the trophoblasts. Based on scRNA-seq analysis and alignment of single scRNA-seq and scATAC-seq cells , we annoCDX2 , 25; TE-2 [ITGA6 ; and TE- (CK7 )   and the  [ GATA3   [ASCL2 (regulator of human EVT differentiation) [GCM1  [A previous study reported that domains of regulatory chromatin (DORCs) are enriched in lineage-determining genes and can be used to infer cell fate choices de novo . To deliiations) . Consisttiation)  and GCM1athways)   , we analFinally, scRNA-seq\u2013based cross-species comparisons between mouse (embryonic day 6.5\u20138.5)  and monkPrevious studies indicated lineage flexibility between naive PSCs and TEs , 35, butGNAO1 in the EPI and INSL4 in the trophoblasts were identified  [Macaca fascicularis genome (Macaca_fascicularis_5.0) using STAR  [RRID:SCR_013027) (v1.3.0) [For the preprocessing of raw sequencing data, we removed adapters and filtered out low-quality reads with an N rate >0.2 using Cutadapt  . Filtere_004463) . Rsem-Ca(v1.3.0)  was usedRRID:SCR_016341) [RRID:SCR_018217) [RRID:SCR_018809) [P < 0.05). The top 2,000 DEGs were selected to construct the trajectory model using Monocle2  [We selected the top 2,000 variable genes based on log-transformed TPM matrices using Seurat  . Princip_018217)  to visua_018809)  to remov_016339) , 51.in vitro and in vivo embryos [in vitro and in vivo datasets. The log-transformed count expression matrices of genes \u00d7 cells were used to create the Seurat object, and then we used normalized values processed by the \u201cNormalizeData\u201d function according to the tutorial of Seurat. The top 2,000 variable genes were identified using the \u201cFindVariableFeatures\u201d function, and scale gene expression values were obtained using the \u201cScaleData\u201d function. The anchors between our and in vivo data were found with the FindTransferAnchors function . The \u201cIntegrateData\u201d function (dims = 1:30) was applied to our and in vivo datasets to get an integrated Seurat object. Then the integrated Seurat object was processed with \u201cScaleData,\u201d \u201cRunPCA,\u201d and \u201cRunUMAP\u201d functions, and a UMAP graph was constructed to visualize the similarities between in vitro and in vivo embryos. Next, we averaged the gene expression levels of each cell cluster in both datasets and calculated the Pearson correlations between the cell clusters in both datasets. The Euclidean distance between the cell clusters was calculated, and unsupervised hierarchical clustering was performed to determine gene expression pattern similarities between the in vitro and in vivo cell clusters.We extracted the overlapping genes in EPI cells between our  embryos . Then R M. fascicularis genome (Macaca_fascicularis_5.0) using Bowtie2  [RRID:SCR_013291) [The raw sequencing data were filtered using Cutadapt (v1.16) , and the_016368) . Fragmen_016368)  was used_013291) . FinallyRRID:SCR_021158) [Signac   was usedtrans correlations. Differential peak-to-gene linkages were visualized by ComplexHeatmap  [RRID:SCR_016884) [The gene activity value was obtained by calculating the fragments in the 2-kb upstream region and gene body, which could be used to measure the gene accessibility and for correlation analysis with the gene expressions. scATAC-seq and scRNA-seq pairs were matched by Seurat\u2019s CCA using the \u201cFindTransferAnchors\u201d function  based on the top 2,000 variable genes identified by \u201cFindVariableFeatures\u201d function, and then scRNA-seq cell-type annotations information was transferred to scATAC-seq using the \u201cTransferData\u201d function. To generate coembedding UMAP, \u201cFindIntegrationAnchors\u201d  and \u201cIntegrateData\u201d (dims = 1:30) functions in Seurat were used. As previously described , we iden_017270) . GO anal_016884) .R\u00b2 as the linkage score for the TF\u2013target pair. NetworkD3 (v0.4) package [For constructing the TF\u2013target gene network, we first identified the TFs and marker genes highly expressed in the same group of cells. Next, we identified TF\u2013target gene pairs, that is, if the marker gene was linked with peaks that matched the corresponding TF motif. For a given marker gene with at least one linked and matched peak, we summed their squared correlation  package  in R V4.ATAC and RNA cells were paired by the \u201cpairCells\u201d function in R package FigR  based onSince ATAC\u2013RNA cell pairs were established, we could get \u201cpseudo cells\u201d in which each \u201cpseudo cell\u201d had chromatin accessibility and gene expression information like multimodal data. Then, paired RNA cells were used to create a pseudotime trajectory using R package monocle2.Q-values <0.1 were retained. Pseudotime-dependent genes were split into 4 main clusters based on expression and further split into some subclusters in each main cluster based on chromatin accessibility using the \u201cplot_pseudotime_heatmap\u201d function. ATAC and RNA pseudotime heatmaps were combined using the \u201cHeatmap\u201d function in the R package ComplexHeatmap. Other pseudotime analysis methods, such as Slingshot (RRID:SCR_017012) [For the trajectory of EPI, we identified pseudotime-dependent genes whose expression varied with pseudotime using the \u201cdifferentialGeneTest\u201d function (fullModelFormulaStr = \u201c\u223csm.ns(Pseudotime)\u201d), and genes with _017012)  and TSCA_017012) , were alQ-values <0.001 were retained. Branch-dependent genes were split into four main clusters based on expression and further split into some subclusters in each main cluster based on chromatin accessibility using the \u201cplot_genes_branched_heatmap\u201d function. ATAC and RNA branched pseudotime heatmaps were combined using the \u201cHeatmap\u201d function in the R package ComplexHeatmap.For the trajectory of TE, we identified branch-dependent genes whose expression varied with the branch using the \u201cBEAM\u201d function, and genes with To get coembedding UMAP for mouse and monkey embryos, we extracted cells in embryonic day 6.5 to 8 mouse embryos. In total, 17,358 common genes were retained in mouse and monkey embryo data based on a homologous gene list from Ensembl BioMart. Then, Seurat objects of mouse and monkey embryos were created, and the Seurat object of mouse embryos was split into three objects based on sequencing batch. Four objects were normalized using the \u201cNormalizeData\u201d function, and their variable genes were identified using the \u201cFindVariableFeatures\u201d function. The integration features between four objects, obtained using the \u201cSelectIntegrationFeatures\u201d function, were inputted into the \u201cScaleData\u201d and \u201cRunPCA\u201d functions to perform PCA. Then, we identified \u201canchors\u201d between four objects by the \u201cFindIntegrationAnchors\u201d function  and integrated four objects using the \u201cIntegrateData\u201d function. Coembedding UMAP was generated by an integrated object using the \u201cRunUMAP\u201d function.P values <0.05 and min log2 fold changes >0.25. DEGs were identified using \u201cFindMarkers\u201d functions in corresponding cell types between species with P values <0.05 and log2 fold changes >0.25. Then, shared genes in DEGs with cell-type markers were retained.Aiming to study the differences between monkey and mouse embryos in gene expressions, we matched cell types of cross-species in the shared neighborhood based on coembedding UMAP and got mouse EPI, VE/YE, EXMC, and TE corresponding cell types in monkey embryos. Conserved cell-type markers were identified using \u201cFindConservedMarkers\u201d functions with max RRID:SCR_017270) [RRID:SCR_016884) [We identified the accessible peaks (peak read count was >0) in each stage (the percentage of cells with accessible peaks was >0.25). The gained peaks at a particular stage were defined as the accessible peaks nonoverlapping with a previous stage. The lost peaks at a specific stage were defined as the peaks nonexisting with this stage compared to a previous stage. Gained and lost peak-to-gene linkages were visualized by ComplexHeatmap (_017270) . GO anal_016884) .P values. EPI and TE upregulated genes were classified based on the log fold change values.To study the genes with increasing expression differences during lineage differentiation, we identified the DEGs in EPI and TE subtypes using the \u201cFindMarkers\u201d function in Seurat and selected genes with increasing log fold change \u2212log P values less than 0.01 in EPI and greater than 0.01 in TE, and the enrichment fold changes in EPI larger than that in TE were identified as EPI-regulated TFs and vice versa.TF motif enrichment analysis was performed for genes upregulated in the EPI and TE. TF motif enrichment in the peaks of EPI and TE genes, To examine the importance of TFs in the corresponding group regulatory network, we calculated the degree centrality, closeness centrality, and eigenvector centrality of each TF in the network and the rank, respectively. The comprehensive rank was obtained by adding the ranks of the 3 centralities, and the higher the rank, the more influential the TFs in the network.GigaScience GigaDB database [All sequencing data were deposited at the National Center for Biotechnology Information Sequence Read Archive under accession no. SRP175059. The data were also deposited at the China National GeneBank (CNGB) Nucleotide Sequence Archive under accession no. CNP0000231. The mouse embryo scRNA-seq dataset was downloaded from EMBL-EBI ArrayExpress under accession no. E-MTAB-6967. All supporting data and materials are available in the database .Supplementary Table S1. Quality control data for the scATAC-seq dataset.Supplementary Table S2. Lineage-specific marker peaks and corresponding genes.Supplementary Table S3. Lineage-specific TFs and their candidate target genes.Supplementary Table S4. Gained or lost peaks and corresponding genes during EPI lineage transition.Supplementary Table S5. EPI subtype lineage-specific TFs and corresponding target genes.Supplementary Table S6. Trophoblast subtype lineage-specific TFs and corresponding target genes.Supplementary Table S7. Gained or lost peaks and corresponding genes during trophoblast lineage transition.Supplementary Table S8. Gene list of 220 genes involved in EPI and trophoblast lineage segregation.Supplementary Fig. S1. Quality control of scATAC-seq data. (A) Bar charts showing the distribution of embryonic day in each cell type. Left panel, scATAC-seq dataset; right panel, scRNA-seq dataset. (B) Violin chart showing the quality control of the scATAC-seq dataset for each embryo. (C) Coembedded UMAP for single-cell pairs of scATAC-seq and scRNA-seq datasets based on geodesic distance-based pairing approach. The colors of cells represent technology and cell type. (D) The distribution of prediction scores calculated in the integrated procedure. (E) Percentage of each cell type in scATAC-seq and scRNA-seq datasets. (F) The chromatin accessibility and gene expression levels of cell-type markers in the individual cell. Each gray line indicates one ATAC\u2013RNA single-cell pair. (G) UMAP plot of gene expression levels and motif deviation scores of lineage-specific TFs.Supplementary Fig. S2. Lineage specification of EPI. (A) Pseudotime trajectories of EPI cells inferred by slingshot (left) and TSCAN (right) analysis. (B) Hierarchical cluster analysis of expression profiles of EPI subtypes between in vivo [in vitro embryos. (C) Bar chart showing the number of genes that gained or lost peaks during EPI subtype transitions. Genes are grouped by the number of peaks changed. (D) The number of accessible peaks changed for each gene during EPI subtype transitions. (E) Heatmap showing gained peaks and corresponding genes during EPI subtype transitions with listed well-studied marker genes, TF binding motifs, candidate TFs, and enriched GO terms. P values derived from the hypergeometric test are shown, and the color indicates the gene ratios. (F) Heatmap showing lost peaks and corresponding genes during EPI subtype transitions with listed well-studied marker genes, TF binding motifs, candidate TFs, and enriched GO terms. P values derived from the hypergeometric test are shown, and the color indicates the gene ratio. in vivo  and in vSupplementary Fig. S3. The correlation between gene expression and chromatin accessibility during EPI specification. (A) Heatmaps showing gene activity scores and expression levels of pseudotime-dependent genes in Fig.\u00a0P values derived from the hypergeometric test are shown, and the color indicates the gene ratio. (B) Pseudotime heatmaps showing gene activity scores and expression levels of EPI-C- and Gast-specific TF target genes in Fig.\u00a0Supplementary Fig. S4. Dynamics of trophoblast cell fate transitions. (A) Coembedded UMAP for single-cell pairs of scATAC-seq and scRNA-seq datasets based on geodesic distance-based pairing approach. The colors of cells represent technology and cell type. (B) Pseudotime trajectory of the scATAC\u2013scRNA paired EPI subtypes with cells colored by trophoblast subtypes and pseudotime. (C) Heatmaps showing z-scores of trophoblast-averaged subtype-specific TFs, their target gene expression levels, and gene activity scores. Averaged gene expression levels and activity scores are calculated from cells aggregated by trophoblast subtypes. (D) Heatmaps showing gene activity scores and expression levels of branch-dependent genes in (B). Columns of the heatmap indicate pseudotime (left panel). Representative genes and GO enrichment terms are listed in the middle and right panels. P values derived from the hypergeometric test are shown, and the color indicates the gene ratio.Supplementary Fig. S5. Dynamics of chromatin accessibility during trophoblast specification. (A) The number of changed accessible peaks of each gene during the transition of trophoblast subtypes. The genes are ranked by changed accessible peaks. (B) Heatmap showing gained peaks and corresponding genes during each trophoblast subtype transitions with listed well-studied marker genes, TF binding motifs, corresponding candidate TFs, and representative enriched GO terms. P values derived from the hypergeometric test are shown, and the color indicates the gene ratio. (C) Heatmap showing lost peaks and corresponding genes during each trophoblast subtype\u2019s transitions with listed well-studied marker genes, TF binding motifs, corresponding candidate TFs, and representative enriched GO terms. P values derived from the hypergeometric test are shown, and the color indicates the gene ratio.Supplementary Fig. S6. Cross-species comparison between mouse and monkey gastrulating embryos. (A) UMAP visualization of cells of in vivo mouse (embryonic days 6.5\u20138) and in vitro monkey (9\u201320 d.p.f.) embryos. Colors encode cell source and type. (B) Heatmap of the top 40 DEG expressions between monkey and mouse embryos. Each column indicates matched cell cluster between monkey and mouse embryos.Supplementary Fig. S7. Transcriptional regulation of EPI and trophoblast lineage segregation. (A) Heatmap showing Pearson correlation coefficient of gene expression profiles between EPI and TE subtypes. The EPI and TE cells were classified into four groups based on development stage and hierarchical clustering . pre, pre-implantation; post, post-implantation; Gast, gastrulating cells. (B) Heatmap showing Pearson correlation coefficient of chromatin accessibility profiles between EPI and TE subtypes. The EPI and TE cells were classified into four groups . (C) Bubble plot showing TF expression and corresponding motif enrichments that regulate lineage differentiation. P values are derived from the hypergeometric test.giad038_GIGA-D-22-00278_Original_SubmissionClick here for additional data file.giad038_GIGA-D-22-00278_Revision_1Click here for additional data file.giad038_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giad038_Reviewer_1_Report_Original_SubmissionZhicheng Ji -- 11/12/2022 ReviewedClick here for additional data file.giad038_Reviewer_1_Report_Revision_1Zhicheng Ji -- 3/30/2023 ReviewedClick here for additional data file.giad038_Reviewer_2_Report_Original_SubmissionIvan Costa -- 11/29/2022 ReviewedClick here for additional data file.giad038_Supplemental_Figures_and_TablesClick here for additional data file.bp: base pair; CCA: canonical correlation analysis; CT: cytotrophoblast; DEG: differentially expressed gene; DORC: domain of regulatory chromatin; DP: differential peak; dpf: day postfertilization; EPI: epiblast; E-T: EPI-trophoblast; EVT: extravillous cytotrophoblast; EXMC: extra-embryonic mesenchyme cell; GO: Gene Ontology; MNN: mutual nearest-neighbor; PBS: phosphate-buffered saline; PCA: principal components analysis; PSC: pluripotent stem cell; scATAC-seq: transposase accessible chromatin sequencing; scRNA-seq: single-cell RNA sequencing; TE: trophectoderm; TF: transcription factor; TPM: transcripts per million mapped reads; UMAP: uniform manifold approximation and projection; VE/YE: visceral endoderm or yolk-sac endoderm; ZNF: zinc finger containing protein.The ethical committee of the State Key Laboratory of Primate Biomedical Research (LPBR) approved all animal and experiment procedures (LPBR-2016\u201301), and the procedures were performed by following the guidelines of the Association for Assessment and Accreditation of Laboratory Animal Care International (AAALAC) for the ethical treatment of nonhuman primates.The authors declare no competing interests.This work was supported by the National Natural Science Foundation of China (82192871), the Natural Science Foundation of Yunnan Province (202001BC070001 and 202102AA100053), and the China National GeneBank (CNGB).H.S., N.S., C.L., L.W., Y.Y., X.W., and L.L. performed most of the experiments. X.D., B.C., and B.B. performed the bioinformatics analysis. X.D., J.W, H.Y., W.J., Z.S., and T.T. participated in discussions. X.D., J.W., Z.S., and T.T. analyzed the data and wrote the manuscript. T.T. and Z.S. conceived and supervised the study."}
+{"text": "Circular RNA (circRNA), as a newly discovered non-coding RNA with important regulatory potential, is closely related to the occurrence and progression of various tumors. This study aimed to investigate has_circ_0000069 expression in breast cancer and its influence on cellular activities. Using real-time quantitative polymerase chain reaction, has_circ_0000069 levels were measured in 137 pairs of tissue specimens, as well as cancer cell lines. The cellular activities of cell lines were determined by cell counting kit-8 (CCK-8) and Transwell assays. The potential targeting miRNAs were predicted and verified using an online database and dual-luciferase reporter assay. Has_circ_0000069 was highly expressed in breast cancer tissues and cells. The expression of has_circ_0000069 was associated with the five-year overall survival of patients. After silencing has_circ_0000069 in breast cancer cells, its expression reduced, and the ability of cell proliferation, migration, and invasion decreased. MiR-432 was verified as a targeting miRNA of has_circ_0000069. Has_circ_0000069 expression increased in breast cancer and was negatively related to patient\u2019s prognosis. Has_circ_0000069 may facilitate breast cancer tumor progression by sponging miR-432. These findings revealed that has_circ_0000069 may be a biomarker for predicting prognosis and a therapeutic target for treating patients with breast cancer. Breast cancer, the most common cancer in women worldwide, is a malignant disease that occurs in the epithelial tissue of the breast . The canCircular RNA (CircRNA) belongs to a special class of non-coding RNAs, which has no 5\u2032 and 3\u2032 end polyadenylate tail and it is a closed circRNA molecule formed by a covalent bond . IncreasHere, the expression of has_circ_0000069 in tumor tissues and cells was detected and its clinical significance was verified. Further studies investigated the oncogenic regulatory role of has_circ_0000069 in breast cancer cellular behaviors via acting as a ceRNA for miR-432. These findings may provide a putative biomarker for the prognosis and therapy of breast cancer.A total of 137 pairs of tumor tissue specimens and adjacent normal tissue specimens from breast cancer patients diagnosed by pathology after surgical resection in the Shanghai Tenth People\u2019s Hospital, Tongji University School of Medicine from July 2016 to May 2019 were collected. No patients received preoperative adjuvant therapy. The specimens were stored in liquid nitrogen. All patients signed informed consent before surgery. All specimens were obtained with the approval of the Ethics Committee of the Shanghai Tenth People\u2019s Hospital, Tongji University School of Medicine. The clinicopathological data of patients were obtained and recorded.2 at 37\u00b0C.Breast cancer cell lines BT549, MCF-7, SK-BR-3, T47D, MDA-MB-231, and normal mammary epithelial cell MCF-10A were obtained from the Type Culture Collection of the Chinese Academy of Sciences . Breast cancer cells were cultured in a DMEM medium with 10% FBS  and penicillin-streptomycin antibody . MCF-10A cells were cultured with DMEM/F12 (1:1) medium, 5% equine serum (Gibco), 10 \u03bcg/mL insulin , epidermal growth factor , and 0.5 \u03bcg/mL hydrocortisone. The culture condition was an incubator containing 5% COWhen the confluence of cells reached 70%, si-NC(5\u2032-GGACUCUCGGAUUGUAAGAUU-3\u2032), si-circRNA-1(5\u2032-CTACTTCAGGCACAGGTCT-3\u2032), si-circRNA-2 (5\u2032-GCACAGGTCTTCCCAAAAG-3\u2032), or si-circRNA-3 (5\u2032-CTTCAGGCACAGGTCTTC-3\u2032)  was transfected into MCF-7 or MDA-MB-231 cells using lipofectamine 2000. The cells were harvested after 24 h for subsequent experiments.TM RT kit . RT-PCR was performed using SYBR Premix Ex TaqTM II  to detect expression levels of has_circ_0000069 in tissues and cells. Using GAPDH as an internal reference and cDNA as a template, the samples were denatured at 95\u00b0C for 3 min, followed by 35 cycles of denaturing at 95\u00b0C for 5 s, 60\u00b0C for 30 s, and extension at 72\u00b0C for 30 s. The 2\u2212\u0394\u0394Ct method was used to detect the relative has_circ_0000069 levels. The primers for RT-qPCR were as follows: has_circ_0000069, F: 5\u2032-CTACTTCAGGCACAGGTCTTC-3\u2032; R: 5\u2032-CTGACTCACTGGATGAGGACT3\u2032; GAPDH F: 5\u2032-AAGGTGAAGGTCGGAGTCA-3\u2032; R: 5\u2032-GGAAGATGGTGATGGGATTT-3\u2032.Total RNA was isolated from cancer tissues, adjacent tissues, and cell lines by the TRIzol  method. A microplate reader was used to measure the concentration and purity of total RNA. Reverse transcription was performed using the PrimeScript4 cells/mL) and seeded into 96-well plates. After incubation at 37\u00b0C and 5% CO2 condition for 0, 24, 48, and 72 h, 10 \u03bcL CCK-8 solution  was added to each well and mixed. Next, the cells were incubated in a temperature box for 2 h, and the absorbance value (450 nm) was measured using a microplate reader.After 24 h of transfection, breast cancer cells (MCF-7 or MDA-MB-231) in each group were prepared into 100 \u03bcL cell suspension  with a pore size of 8 \u03bcm. Matrigel matrix  was used to pre-coat after premelting at 4\u00b0C for the invasion assay but not the migration assay. A total of 100 \u03bcLof treated MCF-7 and MDA-MB-231 cells were seeded in 24-well plates at a density of 1 \u00d7 10The circular RNA interactome online database was used to identify miRNAs targeting has_circ_0000069 . The luct-test. Comparison among three or more groups were performed using one-way ANOVA. The correlation between circRNA expression and the prognosis of breast cancer patients was analyzed by Kaplan-Meier survival analysis. p < 0.05 was considered statistically significant.SPSS software (version 20.0) and GraphPad software (version 7.0) were used for data analysis. Measurement data were expressed as mean \u00b1 SD. Comparison between two groups was performed by student\u2019s p < 0.001, p < 0.05, The expression of has_circ_0000069 in breast cancer was assessed in tissue specimens. RT-qPCR revealed that has_circ_0000069 expression was raised in breast cancer tissues compared with normal tissues (p = 0.007) and lymph node metastasis (p = 0.015).In addition, the average expression value of has_circ_0000069 (2.219) in all tumor tissues was used as the cut-off value to group patients into low circ_0000069 expression group and high has_circ_0000069 expression group. Subsequently, the correlation between has_circ_0000069 expression and clinical parameters of patients was analyzed. The results in p = 0.013, p = 0.027), clinical stage (p = 0.030), and lymph node metastasis (p = 0.040) were prognostic risk factors  for the investigation of the biological role of has_circ_0000069 in MCF-7 and MDA-MB-231 cells, which are high has_circ_0000069 expressed cell lines. The transfection verification results showed si-circRNA-1 significantly decreased the has_circ_0000069 levels in cancer cells .CCK-8 assay revealed that has_circ_0000069 knockdown repressed the growth of treated cells compared with untreated cells (https://circinteractome.nia.nih.gov/index.html) was used to predict putative miRNAs interacted with has_circ_0000069. Based on the search results, we selected miRNAs with context+ score percentile more than 98, which include miR-1253, miR-548c-3p, miR-873, miR-940, and miR-432 . The rescue experiments showed downregulation of miR-432 could attenuate the function of has_circ_0000069 siRNA in breast cancer cells (Moreover, we investigated whether miR-432 was a functional target of has_circ_0000069 in MDA-MB-231 cells. The RT-qPCR assay showed that miR-432 expression was upregulated in si-circRNA-transfected MDA-MB-231 cells, while miR-432 inhibitor decreased miR-432 expression (er cells \u20135D.The present study verified the high has_circ_0000069 expression in 137 pairs of breast cancer tissues and normal tissues. The increased has_circ_0000069 expression was linked to the shorter overall survival of patients. Downregulation of has_circ_0000069 receded breast cancer cellular behaviors by targeting miR-432. These data reveal that targeting has_circ_0000069 may represent a prognostic predictor and therapeutic target for treating patients with breast cancer.Emerging evidence has shown that circRNAs play impotant roles in tumor progression . Many ciHas_circ_001783 , has_cirMany circRNAs come into working with a tumor-promoting role in breast cancer and are involved in tumorigenesis by modulating miRNAs \u201330. For In all, we verified that has_circ_0000069 expression increases in breast cancer tissues and is associated with patients\u2019 overall survival. Knockdown of has_circ_0000069 could reduce the cellular capacities of progression, migration, and invasion of breast cancer cells by targeting miR-432. Therefore, targeting has_circ_0000069 may be a potential prognostic predictor and therapeutic target for treating breast cancer patients."}
+{"text": "A 56-year-old man suffered from epigastric pain for 5 days with elevated amylase (2600\u200aIU/L), and computed tomography indicated acute pancreatitis. Magnetic resonance cholangiopancreatography showed the confluence between dilated biliary and pancreatic ducts , and enHowever, owing to the long common channel and the sharp angle, the guidewire could not be inserted into the pancreatic duct during prior attempts . TherefVideo\u20061\u2002Peroral cholangioscopy-assisted pancreatic duct cannulation in a patient with a pancreaticobiliary maljunction.Peroral cholangioscopy has been widely applied in diagnosing pancreatobiliary diseases and shown its vital role in selective cannulation of complex biliary stricturesEndoscopy_UCTN_Code_TTT_1AR_2AICitation Format10.1055/a-2096-1950.Endoscopy 2023; 55: E792\u2013E793. doi:"}
+{"text": "Median arcuate ligament syndrome (MALS), reported by Harjola in 1963The patient was a 53-year-old man who had undergone prophylactic coil embolization 2 years previously for a 6-cm inferior pancreaticoduodenal artery aneurysm. Contrast-enhanced computed tomography revealed celiac artery stenosis due to the median arch ligament , and heVideo\u20061\u2002The exposed coil is seen in the main pancreatic duct (MPD), which was causing obstructive pancreatitis. The coil is successfully removed from the MPD, but it was not possible to perform transpapillary drainage. Endoscopic ultrasound-guided pancreaticogastrostomy is therefore performed.Endoscopy_UCTN_Code_TTT_1AS_2AD"}
+{"text": "T cells are endowed with T-cell antigen receptors (TCR) that give them the capacity to recognize specific antigens and mount antigen-specific adaptive immune responses. Because TCR sequences are distinct in each na\u00efve T cell, they serve as molecular barcodes to track T cells with clonal relatedness and shared antigen specificity through proliferation, differentiation, and migration. Single-cell RNA sequencing provides coupled information of TCR sequence and transcriptional state in individual cells, enabling T-cell clonotype-specific analyses. In this protocol, we outline a computational workflow to perform T-cell states and clonal analysis from scRNA-seq data based on the R packages Seurat, ProjecTILs, and scRepertoire. Given a scRNA-seq T-cell dataset with TCR sequence information, cell states are automatically annotated by reference projection using the ProjecTILs method. TCR information is used to track individual clonotypes, assess their clonal expansion, proliferation rates, bias towards specific differentiation states, and the clonal overlap between T-cell subtypes. We provide fully reproducible R code to conduct these analyses and generate useful visualizations that can be adapted for the needs of the protocol user.Key featuresComputational analysis of paired scRNA-seq and scTCR-seq dataCharacterizing T-cell functional state by reference-based analysis using ProjecTILsExploring T-cell clonal structure using scRepertoireLinking T-cell clonality to transcriptomic state to study relationships between clonal expansion and functional phenotypeGraphical overview These are heterodimeric proteins in which each of the two protein chains\u2014typically one alpha (\u03b1) and one beta (\u03b2)\u2014is produced through somatic rearrangement of V, (D), and J gene segments, as well as the addition or deletion of nucleotides between spliced gene segments, to form a unique V(D)J exon. This recombination process is largely random and generates a large repertoire of TCRs, with an estimated diversity in the order of 10dividual . Such a The emergence of single-cell technologies has enabled the coupled sequencing of full-length TCRs with transcriptome-wide RNA sequencing in individual cells . This is+ T cells from tumor biopsies, but the protocol is applicable to any single-cell transcriptomics data with TCR sequence information in humans and mice. We invite the reader to follow this protocol while interactively running the associated R Notebook (see Software and datasets section).We have recently proposed computational pipelines for the analysis of single-cell T-cell repertoires  and for Personal computer (minimum 16 GB of RAM) or high-performance computing cluster. All software runs on Linux, Windows, or MacOS machines.This protocol requires basic R programming skills: installing packages, running an R notebook, and adapting the code to the needs of the user.All software used for this protocol is free and open source.R version 4.2 or higherhttps://github.com/ncborcherding/scRepertoire)scRepertoire (version \u2265 1.7)  ProjecTILs (version \u2265 3.0)  Seurat (version \u2265 4.3)  (https:/https://posit.co/downloads/) to interactively run the R Notebook that reproduces the results of this protocol .In addition, it is recommended to install R Studio Desktop  for straightforward installation of all required packages with the correct version and to ensure reproducibility of the results shown in this protocol.Then, move to the newly created directory and open the project file (with .Rproj extension). Open the protocol notebook  in R Studio and execute all commands in order. Note that the R Notebook makes use of the The protocol assumes the user has generated a single-cell transcriptomics dataset with TCR sequencing information for the same T cells or a subset thereof. There is no restriction on the sequencing technology used, if it generates i) a count matrix quantifying gene expression in single cells; and ii) TCR sequences, for paired \u03b1\u03b2 chains or single chains, with barcodes that can be mapped to transcriptomics measurements of the same cells.The protocol details all steps required to go from scRNA-seq and scTCR-seq count matrices to T-cell clonal analysis in the context of a T-cell reference map. Each step includes example code snippets that highlight the R commands that accomplish the step. For the complete list of R commands that reproduce the results of this protocol, refer to the accompanying R Notebook (see Software and datasets section).Single-cell data pre-processingscRNA-seq dataSeveral protocols and technologies are available for transcriptomics quantification using scRNA-seq. Sequencing protocols differ in terms of library preparation, read alignment to a reference genome, and quantification of transcripts, as reviewed in multiple publications . Sequencseurat <- CreateSeuratObject(counts = matrix)Read10X function to read count matrices from the popular 10\u00d7 sequencing platform .Note that Seurat also implements functions to load data from specific technologies, for example the scTCR-seq dataObtaining single-cell TCR sequences requires specific protocols for amplification and sequencing of the V(D)J locus, or their reconstruction from whole-transcriptome sequencing. For an overview of scTCR-seq sequencing approaches, see the comprehensive review byhttps://support.10xgenomics.com/single-cell-vdj/software/pipelines/latest/using/vdj). scRepertoire (We assume that the user has performed V(D)J sequences assembly and clonotype calling. For 10\u00d7 Chromium 5\u2032 V(D)J libraries, such annotated V(D)J sequences (\u201ccontigs\u201d) are obtained from FASTQ files using the Cell Ranger V(D)J pipeline S2 <- read.csv(\"Sample2/outs/filtered_contig_annotations.csv\")contig_list <- listcombined <- combineTCRloadContigs function from scRepertoire, which allows data pre-processing for multiple formats including TRUST4, BD Rhapsody, WAT3R, and AIRR.For V(D)J contigs generated using different pipelines, please see the Note 1: It is often useful for further processing steps to generate keys for unique clonotype\u2013sample combinations. As it may occur by chance that the same clonotype is observed in different individuals, these keys will allow discriminating between T cells with identical TCR but from different samples. For example, generate a clonotype\u2013sample key as a metadata column named \u201ccdr3s_pat\u201d:combined <- lapply{x$cdr3s_pat <- paste; x})Combine scRNA-seq and scTCR-seq datacombineTCR from scRepertoire, you may apply the combineExpression function:Append the TCR information into the previously prepared Seurat object that stores the scRNA-seq counts. If the V(D)J data were processed using seurat <- combineExpression)For V(D)J data pre-processed using different pipelines, add the TCR chains as metadata to the Seurat object:seurat <- AddMetaDataReference-based analysisLoad reference maphttps://github.com/carmonalab/ProjecTILs) and from SPICA (https://spica.unil.ch). For example, to analyze human CD8+ T cells, download and load the corresponding map ref.cd8 <- load.reference.map(\"CD8T_human_ref_v1.rds\")DimPlotProject data into the referenceTo embed query data into the reference space and obtain cell type annotations, apply the ProjecTILs pipeline . If the seurat.list <- SplitObjectseurat.projected <- Run.ProjecTILsIn this case, the output is a list of Seurat objects, each corresponding to a query sample projected in the reference map.+ T cell reference map, ProjecTILs will automatically pre-filter CD8+ T cells from the input data . With ProjecTILs, it is also possible to conduct multi-reference map analysis, for instance using both CD8+ T cells and CD4+ T cells reference maps. An example can be found in the following R notebook:Note 2: For this example, because we chose to use a CD8https://carmonalab.github.io/ProjecTILs_CaseStudies/Bassez_BC.html.Compare marker gene expression profiles of query data with the reference mapTo verify the correspondence of transcriptional phenotypes between the reference and query dataset, visualize the average expression profile of each cell subtype for a panel of marker genes :which.patient <- \"su009\"plot.states.radarCell subtype composition of query dataReference projection of the query data allows embedding them into the same space of the reference. Cell types for the query dataset can be predicted by nearest-neighbor majority voting based on the annotated reference cells. Visualize low-dimensional embeddings and subtype composition for individual samples or other subsets of the projected data :which.patient <- \"su009\"a <- plot.projectionb <- plot.statepred.compositiona | bExclude small samplesRobust analyses require a minimum number of cells in each sample. After projection and annotation, remove all samples with a small number of cells :sizes <- as.vector)keep <- names(sizes)[sizes > 100]seurat.projected <- seurat.projected[keep]For large enough samples, we can compare their composition in terms of cell subtypes :plots <- lapply(names(seurat.projected), function(x) { plot.statepred.composition + ggtitle(x)})wrap_plotsMerge list of objects to obtain a single objectFor some analyses , it is useful to merge individual objects/samples (projected by patient) into a single object:merged.projected <- ReduceIdents(merged.projected) <- \"functional.cluster\"Clonal analysisIf the TCR information was loaded into the query Seurat object as outlined in section A, it will be available as metadata for the projected object. This allows linking the transcriptomics state to clonal information. A few examples of analyses are detailed below.Identify the most expanded clonesCalculate the frequency of unique TCR chains per patient  to identify the most expanded clones per patient:freqs <- lapply { table(x$cdr3s_pat) / sum(!is.na(x$cdr3s_pat))})freqs <- Reducesorted <- sortlargest.clones <- headLocate expanded clones on the reference low-dimensional spaceTCR chains can be used to subset clones of interest  and inspect their distribution on the reference UMAP space :plots <- listfor (i in 1:length(largest.clones)) { ctype <- names(largest.clones)[i] cells <- which(merged.projected[[\"cdr3s_pat\"]]==ctype) plots[[i]] <- plot.projection}wrap_plotsClonal expansion by T-cell subtypescRepertoire implements several useful functions to visualize clonal expansion and clonal diversity. Plot the number of cells in different categories of expansions, from \u201cSingle\u201d clones to large clones (here >50 cells), by T-cell subtype :occupiedscRepertoireClonotype proliferation rateHigh proliferation rate of a specific clonotype may indicate that the T cells with shared specificity are actively recognizing antigens in situ. We can measure proliferation at the clonal level by calculating how many cells of a clone are cycling, according to transcriptomics readouts. ProjecTILs automatically calculates cell cycling signature scores using UCell . These smerged.projected$is.cycling <- ifelse((merged.projected$cycling.score.G1_S > 0.1 |merged.projected$cycling.score.G2_M > 0.1),yes = \"Proliferating\",no = \"Resting\")#Only consider expanded clonesclonotypes <- table(merged.projected$cdr3s_pat)expanded <- names(clonotypes)[clonotypes>=2]frequency.proliferating <- sapply { sub <- subset sum(sub$is.cycling == \"Proliferating\") / ncol(sub) })Note 3: The user may want to use different gene signatures than those automatically applied by ProjecTILs, to quantify activity of additional gene programs. We refer to the UCell online documentation for interacting with Seurat objects and for custom gene signature scoring:https://bioconductor.org/packages/release/bioc/vignettes/UCell/inst/doc/UCell_Seurat.html.Clonal sharing between T-cell subtypesMetrics of clonal overlap  can be clonalOverlapSeveral additional representations of clonal overlap are available in scRepertoire, for example as circos plots  Figure :circles <- getCirclizecirclize::chordDiagram(circles)Note 4: Cell type/state classification algorithms are not perfect, and there is generally some uncertainty in the predicted subtypes, especially among closely related subtypes . Moreover, some cells might display intermediate states of differentiation, transitioning from one state into another. These factors might lead to some background noise for TCR sharing/Morisita index between transcriptionally related cell states exportTable=TRUE, from which we can extract the most significantly biased clones according to their Z-score  most.biased <- biasedplots <- listfor (i in 1:6) { ctype <- most.biased cells <- which(merged.projected [[\"cdr3s_pat\"]]==ctype) title <- sprintf plots[[i]] <- plot.projection}wrap_plotshttps://github.com/carmonalab/Tcell_clonal_analysis. A comprehensive vignette with more information on scRepertoire and its functions can be found at:Fully reproducible R code that generates the results and figures in this protocol, including all pre-processing steps, is available on GitHub: https://ncborcherding.github.io/vignettes/vignette.html. Several case studies of applications of ProjecTILs for reference-based analysis of single-cell data are available at: https://carmonalab.github.io/ProjecTILs_CaseStudies.Commercially available single-cell RNA-sequencing technologies have opened the opportunity to study the association of T-cell states and clonality at large scale. However, scRNA-seq experiments typically produce less than 10,000 high-quality single-cell transcriptomes per sample. Depending on the tissue analyzed, and whether or not T cells have been specifically purified, the number of sequenced T cells obtained, even from inflamed tissues, can be very low. As a result, only a small fraction of the complete TCR repertoire is typically sampled. Under-sampling leads to inaccurate estimations of clonal diversity . For this reason, in this protocol we suggest to exclude from analysis samples with very few cells and we avoided the use of clonal diversity metrics, such as Shannon entropy, Gini-Simpson index, and Gini coefficient, that are particularly sensitive to under-sampling . InsteadTroubleshootingDownload of large objects in R (as in the case of single-cell datasets and reference maps) may occasionally fail due to connection timeout. This commonly manifests in errors such as \u201cobject X is invalid.\u201d Try increasing download timeout using the following command within the R session:options))"}
+{"text": "Third-generation nanopore sequencers offer selective sequencing or \u201cRead Until\u201d that allows genomic reads to be analyzed in real time and abandoned halfway if not belonging to a genomic region of \u201cinterest.\u201d This selective sequencing opens the door to important applications such as rapid and low-cost genetic tests. The latency in analyzing should be as low as possible for selective sequencing to be effective so that unnecessary reads can be rejected as early as possible. However, existing methods that employ a subsequence dynamic time warping (sDTW) algorithm for this problem are too computationally intensive that a massive workstation with dozens of CPU cores still struggles to keep up with the data rate of a mobile phone\u2013sized MinION sequencer.In this article, we present Hardware Accelerated Read Until (HARU), a resource-efficient hardware\u2013software codesign-based method that exploits a low-cost and portable heterogeneous multiprocessor system-on-chip platform with on-chip field-programmable gate arrays (FPGA) to accelerate the sDTW-based Read Until algorithm. Experimental results show that HARU on a Xilinx FPGA embedded with a 4-core ARM processor is around 2.5\u00d7 faster than a highly optimized multithreaded software version (around 85\u00d7 faster than the existing unoptimized multithreaded software) running on a sophisticated server with a 36-core Intel Xeon processor for a SARS-CoV-2 dataset. The energy consumption of HARU is 2 orders of magnitudes lower than the same application executing on the 36-core server.https://github.com/beebdev/HARU, and an example application that uses HARU is at https://github.com/beebdev/sigfish-haru.HARU demonstrates that nanopore selective sequencing is possible on resource-constrained devices through rigorous hardware\u2013software optimizations. The source code for the HARU sDTW module is available as open source at  Key PointsHardware-accelerated signal-matching Read Until designed for resource-constrained embedded platforms.A resource-efficient subsequence dynamic time warping (sDTW) accelerator for selective sequencing.https://github.com/beebdev/HARUFull proposed design : https://github.com/beebdev/sigfish-haru.Example application using HARU and optimized C implementation of RUscripts: https://github.com/beebdev/RUscripts-R9.Modified RUscripts : The latest third-generation nanopore sequencing technology has revolutionized the field of genomics. The portable palm-sized nanopore sequencer called the MinION produced by Oxford Nanopore Technologies (ONT) can perform direct selective sequencing, which rejects the genomic reads that are not of interest. This technique, also known as Read Until, can vastly reduce the sequencing time and cost for applications such as genetic disease identification , 2, cancsubsequence dynamic time warping (sDTW) for direct signal mapping for the early R7 nanopore chemistry, which could sequence at a speed of 70 bases/s. However, with the introduction of the R9 nanopore chemistry with a 450 bases/s speed  \u2254 abs(x[i] \u2212 y[j]) + min can be computed. Normally, 32-bit floating-point data types are used for the sDTW computation to preserve the precision after the sequences are normalized. This is expensive to implement in hardware regarding resources and execution time. By using a fixed-point representation with fewer data bits and scaling the sequence values using a scaling factor, the recurrence equation\u00a0can be computed in hardware rapidly and efficiently while keeping sufficient precision. We chose 16-bit fixed points with a scaling factor of 25 as it gives sufficient precision and keeps II at 1 clock cycle (see section\u00a0Results on accuracy). Using fixed point with a static scaling factor will decrease the accuracy slightly as we are using fewer bits to represent the decimal points compared to floating points. Nevertheless, this data representation will still provide close to zero difference in mapping accuracy compared to using floating points . At each iteration, the costs in the L1 array are shifted into the L2 array, while the current costs are passed onto the L1 array. Each PE computes the recurrence equation, which takes the Manhattan distance (\u03b4 = |x[i] \u2212 y[j]|) and adds the minimum of the 3 neighbor cells . Synthesis was performed using Xilinx\u2019s Vivado 2021.1. The control bus interface for the accelerator uses the AMBA AXI-Lite protocol. We use the AMBA AXI-Stream protocol through the AXI DMA hardware in the FPGA for high-throughput data transfer for the query and reference data.Device drivers were implemented for the hardware accelerator and AXI DMA in the C programming language. The accelerator and AXI DMA drivers memory-map the physical address of corresponding devices into the virtual address space for utilization by the user space applications. The shared communication memory buffers between software and FPGA are preserved on the DDR memory, which is allocated during the initialization stage.The software processing layer that prepares the raw signals and performs the selecting decision was implemented in the C programming language. For benchmarking experiments, the software loads raw signal data in the BLOW5 format  from a UOriginal RUscripts, written by Loose et\u00a0al.  using Pymlpy library [As the Python RUscripts is not efficient enough for a fair comparison, we implemented a multithreaded C implementation that follows similar algorithmic steps. This implementation in C is very similar to the software explained above (see the\u00a0\u201cSoftware processing layer\u201d section), except that sDTW on the CPU is called with multiple threads instead of using the FPGA accelerator. The sDTW computation on the CPU is performed using the optimized sDTW implementation in the  library .HARU was tested against combinations of software running on the systems mentioned in Table\u00a0gettimeofday function in C. This execution time is divided by the number of reads in the dataset to calculate the signal mapping throughput. Note that all our time measurements used in throughput calculation include all the overheads, including reading signal data from the disk, raw signal preprocessing on software, and data transfer time to/from FPGA for HARU.We measure the overall execution time of mapping all reads of the provided datasets by using the dna_r9.4.1_450bps_fast.cfg model was used for Guppy, and a combined reference genome of SARS-CoV-2 and yeast was used for Minimap2 (To compare HARU with DeepSelectNet and the approach in Readfish (Guppy2 followed by Minimap2), we used the curated test data for SARS-CoV-2 and yeast from  that conMinimap2 . For DeeMinimap2 . For HAR\u2013chunk-time and \u2013max-chunks 1 parameters in UNCALLED were used to limit the number of signal samples to 3,200 . UNCALLED was installed on a Rock64 embedded device that has a similar computing power (quad-core ARM Cortex A53 with 4 GB RAM) to the Kria board used for HARU. This is because UNCALLED has many dependencies, and enabling support for the Kria platform, which runs a custom PetaLinux distribution, is laborious. Despite the Rock64 board supporting Ubuntu and the apt package manager along with Python/PIP and C/C++ build tools, we still had to manually intervene in the UNCALLED installation scripts to enable support for HDF5 and BWA dependencies to build on ARM. Both HARU and UNCALLED were executed using the SARS-CoV-2 reference, and the accuracy was calculated by using UNCALLED pafstats by comparing mapping locations to Minimap2 mappings as the truth set . The \u2013chto 3,200 . For genThe field of selective sequencing is a nascent area, and to date, no definitive solution has emerged as the panacea. Both signal-level and base-level approaches to selective sequencing have advantages and disadvantages, and determining which is the optimal approach at this stage is more of a philosophical debate.With the methods available to date, basecalling raw signals obtained from the sequencers to convert signals to the base domain, followed by using optimized alignment tools such as Minimap2 (the approach described in Readfish), is the most practical approach if large genomes are involved because base-level aligners have matured over the past decade of research and development and are highly optimized to make base-level selective sequencing practical. However, for basecalling, regardless of the GPU acceleration effort performed by ONT over the years, basecalling is still the major bottleneck for base-domain selective sequencing, taking 96% of the execution time for Guppy fast basecalling + Minimap2. Furthermore, basecalling is not portable or scalable due to the compute power constraints, and if selective sequencing is ever to be done on an integrated chip within the sequencer, basecalling approaches would require a more costly system and possibly come at a much larger form factor.The goal of signal-level selective sequencing is to completely bypass the basecalling step and, instead, directly map the raw signal to the reference. This is an emerging and immature field and will inevitably require a substantial period of time to achieve the same level of maturity as base-level selective sequencing. Since the concept of nanopore selective sequencing was introduced, a range of different signal-level selective sequencing methods has been explored, including RUscripts , cwDTW , UNCALLEIn addition, directly passing raw signals into neural networks is also being explored as opposed to using classical algorithms for mapping, including works such as SquiggleNet , DeepSelThe data rate of nanopore sequencers is comparable to modern camera sensors on mobile devices today. Considering the amount of raw signal processing being performed for sensors on mobile devices, it is promising to envision signal-level nanopore selective sequencing done efficiently within nanopore sequencers, if this level of miniaturization is ever reached for selective sequencing compute requirements. In summary, signal-level selective sequencing is an exciting area worth investigating together with base-level selective sequencing.In our proof-of-concept implementation of HARU, the reference sequence is first loaded onto the FPGA\u2019s on-chip memory (block RAM) at the beginning of the execution. During alignment, the PE chain streams the reference samples from the block RAM to the first PE Fig.\u00a0. On-chipFuture work can also improve the throughput by implementing multiple parallel sDTW cores for coarse-grain parallelism. Our sDTW processor uses less than 20% of the LUT resources of the FPGA, as mentioned in the\u00a0\u201cResource utilization\u201d section. Thus, resources are sufficient to fit multiple parallel processors, increasing the theoretical throughput. A high-end FPGA board with a larger area could support even more processors; for instance, Xilinx\u2019s Versal VP2802 FPGA has sufficient resources to theoretically fit\u00a0140 parallel processors see  would thOur implementation of HARU loads raw signal from the BLOW5 file format because the slow5lib library is lightweight , thus easily allowing the cross-compilation to target the Kria platform. Running MinKNOW on the Kria platform is theoretically possible but is far from practical due to being closed source. Even if MinKNOW were open source, potential issues with hundreds of bulky dependencies would make cross-compilation impractical. Potential workarounds could include a server\u2013client approach where MinKNOW runs on a laptop and communicates with the Kria board using ethernet. However, such workarounds are not ideal due to network communication overheads. Also, latency in the public-facing ReadUntil API provided by ONT (in Python programming language) would negate the massive benefit of hardware acceleration.Our proof-of-concept HARU implementation is currently limited to DNA on R9.4 chemistry. Future work could focus on extending selective RNA sequencing, the most recent R10.4 chemistry, or upcoming protein sequencing from ONT.The primary sequencer device targeted for HARU running on resource-constrained devices is the palm-sized MinION nanopore sequencer. Sequencers such as ONT\u2019s PromethION provide a much larger throughput than MinION and will vastly increase the selective sequencing processing requirements. Future work could explore the scalability of HARU on higher-end FPGAs with high bandwidth memory and more resources for fast selective sequencing on high-throughput sequencers such as the PromethION.N \u00d7 O(M2) for software approaches) for selective sequencing usage where the reference sequence is static with a known length. sDTW, on the other hand, is a data-reusing version of the approach, and our work exploits the fine-grain parallelism that computes the whole O(M) dimension in parallel, leaving O(M + N) computational time and O(M) space. Furthermore, there is prior work that accelerates DTW using nonvolatile memories [Existing hardware acceleration work targeting the subsequence search problem using the DTW algorithm family is rare. Previous FPGA accelerators such as , 50 implmemories  and usinmemories , 53.For the hardware acceleration on signal-alignment Read Until, the only previous attempt was a simulated Application Specific Integrated Circuit (ASIC) design that accelerates the sDTW algorithm . The proExisting sDTW-based software methods for nanopore selective sequencing are highly computationally intensive, and a large workstation cannot keep up with a portable MinION sequencer. In this article, we present HARU, a resource-efficient design that enables sDTW-based selective sequencing on a low-cost and portable heterogeneous system comprising an ARM processor and an FPGA, which is around 85\u00d7 faster than the original sDTW-based software implementation and around 2.5\u00d7 faster than a highly optimized software version running on a server with a 36-core Xeon processor for a complete SARS-CoV-2 dataset. The energy-delay product for the server is around 650\u00d7 higher than HARU executing on an embedded device.Project name: HARUDescription: Source code for the HARU accelerator, including the Verilog HDL core accelerator and user-space device driverhttps://github.com/beebdev/HARUProject homepage: Operating system(s): Windows 10/11 (building), Custom Embedded Linux image built with PetaLinux 2021.1 (running)Programming language: Verilog, C, PythonOther requirements: Vivado 2022.2, Petalinux 2021.1License: MITbiotools:haruRRID: SCR_023563Project name: Sigfish-HARUDescription: Source code that demonstrates the proof-of-concept integration of HARU accelerator for squiggle mapping. Also contains the optimized RUscripts implementation in C.https://github.com/beebdev/sigfish-haruProject homepage: Operating system(s): Linux (building), embedded Linux built with PetaLinux 2021.1 (running)Programming language: COther requirements: Cross-compilation toolchain for AARCH64License: MITProject name: RUscripts-R9Description: The modified RUscripts to support Python 3.6+, BLOW5 format, and ONT\u2019s current nanopore chemistry R9.4https://github.com/beebdev/RUscripts-R9Project homepage: Operating system(s): Platform independentProgramming language: PythonOther requirements: Python 3.6License: MITgiad046_GIGA-D-22-00317_Original_SubmissionClick here for additional data file.giad046_GIGA-D-22-00317_Revision_1Click here for additional data file.giad046_GIGA-D-22-00317_Revision_2Click here for additional data file.giad046_GIGA-D-22-00317_Revision_3Click here for additional data file.giad046_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giad046_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giad046_Response_to_Reviewer_Comments_Revision_2Click here for additional data file.giad046_Reviewer_1_Report_Original_SubmissionJason Lau -- 12/22/2022 ReviewedClick here for additional data file.giad046_Reviewer_2_Report_Original_SubmissionMohammed Alser -- 12/25/2022 ReviewedClick here for additional data file.giad046_Reviewer_2_Report_Revision_1Mohammed Alser -- 4/29/2023 ReviewedClick here for additional data file.giad046_Reviewer_3_Report_Original_SubmissionDaichi Fujiki -- 12/26/2022 ReviewedClick here for additional data file.giad046_Reviewer_3_Report_Revision_1Daichi Fujiki -- 5/6/2023 ReviewedClick here for additional data file.giad046_Reviewer_4_Report_Original_SubmissionBertil Schmidt -- 12/31/2022 ReviewedClick here for additional data file.giad046_Supplemental_FileClick here for additional data file."}
+{"text": "A 57-year-old woman presented with recurrent epigastric pain. Computed tomography of the abdomen revealed a 26-mm pancreatic stone obstructing the main pancreatic duct (MPD) in the body portion . We perVideo\u20061\u2002Procedural steps of attempts to remove the trapped basket.Here, we report a successful retrieval of an impacted basket with the entrapped pancreatolith. Although the situation may have been reported in the literatureEndoscopy_UCTN_Code_CPL_1AK_2AF"}
+{"text": "Post-translational modifications (PTMs) serve as key regulatory mechanisms in various cellular processes; altered PTMs can potentially lead to human diseases. We present a protocol for using MIND-S (multi-label interpretable deep-learning approach for PTM prediction-structure version), to study PTMs. This protocol consists of step-by-step guide and includes three key applications of MIND-S: PTM predictions based on protein sequences, important amino acids identification, and elucidation of altered PTM landscape resulting from molecular mutations.For complete details on the use and execution of this protocol, please refer to Yan et\u00a0al (2023).  \u2022A protocol on MIND-S, a software program that supports multiple computational analyses on PTMs\u2022Steps described for performing multi-protein multi-PTM prediction\u2022Evaluation of important amino acids for PTM occurrences\u2022Examination of the SNP effect on PTMs Publisher\u2019s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Post-translational modifications (PTMs) serve as key regulatory mechanisms in various cellular processes; altered PTMs can potentially lead to human diseases. We present a protocol for using MIND-S (multi-label interpretable deep-learning approach for PTM prediction-structure version), to study PTMs. This protocol consists of step-by-step guide and includes three key applications of MIND-S: PTM predictions based on protein sequences, important amino acids identification, and elucidation of altered PTM landscape resulting from molecular mutations. Using the same strategy, one can investigate many other proteins and their PTMs of interest.In this protocol, we utilize one protein, Leucine-rich repeat serine/threonine-protein kinase 2  into the input field and select \u2018Map IDs.\u2019c.Wait for the job to complete, then click \u2018Completed\u2019 under the \u2018Status\u2019 column.d.Select \u2018Download\u2019.e.Change \u2018Format\u2019 to \u2018FASTA ,\u2019 and under \u2018Compressed\u2019 select \u2018No,\u2019 and select \u2018Download\u2019.f.Q5S007.fasta.Save the file and rename it to Prepare protein sequence as a fasta file. Optionally, follow the steps below to download it from UniProt :a.NavigaNote: A list of UniProt accession IDs can be entered into the input field to perform batch sequence download.2.a.MIND-S requires the information of mutant amino acid location, wild-type amino acid, and mutant amino acid. For our example, we will examine the effects of a mutation that mutates the arginine at site 1441 to cysteine.Prepare protein mutation of interest.Note: We recommend using Ensembl Variant Effect Predictor (VEP) to convert mutations on the genomic level to the protein level. VEP has an online graphical user interface as well as an API for programmatic access. It accepts inputs in the form of a Variant Call Format (vcf) file or SNP id and outputs detailed information on the protein mutations, if they exist.Note: MIND-S allows users to examine mutation effects on PTM predictions. Here we provide a script for single amino acid mutation. Other mutations can be analyzed similarly.Timing: within 2 h3.https://www.tensorflow.org/install/pip), if not already installed.Follow the instructions to install tensorflow2  and can be specified by the pretrain_name parameter. We recommend using the MIND_fifteenfold model for predicting the 13 PTM types with n_fold\u00a0= 15 for its most robust performance, achieved by repeating the training process 15 times. For predicting the 13 O-PTM types, set pretrain_name to OPTM_fifthteen. The data_path argument is used to provide the path to the protein fasta file and res_path is used to provide the path to the output folder, where the model outputs will be stored.3.a.Two files, \u2018results.json\u2019 and \u2018correct_predictions.csv\u2019, will be returned from MIND-S under the output folder A and 2B.Examine the results.Note: \u2018results.json\u2019 contains the prediction results for every position (indexing starts at 1) in every sequence in json format. Each prediction is labeled as <protein uid_site_ptm_type: prediction score>. \u2018correct_predictions.csv\u2019 contains only predictions with a prediction score >= 0.5, which are defined as the predicted PTMs. The prediction score ranges from 0 to 1, and higher scores indicate a higher predicted probability for PTM occurrence. Details about the PTM type abbreviations can be found in CRITICAL: The above steps illustrate the process of running predictions. All operations in this protocol, including PTM prediction, interpretation and mutation effect inspection, are required to be performed under the MIND directory.Note: MIND-S allows batch predictions on multiple proteins simultaneously. This can be done by simply including all protein sequences of interest in the input fasta file.Given a protein sequence (accepted in fasta format), MIND-S will predict if PTMs will occur on the protein, with detailed information about the PTM type and the PTM site. Predictions will be made on all targeted amino acids in the protein.Timing: 5\u00a0min4.a.From \u2018correct_predictions.csv\u2019 choose the PTM of interest {uid: Q5S007, site: 1444, PTM type: Phos_ST}Determine the PTM of interest.5.Run the following code to make interpretations.>python predict_saliency.py \\> --inter \\> --pretrain_name saved_model/MIND_fifteenfold \\> --data_path sample/Q5S007.fa \\> --res_path result \\> --site 1444> --ptm_type Phos_STNote: Similar to the previous step, pretrain_name specifies the model used; data_path is the path to the protein fasta file and res_path is the path to the output folder. site and ptm_type refer to the position of the PTM interested within the protein sequence (indexing starts at 1) and the PTM type. inter informs the model to execute in interpretation mode. We allow batch interpretation on multiple PTMs within a protein sequence as well. Users can provide the PTM sites and their corresponding types as comma-separated lists as shown below. This step will also generate a figure visualizing the interpretation scores.>python predict_saliency.py \\> --inter \\> --pretrain_name saved_model/MIND_fifteenfold \\> --data_path sample/Q5S007.fa \\> --res_path result \\> --site 6,1269,935,1489 \\> --ptm_type Palm_C,glyco_N,Phos_ST,glyco_N6.Check the saliency scores figure generated in the result directory .Note: The interpretation module will generate a figure showing the saliency scores of 10 flanking amino acids on both sides of the PTM site. In the batch interpretation case, one figure will be generated for each PTM. Peaks in the saliency score figure indicate that the amino acids at these positions are important for the prediction.CRITICAL: For a specific PTM predicted by MIND-S in the previous step, users can leverage the interpretation module in MIND-S to investigate the contribution of adjacent amino acids to that PTM .Note: The supported PTM types and their abbreviations are shown in This step will compute saliency scores to denote the significance of flanking amino acids in the PTM prediction using the integrated gradients method, where a high saliency score of an amino acid indicates a high predicted importance for the PTM.Timing: 5\u00a0min7.Run the following to examine the SNP effect:>python PTMSNP.py \\>--pretrain_name saved_model/MIND_fifteenfold \\>--data_path sample/Q5S007.fa \\>--res_path result \\>--snp R_1441_C \\>--n_fold 15Note: Similarly, we use pretrain_name to specify the model to perform the predictions and n_fold to specify a 15-fold bootstrap method. We provide the path to the fasta file with data_path, and the result will be stored in the res_path. snp indicates the mutation of interest in the format of \u201cWT_site_MUT\u201d, where WT is the wildtype amino acid; site is the location of the amino acid; MUT is the mutant amino acid. In our example, R is the wild-type amino acid, 1441 is the site of the mutation, and C is the mutant amino acid. Two files \u2018Q5S007.json\u2019 and \u2018Q5S007_R1441C.json\u2019 for the wild-type and mutant prediction results will be generated along with a \u2018Q5S007_R1441C.csv\u2019 that summarizes the change in prediction scores. We allow batch evaluation on multiple mutation within a protein sequence as well. Users can provide the mutations in the same format as comma-separated lists as shown below.8.Run the following to visualize the altered PTM landscape by step 1:>python ptmfigure.py \\>--orig_path result/Q5S007.json \\>--mutant_path result/Q5S007_R1441C.json \\>--res_path resultNote: Here we specify the wild-type predictions (orig_path) and mutant predictions (mutant_path) to create a csv file summarizing the predictions as well as a figure highlighting the significant difference between the probabilities of the wild-type and the mutant . PTM information and prediction scores are included; Interpretation figure will be produced as a line plot showing the saliency scores of amino acids surrounding the PTM site of interest ; two JSOWe suggest users be mindful in interpreting the prediction scores. As per PTM, the prediction problem is a binary classification where we used 1 to indicate the positive PTM and 0 for the negative PTM. A score close to 1 indicates that the site is likely to have the PTM and a score close to 0 indicates that the site is unlikely to have the PTM. For evaluating the mutation effect, we suggest only considering the altered PTM with a prediction score difference greater than 0.2 to minimize the impact of noise.In the interpretation step, experimentally identified PTMs can also be used as input. However, we recommend verifying if the PTM is predicted by the MIND-S program, since the interpretation is strongly associated with the model\u2019s specific predictions. The interpretation module is best used for experimental PTMs that are also predicted by MIND-S.>ModuleNotFoundError: No module named 'module'During installation, an error may occur depending on the local environment. (Set up the environment for running the program step 6).>python -m venv venv>source venv/bin/activate>python -m pip install\u00a0\u2013upgrade pip>pip install -r requirementCreate a Python virtual environment and install the required modules according to the environment setup steps.pping38@g.ucla.edu).Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Peipei Ping (This study did not generate new unique reagents."}
+{"text": "Hypoxia is an indispensable factor for cancer progression and is closely associated with the Warburg effect. Circular RNAs (CircRNA) have garnered considerable attention in molecular malignancy therapy as they are potentially important modulators. However, the roles of circRNAs and hypoxia in osteosarcoma (OS) progression have not yet been elucidated. This study reveals the hypoxia-sensitive circRNA, Hsa_circ_0000566, that plays a crucial role in OS progression and energy metabolism under hypoxic stress. Hsa_circ_0000566 is regulated by hypoxia-inducible factor-1\u03b1 (HIF-1\u03b1) and directly binds to it as well as to the Von Hippel-Lindau (VHL) E3 ubiquitin ligase protein. Consequentially, binding between VHL and HIF-1\u03b1 is impeded. Furthermore, Hsa_circ_0000566 contributes to OS progression by binding to HIF-1\u03b1 (while competing with VHL) and by confers protection against HIF-1\u03b1 against VHL-mediated ubiquitin degradation. These findings demonstrate the existence of a positive feedback loop formed by HIF-1\u03b1 and Hsa_circ_0000566 and the key role they play in OS glycolysis. Taken together, these data indicate the significance of Hsa_circ_0000566 in the Warburg effect and suggest that Hsa_circ_0000566 could be a potential therapeutic target to combat OS progression. Osteosarcoma (OS) is a highly malignant bone tumor and a commonly reported type of solid cancer in children and adolescents . OS origHypoxia-inducible factor 1-alpha (HIF-1\u03b1) encodes the \u03b1 subunit of the hypoxia-inducible factor-1 (HIF-1) transcription factor (TF) . HIF-1 iHIF-1\u03b1 activates several enzymes associated with glucose metabolism and glycolysis . AdditioIDH1-AS1, establishes an association of MYC proto-oncogene, BHLH transcription factor (c-MYC), and HIF-1 with mitochondrial respiration and glycolysis regulation via the actions of isocitrate dehydrogenase (NADP (+) 1 and cytoplasmic (IDH1) [IDH1-AS1 expression restoration is a potential metabolic approach for cervical cancer treatment. LnRNAs such as LncRNA-TUG1 and LncRNA-MIF participate in glycolysis and regulate glycolysis metabolism via competitive endogenous RNA (ceRNA) networks [Non-coding RNAs (ncRNAs) are strongly associated with the occurrence and progression of various human diseases . In tumoc (IDH1) . IDH1-ASnetworks -35.Circular RNA (circRNA) is a recently discovered ncRNA that is a new research hot spot . Unlike However, the mechanisms by which circRNAs regulate the Warburg effect are unclear. It is unknown whether circRNAs assist Warburg effect regulation through hypoxia/HIF-1\u03b1. In this study, we demonstrate that the novel HIF-1\u03b1-inducible circRNA, circRNA-circ_0000566, regulates hypoxia-induced glycolysis. Furthermore, we elucidate the hypoxia/HIF-1\u03b1 mechanism of the Warburg effect and indicate that Hsa_circ_0000566 may regulate this process.2 atmosphere. For the hypoxic or normoxic treatment, cells were cultured under hypoxic or normoxic stress for 24 hours. Treated cells were harvested for further experiments.Human hFOB1.19 osteoblasts, HEK-293, and various osteosarcoma cell lines, including 143B, HOS, MG-63, and U2OS, were purchased from the American Type Culture Collection . All cells were cultivated in the Dulbecco\u2019s modified Eagle medium (DMEM) added with 10% (v/v) fetal bovine serum . The cells were cultured at 37 \u00b0C in a humidifying incubator under a 5% COAll animal experimentations were carried out in compliance with the principles outlined by the Guide for the Care and Use of Laboratory Animals published by the National Institutes of Health and performed under the guidelines of the Ethics Committee of Sir Run Run Shaw Hospital.Twelve pairs of clinical osteosarcomas and chondroma samples were obtained from patients who had undergone radical resection at Sir Run Run Shaw Hospital between April 2013 and September 2017. All processes were confirmed by the Institutional Review Board of Sir Run Run Shaw Hospital and were conducted in accordance with the Declaration of Helsinki. All samples were histologically authenticated by pathologists based on the World Health Organization criteria. Informed written consent was provided by all patients prior to study initiation. The basic information of patients was exhibited in -\u2206\u2206Ct method. Nuclear circRNA expression was normalized against U6 expression. Primer sequences are shown in Total RNA extraction was performed with the TRIzol reagent  from certain clinical tumor samples or osteosarcoma cells according to the manufacturer\u2019s instructions. The extraction of cytoplasmic RNA and nuclear RNA from OS cells were performed using the Nuclear/Cytosol Fractionation Kit  according to the manufacturer\u2019s instructions. The RNA samples were stored at -80 ?. The PrimeScript RT reagent kit  and SYBR Premix Ex Taq II  were used to analyze the mRNA and circRNA levels. The \u03b2-actin expression level was considered the reference standard against which the mRNA and circRNA levels were compared. The expression of circRNA and mRNA levels were calculated by using the 2Treated OS cells were subjected to lysis with the radioimmunoprecipitation assay (RIPA) lysis buffer . Protein concentrations were determined with a bicinchoninic acid (BCA) kit . Identical protein quantities were separated via sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). The electrophoresed protein samples were transferred onto a polyvinylidene fluoride (PVDF) membrane which was then treated with 4% skim milk in Tris-buffered saline with Tween (TBST). The membrane was then incubated with anti-LDHA antibody ab52488 , anti-HIF-1\u03b1 antibody ab179483 , anti-VHL antibody ab270968 , anti-PDK1 antibody ab202468 , anti-PDK4 antibody ab110336 , anti-GLUT1 antibody ab115730 , anti-GLUT4 antibody ab188317 , anti-ubiquitin antibody ab134953 , and anti-\u03b2-actin antibody AF2811  at 4 ? overnight. The next day, the membrane was subjected to treatment with secondary antibodies FD0114, FD0115 . The membrane was then washed with TBST, and the signal intensity was measured by using FDbio-Femto enhanced chemiluminescence .After the OS cells were transfected or subjected to treatment, 2.5 \u03bcg of each OS cell extract was mixed with 2 \u03bcg control probe or circular RNA probe for 2 h. The mixtures were incubated with 50 \u03bcL treptavidin agarose beads  for 1.5 h. The beads were then washed thrice, and the protein expression level was examined by conducting western blotting.The RIP assay was conducted using the Magna RIP RNA-binding protein immunoprecipitation kit  in accordance with the manufacturer\u2019s instructions. Pretreated OS cells cultured at 85% confluence were harvested utilizing RIP lysis buffer added with RNase and protease inhibitors. Each cell extract (150 \u03bcL) was subjected to treatment with the RIP buffer containing the magnetic beads bound to the aforementioned antibodies. Mouse anti-IgG antibody was used for normalization.7 stably transfected 143B cells either subcutaneously or in the tibial cavity. Tumor volumes were calculated according to Equation (1): Volume (mm3) = ab2/2 (1).Nude mice  were injected with 1 \u00d7 10Four weeks after treatment, the mice were sacrificed, and their tumors were harvested and weighed. Tumor samples were fixed in 4% (v/v) paraformaldehyde (PFA).Luminescence- and green fluorescent protein (GFP)-labeled OS cells were stably transfected with Luc-vector, Luc-Hsa_circ_0000566-silencing, or Luc-Hsa_circ_ 0000566-silencing and HIF-1\u03b1-overexpressing vectors. Nude mice (age: 4-5 weeks) were subjected to injection in the tail vain with equal amounts of three different stable OS cells. Thirty days after injection, lung metastasis progress was evaluated by conducting in vivo bioluminescence photography.Cell vitality was detected employing the Cell Counting Kit (CCK)-8  according to the manufacturer\u2019s protocols. Transwell\u2122 migration assays were performed in Transwell\u2122 chambers . Cell apoptosis rates were measured by conducting flow cytometry . The FlowJo software  was employed to analyze the result.The proliferation capacities of OS cells were measured using the EdU Cell Proliferation Detection Kit . 48 hours after transfection, OS cells were co-incubated with 500\u03bcL Click Reaction Buffer and were incubated for approximately 30 min. To measure the percentage of cell proliferation, Hoechst 33342 were employed for nuclear staining. Images were captured utilizing a fluorescence microscope. Representative images were selected at least three random fields of view.Transfected OS cells were cultured in semisolid agar medium in 6-well plates at a density of 15000 cells/well. The cells were cultured for 1, 7, 14 days and representative pictures of were taken under a microscope .Ubiquitination immunoprecipitation experiment was carried out utilizing an Immunoprecipitation Kit . Proteins in transfected OS cells were extracted with RIPA lysis buffer . Magnetic beads were washed and were added with 5\u03bcL of HIF-1\u03b1 or IgG antibody. After incubation, a beads-Ab complex was obtained and was incubated with antigen samples to form a beads-Ab-Ag complex. The immune complex eluted from magnetic beads was used for western blotting and the ubiquitination of different samples were detected.Identical amounts of transfected 143B and HOS cells were seeded in 12-well plates at a density of 700 cells/well. Fifteen days after seeding, the colonies were subjected to fixation with 4% (v/v) PFA for 15 min followed by staining with 0.5% (w/v) crystal violet for 15 min. The colonies were enumerated and photographed under a microscope .in situ hybridization (FISH) kit . Images were captured using the Nikon A1Si confocal laser scanning microscope .Alexa Fluor 555-labeled Hsa_circ_0000566 probes were purchased from RiboBio . The probe sequences are validated upon requirement. The probe signals were detected with a fluorescent The experimental procedures used in the current research to determine glucose uptake, lactate production, and ATP concentration have been reported in previous studies. Glucose was detected using a glucose assay kit . Lactate levels were determined using a lactate colorimetric experiment kit . ATP concentrations were examined using an ATP determination kit .ChIP assays were conducted with an EZ-ChIP kit  according to the manufacturer\u2019s instructions.In brief,after dewaxing, rehydration and incubation, slides were blocked in 10% normal goat serum for approximately 20 min. Then, slides were incubated with LDHA, PDK1, PDK4, GLUT1 and GLUT 4 at 4 \u00b0C overnight. On the following day, Samples were washed with PBS and incubated with the second antibody for 50 min at room temperature. Immunoreactivity was detected using the . Iisotype antibody controls, secondary antibody only controls, tissue minus/plus controls were employed to validate antibody specificity and distinguish genuine target staining from background.Statistical and data analyses were conducted using the SPSS software . Results are reported as the mean \u00b1 standard deviation (SD). Statistical tests were performed using non-parametric alternatives. Data from the two experimental groups were compared using the Wilcoxon signed-rank test. Experimental data from multiple groups were evaluated using the Kruskal\u2014Wallis test. Statistical significance was defined as P < 0.05.We first explored potentially hypoxia-responsive circRNAs to determine the circRNAs that has a significant change in expression levels in OS cells under hypoxia. Based on the degree of differential expression, five circRNAs with high and low expression under hypoxic stress from circRNA microarray analysis were selected . The genFluorescence in situ hybridization (FISH) results indicated that Hsa_circ_0000566 expression in OS was significantly higher than in chondrosarcoma . We alsoCCK-8 cell viability experiments were performed to verify Hsa_circ_0000566 function in OS cells . Three dHypoxic stress increases glucose uptake, glycolysis, and lactic acid production. These results indicated that the medium was acidified under hypoxic conditions. All hypoxia-induced effects were substantially reversed by Hsa_circ_0000566 knockdown . In addiThe mechanism by which Hsa_circ_0000566 regulates glycolysis in OS involves HIF-1\u03b1 expression was then investigated. qRT-PCR and western blotting was used to study the regulation of HIF-1\u03b1 expression mediated by the upregulation or downregulation of Hsa_circ_0000566 . Under hCircRNAs regulate their function by binding to their target proteins. RIP and RNA pulldown assays demonstrated that Hsa_circ_0000566 directly bound to HIF-1\u03b1 . To deteT To determine whether Hsa_circ_0000566 could bind to VHL, RIP and RNA pulldown experiments were performed. The results indicated that Hsa_circ_0000566 could directly interact with HIF-1\u03b1 and VHL . HoweverWe examined the rescue effect of HIF-1\u03b1 on Hsa_circ_ 0000566-induced glycolysis. Si-Hsa_circ_ 0000566#2 was used for further research owing to its high knockdown efficiency. Lactate dehydrogenase isoform A (LDHA) enzyme activity assays showed that Hsa_circ_0000566 knockdown in OS cells reduced activity, whereas HIF-1\u03b1 overexpression restored this enzyme activity loss . MoreoveThe mechanism by which Hsa_circ_0000566 is upregulated under hypoxic conditions was explored. Hsa_circ_0000566 induction by HIF-1\u03b1, HIF-2\u03b1, and p53 was examined and only HIF-1\u03b1 expression induced Hsa_circ_0000566 expression under hypoxic stress . The JASSubcutaneous xenograft models were developed and used to investigate the function of Hsa_circ_0000566 in vivo . TransfeOS is a typical solid tumor that readily undergoes metastasis, is insensitive to radiotherapy and chemotherapy, and presents with a hypoxic microenvironment . HypoxiaCircRNAs have several predominant molecular mechanisms. First, circRNAs contain numerous microRNA (miRNA) binding sites . miRNAs The Warburg effect is one among ten characteristics of tumors . The codHIF-1\u03b1 is ubiquitous in mammalian cells and is the only TF that remains active under hypoxic conditions , 54. HIFUnlike OS, chondroma is a type of benign bone tumor that tends to occur in tubular bones. In this study, Hsa_circ_0000566 achieved higher expression in OS than in chondroma, according to FISH assays. This finding was additionally validated by qRT-PCR at the mRNA level. Consequently, we inferred that Hsa_circ_0000566 plays a critical role in OS progression.Our results demonstrate that under hypoxic conditions, HIF-1\u03b1 is a transcriptional regulator upstream of circRNA-Hsa_circ_0000566, whereas Hsa_circ_ 0000566 binds downstream and stabilizes HIF-1\u03b1. These results indicate that HIF-Hsa_circ_0000566 comprises a positive feedback loop that promotes glycolysis, invasion, and metastasis in OS. We also found that Hsa_circ_0000566 inhibits HIF-1\u03b1 ubiquitination and degradation. Several studies have shown that HIF-1\u03b1 is a substrate for the VHL complex. Under normoxia, the prolyl hydroxylase domain (PHD) regulates HIF-1\u03b1 stability, which is in turn recognized by VHL and modified by ubiquitination. We found that Hsa_circ_0000566 binds to both HIF-1\u03b1 and VHL proteins. However, Hsa_circ_0000566 did not regulate VHL protein expression. Instead, it inhibited the binding of VHL to HIF-1\u03b1. Therefore, the Hsa_circ_0000566-VHL combination prevented further binding between HIF-1\u03b1 and VHL. Our experiment ruled out the possibility of a Hsa_circ_0000566-VHL-HIF ternary complex formation. Using rescue experiments, we demonstrated that the regulation of OS glycolysis, invasion, and metastasis by Hsa_circ_0000566 is mediated by HIF-1\u03b1. Hence, Hsa_circ_0000566 is a novel non-coding effector molecule of HIF-1\u03b1 in glycolysis and may serve as a potential target for OS treatment.KDM6B-mediated histone LDHA demethylation promotes lung metastasis in OS . As HIF-Based on the data presented in this study, we propose the existence of a novel HIF-1\u03b1-Hsa_circ_0000566 positive-feedback path. Under normoxic conditions, VHL ubiquitinates and rapidly degrades the hydroxylated HIF-1\u03b1. In the hypoxic environment of OS, HIF-1\u03b1 strongly induces Hsa_circ_0000566. Hsa_circ_0000566 then inhibits HIF-1\u03b1 binding to VHL, hinders HIF-1\u03b1 protein degradation, upregulates the expression of glycolysis-related enzymes downstream of HIF-1\u03b1, and promotes glycolysis by binding to HIF-1\u03b1 and VHL. Therefore, Hsa_circ_0000566 is a potential therapeutic target for treating OS.Our study confirmed that Hsa_circ_0000566 is upregulated in both OS cell lines and tissues. In addition, we discovered that Hsa_circ_0000566 contributes to OS progression and glycolysis by interacting with HIF-1\u03b1 and VHL under hypoxic conditions.www.aginganddisease.org/EN/10.14336/AD.2022.0826.The Supplementary data can be found online at:"}
+{"text": "CTX-M and carbapenemases KPC, IMP, NDM, and VIM, remain taxonomically restricted to Proteobacteria. Even cfiA, the most common carbapenemase gene within the human gut microbiome, remains tightly restricted to Bacteroides, despite being found on a mobilizable plasmid. We confirmed these findings in gut microbiome samples from India, Honduras, Pakistan, and Vietnam, using a high-sensitivity single-cell fusion PCR approach. Focusing on a set of genes encoding carbapenemases and cephalosporinases, thus far restricted to Bacteroides species, we find that few mutations are required for efficacy in a different phylum, raising the question of why these genes have not spread more widely. Overall, these data suggest that globally prevalent, clinically relevant AR genes have not yet established themselves across diverse commensal gut microbiota.The acquisition of antimicrobial resistance (AR) genes has rendered important pathogens nearly or fully unresponsive to antibiotics. It has been suggested that pathogens acquire AR traits from the gut microbiota, which collectively serve as a global reservoir for AR genes conferring resistance to all classes of antibiotics. However, only a subset of AR genes confers resistance to clinically relevant antibiotics, and, although these AR gene profiles are well-characterized for common pathogens, less is known about their taxonomic associations and transfer potential within diverse members of the gut microbiota. We examined a collection of 14,850 human metagenomes and 1666 environmental metagenomes from 33 countries, in addition to nearly 600,000 isolate genomes, to gain insight into the global prevalence and taxonomic range of clinically relevant AR genes. We find that several of the most concerning AR genes, such as those encoding the cephalosporinase  Antimicrobial resistance genes (ARGs) in commensal gut bacteria may act as a reservoir for acquisition by pathogens. Here, the authors assess the distribution and transfer potential of ARGs in gut microbiomes and find that clinically important ARGs are taxonomically restricted despite being associated with mobile plasmids Recognition that the commensal gut microbiota harbor extensive numbers of diverse AR genes4, engage in horizontal gene transfer (HGT) at higher rates than microbiota in other environments6, and may serve as a stable reservoir for pathogenic acquisition8 has prompted broader AR surveillance beyond clinical isolates13. The potential for the spread of AR genes between taxa through HGT17 has raised further concern, especially for mcr genes, which confer resistance against colistin, and carbapenemases. However, many of the annotated AR genes observed to transfer within microbiomes, including many common Class A beta-lactamase variants18, do not confer phenotypic resistance to clinically relevant antibiotics. Understanding the global spread and HGT potential of clinically relevant AR genes\u2014particularly those associated with \u2018last resort\u2019 antibiotics\u2014would be helpful, yet surprisingly little is known about the gene-taxa associations within microbiomes at large.Preventing the spread of multidrug- and pandrug-resistant pathogenic bacteria remains a primary focus of global health effortsHere, using a combined analysis of 14,229 human gut metagenomes and nearly 600,000 isolate genomes, we find that certain AR genes are prevalent throughout global populations\u2019 microbiomes; however, thus far associated with few taxa. To determine whether there may be any additional bacterial hosts for twelve such clinically relevant genes, we applied a single-cell fusion PCR method to associate the genes with the 16S rRNA sequence of the host. We find that these genes are harbored by the set of taxa in which they were previously observed, despite being mobilizable, and, in some instances, functional in taxonomically distant organisms. This study challenges the notion that AR genes have spread more broadly within the gut microbiome than currently realized.19 , however, unexpectedly, a subset of AR gene families was found to be restricted to a single taxonomic class   Fig.\u00a0. This re34. Historical data on global antibiotic use however are sparse, due to the lack of centralized reporting that accounts for human and animal consumption, and environmental use. Nevertheless, recent data from the World Health Organization (WHO) representing 65 countries35 afford the opportunity to examine global use of oral and parenteral use of antibiotics with AR gene prevalence in human gut microbiomes.We next sought to determine the selective pressure imposed by antibiotic use on global AR geographic distribution and taxonomic spread. Antibiotic use on an individual level is generally associated with an increase in AR gene abundance in gut microbiomes35. Despite the copious use of broad-spectrum beta-lactams, only two beta-lactamase genes, cfxA and cblA, were found to be highly prevalent in human gut microbiomes cfiA, which has surprisingly high prevalence, we identified only 8 individuals out of 14,229 harboring any other carbapenemase in their gut microbiomes , was identified in only 5 metagenomes. By contrast, chloramphenicol-resistance genes were found to be pervasive across global metagenomes, even though chloramphenicol has long been considered a last-resort antibiotic due to toxicity and has relatively low usage 35.The most striking observation was a general lack of carbapenemases in gut microbiomes. Watch-group beta-lactams, including carbapenems, penems, monobactams, and third-, fourth-, and fifth-generation cephalosporins, comprised the second most consumed category of antibiotics . Aside from 55  . Although polymyxins, to which mcr genes confer resistance, are infrequently prescribed today, they have historically been used for growth promotion in livestock. Their prevalence may have been limited by the disproportionate fitness defects caused by these genes compared to other AR genes58. Rather, their persistence within communities is thought to be stabilized by being situated within transposons on conjugative plasmids that may be maintained by other selective means60.To address the possibility that AR gene spread, both geographically and between taxa, may simply be the result of time, we used the first report of an identified AR gene family in the literature, a previously established metricel) Fig.\u00a0. Genes wmcr genes in 6 total individuals lacked previously identified hosts at the phylum level, indicating that either there is an undefined reservoir for mcr genes, or known hosts have too low abundances to detect.Because the taxonomic diversity of the AR gene reservoir within human gut microbiomes is unknown, we tested whether clinically relevant AR gene family-taxa associations could be explained solely by using a collection of isolates sequenced thus far. To this end, we examined all 14,229 gut metagenomes from curatedMetagenomicData for the presence of at least one known host for each AR gene family. Save for a few AR gene families in a small number of people, isolate-based associations alone were sufficient to explain the distribution of AR gene families , a sensitive, culture-independent single-cell fusion PCR approach to associate extrachromosomal DNA with their associated genomescblA, cepA, cfxA, CTX-M, cfiA, IMP, NDM, VIM, AAC(6\u2019)-Ib, VanA, QnrS, mcr1, and mcr3) using a probe-based quantitative PCR approach -Ib-cr and qnrS, which have been shown to be mobilizable, and Bacteroides-specific cblA, cepA and cfiA  of each AR gene to ampicillin . Raw reads were then downloaded using the compiled FTP links (wget -c --retry-connrefused -P <download_location\u2009>\u2009<ftp_link\u2009>\u2009). Due to the large variability in Illumina sequencing methods across datasets, all metagenomes were processed as single-end reads by concatenating the fastq read files into a single file. For analysis of inpatient, waste-water, air, and animal metagenomes, metadata and accession numbers were manually curated from multiple studies and downloaded with the same method (Supplementary Data\u00a0The curatedMetagenomicData datasets packagebbduk.sh in=infile.fastq out=trimmed.fastq ref=adapters ktrim\u2009=\u2009r k\u2009=\u200923 mink\u2009=\u200911 hdist\u2009=\u20091 tpe tpo), and aligned to the Comprehensive Antibiotic Resistance Database20 (CARD) protein homolog model database using KMA94 version 1.4.3 (kma -i trimmed.fastq -o kma.txt -t_db card_db). KMA results were analyzed using a custom python script . In short, positive hits were strictly filtered for template identity over 90%, template coverage over 80%, and p-values under 0.0001. Many hits within the KMA results mapped to single nucleotide gene variants within the CARD database, so CD-HIT95 was used to cluster the database to 99% identity to reduce redundancy in the dataset. KMA results were then combined based on the CARD clustering. AR gene family profiles were assembled by counting the presence or absence of AR gene families within each metagenome. Individual sample profiles were then used to calculate the percent prevalence of each gene family by country and in all samples grouped by body site.Concatinated fastq files were rimmed with bbTools version 38.96 https://ftp.ncbi.nlm.nih.gov/genomes/genbank/assembly_summary_genbank.txt 2022-2-11). Only Bacterial genomes with an assigned genus were used. For genomes marked as \u201cexcluded from RefSeq\u201d, only those assonated as \u201cfrom large multi-isolate projects\u201d and \u201cfragmented assemblies\u201d were used, excluding those derived from environmental sources, metagenomes, or single-cell sequencing. Salmonella enterica and Escherichia coli were both randomly subsampled to 100 thousand genomes each due to their overrepresentation in the dataset. FTP links were then compiled from the metadata and sequences downloaded with wget. Plasmid sequences were similarly acquired from RefSeq\u2019s plasmid database (https://ftp.ncbi.nlm.nih.gov/genomes/refseq/plasmid/).Metadata for all genomes withing GenBank were acquired through NCBI . Using a custom python script (process_rgi_V14.ipynb), RGI hits were filtered to remove \u201cloose\u201d hits and AR gene family profiles were constructed for each genome similar to the metagenomic profiles (Supplementary Data\u00a096 version 1.4 (python3 PlasForest.py -f -r --threads 4 -i AR_Contigs.fasta -o out_plasforest.csv)RGI97 were asked to take part in this study. The goal of the microbiome sampling was to be as comprehensive as possible. Pakistani study participants comprised adults (over the age of 18) recruited via the existing community-based antimicrobial surveillance system established by the two union councils of the Matiari district. Participants were stratified based on ethnicity/caste and tribe and random representatives were chose across the communities. Vietnamese participants comprised adult farmers (over the age of 18) involved in studies conducted by the Oxford University Clinical Research Unit (OUCRU) in Vietnam. Indian participants comprised a subset of pregnant women enrolled in the PRACHITi study in Pune, India98. All women were over the age of 18 who presented to the antenatal clinic at BJ Government Medical College in Pune, India, with gestational age between 13\u201334 weeks. After obtaining consent, all human research participants from Honduras, India, Pakistan, and Vietnam were provided with stool acquisition kits and instructed on how to self-collect the samples, store and transport samples to the study coordinators. After voiding, participants kept samples cold using cold packs until they could be frozen at \u221280\u2009\u00b0C.Human participant research was approved by the following committees: Cornell University Institutional Review Board , Aga Khan University Ethics Review Committee (#2019-0550-5166), Ethics Committee of the University of Oxford (OxTREC 38-15) and of Tien Giang Hospital Institutional Review Board (278/BV\u0110K), the Yale University Institutional Review Board (#2000020688), the Indian BJ Government Medical College Ethics Board, and Weill Cornell Medicine Institutional Review Board . Honduran study participants from 9 isolated villages in the western highlands of Honduras that were part of larger population-based cohort assembled for a different purposeAliquots of frozen stool samples from Honduras were shipped on dry ice to Cornell University. 200\u2009mg of stool material was placed in 2\u2009ml PowerBead Tubes with 2.8\u2009mm ceramic beads  for DNA extraction using the Chemagic Stool gDNA Extraction Kit  on the Chemagic 360 Instrument (Perkin Elmer). Resulting genomic DNA quantity, quality, and purity were assessed via determination of the 260/280\u2009nm for values of 1.7\u20132.0, and 260/230 absorbance ratios for values \u2265 and 1% agarose gel electrophoresis to ensure that the gDNA was neither degraded nor displayed RNA contamination. Metagenomic libraries were prepared using KAPA Hyper Library Preparation Kit . Resulting libraries were sequenced on the Illumina NovaSeq (2\u00d7150).61 master mix modified by omitting lysozyme and using a lower concentration of Phusion polymerase (1U per 50\u2009\u00b5l of reaction). Isolates acquired from the CDC & FDA Antimicrobial Resistance Isolate Bank were used as templates for testing primers. Amplification quality was evaluated based on clean bands on a gel as well as efficient nested PCR amplification using NEB Luna Universal qPCR Master Mix. Primer/probe sets were designed based on the validated OIL-PCR primers, using the round 1 and fusion primers for amplification, and the nested round 2 primer sequence for the probes  to degrade the primers . ExoI treated 16S amplicons were indexed as described previously61. To remove clumped cells that can signal false gene associations, purified cells were passed through a 5\u2009\u00b5m nylon syringe filter. To improve the sensitivity of OIL-PCR, we increased the input of cells 10-fold to 400 thousand cells per reaction based on previous experiments showing that OIL-PCR is more accurate with low abundance cells61. In addition, after performing the nested PCR step, multiplexed reactions were recombined and indexed in a single PCR reaction. Lastly, instead of performing Ampure bead purification between the nested PCR and indexing PCR, we used ExoI treatment to degrade nested primers as described for the 16S rRNA library preparations.Nycodenz purification of cells and OIL-PCR was performed with automation as described previously with some adjustmentsusearch -fastq_mergepairs forward.fq -reverse reverse.fq -fastq_pctid 80 -fastq_minmergelen 350 -fastq_maxmergelen 470 -fastq_maxdiffs 10 -fastqout merged.fq), and cutadapt version 3.4 was then used to trim primers (cutadapt -a ^TARGET1_primer\u202616\u2009S_rev$ -a ^ TARGET2_primer\u2026 16S_rev$ -a ^TARGET3_primer\u2026 16S_rev$ --discard-untrimmed -o trimmed.fq merged.fq --cores\u2009=\u20092), before quality filtering with usearch (usearch -fastq_filter trimmed.fq -fastq_maxee 0.5 -fastaout filtered.fa).OIL-PCR sequences were analyzed with a modified pipeline to sort pooled libraries and allow an analysis of individual ASV variants. Using the script \u201cs1.1_run_mrg_trim_flt.qsub.sh\u201d reads were first merged using usearch mergepairs version 11.0.667 (cutadapt -a $target_fusion_primer --discard-untrimmed -o output_target.fa filtere.fa --suffix _target --cores\u2009=\u200910). Cutadapt was run separately for each fusion primer in multiplexed sequencing to simultaneously split and sort the target reads. Cutadapt was run a final time using the 519F 16S sequence to isolate the 16S portion of each read. Duplicate sequences were counted using usearch -fastx_uniques (usearch -fastx_uniques output_target.fa -fastaout unique.fa -relabel target_uniq_ -sizeout). Lastly, 16\u2009S sequences were clustered into OTUs using usearch -cluster_otus (usearch -cluster_otus uniques.fa -otus otus_out.txt -relabel otu -uparseout parsed_file.txt -minsize 1) and taxonomy assigned using MOTHUR RDP classifier with the SILVA version 132 using the script \u201cs3_cluster_assign_tax.sh\u201d.Next the script \u201cs2_combine_split_sort_V2.sh\u201d was used to split target sequences from the 16S sequence using cutadapt to recognize the gene-specific fusion primer in each read (s4_Tax_Blast_Connect_SNPS_V7.ipynb) was used to compile taxonomic-gene associations while keeping track of ASV variants. In short, fasta files were read into tables and sequences were used as identifiers to merge information about the identity of the target gene, assigned taxonomy for the 16\u2009S read, and count the number of reads for the association. A second python script was used to graph the output (graphing_results_V4.ipynb). In summary: 16\u2009S sequencing results were imported and merged with the OIL-PCR association to provide community abundance of each ASV and OUT. Target reads were clustered to 99% identity with CD-HIT. Detected associations were filtered for detection across replicates.A custom python script (99. Plasmid 353 (pARG-test-insulated) was assembled with a p15A origin and chloramphenicol resistance gene amplified from plasmid pdCas9-bacteria  using Gibson assembly (NEBuilder Hi-Fi Assembly Master Mix).We built a strongly insulated expression vector for testing commensal AR genes to reduce the possibility of leaky gene expression from stray RNA polymerase. We designed a gene block containing two strong terminators upstream of TEM-1\u2014a common ampicillin resistance gene used routinely for cloning\u2014expressed from a synthetically designed promoter and RBScfiA-2, cfxA-2, cepA, and cblA-1 were selected from CARD, favoring those that were found on plasmids or other mobile elements. cfiA was selected from the pBFUK1 plasmid. Each gene was synthesized along with 250\u2013350\u2009bp upstream to capture the native promoter region. For the native promoter, the expression plasmid 353 was linearized with primers BH01/BH03 and the ach gene block was inserted using Gibson assembly. For the synthetic promoter constructs, the plasmid was linearized with primers BH01/BH04 and each gene block was amplified with primer BH08 paired with BH09-BH12 to remove the promoter. The fragments were then assembled with Gibson. For the promoter-less construct, plasmid 353 was amplified with primers BH03/BH05, phosphorylated, and circularized with T4 ligase.cfiA2 from plasmid pBFUK1 (accession AB646744)cfxA2 from B. fragilis strain DCMOUH0085B (accession CP037440)cepA from B. fragilis strain 74985 CTn86 mobile element (accession MW169430)cblA1 from Uncultured Bacteroides sp. Clone AMP (accession MH883562)Sequences for Plasmid glycerol stocks were inoculated into 5\u2009ml LB + chloramphenicol and grown at 37\u2009\u00b0C overnight. Cultures were then diluted 1:10 in fresh LB and evenly spread onto Muller-Hinton agar (Hardy Diagnostics G45) with a cotton swab and allowed to dry. MIC test strips (Liofilchem) were then placed onto the agar with tweezers and plates were placed at 37\u2009\u00b0C for at least 24\u2009h before imaging. MIC measurements were evaluated following the manufacturer\u2019s instructions.TEM. Cultures were diluted 1:10 in TE and incubated 10\u2009min at 99\u2009\u00b0C to lyse. The raw lysate was used as a template for PCR with primers BI70/BI71 and processed following the 16\u2009S rRNA library preparation protocol . Illumina reads were processed similar to 16\u2009S rRNA reads, merging with usearch, trimming with cutadapt, quality filtering with usearch100, and counting uniques with usearch. Next, sequences were aligned to the original promoter with bwa mem version 0.7.17, converted to bam, sorted, and indexed with samtools101 version 1.15.1. The program bam-readcount102 was used to count the bases at each position of the reads. A custom python script (s5_parse_tsv_and_graph_V05.ipynb) was then used to parse the bam-readcount output and the results were plotted using Logomaker103.The backbone of plasmid 364 (pARG_cfiA_native) was amplified with primers BH03/BH97. The promoter region was amplified with primers BH98/BH99 using the GeneMorph II random mutagenesis kit (Agilent 200550) with 300\u2009ng template and 30 cycles. Mutated promoters were assembled into the backbone using Gibson assembly, diluted, and plated for individual colonies (~24 thousand). Colonies were pooled and stocks were frozen in 20% glycerol. Mutant libraries were grown overnight in 5\u2009ml LB + chloramphenicol and diluted 1:100 in fresh LB + chloramphenicol with 0, 0.125, 0.5, 2, or 8\u2009\u00b5g/ml of cefotaxime and allowed to grow overnight. Growth occurred at all antibiotic concentrations, likely due to compensatory genomic mutations. This was not observed for DH5a carrying Further information on research design is available in the\u00a0Supplementary InformationPeer Review FileDescirption of Additional Suppelmentary DataSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Reporting Summary"}
+{"text": "Endoscopic retrograde cholangiopancreatography (ERCP) is primarily performed to remove common bile duct (CBD) stones. However, in patients with severe esophageal hiatal hernias, advancing the duodenoscope into the second portion of the duodenum may be challenging. We present a case in which a large-balloon anchor techniqueA 93-year-old woman with cholangitis due to CBD stones, was admitted to our hospital. Computed tomography revealed two 10-mm CBD stones . Most oVideo\u20061\u2002Endoscopic retrograde cholangiopancreatography with large-balloon anchor technique was useful for a 93-year-old woman with severe esophageal hiatal hernias.ERCP with the large-balloon anchor technique has been performed for duodenal stenosis and deformitiesEndoscopy_UCTN_Code_TTT_1AR_2AK"}
+{"text": "Isotopic labeling is an essential relative quantification strategy in mass spectrometry-based metabolomics, ideal for studying large cohorts by minimizing common sources of variations in quantitation. MS-DIAL is a free and popular general metabolomics platform that has isotopic labeling data processing capabilities but lacks features provided by other software specialized for isotopic labeling data analysis, such as isotopic pair validation and tabular light-to-heavy peak ratio reporting.We developed Peak Pair Pruner (PPP), a standalone Python program for post-processing of MS-DIAL alignment matrixes. PPP provides these missing features and innovation including isotopic overlap subtraction based on a light-tagged pool sample as quality control. The MS-DIAL+PPP workflow for isotopic labeling-based metabolomics data processing was validated using light and heavy dansylated amino acid standard mixture and metabolite extract from human plasma.https://github.com/QibinZhangLab/Peak_Pair_Pruner. Raw MS data and .ibf files analyzed are on Metabolomics Workbench with Study ID ST002427.Peak Pair Pruner is freely available on Github: q_zhang2@uncg.eduBioinformatics Advances online. An essential approach for metabolomics studies is stable isotopic labeling, in which analytes in one sample are derivatized with a \u2018light\u2019 tag and those in another sample with a \u2018heavy\u2019 or isotopically enriched tag. These samples undergo pooling and mixing strategies to allow for relative quantification in large cohort studies. MS-DIAL  and is sAlignment matrix import. PPP requires an MS-DIAL alignment matrix. Utilizing keywords and isotopic labeling naming convention, PPP collates metabolite, blank, sample, light QC, heavy QC, mix QC, and replicate data into an internal data array.Isotopic screening. Matching isotopic relationships from MS-DIAL and experimental parameters provided by the user, PPP searches the internal data array for peak pairs that are potentially due to the user\u2019s isotopic labeling experiment, accounting for different charge states and adduct species.Peak pair mass validation. Mass defect filtering is optionally applied based on user-defined upper and lower mass defect limits. Accurate mass difference between paired peaks is validated against user-defined heavy tag shift and mass ppm tolerance.Peak pair quantitative corrections. Background peak values are subtracted utilizing a blank. Isotopic overlap between light and heavy tagged analytes is subtracted utilizing the light pool QC.Peak pair QC ratio validation. Peak pairs are validated against minimum light QC L/H ratio, minimum heavy QC H/L ratio, theoretical mix QC L/H ratio and mix QC L/H ratio tolerance.PPP was implemented in Python utilizing the PySimpleGUI and XlsxWriter packages. We have exported PPP to a single executable program that is independent of its original Python IDE. Technical details are described in the To assess quantitation and demonstrate the MS-DIAL+PPP workflow\u2019s capabilities, we conducted two analyses: (i) dansylation of a 17 amino acid standard mixture and (ii) dansylation of pooled human plasma, both with known L/H ratios of 1:10, 1:2, 1:1, 2:1 and 10:1 with high mass resolution LC-MS data acquisition. In analysis (i), all 17 amino acids were identified by MS-DIAL and validated, quantified by PPP. Further details are in the vbad044_Supplementary_DataClick here for additional data file."}
+{"text": "Mytilus coruscus, represents a model organism for the marine environment and molluscs interaction research. In this study, we used in silico cloning to obtain a small Maf homologue called McMafF_G_K from M. coruscus. McMafF_G_K possesses a typical BZip domain, suggesting its affiliation with the traditional small Maf family and its potential involvement in the Nrf2 signaling pathway. Transcriptional analysis revealed that McMafF_G_K exhibited a robust response to benzo[a]pyrene (Bap) in the digestive glands. However, this response was down-regulated upon interference with McMafF_G_K-siRNA. Interestingly, the expression levels of Nrf2, NAD(P)H: quinone oxidoreductase (NQO-1), and Glutathione Peroxidase (GPx), which are key players in oxidative stress response, showed a positive correlation with McMafF_G_K in digested adenocytes of M. coruscus. Furthermore, in vitro analysis of antioxidant capacity in digestive gland cells demonstrated that Bap exposure led to an increase in reactive oxygen species (ROS) levels, accompanied by an elevation in total antioxidant capacity (T-AOC), potentially counterbalancing the excessive ROS. Strikingly, transfection of McMafF_G_K siRNA resulted in a significant rise in ROS level and a down-regulation of T-AOC level. To validate the functional relevance of McMafF_G_K, a glutathione S-transferase (GST) pull-down assay confirmed its interaction with McNrf2, providing compelling evidence of their protein interaction. This study significantly contributes to our understanding of the functional role of McMafF_G_K in the Nrf2 signaling pathway and sheds light on its potential as a target for further research in oxidative stress response.The nuclear factor erythroid 2-related factor 2 (Nrf2) is a pivotal regulator of antioxidant gene expression in mammals, forming heterodimer complexes with small Maf proteins through its BZip domain. However, the underlying mechanism of Nrf2 action in molluscs remains poorly understood. The thick shell mussel,  In the growth process of aquatic organisms, they are constantly exposed to reactive oxygen species (ROS) and electrophilic substances produced through metabolic and environmental factors ,2. TheseMytilus coruscus pyrene (Bap) irritation induced the expression of Nrf2 and a series of antioxidant enzymes, such as superoxide dismutase (SOD), glutathione peroxidase (GPX), catalase (CAT), and glutathione reductase (GR), in coruscus . Similarnd NQO-1 . In Litotructure . These fMafG and MafK genes from human hematopoietic cells [Nrf2 activity in vivo has shown that the absence of small Maf can effectively reverse the cellular dysfunction caused by Keap1 gene deficiency. Furthermore, it has been observed that this absence also leads to a decrease in the mortality rate in mice resulting from Keap1 loss [Nrf2 and the lack of small Maf both result in highly similar and severe damage in inducing the response of antioxidant and exogenous metabolic enzyme genes to electrophilic agents [Maf exhibit liver steatosis and gene dysregulation related to lipid and amino acid metabolism, as well as proteasome subunit expression. At the same time, the expression levels of many Nrf2 target genes also decrease [Nrf2 functions in cellular transcription by forming heterodimers with members of the small Maf family, including MafG, MafK, and MafF . Throughic cells ,13. Subsic cells . Extensiap1 loss . These dc agents ,16. Thisdecrease .Maf genes in aquatic organisms such as Danio rerio, Cristaria plicata and Procambarus clarkia. Takagi et al. [Danio rerio. Further experiments revealed that the co-overexpression of MafT and Nrf2 resulted in synergistic activation of MARE-mediated gene expression in Danio rerio embryos [MafK in Cristaria plicata, confirming the critical detoxification role of CpMafK in microcystin toxin stress [Procambarus clarkii, the transcriptional expression of the PcMafG-like gene and certain antioxidant genes in the hepatopancreas and gills was significantly up-regulated under Cu2+/Cd2+ stimulation [PcMafG-like was interfered with using dsRNA, the expression of antioxidant genes was inhibited, leading to more severe pathological damage. These findings corroborate the potential of small Mafs in eliciting the activation of antioxidant genes in aquatic organisms. Nevertheless, there remains a dearth of research concerning the involvement of small Mafs in the thick shell mussel M. coruscus, a crucial model organism in marine environmental studies. M. coruscus is primarily distributed in the Yellow Sea and East China Sea, adopting a lifestyle of attachment and filter feeding [M. coruscus. In our previous study, we found that when M. coruscus was exposed to acute Bap stress, the Nrf2-dependent antioxidant system was activated at both the transcriptional and enzyme levels [Maf gene in the Nrf2 pathway and its crucial functional role in the antioxidant process of mussel M. coruscus.Presently, there have been reports of the successful cloning of small i et al.  identifi embryos . In anotn stress . In Procmulation . However feeding ,22. As t feeding . This hae levels . This acMcMafF_G_K revealed a composition of 483 nucleotide residues, encoding a 160 amino acid residue protein. Using Expasy, McMafF_G_K exhibited a molecular weight of 18 kDa and an isoelectric point of 9.85. Structural analysis indicated the presence of an \u03b1-helix structure at the C-terminus  assay. McMafF_G_K mRNA was observed to be highest in the digestive glands, followed by the gills and hemocytes, with the lowest expression detected in the mantle. Furthermore, the transcriptional response of McMafF_G_K to exposure to Bap was also examined, as depicted in McMafF_G_K exhibited a significant up-regulation starting from 24 h post-stimulus (hps), ultimately peaking at 72 hps. Subsequently, although there was a decrease in McMafF_G_K transcription, it remained significantly higher than the control.The distribution profile of McMafF_G_K, McNrf2, McNQO-1, and McGPx were significantly up-regulated compared to the control group. Subsequently, upon knockout of McMafF_G_K, there was a noticeable down-regulation in the expression of McMafF_G_K mRNA when compared to the Bap group, suggesting an effective interference effect of McMafF_G_K-siRNA . The size of the purified His-McMafF_G_K protein was estimated to be approximately 23 kDa based on the analysis performed on an SDS-PAGE gel . The size of the resulting protein band was observed to be around 106 kDa on the SDS-PAGE gel  and 106 kDa (GST), providing clear evidence of the specific interaction between McNrf2 and McMafF_G_K. In contrast, the control group only displayed a single band at 26 kDa (GST)  C.M. coruscus. Our findings revealed that McMafF_G_K, an important Maf variant, contained a DNA-binding domain and the leucine zipper structures that play a crucial role in both self and other BZip transcription factors\u2019 dimerization [McMafF_G_K also possesses the HER domain, a universally observed domain in all Mafs. This conserved structural domain facilitates stable DNA binding [The Nrf2 pathway, as the key defense system against environmental damage and regulator of body homeostasis, has emerged as a critical research focus. Despite its significance, the understanding of the molecular mechanisms of Maf in aquatic organisms is presently limited. In light of this, our study aims to address this knowledge gap through the identification and characterization of Maf in rization ,25. Nota binding . These fMafs were comprehensively characterized in various mouse tissues, yielding valuable insights into their differential gene expression patterns. Specifically, MafK and MafF exhibited prominent upregulation in the lung, underscoring their vital roles in pulmonary physiology, whereas MafG displayed pronounced abundance in the heart, highlighting its significance in cardiac function [MafG mRNA was discernible not only in the heart but also in diverse tissues, including skeletal muscle, cerebral cortex, cerebellum, liver, stomach, and intestine, suggesting its potential involvement in multiple physiological processes in response to this stimulus [Maf gene expression in zebrafish unveiled a widespread distribution of small Mafs across different tissues, with the brain exhibiting particularly elevated expression levels, emphasizing the significance of these proteins in neural functions [Procambarus clarkia, the presence of PcMafG-like mRNA was detected in all examined tissues, with muscle tissue exhibiting the highest expression levels, likely attributable to the integral role of muscle tissue in the organism\u2019s biology [McMafF_G_K mRNA in all examined tissues, with heightened expression levels observed in digestive glands, gills, and hemocytes, all recognised as immune-associated tissues in bivalves, implying their potential contributions to immune responses. These comprehensive analysis of small Maf gene expression profiles in different tissues provides valuable insights into their differential regulation and tissue-specific significance. These findings contribute to our understanding of the multiple physiological roles played by small Mafs. The expression profiles of small function . Furtherstimulus . Intriguunctions . In Proc biology . AdditioNrf2-mediated gene activation. Research has shown that the expression of ARE-dependent genes in the liver of mice is influenced to varying degrees and exhibits high sensitivity to oxidative stress responses. Inducers such as hydrogen peroxide, ARE or electrophile responsive element (ARE/EpRE) inducers, and hypercapnia (elevated carbon dioxide levels) can stimulate the induction of small Mafs, activating the expression of ARE-dependent cell protective genes [MafT and MafG1. Additionally, the co-expression of MafT and Nrf2 synergistically activates gene expression mediated by MARE in zebrafish [2+/Cd2+ stimulation, the expression of PcMafG-like and downstream antioxidant genes is upregulated in the hepatopancreas and gills of Procambarus clarkia [McMafK_G_F, McNrf2, and downstream target genes McGPx and McNQO-1 in the digestive gland cells of M. coruscus following a six-hour Bap exposure. At the same time, we used qPCR technology to assess gene expression after McMafF_G_K gene interference. The results showed significant downregulation in the expression levels of McMafK_G_F, McNrf2, McNQO-1, and McGpx compared to the Bap stimulation group. Specialised research into Mafs regulation unveiled that mice embryos with reduced Maf expression exhibited a decreased basal expression of ARE-dependent cellular protective genes. This decreased expression of oxidative stress response genes may exacerbate cell apoptosis, embryonic growth retardation, and impaired liver function [Small Mafs play a crucial role as transcription factors in the activation of ARE-dependent genes, which are responsible for cellular protection, effectively shielding the organism from environmental harm, and this is essential for ve genes ,28,29. Iebrafish . Upon Cu clarkia . During function . To clarfunction . These fM. coruscus. This suggests that upon exposure to Bap, the antioxidant defense system is triggered to counteract oxidative stress, effectively protecting cells from BaP-induced oxidative damage [Litopenaeus vannamei resulted in decreased activities of SOD, CAT, and GPx enzymes, while malondialdehyde (MDA) activity increased. Similarly, Wang et al. [Cristaria plicata led to significantly higher levels of MDA, indicating elevated lipid peroxidation compared to the control group. In the binding and recognition process of the Nrf2/Maf heterodimer for activating the antioxidant stress response, Nrf2 and Maf play a crucial synergistic role. Interfering with Nrf2 and Maf may lead to similar results [Procambarus clarkii oxidative response, the activities of GSH, Cu/Zn-superoxide dismutase (CZ-SOD), and CAT significantly increased in the Cu2+/Cd2+ stimulation group, while interference with PcMafG-like led to a reduction in enzyme activities [M. coruscus alleviates the damage caused by ROS accumulation by enhancing its total antioxidant capacity. However, McMafF_G_K siRNA interference resulted in increased ROS activity and decreased T-AOC activity, suggesting that Bap activates the Nrf2 signaling pathway to mitigate oxidative stress, and silencing MafF_G_K disrupts the normal antioxidant mechanisms, resulting in decreased total antioxidant capacity and elevated ROS levels. These results suggest that small Mafs may play a crucial regulatory role in the antioxidative stress process of bivalves, and their functional loss could potentially exacerbate damage to the organisms. The continuous generation of ROS is a common response in aquatic animals when facing external stressors. Both non-biological water pollution and biological factors can induce the excessive accumulation of ROS in marine organisms, leading to oxidative stress. In our previous study, we observed that exposure to Bap significantly increased the activities of SOD, CAT, GPx, and GR enzymes in the digestive glands of e damage . Huang ee damage  found thg et al.  demonstr results ,32. Additivities . In thisC. plicata Prx5 within the Nrf2/ARE signaling pathway, mass spectrometry analysis has identified MafK protein as one of the interactors with Nrf2-BZip, which was validated through yeast two-hybrid experiments [McMafF_G_K and GST-McNrf2, as well as the expression of the recombinant proteins in vitro. Our GST-pull down experiments have confirmed the interaction between McNrf2 and McMafF_G_K, thus highlighting the formation of heterodimers between McMafF_G_K and McNrf2 that participate in the transcriptional regulation of antioxidant stress responses.Small Mafs and Nrf2 belong to the CNC transcription factor family, both possessing a BZip structure that mediates DNA binding and dimerization ,24,25. TM. coruscus individuals were collected from Donghe Market in Zhoushan city, Zhejiang province. They were then temporarily cultured for a week in seawater with a temperature of 25 \u00b1 1 \u00b0C and salinity of 30\u2030. During the domestication process, 50% of the seawater was replaced daily, and spirulina powder was fed to them every day. A total of 200 McMafF_G_K cDNA was silicon cloned from the National Center for Biotechnology Information (NCBI) (accession number: CAC5373282). A specific primer pair (McMafF_G_K open reading frame (ORF) region. After DNA sequencing of the PCR products, the putative amino acid sequence was presumed. Theoretical calculations of the isoelectric point and predicted molecular weight of McMafF_G_K were carried out utilizing the EXPASY platform . To predict the presence of conserved domains, we employed the SMART tool . Homologous amino acid sequences of McMafF_G_K were sourced from NCBI utilizing their Blast service .The sequence information of mer pair  was desiMcMafF_G_K.The Bap exposure assay was conducted following the methodology described in our previous publication . In brieMcMafF_G_K-siRNA using GP-Transfect-Mate  was performed when the cell density reached 60% to 80%. To evaluate the impact of McMafF_G_K on the Nrf2 pathway, the cells were then exposed to Bap (50 \u03bcmol/mL) for a period of 6 h.Digestive gland tissue weighing 0.3\u20130.35 g was carefully disinfected by immersing it in 25 mL of Hank\u2019s solution  supplemented with 1% penicillin-streptomycin-gentamicin solution . Thereafter, the tissue was thoroughly rinsed with PBS and finely minced using surgical scissors. To facilitate digestion, 8 mL of 0.25% trypsin solution  was added and allowed to react for 20 min with vigorous shaking and stirring every 5 min. After the hydrolysis was terminated, the resulting filtrate was collected and centrifuged at 3000 rpm for 15 min at 4 \u00b0C. Subsequently, the cell precipitate was suspended in L-15 medium  containing 10% fetal bovine serum . The resultant solution was evenly distributed onto a six-well plate and cultured in a cell incubator at 28 \u00b0C. Transfection with TM Reverse Transcription System kit . For gene detection, the SYBR Green Master Mix, included in the TB Green\u00aeFast qPCR Mix Kit , was employed on the 7500 system. The experiment utilized specific primers, as detailed in \u2212\u0394\u0394Ct method. Finally, data were statistically analyzed and visualized using SPSS 19.0 and GraphPad Prism 8.0.qRT-PCR assays were performed conventionally. Total RNA was extracted from the tissues and cells using Trizol  following established protocols. The extracted RNA was subsequently converted into cDNA using the Go ScriptThe Western blot analysis followed the protocol outlined in our previous study . InitialMcMafF_G_K was successfully transformed into Escherichia coli BL21 host cells  to initiate protein expression. To induce the expression of His-McMafF_G_K protein, the transformed cells were incubated at 37 \u00b0C for 4 h with 0.2 mM isopropyl \u03b2-D-1-thiogalactopyranoside (IPTG) . Similarly, the plasmid pGEX-4T-1-McNrf2 was transformed into TSsetta (DE3)  and allowed to culture overnight at 25 \u00b0C. The induction of GST-McNrf2 recombinant protein expression was carried out using 0.2 mM IPTG . Following cell culture, the culture medium was centrifuged to remove the supernatant. The bacterial cells in suspension were then subjected to sonication for disruption, followed by centrifugation to separate the supernatant from the pellet. Subsequently, the protein samples were verified through 8% and 12% SDS-polyacrylamide gel electrophoresis. The purification of His-McMafF_G_K and GST-McNrf2 proteins was achieved by utilizing Ni-affinity resin and glutathione affinity resin , respectively. Solution exchange was performed using Amicon@Ultra-15 ultrafiltration tubes . Finally, to prevent protein degradation, the His-McMafF_G_K and GST-McNrf2 proteins were stored in BupTM Tris-buffered saline (TBS)  supplemented with 1 mM phenylmethylsulfonyl fluoride (PMSF) .The prokaryotic expression system pet28-McNrf2 protein was initially combined with glutathione agarose resin and gently oscillated at 4 \u00b0C for 1 h. Following this, 150 \u03bcg of His-McMafF_G_K protein was added to the GST-McNrf2 solution, and the mixture was gently oscillated at 4 \u00b0C for 2 h. The eluate was collected and then heated at 95 \u00b0C for 5 min for subsequent Western blotting analysis. For Western blotting, protein separation was performed using a 4\u201320% linear gradient SDS-polyacrylamide gel . After electrophoresis, the proteins were transferred onto a membrane using a standard transfer protocol. The membrane was then subjected to blocking using skim milk powder to minimize non-specific binding. The membrane was then incubated overnight at 4 \u00b0C with mouse monoclonal anti-GST  and anti-His  antibodies . The membrane was washed three times with TBST to remove any unbound antibodies. Following that, the membrane was exposed to a secondary antibody conjugated with horseradish peroxidase  at room temperature for 1 h. The bands corresponding to the target proteins were visualized using the ECL system.In the GST-pull down experiment, 100 \u03bcg of GST-Enzyme activity tests are based on our previous research . The mea\u00ae Multimode Microplate Reader (V2.3). The protein content in the cell samples was determined using the BCA protein concentration determination kit . The total antioxidant capacity of the samples was determined by referencing the standard curve of the Trolox standard solution provided in the kit. The final results of total antioxidant capacity are typically expressed as mmol/mg or mmol/g of protein weight.The assessment of T-AOC was conducted using the Total Antioxidant Capacity Detection Kit  based on the 2,2\u2032-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) method. Following the collection and washing of cells with PBS for the subsequent detection step, the supernatant was transferred to a 96-well plate, and the ABTS working solution was added. Subsequently, the 96-well plate was incubated at room temperature for 6 min and measured using the Sparkp-value less than 0.05 (p < 0.05). Group differences were assessed using either one-way ANOVA or two-way ANOVA. Statistical analyses were conducted using GraphPad Prism software version 6.0.The mean values and standard deviation (SD) were used to describe the data. Statistical significance was set at a McMafF_G_K, a homologue of small Mafs, was computationally identified in Mytilus coruscus. Transcriptional analysis revealed the significant response of McMafF_G_K in the digestive glands to Bap, which was down-regulated upon interference with McMafF_G_K siRNA. Notably, a positive correlation was observed between McMafF_G_K and key players involved in oxidative stress response. Intriguingly, transfection of McMafF_G_K-siRNA resulted in a substantial increase in ROS level and a down-regulation of T-AOC. The functional relevance of McMafF_G_K was further validated through a GST pull-down assay, confirming its interaction with McNrf2 and providing compelling evidence of their protein interaction. Overall, this study provides valuable insights into the functional role of McMafF_G_K in the Nrf2 signaling pathway and its implications for oxidative stress response in molluscs."}
+{"text": "Colorectal liver metastasis (CLM) is a leading cause of colorectal cancer mortality, and the response to immune checkpoint inhibition (ICI) in microsatellite-stable CRC has been disappointing. Administration of cytotoxic chemotherapy may cause increased density of tumor-infiltrating T cells, which has been associated with improved response to ICI. This study aimed to quantify and characterize T-cell infiltration in CLM using T-cell receptor (TCR) repertoire sequencing. Eighty-five resected CLMs from patients included in the Oslo CoMet study were subjected to TCR repertoire sequencing. Thirty-five and 15 patients had received neoadjuvant chemotherapy (NACT) within a short or long interval, respectively, prior to resection, while 35 patients had not been exposed to NACT. T-cell fractions were calculated, repertoire clonality was analyzed based on Hill evenness curves, and TCR sequence convergence was assessed using network analysis.Increased T-cell fractions (10.6%\u00a0vs. 6.3%) were detected in CLMs exposed to NACT within a short interval prior to resection, while modestly increased clonality was observed in NACT-exposed tumors independently of the timing of NACT administration and surgery. While private clones made up >90% of detected clones, network connectivity analysis revealed that public clones contributed the majority of TCR sequence convergence.TCR repertoire sequencing can be used to quantify T-cell infiltration and clonality in clinical samples. This study provides evidence to support chemotherapy-driven T-cell clonal expansion in CLM in a clinical context. Increased understanding of how the immune system is involved in cancer development and progression has resulted in development of therapeutic interventions successfully targeting the immune system, such as the immune checkpoint inhibitors (ICIs) targeting the PD-1/PD-L1 axis. Mismatch repair\u2013deficient cancers with high tumor mutational burden have been shown to respond strongly to ICIs regardless of histologic type . AbnormaColorectal cancer (CRC) accounts for about 10% of all diagnosed cancers and cancer-related deaths worldwide, and colorectal liver metastasis (CLM) is a leading cause of CRC-related mortality . SurgeryIn this work, we have sequenced TCR repertoires in resected CLMs from 85 patients included in the Oslo Randomized Laparoscopic Versus Open Liver Resection for Colorectal Metastases Study (OSLO-COMET) study, and repertoires were compared according to NACT exposure to analyze T-cell fractions and clonality. In addition, network analysis was used to assess sequential convergence reported to be associated with antigen-experienced repertoires.CLM samples were collected from 85 patients included in the OSLO-COMET study NCT01516710) . Briefly, two 16-\u00b5L polymerase chain reaction (PCR) replicates were prepared for each biological sample, with genomic DNA (gDNA) content within the range recommended by the manufacturer for nonlymphoid tissues, which is predicted to yield the optimal 30,000 to 45,000 T cells per PCR replicate. The library preparation protocol uses multiplex PCR with primers for all possible V and J fragments in the TCR-\u03b2 gene with adjustment of primer concentrations to account for variable primer efficiencies. Synthetic repertoires with known quantities are present in each PCR reaction to allow accurate quantification of T cells in each sample . Pooled q parameters was set between q = 0 and q = 10, with steps of 0.2. Evenness profiles are defined by the equation \u03b1E =\u03b1D/SR (or the Hill diversity profile divided by Hill diversity at q = 0), as previously described in ) compared to the no-NACT group (6.3% [4.8\u20137.9%]), .02 Fig.\u00a0. The T-cq = 0) . Both the short-interval  and long-interval groups  exhibited nonsignificant trends toward a higher number of unique clones compared to the no-NACT group . At increasing values of q, the mean in the short- and long-interval groups intersected and fell below the mean of the no-NACT group but with overlapping CIs. When comparing the Hill evenness profiles  . Hill diversity profiles Fig.\u00a0 showed cles Fig.\u00a0, which aly) Fig.\u00a0. Furtherly) Fig.\u00a0. There wly) Fig.\u00a0, but a nNetworks were generated based on the LD of TCR sequences for each repertoire. The purpose of this analysis was to see if there was a tendency of TCR sequence convergence, which might indicate shared specificity to common antigen epitopes. The mean number of nodes detected per repertoire was 16,056 , while the mean number of edges was 4,133 , generating a mean network connectivity fraction of 19.0% (16.5%\u201321.5%). The connectivity fraction increased linearly as a function of the total number of clones, from less than 5% in small networks to greater than 50% in large networks Fig.\u00a0.This observation suggests that the network connectivity fraction was not associated with the clonality of the T-cell repertoire frequency distribution. Furthermore, prior studies on antibody repertoires reported that the clonal degree distribution of LD-based networks resembles a power law distribution, whereas naive networks do not , 22. WhiInstead, connectivity was associated with public clonal sharing level, where the number of degrees increased linearly with increasing sharing level Fig.\u00a0. The majThree representative networks are shown in Fig.\u00a0In this work, using TCR sequencing, we found that the T-cell fraction was significantly higher in tumors with a short interval between NACT exposure and liver resection compared to tumors not exposed to NACT. This finding is in line with previously published work from our group, showing higher T-cell density in the short-interval group compared to the no-NACT and long-interval groups . Consideq = 0 corresponds to the total number of clones in the repertoire . Because clonal richness is influenced by sampling depth and the presence of rare clones, the frequencies should also be assessed across a range of diversity parameters [q values greater than 2 , the inn = 67) [n = 236) [This work currently represents, to our knowledge, the largest study describing TCR sequencing in metastatic CRC. While studies have been performed in other cancer entities, including breast cancer , 24, blan = 67)  with nonn = 236) , where SPairwise comparison of TCR datasets from analysis of 2 tissue aliquots from the same CLM had a high mean MHI (0.9), indicating almost complete overlap of clonal frequencies. In contrast, the MHI for datasets from different CLMs from the same patient was lower (0.3) but still exhibiting a higher degree of overlap than between repertoires from different CLM patients, where the overlap was almost nonexistent (0.0007). Although the numbers are low, this finding indicates high technical reproducibility of the TCR sequencing strategy. It also exemplifies that the immune microenvironment may vary between metastatic lesions from the same cancer within the same organ. In the SCLC and NSCLC comparison study, SCLC exhibited greater intratumoural variability (MHI <0.2) compared to NSCLC (MHI >0.8) . This isB cells, in contrast to T cells, undergo somatic hypermutation. Previous studies of network repertoires generated from analysis of plasma cells show that B cells may exhibit highly centralized networks, with 1 clone highly connected to a large number of peripheral (but very similar) clones. The degree distribution of such networks follows a power law function, suggesting reactivity toward a single antigen , 22. VerAlthough statistically significant, the differences between the NACT-exposed and nonexposed tumors with respect to T-cell infiltration and clonality were moderate, imposing limitations to the interpretation of the data. The methodological approach was also not able to distinguish T-cell subsets such as CD4 or CD8. The observed differences in T-cell infiltration seem to be driven by a subgroup of tumors that had a strong T-cell response to NACT in the short-interval group. Given that all the included patients had microsatellite-stable disease , such reAnalysis of TCR repertoires in CLM confirmed our previous finding that NACT exposure was associated with a transient increase in T-cell infiltration, while a more persistent increase in clonality was observed independently of the timing of NACT administration and liver resection. The findings are consistent with a chemotherapy-driven clonal expansion and T-cell response, possibly to tumor neoantigens. The included samples represent an excellent starting point for further studies to identify potential public and private antigenic drivers. The results underline the importance of attention to the timing of drug administration in combination trials, and the standardized, high-throughput workflow supports the inclusion of TCR sequencing analysis in immunotherapy trials.Project name: airr_toolshttps://github.com/eirikhoye/airr_toolsProject homepage: Data and code DOI: 10.5281/zenodo.7614598biotoolsID: biotools:airr_toolsRRID: SCR_023297Operating system(s): Platform independentProgramming language: R 4.0.5 or higher and Python 3.6 or higherOther requirements: r-tidyverse 1.2.1, r-alakazam 1.0.2, r-bolstad2, r-ggpubr 0.4.0, imnet, pyspark, findsparkLicense: Open Sourcegiad032_GIGA-D-22-00270_Original_SubmissionClick here for additional data file.giad032_GIGA-D-22-00270_Revision_1Click here for additional data file.giad032_GIGA-D-22-00270_Revision_2Click here for additional data file.giad032_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giad032_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giad032_Reviewer_1_Report_Original_SubmissionJames M Heather -- 11/28/2022 ReviewedClick here for additional data file.giad032_Reviewer_2_Report_Original_SubmissionSatoshi Ueha -- 12/2/2022 ReviewedClick here for additional data file.giad032_Reviewer_2_Report_Revision_1Satoshi Ueha -- 2/16/2023 ReviewedClick here for additional data file.giad032_Supplemental_FileClick here for additional data file."}
+{"text": "Bariatric surgery is associated with complications that can be refractory to intervention in malnourished patients. We present a patient with a gastrocutaneous fistula, which occurred as a complication of Roux-en-Y gastric bypass and was initially refractory to endoscopic closure but resolved after nutritional optimization.2. She was started on total parenteral nutrition and underwent percutaneous endoscopic gastrostomy (PEG) placement into the remnant stomach, which was complicated by high-volume leak. Esophagogastroduodenoscopy (EGD) revealed a gastrogastric fistula between the remnant and excluded stomachs, a large gastrocutaneous fistula, and a narrowed pylorus  of 13\u200akg/mpylorus . A fullpylorus . Howevepylorus . The ga2and the gastrocutaneous fistula resolved.Double-channel suturing with the OverStitch device  was utilized to achieve closure of the fistula, and a nasojejunal tube was placed through the gastrojejunal stent . The paPatients with complications of bariatric surgery are often poor surgical candidates owing to malnutrition, which also hinders endoscopic repair. This case demonstrates how advanced endoscopic techniques  enabledVideo\u20061\u2002Multi-step endoscopic management of a large gastrocutaneous fistula.Endoscopy_UCTN_Code_CPL_1AH_2AG"}
+{"text": "A 60-year-old man, with a history of surgery followed by chemoradiotherapy for cardiac cancer 5 years ago, was referred for backache and hematemesis. The abdominal computed tomography angiography (CTA) revealed a pseudoaneurysm of the descending thoracic aorta . EmergeEndoscopy initially revealed profuse bleeding and massive blood clots, making it hard to identify bleeding points . The moVideo\u20061\u2002Endoscopic diagnosis of esophageal fistula hidden below massive bleed assisted by modified external cannula device.The modified external cannula is an innovative device for efficient removal of massive blood clots, especially during bedside endoscopic hemostasis, and has already shown great clinical valueEndoscopy_UCTN_Code_CCL_1AB_2AZ_3AD"}
+{"text": "A 69-year-old man presented with a submucosal tumor in the anterior wall of the lower body of the stomach . He repVideo\u20061\u2002Endoscopic resection of gastritis cystica profunda mimicking a submucosal tumor.The patient fasted for 24 hours and received antibiotic prophylaxis and proton pump inhibitors. He began to drink after 1 day and reported no obvious discomfort. He recovered uneventfully and was discharged after 3 days.Gastric submucosal tumors are commonly detected during endoscopic examinations. Gastrointestinal stromal tumor and neuroendocrine tumor are common typesEndoscopy_UCTN_Code_CCL_1AB_2AD_3AD"}
+{"text": "An 84-year-old man with medical history of T1 bladder cancer underwent radical cystectomy, with subsequent complication of surgical site abscess. Abdominal computed tomography revealed free gas at the surgical site and a rectal wall defect suggestive of fistula. Despite conservative treatment, the patient had persistent penial and anal discharge and was referred for endoscopic closure.Colonoscopy showed a 6-mm fistulous tract between the cystectomy surgical site and the rectal wall . InitiaAfter fluoroscopic characterization of the defect , a 16/4/12\u200amm CSDO was chosen . After Video\u20061\u2002Use of a cardiac septal defect occluder for endoscopic closure of a fistula between the rectum and post-cystectomy surgical site.The patient remains well, without clinical recurrence.CSDO is an off-label device for closure of gastrointestinal fistulas and should be considered for chronic fistulas refractory to conventional endoscopic treatmentsEndoscopy_UCTN_Code_TTT_1AQ_2AG"}
+{"text": "A press-through package (PTP) sheet is sometimes accidentally swallowedTo evaluate the gripping force of various devices, we attached a PTP sheet to the tip of a spring scale . The grA 70-year-old woman presented to our hospital complaining of a sore throat after accidentally ingesting PTP sheets. A computed tomography (CT) scan showed the PTP location in the esophagus . The seVideo\u20061\u2002A Loop Cutter is an ideal gripper for endoscopic removal of press-through packages.Endoscopy_UCTN_Code_TTT_1AO_2AL"}
+{"text": "A 75-year-old man was admitted to our hospital for isolated serum \u03b3-glutamyl transpeptidase  elevation, with fluctuation in the past 2 years . His alVideo\u20061\u2002Endoscopic ultrasound showed a hypoechoic nodule in the common bile duct. Contrast-enhanced endoscopic ultrasound showed the enhancement of the lesion, but the degree was weaker than the peripheral pancreas.The patient was delivered for surgery, and intraoperative choledochoscopy indicated a villous tumor located in the terminal portion of the CBD with a soft texture. Finally, histological diagnosis indicated adenocarcinoma  . The cuEndoscopy_UCTN_Code_CCL_1AZ_2AC"}
+{"text": "Mucosal defect closure after gastric endoscopic submucosal dissection (ESD) is challenging because of the thick mucosal and muscle layersA 60-mm mucosal defect was left after gastric ESD in our patient . A multVideo\u20061\u2002Defect closure after endoscopic submucosal dissection in the gastric antrum with the reopenable clip-over-the-line method using a multibending scope.In conclusion, a multibending scope is effective for closing antral mucosal defects because it prevents stretching of the gastric muscle layer.Endoscopy_UCTN_Code_TTT_1AO_2AG"}
+{"text": "Anisodus tanguticus is a valuable plant for extracting tropane alkaloids. However, the mechanisms by which plant microbiome mediate the accumulation of tropane alkaloids in Anisodus tanguticus are still not well understood. In this study, we collected 55 wild Anisodus tanguticus populations on the Tibetan Plateau and the tropane alkaloids content, and root-related bacteria and fungi diversity were analyzed using HPLC and 16 s rDNA and ITS sequencing. The results showed that tropane alkaloids content has obvious geographical distribution characteristics. Anisodine content had a significant positive correlation with latitude, while anisodamine and atropine content had a significant negative correlation with latitude. Variation partition analysis (VPA) showed that root endophytes play a significant role in promoting tropane alkaloid production in Anisodus tanguticus roots. The root endophytes alone explained 14% of the variation, which was the largest contributor. Soil properties variables could independently explain 5% of the variation, and climate variables could explain 1% of the variation. Of these, endophytic fungi alone accounted for 11%, while bacteria explained only 5%. Random forests and Mantel test showed that different regionally enriched endophytic fungi have a greater impact on the accumulation of tropane alkaloids than the whole endophytic fungi. Richness and relative abundance of enriched endophytic fungi in Hengduan-Qilian Mountains (HQ) group has a significant positive correlation with anisodine content, while richness and relative abundance of enriched endophytic fungi in Himalayas-Hengduan Mountains (HH) group has a significant positive correlation with anisodamine and atropine content. And, these enriched endophytic fungi have high network connectivity and distributed in separate network modules. This study further confirmed that endophytes were closely related to tropane alkaloids accumulation in Anisodus tanguticus and contribute to promote sustainable development, cultivation, and precision medicine of Anisodus tanguticus. Tropane alkaloids are a group of alkaloids that have a pyrrole ring and a piperidine ring that form the basic skeleton of tropane . TropaneAnisodus tanguticus, a solanaceae herbaceous plant, is a tropane alkaloids-producing plant that was used as an anesthetic in in Tibetan medicine . Furthermore, a growing body of modern pharmacological research has shown that A. tanguticus also has anti-inflammatory, anti-oxidation, and anti-cancer activity  clarify the geographical distribution characteristics of tropane alkaloids of wild A. tanguticus populations in Qinghai\u2013Tibetan Plateau; (ii) clarify the diversity and composition of root-related microbiome of A. tanguticus; (iii) explore the correlation between different tropane alkaloids and root-related microbiome of A. tanguticus.Chen et\u00a0al.\u2019s research on ic areas . HoweverA. tanguticus populations on the Tibetan Plateau between August and September 2020  and nitrate nitrogen (NH4+-N) were extracted with 2 M potassium chloride and then measured with the determined by an AQ1 discrete analyzer  and total carbon (TC) were analyzed using a Vario EL Elemental Analyzer . The total phosphorus (TP) was wet digestion with Hc method . The soic method . Soil amanalyzer . Soil orA. tanguticus root were extracted from 0.2 g powder samples with 2% formic acid. The suspension was centrifuged for 10 minutes and then filtered through a 0.22 \u03bcm filter membrane for high-performance liquid chromatography (HPLC) analysis. Anisodine, anisodamine, and atropine were precisely measured and subsequently diluted with methanol to prepare stock solutions with concentrations of 0.36, 0.54, and 0.62 for anisodine, anisodamine, and atropine, respectively. All the commercial standards were purchased from Sigma\u2013Aldrich . Agilent 1260 instrument equipped with a VWD detector was used to determine the tropane alkaloids content . Samples were separated on a ZORBAX Eclipse Plus column (250 mm\u00d74.6mm). The HPLC mobile phases were H2O with 0.1% trifluoroacetic acid (solvent A) and acetonitrile with 0.1% trifluoroacetic acid (solvent B). The elution gradient was 10% B to 10% B in 25 min; The detection wavelength was 215 nm. The assays were conducted in triplicate for accuracy and precision.In brief, tropane alkaloids in We used a FastDNA SPIN Kit to extract Microbial DNA from the root and soil samples. Two primer sets, 799F (5\u2032-AACMGGATTAGATACCCKG-3\u2032)/1392R (5\u2032-ACGGGCGGTGTGTRC-3\u2032) and 799F (5\u2032-AACMGGATTAGATACCCKG-3\u2032)/1193R (5\u2032-ACGTCATCCCCACCTTCC-3\u2032), were utilized to amplify the V5-V7 region of the bacterial 16S rRNA gene . The ITSStatistical analysis was performed in the R program and visualized using the \u201cggplot2\u201d package . Based ohttps://worldclim.org/) as climate variable. Geographic variables include geographic distance and altitude. Soil variables include total nitrogen, total phosphorus, available phosphorus, soil organic matter, ammonium nitrogen, and nitrate nitrogen. Because of a significant distinction in root endophytic bacterial and fungal communities between different groups, microbial variables selected endophytic bacterial and fungal alpha diversity and beta diversity. We included alpha diversity and beta diversity of enriched microbial OTUs for different groups as separate variables in the analysis. The 22 important environmental factors were used for random forest analysis to estimate the contribution of each variable to secondary metabolites variation with the \u201crfPermute\u201d package  and mean annual precipitation (MAP) data at 30-arc seconds from the WorldClim ( package . VariancP < 0.05 (FDR) were used for co-occurrence network construction with the \u2018Hmisc\u2019 package (https://gephi.org/). In order to describe the complexity of the network, the properties  were calculated. The difference in topological features between enriched microbial OTUs and others was assessed using Wilcox test.We constructed co-occurrence networks to evaluate species coexistence across different regions. In order to rule out the influence of rare OTUs, the OTUs with more than 0.01% relative abundance were selected to calculate Spearman\u2019s rank correlation coefficients. Spearman correlation coefficient r > |0.4| and  package . The netA. tanguticus roots and determined the anisodine, anisodamine and atropine content. The content of anisodine was 1.19 mg/g; the content of anisodamine was 0.61 mg/g and the content of atropine was 2.76 mg/g , while anisodamine and atropine content of HH  were significantly higher than HQ  (P < 0.001) (P < 0.001), while anisodamine (P = 0.002) and atropine (P < 0.001) content had a significant negative correlation with latitude (P < 0.001) , however, bacterial and fungal communities in rhizosphere soil were not significantly separated  was used to assess the contribution of climate, soil properties, and root endophytes to the geographic distribution of tropane alkaloids  and alpha diversity  were significantly correlated with secondary metabolites  positive correlation with anisodine content, while have a significant negative relationship with anisodamine  and atropine  content  negative correlation with anisodine content, while have a significant positive correlation with anisodamine  and atropine  content , OTU19 (Flavobacterium), OTU130 (Variovorax), OTU164 , OTU44 (Steroidobacter), OTU1175 (Rhizobacter), OTU2035 , and OTU3504 (Hyphomicrobium) (Nocardioides), OTU984 (Bradyrhizobium), OTU1060 (Candidatus_Phytoplasma), OTU1079 (Amycolatopsis), and OTU1456 (Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium) (Nocardioides) and OTU984 (Bradyrhizobium) (unclassyfied_k_Fungi), OTU58 (unclassyfied_k_Fungi), OTU84 (unclassyfied_k_Fungi), OTU129 (unclassyfied_k_Fungi), OTU221 (unclassyfied_k_Fungi), OTU247 , OTU295 (unclassified_p_Ascomycota), and OTU1764 (unclassyfied_k_Fungi) (unclassified_p_Ascomycota), OTU1679 (unclassified_f_Ceratobasidiaceae), OTU1724 , OTU1845 (unclassified_p_Ascomycota), OTU1850 (unclassyfied_k_Fungi), and OTU1946 (unclassified_p_Ascomycota) (unclassified_p_Ascomycota), OTU1705 (unclassified_p_Ascomycota), OTU1724 , OTU1809 (unclassified_c_Sordariomyc-etes), OTU1842 (unclassified_p_Ascomycota), OTU1850 (unclassyfied_k_Fungi), OTU1919 (unclassified_p_Ascomycota), OTU1946 (unclassified_p_Ascomycota), and OTU1973 (unclassyfied_k_Fungi) , betweenness centrality , and closeness centrality  were significantly lower in enriched OTUs than others (except the enriched OTUs) (P < 0.001), betweenness centrality , and closeness centrality  were significantly higher in enriched OTUs than others , but it is currently unclear whether their involvement in the metabolic process is direct or indirect. But we found that some of these taxa are closely related to plant nitrogen fixation, such as genus Rhizobacter, Rhizobiales_Incertae_Sedis, Bradyrhizobium, Steroidobacter, Hyphomicrobium, and Nocardioides  and SM3B (Marmoricola sp.), which were isolated from capsule of alkaloid rich Sampada, significantly increased morphine content in Papaver somniferum compared with inoculation alone  can be found in the article/BW: Conceptualization, Investigation, Software, Validation, Visualization, Writing \u2013 original draft, Writing \u2013 review & editing. CC: Investigation, Methodology, Validation, Writing \u2013 review & editing. YX: Writing \u2013 review & editing. YH: Investigation, Writing \u2013 review & editing. YG: Investigation, Writing \u2013 review & editing. ZK: Investigation, Writing \u2013 review & editing. XW: Investigation, Writing \u2013 review & editing. YD: Investigation, Writing \u2013 review & editing. SF: Investigation, Writing \u2013 review & editing. GZ: Conceptualization, Funding acquisition, Investigation, Validation, Writing \u2013 review & editing."}
+{"text": "The main reconstruction procedure presently used after total gastrectomy is the Roux-en-Y method, but jejunal interposition was previously performedA 70-year-old woman with a history of jejunal interposition after total gastrectomy was hospitalized for a common bile duct stone. Although ERCP was attempted using a short-type double-balloon endoscope , biliary cannulation failed despite reaching the duodenum. Laparoscopic exploration of the common bile duct and cholecystectomy were subsequently performed. After 2 years, the patient presented with a recurrent common bile duct stone on contrast-enhanced computed tomography . A sideVideo\u20061\u2002Endoscopic removal by over-the-wire technique for common bile duct stone after total gastrectomy with jejunal interposition.This case demonstrates an over-the-wire technique that is effective in ERCP after total gastrectomy with jejunal interposition. By leaving the wire in place, the duodenoscope can be safely advanced under endoscopic and fluoroscopic guidance.Endoscopy_UCTN_Code_TTT_1AR_2AB"}
+{"text": "A 46-year-old woman with a history of hematochezia visited our institution for colonoscopy. A globular submucosal tumor with a diameter of approximately 20\u200amm and mucosal hyperemia was detected in the rectum . FurtheVideo\u20061\u2002Successful resection of a cavernous hemangioma involving the rectal muscularis propria layer by endoscopic full-thickness resection.Although endoscopic mucosal resection and ESD have been reported for treatment of colorectal cavernous hemangiomaEndoscopy_UCTN_Code_TTT_1AQ_2AD"}
+{"text": "During coronavirus infection, in addition to the well-known coronavirus genomes and subgenomic mRNAs, an abundance of defective viral genomes (DVGs) can also be synthesized. In this study, we aimed to examine whether DVGs can encode proteins in infected cells. Nanopore direct RNA sequencing and liquid chromatography-tandem mass spectrometry (LC\u2013MS/MS) analysis were employed. With the protein databases generated by nanopore direct RNA sequencing and the cell lysates derived from the RNA\u2013protein pull-down assay, six DVG-encoded proteins were identified by LC\u2013MS/MS based on the featured fusion peptides caused by recombination during DVG synthesis. The results suggest that the coronavirus DVGs have the capability to encode proteins. Consequently, future studies determining the biological function of DVG-encoded proteins may contribute to the understanding of their roles in coronavirus pathogenesis and the development of antiviral strategies. Coronavirus (CoV), which has the largest known viral RNA genome of ~\u200930 kilobases (kb), is a single-stranded, positive-sense RNA virus , 2. CoVsAccumulated data have shown that DVGs are associated with pathogenesis of RNA viruses , 15. ForThe characteristics of coronavirus DVGs have been previously identified , 26. The2 as previously described.The plaque-purified Mebus strain of bovine coronavirus (BCoV) (GenBank: U00735.2) was obtained from David A. Brian  and grown in human rectal tumor (HRT)-18 cells. HRT-18 cells were obtained from David A. Brian . The aforementioned cells were grown in Dulbecco\u2019s modified Eagle\u2019s medium (DMEM) supplemented with 10% fetal bovine serum  at 37\u00a0\u00b0C with 5% COhttps://osf.io/cm7z6/; file path:Data_analysis/(4)Mass_spectrometer_analysis/BCoV_cell_ORF_DVG_nanopore.xls).The detailed methods for nanaopore direct RNA sequencing for BCoV were described previously , 26. To For identification of DVG-encoded proteins, HRT-18 cells were infected with BCoV at an MOI of 0.1, and cell lysates were collected at 24\u00a0h postinfection. The collected cell lysates were sent directly for LC\u2013MS/MS analysis or for RNA\u2013protein pull-down assay followed by LC\u2013MS/MS analysis. For the RNA\u2013protein pull-down assay , a DNA tFor the treatment of cell lysates for LC\u2013MS analysis \u201330, the For nano-LC/MS/MS analysis, each sample was dissolved in 0.1% FA solution and then separated through UPLC . Samples were trapped and concentrated in a nanoViper C18 trap column  with a flow rate of 10 \u03bcL/min that was connected to a nanoViper C18 analytical column  with a flow rate of 0.3 \u03bcL/min for separation at an oven temperature of 35\u00a0\u00b0C. A binary gradient system was used, consisting of mobile phases A and B, which were 0.1% FA and 0.1% FA in ACN, respectively. The gradient was programmed as follows: 0\u20134.5\u00a0min, 5% B; 4.5\u201331\u00a0min, 5\u201335% B; 31\u201332\u00a0min, 35\u201390% A; 32\u201352\u00a0min, 90% B; 52\u201353\u00a0min, 90\u20135% B; 53\u201370\u00a0min, 5% B. The injection volume was 5 \u03bcL. The TripleToF 6600  was operated in the positive ion mode with an ion spray floating voltage of 2800\u00a0V. The interface heater temperature was set at 150\u00a0\u00b0C. Both the nebulizer gas and curtain gas were nitrogen, which was used at 25 and 20 psi, respectively. The declustering potential was set at 80\u00a0V. The accumulation time was 250 msec. Data-dependent acquisition was scanned in the range of m/z 350 to 1,250 for the collection of MS/MS spectra for the 30 most abundant precursor ions, with two to four charge states (counts\u2009>\u2009100 cps). The exclusion of former target ions was set for 12\u00a0s after 1 occurrence, and the mass tolerance was set to 50 mDa. The MS/MS spectra were accumulated for 80 msec over the range m/z 65 to 1,800 with rolling collision energy. To ensure mass accuracy and sensitivity, 25 fmol/\u03bcL \u03b2-galactosidase (SCIEX) was used for quality control.Homo sapiens database (https://www.uniprot.org/uniprotkb?query=homo+sapiens) downloaded from UniProt (https://www.uniprot.org/) and the in-house database derived from the nanopore direct RNA sequencing of BCoV using the Mascot Server . The search parameters were as follows: type of search: MS/MS ion search; fixed modifications: carbamidomethyl (C); variable modifications: deamidated (NQ), oxidation (HW), oxidation (M); mass values: monoisotopic; protein mass: unrestricted; peptide mass tolerance: \u00b1 0.03 Da; fragment mass tolerance: \u00b1 0.05 Da; max missed cleavages: 1; and instrument type: ESI-QUAD-TOF. The databases of identified proteins by LC-MS/MS analysis are as follows. The databases for DVG-encoded proteins  using protein reference databases derived from nanopore direct RNA sequencing are deposited at https://osf.io/cm7z6/; file path: Data_analysis/(4) Mass_spectrometer_analysis/BCoV-infected_HRT_total_cell_lysate/BCoV-infected_HRT_total_cell_lysate. The databases for DVG-encoded proteins (RNA-protein pull-down lysates) using protein reference databases derived from nanopore direct RNA sequencing are deposited at https://osf.io/cm7z6/; file path: Data_analysis/(4) Mass_spectrometer_analysis/BCoV-infected_HRT_with_RNA_pull-down_cell_lysate/(A) BCoV_DVG_database_result/BCoV with RPDCL by DVG. The databases for encoded proteins from human cells (RNA-protein pull-down lysates) using human protein sequences as reference databases are deposited at https://osf.io/cm7z6/; file path: Data_analysis/(4) Mass_spectrometer_analysis/BCoV-infected_HRT_with_RNA_pull-down_cell_lysate/(B) human_database_result /BCoV with RPDCL by human.All the spectra generated by MS were searched thoroughly against the https://osf.io/cm7z6/), bovine coronavirus (BCoV) DVGs contain open reading frames (ORFs) of various lengths from one or more portions of ORFs in the full-length genome due to recombination during virus replication. These DVGs with diverse genome structures may lead to the synthesis of in-frame, out-of-frame, or fusion proteins when compared with the original ORFs in the full-length genome. Accordingly, to identify the proteins encoded by DVGs bearing diverse ORFs, liquid chromatography-tandem mass spectrometry (LC\u2013MS/MS) analysis was employed. As described above, the diverse genome structures of DVGs may encode in-frame proteins that have the same amino sequences as canonical genome- and sgmRNA-encoded proteins. Consequently, if the amino acid sequences of the peptides determined by LC\u2013MS/MS analysis contain exactly the same in-frame amino acid sequences as those of canonical genome- and sgmRNA-encoded proteins, these peptides cannot be used as markers to determine whether the identified proteins are encoded from coronavirus DVGs. In contrast, if the peptides contain discontinuous in-frame amino acid sequences derived from different portions of amino acid sequences from canonical genome- or sgmRNA-encoded proteins or contain out-of-frame amino acid sequences, they are considered fusion peptides encoded by DVGs caused by recombination of the viral genome. Therefore, these fusion peptides can be used as markers to identify the proteins encoded by coronavirus DVGs.According to our database established by nanopore direct RNA sequencing  Mass_spectrometer_analysis/BCoV_cell_ORF_DVG_nanopore.xlsx) according to the three criteria described in Materials and methods. The databases were then used as references for liquid chromatography-tandem mass spectrometry (LC\u2013MS/MS) analysis to validate the synthesis of BCoV DVG-encoded proteins. A total of 34,104 protein species were identified by LC\u2013MS/MS analysis  Mass_spectrometer_analysis/BCoV-infected_HRT_total_cell_lysate/BCoV-infected_HRT_total_cell_lysate ). However, none of the featured fusion peptides that can be used to represent actual DVG-encoded proteins were detected. These results were not surprising because there were so many species of DVGs in the cells, and thus, the amount of each DVG-encoded protein  in a fixed amount of cell lysate may not be sufficient to be detected by LC\u2013MS/MS analysis. Consequently, it was speculated that with enrichment processes such as RNA\u2013protein pull-down assays, by which the proteins binding to RNA can be isolated, the amount of each protein species can be increased and thus detected by LC\u2013MS/MS, although fewer DVG-encoded protein species may be detected. As a result, 34,056 protein species were identified by LC\u2013MS/MS analysis  Mass_spectrometer_analysis/BCoV-infected_HRT_with_RNA_pull-down_cell_lysate/(A) BCoV_DVG_database_result/BCoV with RPDCL by DVG), but only 7 DVG-encoded proteins with featured fusion peptides were identified. Among the 7 identified peptides, LFLYGGR was identified with three consecutive y ions in both spectra. However, because (i) based on the LC\u2013MS/MS analysis, proteins with a score higher than 41 (p\u2009<\u20090.05) can be considered a confident identification and (ii) in addition to peptide LFLYGGR, there were no other peptides detected, leading to the score [https://osf.io/cm7z6/; file path: Data_analysis/(4) Mass_spectrometer_analysis/BCoV-infected_HRT_with_RNA_pull-down_cell_lysate/(A) BCoV_DVG_database_result/BCoV with RPDCL by DVG). In addition, the 6 featured fusion peptides were not identified using human protein sequences as reference  Mass_spectrometer_analysis/BCoV-infected_HRT_with_RNA_pull-down_cell_lysate/(B) human_database_result /BCoV with RPDCL by human).A total of 145,015 DVG species were identified in the two biological replicates for nanopore direct RNA sequencing, and 189,221 amino acid sequences were converted to protein reference databases  analysis were employed to examine whether DVGs can encode proteins in infected cells. With the protein databases generated by nanopore direct RNA sequencing, six DVG-encoded proteins were identified by LC\u2013MS/MS based on the featured fusion peptides caused by recombination during DVG synthesis. The limitations and the biological significance of the study are discussed.Below, we explain why 34,104  and 34,056 (by cell lysates derived from RNA\u2013protein pull-down assay) protein species were identified by LC\u2013MS/MS analysis. First, coronavirus DVGs are recombination products and thus contain ORFs of various lengths from one or more portions of ORFs in the full-length genome. As a result, many DVG species  are identified by nanopore direct RNA sequencing, and thus, many potential DVG-encoded protein sequences 189,221) can be used as protein reference databases for LC\u2013MS/MS. Second, the diverse genome structures of DVGs may encode in-frame peptides that have the same amino sequences as those encoded from the full-length genome. Consequently, if the peptides determined by LC\u2013MS/MS analysis match the amino acid sequences of the DVG-encoded proteins and the protein scores are higher than 41, the DVG-encoded protein species can be identified based on the provided protein reference databases. Consequently, many DVG-encoded protein species  were identified by LC\u2013MS/MS analysis. However, this may lead to false-positive results because the peptides that match the amino acid sequence of DVG-encoded proteins may also be encoded from the full-length coronavirus genome, as described above, and thus cannot be used as markers to determine whether the identified proteins are encoded by coronavirus DVGs. That is also the reason why we propose that if the peptides contain discontinuous in-frame amino acid sequences derived from different portions of amino acid sequences from full-length genome-encoded proteins or contain out-of-frame amino sequences, the peptides are fusion peptides encoded from DVGs because DVGs are synthesized by recombination of the viral genome. Therefore, these fusion peptides can be used as markers to identify the proteins actually encoded by coronavirus DVGs. Consequently, 6 DVG-encoded proteins were identified through the identification of 6 fusion peptides, as shown in Figs.\u00a021 can beIn addition, because the read number for the 6 DVGs is low (only 1), whether there is a correlation between the abundance of DVGs identified by nanopore direct RNA sequencing and that of their encoded proteins identified by LC-MS/MS remains unknown. Our explanation for the results is as follows. Because coronavirus DVGs are recombination products and thus contain ORFs of various lengths from one or more portions of ORFs derived from the full-length genome, the diverse genome structures of DVGs may encode in-frame peptides that have the same amino sequences as those encoded from the full-length genome. Consequently, if the peptides determined by LC-MS/MS analysis match the amino acid sequences of DVG-encode proteins and the protein scores are higher than 41, the DVG-encoded protein species are identified based on the provided protein reference databases. However, the peptides which match the amino acid sequence of DVG-encoded proteins may also be encoded from full-length coronavirus genome, and thus we cannot determine whether the identified peptides and thus the proteins are encoded from coronavirus DVGs or full-length genome. Consequently, DVG species with higher read numbers may encode more proteins, but without the featured fusion peptides as markers, whether there is a correlation between the abundance of DVGs identified by nanopore direct RNA sequencing and that of their encoded proteins identified by LC-MS/MS still cannot be determined. That is also the reason why we propose that, as described above, if the peptides contain discontinuous in-frame amino acid sequences derived from different portions of amino acid sequences from full-length genome-encoded proteins, or contain out-of-frame amino sequences, they are fusion peptides encoded from DVGs. Thus, at the current stage, we can only conclude that DVG can encode protein, and whether there is a correlation between the abundance of DVGs and that of their encoded proteins remains unknown. However, since the identified 6 DVGs with read number of 1 have the capability to encode proteins as determined by the current study, we can speculate that other DVG species with higher read numbers may also have the capability to encode protein although they cannot encode featured fusion peptide as markers to determine the proteins-coding capability.It has been known that (i) coronavirus DVGs can be packaged , (ii) coThe possible reasons why the featured fusion peptide was not detected in the total cell lysates by LC\u2013MS/MS are as follows. First, because there are too many species of DVGs in cells, the amount of each DVG-encoded protein  in a fixed amount of cell lysate may not be sufficient to be detected by LC\u2013MS/MS. Second, not every DVG-encoded protein contains the featured fusion peptides (based on the protein reference databases generated by nanopore direct RNA sequencing for BCoV), further limiting the identified number of protein species. Third, because SuperScript\u2122 III reverse transcriptase , which is optimized to synthesize first-strand cDNA up to ~\u200912\u00a0kb, was used for nanopore direct RNA sequencing, the identified coronaviral RNA species, including DVGs, may not cover all coronavirus transcripts, especially those of longer size. Thus, the protein reference databases may not contain the full information of the DVG-encoded proteins, limiting the number of protein species identified by LC\u2013MS/MS analysis.As shown in Figs."}
+{"text": "In recent years, endoscopic retrograde appendicitis therapy (ERAT) has been widely used in the treatment of acute uncomplex appendicitisA 34-year-old woman presented with abdominal pain, nausea, vomiting, and no anal exhaust defecation for 2 days. Computed tomography (CT) showed acute appendicitis with a giant periappendiceal abscess and intestinal obstruction . After Video\u20061\u2002Endoscopic retrograde appendicitis therapy for continuous drainage of giant periappendiceal abscess and the process of removing the stent under colonoscopy.Follow-up CT after 2 months showed that the periappendiceal abscess had fully disappeared . The stEndoscopy_UCTN_Code_CCL_1AD_2AG"}
+{"text": "Recently, a new technique for performing underwater endoscopic submucosal dissection (ESD) in a saline solution has been reported2) in the bottle cap is closed using a neoprene rubber plug   . SubseqThe patient in the case presented here had a 30-mm sessile serrated lesion overlying a diverticulum in the sigmoid colon . We perVideo\u20061\u2002A gas-free saline-immersion technique, using a plug that allows saline infusion without carbon dioxide (CO2) gas insufflation, was used to perform endoscopic submucosal dissection (ESD) of a sessile serrated lesion (SSL) in the sigmoid colon.2gas emission during saline immersion.The gas-free saline-immersion technique for ESD is a feasible method with no COEndoscopy_UCTN_Code_TTT_1AQ_2AD"}
+{"text": "Abnormal 5-methylcytosine (m5C) methylation has been proved to be closely related to gastric carcinogenesis, progression, and prognosis. Dysregulated long noncoding RNAs (lncRNAs) participate in a variety of biological processes in cancer. However, to date, m5C-methylated lncRNAs are rarely researched in gastric cancer (GC). Here, we found that RNA cytosine-C(5)-methyltransferase (NSUN2) was upregulated in GC and high NSUN2 expression was associated with poor prognosis. NR_033928 was identified as an NSUN2-methylated and upregulated lncRNA in GC. Functionally, NR_033928 upregulated the expression of glutaminase (GLS) by interacting with IGF2BP3/HUR complex to promote GLS mRNA stability. Increased glutamine metabolite, \u03b1-KG, upregulated NR_033928 expression by enhancing its promoter 5-hydroxymethylcytosine (hm5C) demethylation. In conclusion, our results revealed that NSUN2-methylated NR_033928 promoted GC progression and might be a potential prognostic and therapeutic target for GC. Gastric cancer (GC) is one of the most common malignant tumors, with the fourth lethality and fifth incidence in the world . Almost Helicobacter pylori, intaking excessive food rich in nitrite compounds, having precancerous lesions, and so on . We. We32]. Collectively, \u03b1-KG promoted NR_033928 expression in TETs-dependent DNA demethylation manners.http://kmplot.com/analysis/) showed that patients with high expression of GLS had lower overall survival than those with low GLS expression , which was highly expressed in PDAC and promoted PDAC proliferation, migration, and invasion, and suppressed apoptosis . FunctioNR_033928 was identified as a lncRNA with high m5C modification. In our results, we further explored the mechanism by which m5C modification impacted NR_033928. Sanger sequencing revealed that m5C methyltransferase NSUN2 promoted NR_033928 m5C methylation and specifically methylated C154. Actinomycin D assays indicated that NSUN2 promoted the stability of NR_033928 and upregulated its expression. Except for RNA modifications, there are other factors that may impact the expression of lncRNA, such as DNA methylation or histone modification, which deserves further exploration.To find the downstream of NR_033928, next-generation sequencing was conducted in cells stably transfected with NR_0333928 siRNA and the control group. Through Kyoto Encyclopedia of Genes and Genomes analysis, we found NR_033928 was positively related to glutamine metabolism. Dysregulation of glutamine metabolism widely exists in cancer to support rapid proliferation . And qRTInteracting with RNA binding proteins is the important way by which lncRNAs exert their functions . FISH asMetabolic reprogramming has been proved to participate in various biological processes and regulatory networks in cancers. Interestingly, we found that the downstream metabolite of glutamine, \u03b1-KG, could increase NR_033928 expression in the study. qRT-PCR and hMeDIP assays proved that accumulation of \u03b1-KG upregulated NR_033928 expression by enhancing TETs-dependent NR_033928 promoter demethylation. Hereto, we found that the expression of NR_033928 was regulated synergistically by RNA m5C methylation mediated by NSUN2 and DNA hm5C demethylation mediated by TETs. This shed new light on how lncRNAs are regulated in cancers.We also investigate the clinical value of NR_033928 in this study. Kaplan\u2013Meier analysis indicated that high expression of NR_033928 and GLS was positively correlated with poor prognosis of GC patients. IHC analysis of xenograft in mice showed that silencing NR_033928 significantly decreased the GLS and Ki-67 expression and increased the expression of c-caspase3. This indicated that NR_033928 could serve as a prognostic biomarker and therapeutic target in GC.Collectively, we found that NSUN2 was highly expressed in GC cells and tissues and associated with poor prognosis in GC. Further analysis indicated that NSUN2 maintained NR_033928 stability in an m5C-dependent manner and upregulated its expression. NR_033928 promoted GC proliferation and suppressed apoptosis by increasing GLS expression. NR_033928 acted as a scaffold of the IGF2BP3/HUR complex and GLS. \u03b1-KG could increase NR_033928 expression by enhancing its promoter demethylation, thereby forming a positive feedback loop. Moreover, high NR_033928 expression was associated with patients\u2019 prognosis. Our results highlighted that NR_033928 may serve as a therapeutic target for RNA interference strategies and biomarker of prognosis in GC.A total of 51 pairs of GC and para-cancerous specimens were collected from the first affiliated hospital of Nanjing Medical University. This study was approved by the ethics committee of the hospital. These samples were gathered from GC patients in 2019\u20132022. All samples were collected immediately after radical GC resection and kept in liquid nitrogen. Informed consent was provided by all participants.2.GES-1, HGC-27, MKN28, MKN45, AGS, SNU1 and HEK-293 T cell lines were purchased from ATCC, the Cell Center of Shanghai Institutes for Biological Sciences. GES-1, HGC-27, MKN28, MKN45, and SNU1 cells were cultured in 1640 medium. AGS cells were cultured in F12K medium. HEK-293 T cells were cultured in DMEM medium. All medium were supplemented with 10% fetal bovine serum and 1% penicillin/streptomycin. All cells were incubated in an incubator with constant 37\u2009\u00b0C and 5% COhttps://xenabrowser.net/datapages/), including 375 gastric cancers and 32 normal tissues. The \u201climma\u201d R package was used to analyze the differentially expressed genes, and \u201cggplot2\u201d and \u201cRCircos\u201d were employed to visualize. We perform KEGG enrichment analysis by DAVID Bioinformatics Resources (https://david.ncifcrf.gov/). Overall survival was calculated using Kaplan\u2013Meier model . Several online databases were used to predict the function of NR_033928, including CPC2 (http://cpc2.gao-lab.org/), CPAT (http://rna-cpat.sourceforge.net/), LncACTdb 3.0 (http://bio-bigdata.hrbmu.edu.cn/LncACTdb/), starBase v2.0 (http://starbase.sysu.edu.cn/), ViennaRNA Web Services (http://rna.tbi.univie.ac.at/), and RNAstructure (http://rna.urmc.rochester.edu/RNAstructure).The public data TCGA-STAD was downloaded from The Cancer Genome Atlas (TCGA) database . The input samples with or without m5C immunoprecipitation samples were used for RNA-seq library generation with the NEBNext\u00ae Ultra II Directional RNA Library Prep Kit . cDNA library sequencing was performed on an Illumina HiSeq4000. For the m5C-RIP-qPCR, the RNA enrichment was obtained from the IP beads and was analyzed by qPCR .Total RNA of GC cells was extracted and purified using TRIzol reagent  under the instructions of the manufacturer\u2019s recommendations. lncRNA expression profiles were investigated using SBC Human lncRNA Microarray. Agilent Feature Extraction software was used to extract the raw data. The Quantile algorithm, Gene Spring (Agilent Technologies) was used for microarray statistics analysis.Total RNA was isolated from wild MKN45 cells and si-NR_033928 MKN45 cells, each with three replicates. mRNA expression profiling on total RNA was performed by GENEWIZ. Next-generation sequencing libraries were constructed using the NEBNextR Ultra\u2122 RNA Library Prep Kit for Illumina\u00ae and sequenced on an Illumina HiSeq instrument according to the manufacturer\u2019s instructions (Illumina).LncRNA and mRNA siRNAs were purchased from RiboBio . The lentiviral targeting lncRNA and mRNA were generated by GenePharma . Overexpressing plasmids were purchased from WZ Biosciences . All cells were transfected with siRNAs and plasmids by using Lipo3000 under the product instructions. The detailed sequences are listed in Supplementary Table The RNA extraction and PCR were performed as described previously . PrimersFluorescence in situ hybridization (FISH) was performed as described previously . Fam-labWestern blotting (WB) was performed as described previously . AntibodThe stability of NR_033928 was assessed by additionally adding actinomycin D(5\u2009\u03bcg/ml) into the cell medium at the indicated time. Then total RNA was used for RT-PCR to calculate the half-life of NR_033928.The colony formation assay was performed as described previously .The 5-Ethynyl-2\u2032-deoxyuridine assay (EDU) was performed with a Cell-Light EDU Cell Proliferation Kit (RiboBio) as described previously .The apoptosis assay was performed through Annexin V and PI Apoptosis Kit  in the Cytoflex flow cytometer , as described previously .A Glutamate Assay Kit and alpha-Ketoglutarate Assay Kit were purchased from Abcam  and Sigma . The detection was performed under the instructions of product manuals.CB-839 (HY-12248), GSK J4 (HY-15648B), and isocitrate (HY-W009362) were purchased from MCE (USA). Fumarate (47910), succinate (S9512), citrate (C0759), diethyl malate (7554-12-3) and diethyl-ester OAA (40876-98-0) purchased from Sigma-Aldrich (USA). All reagents were dissolved to the indicated concentration in dimethyl sulfoxide.The hydroxymethylated DNA immunoprecipitation(hMeDIP) was performed by using EpiQuik\u2122 hydroxymethylated DNA immunoprecipitation kit . Briefly, the DNA was exacted and sonicated to 200\u20131000\u2009bp fragments by ultrasonic shredding. 5-hmC antibody and IgG were added to DNA separately. Next, the purified DNA was obtained by washing, release and elution of DNA. Then the purified products were analyzed by qPCR. The primer sequences used were shown in Supplementary Table The xenograft tumor model was performed as described previously . The tumImmunohistochemistry (IHC) analysis was performed as described previously . IndicatThe RNA Binding Protein Immunoprecipitation Assay (RIP) assay was performed as described previously . ImprintThe Co-immunoprecipitation assay (CO-IP) assay was performed as described previously by using anti-IGF2BP3 and anti-HUR under the instructions of Pierce Co-Immunoprecipitation Kit  protocols .TM RNA-Protein pull-down Kit (Geneseed) was purchased for RNA pull-down assay. The bio-labeled RNA and protein complex was used for western blotting and mass spectrometry analysis. The probe for RNA pull-down was listed in Supplementary Table RNA pull-down and mass spectrometry were performed as described previously . Pure-BiFor the validation of NR_033928 methylated sites, bisulfite converted RNA was reverse transcribed into cDNA using the PrimeScript\u2122 II 1st Strand cDNA Synthesis Kit  according to the manufacturer\u2019s instructions. cDNA was amplified by PCR using specific primers for bisulfite-treated RNAs and the PyroMark PCR Kit . Then PCR products were used for Sanger sequencing.t test were used to test the difference in most of experiments. The chi-square test was used to analyze the association between the expression of NR_033928 and clinicopathological parameters. Overall survival was calculated using Kaplan\u2013Meier model . Differences were considered significant with a value of p\u2009<\u20090.05.All statistical analyses were carried out using the GraphPad Prism 7.0 or SPSS . One-Way ANOVA test and the Student\u2019s Further information on research design is available in the Original Data Filessupplementary materialsReporting Summary"}
+{"text": "Cytidine and uridine are endogenous metabolites in the pyrimidine metabolism pathway, and cytidine is a substrate that can be metabolized into uridine via cytidine deaminase. Uridine has been widely reported to be effective in regulating lipid metabolism. However, whether cytidine could ameliorate lipid metabolism disorder has not yet been investigated. In this research, ob/ob mice were used, and the effect of cytidine (0.4 mg/mL in drinking water for five weeks) on lipid metabolism disorder was evaluated in terms of an oral glucose tolerance test, serum lipid levels, liver histopathological analysis and gut microbiome analysis. Uridine was used as a positive control. Our findings reveal that cytidine could alleviate certain aspects of dyslipidemia and improve hepatic steatosis via modulating the gut microbiota composition in ob/ob mice, especially increasing the abundance of short-chain fatty acids-producing microbiota. These results suggest that cytidine supplementation could be a potential therapeutic approach for dyslipidemia. Dyslipidemia is a lipid disorder characterized by an elevation in the level of serum total cholesterol (TC), triglyceride (TG) or low-density lipoprotein cholesterol (LDL-C) and a low level of high-density lipoprotein cholesterol (HDL-C). Various diseases, such as cardiovascular complications, fatty liver, diabetes, obesity and atherosclerosis, are clearly associated with dyslipidemia . InterveNucleotides, the building blocks of RNA and DNA, play important roles in various biological processes, including gastrointestinal function, energy metabolism and immune regulation . NucleotThe aim of this study was to investigate whether cytidine treatment could alleviate metabolic disorders. Uridine was applied as a positive control, and ob/ob mice were used in this study. Moreover, the effect of cytidine on the gut composition in ob/ob mice was also studied.Cytidine (99% purity) and uridine (99% purity) were purchased from Shanghaiyuanye Bio-Technology Co., Ltd. ; Ultrapure water was purchased from Watsons Group, and all other reagents used in this study were of analytical grade. Male SPF (specific pathogen-free)-grade ob/ob mice (5 weeks old) were obtained from GemPharmatech Biotechnology Co., Ltd. , and male SPF-grade C57BL/6J mice (5 weeks old) were obtained from SPF  Biotechnology Co., Ltd. . All animals were kept in a controlled environment with free access to food and water  and allowed to adapt to their living conditions for one week.After acclimatization, 24 ob/ob mice aged six weeks were randomly divided into three groups as follows: model group, cytidine group, and uridine group (n = 8 per group). C57BL/6J mice were applied as a control group (n = 8). Mice in the model group and control group were fed with normal drinking water. Mice in the cytidine group and uridine group were fed with drinking water containing cytidine or uridine at a dose of 0.4 mg/mL for 5 consecutive weeks. All mice were fed a standard diet. Body weights were recorded every week, and water and food consumption were measured every day. By the end of the experiment, mice were anesthetized with pelltobarbitalum natricum. Serum samples used for the determination of biochemical indicators were collected before the mice were sacrificed via cervical dislocation. The liver and cecum were manually isolated. In order to conduct the histological examination, liver tissues were preserved in 4% paraformaldehyde. The cecal contents were collected, and five samples for each group were randomly selected for microbiome analysis. All samples were stored at \u221280 \u00b0C.After the mice were treated with cytidine or uridine for 5 weeks, oral glucose tolerance was tested. Briefly, all mice first fasted for 12 h, and then each mouse was intragastrically administered glucose solution at a dose of 2 g/kg. Blood glucose concentrations were determined using tail\u2013tip blood sampling at 0, 15, 30, 60, 90, and 120 min after glucose was administered, and the area under the curve (AUC) was calculated.The contents of serum TC, TG, HDL-C and LDL-C were determined using commercial assay kits supplied by Nanjing Jiancheng Bioengineering Institute .Liver tissues were fixed in 4% paraformaldehyde, dehydrated, blocked with paraffin, and then sectioned and stained with hematoxylin and eosin (H&E). Histopathological changes were analyzed and recorded using a light microscope (200\u00d7) . The hepatic fatty vacuole area ratio in liver tissue slices was determined by quantifying the amount of stained area using Image J software .\u00ae9700 , and the following reaction conditions were used: 95 \u00b0C for 3 min, 27 cycles at 95 \u00b0C for 30 s, 55 \u00b0C for 30 s, 72 \u00b0C for 45 s and finally 72 \u00b0C for 10 min.Regarding gut microbiome analysis, bacterial DNA from the cecal contents was extracted, and the DNA concentration was detected using 1% agarose gel electrophoresis. DNA encoding the 16S rRNA V3\u2013V4 region was amplified using a pair of universal primers (338F and 806R). The PCR instrument was ABI GeneAmphttps://github.com/OpenGene/fastp, accessed on 22 February 2023, v0.19.6) and FLASH . Chimeras were removed, and operational taxonomic unit (OTU) clustering was obtained according to 97% similarity using the UPARSE software platform . The RDP classifier  was applied for the acquisition of species classification information for each OTU, and the database SILVA  was utilized to classify OTUs taxonomically. Bioinformatics, including \u03b1-diversity and \u03b2-diversity analyses, were subsequently conducted. The Kruskal\u2013Wallis rank sum test was conducted for statistical difference analysis among groups (the significance level was 0.95). The false discovery rate (FDR) approach was applied for multiple comparisons. LEfSe analysis was performed to detect features differentially represented between different groups. The different species in each group were ranked by effect size after linear discriminant analysis (LDA).The sequencing and data analysis were performed based on the quality control software fastp  followed by one-way analysis of variance (ANOVA) or the Kruskal\u2013Wallis rank sum test. All data are expressed as the mean \u00b1 standard deviation. A p < 0.001). Cytidine and uridine treatment had no effect on the body weight and food intake of ob/ob mice, indicating that cytidine and uridine had no obvious toxic effects. Compared with the model group, uridine treatment significantly increased the water intake (p < 0.05). Even though no significant difference in water intake was observed between the model group and the cytidine group, an increasing trend was observed after cytidine treatment. Regarding the serum lipid levels, compared with C57BL/6J mice, ob/ob mice showed dyslipidemia symptoms with significantly higher levels of serum TC, HDL-C and LDL-C . After five weeks of treatment, cytidine and uridine significantly reduced the levels of TC and LDL-C . Cytidine treatment significantly reduced the serum HDL-C level (p < 0.05). Regarding the level of HDL-C, a decreasing trend was observed in the uridine group when compared with the model group; however, there was no significant difference.As shown in In order to investigate whether cytidine could alleviate glucose intolerance in ob/ob mice, OGTT was conducted. As shown in The liver index was calculated at the end of the treatment C. A signp < 0.001). The principal coordinate analysis (PCoA) was then performed on all samples using the unweighted-unifrac distance algorithm to investigate the similarities and differences in bacterial community structures. As shown in The 16S rRNA sequencing analysis was applied to assess the effects of cytidine and uridine treatment on the composition and abundance of the gut microbiota in the ob/ob mice. Both the Chao and Shannon indexes revealed A,B that Firmicutes and Bacteroidetes were the dominant phyla. After supplementation with dietary cytidine and uridine, the gut microbiota composition returned to normal at the phylum level. At the genus level, when compared with the control group, the abundances of several gut microbiota taxa were significantly decreased in ob/ob mice, including Lachnospiraceae_NK4A136_group (p < 0.05), unclassified_f__Lachnospiraceae (p < 0.05), Dubosiella (p < 0.05), norank_f__Lachnospiraceae (p < 0.01), Eubacterium_xylanophilum_group (p < 0.01), Roseburia (p < 0.01), norank_f__norank_o__Clostridia_UCG-014 (p < 0.01) and Lachnospiraceae_UCG-006 (p < 0.01). Compared with the model group, all of the above-mentioned gut microbiota taxa except for Dubosiella were significantly increased after cytidine or uridine treatment (p < 0.05 or p < 0.01) , and after treatment with cytidine or uridine, the abundance of norank_f__Muribaculaceae was significantly decreased compared with the model group (p < 0.05) ( < 0.01) E. Regard < 0.05) E. g__Lachnospiraceae_NK4A136_group, g__Dubosiella, g__unclassified_f__Lachnospiraceae, g__norank_f__Lachnospiraceae, g__Eubacterium_xylanophilum_group and g__Roseburia . The results show that when compared with the model group, the differential gut microbiota taxa enriched in the cytidine group included oseburia F, and thum_group G.norank_f__ Muribaculateae showed a significant positive correlation with all serum lipid indicators, while other gut microbiota taxa (except for Dubosiella) showed a significant negative correlation with serum lipid indicators.The correlation between serum TC, TG, HDL-C, LDL-C and gut microbiota taxa in the control, model, cytidine and uridine groups were analyzed according to the Spearman correlation algorithm. As revealed in Ob/ob mice possess a mutation in the leptin gene, and as a result, ob/ob mice are hyperphagic, obese, and hyperglycemic and display dyslipidemia . Thus, oFirmicutes/Bacteroidetes was closely correlated with obesity [Bacteroidetes/Firmicutes in ob/ob mice is usually evidently higher, indicating that ob/ob mice suffer from metabolic disorders [Bacteroidetes/Firmicutes in ob/ob mice was 0.73, which was higher than that of C57BL/6J mice. This is consistent with the literature reported [Lachnospiraceae_NK4A136_group, unclassified_f__Lachnospiraceae, Dubosiella, norank_f__Lachnospiraceae, Eubacterium_xylanophilum_group, Roseburia, norank_f__norank_o__Clostridia_UCG-014 and Lachnospiraceae_UCG-006 were significantly decreased, and the abundance of norank_f__Muribaculaceae was significantly increased in ob/ob mice. Regarding Lachnospiraceae, Dubosiella, Eubacterium_xylanophilum and Roseburia, they all produce short-chain fatty acids [norank_f__norank_o__Clostridia_UCG-014, no clear evidence showed that it could produce short-chain fatty acids; however, Clostridium species were reported to be closely related to diabetics [norank_f__Muribaculaceae was revealed to be related to bile acid metabolism [Dubosiella was significantly increased after cytidine treatment, and all these bacteria were negatively correlated with serum lipid indicators. These results suggest that cytidine could alleviate certain aspects of dyslipidemia via modulating the gut microbiota composition in ob/ob mice, especially increasing the abundance of short-chain fatty acids-producing microbiota.Microbiota characteristics of ob/ob mice have been extensively investigated. The ratio of  obesity . Compareisorders . In our reported . The gutreported . Variousreported . In patireported ,20,21. Ity acids ,23,24. Riabetics . norank_tabolism . TherefoNucleotides have a role in umami taste perception, and C57BL/6J mice have higher preferences for umami-tasting solutions . In the Odoribacter, Ruminococcaceae, Intestinimonas, Ruminiclostridium, and Lachnospiraceae [g__norank_f__Muribaculaceae, g__Lachnospiraceae_NK4A136_group, g__unclassified_f__Lachnospiraceae, g__norank_f__Lachnospiraceae, g__Lachnospiraceae_UCG-006 and g__Eubacterium_xylanophilum_group. When comparing our results with previously reported results in both ob/ob mice and high-fat-diet mice, uridine treatment could significantly promote the growth of Lachnospiraceae. However, the changed gut microbiota taxa after uridine treatment obtained from ob/ob mice and high-fat diet mice were different. This may be caused by the fact that the gut microbiota composition and structure of ob/ob mice and high-fat-diet mice were different [It was reported that uridine treatment could ameliorate hepatic lipid accumulation in mice by modulating the gut microbiota composition . Gut micpiraceae . In thisifferent . While this study has demonstrated that cytidine supplementation could be a potential therapeutic approach for dyslipidemia, there were limitations in this study. First, the concentrations of short-chain fatty acids were not determined, and the lipid profiles in liver tissue were not fully investigated. Second, only one dose of treatment was evaluated in this study. Third, OTUs were used in 16S rRNA amplicon data analysis instead of the more modern method of ASVs (amplicon sequence variants), which could be beneficial as a comparative assessment of the data set. Fourth, additional experiments, such as fecal microbiota transplantation, are still needed to fully elucidate the relationship between the improvement in lipid metabolism and the change in gut microbiota. In summary, the present study suggested that cytidine supplementation could be a potential therapeutic approach for dyslipidemia. However, future studies to validate the efficacy and better understand the mechanism are still needed.In summary, our results reveal that cytidine treatment could reduce serum lipid levels and alleviate hepatic steatosis in ob/ob mice. Modulating the composition of gut microbiota, especially promoting the growth of short-chain fatty acids-producing bacteria, might at least partially account for the activity of cytidine. Thus, cytidine supplementation could be a potential therapeutic approach for dyslipidemia."}
+{"text": "Enterococcus faecalis and Enterococcus faecium bacteriophages were analysed for gene shuffling in the lytic cassettes of bacteriophages infecting. It was found that enterococcal bacteriophages could be classified into well-defined groups based on the size of their genomes and each size group had its own conserved gene composition of lytic cassettes. Enterococcal bacteriophages use a relatively broad spectrum of holins and endolysins with variable cell-wall binding (CWB) and catalytic domains, and most of them utilise a lytic cassette with more than two genes. Enterococcal bacteriophages most commonly use endolysins with amidase catalytic domains and the CWB domain SH3_5. Some bacteriophages possess in their lytic cassette a holin-like gene with the XhlA domain protein, characteristic of hemolysin. Regardless of the shuffling of genes encoding holins and endolysins in lytic modules, a novel example of CWB domain shuffling within enterococcal endolysins was identified.The genomes of The online version contains supplementary material available at 10.1007/s13205-023-03775-w. Enterococcus faecalis and Enterococcus faecium, are known to cause secondary infections in hospital environments. With the emergence of multidrug-resistant strains over the past two decades, they have become an important nosocomial pathogen causing urinary tract infections, bacteremia, and endocarditis  species, have become one of the major nosocomial pathogens worldwide  domain and the N\u2014terminal catalytic domain. The CWB domain keeps endolysin tightly bound to the bacterial cell wall, while the catalytic domain cleaves specific bonds in peptidoglycan Donovan . The CWBEnterococcus spp. bacteriophages were obtained from the GenBank database , Holin_BhlA (pfam: PF10960), Phage_holin_1 (pfam: PF04531), Phage_holin_Dp1 (pfam: PF16938), Phage_holin_2_2 (pfam: PF10746), Phage_holin_5_2 (pfam: PF16079), XhlA (pfam: PF10779), and Holin_SPP1 (pfam: PF04688).Endolysins of enterococcal bacteriophages were found to possess 7 different catalytic domains already known to protein databases: Amidase_2 (pfam: PF01510), endopeptidase domain like (CATH Superfamily 3.90.1720.10), CHAP (pfam: PF05257), Glyco_Hydro_25 (pfam: PF01183), Amidase_5 (pfam: PF05382), Peptidase_M23 (pfam: PF01551), and Glucosaminidase (pfam: PF01832); and 4 types of CWB domains: ZoocinA_TRD (pfam: PF16775), SH3_3 (pfam: PF08239), SH3_5 (pfam: PF08460), and LysM (pfam: PF01476).Based on sequence comparisons, there were 21 different combinations of proteins and their domains in lytic cassettes observed in enterococcal bacteriophages, and the distribution and composition of individual types of lytic cassettes were non-random regarding the size groups of bacteriophages. All types of lytic cassettes, together with corresponding numbers of bacteriophages possessing particular cassettes, are shown in Fig.\u00a0The bacteriophages with the smallest genomes  had three types of lytic cassettes. The first type had holin with a Phage_holin_4_1 domain, followed by endolysin with an Amidase_2 domain, a protein with an unknown function, DUF3310, and an endopeptidase domain-containing protein. Based on multiple sequence alignments, Amidase_2 endolysin from phage MDA1 (MW623430.1) was used as the type endolysin for this lytic cassette. Secondary structure prediction showed that the Amidase_2 domain is connected with an unknown CWB domain via a proline-rich interdomain linker. The second type of lytic cassette in Group 1 contained holin with the HolinBhlA domain, endolysin with the Amidase_2 domain, and a CHAP  domain-containing protein, which functions mainly in peptidoglycan hydrolysis. The predicted structure of Amidase_2 endolysin from this type of lytic cassette showed a similar proline-rich interdomain linker connecting another unknown CWB domain. Only one bacteriophage had the third type of lytic cassette consisting of protein with domains Phage_lysozyme2 and Peptidase_M23, holin with domain Phage_holin_4_1, and a second lytic protein with the Glyco_hydro_25 catalytic domain.Group 2, consisting of 73 bacteriophages with genome sizes ranging from 20 to 50 kbp, was the most diverse in lytic cassette types. Most of the lytic cassettes had an endolysin with an N-terminal Amidase_2 domain, followed by a C-terminal SH3_5 or ZoocinA CWB domain, and a holin with a Phage_holin_1 domain. In some of these lytic cassettes, no known CWB domain was identified using InterProScan, but multiple sequence alignment showed that multiple different Amidase_2 sequences contained various C-terminal sequences, probably representing unknown CWB domains. Bacteriophages MSF2 (MK982307.1) and AUEF3 (KJ127304.1) were used for secondary structure prediction of endolysins with SH3_5 and ZoocinA domains. Surprisingly, the predicted structure of endolysin from MSF2 revealed another possible CWB domain between Amidase_2 and SH3_5. The second most common type of lytic cassette comprises endolysin with N-terminal Glyco_hydro_25 catalytic domain, followed by two C-terminal LysM domains, and holin with Phage_holin_Dp1, except for two bacteriophages with Phage_holin_1. All lytic cassettes either with Amidase_2 or Glyco_hydro_25 domains had holin-like protein with XhlA hemolysin domain. Several bacteriophages had unique lytic cassettes  was the only one with a holin-like protein with an XhlA domain, a holin with a Phage_holin_1 domain, an endolysin with an N-terminal Amidase_2 domain, and an unknown C-terminal CWB domain. Although this phage belongs to size Group 4, multiple sequence alignment of Amidase_2 endolysins clearly showed that its endolysin is almost identical to endolysins from phages Aramis (LR990833.1) and dArtagnan (LR991625.1) from Group 2. The rest of the bacteriophages had holin with the Phage_holin_5_2 domain and endolysin with N-terminal Amidase_2 domain and C-terminal SH3_5 CWB domain. Multiple sequence alignment showed that bacteriophage nattely (MT119360.1) had the same sequence of the Amidase_2 domain as other phages in this group but a slightly different sequence of the putative CWB domain. However, the predicted secondary structure showed a surprisingly similar domain composition with the Amidase_2 catalytic domain, a putative unknown CWB domain, and the SH3_5 CWB domain. Bacteriophage 9183 (MT939241.1) had a unique amino acid sequence of endolysin with an Amidase_2 domain and two CWB domains, SH3_5 and SH3_3. The secondary structure of this endolysin resembled that of MSF2 (Group 2), nattely phage, and other phages from Group 4, but their amino acid sequences were different.Group 5 was formed by 41 bacteriophages encoding three types of lytic cassettes. All three types had endolysin with an N-terminal Amidase_2 domain, an aggregation-promoting factor containing a LysM domain, and holin with a Holin_SPP1 domain, except for one bacteriophage with a Phage_holin_4_1 domain similar to the holins of Group 1. All bacteriophages from this group had a similar composition of lytic cassettes, but sequence analysis of endolysins revealed that there were two different types of endolysins with the Amidase_2 domain. The first was a protein with a shorter sequence of 289 amino acid residues. The second type had the same N-terminal amidase domain but consisted of 416 amino acid residues. Multiple sequence alignment and secondary structure prediction showed that there were CWB domains not previously identified by protein databases. Surprisingly, bacteriophages with longer endolysin contained another protein in their lytic cassette with an Amidase_5 catalytic domain and an SH3_5 CWB domain.In summary, the most common catalytic domain was Amidase_2, followed by the CHAP domain, the Amidase_5 domain, and the Glyco_hydro_25 domain. Considering only known domains from the Pfam database, the most common combination of catalytic and CWB domains was found to be Amidase_2\u2009+\u2009SH3_5, followed by Amidase_2\u2009+\u2009ZoocinA, Amidase_5\u2009+\u2009SH3_5, and Glyco_hydro_25\u2009+\u2009LysM Table . CombinaMany bacteriophages from Group 2 with either Amidase_2 domain endolysins or Glyco_hydro_25 domain endolysins had in their lytic cassettes the holins Phage_holin_1 and Phage_holin_Dp1. However, GenBank records for another holin-like gene were found in the majority of enterococcal bacteriophage genomes from Group 2 with those specific lytic cassettes. Analysis using InterproScan revealed that this holin-like protein contained an XhlA hemolysin domain. This gene was always present in combination with Phage_holin_1 and Phage_holin_Dp1. However, in many genome annotations, this gene was misidentified as \"tail fiber protein\" or simply denoted as \"hypothetical protein\". Close localization of these holin-like protein genes to genes encoding holins might suggest that this gene plays an important role in bacteriophage lytic activity as well.Gene shuffling in lytic cassettes of Group 2 and Group 5 bacteriophages In most lytic cassettes, holins were always paired with specific endolysins. Holins were also highly specific for each group. Surprisingly, some holins as well as endolysins were found in groups in which they were not expected. Some genes encoding holins were shuffled among bacteriophages with similar but not identical compositions of lytic cassettes. Gene shuffling was identified in lytic cassettes of bacteriophages from Group 2 and Group 5.Two types of lytic cassettes were detected in Group 2 bacteriophages, consisting of identical endolysins with Glyco_hydro_25 catalytic domains and LysM CWB domains. These bacteriophages mostly encoded Phage_holin_Dp1, which we considered specific for this holin-endolysin pair. However, two of those bacteriophages were found to use Phage_holin_1 instead. Both lytic cassettes contained an XhlA holin-like protein. Fig.\u00a0A.Fig. 3AA similar situation could be seen in Group 5 bacteriophages, which contained endolysin with an Amidase_2 domain and a separate protein containing a LysM CWB domain. All bacteriophages in Group 5 used HolinSPP1 except one, which had Phage_holin_4_1 in its lytic cassette Fig.\u00a0B.We also discovered that there were two types of Amidase_2 endolysins in Group 5. The endolysin with the longer amino acid sequence was present in lytic cassettes with another endolysin consisting of Amidase_5 and SH3_5 domains. Both types of lytic cassettes, the ones with longer Amidase_2 and Amidase_5 endolysins and the ones with shorter Amidase_2, had HolinSPP1. Multiple sequence alignments showed that the Amidase_2 domain regions of these endolysins were almost identical in sequence but differed in their putative CWB domain regions. This suggests that both gene and domain shuffling might have occurred in the evolution of the largest enterococcal bacteriophages , vB_EfaS-DELF1 (LC513943.1), vB_GEC_Ef_S_9 (MW672041.1), and Aramis (LR990833.1) were used. Endolysins from bacteriophages 9184, vB_EfaS-DELF1, and vB_GEC_Ef_S_9 had the same amino acid sequence of the Amidase_2 domain with differences in their putative CWB domain regions. Putative CWB domains were denoted as UCWB (unknown CWB) domains. Secondary structure predictions of representative endolysins showed that those UCWB domain regions contained large CWB domains as opposed to SH3_5-containing endolysins. Since the Amidase_2 domain and interdomain linkers had high sequence similarity, domain shuffling may have happened between UCWB domains in these endolysins. Surprisingly, endolysin from bacteriophage Aramis had a different Amidase_2 domain but the same UCWB as endolysin from bacteriophage 9184. That suggests possible shuffling not only between CWB domains but between catalytic domains as well. The putative interdomain linker of this endolysin slightly differed from the consensus sequence. The same Amidase_2 and UCWB domains were found in Group 4 bacteriophage 9181. A similar situation probably happened with bacteriophages vB_EfaP_Zip (MK360025.1) and vB_EfaP_IME199 (KT945995.1) from Group 1. Their endolysin contained the same UCWB domain as previously mentioned bacteriophages 9184 and Aramis but a completely different Amidase_2 domain . Most Amidase_2 domain-containing endolysins had unknown CWB domains (32.75% of lytic cassettes). Domain SH3_5 was found to be typical in the Amidase_2 endolysins of enterococcal bacteriophages. The common lytic cassettes of Enterococcus spp. phages were different from those of other Lactobacillales phages. The combination of Amidase_2\u2009+\u2009SH3_5 domains is typical for Enterococcus phages and prophages. Lactobacillus prophages lytic cassettes have the majority of endolysins with domains Glyco_hydro_25\u2009+\u2009LysM Below is the link to the electronic supplementary material."}
+{"text": "The combination of hsa_circ_0003570 and hsa_circ_0004018 may be a potential prognostic biomarker for HBV-HCC.The clinical significance of hsa_circ_0004018 and hsa_circ_0003570 in patients with hepatitis B virus-related hepatocellular carcinoma (HBV-HCC) is unclear. We aimed to explore the clinical significance and prognostic utility of these two circular RNAs (circRNAs) in patients with HBV-HCC. Based on 86 paired tissue samples of HCC and adjacent non-HCC, the relative expression profiles of hsa_circ_0004018 and hsa_circ_0003570 were determined using quantitative real-time polymerase chain reactions. The cut-off values were the median expression of each of the two circRNAs in 86 patients with HBV-HCC. The combination group comprised patients with high levels of the two circRNAs. Clinicopathological features, body composition profiles at the L3 level, and survival rates were investigated. The expression of hsa_circ_0004018 and hsa_circ_0003570 was downregulated in HCC tissues compared with non-HCC tissues. High expression levels of hsa_circ_0003570 (hazard ratio (HR), 0.437;  Hepatocellular carcinoma (HCC) has the third highest cancer-related mortality and the sixth highest cancer incidence worldwide . In KoreCircular RNAs (circRNAs) have emerged as novel prognostic biomarkers in patients with HBV-HCC ,6. CircRIn this study, we evaluated the clinical outcomes, including survival and cancer progression, of hsa_circ_0004018 and hsa_circ_0003570 in 86 patients with HBV-HCC. Furthermore, we investigated the clinical significance of the combination of these two circRNAs as potential prognostic biomarkers independent of traditional risk factors.This retrospective, single-institution study investigated the clinical significance of two circRNAs (hsa_circ_0004018 and hsa_circ_0003570) as prognostic biomarkers for patients with HBV-HCC. In our previous study, 121 patients with HCC who performed diagnostic biopsies or surgical resections were included . Of thes9/L] \u00d7 alanine aminotransferase [U/L]) ) ,17. Respe [U/L]) . During e [U/L]) .TM; Thermo Fisher Scientific, Waltham, MA, USA) and then stored at \u221280 \u00b0C. This research was approved by the Institutional Review Board  and was done in accordance with the ethical guidelines of the 1975 Declaration of Helsinki. Written informed consent was obtained from all patients prior to sampling.All tissue specimens with tumors and adjacent nontumor tissues (NT) from 86 patients with HCC were immediately stored at 4 \u00b0C overnight in an RNAlater\u2122 Stabilization Solution , according to the manufacturer\u2019s instructions. The quantity, quality, and purity of the extracted RNA were evaluated using a NanoPhotometer\u00ae N60 .Total RNA was extracted from homogenized frozen tissues obtained from 86 patients with HCC using the QIAzol\u0394\u0394Ct method.Complementary DNA (cDNA) was synthesized using a high-capacity cDNA Reverse Transcription Kit , according to the manufacturer\u2019s instructions. Quantitative real-time polymerase chain reactions (qRT-PCR) were performed using a Power SYBR\u2122 Green PCR Master Mix (Applied Biosystems) and the QuantStudio 6 RT-PCR System (Applied Biosystems). All qRT-PCR experiments were performed in triplicates. For the analysis of relative circRNA expression, divergent primers for each of the two hsa_circRNAs, including the gap junction of circRNA, were designed as previously described ,19. The https://gitlab.com/Michael_Paris/AutoMATiCA (accessed on 12 September 2022.)) [2) divided by the square of the height (m2). Sarcopenia was classified as a skeletal muscle index <50 cm2/m2 for men and <39 cm2/m2 for women [Based on the Picture Archiving and Communications System , cross-sectional images at the L3 level from an abdominal CT scan were obtained. The specific software that measured the areas of the skeletal muscle mass and adipose tissues using varying Hounsfield unit thresholds was AutoMATiCA  for normally distributed data, while categorical data were presented as percentages. For non-normally distributed data, medians and interquartile ranges (IQRs) were reported. We used the chi-square test, Fisher\u2019s exact probability test, and the Mann\u2013Whitney U test to compare the clinicopathological characteristics of the groups according to the expression of hsa_circ_0004018 and hsa_circ_0003570. To examine the contrast in the expression of the two hsa_circRNAs between tumors and adjacent NT tissues, a paired t-test was performed. The combination group was defined as having a high expression of both hsa_circ_0003570 and hsa_circ_0004018. We analyzed patient survival and compared survival between the groups using the Kaplan\u2013Meier method and log-rank test. To identify predictors of survival, we performed logistic regression analyses based on the Cox proportional hazards model. All statistical analyses were performed using the R software , and statistical significance was set at The baseline characteristics are shown in Of the 86 patients with HBV-HCC, 50 (58.1%) died during the follow-up period. The median age was 57.0 years (interquartile range (IQR), 52.0\u201365.0 years), and 73 patients (84.9%) were men. Regarding tumor profiles, 39 (45.3%) had tumors in the T3 and T4 stages, 5 (5.8%) had tumors in the N1 stage, and 9 (10.5%) had tumors in the M1 stage. The median \u03b1-fetoprotein (AFP) levels were 137.0 ng/mL . Forty-seven (54.7%) patients were found to have advanced fibrosis. Of the 86 patients, 76 (88.4%), 9 (10.4%), and 1 (1.2%) were classified as CTP-A, CTP-B, and C, respectively. On the circRNA profiles, the expression of hsa_circ_0004018 and hsa_circ_0003570 in HCC tumor tissues was 0.0003  and 0.0006 , respectively.p < 0.001 and hsa_circ_0003570: p < 0.05: In this study, qRT-PCR was performed on the nontumor (NT) tissues and tumor tissues of patients with HBV-HCC. The CT values of GAPDH were measured to be 17.078\u201319.851 in NT tissues and 17.02\u201319.673 in tumor tissues, respectively. We verified that there was no difference in the expression levels of GAPDH between tissue samples, and we used GAPDH as a normalizer . In addiRegarding body composition profiles, 48 (55.9%) and 27 (31.4%) patients had sarcopenia and visceral adiposity, respectively. The median durations of OS and PFS were 73.6 months  and 34.4 months .p = 0.031) and lower AFP levels  than the low expression group. However, no significant differences were observed in the hsa_circ_0004018 expression group.Patients with HBV-HCC were divided into high- and low-expression groups, based on the median expression levels of the two circRNAs . Since bp = 0.005), whereas the OS was not statistically significant according to the hsa_circ_0004018 expression group (p = 0.32)  .In the group expressing hsa_circ_0004018, the cumulative 1-, 3-, and 5-year OS rates were 69.8%, 51.2%, and 44.4% for patients with high expression levels, compared with 53.5%, 37.2%, and 37.2% for those with low expression levels. In the hsa_circ_0003570 expression group, the cumulative 1-, 3-, and 5-year OS rates were 76.7%, 58.1%, and 51.2% for patients with high expression levels, compared with 46.5%, 30.2%, and 30.2% for those with low expression levels.p < 0.001), the presence of nodal involvement , metastasis , decompensated liver cirrhosis (LC) defined as CTP classes B and C , and sarcopenia  were significant risk factors for OS. In particular, high hsa_circ_0003570 expression  was found to have an inverse association with OS in patients with HBV-HCC.p = 0.038), whereas the PFS was not statistically significant according to the hsa_circ_0004018 expression group (p = 0.195)  . In the group expressing hsa _circ_0004018, the cumulative 1-, 3-, and 5-year PFS rates were 64.3%, 34.5%, and 23.2% for patients with high expression levels, compared with 46.3%, 21.4%, and 21.4% for those with low expression levels. In the hsa_circ_0003570 expression group, the cumulative 1-, 3-, and 5-year PFS rates were 71.4%, 34.3%, and 21.8% for patients with high expression levels, compared with 39%, 21.8%, and 21.8% for those with low expression levels.p < 0.001) and decompensated LC  were associated with PFS. In addition, the presence of visceral adiposity  and the high expression of hsa_circ_0004018  had an inverse association with PFS in HBV-HCC , and tended to have a longer OS than did the non-combination group, but it was not statistically significant .We found that hsa_circ_0004018 was related to PFS, while hsa_circ_0003570 was related to OS in patients with HBV-HCC, independent of traditional risk factors, including tumor and liver function-related factors. To evaluate the impact of the combination of the two circRNAs, a group satisfying the high expression of both hsa_circRNAs was defined as the \u201ccombination group.\u201d p = 0.078 in OS and p = 0.08 in PFS, respectively). p < 0.001), the presence of nodal involvement , metastasis , decompensated LC , and sarcopenia  were significant risk factors for OS based on multivariate analysis. Similar to previous results, the combination group  had an inverse association with OS in patients with HBV-HCC. In addition, advanced T stage , decompensated LC , and sarcopenia  were significant risk factors for PFS. Visceral adiposity  and the high expression levels of both circRNAs (the combination group)  were found to have an inverse association with PFS in patients with HBV-HCC (Advanced T stage (HR, 7.613; 95% CI, 3.792\u201315.284;  HBV-HCC .Compared with the expression of hsa_circ_0004018 and hsa_circ_0003570 in normal tissues, this study revealed that the expression of the two circRNAs was low in HBV-HCC tissues. We have shown that tissue hsa_circ_0004018 and hsa_circ_0003570 are associated with a favorable survival outcome including OS and PFS in patients with HBV-HCC. In addition, the combination of the two circRNAs may be a potential predictor for survival and progression in HBV-HCC patients, independent of traditional risk factors, including tumor, liver function-related, and body composition variables based on CT.In previous studies, hsa_circ_0004018 was well known be a potential tumor suppressor in patients with HCC, which was transcribed from SMTD4 . Fu et aHowever, no study has identified the clinical impact of hsa_circ_0004018 in patients with HBV-HCC. Our study focused on clinical outcomes, including survival and progression, in accordance with hsa_circ_0004018 expression in patients with HBV-HCC. Similar to the results of previous studies, the expression of hsa_circ_0004018 was decreased in HBV-HCC tissues compared to that in nontumorous tissues in the current study ,23. In aThe role of hsa_circ_0003570 in patients with HCC is relatively unknown compared with that of hsa_circ_0004018. Fu et al.  demonstrIn the current study, hsa_circ_0003570 was inversely associated with OS but not with PFS, which was contrary to the findings of a previous study . SimilarIn our study, we aimed to determine the clinical implications based on the combination of the two circRNAs with the aforementioned tumor suppressor roles. Although the molecular pathway was not directly identified in this study, the putative mechanism of the combination of the two circRNAs is considered to involve the Wnt/\u03b2-catenin signaling pathway. In a previous study, the high expression of miR-182-5p activated the Wnt/\u03b2-catenin signaling pathway by interfacing with \u03b2-catenin degradation, leading to unfavorable prognoses, including early recurrence in HCC patients that underwent surgery . RecentlFurthermore, owing to the discordance in the clinical impact of the two tissue circRNAs on OS and PFS, we investigated the clinical significance of survival and progression using a combination of the two circRNAs. Studies on the clinical significance of tissue circRNA combinations in patients with HCC are lacking. In our study, there were no differences in the tumor, liver function, and clinical outcome profiles, except for the duration of PFS in the combination group, defined as having a higher expression of hsa_circ_0004018 and hsa_circ_0003570 than in the noncombination group. However, considering that the high expression combination group was independently correlated with survival and progression in patients with HBV-HCC, the combination of the two differentially expressed circRNAs could be a method for discovering prognostic targets for HCC in the future.Recent studies have shown that sarcopenia and visceral adiposity are related to worse survival outcomes in patients with HCC ,27. SimiThe limitations of this study were as follows: First, it was difficult to extend the application to the entire population due to the Korean cohort-based study, small tissue sample, retrospective nature of the study, and potential for selection bias. Second, we could not identify the potential circRNA/miRNA/mRNA molecular mechanisms of these two circRNAs. Considering that the progression of sarcopenia is related to the activation of the Wnt/\u03b2-catenin signaling pathway, additional molecular studies may reveal a link between sarcopenia and the two circRNAs which inhibit the Wnt/\u03b2-catenin signaling pathway ,33. HoweIn conclusion, the combination of the high tissue expression of hsa_circ_0004018 and hsa_circ_0003570 is strongly associated with favorable clinical outcomes and can be a novel prognostic marker in patients with HBV-HCC. Further, larger sample sizes and well-validated studies are needed to elucidate the mechanisms of hsa_circ_0004018 and hsa_circ_0003570 and their potential as prognostic factors and circulating biomarkers for HCC."}
+{"text": "SATB1 overexpression or miR-590-5p inhibition reversed glioma cells proliferation and migration post-silencing of hsa_circ_0010889. Taken together, our study demonstrates that hsa_circ_0010889 downregulation inhibits glioma progression through the miR-590-5p/SATB1 axis.Glioma is a general neurological tumor and circular RNAs (circRNAs) have been implicated in glioma development. However, the underlying mechanisms and circRNA biological functions responsible for the regulation of glioma progression remain unknown. In this study, we employ next-generation sequencing (NGS) to investigate altered circRNA expression in glioma tissues. Regulatory mechanisms were studied using luciferase reporter analyses, transwell migration, CCK8, and EdU analysis. Tumorigenesis and metastasis assays were utilized to determine the function of hsa_circ_0010889 in glioma. Our results showed that hsa_circ_0010889 expression increased in glioma cell lines and tissues, indicating that hsa_circ_0010889 may be involved in glioma progression. Downregulation of hsa_circ_0010889 inhibited glioma invasion and proliferation in both  Glioma is a major primary brain tumor, resulting in ~75% of malignant central nervous system (CNS) cancers among adults , 2. CurrCircular RNAs (circRNAs) is a type of single-stranded noncoding RNA having covalently closed-loop structures, which are considered as promising biomarkers and targets for the diagnosis and treatment of many diseases, particularly cancer. Accumulating evidence suggests that EWSR1-induced circNEIL3/IGF2BP3 enhances glioma progression by regulating macrophage polarization  and circin vitro and in vivo experiments. Luciferase reporter assay results showed that miR-590-5p and SATB1 were downstream targets for hsa_circ_0010889. The aim of the present study was to discuss the regulation mechanism of hsa_circ_0010889 in glioma.The present study found that hsa_circ_0010889 expression increased in glioma cell lines and tissues. Downregulation of hsa_circ_0010889 inhibited glioma progression in both Nude BALB/c female mice  were obtained from SLAC Laboratory Animal Co. Ltd., Shanghai, China. They were housed in independently ventilated cages and kept at 24\u201326\u00b0C, with constant humidity and a 12-h light/dark cycle. The Ethics Committee of The First Hospital of Lanzhou University oversaw all the procedures (LDYYLL2021-23).Total RNA was extracted from pairs of freshly frozen glioma and adjacent tissues. An Agilent 2200 system  was used to confirm the RNA quality. The RiboMinus eukaryote kit  was used to remove ribosomal RNA followed by cDNA library construction. NGS was carried out using the Illumina HiSeq 3000  and the reads were aligned to the GRCH37.p13 reference. Unmapped reads were collected to characterize circRNAs. The reads were counted and used for mapping to the circRNA junction with an overhang of \u22656 nt for each candidate.2.Human glioma cell lines LN229, SHG44, U251, and T98G along with normal glial HEB cell lines were purchased from the Chinese Academy of Sciences Cell Bank . We cultured HEB cells in RPMI-1640 medium and cultured Glioma cell lines in DMEM medium supplied with 10% FBS and 1% Penicillin-Streptomycin Solution . The cells were maintained in an incubator at 37\u00b0C with 5% COmiR-590-5p mimics and hsa_circ_0010889 siRNA were synthesized by Genepharma . Cell transfection was performed at 70% confluency according to Lipofectamine 2000 manufacturer\u2019s instructions. After two days, the cells were harvested for downstream experiments.SATB1 overexpression vector was constructed by inserting SATB1 cDNA into the pcDNA3.1 vector. Then, Specific probes for hsa_circ_0010889 (Dig-5\u2032-CTTGCCAGACTTAAGCTTTTTACGACGCG-3\u2032-Dig) were synthesized  and signals were captured via Cy3-conjugated anti-biotin antibodies . 4,6-diamidino-2-phenylindole (DAPI) was also utilized to counterstain for cell nuclei. Finally, we imaged the cells using a Zeiss LSM 700 confocal microscope .Protein samples were extracted from cells with RIPA lysis buffer for western blot assays. Anti-E-cadherin , anti-N-cadherin , and anti-GAPDH  primary antibodies were obtained from Cell Signaling Technology  and used to stain protein blots following the manufacturer\u2019s instructions. Immunoreactivity was visualized using a chemiluminescence detection kit .\u2212\u0394\u0394CT approach was used to obtain relative expression fold changes. U6 and GAPDH were employed as internal references. The following primers were used: hsa_circ_0010889 primers forward, 5\u2032-CCTAATAAATCCTTGC-3\u2032 and reverse, 5\u2032-CAGCTCCGGCAACTAAGCGCGC-3\u2032. miR-590-5p primers forward, 5\u2032-GAGCTTATTCATAAAAGT-3\u2032; and reverse: 5\u2032-TCCACGACACGCACTGGATACGAC-3\u2032. U6 primers were forward, 5\u2032-CTCGCTTCGGCAGCACA-3\u2032; and reverse: 5\u2032-AACGCTTCACGAATTTGCGT-3\u2032; GAPDH primers were forward, 5\u2032-AATGGGCAGCCGTTAGGAAA-3\u2032; and reverse: 5\u2032-TGAAGGGGTCATTGATGGCA-3\u2032.Total cellular RNA was extracted using a TRIzol reagent kit  and cDNA was synthesized for subsequent qPCR using a TaqMan Assay Kit  and the 24 of LN229 and U251 cells were seeded into 96-well plates overnight. On the second day, EdU solution (25 \u03bcM) was added to the wells and incubated for 24 h. Then, 4% formalin was utilized to fix the cells for 2 h at room temperature. We utilized Triton X-100 to permeabilize the cells for ten minutes, and then added 200 \u03bcL Apollo reaction solution to stain EdU and 200 \u03bcL of DAPI solution to stain cell nuclei for 0.5 h. DNA synthesis and cell proliferation were measured using a fluorescence microscope .DNA synthesis and cell proliferation were analyzed using an EdU assay kit . Here, 1 \u00d7 103 cells into 96-well plates and the absorbance at 450 nm was read for every sample using the CCK-8 assay . Finally, we constructed a cell viability curve.We seeded 2 \u00d7 105/mL and briefly, 200 \u03bcL/well of cell suspension was added to the Transwell chamber  upper side. At the same time, we added 500 \u03bcL of medium containing 10% FBS to the lower chamber. After a 24 h incubation, we fixed the cells that had migrated to the bottom side with paraformaldehyde for 15 min, and then stained them with crystal violet for 5 mins. Cells were observed under a microscope and the migration cell numbers were counted. We randomly selected and counted five fields of view for every sample.Transfected cells after 48 h were diluted 2.0 \u00d7 10Putative miR-590-5p binding site in the 3\u2032-UTR for the target gene SATB1 and hsa_circ_0010889 (Mut/WT) were cloned into the psi-CHECK  vector downstream of firefly luciferase 3\u2032-UTR or hsa_circ_0010889. The primary luciferase signal was normalized to Renilla luciferase as the normalization signal. The relative Renilla luciferase activity was analyzed according to the provided protocols .6) with sh-NC or sh-hsa_circ_0010889 were injected into the flank of nude mice. We then measured tumor volume and weight.To establish the nude mouse model for glioma, LN229 cells (1 \u00d7 105) were injected into each nude mouse tail vein and post 4 weeks, lung metastasis was assessed using an in vivo bioluminescence imaging system and computed metastatic foci counts in lung tissues post H&E staining.For tumor metastasis experiments, luminescence-labeled LN229 cells transfected with sh-NC or sh-hsa_circ_0010889  and statistical analyses were performed using GraphPad Prism  to determine statistical significance between groups. A The datasets used and/or analyzed during the present study are available from the corresponding author on reasonable request.Accumulating evidence suggests that circRNA has an important function in glioma progression , 9. Howein vitro and in vivo experiments.To determine a role for hsa_circ_0010889 in glioma progression, we constructed an siRNA against hsa_circ_0010889 (si-hsa_circ_0010889), and transfected this into both LN229 and U251 cells. Results showed that hsa_circ_0010889 significantly decreased post hsa_circ_0010889 silencing in U251 and LN229 cells . Our CCKTranswell assays looking at migration showed that hsa_circ_0010889 silencing inhibited migration in both LN229 and U251 cells , 3B and Bioinformatics results found that hsa_circ_0010889 interacted with miR-590-5p. A luciferase reporter analysis further confirmed that miR-590-5p inhibited luciferase function in WT cells , 4B, sugBioinformatics data also found that SATB1 was the miR-590-5p downstream target. Therefore, to better validate the association between SATB1 and miR-590-5p, WT/MUT 3\u2032UTR-SATB1 sequences including a miR-590-5p binding sequence were constructed into a luciferase reporter vector , which wFurthermore, RT-qPCR results showed that hsa_circ_0010889 expression decreased post transfected with hsa_circ_0010889 silencing vector. But a miR-590-5p inhibitor or overexpression of SATB1 had no effect on hsa_circ_0010889 expression in U251 and LN229 cells , 4F, sugUsing EdU \u20135D we foAgain, using EdU \u20136D detecAccumulating studies have found that circRNAs are partially responsible for tumorigenesis and glioma progression . |Our prBioinformatics data found that SATB1 and miR-590-5p were downstream targets for hsa_circ_0010889 and this was confirmed by luciferase reporter assay. hsa_circ_0010889 downregulation promoted miR-590-5p expression and importantly previous investigations found that circ_0069718 promotes breast cancer via upregulation of NFIB and targets miR-590-5p directly . miR-590Further investigations have found that SATB1 is a downstream target for miR-590-5p which was confirmed by luciferase reporter assay. hsa_circ_0010889 downregulation inhibits SATB1 expression but inhibition of miR-590-5p reversed this inhibitory effect with respect to si-hsa_circ_0010889 and SATB1 expression. Previous studies have found that SATB1 has an important function to regulate invasion and metastasis in breast cancer . FurtherOur investigation provides evidence that hsa_circ_0010889 downregulation can reduce glioma proliferation and invasion via miR-590-5p/SATB1 signaling mediated by the regulation of aerobic glycolysis. Our data has revealed that hsa_circ_0010889 is a promising marker for glioma diagnostics, and which may be extended to the development of drugs targeting hsa_circ_0010889, for the treatment of glioma."}
+{"text": "Sinorhizobium comprises rhizobia that fix nitrogen in symbiosis with legumes. To support taxonomic studies of this genus and of rhizobia more broadly, we report complete genome sequences and annotations for the species type strains Sinorhizobium garamanticum LMG 24692 and Sinorhizobium numidicum LMG 27395 and CIP 109850.The genus  Sinorhizobium currently consists of 17 validly published species , in June 2010 and November 2022, respectively, while S. numidicum CIP 109850T was obtained from the Institute Pasteur Collection in November 2022. Following rehydration, single colonies were immediately used to prepare 20% glycerol stocks for storage at \u221280\u00b0C until use. For DNA isolation, strains were streaked from the frozen stocks onto tryptone-yeast extract (TY) medium (T) or 6.4.6+ae70e8f (LMG 27395T and CIP 109850T), with the r941_min_sup_g507 model (ONT). Illumina sequencing was performed at SeqCenter  using the Illumina DNA prep kit and IDT 10-bp unique dual indexes (UDI) and sequenced on an Illumina NextSeq 2000 instrument to produce 2\u2009\u00d7\u2009151-bp reads. The Illumina reads were filtered using BBDuk version 38.96  medium . Single on 38.96  and trimon 38.96  with thednaA gene (if found) or otherwise at a gene nearest to the middle of the replicon. Finally, the genomes were annotated using PGAP version 2022-10-03.build6384 . The. TheSinoequences .https://github.com/diCenzo-Lab/008_2023_Sinorhizobium_species_type_strains).The annotated genome assemblies and raw sequencing reads were deposited at NCBI GenBank and the Sequence Read Archives, respectively, and are accessible via the accession numbers listed in"}
+{"text": "FOXP4. This remarkable discovery suggests that individuals infected with SARS-CoV-2 who also carry the rs9367106 SNP have a 1.65-fold increased likelihood of experiencing long COVID  closely linked to rs9367106 to represent the significant association between rs9367106 and elevated FOXP4 expression in lung tissue according to data from the Genotype-Tissue Expression database (GTEx)  . FurtherFOXP4 has been reported to be linked to lung cancer :n = 3,018) vs. general population controls , with its download link provided as (https://my.locuszoom.org/gwas/192226/?token=09a18cf9138243db9cdf79ff6930fdf8).Strict cases of long COVID after test-verified SARS-CoV-2 infection  vs. general population controls , with its download link provided as (https://my.locuszoom.org/gwas/826733/?token=c7274597af504bf3811de6d742921bc8).Broad long COVID cases identified as infected by any SARS-CoV-2 virus  vs. strict controls restricted to individuals who were infected by SARS-CoV-2 but were not diagnosed with long COVID : with its download link provided as (https://my.locuszoom.org/gwas/793752/?token=0dc986619af14b6e8a564c580d3220b4).Strict long COVID cases defined  vs. strict controls as defined in : with its download link provided as (https://my.locuszoom.org/gwas/91854/?token=723e672edf13478e817ca44b56c0c068).Broad long COVID cases defined . These GWASs investigated severe respiratory-confirmed COVID-19 in comparison to the general population across mixed and ancestry-specific subpopulations, including European (EUR), East Asian (EAS), South Asian (SAS), and African (AFR) populations. Only SNPs that met the criteria of a minor allele frequency (MAF) \u2265 1% and an imputation score > 0.6 were selected for subsequent analysis.The severe COVID-19 GWASs involved in this study feature the following sample sizes and associated download links:https://storage.googleapis.com/covid19-hg-public/freeze_7/results/20220403/main/sumstats/COVID19_HGI_A2_ALL_leave_23andme_20220403_GRCh37.tsv.gz.A2_ALL for mixed populations , with the following download link: https://storage.googleapis.com/covid19-hg-public/freeze_7/results/20220403/pop_spec/sumstats/COVID19_HGI_A2_ALL_afr_leave23andme_20220403_GRCh37.tsv.gz.A2_AFR for populations with African ancestry , with the following download link: https://storage.googleapis.com/covid19-hg-public/freeze_7/results/20220403/pop_spec/sumstats/COVID19_HGI_A2_ALL_eas_leave23andme_20220403_GRCh37.tsv.gz.A2_EAS for populations with East Asian ancestry , with the following download link: https://storage.googleapis.com/covid19-hg-public/freeze_7/results/20220403/pop_spec/sumstats/COVID19_HGI_A2_ALL_eur_leave23andme_20220403_GRCh37.tsv.gz.A2_EUR for populations with European ancestry , with the following download link: https://storage.googleapis.com/covid19-hg-public/freeze_7/results/20220403/pop_spec/sumstats/COVID19_HGI_A2_ALL_sas_leave23andme_20220403_GRCh37.tsv.gz.A2_SAS for populations with South Asian ancestry , with the following download link: FOXP4 locus with both the long COVID GWAS of mixed ancestries and the severe COVID-19 GWAS of European ancestry. The self-reported lung cancer GWAS was generously provided by Neale\u2019s lab , and the GWAS dataset consists of 360,938 controls and 203 cases, which is publicly accessible via the following link:The self-reported lung cancer GWAS data from UK Biobank was obtained to perform a comparative analysis of association signals around the https://broad-ukb-sumstats-us-east-1.s3.amazonaws.com/round2/additive-tsvs/20001_1001.gwas.imputed_v3.both_sexes.tsv.bgz.Following the acquisition of these long COVID GWAS summary statistics, a custom Perl script was employed to merge the association signals of each SNP by rsID across the four long COVID GWAS summary statistics. This procedure resulted in a final table where each row is specific to a single SNP, with corresponding columns housing the summary statistics extracted from the four GWASs. To reduce file size, this merged table was subsequently compressed into a \u201cgz\u201d file format using 7zip. It was then uploaded to the workspace of the freely accessible online software SAS OnDemand for Academics. Within this platform, the integrated data underwent comprehensive analysis alongside summary statistics from severe COVID-19 GWASs, which were downloaded from the HGI website.FOXP4 gene, a custom SAS script leveraging the SAS package COVID19_GWAS_Analyzer was developed. This script automates the extraction of FOXP4 SNPs from the aforementioned GWASs and visualizes their association signals in a localized Manhattan plot.To facilitate the analysis and visualization of association signals related to both long COVID and severe COVID-19 around the https://github.com/chengzhongshan/COVID19_GWAS_Analyzer), where the corresponding SAS codes for extracting FOXP4 SNPs from these GWASs and generating the local Manhattan plot can be found at this link .The COVID19_GWAS_Analyzer package is openly accessible on GitHub  in the vicinity of the FOXP4 locus in relation to self-reported lung cancer. These selected SNPs underwent expression quantitative trait locus (eQTL) analysis using the GTEx API , and COVID19_GWAS_Analyzer implemented a SAS Macro to facilitate the automation of the eQTL analysis.Details for the analysis process were divided into four distinct steps. First, the merged long COVID GWASs were screened for association signals of SNPs within the genomic region chr6:41000000\u201342000000 (hg38), where the https://github.com/chengzhongshan/COVID19_GWAS_Analyzer/tree/main/LongCOVID_data_and_scripts/MergeBigFiles.PL).All the analyses described above were executed within the SAS On Demand for Academics. Additionally, the customized Perl script used for merging the association signals of each SNP by rsID across the four long COVID GWAS summary statistics is available on GitHub  in the GWAS of severe COVID-19. This association also held true for the mixed population as well as the South Asian\u2014SAS population, with a somewhat weaker association observed in the East Asian\u2014EAS population, nearing genome-wide significance, as illustrated in 2 value of 0.88 in the European population). Interestingly, rs2496644 demonstrated similar association signals across four GWAS datasets, encompassing the long COVID GWAS and the severe COVID-19 GWAS conducted in mixed populations, as well as in EAS and SAS populations. These findings align with the understanding that severe COVID-19 patients are more likely to develop long COVID.The FOXP4 gene in the self-reported lung cancer GWAS with those from the severe COVID-19 GWAS of European samples and the long COVID GWAS, which includes individuals of mixed ancestries. As illustrated in Ps > 0.05). However, it is noteworthy that 20 SNPs located within the gene body of FOXP4 displayed suggestive associations with lung cancer (Ps < 5 \u00d7 10\u22124). Interestingly, these SNPs did not achieve nominal significance in the two COVID-related GWAS datasets. This observation strongly suggests that different SNPs may underlie the potential associations of FOXP4 with lung cancer, as well as with long COVID and severe COVID-19. In summary, our findings indicate that FOXP4 is indeed implicated in lung cancer, but the long COVID and severe COVID-19 risk SNP rs9367106 does not exhibit an association with lung cancer.To explore the potential relationship between rs9367106 and lung cancer, we conducted an analysis using the UK Biobank self-reported lung cancer GWAS dataset, which primarily comprises individuals of European ancestry. Additionally, we compared the association signals around the FOXP4 expression, we conducted an extensive eQTL analysis across 49 GTEx tissues. This analysis involved correlating the genotypes of each SNP with FOXP4 expression in each GTEx tissue. Given the absence of rs9367106 in GTEx, we focused on its highly linked SNP, rs12660421. Our findings revealed that both COVID risk SNPs, rs2496644 and rs12660421, exhibited robust eQTL effects on FOXP4 expression in the lung and brain hippocampus tissues. Notably, the lung tissue displayed the most pronounced association between FOXP4 expression and rs12660421  displayed varying minor allele frequencies across populations, with the lowest in Europeans and higher frequencies in Asian populations. It showed nominal significance in only a few sub-cohorts used for the meta-analyses of long COVID GWAS , indicatIt is essential to emphasize that the relationship between long COVID and other COVID phenotypes is intricately intertwined. Long COVID represents a broad phenotype encompassing persistent post-COVID symptoms, which endure for more than 6\u00a0months following SARS-CoV-2 infection . ResearcDespite limitations,"}
+{"text": "The effects of Acid-Enz TC (obtained from a powder preparation of 85% H3PO4-Enz_TC) at different weight amounts  blending with WF-rPET powder prepared by white recycled polyester fabric were evaluated for fiber spinnability at different winding speeds of 1000 and 1500 m/min. The results revealed that recycled PET fiber spun by adding Acid-Enz_TC up to 10 %wt gave uniformly distributed filament fibers. A comparative study of the physical, thermal, and mechanical properties also investigated the relationship between the effect of Acid-Enz_TC and the structure of the obtained fibers. Acid-Enz_TC:WF-rPET (5:95) was the optimal ratio. The thermal values were analyzed by DSC and TGA and crystallinity was analyzed by XRD, with mechanical strength closed to 100% WF-rPET. The FTIR analysis of the functional groups showed the removal of cotton from the blended fabrics. Other factors such as the Acid-Enz_TC component in WF-rPET, extraction conditions, purity, thermal, chemical, and exposure experiences also affected the formability and properties of recycled PET made from non-single-component raw materials. This study advanced the understanding of recycling PET from TC fabrics by strategically removing cotton from polyester\u2013cotton blends and then recycling using controlled conditions and processes via the melt spinning method.Polyester/cotton fabrics with different proportions of Tetron Cotton, TC (35% Cotton/65% PET), and Chief Value Cotton, CVC (60% Cotton/40% PET), were investigated by removing the cotton component under various phosphoric acidic conditions including the use of cellulase enzymes. The remaining polyethylene terephthalate (PET) component was spun using the melt spinning method. Only 85% H The global production of textile fibers has almost doubled over the past 20 years, from 58 million metric tons in 2010 to 109 million metric tons in 2020  due to tAmong clothing textiles, mixed fibers of PET and cotton such as Chief Value Cotton  or Tetron Cotton  predominate. Combining polyester fibers with cotton fibers gives low-cost cool clothing products that dry quickly, retain their shape, and are not easily wrinkled. Before recycling, these material combinations require careful sorting and mechanical separation  under dirPET properties including viscosity, purity, ash content, and moisture levels.In the quest to find sustainable solutions for recycling the substantial surplus of polyester fabric produced by the textile industry, this initiative departed from traditional recycling methods. Recycled PET is typically derived from bottles and can be transformed into either bottles or fiber grades through a series of modifications. The primary objective was to control critical rPET) from 100% white polyester fabric waste using a single-component recycling process. This method involved thermal and mechanical preparation through compression and grinding. Essential aspects were temperature and heat management, which included the precise regulation of time, temperature, and residence time while avoiding high-shear force processes. Ultimately, this innovative method proved effective for melt-spinning rPET fibers derived from PET fabric.Previous research  concentrrPET fibers were spun using different proportions of extracted PET powders from white TC fabrics, with the blending of pure PET powder obtained from white recycled polyester fabric  as received from industrial waste polyester raw materials, compared to 100% polyester (single component) by a cotton removal method under acidic and enzymatic conditions. Fiber/Fabric to Fiber/Fabric was the main concept of this research. Compared to the traditional method (Bottle to Fiber/Fabric), more complicated mechanisms of modifying Fiber/Fabric-grade polyester for use in the fiber-forming process involved the pretreatment of the blended yarn of cotton\u2013polyester (TC fabrics) using different proportions of acid and enzymatic pretreatment to extract only PET fibers for the recycling process ,10. Grinescribed ). The sp2 and white CVC knitted fabric of 60% cotton/40% PET (code WF_CVC) at basis weight 180 g/m2 sponsored from Yong Udom Karn Tho Co., Ltd.  and L.V.W. Group Co., Ltd. , respectively, were used as textile wastes to recover PET. White PET knitted fabric (code WF_PET) at basis weight 136.87 g/m2 obtained from the stock of Jong Stit Co., Ltd. , while recycled bottle PET (code BO_PET) sponsored by Teijin Co., Ltd.  were used as control samples. All fabrics were defibrillated using a recycling machine.White TC knitted fabric of 35% cotton/65% PET (code WF_TC) at basis weight 250 g/m3PO4_TC or H3PO4_CVC at LR 1:15 (w/v) at 50 \u00b0C for 7 h following the method of  is the peak area  and \u0394mH0 [J/g] is the melting enthalpy of a perfect PET crystal, equal to 140.1 J/g [The melting, crystallization, and glass transition behavior of different ratios of Acid-Enz_TC/WF_rPET blended compounds were achieved using a Bruker D8 Advance model with CuK\u03b1 radiation at a wavelength of 1.54 \u00c5. The diffraction spectrogram was recorded as 2\u03b8 (2 theta) ranging from 5 to 80 \u00b0C.XRD patterns of different ratios of Acid-Enz_TC/WF_rPET blended compounds in each step from 25 to 800 \u00b0C. All rPET filaments were measured by a thermogravimetric analyzer . The samples were weighed (2\u20135 mg) and heated at a rate of 10 \u00b0C/min under nitrogen gas at a constant flow rate of 20 mL/min.TGA was used to determine the thermal properties of different ratios of Acid-Enz_TC/WF_rPET blended compounds were studied after melt spinning under an optical microscope . Fiber diameter was measured using software and fiber fineness (denier) was calculated using Equation (2):3) and a is the diameter (cm). In Equation (3):Cross section and longitudinal section fiber morphologies of different ratios of Acid-Enz_TC/WF_rPET blended compounds were measured following the standard method of ASTM D3822 [Tensile strength and percentage elongation at break of different ratios of Acid-Enz_TC/WF_TM D3822  using a \u22121 attributed to the O-H stretching vibration of cellulose [\u22121 attributed to the C\u2013H stretching vibration of cellulose [\u22121 was due to the \u2013C\u2013O\u2013C pyranose ring skeletal vibration and at 897 cm\u22121 due to \u03b2-glycosidic linkages of cellulose [\u22121, attributed to carbonyl (C=O) stretching, 1265 cm\u22121, attributed to the C(=O)-O stretching of the terephthalate group, and 1102 cm\u22121, attributed to the C-O stretching of ethylene glycol [3PO4_TC was found in the range 1700\u20131102 cm\u22121 with no peaks of cellulose remaining. The results showed that conditions for dissolving cotton apart from PET should be carried out with a cotton content of less than 50%, which is suitable for dissolving TC to reuse as rPET. For reusing CVC, conditions for cotton dissolving involved using a phosphoric acid treatment 2\u20133 times (All PET textile wastes from different ratios of cotton and PET at 60:40 (code CVC) and 35:65 (code TC) were defibrillated using a recycling machine, before pretreating with phosphoric acid at 85% to dissolve the cotton part. The remaining PET in all samples was then analyzed by FTIR spectroscopy. For the CVC fabric, the structure of the cotton was shown mainly at 3342 cmellulose , and at ellulose . The FTIellulose . The PETe glycol . Compare\u20133 times .3PO4_CVC or 85% H3PO4_TC) are shown in 3PO4_TC was similar to WF_PET (knitted PET fabric as the reference). Phosphoric acid pretreatment at 85% completely hydrolyzed the cotton component in TC.The thermal properties of the remaining PET fibers in treated CVC or TC fabrics after phosphoric acid pretreatment . The remaining PET fibers (H3PO4_TC) had Tm and Tc values comparable to PET fibers obtained from white knitted fabrics of PET (WF_PET). The remaining PET fibers from H3PO4_TC were used to prepare PET powder by a compression machine at the melting temperature of PET and then cooled to room temperature to prepare the PET sheet before grinding into powder for melt spinning. After compression, the H3PO4_TC sheet was burned at the edge because some cotton remained in the components. The phosphoric acid method did not completely remove the cotton in TC. Therefore, an enzymatic treatment was applied after phosphoric acid pretreatment following previous research [The thermal properties were analyzed by DSC, with both the absorption and exothermic thermograms presented in research .\u22121, 2923 cm\u22121, and 2854 cm\u22121. A pronounced peak of PET appeared at a wavenumber in the range of 1700\u20131102 cm\u22121. The 85% H3PO4-Enz_TC was hot pressed with compression at 250 \u00b0C without burning, and then ground to a powdered state using a combination of compression molding and mechanical grinding. This process was also applied to white PET fabric samples (WF_PET), as studied by [rPET uniform filament.udied by . SubsequrPET powders derived from white fabric and TC fabric waste, the predominant component was WF_rPET powder, which was blended with varying percentages of Acid_Enz_TC powder. The maximum allowable loading capacity and winding speed were set at 10% Acid_Enz_TC + 90% WF_rPET and 1500 m/min, respectively, due to limitations in spinnability. In processing, it is imperative to meticulously adjust key parameters, including the throughput rate, melting temperature, and winding speeds. This adjustment is critical to achieve consistent fiber formation with optimal performance  patterns at 16, 17.5, 22.5, and 25.5 degrees . The XRDrPET fiber samples that were either non-blended or blended with Acid-Enz_TC in varying quantities were determined by thermogravimetric analysis (TGA). The results showed no significant differences in the ability to withstand temperature increases within the range of 30\u2013392 \u00b0C, as depicted in rPET 100% exhibited the highest tendency to retain weight compared to Acid-Enz_TC+WF-rPET.The thermal stability properties of WF-Several factors contributed to this phenomenon. For instance, the white polyester fabric may contain trace additives that result in ash residuals, while blended fibers may exhibit fluctuations in weight loss due to the post-effects of the etching cellulose system that can alter some components and change physical properties. Moreover, the composition of the original fabric between 100% white polyester and TC fabric may differ in terms of properties and additive ratios, leading to variations in weight loss tendencies .rPET fibers prepared from recycled PET materials with different sources, specifically r-BO_PET grade , white fabric (WF-rPET), and varying %Acid_Enz_TC in WF-rPET. These fibers were manufactured through melting in situ with the increase of the winding speeds for each ratios.rPET exhibited a relatively low crystallization rate. Consequently, incomplete structures or the amorphous phase led to recrystallization when exposed to temperatures higher than Tg, as indicated by the significant exothermic cold crystallization area [ion area .rPET, the position of TC did not exhibit significant deviations from the references (r-BO and WF-rPET 100%). Conversely, 5% Acid_Enz_TC in WF_rPET induced the crystallization rate or faster nucleation, accompanied by an increase in the enthalpy of crystallization. The graph also displayed a sharp characteristic signifying the presence of Acid_Enz_TC in WF_rPET crystallinity. This potentially impacted the fiber formation behavior spin-line or processability. Furthermore, thermal experiences encountered during the fiber formation process may have consequential effects on the resulting fiber properties.rPET fibers between pure WF-rPET and WF-rPET blended with Acid-Enz_TC at different quantities are shown in rPET increased the crystallinity, this led to a reduction in the tenacity value due to the increased brittleness of the fibers. In industrial production, additional modifiers may need to be introduced to balance the flow properties and other factors to efficiently enable the production of recycled PET fibers.The graphs obtained from the tenacity and percentage elongation testing of rPET fibers, both non-blended and blended with varying quantities of Acid-Enz_TC, at a winding speed of 1000 m/min confirmed that these fibers were rPET fibers, consistent with research conducted by [rPET fibers blended with Acid-Enz_TC may still contain residual cellulose, as indicated by the FTIR results.As depicted in ucted by , and exhrPET materials extracted from the TC fabric were then converted into a powdered state using a combination of compression molding and mechanical grinding. This process was the same as that applied to white PET fabric samples (WF_rPET). Subsequently, both types of rPET, sourced from distinct origins, were subjected to the melt-spinning process with different ratios to produce uniform fibers.After effectively eliminating cellulose from the TC fabric through the application of an acid and enzyme system, the resulting Acid-Enz_TC fabrics were subjected to FTIR analysis to conclusively verify the presence of the PET component, as shown in rPET fibers. Previous researches had explored cellulose extraction from fabrics, but no subsequent steps were taken to reconvert this cellulose into fibers. In our experiment, TC (35% cotton/65% polyester) was employed to remove cotton components under varying phosphoric acid conditions, enhancing the rPET purity and yield. Enzyme pretreatment was also applied to degrade the remaining cotton after acid extraction. Significantly, only the combination of 85% H3PO4 and enzymes, referred to as 85%H3PO4-Enz_TC, enabled the continuation of fiber formation. The optimal condition for rPET fiber production involved melt spinning using 85% H3PO4-Enz_TC blended with pure rPET powder obtained from white recycled polyester fabric (WF_rPET) at a 5:95 ratio. The rPET displayed good stability, as indicated by its thermal profile via TGA and DSC, along with mechanical strength nearly comparable to 100% WF_rPET. These extensive analyses have provided valuable insights for further research into the recycling of multi-component polymer materials, particularly polyester\u2013cotton blends, as surplus products in the textile industry.In summary, this study focused on the conversion of a blend of cotton and polyester fabrics, constituting a \u201cmulti-component recycling process,\u201d into usable"}
+{"text": "Myxococcus xanthus that assembles following an outside-in pathway, starting with the polar incorporation of the PilQ secretin forming a multimeric T4aP conduit in the outer membrane. We demonstrate that PilQ recruitment to the nascent poles initiates during cytokinesis, but most are recruited to the new poles in the daughters after the completion of cytokinesis. This recruitment depends on the peptidoglycan-binding AMIN domains in PilQ. Moreover, the pilotin Tgl stimulates PilQ multimerization in the outer membrane, is transiently recruited to the nascent and new poles in a PilQ-dependent manner, and dissociates after the completion of secretin assembly. Altogether, our data support a model whereby PilQ polar recruitment and multimerization occur in two steps: the PilQ AMIN domains bind septal and polar peptidoglycan, thereby enabling polar Tgl localization, which then stimulates secretin multimerization in the outer membrane. Using computational analyses, we provide evidence for a conserved mechanism of T4aPM pilotins whereby the pilotin transiently interacts with the unfolded \u03b2-lip of the secretin monomer, i.e., the region that eventually inserts into the outer membrane. Finally, we suggest that the presence/absence of AMIN domain(s) in T4aPM secretins contributes to the different T4aPM localization patterns across bacteria.Type IVa pili (T4aP) are important for bacterial motility, adhesion, biofilm formation, and virulence. This versatility is based on their cycles of extension, adhesion, and retraction. The conserved T4aP machine (T4aPM) drives these cycles; however, the piliation pattern varies between species. To understand how these patterns are established, we focused on the T4aPM in Myxococcus xanthus by studying the localization of the PilQ secretin, the first component of this machine that assembles at the poles. Based on experiments using a combination of fluorescence microscopy, biochemistry, and computational structural analysis, we propose that PilQ, and specifically its AMIN domains, binds septal and polar peptidoglycan, thereby enabling polar Tgl localization, which then stimulates PilQ multimerization in the outer membrane. We also propose that the presence and absence of AMIN domains in T4aP secretins contribute to the different piliation patterns across bacteria.Type IVa pili (T4aP) are widespread bacterial cell surface structures with important functions in motility, surface adhesion, biofilm formation, and virulence. Different bacteria have adapted different piliation patterns. To address how these patterns are established, we focused on the bipolar localization of the T4aP machine in the model organism  In bacteria, motility is important for a wide range of processes, including virulence, colonization of habitats, and biofilm formation , 2. Two Myxococcus xanthus and Thermus thermophilus revealed that both forms are multilayered structures M. xanthutin PilQ \u201339. Setin PilQ . The N-ttin PilQ , 35, 37.tin PilQ . The pertin PilQ . For thetin PilQ \u201343,  protein PilO  . Because PilQ\u0394AMIN\u00d73-sfGFP was not detected by the PilQ antibodies   by deletdetected . Consisttibodies , we suggclusters . We concAMIN\u00d73-sfGFP)   and replEscherichia coli binds to septal PG during cytokinesis (pilQ mutant under the control of the vanillate-inducible promoter (Pvan) and then followed its polar recruitment , in which the conserved Cys residue (+1 in the mature protein)  was substituted to Gly to prevent its acylation and, therefore, transport to and anchoring in the OM. Additionally, because an Asp in position +2 of mature lipoproteins in E. coli can cause their retention in the IM , thus precluding their further analyses.To evaluate whether OM localization of Tgl is important for its function, we generated a strain expressing Tgln the IM , we alsotgl cell extracts, monomeric PilQ was enriched in the membrane fraction. Control proteins previously shown to localize to the IM or OM were enriched in the membrane fraction and a cytoplasmic control protein was enriched in the soluble fraction documenting that the fractionation procedure worked properly  cells, the PilQ monomer and multimer were enriched in the membrane fraction . Similarproperly .tgl cells after osmotic shock with sucrose and EDTA treatment. Monomeric PilQ was detected in the OM fraction of both strains   is divided into four main regions: the three AMIN domains connected by flexible linkers, the N0- and N3-domains, the \u03b2-lip region, and the C-terminal secretin domain (Mxa was modeled with high confidence using AlphaFold (P. aeruginosa PilQ (PilQPae) , supporting that the predicted structures are modeled with high confidence.Similar to other T4aPM secretins, PilQ from n domain . MonomerPilQPae)  .It is currently not known how pilotins of T4aPM secretins interact with their cognate secretin monomer. Therefore, to gain insights into how T4aPM secretins and their pilotins interact, we started with the  monomer . Remarka monomer . UnderscIn conclusion, we suggest that T4aPM pilotins by associating with the unfolded \u03b2-lip of their cognate monomeric secretin keep this region, part of which will ultimately be inserted into the OM, in a conformation optimal for oligomerization and OM insertion. Once the secretin monomers multimerize and the correctly folded \u03b2-lip integrates into the OM, the interaction with the pilotin would be lost, thus explaining why the pilotin only transiently associates with the secretin.M. xanthus depends on its AMIN domains. Because AMIN domains are not universally conserved in T4aPM secretins  do not follow this overall correlation.The PilQ secretins of ll poles \u201319, 25N domain ; Fig. S5N domain , 23, 24.ous T4aP , 22, botous T4aP ; Fig. S5 domains ; Fig. S5M. xanthus to understand how different localization patterns of T4aP are ultimately established. M. xanthus is an ideal system to address this question because the T4aPM assembly pathway is well-understood and initiates with the PilQ secretin in the OM  stimulating multimerization by maintaining an oligomerization-ready conformation of the PilQ monomer, (ii) protecting monomeric PilQ from proteolytic degradation, and (iii) ensuring that the assembled secretin only forms at the OM. Because Tgl is associated with the OM via its acylated N-terminus, CS1 and CS2 are close to the OM and, therefore, ideally positioned to assist in PilQ secretin integration into the OM. Once PilQ monomers multimerize and integrate into the OM, the interaction with Tgl would be lost because the \u03b2-lip is integrated into the OM and the interaction surfaces no longer available for interaction with Tgl  correlation did not match was A. baylyi. This species has a unique lateral piliation pattern, and its T4aPM secretin contains two AMIN domains. Interestingly, the lateral localization of the T4aPM, but not the assembly of the T4aPM, depends on the FimV protein  Bacto Casitone, 10 mM Tris-HCl pH 8.0, 1 mM K2HPO4/KH2PO4 pH 7.6, and 8 mM MgSO4] or on 1% CTT 1.5% agar (\u22121) or oxytetracycline (10 \u00b5g mL\u22121).All e DK1622  and are e DK1622  and were.5% agar  supplemeE. coli Mach1 , which was grown at 37\u00b0C in lysogeny broth  supplemented when required with kanamycin (50 \u00b5g mL\u22121).Plasmids used in this study are listed in All oligonucleotides used are listed in Table S1. All constructed plasmids were verified by DNA sequencing.pLC220 (plasmid for replacement of tgl with tgl-sfGFP in the native site): the tgl-sfGFP fragment was amplified from pSC104 : up- and downstream fragments were amplified using genomic DNA from M. xanthus DK1622 as DNA template and the primer pairs tgl_CtoG_A_HindIII/tgl_CtoG_Bov and tgl_CtoG_Cov/ tgl_CtoG_D_BamHI, respectively. To generate the full-length insert, an overlapping PCR using the two fragments as DNA templates and the primer pair tgl_CtoG_A_HindIII/ tgl_CtoG_D_BamHI was performed. Subsequently, the fragment was digested with HindIII and BamHI, and cloned into pBJ114.For pMH118 (plasmid for expression of pilQ-sfGFP from the 18\u201319 site under the control of the vanillate promoter): pilQ-sfGFP was amplified using genomic DNA from M. xanthus SA7192 (pilQ::pilQ-sfGFP) (For Q-sfGFP)  as DNA tpMH119 (plasmid for expression of tgl-sfGFP from the 18\u201319 site under the control of the vanillate promoter): tgl-sfGFP was amplified using genomic DNA from M. xanthus SA12016 (tgl::tgl-sfGFP) as DNA template and the primer pair Pvan_tgl_fw_NdeI/ sfGFP_rv_tgl_EcoRI. The fragment was digested with NdeI and EcoRI, and cloned into pMR3690.For pMH120 (plasmid for expression of tglC20G-sfGFP from the 18\u201319 site under the control of the vanillate promoter): tglC20G-sfGFP was amplified using genomic DNA from M. xanthus SA12035 (tglC20G::tgl-sfGFP) as DNA template and the primer pair Pvan_tgl_fw_NdeI/sfGFP_rv_tgl_EcoRI. The fragment was digested with NdeI and EcoRI, and cloned into pMR3690.For pMH121 (for generation of an in-frame deletion of the AMIN \u00d7 3 domains of native pilQ): up- and downstream fragments were amplified from genomic DNA from M. xanthus DK1622 using the primer pairs PilQ_dAMIN_A_XbaI/PilQ_dAMIN_B and PilQ_dAMIN_C/pilQ_dAMIN_D_HindIII, respectively. Subsequently, the up- and downstream fragments were used as a template for an overlapping PCR with the primer pair PilQ_dAMIN_A_XbaI/pilQ_dAMIN_D_HindIII, to generate the AD fragment. The AD fragment was digested with XbaI and HindIII, and cloned in pBJ114.For pMH122 (for generation of an in-frame deletion of tgl): up- and downstream fragments were amplified from genomic DNA of SA6053 (\u0394tgl) (For 3 (\u0394tgl)  using th3 (\u0394tgl) . The AD pMH125 (for replacement of pilQ with pilQAMINs\u00d73 (1\u2013475)-sfGFP in the native site of the pilQ::pilQ-sfGFP strain): up- and downstream fragments were amplified from pMH118 using the primer pairs PilQAMIN_A_KpnI/PilQAMIN_sfGFP_overlay_rev and PilQamin_sfGFP_overlay_fwd/sfGFP_rev_pilQ_EcoRI, respectively. Subsequently, the up- and downstream fragments were used as a template for an overlapping PCR with the primer pair PilQAMIN_A_KpnI/sfGFP_rev_pilQ_EcoRI, to generate the AD fragment. The AD fragment was digested with KpnI and EcoRI, and cloned in pBJ114.For pMH127 (plasmid for expression of tglS21D-sfGFP from the 18\u201319 site under the control of the vanillate promoter): tglS21D-sfGFP was amplified using pMH119 as DNA template and the primer pairs Pvan forw/Tgl_S21G_overlay_rev and Tgl_S21G_overlay_fwd/sfGFP_rv_tgl_EcoRI to introduce the point mutation. Subsequently, both PCR fragments were used as a template for an overlapping PCR with the primer pair Pvan forw/sfGFP_rv_tgl_EcoRI, to generate the full-length fragment. The fragment was digested with NdeI and EcoRI, and cloned into pMR3690.For pMP183 (plasmid for expression of pilQ from the 18\u201319 site under the control of the vanillate promoter): pilQ was amplified using pMH118 as DNA template and the primer pair Pvan_PilQ_fwd_NdeI/PilQ_rev_EcoRI. The fragment was digested with NdeI and EcoRI, and cloned into pMR3690.For M. xanthus cultures were harvested  and resuspended in 1% CTT to a calculated density of 7 \u00d7 109 cells mL\u22121. 5 \u00b5L aliquots were spotted on 0.5% CTT supplemented with 0.5% select-agar (Invitrogen). After 24 h incubation at 32\u00b0C, cells were imaged using an M205FA Stereomicroscope (Leica) equipped with a Hamamatsu ORCA-flash V2 Digital CMOS camera (Hamamatsu Photonics), and images were analyzed using Metamorph v7.5 (Molecular Devices).T4aP-dependent motility assays were performed as described . Briefly2HPO4/KH2PO4 pH 7.6, and 8 mM MgSO4), and supplemented with vanillate or cephalexin as indicated. For long time-lapse microscopy, the pad was additionally sealed with parafilm to reduce evaporation. Additionally, to prevent motility during time-lapse microscopy, strains contained an in-frame deletion of gltB (\u0394gltB), which encodes a component of the M. xanthus gliding motility machine  and concentrated to an optical density at 550 nm (OD550) of 28 in resuspension buffer . Cells were lysed by sonication with 5 \u00d7 30 pulses, pulse 60%, amplitude 60% with a UP200St sonifier and microtip (Hielscher), and the lysate was cleared by centrifugation . As a sample for total cellular protein, an aliquot of the cleared lysate was taken and mixed with 4 \u00d7 SDS lysis buffer . A 200 \u00b5L aliquot of the remaining supernatant was subjected to ultracentrifugation using an Air-Fuge (Beckman) . The resulting supernatant is enriched in soluble proteins and a sample was taken and mixed with 4 \u00d7 SDS lysis buffer. The pellet was washed by resuspension in 200 \u00b5L resuspension buffer and was subjected to ultracentrifugation as above. The remaining pellet, which is enriched in IM and OM membrane proteins, was resuspended in 100 \u00b5L 1 \u00d7 SDS lysis buffer. All samples were heated for 10 min at 95\u00b0C, separated by SDS-PAGE and analyzed by immunoblot.To fractionate M. xanthus cell suspension was harvested by centrifugation  and concentrated to an OD550 of 7 in 1 \u00d7 SDS lysis buffer. To isolate a fraction enriched for OM proteins, 50 mL of the cell suspension was harvested , and the pellet was gently resuspended in TSE8-buffer  to a concentration corresponding to OD550 = 50. The sample was incubated for 30 min at 4\u00b0C to release the OM, followed by centrifugation of the samples . The resulting supernatant is enriched in OM and periplasmic proteins and was recovered for the following steps, while the pellet, containing cells without OM or where the OM had not been released, was frozen at \u221220\u00b0C. Next, 150 \u00b5L of the supernatant was ultra-centrifuged using an Air-Fuge (Beckman)  to separate the OM from periplasmic proteins. The resulting supernatant was discarded and the OM-enriched pellet (OM fraction) was resuspended in 150 \u00b5L 1 \u00d7 SDS lysis buffer. The frozen pellet was thawed, resuspended to OD550 = 50 in resuspension buffer  and lysed by sonication. Cell debris was removed by centrifugation . The cell-free supernatant (~150 \u00b5L) was subjected to ultra-centrifugation as described above. The resulting supernatant contained cytoplasmic proteins and was mixed with 4 \u00d7 SDS lysis buffer (cytoplasmic fraction). All samples were boiled for 10 min at 95\u00b0C, separated by SDS-PAGE, and analyzed by immunoblot.As a sample for total cellular protein , 2 mL of an exponentially growing http://www.pymol.org/pymol) was used to analyze and visualize the models. Structural alignments were performed using the PyMOL Alignment plugin with default settings. Hydrophobicity was calculated in PyMOL according to the hydrophobicity scale (Full-length protein sequences or sequences in which the signal peptide was identified with SignalP 6.0  and remoty scale . Conservty scale . Proteinty scale  and the ty scale ."}
+{"text": "To explore the mechanisms of radiotherapy resistance and search for prognostic biomarkers for prostate cancer.The GSE192817 and TCGA PRAD datasets were selected and downloaded from the GEO and UCSC Xena databases. Differential expression and functional annotation analyses were applied to 52 tumour cell samples from GSE192817. Then, the ssGSEA or GSVA algorithms were applied to quantitatively score the biological functional activity of samples in the GSE192817 and TCGA PRAD datasets, combined with specific gene sets collected from the Molecular Signatures Database (MSigDB). Subsequently, the Wilcoxon rank-sum test was used to compare the differences in ssGSEA or GSVA scores among cell types or PRAD patients. Moreover, radiotherapy resistance-associated gene screening was performed on DU145 and PC3 cells (prostate cancer cells), and survival analysis was used to evaluate the efficacy of these genes for predicting the prognosis of PRAD patients.p value for PFS was 0.0072.A total of 114 genes that were differentially expressed in more than two different cancer cell types and associated with either sham surgery or radiotherapy treatment (X-ray or photon irradiation) were detected in cancer cells from GSE192817. Comparison of DNA damage-related ssGSEA scores between sham surgery and radiotherapy treatment in prostate cancer cells (DU145 and PC3) showed that photon irradiation was potentially more effective than X-ray treatment. In the TCGA PRAD dataset, patients treated with radiotherapy had much higher \u201cGOBP_CELLULAR_RESPONSE_TO_DNA_DAMAGE_STIMULUS\u201d, \u201cGOBP_G2_DNA_DAMAGE_CHECKPOINT\u201d and \u201cGOBP_INTRA_S_DNA_DAMAGE_CHECKPOINT\u201d GSVA scores, and the Wilcoxon rank-sum test p values were 0.0005, 0.0062 and 0.0800, respectively. Furthermore, SRXN1 was upregulated in DU145 cells (resistant to X-ray irradiation compared to PC3 cells) after radiotherapy treatment, and low SRXN1 expression in patients was beneficial to radiotherapy outcomes. The log-rank test Radiotherapy can damage DNA and induce oxidative stress to kill tumour cells. In this study, we found that SRXN1, as an antioxidative stress gene, plays an important role in radiotherapy for prostate cancer treatment, and this gene is also a potential biomarker for predicting the prognosis of patients treated with radiotherapy. Prostate cancer ranks second among all male malignancies in the world . In receIonizing radiation is an important way to treat malignant diseases . RadiatiIn this study, according to the differential expression analysis and quantitative scoring based on the ssGSVA algorithm in GSE192817, radiotherapy was found to significantly enhance DNA damage and cause tumour cell death. According to the analysis of prostate cancer cells, eight genes related to radiotherapy resistance were screened, and SRXN1 was identified as a potential biomarker for predicting the prognosis of prostate cancer in patients treated with radiotherapy.https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc\u2009=\u2009GSE192817). A total of 52 cell samples from GSE192817 were treated with sham surgery, proton irradiation or X-ray irradiation, and the majority of samples were collected after approximately 7\u00a0days. Combined with the sequencing platform GPL20844 of this dataset and the R package \"biomaRt\" (Version: 2.48.3), the microarray probe IDs were converted into gene symbols, and the gene types were classified. Moreover, TCGA prostate adenocarcinoma (PRAD) was downloaded from the UCSC Xena database (https://tcga.xenahubs.net), which included gene expression data and clinical follow-up data for patients.With \u201cradiotherapy\u201d and \u201ccancer\u201d as the keywords, we searched and selected the GSE192817 dataset from the GEO database  was used to perform GO functional annotation on the above differentially expressed genes.Differences in protein-coding genes were compared between the radiotherapy group (X-ray radiotherapy and proton radiotherapy) and the control group (sham surgery) based on the Wilcoxon rank sum test. Potentially differentially expressed genes in six types of cells were obtained based on a http://software.broadinstitute.org/gsea/msigdb). Subsequently, the \"ssGSEA\" algorithm in the R package \"GSVA\" (Version: 1.42.0) was used to quantitatively score the samples from the six types of cells in the GSE192817 dataset after 420\u00a0h of radiotherapy. For patients in the TCGA PRAD dataset, we used the \"GSVA\" algorithm for quantitative scoring and applied the Wilcoxon rank sum test for different comparisons.A total of four gene sets, namely, \"GOBP_CELLULAR_RESPONSE_TO_, DNA_DAMAGE_STIMULUS\", \"GOBP_G2_DNA_DAMAGE_CHECKPOINT\", \"GOBP_INTRA_S_DNA_DAMAGE_CHECKPOINT\" and \"GOBP_INTRINSIC_, APOPTOTIC_SIGNALING_PATHWAY_IN_RESAMAGE_TO_TO\", were downloaded from the MSigDB  was used to perform survival analysis on the above candidate genes, a univariate Cox proportional hazards model was constructed, and the log-rank test was performed. Finally, the HR [95% CI] of the risk model and the p-value test results were recorded.The Wilcoxon rank-sum test was used to compare the differences in genes between two prostate cancer cell types (DU145 and PC3) in the TCGA PRAD dataset according to either X-ray radiotherapy or sham surgery treatment, and FC values were calculated. Differentially expressed genes with a p-value test results were finally recorded.The gene set \"GOBP_RESPONSE_TO_OXIDATIVE_STRESS\" was identified and downloaded from the MSigDB, and a correlation analysis was performed between the gene expression of SRXN1 in the TCGA-PRAD dataset and the GSVA of the patient's oxidative stress response; the Pearson correlation coefficient (PCC) was calculated, and a correlation test (cor.test) was performed. The Wilcoxon rank sum test was used to compare the differences between the SRXN1 high-expression group and the SRXN1 low-expression group. The R package \"survival\" (Version: 3.2\u201311) was used to evaluate the oxidation. Survival analysis was performed on the GSVA score of the stress response, a univariate Cox proportional hazards model was constructed, the log-rank test was performed, and the HR [95% CI] of the risk model and the To explore the intrinsic molecular mechanism of radiotherapy resistance in tumour cells, with \u201cradiotherapy\u201d and \u201ccancer\u201d as the keywords, we searched and selected the GSE192817 dataset from the GEO database. In GSE192817, we analysed 52 cell samples  than after 4\u00a0h of radiation (only one sample was sequenced), but the DU145 ssGSEA score after 12\u00a0h of radiation was slightly higher than that after 420\u00a0h of radiotherapy  based on radiotherapy treatment, and the GSVA algorithm was used for quantitative scoring. The p values of the \u201cGOBP_CELLULAR_RESPONSE_TO_DNA_DAMAGE_STIMULUS\u201d, \u201cGOBP_G2_DNA_DAMAGE_CHECKPOINT\u201d and \u201cGOBP_INTRA_S_DNA_DAMAGE_CHECKPOINT\u201d GSVA scores between patients treated with and without radiotherapy were 0.0005, 0.0062 and 0.0800, respectively  and a low-expression group . The HR [95% CI] was 4.326 1.472\u201312.72], and the log-rank test2\u201312.72, p value was 0.0072   Fig.\u00a0.p value of 0.01  mainly exists in the endoplasmic reticulum and nuclear membrane, and it plays an important role in maintaining the integrity of the nuclear membrane and endoplasmic reticulum . In receIn conclusion, we have confirmed that radiotherapy can kill tumour cells through DNA damage and induce oxidative stress. However, in prostate cancer, upregulation of the antioxidative stress factor SRXN1 leads to radiotherapy tolerance, which can potentially be used to predict the prognosis of prostate cancer patients after radiotherapy. Due to limited experimental conditions, we plan to knock out or knock down SRXN1 expression in PC3 and DU145 cells and verify whether tumour cell sensitivity to radiotherapy will be increased after inhibiting SRXN1 expression."}
+{"text": "Gastric submucosal tumors (SMTs) can be reliably resected in a minimally invasive manner using endoscopic full-thickness resection (EFTR) because, for example, they do not require mesenteric processing. However, there are many issues regarding suture methods, device costs, technique difficulty, and suturing certainty. Laparoscopic intragastric surgery allows secure suturing of the stomach wall from within the stomach by laparoscopyCASE 1: A 76-year-old man had a 30-mm SMT with delle at the lesser curvature of the upper gastric body . EmergeVideo\u20061\u2002This video demonstrates the clinical utility of combining endoscopic full thickness resection and laparoscopic intragastric surgery to treat a gastric submucosal tumor.CASE 2: A 75-year-old woman had a 25-mm SMT at the posterior wall of the middle gastric body. After the tumor had been resected by EFTR , the paEndoscopy_UCTN_Code_CCL_1AB_2AD_3AB"}
+{"text": "Background: Expression proteomics involves the global evaluation of protein abundances within a system. In turn, differential expression analysis can be used to investigate changes in protein abundance upon perturbation to such a system.Methods: Here, we provide a workflow for the processing, analysis and interpretation of quantitative mass spectrometry-based expression proteomics data. This workflow utilizes open-source R software packages from the Bioconductor project and guides users end-to-end and step-by-step through every stage of the analyses. As a use-case we generated expression proteomics data from HEK293 cells with and without a treatment. Of note, the experiment included cellular proteins labelled using tandem mass tag (TMT) technology and secreted proteins quantified using label-free quantitation (LFQ).Results: The workflow explains the software infrastructure before focusing on data import, pre-processing and quality control. This is done individually for TMT and LFQ datasets. The application of statistical differential expression analysis is demonstrated, followed by interpretation via gene ontology enrichment analysis.Conclusions: A comprehensive workflow for the processing, analysis and interpretation of expression proteomics is presented. The workflow is a valuable resource for the proteomics community and specifically beginners who are at least familiar with R who wish to understand and make data-driven decisions with regards to their analyses. Proteins are responsible for carrying out a multitude of biological tasks, implementing cellular functionality and determining phenotype. Mass spectrometry (MS)-based expression proteomics allows protein abundance to be quantified and compared between samples. In turn, differential protein abundance can be used to explore how biological systems respond to a perturbation. Many research groups have applied such methodologies to understand mechanisms of disease, elucidate cellular responses to external stimuli, and discover diagnostic biomarkers  label-free or (2) label-based quantitation. Moreover, the latter can be implemented with a number of different peptide labelling chemistries, for example, using tandem mass tag (TMT), stable-isotope labelling by amino acids in cell culture (SILAC), isobaric tags for relative and absolute quantitation (iTRAQ), among others. MS analysis can also be used in either data-dependent or data-independent acquisition (DDA or DIA) mode., Although all of these experimental methods typically result in a similar output, a matrix of quantitative values, the data are different and must be treated as such. Secondly, data processing is dependent upon the experimental goal and biological question being asked.The data generated during an expression proteomics experiment are complex, and unfortunately there is no one-size-fits-all method for the processing and analysis of such data. The reason for this is two-fold. Firstly, there are a wide range of experimental methods that can be used to generate expression proteomics data. Researchers can analyse full-length proteins (top-down proteomics) or complete an enzymatic digestion and analyse the resulting peptides. This proteolytic digestion can be either partial (middle-down proteomics) or complete (bottom-up proteomics). The latter approach is most commonly used as peptides have a more favourable ionisation capacity, predictable fragmentation patterns, and can be separated via reversed phase liquid chromatography, ultimately making them more compatible with MS..txt file. Such files are the outputs of most major third party search software . We begin with data import and then guide users through the stages of data processing including data cleaning, quality control filtering, management of missing values, imputation, and aggregation to protein-level. Finally, we finish with how to discover differentially abundant proteins and carry out biological interpretation of the resulting data. The latter will be achieved through the application of gene ontology (GO) enrichment analysis. Hence, users can expect to generate lists of proteins that are significantly up- or downregulated in their system of interest, as well as the GO terms that are significantly over-represented in these proteins.Here, we provide a step-by-step workflow for processing, analysing and interpreting expression proteomics data derived from a bottom-up experiment using DDA and either LFQ or TMT label-based peptide quantitation. We outline how to process the data starting from a peptide spectrum match (PSM)- or peptide- level we make use of several state-of-the-art packages from the open-source, open-development Bioconductor project to analyse use-case expression proteomics datasets from both LFQ and label-based technologies.Using the R statistical programming environmentR Bioconductor project. TheBioconductor initiative provides R software packages dedicated to the processing of high-throughput complex biological data. Packages are open-source, well-documented and benefit from an active community of developers. We recommend users to download the RStudio integrated development environment (IDE) which provides a graphical interface to R programming language.In this workflow we make use of open-source software from theBioconductor Installation page. The main packages required for this workflow are installed using the code below.if ) {install.packages(\"BiocManager\")}BiocManager::install)Detailed instructions for the installation of Bioconductor packages are documented on thelibrary function. For example, to load theQFeatures package one would typelibrary(\"QFeatures\") after installation. Here we load all packages included in this workflow.\"QFeatures\")library(\"ggplot2\")library(\"stringr\")library(\"dplyr\")library(\"tibble\")librarylibrary(\"corrplot\")library(\"Biostrings\")library(\"limma\")library(\"org.Hs.eg.db\")library(\"clusterProfiler\")library(\"enrichplot\")library left untreated, or (ii) provided with the treatment of interest. These two conditions are referred to as \u2018control\u2019 and \u2018treated\u2019, respectively. Each condition was evaluated in triplicate. At 96-hours post-treatment, samples were collected and separated into cell pellet and supernatant fractions containing cellular and secreted proteins, respectively. Both fractions were denatured, alkylated and digested to peptides using trypsin., Of note, TMT labelling of cellular proteins was achieved using a single TMT6plex. Hence, this workflow will not include guidance on multi-batch TMT effects or the use of internal reference scaling. For more information about the use of multiple TMTplexes users are directed to Refs.The supernatant fractions were de-salted and analysed over a two-hour gradient in an Orbitrap Fusion\u2122 Lumos\u2122 Tribrid\u2122 mass spectrometer coupled to an UltiMate\u2122 3000 HPLC system (Thermo Fisher Scientific). LFQ was achieved at the MS1 level based on signal intensities. Cell pellet fractions were labelled using TMT technology before being pooled and subjected to high pH reversed-phase peptide fractionation giving a total of 8 fractions. As before, each fraction was analysed over a two-hour gradient in an Orbitrap Fusion\u2122 Lumos\u2122 Tribrid\u2122 mass spectrometer coupled to an UltiMate\u2122 3000 HPLC system (Thermo Fisher Scientific). To improve the accuracy of the quantitation of TMT-labelled peptides, synchronous precursor selection (SPS)-MS3 data acquisition was employed.The cell pellet and supernatant datasets were handled independently and we take advantage of this to discuss the processing of TMT-labelled and LFQ proteomics data. In both cases, the raw MS data were processed using Proteome Discoverer v2.5 (Thermo Fisher Scientific). While the focus in the workflow presented below is differential protein expression analysis, the data processing and quality control steps described here are applicable to any TMT or LFQ proteomics dataset. Importantly, however, the experimental aim will influence data-guided decisions and the considerations discussed here likely differ from those of spatial proteomics, for example., partner repository with the dataset identifier PXD041794, Zenodo athttp://doi.org/10.5281/zenodo.7837375 and at the Github repositoryhttps://github.com/CambridgeCentreForProteomics/f1000_expression_proteomics/. Users are advised to download these files into their current working directory. In R thesetwd function can be used to specify a working directory, or if using RStudio one can use the Session -> Set Working Directory menu.The files required for this workflow can be found deposited to the ProteomeXchange Consortium via the PRIDEQFeatures, Bioconductor package. Prior to utilising theQFeatures infrastructure, it is first necessary to understand the structure of aSummarizedExperiment object asQFeatures objects are based on theSummarizedExperiment class. ASummarizedExperiment, often referred to as an SE, is a data container and S4 object comprised of three components: (1) thecolData (column data) containing sample metadata, (2) therowData containing data features, and (3) theassay storing quantitation data, as illustrated incolData function. Data features, accessed via therowData function, represent information derived from the identification search. Examples include peptide sequence, master protein accession, and confidence scores. Finally, quantitative data is stored in theassay slot. These three independent data structures are neatly stored within a singleSummarizedExperiment object.To be able to conveniently track each step of this workflow, users should make use of the Quantitative features for mass spectrometry, orQFeatures object holds each level of quantitative proteomics data, namely (but not limited to) the PSM, peptide and protein-level data. Each level of the data is stored as its ownSummarizedExperiment within a singleQFeatures object. The lowest level data e.g. PSM, is first imported into aQFeatures object before aggregating upward towards protein-level . In the current experiment the order of TMT labels was randomised in an attempt to minimise the effect of TMT channel leakage. For ease of grouping and simplification of downstream visualisation, samples are re-ordered during the import step. This is done by creating a vector containing the sample column names in their correct order. If samples are already in the desired order, the vector can be created by simply indexing the quantitative columns.## Locate the PSM .txt filecp_psm <- \"cell_pellet_tmt_results_psms.txt\"## Identify columns containing quantitative datacp_psm %>%  read.delim %>%  names##  [1] \"PSMs.Workflow.ID\"                  \"PSMs.Peptide.ID\"##  [3] \"Checked\"                           \"Tags\"##  [5] \"Confidence\"                        \"Identifying.Node.Type\"##  [7] \"Identifying.Node\"                  \"Search.ID\"##  [9] \"Identifying.Node.No\"               \"PSM.Ambiguity\"## [11] \"Sequence\"                          \"Annotated.Sequence\"## [13] \"Modifications\"                     \"Number.of.Proteins\"## [15] \"Master.Protein.Accessions\"         \"Master.Protein.Descriptions\"## [17] \"Protein.Accessions\"                \"Protein.Descriptions\"## [19] \"Number.of.Missed.Cleavages\"        \"Charge\"## [21] \"Original.Precursor.Charge\"         \"Delta.Score\"## [23] \"Delta.Cn\"                          \"Rank\"## [25] \"Search.Engine.Rank\"                \"Concatenated.Rank\"## [27] \"mz.in.Da\"                          \"MHplus.in.Da\"## [29] \"Theo.MHplus.in.Da\"                 \"Delta.M.in.ppm\"## [31] \"Delta.mz.in.Da\"                    \"Ions.Matched\"## [33] \"Matched.Ions\"                      \"Total.Ions\"## [35] \"Intensity\"                         \"Activation.Type\"## [37] \"NCE.in.Percent\"                    \"MS.Order\"## [39] \"Isolation.Interference.in.Percent\" \"SPS.Mass.Matches.in.Percent\"## [41] \"Average.Reporter.SN\"               \"Ion.Inject.Time.in.ms\"## [43] \"RT.in.min\"                         \"First.Scan\"## [45] \"Last.Scan\"                         \"Master.Scans\"## [47] \"Spectrum.File\"                     \"File.ID\"## [49] \"Abundance.126\"                     \"Abundance.127\"## [51] \"Abundance.128\"                     \"Abundance.129\"## [53] \"Abundance.130\"                     \"Abundance.131\"## [55] \"Quan.Info\"                         \"Peptides.Matched\"## [57] \"XCorr\"                             \"Number.of.Protein.Groups\"## [59] \"Percolator.q.Value\"                \"Percolator.PEP\"## [61] \"Percolator.SVMScore\"## Store location of quantitative columns in a vector in the desired orderabundance_ordered <- cThe columns containing quantitative data also need to be identified before import. To check the column names we usereadQFeatures function and provide these two pieces of information. We also specify that the file is tab-delimited by includingsep = \u201c\\t\u201d . Of note, thereadQFeatures function can also takefnames as an argument to specify a column to be used as the row names of the imported object. Whilst previousQFeatures vignettes used the \u201cSequence\u201d or \u201cAnnotated.Sequence\u201d as row names, we advise against this because of the presence of PSMs matched to the same peptide sequence with different modifications. In such cases, multiple rows would have the same name forcing thereadQFeatures function to output a \u201cmaking assay row names unique\u201d message and add an identifying number to the end of each duplicated row name. These sequences would then be considered as unique during the aggregation of PSM to peptide, thus resulting in two independent peptide-level quantitation values rather than one. Therefore, we do not pass afnames argument and the row names automatically become indices. Finally, we pass the name argument to indicate the type of data added.## Create QFeaturescp_qf <- readQFeaturesNow that the necessary file and its quantitative data columns have been identified, we can pass this to theQFeatures data object is a list ofSummarizedExperiment objects. As such, an individualSummarizedExperiment can be accessed using the standard double bracket nomenclature, as demonstrated in the code chunk below.## Index using positioncp_qf[[1]]## class: SummarizedExperiment## dim: 48832 6## metadata(0):## assays(1): \u201d## rownames(48832): 1 2 \u2026 48831 48832## rowData names(55): PSMs.Workflow.ID PSMs.Peptide.ID \u2026 Percolator.PEP##   Percolator.SVMScore## colnames(6): Abundance.128 Abundance.127 \u2026 Abundance.126##   Abundance.130## colData names(0):## Index using namecp_qf[[\"psms_raw\"]]## class: SummarizedExperiment## dim: 48832 6## metadata(0):## assays(1): \u201d## rownames(48832): 1 2 \u2026 48831 48832## rowData names(55): PSMs.Workflow.ID PSMs.Peptide.ID \u2026 Percolator.PEP##   Percolator.SVMScore## colnames(6): Abundance.128 Abundance.127 \u2026 Abundance.126##   Abundance.130## colData names(0):As outlined above, arowData,colData orassay data from a particularSummarizedExperiment within aQFeatures object users can make use of therowData,colData andassay functions. For plotting or data transformation it is necessary to convert to adata.frame ortibble.## Access feature information with rowData## The output should be converted to data.frame/tibble for further processingcp_qf[[\"psms_raw\"]] %>%  rowData %>%  as_tibble %>%  summarise(mean_intensity = mean(Intensity))## # A tibble: 1 x 1##    mean_intensity##             <dbl>## 1       13281497.A summary of the data contained in the slots is printed to the screen. To retrieve thecolData slot. It is also useful to clean up sample names such that they are short, intuitive and informative. This is done by editing thecolnames. These steps may not always be necessary depending upon the identification search output.## Clean sample namescolnames(cp_qf[[\"psms_raw\"]]) <- paste0## Add sample info as colData to QFeatures objectcp_qf$label <- ccp_qf$sample <- paste0cp_qf$condition <- rep, each = 3)## VerifycolData(cp_qf)## DataFrame with 6 rows and 3 columns##           label        sample     condition##     <character>   <character>   <character>## S1       TMT128            S1       Treated## S2       TMT127            S2       Treated## S3       TMT131            S3       Treated## S4       TMT129            S4       Control## S5       TMT126            S5       Control## S6       TMT130            S6       Control## Assign the colData to first assay as wellcolData(cp_qf[[\"psms_raw\"]]) <- colData(cp_qf)Having imported the data, each sample is first annotated with its TMT label, sample reference and condition. As this information is experimental metadata, it is added to the## Check what information has been importedcp_qf[[\"psms_raw\"]] %>%  rowData %>%  colnames##  [1] \"PSMs.Workflow.ID\"                  \"PSMs.Peptide.ID\"##  [3] \"Checked\"                           \"Tags\"##  [5] \"Confidence\"                        \"Identifying.Node.Type\"##  [7] \"Identifying.Node\"                  \"Search.ID\"##  [9] \"Identifying.Node.No\"               \"PSM.Ambiguity\"## [11] \"Sequence\"                          \"Annotated.Sequence\"## [13] \"Modifications\"                     \"Number.of.Proteins\"## [15] \"Master.Protein.Accessions\"         \"Master.Protein.Descriptions\"## [17] \"Protein.Accessions\"                \"Protein.Descriptions\"## [19] \"Number.of.Missed.Cleavages\"        \"Charge\"## [21] \"Original.Precursor.Charge\"         \"Delta.Score\"## [23] \"Delta.Cn\"                          \"Rank\"## [25] \"Search.Engine.Rank\"                \"Concatenated.Rank\"## [27] \"mz.in.Da\"                          \"MHplus.in.Da\"## [29] \"Theo.MHplus.in.Da\"                 \"Delta.M.in.ppm\"## [31] \"Delta.mz.in.Da\"                    \"Ions.Matched\"## [33] \"Matched.Ions\"                      \"Total.Ions\"## [35] \"Intensity\"                         \"Activation.Type\"## [37] \"NCE.in.Percent\"                    \"MS.Order\"## [39] \"Isolation.Interference.in.Percent\" \"SPS.Mass.Matches.in.Percent\"## [41] \"Average.Reporter.SN\"               \"Ion.Inject.Time.in.ms\"## [43] \"RT.in.min\"                         \"First.Scan\"## [45] \"Last.Scan\"                         \"Master.Scans\"## [47] \"Spectrum.File\"                     \"File.ID\"## [49] \"Quan.Info\"                         \"Peptides.Matched\"## [51] \"XCorr\"                             \"Number.of.Protein.Groups\"## [53] \"Percolator.q.Value\"                \"Percolator.PEP\"## [55] \"Percolator.SVMScore\"## Find out how many PSMs are in the datacp_qf[[\"psms_raw\"]] %>%  dim##\u2003\u2003[1]\u2003\u200348832\u2003\u20036<- cp_qf[[\"psms_raw\"]] %>%  nrow %>%  as.numericoriginal_psms As well as cleaning and annotating the data, it is always advisable to check that the import worked and that the data looks as expected. Further, preliminary exploration of the data can provide an early sign of whether the experiment and subsequent identification search were successful. Importantly, however, the names of key parameters will vary depending on the software used, and will likely change over time. Users will need to be aware of this and modify the code in this workflow accordingly.## Find out how many peptides and master proteins are in the dataoriginal_peps <- cp_qf[[\"psms_raw\"]] %>%  rowData %>%  as_tibble %>%  pull(Sequence) %>%  unique %>%  length %>%  as.numericoriginal_prots <- cp_qf[[\"psms_raw\"]] %>%  rowData %>%  as_tibble %>%  pull(Master.Protein.Accessions) %>%  unique %>%  length %>%  as.numericprint)##\u2003\u2003[1]\u2003\u200325969\u2003\u20035040We can see that the original data includes 48832 PSMs across the 6 samples. It is also useful to make note of how many peptides and proteins the raw PSM data corresponds to, and to track how many we remove during the subsequent filtering steps. This can be done by checking how many unique entries are located within the \u201cSequence\u201d and \u201cMaster.Protein.Accessions\u201d for peptides and proteins, respectively. Of note, searching for unique peptide sequences means that the number of peptides does not include duplicated sequences with different modifications.table function for discrete parameters andsummary for those which are continuous. This is also helpful for users who are analysing publicly available data and have limited knowledge about the identification search parameters.## Check missed cleavagescp_qf[[\"psms_raw\"]] %>%  rowData %>%  as_tibble %>%  pull(Number.of.Missed.Cleavages) %>%  table## .##     0     1    2## 46164  2592   76## Check precursor mass tolerancecp_qf[[\"psms_raw\"]] %>%  rowData %>%  as_tibble %>%  pull(Delta.M.in.ppm) %>%  summary##    Min.   1st Qu.  Median    Mean  3rd Qu.   Max.## -8.9300  -0.6000  0.3700  0.6447  1.3100  9.6700## Check fragment mass tolerancecp_qf[[\"psms_raw\"]] %>%  rowData %>%  as_tibble %>%  pull(Delta.mz.in.Da) %>%  summary##       Min.      1st Qu.    Median       Mean     3rd Qu.      Max.## -0.0110400  -0.0004100  0.0002500  0.0006812  0.0010200  0.0135100## Check PSM confidence allocationscp_qf[[\"psms_raw\"]] %>%  rowData %>%  as_tibble %>%  pull(Confidence) %>%  table## .##   High##  48832Hence, the output of the identification search contains 48832 PSMs corresponding to 25969 peptide sequences and 5040 master proteins. Finally, we confirm that the identification search was carried out as expected. For this, we print summaries of the key search parameters using theExperimental quality control of TMT-labelled quantitive proteomics data takes place in two steps: (1) assessment of the raw mass spectrometry data, and (2) evaluation of TMT labelling efficiency.Spectra Bioconductor package which allows for visualisation and exploration of raw chromatograms and spectra, among other features.Having taken an initial look at the output of the identification search, it is possible to create some simple plots to inspect the raw mass spectrometry data. Such plots are useful in revealing problems that may have occurred during the mass spectrometry run but are far from extensive. Users who wish to carry out a more in-depth evaluation of the raw mass spectrometry data may benefit from use of the## Generate scatter plot of mass accuracycp_qf[[\"psms_raw\"]] %>%  rowData %>%  as_tibble %>%  ggplot) +  geom_point +  geom_hline +  geom_hline +  labs(x = \"RT (min)\", y = \"Delta precursor mass (ppm)\") +  scale_x_continuous, breaks = seq) +  scale_y_continuous, breaks = c) +  ggtitle(\"PSM retention time against delta precursor mass\") +  theme_bwThe first plot we generate looks at the delta precursor mass, that is the difference between observed and estimated precursor mass, across retention time. Importantly, exploration of this raw data feature can only be done when using the raw data prior to recalibration. For users of Proteome Discoverer, this means using the spectral files node rather than the spectral files recalibration node.Since we applied a precursor mass tolerance of 10 ppm during the identification search, all of the PSMs are within## Generate scatter plot of ion inject time across retention timecp_qf[[\"psms_raw\"]] %>%  rowData %>%  as_tibble %>%  ggplot) +  geom_point +  geom_hline +  labs(x = \"RT (min)\", y = \"Ion inject time (ms)\") +  scale_x_continuous, breaks = seq) +  scale_y_continuous, breaks = seq) +  ggtitle(\"PSM retention time against ion inject time\") +  theme_bwThe second quality control plot of raw data is that of MS2 ion inject time across the retention time gradient. Here, it is desirable to achieve an average MS2 injection time of 50 ms or less, although the exact target threshold will depend upon the sample load. If the average ion inject time is longer than desired, then the ion transfer tube and/or front end optics of the instrument may require cleaning.## Plot histogram of PSM ion inject timecp_qf[[\"psms_raw\"]] %>%  rowData %>%  as_tibble %>%  ggplot(aes(x = Ion.Inject.Time.in.ms)) +  geom_histogram(binwidth = 1) +  labs(x = \"Ion inject time (ms)\", y = \"Frequency\") +  scale_x_continuous, breaks = seq) +  ggtitle(\"PSM frequency across ion injection time\") +  theme_bw## Plot histogram of PSM retention timecp_qf[[\"psms_raw\"]] %>%  rowData %>%  as_tibble %>%  ggplot(aes(x = RT.in.min)) +  geom_histogram(binwidth = 1) +  labs(x = \"RT (min)\", y = \"Frequency\") +  scale_x_continuous) +  ggtitle(\"PSM frequency across retention time\") +  theme_bwFrom this plot we can see that whilst there is a high density of PSMs at low inject times, there are also many data points found at the 50 ms threshold. This indicates that by increasing the time allowed for ions to accumulate in the ion trap, the number of PSMs could also have been increased. Finally, we inspect the distribution of PSMs across both the ion injection time and retention time by plotting histograms.The four plots that we have generated look relatively standard with no obvious problems indicated. Therefore, we continue by evaluating the quality of the processed data.The most fundamental data quality control step in a TMT experiment is to check the TMT labelling efficiency. TMT labels react with amine groups present at the peptide N-terminus as well as the side chain of lysine (K) residues. Of note, lysine residues can be TMT modified regardless of whether they are present at the C-terminus of a trypic peptide or internally following miscleavage.To evaluate the TMT labelling efficiency, a separate identification search of the raw data was completed with lysine (K) and peptide N-termini TMT labels considered as dynamic modifications rather than static. No additional residues (S or T) were evaluated for labelling in the search. This allows the search engine to assess the presence of both the modified (TMT labelled) and unmodified  forms of each peptide. The relative proportions of modified and unmodified peptides can then be used to calculate the TMT labelling efficiency. To demonstrate how to check for TMT labelling efficiency, only two of the eight fractions were utilised for this search..txt file directly as aSummarizedExperiment rather than aQFeatures object. This is done using thereadSummarizedExperiment function and the same arguments as those inreadQFeatures.## Locate the PSM .txt filetmt_psm <- \"cell_pellet_tmt_efficiency_psms.txt\"## Identify columns containing quantitative datatmt_psm %>%  read.delim %>%  names##  [1] \"PSMs.Workflow.ID\"                  \"PSMs.Peptide.ID\"##  [3] \"Checked\"                           \"Tags\"##  [5] \"Confidence\"                        \"Identifying.Node.Type\"##  [7] \"Identifying.Node\"                  \"Search.ID\"##  [9] \"Identifying.Node.No\"               \"PSM.Ambiguity\"## [11] \"Sequence\"                          \"Annotated.Sequence\"## [13] \"Modifications\"                     \"Number.of.Proteins\"## [15] \"Master.Protein.Accessions\"         \"Master.Protein.Descriptions\"## [17] \"Protein.Accessions\"                \"Protein.Descriptions\"## [19] \"Number.of.Missed.Cleavages\"        \"Charge\"## [21] \"Original.Precursor.Charge\"         \"Delta.Score\"## [23] \"Delta.Cn\"                          \"Rank\"## [25] \"Search.Engine.Rank\"                \"Concatenated.Rank\"## [27] \"mz.in.Da\"                          \"MHplus.in.Da\"## [29] \"Theo.MHplus.in.Da\"                 \"Delta.M.in.ppm\"## [31] \"Delta.mz.in.Da\"                    \"Ions.Matched\"## [33] \"Matched.Ions\"                      \"Total.Ions\"## [35] \"Intensity\"                         \"Activation.Type\"## [37] \"NCE.in.Percent\"                    \"MS.Order\"## [39] \"Isolation.Interference.in.Percent\" \"SPS.Mass.Matches.in.Percent\"## [41] \"Average.Reporter.SN\"               \"Ion.Inject.Time.in.ms\"## [43] \"RT.in.min\"                         \"First.Scan\"## [45] \"Last.Scan\"                         \"Master.Scans\"## [47] \"Spectrum.File\"                     \"File.ID\"## [49] \"Abundance.126\"                     \"Abundance.127\"## [51] \"Abundance.128\"                     \"Abundance.129\"## [53] \"Abundance.130\"                     \"Abundance.131\"## [55] \"Quan.Info\"                         \"Peptides.Matched\"## [57] \"XCorr\"                             \"Number.of.Protein.Groups\"## [59] \"Contaminant\"                       \"Percolator.q.Value\"## [61] \"Percolator.PEP\"                    \"Percolator.SVMScore\"## Read in as a SummarizedExperimenttmt_se <- readSummarizedExperiment## Clean sample namescolnames(tmt_se) <- paste0## Add sample info as colData to QFeatures objecttmt_se$label <- ctmt_se$sample <- paste0tmt_se$condition <- rep, each = 3)## VerifycolData(tmt_se)## DataFrame with 6 rows and 3 columns##          label      sample   condition##    <character> <character> <character>## S1      TMT128          S1     Treated## S2      TMT127          S2     Treated## S3      TMT131          S3     Treated## S4      TMT129          S4     Control## S5      TMT126          S5     Control## S6      TMT130          S6     ControlAs we will only look at TMT efficiency at the PSM-level, here we upload therowData. Using this information, the TMT labelling efficiency of the experiment is calculated using the code chunks below. Users should alter this code if TMTpro reagents are being used such that \u201cTMT6plex\u201d is replaced by \u201cTMTpro\u201d.Information about the presence of labels is stored within the \u2018Modifications\u2019 feature of the## Count the total number of PSMstmt_total <- length(tmt_se)## Count the number of PSMs with an N-terminal TMT modificationnterm_labelled_rows <- grep(\"N-Term\\\\(TMT6plex\\\\)\",                            rowData(tmt_se)$Modifications)nterm_psms_labelled <- length(nterm_labelled_rows)## Calculate N-terminal TMT labelling efficiencyefficiency_nterm <-  * 100efficiency_nterm %>%  round(digits = 1) %>%  print## [1] 96.8First we consider the efficiency of peptide N-termini TMT labelling. We use the grep function to identify PSMs which are annotated as having an N-Term TMT6plex modification. We then calculate the number of PSMs with this annotation as a proportion of the total number of PSMs.## Count the number of lysine TMT6plex modifications in the PSM datak_tmt <- str_count(string = rowData(tmt_se)$Modifications,                   pattern = \"K[0-9]{1,2}\\\\(TMT6plex\\\\)\") %>%  sum %>%  as.numeric## Count the number of lysine residues in the PSM datak_total <- str_count(string = rowData(tmt_se)$Sequence,                     pattern = \"K\") %>%  sum %>%  as.numeric## Determine the percentage of TMT labelled lysinesefficiency_k <-  * 100efficiency_k %>%  round(digits = 1) %>%  print## [1] 98.5Secondly, we consider the TMT labelling efficiency of lysine (K) residues. As mentioned above, lysine residues can be TMT labelled regardless of their position within a peptide. Hence, we here calculate lysine labelling efficiency on a per lysine residue basis.Users should aim for an overall TMT labelling efficiency >90% in order to achieve reliable quantitation. In cases where labelling efficiency is towards the lower end of the acceptable range, TMT labels should be set as dynamic modifications during the final identification search, although this will increase the search space and time as well as influencing false discovery rate (FDR) calculations. A summary of the current advice from Thermo Fisher is provided inSince the use-case data has a sufficiently high TMT labelling efficiency, we can continue to use the output of the identification search. This search considered TMT labelling of lysines as a static modification whilst N-terminal labelling was kept as dynamic, to investigate the presence of protein N-terminal modifications.SummarizedExperiment, called \u201cpsms_filtered\u201d, and add it to theQFeatures object. This is done using theaddAssay function. All changes made at the PSM-level will then only be applied to this second copy, so that we can refer back to the original data if needed.## Extract the \"psms_raw\" SummarizedExperimentdata_copy <- cp_qf[[\"psms_raw\"]]## Add copy of SummarizedExperimentcp_qf <- addAssay## Verifycp_qf## An instance of class QFeatures containing 2 assays:## [1] psms_raw: SummarizedExperiment with 48832 rows and 6 columns## [2] psms_filtered: SummarizedExperiment with 48832 rows and 6 columnsBeing confident that the experiment and identification search were successful, we can now begin with some basic data cleaning. However, we also want to keep a copy of the raw PSM data. Therefore, we first create a second copy of the PSMassay (orSummarizedExperiment) to theQFeatures object does not automatically generate links between theseassays. We will manually add the explicit links later, after we complete data cleaning and filtering.Of note, manually adding andplyr::count function. The unwanted rows are removed using thefilterFeatures function. Since we only wish to apply the filters to the \u201cpsms_filtered\u201d level, we specify this by using thei = argument. If this argument is not used,filterFeatures will remove features from allassays within aQFeatures object.The cleaning steps included in this section are non-specific and should be applied to all quantitative proteomics datasets. The names of key parameters will vary in data outputs from alternative third party software, however, and users should remain aware of both terminology changes over time as well as the introduction of new filters. All data cleaning steps are completed in the same way. We first determine how many rows, here PSMs, meet the conditions for removal. This is achieved by using thedplyr::count on the master protein column. Any master proteins that returnTRUE will be removed by filtering. If this returns noTRUE values, users should move on to the next filtering step without removing rows as this will introduce an error.## Find out how many PSMs we expect to losecp_qf[[\"psms_filtered\"]] %>%  rowData %>%  as_tibble %>%  dplyr::count(Master.Protein.Accessions == \"\")## # A tibble: 2 x 2##   \u2018Master.Protein.Accessions == \"\"\u2018 n##   <lgl>                         <int>## 1 FALSE                         48660## 2 TRUE                            172The first common cleaning step we carry out is the removal of PSMs that have not been assigned to a master protein during the identification search. This can happen when the search software is unable to resolve conflicts caused by the presence of the isobaric amino acids leucine and isoleucine. Before implementing the filter, it is useful to find out how many PSMs we expect to remove. This is easily done by using the\"Removing\",      length(which(rowData(        cp_qf[[\"psms_filtered\"]])$Master.Protein.Accessions == \"\")),      \"PSMs without a master protein accession\") %>%  messagepasteThis code could be adapted to each cleaning and filtering step. To maintain simplicity of this workflow, we will not print explicit messages at each step. Instead, the decision to do so is left to the user.filterFeatures function on a contaminants annotation column (as per theQFeatures processing vignette), we demonstrate how to filter using only contaminant protein accessions for users who do not have contaminant annotations within their identification data.Next we remove PSMs corresponding to contaminant proteins. Such proteins can be introduced intentionally as reagents during sample preparation, as is the case for digestive enzymes, or accidentally, as seen with human keratins derived from skin and hair. Since these proteins do not contribute to the biological question being asked and it is standard practice to remove them from the data. This is done by using a carefully curated, sample-specific contaminant database. Critically, the database used for filtering should be the same one that was used during the identification search. Whilst it is possible to remove contaminants using the.fasta file for this database is available at the Hao Group\u2019s Github Repository for Protein Contaminant Libraries for DDA and DIA Proteomics and specifically can be found athttps://github.com/HaoGroup-ProtContLib/Protein-Contaminant-Libraries-for-DDA-and-DIA-Proteomics/tree/main/Universal%20protein%20contaminant%20FASTA. Here, we import this file using thefasta.index function from theBiostrings package. This function requires a file path to the .fasta file and then asks users to specify the sequence type. In this case we have amino acid sequences so passseqtype = \"AA\". The function returns adata.frame with one row per FASTA entry. We then can extract the protein accessions from the fasta file. Users will need to alter the below code according to the contaminant file used.## Load Hao group .fasta file used in searchcont_fasta <- \"220813_universal_protein_contaminants_Haogroup.fasta\"conts <- Biostrings::fasta.index## Extract only the protein accessions (not Cont_ at the start)cont_acc <- regexpr(\"(?<=\\\\_).*?(?=\\\\|)\", conts$desc, perl = TRUE) %>%  regmatchesFor this experiment, a contaminant database from Ref.## Define function to find contaminantsfind_cont <- function {  cont_indices <- c  for (i in 1:length(cont_acc)) {    cont_protein <- cont_acc[i]    cont_present <- grep$Protein.Accessions)    output <- c(cont_present)    cont_indices <- append  }  cont_psm_indices <- cont_indices}## Store row indices of PSMs matched to a contaminant-containing protein groupcont_psms <- find_cont## If we find contaminants, remove these rows from the dataif (length(cont_psms) > 0)  cp_qf[[\"psms_filtered\"]] <- cp_qf[[\"psms_filtered\"]]Now we have our contaminant list by accession number, we can identify and remove PSMs with any contaminant protein within their \u201cProtein.Accessions\u201d. Importantly, filtering on \u201cProtein.Accessions\u201d ensures the removal of PSMs which matched to a protein group containing a contaminant protein, even if the contaminant protein is not the group\u2019s master protein.At this point, users can also remove any additional proteins which may not have been included in the contaminant database. For example, users may wish to remove human trypsin (accession P35050) should it appear in their data.Several third party softwares also have the option to directly annotate which fasta file  a PSM is derived from. In such cases, filtering can be simplified by removing PSMs annotated as contaminants in the output file.## Find out how many PSMs we expect to losecp_qf[[\"psms_filtered\"]] %>%  rowData %>%  as_tibble %>%  dplyr::count(Quan.Info == \"NoQuanLabels\")## # A tibble: 2 x 2##  \u2018Quan.Info == \"NoQuanLabels\"\u2018    n##  <lgl>                        <int>## 1 FALSE                       47241## 2 TRUE                          228## Drop these rows from the datacp_qf <- cp_qf %>%  filterFeaturesNow that we are left with only PSMs matched to proteins of interest, we filter out PSMs which cannot be used for quantitation. This includes some PSMs which lack quantitative information altogether. In outputs derived from Proteome Discoverer this information is included in the \u201cQuan.Info\u201d column where PSMs are annotated as having \u201cNoQuanLabels\u201d. For users who have considered both lysine and N-terminal TMT labels as static modifications, the data should not contain any PSMs without quantitative information. However, since the use-case data was derived from a search in which N-terminal TMT modifications were dynamic, the data does include this annotation. Users are reminded that column names are typically software-specific as the \u201cQuan.Info\u201d column is found only in outputs derived from Proteome Discoverer. However, the majority of alternative third party softwares will have an equivalent column containing the same information.## Are there any remaining annotations in the Quan.Info column?cp_qf[[\"psms_filtered\"]] %>%  rowData %>%  as_tibble %>%  pull(Quan.Info) %>%  table## .#### 47241This point in the workflow is a good time to check whether there are any other annotations within the \u201cQuan.Info\u201d column. For example, if there are any PSMs which have been \u201cExcludedByMethod\u201d, this indicates that a PSM-level filter was applied in Proteome Discoverer during the identification search. If this is the case, users should determine which filter has been applied to the data and decide whether to remove the PSMs which were \u201cExcludedByMethod\u201d (thereby applying the pre-set threshold) or leave them in (disregard the threshold).In the above code chunk we see there are no remaining annotations in the \u201cQuan.Info\u201d column so we can continue.The next step is to consider which PSMs are to be used for quantitation. There are two ways in which a PSM can be considered as unique. The first and most pure form of uniqueness comes from a PSM corresponding to a single protein only. This results in the PSM being allocated to one protein and one protein group. However, it is common to expand the definition of unique to include PSMs that map to multiple proteins within a single protein group. That is PSMs which are allocated to more than one protein but only one protein group. This distinction is ultimately up to the user. By contrast, PSMs corresponding to razor and shared peptides are linked to multiple proteins across multiple protein groups. In this workflow, the final grouping of peptides to proteins will be done based on master protein accession. Therefore, differential expression analysis will be based on protein groups, and we here consider unique as any PSM linked to only one protein group. This means removing PSMs where \u201cNumber.of.Protein.Groups\u201d is not equal to 1.## Find out how many PSMs we expect to losecp_qf[[\"psms_filtered\"]] %>%  rowData %>%  as_tibble %>%  dplyr::count(Number.of.Protein.Groups != 1)## # A tibble: 2 x 2##   \u2018Number.of.Protein.Groups != 1\u2018     n##   <lgl>                           <int>## 1 FALSE                           44501## 2 TRUE                             2740In the below code chunk we count the number of PSMs linked to more than 1 protein group.filterFeatures function to retain PSMs linked to only 1 protein group and discard any PSMs linked to more 1 group.## Remove these rows from the datacp_qf <- cp_qf %>%  filterFeaturesWe again use theAdditional considerations regarding protein isoformsUsers searching against a database that includes protein isoforms must take extra caution when defining \u2018unique\u2019 PSMs. A PSM that corresponds to a single protein when data is searched against the proteome without isoforms may correspond to multiple proteins once additional isoforms are included. As a result, PSMs or peptides that were previously mapped to one protein and one protein group could instead be mapped to multiple proteins and one protein group. These PSMs would be filtered out by defining \u2018unique\u2019 as corresponding to only one protein and one protein group, but would be retained if the definition was expanded to multiple proteins and one protein group. Users should be aware of these possibilities and select their filtering strategy based on the biological question of interest.## Find out how many PSMs we expect to losecp_qf[[\"psms_filtered\"]] %>%  rowData %>%  as_tibble %>%  dplyr::count(Rank != 1)## # A tibble: 2 x 2##  \u2018Rank != 1\u2018      n##  <lgl>        <int>## 1 FALSE       43426## 2 TRUE         1075## Drop these rows from the datacp_qf <- cp_qf %>%  filterFeaturesAnother filter that is important for quantitation is that of PSM rank. Since individual spectra can have multiple candidate peptide matches, Proteome Discoverer uses a scoring algorithm to determine the probability of a PSM being incorrect. Once each candidate PSM has been given a score, the one with the lowest score (lowest probability of being incorrect) is allocated rank 1. The PSM with the second lowest probability of being incorrect is rank 2, and so on. For the analysis, we only want rank 1 PSMs to be retained.## Find out how many PSMs we expect to losecp_qf[[\"psms_filtered\"]] %>%  rowData %>%  as_tibble %>%  dplyr::count(Search.Engine.Rank != 1)## # A tibble: 2 x 2##   \u2018Search.Engine.Rank != 1\u2018     n##   <lgl>                     <int>## 1 FALSE                     43153## 2 TRUE                        273## Drop these rows from the datacp_qf <- cp_qf %>%  filterFeaturesThe majority of search engines, including SequestHT, also provide their own PSM rank. To be conservative and ensure accurate quantitation, we also only retain PSMs that have a search engine rank of 1.Finally, we retain only unambiguous PSMs. Since there are several candidate peptides for each spectra, Proteome Discoverer allocates each PSM a level of ambiguity to indicate whether it was possible to determine a definite PSM or whether one had to be selected from a number of candidates. The allocation of PSM ambiguity takes place during the process of protein grouping and the definitions of each ambiguity assignment are given below in## Find out how many PSMs we expect to losecp_qf[[\"psms_filtered\"]] %>%  rowData %>%  as_tibble %>%  dplyr::count(PSM.Ambiguity != \"Unambiguous\")## # A tibble: 1 x 2##   \u2018PSM.Ambiguity != \"Unambiguous\"\u2018     n##   <lgl>                            <int>## 1 FALSE                            43153## No PSMs to remove so proceedImportantly, depending upon the software being used, output files may already have excluded some of these categories. It is still good to check before proceeding with the data.## Determine number and proportion of PSMs removedpsms_remaining <- cp_qf[[\"psms_filtered\"]] %>%  nrow %>%  as.numericpsms_removed <- original_psms - psms_remainingpsms_removed_prop <-  * 100) %>%  round(digits = 2)## Determine number and proportion of peptides removedpeps_remaining <- cp_qf[[\"psms_filtered\"]] %>%  rowData %>%  as_tibble %>%  pull(Sequence) %>%  unique %>%  length %>%  as.numericpeps_removed <- original_peps - peps_remainingpeps_removed_prop <-  * 100) %>%  round(digits = 2)## Determine number and proportion of proteins removedprots_remaining <- cp_qf[[\"psms_filtered\"]] %>%  rowData %>%  as_tibble %>%  pull(Master.Protein.Accessions) %>%  unique %>%  length %>%  as.numericprots_removed <- original_prots - prots_remainingprots_removed_prop <-  * 100) %>%  round(digits = 2)## Print as a tabledata.frame,           \"Number lost\" = c,           \"Percentage lost\" = c)##    Feature Number.lost Percentage.lost## 1     PSMs        5679           11.63## 2 Peptides        1565            6.03## 3 Proteins         452            8.97Now that we have finished the non-specific data cleaning, we can pause and check to see what this has done to the data. We determine the number and proportion of PSMs, peptides, and proteins lost from the original dataset.The next step is to take a look at the data and make informed decisions about in-depth filtering. Here, we focus on three key quality control filters for TMT data: 1) average reporter ion signal-to-noise (S/N) ratio, 2) percentage co-isolation interference, and 3) percentage SPS mass match. It is possible to set thresholds for these three parameters during the identification search. However, specifying thresholds prior to exploring the data could lead to unnecessarily excessive data exclusion or the retention of poor quality PSMs. We suggest that users set the thresholds for all three aforementioned filters to 0 during the identification search, thus allowing maximum flexibility during data processing. In all cases, quality control filtering represents a trade-off between ensuring high quality data and losing potentially informative data. This means that the thresholds used for such filtering will likely depend upon the initial quality of the data and the number of PSMs, as well as the experimental goal being stringent or exploratory.Intensity measurements derived from a small number of ions tend to be more variable and less accurate. Therefore, reporter ion spectra with peaks generated from a small number of ions should be filtered out to ensure accurate quantitation and avoid stochastic ion effects. When using an orbitrap analyser, as was the case in the collection of the use-case data, the number of ions is proportional to the S/N value of a peak. Hence, the average reporter ion S/N ratio can be used to filter out quantification based on too few ions.## Get summary informationcp_qf[[\"psms_filtered\"]] %>%  rowData %>%  as_tibble %>%  pull(Average.Reporter.SN) %>%  summary##   Min.   1st Qu.   Median   Mean   3rd Qu.     Max.   NA\u2019s##    0.3      84.2    215.8  321.8     450.3   3008.2    140## Plot histogram of reporter ion signal-to-noisecp_qf[[\"psms_filtered\"]] %>%  rowData %>%  as_tibble %>%  ggplot(aes(x = log10(Average.Reporter.SN))) +  geom_histogram(binwidth = 0.05) +  geom_vline +  labs(x = \"log10(average reporter SN)\", y = \"Frequency\") +  ggtitle(\"Average reporter ion S/N\") +  theme_bwTo determine an appropriate reporter ion S/N threshold we need to understand the original, unfiltered data. Here, we print a summary of the average reporter S/N before plotting a simple histogram to visualise the data. The default threshold for average reporter ion S/N when filtering within Proteome Discoverer is 10, or 1 on the base-10 logarithmic scale displayed here. We include a line to show where this threshold would be on the data distribution.na.rm = TRUE.## Find out how many PSMs we expect to losecp_qf[[\"psms_filtered\"]] %>%  rowData %>%  as_tibble %>%  dplyr::count(Average.Reporter.SN < 10)## # A tibble: 3 x 2##   \u2018Average.Reporter.SN < 10\u2018     n##   <lgl>                      <int>## 1 FALSE                      42066## 2 TRUE                         947## 3 NA                           140## Drop these rows from the datacp_qf <- cp_qf %>%  filterFeaturesFrom the distribution of the data it is clear that applying such a threshold would not result in dramatic data loss. Whilst we could set a higher threshold for more stringent analysis, this would lead to unnecessary data loss. Therefore, we keep PSMs with an average reporter ion S/N threshold of 10 or more. We also remove PSMs that have an NA value for their average reporter ion S/N since their quality cannot be guaranteed. This is done by includingA second data-dependent quality control parameter which should be considered is the isolation interference. The first type of interference that occurs during a TMT experiment is reporter ion interference, also known as cross-label isotopic impurity. This type of interference arises from manufacturing-level impurities and experimental error. The former should be reduced somewhat by the inclusion of lot-specific correction factors in the search set-up and users should ensure that these corrections are applied. In Proteome Discoverer this means setting \u201cApply Quan Value Corrections\u201d to \u201cTRUE\u201d within the reporter ions quantifier node. The second form of interference is co-isolation interference which occurs during the MS run when multiple labelled precursor peptides are co-isolated in a single data acquisition window. Following fragmentation of the co-isolated peptides, this results in an MS2 or MS3 reporter ion peak  derived from multiple precursor peptides. Hence, co-isolation interference leads to inaccurate quantitation of the identified peptide. This problem is reduced by filtering out PSMs with a high percentage isolation interference value. As was the case for reporter ion S/N, Proteome Discoverer has a suggested default threshold for isolation interference - 50% for MS2 experiments and 75% for SPS-MS3 experiments.## Get summary informationcp_qf[[\"psms_filtered\"]] %>%  rowData %>%  as_tibble %>%  pull(Isolation.Interference.in.Percent) %>%  summary##   Min.  1st Qu.   Median    Mean  3rd Qu.    Max.##  0.000    0.000    8.385  12.637  21.053   84.379## Plot histogram of co-isolation interferencecp_qf[[\"psms_filtered\"]] %>%  rowData %>%  as_tibble %>%  ggplot(aes(x = Isolation.Interference.in.Percent)) +  geom_histogram(binwidth = 2) +  geom_vline +  labs(x = \"Isolation inteference (%)\", y = \"Frequency\") +  ggtitle(\"Co-isolation interference %\") +  theme_bwAgain, we get a summary and visualise the data using the code chunk below.## Find out how many PSMs we expect to losecp_qf[[\"psms_filtered\"]] %>%  rowData %>%  as_tibble %>%  dplyr::count(Isolation.Interference.in.Percent > 75)## # A tibble: 2 x 2##   \u2018Isolation.Interference.in.Percent > 75\u2018      n##   <lgl>                                     <int>## 1 FALSE                                     42007## 2 TRUE                                         59## Remove these rows from the datacp_qf <- cp_qf %>%  filterFeaturesLooking at the data, very few PSMs have an isolation interference above the suggested threshold, and hence minimal data will be lost. Again, we choose to apply the standard threshold with the understanding that decreasing the threshold would result in greater data loss. Importantly, we are able to apply relatively standard thresholds here as the preliminary exploration did not expose any problems with the experimental data . If users have reason to believe the data is of poorer quality then more stringent thresholding should be considered.The final quality control filter that we will apply is a percentage SPS mass match threshold. SPS mass match is a metric which has been introduced by Proteome Discoverer versions 2.3 and above to quantify the percentage of SPS-MS3 fragments that can still be explicitly traced back to the precursor peptide. This parameter is of particular importance given that quantitation is based on the SPS-MS3 spectra. Unfortunately, the SPS Mass Match percentage is currently only a feature of Proteome Discoverer (2.3 and above) and will not be available to users of other third party software.## Get summary informationcp_qf[[\"psms_filtered\"]] %>%  rowData %>%  as_tibble %>%  pull(SPS.Mass.Matches.in.Percent) %>%  summary##   Min.  1st Qu.   Median   Mean  3rd Qu.    Max.##   0.00    50.00    70.00  64.31   80.00   100.00## Plot histogram of SPS mass match %cp_qf[[\"psms_filtered\"]] %>%  rowData %>%  as_tibble %>%  ggplot(aes(x = SPS.Mass.Matches.in.Percent)) +  geom_histogram(binwidth = 10) +  geom_vline +  labs(x = \"SPS mass matches (%)\", y = \"Frequency\") +  scale_x_continuous) +  ggtitle(\"SPS mass match %\") +  theme_bwWe follow the same format as before to investigate the SPS Mass Match (%) distribution of the data. The default threshold within Proteome Discoverer is a SPS Mass Match above 65%. In reality, since SPS Mass Match is only reported to the nearest 10%, removing PSMs annotated with a value below 65% means removing those with 60% or less. Hence, only PSMs with 70% SPS Mass Match or above would be retained. We can see how many PSMs would be lost based on such thresholds using the code chunk below.## Find out how many PSMs we expect to losecp_qf[[\"psms_filtered\"]] %>%  rowData %>%  as_tibble %>%  dplyr::count(SPS.Mass.Matches.in.Percent < 65)## # A tibble: 2 x 2##   \u2018SPS.Mass.Matches.in.Percent < 65\u2018      n##   <lgl>                               <int>## 1 FALSE                               21697## 2 TRUE                                20310## Drop these rows from the datacp_qf <- cp_qf %>%  filterFeaturesFrom the summary and histogram we can see that the distribution of SPS Mass Matches is much less skewed than that of average reporter ion S/N or isolation interference. This means that whilst the application of thresholds on average reporter ion S/N and isolation interference led to minimal data loss, attempting to impose a threshold on SPS Mass Match represents a much greater trade-off between data quality and quantity. For simplicity, here we choose to use the standard threshold of 65%.## Summarize the effect of data-specific filtering## Determine the number and proportion of PSMs removedpsms_remaining_2 <- cp_qf[[\"psms_filtered\"]] %>%  nrow %>%  as.numericpsms_removed_2 <- psms_remaining - psms_remaining_2psms_removed_prop_2 <-  * 100) %>%  round(digits = 2)## Determine number and proportion of peptides removedpeps_remaining_2 <- rowData(cp_qf[[\"psms_filtered\"]])$Sequence %>%  unique %>%  length %>%  as.numericpeps_removed_2 <- peps_remaining - peps_remaining_2peps_removed_prop_2 <-  * 100) %>%  round(digits = 2)## Determine number and proportion of proteins removedprots_remaining_2 <- cp_qf[[\"psms_filtered\"]] %>%  rowData %>%  as_tibble %>%  pull(Master.Protein.Accessions) %>%  unique %>%  length %>%  as.numericprots_removed_2 <- prots_remaining - prots_remaining_2prots_removed_prop_2 <-  * 100) %>%  round(digits = 2)## Print as a tabledata.frame,           \"Number lost\" = c,           \"Percentage lost\" = c)##    Feature Number.lost Percentage.lost## 1     PSMs       21456           43.94## 2 Peptides       10162           39.13## 3 Proteins        1299           25.77As we did after the non-specific cleaning steps, we check to see how many PSMs, peptides and proteins have been removed throughout the in-depth data-specific filtering.,, In many cases this is due to the biological condition being evaluated, for example the cell type or treatment applied.Having finished the data cleaning at the PSM-level, the final step is to deal with missing data. Missing values represent a common challenge in quantitative proteomics and there is no consensus within the literature on how this challenge should be addressed. Indeed, missing values fall into different categories based on the reason they were generated, and each category is best dealt with in a different way. There are three main categories of missing data: missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR). Within proteomics, values which are MCAR arise due to technical variation or stochastic fluctuations and emerge in a uniform, intensity-independent distribution. Examples include values for peptides which cannot be consistently identified or are unable to be efficiently ionised. By contrast, MNAR values are expected to occur in an intensity-dependent manner due to the presence of peptides at abundances below the limit of detection.To simplify this process, we consider the management of missing data in three steps. The first step is to determine the presence and pattern of missing values within the data. Next, we filter out data which exceed the desired proportion of missing values. This includes removing PSMs with a greater number of missing values across samples than we deem acceptable, as well as whole samples in cases where the proportion of missing values is substantially higher than the average. Finally, imputation can be used to replace any remaining NA values within the dataset. This final step is optional and can equally be done prior to filtering if the user wishes to impute all missing values without removing any PSMs, although this is not recommended. Further, whilst it is possible to complete such steps at the peptide- or protein-level, we advise management of missing values at the lowest data level to minimise the effect of implicit imputation during aggregation.nNA function within theQFeatures infrastructure. This function will return the absolute number and percentage of missing values both per sample and as an average. Importantly, alternative third-party software may output missing values in formats other than NA, such as zero, or infinite. In such cases, missing values can be converted directly into NA values through use of thezeroIsNA orinfIsNA functions within theQFeatures infrastructure.## Determine whether there are any NA values in the datacp_qf[[\"psms_filtered\"]] %>%  assay %>%  anyNA## [1] TRUE## Determine the amount and distribution of NA values in the datacp_qf[[\"psms_filtered\"]] %>%  nNA## $nNA## DataFrame with 1 row and 2 columns##         nNA        pNA##   <integer>  <numeric>## 1         4 0.00307262#### $nNArows## DataFrame with 21697 rows and 3 columns##              name       nNA       pNA##       <character> <integer> <numeric>## 1              13         0         0## 2              20         0         0## 3              25         0         0## 4              26         0         0## 5              29         0         0## ...           ...       ...       ...## 21693       48786         0         0## 21694       48792         0         0## 21695       48797         0         0## 21696       48810         0         0## 21697       48819         0         0#### $nNAcols## DataFrame with 6 rows and 3 columns##          name       nNA        pNA##   <character> <integer>  <numeric>## 1          S1         0 0.00000000## 2          S2         2 0.00921786## 3          S3         0 0.00000000## 4          S4         1 0.00460893## 5          S5         1 0.00460893## 6          S6         0 0.00000000First, to determine the presence of missing values in the PSM-level data we use the## Plot histogram to visualize the distribution of NAsnNA(cp_qf[[\"psms_filtered\"]])$nNAcols %>%  as_tibble %>%  mutate, each = 3)) %>%  ggplot) +  geom_bar +  geom_hline +  labs\") +  theme_bwWe can see that the data only contains 0.003% missing values, corresponding to 4 NA values. This low proportion is due to a combination of the TMT labelling strategy and the stringent PSM quality control filtering. In particular, co-isolation interference when using TMT labels often results in very low quantification values for peptides which should actually be missing or \u2018NA\u2019. Nevertheless, we continue and check for sample-specific bias in the distribution of NAs by plotting a simple histogram. We also use colour to indicate the condition of each sample as to check for condition-specific bias.## Find out the range of missing values per PSMnNA(cp_qf[[\"psms_filtered\"]])$nNArows$nNA %>%  table## .##     0   1## 21693   4The percentage of missing values is sufficiently low that none of the samples need be removed. Further, there is no sample- or condition-specific bias in the data. We can get more information about the PSMs with NA values using the code below.## Get indices of rows which contain NArows_with_na_indices <- which(nNA(cp_qf[[\"psms_filtered\"]])$nNArows$nNA != 0)## Subset rows with NArows_with_na <- cp_qf[[\"psms_filtered\"]]## Inspect rows with NAassay(rows_with_na)##         S1   S2   S3   S4   S5   S6## 12087 11.0 17.0 13.3 22.1   NA 30.6## 30824 45.0   NA 43.1 66.7 69.7 62.1## 30846 34.3   NA 47.9 56.8 65.5 57.2## 44791 22.8 28.7 19.6   NA  3.8 12.2From this output we can see that the maximum number of NA values per PSM is one. This information is useful to know as it may inform the filtering strategy in the next step.filterNA function inQFeatures, as outlined below. We pass the function theSummarizedExperiment and use thepNA = argument to specify the maximum proportion of NA values to allow.## Check how many PSMs we will removenNA(cp_qf[[\"psms_filtered\"]])$nNArows %>%  as_tibble %>%  dplyr::count(pNA >= 20)## # A tibble: 1 x 2##   \u2018pNA >= 20\u2018     n##   <lgl>       <int>## 1 FALSE       21697First we apply some standard filtering. Typically, it is desirable to remove features, here PSMs, with greater than 20% missing values. We can do this using the## Remove PSMs with more than 20 % (0.2) NA valuescp_qf <- cp_qf %>%  filterNAAlthough the use-case data does not contain any PSMs with >20% missing values, we demonstrate how to apply the desired filter below.Since previous exploration of missing data did not reveal any sample with an excessive number of NA values, we do not need to remove any samples from the analysis.Although not covered here, users may wish to carry out condition-specific filtering in cases where the exploration of missing values revealed a condition- specific bias, or where the experimental question requires. This would be the case, for example, if one condition was transfected to express proteins of interest whilst the control condition lacked these proteins. Filtering of both conditions together could, therefore, lead to the removal of proteins of interest.The final step is to consider whether to impute the remaining missing values within the data. Imputation refers to the replacement of missing values with probable values. Since imputation requires complex assumptions and can have substantial effects on downstream statistical analysis, we here choose to skip imputation. This is reasonable given that we only have 3 missing values at the PSM-level, and that some of these will likely be removed by aggregation. A more in-depth discussion of imputation will be provided below in the LFQ workflow.## Determine final number of PSMs, peptides and master proteinspsms_final <- cp_qf[[\"psms_filtered\"]] %>%  nrow %>%  as.numericpsms_removed_total <- original_psms - psms_finalpsms_removed_total_prop <-  * 100) %>%  round(digits = 2)peps_final <- cp_qf[[\"psms_filtered\"]] %>%  rowData %>%  as_tibble %>%  pull(Sequence) %>%  unique %>%  length %>%  as.numericpeps_removed_total <- original_peps - peps_finalpeps_removed_total_prop <-  * 100) %>%  round(digits = 2)prots_final <- cp_qf[[\"psms_filtered\"]] %>%  rowData %>%  as_tibble %>%  pull(Master.Protein.Accessions) %>%  unique %>%  length %>%  as.numericprots_removed_total <- original_prots - prots_finalprots_removed_total_prop <-  * 100) %>%  round(digits = 2)## Print as tabledata.frame,           \"Number lost\" = c,           \"Percentage lost\" = c,           \"Number remaining\" = c)##    Feature Number.lost Percentage.lost Number.remaining## 1     PSMs       27135           55.57            21697## 2 Peptides       11727           45.16            14242## 3 Proteins        1751           34.74             3289Thus far we have checked that the experimental data we are using is of high quality by visualising the raw data and calculating TMT labelling efficiency. We then carried out non-specific data cleaning, data-specific filtering steps and management of missing data. Here, we present a combined summary of these PSM processing steps.QFeatures object to thelogTransform function, as per the below code chunk.## log2 transform quantitative datacp_qf <- logTransform## Verifycp_qf## An instance of class QFeatures containing 3 assays:##  [1] psms_raw: SummarizedExperiment with 48832 rows and 6 columns##  [2] psms_filtered: SummarizedExperiment with 21697 rows and 6 columns##  [3] log_psms: SummarizedExperiment with 21697 rows and 6 columnsOnce satisfied that the PSM-level data is clean and of high quality, the PSM-level quantitative data is log transformed. log2 transformation is a standard step when dealing with quantitative proteomics data since protein abundances are dramatically skewed towards zero. Such a skewed distribution is to be expected given that the majority of cellular proteins present at any one time are of relatively low abundance, whilst only a few highly abundant proteins exist. To perform the logarithmic transformation and generate normally distributed data we pass the PSM-level data in theaggregateFeatures function and provide the base level from which we wish to aggregate, the log PSM-level data in this case. We also tell the function which column to aggregate, which is specified by the fcol argument. We will first aggregate from PSM to peptide to create explicitQFeatures links. This means grouping by PSM \u201cSequence\u201d.For the aggregation itself we use theaggregateFeatures is therobustSummary function from theMsCoreUtils package. This method is a form of robust regression and is described in detail elsewhere. Nevertheless, the user must decide which aggregation method is most appropriate for their data and biological question. Further, an understanding of the selected method is critical given that aggregation is a form of implicit imputation and has substantial effects on the downstream data. Indeed, aggregation methods have different ways of dealing with missing data, either by removal or propagation. Options of aggregation methods within the aggregateFeatures function includeMsCoreUtils::medianPolish,MsCoreUtils::robustSummary,base::colMeans,base::colSums, andmatrixStats::colMedians. Users should also be aware that some methods have specific input requirements. For example,robustSummary assumes that intensities have already been log transformed.As well as grouping PSMs according to their peptide sequence, the quantitative values for each PSM must be aggregated into a single peptide-level value. The default aggregation method withinrobustSummary to aggregate from PSM to peptide-level. This method is currently considered to be state-of-the-art as it is more robust against outliers than other aggregation methods., We also includena.rm = TRUE to exclude any NA values prior to completing the summarisation.## Aggregate PSM to peptidecp_qf <- aggregateFeatures## Your quantitative and row data contain missing values. Please read the## relevant section(s) in the aggregateFeatures manual page regarding the## effects of missing values on data aggregation.## Verifycp_qf## An instance of class QFeatures containing 4 assays:##  [1] psms_raw: SummarizedExperiment with 48832 rows and 6 columns##  [2] psms_filtered: SummarizedExperiment with 21697 rows and 6 columns##  [3] log_psms: SummarizedExperiment with 21697 rows and 6 columns##  [4] log_peptides: SummarizedExperiment with 14242 rows and 6 columnsHere, we useQFeatures object holding the PSM and peptide-level data in their ownSummarizedExperiments. Importantly, an explicit link has been maintained between the two levels and this makes it possible to gain information about all PSMs that were aggregated into a peptide.We are now left with a## Confirm the presence of NaNassay(cp_qf[[\"log_peptides\"]]) %>%  is.nan %>%  table## .## FALSE## 85452## Replace NaN with NAassay(cp_qf[[\"log_peptides\"]])[is.nan(assay(cp_qf[[\"log_peptides\"]]))] <- NAIf users did not impute prior to aggregation, NA values within the PSM-level data may have propagated into NaN values. This is because peptides only supported by PSMs containing missing values would not have any quantitative value to which a sum or median function, for example, can be applied. Therefore, we check for NaN and convert back to NA values to facilitate compatibility with downstream processing.aggregateFeatures function to assemble the peptides into proteins. As before, we must pass several arguments to the function. Namely, theQFeatures object i.e.cp_qf, the data level we wish to aggregation from i.e.log_peptides, the column of therowData defining how to aggregate the features i.e. by\"Master.Protein.Accessions\" and a name for the new data level e.g.\"log_proteins\". We again choose to userobustSummary as our aggregation method and we passna.rm = TRUE to ignore NA values. Users can type?aggregateFeatures to see more information. Users should be aware that peptides are grouped by their master protein accession and, therefore, downstream differential expression analysis will consider protein groups rather than individual proteins.## Aggregate peptides to proteincp_qf <- aggregateFeatures## Your quantitative and row data contain missing values. Please read the## relevant section(s) in the aggregateFeatures manual page regarding the## effects of missing values on data aggregation.## Verifycp_qf## An instance of class QFeatures containing 5 assays:##  [1] psms_raw: SummarizedExperiment with 48832 rows and 6 columns##  [2] psms_filtered: SummarizedExperiment with 21697 rows and 6 columns##  [3] log_psms: SummarizedExperiment with 21697 rows and 6 columns##  [4] log_peptides: SummarizedExperiment with 14242 rows and 6 columns##  [5] log_proteins: SummarizedExperiment with 3289 rows and 6 columnsNext, using the same approach as above, we use theFollowing aggregation, we have a total of 3289 proteins remaining within the data.After transforming the data, we normalise the protein-level abundances. Normalization is a process of correction whereby quantitative data is returned to its original, or \u2018normal\u2019, state. In expression proteomics, the aim of post-acquisition data normalization is to minimise the biases that arises due to experimental error and technological variation. Specifically, the removal of random variation and batch effects will allow samples to be aligned prior to downstream analysis. Importantly, however, users must also be aware of any normalization that has taken place within their sample preparation, as this will ultimately influence the presence of differentially abundant proteins downstream. An extensive review on normalization strategies, both experimental and computational, is provided in Ref.NormalyzerDE, a tool for evaluating different normalisation methods. By passing aSummarizedExperiment object to thenormalyzer function it is possible to generate a report comparing common normalisation strategies, such as total intensity (TI), median intensity (MedI), average intensity (AI), quantile (from thepreprocessCore package), NormFinder (NM), Variance Stabilising Normalization , Robust Linear Regression (RLR), and LOESS (from thelimma package). A number of qualitative and quantitative evaluation measures are provided within the report, including total intensity, Pooled intragroup Coefficient of Variation (PCV), Pooled intragroup Median Absolute Deviation (PMDA), CV-intensity plots, MA-plots, Pearson and Spearman correlation.Unfortunately, there is not currently a single normalization method which performs best for all quantitative proteomics datasets. Within the Bioconductor packages, however, existsNormalyzer accepts intensity data in a raw format, prior to log transformation. Therefore, we first generate a protein-levelSummarizedExperiment from our PSM-level data prior to transformation.## Aggregate from PSM directly to proteincp_qf <- aggregateFeaturesSummarizedExperiment here and the function will do the log2 transformation for us. A second important consideration is that missing values must be denoted \u2018NA\u2019, not zero, NaN or infinite. We can pass theSummarizedExperiment containing the protein data to thenormalyzer function. With this, we provide a name for the report and the directory in which to save the report. Thenormalyzer function also expects two pieces of information, the sample name and corresponding experimental group. We previously annotated the data with this information through the sample and condition columns of thecolData, so we tell thenormalyzer function to look here.## Generate normalyzer reportnormalyzerHence, we will use the \u201cproteins_direct\u201d.pdf file containing plots such as those displayed inThe function will take a few minutes to run, particularly if there are many samples. Once complete, the report can be accessed as anormalyzer report did not indicate any superior normalisation method in this case, we will apply a center median approach here. To do this, we pass the log transformed protein-level data to thenormalize function inQFeatures. We specify the method of normalisation that we wish to apply i.e.method = \"center.median\" and name the new data level e.g.name = \"log_norm_proteins\". Of note, for users who wish to apply VSN normalisation the raw protein data must be passed (prior to any log transformation) as the log transformation is done internally when specifymethod = \"vsn\". All other methods require users to explicitly perform log transformation on their data before use. More details can be found in theQFeatures documentation, please typehelp.## normalize the log transformed peptide datacp_qf <- normalize## Verifycp_qf## An instance of class QFeatures containing 7 assays:##  [1] psms_raw: SummarizedExperiment with 48832 rows and 6 columns##  [2] psms_filtered: SummarizedExperiment with 21697 rows and 6 columns##  [3] log_psms: SummarizedExperiment with 21697 rows and 6 columns##  [4] log_peptides: SummarizedExperiment with 14242 rows and 6 columns##  [5] log_proteins: SummarizedExperiment with 3289 rows and 6 columns##  [6] proteins_direct: SummarizedExperiment with 3289 rows and 6 columns##  [7] log_norm_proteins: SummarizedExperiment with 3289 rows and 6 columnsSince the## Evaluate the effect of data normalizationpre_norm <- cp_qf[[\"log_proteins\"]] %>%  assay %>%  longFormat %>%  mutate,                            \"Treated\", \"Control\")) %>%  ggplot) +  geom_boxplot +  labs\", title = \"Pre-normalization\") +  theme_bwpost_norm <- cp_qf[[\"log_norm_proteins\"]] %>%  assay %>%  longFormat %>%  mutate,                            \"Treated\", \"Control\")) %>%  ggplot) +  geom_boxplot +  labs\", title = \"Post-normalization\") +  theme_bw(pre_norm + theme(legend.position = \"none\")) +  post_norm & plot_layout(guides = \"collect\")To evaluate the effect of normalisation we plot a simple boxplot.plotDensities function from thelimma package.## visualize the process of log transformation and normalizationpar)cp_qf[[\"psms_filtered\"]] %>%  assay %>%  plotDensitiescp_qf[[\"log_psms\"]] %>%  assay %>%  plotDensities\")cp_qf[[\"log_norm_proteins\"]] %>%  assay %>%  plotDensities\")We can now generate a density plot to help us visualise what the process of log transformation and normalisation has done to the data. This is done using theaddAssayLinks function, demonstrated below. We can check that theassay links have been generated correctly by passing ourQFeatures object to the AssayLink function along with theassay of interest (i =).## Add assay links from log_norm_proteins to psms_rawcp_qf <- addAssayLink## VerifyassayLink## AssayLink for assay <log_norm_proteins>## [from:psms_raw|fcol:Master.Protein.Accessions|hits:42678]After completing all data pre-processing, we now add explicit links between our final protein-level data and the raw PSM-level data which we created as an untouched copy. This allows us to investigate all data corresponding to the final proteins, including the data that has since been removed. To do this, we use theQFeatures infrastructure is that explicit links have been maintained throughout the aggregation process. This means that we can now access all data corresponding to a protein, its component peptides and PSMs. One way to do this is through the use of thesubsetByFeature function which will return a newQFeatures object containing data for the desired feature across all levels. For example, if we wish to subset information about the protein \u201cQ01581\u201d, that is hydroxymethylglutaryl-CoA synthase, we could use the following code:## Subset all data linked to the protein with accession Q01581Q01581 <- subsetByFeature## VerifyQ01581## An instance of class QFeatures containing 7 assays:##  [1] psms_raw: SummarizedExperiment with 42 rows and 6 columns##  [2] psms_filtered: SummarizedExperiment with 27 rows and 6 columns##  [3] log_psms: SummarizedExperiment with 27 rows and 6 columns##  [4] log_peptides: SummarizedExperiment with 15 rows and 6 columns##  [5] log_proteins: SummarizedExperiment with 1 rows and 6 columns##  [6] proteins_direct: SummarizedExperiment with 1 rows and 6 columns##  [7] log_norm_proteins: SummarizedExperiment with 1 rows and 6 columnsOne of the characteristic attributes of theWe find that in this data the protein Q01581 has 15 peptides and 27 supporting its identification and quantitation. We also see that the original data prior to processing contained 42 PSMs in support of this protein.## Define conditionstreament <- ccontrol <- c## Plot abundance distributions across samples at PSM, peptide and protein-levelQ01581 %>%  longFormat %>%  as_tibble %>%  mutate,    labels = c),    condition = ifelse) %>%  ggplot) +  geom_point(size = 3) +  geom_line(aes(group = rowname)) +  scale_x_discrete) +  facet_wrap(~assay_order) +  labs +  ggtitle(\"log2 Q01581 abundance profiles\") +  theme_bwFurther, we can visualise the process of aggregation that has led to the protein-level abundance data for Q01581, as demonstrated below. Of note, this plot shows the protein data prior to normalisation.QFeatures object is the ease at which we can determine PSM and peptide support per protein. When applying theaggregateFeatures function a column, termed\".n\", is created within the rowData of the newSummarizedExperiment. This column indicates how many lower-level features were aggregated into each new higher-level feature. Hence,\".n\" in the peptide-level data represents how many PSMs were aggregated into a peptide, whilst in the protein-level data it tells us how many peptides were grouped into a master protein. For ease of plotting, we will use the\"proteins_direct\" data generated above. Since this data was generated via direct aggregation of PSM to protein,\".n\" this will tell us PSM support per protein. We plot these data as simple histograms.## Plot PSM support per protein - .n in the proteins_direct SEpsm_per_protein <- cp_qf[[\"proteins_direct\"]] %>%  rowData %>%  as_tibble %>%  ggplot(aes(x = .n)) +  geom_histogram +  labs(x = \"PSM support (shown up to 20)\",       y = \"Frequency\") +  scale_x_continuous,                     limits = c,                     breaks = seq) +  scale_y_continuous,                     limits = c,                     breaks = seq) +  ggtitle(\"PSM support per protein\") +  theme_bw## Plot peptide support per protein - .n in the proteins SEpeptide_per_protein <- cp_qf[[\"log_proteins\"]] %>%  rowData %>%  as_tibble %>%  ggplot(aes(x = .n)) +  geom_histogram +  labs(x = \"Peptide support (shown up to 20)\",       y = \"Frequency\") +  scale_x_continuous,                     limits = c,                     breaks = seq) +  scale_y_continuous,                     limits = c,                     breaks = seq) +  ggtitle(\"Peptide support per protein\") +  theme_bwpsm_per_protein + peptide_per_proteinAnother benefit of the explicit links maintained within aAt this point, users may wish to include additional quality control filtering based on PSM and/or peptide support per protein. Given the extensive quality control filtering already applied in this workflow, we decide not to remove additional proteins based on PSM or peptide support.QFeatures object into an.rda file so that we can re-load it later at convenience.## Save protein-level SEcp_proteins <- cp_qf[[\"log_norm_proteins\"]]## Export the final TMT QFeatures objectsaveFinally, we save the protein-level data and export theHaving discussed the processing of quantitative TMT-labelled data, we now move on to consider that of label-free quantitative (LFQ) data. As described previously, the cell culture supernatant fractions of triplicate control and treated HEK293 cells were kept label-free. As such, each sample was analysed using an independent mass spectrometry run without pre-fractionation. Again, a two-hour gradient in an Orbitrap Lumos Tribrid mass spectrometer coupled to an UltiMate 3000 HPLC system was applied. Given that much of the TMT pre-processing workflow also applies to label-free data, we only discuss steps which are different to those previously described. Readers are advised to refer to the TMT processing workflow for a more in-depth explanation of any shared steps.https://github.com/CambridgeCentreForProteomics/f1000_expression_proteomics which contains the identification search along with an additional explanation of key parameters in an appendix. To begin processing LFQ data, users should export a peptide-level.txt file from the results of their identification search.As was the case for TMT labelled cell pellets, raw LFQ data from supernatant samples was searched using Proteome Discoverer 2.5. The GitHub repository associated with this manuscript can be found atUnlike the TMT-labelled use-case data which was processed from the PSM-level, the label-free use-case data can only be considered from the peptide-level up. This is because a retention time alignment algorithm  was applied to the PSM-level data. This means that peptides can be identified in samples even without a corresponding PSM, simply by sharing feature information across runs..txt file and upload this into aQFeatures data container in the same way as before. Since the samples are already stored in the correct order, we simply identify the quantitative columns by their indices.## Locate the PeptideGroups .txt filesn_peptide <- \"supernatant_lfq_results_peptides.txt\"## Identify columns containing quantitative datasn_peptide %>%  read.delim %>%  names##  [1] \"Peptide.Groups.Peptide.Group.ID\"##  [2] \"Checked\"##  [3] \"Tags\"##  [4] \"Confidence\"##  [5] \"PSM.Ambiguity\"##  [6] \"Sequence\"##  [7] \"Modifications\"##  [8] \"Modifications.all.possible.sites\"##  [9] \"Qvality.PEP\"## [10] \"Qvality.q.value\"## [11] \"SVM_Score\"## [12] \"Number.of.Protein.Groups\"## [13] \"Number.of.Proteins\"## [14] \"Number.of.PSMs\"## [15] \"Master.Protein.Accessions\"## [16] \"Master.Protein.Descriptions\"## [17] \"Protein.Accessions\"## [18] \"Number.of.Missed.Cleavages\"## [19] \"Theo.MHplus.in.Da\"## [20] \"Sequence.Length\"## [21] \"Abundance.F1.Sample\"## [22] \"Abundance.F2.Sample\"## [23] \"Abundance.F3.Sample\"## [24] \"Abundance.F4.Sample\"## [25] \"Abundance.F5.Sample\"## [26] \"Abundance.F6.Sample\"## [27] \"Abundances.Count.F1.Sample\"## [28] \"Abundances.Count.F2.Sample\"## [29] \"Abundances.Count.F3.Sample\"## [30] \"Abundances.Count.F4.Sample\"## [31] \"Abundances.Count.F5.Sample\"## [32] \"Abundances.Count.F6.Sample\"## [33] \"Quan.Info\"## [34] \"Found.in.File.in.F1\"## [35] \"Found.in.File.in.F2\"## [36] \"Found.in.File.in.F3\"## [37] \"Found.in.File.in.F4\"## [38] \"Found.in.File.in.F5\"## [39] \"Found.in.File.in.F6\"## [40] \"Found.in.Sample.in.S1.F1.Sample\"## [41] \"Found.in.Sample.in.S2.F2.Sample\"## [42] \"Found.in.Sample.in.S3.F3.Sample\"## [43] \"Found.in.Sample.in.S4.F4.Sample\"## [44] \"Found.in.Sample.in.S5.F5.Sample\"## [45] \"Found.in.Sample.in.S6.F6.Sample\"## [46] \"Found.in.Sample.Group.in.S1.F1.Sample\"## [47] \"Found.in.Sample.Group.in.S2.F2.Sample\"## [48] \"Found.in.Sample.Group.in.S3.F3.Sample\"## [49] \"Found.in.Sample.Group.in.S4.F4.Sample\"## [50] \"Found.in.Sample.Group.in.S5.F5.Sample\"## [51] \"Found.in.Sample.Group.in.S6.F6.Sample\"## [52] \"Confidence.by.Search.Engine.Sequest.HT\"## [53] \"Charge.by.Search.Engine.Sequest.HT\"## [54] \"Delta.Score.by.Search.Engine.Sequest.HT\"## [55] \"Delta.Cn.by.Search.Engine.Sequest.HT\"## [56] \"Rank.by.Search.Engine.Sequest.HT\"## [57] \"Search.Engine.Rank.by.Search.Engine.Sequest.HT\"## [58] \"Concatenated.Rank.by.Search.Engine.Sequest.HT\"## [59] \"mz.in.Da.by.Search.Engine.Sequest.HT\"## [60] \"Delta.M.in.ppm.by.Search.Engine.Sequest.HT\"## [61] \"Delta.mz.in.Da.by.Search.Engine.Sequest.HT\"## [62] \"RT.in.min.by.Search.Engine.Sequest.HT\"## [63] \"Percolator.q.Value.by.Search.Engine.Sequest.HT\"## [64] \"Percolator.PEP.by.Search.Engine.Sequest.HT\"## [65] \"Percolator.SVMScore.by.Search.Engine.Sequest.HT\"## [66] \"XCorr.by.Search.Engine.Sequest.HT\"## [67] \"Top.Apex.RT.in.min\"We locate the PeptideGroupsreadQFeatures function to import our data intoR and create aQFeatures object. We find the abundance data is located in columns 21 to 26 and thus pass this to ecol. After import we annotate thecolData.## Create QFeatures objectsn_qf <- readQFeatures## Clean sample namescolnames(sn_qf[[\"peptides_raw\"]]) <- paste0## Annotate samplessn_qf$sample <- paste0sn_qf$condition <- rep, each = 3)## Verify and allocate colData to initial SummarizedExperimentcolData(sn_qf)## DataFrame with 6 rows and 2 columns##         sample   condition##    <character> <character>## S1          S1     Treated## S2          S2     Treated## S3          S3     Treated## S4          S4     Control## S5          S5     Control## S6          S6     Control\"peptides_raw\"]]) <- colData(sn_qf)colData %>%  sum## Determine the number of peptidesoriginal_peps <- sn_qf[[\"peptides_raw\"]] %>%  rowData %>%  as_tibble %>%  pull(Sequence) %>%  unique %>%  length %>%  as.numeric## Determine the number of proteinsoriginal_prots <- sn_qf[[\"peptides_raw\"]] %>%  rowData %>%  as_tibble %>%  pull(Master.Protein.Accessions) %>%  unique %>%  length %>%  as.numeric## Vieworiginal_psms## [1] 144302original_peps## [1] 20312original_prots## [1] 3941We also determine the number of PSMs, peptides and proteins represented within the initial data. Since identical peptide sequences with different modifications are stored as separate entities, the output of## Check missed cleavagessn_qf[[\"peptides_raw\"]] %>%  rowData %>%  as_tibble %>%  pull(Number.of.Missed.Cleavages) %>%  table## .##     0    1  2## 22055 1248 72## Check precursor mass tolerancesn_qf[[\"peptides_raw\"]] %>%  rowData %>%  as_tibble %>%  pull(Delta.M.in.ppm.by.Search.Engine.Sequest.HT) %>%  summary##     Min. 1st Qu. Median   Mean 3rd Qu.   Max.## -9.9600  -0.2500 0.1500 0.6576  0.6900 9.9900## Check fragment mass tolerancesn_qf[[\"peptides_raw\"]] %>%  rowData %>%  as_tibble %>%  pull(Delta.mz.in.Da.by.Search.Engine.Sequest.HT) %>%  summary##       Min.     1st Qu.   Median      Mean   3rd Qu.      Max.## -0.0113400 -0.0001400 0.0000900 0.0006618 0.0004800 0.0142300## Check peptide confidence allocationssn_qf[[\"peptides_raw\"]] %>%  rowData %>%  as_tibble %>%  pull(Confidence) %>%  table## .##  High## 23375Thus, the search identified 144302 PSMs corresponding to 20312 peptides and 3941 proteins. Finally, we take a look at some of the key parameters applied during the identification search. This is an important verification step, particularly for those using publicly available data with limited access to parameter settings.The preliminary data is as expected so we continue on to evaluate the quality of the raw data.## Plot scatter plot of mass accuracysn_qf[[\"peptides_raw\"]] %>%  rowData %>%  as_tibble %>%  ggplot) +  geom_point +  geom_hline +  geom_hline +  labs(x = \"RT (min)\", y = \"Delta precursor mass (ppm)\") +  scale_x_continuous, breaks = seq) +  scale_y_continuous, breaks = c) +  ggtitle(\"Peptide retention time against delta precursor mass\") +  theme_bw## Plot histogram of peptide retention timesn_qf[[\"peptides_raw\"]] %>%  rowData %>%  as_tibble %>%  ggplot(aes(x = RT.in.min.by.Search.Engine.Sequest.HT)) +  geom_histogram(binwidth = 1) +  labs(x = \"RT (min)\", y = \"Frequency\") +  scale_x_continuous) +  ggtitle(\"Peptide frequency across retention time\") +  theme_bwTo briefly assess the quality of the raw mass spectrometry data from which the search results were derived, we create simple plots. In contrast to the previous PSM processing workflow, we do not have access to information about ion injection times from the peptide-level file. However, we can still look at the peptide delta mass across retention time, as well as the frequency of peptides across the retention time gradient.For a more in-depth discussion of these plots users should refer back to the TMT processing workflow. Since neither plot indicates any major problems with the MS runs, we continue on to basic data cleaning.1.Removal of features without a master protein accession2.Removal of features corresponding to protein groups which contain a contaminant3.Removal of features without quantitative data4. Removal of features which are not unique to a protein group5.Removal of features not allocated rank 1 during the identification search6.Removal of features not annotated as unambiguousAs discussed in detail above, there are several basic data cleaning steps which are non-specific and should be applied to all quantitative datasets, regardless of the quantitation method or data level . These steps are as follows:In addition to these standard steps, LFQ data should be filtered to remove peptides that were not quantified based on a monoisotopic peak. The monoisotopic peak is that which comprises the most abundant natural isotope of each constituent element. For bottom-up proteomics, this typically translates to the peptides containing carbon-12 and nitrogen-14. When the different isotopes are well resolved, the monoisotopic peak usually provides the most accurate measurement.SummarizedExperiment, as to retain a copy of the raw data for reference. As before we use theaddAssay function.## Add second copy of data to be filtereddata_copy <- sn_qf[[\"peptides_raw\"]]sn_qf <- addAssay## Verifysn_qf## An instance of class QFeatures containing 2 assays:##  [1] peptides_raw: SummarizedExperiment with 23375 rows and 6 columns##  [2] peptides_filtered: SummarizedExperiment with 23375 rows and 6 columnsBefore we remove any data, we first create a second copy of the originalfind_cont function. Refer back to the TMT processing workflow for more details.## Store row indices of peptides matched to a contaminant-containing protein groupcont_peptides <- find_cont## Remove these rows from the dataif (length(cont_peptides) > 0)  sn_qf[[\"peptides_filtered\"]] <- sn_qf[[\"peptides_filtered\"]]Here, cleaning is done is two steps. The first is the removal of contaminant proteins using the self-definedfilterFeatures function as before.<- sn_qf %>%  filterFeatures %>%  filterFeatures %>%  filterFeatures %>%  filterFeatures %>%  filterFeatures %>%  filterFeaturessn_qf Second, we carry out all remaining cleaning using the## Check for remaining annotationssn_qf[[\"peptides_filtered\"]] %>%  rowData %>%  as_tibble %>%  pull(Quan.Info) %>%  table## .#### 17999As before, we check to see whether additional annotations remain within the \u201cQuan.Info\u201d column.## Determine number of PSMs, peptides and proteins remainingpsms_remaining <- sn_qf[[\"peptides_filtered\"]] %>%  rowData %>%  as_tibble %>%  pull(Number.of.PSMs) %>%  sumpeps_remaining <- sn_qf[[\"peptides_filtered\"]] %>%  rowData %>%  as_tibble %>%  pull(Sequence) %>%  unique %>%  length %>%  as.numericprots_remaining <- sn_qf[[\"peptides_filtered\"]] %>%  rowData %>%  as_tibble %>%  pull(Master.Protein.Accessions) %>%  unique %>%  length %>%  as.numeric## Determine the number of proportion of PSMs, peptides and proteins removedpsms_removed <- original_psms - psms_remainingpsms_removed_prop <-  * 100) %>%  round(digits = 2)peps_removed <- original_peps - peps_remainingpeps_removed_prop <-  * 100) %>%  round(digits = 2)prots_removed <- original_prots - prots_remainingprots_removed_prop <-  * 100) %>%  round(digits = 2)## Present in a tabledata.frame,           \"Number lost\" = c,           \"Percentage lost\" = c,           \"Number remaining\" = c)##    Feature Number.lost Percentage.lost Number.remaining## 1     PSMs       28140           19.50           116162## 2 Peptides        3767           18.55            16545## 3 Proteins         690           17.51             3251As in the previous example, we assess the impact that cleaning has had on the data. Specifically, we determine the number and proportion of PSMs, peptides and proteins lost. Again, when we refer to the number of peptides we only consider unique peptide sequences, not those that differ in their modifications..txt file rather than aggregating up from a PSM file, additional parameters exist within the peptiderowData. Such parameters include Quality PEP, Quality q-value, and SVM score, as well as similar scoring parameters provided by the search engine. Although we will not complete additional filtering based on these parameters in this workflow, users may wish to explore this option.When extracting data from the peptide-levelHaving cleaned the peptide-level data we now move onto the management of missing data. This is of particular importance for LFQ workflows where the missing value challenge is amplified by intrinsic variability between independent MS runs. As before, the management of missing data can be divided into three steps: 1) exploring the presence and distribution of missing values, (2) filtering out missing values, and (3) optional imputation.## Are there any NA values within the peptide data?sn_qf[[\"peptides_filtered\"]] %>%  assay %>%  anyNA## [1] TRUE## How many NA values are there within the peptide data?sn_qf[[\"peptides_filtered\"]] %>%  nNA## $nNA## DataFrame with 1 row and 2 columns##         nNA       pNA##   <integer> <numeric>## 1     15863   14.6888#### $nNArows## DataFrame with 17999 rows and 3 columns##              name       nNA       pNA##       <character> <integer> <numeric>## 1               1         4   66.6667## 2               2         1   16.6667## 3               3         0    0.0000## 4               4         1   16.6667## 5               5         0    0.0000## ...           ...       ...       ...## 17995       23371         0         0## 17996       23372         0         0## 17997       23373         0         0## 17998       23374         0         0## 17999       23375         0         0#### $nNAcols## DataFrame with 6 rows and 3 columns##          name       nNA       pNA##   <character> <integer> <numeric>## 1          S1      3699   20.5511## 2          S2      1945   10.8062## 3          S3      2048   11.3784## 4          S4      3674   20.4122## 5          S5      2673   14.8508## 6          S6      1824   10.1339The aim of the first step is to determine how many missing values are present within the data, and how they are distributed between samples and/or conditions.## Plot histogram to visualize sample-specific distribution of NAsnNA(sn_qf[[\"peptides_filtered\"]])$nNAcols %>%  as_tibble %>%  mutate, each = 3)) %>%  ggplot) +  geom_bar(stat = \"identity\") +  geom_hline +  labs\") +  theme_bwAs expected, the LFQ data contains a higher proportion of missing values as compared to the TMT-labelled data. There are 15863 missing (NA) values within the data, which corresponds to 15%. We check for sample- and condition-specific biases in the distribution of these NA values.Whilst S1 and S4 have a slightly higher proportion of missing values, all of the samples are within an acceptable range to continue. Again, there is no evidence of a condition-specific bias in the data.## Check how many peptides we will removewhich(nNA(sn_qf[[\"peptides_filtered\"]])$nNArows$pNA >= 20) %>%  length## [1] 4364## Remove peptides with 2 or more NA valuessn_qf <- sn_qf %>%  filterNAWe next filter out features, here peptides, which comprise 20% or more missing values.\"peptides_filtered\"]])$nNAnNA have proven favorable for data with a high proportion of MNAR values whilst hot deck methods  are more appropriate when the majority of missing data is MCAR ] %>%  assay %>%  log2 %>%  plotDensitiessn_qf[[\"peptides_imputed\"]] %>%  assay %>%  log2 %>%  plotDensitiesFollowing imputation we check to ensure that the distribution of the data has not dramatically changed. To do so we create a density plot of the data pre- and post-imputation.## Determine the impact of imputation on summary statisticspre_imputation_summary <- sn_qf[[\"peptides_filtered\"]] %>%  assay %>%  longFormat %>%  group_by(colname) %>%  summarise,            max_intensity = max,            median_intensity = median)post_imputation_summary <- sn_qf[[\"peptides_imputed\"]] %>%  assay %>%  longFormat %>%  group_by(colname) %>%  summarise,            max_intensity = max,            median_intensity = median)print(pre_imputation_summary)## # A tibble: 6 x 4##   colname sum_intensity max_intensity median_intensity##   <chr>           <dbl>         <dbl>            <dbl>## 1 S1       98919496611.    1477162278.        1948794.## 2 S2      155722262777.    1678988256.        3553168.## 3 S3      145509803642.    1804842981.        3251578.## 4 S4       94892286529.    1087946291.        1873948.## 5 S5      121590387110.    1307181986         2503109## 6 S6      143538084562.    1608003894.        3282077.print(post_imputation_summary)## # A tibble: 6 x 4##   colname sum_intensity max_intensity median_intensity##   <chr>           <dbl>         <dbl>            <dbl>## 1 S1       99811359317.    1477162278.        1812200.## 2 S2      156124619343.    1678988256.        3478920.## 3 S3      145754574641.    1804842981.        3201591## 4 S4       96201734159.    1087946291.        1720642.## 5 S5      122113590867.    1307181986         2440628.## 6 S6      143994721088.    1608003894.        3199278.From this plot the change in the data appears to be minimal. We can further validate this by comparing the summary statistics of the data pre- and post-imputation.Comparison of the two tables reveals minimal change within the data. However, we find that S1 and S4 display greater differences between pre- and post-imputation statistics because of the higher number of missing values which required imputation.robustSummary aggregation.## log2 transform the quantitative datasn_qf <- logTransform## Verifysn_qf## An instance of class QFeatures containing 4 assays:##  [1] peptides_raw: SummarizedExperiment with 23375 rows and 6 columns##  [2] peptides_filtered: SummarizedExperiment with 13635 rows and 6 columns##  [3] peptides_imputed: SummarizedExperiment with 13635 rows and 6 columns##  [4] log_peptides: SummarizedExperiment with 13635 rows and 6 columnsIn the following code chunk we log2 transform the peptide-level data to generate a near-normal distribution within the quantitative data. This is necessary prior to the use ofaggregateFeatures function.## Aggregate peptide to proteinsn_qf <- aggregateFeatures## Your row data contain missing values. Please read the relevant## section(s) in the aggregateFeatures manual page regarding the effects## of missing values on data aggregation.## Verifysn_qf## An instance of class QFeatures containing 5 assays:##  [1] peptides_raw: SummarizedExperiment with 23375 rows and 6 columns##  [2] peptides_filtered: SummarizedExperiment with 13635 rows and 6 columns##  [3] peptides_imputed: SummarizedExperiment with 13635 rows and 6 columns##  [4] log_peptides: SummarizedExperiment with 13635 rows and 6 columns##  [5] log_proteins: SummarizedExperiment with 2837 rows and 6 columnsNow that we are happy with the peptide-level data, we aggregate upward to proteins using thecenter.median\" method via thenormalize function.## normalize protein-level quantitation datasn_qf <- normalize## Verifysn_qf## An instance of class QFeatures containing 6 assays:##  [1] peptides_raw: SummarizedExperiment with 23375 rows and 6 columns##  [2] peptides_filtered: SummarizedExperiment with 13635 rows and 6 columns##  [3] peptides_imputed: SummarizedExperiment with 13635 rows and 6 columns##  [4] log_peptides: SummarizedExperiment with 13635 rows and 6 columns##  [5] log_proteins: SummarizedExperiment with 2837 rows and 6 columns##  [6] log_norm_proteins: SummarizedExperiment with 2837 rows and 6 columnsFinally, we complete the data processing by normalising quantitation between samples. This is done using the \"SummarizedExperiment file as well as exporting the finalQFeatures object.## Save protein-level SEsn_proteins <- sn_qf[[\"log_norm_proteins\"]]## Export TMT final QFeatures objectsaveThe final dataset is comprised of 2837 proteins. We will save the protein-levelHaving described the processing steps for quantitative proteomics data, we next demonstrate how to explore the protein-level data prior to statistical analysis. For this we will utilise the TMT-labelled cell pellet dataset since it contains a greater number of proteins.corrplot package to calculate and plot the Pearson\u2019s correlation coefficient between each sample pair. Thecor function within thecorrplot package will create a correlation matrix but requires adata.frame,matrix or avector of classnumeric as input. To convert theQFeatures assay data into adata.frame we use the as.data.frame function.## Convert TMT CP protein assay into a dataframeprot_df <- cp_qf[[\"log_norm_proteins\"]] %>%  assay %>%  as.data.frame## Calculate a correlation matrix between samplescorr_matrix <- corprint(corr_matrix)##           S1        S2        S3        S4        S5        S6## S1 1.0000000 0.9863382 0.9927432 0.9447089 0.9627190 0.9628944## S2 0.9863382 1.0000000 0.9886960 0.9206049 0.9535162 0.9501522## S3 0.9927432 0.9886960 1.0000000 0.9344640 0.9551061 0.9548680## S4 0.9447089 0.9206049 0.9344640 1.0000000 0.9822722 0.9867422## S5 0.9627190 0.9535162 0.9551061 0.9822722 1.0000000 0.9928376## S6 0.9628944 0.9501522 0.9548680 0.9867422 0.9928376 1.0000000We will first generate correlation plots between pairs of samples. To do this we use the## Plot correlation between two samples - S1 and S2 used as exampleprot_df %>%  ggplot) +  geom_point +  geom_abline +  theme,        panel.background = element_rect(fill = \"white\"),        axis.title.x = element_text,        axis.title.y = element_text,        axis.text.x = element_text,        axis.text.y = element_text(size = 12),        axis.line = element_line,        plot.margin = margin) +  xlim +  ylim +  labs(x = \"log2(abundance S1)\", y = \"log2(abundance S2)\") +  coord_fixed(ratio = 1)## Create colour palette for continuumcol <- colorRampPalette)## Plot all pairwise correlationsprot_df %>%  cor %>%  corrplot,           type = \"upper\",           addCoef.col = \"white\",           diag = FALSE,           tl.col = \"black\",           tl.srt = 45,           outline = TRUE)Now we can visualise the correlation data using pairwise scatter plots and a correlation heat map.From these plots we can see that all replicate pairs have a Pearson\u2019s correlation coefficient >0.98 whilst the correlation between pairs of control and treated samples is somewhat lower. Users may interpret this information as an early indication that some proteins may be differentially abundant between the two groups., This is especially true for expression proteomics data in which high correlation values are likely due to the majority of proteins remaining at similar levels regardless of cellular perturbation. Users are directed to Ref.Of note, whilst correlation is widely applied as a measure of reproducibility, users are reminded that correlation coefficients alone are not informative of reproducibility.stats package to perform the PCA. Since PCA does not accept missing values and we did not impute the TMT data, thefilterNA function can be used to remove any missing values that may be present in the protein-level data. We then extract and transpose the assay data before passing it to the prcomp function to carry out PCA.## Carry out principal component analysisprot_pca <- cp_qf[[\"log_norm_proteins\"]] %>%  filterNA %>%  assay %>%  t %>%  prcompPrincipal Component Analysis (PCA) is a dimensionality reduction method which aims to simplify complex datasets and facilitate the visualisation of multi-dimensional data. Here we use the prcomp function from the## Get a summary of the PCAsummary(prot_pca)## Importance of components:##                           PC1     PC2     PC3      PC4      PC5       PC6## Standard deviation    42.4845 26.7522 18.1650 15.15893 14.44395 5.474e-14## Proportion of Variance 0.5488  0.2176  0.1003  0.06987  0.06343 0.000e+00## Cumulative Proportion  0.5488  0.7664  0.8667  0.93657  1.00000 1.000e+00We can get an idea of the outcome of the PCA by running the summary function on the results of the PCA.factoextra package.## Generate dataframe of each sample's PCA resultspca_df <- as.data.frame(prot_pca$x)## Annotate samples with their corresponding conditionpca_df$condition <- cp_qf[[\"psms_raw\"]]$condition## Generate a PCA plot using PC1 and PC2pca_df %>%  ggplot) +  geom_point(size = 4) +  scale_color_brewer +  labs(colour = \"Condition\") +  geom_hline +  geom_vline +  guides(colour = guide_legend(override.aes = list(size = 3))) +  labs(x = \"PC1 (42.5 %)\", y = \"PC2 (26.8 %)\") +  ggtitle(\"Protein-level PCA plot\") +  xlim +  ylim +  coord_fixed(ratio = 1) +  theme_bwFinally, we create a PCA plot. For additional PCA exploration and visualization tools users are directed to theBefore carrying out differential expression analysis, it is first necessary to explore the presence of batch effects within the data. Batch effects are derived from non-biological factors which impact the experimental data. These include reagents, instrumentation, personnel and laboratory conditions. In most cases the increased variation caused by batch effects will lead to reduced downstream statistical power. On the other hand, if correlated with the experimental sub- groups, batch effects can also lead to confounded results and the incorrect biological interpretation of differential expression.removeBatchEffect function from the limma package.Given that the use-case data was derived from a small experiment with only six samples and a single TMTplex, there are minimal batch effects to explore here. For users analysing larger experiments completed over long period of time, across several laboratories/individuals, or using multiple TMTplex reagents, it is advisable to annotate the PCA plot with all potential batch factors. If data is found to cluster based on any of these factors, batch effects should be incorporated into downstream analyses. For example, users can apply theThe last section of this workflow demonstrates how to gain biological insights from the resulting list of proteins. Again we will utilise the TMT-labelled cell pellet data, although the process would be exactly the same for the LFQ supernatant protein list. Users are reminded that although referred to as differential \u2018expression\u2019 analysis, abundance is determined by both protein synthesis and degradation.SummarizedExperiment from the cell pellet TMTQFeatures object and specify the study factors. Here we are interested in discovering differences between conditions, control and treated. As well as assigning these conditions to each sample, we can define the control group as the reference level such that differential abundance is reported relative to the control. This means that when we get the results of the statistical analysis, \u2018upregulated\u2019 will refer to increased abundance in treated cells relative to control controls.## Extract protein-level data and associated colDatacp_proteins <- cp_qf[[\"log_norm_proteins\"]]colData(cp_proteins) <- colData(cp_qf[[\"log_norm_proteins\"]])## Create factor of interestcp_proteins$condition <- factor(cp_proteins$condition)## Check which level of the factor is the reference level and correctcp_proteins$condition## [1] Treated Treated Treated Control Control Control## Levels: Control Treated<- relevelcp_proteins$condition We first extract the protein-levelMSstats andMSstatsTMT can be used to determine differential protein expression within both DDA and DIA datasets for LFQ and TMT, respectively., Of note,MSstatsTMT includes additional functionality for dealing with larger, multi-plexed TMT experiments. For LFQ experiments,proDA,prolfqua andMSqRob2 can be utilised, among others., Here, we will use thelimma package. limma is widely used for the analysis of large omics datasets and has several models that allow differential abundance to be assessed in multifactorial experiments. This is useful because it allows multiple factors, including TMTplex, to be integrated into the model itself, thus minimising the effects of confounding factors. In this example we will applylimma\u2019s empirical Bayes moderated t-test, a method that is appropriate for small sample sizes.Bioconductor contains several packages dedicated to the statistical analysis of proteomics data. For example,model.matrix function to create a matrix in which each of the samples are annotated based on the factors we wish to model, here the condition group. This ultimately defines the \u2018design\u2019 of the model, that is how the samples are distributed between the groups of interest. We then fit a linear model to the abundance data of each protein by passing the data and model design matrix to thelmFit function. Finally, we update the estimated standard error for each model coefficient using the eBayes function. This function borrows information across features, here proteins, to shift the per-protein variance estimates towards an expected value based on the variance estimates of other proteins with similar mean intensity. This empirical Bayes technique has been shown to reduce the number of false positives for proteins with small variances as well as increase the power of detection for differentially abundant proteins with larger variances. Further, we use thetrend = TRUE argument when passing theeBayes function so that an intensity-dependent trend can be fitted to the prior variances. For more information about thelimma trend method users are directed to Ref.## Design a matrix containing all of the factors we wish to model the effects ofmodel_design <- model.matrix(~ cp_proteins$condition)## Verifyprint(model_design)##   (Intercept) cp_proteins$conditionTreated## 1           1                            1## 2           1                            1## 3           1                            1## 4           1                            0## 5           1                            0## 6           1                            0## attr## [1] 0 1## attr## attr$\u2018cp_proteins$condition\u2018## [1] \"contr.treatment\"## Create a linear model using this designfitted_lm <- cp_proteins %>%  assay %>%  lmFit(design = model_design)## Update the model based on Limma eBayes algorithmfitted_lm <- eBayes## Save results of the testlimma_results <- topTable %>%rownames_to_column(\"Protein\") %>%as_tibble %>%mutate)We first use theplotSA function withinlimma. An SA plot shows the log2 residual standard deviation (sigma) against log average abundance and is a simple way to visualise the trend that has been fitted to the data.## Plot residual SD against average log abundanceplotSA\",       ylab = \"log2(sigma)\",       cex = 0.5)Having applied the model to the data, we need to verify that this model was appropriate and that the statistical assumptions were met. To do this we first generate an SA plot using theThe residual standard deviation is a measure of model accuracy and is most easily conceptualised as a measurement of how far from the model prediction each data point lies. The smaller the residual standard deviation, the closer the fit between the model and observed data.P.value variable, not theadj.P.Val.## Plot histogram of raw p-valueslimma_results %>%  ggplot) +  geom_histogram(binwidth = 0.025) +  labs +  ggtitle +  theme_bwNext we will plot a p-value histogram. Importantly, this histogram shows the distribution of p-values prior to any multiple hypothesis test correction or FDR control. This means plotting the## Look at limma results tablehead(limma_results)## # A tibble: 6x8##   Protein logFC AveExpr     t  P.Value  adj.P.Val      B TP##   <chr>   <dbl>   <dbl> <dbl>    <dbl>      <dbl>  <dbl> <lgl>## 1 Q9C0G0  2.97   -0.814  33.7 1.81e-10 0.000000596 14.1  FALSE## 2 Q01581  1.50    0.588  28.4 7.87e-10 0.00000129  13.0  FALSE## 3 P15104  1.32     1.36  23.7 3.69e- 9 0.00000404  11.7  FALSE## 4 Q9UK41  1.46    -1.49  22.0 7.05e- 9 0.00000553  11.1  FALSE## 5 P37268  1.32   -0.678  21.1 9.80e- 9 0.00000553  10.8  FALSE## 6 P04183  1.29    0.939  21.1 1.01e- 8 0.00000553  10.8  FALSEThe figure displayed shows an anti-conservative p-value distribution. The flat distribution across the base of the graph represents the non-significant p-values spread uniformly between 0 and 1, whilst the peak close to 0 contains significant p-values, along with some false positives. For a more thorough explanation of interpreting p-value distributions, including why your data may not produce an anti-conservative distribution if your statistical model is inappropriate, please see Ref.AveExpr. Since we carried out an empirical Bayes moderated t-test, each protein also has a moderated t-statistic and associated p-value. The moderated t-statistic can be interpreted in the same way as a standard t-statistic. Each protein also has an adjusted p-value which accounts for multiple hypothesis testing to control the overall FDR. The default method for multiple hypothesis corrections within thetopTable function that we applied is the Benjamini and Hochberg (BH) adjustment, although we could have specified an alternative. Finally, the B-statistic represents the log-odds that a protein is differentially abundant between the two conditions, and the data is presented in descending order with those with the highest log-odds of differential abundance at the top.The results table contains several important pieces of information. Each master protein is represented by its accession number and has an associated log2 fold change, that is the log2 difference in mean abundance between conditions, as well as a log2 mean expression across all six samples, termedlogFC) threshold is at the users discretion and can be useful to determine significant results of biological relevance. When using a TMT labelling strategy the co-isolation interference can lead to substantial and uneven ratio compression, thus it is not recommended to apply a fold change threshold here.## Add direction of log fold change relative to controllimma_results$direction <- ifelse %>%  as.factor## Add significance thresholdslimma_results$significance <- ifelse %>%  as.factor## Verifystr(limma_results)## tibble  (S3: tbl_df/tbl/data.frame)##  $ Protein      : chr [1:3289] \"Q9C0G0\" \"Q01581\" \"P15104\" \"Q9UK41\" \u2026##  $ logFC        : num [1:3289] 2.97 1.5 1.32 1.46 1.32 \u2026##  $ AveExpr      : num [1:3289] -0.814 0.588 1.359 -1.489 -0.678 \u2026##  $ t            : num [1:3289] 33.7 28.4 23.7 22 21.1 \u2026##  $ P.Value      : num [1:3289] 1.81e-10 7.87e-10 3.69e-09 7.05e-09 9.80e-09 \u2026##  $ adj.P.Val    : num [1:3289] 5.96e-07 1.29e-06 4.04e-06 5.53e-06 5.53e-06 \u2026##  $ B            : num [1:3289] 14.1 13 11.7 11.1 10.8 \u2026##  $ TP           : logi [1:3289] FALSE FALSE FALSE FALSE FALSE FALSE \u2026##  $ direction    : Factor w/ 2 levels \"down\",\"up\": 2 2 2 2 2 2 2 1 1 2 \u2026##  $ significance : Factor w/ 2 levels \"not.sig\",\"sig\": 2 2 2 2 2 2 2 2 2 2 \u2026We can add annotations to this results table based on the user-defined significance thresholds. In the literature, for stringent analyses an FDR-adjusted p-value threshold of 0.01 is most frequently used, or 0.05 for exploratory analyses. Ultimately these thresholds are arbitrary and set by the user. The addition of a log-fold change  %>%  summary##       (Intercept) cp_proteins$conditionTreated## Down         1448                          395## NotSig        414                         2569## Up           1427                          325In the next code chunk, we use the## Subset proteins that show significantly different abundancesig_proteins <- subsetlength(sig_proteins$Protein)## [1] 720From this table we can see that 395 proteins were downregulated in treated HEK293 cells compared to the control group whilst 325 were upregulated. Given that no logFC threshold was applied some of the significant differences in abundance may be small. Further, these results mean little without any information about which proteins these were and what roles they play within the cell. We subset the significant proteins so that we can investigate them further.## Generate a volcano plotlimma_results %>%  ggplot)) +  geom_point(aes(colour = significance:direction), size = 0.5) +  scale_color_manual, name = \"\",  labels = c) +  theme,        axis.title.y = element_text,        axis.text.x = element_text,        axis.text.y = element_text(size = 12),        plot.background = element_rect(fill = \"white\"),        panel.background = element_rect(fill = \"white\"),        axis.line = element_line,        plot.margin = margin,        legend.position = c) +  labs(x = \"log2(FC)\", y = \"-log10\") +  xlim## Generate MA plotlimma_results %>%  ggplot) +  geom_point(aes(colour = significance:direction), size = 0.5) +  scale_color_manual, name = \"\",  labels = c) +  theme,        axis.title.y = element_text,        axis.text.x = element_text,        axis.text.y = element_text(size = 12),        plot.background = element_rect(fill = \"white\"),        panel.background = element_rect(fill = \"white\"),        axis.line = element_line,        plot.margin = margin,  \u2003\u2003\u2003\u2003\u2003\u2003legend.position = c) +  xlab(\"log2(mean abundance)\") +  ylab(\"log2(FC)\") +  xlimBefore looking deeper into which proteins have differential abundance, we first create some simple plots to visualise the results. Volcano plots and MA plots are two of the common visualisations used in this instance. When plotting the former, users are advised to plot raw p-values rather than their derivative BH-adjusted p-values. Point colours can be used to indicate significance based on BH-adjusted p-values, as is shown in the code chunk below.The final step in the processing workflow is to apply Gene Ontology (GO) enrichment analyses to gain a biological understanding of the proteins which were either up or downregulated in HEK293 cells upon treatment. GO terms provide descriptions for genes and their corresponding proteins in the form of Molecular Functions (MF), Biological Processes (BP) and Cellular Components (CC). By carrying out GO enrichment analysis we can determine whether the frequency of any of these terms is higher than expected in the proteins of interest compared to all of the proteins which were detected. Such results can indicate whether proteins that were increased or decreased in abundance in treated HEK293 cells represent particular cellular locations, biological pathways or cellular functions.GOrilla orPantherDB,, we advise against this due to a lack of traceability and reproducibility. Instead, readers are advised to make use of GO enrichment packages within the Bioconductor infrastructure. Many such packages exist, includingtopGO,GOfuncR, andclusterProfiler. Here we will useenrichGO function in theclusterProfiler package.Although GO enrichment analysis can be carried out online using websites such as## Subset significantly upregulated and downregulated proteinssig_up <- limma_results %>%  filter(direction == \"up\") %>%  filter(significance == \"sig\") %>%  pull(Protein)sig_down <- limma_results %>%  filter(direction == \"down\") %>%  filter(significance == \"sig\") %>%  pull(Protein)First, we subset the accessions of proteins that we consider to be significantly up or downregulated. These will be our proteins of interest.org.Hs.eg.db). We also inform the function which GO categories we wish to consider, here \u201cALL\u201d, meaning BP, MF and CC.Next, we input the UniProt IDs of up and downregulated proteins into the GO enrichment analyses, as demonstrated below. Importantly, we provide the protein list of interest as the foreground and a list of all proteins identified within the study as the background, or \u2018universe\u2019. The keyType argument is used to tell the function that our protein accessions are in UniProt format. This allows mapping from UniProt ID back to a database containing the entire human genome  as well as q-values . Although many papers often use \u2018q- value\u2019 to mean \u2018BH-adjusted p-value\u2019, the two are not always the same and users should be explicit about the statistical thresholds that they have applied. For exploratory purposes we will use the standard BH method for FDR control and set p-value, BH-adjusted p-value, and q-value thresholds of 0.05.## Search for enriched GO terms within upregulated proteinsego_up <- enrichGO## Check resultsego_up## ### # over-representation test## ### #\u2026@organism     Homo sapiens## #\u2026@ontology     GOALL## #\u2026@keytype      UNIPROT## #\u2026@gene     chr [1:325] \"Q9C0G0\" \"Q01581\" \"P15104\" \"Q9UK41\" \"P37268\" \"P04183\" \"Q9UHI8\" \u2026## #\u2026pvalues adjusted by \u2019BH\u2019 with cutoff <0.05## #\u20262 enriched terms found## \u2019data.frame\u2019: 2 obs. of 10 variables:##  $ ONTOLOGY    : chr \"CC\" \"CC\"##  $ ID          : chr \"GO:0005758\" \"GO:0031970\"##  $ Description : chr \"mitochondrial intermembrane space\" \"organelle envelope lumen\"##  $ GeneRatio   : chr \"15/319\" \"15/319\"##  $ BgRatio     : chr \"45/3228\" \"49/3228\"##  $ pvalue      : num 1.32e-05 4.18e-05##  $ p.adjust    : num 0.00507 0.008##  $ qvalue      : num 0.00506 0.00798##  $ genelD      : chr \"CHCHD2/TIMM9/AK2/TIMM8B/COA4/COA6/MIX23/TIMM8A/DIABLO/TIMM13/TIMM10/TRIAP1/CYCS/COX17/CAT\"##  $ Count : int 15 15## #\u2026Citation##  T Wu, E Hu, S Xu, M Chen, P Guo, Z Dai, T Feng, L Zhou, W Tang, L Zhan, X Fu, S Liu, X Bo, and G Yu.##  clusterProfiler 4.0: A universal enrichment tool for interpreting omics data.##  The Innovation. 2021, 2(3):100141As well as the information outlined above, there is the opportunity for users to specify various thresholds for statistical significance. These include thresholds on original and adjusted p-values ## Check resultsego_down## ### # over-representation test## ### #\u2026@organism     Homo sapiens## #\u2026@ontology     GOALL## #\u2026@keytype      UNIPROT## #\u2026@gene     chr [1:395] \"Q53EL6\" \"P08243\" \"P35716\" \"Q92878\" \"P26583\" \"Q92522\" \"O43657\" \u2026## #\u2026pvalues adjusted by \u2019BH\u2019 with cutoff <0.05## #\u202669 enriched terms found## \u2019data.frame\u2019:    69 obs. of 10 variables:##  $ ONTOLOGY    : chr \"BP\" \"BP\" \"BP\" \"BP\" \u2026##  $ ID          : Chr \"GO:0006310\" \"GO:0006520\" \"GO:0000725\" \"GO:0006302\" \u2026##  $ Description : chr \"DNA recombination\" \"amino acid metabolic process\" \"recombinational repair\" \"double-strand break repair\" \u2026##  $ GeneRatio   : chr \"36/378\" \"35/378\" \"22/378\" \"31/378\" \u2026##  $ BgRatio     : chr \"94/3166\" \"112/3166\" \"55/3166\" \"98/3166\" \u2026##  $ pvalue      : num 2.27e-11 2.48e-08 8.41e-08 1.20e-07 1.01e-06 \u2026##  $ p.adjust    : num 5.66e-08 3.09e-05 7.00e-05 7.48e-05 5.06e-04 \u2026##  $ qvalue      : num 5.37e-08 2.93e-05 6.63e-05 7.09e-05 4.80e-04 \u2026##  $ genelD      : chr \"RAD50/HMGB2/H1-10/RADX/MRE11/H1-0/H1-2/ZMYND8/HMGB3/MCM5/NUCKS1/RAD21/PRKDC/SFPQ/MCM4/XRCC6/H1-3/MCM7/TFRC/XRCC\"| __truncated__ \"ASNS/PHGDH/SDSL/SARS1/YARS1/AARS2/HMGCL/IARS2/GARS1/AARS1/HIBADH/PYCR1/MCCC2/ACADSB/DHFR/MARS1/SLC25A12/ETFA/PS\"| __truncated__ \"RADX/MRE11/ZMYND8/MCM5/NUCKS1/RAD21/SFPQ/MCM4/XRCC6/MCM7/XRCC5/PPP4R2/POGZ/YY1/MCM3/MCM2/VPS72/PARP1/BRD8/MCM6/FUS/RECQL\" \"RAD50/HMGB2/RADX/MRE11/DEK/ZMYND8/MCM5/NUCKS1/RAD21/PRKDC/TP53/SFPQ/SMARCC2/MCM4/XRCC6/HPF1/MCM7/XRCC5/HMGB1/PP\"| __truncated__ \u2026##  $ Count       : int 36 35 22 31 20 56 57 18 14 56 \u2026## # \u2026Citation##  T Wu, E Hu, S Xu, M Chen, P Guo, Z Dai, T Feng, L Zhou, W Tang, L Zhan, X Fu, S Liu, X Bo, and G Yu.##  clusterProfiler 4.0: A universal enrichment tool for interpreting omics data.##  The Innovation. 2021, 2(3):100141We can see from the results that there are 2 significantly enriched terms associated with the upregulated proteins. Next, we take a look at the downregulated proteins.barplot function from theenrichplot package. Users are directed to the vignette of theenrichplot package for additional visualisation options and guidance. We plot the first 10 GO terms i.e. the 10 GO terms with the greatest enrichment.## Plot the resultsbarplotThe downregulated proteins contain 69 significantly enriched GO terms. There are many ways in which users can represent these results visually. Here, we create a barplot using the.csv files.## Save results of Limma statisticswrite.csv## Save subsets of upregulated and downregulated proteinswrite.csvwrite.csv)## Save results of GO enrichmentwrite.csvwrite.csvFinally, we export the results of our statistical analyses asggsave function to export any of the figures generated.Users can also use theExpression proteomics is becoming an increasingly important tool in modern molecular biology. As more researchers participate in expression proteomics, either by collecting data or accessing data collected by others, there is a need for clear illustration(s) of how to deal with such complex data.MSstats andMSstatsTMT,), can only be applied to specific data formats , or provide very limited commentary. The latter directly contributes to a problematic disconnect between researchers and their data whereby the users do not understand if or why each step is necessary for their given dataset and biological question. This can prevent researchers from refining a workflow to fit their specific needs. Finally, the majority of proteomics workflows utilisedata.frame ortibble structures which limits their traceability, as is the case forprotti,promor andprolfqua.\u2013Existing bottom-up proteomics workflows for differential expression analysis either provide pipelines with limited user control and flexibility . Our workflow takes advantage of the relatively recentNo single workflow can demonstrate the processing, analysis and interpretation of all proteomics data. Our workflow is currently suitable for DDA datasets with label-free or TMT-based quantitation. We do not include examples of experiments that combine data from multiple TMTplexes, although the code provided could easily be expanded to include such a scenario. This workflow provides an in-depth user-friendly pipeline for both new and experienced proteomics data analysts.## Print session informationsessionInfo## R version 4.3.0 (2023-04-21)## Platform: x86_64-apple-darwin20 (64-bit)## Running under: macOS Ventura 13.4#### Matrix products: default## BLAS: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0#### locale:## [1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8#### time zone: Europe/London## tzcode source: internal#### attached base packages:## [1] stats4    stats    graphics    grDevices    utils   datasets   methods## [8] base#### other attached packages:##  [1] patchwork_1.1.2             enrichplot_1.20.0##  [3] clusterProfiler_4.8.1       org.Hs.eg.db_3.17.0##  [5] AnnotationDbi_1.62.1        limma_3.56.2##  [7] Biostrings_2.68.1           XVector_0.40.0##  [9] corrplot_0.92               NormalyserDE_1.18.0## [11] tibble_3.2.1                dplyr_1.1.2## [13] stringr_1.5.0               ggplot2_3.4.2## [15] QFeatures_1.10.0            MultiAssayExperiment_1.26.0## [17] SummarizedExperiment_1.30.2 Biobase_2.60.0## [19] GenomicRanges_1.52.0        GenomeInfoDb_1.36.0## [21] IRanges_2.34.0              S4Vectors_0.38.1## [23] BiocGenerics_0.46.0         MatrixGenerics_1.12.2## [25] matrixStats_1.0.0#### loaded via a namespace (and not attached):## [1] splines_4.3.0           bitops_1.0-7               ggplotify_0.1.0## [4] cellranger_1.1.0        polyclip_1.10-4            preprocessCore_1.62.1## [7] rpart_4.1.19            lifecycle_1.0.3            lattice_0.21-8## [10] MASS_7.3-60            backports_1.4.1            magrittr_2.0.3## [13] Hmisc_5.1-0            rmarkdown_2.22             yaml_2.3.7## [16] sp_1.6-1               cowplot_1.1.1              MsCoreUtils_1.12.0## [19] DBI_1.1.3              RColorBrewer_1.1-3         abind_1.4-5## [22] zlibbioc_1.46.0        purrr_1.0.1                AnnotationFilter_1.24.0## [25] ggraph_2.1.0           RCurl_1.98-1.12            yulab.utils_0.0.6## [28] nnet_7.3-19            tweenr_2.0.2               sandwich_3.0-2## [31] git2r_0.32.0           GenomeInfoDbData_1.2.10    ggrepel_0.9.3## [34] tidytree_0.4.2         terra_1.7-37               nortest_1.0-4## [37] codetools_0.2-19       DelayedArray_0.26.3        DOSE_3.26.1## [40] ggforce_0.4.1          tidyselect_1.2.0           RcmdrMisc_2.7-2## [43] aplot_0.1.10           raster_3.6-20              farver_2.1.1## [46] viridis_0.6.3          base64enc_0.1-3            jsonlite_1.8.5## [49] e1071_1.7-13           tidygraph_1.2.3            Formula_1.2-5## [52] tools_4.3.0            treeio_1.24.1              Rcpp_1.0.10## [55] glue_1.6.2             BiocBaseUtils_1.2.0        gridExtra_2.3## [58] xfun_0.39              qvalue_2.32.0              usethis_2.2.0## [61] withr_2.5.0            BiocManager_1.30.21        fastmap_1.1.1## [64] fansi_1.0.4            digest_0.6.31              R6_2.5.1## [67] gridGraphics_0.5-1     colorspace_2.1-0           GO.db_3.17.0## [70] RSQLite_2.3.1          utf8_1.2.3                 tidyr_1.3.0## [73] generics_0.1.3         data.table_1.14.8          class_7.3-22## [76] graphlayouts_1.0.0     httr_1.4.6                 htmlwidgets_1.6.2## [79] S4Arrays_1.0.4         scatterpie_0.2.1           pkgconfig_2.0.3## [82] gtable_0.3.3           blob_1.2.4                 impute_1.74.1## [85] shadowtext_0.1.2       htmltools_0.5.5            carData_3.0-5## [88] bookdown_0.34          fgsea_1.26.0               ProtGenerics_1.32.0## [91] clue_0.3-64            scales_1.2.1               png_0.1-8## [94] ggfun_0.1.0            knitr_1.43                 rstudioapi_0.14## [97] reshape2_1.4.4         nlme_3.1-162               checkmate_2.2.0## [100] proxy_0.4-27          cachem_1.0.8               zoo_1.8-12## [103] parallel_4.3.0        HDO.db_0.99.1              foreign_0.8-84## [106] pillar_1.9.0          grid_4.3.0                 vctrs_0.6.3## [109] car_3.1-2             cluster_2.1.4              htmlTable_2.4.1## [112] evaluate_0.21         cli_3.6.1                  compiler_4.3.0## [115] rlang_1.1.1           crayon_1.5.2               labeling_0.4.2## [118] plyr_1.8.8            forcats_1.0.0              fs_1.6.2## [121] stringi_1.7.12        viridisLite_0.4.2          BiocParallel_1.34.2## [124] munsell_0.5.0         lazyeval_0.2.2             GOSemSim_2.26.0## [127] Matrix_1.5-4.1        hms_1.1.3                  bit64_4.0.5## [130] KEGGREST_1.40.0       haven_2.5.2                igraph_1.5.0## [133] memoise_2.0.1         BiocWorkflowTools_1.26.0   ggtree_3.8.0## [136] fastmatch_1.1-3       bit_4.0.5                  readxl_1.4.2## [139] downloader_0.4        gson_0.1.0                 ape_5.7-1The workflows provided involve use of functions from many different R/Bioconductor packages. The sessionInfo function provides an easy way to summarize all packages and corresponding their versions used to generate this document. Should software updates lead to the generation of errors or different results to those demonstrated here, such changes should be easily traced.R itself as well as packages as required. Bioconductor packages can be updated using theBiocManager::install function, as shown below.\"BiocManager\", quietly = TRUE)) {  install.packages(\"BiocManager\")}BiocManager::installif . It makes use of QFeature objects, a structure that is very well suited for bottom-up proteomics data analysis. I think that this is a very nice step-by-step tutorial for MS-based proteomics data analysis with R for people who start in the field and have limited coding skills. Beyond its educational aspect, it provides the backbones of a data analysis pipeline that can be modified and published alongside any manuscript that includes MS-based proteomics data analysis. The manuscript is very well written, and the authors provide very clear and detailed instructions on how to use the workflow. The figures are also very clear and informative. They present the data, detail what quality control to perform to assess if the data are suitable for statistical analysis. This workflow contains a lot of useful QC plots and checks that are often overlooked, including TMT labelling efficiency calculation. It makes it a good step-by-step guide on how to analyze TMT-labelled bottom-up data. I really like the description on how to double check quality metrics such as S/N and isolation interference to adapt them to the data. The raw data is available in PRIDE and all the fasta files and the Proteome Discoverer output files are in Zenodo. The code is in a github repository. I easily found all the data associated with this manuscript.It should be made clear that this script is tailored for Proteome Discoverer outputs (and maybe even for a given version of Proteome Discoverer). For most of the plotting/filtering/analysis steps, changing input would only require adapting the column names/headers, and most of the time it is very well discussed by the authors. Nevertheless, right now this script is only adapted to Proteome Discoverer outputs and this should be made clear in the abstract and the introduction, as well as the discussion.Aggregation of PSM quantification values to peptides and proteins: Here, the authors choose to aggregate all PSM intensities corresponding to the same sequence (stripped of its modifications). I would aggregate the PSM quantities per peptidoform  since two ions can have the same sequence but with modifications that are differentially regulated between the conditions compared. This should be mentioned.For the GO-term enrichment: only the majority protein accessions are reported in the output of the limma analysis. So there is only one gene per feature. How do you make sure that it is the most representative annotation for a given protein group? I am fully aware that this issue is ignored by the community , and that most of the time people just pick one accession per group for GO-term enrichment analysis. So, I am not asking the authors to find a solution. Nevertheless, it would be nice to mention that the output of the enrichment will depend on what accession is picked per group. Minor comments:I would remove contaminants before estimation of TMT labelling efficiency.I find the paragraph \u201cAdditional considerations regarding protein isoforms\u201d unclear. The authors should rephrase sentences such as this one: \u201cPSMs or peptides that were previously mapped to one protein and one protein group could instead be mapped to multiple proteins and one protein group.\u201d Maybe they should use the term \u201ccanonical protein\u201d to be more precise? In any cases, the issue of peptide uniqueness does not depend only on the presence of isoforms in the fasta file but also on the strategy that was chosen for protein inference. I agree with the authors that people should precisely describe what peptides/PSMs were used for quantification, and it is good to mention it. Nevertheless, this level of detail on general MS-based proteomics concept may not be necessary here. .In the paragraph \u201cRemoving PSMs that are not rank 1\u201d: I think that the \u201cPSM category\u201d that is discussed a bit later is specific of Proteome Discoverer. Some search engines report PSMs of equal score (this would correspond to the \u201cpretty rank\u201d in Mascot). I don\u2019t think that all this should necessarily be discussed in this manuscript, but I insist on the fact that the workflow is tuned for Proteome Discoverer output and this should be made clear in the introduction/abstract.In the paragraph \u201cManaging missing data\u201d for the TMT: the authors mention MCAR, MAR, and MNAR, but do they all apply here? I would expect TMT-labelled data to mostly have missing values due to ions being under the limit of detection because when an ion is fragmented, the reporter ions used for relative quantification are often all detected. Isn\u2019t it the case? Knowing this should restrict the choice for a more suited strategy of replacement of missing values. Still on missing values: page 33, it is stated that \u201cTypically, it is desirable to remove features, here PSMs, with greater than 20% missing values\u201d. Why this number? Is this accepted by the entire community? : it is really nice to provide references of strategies applied to replacement of missing values. It is indeed a tricky decision to make (how to replace? Should we replace?) and there is no one-size-fits-them-all method. Why do you choose to replace at peptide level and not protein level?Page 38: what do the authors mean by \u201creport did not indicate any superior normalization method\u201d? How would we know what normalization method works best? This point is because I am curious, not to correct any obvious mistake. It is great to try out different normalization strategies, but I don\u2019t really see how to pick one based on the boxplots of normalized intensities .The authors normalize the data after protein aggregation and not at the PSM level. Is this general practice? Wouldn\u2019t it make sense to normalize before aggregation?Page 44; \u201cData import, housekeeping and exploration\u201d: the authors mention that LFQ analysis cannot be performed at PSM level but has to be done at peptide level. I think that this is dependent on the software tool that is used, and this may be specific of Proteome Discoverer. Match between run is performed at the ion level, but the intensity retrieved at this step can be reported at PSM level. I think that this is the case in MaxQuant \u201cevidence.txt\u201d tables, if I am not mistaken.Page 63: DEqMS could be mentioned since it is specifically developed for proteomics statistical analysis. (DOI: 10.1074/mcp.TIR119.001646)This is more of a na\u00efve question regarding using Limma in this context: does it make sense to model the variance depending on intensity after missing value replacement? I know that this manuscript may not be the place to discuss this, but I would be interested to know what the authors\u2019 opinion is on this question.Additional suggestions :https://www.nature.com/articles/s41467-021-26111-3; there is a GUI to generate these files now: https://lessdrf.streamlit.app/). This would facilitate data reuse and transparency. The data is available in PRIDE (PXD041794), but the information necessary to match TMT channel/sample to conditions/replicates is only available in the associated paper. It would be great to add the Table 1 of the manuscript and a link to the zenodo repository in PRIDE alongside the data. An even better solution would be to provide the metadata in the SDRF-Proteomics format : \u201cproteins\u201d should be replaced by \u201cprotein groups\u201d since this is what is actually counted. If the authors wanted to be more precise, they could also specify that the \u201cpeptides\u201d correspond to peptide sequences stripped of all modifications.Bar plot of missing value proportion page 32: what does the red dashed line correspond to?Page 37, when running the function `normalizer`, I got an error \u201cNo RT column specified (column named 'RT') or option not specified Skipping RT normalization.\u201d And could not get the expected report. I am not familiar with the tool and did not investigate further.I don\u2019t think that \u201csoftwares\u201d can be used. There is no \u201cs\u201d at the end.Page 51: \u201ccleaning is done is two steps\u201d should be \u201ccleaning is done in two steps\u201dPage 54: in the bar plot of missing value count, what is the dashed red line?Page 55: \u201cIf the method requires data to display a normal distribution, users must log2 transform the data prior to imputation.\u201d -> is there a reason to perform missing values replacement before log transformation? If not, this step could be moved to after log transform?lfc = as\u201d are in a different police, I think that this is a bit unclear since the \u201cas\u201d should be regular text. Also, maybe you could explain \u201chave includedlfc = followed by the minimum absolute log2-transformed fold change\u201d. \u00a0Page 66: \u201cIf we had not used TMT labels and wished to include a logFC threshold, we could have included lfc = as an argument\u201d. The characters of \u201cPage 72: \u201csummarize all packages and corresponding their versions used to generate\u201d -> the sentence does not seem correct to me. Major comments:Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?YesIs the rationale for developing the new software tool clearly explained?YesIs the description of the software tool technically sound?YesAre sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?YesIs sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?YesReviewer Expertise:I work on MS-based proteomics data analysis and computational mass spectrometry.I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. The article entitled \"A Bioconductor workflow for processing, evaluating, and interpreting expression proteomics data\" regards a full and comprehensive workflow for performing not only qualitative but also quantitative proteomics analysis. The manuscript is very well written and describes in detail the whole workflow for quantifying proteomics data. The authors focused on the LFQ analysis and TMT (for labeled datasets). However, this workflow relies on the results from a search engine that will provide the identified peptides. On this manuscript the authors used results from Proteome Discoverer. I recommended the publication, but I would like to pinpoint some minor comments: a) Although the authors mentioned the quantitation analyses by using TMT for labeled datasets, why they didn't show the analysis by using SILAC, once it uses XIC (the same strategy used by LFQ)? b) The authors used Proteome Discoverer as search engine, and the provided a template to import the results into the workflow. They could also provide other templates to turn this workflow more versatile, such as FragPipe, Patternlab for Proteomics, etc.Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?YesIs the rationale for developing the new software tool clearly explained?YesIs the description of the software tool technically sound?YesAre sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?YesIs sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?YesReviewer Expertise:I have experience in proteomics analyses ; XL-MS analysis. I also develop software for identifying and quantifying proteomics datasets.I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard."}
+{"text": "In this manuscript, we introduce and benchmark Mandalorion v4.1 for the identification and quantification of full-length transcriptome sequencing reads. It further improves upon the already strong performance of Mandalorion v3.6 used in the LRGASP consortium challenge. By processing real and simulated data, we show three main features of Mandalorion: first, Mandalorion-based isoform identification has very high precision and maintains high recall even in the absence of any genome annotation. Second, isoform read counts as quantified by Mandalorion show a high correlation with simulated read counts. Third, isoforms identified by Mandalorion closely reflect the full-length transcriptome sequencing data sets they are based on.The online version contains supplementary material available at 10.1186/s13059-023-02999-6. In any eukaryotic cell, alternative splice site, transcription start site, and polyA site usage shape transcriptomes by enabling the expression of multiple unique isoforms for any one gene . UnderstTools designed to process data generated by the ubiquitous RNA-seq assay fail at the isoform-level analysis of transcriptomes \u20135 becausLong-read platforms therefore made it possible to sequence transcripts end-to-end which in turn gave rise to the growing field of full-length transcriptome sequencing assays. To overcome the high error rate inherent to both PacBio and ONT platforms, the PacBio CCS/Iso-Seq method and the ONT-based R2C2 method generate consensus sequences from very long but error-prone raw reads. The millions of highly accurate end-to-end transcript sequences that PacBio Iso-Seq , 8 and OIndeed, new computational tools have been developed or existing tools adapted to take full advantage of this new data type. However, many of these tools rely heavily on previously generated genome annotations. As part of the LRGASP consortium, several isoform identification tools, including StringTie , 12, IsoHere, we introduce and benchmark version 4.1 of Mandalorion. Mandalorion v4.1 identifies isoforms with very high recall and precision when applied to either spike-in or simulated data with known ground-truth isoforms. Mandalorion v4.1 outperforms or matches StringTie (v2.2.1), Bambu (v3.08), and IsoQuant (v3.2.0) when identifying and quantifying isoforms with annotation files provided. Importantly, Mandalorion had a distinct performance lead when tools were run entirely without annotation files. Running tools entirely without an annotation allows us to evaluate performance within poorly or incompletely annotated genomes like those of non-model organisms. We also show that Mandalorion not only accurately identifies isoforms but accurately quantifies isoform levels. Finally, by analyzing public PacBio Iso-Seq data, we show that, in contrast to isoforms identified by the other tools, isoforms identified by Mandalorion closely reflect the data set they are based on. Together, this establishes Mandalorion as an excellent choice when analyzing any high-accuracy long-read transcriptome data set in any context.Mandalorion (v4.1) accepts an arbitrary number of FASTA/Q files containing accurate full-length transcriptome sequencing data. While Mandalorion is generally platform and method agnostic, it is unlikely to work with data sets with error rates higher than 3% and has only been tested on high-accuracy end-to-end transcript sequences which can be generated by either the ONT-based R2C2 method or the PacBio Iso-Seq method\u2014both of which have median error rates lower than 0.5%.Mandalorion is organized into several modules  which by default are all run sequentially Fig.\u00a0. The \u201cA\u201dWe compared Mandalorion (v4.1), StringTie (v.2.21), Bambu (v3.08), and IsoQuant (v3.2.0) for the identification of isoforms from both simulated and real PacBio reads\u2014whether or not a genome annotation was provided. We focused our comparison on StringTie, Bambu, and IsoQuant because they do not require a genome annotation file as input.First, we ran these tools on mouse data produced or simulated for the LRGASP consortium and available from ENCODE. Second, we ran the tools on publicly available Universal Human Reference (UHR) PacBio Iso-Seq data.The simulated mouse data contained a subset of annotated isoforms present in the GENCODE genome annotation as well as artificial isoforms not present in the GENCODE genome annotation provided to the tools. The genome annotation\u2014if provided to the tools\u2014therefore contained many annotated transcripts that were not simulated and did not contain any of the simulated artificial transcripts. To evaluate the performance of each tool on this simulated data set\u2014with or without provided genome annotation\u2014we compared the isoforms they identified to the ground truth annotation\u2014all isoforms that were actually present in the simulated data set .The real mouse PacBio data contained SIRV  synthetic spike-in transcript isoforms of known sequence. All SIRV isoforms were present in the genome annotation provided to the tools as part of synthetic gene loci. No additional \u201cdecoy\u201d isoforms were present in the annotation either, meaning that tools were provided the ground truth for the SIRV isoforms. To evaluate the performance of each tool on these synthetic transcript isoforms\u2014with or without provided genome annotation\u2014we compared the isoforms they identified to the same ground truth annotation.To compare the isoforms identified by each tool for each data set, we used SQANTI  categoriFor the analysis of the UHR data, which lacks a ground truth, we evaluated how isoforms identified by each tool and condition were categorized by SQANTI, supported by read alignments, and shared between conditions and tools.To start, we analyzed simulated and real PacBio Iso-seq data with Mandalorion, StringTie, Bambu, and IsoQuant and calculated recall and precision for each tool.With an annotation file available, Mandalorion closely matched all other tools when analyzing simulated or real PacBio Iso-Seq data. When analyzing simulated PacBio Iso-Seq data, Mandalorion reached recall and precision of 87.89% and 92.05% compared to 77.91% and 91.86% reached by StringTie, 85.44% and 92.94% reached by Bambu, and 77.10% and 93.43% reached by IsoQuant, respectively .r value of 0.992 compared to\u2009~\u20090.95 reached by the other tools ) as determined by the tools for these isoforms to the known TPM simulated for these isoforms. We found that Mandalorion TPM values showed the highest correlation with the ground truth, reaching a Pearson\u2019s ols Fig.\u00a0. The mear value of 0.992 compared to the\u2009~\u20090.985 reached by the other tools  Iso-Seq data. This data set is generated from Universal Human Reference RNA (Agilent), which is composed of the RNA of 10 diverse human cell lines and is therefore highly complex\u2014probably more complex than most tissue or cell line samples. This data set was generated and released by PacBio and contains about 6.7 million full-length cDNA reads.Because there is no available ground truth for this data set, we focused on how the identified isoforms matched the GENCODE annotation and whether they reflected the actual read alignments they were based on.We used SQANTI to match isoforms identified by the tools to the GENCODE annotation and found that in contrast to the other tools, Mandalorion identified a mix of annotated and novel isoforms which did not substantially change in number or composition, whether or not an annotation file was available.With an annotation file available, Mandalorion generated 53,493 isoforms of which 57.03% were categorized by SQANTI as FSM, i.e., its entire SJC was present in the GENCODE v38 annotation. StringTie generated 93,611 isoforms of which 36.48% were categorized as FSM. Furthermore, Bambu generated 39,537 isoforms of which 98.78% were categorized as FSM, while IsoQuant generated 57,068 isoforms of which 67.13% were categorized as FSM  \u201cSJC\u201d support, which required at least one read alignment to contain the same SJC as an isoform, and (2) \u201cSJC and isoform ends (SJC\u2009+\u2009E)\u201d support, which required at least one read alignment to contain the same SJC as an isoform and for the alignment to end within 50 nt of both of the isoform\u2019s ends.In contrast to the other tools, Mandalorion showed nearly universal read support for the isoforms it identified. 99.6% and 98.9% of isoforms identified by Mandalorion with annotation showed SJC and SJC\u2009+\u2009E read support, respectively. In contrast, 80\u201393% of isoforms identified by StringTie, Bambu, and IsoQuant showed SJC read support, but only 46\u201358% of these isoforms showed SJC\u2009+\u2009E support , while SJC\u2009+\u2009E support diverged between the tools. 39.7% of StringTie isoforms, 93.9% of Bambu isoforms, and 64.4% of IsoQuant isoforms identified without annotation had SJC\u2009+\u2009E support Fig.\u00a0C. IntereBecause of the apparent difference in read support between tools and conditions, we next wanted to investigate the overlap of isoforms identified by Mandalorion, StringTie, Bambu, and IsoQuant. We used GffCompare  to groupBy comparing the same tool run with or without annotation available, we found that Mandalorion had the highest overlap with itself at 85.54%, followed by IsoQuant with 58.04% and StringTie at 48.22%. Bambu produced very different isoforms when run with or without annotation with 32.83% overlap. Between tools, the most similar isoform sets were Bambu and IsoQuant run with annotation (41.75% overlap), indicating their mutual heavy reliance on transcript annotations  that more closely resemble the read alignments they are based on.We show two examples of this behavior Fig.\u00a0E where SUltimately, this highlights that Mandalorion relies on the fact that the majority of accurate full-length cDNA reads truly cover RNA transcripts end-to-end. We believe this is a strength when analyzing this data type, but it also means that, in contrast to other tools, Mandalorion will identify isoforms that contradict annotations if they are supported by reads and will not identify annotated isoforms unless they are supported by reads, which may not be the case due to molecular biology or sequencing technology limitations for, e.g., very long isoforms.We initially released Mandalorion in 2017 to identify isoforms based on the then fairly new full-length transcriptome data type . Over thAlongside Mandalorion, the full-length transcriptome sequencing field has matured as well and other tools have been designed for isoform identification based on this now-established data type. These tools, which include but are not limited to FLAIR, IsoQuant, IsoTools, TALON, StringTie, Bambu, and FLAMES, present other approaches to the isoform identification problem and their \u201cbig-picture\u201d differences can be compared in the LRGASP manuscript .Here, we perform a separate, distinct analysis to show that Mandalorion represents a strong combination of recall and precision when analyzing PacBio Iso-Seq data\u2014although LRGASP shows that Mandalorion shows the equivalent performance when run on ONT-based R2C2 data or a mix of the two data types.In our comparison based on publicly available LRGASP and UHR data, Mandalorion compares favorably to StringTie, Bambu, and IsoQuant\u2014especially in the absence of genome annotation. While running a tool entirely without genome annotation does not reflect their likely usage on model organism data, it does allow us to predict performance in poorly annotated gene loci or in any transcriptome/genome combination that lacks a highly curated annotation, i.e., anything that is not human or mouse. Based on its performance here, we make a strong case that Mandalorion is a powerful tool for de novo genome annotation based on full-length transcriptome data.What sets Mandalorion apart from other tools is how it treats genome annotations (if available), as well as read alignments and their underlying sequences. The only information that Mandalorion extracts from genome annotations is the location of splice sites. It does not collect information about how these sites are connected into splice junctions. It also entirely ignores the transcription start and polyA sites present in the genome annotation. As a consequence of its minimal usage of genome annotation information, Mandalorion is only minimally biased by it. Furthermore, Mandalorion uses alignment quality, specifically around splice sites, to filter reads. We therefore recommend the use of Mandalorion only for high-quality data sets generated by PacBio or ONT-based R2C2. Finally, Mandalorion is unique in generating read-based consensus sequences for each putative isoform which it realigns to define the isoform\u2019s genomic coordinates. This means that Mandalorion is unlikely to report an isoform that is not present in the full-length cDNA data it is processing. To then remove cDNA fragments\u2014the most likely the cause of remaining false-positive isoforms\u2014Mandalorion will discard isoforms that are likely to be internal fragments of other, longer isoforms.The result of this unique workflow\u2014Mandalorion v4.1\u2014is a strong addition to the toolbox of researchers analyzing full-length transcriptome data. As this data type becomes more common, additional tasks like variant detection and allele-specific isoform analysis will represent new challenges for tools like Mandalorion and represent exciting opportunities for further tool development.https://github.com/yunhaowang/IsoSeqSim). IsoSeqSim simulates truncation and errors at rates estimated using real PacBio cDNA CCS reads. Pre-computed Sequel II truncation probabilities included in IsoSeqSim were used for this purpose. A GTF file containing a subset of GENCODE vM27 and a list of artificial isoforms paired with a file containing abundances for each isoform were used as the underlying isoforms for simulation and served as ground truth for our analysis.PacBio Iso-Seq data was simulated for the LRGASP consortium effort using IsoSeqSim  was prepped using the Iso-Seq Template Preparation for Sequel Systems (PN 101\u2013070-200) protocol which is also based on the Smart-Seq2 protocol. The resulting libraries were sequenced on the PacBio Sequel II.python3 Mandalorion/utils/removePolyA_nonDirectionalInput.py -i input.fasta -o output.trimmed.fasta -t 1,1With annotation: python3 Mando.py -p./ -f reads.fofn -W basic,SIRV -G lrgasp_grcm39_sirvs.fasta -t 50 -g lrgasp_gencode_vM27_sirvs.gtfWithout annotation: python3 Mando.py -p./ -f reads.fofn -G lrgasp_grcm39_sirvs.fasta -t 50For StringTie we used the alignments generated by running Mandalorion after sorting and converting to bam using samtools .With annotation: stringtie mm2Alignments.sorted.bam -o stringtie_annot.gtf -L -p 50 -G lrgasp_gencode_vM27_sirvs.gtfWithout annotation: stringtie mm2Alignments.sorted.bam -o stringtie_annot.gtf -L -p 50library(bambu)With annotations: bambuAnnotations\u2009<\u2014prepareAnnotations(\u201clrgasp_gencode_vM27_sirvs.gtf\u201d).se\u2009<\u2014bambuwriteBambuOutputBecause Bambu reports newly identified isoforms alongside the entire provided genome annotation in a combined GTF, we parsed this GTF to only keep isoforms if they had a bambu reported read count of at least 3.Without annotations: se\u2009<\u2014bambuwriteBambuOutputWith annotation: isoquant.py \u2013genedb isoquant/lrgasp_gencode_vM27_sirvs.gtf \u2013reference lrgasp_grcm39_sirvs.fasta \u2013bam mm2Alignments.sorted.bam \u2013data_type pacbio_ccs -o isoquant/Without annotation: isoquant.py \u2013reference lrgasp_grcm39_sirvs.fasta \u2013bam mm2Alignments.sorted.bam \u2013data_type pacbio_ccs -o isoquantNoAnnot/python3 sqanti3_lrgasp.challenge1.py isoform.gtf simulated_isoforms.gtf lrgasp_grcm39_sirvs.fasta \u2013json experiment.json \u2013cage_peak refTSS.mouse.bed \u2013polyA_motif_list polyA_list.txt -c ES_Illumina_STARpass1_SJ.out.tab -d./SQANTI -o output \u2013gtfpython3 sqanti3_lrgasp.challenge1.py isoform.gtf lrgasp_gencode_vM27_sirvs.gtf lrgasp_grcm39_sirvs.fasta \u2013json experiment.json \u2013cage_peak refTSS.mouse.bed \u2013polyA_motif_list polyA_list.txt -c ES_Illumina_STARpass1_SJ.out.tab -d./SQANTI -o output \u2013gtfpython3 sqanti3_lrgasp.challenge1.py isoform.gtf lrgasp_gencode_v38_sirvs.gtf lrgasp_grch38_sirvs.fasta \u2013json experiment.json \u2013cage_peak refTSS.human.bed \u2013polyA_motif_list polyA_list.txt -c WTC11_Illumina_STARpass1_SJ.out.tab -d./SQANTI -o output \u2013gtfSamtools , NumPy , 29, SciAdditional file 1: Table S1. Recall and precision of Mandalorion and StringTie versions.\u00a0Table S2. (SIRV tool assignment).Additional file 2. Review history."}
+{"text": "Salmonella enterica. vB_Hercules_Set was isolated from a slurry of soil and deli meat collected in New Hampshire in 2021. The genome length is 157,338 nucleotides, containing 210 protein-coding genes and five tRNAs.We report the isolation, sequencing, and annotation of bacteriophage vB_Hercules_Set, a kuttervirus infecting human pathogen  Salmonella enterica serovar Typhimurium is a pathogen in humans and other animals, a common culprit of gastrointestinal disease from contaminated food  and then overlaid with S. enterica serovar Typhimurium in 0.4% nutrient broth agar. Plaques were ~1\u2009mm in size and mildly turbid after 24 h at 37\u00b0C. Purification was accomplished by two rounds of 10-fold dilutions and plating from a single plaque. Amplification was achieved by soaking three overlay plates in buffer   and ~3,240-fold coverage of the genome.Double-stranded genomic DNA was isolated from vB_Hercules_Set using the Quick-DNA viral kit (Zymo) and quantitated by a NanoDrop spectrophotometer (ThermoFisher) and assessed by a Qubit fluorometer (ThermoFisher). Library preparation, sequencing, and assembly were performed at the North Carolina State University Genomic Sciences Laboratory (NCSUGSL). The DNA was assessed using ScreenTape (Agilent), prepared for sequencing using the TruSeq DNA Nano library prep kit (Illumina) via random fragmentation, and subjected to bead-based size selection followed by ligation of dual-index adapters in multiplex runs. Pair-end sequencing (Illumina NovaSeq6000) yielded 3.38 \u00d7 10Software was run using default settings except where otherwise noted. Assessment of read quality, adapter trimming, and assembly were done using the CLC Genomics Workbench v.21 assembly tool v.6.5.1 (Qiagen) at NCSUGSL.Autoannotation was performed at Franklin Pierce University with DNA Master v.5.23.6 . AnnotatSalmonella phage Vi01 and Escherichia phage vB_EcoM_Sa157lw (each with >98% identity and >92% coverage). Negative staining with 1% uranyl acetate (vB_Hercules_Set was identified as a kuttervirus based on nucleotide similarity. Its closest relatives are  acetate  was visu acetate . A summaOP423032.1, BioProject no. PRJNA889411, BioSample no. SAMN31243182, and Sequence Read Archive no. SRR21863082.vB_Hercules_Set is available at GenBank under accession no."}
+{"text": "Introduction: Sugar beets are an important crop for global sugar production. Intense drought and the increasing lack of water resources pose a great threat to sugar beet cultivation. It is a priority to investigate favourable germplasms and functional genes to improve the breeding of drought tolerant plants.Methods: Thus, in this study, 328 sugar beet germplasms were used in a genome-wide association study (GWAS) to identify single nucleotide polymorphism (SNP) markers and candidate genes associated with drought tolerance.Results: The results showed that under drought stress (9% PEG-6000), there were 11 significantly associated loci on chromosomes 2, 3, 5, 7, and 9 from the 108946 SNPs filtered using a mixed linear model (MLM). Genome-wide association analysis combined with qRT-PCR identified 13 genes that were significantly differentially expressed in drought-tolerant extreme materials.Discussion: These candidate genes mainly exhibited functions such as regulating sugar metabolism, maintaining internal environmental stability and participating in photosystem repair. This study provides valuable information for exploring the molecular mechanisms of drought tolerance and improvement in sugar beet. Sugar beet is an important sugar crop. It has strong drought tolerance in the middle and late growth period, but weak drought tolerance at seedling stage. Most regions in the world are facing the problem of drought, and the sugar beet plant area of Heilongjiang Province in China is no exception. In spring, there are many southerly winds, large evaporation and small precipitation. The development of drought-tolerant varieties becomes more and more important , and theMapping of quantitative trait loci (QTLs) is an effective tool often used to reveal the genetic basis of complex quantitative traits in crops . HoweverDrought tolerance in plants is a complex quantitative trait that is controlled by multiple genes and involves multiple physiological and biochemical metabolic pathways. Although QTL mapping is a powerful method for detecting genomic regions associated with complex traits, the genetic effects of QTLs may not exist or may simply not be tested in different genetic backgrounds and environments . CompareThe material for this study was provided by the National Beet Medium-Term Gene Bank at Heilongjiang University. The 328 sugar beet germplasm resources used in the trial were from 17 countries . 200 froThe following traits were measured in this test: embryonic axis diameter  , maximumRelative leaf water = (Leaf fresh weight-Leaf dry weight)/(Leaf saturated fresh weight-Leaf dry weight).Superoxide dismutase activity was evaluated by the nitrogen blue tetrazolium method . SolubleThe drought tolerance coefficient (DTC) was calculated for each trait:ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/511/025/GCF_000511025.2_RefBeet-1.2.2/GCF_000511025.2_RefBeet-1.2.2_genomic.fna.gz). Samtools  and sequenced using the Illumina HiSeq sequencing platform with double-end (Paired-End) 150 sequencing. BWA  (0.7.17)Samtools  (1.9): SSamtools  (4.2.6.1Samtools  (0.1.17)VCFtools (0.1.17) was first used to convert the.vcf files ped files and then plink (1.9) software was used to convert ped format to bed format . PopulatThe kinship analysis was performed using Tassel (5.2.82) software to obtain the kinship matrix  and the In the equation, Y is the vector of measured phenotypes, X is the fixed effect of SNPs, P is the fixed effect of population structure, K is the random effect of kinship, and e is the random error .We used the powerful annotation function of the internationally recognized UniProt database  to annotThe SNPs were screened to obtain high-quality and significantly correlated SNPs with a threshold of \u2212log10p> 6. The size of the screened SNP interval was estimated based on LD block analysis, and genes in the region near the locus were obtained and functionally annotated. Based on the functional annotations, we could roughly determine which genes might be associated with the traits of interest and use in subsequent analysis .\u2212\u0394\u0394CT method (BvGAPDH (NC_ 024800) was used as an internal control to standardize the expression levels of the different samples. All assays were performed in two independent experiments and replicated three times. Primers were summarized in Four drought-tolerant  and four drought-sensitive germplasms  were used as extreme materials to extract total RNA according to the Trizol method. cDNA was synthesized according to the TranScript One-Step gDNA Removal and cDNA Synthesis SuperMix protocol . qPCR was performed using the SuperReal PreMix Plus kit  on a real-time fluorescent quantitative PCR instrument  . ExpressT method . BvGAPDHThe statistical results showed that the measured data were all close to a normal distribution, indicating that the 12 traits were all typical quantitative traits. Eight traits, including embryonic axis diameter (EAD), plant height (PH), root length (RL), leaf fresh weight (LFW), root fresh weight (RFW), root dry weight (RDW), leaf dry weight  and leafPopulation stratification refers to the existence of subgroups within a population, where the diversity among individuals within the subgroups is greater than the average diversity among individuals within the entire population . DiffereA linkage disequilibrium (LD) analysis was carried out, and the LD distance decreased as the physical location of the SNPs on chromosomes gradually increased. The LD distance of 50\u00a0kb was equal to half of the maximum value .The visualization of the kinship matrix showed that the variation between most of the 328 genotypes was low (light blue), and the genetic similarity between individual genotypes was high. The low genetic variation in this study would reduce the occurrence of false positive results .The GWAS results showed  that a tIn the five traits (\u2212log10p> 6) of DTC_RLW, DTC_LFW, DTC_LDW, DTC_RDW, and DTC_SS, 50\u00a0kb upstream and downstream of the significant loci were analyzed by LD decay. A total of 24 genes in sugar beet associated with drought tolerance were identified and annotated .BVRB_7g160020 and BVRB_7g160030) were identified on chromosome 7\u00a0at position 4,745,224 may be associated with DTC_RLW  family  glycosidic bond branching synthesis. We believe that BVRB_7g160030 is a potential target gene for the drought stress response in sugar beet.Two genes ( DTC_RLW . BVRB_7g) family . BVRB_7gogenesis . It was BVRB_2g033710, BVRB_2g033720 and BVRB_2g033730) were found on chromosome 2. Two genes (BVRB_5g114800 and BVRB_5g114810) were found on chromosome 5. BVRB_2g033720, BVRB_2g033730 and BVRB_5g114800 were of unknown function  were identified on chromosome 3 position: 998,024, may be associated with DTC_LDW , which functions as a phosphate donor using ATP to phosphorylate serine and threonine residues on proteins to regulate abscisic acid, growth hormone, glucose and sucrose-mediated signalling . BVRB_9g203050 functions as photosystem II (PSII) stability/assembly factor HCF136 and is essential for PSII biogenesis. BVRB_9g203030 functions as eukaryotic initiation factor 4A-9, which promotes the hydrolysis of ATP and drives RNA deconvolution against abiotic stress; BVRB_9g203000 functions as enhancer of AG-4 protein 2, which possesses the dual ability to induce apoptosis and autophagy  on chromosome 9 were found to be associated with DTC_RDW . Six genutophagy , and is utophagy .BVRB_2g036960 and BVRB_2g036950) were found to be associated with DTC_SS  were found near position 15,170,171 and they are hypothetical and uncharacterized proteins. Three genes  were found near position 27,948,364, and BVRB_7g166470 functions as glycine-rich cell wall structural protein 1, a process that results in the assembly, arrangement of constituent parts, or disassembly of the cell wall. BVRB_7g166480 functions as PROTEIN LOW PSII ACCUMULATION 1, which is mainly involved in the efficient assembly of photosynthetic system II. BVRB_7g166490 functions as photosystem I reaction centre subunit IV. Two gene (BVRB_7g166670 and BVRB_7g166680) were found near positions 28,950 and BVRB_7g166670 is an uncharacterized protein, and BVRB_7g166680 functions as a ras GTPase-activating protein-binding protein.Four SNP loci significantly associated with DTC_SS  were fouBVRB_2g036960, the other 13 genes were all significantly differently expressed between the drought tolerant and sensitive groups. The expression levels of BVRB_7g160020 and BVRB_5g114810 were 3.30-fold and 2.57-fold higher in the drought-sensitive group than them in the drought-tolerant group, respectively. BVRB_7g160030, BVRB_2g033710, BVRB_3g048910, BVRB_3g048900, BVRB_9g203050, BVRB_9g203030, BVRB_9g203000, BVRB_7g166680, BVRB_7g166470 and BVRB_7g166490 in drought-tolerant group were expressed 1.47-4.23-fold of them in the drought-sensitive group. Notably, the expression level of BVRB_7g166480 in the drought-tolerant group was 46.69-fold higher than that of the drought-sensitive group.By examining the overlap of the identified candidate genes with known drought stress response genes in other crop species, we identified 14 genes that may be involved in the drought tolerance process in sugar beet. In order to validate the GWAS results, qRT-PCR was performed on these genes  glycosidic bond branching increases the water solubility of glycogen for storage and increases the number of nonreducing ends, making it easier for biological organisms to aggregate when glycogen is needed for energy supply, and the 1,4-alpha-glucan-branching enzyme regulates glycogen metabolism and participates in the abiotic stress response , which is an important precursor of lipid signalling . Genome-wide association analysis combined with qRT-PCR revealed that 13 genes were significantly differentially expressed between the two extreme groups of materials, with"}
+{"text": "Bile leakage after hepatectomy has been reported to occur in 5\u200a%\u20138\u200a% of casesA 74-year-old man with gallbladder cancer underwent cholecystectomy with partial hepatectomy and bile duct resection. Following the surgery, isolated bile leakage occurred at the right posterior branch (RPB) . InitiaVideo\u20061\u2002Successful endoscopic ultrasound-guided biliary recanalization with rendezvous balloon-inflation assistance and cholangioscopy to manage isolated bile leakage.Rendezvous cholangioscopic assistance has been reported to be a useful technique for successful recanalization of postoperative biliary disconnectionEndoscopy_UCTN_Code_TTT_1AS_2AD"}
+{"text": "The rapid expansion of Whole-Genome Sequencing has revolutionized the fields of clinical and food microbiology. However, its implementation as a routine laboratory technique remains challenging due to the growth of data at a faster rate than can be effectively analyzed and critical gaps in bioinformatics knowledge.Campylobacter, which is the main cause of gastroenteritis worldwide making a negative impact on the economy of the public health systems. CamPype allows fully customization of stages to run and tools to use, including read quality control filtering, read contamination, reads extension and assembly, bacterial typing, genome annotation, searching for antibiotic resistance genes, virulence genes and plasmids, pangenome construction and identification of nucleotide variants. All results are processed and resumed in an interactive HTML report for best data visualization and interpretation.To address both issues, CamPype was developed as a new bioinformatics workflow for the genomics analysis of sequencing data of bacteria, especially https://github.com/JoseBarbero/CamPype.The minimal user intervention of CamPype makes of this workflow an attractive resource for microbiology laboratories with no expertise in bioinformatics as a first line method for bacterial typing and epidemiological analyses, that would help to reduce the costs of disease outbreaks, or for comparative genomic analyses. CamPype is publicly available at The online version contains supplementary material available at 10.1186/s12859-023-05414-w. Since the Human Genome Project was completed in 2003 , Whole-GThe development of WGS has revolutionized microbiology research practices by replacing many traditional time-consuming and labor-intensive techniques . Genome 3P [Campylobacter, that is the main cause of gastroenteritis worldwide [Campylobacter jejuni, while Campylobacter coli is responsible for almost 10%. These bacteria are ubiquitous and live in the intestinal tract of poultry, pigs and cattle, but they may also be found in the feces [Campylobacter spp. would accelerate epidemiological studies through the different sequencing-based typing methods that have arisen since the first Campylobacter genome was published in 2000 [The implementation of WGS in clinical and food microbiology laboratories has led to the establishment of large public databases comprising thousands of genomes available . The vas3P  and Bact3P . Howeverorldwide . Most cahe feces . Their ghe feces . Moreovehe feces . Thus, a in 2000 , such asC. jejuni and C. coli, although any other bacterial genus can be analyzed as well. CamPype includes a fully customizable configuration, leading to the specific results the researchers want and saving time running steps they do not need. The entire workflow can be run using one single command, making it easy to use for researchers that are not familiar with the command line. Also, CamPype provides conda environments (https://docs.anaconda.com/) with Bioconda packages [In this work, we present CamPype, an open-source workflow for the WGS analysis of paired-end Illumina reads from packages  for all https://github.com/JoseBarbero/CamPype). Users can skip certain processes and adjust the configuration of parameters and databases from among the different options included for each stage in the campype_config.py file. An overview of the structure of CamPype is summarized in Fig.\u00a0The CamPype workflow comprises three main stages  that include several processes conducted by different tools. CamPype can take raw reads or assembled genomes in contigs as inputs. Instructions to set up the input files and workflow configuration are addressed in the CamPype repository (http://www.bioinformatics.babraham.ac.uk/projects/fastqc) to assess the quality of raw reads and optimize the step of read quality control. For fast visualization of this analysis, MultiQC [trim_adaptors option. CamPype includes all possible Illumina adaptor sequences in the file indicated in the option adapters_reference_file, although users can include their own. Then, reads are quality filtered and trimmed by PRINSEQ [min_len), minimum read quality , quality threshold score from the 3'-end to trim sequence by  and sliding window size . The reads that pass the quality control can then be used for bacterial identification and read contamination by Kraken2 [species_identification option.Previously to CamPype, a sequencing data analysis can be performed with FastQC  and merged and unmerged reads are further de novo assembled using SPAdes [mode and k options, respectively. Quality assembly of genomes is evaluated with QUAST [min_contig_len are discarded. Resulted contigs are ordered using progressiveMauve [reference_genome block.The reads that pass the quality control can be extended using FLASH  (merge_rg SPAdes . The assth QUAST  and contiveMauve  against species_identification) when assembled genomes are used as inputs. For subtyping purposes, MLST is performed through mlst  using the run_mlst option, and Clonal Complexes (CCs) are assigned with the Campylobacter jejuni/coli PubMLST scheme [include_cc option. Prokka [annotator option, although this stage can be disabled by using the run_annotation option. A reference genome annotation in GenBank format to first annotate from can be used in Prokka [reference_annotation option. Keeping the raw product annotation (rawproduct) in Prokka [roary_plots.py script by Marco Galardini (https://github.com/sanger-pathogens/Roary/blob/master/contrib/roary_plots/roary_plots.py) with minor modifications to show isolate labels and CCs (when possible) in the presence/absence accessory genome tree. Paralogs split can be disabled by the option split_paralogs and minimum percentage of identity for blastp can be selected by using the minid option. Moreover, pangenome analysis can be skipped by using the run_pangenome tool. Antibiotic resistance genes can be searched using protein alignments with AMRFinderPlus [Campylobacter spp., or/and using nucleotide alignments with ABRicate  against any of the databases provided by this tool , such as ARG-ANNOT [run_antimicrobial_resistance_genes_prediction option and specific tools can be selected in the antimicrobial_resistance_genes_predictor_tool option. Draft genomes can be also screened for virulence genes using tBLASTn against an in-house database (proteins_reference_file), or/and BLASTn with ABRicate  against any of the databases provided by this tool (virulence_factors_databases), such as the Virulence Factors Database (VFDB) [Campylobacter spp. database provided with more sequences of interest or create a new one for other species, while checking the databases available in ABRicate at its repository . Activation of soft_masking is highly encouraged to find initial matches when using tBLASTn. Virulence genes search can be skipped using the run_virulence_genes_prediction option and specific tools can be selected in the virulence_genes_predictor_tool option. Minimum identity (minid) and coverage (mincov) can be selected within each tool for considering an antibiotic resistance gen and virulence gen as present. Plasmids are searched using BLASTn and ABRicate  against the PlasmidFinder database [run_plasmid_prediction option. Genetic variants identification is performed through snippy  using the reference genome indicated in the file option below the reference genome options, as mentioned before.Draft genomes (ordered or not) can be further characterized through different tools, which can be selected or disabled by using the corresponding options. These include software for taxonomic classification, Multi-Locus Sequence Typing (MLST), genome annotation, detection of antibiotic resistance genes, virulence genes and plasmids, pangenome construction and identification of SNPs. For bacterial identification, Kraken2  assigns T scheme  using th. Prokka  or DFAST. Prokka  can be un Prokka  through n Prokka  is highln Prokka  includesn Prokka  to constnderPlus  against RG-ANNOT , CARD [3RG-ANNOT , MEGAResRG-ANNOT , the NCBRG-ANNOT . Antibioe (VFDB) . Users adatabase , althoughttps://www.R-project.org/) using the following R packages: ape [https://CRAN.R-project.org/package=dplyr), DT (https://CRAN.R-project.org/package=DT), ggplot2 [https://CRAN.R-project.org/package=pander), plotly [https://CRAN.R-project.org/package=rjson), rmarkdown (https://rmarkdown.rstudio.com) and tidyverse [https://josebarbero.github.io/CamPype/example_report/CamPype_Report_long_first_case_study.html.Last, a summary HTML report is generated to resume the results of CamPype and can be displayed on any web browser. The report is generated in R environment .The results of CamPype are stored in specific directories for each stage and tool, with separate folders for each isolate, and include log files for analysis tracking and results standardization across different users together with files that compare the results across all analyzed samples. The location and name of the CamPype output directory can be set using the options https://www.gnu.org/software/bash/) and R v4.1.3. CamPype is freely available at https://github.com/JoseBarbero/CamPype with a detailed instruction manual for its installation and use on any UNIX operating system. The CamPype workflow, including all required tools and dependencies, can be automatically installed using the conda environment provided. The execution of CamPype requires enough storage space. It is recommended to have available at least three times the size of the input data for a successful complete execution when raw reads are taken as inputs and one or two GB of free space in hard disk when contigs are taken as inputs . For the study case reported here, we used a high computational capability (28 CPU cores and 64\u00a0GB RAM), even though CamPype can be run in any standard computer.The CamPype workflow was developed using a combination of python v3.7.8, GNU bash v5.0.17 (C. jejuni (5) and C. coli (5) strains isolated from faeces of Bos taurus and Ovis aries [Ten previously published and WGS analyzed (raw reads) is aries  were useEscherichia coli randomly selected from the RefSeq database were used as input for CamPype: GCF_003017915.1 (strain 2014C-3051), GCF_003018035.1 (strain 2015C-4944), GCF_003018055.1 (strain 2013C-3252), GCF_003018135.1 (strain 2014C-3050), GCF_003018315.1 (strain 2013C-3513), GCF_003018455.1 (strain 97\u20133250), GCF_003018555.1 (strain 2013C-4225), GCF_003018575.1 (strain 2013C-4538), GCF_003018795.1 (strain 2012C-4606), GCF_003018895.1 (strain 2014C-3057), GCF_003019175.1 (strain 2013C-4187), GCF_004010675.1 (strain 2010C-3347), GCF_004010715.1 (strain 08\u20133914), GCF_025995195.1 (strain F690), GCF_025995255.1 (strain F765), GCF_025995315.1 (strain H52_982342), GCF_025995355.1 (strain 8_140198), GCF_025995415.1 (strain 26_141088), GCF_025995475.1 (strain 27_141091), GCF_025995535.1 (strain 53_142304), GCF_025995615.1 (strain 57_142493), GCF_025995675.1 (strain 61_150228), GCF_025995735.1 (strain 93_161312), GCF_025995895.1 (strain CEC96047), GCF_025996315.1 (strain CEC13091), GCF_025996495.1 (strain CEC08123), GCF_025996555.1 (strain CEC03102), GCF_025996675.1 (strain CEC13002), GCF_025996735.1 (strain CEC13004), GCF_027925505.1 (strain 2313), GCF_027925565.1 (strain EH031), GCF_027925625.1 (strain H19), GCF_027925685.1 (strain 20\u20131), GCF_027925745.1 (strain EH2252), GCF_027925765.1 (strain 98E11), GCF_027925785.1 (strain NIID080884), GCF_027925805.1 (strain PV0838), GCF_027925825.1 , GCF_027925845.1 (strain 02E060), GCF_008926165.1 (strain ERL06-2442), GCF_005221885.1 (strain 143), GCF_008931135.1 (strain ERL04-3476), GCF_005221505.1 (strain 150) and GCF_008926185.1 (strain ERL05-1306). The default configuration of CamPype was modified as follows. The genome and annotation of Escherichia coli strain K-12 from NCBI (NZ_CP047127) were used as reference (reference_genome), the assembled_genomes option was set to True, the include_cc option was set to False, ABRicate was used for virulence genes screening (virulence_genes_predictor_tool), and variant calling was set to True .A total of 44 assembled genomes of C. jejuni and C. coli isolate sequences is reported to validate CamPype workflow. The analysis took 5.4\u00a0h using 28 CPUs and generated a result directory of 17.2\u00a0GB (from 9\u00a0GB of compressed input data). The results of the raw reads quality control can be found in https://josebarbero.github.io/CamPype/example_report/multiqc_report_first_case_study.html, and the report with the summarized results generated by CamPype can be visualized in https://josebarbero.github.io/CamPype/example_report/CamPype_Report_long_first_case_study.html. A total of 13.0\u00a0M\u2009\u00b1\u20092.5\u00a0M reads per sample were directly submitted to CamPype and reduced to 12.2\u00a0M\u2009\u00b1\u20092.3\u00a0M reads per sample by the quality control stage; i.e., 97% of reads survived overall and 30% were then merged   that were grouped into Clonal Complexes (CCs) CC21 (C. jejuni) and CC828 (C. coli). Most isolates (100% C. jejuni and 60% C. coli) harbored a blaOXA gen and tet(O), conferring resistance to beta-lactams and tetracyclines, respectively. C. jejuni strains harbored blaOXA-193 or blaOXA-611, whereas C. coli strains C0551, C0561 and C0663 harbored blaOXA-489  gen. Moreover, resistance to aminoglycosides (aadE or aadE-Cc) was only found in C. coli (60%). The efflux systems CmeABC and CmeDEF and the CmeR repressor were present in all isolates   were not found in any of the isolates. No plasmids were found in any of the isolates. A total of 1657\u20131806 Coding DNA Sequences (CDS) were annotated among all isolates , of which 10.4\u00a0GB constituted the genomic variants calling directory. The CamPype\u2019s HTML report for the analysis of the E. coli genomes can be found in https://josebarbero.github.io/CamPype/example_report/CamPype_Report_short_second_case_study.Besides, the analysis of the 44 genomes of Campylobacter WGS reads obtained from Illumina paired-end sequencing technologies is demonstrated through two different scenarios. Ten previously published C. jejuni and C. coli genomes were analyzed from the sequencing raw data using CamPype in a single command and produced same results to that of the multi-stage analyses included in the publication of Ocejo et al. [E. coli genomes using contigs as input and results were accurately reported for each genome, including bacterial typing (MLST), assembly analysis and genome annotation, searching for antibiotic resistance genes, virulence genes and plasmids, pangenome construction and identification of nucleotide variants against E. coli str. K-12 as reference genome. The most outstanding and promising tools hitherto for WGS are available for the users to include in the analysis, and their parameters can also be adjusted to meet their preferences. CamPype integrates various alternatives to identify antibiotic resistance genes and virulence genes since there is no single standardized and open-access database for antimicrobial resistance targets or virulence factors identification, so that the supplementary use of sequence databases generates the most complete results possible [Advances in NGS has transformed the fields of clinical and food microbiology , 52. Theo et al. , and evepossible . The compossible . This ispossible . MoreoveC. jejuni and C. coli as they are the main responsible of gastroenteritis in humans with a frequency of about 3\u20134 times higher than in Salmonella or E. coli [C. jejuni and C. coli Sequence Types into Clonal Complexes while providing a specific virulence genes database of this genus were not found in any of the existing microbial analysis pipelines to date, such as TORMES [3P [Campylobacter infection control actions to minimize adverse patient outcome and in outbreak investigation. Besides, the workflow has been already used for the characterization of Campylobacter jejuni-associated with perimyocarditis [Campylobacter spp. isolated from Spain (in prep.).CamPype is specially developed for  E. coli . The poss TORMES , BacPipes TORMES , ASA3P [RMES [3P  and BactRMES [3P . MoreoveRMES [3P . A matheRMES [3P . The webRMES [3P . Thus, Ccarditis  and alsoCamPype was developed with the needs of microbiology laboratories in mind and obstacles that restrict the use of WGS for clinical/public health microbiology investigations . Along wImplementing WGS in clinical and food microbiology laboratories has led to an increase in the amount of raw data and genomes publicly available. However, the use of WGS as a routine method is unfeasible without the application of bioinformatics resources and remains a challenge due to the required specific skill set. CamPype is a reliable solution for integration WGS into routinely use and overcome these barriers because it enables easy and automated analysis of large genome datasets, providing a quick visualization of results that facilitates data interpretation.Additional file 1: Genomic characteristics of the Campylobacter jejuni and Campylobacter coli isolates included in the first case study."}
+{"text": "Endoscopy has been increasingly utilized to manage esophageal perforationsWe present in this case a successful endoscopic closure of a large esophageal perforation using the purse-string techniqueVideo\u20061\u2002Endoscopic closure of a large esophageal perforation.A 79-year-old woman with multiple comorbidities underwent a savary dilation of esophageal stricture at an outside institution. She presented with septic shock, lactic acidosis, and respiratory failure.A chest x-ray revealed pneumomediastinum and a right-sided pneumothorax. A chest tube was inserted. She was resuscitated with intravenous fluids, bicarbonate, antibiotics, and three vasopressors. A chest computed tomography (CT) revealed pneumomediastinum and improved pneumothorax after chest tube insertion without mediastinal fluid collections. She was deemed a poor candidate for surgery.Endoscopy revealed a large mid-esophageal perforation . EndoscAn endoloop catheter and a clip were advanced through the two channels of a double-channel endoscope. The endoloop was fixed to the margins of the perforation by clips. The endoloop was then tightened approximating the margins of the defect  and depA through-the-scope esophageal stent (18\u200amm\u200a\u00d7\u200a149\u200amm) was deployed covering the site of perforation . The prFive days after the procedure, she was extubated and weaned off vasopressors. Two weeks after the procedure, chest CT showed a remarkable decrease in the pneumomediastinum.She was weaned off oxygen, and the chest tube was removed. Enteral feeding was started through a jejunal extension of a preexisting gastrostomy tube.Three weeks after the procedure, an endoscopy was performed. The esophageal stent was retrieved. The perforation site had healed . ContraEndoscopy_UCTN_Code_TTT_1AO_2AI"}
+{"text": "Recently, a newly designed endoscopic sheath  has become available in Japan . Here, A 90-year-old man was referred to our hospital because of multiple pericholecystic abscesses caused by acute cholecystitis . EUS-guVideo\u20061\u2002Endoscopic ultrasound-guided drainage for pericholecystic abscess. Intra-abscess irrigation and 5-Fr plastic stent placement through the novel endoscopic sheath.This technique may be useful for EUS-guided abscess drainage. However, in cases without adhesion to the stomach, it should be performed with a covered metal stent to prevent fluid leakage into the abdominal cavity.Endoscopy_UCTN_Code_TTT_1AS_2AD"}
+{"text": "Genome sequencing efforts for individuals with rare Mendelian disease have increased the research focus on the noncoding genome and the clinical need for methods that prioritize potentially disease causal noncoding variants. Some tools for assessment of variant pathogenicity as well as annotations are not available for the current human genome build (GRCh38), for which the adoption in databases, software, and pipelines was slow.Here, we present an updated version of the Regulatory Mendelian Mutation (ReMM) score, retrained on features and variants derived from the GRCh38 genome build. Like its GRCh37 version, it achieves good performance on its highly imbalanced data. To improve accessibility and provide users with a toolbox to score their variant files and look up scores in the genome, we developed a website and API for easy score lookup.https://remm.bihealth.org.Scores of the GRCh38 genome build are highly correlated to the prior release with a performance increase due to the better coverage of features. For prioritization of noncoding mutations in imbalanced datasets, the ReMM score performed much better than other variation scores. Prescored whole-genome files of GRCh37 and GRCh38 genome builds are cited in the article and the website; UCSC genome browser tracks, and an API are available at  The Regulatory Mendelian Mutation (ReMM) score predicts the potential pathogenicity of noncoding variants . It is sRRID:SCR_018160) v377 [RRID:SCR_003496) [The ReMM score is based on an imbalance-aware machine learning algorithm, hyperSMURF , trainedn nonoverlapping partitions, which then are subsampled according to a ratio parameter. The minority class is oversampled by factor 2, and the majority class is undersampled by factor 3, which leads to the ration of pathogenic versus benign variants of 2\u20133 in a more balanced dataset with around 2,000 data points. However, each balanced dataset alone provides insufficient coverage of the large data space of the majority class. That is why hyperSMURF applies an ensemble method: it divides the dataset into 100 partitions, each containing all oversampled pathogenic and 1 partition of downsampled proxy-benign variants. On each partition, a random forest [The hyperSMURF algorithm applies a special sampling technique essential for the highly imbalanced data of human pathogenic variants , 5. The m forest  is trainm forest .Genomic data are confounded by local correlation of annotations . Further, known pathogenic variants are not distributed evenly across the genome  but rather cluster around certain well-studied genes and share certain molecular functions or properties. When not accounted for, learners might infer superior hold-out performance because of genomic proximity of variants. To handle the local correlation structure in the genome, we apply 10-fold cytogenic band-aware cross-validation (CV) [P values, we use the value 1.Twenty-six selected features see  capture Prescored, block-gzip compressed and indexed whole-genome files  were genRRID:SCR_006169) was downloaded on 19 December 2022. Variants were filtered for single-nucleotide changes with unambiguous clinical assertions of \u201cpathogenic,\u201d \u201clikely pathogenic,\u201d \u201clikely benign,\u201d and \u201cbenign.\u201d The set was annotated using Jannovar as described above and filtered for noncoding effects. Variants overlapping the training dataset as well as mitochondrial single-nucleotide variants (SNVs) were excluded .Version 2022\u201312-03 of NCBI ClinVar (For performance comparison on the GRCh38 training set (CV results as described above) of ReMM v0.4.hg38 with other scores, prescored GRCh38 whole-genome files of CADD  version After 100 training cycles using different random seeds and 10-fold cytoband cross-validation, we achieved a performance with an average area under the precision recall curve (AUPRC) of 0.613 \u00b1 0.005 . We randRather than using ReMM scores for ranking, some users choose to specify score thresholds for classifying into pathogenic and benign variants. Using a cutoff of 0.5 yields a good result in terms of retrieving known pathogenic noncoding variants , but the number of benign variants might be extremely large. For ReMM v0.4.hg38, recall is 92% (375 of 406) at a cutoff of 0.5 Fig.\u00a0, but preTo compare both genome builds, we correlate ReMM scores from three genomic regions without assembly gap changes  and >100,000 randomly sampled autosomal positions with successful reciprocal liftover . Here, RP values . The replaced encRegTfbsClustered feature achieves a similar average Gini index (rank 6 on v0.4.hg19) as the previous numTFBSConserved feature .From the underlying Ranger random forest (RF) models , we retrA number of different tools for scoring pathogenicity of noncoding variants exist . HoweverDue to the very limited availability of noncoding scores on GRCh38, we compared ReMM on GRCh37 with multiple other scores and on a set of noncoding variants from NCBI ClinVar that do not overlap its training set. We only used variants where all scores were able to provide a prediction  and plotted PR and ROC curves Fig.\u00a0,B. CADD de facto standard in research and routine diagnostics. Scores over the GRCh38 genome are highly correlated to the prior release with a performance increase for GRCh38 due to the better coverage of features. On imbalanced data , ReMM scores outperform other noncoding effect scores. However, our analysis of new noncoding ClinVar variants also highlights limitations when scores are applied to variants (here splice variants) missing from the training data or for which no specific model features were included. In summary, we established a reproducible and scalable framework for integration of new features or new training data for further development of ReMM. The prescored whole-genome files, UCSC genome browser annotation tracks [The ReMM v0.4 score is a fully retrained noncoding score available for both the GRCh37 and GRCh38 genome builds. This fills the high need of supporting variant prioritization on the GRCh38 genome release, which is the n tracks , and a wn tracks  can now Project name: ReMM scorehttps://remm.bihealth.orgProject homepage: Operating system(s): Platform independent (website), Linux (workflow)Programming language: Python, Java, C++, BashOther requirements: browser (website); conda, snakemake, parSMURF (workflow)License: MIT LicenseRRID:SCR_023095giad024_GIGA-D-22-00232_Original_SubmissionClick here for additional data file.giad024_GIGA-D-22-00232_Revision_1Click here for additional data file.giad024_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giad024_Reviewer_1_Report_Original_SubmissionYan Guo -- 10/11/2022 ReviewedClick here for additional data file.giad024_Reviewer_1_Report_Revision_1Yan Guo -- 2/27/2023 ReviewedClick here for additional data file.giad024_Reviewer_2_Report_Original_SubmissionMulin Jun Li -- 11/18/2022 ReviewedClick here for additional data file.giad024_Reviewer_3_Report_Original_SubmissionWyeth Wasserman -- 11/22/2022 ReviewedClick here for additional data file.giad024_Supplemental_FileClick here for additional data file."}
+{"text": "Tumor progression and the therapeutic resistance associated with cancer agents are thought to be modulated by circular RNAs (circRNAs); however, its mechanism associated with nonsmall cell lung cancer (NSCLC) is still undetermined. The following investigation aimed to evaluate the involvement of circRNAs with NSCLC.  The serum specimens of 146 NSCLC individuals who received complete four cycles of PTX chemotherapy were collected. The serum concentration of hsa_circ_0005962 of these individuals was assessed with quantitative real-time polymerase chain reaction (qRT-PCR), followed by the evaluation of demographic and survival consequences for further assessments.  It was revealed that hsa_circ_0005962 is substantially increased in NSCLC chemoresistant patients and was positively correlated with the disease stage. Furthermore, the hsa_circ_0005962 value of the area under the curve was moderate, and increased hsa_circ_0005962 expression was linked with shorter overall survival (OS). Hsa_circ_0005962 stimulated paclitaxel resistance (PTX-R) in resistant NSCLC cells by regulating the axis of miR-126-5p/insulin-like growth factor 1 (IGF1).  The results of this investigation highlight that hsa_circ_0005962 induces chemoresistance in NSCLC patients and, therefore, can act as a physiological target to treat NSCLC. Of all the different types of cancers, that of the lung is frequently occurring malignant tumors and its type; nonsmall cell lung cancer (NSCLC) is believed to account for 80% to 85% of lung cancers , 2. AlthCircular RNAs (circRNAs) are in the form of a loop, with the 3\u2032 and 5\u2032 ends covalently connected thereby forming a circle . In moleIn this investigation, the circular RNA hsa_circ_0005962 locates at chr8:101936182-101937267, and its associated-gene symbol is YWHAZ. The serum concentration of hsa_circ_0005962 in NSCLC individuals and its correlation with clinical outcomes were determined. It was revealed that PTX-R NSCLC individuals had increased hsa_circ_0005962 expressions, which were associated with substandard overall survival (OS). Hsa_circ_0005962 induced NSCLC individuals PTX resistance by regulating miR-126-5p/IGF1 axis. Furthermore, hsa_circ_0005962 presented a reasonable area under the receiver operating characteristic (AUC-ROC) curve data, indicating its importance as a new prognostic bio-index of PTX-R NSCLC.2 percentage of 5. Additionally, 5\u2009nM of PTX (Solarbio) was introduced in the growth media to conserve H460-PTX and A549-PTX cell resistance.Serum specimens of 146 NSCLC and 142 healthy control individuals enrolled at First People's Hospital of Jiujiang were obtained. This investigation was authenticated by the ethical board of the First People's Hospital of Jiujiang, and all the subjects were initially informed about the research, and then, their signed consent was taken. Primary human bronchial epithelial (HBE) and NSCLC (A549 and H460) cells were acquired from Procell . The PTX-R corresponding NSCLC cells (H460-PTX and A549-PTX) was developed by augmenting the parental cells with accelerating PTX concentrations . All the cultures were propagated in RPMI1640 media  augmented with 10% fetal bovine serum  and 1% penicillin\u2013streptomycin (Invitrogen) at the standard temperature of 37\u00b0C and COWhole cellular RNA was procured via the RNeasy Mini kit  and assessed by NanoDrop 2000c Spectrophotometer . Then, with the help of an M-MLV reverse transcriptase kit  or TaqMan MicroRNA reverse transcription kit , the cDNA was prepared, followed by qRT-PCR evaluation on the StepOnePlus Real-Time PCR System (Applied Biosystems) with SYBR Green PCR Master Mix (Invitrogen) and specified primers . The primers used in this investigation were hsa_circ_0005962 forward: 5\u2032AACTCCCCAGAGAAAGCCTGC3\u2032 and reverse: 5\u2032TGCTTGTGAAGCATTGGGGAT3\u2032; IGF1 forward: 5\u2032CGTCTCCCGTTCGCTAAATC 3\u2032 and reverse: 5\u2032AATAAAAGCCCCGGTCTCCA3\u2032; miR-126-5p forward: 5\u2032GCCGAGCATTATTACTTTT3\u2032 and reverse: 5\u2032CAGTGCAGGGTCCGAGGTAT3\u2032;glyceraldehyde-phosphate dehydrogenase (GAPDH) forward: 5\u2032AGAAGGCTGGGGCTCATTTG3\u2032 and reverse: 5\u2032AGGGGCCATCCACAGTCTTC3\u2032; U6 forward: 5\u2032GGAACGATACAGAGAAGATTAGC3\u2032 and reverse: 5\u2032TGGAACGCTTCACGAATTTGCG3\u2032.\u03bcl). Lastly, the absorbance of cells (470\u2009nm) was determined via a microplate reader. IC50 of DTX, DDP, and PTX was assessed on GraphPad Prism 7 software .After siRNA/plasmid was incorporated, 5,000 cells (H460-PTX and A549-PTX) were propagated in 96-well plates and then allowed 48\u2009h of DTX, DDP, and PTX exposure. Thereafter, MTT reagent (2\u2009mg/mL) (Sigma-Aldrich) was introduced to react with cells for 4\u2009h. Living cells formed formazan, and these were then resolved in dimethylsulfoxide  were carried out for differential analysis. P value <00.5 was termed significant.All the protocols were performed thrice, and their data were assessed with GraphPad Prism 7 and depicted by values of the mean of\u2009\u00b1\u2009standard deviation. Student's n\u2009=\u200964) than in PTX-sensitive patients (n\u2009=\u200982)  . The dat(n\u2009=\u200982) . The res(n\u2009=\u200982) .Next, the NSCLC individuals were stratified into groups with high and low hsa_circ_0005962 concentrations in serum, according to their average expression in this cohort. Furthermore, the clinical manifestations were also compared between the two cohorts. Chi-squared analyses indicated the association of hsa_circ_0005962 expressions with tumor size, TNM stages, distant metastasis or recurrence, and lymph node metastasis ; howeverKaplan\u2013Meier and log-rank tests revealed that chemoresistant NSCLC individuals have substantially decreased OS and progression-free survival (PFS) than chemosensitive individuals . Cox prop < \u20090.0001), compatible with its application as a physiological marker for differentiating NSCLC suffering individuals from healthy subjects.To determine the diagnostic efficiency of concerned circRNA in serum, the AUC-ROC was assessed and observed to be 0.9014  indicated that serum expression of hsa_circ_0005962 can be reliably utilized for differentiating NSCLC individuals from healthy subjects.This investigation summarizes that the upregulation of hsa_circ_0005962 is prominently evident in the NSCLC individuals' serum samples, where this upregulation was substantially pronounced in the serum of chemosensitive rather than chemoresistant subjects. Furthermore, hsa_circ_0005962 promoted PTX resistance in PTX-R NSCLC cells by modulating miR-126-5p/IGF1 axis. Therefore, hsa_circ_0005962 is an effective target that opens a new path for further studies to evaluate the underlying processes involved in NSCLC chemoresistance."}
+{"text": "Dear Editor,Embryonic stem cells (ESCs) have been assumed to possess immature mitochondria and to favor anaerobic glycolysis over oxidative phosphorylation (OXPHOS) for energy production. This proposition is largely based on the findings that ESCs possess globular mitochondria with blurred cristae, and the facts that ESCs have higher glycolysis activity and lower mitochondrial respiration capacity than somatic cells . HoweverWe firstly determined the total cellular and mitochondrial volumes of individual mouse na\u00efve ESCs (ESCs), primed ESCs (EpiLCs), neural stem cells (NSCs), embryonic fibroblasts (MEFs), and cardiomyocyte cells (HL-1) . The totThe oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) were simultaneously measured in ESCs, EpiLCs, NSCs, MEFs, and HL-1 cells  and S1E.Interestingly, when the OCR was normalized to mitochondrial volume, the ESC mitochondria consumed significantly more oxygen than mitochondria in EpiLCs, NSCs, MEFs, and even HL-1 cardiomyocytes for ATP-generation-related respiration . Correspd-glucose (2-DG), a glucose analog, was titrated to inhibit 20% and 50% of the total glycolysis\u2013ATP generation (designated 20%GI and 50%GI)  . Meanwhid 50%GI) . It is wd 50%GI) .Transcriptome profiling was employed to investigate gene expression reprogramming in response to OXPHOS or glycolysis inhibition using the titrated concentrations of oligomycin and 2-DG. Surprisingly, the results indicate that OXPHOS inhibition in ESCs results in much more extensive effects on gene expression than glycolysis inhibition at the whole transcriptome level .Inhibition of OXPHOS not only decreased expression of pluripotency genes, but also disrupted expression of genes in the tricarboxylic acid (TCA) cycle, the amino acid biosynthesis, fatty acid metabolism, and pentose phosphate pathways as well as the glycolysis/gluconeogenesis pathways in ESCs . The ranBoth 20%OI and 50%OI inhibition significantly decreased ESC colony formation and expression of pluripotency genes, whereas 20%GI did not affect ESC self-renewal and pluripotency, and 50%GI inhibited ESC self-renewal and pluripotency to a lesser extent than 20%OI  and S5B.ESCs treated with 20%OI or 50%OI did not form any visible teratomas, while 20%GI or 50%GI had no obvious effects on teratoma formation . AccordiConsistent with the chemical treatment results, the mitochondrial respiration, self-renewal, pluripotency, and differentiation capability of ESCs were inhibited by ATP5a1 knockdown  and S7. N-acetylglucosamine (UDP-GlcNAc), an amino sugar produced by the hexosamine biosynthetic pathway (HBP), was identified at the top of the metabolite list with a dramatic reduction upon inhibition of OXPHOS, and the expression levels of enzymes involved in UDP-GlcNAc biosynthesis were dramatically disturbed  expression (l-norleucine (Don) inhibited colony formation and expression of pluripotency genes in ESCs  of blastocysts (in vivo.Importantly, inhibition of OXPHOS, but not glycolysis, resulted in decreased O-GlcNAcylation and expression of Oct4 and Sox2 in the inner cell mass (ICM, the stocysts  and S8I.As PSCs undergo differentiation, many cellular parameters change, like cell volume, cell mass, mitochondrial volume, mitochondrial mass, and expression of the mitochondrial house-keeping genes TOM40, TIM23, ATP5A, etc.  and S1K.We established that OXPHOS accounts for ~65% of total cellular ATP generation in na\u00efve ESCs and ~51% of total cellular ATP generation in primed ESCs  and S1F.Both diffusion map and scatterplots analyses indicate that OXPHOS inhibition induces ESCs into a unique state that is different from either the diapause or primed state . Our intIn conclusion, the current study demonstrates that PSC mitochondria are in a super-active state and OXPHOS produces the majority of cellular ATP, which challenges the traditional concept that ESCs rely on glycolysis as their major source of energy. In addition, this study uncovered a previously unknown mechanism in which OXPHOS couples with the HBP pathway for UDP-GlcNAc generation to regulate pluripotency in mouse ESCs . The undpwac009_suppl_Supplementary_MaterialClick here for additional data file.pwac009_suppl_Supplementary_Table_S1Click here for additional data file.pwac009_suppl_Supplementary_Table_S2Click here for additional data file.pwac009_suppl_Supplementary_Video_S1Click here for additional data file.pwac009_suppl_Supplementary_Video_S2Click here for additional data file.pwac009_suppl_Supplementary_Video_S3Click here for additional data file.pwac009_suppl_Supplementary_Video_S4Click here for additional data file.pwac009_suppl_Supplementary_Video_S5Click here for additional data file."}
+{"text": "The Lycophyta species are the extant taxa most similar to early vascular plants that were once abundant on Earth. However, their distribution has greatly diminished. So far, the absence of chromosome-level assembled lycophyte genomes has hindered our understanding of evolution and environmental adaption of lycophytes.Isoetes sinensis, a lycophyte. This genome represents the first chromosome-level assembled genome of a tetraploid seed-free plant. Comparison of genomes between I. sinensis and Isoetestaiwanensis revealed conserved and different genomic features between diploid and polyploid lycophytes. Comparison of the I. sinensis genome with those of other species representing the evolutionary lineages of green plants revealed the inherited genetic tools for transcriptional regulation and most phytohormones in I. sinensis. The presence and absence of key genes related to development and stress responses provide insights into environmental adaption of lycophytes.We present the reference genome of the tetraploid aquatic quillwort, I. sinensis but also other lycophytes.The high-quality reference genome and genomic analysis presented in this study are crucial for future genetic and environmental studies of not only  Some members of the Lycophyta can survive in a variety of extreme environments, such as deserts , and even arctic and alpine regions [Isoetes, are endangered [The vascular plants that currently dominate the land can be categorized into 2 major phyla: Euphyllophyta and Lycophyta. Euphyllophyta includes seed plants and ferns, while Lycophyta comprises spore-bearing species that exhibit the greatest similarity to the early vascular plants found in the fossil record. Lycophytes have the longest evolutionary history among all groups of vascular plants and have had major impacts on biodiversity, soil formation , and CO2legmaria ) to the dophylla ), humid  regions . Howeverdangered , 6. The Selaginella moellendorffii [Selaginella tamariscina [Lycopodium clavatum [Isoetes taiwanensis [Isoetes sinensis (NCBI:txid283158)  system that is crucial for the plant adaptation to a low CO2 environment underwater [Lycophytes included diploid and polyploid species in many lineages. So far, 4 genomes of diploid lycophytes, including ndorffii , Selaginariscina , Lycopodclavatum , and Isowanensis , are ava58) Fig.\u00a0, is a tein China . Like otI. sinensis assembled into 22 pseudochromosomes. Our comparative analyses of its genome with I. taiwanensis and those of green algae and land plants allow us to better understand the evolution of lycophytes and the genetic basis of the environmental adaptability of lycophytes.Here, we report a reference genome sequence of k-mer analysis revealed the genome size of I. sinensis to be approximately 2.25 Gb with a heterozygosity value of 0.26%. We sequenced the I. sinensis genome by generating 176.46 Gb (79.17\u00d7 coverage) Illumina short reads, 97.01 Gb (43.52\u00d7 coverage) PacBio SMRT HiFi long reads, and 237.7 Gb (111.50\u00d7 coverage) Hi-C data. We subsequently assembled the 2.13 Gb I. sinensis genome into 22 pseudochromosomes consisting of 3,741 scaffolds with N50 length of 86.66 Mb  phylogeny of 19 species of evolutionarily representative land plants and green algae indicates that wanensis . We attemparison  and phylmparison  showed te groups . In addie groups . These rassembly  and artime pairs . Gene nume pairs . We founme pairs , indicatwanensis , suggestZea mays , which iI. sinensis, respectively  relative to that of I. taiwanensis (150.9 Mb). Furthermore, 6,578 genes that exist as a single copy in I. taiwanensis still exist as a single copy (1 copy per subgenome) in each of the 2 I. sinensis subgenomes. To understand the effect of polyploidization on gene expression, we analyzed the gene expression bias between pairs of chromosomes in I. sinensis by using a similar approach reported in Brassica juncea [Diploid A and B subgenomes shared 15,280 orthologous gene families, which include 3,007 and 2,103 multicopy gene families in the A and B subgenomes, respectively. Of the orthologous single-copy gene sets in a juncea . On averKs) suggests the occurrence of 2 whole-genome duplications (WGDs) with median values of 0.4 and 1.8 in I. sinensis, and the strong peak \u223c1.8 may represent the Ks values of homeologs of the A and B subgenomes  are long terminal repeat (LTR) retrotransposons  belonging to 52 families in sinensis . We founen algae . Genes tsis Fig.\u00a0. When weceptions . For exarns Fig.\u00a0. Interestes Fig.\u00a0. Next, welopment , 25. Amosis Fig.\u00a0. We obsesinensis , which mI. taiwanensis and S. moellendorffii are available, little is yet known about phytohormone in the Lycophyta. To better understand phytohormone regulation in I. sinensis, we investigated both conserved and lost genes that related to synthesis, transport, and signal transduction of phytohormones.Although the genome sequences of TAA (encoding tryptophan aminotransferase in Arabidopsis) and 5 YUCCA homologs encoding flavin monooxygenase-like enzymes [YUC was found in I. sinensis. There is no TAA-encoding gene in I. sinensis, although its paralog TAR was detected  was found. Genes encoding downstream TFs, such as AREB/ABFs, which are involved in desiccation tolerance, were also detected in I. sinensis. In addition, almost all of the genes involved in the cytokinin/ethylene-controlled signal transduction pathways exist in I. sinensis, except for those encoding the receptor CKR in the cytokinin signaling pathway, and 1-aminocyclopropane-1-carboxylate oxidase, which exists only in seed plants [I. sinensis  is generated under environmental stress and leads to a series of reactions that allow plants to adapt to adverse conditions . Almost sinensis . The PYLd plants   can be detected in I. sinensis  and salicylic acid (SA) signaling pathways in sinensis . For exasinensis . As for I. sinensis and I. taiwanensis. Except for a small number of genes found only in I. sinensis, such as GA2OX and AOC3, and the genes found only in I. taiwanensis, such as BAK1, ACO4, ACS2, ACS4, and JAZ, most of genes are conserved with slight copy number variation between these 2 Isoetes species   , a key ewanensis . In addixykinase , which pIsoetes to adapt to amphibiotic conditions. However, we found that some key genes for stomata development, such as SPEECHLESS (SPCH), MYB88, and MUTE [I. sinensis or I. taiwanensis  pathway comprises the SOS3 and SCaBP8 calcium sensors, the SOS2 protein kinase, and the SOS1 plasma membrane Na+/H+ antiporter. When an Arabidopsis plant experiences salt stress, SOS3 and ScaBP8 sense the calcium signal, interact with SOS2, and activate its kinase activity, which then activates the reverse transport activity of SOS1 [Arabidopsis are mediated by the Ca2+-permeable transporters AtANN1 and AtANN4 [2+ transporters ANN1 and ANN4 and those encoding the downstream sensor SOS3 and ScaBP8 might thus limit the adaptability of lycophytes to salt stress  encodes a defensin-like protein that can chelate cytosolic Cd and promotes secretion of Cd into intercellular spaces such as the cell wall apoplast and xylem to decrease the concentration of Cd in the cytosol during transport of Cd within the plant [HMA3 and CAL1 are not present in the I. sinensis and many lycophytes , a gene present in algae and land plants that reduces the level of active indole-3-acetic acid (IAA) by esterifying it with an amino acid, resulting in increased lignin synthesis and peroxidase activity during plant defenses to heavy metal toxicity [ETR2 and ERF1, which encode ethylene receptors, whereas the abundance of transcripts for brassinosteroid (BR)\u2013related genes such as DWARF and BR6ox, decreased, suggesting that Cd-mediated BR biosynthesis feedback is inhibited when the BR contents increase [I. sinensis and those of most lycophytes , have been lost or not detected in the I. sinensis and many lycophytes. These findings are crucial for the understanding of lycophyte development and their adaptation to adverse abiotic environmental conditions.Here, we present a high-quality assembly and annotation of the I. sinensis shoot materials were harvested from Yangdongcun, Beilun District, Ningbo, Zhejiang Province of China. DNA was extracted using a modified cetyltrimethylammonium bromide procedure. DNA concentrations and purity were evaluated by NanoDrop and its quality analyzed by agarose gel electrophoresis. Paired-end libraries with a 350-bp inserts were prepared by following the Illumina protocols and then sequenced in PE150 mode on the Illumina HiSeq X Ten platform (RRID:SCR_016385). A total of 176.46 Gb paired-end reads were obtained for genome survey. The read mapping rate of the Illumina sequencing was 98.58%, covering 99.95% of the I. sinensis genome. For the PacBio Sequel analysis, the libraries for single-molecule real-time (SMRT) genome sequencing were prepared according to the manufacturer's protocol for the sequencing platform and then sequenced with SMRT sequencing at 43.52\u00d7 coverage using 4 cells. A total of 97.01 Gb reads were obtained for the genome assembly. High-throughput chromosome conformation capture (Hi-C) sequencing libraries were produced as follows: nuclei were isolated and fixed with the cross-linking agent paraformaldehyde and then the cross-linked DNA was treated with restriction enzymes. Biotin was then added to label the ends of oligonucleotides during terminal repair. Adjacent DNA fragments were joined using nuclease ligases. Protein was digested with a protease to dissociate the protein from the DNA. Then the genomic DNA was extracted and randomly sheared into 350-bp fragments using a Covaris crusher. The library was prepared according to manufacturer's instructions (Illumina) and sequenced on a HiSeq X Ten DNA system to obtain 150-bp paired-end sequences.I. sinensis were extracted using a RNeasy Plus Mini Kit (Qiagen). After that, rRNA was removed from total RNA samples using the RiBO-Zero Kit (Illumina). The isolated mRNA  was used as template to synthesize complementary DNA (cDNA), then the cDNA was sheared into small fragments. Paired-end libraries were prepared from various tissues by following the Illumina protocols and sequenced with PE150 mode on the Illumina HiSeq X Ten platform. Pooled samples from the roots, shoots, and sporangia pooling sample were used for the PacBio Sequel analysis. The libraries for SMRT genome sequencing were prepared according to the manufacturer's protocol for the sequencing platform and then sequenced on a PacBio Sequel II with SMRT sequencing.RNAs from roots, shoots, and sporangia of de novo genome assembly, Illumina short reads were used for preliminary evaluation of the genome size, heterozygosity, and repeat sequence proportions by k-mer analysis. After data filtering and quality control, the short reads were first assembled using SOAPdenovo (RRID:SCR_010752) software to generate contigs. These contigs were further used to construct scaffolds according to their pair-end relationships. The quality value (QV) score generated from Merqury (RRID:SCR_022964) was 46.1448, and the corresponding error rate was 2.4295e-05.Before De novo genome assembly of the PacBio long reads from I. sinensis genomes was performed using Hifiasm (RRID:SCR_021069) [RRID:SCR_014731). The total length of this assembly was 2,131.51 Mb, with a contig N50 up to 2,673\u00a0kb._021069) . The pri_021069) . The conRRID:SCR_005227) [RRID:SCR_022750) [RRID:SCR_000262) (v.1.3.1) [For the chromosome-level assembly, the clean Hi-C sequencing data were mapped to the draft genome using the Burrows\u2013Wheeler Aligner (BWA) , and the_005227) . Only un_022750) . Finally_022750)  softwarev.1.3.1)  with defRRID:SCR_015008) [RRID:SCR_015055) [RRID:SCR_010910). Finally, 98.58% of small fragment reads mapped to the I. sinensis genome. LAI was evaluated by LTR_retriever (RRID:SCR_017623) (v2.9.0) [Genome completeness was evaluated using BUSCO (_015008)  and CEGM_015055)  analyses(v2.9.0) .I. sinensis were estimated by de novo strategies using RepeatModeler (RRID:SCR_015027), RepeatScout (RRID:SCR_014653), LTR_FINDER (RRID:SCR_015247) [RRID:SCR_020946) [RRID:SCR_012954) [RRID:SCR_021169).The repetitive sequences in _015247) , MITE-Hu_020946) , and PIL_020946) . A homol_012954)  to searcRRID:SCR_018970) [LTRs were identified using LTR_FINDER  and LTRh_018970) , the res_018970)  to buildI. sinensis sample using the RNeasy Plus Mini Kit, and rRNA removal was performed using a RiBO-Zero Kit. Isolated RNA was used for cDNA library construction, using the dUTP method [RRID:SCR_004726), and PhyloCSF [Total RNA was extracted from each P method . These lP method . TranscrP method , CNCI [7P method , Pfam  v1.9.1. Clean reads of 18 to 30\u00a0nucleotides were screened for subsequent analysis. The clean reads were mapped to Silva (RRID:SCR_006423), GtRNAdb database (RRID:SCR_006939), Rfam (RRID:SCR_007891), and Repbase (RRID:SCR_021169) to remove rRNAs, tRNAs, snRNAs, snoRNAs, and other ncRNAs and repeats. The remaining reads were compared with reference miRNAs in the miRbase (RRID:SCR_003152) to annotate miRNAs. These reads were then mapped to the genome using Bowtie 2 (RRID:SCR_016368) [Small RNA libraries for ab initio prediction, homology-based gene prediction, and transcript evidence from RNA-seq data for I. sinensis. The ab initio gene prediction was conducted using 2 ab initio gene predictors, Augustus (RRID:SCR_008417) [RRID:SCR_013362), with default parameters. Orthologous protein sequences were then aligned to the genome assembly using GeneWise (RRID:SCR_015054) [I. sinensis and all protein-coding genes were annotated to the public protein databases at KEGG (RRID:SCR_012773), SwissProt (RRID:SCR_021164), TrEMBL, and InterProScan v5.11\u201351.0 (RRID:SCR_005829), with an E-value cutoff of 1e\u22125. Pseudogenes were detected by exonerate (RRID:SCR_016088) (v.2.4) using the protein data of Salvinia cucullata, Azolla filiculoides [I. sinensis.Gene annotation was performed by combining evidence drawn from _008417)  and Gens_015054) . In addi_015054) . Evidenc_015054)  was usedde novo prediction and direct RNA sequencing of small RNAs and lncRNAs. rRNA fragments were identified using BLAST against rRNA sequences of reference species in the Pfam database. tRNAs were identified using tRNAscn-SE. Additionally, other types of noncoding RNA, including miRNAs and snRNAs, were identified at the Rfam database using INFERNAL (RRID:SCR_011809) [We used 2 strategies to annotate noncoding RNAs, including _011809) .I. sinensis genome, we used the Whole-Genome Duplication Integrated analysis tool for WGD and intragenomic collinearity detection as well as Ks estimation and peak fitting [In order to search for genome-wide duplications in the  fitting . The WGDRRID:SCR_007839) (v. 2.0.9) with default parameters [A. thaliana, Vitis vinifera, Z. mays, Oryza sativa [Physcomitrella patens [Marchantia polymorpha [A. filiculoides, S. cucullata, Amborella trichopoda [Cycas panzhihuaensis [Picea abies [Gnetum montanum [S. moellendorffii [I. sinensis, I. taiwanensis [Mesostigma viride [Chlamydomonas reinhardtii [Klebsormidium nitens [Chara braunii [RRID:SCR_011811) (v.7.490), and then ProTest (v.3.4.2) was used to find the best model of amino acid replacement in the single-copy gene alignments. Before phylogeny construction, Gblocks (RRID:SCR_015945) (v.0.91b) [RRID:SCR_006086) (v.8.2.12) [Gene families for the 19 species were analyzed and clustered using OrthoMCL   (-b5 = h.8.2.12)  with theC. reinhardtii and G. montanum from TimeTree (RRID:SCR_021162), the divergence times for the inferred species tree were calculated using r8s (RRID:SCR_021161) (v.1.81) [RRID:SCR_005983) (v.4.2.1) with P < 0.05 [P values were used to estimate the likelihood of the observed gene family sizes given average rates of gain and loss and were also used to determine expansion or contraction for individual gene families in each node.Based on a calibration of divergence times using (v.1.81) . Gene faI. sinensis and 13 representative plants or algae  and transcriptomes of the other 19 lycophytes from the 1KP project [P < 1e-5) was performed using well-studied proteins  as queries to identify the homolog genes in I. sinensis. The redundant sequences were deleted, and subsequently, candidates were examined for the conserved domain(s) of respective gene families using SMART (RRID:SCR_005026). Amino acid sequences of our target genes were aligned using Muscle. The alignments were then manually inspected using MEGA 7. MEGA 7 was run with 1,000 bootstrap replicates to generate the neighbor-joining phylogenetic trees [To identify TF, phytohormone, CAM and stress response related genes, we performed comparative genomic analysis of the genomes of  project . BLASTP ic trees .Brassica juncea [I. sinensis. DEG pairs with fold change >2 were defined as dominant gene pairs. The dominant genes were defined as the genes with higher expression in dominant gene pairs, and the lower ones within dominant gene pairs were defined as subordinate genes. The rest of the genes with 1:1 homoeologs were defined as neutral genes.We adopted the method used to analyze homoeolog expression in a juncea  and focugiad079_GIGA-D-23-00116_Original_SubmissionClick here for additional data file.giad079_GIGA-D-23-00116_Revision_1Click here for additional data file.giad079_GIGA-D-23-00116_Revision_2Click here for additional data file.giad079_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giad079_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giad079_Reviewer_1_Report_Original_SubmissionDongya Wu -- 7/3/2023 ReviewedClick here for additional data file.giad079_Reviewer_1_Report_Revision_1Dongya Wu -- 8/17/2023 ReviewedClick here for additional data file.giad079_Reviewer_2_Report_Original_SubmissionYongzhi Yang, Ph.D. -- 7/5/2023 ReviewedClick here for additional data file.giad079_Reviewer_2_Report_Revision_1Yongzhi Yang, Ph.D. -- 8/21/2023 ReviewedClick here for additional data file.giad079_Supplemental_FilesClick here for additional data file."}
+{"text": "There are two types of Tl+ ions in the complex, with coordination numbers of eight and seven and with stereochemically active and inactive lone-pair electrons, respectively. In the crystal, the doubly deprotonated ligands form two-dimensional hydrogen-bonded layers through O\u2014H\u22efO hydrogen bonds. The NH group is involved in a trifurcated intra\u00admolecular hydrogen bond. Coordination of the phospho\u00adnate ligands to the Tl+ ions creates a three-dimensional structure.The title compound, [Tl DOI: 10.1107/S1600536809031006/su2128Isup2.hkl            Structure factors: contains datablocks I. DOI:  crystallographic information; 3D view; checkCIF report            Additional supplementary materials:"}
+{"text": "We studied the role of peroxisomal catalase in chronological aging of the yeastHansenula polymorpha in relation to various growth substrates. Catalase-deficient (cat) cells showed a similar chronological life span (CLS) relative to the wild-type control upon growth on carbon and nitrogen sources that are not oxidized by peroxisomal enzymes. However, when media contained methylamine, which is oxidized by peroxisomal amine oxidase, the CLS of cat cells was significantly reduced. Conversely, the CLS of cat cells was enhanced relative to the wild-type control, when cells were grown on methanol, which is oxidized by peroxisomal alcohol oxidase. At these conditions strongly enhanced ROS levels were observed during the exponential growth phase of cat cells. This was paralleled by activation of the transcription factor Yap1, as well as an increase in the levels of the antioxidant enzymes cytochrome c peroxidase and superoxide dismutase. Upon deletion of the genes encoding Yap1 or cytochrome c peroxidase, the CLS extension of cat cells on methanol was abolished. These findings reveal for the first time an important role of enhanced cytochrome c peroxidase levels in yeast CLS extension. Aging is defined as progressive deterioration of cellular components resulting in loss of function and cell death. Reactive oxygen species (ROS) are considered to play a pivotal role in this process. Until recently ROS were assumed to represent toxic by-products of cellular metabolism, which inflict damage to important macromolecules such as DNA, lipids and proteins , 2. IndeSince depolarized mitochondria are a main source of ROS, homeostasis of this organelle is considered as a major determinant of lifespan -9. A sec2O2 scavenging enzyme. Studies in human fibroblasts indicated that during aging import of peroxisomal catalase is compromised, associated with enhanced intracellular H2O2 levels, indicative for a function in cellular ROS homeostasis [Catalase is an important conserved peroxisomal Heostasis , 16. Cheeostasis . Moreoveeostasis , which fSaccharomyces cerevisiae is widely used as a model organism to study the molecular mechanisms of aging [CTA1 was shown to cause a decrease in the chronological lifespan (CLS) [CTA1 caused an increase in CLS. This was explained by the elevation of H2O2 levels, which triggered expression of superoxide dismutase (SOD2) thereby decreasing the level of superoxide anions [CTA1 was shown to decrease the CLS of S. cerevisiae. Both observations would be in line with the redox stress hypothesis.of aging . In contof aging , 21. Delan (CLS) . Howevere anions . MoreoveHansenula polymorpha as a model organism. Akin to mammalian cells, this organism has only one catalase, which is peroxisomal [S. cerevisiae which contains only one peroxisomal oxidase, acyl CoA oxidase, H. polymorpha peroxisomes contain in addition multiple other oxidases, like in mammals.To better understand the role of peroxisomal catalase in aging, we used the yeast oxisomal , 25. In cat)H. polymorpha cells upon cultivation on media containing different carbon and nitrogen sources that do or do not involve peroxisome function. During growth on glucose or glycerol as carbon source in the presence of ammonium sulfate as nitrogen source peroxisomal enzymes are not required for the metabolism of the primary carbon and nitrogen sources. However, when media contain methylamine as sole nitrogen source or methanol as carbon source, the peroxisomal oxidases amine oxidase (AMO) and alcohol oxidase (AO) are required for growth. Our data indicate that the effects of the absence of peroxisomal catalase on the CLS is highly variable (ranging from a negative to a positive effect) and depends on the growth substrates.In this study we analyzed the CLS of wild-type (WT) and catalase-deficient  when the cultures had entered the stationary phase (cat culture grown on glucose/ammonium sulfate (Glc/AS) or glycerol/ammonium sulfate (Gly/AS)  Fig. , Table 1catcultures showed a significant reduction in both median and maximum lifespan compared to the WT control , rol Fig. , Table 1rol Fig. .catcultures was significantly enhanced relative to the WT control  the median lifespans were similar, but the maximum lifespan of rol Fig. , Table 1rol Fig. . The fineviously   and FACS .To check the impact of cat cultures  encodes a protein of 420 amino acids containing a predicted nuclear localization signal (NLS) [In tor Yap1 , 31. We  pastoris, S. pomb[S. pombe and S. cerevisiae. The putal (NLS)  at the CMoreover, it contains 6 cysteines, two of which are present within the NES. Sequence alignments indicated that those features are conserved .yap1 and cat yap1 deletion strains. These strains showed similar growth profiles as cat and WT cultures . Upon growth on Glc/AS, Glc/MA or Gly/AS, mGFP-Yap1 was predominantly localized to the cytosol in WT and cat cells (data not shown). However, after the shift of Glc/AS-grown cells to Gly/MeOH/AS, mGFP-Yap1 rapidly migrated (within two hours) to the nucleus of cat cells, but not of WT cells , superoxide dismutase (SOD) and glutathione reductase (GLR) in crude extracts of WT and ins Fig. .yap1 and cat yap1 cells, using WT and cat cells as controls. Deletion of YAP1 alone did not affect activity of this enzyme relative to WT. CCP activity was enhanced in cat cells but not in cat yap1 cells relative to the WT control  is most likely a Mn containing SOD, as it appeared to be resistant to both compounds.SOD isozyme profiling of stationary WT cells grown on Glc/AS medium revealed the presence of 3 bandsFig. . The bancat cells grown on Glc/MA or Gly/MeOH/AS, revealed that cat cells grown on Gly/MeOH/AS showed the highest SOD1 activity  via no effect (Glc/AS and Gly/AS) to a positive effect (Gly/MeOH/AS). An important difference between MA and MeOH utilization is the amount of H2O2 that is generated during the growth phase. This is higher for MeOH, which is used as carbon source, relative to MA, which serves as nitrogen source (C/N ratio of H. polymorpha cells = 7).Our data indicate that in CAT has no effect on the CLS upon growth on these substrates  [Our data suggest that enhanced ROS levels during the exponential growth phase on Gly/MeOH/AS Fig.  is benefme (OYE) .H. polymorpha genes for putative Yap1 binding sites [H. polymorpha genes encoding CCP, SOD and OYE , but not in Cu/Zn SOD  or on YPD  supplemented with 300 \u03bcg/ml hygromycin B (Sigma) or 100 \u03bcg/ml nourseothricin. For viability determination cells were plated on YPD agar plates. For cloning purposes, E. coli DH5\u03b1 or GM48 were used. Bacteria were grown at 37\u00b0C in LB media supplemented with 100 \u03bcg/ml ampicillin or 50 \u03bcg/ml kanamycin when required.The H. polymorpha was performed as described previously [The plasmids and primers used in this study are listed in Tables eviously . All delcat) was constructed by replacing the genomic region of CAT (P30263) comprising nucleotides +1 to +1256 by the auxotrophic marker for uracil (URA3). To this end the first region -399 to 0 of the CAT gene was amplified using primers 41_5CAT_F and 41_5CAT_R with attB sites and recombined into pDONR41 yielding pENTR_41_5'CAT. At the same time region +1256 to +1665 was amplified using primers 23_3CAT_F and 23_3CAT_R and recombined with pDONR23 yielding pENTR_23_3'CAT.A catalase deletion strain  was deleted by replacing nucleotides -2 to +1262 by a hygromycin B resistance cassette (HPH). To this end a fragment containing region -280 to -3 from the start codon was first amplified from H. polymorpha genomic DNA using primers 41_YAP1_F and 41_Yap1_R and recombined in Gateway BP reaction into pDONR_41 yielding plasmid pENTR_41_5'YAP1. Similarly region +1263 to +1639 was amplified using primers 23_YAP1_F and 23_YAP1_R and recombined into pDONR_23 yielding pENTR_23_3'YAP1. Plasmids pENTR_41_5'YAP1, pENTR_221_HPH and pENTR_23_3'YAP1 were recombined in Gateway LR reaction with pDEST_43_ NAT yielding plasmid pDEL_YAP1_HPH. A deletion cassette of 2417bp was amplified from this plasmid using primers Yapdelcas_F and Yapdelcas_R and used for transformation of H. polymorpha WT and cat cells. Transformants were selected on YPD with hygromycin.YAP1 deletion strain was complemented by insertion of an expression cassette containing the YAP1 promoter, followed by a gene encoding the N terminally mGFP tagged Yap1 and the AMO terminator into the YAP1 promoter region. To this end first the YAP1 promoter (region -400 to 0) was amplified from genomic DNA using primers 41_Prom_Yap1F and 2PGFP_O_R. The mGFP ORF without a stop codon was amplified from the pHIPZ_mGFP fusinator plasmid using primers 2PGFP_O_F and 41_mGFP_R. PCR fragments were combined and used as a template in second overlay PCR using primers 41_Prom_Yap1F and 41_mGFP_R. The obtained DNA fragment was used in a Gateway BP reaction with pDONR_41 yielding pENTR_41_ PYAP1_mGFP. Next YAP1 ORF was amplified from genomic DNA using primers 221_YAP1_F and 221_YAP1_R and recombined in Gateway BP reaction with pDONR_221 resulting in plasmid pENTR_221_YAP1. Plasmids pENTR_41_PYAP1_mGFP, pENTR_221_YAP1, pENTR_23_TAMO were recombined in a Gateway LR reaction with pDEST_43_NAT yielding plasmid pEXP_PYAP1_mGFP-YAP1_TAMO. This plasmid was transformed to E.coli GM48 to obtain unmethylated DNA. After linearization with XbaI plasmid DNA was transformed into yap1 and cat yap1 cells. Colonies were selected on YPD supplemented with nourseothricin.The CCP (EFW94326) was deleted by replacing nucleotides -7 to +376 by a HPH cassette. Region -280 to -3 from the start codon was first amplified from H. polymorphagenomic DNA using primers 41_CCP_F and 41_CCP_R and recombined in Gateway BP reaction into pDONR_41 yielding plasmid pENTR_41_5'CCP. Similarly, region +376 to +795 was amplified using primers 23_CCP_F and 23_CCP_R and recombined into pDONR_23 yielding pENTR_23_3'CCP. Plasmids pENTR_41_5'CCP, pENTR_221_HPH and pENTR_23_ 3'CCP were recombined in a Gateway LR reaction with pDEST_43_NAT yielding plasmid pDEL_CCP_HPH. A deletion cassette of 2633 bp was amplified from this plasmid using primers Ccpdelcas_F and Ccpdelcas_R and used for transformation of H. polymorpha WT and cat cells. Transformants were selected on YPD with hygromycin.600nm = 1.8) cultures were diluted to an OD600 nm of 0.1 in the final medium. Survival measurements were started after the cultures reached the stationary phase. This was 16 h for cultures on Glc/AS,Glc/MA and Gly/AS and 40 h for cultures grown on Gly/MeOH/AS unless stated otherwise. The number of cells per ml of culture was determined using CASY\u00ae Model TT (Roche Applied Science). 500 cells were plated on YPD agar plates in triplicate. Plates were incubated at 37\u00b0C for 36 to 48 hours and photographed. Colony numbers were counted using an ImageJ plugin. The number of colony forming units at the first time point (invariably approximately 500) was set to 100%.Cells were extensively precultivated on media containing 0.25% glucose and 0.25% ammonium sulfate. Mid-exponential cultures  for 10.000 events at the speed of 500-1000 events per second using a 488nm laser, 505nm long pass mirror and 525/50nm band-pass filter. FACSDiva software version 6.1.2 was used for data acquisition and analysis. The presented data represent differences in mean fluorescence between stained cells and the background.ROS accumulation was measured using dihydrorhodamine 123 . 10Fluorescence microscopy images were captured using a Zeiss Axioskop 50 with a 100x 1.30 NA Plan Neofluar objective using MetaVue software and a digital camera . GFP signal was visualized with a 470/40 nm bandpass excitation filter, a 495 nm dichromatic mirror, and a 525/50-nm bandpass emission filter. ImageJ and Adobe Photoshop CS2 were used for image analysis and figure preparation. In overlay figures bright field images were false colored in blue to mark cell edges."}
+{"text": "The zinc(II) atoms are further connected via a \u03bc3-hydroxido anion into trinuclear building blocks. The formula unit consists of three zinc cations, four seleno\u00adcyanato anions, one \u03bc3-hydroxido anion, four pyridazine mol\u00adecules as well as one cyanido anion. The asymmetric unit contains half of a formula unit. One of the zinc atoms, two seleno\u00adcyanato anions, two pyridazine ligands and the \u03bc3-hydroxido anion are located on a crystallographic mirror plane, whereas the cyanido anion is located on a twofold rotation axis. Therefore, this anion is disordered due to symmetry. The cyanido anions connect the metal centres into polymeric zigzag chains propagating along the a axis.In the crystal structure of the title compound, [Zn DOI: 10.1107/S1600536810029107/bt5302Isup2.hkl            Structure factors: contains datablocks I. DOI:  crystallographic information; 3D view; checkCIF report            Additional supplementary materials:"}
+{"text": "Staphylococcus aureus. The revised sequence presented here includes 12,436\u00a0bp of additional sequence not present in the previously available phage K genome (GenBank accession no. NC_005880) and updated annotations, and has been reopened at the predicted terminal repeat boundary.Bacteriophage K is a member of the virulent Twort-like group of myophages infecting  Staphylococcus aureus is a Gram-positive opportunistic pathogen, many strains of which are antibiotic resistant  (esistant . Among tS.\u00a0aureus strain Newbould 305. Phage genomic DNA was extracted using previously described methods (Bacteriophage K ATCC 19685-B1) was obtained from the ATCC and routinely propagated on  methods  to produ-B1 was o methods  and Inte methods .The phage K unit genome presented here is 139,831\u00a0bp in length and contains two large regions of additional sequence not present in the published phage K genome (GenBank accession no. NC_005880) . The firS.\u00a0aureus bacteriophage (unpublished data). Phage K is predicted to contain an 8,486-bp terminal redundancy, making the entire packaged genome 148,317\u00a0bp in length.Based on a recent analysis of Twort-like phages, phage K is predicted to possess a terminally redundant nonpermuted genome as demonstrated for other members of the SPO1-like family . The phaKF766114.This genome sequence was deposited into GenBank under accession no."}
+{"text": "The ZnII ion is four-coordinated by two O atoms from two carboxlate ligands, two N atoms from two imidazole ligands. Two ZnII ions are bridged by two carboxyl\u00adate groups in chelating mode, generating a binuclear secondary building unit (SBU), which is further coordinated by two N atoms from two imidazole ligands in monodentate mode. Thus, the binuclear SBUs are further bridged by imidazole ligands in two different directions, giving rise to a chain. The water solvent mol\u00adecules are hydrogen bonded within the chain along the c axis.The asymmetric unit of the title compound, {[Zn(C DOI: 10.1107/S1600536812019642/ds2187Isup2.hklStructure factors: contains datablock(s) test. DOI:  crystallographic information; 3D view; checkCIF reportAdditional supplementary materials:"}
+{"text": "Sinorhizobium meliloti strain 1021, a nitrogen-fixing, root-nodulating bacterial microsymbiont of alfalfa, has a 3.5 Mbp circular chromosome and two megaplasmids including 1.3 Mbp pSymA carrying nonessential \u2018accessory\u2019 genes for nitrogen fixation (nif), nodulation and host specificity (nod). A related bacterium, psyllid-vectored \u2018Ca. Liberibacter asiaticus,\u2019 is an obligate phytopathogen with a reduced genome that was previously analyzed for genes orthologous to genes on the S. meliloti circular chromosome. In general, proteins encoded by pSymA genes are more similar in sequence alignment to those encoded by S. meliloti chromosomal orthologs than to orthologous proteins encoded by genes carried on the \u2018Ca. Liberibacter asiaticus\u2019 genome. Only two \u2018Ca. Liberibacter asiaticus\u2019 proteins were identified as having orthologous proteins encoded on pSymA but not also encoded on the chromosome of S. meliloti. These two orthologous gene pairs encode a Na+/K+ antiporter (shared with intracellular pathogens of the family Bartonellacea) and a Co++, Zn++ and Cd++ cation efflux protein that is shared with the phytopathogen Agrobacterium. Another shared protein, a redox-regulated K+ efflux pump may regulate cytoplasmic pH and homeostasis. The pSymA and \u2018Ca. Liberibacter asiaticus\u2019 orthologs of the latter protein are more highly similar in amino acid alignment compared with the alignment of the pSymA-encoded protein with its S. meliloti chromosomal homolog. About 182 pSymA encoded proteins have sequence similarity (\u2264E-10) with \u2018Ca. Liberibacter asiaticus\u2019 proteins, often present as multiple orthologs of single \u2018Ca. Liberibacter asiaticus\u2019 proteins. These proteins are involved with amino acid uptake, cell surface structure, chaperonins, electron transport, export of bioactive molecules, cellular homeostasis, regulation of gene expression, signal transduction and synthesis of amino acids and metabolic cofactors. The presence of multiple orthologs defies mutational analysis and is consistent with the hypothesis that these proteins may be of particular importance in host/microbe interaction and their duplication likely facilitates their ongoing evolution. Sinorhizobium meliloti is a member of a diverse bacterial family, RhizobiaceaeRhizobiaceae and indeed the Rhizobium type genus Agrobacterium and RhizobiumRhizobiaceae and of the order Rhizobiales are becoming recognized based on 16 S RNA gene sequence data. Many species interact with plants and other eukaryotes to produce important agricultural, economic and/or environmental consequences. \u2018Ca. Liberibacter asiaticus\u2019 is recognized as the causal agent of huanglongbing (HLB), also known as citrus greening disease. This phytopathogen was introduced into the New World from Asia Diaphorina citriCa. Liberibacter asiaticus\u2019 was determined by deep sequencing of total DNA obtained from a single citrus psyllid which contained more than 108 cells of \u2018Ca. Liberibacter asiaticus.\u2019 Phylogenetic analysis indicated that \u2018Ca. Liberibacter asiaticus\u2019 and S. meliloti are close bacterial relatives Ca. Liberibacter asiaticus\u2019 has a single small chromosome of 1.23 Mbps with 36.5% GC content compared to the chromosome of S. meliloti which is 3.65 Mbps and 62.7% GC Ca. Liberibacter asiaticus\u2019 is not available in culture but can colonize intracellularly and move systemically in the phloem vessels of citrus trees D. citri. \u2018Ca. Liberibacter asiaticus\u2019 also systemically and intracellularly colonizes citrus psyllid salivary glands Ca. Liberibacter asiaticus\u2019 has many adaptations to facilitate an intracellular lifestyle in both plant and insect cells Within the order Rhizobiales of the class alpha-Proteobacteria, S. meliloti 1021, free living in the soil, also lives in specialized root nodules in symbiosis with alfalfa, Medicago sativa L., where S. meliloti reduces atmospheric nitrogen and thereby confers plant nitrogen sufficiency. S. meliloti 1021 has a composite genome comprised of the chromosome and two megaplasmids, pSymA and pSymB, of 1.3 Mbps and 1.7 Mbps pSymA and pSymB encode 1293 and 1570 protein-coding genes, respectively Ca. Liberibacter asiaticus\u2019 were compared by protein BLAST against all of the predicted gene products encoded by chromosomal genes of S. meliloti 1021 S. meliloti megaplasmid pSymA had not been previously subjected to protein BLAST compared with the predicted gene products encoded in the \u2018Ca. Liberibacter asiaticus\u2019 genome. As an \u201caccessory\u201d genome component pSymA may have originated from an ancestral plasmid and may have maintained individual and some small blocks of genes originally from host chromosomes. It has long been known that the pSymA megaplasmid of S. meliloti, in contrast to the circular chromosome or the other S. meliloti megaplasmid pSymB, specializes in carrying genes essential for establishing and maintaining intimate intracellular plant interactions with alfalfa. The megaplasmid pSymA itself is self-transmissible and upon transfer confers nodulation (nod), nitrogen fixation (nif) and other symbiotic abilities in related bacteria with a similar genetic background or complement of chromosomal genes Agrobacterium tumefaciens encodes characteristics practically identical to those of Rhizobium microsymbionts, and members of the species also maintain pTi as an accessory genomic component. When bacterial cells recruit or accept transfer of the Ti plasmid or pSymA, either tumor-inducing pathogenicity or nitrogen-fixing symbiotic ability, respectively, is conferred on the host bacterium. Agrobacterium and Rhizobium were combined into a single genus within the Rhizobiaceae largely for this reason and to reject taxonomy based largely on plant interactions determined by extrachromosomal elements, i.e., to avoid plasmid-based nomenclature Predicted gene products of all open-reading frames (ORFs) in the genome of \u2018S. meliloti is a close phylogenetic relative of \u2018Ca. Liberibacter asiaticus,\u2019 it is likely that the two bacteria deploy a similar repertoire of mechanisms for avoiding defenses elicited in host plant cells by their invasion, or, in the case of beneficial root nodule bacteria, recruitment or \u201cwelcomed entry\u201d Ca. Liberibacter asiaticus\u2019 with those encoded by genes carried on the megaplasmid pSymA of S. meliloti strain 1021. The rationale for identifying protein products of \u2018Ca. Liberibacter asiaticus\u2019 genes orthologous to those encoded by genes on pSymA is simply that such genes and their protein products may play direct or indirect role(s) in establishing and maintaining an intimate intracellular interaction with eukaryotes. We have identified a number of such interesting protein products.As pSymA genes are more similar in sequence alignment to those encoded by S. meliloti chromosomal orthologs than to orthologous proteins encoded by genes carried on the \u2018Ca. Liberibacter asiaticus\u2019 genome. There are two very notable exceptions where orthologs are shared only by pSymA and \u2018Ca. Liberibacter asiaticus\u2019 and not with the chromosome of S. meliloti. YP_003064907, the predicted protein product of a \u2018Ca. Liberibacter asiaticus\u2019 gene, is orthologous to NP_435608.2 (E\u200a=\u200a5e-71) encoded by pSymA yet lacks an orthologous protein encoded by the chromosome of S. meliloti (pSymA-encoded NP_436297.2 is orthologous to YP_003064775 (E\u200a=\u200a3e-30) encoded on the chromosome of \u2018Ca. Liberibacter asiaticus\u2019, but neither has an ortholog encoded by the S. meliloti chromosome  at residues 4 to 379 of the 381 amino acid protein pSymA and \u2018Ca. Liberibacter asiaticus\u2019. A third orthologous protein pair comprised of NP_436118.1 (pSymA) and YP_003065337 (\u2018Ca. Liberibacter asiaticus\u2019) is much more similar in amino acid sequence (E\u200a=\u200a1e-134) than is NP_436118.1 and its ortholog encoded by the S. meliloti chromosome, NP_384912 (E\u200a=\u200a1e-22). Bioinformatic analyses revealed that YP_003065337 is a potassium efflux protein. Control of intracellular pH is ascribed in its domain architecture. SMART analysis of YP_003065337 confidently predicts two domains as follows: an amino-terminal sodium/proton exchanger domain (E\u200a=\u200a2.7e-72) from residues 4 to 394 and a non-overlapping TrkA domain (E\u200a=\u200a8.3e-25) for redox regulation, from amino acids 451\u2013566 of the 609 amino acid protein. \u2018Ca. Liberibacter asiaticus\u2019 YP_003065245, a potassium uptake protein, has E\u200a=\u200a0 alignments with both pSymA (NP_436237.1) and S. meliloti chromosomal orthologs   .Ca. Liberibacter asiaticus\u2019, pSymA and the S. meliloti chromosome , and all are annotated as general amino acid transporters with an ATP binding site and other characteristics of an ABC type amino acid transporter. The corresponding genes are dispersed across the pSymA . The enzyme 8-amino-7-oxononanoate synthase (AONS), which is pyridoxal 5\u2032-phosphate-dependent, catalyzes 8-amino-7-oxononanoate synthesis, needed for biosynthesis of biotin. \u2018Ca. Liberibacter asiaticus\u2019 YP_003064762 is a serine hydroxymethyltransferase (SHMT) that catalyzes production of L-serine and tetrahydrofolate with an exceptionally low E-value when compared with its pSymA ortholog NP_436409. YP_003065324 encodes formyltetrahydrofolate deformylase, active in glyoxylate and dicarboxylate metabolism. YP_003064946 is a 3-oxoacyl-(acyl carrier protein) synthase, presumably involved in fatty acid synthesis, orthologous to NodE, a pSymA-encoded protein NP_435712.1 (E\u200a=\u200a4e-63). YP_003064948, annotated as a 3-ketoacyl-(acyl carrier protein) reductase, is a dehydrogenase involved in fatty acid biosynthesis (COG1028). Megaplasmid pSymA encodes 20 proteins similar to YP_003064948 (E\u200a=\u200a8e-78 to E\u200a=\u200a2e-11)  .Ca. Liberibacter asiaticus\u2019 and S. meliloti chromosomes also encode enzymes involved in the synthesis of amino acids (Ca. Liberibacter asiaticus\u2019 YP_003064878 and pSymA NP_435501.1 (E\u200a=\u200a3e-75) have domains expected of threonine synthase, ThrC, a member of the TrpB tryptophan synthase superfamily. YP_003064845 is an acetylornithine transaminase protein, part of the arginine metabolism and lysine biosynthesis pathways. \u2018Ca. Liberibacter asiaticus\u2019 YP_003064846 and its orthologs in S. meliloti, encoded by genes on pSymA and its chromosome, are predicted to be an ornithine carbamoyl transferase, involved in arginine metabolism. YP_003065195 and its S. meliloti homolog are succinyl diaminopimelate desuccinylase involved in lysine biosynthesis. However, NP_436258.1, a similar protein encoded by pSymA, is annotated as an acetylornithine deacetylase, an enzyme which catalyzes a step in arginine metabolism. Plant hosts of \u2018Ca. Liberibacter asiaticus\u2019 are presumed to supply some amino acids to the obligate phytopathogen since certain genes for the biosynthesis of several amino acids were not found in the comparison of the chromosomes of \u2018Ca. Liberibacter asiaticus\u2019 and S. melilotiGenes on pSymA with orthologs on the \u2018no acids . The preCa. Liberibacter asiaticus\u2019 shares genes with pSymA and the S. meliloti chromosomes for several proteins used to export small bioactive molecules (chvD) in Agrobacterium, Bartonella and BrucellaIn addition to the proteins involved in the import or potential export of amino acids and metabolic cofactors, \u2018olecules . These iCa. Liberibacter asiaticus\u2019 genome encodes two nodI homologs, likely involved in exporting lipid virulence factors. YP_003065157, an ATP-binding ABC-type transporter protein with a yhbG domain, is similar to pSymA nodI, a multidrug efflux-like protein that secretes lipids and lipopolysaccarides including cell wall and outer membrane or Nod factor components. Interestingly, pSymA encodes six homologs of YP_003065157, consistent with important roles for these proteins in the S. meliloti/alfalfa interaction. When ABC-type transporters of \u2018Ca. Liberibacter asiaticus\u2019 were blasted against the Transporter Classification Database (http://www.tcdb.org/), YP_003065330, annotated as an ABC-type transporter nucleotide binding/ATPase, was homologous with Q2G2M9 (4.00E-088), a putative multi-drug export ATP-binding/permease protein of Staphylococcus aureus,SAOUHSC_02003. Like YP_003065157, YP_003065330 aligns best with nodI NP_435718.1 (2.00E-021) encoded by pSymA.The \u2018Ca. Liberibacter asiaticus\u2019 YP_003065113, orthologous to pSymA-encoded protein NP_435856.2 (E\u200a=\u200a5e-33), a serine protease DO protease, has a PDZ-metalloprotease domain, a PDZ serine protease domain and a trypsin domain, all fused into a single multi-domain protease product. This protein, a probable virulence factor, is likely to protrude from \u2018Ca. Liberibacter asiaticus\u2019 into the host cell where it can presumably digest host proteins using zinc as a cofactor.\u2018Ca. Liberibacter asiaticus\u2019, pSymA and S. meliloti  are encoded by orthologous genes for a UvrD2 DNA helicase II, or superfamily I DNA and RNA helicase. \u2018Ca. Liberibacter asiaticus\u2019 YP_003065000 and pSymA orthologs NP_436130.1 and NP_436426.1 encodes a XerD recombinase/integrase. YP_003064676, a Tu translation elongation factor, is orthologous to pSymA-encoded NP_435253 (E\u200a=\u200a7e-19) and to NP_435715.1 (E\u200a=\u200a6e-18), and is involved in the synthesis of proteins. YP_003065391 encodes aspartyl/glutamyl-tRNA amidotransferase, an enzyme known to allow formation of correctly charged asparagine or glutamine tRNAs in organisms which lack either or both a asparaginyl-tRNA synthetases or a glutaminyl-tRNA synthetase.\u2018cription . YP_0030cription . Other gCa. Liberibacter asiaticus\u2019 and pSymA share several genes encoding proteins that contribute to a functional architecture of the cell surface (S. meliloti pSymA NP_435574.1 (E\u200a=\u200a8e-14) and the S. meliloti chromosome NP_384380 (E\u200a=\u200a2e-16). Pilus assembly gene clusters on pSymA have orthologs on the \u2018Ca. Liberibacter asiaticus\u2019 chromosome. Proteins YP_003065134 , YP_003065135 , YP_003065137 , YP_003065140  and YP_003065141  are all components of a pilus assembly for \u2018Ca. Liberibacter asiaticus\u2019. YP_003065138, a pilus-associated response regulator receiver protein, likely regulates production of pili based on signals received from the environment.\u2018 surface . A UDP-gCa. Liberibacter\u2019 asiaticus\u2019 and pSymA-encoded NP_436359.1 (E\u200a=\u200a3e-40), could produce cell wall degradation products. YP_003064721 signal peptide protein orthologous to pSymA NP_436359.1 and is a putative membrane-bound lytic murein transglycosylase (E\u200a=\u200a3e-40).A transglycosylase, YP_003064721 in \u2018Ca. Liberibacter asiaticus\u2019 YP_003065356 . A transmembrane protein that also binds penicillin, YP_003065516 is orthologous to pSymA NP_436482.1 (E\u200a=\u200a1e-93). \u201803065356 , a glucoCa. Liberibacter asiaticus\u2019 genome and in the genomes of four other members of the Rhizobiales that were analyzed previously Ca. Liberibacter asiaticus\u2019 chromosomal genes include the following: Chain A, NuoA2 NADH: ubiquinone oxidoreductase YP_003065265 and NP_436080.1 (E\u200a=\u200a9e-14); Chain B, or NuoB2 YP_003065266 and NP_436079 (E\u200a=\u200a6e-48); Chain C, or NuoC2 YP_003065267 and NP_436078.1 (E\u200a=\u200a6e-33); Chain D, or NuoD2 YP_003065268 and NP_436077.1 (E\u200a=\u200a1e-110); Chain E or NuoE2, YP_003065269 and NP_436076.1 (E\u200a=\u200a4e-24) and Chain F or NuoF2 YP_003065271 and NP_436075.1 (E\u200a=\u200a1e-104). Chains G, H and I/J or NuoG2, NuoH2, a multi-subunit ubiquinone oxidase have the following orthologs: YP_003065272 and NP_436074.1 (E\u200a=\u200a2e-62); YP_003065273 and NP_436071.1 (E\u200a=\u200a1e-51), YP_003065274 and NP_436072.1 (E\u200a=\u200a1e-22), and YP_003065275 and NP_436087.2 (E\u200a=\u200a8e-11). YP_003065278 is orthologous with pSym proteins NP_436085.1 and NP_436083.4 annotated as, respectively, a multidomain K/L oxidoreductase, a NADH ubiquinone oxidase (COG1009), or a multi-domain Na+/K+ antiporter, largely synonomous, involved in energy generation, cation efflux or intracellular pH control. \u2018Ca. Liberibacter asiaticus\u2019 YP_003065279 and pSymA NP_436082.1 (E\u200a=\u200a2e- 92) are chain M of ubiquinone oxidoreductase. \u2018Ca. Liberibacter asiaticus\u2019 YP_003065280 and pSymA NP_436081.1 are chain N (E\u200a=\u200a4e-57). Nearly all orthologous proteins shared between pSymA and S. meliloti chromsome have lower E-values aligned with each other than do orthologs shared between pSymA and the \u2018Ca. Liberibacter asiaticus\u2019 chromosome  on the S. meliloti chromosome is a NAD-dependent formate dehydrogenase alpha subunit . The NADH dehydrogenase subunit G  is the second best result. This protein is transcribed and translated from a gene with the expected position in the NADH dehydrogenase MOG and aligns well with the \u2018Ca. Liberibacter asiaticus\u2019 NuoG (e\u200a=\u200a0). A similar finding was obtained when pSymA NP_436085 encoding the NuoL subunit was used in a blast search of the chromosome of S. meliloti. The best matches were to proteins annotated as monovalent cation/H+ antiporter subunits located at different positions on the chromosome. NADH dehydrogenase subunit L  encoded by the chromosome was significantly less well-aligned. As was seen for NuoG, this protein is transcribed from a gene with the expected position in the NADH dehydrogenase MOG and aligns well with the \u2018Ca. Liberibacter asiaticus\u2019 NuoL (e\u200a=\u200a0). Thus these proteins likely function in the role of NuoL. The NADH dehydrogenase gene cluster on pSymA has a different arrangement of genes (An important conserved microsyntenous orthologous gene (MOG) cluster, occurs in the \u2018romosome \u20134. Howevch other . The orgiaticus\u2019 . NuoG anof genes . Genes eCa. Liberibacter asiaticus\u2019, pSymA and the chromosome of S. meliloti, and have roles in energy generation. YP_003065437, is a probable electron transfer flavoprotein-quinone oxidoreductase or FixC. YP_003064703 is orthologous with both pSymA-borne NP_435265.2 and NP_435950.1, which are zinc-dependent NADPH quinone oxidoreductases, COG0604, and are important in energy production. YP_003064781 and YP_003064782, encoded by adjacent genes, are orthologs of NP_436008.1 and NP_436007.1 (E\u200a=\u200a3e-73 and 2e- 77 respectively). Both genes have two adjacent ortholog pairs on both pSymA and the S. meliloti chromosome. YP_003065174 and NP_435375.1 (E\u200a=\u200a3e-76) are annotated as energy yielding FAD-dependent dehydrogenases. YP_003065041 is coproporphyrinogen III oxidase and is orthologous to a pSymA-borne locus NP_435937.1. The protein has two domains; N-terminal Radical-SAM super family and a C-terminal HemN_C superfamily, which obtains electrons from a flavodoxin-like system regenerated by a nicotinamide cofactor-dependent flavodoxin Ca. Liberibacter asiaticus\u2019 YP_003064696 is a NADPH thioredoxin reductase orthologous to NP_436285.1.Other proteins are encoded by genes shared by \u2018Ca. Liberibacter asiaticus\u2019 orthologous to those encoded by genes on pSymA was an hypothesis that such genes and their protein products in all likelihood play direct or indirect role(s) in establishing and maintaining an intimate intracellular interaction with eukaryotes. \u2018Ca. Liberibacter asiaticus\u2019 has an obligatory requirement for a eukaryotic host, and there are multiple orthologs of these genes in Sinorhizobium meliloti. These factors preclude mutagenesis studies at the present time to test this hypothesis. In particular, we discovered that there are several orthologous genes in the \u2018Ca. Liberibacter asiaticus\u2019 genome and on pSymA that encode proteins with vital roles in maintaining intracellular homeostasis for mono- and divalent cations and pH. Genes encoding a Na+/H+ antiporter ++, Zn++ or Co++ in exchange for K+ and H+pSymA and \u2018Ca. Liberibacter asiaticus,\u2019 i.e., not found on the S. meliloti chromosome. In a previous study we compared the proteins encoded by the circular chromosomes of \u2018Ca. Liberibacter asiaticus\u2019 and four other members of the Rhizobiales. In that study the cation efflux antiporter (YP_003064907) was found to be uniquely shared between \u2018Ca. Liberibacter asiaticus\u2019 and phytopathogenic Agrobacterium tumefaciens C58 . The Na+/H+ antiporter (YP_003064775) shared between pSymA and \u2018Ca. Liberibacter asiaticus\u2019 is also shared with 10 species or strains of the Bartonellaceae (6E-60\u20134E-48), but not with the other members of the Rhizobiales studied pSymA and the \u2018Ca. Liberibacter asiaticus\u2019 and S. meliloti chromosomes, is a glutathione-gated potassium efflux protein that exchanges potassium for sodium and protons and thereby modulates cytoplasmic pH pSymA and the \u2018Ca. Liberibacter asiaticus\u2019 genome align with an e-value that is 110 orders of magnitude lower than the alignment of the orthologous proteins encoded by pSymA and the S. meliloti chromosome. Thus, the maintenance of intracellular pH and ion homeostasis are likely critical functions of proteins encoded by genes shared uniquely by \u2018Ca. Liberibacter asiaticus\u2019 and pSymA (but not the S. meliloti chromosome). Other \u2018Ca. Liberibacter asiaticus\u2019 genes with orthologs on pSymA likely encode proteins with either direct or indirect nutritional or energy roles in establishing and maintaining an intimate intracellular interaction with eukaryotic hosts.The premise for identifying protein products of \u2018Ca. Liberibacter asiaticus\u2019 genes with 182 probable orthologs on pSymA and the S. meliloti chromosome. Most of the orthologous proteins encoded by pSymA and the S. meliloti chromosome produced alignments with substantially more significant E-values than orthologous proteins encoded by \u2018Ca. Liberibacter asiaticus\u2019 and pSymA. Individual genes encoded by the reduced genome of \u2018Ca. Liberibacter asiaticus\u2019 may have many orthologs or paralogs carried by the pSymA megaplasmid besides additional similar genes on the chromosome of S. meliloti. An example is YP_003064948, a 3-ketoacyl-(acyl-carrier-protein) reductase with 20 similar proteins encoded by pSymA. Thus, numerous physiological and niche adaptations conferred upon S. meliloti by pSymA may not be available to \u2018Ca. Liberibacter asiaticus.\u2019 However, in a reduced genome such as that of \u2018Ca. Liberibacter asiaticus\u2019, genes that are retained may function with relaxed substrate specificity Ca. Liberibacter asiaticus\u2019 genome. The multiplicity of pSymA orthologs complicates any mutagenesis strategy to definitively establish their roles in S. meliloti.We identified 86 \u2018S. meliloti has conditionally expressed genes that are especially important for nitrogen fixation and nodulation symbiosis. YP_003064695 is a putative regulator of transcription by \u2018Ca. Liberibacter asiaticus\u2019, with 14 related proteins in pSymA, many of which are annotated as LysR transcriptional regulatory proteins. Despite considerable conservation both structurally and functionally, LysR-type transcriptional regulators regulate a diverse set of genes, including those involved in virulence, metabolism, quorum sensing and motility S. meliloti, LysR proteins function in exquisite control of a sequence of events leading to root colonization, initiation of nodulation and subsequent differentiation of nodules containing S. melilotipSymA are not found on the \u2018Ca. Liberibacter asiaticus\u2019 chromosome. Thus, \u2018Ca. Liberibacter asiaticus\u2019 may have much less capability for finely nuanced control of the expression of genes, consistent with its obligatory intracellular lifestyle. Parasitism of citrus by \u2018Ca. Liberibacter asiaticus\u2019 follows direct injection of the pathogen into the host and results in systemic invasion of phloem tissues throughout the host Protein products not constitutively expressed may be considered as candidates for being involved with niche specialization or pathogenesis; S. meliloti are largely specialized for symbiosis and nitrogen metabolism in S. meliloti and include 48 ABC-type transporter genes. The genome of \u2018Ca. Liberibacter asiaticus\u2019 encodes only 11 ABC-type transporter proteins that are orthologous to pSymA-encoded proteins, compared with a total of about 40 ABC-type transporters encoded by the chromosome Ca. Liberibacter asiaticus\u2019 from the host. The genome of \u2018Ca. Liberibacter asiaticus\u2019 is known to be deficient in genes used in the biosynthetic pathways of these compounds Genes with orthologs on both pSymA and the chromosome of Ca. Liberibacter asiaticus\u2019. YP_003064989 is an ABC-type transporter protein encoded by \u2018Ca. Liberibacter asiaticus\u2019 that likely functions to export glucans for osmoprotection. A class of proteins that confer multiple drug resistance, are called \u2018RND\u2019 for Resistance, Nodulation and Division pSymA NodI, a protein that is required for nodulation of alfalfa by S. meliloti. NodI-like proteins secrete lipids and lipopolysaccarides such as cell wall, outer membrane or Nod factor components needed to stimulate the differentiation of nodules by the host. Very interestingly pSymA encodes six homologs of YP_003065157. Orthologs of ABC-type transporters encoded by \u2018Ca. Liberibacter asiaticus\u2019 were identified with the \u201cTransporter Classification Database\u201d (http://www.tcdb.org/). YP_003065330, annotated as an ABC-type transporter with nucleotide binding and ATPase domains was homologous to Q2G2M9 (4.00E-88), annotated as a putative multi-drug export ATP-binding/permease protein, Staphylococcus aureus SAOUHSC_02003. YP_003065330 aligns best with nodI NP_435718.1 (2.00E-021) encoded by pSymA. Thus these proteins at the least share similar structural motifs.ABC-type transporters may also be used to export small molecules by \u2018Ca. Liberibacter asiaticus\u2019, S. meliloti, and pSymA. Effectors directly involved with the establishment of \u2018Ca. Liberibacter asiaticus\u2019 as an intracellular pathogen living within the phloem cells of citrus, may be transferred from the bacterium to its host by type IV secretion involving pilus assembly proteins. If pili are produced by \u2018Ca. Liberibacter asiaticus\u2019, they may also have roles in twitching motility and biofilm formation as in Xylella fastidiosaGenes encoding type IV pili may have evolved from structures used for DNA transfer but it is now known that virulence proteins may be secreted by components of a type IV pili S. meliloti chromosomal respiratory complex I gene set duplicated on pSymA? Plants respond to bacterial pathogens by producing a sustained oxidative burst leading to apoptosis. The respiratory complex protein NuoG can also be used by Mycobacterium tuberculosis to pump protons out of the cell to neutralize such superoxide radicals NuoG in the \u2018Ca. Liberibacter asiaticus\u2019/citrus interaction. Homeostasis with respect to sodium is also of critical importance to both S. meliloti and \u2018Ca. Liberibacter asiaticus\u2019 as discussed above, and in addition to pumping protons out of the cell to create the proton motive force needed for the synthesis of ATP, respiratory complex I of at least some Gram negative bacteria can also transport sodium ions out of the cells Ca. Liberibacter asiaticus\u2019 and S. meliloti are very similar.Why is the entire Ca. Liberibacter asiaticus\u2019 protein products that align significantly with proteins encoded by pSymA also align significantly with predicted protein products encoded by the S. meliloti chromosome. Thus, although present as single copies for the most part in \u2018Ca. Liberibacter asiaticus\u2019, these genes are present in multiple copies per replicon in the free-living microsymbiont. The presence of multiple orthologs in pSymA of \u2018Ca. Liberibacter asiaticus\u2019 proteins such as the amino acid transporter YP_003064586, the fatty acid dehydrogenase YP_003064948 and the LysR transcriptional regulator YP_003064695, suggests that their products are of particular importance in host/microbe interactions, and are likely to be examples of a single protein acquiring multiple functions in an organism with a reduced genome Ca. Liberibacter asiaticus\u2019 very notably also has two or three genes shared with pSymA but not the S. meliloti chromosome. These genes are predicted to produce proteins that generate a proton gradient with ATP-producing potential, maintain sodium and potassium homeostasis, prevent over accumulation of divalent metal cations and maintain intracellular pH. Thus the maintenance of intracellular homeostasis is potentially a vital requirement for successful colonization of citrus and psyllids by \u2018Ca. Liberibacter asiaticus\u2019. In an obligate pathogen, cellular vitality and pathogenesis are tied together.Most \u2018Ca. Liberibacter asiaticus\u2019 was compared with both the circular chromosome and megaplasmids, especially pSymA, of S. meliloti. To identify orthologous proteins, predicted amino acid sequences  were downloaded from NCBI. Using default BLAST parameters, each predicted amino acid sequence from the ORFs identified on pSymA of the S. meliloti 1021 genome was BLASTed against the predicted amino acid sequences of the ORFs on the chromosomes of \u2018Ca. Liberibacter asiaticus\u2019 and Sinorhizobium meliloti strain 1021. Perl scripts and Excel spreadsheets were created to identify hits between genomes with low, negative e-values and to extract annotations from Genbank. In addition, amino acid similarity and length of the blast hit was extracted from each top hit from the BLAST output. First, a general analysis was done extracting predicted protein products having BLAST alignment values of e -10 or lower. Similar or homologous proteins were also identified manually where possible to be consistent with the annotations from the authors of each respective genome. That is, annotations of ORFs were extracted from NCBI annotations of the genome of \u2018Ca. Liberibacter asiaticus\u2019 and megaplasmid pSymA of Sinorhizobium meliloti strain 1021, respectively. Thus, the matching homolog might not have the best e-value or match with the top hit as many proteins share the same domain structure or amino acid similarity, but are functionally quite different. Similarly, for proteins of interest that were unique to each genome, proteins were sorted for positive e-values, signifying that an orthologous protein was not encoded in a respective genome. Orthologous genes from the \u2018Ca. Liberibacter asiaticus chromosome, the S. meliloti chromosome and megaplasmid pSymA were mapped on linear representations of the respective genophores opened at their origins of replication.The genome of \u2018Table S1Proteins shared between the Sinorhizobium meliloti plasmid pSymA, and the \u2018Ca. Liberibacter asiaticus\u2019 and Sinorhizobium meliloti chromosomes. The protein IDs and annotations used by NCBI and the e-values of pairwise comparisons are provided.(XLSX)Click here for additional data file."}
+{"text": "Genomic position (GP) files currently used in next-generation sequencing (NGS) studies are always difficult to manipulate due to their huge size and the lack of appropriate tools to properly manage them. The structure of these flat files is based on representing one line per position that has been covered by at least one aligned read, imposing significant restrictions from a computational performance perspective.PileLine implements a flexible command-line toolkit providing specific support to the management, filtering, comparison and annotation of GP files produced by NGS experiments. PileLine tools are coded in Java and run on both UNIX  and Windows platforms. The set of tools comprising PileLine are designed to be memory efficient by performing fast seek on-disk operations over sorted GP files.http://sourceforge.net/projects/pilelinetools under the GNU LGPL license. Full documentation including common use cases and guided analysis workflows is available at http://sing.ei.uvigo.es/pileline.Our novel toolbox has been extensively tested taking into consideration performance issues. It is publicly available at  In this context, SAMtools [Nowadays, commercially available NGS technologies are able to generate in a single run millions of DNA short reads producing a gigabasepair (Gbp) scale throughput at relatively low cost . A cruciSAMtools  is a wel9 lines for human genome) and the lack of specific tools able to efficiently manage them from a disk, memory and CPU point of view.As an example, Ding et al.  have chai) full standard annotation with human dbSNP, HGNC Gene Symbol and Ensembl IDs, (ii) custom annotation through standard .bed or .gff files, (iii) two sample (i.e.: case VS control) and n sample comparison at variant level, (iv) generation of SIFT [v) a genotyping quality control (QC) test for estimating performance metrics on detecting homo/heterozygote variants against a given gold standard genotype [Starting from our experience in giving direct support to wet-lab users requiring NGS analysis we have developed PileLine, a novel and flexible command-line toolbox for efficient management, filtering, comparison and annotation of GP files. The toolbox has been designed to be memory efficient by performing fast seek on-disk operations over sorted GP files. Based on the combination of basic core operations, PileLine provides several functionalities, including  processing and annotation, implementing simple but reusable operations over input GP files and (ii) analysis, giving support to more advanced and specific requirements  containing the chromosome name and the coordinate position as the two first columns .The second design principle of PileLine is focussed on flexibility and modularity. Thus, PileLine tools may be combined with standard UNIX commands allowing custom data analysis workflows. Moreover, the modular design of our toolbox facilitates the inclusion of additional functionalities. With respect to the file formats, while PipeLine toolbox contains 10 command-line utilities that have been designed to be memory efficient by performing on-disk operations over sorted GP files. By combining their execution using different arguments and several options the user is able to sketch and execute diverse workflows that can be enhanced by using third party software applications. Here we report several example PileLine applications, further commands and examples of use can be found on the PileLine web site:.pileup files:a) Case-Control comparisons working with pileline-2smc.sh -a <Control1.pileup> -b <Case1.pileup> -v <Control1.variants.pileup> -w <Case1.variants.pileup> -o <myoutput1.txt>$ sh YOUR_PATH_TO_PILELINE/cmd/N sample comparisons reporting consistent variants amongst .pileup files:b) pileline-nsmc.sh --a-samples <Control1.variants.pileup>,<Control2.variants.pileup>,<Control3.variants.pileup> --b-samples <Case1.variants.pileup>,<Case2.variants.pileup>,<Case3.variants.pileup> -o <mycommonvariants_in_Cases.txt>$ sh YOUR_PATH_TO_PILELINE/cmd/c) Full annotation of GP files with human dbSNP:pileline-fastjoin.sh -a <GP_file.txt> -b ./dbSNP_36.3.txt --left-outer-join ><mydbSNPannotation1.txt>$ sh YOUR_PATH_TO_PILELINE/cmd/.bed or .gff files.#HGNC Gene Symbol, Ensembl IDs and custom annotations are also allowed and may be supplied through standard d) Generate input for external mutational effects prediction software (i.e. SIFT):pileline-pileup2sift.sh -i <myfile.pileup> > <mysiftinput.txt>$ sh YOUR_PATH_TO_PILELINE/cmd/# Polyphen2 and Firestar inputs are also allowed.e) Print a given range of a GP file (without indexing).pileline-fastseek.sh -p <GP_file> -s chr10:100:10000 ><mychrrange.txt>$ sh YOUR_PATH_TO_PILELINE/cmd/fastseek command showed good performance being able to retrieve 1400 random positions per second on a file of ~174 millions of lines . This behaviour allows, for example, to retrieve all positions from a .pileup file containing known SNPs (~4 million) in approximately 45 minutes consuming less than 150 Mb of RAM.PileLine performs efficiently on a standard PC , where initial tests with the 2N), where N is the number of lines of the input GP file.Although an optimal search performance could be attained by using auxiliary indexes, this approach requires an additional step for building the supporting files. Moreover, the performance degrades linearly as the input GP file grows in size, and its generation takes a considerable amount of time. PileLine was designed to avoid indexing but, by performing binary searches instead of sequential searches (taking advantage of sorted GP files), it scales reasonably well since its complexity is OProgramming language: JavaOther requirements: Java Runtime Environment (JRE) 1.6, Apache Ant 1.7License: GNU LGPLDG-P programmed the PileLine application. GGL provided use cases, tested the usability of the software and generated PileLine documentation. MRJ tested the performance of PileLine tools. DG-P and GGL wrote the paper while FFR and DGP provided comments and discussion. All authors read and approved the final manuscript.Example output of a genotyping test for quality control. Genotest metrics table description. It may be obtained by using --print-help-table argument.Click here for file"}
+{"text": "Vibrio cholerae is complex, with ToxRS being an important part of the regulatory cascade. Additionally, ToxR is the transcriptional regulator for the genes encoding the major outer membrane porins OmpU and OmpT. ToxR is a transmembrane protein and contains two cysteine residues in the periplasmic domain. This study addresses the influence of the thiol-disulfide oxidoreductase system DsbAB, ToxR cysteine residues and ToxR/ToxS interaction on ToxR activity. The results show that porin production correlates with ToxR intrachain disulfide bond formation, which depends on DsbAB. In contrast, formation of ToxR intrachain or interchain disulfide bonds is dispensable for virulence factor production and in vivo colonization. This study further reveals that in the absence of ToxS, ToxR interchain disulfide bond formation is facilitated, whereat cysteinyl dependent homo- and oligomerization of ToxR is suppressed if ToxS is coexpressed. In summary, new insights into gene regulation by ToxR are presented, demonstrating a mechanism by which ToxR activity is linked to a DsbAB dependent intrachain disulfide bond formation.Virulence factor production in  Vibrio cholerae is a Gram-negative, facultative anaerobic bacterium. It is the causative agent of cholera, which is endemic in India, Bangladesh, Southeast Asia, Africa and South America V. cholerae bacteria from the environment through contaminated food or water supplies V. cholerae bacteria pass through the gastric acid compartment of the stomach, penetrate the mucus lining of the intestinal epithelia and start colonizing the small intestine. This compartment contains growth inhibitory substances, such as bile salts and organic acids and also factors of the innate immune system, e.g., complement secreted by intestinal epithelial cells V. cholerae has developed the ability to survive, colonize and produce virulence factors toxTctx and tcp loci, as well as additional genes V. cholerae strains lacking ToxT or ToxR do not produce CT or TCP and are avirulent ompT and ompU, which encode the outer membrane proteins OmpT and OmpU. Both porin genes are inversely regulated ompU transcription is activated, whereas ompT is repressed by ToxR as determined by OMP analysis and in vivo colonization toxT promoter, ToxR and TcpP binding occurs such that ToxR binds on the distal end and acts as a \u201cscaffold\u201d protein by facilitating TcpP binding adjacent to the RNA polymerase binding site ompU and toxT transcription. Based on this observation it was proposed that the orientation of ToxR on its corresponding operators differs for the ompU and toxT promoter regions Extensive studies of cholera pathogenesis revealed that production of the main virulence factors, namely cholera toxin (CT) and toxin-coregulated pili (TCP), is coordinated by a regulatory network Escherichia coli, ToxR-fusion proteins containing defined dimerization signals, were transcriptionally active. However, this activity was not conclusively confirmed in V. cholerae using the same or similar ToxR variants toxS gene is cotranscribed downstream of toxRtoxS negatively influence the transcriptional activity of ToxR The N-terminus of ToxR is located in the cytoplasm and contains the DNA-binding motif, followed by a transmembrane domain and then the periplasmic C-terminus dsbAB and cysteine to serine substitutions in ToxR and ToxR activities were determined for virulence factor and porin expression.In this report, the molecular mechanisms that control activity of the membrane bound transcription factor ToxR were addressed using epidemic O1 El Tor and O395 classical strains. The study includes the interplay between ToxR and ToxS and the formation of ToxRS heterodimer. Furthermore, the redox state of ToxR cysteines were characterized in strains encoding knockout mutations in Mice were used for competition colonization experiments in strict accordance to the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health, the national \u201cBundesgesetzblatt fuer die Republik Oesterreich\u201d. Animal protocol (39/158 ex 2000/10), has been approved by the Austrian Federal Ministry of Science and Research Ref. II/10b and the Committee on the Ethics of Animal Experiments of the University of Graz. Housing of mice was conducted with food and water ad libitum and monitored in accordance with the rules of the Institute of Molecular Biosciences at the University of Graz.toxR, the suicide vectors pCVD442 and pKEK229 were used. If not stated otherwise, E. coli and V. cholerae strains were transformed by electroporation. E. coli strain SM10\u03bbpir was used to introduce plasmids into V. cholerae by conjugation. V. cholerae P27459-S, a spontaneous streptomycin resistant mutant of V. cholerae O1 El Tor clinical isolate P27459 V. cholerae O1 classical clinical isolate were used as WT strains in all experiments. E. coli strains were grown using LB broth at 37\u00b0C. Unless stated otherwise, V. cholerae strains were grown using LB broth or minimal medium M9 supplemented with glycerol (0.4%) as a carbon source at 37\u00b0C. For optimal induction of virulence genes, V. cholerae O1 El Tor was grown using AKI conditions V. cholerae O1 classical strain was grown in LB broth (pH 6.5) at 30\u00b0C E. coli refers to XL1-Blue, V. cholerae to O1 El Tor isolate P27459-S and classical to classical O1 isolate O395.Strains and plasmids used in this study are listed in Oligonucleotide primers used in this study are listed in E. coli SM10\u03bbpir and were subsequently conjugated into V. choleraedsb deletion mutants, a kmr cassette was derived from pKan\u03c0, EcoRI fragment and ligated in between the dsb up and down flanking DNA fragments. The resulting dsb mutant strains carried chromosomal replacements of the dsb genes by kmr cassettes. The correct deletion for all mutants was confirmed by PCR  (data not shown). Insertion mutants where constructed by using suicide plasmid pGP704 E. coli SM10\u03bbpir and further conjugated into V. cholerae. Correct homologous integration of the plasmid into the chromosome was confirmed by PCR  (data not shown) and maintenance of the plasmid was ensured by culturing the respective strains on media containing ampicillin.Deletion mutations were generated as described by Donnenberg and Kaper toxR was generated by using the oligonucleotide KpnI_toxRC293S_3\u2032_ FLAG, which contains the required amino acid substitution for cysteine to serine residue. The C236S mutation was generated by SOE PCR (splicing by overlap extension), using pFLAGtoxR as a template and oligonucleotide pairs HindIII_toxR_5\u2032_FLAG and toxRC236S_3\u2032 as well as toxRC236S_5\u2032 and KpnI_toxRC293S_3\u2032_FLAG HindIII and KpnI and ligated into similarly digested pFLAG-MAC\u2122. pFLAGtoxRS and pFLAGtoxRCCS were constructed by using P27459-S \u0394toxR::FLAGtoxR or P27459-S \u0394toxR::FLAGtoxRCC as templates and oligonucleotides KpnI_toxRS_5\u2032_FLAG and BglII_toxRS_3\u2032_FLAG. PCR fragments were digested with KpnI and BglII and ligated into similar digested pFLAG-MAC\u2122. The resulting plasmids were digested with BamHI to integrate ompU or toxT operator sites. ompU and toxT operator fragments were amplified with oligonucleotides BamHI_ompU_5\u2032 and BamHI_ompU_3\u2032 or BamHI_toxT_5\u2032 and BamHI_toxT_3\u2032, respectively, and also digested with BamHI. Similar constructs were digested with AccI to generate a 264 bp deletion of toxS. Constructs were confirmed by PCR and DNA sequencing (data not shown).For construction of expression plasmids, using pFLAG-MAC\u2122 or pBAD18, the respective genes were amplified by PCR using oligonucleotides labeled in the format X_Y_5\u2032 and X_Y_3\u2032, in which X stands for the restriction enzyme and Y for the respective genes, toxR and amino acid substitution mutants were constructed by using SOE PCR. For amplification of PCR fragments, pFLAGtoxR and pFLAGtoxRCC were used as templates. Oligonucleotides c_FLAGtoxR_5\u2032_F2 and c_FLAGtoxR_3\u2032_F2 respectively c_FLAGtoxRC293S_3\u2032_F2 were used for generation for P27459-S \u0394toxR::FLAGtoxR and P27459-S \u0394toxR::FLAGCCtoxR. For construction of P27459-S \u0394toxR::FLAGtoxR \u0394toxS oligonucleotides c_FLAGtoxR_5\u2032_F2 and c_FLAGtoxRtoxS_3\u2032_F2 were used. Fragments with about 800 bp each of flanking DNA regions of toxR with one end complementary to the first PCR fragment (see above) were amplified by PCR using oligonucleotides SacI_toxRS_1 and c_FLAGtoxR_3\u2032_F1 and c_FLAGtoxR_5\u2032_F3 or c_FLAGtoxRC293S_5\u2032_F3 and XbaI_toxRS_4 for P27459-S \u0394toxR::FLAGtoxR and P27459-S \u0394toxR::FLAGCCtoxR, respectively. For construction of P27459-S \u0394toxR::FLAGtoxR \u0394toxS oligonucleotides c_FLAGtoxR toxS_5\u2032_F3 and XbaI_toxRS_4 were used. The three PCR products were used as templates in the second PCR with SacI_toxRS_1 and XbaI_toxRS_4 and the resulting PCR fragments were digested with SacI and XbaI and ligated into pCVD442 that had been digested with same restriction enzymes. Resulting ligation products were transformed into E. coli SM10\u03bbpir and were further transferred into V. cholerae \u0394toxR or \u0394toxRS by conjugation. V. cholerae cells in which integration of the plasmid occurred by homologous recombination via one of the two fragments and a second homologous recombination step via the other fragment resulted in mutant strains harboring an integration of either FLAG-tagged toxR or FLAG-tagged toxR cysteine to serine substitution mutant in the toxR gene locus. The correct integration of all mutants was confirmed by PCR and DNA sequencing (data not shown).Chromosomal FLAG-tagged V. cholerae strains were prepared from cells either grown in LB medium or minimal medium M9 glycerol (0.4%). Cells were harvested by centrifugation, washed with HEPES pH 7.5 (10 mM) and lysed by sonification on ice according to standard protocols . OMP preparations were performed as described previously Proteins of the membrane and outer membrane of E. coli or V. cholerae cultures were either grown in LB, induced with IPTG (0.005 to 0.05 mM) for one to two hours or in M9 glycerol (0.4%) minimal media and induced with IPTG (0.005 to 0.05 mM) for 6.5 h. Equal amounts of cells were harvested by centrifugation in an Eppendorf centrifuge. For immunoblot analyses of whole cell extracts the overall protein contents were assessed to contain similar protein levels by SDS-PAGE coomassie blue staining. Cell pellets were washed with media, resuspended in sample buffer either with or without the reducing agent \u03b2-mercaptoethanol and boiled for 10 min. OMP preparations and whole cell extracts were then separated by SDS-PAGE in polyacrylamide (15%) gels, using Mini-PROTEAN Tetra cell . For detection of membrane and outer membrane proteins, equal protein amounts  were loaded. After SDS-PAGE, proteins were either stained with Coomassie brilliant blue as previously described For whole cell extracts, rpoBrpoB or 16 s rRNA reference sample of the WT. Values above or below 1 indicate that the transcript is present in higher or lower numbers, respectively, in the mutant compared to the WT strain.Primers used for quantitative reverse transcriptase PCR (qRT-PCR) are listed in V. cholerae O1 El Tor was grown in AKI and V. cholerae O1 classical in LB broth (pH 6.5) at 30\u00b0C. Cells were incubated with CTX-km\u03a6 lysate for 30 min and subsequently plated in parallel on LB plates with sm and sm/km to determine transduction frequency.TCP production was determined by phage transduction frequency, utilizing phage CTX-km\u03a6 and TCP producing cells M1 ELISA V. cholerae O1 El Tor was grown in AKI and V. cholerae O1 classical in LB broth (pH 6.5) at 30\u00b0C to induce CT production. Cells were removed from CT containing supernatants by centrifugation and supernatants were stored at \u221220\u00b0C. ELISA plates  were coated with GM1 ganglioside (10 \u00b5g/ml) in Na2CO3 (10 mM)  for 4 h at 37\u00b0C and washed four times with PBS-T pH 7.4 consisting of NaCl (137 mM), KCl (2.7 mM), Na2HPO4\u00d72 H2O (8.1 mM), KH2PO4 (1.76 mM), Tween-20 (0.05%). Free binding sites were blocked with BSA (4 mg/ml) for 1 h at RT. After washing as described above, CT containing supernatants were diluted in PBS and added to the plate. Additionally, purified CT in PBS  was inoculated in separate wells to generate a standard curve. ELISA plates were incubated with supernatants and purified CT for 1 h at RT and were washed again as described above. After incubation with the primary antibody , diluted 1\u22362,000 in PBS containing BSA (4 mg/ml) for 1 h at RT, ELISA plates were washed four times with PBS-T. After incubation with the secondary antibody , diluted 1\u22365,000 in PBS containing BSA (4 mg/ml) for 1 h at RT and subsequent washing, the ELISA plates were incubated with TMB Substrate Reagent Set  for detection of CT. The reaction was stopped, by adding H3PO4 (1 M) and ELISA plates were measured at OD450 by using a microplate reader .CT production in culture supernatants was determined by the ganglioside G\u2212) and isogenic WT (LacZ+) strains. The competitive index (CI) is the ratio calculated of CFU of mutant to WT recovered at 24 h, normalized to the input ratio.Competition assays for intestinal colonization in infant C57Bl/6 mice  (in vivo) and for growth in LB broth (in vitro) were performed as previously described P values of <0.05.For data analyses Mann-Whitney U test, Kruskal-Wallis followed by Dunns test of selected pairs of columns or unpaired t test were used. Differences were considered significant for V. cholerae WT strain and corresponding dsbA, dsbB and dsbC deletion mutants. WT and the dsbA mutant showed similar growth kinetics under the conditions tested .ToxR contains two cysteine residues in its periplasmic domain at amino acid position 236 and 293 s tested . It is e mutants . Complem strains . ImportatoxR, ompU and ompT transcription in WT and dsbA mutant strains derived from late stationary grown cells (72 h) in M9 glycerol medium  fused in frame to the toxR 5\u2032 end, termed as FLAG-tagged toxRS. Cysteine residue 236 in ToxR was shown to contribute to ToxR intrachain disulfide bond formation CCStoxR mutant. Furthermore, to address ToxS function on ToxR disulfide bond formation a control plasmid pFLAGtoxRS(\u0394264) was constructed, harboring a 264 bp internal deletion in toxS, yielding an incomplete ToxS protein. Immunoblot analysis was performed using specific FLAG-antibodies to detect ToxR proteins. The latter were produced by plasmid encoded FLAG-tagged toxRS, toxRS(\u0394264) and CCStoxR in a V. cholerae \u0394toxRS strain grown in LB medium. Cell extracts were sampled in Laemmli buffer, both with and without the reducing agent \u00df-mercaptoethanol. In the presence of \u00df-mercaptoethanol, only a single reduced ToxR or ToxRCC protein band with 35 kDa was observed (data not shown). If cell extracts were not treated with reducing agent, then additional ToxR protein bands became visible. As shown (CCStoxR expression (toxRS(\u0394264), the formation of ToxR disulfide bond dependent homodimer and oligomers was enhanced (E. coli (see below) and demonstrates that toxRS coexpression negatively influences the formation of cysteinyl dependent ToxR homodimers and oligomers.In order to monitor ToxR protein in toxRS was monitored in V. cholerae \u0394toxRS and +/\u2212dsbA strain backgrounds. Cells were grown under two different conditions, M9 glycerol and LB broth. As shown . Therefore, single copy gene number and chromosomally expressed toxR and CCtoxR were tested under different growth conditions. FLAG-tagged toxR and CCtoxR gene alleles were transferred into the chromosomal toxR expression locus of a V. cholerae \u0394toxR strain . Subsequently, chromosomally produced FLAG-tagged ToxR proteins were collected from membrane extracts derived from cells grown to stationary phase in LB broth and monitored by immunoblot analysis. It is important to note that ToxR proteins could be detected under these conditions, however, the signal intensity was weak such that monitoring ToxR was only possible under high magnification sensitivity . Considering the recently published effects of NRES dsbA mutation does not disrupt NRES activated OmpU production, but CCtoxR mutation does. To quantify the effects of CCtoxR on transcription, ompU, ompT and toxR mRNA levels were determined by qRT-PCR analysis of cells grown in M9 glycerol medium for 24 h. The results showed that toxR gene transcript levels of FLAG-tagged toxR compared to CCtoxR were similar , a novel and stable SDS-resistant FLAG related protein band of about 55 kDa was observed   and FLAG-tagged CCtoxR (LacZ+) strains was administered orally to infant mice (in vivo). As a control, LB broth was inoculated with this mixture and incubated for 24 h at 37\u00b0C (in vitro). Competitive indices from at least four independent competition experiments were obtained, CCtoxR mutant strain grown in AKI medium showed a similar porin pattern as obtained for a WT strain . Furthermore, no difference was observed between toxR or CCtoxR transcription levels, hence we argue that CCtoxR represents a mutation affecting ToxR activity. Therefore, we provide evidence that DsbA activity is targeting cysteine residues of ToxR and this influences ToxR activation.To confirm V. cholerae strain was grown in minimal medium OmpT was expressed as a major porin and no OmpU protein was observed. In LB broth, the porin production pattern was reversed. If NRES amino acids were added to the minimal medium, the porin production profile appeared similar to that observed for cells cultured in LB broth. Mey et al. further showed that NRES amino acids added to the minimal medium led to elevated toxR transcription, which they concluded, is the cause for the switched porin production. As shown in here, we also confirmed the NRES effect in showing that addition of NRES to M9 glycerol medium enhances OmpU production for WT and FLAG-tagged toxR strains. Also FLAG-tagged CCtoxR mutant cells responded to NRES but to a lower extend. However, CCtoxR mutant cells grown in AKI showed maximum OmpU production, similar as observed for FLAG-tagged toxR or WT strains. Hence, we assumed that under AKI growth conditions, elevated toxR and CCtoxR transcription would occur, explaining the increase in OmpU production. This assumption was not confirmed, since qRT-PCR analysis of WT cells cultured in M9 glycerol compared with AKI cultures showed no significant difference in toxR transcription. These data indicate, that other mechanisms exist, which influence ToxR activity. For example, we cannot rule out, that DsbAB activity influences ompU post-transcriptional, -translational or secretion pathways, neither can we exclude that dsbA or CCtoxR mutants are solely responsible for the observed decreased ompU transcription. Therefore, other yet unknown factors may contribute or facilitate OmpU expression, especially under growth conditions such as AKI or LB broth media. Also to mention is that if CCtoxR is expressed from multi-copy plasmid, then we observed that cells were producing high OmpU levels. Similar behavior of ToxR activity was observed earlier toxR is expressed from its chromosomal loci.As shown recently toxS was coexpressed along with toxR, cysteinyl dependent ToxR homodimer and oligomer formation was decreased as shown in our results. The latter finding is supported by earlier published data toxR overexpression, since this form was not observed earlier, if chromosomal expressed toxR was monitored E. coli, it was SDS- and heat-resistant and was stably detectable without any cross-linking chemistry. This finding is not exceptional, since other SDS-resistant protein-protein interactions are known to occur V. cholerae. To further characterize this, defined ToxR binding sites, located upstream of ompUtoxTtoxRS expression plasmids and analyzed in E. coli. Interestingly, if ToxR binding sites were present, heterodimer signals were strongly diminished, indicating that the presence of operator sites negatively interfered with ToxRS heterodimerization. Earlier data also described ToxRS heterodimerization in V. cholerae, using cross-linking chemistry V. cholerae contained multiple ToxR regulated genes If CCtoxR and toxR strains. This indicated that under virulence factor inducing conditions, ToxRCC can participate in the regulation cascade of the virulence factor production system. Moreover, the CCtoxR mutant in O1 El Tor V. cholerae strain did also not display a phenotype for in vivo colonization. In summary, this suggests that cysteine associated ToxR forms are dispensable for ToxR activity under virulence factor inducing conditions. Thereby, we argue that multiple ToxRS activation conditions may exist, which do not rely on thiol-dependent disulfide bond forms. So far, we cannot explain why porin gene regulation seems to respond sensitive to ToxR disulfide bond formation, whereas virulence gene expression is not. Recently, a possible hint was provided by Morgan and colleagues toxR point mutations, which differentially target toxT and ompU transcription. All of their toxR mutations were found in the cytoplasmic part of ToxR. For example, for amino acid residues V71, F69 and E39 it was proposed that they interfere with RNAP engagement on the ompU promoter, rather then with DNA binding. Interestingly, the same point mutants had much less effect on toxT activation. In contrast, amino acids R65 and D73 affected more severely ompU activation than toxT. Based on these results, the authors concluded that the facing of ToxR upon operator binding seems differently oriented for the promoter regions of ompU and toxT. Thereby, it can only be speculated that ToxRCC protein configuration is sufficient to serve for toxT activation by correctly facing to the toxT promoter, but becomes conditionally insufficient for ompU activation for yet unknown reasons.Finally, we tested virulence factor production in O1 El Tor or classical strains and neither the level of CT, nor CTX-km\u03a6 transduction frequencies showed any significant differences between FLAG-tagged dsb gene transcription is under the control of the \u03c3E-membrane stress pathway in V. cholerae and additionally of the Cxp regulon as shown in E. coli\u2212 derived from disulfide bond formations Finally, the question arises whether ToxR may represent a thiol-based redox switch regulator. Although, several periplasmic proteins depend on DsbAB folding activity to obtain function, e.g. PhoA Figure S1dsbA mutant strains. Shown are growth curves (OD600 left Y axis) and colony forming units (cfu/ml right Y axis) of WT strain P27459-S and corresponding \u0394dsbA strains over 72 h in M9 minimal media supplemented with glycerol (0.4%).Growth and cell survival for P27459-S and \u0394(TIF)Click here for additional data file.Figure S2V. cholerae O1 classical strain O395. Arrows indicate OmpU and OmpT. Panel A, shown are WT O395, \u0394toxR, \u0394dsbA and \u0394dsbB strains grown to stationary phase in M9 glycerol medium. Panel B, shown are WT O395, \u0394toxR, \u0394toxR::FLAGtoxR and \u0394toxR::FLAGCCtoxR strains. Cells were grown to stationary phase in LB broth medium.. Arrowheads on the right indicate a ToxR independent protein band used as loading control.OMP profiles of (TIF)Click here for additional data file.Figure S3toxR and CCtoxR in V. cholerae P27459-S and mutant strains \u0394toxR and \u0394toxRS of isolated membrane fractions. Cross-reacting background bands are marked with asterisks and ToxR is indicated by an arrow. Molecular size markers are indicated on the left. Immunoblot analysis was performed at least two times, and results were reproducible.Detection of chromosomal encoded FLAG-tagged ToxR expressed fusion proteins. Immunoblot analysis is shown, using anti-FLAG antibodies to detect chromosomal expression of FLAG-tagged (TIF)Click here for additional data file.Figure S4toxR and porin gene ompU in V. cholerae P27459-S grown in M9 glycerol compared to AKI conditions. The WT strain was cultured in M9 glycerol medium to mid log growth phase and shifted to fresh M9 glycerol or AKI medium for 45 min. Subsequently mRNA was prepared and qRT-PCR was performed for the ompU porin gene and also for toxR. mRNA level of 16S rRNA was determined as a reference and correlated with the mRNA level of the genes of interest. Experiments were performed with three independent samples and data represent means and standard deviations. The unpaired t test was used, P<0.05.Transcriptional analysis of (TIF)Click here for additional data file."}
+{"text": "Bioinformatic analyses typically proceed as chains of data-processing tasks. A pipeline, or 'workflow', is a well-defined protocol, with a specific structure defined by the topology of data-flow interdependencies, and a particular functionality arising from the data transformations applied at each step. In computer science, the dataflow programming (DFP) paradigm defines software systems constructed in this manner, as networks of message-passing components. Thus, bioinformatic workflows can be naturally mapped onto DFP concepts.e.g., for biomolecular sequences, alignments, structures) and functionality .To enable the flexible creation and execution of bioinformatics dataflows, we have written a modular framework for parallel pipelines in Python ('PaPy'). A PaPy workflow is created from re-usable components connected by data-pipes into a directed acyclic graph, which together define nested higher-order map functions. The successive functional transformations of input data are evaluated on flexibly pooled compute resources, either local or remote. Input items are processed in batches of adjustable size, all flowing one to tune the trade-off between parallelism and lazy-evaluation (memory consumption). An add-on module ('NuBio') facilitates the creation of bioinformatics workflows by providing domain specific data-containers (http://muralab.org/PaPy, and includes extensive documentation and annotated usage examples.PaPy offers a modular framework for the creation and deployment of parallel and distributed data-processing workflows. Pipelines derive their functionality from user-written, data-coupled components, so PaPy also can be viewed as a lightweight toolkit for extensible, flow-based bioinformatics data-processing. The simplicity and flexibility of distributed PaPy pipelines may help users bridge the gap between traditional desktop/workstation and grid computing. PaPy is freely distributed as open-source Python code at  To this end, NuBio represents all biomolecular data as hierarchical, multidimensional entities, and uses standard programming concepts (such as 'slices') to access and manipulate these entities. For instance, in this frame-work, a single nucleotide is a scalar object comprised of potentially n-dimensional entities , a DNA sequence or other nucleotide string is a vector of rank-1 objects (nucleotides), a multiple sequence alignment of n sequences is analogous to a rank-3 tensor (an (n-dim) array of (1-dim) strings, each composed of characters), and so on. The following blocks of code tangibly illustrate these concepts (output is denoted by '- > '):As outlined in the earlier from nubio import NtSeq, CodonSeqfrom string import upper, lower# A sequence of eight codons:my_codons_1 = CodonSeq('GUUAUUAGGGGUAUCAAUAUAGCU')# ...and the third one in it, using the 'get_child' method:my_codons_1_3 = my_codons_1.get_child(2)# ...and its raw  representation as a byte string# (ASCII char codes):print my_codons_1_3-> Codon# Use the 'tobytes' method to dump as a char string: print my_codons_1_3.tobytes-> AGG# 'get_items' returns the codon as a Python tuple: print my_codons_1.get_item(2)-> # The string 'UGUGCUAUGA' isn't a multiple of 3 (rejected# as codon object), but is a valid NT sequence object:my_nts_1 = NtSeq('UGUGCUAUGA')# To make its (DNA) complement:my_nts_1_comp = my_nts_http://1.complement print my_nts_1_complement -> ACACGATACT# Sample application of a string method, rendering the# original sequence lowercase (in-place modification):my_nts_1.str(method=\"lower\")print my_nts_1.tobytes -> ugugcuauga# Use NuBio's hierarchical representations and data conta-# iners to perform simple sequence(/string) manipulation:# grab nucleotides 3-7 (inclusive) from the above NT string:my_nts_1_3to7 = my_nts_1.get_chunk, slice)) print my_nts_1_3to7.tobytes-> ugcua# Get all but the first and last (-1) NTs from the above NT# string:my_nts_1_NoEnds = my_nts_1.get_chunk, \\ slice))print my_nts_1_NoEnds.tobytes-> gugcuaug# Get codons 2 and 3 (as a flat string) from the codon string:my_codons_1_2to3 = my_codons_1.get_chunk, \\slice))print my_codons_1_2to3.tobytes -> AUUAGG# Grab just the 3rd (wobble) position NT from each codon:my_codons_1_wobble = my_codons_1.get_chunk, nslice))print my_codons_1_wobble.tobytes -> UUGUCUAUe.g., the genetic code). In the following example, a sequence of codons is translated:For general convenience and utility, NuBio's data structures can access built-in dictionaries provided by this package # Instantiate a (translate-able) CodonSeq object from this:codon_start_stop = CodonSeq(nt_start_stop.data)# ...and translate it:print(codon_start_stop.translate) ->-> AaSeq(M*)print(codon_start_stop.translate(strict = True))-> AaSeq(M)The follflowing block illustrates manipulations with protein sequences:from nubio import AaSeq, AaAln# Define two protein sequences. Associate some metadata  with the second one, as key/value pairs:seq1 = AaSeq('MSTAP')seq2 = AaSeq# Create an 'alignment' object, and print its sequences:aln = AaAln)for seq in aln: print seq-> AaSeq(MSTAP)-> AaSeq(M-TAP)# Print the last 'seq' (\"M-TAP\"), sans gapped residues# :print seq.keep(seq.meta['ALPHABET'])-> AaSeq(MTAP)# Retrieve metadata associated with 'my_key': aln[1].meta['my_key']-> 'my_data'via the produce/consume/spawn idiom  and a sample workflow that illustrates papy/nubio integration . The prototype pipeline includes commonly encountered workflow features, such as the branch/merge topology. Most importantly, the example code is annotated with descriptive comments, and is written in a highly modular manner .To assist one in getting started with bioinformatic pipelines, PaPy also includes a generic pipeline template . A possg. [e.g. ). Though, Stride  to compu, Stride  for ener(i) computational overhead from serialization; (ii) data transmission over potentially low-bandwidth/high-latency communication channels; (iii) process synchronization, and the associated waiting periods; and (iv) a potential bottelneck from the sole manager process  complete, step-by-step installation instructions for the Unix/Linux platform; (ii) a Quick Introduction describing PaPy's basic design, object-oriented architecture, and core components (classes), in addition to hands-on illustrations of most concepts via code snippets; (iii) an extensive presentation of parallelism-related concepts, such as maps, iterated maps, NuMap, and so on; (iv) a glossary of PaPy-related terms; and (v) because PaPy is more of a library than a program, a complete description of its application programming interface (API).In addition to full descriptions of the generic PaPy pipeline template and the sample loop-refinement workflow .Although a thorough analysis of PaPy's relationship to existing workflow-related software solutions lies beyond the scope of this report, Additional File reduction), it also could be useful for replicated simulations and other types of workflows which involve computationally-expensive components that generate large volumes of data.PaPy is a Python-based library for the creation and execution of cross-platform scientific workflows. Augmented with a 'NuMap' parallel execution engine and a 'NuBio' package for generalized biomolecular data structures, PaPy also provides a lightweight tool for data-processing pipelines that are specific to bioinformatics. PaPy's programming interface reflects its underlying dataflow and object-oriented programming paradigms, and it enables parallel execution through modern concepts such as the worker-pool and producer/consumer programming patterns. While PaPy is suitable for pipelines concerned with data-processing and analysis  is advised; the standard, freely-available Python package RPyC is an optional dependency (for distributed computing).\u2022 License: New BSD License\u2022 Any restrictions to use by non-academics: None; the software is readily available to anyone wishing to use it.\u2022 This supplementary file provides the following material, along with complete and fully annotated source-code for each example: (\u00a71). Two simple examples of workflows (useful as pipeline templates), one showing a generic forked pipeline and the other focusing on the usage of NuBio; (\u00a72) A detailed description of our more complicated case-study (simulation-based refinement of homology model loops); (\u00a73) Further notes on PaPy's platform independence, as well as the relationship between the Dagger and Plumber classes; (\u00a74) A brief overview of PaPy's scope and implementation, in relation to a fully-integrated WMS suite(KNIME).Click here for file"}
+{"text": "Mycoplasma agalactiae MAG_5040, a magnesium-dependent nuclease homologue to the staphylococcal SNase was characterized and its antigenicity during natural infections was established. A UGA corrected version of MAG_5040, lacking the region encoding the signal peptide, was expressed in Escherichia coli as a GST fusion protein. Recombinant GST-MAG_5040 exhibits nuclease activity similar to typical sugar-nonspecific endo- and exonucleases, with DNA as the preferred substrate and optimal activity in the presence of 20 mM MgCl2 at temperatures ranging from 37 to 45\u00b0C. According to in silico analyses, the position of the gene encoding MAG_5040 is consistently located upstream an ABC transporter, in most sequenced mycoplasmas belonging to the Mycoplasma hominis group. In M. agalactiae, MAG_5040 is transcribed in a polycistronic RNA together with the ABC transporter components and with MAG_5030, which is predicted to be a sugar solute binding protein by 3D modeling and homology search. In a natural model of sheep and goats infection, anti-MAG_5040 antibodies were detected up to 9 months post infection. Taking into account its enzymatic activity, MAG_5040 could play a key role in Mycoplasma agalactiae survival into the host, contributing to host pathogenicity. The identification of MAG_5040 opens new perspectives for the development of suitable tools for the control of contagious agalactia in small ruminants.In this study the enzymatic activity of  Proteins homologues to SNase have been identified in many bacteria, and in several mycoplasmas in vitro activity of the putative M. agalactiae SNase MAG_5040, and we investigated its expression and antigenic properties during natural infection. The gene encoding MAG_5040 was cloned and GST-tagged in an E. coli expression system. Substrate specificity and biochemical properties of the purified recombinant protein (rGST-MAG_5040) were examined. Recombinant cleaved MAG_5040 was also used to detect specific antibodies during different stages of infection in the natural hosts (sheep and goats), and to determine its reactivity with hyperimmune sera raised against selected mycoplasma species, as a preliminary investigation of potential SNase homologues expressed in other Mycoplasma species.Mycoplasmas are the smallest and simplest self-replicating prokaryotes. They evolved from Gram-positive bacteria following a regressive process that led to the reduction of genomic resources to an essential minimum This study was approved by the ethics committee of the University of Sassari. Blood sampling and pharmacological treatment of infected animals were operated by a veterinary practitioner authorized by the National Health System, after obtaining permission from the sheep owner. Animals where moved and transported by the shepherd during routine management of the flock in accordance with D.P.R. 8 Febbraio 1954, n. 320. Rabbit hyperimmune sera were kindly provided in 1996 by E.A. Freundt .M. agalactiae MAG_5040 protein sequence (YP_001256642) was submitted to BLASTP M. hominis group were selected. Sequences of the M. sualvi, M. lipophilum, and M. equigenitalium clusters were not available, since the genomes of these mycoplasmas have not yet been sequenced. Regions flanking MAG_5040 homologs were also investigated by homology search in the 8 mycoplasmas. These analyses were extended to three additional sequences selected outside the M. hominis group  and outside mycoplasmas (S. aureus subspecies aureus). MAG_5040 protein sequence was aligned to the homologues sequences identified in M. bovis (YP_006471195), M. fermentans (YP_004136712), M. synoviae (YP_278410), M. hyorhinis (YP_003856075), M. hyopneumoniae (YP_115890), M. ovipneumoniae (ZP_09312358), M. pulmonis (NP_325856), M. hominis (YP_003302610), M. genitalium (NP_072849), M. pneumoniae (NP_109821), and S. aureus (YP_001316549) by CLUSTALW The M. agalactiae PG2T was grown in PPLO medium supplemented with 20% heat inactivated horse serum and 500 \u00b5g/ml ampicillin, at 37\u00b0C with constant agitation. Mycoplasmas were collected by centrifugation , and washed three times with ice-cold PBS. Pellets were stored at \u221280\u00b0C until use. E. coli strains were grown in Luria-Bertani broth or on Luria-Bertani agar E. coli (Invitrogen) were transformed with the ligation product, and clones containing the recombinant vector (pGEX-2T/MAG_5040) were selected for ampicillin resistance. pGEX-2T/MAG_5040 was purified with the PureLink\u2122 Quick Plasmid Miniprep Kit (Invitrogen). Automated Sanger sequencing confirmed the correct cloning of MAG_5040 sequence into the vector. To avoid the expression of truncated proteins, 7 mycoplasma TGA tryptophan codons contained in MAG_5040 were mutagenized to TGG by using the QuikChange\u00ae Site-Directed Mutagenesis Kit (Stratagene), following manufacturer\u2019s instructions. Primers used for mutagenesis (MAG_5040/MUT) are summarized in E. coli BL21(DE3) were transformed with pGEX-2T/rMAG_5040 by means of RapidTransit\u2122 Transformation Kit (Sigma-Aldrich) according to manufacturer\u2019s instructions, and positive clones were selected for ampicillin and chloramphenicol resistance. Expression of rMAG_5040 was induced by adding IPTG  and incubating at 30\u00b0C under constant agitation for 4 hours. The rGST-MAG_5040 fusion protein was purified by means of affinity chromatography with Glutathione Sepharose\u2122 High Performance , and buffer-exchanged to PBS in a 30 kDa NMWL Amicon Ultra-15 centrifugal filter unit (Millipore). In order to obtain rMAG_5040, GST was cleaved from rGST-MAG_5040 by using thrombin . Both rGST-MAG_5040 and rMAG_5040 concentrations were evaluated with the BCA Protein Assay kit (Pierce).Total DNA was extracted from mycoplasma pellets with DNeasy Blood & Tissue Kit (Qiagen). In order to express MAG_5040 in fusion with glutathione-S-transferase (GST), a version of the MAG_5040 gene excluding the region encoding the signal peptide (amino acids 1 to 25) was amplified with primers MAG_5040/BamHI/F and MAG_5040/EcoRI/R . PCR rec2, 5 mM CaCl2) containing 1 to 5 \u00b5g of nucleic acid substrate. Aliquots (10 \u00b5l) were sampled at different times of incubation, and reaction was stopped in each collected tube by adding EDTA at the final concentration of 20 mM. Digestion products were analyzed by agarose gel electrophoresis and documented as described above. Exonuclease and endonuclease activities were evaluated by digesting both linear DNA  and the circular plasmid pGEX-2T/rMAG_5040. Substrate specificity was investigated with ssDNA  and total RNA purified from mid-log E. coli cultures. Optimal reaction conditions were defined by varying calcium and magnesium concentrations, ionic strength, and temperature in triplicate digestion reactions of the plasmid pGEX-2T/rMAG_5040. In order to examine the nuclease activity of rGST-MAG_5040 in the absence of exogenously supplied divalent cations, EDTA was added to the reaction . In order to rule out carryover of E. coli nucleases along with the fusion protein, GST was expressed in E. coli, purified, and used as a negative control in all assays.Approximately 4 \u00b5g of rGST-MAG_5040 were incubated at 37\u00b0C in 100 \u00b5l reaction buffer  were tested by milk mycoplasma isolation in PPLO M. agalactiae rP48 ELISA M. agalactiae PG2T. Five sheep belonging to group A and negative to the three tests were moved into an infected flock (group B), and placed in close contact with symptomatic animals actively shedding M. agalactiae in milk. Sera were collected at time of arrival (T0) from the 5 negative group A sheep, and from both positive (n\u200a=\u200a5) and negative (n\u200a=\u200a5) animals of the group B. Sera sampling was repeated every other week for a total of 36 weeks (9 months). In order to confirm infection of negative sheep during the longitudinal study, animals were tested both serologically and culturally as described above, at each sampling occasion. Sera obtained from both positive and negative group B sheep where pooled and used as positive and negative controls, respectively.Ten sheep were selected from a Contagious Agalactia (CA) free flock in North Sardinia . In order to confirm the absence of M. agalactiae PG2T, M. mycoides subsp. capri PG3, M. capricolum subsp. capricolum CK, M. arginini G230, M. canadense C275, M. ovipneumoniae Y98, M. putrefaciens KS1, M. mycoides subsp. capri LC, and M. capricolum subsp. capripneumoniae were also used in this study.Also, a panel of 10 well-characterized sera of an outbreak of CA occurred in Sicily were kindly provided by the \u201dIstituto Zooprofilattico della Sardegna\u201d. Furthermore, 16 high titer sera were obtained from the Department of Veterinary Science (University of Turin) and came from 8 naturally infected goats sampled at two weeks distance in Piedmont . Nine rabbit hyperimmune sera raised against whole cell preparations of eight mycoplasma species, namely M. agalactiae PG2T were run in single well 10% polyacrylamide gels (corresponding to 6\u201310 ng/lane), and transferred into nitrocellulose membranes with a Mini-Trans-Blot Cell (Bio-Rad), at 250 mA for one hour. After blotting, membranes were blocked with PBS-0.05% Tween-20 (PBS-T) containing 5% skim milk, and then incubated for one hour in a Multiscreen apparatus (Bio-Rad) alternatively with sera obtained from M. agalactiae naturally infected sheep and goats of geographically distant Italian Regions , or with rabbit hyperimmune sera raised against different Mycoplasma species. After incubation with primary antibodies, membranes were washed five times with PBS-T and incubated with the appropriate HRP-conjugated secondary antibodies (Southern Biotech). After five washes, membranes were developed with Chemiluminescent Peroxidase Substrate (Sigma-Aldrich) and images were acquired with a VersaDoc MP 4000 Imaging System (Bio-Rad).For SDS-PAGE, approximately 200 ng of cleaved rMAG_5040 or alternatively total protein extracts of pI of about 8.5. LipoP analysis classifies MAG_5040 as a putative lipoprotein, with a classical signal peptide of 25 amino acids and a typical cysteine cleavage site at residue 25 . Similarly, MAG_5040 homologues are present in about 60 Gram-negative and Gram-positive bacterial species. With one exception . Significant values of confidence >90% are also observed with families of solute binding proteins mostly associated with sugar transport  cells transformed with pGEX-2T/rMAG_5040 revealed the overexpression of a soluble protein of about 68 kDa, according to the predicted rGST-MAG_5040 molecular mass. This band was absent in uninduced E. coli cells  was incubated with GST-rMAG_5040, the amount of intact dsDNA decreased during incubation, and that was associated with the appearance of a smear in the gel, whose average size became lower over time , and by varying ionic strength (Na+ and K+), and temperature  up to T18  in 3 out of 5 sheep. Western blotting evaluation performed on total protein lysates of M. agalactiae PG2T against the same sera showed that the 3 group A sheep were already infected at T1. Mycoplasma isolation and rP48 ELISA were consistent with western blotting. Sera collected during the same sampling occasions from culturally and serologically negative sheep never reacted with rMAG_5040 (data not shown). In order to select a number of negative sheep from a flock with no history of CA, 10 sheep (group A) where tested by mycoplasma isolation, rP48 ELISA, and western blotting . Five group A sheep negative both serologically and culturally were moved to a mycoplasma-infected flock (group B). When rMAG_5040 was probed with sera collected from group A sheep placed in close contact with group B sheep, antibodies against MAG_5040 could be detected starting from TM. mycoides subsp. capri PG3, M. capricolum subsp. capricolum CK, M. arginini G230, M. canadense C275, M. mycoides subsp. capri LC, M. capricolum subsp. capripneumoniae.To tentatively investigate the presence of expressed MAG_5040 homologues in selected mycoplasma species, we tested the reactivity of specific rabbit hyperimmune sera against rMAG_5040 . A positM. agalactiae is the etiological agent of contagious agalactia (CA), a serious disease of sheep and goats reported worldwide and endemic in most Mediterranean countries M. agalactiae virulence and host interaction. Full genome sequencing of two M. agalactiae strains combined to gene ontology analyses M. agalactiae pathogenicity does not relate to primary virulence factors such as cytolysins, invasins, or toxins. Few genes, mostly involved in adhesion, have been identified thus far as related to pathogenicity M. agalactiae membrane M. fermentans product with a macrophage-stimulatory activity M. agalactiae a mechanism by which it potentially avoids opsonization, phagocytosis, macrophage killing, and antibodies neutralization 2, and NH3 are combined in successive reactions to form nucleotides (de novo pathway), these microorganisms optimized the use of free bases and nucleosides released from the breakdown of the host nucleic acids by converting them back into nucleotides  can be identified upstream the SNase gene at least in the M. hominis group, suggesting its conserved role as solute binding protein. Indeed, MAG_5030 3D modeling and structure prevision designate this protein as belonging to families including solute binding proteins mostly associated with sugar transport. On the contrary, no conserved positions are observed downstream the ABC transporter.In M. agalactiae persistence by providing essential nucleotide precursors for biosynthesis and replication, while competing with the host for nucleotide pools. The involvement of MAG_5030 and MAG_5040 in an active ABC transport system is supported by the identification of overlapping transcripts between MAG_5030 and MAG_5070 in RT-PCR analyses (data not shown). Moreover a putative transcription promoter is present upstream MAG_5030 while a Rho-independent termination signal can be identified downstream MAG_5080. Notably, MAG_5030, MAG_5040, MAG_5050, MAG_5060, and MAG_5070 are all expressed in cultured M. agalactiae PG2TTherefore in a hypothetic model, MAG_5040 could provide nucleotide precursors to the ABC transporter by \u201cstealing\u201d them from the host nucleic acids, with MAG_5030 (P80) acting as solute binding protein. Consequently, MAG_5040 could be a critical pathogenic contributor to M. agalactiae collected at different infection times reacted with the recombinant cleaved MAG_5040. On the one hand, the reactivity of sera obtained from outbreaks occurred in distant geographic regions in different years with rMAG_5040, recorded up to 9 months post infection, reinforces the key role of this protein in the interaction with the natural hosts. On the other hand, the establishment of the antigenic properties of MAG_5040 opens new perspectives in the development of both high throughput diagnostic and prophylactic tools for the control of contagious agalactia. If confirmed, the importance of MAG_5040 nuclease in promoting M. agalactiae survival and persistence could suggest focusing on this protein as a target for the development of chemotherapics active against nucleotide recycling.Sera of sheep and goats naturally infected with M. capricolum and M. mycoides. It should be pointed out that a SNase homolog could not be identified by homology search in the genomes of these two latter mycoplasma species. However, our results are in accordance with what experimentally observed by Minion and coworkers, that reported a Mg2+ dependent nuclease activity in M. capricolumThe reactivity of MAG_5040 with rabbit sera raised against selected mycoplasmas suggests the expression of SNase homologs in most of the species examined, including M. agalactiae, potentially involved in pathogenicity and playing an important role in the interaction and survival of this mycoplasma in the host. Further studies, such as functional proteomics assays, might hopefully help to elucidate the interconnected role of MAG_5030, MAG_5040 and of the other components of the putative nucleoside uptake machinery, for the full comprehension of mycoplasmas life cycle, as well as to develop effective tools for the control of mycoplasmosis.MAG_5040 is the first antigenic protein with nuclease activity characterized in Figure S1Phyre software results. Alignment coverage, 3D model, confidence, and percentage of identity of the most similar proteins are shown.(PDF)Click here for additional data file.Figure S2Expression and purification of rMAG_5040. MW indicates the molecular weight marker . Lane 1, uninduced E. coli. Lane 2, E. coli expressing recombinant GST-MAG_5040 after 4 hours induction. Lanes 3 and 4, purified GST-MAG_5040 and its thrombin cleavage products, respectively.(PDF)Click here for additional data file.Table S1Primers used in this study.(PDF)Click here for additional data file."}
+{"text": "Histophilus somni, an opportunistic pathogen of the reproductive and respiratory tracts of cattle. Thus far only a few genes involved in metabolic and virulence functions have been identified and characterized in H. somni using traditional methods. Analyses of the genome sequences of several Pasteurellaceae species have provided insights into their biology and evolution. In view of the economic and ecological importance of H. somni, the genome sequence of pneumonia strain 2336 has been determined and compared to that of commensal strain 129Pt and other members of the Pasteurellaceae.Pneumonia and myocarditis are the most commonly reported diseases due to Pasteurellaceae, several H. somni genes that may encode proteins involved in virulence  were identified. The two strains contained a total of 17 ORFs that encode putative glycosyltransferases and some of these ORFs had characteristic simple sequence repeats within them. Most of the genes/loci common to both the strains were located in different regions of the two chromosomes and occurred in opposite orientations, indicating genome rearrangement since their divergence from a common ancestor.The chromosome of strain 2336  contained 1,980 protein coding genes, whereas the chromosome of strain 129Pt  contained only 1,792 protein coding genes. Although the chromosomes of the two strains differ in size, their average GC content, gene density , and percentage of sequence (number of genes) that encodes proteins were similar. The chromosomes of these strains also contained a number of discrete prophage regions and genomic islands. One of the genomic islands in strain 2336 contained genes putatively involved in copper, zinc, and tetracycline resistance. Using the genome sequence data and comparative analyses with other members of the H. somni strains.Since the genome of strain 129Pt was ~256,000 bp smaller than that of strain 2336, these genomes provide yet another paradigm for studying evolutionary gene loss and/or gain in regard to virulence repertoire and pathogenic ability. Analyses of the complete genome sequences revealed that bacteriophage- and transposon-mediated horizontal gene transfer had occurred at several loci in the chromosomes of strains 2336 and 129Pt. It appears that these mobile genetic elements have played a major role in creating genomic diversity and phenotypic variability among the two  Histophilus somni is a commensal or opportunistic pathogen of the reproductive and respiratory tracts of cattle. H. somni was initially identified as the etiologic agent of bovine thrombotic meningoencephalitis (TME), but also causes bovine shipping fever pneumonia, either independently or in association with Mannheimia haemolytica and Pasteurella multocida. Pneumonia and myocarditis are currently the most commonly reported diseases due to H. somni  and 9 ORFs of unknown function. A similar sequence was not found in strain 2336 or other members of the Pasteurellaceae. GI III of strain 129Pt contained ORFs encoding a putative phage terminase protein [GenBank:HS_1334], a prophage regulatory element [GenBank:HS_1335], an integrase [GenBank:HS_1337], and 4 ORFs of unknown function. A similar sequence was not found in strain 2336, but Aggregatibacter aphrophilus strain NJ8700 contained several short sequences that had homology to this region . All predicted ORFs from GIs IV and V of strain 129Pt were of unknown function and strain 2336 contained several short sequences that had homology to these regions . The chromosome of strain 2336 had no regions of homology to GI VI of strain 129Pt, but H. parasuis strain SH0165 contained several short sequences that had homology to this region .GI I of strain 129Pt contained an ORF encoding a resolvase/integrase-like protein  flanked by 18-29 bp of terminal inverted repeats, is a member of the IS1595 superfamily [HSM_0851], [GenBank:HSM_1211], [GenBank:HSM_1267], and [GenBank:HSM_1883]) and three truncated copies of IS1016. GI III of strain 2336 included [GenBank:HSM_1883] and one of the three truncated copies of IS1016. Strain 129Pt contained 5 full-length copies of IS1016 . A gene in strain 129Pt , encoding a choline kinase that is necessary for the synthesis of phosphorylcholine . Strain 2336 contained a full-length member of the family IS30 ([GenBank:HSM_1680]) and a truncated member of the family IS1182/IS5 ([GenBank:HSM_1887]). Strain 2336 contained a full-length member of the family IS481 ([GenBank:HSM_0451]), whereas strain 129Pt contained a truncated copy of the same ORF ([GenBank:HS_1518]). Strain 2336 also contained truncated members of the family IS3 ([GenBank:HSM_0531] and [GenBank:HSM_0532]). The closest homologs of H. somni IS200/IS605 and IS1595 elements were found in M. haemolytica  and H. influenzae . However, the closest homologs of the IS30 element from strain 2336 were found in A. pleuropneumoniae  and H. parasuis .Strain 2336 contained 4 full-length members of the family ISHSM_0528], [GenBank:HSM_0530], [GenBank:HSM_0532], [GenBank:HSM_0603], [GenBank:HSM_1185], [GenBank:HSM_1483], [GenBank:HSM_1636], [GenBank:HSM_1742], and [GenBank:HSM_1743]) had 38-50 aa. Strain 129Pt contained 165 putative protein coding genes with no homologs in strain 2336 . In strain 2336, 440 ORFs could not be assigned a function based on BLAST analysis and were therefore annotated as encoding hypothetical or conserved hypothetical proteins. In strain 129Pt, 429 ORFs were annotated as encoding hypothetical or conserved hypothetical proteins. Among hypothetical proteins that were common to both strains, 30 did not have homologs outside the genus. Pairwise BLAST comparisons indicated that strain 2336 contained 311 putative protein coding genes with no homologs in strain 129Pt . A complete list of these genes is available at the 'Organism Details' sections for strains 2336 and 129Pt within IMG. Other strain-specific genes identified encoded DNA methylases , transposases , ABC transporters , ATPases , transcriptional regulators , kinases , and several proteins related to bacteriophage functions . Excluding intergenic regions, the total length of sequence that was associated with specific genes in strain 2336 was 254,052 bp (~11% of the genome), and was 98,016 bp (~5% of the genome) in specific genes of strain 129Pt.In both strains, a vast majority of putative HTGs appeared to have had their origins among members of gammaproteobacteria . Putative HTGs with possible origins among members of betaproteobacteria  and alphaproteobacteria  were also identified. Among HTGs identified were those encoding proteins putatively involved in virulence  had no homologs among other members of the Pasteurellaceae and six  had distant homologs (< 50% identity) among other members of the Pasteurellaceae , comparative analyses using the Swiss-Prot database , and their homology to genes encoding putative GTs in non-Pasteurellaceae genomes .A search of the NCBI non-redundant protein database using the BLASTP algorithm identified 17 ORFs that encode putative glycosyltransferases (GTs) in the genomes of strains 2336 and 129Pt. Seven of these ORFs were common to both genomes (at least 96% identity at the predicted protein level), 8 were found only in strain 2336, and 2 were found only in strain 129Pt. Among the ORFs encoding putative GTs common to both strains, 5 contained simple sequence repeats (SSRs), and 4 of the 8 ORFs encoding GTs found in strain 2336 contained SSRs. A list of putative GTs and their SSRs identified in both strains are shown in Table lipooligosaccharide biosynthesis (lob) gene cluster consisting of lob1 and lob2ABCD ORFs encoding glycosyltransferases involved in attaching the outer core glycoses of the LOS was previously identified in strain 738, which is an LOS phase variant of strain 2336  or csrA, carbon storage regulator; [GenBank:HSM_1062] or manB phosphomannomutase; [GenBank:HSM_1063] or galU, UTP-glucose-1-phosphate uridylyltransferase). Strain 129Pt contained a similar locus ([GenBank:HS_1117] to [GenBank:HS_1119]) that had an IS1016 element  adjacent to it. Furthermore, both strains contained ORFs encoding a putative UDP-glucose 4-epimerase , phosphoglucomutase , and dTDP-glucose 4,6-dehydratase . These three genes/enzymes were predicted to be involved in polysaccharide biosynthesis/modification.Strain 2336 also contained a locus that encodes proteins putatively involved in exopolysaccharide (EPS) and/or LOS biosynthesis  and contained genes encoding four FhaB homologs  and one FhaC homolog ([GenBank:HSM_0267]). No transposon or phage sequences were present in this locus. In contrast, strain 129Pt had a locus containing genes that flanked locus I of strain 2336, but did not contain the FhaB and FhaC homologs  and FhaC ([GenBank:HSM_1089]), which appeared to be associated with a transposon. Strain 129Pt had a locus containing genes that flanked locus II of strain 2336, but did not contain the FhaB and FhaC homologs  and FhaC ([GenBank:HSM_1490]). The fhaB  of this locus was the second largest gene in the genome and the largest among the 12 homologs. This gene encoded a putative protein homologous to the high molecular weight immunoglobulin-binding protein of H. somni and the large supernatant proteins (Lsp1 and Lsp2) of H. ducreyi that have been previously described [HSM_1638], [GenBank:HSM_1641], [GenBank:HSM_1643], [GenBank:HSM_1646], [GenBank:HSM_1647], [GenBank:HSM_1651]). A truncated gene encoding FhaC  was also found in this locus. A partial homolog of [GenBank:HSM_1647] was found in strain 129Pt , which appeared to be the only region in the chromosome of strain 129Pt with a sequence related to the fhaB genes  and contained genes encoding FhaB  were conserved in all six homologs and 2 (an asparagine and a methionine) were conserved in five of the homologs . The FhaC homologs of loci I (581 aa) and III (586 aa) were more closely related to each other than to FhaC from locus II (450 aa). Multiple sequence alignment of N-terminal fragments of FhaB homologs from the four loci of strain 2336 with those of H. somni strain 2336 GI III contained a gene encoding a putative subtilisin-like serine protease . The NCBI protein clusters database lists [GenBank:HSM_1889] as one of the 16 members of the [GenBank:CLSK923564] cluster . Among the proteins of this cluster, homologs from Ralstonia eutropha JMP134 , Pseudomonas fluorescens Pf0-1 , and Pseudomonas syringae pv. phaseolicola 1448A  are listed in the prokaryotic subtilase database as subtilases of the D-H-S family.CLSK923564] cluster revealed that in 3 cases , the ORF encoding subtilisin was found on plasmids. In the case of Delftia acidovorans SPH-1, Burkholderia ambifaria AMMD, Polaromonas naphthalenivorans CJ2, Chelativorans sp. BNC1, and E. coli O127:H6 str. E2348/69, the ORF encoding subtilisin was found within a prophage region. Furthermore, in the case of Photorhabdus luminescens subsp. laumondii TTO1, P. syringae pv. phaseolicola 1448A, P. fluorescens Pf0-1, Verminephrobacter eiseniae EF01-2, and Xanthomonas oryzae pv. oryzae PXO99A, the ORF encoding subtilisin appeared to be associated with genes encoding transposases. However, in Chromohalobacter salexigens DSM 3043 and Anaeromyxobacter sp. K, the ORF encoding subtilisin was not associated with prophage or transposase sequences. Interestingly, in 15 host species the subtilisin ORF formed an operon with an ORF encoding homologous ATPases of the AAA family. In the case of A. aurescens TC1, a transposon insertion appears to have disrupted the AAA ATPase-subtilisin operon. The two ORFs have a 4 bp overlap in 8 species and appear to be co-transcribed  and TbpB ([GenBank:HS_0448] and [GenBank:HSM_0749]). In strain 129Pt, GI I was identified immediately upstream of the tbp locus. Furthermore, homologs of H. somni strain 649 TbpA2 were present in strain 129Pt  and strain 2336 . Pairwise BLAST analysis identified several other putative genes encoding proteins that may also be involved in iron transport. In strain 129Pt these included [GenBank:HS_0069], iron-regulated outer membrane protein; [GenBank:HS_0181], TonB-dependent outer membrane receptor; [GenBank:HS_0728], hemin receptor outer membrane protein; and [GenBank:HS_1306], iron-regulated outer membrane protein. In strain 2336 they included [GenBank:HSM_0047], TonB-dependent receptor; [GenBank:HSM_1168], TonB-dependent hemoglobin/transferrin/lactoferrin family receptor; [GenBank:HSM_1176], outer membrane hemin receptor protein, and [GenBank:HSM_1962], TonB-dependent receptor. Two of the genes, [GenBank:HS_1306] and [GenBank:HSM_1168], were strain-specific. Among others, HS_0582 was associated with a transposase ([GenBank:HS_0583]) and [GenBank:HSM_1168] was found near PR IV.A comparison of the genomes of HS_0209], [GenBank:HS_0383], [GenBank:HS_0478], [GenBank:HS_0589], [GenBank:HS_0602], [GenBank:HS_0790], [GenBank:HS_1058], [GenBank:HS_1085], [GenBank:HS_1154], [GenBank:HS_1185], [GenBank:HS_1234], [GenBank:HS_1512], [GenBank:HS_1543], [GenBank:HS_1563], [GenBank:HS_1616], and [GenBank:HS_1632]) with homology to genes that encode proteins of the Yersinia adhesin (YadA) superfamily [HSM_0077], [GenBank:HSM_0338], [GenBank:HSM_0346], [GenBank:HSM_0377], [GenBank:HSM_0394], [GenBank:HSM_0708], [GenBank:HSM_0844], [GenBank:HSM_0938], [GenBank:HSM_0953], [GenBank:HSM_1022], [GenBank:HSM_1212], [GenBank:HSM_1257], [GenBank:HSM_1484], [GenBank:HSM_1542], [GenBank:HSM_1571], [GenBank:HSM_1793]) with homology to genes that encode proteins of the YadA superfamily. Adhesin-encoding genes [GenBank:HS_0209]  and [GenBank:HSM_1257]  were the largest among all protein coding genes predicted in the chromosomes of strains 129Pt and 2336, respectively. Within this gene repertoire, [GenBank:HSM_0938] (388 aa) and [GenBank:HS_0589] (386 aa) were 85% similar to each other at the predicted protein level  and were associated with genes encoding putative ABC transporters . In addition, [GenBank:HSM_0077] (4063 aa) and [GenBank:HS_0209] (5143 aa) were 65% similar to each other at the predicted protein level  and were also associated with genes encoding putative ABC transporters . Furthermore, although homologs of the H. influenzae fimbrial gene cluster (hifABCDE) were absent in both strains of H. somni, homologs of type IV pili genes (pilABCD) occurred in strains 2336 and 129Pt . In addition, both strains contained a gene that encoded a putative pseudopilin  that may facilitate type II secretion.Strain 129Pt contained sixteen genes randomly distributed throughout the chromosome ([GenBank:erfamily . Strain HSM_1191] and [GenBank:HSM_1734]) and MerR ([GenBank:HSM_1728] and [GenBank:HSM_1741]) families, one each that belonged to the LysR ([GenBank:HSM_0806]), OmpR ([GenBank:HSM_0817]), and MarR ([GenBank:HSM_1737]) families, and a member of an unassigned family . Among these, members of the TetR, MerR, and MarR families were found within GI II. Strain 2336 also contained a gene adjacent to tetR encoding a tetracycline resistance antiporter . The NCBI protein clusters database lists [GenBank:HSM_1734] as a member of the PRK13756 cluster . The closest homologs of [GenBank:HSM_1734] and [GenBank:HSM_1735] were found in P. multocida . In addition to [GenBank:HSM_1737], strain 2336 also contained [GenBank:HSM_1736] . [GenBank:HSM_1191] is listed as one of the members of the [GenBank:CLSK391246] cluster . This regulator gene was associated with two genes encoding an ABC-type transport system ([GenBank:HSM_1192] and [GenBank:HSM_1193]). The closest homologs of [GenBank:HSM_1191] were found among members of Firmicutes, Spirochaetes, and Fusobacteria , but not the Pasteurellaceae. The two MerR homologs of strain 2336 had only 39% identity to each other  and both were conserved in members of the Pasteurellaceae . However, the MerR homologs have been included in different protein clusters ([GenBank:HSM_1728] in [GenBank:CLSK892364] and [GenBank:HSM_1741] in [GenBank:CLSK2299246]) and appear to regulate different functions. Since [GenBank:HSM_1728] was associated with genes involved in copper metabolism , it may encode a copper efflux regulator, CueR. However, [GenBank:HSM_1741] may be involved in zinc homeostasis since it was associated with zinc-responsive genes ([GenBank:HSM_1740] encodes a zinc efflux protein and [GenBank:HSM_1739] encodes a zinc-dependent hydrolase). The NCBI protein clusters database lists [GenBank:HSM_0806] as one of the members of the [GenBank:CLSK797597] cluster . The closest homologs of [GenBank:HSM_0806] were found in Pasteurella dagmatis , H. influenzae , and several species of Streptococci . In strain 2336, [GenBank:HSM_0817] encoded a putative OmpR family response regulator whereas [GenBank:HSM_1124] encoded a putative ArcA family response regulator. These response regulators have very low homology to each other . However, their amino-termini contained two conserved aspartic acid residues (Asp-11 and Asp-56) that may serve as phosphate acceptors and their carboxy-termini contained several conserved residues that may be involved in DNA binding (data not shown). Strains 2336 and 129Pt also contained genes that encoded putative histidine kinases . Whereas [GenBank:HSM_1378] and [GenBank:HS_0900] had an ORF downstream that encodes a putative cognate response regulator ([GenBank:HSM_1379] and [GenBank:HS_0901]), [GenBank:HSM_0727] and [GenBank:HS_0402] had an ORF downstream that encodes a transcriptional regulator with an N-terminal XRE-type helix-turn-helix domain ([GenBank:HSM_0728] and [GenBank:HS_0403]). Strain 2336 contained an additional gene that encoded a putative histidine kinase ([GenBank:HSM_0824]) that had a homolog in P. multocida strain Pm70 , but not in strain 129Pt.Pairwise BLAST comparisons indicated that strain 2336 contained 14 genes encoding transcriptional regulators with no homologs in strain 129Pt. These included two regulators each that belong to the TetR . Although strain 129Pt lacked hsdM, it contained a full-length hsdR ([GenBank:HS_0559]) and two truncated hsdS ([GenBank:HS_0554] and [GenBank:HS_0556]) orthologs. Strain 2336 also contained an operon putatively encoding enzymes of the Type II RM system . At the predicted protein level, M.HsoI has 68% identity to M.HinP1I  and R.HsoI has 72% identity to R.HinP1I  of H. influenzae P1. Strain 129Pt lacked M.hsoI and R.hsoI but contained an operon putatively encoding a BcgI-like RM system  that is absent in strain 2336.Type I and Type II restriction-modification (RM) systems comprise the most frequently encountered DNA modifying enzymes among eubacteria. Strain 2336 contained ORFs that putatively encode proteins of the Type I RM system ([GenBank:H. somni strains contained ORFs encoding proteins putatively involved in the transport and metabolism of fucose ([GenBank:HSM_0580] and [GenBank:HS_1451] to [GenBank:HSM_0585] and [GenBank:HS_1446]), mannose ([GenBank:HSM_0956] and [GenBank:HS_0605] to [GenBank:HSM_0960] and [GenBank:HS_0609]), xylose ([GenBank:HSM_0933] and[GenBank:HS_0584] to [GenBank:HSM_0937] and [GenBank:HS_0588]), galactitol ([GenBank:HSM_1030] and [GenBank:HS_1146] to [GenBank:HSM_1036] and [GenBank:/HS_1140]), mannitol ([GenBank:HSM_0825] and [GenBank:HS_1252] to [GenBank:HSM_0827] and [GenBank:HS_1250]), and myo-inositol ([GenBank:HSM_0425] and [GenBank:HS_1586] to [GenBank:HSM_0435] and [GenBank:HS_1576]). Furthermore, strain 2336 contained a locus with three ORFs encoding proteins putatively involved in galactose utilization , whereas strain 129Pt contained only galM ([GenBank:HS_0236]) and truncated galK ([GenBank:HS_0235]) orthologs. However, both strains have galE, and strain 129Pt has galactose in its LOS (38), indicating that it can metabolize galactose.Both HSM_0818] (ribokinase-like domain-containing protein), [GenBank:HSM_0819] , [GenBank:HSM_0820] (sugar-related regulatory protein), [GenBank:HSM_0821] (sugar binding protein), [GenBank:HSM_0822] (monosaccharide-transporting ATPase), and [GenBank:HSM_0823] (ABC type sugar transporter). Strain 2336 also contained ORFs encoding a cyclase family protein and a membrane-spanning protein ([GenBank:HSM_1907] and [GenBank:HSM_1908]). These ORFs appeared to form an operon with ORFs encoding proteins putatively involved in butyrate/pyruvate metabolism ([GenBank:HSM_1903] to [GenBank:HSM_1906]). Furthermore, strain 2336 contained an ORF that encoded an oligopeptide permease ABC transporter homolog ([GenBank:HSM_0695]). The putative Opp protein of strain 2336 was related to OppB proteins involved in peptide transport in E. coli and Bacillus subtilis .Strain 2336-specific genes encoding proteins putatively involved in carbohydrate metabolism included [GenBank:HS_0437], cellobiose-specific IIC component) whose homologs are found in M. haemolytica  and H. parasuis . Strain 129Pt also contained a gene encoding a virulence-associated protein E ([GenBank:HS_0427]). This protein was related to the VirE proteins encoded by genes found on Staphylococcus phage phi2958PVL  and Enterococcus phage phiFL4A . Strain 129Pt contained genes ([GenBank:HS_0631] and [GenBank:0632]) encoding a putative serine/threonine protein kinase-phosphatase pair. Strain 129Pt also contained loci encoding proteins putatively involved in thiamine biosynthesis , tellurite resistance , and lysine degradation .GI I of strain 129Pt contained a gene encoding a putative phosphotransferase system protein , it is interesting to note that both strains contain several prophage-like sequences despite the presence of genes encoding putative RM systems in their chromosomes. The lack of ORFs encoding HsdM, M.HsoI, and R.HsoI in strain 129Pt indicates that these systems are not absolutely essential for cell survival. Their absence may also partially explain the relative ease with which this strain can be transformed in the laboratory.The presence of PRs in the chromosomes of strains 2336 and 129Pt was a notable feature since the number and diversity of genes associated with these PRs far exceeded those described in 86-028NP ,45. The 86-028NP . AlthougH. somni. Phase-variation of H. somni LOS has been shown to be due to the presence of SSRs in genes that encode GTs and enzymes involved in assembling non-glycose LOS components such as phosphorylcholine , [GenBank:HSM_0164], [GenBank:HSM_0975], and [GenBank:HSM_1552] also contain SSRs that may be responsible for LOS phase variation, but require additional experimental investigation. Most H. somni strains also produce a biofilm-associated EPS consisting primarily of mannose and galactose ; subtilisin-like serine proteases) are a large superfamily of functionally diverse endo- and exo-peptidases that occur in prokaryotes and eukaryotes [Pasteurellaceae have not been characterized previously. The presence of a gene encoding a putative subtilase whose homologs were not found in other members of the Pasteurellaceae was yet another example of HGT in strain 2336. Although H. ducreyi strain 35000HP contains genes ([GenBank:HD1094] and [GenBank:HD1278]) encoding serine proteases that belong to the D-H-S family, they are unrelated to each other and to [GenBank:HSM_188]. In Agrobacterium tumefaciens, genes encoding AAA-ATPase and subtilisin-like serine protease have been shown to be functionally related and this pair has been proposed to constitute a toxin-antitoxin system that contributes to stability of plasmid pTiC58   in H. influenzae and P. dagmatis are associated with genes encoding proteins involved in fatty acid metabolism . Therefore, this cluster may represent a novel class of metabolic regulators within the LysR family. Most members of the [NCBI:PRK13756] cluster are involved in regulation of antibiotic resistance genes [HSM_1734] and [GenBank:HSM_1735] among members of the Pasteurellaceae encode tetracycline resistance and are associated with mobile genetic elements [HSM_1736] and [GenBank:HSM_1737] in other bacteria are known to be horizontally transferred and may mediate resistance to antibiotics [HSM_1192] and [GenBank:HSM_1193] are predicted to be involved in multidrug resistance [Pasteurellaceae is clinically significant.Transcriptional regulators play crucial roles in bacterial functions and they have been classified into a number of families . The homce genes . Homologelements -86. Homoibiotics . Furthersistance ,89. In sH. somni 2336, H. somni 129Pt, H. influenzae 86-028NP, H. ducreyi 35000HP, and P. multocida Pm70 contain 49, 49, 58, 46, and 57 tRNA genes, respectively). Whether the lower number of tRNA genes found in H. somni strains is due to disruptive integration of bacteriophages into tRNA genes  or is ahnsonii' ) is unknH. somni strain 2336 contains a larger chromosome when compared to other Haemophilus and Histophilus strains whose genome sequences are available. Several regions that resemble the pathogenicity islands of other virulent bacteria are present in strain 2336. There is evidence to suggest that most of these regions were acquired by HGT mechanisms, whereas similar regions were not found in the commensal strain 129Pt. Although previous studies have discovered the genetic basis for some of the phenotypic dissimilarities between strains 2336 and 129Pt, complete genome sequence analyses have provided a comprehensive account of innate and acquired genetic traits. Furthermore, comparisons of the genomes of strains 2336 and 129Pt have contributed to our understanding of the biology and pathogenic evolution of these bacteria. The post-genomic era for H. somni poses new challenges and opportunities in terms of functional characterization of genes and deciphering their roles in colonization, survival, and pathogenesis. Continued analyses of the genomes of H. somni strains and comparison with newly sequenced genomes of other bacteria should enhance the current knowledge on virulence mechanisms. Nevertheless, the results from this study are expected to facilitate the development of improved diagnostic tests for and vaccines against H. somni.SS performed the annotation using RAST, planned the comparative analysis, generated all the figures, and drafted most of the manuscript. JFC, AJD, and AFG contributed to whole genome comparisons and identification of putative virulence genes. MC, JG, JO, JZ, and GB contributed to the whole genome shotgun sequencing of strain 2336 at OUHSC. DB, OC, JCD, CSH, and RT contributed to the finishing of the genome of strain 2336 at LANL and participated in the study design and comparative analysis of the two genomes. TJI and DWD conceived the study, participated in genome analyses, and helped draft the manuscript. All authors read and approved the final manuscript.H. somni strain 2336 specific genesAdditional file 1 List of . This table lists the strain-specific genes found in H. somni strain 2336. This data was obtained by cross-comparison of the genomes of strains 2336 and 129Pt using blastn.Click here for fileH. somni strain 129Pt specific genesAdditional file 2 List of . This table lists the strain-specific genes found in H. somni strain 129Pt. This data was obtained by cross-comparison of the genomes of strains 129Pt and 2336 using blastn.Click here for file"}
+{"text": "Saccharomyces sensu stricto clade whose phylogeny is well-established. We identified 2,647 sites containing RGC_CAM substitutions, a number that contrasts sharply with the 100,887 sites containing RGC_non-CAM substitutions . We found that RGC_CAM substitutions had significantly lower homoplasy than RGC_non-CAM ones; specifically RGC_CAM substitutions showed a per-site average homoplasy index of 0.100, whereas RGC_non-CAM substitutions had a homoplasy index of 0.215. Internode certainty values were also higher for sites containing RGC_CAM substitutions than for RGC_non-CAM ones. These results suggest that RGC_CAM substitutions possess a strong phylogenetic signal and are useful markers for phylogenetic inference despite their rarity.When inferring phylogenetic relationships, not all sites in a sequence alignment are equally informative. One recently proposed approach that takes advantage of this inequality relies on sites that contain amino acids whose replacement requires multiple substitutions. Identifying these so-called RGC_CAM substitutions (after Rare Genomic Changes as Conserved Amino acids-Multiple substitutions) requires that, first, at any given site in the amino acid sequence alignment, there must be a minimum of two different amino acids; second, each amino acid must be present in at least two taxa; and third, the amino acids must require a minimum of two nucleotide substitutions to replace each other. Although theory suggests that RGC_CAM substitutions are expected to be rare and less likely to be homoplastic, the informativeness of RGC_CAM substitutions has not been extensively evaluated in biological data sets. We investigated the quality of RGC_CAM substitutions by examining their degree of homoplasy and internode certainty in nearly 2.7 million aligned amino acid sites from 5,261 proteins from five species belonging to the yeast  Recent advances in genomics, computer science and systematics theory have invigorated the pursuit of assembling the tree of life It was recently proposed that conserved amino acids whose replacement requires multiple substitutions, known as RGC_CAM substitutions, represent a novel type of rare genomic change Saccharomyces sensu stricto species that possess a well-resolved phylogeny: Saccharomyces cerevisiae, Saccharomyces paradoxus, Saccharomyces mikatae, Saccharomyces kudriavzevii, and Saccharomyces bayanusWhile RGC_CAM substitutions have been unable to resolve these controversial and much debated relationships between metazoan phyla, this does not necessarily mean that their quality is not as good as, or better than standard amino acid substitutions [see also Ref. 13]. To further evaluate the informativeness and utility of RGC_CAM substitutions, we identified and examined all 2,647 RGC_CAM substitutions present in amino acid sequence alignments from 5,261 orthologous groups of proteins obtained from five We define RGC_CAM substitutions as substitutions that occur at a site in an amino acid sequence alignment that fulfill three criteria: a) a minimum of two different amino acids must be present at the site, b) each amino acid must be present in at least two taxa, and c) a replacement of one amino acid for the other must take a minimum of two nucleotide substitutions . Determihttp://as.vanderbilt.edu/rokaslab/data/Polzin_Rokas_PLoSONE_2014.zip).A Perl script (list_rgc_cams.pl) was written to use the standard genetic code table and systematically compare the codons of each amino acid to the codons of every other amino acid. Doing so created a matrix of all the amino acid pairs that require a minimum of two nucleotides to change from one amino acid to the other, which we name RGC_CAM substitution matrix . A seconSaccharomyces sensu stricto genus were obtained from www.SaccharomycesSensuStricto.orgThe amino acid alignments from the 5,261 high-quality groups of orthologous proteins across five species in the genus Saccharomyces sensu stricto sequence length, the number of proteins containing RGC_CAM sites, and the number of RGC_CAM sites per protein. We also determined the frequency with which amino acids were present with specific other amino acids at sites along the sequences.To measure the prevalence of sites containing RGC_CAM substitutions and of specific amino acids, we calculated the ratio of RGC_CAM sites to the full Phylogenetic analyses were performed using parsimony analysis on the RGC_CAM and RGC_non-CAM datasets using PAUP* 4.10b The degree of homoplasy in RGC_CAM and RGC_non-CAM sites was measured using the homoplasy index (HI) To visualize the comparison of the distribution of HI values between RGC_CAM sites and RGC_non-CAM sites, we also calculated HI on 52 bins of 50 RGC_CAM sites, which were generated by randomly distributing the 2,647 RGC_CAM sites . To compare these 52 bins of RGC_CAM sites, we generated 52 equally sized bins of randomly picked RGC_non-CAM sites. For each bin, the mean HI was calculated and used in a Wilcoxon Rank-Sum test to determine if the HI values of RGC_CAM site bins and RGC_non-CAM site bins were significantly different.Saccharomyces species resulted in the identification of 2,647 sites containing RGC_CAM substitutions and 100,887 sites containing RGC_non-CAM substitutions; thus, approximately 0.1% and 3.8% of sites in the alignment contain RGC_CAM and RGC_non-CAM substitutions, respectively. Although sites containing RGC_CAM substitutions are found in 1,723 protein alignments, 1,142 of them contain only one RGC_CAM site, with the remaining 581 protein alignments containing two or more. In contrast, 5,079 of the 5,261 protein alignments (\u223c96.5%) contain one or more RGC_non-CAM sites. Interestingly, the protein alignments for the genes YDL081C, YGL123W, YLR044C, YMR260C, YMR290C, YNL190W, YNL090W, and YPL025C contained one or two RGC_CAM sites but no RGC_non-CAM ones. These results show that RGC_CAM substitutions comprise a very small fraction of the total number of informative sites in protein sequence alignment data, suggesting that they may be of practical use only when large amounts of sequence data are available.Examination of 2,668,077 columns of amino acid sequence alignment from five S. cerevisiae and S. paradoxus. Focusing on the IC metric, a value of 0.66 means that the most prevalent grouping of S. cerevisiae and S. paradoxus received much more support than the next most well-supported, but conflicting, grouping of S. cerevisiae and S. bayanus; specifically, the S. cerevisiae \u2013 S. paradoxus grouping was supported by 937 RGC_CAM sites whereas the S. cerevisiae \u2013 S. bayanus grouping was supported by only 62 RGC_CAM sites (a greater than 9:1 ratio). For RGC_non-CAM sites, the IC value of 0.36 for the same S. cerevisiae \u2013 S. paradoxus grouping stems from the fact that there was much larger support for a conflicting grouping; specifically, whereas the S. cerevisiae \u2013 S. paradoxus grouping was supported by 25,625 RGC_non-CAM sites, the S. cerevisiae \u2013 S. bayanus grouping was supported by 5,008 RGC_non-CAM sites (approximately a 5:1 ratio). Similarly, IC/ICA values were 0.44/0.40 for RGC_CAM sites but only 0.22/0.17 for RGC_non-CAM sites for the clade that groups S. cerevisiae, S. paradoxus and S. mikatae. Thus, the TC/TCA values for the phylogeny constructed based on RGC_CAM substitutions were 1.10/1.06 whereas those for the phylogeny constructed based on RGC_non-CAM substitutions were 0.58/0.53.Phylogenetic analysis of both RGC_CAM and RGC_non-CAM sites recovered the established species phylogeny  [20], [2p-value < 2.2\u00d710\u221216).To further investigate the degree of homoplasy in sites containing RGC_CAM and RGC_non-CAM substitutions, we calculated the average HI per site across the two data sets. The mean HI per site was 0.100 for RGC_CAM sites and 0.215 for RGC_non-CAM sites. We also measured the numbers of RGC_CAM sites that support the accepted yeast phylogeny , of RGC_CAM sites that do not support the yeast phylogeny (530), as well as the numbers of RGC_non-CAM sites that support , or do not support  the yeast phylogeny . A Fishep-value \u200a=\u200a 1.55\u00d710\u221233; 52 bins versus 52 RGC_non-CAM bins: p-value \u200a=\u200a 5.46\u00d710\u221218)  . The low6\u00d710\u221218) , suggestSaccharomyces yeast species and identify 2,647 sites that contain RGC_CAM substitutions and 100,887 sites that contain RGC_non-CAM substitutions. Comparison of the congruence (by means of examining internode certainty) and homoplasy (by means of examining the homoplasy index) of RGC_CAM and RGC_non-CAM sites indicated that RGC_CAM substitutions show much higher levels of congruence and significantly lower levels of homoplasy relative to RGC_non-CAM substitutions, although it should be noted that both types of markers are able to accurately resolve the phylogeny of these five species. Our results suggest that, although very rare, RGC_CAM substitutions are high quality markers for phylogenetic inference and might be a very attractive source of alternative markers in large datasets showing high levels of homoplasy.In conclusion, we have developed computational scripts that allowed us to examine nearly 2.7 million sites of amino acid alignment from 5 closely related Table S1RGC_CAM substitution matrix.(PDF)Click here for additional data file."}
+{"text": "M. tuberculosis and BCG. We therefore used a bioinformatic pipeline to define proteins which are present in common NTM and absent from the M. tuberculosis complex, using protein BLAST, TBLASTN and a short sequence protein BLAST to ensure the specificity of this process. We then assessed immune responses to these proteins, in healthy South Africans and in patients from the United Kingdom and United States with documented exposure to NTM. Low level responses were detected to a cluster of proteins from the mammalian cell entry family, and to a cluster of hypothetical proteins, using ex vivo ELISpot and a 6 day proliferation assay. These early findings may provide a basis for characterising exposure to NTM at a population level, which has applications in the field of TB vaccine design as well as in the development of diagnostic tests.The lack of an effective TB vaccine hinders current efforts in combating the TB pandemic. One theory as to why BCG is less protective in tropical countries is that exposure to non-tuberculous mycobacteria (NTM) reduces BCG efficacy. There are currently several new TB vaccines in clinical trials, and NTM exposure may also be relevant in this context. NTM exposure cannot be accurately evaluated in the absence of specific antigens; those which are known to be present in NTM and absent from  Tuberculosis (TB) remains a major threat to global public health, with an estimated 9.27 million new cases occurring worldwide in 2007 BCG is currently the only licensed TB vaccine. It protects against severe forms of the disease in childhood, but has very poor efficacy in preventing adult pulmonary TB where it is most needed, in tropical countries which have a high incidence of TB. Protection ranges from 80% in the UK M. avium had a reduced protective immune response to subsequent BCG vaccination, cleared the live BCG vaccine more rapidly than mice not exposed to M. avium, and were more susceptible to M. tuberculosis infection following BCG vaccination M. avium was administered after BCG vaccination showed declining efficacy of BCG with ongoing exposure to M. aviumM. tuberculosis infection following NTM exposure M. tuberculosisOne theory as to why BCG works less well in the tropics than in temperate regions is exposure to non-tuberculous mycobacteria (NTM) et al have shown that adolescents living in Malawi had marked pre-existing T cell responses to purified protein derivative (PPD) prior to BCG vaccination. After vaccination with BCG the increase in response to PPD was minimal, in contrast to adolescents in the UK, in whom baseline responses were very low and a marked increase in PPD-specific responses post BCG-vaccination was detected In humans, Black M. tuberculosis and BCG. Although previous studies have investigated NTM exposure using PPD derived from NTM species It is, however, hard to characterise NTM exposure with certainty, as there are currently no defined antigens which are specific to NTM and not also present in (and therefore confounded by exposure to) Further, the nature of the T cell response necessary for protection against mycobacterial infection is not known: a limited number of strong responses to critical epitopes or antigens may be required; alternatively multiple low affinity T cell responses to various antigens may provide protection, including cross-species protection.M. paratuberculosis) cause considerable morbidity and economic losses in animal husbandry If exposure to NTM has an effect on BCG replication and hence immunogenicity and protective efficacy, this effect might also be seen with novel TB vaccines based on BCG. Viral vectored and protein/ adjuvant subunit vaccines do not replicate, so may not be susceptible to interference by this mechanism M. tuberculosis complex), and which could be used to study NTM exposure with a high degree of specificity. This is relevant for studies both of BCG efficacy and of novel TB vaccines currently in development. We used a bioinformatic approach to define families of proteins which are present in common NTM and not in the M. tuberculosis complex, and investigated the T cell immune response to these antigens in patients in the UK and US who had been exposed to NTM, in cord blood samples with a low chance of NTM exposure, and in healthy South Africans from the Western Cape, an area with documented NTM exposure In this study, we aimed to define antigens which are specific to NTM , was also loaded into the database. Mycobacterial species were classified into 3 groups: group 1\u200a=\u200a NTM species of interest, group 2\u200a=\u200aM. tuberculosis complex, group 3\u200a=\u200a all other mycobacterial species  and Sanger websites on 2 December 2009 and stored in a BioSql database  species . NTM speM. tuberculosis-specific RD1 gene products M. tuberculosis CDC1551 and M. bovis BCG (data not shown).NCBI protein BLAST was used to compare all protein sequences in the database against each other using NCBI default parameters M. tuberculosis complex). Selected proteins were subsequently compared with all group 2 mycobacterial genome sequences using TBLASTN, and excluded if significant matches were found. NCBI BLASTClust A protein was selected if present in at least 3 other species from group 1 (NTM of interest) and absent from group 2 (M. tuberculosis complex) during TBLASTN, or (2) either (a) contained a majority of proteins with a prediction to be secreted or (b) contained proteins for which there was experimental evidence of immunogenicity.Clusters were selected for experimental testing if they: (1) contained no proteins which hit nucleotide sequences in group 2 , from Oxfordshire, UK  and from Portland VA Medical Center, Portland, United States (US) (reference IRB00004835). Written informed consent was obtained from all study participants. Healthy individuals living in the Western Cape, South Africa, who were known to have a negative result to the QuantiFERON\u00ae-TB Gold In-Tube test (Cellestis) were recruited. An additional cohort of individuals who were known to have a positive result was also recruited. Patients from whom NTM had been isolated from sputum on at least 2 occasions  and with a low risk of TB exposure were recruited from the Churchill Hospital, Oxford, UK, and from Portland VA Medical Center, US. Cord blood was collected at the John Radcliffe Hospital, Oxford, UK.Peptides were dissolved in DMSO, stored at 1 mg/ml at \u221220\u00b0C and used at a final DMSO concentration of <0.35%. Pools were arranged such that each pool contained peptides from only one cluster except in the case of very small clusters, which were combined .2 in a humidified incubator 50 ml blood was taken from adult volunteers into sodium heparin tubes. Cord blood was taken into a standard blood donor collection bag, containing citrate phosphate dextrose anticoagulant. Peripheral blood mononuclear cells (PBMC) were separated and cryopreserved from the UK blood samples as previously described ex vivo ELISpot assay. Peptides were tested in 20 pools, each pool containing between 58 to 85 peptides. Briefly, nitrocellulose bottomed 96-well Multiscreen HA filtration plates  were coated with anti-human-IFN-\u03b3-mAb  overnight at 4\u00b0C. 3\u00d7105 PBMC were plated in 100 \u00b5l final volume and plates were incubated for 18\u201320 h in a humidified 37\u00b0C 5% CO2 incubator with peptide, PPD, PHA/PMA or media alone. Assays were performed in duplicate and the results averaged. Plates were washed and developed as previously described 6 PBMC in at least one positive control well, and if both negative control wells had <20 SFC. Cut off for a positive response was calculated as 3 median absolute deviations (MADs) above the median.PBMC IFN-\u03b3 responses to NTM-specific peptides , PPD , phytohaemagluttinin/ phorbol 12-myristate 13-acetate  and pool of ESAT-6/ CFP-10 peptides ((negative control), 15-mers overlapping by 10 amino acids, 4 \u00b5g/ml) were measured using overnight 2. On day 3, PHA (1 \u00b5g/ml) was added to one of the \u2018media only\u2019 wells. On day 6, PBMC were stained with LIVE/DEAD Fixable Violet Dead Cell Stain (Invitrogen) and fixed with BD FACS Lysing Solution (BD Biosciences). Following permeabilisation with Perm/Wash (BD Biosciences) cells were incubated with the following monoclonal antibodies: anti-CD3-QDot 605, anti-CD4-APC, anti-CD8-PerCP-Cy5.5 and anti-Ki67-PE. All antibodies were from BD Biosciences except for CD3-QDot 605, which was from Invitrogen, and were used in pre-determined optimal concentrations. Following washes, samples were acquired on a BD LSRII flow cytometer . Time gates (excluding fluctuations in fluorescence), antibody aggregate exclusion gates and forward scatter/ side scatter gates (selecting singlets) were followed by gating on live CD3 positive cells, then CD4 or CD8 positive cells, then Ki67 positive cells. Data were analysed using FlowJo Software version 8.8.3 (Treestar Inc.) and GraphPad Prism (version 5). Proportion of Ki67 positive was used as the readout of proliferation All cryopreserved PBMC samples from UK-based NTM-exposed patients and cord blood samples were analysed, where remaining cell numbers allowed M. tuberculosis complex (group two) and all other mycobacterial species (group three)  .M. fortuitum isolate, and genome assembly was undertaken using the programme Velvet M. fortuitum), and predicted protein sequences were derived .Next-generation (Illumina GAIIx) sequencing was performed on a clinical  derived . Median M. tuberculosis complex). These proteins were selected for further analysis.NCBI protein BLAST was used to compare all protein sequences against themselves see ; 4048 grM. tuberculosis complex during the TBLASTN process was excluded, leaving 78 clusters varying in size from 2\u201317 proteins. These clusters were biologically relevant, containing families such as the Mce family, the DoxX and the 27kDa lipoprotein antigen. From these, 12 clusters were selected for experimental testing  and not in the M. tuberculosis complex (group 2 organisms). TBLASTN, which is independent of genome annotation, was then used to increase the specificity of the results, comparing protein sequences against translated nucleotide sequences. This was done as protein sequences derived from genome annotation are not always experimentally confirmed, so accuracy of annotation may vary M. tuberculosis complex or common bacterial peptides. We focussed on secreted proteins since this group of proteins is associated with the induction of strong immune responses in mycobacteria We used a bioinformatic pipeline to define peptides which are shared between common NTM, eliminating peptides with sequence homology to either the ex vivo IFN-\u03b3 ELISpot, statistically significant responses were seen to pools 5 and 6 of the 20 NTM-specific peptide pools in the healthy South African cohort. These pools represent the Mce family of proteins (pool 5) and a cluster of hypothetical proteins  family of proteins are virulence factors which are involved in mycobacterial entry and survival in macrophages M. tuberculosis-associated mycobacterial antigens in individuals with latent M. tuberculosis infection. There are a number of possible explanations for this. Unlike M. tuberculosis, NTM are not highly pathogenic. About 80% of the predicted proteome of group 1 NTM is shared by members of the M. tuberculosis complex, and it is possible that these \u2018core proteins\u2019 of all mycobacteria are the most immunogenic. Secondly, in the absence of a gold standard test for NTM exposure we could not determine whether all South African volunteers had been exposed, nor how recently. The individuals in whom we detected positive responses may have been the only individuals with recent and significant exposure. Proliferative studies in a larger cohort with prospective assessment of NTM exposure might address this, although this would be associated with significant challenges. In the absence of a defined clinical phenotype, it remains extremely difficult to define populations of individuals with NTM exposure, and this is a significant limitation in the conduct of studies such as this.It is promising that we appear to have demonstrated responses to NTM-specific antigens. However, responses were significantly lower than are routinely seen to immunodominant Responses seen in UK and the US patients, where the exposure was definite but the duration of the NTM exposure cannot be quantified, were weaker than in South Africans. There are several possible explanations for this. These individuals suffered from chronic lung diseases, some of which are associated with low nutritional state and immune dysregulation M. bovis infection in cattle M. ulceransM. bovis pipeline consisted of a genome BLAST using the NCBI and Tuberculist servers. Cross-reactivity occurred between M. bovis-infected and BCG-exposed cattle, and further examination of the individual peptides responsible for cross-reactive responses highlighted that cross-reacting peptides hit similar sequences from M. tuberculosis on protein BLAST. Similarly, in the M. ulcerans pipeline, 11 out of 34 protein clusters identified using BLASTClust were found on PCR to have previously unknown homologues in strains of M. marinum. The M. leprae pipeline compared the M. leprae genome and predicted protein sequences with genome and predicted protein sequences of other published mycobacteria using BLAST and FASTA. Proteins were recognised by T cells from patients infected with M. tuberculosis as well as by those with leprosy M. kansasii, M. szulgai and M. marinum, but these species were not isolated from samples from any of the UK or US patients; we do not know which species the South Africans were exposed to.Bioinformatic pipelines have been used for the purposes of identifying antigens for the diagnosis of M. tuberculosis, BCG and other members of the M. tuberculosis complex. In South Africans, we detected low level T cell responses to multiple proteins, including the Mce family of proteins, which are virulence factors in mycobacteria. Mce proteins and a pool from a cluster of hypothetical proteins were also recognised using a proliferation assay on PBMC from UK and US patients with NTM isolated from sputum samples. Further exploration of this approach is warranted, and the specificity of these promising pools could be confirmed by investigating larger cohorts of individuals from rural tropical areas with NTM exposure defined by surrogates such as strong PPD responses in the absence of any response to RD1 antigens such as ESAT-6 / CFP-10.In conclusion, we used a novel, comprehensive and stringent approach to define clusters of proteins which are predicted to be secreted and are present in common species of NTM but absent from Figure S1Peptide \u2018hits\u2019 on bacterial reference proteins following Protein BLAST. Protein BLAST was carried out on all peptide sequences against all bacterial reference proteins. The resulting hits are shown by bacterial genus. Y axis: median number peptide hits per genus. X axis: bacterial genus.(TIF)Click here for additional data file.Figure S2Correlation between response to ESAT-6/CFP-10 peptide pool and NTM-specific peptide pools. Correlation between response to pool of ESAT-6/ CFP-10 peptides and NTM-specific peptide pools, in all healthy South African volunteers.(TIF)Click here for additional data file.Figure S3Peptide pools 5 and 6: constituent proteins and peptides. Showing the identities of proteins making up the clusters in pools 5 and 6, and the amino acid sequences for the peptides derived from them.(PDF)Click here for additional data file.Figure S4Protein clusters to which immune responses were detected using ex vivo ELISpot and proliferation assays. A. Protein sequences making up cluster A: NP_960785.1: hypothetical protein MAP1851 [Mycobacterium avium subsp. paratuberculosis K-10], YP_881588.1: mce related protein [Mycobacterium avium 104], NZ_ABIN01000058_P_11090: predicted protein sequence from M. intracellulare genome, YP_001852130.1: Mce protein, Mce5A [Mycobacterium marinum M], YP_907368.1: Mce protein, Mce5A [Mycobacterium ulcerans Agy99], NOTNCBI_FOR1052_P_8286: predicted protein sequence from M. fortuitum genome, YP_879398.1: mce related protein [Mycobacterium avium 104], NP_959042.1: hypothetical protein MAP0108 [Mycobacterium avium subsp. paratuberculosis K-10], NZ_ABIN01000014_P_12211: predicted protein sequence from M. intracellulare genome, YP_001705239.1: putative Mce family protein [Mycobacterium abscessus ATCC 19977], YP_001701754.1: putative MCE family protein [Mycobacterium abscessus ATCC 19977], YP_001705291.1: putative Mce family protein [Mycobacterium abscessus ATCC 19977], YP_001848502.1: MCE-family protein Mce6A [Mycobacterium marinum M], YP_908277.1: MCE-family protein Mce6A [Mycobacterium ulcerans Agy99], YP_001702434.1: putative Mce family protein [Mycobacterium abscessus ATCC 19977], YP_001705322.1: putative Mce family protein [Mycobacterium abscessus ATCC 19977]. B. Protein sequences making up cluster B: YP_001852137.1: hypothetical protein MMAR_3871 [Mycobacterium marinum M], YP_907374.1: hypothetical protein MUL_3803 [Mycobacterium ulcerans Agy99], NP_960792.1: hypothetical protein MAP1858 [Mycobacterium avium subsp. paratuberculosis K-10], YP_881582.1: hypothetical protein MAV_2381 [Mycobacterium avium 104], NZ_ABIN01000058_P_11097: predicted protein sequence from M. intracellulare genome, YP_001848485.1: hypothetical protein MMAR_0160 [Mycobacterium marinum M], YP_908294.1: hypothetical protein MUL_4936 [Mycobacterium ulcerans Agy99], YP_001701747.1: hypothetical protein MAB_1003c [Mycobacterium abscessus ATCC 19977], NP_959049.1: hypothetical protein MAP0115 [Mycobacterium avium subsp. paratuberculosis K-10], YP_879405.1: hypothetical protein MAV_0109 [Mycobacterium avium 104], NZ_ABIN01000160_P_8921: predicted protein sequence from M. intracellulare genome, NOTNCBI_FOR324_P_3844: predicted protein sequence from M. fortuitum genome. Note: protein sequences from M. marinum and M. ulcerans are shown in these clusters, but peptides were not picked from these species. Amino acid sequence is shown for each protein, with protein sequences identified by NCBI accession number. Numbers under the clusters indicate the amino acid number in the sequence; grey bars under the clusters indicate the degree of similarity between the sequences .(TIF)Click here for additional data file."}
+{"text": "Pichia pastoris, simultaneously employs at least two types of replication origins\u2014a G/C-rich type associated with transcription start sites and an A/T-rich type more reminiscent of typical budding and fission yeast origins. We used a suite of massively parallel sequencing tools to map and dissect P. pastoris origins comprehensively, to measure their replication dynamics, and to assay the global positioning of nucleosomes across the genome. Our results suggest that some functional overlap exists between promoter sequences and G/C-rich replication origins in P. pastoris and imply an evolutionary bifurcation of the modes of replication initiation.The well-studied DNA replication origins of the model budding and fission yeasts are A/T-rich elements. However, unlike their yeast counterparts, both plant and metazoan origins are G/C-rich and are associated with transcription start sites. Here we show that an industrially important methylotrophic budding yeast,  Pichia pastoris. We show that P. pastoris has two general classes of origins\u2014A/T-rich origins resembling those of most other yeasts, and a novel, G/C-rich class, that appear more robust and are associated with promoters. P. pastoris is the first known species using two kinds of origins and the first known budding yeast to use a G/C-rich origin motif. Additionally, the G/C-rich motif matches one of the motifs annotated as binding sites of the human Hsf1 transcriptional regulator suggesting that in this species there may be a link between transcriptional regulation and DNA replication initiation.Genome duplication in eukaryotes initiates at loci called replication origins. Origins in most budding and fission yeasts are A/T-rich DNA sequences, while metazoan origins are G/C-rich and are often associated with promoters. Here we have globally mapped replication origins and nucleosome positions in an industrially important methylotrophic yeast,  While the initiation of DNA replication at origins is a key regulatory feature of genome replication in all organisms studied, the structural components of these Saccharomyces cerevisiae, where origin fragments shorter than 100 bp can act as autonomously replicating sequences (ARSs) sufficient for episomal plasmid maintenance S. cerevisiae are the product of a temporal timing program acting on origins with variable initiation efficiencies, the underlying regulators of replication dynamics are incompletely understood Schizosaccharomyces pombe where longer (500 bp to 1 kb) stretches of A/T DNA are stochastically recognized by a domain of nine AT-hooks on the N-terminus of one of the ORC subunits\u2014Orc4 Replication origins have been best defined in the budding yeast Replication origins in metazoans have not been delineated to the same extent as in yeast. Metazoan replication initiates in broad replication zones that range up to 500 kb in length. Replication timing is controlled by both stochastic and regulated forces and is highly plastic throughout developmental transitions Kluyveromyces lactis has a 50 bp ARS consensus motif that can be accurately used to predict origin locations Lachancea kluyveri recognizes sequences similar to the S. cerevisiae ACS, but with a much relaxed requirement for specific sequences L. waltii requires a consensus motif that bears similarities to aspects of both the S. cerevisiae and the K. lactis ACS motifs S. japonicus and S. octosporus implicated G/C-rich elements in origin function Recent studies in non-canonical yeast species have elucidated that, even in related species, a diversity of consensus motifs are implicated in origin function. All budding yeast species tested so far have short A/T-rich origins with different consensus motifs. Pichia pastoris (Komagataella phaffii) P. pastoris ARSs require a G/C-rich motif that closely matches one form of the binding site of the well-studied Hsf1 transcriptional regulator P. pastoris contains a member of the AT-rich class of origin, suggesting that use of plasmids bearing a G/C-rich origin will yield immediate improvements for strain engineering.In this study we have comprehensively profiled replication origin location, structure, and dynamics in the methylotrophic budding yeast P. pastoris genome that have ARS function, but do not have ACS elements seen in S. cerevisiae ARSs P. pastoris (PpARSs) we utilized ARS-seq, a high-throughput ARS screen combined with deep sequencing (URA3 shuttle vector. A P. pastoris ura3 strain (JC308) was transformed with this library and plated on medium lacking uracil (C-Ura) resulting in \u223c20,000 colonies from an estimated 2\u20133\u00d7106 transformants. Colonies were replica-plated on C-Ura plates and grown for four additional days before the growing colonies were pooled. Total DNA was extracted from pooled cells. ARS inserts were amplified using vector-specific Illumina primers and sequenced using paired-end deep sequencing. The sequencing reads were assembled into 971 unique genomic fragments  setting. MEME identified a 20 bp G/C-rich consensus motif  with a TYGAAC core . Furthermore, the 107 ARSs bearing the GC-ACS motif (\u201cGC-ARSs\u201d) were significantly enriched  for G/C-content relative to the 204 ARSs without the motif (\u201cAT-ARSs\u201d). In fact, the AT-ARSs alone are not significantly enriched for G/C or A/T content relative to all of intergenic DNA , suggesting that GC-ARSs are chiefly responsible for the overall G/C enrichment in the ARS dataset. Additionally, while both classes of ARSs are predominantly intergenic, GC-ARSs associate with longer intergenes whereas AT-ARSs do not. The median length of all intergenes in the P. pastoris GS115 strain background is 216 bp We found that S. cerevisiae, where replication origins are enriched in convergently transcribed intergenes (where both adjacent genes are transcribed toward the intergene), P. pastoris ARSs are depleted in convergent intergenes . However, unlike bona fide replication origins in their chromosomal context, we assayed genomic origin firing by 2D-gel electrophoresis at two genomic loci  across the genome. The remaining twenty-eight intergenic occurrences of this motif that were not detected by ARS-seq have significantly lower match scores than the motifs within ARS fragments (T-test P\u200a=\u200a1.49e-07) suggesting that strong matches to the GC-ACS are good indicators of ARS activity.P. pastoris ARS-C379 and ARS-A2772. This method involves competitively growing yeast transformed with a library of randomly mutagenized variants of a given ARS and measuring the enrichment of each allele through paired-end deep sequencing of samples over time . Resulting colonies on selective medium plates  were pooled and the cell mixture was used to inoculate a 1 L culture of liquid selective medium. The culture was grown at 30\u00b0C and the abundance of each ARS variant at different times was measured by 101 bp paired-end sequencing.To assay directly the sequence determinants of ARS function, we applied a deep mutational scanning ver time , and S5.P. pastoris can utilize at least two different non-overlapping sequence motifs for the initiation of DNA replication. We also found that these ARSs retained function in both orientations within the vector, on different length inserts, and in other plasmid contexts (data not shown), suggesting that at least one of these sequences, or an equivalent, must be present for the initiation of plasmid replication and that each is sufficient for initiation.The results of mutARS-seq show a striking difference in the sequences required for function of the two types of PpARSs. ARS-C379 shows a zone of constraint within the region corresponding to the match of the GC-ACS motif  and S3 fS. cerevisiae or S. pombe, presumably due to higher-level regulation of timing that is absent on plasmids. To overcome this limitation of the ARS assay, we used an approach that combines cell sorting and deep sequencing P. pastoris genome. This method calculates the DNA copy number ratio between S phase and G1 phase cells in sliding windows across the genome. Since a replicated region is present in twice the copy number of a non-replicated region, this copy number ratio is proportional to the relative mean replication time of a given locus While the ARS assay can be used for high-precision mapping of sequences required for replication initiation, it is not an accurate measure of origin activity in the genomic context. No correlation between ARS activity and genomic replication timing has been detected in either Approximately 1.5 million G1 and S phase cells were sorted from an exponentially growing culture using FACS. Total genomic DNA was isolated, randomly sheared, and sequenced to high coverage to measure the relative DNA copy number of all genomic loci. The ratios of sequence reads between G1 and S phase samples were calculated in non-overlapping 1 kb sliding windows across the genome and normalized based on the total number of reads within each sample (Methods). The resulting ratios from biological replicates were LOESS smoothed, yielding highly reproducible replication timing curves  close to the site of initiation leosomes . On the http://www.factorbook.org/mediawiki/index.php/HSF1). Additionally, when centered on the GC-ACS motif (in the TYGAAC orientation), GC-ARSs show a pronounced poly(dA) region around 10 bp to 35 bp upstream of the motif  and 16 genes had GC-ACSs within 500 bp upstream of the start codon (hypergeometric test P\u200a=\u200a0.037).The underrepresentation of GC-ARSs in convergently transcribed intergenes  suggestshe motif  and S7A.he motif . It has P. pastoris. From these, 1,188 unique genes were assigned as closest gene to an occurrence of the GC-ACS and 1,236 unique genes were assigned as closest to an HSE. A significant number (524) of unique genes were present in both lists, suggesting an association between GC-ACS and HSE motifs (hypergeometric test P\u200a=\u200a4.6e-67). While HSF function in P. pastoris has not been studied, these results show an enrichment of GC-ACS motifs in regions likely to be regulated by HSF. Furthermore, the GC-ACS motif is positioned close to TSSs .Since the GC-ACS is associated with promoters, it raises the possibility that transcription is required for origin activation. If this possibility were true, then the DNA between the GC-ACS and the TSS may be required for ARS function. Since miniARS-seq screens large numbers of randomly sheared ARS sub-fragments, we were able to test this possibility by determining what sequences flanking the GC-ACS are required for ARS function. Using the full list of inferred functional ARS cores we calculated the length of sequence between the edge of the consensus motif and the edge of the ARS core on either side of the motif . The disThe majority of ARSs in budding yeast require sequences on the 3\u2032 side of the ACS (on the T-rich strand) collectively called \u201cB-elements\u201d S. cerevisiae and S. pombe have yielded great insights into origin function, but lack several properties exhibited by metazoan origins. For one, metazoan origins have G/C-rich signatures whereas all yeast origin sequence determinants described to date are A/T-rich with the possible exception of fission yeast S. japonicus, where GC-rich motifs have been implicated in origin function through sequence analysis. Another key difference between yeast and metazoan origins is the connection between replication initiation and transcription. While promoter-associated origins tend to be early-firing in metazoans, this phenomenon has not been previously described in yeast. These discrepancies limit the value of most yeast species as models for the study of replication origins from higher eukaryotes. A better model would ideally possess the beneficial characteristics of yeast (genetic and molecular tools) while also recapitulating more of the traits displayed by metazoans.Faithful genome duplication is essential to all living organisms. Like many other cellular processes, DNA replication is primarily regulated at the initiation step. Understanding the regulation of initiation at replication origins is therefore key to understanding how different species replicate their genomes. The extensively studied yeasts P. pastoris, a budding yeast that is very distantly related to both the S. cerevisiae and S. pombe yeasts P. pastoris ARSs did not function in S. cerevisiae, suggesting key mechanistic differences in replication initiation between the two species P. pastoris and were able to delineate the essential functional regions to <200 bp in most cases. As in other budding yeasts we found PpARSs to reside predominantly in intergenic regions. However, unlike other studied yeasts, P. pastoris displayed a conserved G/C-rich motif (GC-ACS) in approximately 35% of its ARSs. In fact, almost all strong intergenic matches to this motif were isolated in our ARS screen, suggesting a causal role for this motif in origin function. We were unable to detect a strong conserved motif within the other origins (AT-ARSs). It is possible that the AT-ARSs function with an ill-defined sequence determinant similar to those seen in S. pombe and L. kluyveriIn this study we generated a comprehensive profile of replication origins in S. cerevisiae \u201cB-elements\u201d. The fact that the GC-ACS motif retains function within different plasmid contexts supports this hypothesis. The mutARS-seq experiment on ARS-A2772, an AT-ARS, revealed a very different region of functional constraint  genus, or perhaps only P. pastoris. Another observation that points to this motif being used for multiple functions is that a G/C-rich motif constructed from mutARS-seq data , an ARS discovered almost three decades ago S. cerevisiaeP. pastoris, facilitating strain engineering efforts in this system.In addition to elucidating the features of replication dynamics, our data offer useful tools and data resources for this industrially important yeast. We anticipate that our nucleosome position map will be useful for studies of chromatin and gene expression, especially when combined with transcriptome data P. pastoris strain used in these studies was JC308 (James Cregg), a ura3 auxotroph of the GS115 background strain. All yeast growth was performed at 30\u00b0C; all bacterial growth was performed at 37\u00b0C. The plasmid vectors used in this study were previously described E. coli work was done using Alpha-Select Gold Efficiency competent cells (Bioline). All enzymes used were from New England Biolabs unless otherwise noted. Primers were purchased from IDT unless otherwise noted. PCR purification and purification of digested plasmids was done using the DNA Clean and Concentrator-5 Kit (Zymo Research). Plasmid DNA was purified using the Wizard Plus SV Miniprep Kit (Promega).The P. pastoris genomic DNA was isolated from cells grown in YPD using a phenol/chloroform bead-disruption method followed by ultracentrifugation in a CsCl gradient  followed by EtOH precipitation. Genomic DNA was fragmented and ligated as described P. pastoris cells were transformed with libraries using a custom lithium acetate protocol as follows. To make competent cells yeast were grown in YPG medium  until OD600 density of 1. Cells from 1L of culture were spun down, rinsed and resuspended in 10 mL of TE/LiOAc . Cell suspensions were incubated at 30\u00b0C with shaking for 30 minutes, dispensed into 100 \u00b5L aliquots and frozen at \u221280\u00b0C. For transformations competent cells were thawed at room temperature, mixed with 1\u20135 \u00b5g of plasmid DNA, 600 \u00b5L of \u201ctwo-step\u201d transformation buffer  and incubated at 30\u00b0C with gentle rotation for 30 minutes. The cell mixture was then heat-shocked at 42\u00b0C for 30 minutes and plated. Cells were grown for five days, replica-plated, and grown for three more days before cells were pooled for plasmid extraction. DNA shearing for miniARS-seq, plasmid recovery from yeast, and Illumina sequencing were performed as described ARS-seq and miniARS-seq screens were performed largely as described Illumina paired end sequencing reads were uniquely mapped to the GS115 genome mutARS-seq was performed largely as described ARS sequences bearing mutations  were ordP. pastoris intergenic sequences. Both MAST The MEME de novo motif discovery tool P. pastoris was grown to early log phase in YEPD and harvested for genomic DNA isolation 32P-dATP labeled PCR probes.A 1 L culture of P. pastoris cells were subjected to flow sorting using standard techniques on a BD FACsAria II cell-sorter. The purity of each sorted sample was determined to be \u223c95%. Genomic DNA from 1.5\u20132 million G1 and S-phase cells was isolated using the YeaStar Genomic DNA Kit (Zymo Research). Randomly fragmented sequencing libraries were prepared using the Nextera DNA Sample Preparation Kit (Illumina) P. pastoris GS115 reference genome and \u223c1% of the reads in each sample were removed due to multiple mapping sites. After processing, 25\u201327 million reads were assigned to 1 kb bins across the genome resulting in average count-depth of 2936 reads/bin for G1 sample of replicate 1, 2796 reads/bin for G1 sample of replicate 2, 2843 reads/bin for S sample of replicate 1, and 2913 reads/bin for S sample of replicate 2. Reads were mapped using Bowtie and custom scripts were used to generate replication timing profiles as described Replication timing experiments were performed largely as described 600 of 1 and then cross-linked with formaldehyde. The two samples were bead disrupted in 10 mM Tris-HCl pH 8.0 with 1 mM CaCl2. Visually lysed samples were then MNase digested for 30 minutes at increasing concentrations of MNase. Cross-links were removed by overnight incubation at 65\u00b0C followed by DNA extraction with phenol/chloroform. Extracted DNA was separated using a 2% agarose gel to visualize the mononucleosome enriched band. DNA corresponding to \u223c150 bp was then extracted and sequenced using the Illumina HiSeq platform. The samples were divided in half to provide technical replicates.Nucleosome positions were mapped similarly to the method described All sequencing data presented are available from the National Center for Biotechnology Information Sequence Read Archive .Figure S1Summary of ARS-seq and miniARS-seq results. (A) ARS fragment length distributions. The distribution of lengths of unique ARS-seq and miniARS-seq inserts as shown in (PDF)Click here for additional data file.Figure S2The direction of genes flanking intergenic ARSs. Intergenic regions >1 bp in length (PDF)Click here for additional data file.Figure S3ARS-C379 mutARS-seq data during competitive growth. Data processed as described (Methods) is shown as the average of two replicates for 12-, 24-, and 36-hour timepoints normalized against the same input sample. Data are plotted on the same y-axis scale to aid visual comparison. Scatterplots show correlations between replicates of the same timepoint samples (lower panels).(PDF)Click here for additional data file.Figure S4ARS-A2772 mutARS-seq data during competitive growth. Data processed as described (Methods) is shown as the average of two replicates for 12-, 24-, and 36-hour timepoints normalized against the same input sample. Data are plotted on the same y-axis scale to aid visual comparison. Scatterplots show correlations between replicates of the same timepoint samples (lower panels).(PDF)Click here for additional data file.Figure S5Comparisons of mutARS-seq data during competitive growth. Averaged mutARS-seq data from 12-, 24-, and 36-hour timepoints are plotted as scatterplots.(PDF)Click here for additional data file.Figure S6P. pastoris chromosomes. Replication timing profiles were computed as discussed Click here for additional data file.Figure S7Comparison of GC-ARS motifs. (A) The ACS motif identified from 107 GC-ARSs when the motif length is forced to be 50 bp. (B) The motif obtained from mutARS-seq of ARS-C379 using a procedure identical to the one used to obtain the AT-rich motif in (PDF)Click here for additional data file.Figure S8Distance between GC-ACS and flanking ATGs. For all instances where the GC-ARS is found adjacent to the 5\u2032 end of a gene, we calculated the distance between the start ATG codon of the ORF and the closest edge of the GC-ACS match. The distributions of ATG-to-ACS distances are plotted as histograms based on the direction relative to the ACS.(PDF)Click here for additional data file.Table S1List of ARS-seq fragments. \u201cfragment_name\u201d: a unique identifier for each fragment consisting of the fragment's endpoint coordinates. \u201cchrom\u201d: chromosome containing the fragment. \u201cstart\u201d: the leftmost coordinate of the fragment. \u201cend\u201d: the rightmost coordinate of the fragment. \u201crd\u201d: the read depth of the fragment. \u201crestriction_fragment\u201d: the restriction enzyme digest which made this fragment, \u201cunk\u201d\u200a=\u200acannot assign to a single site.(XLSX)Click here for additional data file.Table S2List of miniARS-seq fragments. \u201cminiARS_fragment_name: a unique identifier for each fragment consisting of the fragment's endpoint coordinates. \u201cchrom\u201d: chromosome containing the fragment. \u201cstart\u201d: the leftmost coordinate of the fragment. \u201cend\u201d: the rightmost coordinate of the fragment. \u201crd\u201d: the read depth of the fragment. \u201cARSseq_contig_name\u201d: the parent ARS-seq contig of this sub-fragment. \u201ccontig_count\u201d: the total number of miniARS fragments assigned to this parent contig.(XLSX)Click here for additional data file.Table S3Compiled list of PpARSs. \u201cARS_name\u201d: The systematic name of the ARS, based on chromosomal location. \u201ccontig_name\u201d: a unique identifier for each fragment consisting of the fragment's endpoint coordinates. \u201cchrom\u201d: chromosome containing the fragment. \u201cstart\u201d: the leftmost coordinate of the fragment. \u201cend\u201d: the rightmost coordinate of the fragment. \u201ccombined_rd\u201d: the combined read depth of all ARS-seq fragments in this contig. \u201cfragment_count\u201d: the number of ARS-seq fragments assigned to this contig. \u201cARSseq_core_start\u201d: leftmost coordinate of the inferred functional core of this ARS. \u201cARSseq_core_end\u201d: rightmost coordinate of the inferred functional core of this ARS. \u201cARSseq_core_len\u201d: length of the inferred functional core of this ARS. \u201cminiARS_core_start\u201d: leftmost coordinate of the inferred functional core of this miniARS . \u201cminiARS_core_end\u201d: leftmost coordinate of the inferred functional core of this miniARS. \u201cminiARS_core_len\u201d: length of the inferred functional core of this miniARS. \u201cmanually_confirmed\u201d: has some fragment of this ARS been manually validated?(XLSX)Click here for additional data file.Table S4Manual validation results. \u201cfragment_name\u201d: the fragment orARS tested. \u201cchrom\u201d: chromosome containing the fragment. \u201cstart\u201d: the leftmost coordinate of the fragment. \u201cend\u201d: the rightmost coordinate of the fragment. \u201clength\u201d: the length of the fragment tested. \u201ccombined_rd\u201d: the read depth of the ARS .(XLSX)Click here for additional data file.Table S5Replication timing results. \u201cbin_name\u201d: a unique identifier for each 1 kb bin within the genome. \u201cchrom\u201d: chromosome containing the bin. \u201ccoord_kb\u201d: the value of the first position coordinate of the corresponding bin. \u201creplicate1_ratio\u201d: the relative replication ratio for biological replicate 1. \u201creplicate1_ratio\u201d: the relative replication ratio for biological replicate 2. \u201cnormalized_ratio\u201d: averaged, smoothed, and normalized replication ratio as described.(XLSX)Click here for additional data file.Table S6Locations of replication timing peaks. \u201cbin_name\u201d: the unique identifier of the 1 kb bin containing the center of the peak. \u201cchrom\u201d: chromosome containing the bin \u201ccoord_kb\u201d: the value of the first position coordinate of the corresponding bin.(XLSX)Click here for additional data file.Table S7Nucleosome position values. \u201cchrom\u201d: chromosome. \u201ccoord\u201d: coordinate within the chromosome. \u201cP1_A\u201d: nucleosome density in biological replicate 1, technical replicate 1. \u201cP1_B\u201d: nucleosome density in biological replicate 1, technical replicate 2. \u201cP2_A\u201d: nucleosome density in biological replicate 2, technical replicate 1. \u201cP2_B\u201d: nucleosome density in biological replicate 2, technical replicate 2. \u201cmean\u201d: the mean value of the four samples.(ZIP)Click here for additional data file."}
+{"text": "In the past, several methods have been developed for the prediction of conformational and linear (or continuous) B-cell epitopes. However, the existing methods for predicting linear B-cell epitopes are far from perfection. In this study, an attempt has been made to develop an improved method for predicting linear B-cell epitopes. We have retrieved experimentally validated B-cell epitopes as well as non B-cell epitopes from Immune Epitope Database and derived two types of datasets called Lbtope_Variable and Lbtope_Fixed length datasets. The Lbtope_Variable dataset contains 14876 B-cell epitope and 23321 non-epitopes of variable length where as Lbtope_Fixed length dataset contains 12063 B-cell epitopes and 20589 non-epitopes of fixed length. We also evaluated the performance of models on above datasets after removing highly identical peptides from the datasets. In addition, we have derived third dataset Lbtope_Confirm having 1042 epitopes and 1795 non-epitopes where each epitope or non-epitope has been experimentally validated in at least two studies. A number of models have been developed to discriminate epitopes and non-epitopes using different machine-learning techniques like Support Vector Machine, and K-Nearest Neighbor. We achieved accuracy from \u223c54% to 86% using diverse s features like binary profile, dipeptide composition, AAP (amino acid pair) profile. In this study, for the first time experimentally validated non B-cell epitopes have been used for developing method for predicting linear B-cell epitopes. In previous studies, random peptides have been used as non B-cell epitopes. In order to provide service to scientific community, a web server LBtope has been developed for predicting and designing B-cell epitopes ( Identification of smallest regions in an antigen also called an antigenic region that can activate immune system is one of the major challenges in designing of a subunit or peptide-based vaccine. These antigenic regions, which stimulate B-cell response, are known as B-cell epitopes. Prediction of B-cell epitope is difficult but important for designing a peptide-based vaccine et al, have analyzed conformational B-cell epitopes from antigen-antibody complexes and reported average length of conformation epitope as 15 residues In order to overcome limitations of experimental techniques, in the past several algorithms have been developed to predict linear B-cell epitopes In this study, for the first time, we have exploited the availability of several thousands of experimentally verified epitopes and non-epitopes. We have derived five datasets from Immune Epitope Database (IEDB) called Lbtope_Fixed, Lbtope_Fixed_non_redundant, Lbtope_Variable, Lbtope_Variable_non_redundant and Lbtope_Confirm dataset. We developed various models on these datasets for discriminating B-cell epitopes from non-epitopes. A web server has been developed for predicting B-cell epitopes using best models developed on these datasets.We have obtained experimentally validated 49694 B-cell epitopes and 50324 non B-cell epitopes from Immune Epitope Database (IEDB) in Jan 2012 Most of the machine learning techniques commonly used for developing prediction or class discrimination need definite length patterns. Since B-cell epitopes have variable length, we used truncation and extension technique used in previous studies to generate definite length peptides (epitopes & non-epitopes) of 20 residues . The redundant dataset contains 7824 B-cell epitopes and 7853 non-epitopes.Using Lbtope_Fixed dataset, we have created an 80% non-redundant dataset using CD-HIT First, we removed all epitopes or non-epitopes having less than five residues or more than fifty residues. All epitopes common in B-cell epitopes and non B-cell epitopes were also removed. We found that majority of common epitopes are related to autoimmunity. Our final dataset Lbtope_Variable contains 14876 unique B-cell epitopes and 23321 unique non B-cell epitopes.We again created an 80% non-redundant Lbtope_Variable dataset using CD-HIT. We obtained 8011 B-cell epitopes and 10868 non-epitopes.One of the challenges in creating dataset is its validity, though all epitopes, which we have extracted from IEDB, are experimentally tested. In order to improve the quality of epitopes/non-epitopes, we used only those epitopes/non-epitopes which reported in at least two studies. The final dataset Lbtope_Confirm contains 1042 unique B-cell epitopes and 1795 non B-cell epitopes.In this study, we generated and used various types of features of peptides that include binary profile or sparse matrix http://svmlight.joachims.org/) package for implementing SVM technique. SVM has been used in several biological problems, including functional characterization of proteins In this study, we used SVM_light . Sparse matrix contains information for each position and each type of amino acids in the pattern. We achieved accuracy range from 37\u201367% with MCC of 0.03\u20130.22 and AUC of 0.65, which is better than random prediction .It is already known that physico-chemical properties of amino acids are responsible for structural and functional behavior of peptides and proteins. In our study, we have tested few topological properties , which wBesides understanding the positional effect of amino acids, we also computed and compared the overall composition of epitopes and non-epitopes . SimilarSince only composition-based method can be applied to variable data, we applied amino acid composition, CTD, AAP and dipeptide composition methods on Lbtope_Variable and Lbtope_Confirm datasets. It was observed that dipeptide-based method performed best among other methods with accuracy 75.89, 82.33 and MCC 0.51, 0.64 on Lbtope_Variable, Lbtope_Confirm dataset respectively , S17. PeAlthough we have considered unique epitopes, the redundancy could be expected among them similar to protein sequences. However, it is known that properties of peptide could change with a single amino acid variation. Nevertheless, to have an idea of redundancy and model performance, we created non-redundant dataset corresponding to both Lbtope_Fixed and Lbtope_Variable databases. We found that the number of peptides decreased as expected, but the performance remained significant.We observed AUC of 0.61, 0.66 and 0.69 for simple amino acid, AAP and dipeptide composition respectively on Lbtope_Fixed_non_redundant dataset , S21. WeIt is important to compare the newly developed algorithm with existing algorithms, which requires testing of all methods on same dataset. Unfortunately, our dataset is different than datasets used in previous studies. Thus one to one comparison is not feasible. In order to understand differences and similarities in our and existing models, we tested our models on datasets used in earlier methods. Similarly, we tested previously developed methods on our datasets. It was observed that earlier models failed on datasets used in this study, and our models failed on existing datasets , S32. AuSimilarly, we evaluated existing methods (BCPred and Chen\u2019s method) on datasets used in this study. It was observed that these methods performed reasonably well on B-cell epitopes but failed on non B-cell epitopes , S29 [9]It can be suggested that existing models/methods perform reasonably fine on our B-cell epitopes, but failed on non B-cell epitopes. Similarly, our models failed on random peptides used in previous studies as non B-cell epitopes. This could be due to fact that our negative dataset comprised of experimentally verified non B-cell epitopes where as negative datasets of existing methods consist of random peptides generated from proteins.To know the effect of using experimental proved non B-cell epitope instead of random peptides from Swiss-Prot in development of model. We created another dataset Lbtope-positive-fbcpred-negative, in which instead of experimental non B-cell epitopes, we used random peptides from FBCPred dataset as non B-cell epitopes. Next, we performed a five-fold cross validation on the above- dataset and obtained 85% sensitivity with 0.71 MCC. On the other hand, Lbtope_Confirm has achieved 81% sensitivity with 0.65 MCC, a bit poorer than Lbtope-positive-fbcpred-negative \u2013S34. Takhttp://crdd.osdd.net/raghava/lbtope/.In order to provide prediction service to scientific community, we have developed a user-friendly web server based on the model developed in this study. The server is developed using PHP 5.2.9, HTML and JavaScript as the front end and installed on a Red Hat Enterprise Linux 6 server environment. The server takes antigen primary amino acid sequence (s) in \u2018FASTA\u2019 format, generates 20 amino acids overlapping peptides for Lbtope_Fixed dataset model, 5\u201330 amino acids overlapping peptides for variable datasets model and predicts the linear epitopes. The non-redundant model is also implemented in case of very high specificity. The output is antigen sequence (s) mapped with B-cell epitopes with a probability scale of 20\u201380%. A higher score implies higher probability of peptide to be B-cell epitope. We have developed separate dedicated pages for antigen and peptide submission to avoid any complexity. In addition, we have developed a peptide mutation tool, which creates all possible single point mutations in given peptide and calculates the probability score based on the algorithm and also predicts the other properties. Using the mutation tool, user can design better epitopes or even choose fewer epitopic peptides for the de-immunization of therapeutic proteins. The web server is freely available at Epitope mapping is no doubt a very useful procedure, which has vast applications in the area of therapy and diagnostics. Experimental methods do exist, but they require time, resources and cannot handle the pace with which biological data is generated. Therefore, computer algorithms have been developed over the decades to predict the B- and T-cell epitopes from antigen sequence or structure if available. It\u2019s observed that linear B-cell epitope prediction is more challenging than other epitope types like conformational B-cell or T-cell epitopes. This might be due to the reason that linear B-cell epitope posse\u2019s variable length from 2\u201385 amino acids as compared to the almost fixed length core of the T-cell epitopes. This variability imposes several obstacles for algorithm developers. Besides variability, all the methods to date have been developed on very small data set with negatives examples obtained from randomly chosen UniProt peptides or same antigens, which are not experimentally validated. In the present study, for the first time we have used experimentally verified B-cell and non B-cell epitopes from IEDB database, which are much more in the number and rationally, created to previous methods. We created the 20 mer epitopes using corresponding \u2018truncation-extension\u2019 methodology and similar length, which were used in earlier methods. By using simple composition technique in combination with SVM and Weka implemented IBk; we came up with an algorithm, which is as good as existing tools. Performance of LBtope models decreased on non-redundant datasets, still performance remained as good as existing methods. It is also observed that model developed on Lbtope_Confirm dataset performed better than the models developed on Lbtope_Variable dataset. We have compared the performance of LBtope models on Lbtope and existing datasets. LBtope performs poor on negative dataset of existing methods, and they also performed poor on our negative dataset. It is because our negative dataset is experimentally verified B-cell epitope, whereas existing method, negative dataset were randomly generated from UniProt. We have implemented the algorithm in the form of a user-friendly web server: LBtope. The user can create the mutants of each peptide and test its epitopic or other desired probability using our server\u2019s mutant tool. We hope that present model will aid the researchers in the field of linear B-cell epitope prediction.Figure S1Diagram showing calculation of Dipeptide composition, AAP and modified AAP (AAP*) from patterns.(TIF)Click here for additional data file.Figure S2Diagram showing % composition of B-cell epitopes and non-epitopes .(TIF)Click here for additional data file.Figure S3Flowchart showing preparation of LBtope dataset from IEDB database.(TIF)Click here for additional data file.Table S1Datasets used so far in the linear B-cell epitope prediction.(DOC)Click here for additional data file.Table S2Amino acid indices as obtained from Huang et al 2007.(DOC)Click here for additional data file.Table S3The performance of SVM models developed on Lbtope_Fixed dataset using binary profile. These models were developed using 5-fold cross-validation on 90% data and tested on remaining 10% data.(DOC)Click here for additional data file.Table S4The performance of SVM models developed on Lbtope_Fixed dataset using Physico-chemical property (4 R indices). These models were developed using 5-fold cross-validation on 90% data and tested on remaining 10% data.(DOC)Click here for additional data file.Table S5The performance of SVM/IBK models developed on Lbtope_Fixed dataset using Amino acid composition. These models were developed using 5-fold cross-validation on 90% data and tested on remaining 10% data.(DOC)Click here for additional data file.Table S6The performance of SVM/IBK models developed on Lbtope_Fixed dataset using Composition Transition. These models were developed using 5-fold cross-validation on 90% data and tested on remaining 10% data.(DOC)Click here for additional data file.Table S7The performance of SVM/IBK models developed on Lbtope_Fixed dataset using AAP profile. These models were developed using 5-fold cross-validation on 90% data and tested on remaining 10% data.(DOC)Click here for additional data file.Table S8The performance of SVM/IBK models developed on Lbtope_Fixed dataset using AAA profile. These models were developed using 5-fold cross-validation on 90% data and tested on remaining 10% data.(DOC)Click here for additional data file.Table S9The performance of SVM/IBK models developed on Lbtope_Fixed dataset using Dipeptide composition. These models were developed using 5-fold cross-validation on 90% data and tested on remaining 10% data.(DOC)Click here for additional data file.Table S10The performance of SVM/IBK models developed on Lbtope_Variable dataset using Amino acid composition. These models were developed using 5-fold cross-validation on 90% data and tested on remaining 10% data.(DOC)Click here for additional data file.Table S11The performance of SVM/IBK models developed on Lbtope_Variable dataset using Composition Transition. These models were developed using 5-fold cross-validation on 90% data and tested on remaining 10% data.(DOC)Click here for additional data file.Table S12The performance of SVM/IBK models developed on Lbtope_Variable dataset using AAP profile. These models were developed using 5-fold cross-validation on 90% data and tested on remaining 10% data.(DOC)Click here for additional data file.Table S13The performance of SVM/IBK models developed on Lbtope_Variable dataset using Dipeptide composition. These models were developed using 5-fold cross-validation on 90% data and tested on remaining 10% data.(DOC)Click here for additional data file.Table S14The performance of SVM/IBK models developed on Lbtope_Confirm (epitope tested by at least two studies) dataset using Amino acid composition. These models were developed using 5-fold cross-validation on 90% data and tested on remaining 10% data.(DOC)Click here for additional data file.Table S15The performance of SVM/IBK models developed on Lbtope_Confirm (epitope tested by at least two studies) dataset using Composition Transition. These models were developed using 5-fold cross-validation on 90% data and tested on remaining 10% data.(DOC)Click here for additional data file.Table S16The performance of SVM/IBK models developed on Lbtope_Confirm (epitope tested by at least two studies) dataset using AAP profile. These models were developed using 5-fold cross-validation on 90% data and tested on remaining 10% data.(DOC)Click here for additional data file.Table S17The performance of SVM/IBK models developed on Lbtope_Confirm (epitope tested by at least two studies) dataset using Dipeptide composition. These models were developed using 5-fold cross-validation on 90% data and tested on remaining 10% data.(DOC)Click here for additional data file.Table S18The performance of SVM/IBK models developed on Lbtope_Fixed_non_redundant dataset using amino acid composition. These models were developed using 5-fold cross-validation on 90% data and tested on remaining 10% data.(DOC)Click here for additional data file.Table S19The performance of SVM/IBK models developed on Lbtope_Fixed_non_redundant dataset using composition-transition. These models were developed using 5-fold cross-validation on 90% data and tested on remaining 10% data.(DOC)Click here for additional data file.Table S20The performance of SVM/IBK models developed on Lbtope_Fixed_non_redundant dataset using composition-transition. These models were developed using 5-fold cross-validation on 90% data and tested on remaining 10% data.(DOC)Click here for additional data file.Table S21The performance of SVM/IBK models developed on Lbtope_Fixed_non_redundant dataset using dipeptide composition. These models were developed using 5-fold cross-validation on 90% data and tested on remaining 10% data.(DOC)Click here for additional data file.Table S22The performance of SVM/IBK model developed on Lbtope_Variable_non_redundant dataset using amino acid composition. These models were developed using 5-fold cross-validation on 90% data and tested on remaining 10%.(DOC)Click here for additional data file.Table S23The performance of SVM/IBK models developed on Lbtope_Variable_non_redundant dataset using composition-transition. These models were developed using 5-fold cross-validation on 90% data and tested on remaining 10% data.(DOC)Click here for additional data file.Table S24The performance of SVM/IBK models developed on Lbtope_Variable_non_redundant dataset using composition-transition. These models were developed using 5-fold cross-validation on 90% data and tested on remaining 10% data.(DOC)Click here for additional data file.Table S25The performance of SVM/IBK models developed on Lbtope_Variable_non_redundant dataset using dipeptide composition. These models were developed using 5-fold cross-validation on 90% data and tested on remaining 10% data.(DOC)Click here for additional data file.Table S26The performance of Chen\u2019s AAP model on Lbtope_Fixed data.(DOC)Click here for additional data file.Table S27Performance of BCPred model (20 mer) on Lbtope_Fixed dataset.(DOC)Click here for additional data file.Table S28The performance of SVM models developed on Lbtope_Fixed dataset tested on Chen dataset.(DOC)Click here for additional data file.Table S29The performance of SVM models developed on Lbtope_Fixed dataset tested on BCPred dataset.(DOC)Click here for additional data file.Table S30Performance of ABCPred model (20 mer) on Lbtope_Fixed dataset.(DOC)Click here for additional data file.Table S31The performance of SVM models developed on Lbtope_Fixed and tested on ABCPred dataset.(DOC)Click here for additional data file.Table S32The performance of SVM models developed on Lbtope_Variable and tested on bcpred variable data.(DOC)Click here for additional data file.Table S33SVM results of five-fold cross validation on Lbtope_Confirm (epitope tested by at least two studies) dataset using dipeptide composition.(DOC)Click here for additional data file.Table S34SVM results of five-fold cross validation on Lbtope_positive_fbcpred_negative (B-cell epitope (positive) from Lbtope_Confirm dataset and random peptides (negative) from fbcpred) using dipeptide composition.(DOC)Click here for additional data file.Text S1Different B-cell epitope prediction techniques already used in earlier methods.(DOC)Click here for additional data file."}
+{"text": "Pucciniamonoica is a spectacular plant parasitic rust fungus that triggers the formation of flower-like structures (pseudoflowers) in its Brassicaceae host plant Boecherastricta. Pseudoflowers mimic in shape, color, nectar and scent co-occurring and unrelated flowers such as buttercups. They act to attract insects thereby aiding spore dispersal and sexual reproduction of the rust fungus. Although much ecological research has been performed on Pmonoica. -induced pseudoflowers, this system has yet to be investigated at the molecular or genomic level. To date, the molecular alterations underlying the development of pseudoflowers and the genes involved have not been described. To address this, we performed gene expression profiling to reveal 256 plant biological processes that are significantly altered in pseudoflowers. Among these biological processes, plant genes involved in cell fate specification, regulation of transcription, reproduction, floral organ development, anthocyanin  and terpenoid biosynthesis  were down-regulated in pseudoflowers. In contrast, plant genes involved in shoot, cotyledon and leaf development, carbohydrate transport, wax biosynthesis, cutin transport and L-phenylalanine metabolism  were up-regulated. These findings point to an extensive reprogramming of host genes by the rust pathogen to induce floral mimicry. We also highlight 31 differentially regulated plant genes that are enriched in the biological processes mentioned above, and are potentially involved in the formation of pseudoflowers. This work illustrates the complex perturbations induced by rust pathogens in their host plants, and provides a starting point for understanding the molecular mechanisms of pathogen-induced floral mimicry. Pucciniamonoica that manipulates its plant host Boecherastricta (syn. Arabisdrummondii) to create elaborate pseudoflowers. These structures are completely novel to the plant\u2019s native architecture , with \u2018X\u2019 corresponding to the gene of interest. The statistical T-test was performed using R software to determine differences between \u2018Pf\u2019 vs. \u2018SL\u2019 and \u2018F\u2019 vs. \u2018SL\u2019 group. A P-value < 0.05 was defined as statistically significant. To indicate the mode of regulation we used two symbols: \u2018*\u2019 for significant up-regulation and \u2018#\u2019 for significant down-regulation. The number of symbols indicates level of significance: one for P < 0.05, two for P < 0.01 and three for P < 0.001. Data is presented as average \u00b1 SEM.Seven candidate sin) see . All ampArabidopsis thaliana was extracted from The Arabidopsis Information Resource (TAIR) database [P-value threshold of 0.05. This test identified significantly enriched GO categories by comparing Arabidopsis thaliana 27822 GO annotated genes with the 1036 and 910 genes that showed significant changes in gene expression in (i) Pucciniamonoica-induced pseudoflowers \u2018Pf\u2019 vs. uninfected Boecherastricta stems and leaves \u2018SL\u2019 and (ii) uninfected Bstricta.  flowers \u2018F\u2019 vs. \u2018SL\u2019 comparisons, respectively. Using Cytoscape visualization tools we constructed a network map to illustrate significantly enriched Gene Ontology terms describing Biological Processes (GOBP) in (i) \u2018Pf\u2019 vs. \u2018SL\u2019 and (ii) \u2018F\u2019 vs. \u2018SL\u2019 comparisons, respectively. The size of the node in the network map corresponds to -log10 of the corrected P-value of enrichment within a GOBP term. In addition, snapshots of A. thaliana AraCyc and PlantCyc browsers (http://plantcyc.org/) were used to visualize specific metabolic pathways that were significantly regulated in \u2018Pf\u2019 vs. \u2018SL\u2019.A list of GO annotations for database . Using tdatabase , over-reTable S1List of 1036 significantly differentially expressed genes in pseudoflowers.'Pf': Pucciniamonoica-induced pseudoflowers. 'SL': Boecherastricta uninfected stems and leaves. *False Discovery Rate (FDR) estimated using Rank Products (RP) to detect genes that are differentially expressed. Genes with RP FDR value < 0.05 are considered significant.(XLSX)Click here for additional data file.Table S2Boecherastricta flowers.List of 910 significantly differentially expressed genes in uninfected 'F': Boecherastricta uninfected flowers. 'SL': Boecherastricta uninfected stems and leaves. *False Discovery Rate (FDR) estimated using Rank Products (RP) to detect genes that are differentially expressed. Genes with RP FDR value < 0.05 are considered significant.(XLSX)Click here for additional data file.Table S3List of 256 gene ontology biological processes (GOBP) enriched in pseudoflowers.'Pf': Pucciniamonoica-induced pseudoflowers. 'SL': Boecherastricta uninfected stems and leaves. aNumber of genes within found within the biological process. bP-value showing the significance for enrichment of genes within the biological process.(XLSX)Click here for additional data file.Table S4Boecherastricta flowers.List of 199 gene ontology biological processes (GOBP) enriched in uninfected 'F': Boecherastricta uninfected flowers. 'SL': Boecherastricta uninfected stems and leaves. aNumber of genes within found within the biological process. bP-value showing the significance for enrichment of genes within the biological process.(XLSX)Click here for additional data file.Table S5Primer sequences used for qRT-PCR assay.(XLSX)Click here for additional data file."}
+{"text": "Emerging evidence suggests that maternal obesity (MO) predisposes offspring to obesity and the recently described non-alcoholic fatty pancreas disease (NAFPD) but involved mechanisms remain unclear. Using a pathophysiologically relevant murine model, we here investigated a role for the biological clock - molecular core circadian genes (CCG) in the generation of NAFPD.Female C57BL6 mice were fed an obesogenic diet (OD) or standard chow (SC) for 6 weeks, prior to pregnancy and throughout gestation and lactation: resulting offspring were subsequently weaned onto either OD (Ob_Ob and Con_Ob) or standard chow (Ob_Con and Con_Con) for 6 months. Biochemical, pro-inflammatory and pro-fibrogenic markers associated with NAFPD were then evaluated and CCG mRNA expression in the pancreas determined.Offspring of obese dams weaned on to OD (Ob_Ob) had significantly increased (p\u22640.05): bodyweight, pancreatic triglycerides, macrovesicular pancreatic fatty-infiltration, and pancreatic mRNA expression of TNF-\u03b1, IL-6, \u03b1-SMA, TGF-\u03b2 and increased collagen compared to offspring of control dams weaned on to control chow (Con_Con). Analyses of CCG expression demonstrated a phase shift in CLOCK , REV-ERB-\u03b1  and Per2  in association with decreased amplitude in BMAL-1  and PER2  in Ob_Ob compared to Con_Con. 2-way ANOVA revealed significant interaction between MO and post-weaning OD in expression of CLOCK (p<0.005), PER1 (p<0.005) and PER2 (p<0.05) whilst MO alone influenced the observed rhythmic variance in expression of all 5 measured CCG.Fetal and neonatal exposure to a maternal obesogenic environment interacts with a post-natal hyper-calorific environment to induce offspring NAFPD through mechanisms involving perturbations in CCG expression. Non-alcoholic fatty pancreas disease (NAFPD) is a recently described disease entity associated with an obese and/or dysmetabolic phenotype The prevalence of maternal obesity is increasing worldwide in parallel with adult obesity rates The concept of developmental programming suggests that the early environment from conception through to the early post-natal period can alter gene expression through epigenetic processes in the developing offspring, resulting in a permanent alteration in offspring physiology Circadian clocks are molecular oscillators, which drive daily rhythms of physiology and behaviour Homeostasis is achieved through interactions between the pace-setting hypothalamic suprachiasmatic nucleus (SCN), also known as the master clock, and the peripheral clocks, located in all body cells. The master clock acts as a pacesetter for all peripheral clocks and is predominantly entrained by light CLOCK gene mutations are reported to be hyperphagic and obese Mice with Specifically, we interrogated the potential for mechanistic involvement of CCG in the pathogenesis of NAFPD arising from an interaction between maternal obesity and a post-weaning nutritional status.ad libitum. They were then randomly allocated to either a control standard chow  or a semi-synthetic energy-rich and highly palatable obesogenic diet , Special Dietary Services, UK, energy 4.5 kcal/g). The pelleted obesogenic diet was supplemented with ad libitum access to sweetened condensed milk  with added micronutrient mineral mix . Combined intake calculated from measured daily intake of pellets and milk  ad libitum access to food and water and were maintained in a 12-hour light/dark cycle in a thermostatically controlled environment (22\u00b0C). Each of the four groups included pups randomly selected from litters born to different dams.All studies were approved by Local University College London Ethics Committee, and conducted under UK Home Office, Animal in Science Regulation Unit (Scientific Procedures) Act 1986 guidelines. Female C57BL/6J mice (n\u200a=\u200a60), proven breeders (one previous litter) and approximately 100-day-old  were maintained under controlled conditions  and fed either a standard chow diet  The allocation of maternal and offspring post-weaning diet provided 4 groups:maternal diet of SC followed by a post-weaning diet of SC .maternal diet of SC followed by a post-weaning OD .an obesogenic maternal diet followed by a post-weaning diet of SC .an obesogenic maternal diet followed by a post-weaning obesogenic diet .An oral glucose tolerance test (OGTT) was performed at 6 months, as previously described, with some modification ad libitum, were sacrificed at 4 hourly intervals over a 24-hour period, to allow analyses of CCG expression over a 24-hour period (Zeitgeber Time (ZT) 0, 4, 8, 12, 16, and 20, where ZT0\u200a=\u200alight on and ZT12\u200a=\u200alight off). Following sacrifice, blood samples were taken and harvested organs appropriately stored until analysed. For sample collection in the dark period, mice were transferred to a lit room and terminated within a few minutes of transfer.At 6 months of age, offspring being maintained on a 12 h light/12 h dark cycle with food and water available Clock, BMAL-1, Period 1, Period 2 and REVERB- \u03b1) using pups from all time points. Primer sequences were as shown in Pancreatic tissue mRNA was assayed by quantitative 2-step PCR for expression of pro-inflammatory markers (Interleukin-6 (IL-6) and tumour necrosis factor-\u03b1 (TNF\u03b1), pro-fibrogenic markers,  and CCG  Following sacrifice, sections of pancreas were fixed in formalin with the spleen attached to aid orientation in histological analysis. Sections were embedded in paraffin and stained with both H&E and Masson's Trichrome to determine adipocyte infiltration and pericellular fibrosis respectively. Fat infiltration and extent of fibrosis was graded as previously described Data are expressed as mean \u00b1 standard error of the mean (SEM). Means of each group were compared using both one-way and two-way ANOVA, as indicated. Statistical significance was assumed as p<0.05. Cosinor analysis was employed, in addition to ANOVA, to determine rhythmicity of circadian gene expression within a 24-hour period. Cosinor analysis evaluates the \u2018mesor\u2019 , timing of the oscillatory crest and amplitude, p<0.05 regarded as significant. Two-way ANOVA was also used to determine the effect of gender and nutritional group on the observed variance between offspring and the relative influence of maternal verses postnatal diet on offspring phenotype. Since no main effect of gender was observed for the reported biomarkers  male andAs described in detail above, the allocation of maternal and offspring post-weaning diets provided 4 groups: Con_Con; Con_Ob; Ob_Con; Ob_Ob. Offspring exposed to a post-weaning OD (Con_Ob and Ob_Ob) were significantly heavier than control . Two-way ANOVA further revealed 83% of the variance seen between the groups to be attributable to the post-weaning diet (p<0.0001), with no apparent main effect of MO on body weight, and no interaction between MO and post-weaning diet attributed to variance .At 6 months, only offspring exposed to both interventions (Ob_Ob) were found to have significantly heavier pancreas weights compared to control . Moreover, these offspring also displayed heavier pancreata compared to offspring exposed to MO alone . Two-way ANOVA attributed the post-weaning diet to 64.18% of the observed variance between the groups (p<0.001), with no statistical main effect of MO, nor an interaction between the variables as responsible for the variance .As for pancreatic triglyceride content, this was significantly greater in Ob_Ob offspring than both Ob_Con offspring (p<0.001) and Con_Con offspring (p<0.001). There was no significant difference between offspring exposed to a post-weaning OD alone (Con_Ob) and control. Two-way ANOVA attributed 54.38% of the observed variance to the post-weaning diet (p<0.01) but no statistical power was attributed to the effect of MO .Pancreatic Macrovesicular Fat Infiltration was significantly greater in Ob_Ob compared to all other groups: Ob_Con (p<0.05), Con_Ob (p<0.05) and Con_Con (p<0.05) ((p<0.05) , n\u200a=\u200a4\u20135(p<0.05) .The influence of maternal obesity on offspring glucose tolerance was evident following an oral glucose tolerance test (OGTT), with both Ob_Con offspring (p<0.05) and Ob_Ob offspring (p<0.0001) displaying a significant increase in AUC compared with Con_Con offspring . MoreoveHistological analysis (H&E staining) of pancreata revealed a marked increase in macrovesicular adipocyte infiltration in offspring exposed to both obesogenic interventions (i.e. Ob_Ob) . Upon scWe then sought to analyze the gene expression levels of the major inflammatory cytokines  and the fibrotic indicators  in the pancreata of the 4 mice groups, to test the potential effects of MO and post-weaning diets on these pathogenic processes. All the marker tested in PCR showed same trend across the gropus in all time points, therefore, ZT 8 was chosen for further analysis in all groups.IL-6 mRNA expression in Ob_Ob offspring was higher when compared to all other groups: versus Ob_Con group (p<0.05), versus Con_Ob group (p<0.05), and versus Con_Con (p<0.001). There was no significant difference between offspring exposed to a post-weaning OD alone (Con_Ob) or MO alone (Ob_Con) compared to controls (Con_Con), indicating the need for the combined presence of both interventions to induce upregulation of IL-6 expression. Two-Way ANOVA further corroborated these results, revealing MO and the post-weaning OD to be attributable to 43.71% (p<0.001) and 34.53% (p<0.01) of the overall variance in IL-6 expression respectively, but also, that there was a significant interaction between these variables, contributing 10.18% (p<0.05) of the overall variance .The influence of the interventions on IL-6 was mirrored in TNF-\u03b1, another marker of pancreatic inflammation, with Ob_Ob offspring demonstrating a significantly increased expression of this cytokine compared to the other offspring groups: versus Ob_Con (p<0.001), versus Con_Ob (p<0.0001) and versus Con_Con (p<0.0001). In addition, MO was found to have an independent effect with Ob_Con offspring exhibiting significant upregulation of TNF-\u03b1 compared with control (p<0.05). Two-way ANOVA attributed 61.22% and 18.35% of the overall variance to MO (p<0.0001) and the post-weaning diet to (p<0.001), respectively. An overall significant interaction between the variables attributed 4.63% (p<0.05) of the total variance. As with IL-6, there was no significant upregulation of TNF-\u03b1 by a post-weaning OD in isolation, compared with control .The influence of MO on markers of offspring pancreatic fibrotic injury was evident in \u03b1-SMA expression, with Ob_Ob displaying upregulation compared to the other offspring groups: versus Ob_Con (p<0.001); versus Con_Ob (p<0.001) and versus Con_Con (p<0.0001). Two-way ANOVA further supported these results, attributing 63.77% (p<0.0001) and 13.08% (p<0.01) of the observed variance to MO and post-weaning OD respectively, and 5.53% (p<0.05) of the variance to an overall interaction between the variables. No significant difference was noted between offspring exposed to a post-weaning OD alone (Con_Ob) and control .Similarly, Ob_Ob collagen mRNA expression was significantly greater than the other 3 offspring groups: versus Ob_Con (p<0.001), versus Con_Ob (p<0.0001), and versus Con_Con (p<0.0001). Two-way ANOVA attributed 61.26% (p<0.0001) and 28.39% (p<0.001) of observed variance to MO and post-weaning OD respectively and 7.14% (p<0.05) of the variance to an overall interaction between the variables. Again, no significant difference was observed between Con_Oband Con_Con .TGF-\u03b2 mRNA expression was significantly greater in Ob_Ob compared with Ob_Con (p<0.05) and Con_Con (p<0.001) indicating an additive effect of the postweaning obesogenic diet. No significant difference was noted between Ob_Ob and Con_Ob, nor was a significant difference noted between Con_Ob and control. Two-way ANOVA revealed MO contributing 64% (p\u200a=\u200a0.0009) of the observed variance, indicating MO as having the main effect on the observed variance in results .Results are divided into ANOVA and cosinor analysis of CCG of offspring over 6 separate time points :Both CLOCK and Per2 gene expression displayed differences as a result of MO and a post-weaning OD. Analysis of CLOCK gene expression revealed a significant difference between Con_Con expression and the 3 other groups at ZT4: vs Ob_Ob (p<0.001), vs Ob_Con (p<0.0001) and vs Con_Ob (p<0.05). Two-way ANOVA revealed that MO accounted for 64% of the total variance seen and an interaction between the variables as accountable for 23% (p<0.05) . Per-2 gAt ZT16, a perturbation was also observed for BMAL-1 as noted for Per-2 expression at ZT8 . Two-way ANOVA revealed that MO and an overall interaction between MO and a post-weaning OD accounted for 18%, and 54% variance respectively, p<0.05. Additionally, the post-weaning diet contributed a further 44% (p<0.05) of the overall variance .Further interactions between the sequential circadian genes were observed with the same patterns of gene expression noted for CLOCK at ZT4 and Per-1 at ZT16. As observed with CLOCK at ZT4, Per-1 gene expression was different between Con_Con and Con_Ob (p<0.0001), Ob_Con (p<0.0001) and Ob_Ob (p<0.0001). Two-way ANOVA revealed an overall interaction between MO and OD as contributing 10% of total variance (p<0.05), MO alone contributing 49% (p<0.0001) and the post-weaning OD 24% (p<0.001), . Two-wayTo determine if variations observed at the mRNA level were mirrored at the protein level, we now analyzed protein expression levels of the core components of the circadian clock machinery, CLOCK and BMAL1, in the pancreata of the 4 experimental groups at ZT 0 h, 8 h and 16 h by immunoblotting . StrikinCosinor analysis revealed a phase shift of \u22124.818005 Hours (p<0.01) when comparing Ob_Ob with Ob_Con, suggesting a main effect of the postweaning diet. No other significant differences were seen between the groups in relation to CLOCK gene expression ; Table 3Cosinor analysis revealed significantly reduced amplitude, with respect to Con_Con, in Ob_Ob offspring  and Con_Ob offspring , suggesting main effect of postweaning diet. Furthermore, maxima were calculated to be significantly reduced in Ob_Ob offspring when compared with Con_Con (\u22121.86 p<0.05). No other significant differences were seen between the groups in relation to BMAL-1 gene expression ; Table 3Cosinor analysis yielded no statistically significant difference between the groups in Per 1 ; Table 3There was a phase shift of 3.27 Hours (p<0.05) between Ob_Ob and Ob_Con offspring suggesting main effect of MO. Amplitude was also calculated as increased in Ob_Ob  and Ob_Con , when compared with Con_Con suggesting evidence of both MO and an interaction between the variables as having a significant effect. Furthermore, maxima were increased in Ob_Ob compared with Con_Con , and minima decreased Ob_Con compared with Con_Con . No other significant differences were seen between the groups in relation to Per-2 gene expression ; Table 3There was a phase shift of \u22121.4 Hours (p<0.05) when comparing offspring exposed to a post-weaning OD  with COn_Con offspring. No other significant differences were seen between the groups in relation to REVERB-\u03b1 gene expression ; Table 3NAFPD is an emerging clinical entity associated with dysmetabolism and characterized by pancreatic fat deposition, inflammation, fibrosis and pancreatitis. This pancreatic phenotype is strikingly similar to that of obesity-induced NAFLD, which describes a spectrum characterized by hepatic steatosis, steatohepatitis and cirrhosis. The similarity in phenotypes could be due to the common embryonic origins of the liver and pancreas. We have previously shown that offspring exposure to MO throughout pregnancy and lactation induces a significant increase in markers indicative of a NAFPD phenotype, when compared to offspring exposed to a normal intrauterine and perinatal environment Moreover, offspring glucose tolerance was impaired not only in Ob_Ob but also in offspring exposed to MO in isolation (Ob_Con), accentuating an independent effect of MO. Thus, MO appears to have a deleterious effect on offspring phenotype, particularly when coupled with a post-weaning OD, which in isolation only significantly influenced offspring bodyweight. Since the offspring of obese mothers, weaned onto an OD (Ob_Ob) demonstrated the highest pancreatic triglyceride (TG) concentration, with no change in TGs in Con_Ob, this was most likely due to an interaction between the two interventions  rather than the post-weaning diet alone . The TG However, as Ob_Ob fat infiltration was higher than Con_Ob, this exposed an independent influence of MO in these offspring . Thus, MMoreover, we found that control offspring exposed to a post-weaning OD showed less evidence of inflammation than offspring of the obese dams, on the same diet adding to evidence for a priming effect of MO. The higher TNF-\u03b1 expression in the MO offspring, strengthens a role for MO in the programming of offspring disease. This is not the first study to demonstrate a role for MO in activation of inflammatory cytokines in offspring Support for the concept of programming of clock genes by nutrition is provided by a recent report in which adult offspring of protein-restricted rat dams demonstrated permanently altered expression in a functional network of hypothalamic nuclear receptors and co-regulators of the circadian clock involved in lipid metabolism Our results further report a significant disruption in CCG, which could have a mechanistic role for CCG in developmentally programmed NAFPD. Moreover, analysis of CCG expression at different stages of daylight revealed corresponding genes being affected in the same pattern but at opposite times of day , are represented. Ob_Ob showed significantly higher glucose levels at 15 minutes than Con_Con and Con_Ob . **p<0.001 vs. Con_Ob, # p<0.05 vs. Con_Con.(TIF)Click here for additional data file.Figure S2Upper panel: pancreatic tissues from 4 different animals per group/time points were pooled together as previously described (TIF)Click here for additional data file."}
+{"text": "Clostridium thermocellum is a candidate consolidated bioprocessing (CBP) biocatalyst for cellulosic ethanol production. The aim of this study was to investigate C. thermocellum genes required to ferment biomass substrates and to conduct a robust comparison of DNA microarray and RNA sequencing (RNA-seq) analytical platforms.The thermophilic anaerobe C. thermocellum ATCC 27405 fermentations were conducted with a 5\u00a0g/L solid substrate loading of either pretreated switchgrass or Populus. Quantitative saccharification and inductively coupled plasma emission spectroscopy (ICP-ES) for elemental analysis revealed composition differences between biomass substrates, which may have influenced growth and transcriptomic profiles. High quality RNA was prepared for C. thermocellum grown on solid substrates and transcriptome profiles were obtained for two time points during active growth (12\u00a0hours and 37\u00a0hours postinoculation). A comparison of two transcriptomic analytical techniques, microarray and RNA-seq, was performed and the data analyzed for statistical significance. Large expression differences for cellulosomal genes were not observed. We updated gene predictions for the strain and a small novel gene, Cthe_3383, with a putative AgrD peptide quorum sensing function was among the most highly expressed genes. RNA-seq data also supported different small regulatory RNA predictions over others. The DNA microarray gave a greater number  of significant genes relative to RNA-seq  method) in an analysis of variance (ANOVA) testing method with a 5% false discovery rate (FDR). When a 2-fold difference in expression threshold was applied, 73 genes were significantly differentially expressed in common between the two techniques. Sulfate and phosphate uptake/utilization genes, along with genes for a putative efflux pump system were some of the most differentially regulated transcripts when profiles for C. thermocellum grown on either pretreated switchgrass or Populus were compared.Our results suggest that a high degree of agreement in differential gene expression measurements between transcriptomic platforms is possible, but choosing an appropriate normalization regime is essential. Clostridium thermocellum exhibits one of the highest rates of degradation of cellulosic substrates, which is facilitated by large extracellular multi-subunit enzyme systems termed cellulosomes , and represented the first genome sequence for this species. Repetitive sequences such as transposases and those present in cohesin domains made closing this genome challenging and the genome sequence was not finished until 2007. The C. thermocellum ATCC 27405 genes were originally predicted using two gene modeling programs, Glimmer   and we have made mapped reads and data available through the BioEnergy Science Center (BESC) KnowledgeBase http://bobcat.ornl.gov/besc/index.jsp[C. thermocellum harvested after growth on Populus for 12\u00a0hours: F185_Ctherm_Pop_12 hr [SRR:620218] and F188_Ctherm_Pop_12 hr [SRR:620325]. C. thermocellum harvested after growth on Populus for 37\u00a0hours: F185_Ctherm_Pop_37 hr [SRR:620219] and F188_Ctherm_Pop_37 hr [SRR:620327]. C. thermocellum harvested after growth on switchgrass for 12\u00a0hours: F186_Ctherm_Swg_12 hr [SRR:620229] and F187_Ctherm_Swg_12 hr [SRR:620532]. C. thermocellum harvested after growth on switchgrass for 37\u00a0hours: F186_Ctherm_Swg_37 hr [SRR:620238] and F187_Ctherm_Swg_37 hr [SRR:620324]. Note that the same nomenclature of fermenter number , biomass substrate (Pop and Swg), and time point of sampling (12\u00a0hours and 37\u00a0hours) is used for naming the samples in the microarray Gene Expression Omnibus (GEO) submission, see details below.Raw reads were mapped to genome [GenBank:CP000568.1] using CLC Genomics Workbench version 5.5.1  using the default settings for prokaryote genomes. Uniquely mapped reads were logindex.jsp. SamplesRNA-seq libraries were also used for hybridization to the microarray. Beginning with 100\u00a0ng of cDNA, half volume Cy3 labeling reactions were undertaken for all eight samples according to the manufacturer\u2019s protocols. Cy3 labeling efficiency was assessed by NanoDrop ND-1000 spectrophotometer  and determined to fall within the range of 20 to 24 pmol/\u03bcg. Hybridizations were conducted using a 12-bay hybridization station  and the arrays dried using a MAUI Wash System (BioMicro Systems). Microarrays were scanned with a SureScan High-Resolution DNA Microarray Scanner (5\u00a0\u03bcm) (Agilent), and the images were quantified using NimbleScan software .2 transformed and imported into the statistical analysis software JMP Genomics 6.0 software (SAS Institute). The data were normalized together using a single round of the LOESS normalization algorithm within JMP Genomics, and distribution analyses conducted before and after normalization were used as a quality control step. An ANOVA was performed in JMP Genomics to determine differential gene expression levels via a direct comparison of the two biomasses and time points using the FDR testing method (P <0.05) and Kenward-Roger degrees of freedom method. Microarray data have been deposited in the NCBI GEO database [GSE:47010]. Samples in the GEO series [GSE:47010] are labeled accordingly with the specific GEO sample accession number given in square brackets. C. thermocellum harvested after growth on Populus for 12\u00a0hours: F185_Pop_12 hr_rep1 [GSM:1142896] and F188_Pop_12 hr_rep1 [GSM:1142902]. C. thermocellum harvested after growth on Populus for 37\u00a0hours: F185_Pop_37 hr_rep1 [GSM:1142897] and F188_Pop_37 hr_rep1 [GSM:1142903]. C. thermocellum harvested after growth on switchgrass for 12\u00a0hours: F186_Swg_12 hr_rep1 [GSM:1142898] and F187_Swg_12 hr_rep1 [GSM:1142900]. C. thermocellum harvested after growth on switchgrass for 37\u00a0hours: F186_Swg_37 hr_rep1 [GSM:1142899] and F187_Swg_37 hr_rep1 [GSM:1142901].Raw data was logCthe_0344_F CGACTTCCCGAACCAGATAA, Cthe_0344_R GCAGCGGCTATCTTCATTTC; Cthe_0482_F GAGCAGGGATTGGTAATGGA, Cthe_0482_R TACCGCAAGACCTACAAGCA; Cthe_1481_F AGTCATATCCGAAAACATGG, Cthe_1481_R TTGTAGTCGTCAAGGGAAGT; Cthe_1604_F GTGTCCCCGCTATTGCTAAA, Cthe_1604_R ATGGGTAAAATGCCGAATGA; Cthe_1951_F AAAATAAAAGCCCAGGATTC, Cthe_1951_R GCATTATCCTGAAGTTCGTC; and Cthe_2531_F CGGAAAGGACATTGTCATCC, Cthe_2531_R CAAAGCCAGGGTTACGACAT.Microarray data were validated using RT-qPCR, as described previously . Six genANOVA: Analysis of variance; BESC: BioEnergy Science Center; BLAST: Basic Local; Alignment Search Tool; CBP: Consolidated bioprocessing; CDS: Coding sequence; DI: Deionized; DOE: Department of Energy; FDR: False discovery rate; GEO: Gene Expression Omnibus; HMF: Hydroxymethylfurfural; HPLC: High performance liquid chromatography; ICP-ES: Inductively coupled plasma emission spectroscopy; JGI: Joint Genome Institute; KDMM: Kernel density mean of M component; LC: Liquid chromatography; MAQC: MicroArray Quality Control; MS/MS: Tandem mass spectrometry; MTC: Medium for Thermophilic Clostridia; NCBI: National Center for Biotechnology Information; NREL: National Renewable Energy Laboratory; ORF: Open reading frame; ORNL: Oak Ridge National Laboratory; PCR: Polymerase chain reaction; RBS: Ribosome binding site; RIN: RNA integrity number; RNA-seq: RNA sequencing; RPKM: Reads per kilobase per million; RPM: Reads per million; RT: Reverse transcriptase; SRA: Sequence Read Archive; TMM: Trimmed mean of M component; UQS: Upper quartile scaling; USEPA: United States Environmental Protection Agency.SLM, TMC, and RDW are employees of the SAS Institute and developers of JMP Genomics.SDB, MR, JRM, and CMW designed the experiments. MR, CMJ, DMK, AJR, and CMW carried out the experiments. CMW, SLM, TMC, RDW, MR, JRM, LJH, MLL, MHS, AJR, TJT, and SDB analyzed the data. CMW and SDB wrote the manuscript. All authors read and approved the final manuscript.Peptides BLAST output. Complete output from BLAST search of peptides against the [GenBank:CP000568.1] version of the C. thermocellum ATCC 27405 genome. The query name given in the first column includes the ORF name, the genome coordinates of the ORF (ORF start to ORF stop), the peptide ID, and the spectral counts of each mapped peptide. The subject is the [GenBank:CP000568.1] version of the C. thermocellum ATCC 27405 genome. The remaining columns are standard output from the BLAST search.Click here for filePeptides used to manually curate the C. thermocellum genome. A subset of Additional file Click here for filePeptide support for updates to the C. thermocellum genome. Examples of where peptides were used to update the C. thermocellum ATCC 27405 genome annotation. (A) Illustration of where peptide hits were used to update the predicted start site of an ORF; (B) illustration of peptide support for the addition of a new gene; and (C) illustration of peptide support for the expression of an existing pseudogene. Within each image: 1. represents the genome coordinates; 2. RNA-seq data from one replicate of C. thermocellum grown on Populus for 12\u00a0hours; 3. existing gene coding sequence; 4. updated ORF; and 5. mapped peptides.Click here for fileMicroarray probe assignment update. The methods and results from the update to the microarray probe gene assignment.Click here for fileTable of BLAST results for the new probe assignment. Dataset of results from a BLAST search of probes (60\u00a0bp in length) from the microarray platform (GEO platform GPL15992). The best hit against the C. thermocellum ATCC 27405 genome [GenBank:CP000568.1] is given in the column Gene, with the percentage of identical nucleotides and alignment between the query and result sequence given in the ID column and Alignment column, respectively. The proportion of the alignment length or accuracy of the alignment is given in the column Proportion of alignment length: ID/100*Alignment length for those alignments greater than 36.Click here for fileNew probe assignments. Dataset containing a subset of probes from Additional file C. thermocellum ATCC 27405 genome. Results of BLAST search of probes (60\u00a0bp in length) from the microarray platform (GEO platform GPL15992). The best hit against the C. thermocellum ATCC 27405 genome [GenBank:CP000568.1] is given in the column Gene, with the percentage of identical nucleotides and alignment between the query and result sequence given in the ID column and Alignment column, respectively.Click here for fileICP-ES elemental analysis results. Table of results from the compositional analysis of the pretreated and unpretreated biomass substrates. Samples of dried biomass substrates were analyzed for elemental composition (mg/kg) by ICP-ES.Click here for fileFermentation products and cell counts. Fermentation products and cell counts of C. thermocellum grown in duplicate batch fermenters. Arrows correspond to time points sampled for transcriptomic analyses. Fermentation products were determined by HPLC.Click here for fileSummary of RNA-seq reads. Table summarizing the RNA-seq reads mapped to the C. thermocellum ATCC 27405 genome [GenBank:CP000568.1] using CLC Genomics Workbench version 5.5.1 (CLC bio) using the default settings for prokaryote genomes. Reads that were uniquely mapped to a single locus in the genome [GenBank:CP000568.1] were used in further analyses.Click here for fileCorrelation curves of biological replicates. Figure of the gene-wise correlation of transcriptome data of pre-normalized reads (RNA-seq) or pre-normalized intensity values (microarray) of biological replicates log2 transformed and plotted against each other; each axis corresponds to a single biological replicate for each condition. Pearson R values are given for each correlation. If values for the RNA-seq were missing, that is, no reads for a particular gene, values were estimated by the REML method in JMP Genomics 6.Click here for fileSpearman correlation of RNA-seq and array for each averaged sample. Figure showing the gene-wise correlation of transcriptome data from averaged biological duplicates of pre-normalized microarray log2 transformed intensity values and pre-normalized RNA-seq log2 transformed reads. The color intensities  indicate the level of Spearman correlation coefficients of the sets of data.Click here for filePre- and post-normalization distribution curves. Figure of the distribution curves of pre- and post-normalization log2 transformed intensity values or reads  of each gene for the microarray and RNA-seq, respectively.Click here for fileHierarchical clustering of gene abundance profiles. Dataset of the abundance profiles of C. thermocellum ATCC 27405 genes detected in both the microarray and RNA-seq datasets. Given are log2 transformed values of normalized data for each gene. The cluster that each gene was grouped in Figure\u00a0Click here for fileRNA-seq reads mapped to sRNA and 3383. Figure showing the RNA-seq reads from a representative of each biomass fermentation mapped to the updated C. thermocellum genome [GenBank:CP000568.1]. (A) Rfam and mBio predictions for sRNA gene structure, blue indicates high levels of gene expression. (B) High levels of expression from a newly annotated gene, Cthe_3383 (black arrow), with predicted functions as an AgrD-like signaling peptide. (C) Multiple sequence alignments of small newly predicted C. thermocellum proteins, Cthe_3383 and Cthe_3348, against C. acetobutylicum ATCC 824 and Staphylococcus aureus ArgD sequences. (D) Pairwise percent identical residue comparisons. CLC Genomics Workbench (version 6.0.1) was used to create alignments and comparisons.Click here for fileSignificantly differentially expressed genes. Dataset of differential gene expression expressed as a ratio between stated conditions. Included is the FDR adjusted P value for each gene comparison, with an FDR adjusted P value <0.05 and greater than\u2009\u00b1\u20091 log2 transformed ratio between the conditions indicative of altered gene regulation.Click here for fileqPCR validation of microarray and RNA-seq expression data. Figure of the RT-qPCR confirmation of differential gene regulation when C. thermocellum ATCC 27405 was harvested at 12\u00a0hours postinoculation on the biomass substrates Populus and switchgrass. R2 values are given for the RT-qPCR correlation with both the array and RNA-seq analytical platforms.Click here for file"}
+{"text": "Campylobacter jejuni is a major cause of bacterial gastroenteritis worldwide, and the capsular polysaccharide (CPS) of this organism is required for persistence and disease. C. jejuni produces over 47 different capsular structures, including a unique O-methyl phosphoramidate (MeOPN) modification present on most C. jejuni isolates. Although the MeOPN structure is rare in nature it has structural similarity to some synthetic pesticides. In this study, we have demonstrated, by whole genome comparisons and high resolution magic angle spinning NMR, that MeOPN modifications are common to several Campylobacter species. Using MeOPN biosynthesis and transferase mutants generated in C. jejuni strain 81\u2013176, we observed that loss of MeOPN from the cell surface correlated with increased invasion of Caco-2 epithelial cells and reduced resistance to killing by human serum. In C. jejuni, the observed serum mediated killing was determined to result primarily from activation of the classical complement pathway. The C. jejuni MeOPN transferase mutant showed similar levels of colonization relative to the wild-type in chickens, but showed a five-fold drop in colonization when co-infected with the wild-type in piglets. In Galleria mellonella waxmoth larvae, the MeOPN transferase mutant was able to kill the insects at wild-type levels. Furthermore, injection of the larvae with MeOPN-linked monosaccharides or CPS purified from the wild-type strain did not result in larval killing, indicating that MeOPN does not have inherent insecticidal activity. Campylobacter jejuni is a Gram-negative bacterium that is a leading cause of bacterial gastroenteritis worldwide C. jejuni, CPS is the major determinant of the Penner serotyping scheme C. jejuni strains are structurally complex and highly variable, due in part to the addition of phase-variable modifications such as O-methyl, ethanolamine, aminoglycerol, and O-methyl phosphoramidate (MeOPN) groups C. jejuni isolates C. jejuni strain 11168H, we identified four genes required for the biosynthesis of MeOPN, cj1415\u2212cj1418, and two phase variable genes, cj1421 and cj1422, that encode transferases responsible for the addition of MeOPN to C-3 of \u03b2-D-GalfNAc or to C-4 of D-glycero-\u03b1-L-gluco-Hep, respectively Capsular polysaccharide (CPS) forms the outermost structure on most bacteria and plays key roles in the interaction between the organism, host, and environment. In the case of Campylobacteraceae, their commonality among C. jejuni strains suggests an important biological role for these surface-expressed modifications within the species, and we have recently demonstrated that MeOPN is a receptor for several C. jejuni lytic bacteriophages C. jejuni 11168H in the Galleria mellonella model showed a substantial decrease in insecticidal activity for a MeOPN biosynthesis mutant relative to the wild-type strain et al. C. jejuni 81\u2013176 strain correlated to reductions in both serum resistance and colonization in a mouse intestinal model, relative to the wild-type strain. In combination, these studies indicate that, when present, MeOPN biosynthesis is important for C. jejuni cellular interactions and infection.While it is unknown whether MeOPN modifications are present in other Campylobacter species, and not limited to C. jejuni. We have mutated the MeOPN transferase gene homologues in C. jejuni strains 81\u2013176 and 11168H. Both mutants exhibited similar levels of insecticidal activity compared to wild-type in the G. mellonella infection model, and no insecticidal activity was observed when G. mellonella was injected with either purified CPS or synthesized compounds containing MeOPN. However, the MeOPN mutant exhibits enhanced invasion of Caco-2 cells and reduced resistance to serum, primarily due to activation of the classical complement pathway. The mutant shows no difference in colonization of chickens compared to the wild-type, but the mutant shows a drop in colonization in piglets in co-infection studies with the wild-type. Our data suggests that, when present, MeOPN has a contributory role in pathogenesis.In this study, we have determined that MeOPN modifications are prevalent among several Campylobacter strains  at 37\u00b0C. For NMR experiments, Campylobacter strains were grown on Brain Heart Infusion agar supplemented with 5% (v/v) defibrinated horse blood. Otherwise, C. jejuni 81\u2013176 wild-type and mutant strains were routinely propagated on Mueller-Hinton (MH) agar or in MH broth with agitation at 100 r.p.m. Where appropriate, media were supplemented with 30 \u00b5g ml\u22121 kanamycin (Km) and 20 \u00b5g ml\u22121 chloramphenicol (Cm).Unless otherwise specified, all reagents were obtained from Sigma-Aldrich Canada , and all media were obtained from Difco Laboratories . Restriction enzymes were obtained from New England Biolabs  and T4 DNA ligase was obtained from Promega . All  strains  were groCampylobacter taxa  indirect-detection nanoprobe. The first increment of 1H\u201331P heteronuclear signal quantum correlation spectra (1D HSQC spectra) were acquired with the transients noted: 1780 for C. insulaenigrae RM5435; 2048 for C. lari UPTC NCTC 11845; 12,288 for C. lari subsp. concheus LMG 11760; 1320 for C. subantarcticus RM8523; 1532 for C. cuniculorum LMG 24588; 3072 for C. upsaliensis RM3940; 512 for C. upsaliensis RM3195, C. helveticus CCUG 30566, C. jejuni NCTC 11168, and the C. jejuni 81\u2013176 cjj81176_1415 mutant; 256 for C. jejuni 81\u2013176 and the C. jejuni 81\u2013176 cjj81176_1420/cjj81176_1435 mutant. Note that the spectrum for the C. jejuni 81\u2013176 cjj81176_1415 mutant was obtained on a 500 MHz spectrometer.The methods were followed as previously described cjj81176_1420 and cjj81176_1435 genes of C. jejuni 81\u2013176 (Genome accession number: NC_008787) were disrupted by insertional inactivation and allelic replacement. Briefly, cjj81176_1420 and cjj81176_1435 were PCR amplified using the primer pairs ak265/ak266 and ak265/267 with the following sequences: ak265\u22365\u2032-GCTCTAGAAGGAGTTTAAAATGTATAACCCAAACTCAGCTATAGAAAGAG-3\u2032; ak266\u22365\u2032-GCTCTAGATTACAAATCTTTTTCCTGAATATCACCATCCAAC-3\u2032; ak267\u22365\u2032-GCTCTAGACTATGTTTTAATTTCTTTATAACTATACCAATTTTTAC-3\u2032. The 1.8 kb PCR products were cloned into the pGEM-T Easy vector (Promega). After confirmation of the inserts via restriction analysis, the recombinant plasmids were used for construction of rkan and rcam derivatives for insertional inactivation of genes cjj81176_1420 and cjj81176_1435, respectively. The blunt-ended BamHI fragment of pJMK30 (sourse of the rkan cassette) was inserted into a unique HindIII site (after blunt-ending) of cjj81176_1420, whilst the rcam cassette-carrying the SmaI fragment of plasmid pAV35 was inserted into a unique SwaI site of gene cjj81176_1435rcam and rkan cassettes were verified using restriction analysis to make sure that the antibiotic resistance genes are in colinear orientation with the target genes. The latter is essential for prevention of a negative polar effect.The cjj81176_1420::rkan derivative into 81\u2013176 and selection of colonies on a Km plate (50 \u00b5g ml\u22121) the products of recombination were confirmed by PCR using primers ak265/ak266. The derived 81\u2013176 cjj81176_1420::rkan mutant was then used for insertional inactivation of gene cjj81176_1435 via trasformation with the cjj81176_1435::rcam construct and selection of recombinant clones on plates supplemented with both Km (50 \u00b5g ml\u22121) and Cm (10 \u00b5g ml\u22121). The resultant cjj81176_1420::rkan/cjj81176_1435::rcam MeOPN transferase double mutant isolates were verified by PCR with ak265/266, ak265/ak267 and ak265 with ak237  producing products of 3.3 kb, 2.7 kb and 1.4 kb respectively as expected. Several independent confirmed clonal isolates were stored in 50% glycerol at \u221280\u00b0C.After transformation of the cjj81176_1415 gene of C. jejuni 81\u2013176 was disrupted by insertional inactivation and allelic replacement. Briefly, a mutagenesis construct was generated by PCR amplification of the disrupted rcj1416-kan gene from the 11168H cj1416 mutant we used previously et al. 5\u2032- CACTAAATCAGCCTCTGGTTTATC-3\u2032) and CS-351 (5\u2032- AAAAGAAGATTTGGCTCATCTTG-3\u2032), followed by ligation of the 3.2 kb PCR product into the pGEM-T Easy vector (Promega). After natural transformation of the cjj81176_1415::rkan derivative into 81\u2013176 and selection of colonies on a BHI-Blood Km agar plate (25 \u00b5g ml\u22121 Km), 81\u2013176 cjj81176_1415::rkan mutant isolates were verified by PCR using the CS-350/CS-351 primer pair, producing a 3.2 kb product as expected.The C. jejuni 11168H and 81\u2013176 wildtype, 11168H cj1420::rkan mutant, 11168H cj1421/cj1422::rkan mutant, 81\u2013176 cjj81176_1415::rkan mutant, and 81\u2013176 cjj81176_1420::rkan/cjj81176_1435::rcam mutant strains using the Aurum\u2122 Total RNA Mini Kit . First strand synthesis of cDNA from RNA preparations was performed using the SuperScript III First-Strand Synthesis system , using the following specific primers: CS-645 (5\u2032- CATTAGCTCGAGGTTTTTTTGATATTG-3\u2032) for cjj81176_1414 and cj1415; CS-1247 (5\u2032- GGAATCTCTATACTTTGCATATG-3\u2032) for cjj81176_1419 and cj1420; CS-1249 (5\u2032- GGACACTAACTCAAATGGTAG-3\u2032) for cjj81176_1434; CS-869 (5\u2032- ATGACTTGACGTCGTCCACACCTT -3\u2032) for 16S rRNA as an internal control. PCR reactions were performed using resultant cDNA preparations with Taq polymerase (Life Technologies) and the following primer pairs: CS-644 (5\u2032- GAGGTGCCATATGAAAAATAATCC-3\u2032) and CS-645 for cjj81176_1414 and cj1415; CS-1246 (5\u2032- CAATAGACCTGCTTATAGTCC-3\u2032) and CS-1247 for cjj81176_1419 and cj1420; CS-1248 (5\u2032- GTAAAATACGTGGCTGTTGTC-3\u2032) and CS-1249 for cjj81176_1434; CS-868 (5\u2032- GCGCAACCCACGTATTTAGTTGCT -3\u2032) and CS-869 for 16S rRNA as an internal control. For each PCR reaction, a 75-\u00b5l reaction mix was generated and aliquoted into 7 individual 10-\u00b5l aliquots. These aliquots were placed in a thermocycler and subjected to PCR, with individual aliquots being removed in 5-cylce increments from 5 to 35 cycles, and analyzed by agarose gel electrophoresis.Reverse transcriptase PCR experiments were performed with wildtype and mutant strains to examine whether transcript levels of genes located immediately downstream from insertionally disrupted genes were affected in the MeOPN biosynthesis and MeOPN transferase knockout mutant strains used in this study . Total R2. Two days prior to infection, cells were trypsinized and seeded into a 24-well plate at 2.5\u00d7105 cells ml\u22121, according to the method of Oelschlaeger et al. g, 10 min, 20\u00b0C), washed with phosphate buffered saline (PBS), and added to Caco-2 cells to a multiplicity of infection of 100. After a 3 h incubation at 37\u00b0C in 5% CO2, Caco-2 cells were rinsed with 3\u00d71 ml of PBS, lysed with 250 \u00b5l of 0.1% Triton X-100 in PBS , and dilutions of each well were plated on MH agar to yield combined adherence and invasion numbers. Invasion levels were determined after a 3 h incubation in 0.5 ml of medium containing 250 \u00b5g ml\u22121 of gentamicin (Gm), then extensive washing prior to lysis with Triton X-100. Adherence values were determined by subtraction of the invasion values from the combined adherence and invasion values. Experiments were performed in duplicate and the averages (mean \u00b1 SEM) of three separate experiments are presented, and significance was assessed by an unpaired t-test.The human Caco-2 epithelial cell line was obtained from the American Type Culture Collection (ATCC HTB-37) and was routinely grown in Dulbecco\u2019s minimal essential medium (DMEM) supplemented with 10% (v/v) fetal bovine serum, 2 mM L-glutamine and 1.0 mM sodium pyruvate and incubated at 37\u00b0C in a humidified atmosphere with 5% COC. jejuni 81\u2013176 cjj81176_1415::rkan, cjj81176_1420::rkan/cjj81176_1435::rcam and kpsM::rkan mutants relative to the wild-type strain, sterile needles were dipped into liquid cultures of each strain and stabbed into semi-solid medium (thioglycollate medium containing 0.4% agar). Swarming was assessed after incubation under microaerobic conditions at 37\u00b0C for 24 h.To assess the motility of the et. al. C. jejuni strains were grown for 16 h in biphasic MH medium, then washed and resuspended in HEPES buffer  to a final concentration of 106 CFU ml\u22121. For each culture, 100 \u00b5l aliquots of cell suspension were combined with 350 \u00b5l HEPES buffer and 50 \u00b5l of either fresh pooled normal human complement serum (NHS)  or heat-inactivated NHS (56\u00b0C for 45 min) . Aliquots were taken at 0 min, 30 min, and 60 min, and bacterial counts were enumerated via serial dilution on MH agar. Bacterial survival in active serum was calculated as percentage of survival in inactive serum. Experiments were performed in triplicate and the averages (mean \u00b1 SEM) of three separate experiments are presented, and significance was assessed by an unpaired t-test.Serum resistance assays were performed according to the method of Blaser To determine if the classical complement activation pathway was involved in the observed killing, serum resistance assays were performed as above in both the presence and absence of 50 mM EGTA and incubated as above for 60 min before serial dilution and plating on MH to enumerate surviving bacteria. Experiments were performed in triplicate and the averages (mean \u00b1 SEM) of three separate experiments are presented, and significance was assessed by an unpaired t-test.C. jejuni strains were grown overnight on MH agar with the appropriate antibiotic. Bacteria were harvested in PBS and adjusted to 107 CFU/ml. One-day-old chicks were infected by oral gavage with 106 CFU of C. jejuni 81\u2013176 wild-type and the cjj81176_1420::rkan/cjj81176_1435::rcam mutant in 100 \u00b5l of PBS or with PBS alone. Chicks were euthanized 6 days post-infection and the caecal content was aseptically removed, serially diluted in PBS, and plated onto Karmali selective agar plates. Colonies were counted after 2 days of incubation under microaerobic conditions  at 37\u00b0C to determine colonization levels.Animal procedures were approved by the Biosciences Animal Care and Use Committee at the University of Alberta. The animals were maintained and used in accordance with the recommendations of the Canadian Council on Animal Care. Chickens were obtained from the Poultry Research Facility, Department of Agriculture, Food and Nutrition Science, University of Alberta and separated into three groups of eight birds. C. jejuni 81\u2013176 wild-type and the cjj81176_1420::rkan/cjj81176_1435::rcam mutant were adjusted to an OD600 nm of 1.0, combined to create a 1\u22361 ratio of mutant to wild-type, and were diluted in fresh MH broth to a 10 ml culture of approximately 107 CFU/ml and used to inoculate each piglet. To confirm the exact ratio of wild-type to mutant, samples of the inoculum were serially diluted, plated on MH agar with and without Cm and enumerated.Animal procedures were approved by the Animal Care and Use Committee at the University of Ottawa. The animals were maintained and used in accordance with the recommendations of the Canadian Council on Animal Care. Five male, specific pathogen free neonatal colostrum-deprived piglets were acquired from the Canadian Food Inspection Agency and were allowed to acclimatize for at least 24 h to their environment. Overnight cultures of \u22121 kanamycin supplement for the selection of the cjj81176_1420::rkan/cjj81176_1435::rcam mutant. The plates were incubated at 37\u00b0C, under microaerobic conditions for 48 h and C. jejuni colonies were counted to determine colonization levels. Statistical significance was determined using a Mann\u2212Whitney test comparing the measured wild-type to mutant ratio of the inoculum, to the ratio recovered from the piglet intestine following infection (p\u200a=\u200a0.0005).The piglets developed a severe infection and were euthanized within 48 h post-infection. Samples were taken from the duodenum, jejunum, ileum, cecum, and colon, were homogenized, serially diluted in MH broth and plated onto Campylobacter agar base (Oxoid CM935) containing Campylobacter-selective Karmali supplements (Oxoid SR167E) with and without a 10 \u00b5g mlG. mellonella larvae  were stored at 4\u00b0C in woodchips until needed, and used within 7 days of being received. Overnight cultures of C. jejuni (grown on MH agar) were harvested and resuspended in 10 mM MgSO4 to a final OD550 nm\u200a=\u200a1.0 (\u223c1.0\u00d7109 cfu ml\u22121). Stock solutions (10 mg ml\u22121) of synthesized MeOPN-linked monosaccharides (see below)  and 1 mg ml\u22121 CPS were prepared immediately prior to injection. After swabbing larvae with ethanol, 5 \u00b5l aliquots of cell suspension or diluted compound were injected into the left hindmost proleg using a 10 \u00b5l Hamilton syringe. To prevent cross-contamination, the syringe was rinsed sequentially with methanol then water between groups. After injection, larvae were incubated at 37\u00b0C for 24 h, and survival numbers recorded. Larvae that were not responsive to touch were scored as dead. Campylobacter injection results represent the mean (\u00b1SEM) of five independent experiments, MeOPN-containing compound injection results represent the mean (\u00b1SEM) of four independent experiments, and the 81\u2013176 CPS injection data was the result of a single experiment. In all cases, each individual experiment consisted of three technical replicates, each containing 10 larvae.C. jejuni 81\u2013176 was purified according the enzymatic isolation method of McNally et al. The CPS of O-(methylphosphoramidyl)-\u03b1-D-glucopyranoside (2-MeOPN-Glc), methyl 6-O-(methylphosphoramidyl)-\u03b1-D-glucopyranoside (6-MeOPN-Glc), methyl 2-O-(methylphosphoramidyl)-\u03b1-D-galactopyranoside , and methyl 6-O-(methylphosphoramidyl)-\u03b1-D-galactopyranoside (6-MeOPN-Glc) will be described in a separate publication (unpublished data).The synthesis of methyl 2-Campylobacter genus resulted in the identification of orthologues to the C. jejuni MeOPN biosynthesis genes cj1416\u20131418 in 13 of the 30 Campylobacter strains analyzed .A selection of the examined . SignalsC. jejuni 81\u2013176 strain. In silico analysis of the 81\u2013176 genome led to the identification of a locus (cjj81176_1414-cj81176_j1417) with homology to the MeOPN biosynthesis locus of C. jejuni strain NCTC 11168 (cj1415-cj1418), as well as two putative MeOPN transferases (cjj81176_1420 and cj81176_j1435) with homology to the cj1422 MeOPN transferase gene from NCTC 11168. Unlike cj1421 and cj1422 in NCTC 11168, cjj81176_1420 and cjj81176_1435 are not adjacent to each other and cjj81176_1435 is significantly downstream of the MeOPN biosynthesis locus but, similar to cj1422, both contain polyG:C tracts, causing them to be prone to phase variation. Sequencing results from our 81\u2013176 wild-type strain indicated that cjj81176_1435 is phased on while cjj81176_1420 is phased off, but a cjj81176_1420::rkan/cjj81176_1435::rcam double knockout was nonetheless generated to avoid any risk of cjj81176_1420 turning back on. HR-MAS NMR analyses indicated the presence of a single MeOPN in the 81\u2013176 wild-type strain which was absent in the cjj81176_1420::rkan/cjj81176_1435::rcam MeOPN transferase mutant  with homology to cj1416 from NCTC 11168. Similar to the MeOPN transferase mutant, the biosynthesis mutant did not show a resonance for MeOPN in the HR-MAS NMR analysis , the cjj81176_1415::rkan and cjj81176_1420::rkan/cjj81176_1435::rcam mutants and the acapsular kpsM::rkan mutant exhibited marked sensitivity to serum-mediated killing relative to wild-type . Therefore, we examined the effects of the presence or absence of 50 mM EGTA on the level of serum resistance observed , and the ratio of cjj81176_1420::rkan/cjj81176_1435::rcam versus wild-type recovered was determined. The mutant was significantly outcompeted by the wild-type strain with a 5-fold reduction in colonization level  G. mellonella were also assessed. In this background, the MeOPN transferase mutant in the C. jejuni 11168H background was also able to kill G. mellonella to similar levels to the wild-type, indicating that the presence of MeOPN on the CPS surface is not required for insecticidal activity for either C. jejuni 11168H or C. jejuni 81\u2013176. In contrast, both the C. jejuni 81\u2013176 cjj81176_1415::rkan and C. jejuni 11168H cj1416::rkanG. mellonella . One possible reason why we did not detect MeOPN in the other Campylobacter species is that these strains could produce a phosphoramidate lacking the O-methyl group, as has been observed in the lipopolysaccharides of Xanthomonas campestrisShewanellaO-methyl of the MeOPN group. Alternatively, the MeOPN biosynthesis or transferase genes in these strains could be phased off, given that the MeOPN biosynthesis and transferase genes identified in both C. jejuni 11168H and C. jejuni 81\u2013176 were found to be phase variable cj1416-cj1418 orthologues of C. fetus subsp. fetus, C. fetus subsp. venerealis, C. lari subsp. lari, and C. sputorum bv. sputorum strains tested in this study do not contain GC tracts typically associated with phase variability in Campylobacter. Most likely the reason for not detecting MeOPN in these strains is due to the lack of MeOPN transferase orthologues (cj1421 or cj1422) although it is possible they may contain MeOPN transferase genes with low similarity to cj1421 or cj1422.Genetic analyses of representative members of the . jejuni , they weC. jejuni 81\u2013176 due to the virulent nature of this strain. We generated mutants that were deficient in both the biosynthesis and transfer of MeOPN, to be confident that any effects observed could be correlated specifically to loss of MeOPN, and confirmed that there were no obvious changes to CPS besides the lack of MeOPN. The identification of two orthologues of cj1422, with one phased off in our 81\u2013176 wild-type strai\u0146 is interesting because it is unclear whether an isolate with both these genes phased on would display a second distinct MeOPN modification or whether the transferases play redundant roles. The published structure of the 81\u2013176 CPS indicates only one MeOPN modification To investigate the biological role of MeOPN, we used C. jejuni. The results obtained from the assays indicated that MeOPN influences the ability of C. jejuni to invade and resist serum killing. It is noteworthy that the serum resistance results with the cjj81176_1420::rkan/cjj81176_1435::rcam mutant are consistent with those recently reported by Maue et al. C. jejuni 81\u2013176 MeOPN biosynthetic mutant, which we also tested. Because C. jejuni is primarily considered a gastrointestinal pathogen, these results are relevant as they indicate that CPS MeOPN modifications play a role in protection against humoral immunity. However, because C. jejuni can also cause bacteremia 2+; therefore, the addition of EGTA acts as an effective inhibitor of the classical pathway of complement activation, which is Ca2+-dependent C. jejuni antibodies. The intermediate level of serum survival observed with the wild-type strain in the absence of EGTA indicates that the CPS MeOPN modifications offer a moderate degree of protection by interfering with serum killing.We first examined whether or not the presence/absence of MeOPN on the cell surface affects the adherence/invasion and serum resistance of cjj81176_1420 and cjj81176_1435 genes suggests that MeOPN expression is desirable early in the infection process for protection against humoral immunity, but that phase variation to a MeOPN\u2212 phenotype later in the infection process would be beneficial and enhance the invasiveness and virulence of C. jejuni. Interestingly, the C. jejuni NCTC 11168 variant that we isolated, which became bacteriophage resistant through the loss of MeOPN et al., unpublished data). It is not yet clear whether or not the presence of MeOPN enhances serum resistance by preventing antibody deposition, by inhibiting binding by CRP or another acute phase serum protein, or by functioning through another mechanism. Studies are currently underway to determine if CRP plays a role in the observed serum mediated killing and whether other components of human serum bind preferentially to whole cells and/or CPS lacking MeOPN relative to wild-type.The phase variability of the in vivo in both commensal (chicken) and mammalian (piglet) colonization models. Loss of MeOPN in the 81\u2013176 transferase mutant was found to have no effect on chicken colonization. This is consistent with our recent results demonstrating that C. jejuni NCTC 11168 fed to chickens along with bacteriophages recognizing the MeOPN modification caused a selection for C. jejuni variants without the CPS modification, but did not change levels of C. jejuni colonization et al. C. coli strains that we have examined by HR-MAS NMR to date Campylobacter species and phages and we have already demonstrated that this relationship indeed exists for C. jejuniGiven that MeOPN was found to influence both serum resistance and invasion, we proceeded to examine the effects of loss of MeOPN C. jejuni 11168H to kill G. mellonella larvae, a MeOPN biosynthetic mutant showed a marked decrease in larval killing relative to wild-type and complementation of the mutation was able to restore wild-type killing, suggesting that MeOPN has insecticidal activity against G. mellonellaC. jejuni 81\u2013176 and C. jejuni 11168H MeOPN transferase mutants to kill G. mellonella larvae to wild-type levels was unexpected, and suggested that MeOPN might be accumulating to significant levels within the bacterial cell in our mutants. However, we were unable to detect MeOPN or MeOPN intermediates in metabolomics studies with the cjj81176_1420::rkan/cjj81176_1435::rcam mutant (results not shown), so we examined potential insecticidal activity directly. MeOPN-linked monosaccharides were synthesized and injected into G. mellonella larvae, but none of the compounds showed any level of insecticidal activity. To rule out the possibility that MeOPN needs to be presented in a specific context to elicit the insecticidal activity, purified CPS from 81\u2013176 wild-type was injected into G. mellonella, but again no insecticidal activity was observed. It is worth noting that concentrated solutions were used for both the MeOPN-linked monosaccharides and the purified CPS injections, and therefore the MeOPN levels in these injections are significantly higher than those in the C. jejuni injections. The inability of MeOPN, MeOPN-linked oligosaccharides, or purified 81\u2013176 CPS to induce larval killing provided conclusive evidence that MeOPN does not have insecticidal activity as previously suggested In a previous study investigating the ability of cjj81176_1420::rkan/cjj81176_1435::rcam mutant to kill larvae at wild-type levels suggests that larval killing by C. jejuni is MeOPN-independent. The observation that MeOPN biosynthetic mutants show a decreased ability to kill G. mellonella larvae as observed by Champion et al. and by us in this study, is contrary to the other findings, and suggests that MeOPN biosynthetic intermediates may accumulate in the cytoplasm, causing the mutant to be less able to survive inside the larvae, or that the mutation causes downstream effects. Examination of all the mutants in this study by RT-PCR demonstrated wild-type levels of gene transcripts downstream of both MeOPN transferase mutants. In contrast, mutants in the biosynthesis homologue cj1416  show slightly less cj1415 transcription in both strain backgrounds. This is of relevance because in our earlier study cj1415 mutation results in reduced CPS expression . So, the MeOPN biosynthesis mutants may show higher levels of survival compared to the wild-types because they are actually producing less CPS. Future studies to elucidate the biochemical pathway for MeOPN biosynthesis and to develop methods to accurately quantitate CPS expression will be invaluable to advance our understanding of this unique modification.While it is possible that MeOPN could contribute indirectly to larval killing by protecting the bacteria from the humoral responses of the larval immune system, the ability of the Campylobacter genus, and especially prevalent in species more closely related to C. jejuni, with the possible exception of C. coli. We identified MeOPN biosynthesis and transferase genes in C. jejuni 81\u2013176, and generated mutants in this pathway. We demonstrated that MeOPN moieties are not inherently insecticidal against G. mellonella and that larval killing by C. jejuni is not directly related to MeOPN. We further showed that loss of MeOPN on the cell surface results in a drastic decrease in serum resistance while enhancing invasion of Caco-2 cells. Loss of MeOPN was found to have no effect on colonization in a commensal chicken model, but showed a reduction in colonization relative to wild-type in a piglet model, suggesting that MeOPN has a contributory role in pathogenesis.In summary, we have demonstrated that MeOPN modifications are common within the Figure S1RT-PCR analyses of genes located downstream from the Campylobacter jejuni mutated genes in this study. Reverse Transcriptase PCR analyses of genes downstream of the chromosomally disrupted genes indicates similar transcript levels between wild-type and MeOPN transferase mutants for (A) cj1420 downstream of cj1421 and cj1422 in C. jejuni 11168H and cjj81176_1419 downstream of cjj81176_1420 in C. jejuni 81\u2013176, and (B) cjj81176_1434 downstream of cjj81176_1435 in the 81\u2013176 background. Lower levels of transcripts were observed in the MeOPN biosynthesis mutant backgrounds relative to the wild-type strain for (C) cj1415 downstream of cj1416 in 11168H and cjj81176_1414 downstream of cjj81176_1415 in 81\u2013176.(DOC)Click here for additional data file.Table S1Accession numbers for the Cj1416-Cj1418 homologues and AtpA sequences from Campylobacter species used in this study.(DOC)Click here for additional data file."}
+{"text": "The field of genetics has come to rely heavily on commercial genotyping arrays and accompanying annotations for insights into genotype-phenotype associations. However, in order to avoid errors and false leads, it is imperative that the annotation of SNP chromosomal positions is accurate and unambiguous. We report on genomic positional discrepancies of various SNP chips for human, cattle and mouse species, and discuss their causes and consequences. Array based genotyping provide a powerful tool to interrogate genetic variation. It enables a broad variety of applications such as genome-wide association studies, evaluation of genetic merit in breeding applications, linkage disequilibrium studies, comparative genetic studies, as well as for characterizing biodiversity To detect genomic position discrepancies of SNPs in genotyping arrays, we used BLAST http://www.illumina.com/products/bovine_snp50_whole-genome_genotyping_kits.ilmn) with the ones made by Illumina\u2122. From the 54,001 SNPs present on the chip, we found that only 41,496 (77%) had a unique and perfect match in both our and Illumina\u2122 mappings  came into the market which includes 54,609 SNPs in comparison to 54,001 SNPs from the previous version. Using the same procedure we mapped the SNP postions for this beadchip using only the SNPs that have a unique perfect hit in the genome assembly (UMD3.1 in this case). From 48,284 SNPs, we detected 449 SNP genomic position discrepancies, of which 248 (55%) were in different genes. These 449 discrepancies lead to a change in the genomic order of 13,133 SNPs, with 90% being less or equal than 2 index positions away (updated mapping file provided as http://www.illumina.com/products/bovinehd_whole-genome_genotyping_kits.ilmn), and found that only 14 SNPs (2 in different chromosomes) retrieved mapping to different genomic positions, of which 2 (14%) were observed in different genes. These 14 discrepancies lead to a change in genomic order of 182 SNPs, with 98% being less or equal than 1 index away . We detected that 620 SNPs (5 in different chromosomes) retrieved different genomic mapping positions, of which 66 (11%) are in different genes. These 620 differences lead to a change in genomic order of 271,325 SNPs, with 98% being less or equal than 2 indexes away  and the Affymetrix's\u2122 Genome-Wide Human SNP Array 6.0 . Concerning the Affymetrix\u2122 human genotyping array, we detected 25 differences (5 in different chromosomes), of which 10 (40%) were in different genes. These 25 discrepancies lead to 61,916 SNPs being in a different genomic order, with 89% being less or equal than 2 indexes away  should be used in order to achieve an accurate chromosomal alignment instead of retrieving a partial best alignment with extra SNPs, indels or less SNP flanking sequence aligned .Since wrongly mapped SNPs can change in which genic and regulatory regions they overlap, it can trigger erroneous variant effect conclusions. Large SNP positional discrepancies can also affect studies of genotype imputation and linkage disequilibrium, leading to false coverage and power of genome-wide association analysis and erroneous evaluation of the choice of SNP platform to use We would recommend the providers of commercial SNP chips to always provide (for each chip) a technical report on how they exactly did the mapping. Specifically, refer to which mapping algorithm and its parameters used, genome assembly version, and the location of SNP flanking sequences in their websites. It was our experience that trying to retrieve this information revealed to be a cumbersome task, with little or no information provided regarding the SNP mapping procedure.In Supplementary data we provide our mappings for the genotyping platforms tested here, and we hope that investigators using different genotyping platforms are encouraged to map them using an accurate and sensitive procedure . The SNPftp://hgdownload.cse.ucsc.edu/goldenPath/mm9/bigZips/chromFa.tar.gz (mouse assembly mm9), ftp://hgdownload.cse.ucsc.edu/goldenPath/hg18/bigZips/chromFa.zip (human assembly hg18), ftp://ftp.cbcb.umd.edu/pub/data/assembly/Bos_taurus/Bos_taurus_UMD_3.1/bos_taurus.fa.gz (cow assembly UMD3.1) and ftp://hgdownload.cse.ucsc.edu/goldenPath/bosTau4/bigZips/bosTau4.fa.gz (cow assembly BosTau4.0).All SNP discrepancies reported here are relatively to the genome build to which the chip was initially mapped to. The fasta files for the genome assemblies of each species queried were retrieved from The cow genome has currently two genome versions available, one (Btau4.0) from the public consortium that sequenced the bovine genome ftp://ftp.illumina.com/Whole%20Genome%20Genotyping%20Files/. This site is only accessible through password that can be provided by Illumina\u2122 customer services. Inside this folder there are subfolders containing the four type of arrays tested here . The files used were BovineHD_B.csv, BovineHD_777962_Name_Chr_Coord.csv, BovineSNP50_B.csv, BovineSNP50_Final_SNPs_54001.csv, BovineSNP50v2_AlleleReport_revB, BovineSNP50v2_FinalSNPList_54609_09Apr10.csv, Human1M-Duov3_B_csv, Human1M-Duov3_FinalMarkerList_1199187.txt.The genomic coordinates of each SNP and the fasta files for the oligomer sequences flanking the SNPs in each chip were taken from different sources.For the Illumina\u2122 arrays, these data were fetched from http://www.affymetrix.com/browse/products.jsp?productId=131533&navMode=34000&navAction=jump&aId=productsNav#1_3 and from http://www.affymetrix.com/estore/browse/products.jspjsessionid=07BF945B0A18133EA55E7EE9D965B154?productId=prod100002&categoryId=cat30008#1_3. The files used were GenomeWideSNP_6.bed, GenomeWideSNP_6_flanking_sequences_fasta and MOUSEDIVm520650.na31.annot.csv. Contrary to the human array, the Affymetrix\u2122 website for the mouse array did not contain the flanking sequences for the respective SNPs. After contacting Affymetrix\u2122 support, they told us that The Mouse Diversity Genotyping array was not designed by Affymetrix, but by The Jackson Laboratory and the University of North Carolina. As such, they suggested us to take a look at the website of the Jackson Laboratory (http://cgd.jax.org/tools/diversityarray.shtml) to see whether we could find the flanking sequences files. Unfortunately we were not able to get it and therefore we retrieved this information from dbSNP mouse build 129 (http://www.ncbi.nlm.nih.gov/projects/SNP/), where the SNP flanking information was stored. Consequently, for this SNP chip we did not get all the data from the primary source but from a secondary source which might add to the reasons for this array having the biggest number of diverging SNP positions.For the Affymetrix\u2122 arrays, the data was fetched from http://genome.ucsc.edu/). For human and mouse, the gene track \u2018UCSC Genes\u2019 table \u2018knownGene\u2019 was used, while for cow the gene track \u2018Ensembl Genes\u2019 table \u2018ensGene\u2019 was used for assembly Btau 4.0 and the file ftp://ftp.cbcb.umd.edu/pub/data/assembly/Bos_taurus/Bos_taurus_UMD_3.1/annotation/UMD3.1.gff.gz was used for assembly UMD3.1.\u201dThe NHGRI catalog of GWAS studies (http://www.genome.gov/gwastudies/) was used to select papers published in 2010 from which significant SNPs were detected to have mapping discrepancies.\u201cThe gene annotations were retrieved from UCSC genome browser  T (FULL SW ALIGN) OR F[GAPPED ALIGNMENT] T[OPEN PENALTY] -2[EXTEND PENALTY] -1It should be noted that the performance speed depends on the WORD SIZE and QUERY INCREMENT, with lower word size and lower query increment increasing the sensitivity of the alignment.The aligned output files were formatted with Unix commands. After, the genomic coordinates of each perfectly unique mapped SNP were compared with the original genomic coordinates annotated by Affymetrix\u2122 and Illumina\u2122. This was done with custom R scripts. Both Unix and R commands are provided in Data S1BovineSNP50 v1 beadchip uniquely mapped SNPs .(RAR)Click here for additional data file.Data S2BovineSNP50 v2 beadchip uniquely mapped SNPs .(RAR)Click here for additional data file.Data S3BovineHD beadchip uniquely mapped SNPs .(RAR)Click here for additional data file.Data S4Affymetrix mouse diversity genotyping array uniquely mapped SNPs .(RAR)Click here for additional data file.Data S5Affymetrix Genome-Wide Human SNP Array 6.0 uniquely mapped SNPs .(RAR)Click here for additional data file.Data S6Illumina Human1M-Duo DNA Analysis beadchip uniquely mapped SNPs .(RAR)Click here for additional data file.Data S7Unix and R commands used to post-process the aligned sequences.(TXT)Click here for additional data file."}
+{"text": "Saccharopolyspora erythraea, a mycelium-forming actinomycete, produces a clinically important antibiotic erythromycin. Extensive investigations have provided insights into erythromycin biosynthesis in S. erythraea, but knowledge of its morphogenesis remains limited. By gene inactivation and complementation strategies, the TetR-family transcriptional regulator SACE_0012 was identified to be a negative regulator of mycelium formation of S. erythraea A226. Detected by quantitative real-time PCR, the relative transcription of SACE_7115, the amfC homolog for an aerial mycelium formation protein, was dramatically increased in SACE_0012 mutant, whereas erythromycin biosynthetic gene eryA, a pleiotropic regulatory gene bldD, and the genes SACE_2141, SACE_6464, SACE_6040, that are the homologs to the sporulation regulators WhiA, WhiB, WhiG, were not differentially expressed. SACE_0012 disruption could not restore its defect of aerial development in bldD mutant, and also did not further accelerate the mycelium formation in the mutant of SACE_7040 gene, that was previously identified to be a morphogenesis repressor. Furthermore, the transcriptional level of SACE_0012 had not markedly changed in bldD and SACE_7040 mutant over A226. Taken together, these results suggest that SACE_0012 is a negative regulator of S. erythraea morphogenesis by mainly increasing the transcription of amfC gene, independently of the BldD regulatory system.The online version of this article (doi:10.1007/s00284-013-0410-x) contains supplementary material, which is available to authorized users. Actinomycetes undergoes a complex morphological differentiation to adapt to adverse environments [Actinomycetes begins with spore germination and hyphal outgrowth, leading to the formation of a vegetative, or substrate mycelium. Sensing of nutrient deprivation stimulates reproductive growth resulting in the development of aerial hyphae and spore chains [Saccharopolyspora erythraea could form the aerial hyphae, and produce erythromycin, which is a macrolide antibiotic with broad-spectrum antimicrobial activity. Extensive genetic and biochemical studies have provided detailed insights into the genes involved in erythromycin biosynthesis in S. erythraea [During its life cycle, the soil-inhabiting ronments . Growth e chains . Saccharrythraea , 4, yet S. erythraea allowed a deeper exploration of the molecular processes controlling its morphogenesis [S. erythraea [S. erythraea mycelium formation, and established genetic evidence for the crosstalk between SACE_7040 and BldD [S. erythraea. Deletion of SACE_0012 principally influences the transcription of a putative aerial mycelium formation gene SACE_7115, that is homologous to amfC of Streptomyces.In recent years, the availability of the complete genome sequence of ogenesis . Guided ogenesis , controlrythraea . FurtherSaccharopolyspora erythraea A226 and its derivatives were incubated in TSB medium at 30\u00a0\u00b0C for DNA extraction, protoplast preparation, and in liquid fermentation medium R5 for analysis of erythromycin production. R3M agar medium was used for protoplast regeneration, phenotypic observations, and RNA extraction [Escherichia coli DH5\u03b1 was the host for plasmid construction [Bacillus subtilis PUB110 was used for an inhibition test of erythromycin production in S. erythraea.traction . Escheritruction . Bacillutsr) cloned into the BamHI/SmaI sites. The E. coli-S. erythraea integrative shuttle expression vector pZMW [E. coli and S. erythraea were carried out according to the standard methods [Plasmid pUCTSR  was a pUtor pZMW , 10 was  methods , 11.SACE_0012 gene were amplified from genomic DNA of S. erythraea A226 by PCR using the primer pairs P1/P2  and P3/P4 . Then, the two DNA PCR products were inserted into the EcoRI/KpnI and XbaI/HindIII sites of pUCTSR, respectively, yielding pUCTSR\u03940012. By linearized fragment homologous recombination [SACE_0012 gene was replaced with the thiostrepton resistance gene in the S. erythraea A226 chromosome, and the selected mutants were verified by PCR using the primers P1/P4  and P6 (5\u2032-CGC GAT ATC TCA GCG ATC GGC GGT AGT CG-3\u2032) from genomic DNA of S. erythraea A226, and was ligated into the NdeI/EcoRV sites of pZMW [0012. Then, pZMW-0012 was introduced into SACE_0012 mutant by PEG-mediated protoplast transformation, generating the complemented strain \u0394SACE_0012/pZMW-0012.For complementation, the  of pZMW  to genereryA, bldD, SACE_0012, and homologous genes of whiA, whiB, whiG, and amfC associated with morphogenesis in Streptomyces (Table S1) [S. erythraea A226 and the mutants of SACE_0012, bldD, and SACE_7040 after 2 or 4\u00a0days growth on R3M agar medium. Then, extracted RNA was treated with DNase I (Fermentas), and reverse transcription was accomplished using a cDNA synthesis kit (Fermentas). qRT-PCR reactions were performed on the Applied Biosystems StepOnePlus system with Maxima\u2122 SYBR Green/ROX qPCR Master Mix (Fermentas). The hrdB gene encoding the major sigma factor in S. erythraea was used as an internal control, and relative quantification was evaluated using a comparative cycle threshold method as described by Livak and Schmittgen [The transcriptional levels of hmittgen .SACE_0012, and \u0394bldD were grown in 30\u00a0ml R5 liquid medium in 250\u00a0ml baffled flasks for 6\u00a0days at 30\u00a0\u00b0C. 5\u00a0\u03bcl fermentation supernatant from these cultures was added to LB agar plates, which was sprayed with an overnight culture of B. subtilis PUB110. The plates were incubated at 37\u00a0\u00b0C for 12\u00a0h, and the erythromycin production was estimated by scoring the growth-inhibition zones. Furthermore, erythromycin A produced by these cultures were quantitatively analyzed by high performance liquid chromatograph (HPLC) as described previously [Wild-type strain A226, \u0394eviously . Erythroeviously .S. erythraea, including the SACE_7040 gene previously reported [SACE_0012 gene has a full-length of 690\u00a0bp  and is a member of the TetR-family regulators that consists of 229 amino acids with a molecular mass of 25\u00a0kDa. To investigate its function, SACE_0012 was inactivated by replacing the 690\u00a0bp gene with a thiostrepton resistance cassette in S. erythraea A226 by the linearized fragment homologous recombination. A thiostrepton resistant mutant \u0394SACE_0012 was formed and confirmed by PCR analysis , the \u0394SACE_0012/pZMW-0012 strain had restored the timing of aerial mycelium on R3M agar medium (data not shown). After a longer cultivation to the sixth day, no significant phenotypic difference was observed between the wild-type strain A226, mutant \u0394SACE_0012, and \u0394SACE_0012/pZME-0012 (data not shown), revealing that SACE_0012 was responsible for the early aerial hypha formation of S. erythraea. Moreover, \u0394SACE_0012 and A226 strains had comparable inhibition activity for B. subtilis, and produced similar amount of erythromycin A by HPLC analysis of fermentation products , confirming that SACE_0012 was specifically involved in the morphological differentiation of S. erythraea.When grown on R3M medium, the mutant \u0394SACE_0012 disruption on the expression of morphogenesis and erythromycin biosynthesis genes, we compared A226 and mutant \u0394SACE_0012 for the transcriptional change to sporulation genes (whi and bldD), an aerial mycelium formation gene amfC [eryA (Table S1). The homologous genes to whiA, whiB, whiG involved in the regulation of sporulation in Streptomyces [SACE_2141, SACE_6464, SACE_6040, respectively) were examined by qRT-PCR. SACE_2141, SACE_6464, SACE_6040 transcriptions were slightly increased but not statistically different in mutant \u2206SACE_0012 over strain A226. bldD and eryA were also not differentially expressed. However, the transcriptional levels of the amfC homolog SACE_7115 (Table S1), an aerial mycelium formation gene conserved presented in Streptomyces [SACE_0012 , BN6_77090 from Saccharothrix espanaensis (identities 50\u00a0%), AMED_0889 from Amycolatopsis mediterranei (identities 48\u00a0%), etc. . However, functional analysis of the TetR-family regulator has been never reported, signifying a new regulatory mechanism for mycelial formation in actinomycetes, such as how it works with its ligand and target [S. erythraea developmental biology. In the future, more detailed regulatory mechanism of the SACE_0012 gene will likely be valuable to deepening the understanding of the modulation of S. erythraea morphogenesis.With structural and sequence conserved analysis , homologd target . TherefoSupplementary material 1 (DOC 703\u00a0kb)Below is the link to the electronic supplementary material."}
+{"text": "Salmonella paratyphi C is one of the few human-adapted pathogens along with S. typhi, S. paratyphi A and S. paratyphi B that cause typhoid, but it is not clear whether these bacteria cause the disease by the same or different pathogenic mechanisms. Notably, these typhoid agents have distinct sets of large genomic insertions, which may encode different pathogenicity factors. Previously we identified a novel prophage, SPC-P1, in S. paratyphi C RKS4594 and wondered whether it might be involved in pathogenicity of the bacteria.Salmonella phages such as P22 and ST64T but significantly lower than the 52.16% average of the RKS4594 chromosome. Electron microscopy showed short-tailed phage particles very similar to the lambdoid phage CUS-3. To evaluate its roles in pathogenicity, we lysogenized S. paratyphi C strain CN13/87, which did not have this prophage, and infected mice with the lysogenized CN13/87. Compared to the phage-free wild type CN13/87, the lysogenized CN13/87 exhibited significantly increased virulence and caused multi-organ damages in mice at considerably lower infection doses.We analyzed the sequence of SPC-P1 and found that it is an inducible phage with an overall G+C content of 47.24%, similar to that of most S. paratyphi C in animal infection models, so it is possible that this prophage is involved in typhoid pathogenesis in humans. Genetic and functional analyses of SPC-P1 may facilitate the study of pathogenic evolution of the extant typhoid agents, providing particular help in elucidating the pathogenic determinants of the typhoid agents.SPC-P1 contributes pathogenicity to  Salmonella contains more than 2600 very closely related serovars, classified by the Kauffmann-White Scheme according to their differences in the somatic (O) and flagellar (H) antigens ; S. choleraesuis [SC-B67 NC_006905]; S. paratyphi A ATCC9150 [NC_006511]; S. typhi CT18 [NC_003198]; S. typhi Ty2 [NC_004631]; S. paratyphi C RKS4594 [CP000857]; S. schwarzengrund CVM19633 [NC_011094]; S. paratyphi A AKU_12601 [NC_011147]; S. newport SL254 [NC_011080]; S. heidelberg SL476 [NC_011083]; S. gallinarum 287/91 [NC_011274]; S. enteritidis P125109 [NC_011294]; S. dublin CT_02021853 [NC_011205]; S. agona SL483 [NC_011149]; S. arizonae 62:z4,z23:-- [NC_010067]; Enterobacteria phage ES18 [NC_006949]; Enterobacteria phage ST64T [NC_004348]; Enterobacteria phage ST104 [AB102868]; Enterobacteria phage CUS-3 [CP000711]; Enterobacteria phage HK620 [NC_002730]; Enterobacteria phage Sf6 [NC_005344]; Enterobacteria phage HK022[NC_002166]; Enterobacteria phage HK97 [NC_002167]; Enterobacteria phage lambda [NC_001416]; Bacteriophage P22[AF217253]; Salmonella typhimurium phage ST64B [AY055382]; Enterobacteria phage Min27 [NC_010237].Genbank: 50 of the bacteria on mice; QHZ and HYZ did phage induction, morphologic examination, lysogenization and PFGE experiments; YF was involved in bioinformatic analysis; YGL contributed reagents; RNJ, GRL and SLL coordinated the work; QHZ and SLL produced the manuscript. All authors read and approved the final manuscript.QHZ initiated the project and carried out the bioinformatic analysis; QHL carried out animal experiments and determined IDThe ORFs in SPC-P1 DNA whose putative products exhibit significant homology to extant protein sequences. This file includes the position of each ORFs in the chromosome of RKS4594, the start and stop codon, the size, %G+C, homology proteins and the % Identity range, E-value of each ORFs.Click here for file"}
+{"text": "Campylobacter jejuni, a major foodborne pathogenic species causing human gastroenteritis. Although CosR is a response regulator, its cognate sensor kinase has not been identified in C. jejuni. In this study, DNA sequence analysis of the cosR flanking regions revealed that a gene encoding a putative sensor kinase, which we named cosS, is prevalent in non-thermotolerant Campylobacter spp., but not in thermotolerant campylobacters. Phosphorylation assays indicated that C. fetus CosS rapidly autophosphorylates and then phosphorylates C. fetus CosR, suggesting that the CosRS system constitutes a paired two-component signal transduction system in C. fetus. However, C. fetus CosS does not phosphorylate C. jejuni CosR, suggesting that CosR may have different regulatory cascades between thermotolerant and non-thermotolerant Campylobacter species. Comparison of CosR homolog amino acid sequences showed that the conserved phosphorylation residue (D51), which is present in all non-thermotolerant Campylobacter spp., is absent from the CosR homologs of thermotolerant Campylobacter species. However, C. jejuni CosR was not phosphorylated by C. fetus CosS even after site-directed mutagenesis of N51D, implying that C. jejuni CosR may possibly function phosphorylation-independently. In addition, the results of cosS mutational analysis indicated that CosS is not associated with the temperature dependence of the Campylobacter spp. despite its unique divergent distribution only in non-thermotolerant campylobacters. The findings in this study strongly suggest that thermotolerant and non-thermotolerant Campylobacter spp. have different signal sensing mechanisms associated with the CosR regulation.Two-component signal transduction systems are commonly composed of a sensor histidine kinase and a cognate response regulator, modulating gene expression in response to environmental changes through a phosphorylation-dependent process. CosR is an OmpR-type response regulator essential for the viability of  Campylobacter spp. are associated with various forms of infectious diseases in animals and humans  Campylobacter genus, most species are microaerophilic and grow at \u223c35\u201337\u00b0C; however, thermotolerant species, such as Campylobacter jejuni, Campylobacter coli, Campylobacter lari and Campylobacter upsaliensis, are able to grow at 42\u00b0C and constitute a distinct assemblage in the phylogenetic tree of CampylobacterCampylobacter spp., thermotolerant C. jejuni account for >90% of human campylobacteriosis, resulting in fever, diarrhea, and in some cases Guillain-Barr\u00e9 syndrome as a post-infection complication C. jejuni  is close to the body temperature of avian species C. jejuni colonizes the gastrointestinal tracts of poultry, but as a commensal organism without causing any clinical symptoms Campylobacter are caused by the consumption of contaminated poultry C. jejuni's fastidious nature, increasing numbers of human campylobacteriosis cases around the world suggest that this pathogenic bacterium may have many, but yet-unidentified, adaptation mechanisms to survive under harsh environmental conditions during its transmission from animal reservoirs, particularly poultry, to humans. To sense and respond to environmental changes by altering gene expression, bacteria possess efficient regulatory mechanisms, such as two-component regulatory systems (TCRSs) C. jejuni NCTC 11168 identified the presence of seven histidine kinases and 12 response regulators C. jejuni are known to be involved in various pathogenic characteristics of C. jejuni, including bacterial motility, animal colonization, biofilm formation and bile acid resistance Despite C. jejuniCampylobacter, Helicobacter and WolinellaC. jejuni's stress resistance by regulating the expression of key determinants of oxidative stress response and antibiotic resistance C. jejuni, there is no sensor kinase gene in the vicinity of cosR in C. jejuni, leaving a question on whether CosR is an orphan regulator or functionally linked to an unknown histidine kinase. In this study, we report that CosS, the cognate histidine kinase of CosR, is well conserved and present in non-thermotolerant Campylobacter spp., but absent from thermotolerant Campylobacter species. However, CosS from non-thermotolerant Campylobacter spp. does not phosphorylate C. jejuni CosR, suggesting that CosS in non-thermotolerant Campylobacter spp. is not functionally compatible with the response regulator CosR in C. jejuni despite its unique genetic organization.CosR is an OmpR-type response regulator essential for the viability of C. jejuni subsp. jejuni NCTC 11168 and C. fetus subsp. fetus 82-40 are genome-sequenced strains and were used in this study. C. jejuni NCTC 11168 was routinely grown at 42\u00b0C on Mueller-Hinton  media microaerobically , and C. fetus 82-40 was cultured at 37\u00b0C on Brain Heart Infusion  media in a gas condition . The different gas compositions were generated using an Anoxomat\u2122 . To investigate whether cosS contributes to different growth temperature dependence between thermotolerant and non-thermotolerant campylobacters, a cosS knockout mutant of C. fetus, a C. jejuni strain harboring C. fetus cosS, and their parental strains were cultured with shaking at 37\u00b0C or 42\u00b0C. The culture media were occasionally supplemented with chloramphenicol (10 \u00b5g ml\u22121) or kanamycin (50 \u00b5g ml\u22121), where required.cosS knockout mutant was constructed in C. fetus 82-40 by using a suicide plasmid as described previously cosS and its flanking region were amplified with the primers fetus_cosS_F (Xba): GCA GCT TCT AGA TGC TAT TTG G and fetus_cosS_R (Xba): AGA CAT CTA GAA CCT TTC AGT AC, and was cloned into an Xba\u0399 site on pUC19. The chloramphenicol resistance cassette (cat) amplified from pRY112 cosS on pUC19 to generate pUC19-cosS::cat, and the orientation of the antibiotic marker was confirmed by sequencing. After introducing the constructed suicide plasmid by electroporation, the cosS mutant was selected by growing on MH agar plates supplemented with chloramphenicol (10 \u00b5g ml\u22121). For the cosS complementation of the C. fetus cosS mutant and the heterogenous expression of cosS in C. jejuni, the cosS gene was amplified from C. fetus and integrated into a non-coding spacer region of rRNA gene clusters in the chromosome of the C. fetus cosS mutant and C. jejuni using a methodology reported previously cosS and its flanking region was cloned into an XbaI site of pFMB that carries an rRNA gene cluster and a kanamycin resistance cassette C. fetus cosS mutant or C. jejuni strains by electroporation.A GAT TGG ACT TTA CCT GAT GG GGC ATT AGA CAT TAT GAT TTA GTT TTA GCA  and a151g_c153t_R: CCAA TCT GCT AAA ACT AAA TCA TAA TGT CTA ATG CCG AT TCA GGT AAA GTC CA. For the purification of C. fetus CosR (CosR_F), the cosR gene C. fetus was PCR-amplified using primer pairs of CosRF_His(Nde)-F: TTCAT ATG AGA ATT TTG ATA GT AAG GAA AGT  & CosRF_His(BamH)-R: TTGGG ATC CCT TAA GC TAG AGC AAA T. After digestion with NdeI and BamHI, the PCR product was cloned into pET15b, which had been digested with the same enzymes, to generate pET15b-cosRF. Histidine-tagged recombinant C. jejuni CosR (rCosR_J), CosR_J mutant (CosRJ_N51D) and C. fetus CosR (rCosR_F) proteins were overexpressed and purified under the native conditions using Ni2+ affinity chromatography as previously described cosS gene in C. fetus was amplified by PCR using the primers TrCosSF_MBP_F (Nco): TGCCCA TGG TTA GC TTT TAC CTA TAA  and TrCosSF_MBP_R (Xba): AAAT CTA GAC AAT ATT TTT AC GCC AC. The resulting products were digested with NcoI and XbaI, and cloned into NcoI and XbaI sites of pMBP-parallel1 E. coli BL21 (DE3) carrying plasmid pMBPtrCosS was grown to an optical density of approximately 0.5 at 600 nm at 37\u00b0C. After induction with 0.1 mM IPTG at 30\u00b0C for 5 h, MBP  tagged trCosS (MBP-trCosS) was purified under a native condition using an amylose resin.To prepare CosR_J mutant in which an asparagine residue at position 51 was substituted with an aspartate residue (CosRJ_N51D), pET15b-cosRJ_N51D was generated by site-directed mutagenesis , using CosR_J overexpressing plasmid pET15b-cosRJ constructed in our previous research as a template 32P]ATP in 20 \u00b5l of a buffer containing 50 mM Tris-Cl (pH 8.0), 75 mM KCl, 2 mM MgCl2, and 1 mM DTT at 37\u00b0C MBP-trCosSF (2 \u00b5M) was incubated with 10 \u00b5Ci of  ATP  as described previously 32P-labeled DNA probe was incubated with 3.2 nM concentration of the purified rCosR_F, rCosR_J or CosRJ_N51D protein at 37\u00b0C for 15 min in 10 \u00b5l of the gel-shift assay buffer (20 mM HEPES (pH 7.6), 1 mM EDTA, 10 mM (NH4)2SO4, 5 mM DTT, 0.2% Tween 20, 30 mM KCl, 0.1 \u00b5g poly (dI-dC)). The reaction mixtures were resolved in a 6% polyacrylamide gel, and the radiolabeled DNA fragments were visualized using the BAS2500 system (Fuji Film).To perform EMSA, the DNA fragments containing the promoter region of cosR, whose homologs are prevalent in all genome-sequenced Campylobacter species. In our previous study, no sensor kinase gene was found near cosR in the C. jejuni genome, raising a question that CosR may be an orphan response regulator H. pylori HP1043, a CosR homolog, is an orphan response regulator and functions in a phosphorylation-independent manner C. jejuni. In this study, DNA sequence analysis of cosR homologs and their flanking regions in Campylobacter spp. revealed that several Campylobacter spp. have a gene downstream of a cosR homolog which encodes a histidine kinase with several highly-conserved and well-known motifs in the cytoplasmic portion, such as the histidine phosphotransfer domain containing the histidine phosphorylation site (at His-190 in C. fetus) and the C-terminal catalytic and ATP-binding domain  whereas Helicobacter pylori does not cosR is encoded by all campylobacters, whereas cosS is encoded by all validly-described taxa only within the non-thermotolerant group of campylobacters, including C. hyointestinalis, C. lanienae, C. mucosalis, C. sputorum and C. ureolyticus (unpublished data). The clear difference in cosS prevalence between thermotolerant and non-thermotolerant Campylobacter spp. raised two research questions, whether: (i) CosS is responsible, in part, for the temperature dependence of the two Campylobacter groups; and (ii) CosS in non-thermotolerant Campylobacter spp. might be functionally linked to CosR in C. jejuni.CosR is an OmpR-type response regulator encoded by g domain . Interes C. lari . Other mhomologs . Like caCampylobacter spp. revealed that the C-terminal DNA-binding domain was highly conserved, but the N-terminal receiver domain was more variable (Campylobacter group (about 80%). Unlike non-thermotolerant Campylobacter spp., all thermotolerant Campylobacter spp. have an amino acid substitution at the conserved aspartate residue D51  and C. jejuni CosR (CosR_J) were not phosphorylated by ATP in the absence of CosS  and atmospheric conditions. Although the aerotolerance of cosS mutant was slightly decreased compared with that of the wild type and the complementation strain  (data not shown), while C. jejuni harbors only sodBcosR mutation on the oxidative stress resistance of C. fetus. However, cosR appears to be essential in C. fetus, because its knockout mutants were not generated despite our multiple attempts (data not shown). In addition, knockdown of cosR in C. fetus using peptide nucleic acids was not as effective as that in C. jejuni (data not shown). Instead, a gel-shift assay was carried out to compare the binding affinity of C. jejuni CosR and C. fetus CosR to the promoters of oxidative stress genes that are regulated by C. jejuni CosR. In this assay, if C. jejuni CosR and C. fetus CosR recognizes similar DNA sequences, their binding efficiencies will be comparable to each other. C. jejuni CosR and CosRJ_N51D bound to the promoters effectively as reported previously C. fetus CosR to the tested promoters was extremely weak  in non-thermotolerant Campylobacter spp.: C. fetus CosS (YP_891447.1), C. concisus CosS (YP_001466302.1), C. curvus CosS (YP_001408853.1), and C. hominis CosS (YP_001406323.1). The predicted conserved domains of histidine sensor kinases (histidine phosphotransfer domain and ATP-binding domain) and the histidine phosphorylation site are indicated by boxes and a star, respectively.(TIF)Click here for additional data file."}
+{"text": "Allostery in bacterial transcription factors arises from changes in global low-frequency protein dynamics. Amino acids that regulate low-frequency dynamics are identified and seen to be evolutionarily conserved. Escherichia coli and GlxR of Corynebacterium glutamicum. The latter we demonstrate as a new exemplar for allostery without conformation change. We observe that binding the first molecule of cAMP ligand is correlated with modulation of the global normal modes and negative cooperativity for binding the second cAMP ligand without a change in mean structure. The theory makes key experimental predictions that are tested through an analysis of variant proteins by structural biology and isothermal calorimetry. Quantifying allostery as a free energy landscape revealed a protein \u201cdesign space\u201d that identified the inter- and intramolecular regulatory parameters that frame CRP/FNR family allostery. Furthermore, through analyzing CAP variants from diverse species, we demonstrate an evolutionary selection pressure to conserve residues crucial for allosteric control. This finding provides a link between the position of CRP/FNR transcription factors within the allosteric free energy landscapes and evolutionary selection pressures. Our study therefore reveals significant features of the mechanistic basis for allostery. Changes in low-frequency dynamics correlate with allosteric effects on ligand binding without the requirement for a defined spatial pathway. In addition to evolving suitable three-dimensional structures, CRP/FNR family transcription factors have been selected to occupy a dynamic space that fine-tunes biological activity and thus establishes the means to engineer allosteric mechanisms driven by low-frequency dynamics.Allostery is a fundamental process by which ligand binding to a protein alters its activity at a distinct site. There is growing evidence that allosteric cooperativity can be communicated by modulation of protein dynamics without conformational change. The mechanisms, however, for communicating dynamic fluctuations between sites are debated. We provide a foundational theory for how allostery can occur as a function of low-frequency dynamics without a change in structure. We have generated coarse-grained models that describe the protein backbone motions of the CRP/FNR family transcription factors, CAP of  Allostery is a process by which a molecule binding to one site of a protein alters the activity of the protein at another site. Allostery is typically thought to occur through a change in protein structure, but there is now clear evidence that the dynamic properties of a protein can also regulate allostery without a change in overall conformation. Here we examine two members of a large family of bacterial transcription factors and provide a mechanism to describe the allosteric binding of their activating ligands. We demonstrate, in these systems, that allostery arises as a natural consequence of changes in global low-frequency protein fluctuations on ligand binding. We further demonstrate that the higher dimensional parameter space that describes all potential variant transcription factors can be reduced to a two-dimensional free energy landscape that determines the key molecular parameters that predominantly regulate allostery. We additionally show that the amino acids we determine as contributing sensitively to allosteric control tend to be conserved in diverse bacteria; thus we identify a link between residues that contribute to low-frequency fluctuations and evolutionary selection pressures. Small regulatory molecules frequently bind proteins at regions remote from the active site. These allosteric events can switch proteins between inactive and active states Escherichia coli and GlxR of Corynebacterium glutamicum.One hypothesis for fluctuation-induced allostery is that binding modifies the structure of the thermally excited global normal modes and thence the coupling interaction between cooperative elements. This in turn affects the structural ensemble of the distant sites and so the free energy of binding Glc (phosphorylated in response to the phosphoenolpyruvate-carbohydrate phosphotransferase system) G and the adiabatic compressibility (\u03b2s\u00b0) where proteins with a higher \u03b2s\u00b0  demonstrated enhanced negative cooperativity CAP is a 210-amino-acid transcription factor that binds cAMP generated by adenylyl cyclase in response to the phosphorylated form of Enzyme IIAC. glutamicum. We unite our findings for CAP and GlxR to determine the extent to which key inter- and intramolecular parameters contribute to negative cooperativity in CRP/FNR family transcription factors. We further demonstrate that amino acids that contribute significantly to allosteric control are more likely to be conserved in variant proteins from diverse species. The theoretical and experimental work and associated data analysis provide both a significant advance in our understanding of the mechanisms that underpin the dynamic regulation of allostery and also a means for informed rational engineering of cooperativity in proteins.Here we propose that changes to global low-frequency protein backbone fluctuations are carriers of an allosteric signal in CAP and present this in the context of a significant new quantitative theory for allosteric coupling. We produce coarse-grained models that describe global low-frequency protein backbone motions of CAP and show a strong correlation between negative cooperativity for cAMP and modulation of the delocalised normal modes on ligand binding without a requirement for a spatially distinct physical pathway or conformational change. We demonstrate experimentally that altered connectivity between backbone elements in CAP can give predictable alterations to cooperativity for cAMP binding through altered mode amplitudes. We further demonstrate a broader applicability for this theory using an additional CRP/FNR family transcription factor, GlxR of G, were calculated using the full harmonic solution, and the negatively cooperative binding of cAMP to wild-type full-length CAP confirmed by calculating a positive value for \u0394\u0394G\u200a=\u200a(\u0394Gholo2\u2212\u0394Gholo1)\u2212(\u0394Gholo1\u2212\u0394Gapo)\u200a=\u200a179 cal mol\u22121 consistent with experimentally obtained values  as a function of altering the entire primary amino acid sequence (one residue at a time) can therefore be viewed as a quantitative map of the contribution of the normal modes to cooperativity. Such a quantitative map can be constructed either by simulation or experiment; in practice, it is convenient, as we demonstrate below, to use simulation of the entire allosteric map to guide mutagenesis for experimental study. We therefore performed a scanning computational mutagenesis of the entire CAP protein to investigate the influence of side chain connectivity on cooperativity via their influence on the normal modes.We hypothesized that if side-chain replacement on amino acids at sites distinct from the cAMP binding site of CAP do not cause conformational rearrangement, yet increase or decrease amino acid side chain hydrophobic or electrostatic forces in their local environments, the normal modes of protein motion would be altered without significant structural changes. If these changes to the normal modes have sufficiently global effects, they will in turn modify cooperativity between the cAMP binding sites through an entropic contribution to the binding free energy. Amino acid side chain replacement can therefore act as a sensitive probe of the contribution of side chain connectivity to cooperativity and the underlying mechanism for allostery within the elastic structure of the protein. The change in allosteric free energy  and kV132/k\u200a=\u200a4 (V132L). For example, kV132/k\u200a=\u200a4 shows significant tightening of the protein  and V140A , the simulated mutations create a uniform decrease in flexibility throughout the monomer except for the straightforward loosening/tightening at the site of the mutations. There is a general trend, therefore, for those simulated mutations that decrease negative cooperativity to be associated with decreased protein backbone motion nonlocally.The ENM can provide further insight into the mechanism by which allosteric control is associated with alterations to the normal modes. No global structural changes were induced in the ENM simulations or were evident from crystal structures of variant proteins; only the pattern of coupled low-frequency fluctuations was modified by the simulated side-chain mutations. This appearance of \u201ccontrol at a distance\u201d in the CAP homodimer is explained, through contributions to binding entropy, if there are correlations in the low-frequency motions between cAMP binding sites and if ligand binding or side chain mutation modifies this correlation  compare . An exam compare . The prekR/k\u200a=\u200a0.25) with cooperativity. Mutations that increase motion at the ligand bind site are associated with an increase in the extent of negative cooperativity and vice versa. This is entirely consistent with the controlling entropic allosteric mechanism in these cases, providing that cAMP binding has the effect of increasing local rigidity. This interaction between the heightened local motions following the first cAMP-binding event creates an entropic contribution to negative cooperativity in \u0394\u0394GE. coliA specific requirement of global low-frequency motion as an underpinning mechanism for allostery at a distance is a coupling between protein motion and the behaviour of the cAMP-binding site. We find that the loosening and tightening effects of simulated mutations is correlated with significant modulation of backbone flexibility in the region of the cAMP-binding site  . The figC. glutamicum is a cAMP binding homodimeric transcription factor of the CRP/FNR family that activates genes required for aerobic respiration, glycolysis, and ATP synthesis K2/K1\u200a=\u200a2.37; \u0394\u0394G\u200a=\u200a513 cal mol\u22121) than for CAP . This prediction of enhanced negative cooperativity was confirmed on experiment with an observed value for K2/K1 of 19.47  and between monomers (k12). Points below and above the z\u200a=\u200a0 plane correspond to positive and negative cooperativity, respectively. The wild-type proteins for both CAP and GlxR are offset from the maxima of anti-cooperative ridges in the two-dimensional free energy landscapes that emerge. At this position, loosening coupling internal to monomers (k1) moves the system into a basin of less negative cooperativity (GlxR) or positive cooperativity (CAP), while loosening in the coupling region (k12) moves the system for both CAP and GlxR to the top of the ridge (red) to increase negative cooperativity. Further analysis demonstrated consistency in the negative cooperativity arising through the normal modes in the ENM and in the super-coarse-grained model. For example, the simulated loosening  and tightening  mutations of the CAP ENM and the tightening  mutation of GlxR alter cooperativity through generating effective changes in k12 at the super-coarse-grained level. The super-coarse-grained model therefore effectively reveals the critical intra- and intermolecular parameters that associate with cooperativity and how these parameters can be altered to move within the allosteric free energy landscape.Our findings indicate general biophysical principles that describe the emergence of negative cooperativity in CRP/FNR family transcription factors through the allosteric modulation of normal modes. The property that allosteric effects are carried in general by the more globally distributed, and so typically longer wavelength, normal modes motivated the exploration of the underlying physics by coarse-graining the CAP and GlxR representations even further into rotational-translational block representations k1 and k12. This general hypothesis can be used to make an additional significant experimental prediction. If the similar position of CAP and GlxR within their respective free energy landscapes is the result of a selection pressure, then we predict that amino acids that contribute significantly to quantitative allosteric control . We found evidence that the rate at which an amino acid mutates is negatively related to \u0394K2/K1 ]. Amino acids of CAP that contribute to allostery through regulation of low-frequency protein dynamics are therefore more likely to be conserved in CAP variants through their contribution to protein function. Note that a test for overdispersion was significant, even after allostery had been accounted for , suggesting that other variables also have an influence on mutation rates.If cooperativity confers a selective advantage on the organism, then the allosteric free energy landscape can also be viewed as evolutionary landscape. In this case, the position of a protein within the landscape depends upon selection pressures that impact upon  control  and 6d wHere we demonstrate that negative allostery in CRP/FNR family transcription factors is correlated with modulation of the normal modes of protein motion on ligand binding in the absence of conformational change. The model makes key predictions that we test at select sites of the CAP and GlxR proteins, the latter identified as an important new exemplar for allostery in the absence of conformation change. The alterations in protein flexibility that are a signature for allostery in CRP/FNR family transcription factors are a consequence of the global nature of those normal modes responsible and mutations that predictably alter cooperativity do so by influencing protein backbone flexibility. Our theory describes how allostery can arise from changes to low-frequency dynamics in the absence of any mean structural change. The theory is particularly significant as it describes allostery as a natural consequence of the dynamic properties of a protein without a requirement for spatially localised dynamic pathways between allosteric sites. The allostery observed is unlikely to have microheterogeneity as an alternative explanation as all CAP proteins crystallised as a single superimposable structure. Any form of heterogeneity reduces the likelihood of forming ordered crystals The possibility of a direct interaction between cAMP binding sites might also be considered as a mechanism to explain the allostery observed. The closest distance between the two cAMP molecules in the CAP dimer is 9.5 \u00c5 (the distance between the N6 atoms of the adenine ring). Although it is impossible to conclusively eliminate small local changes that binding of the first molecule of cAMP has at the second site, no conformational changes have been reported in this region in previous NMR studies, making this explanation unlikely. The possibility of a direct interaction is made even more unlikely as, similar as to that described above, any invoked direct interaction between cAMP binding sites would have to consistently match not only the qualitative aspects of the computational predictions for the role of the global modes, but also their quantitative correlation with the observed experimental values. Analysis of the relationship between Cartesian distance and protein motions demonstrated strongly correlated motions between allosteric sites at distances of <10\u201320 \u00c5 G\u200a=\u200a0.3 kcal mol\u22121). However, the scale of biologically relevant cooperative effects is set by the thermal energy . The values of \u0394\u0394G observed and manipulated experimentally are those that modulate the concentration range of cAMP to which the system is sensitive by an order of 1. Engineering of cooperativity is therefore possible by manipulating \u0394\u0394G, as described here, with the caveat that it is likely only possible over a thermodynamic range to which the protein is responsive.The range of available sites for side chain mutagenesis of CRP/FNR family transcription factors do not constitute as large a set of separate and independent control parameters as at first seems, but in a good approximation explore a lower dimensional space  of the underlying allosteric signal. The delicate interactions of effects at different length scales are missed without such a multiscale approach to the physics of protein dynamics. Changes to the normal modes are presented as an important new theory to describe how allostery can arise in the absence of structural change and provide an important theoretical context within which to frame global issues of allostery in proteins.BamHI and HindIII sites of pQE30 and mutant variants constructed by site-directed mutagenesis. Wild-type and mutant recombinant protein was expressed from E. coli M182 \u0394CAP \u2212F \u0394(lacIPOZY)X74 galE15 galK16 + lambda\u2212rpsL thi [pREP4] for 2 h at 37\u00b0C with 1 mM IPTG. Protein was purified using sequential nickel-chelated sepharose affinity and Superdex 75 16/60 size exclusion columns . Protein concentration was calculated using the Beer-Lambert Law and a molar extinction coefficient of 20,065 M\u22121 cm\u22121 at 280 nm. Full-length GlxR protein was expressed and purified as previously described The open reading frame corresponding to the full-length CAP protein was cloned into the 4 pH 7.8, 200 mM KCl, 2 mM 1-thioglycerol at 4\u00b0C. Protein and buffer were degassed under vacuum and degassed buffer used to dilute cAMP ligand. cAMP concentration was calculated using the Beer-Lambert Law and a molar extinction coefficient of 14,650 M\u22121 cm\u22121 at 259 nm. Data were generated using an iTC200  by typically 40 sequential 1 \u00b5L injections of 4\u20136 mM cAMP into 202 \u00b5L 130\u2013400 \u00b5M protein. Data for the first injection was routinely discarded as this is affected by diffusion between the syringe and the protein solution during equilibration prior to data collection.Protein was dialyzed against 100 mM KPOiF:Q, to the experimental value allowed calculation of the best fit of the binding constants, iK, and the binding enthalpies, \u0394iH, using the solver plug-in for Excel:Ligand binding for cAMP to CAP was described by a sequential three-site model , two dimers (wild-type in space group P1), and three dimers (V140A CAP in space group I2)  polyethylene glycol 3350 and 15\u201320% (v/v) 2-methyl-2,4-pentanediol with 2 mM cAMP in 24-well hanging-drop vapour diffusion plates. Crystals were cryoprotected using mother liquor containing 30% (v/v) glycerol and flash cooled in liquid nitrogen  I2) see . In all \u22121 \u00c5\u22122 with a cutoff radius of 8 \u00c5, and only the C\u03b1 atoms in the protein were considered. The presence of cAMP effector at the binding site was treated by the addition of one node at the mass weighted average coordinate for each ligand. Varying the spring constant of any springs attached to a single residue of the protein was used to represent side chain mutations. The allosteric free energy was calculated by summing over modes 1 to n. n was determined by examining where values K2/K1 converged , and an additional in-house file isostructural to 2GZW. The PDB file for constructing the GlxR ENM was 3R6S.ENM simulations were performed using our own code based on the regular implementation onverged . The fink1 though k4 and the intersubunit couplings by k12, k13, and k24  simulations employed the harmonic force field equations used in the ff99SB and GAFF force fields within the AMBER simulation program K2/K1, hereon denoted x, is associated with the mutation rate of amino acids, we first estimated the relative amino acid mutation rate using the sequence data for CAP variants and we then statistically tested for an effect of x on this rate. Relative mutation rate was estimated by finding the minimum number of amino acid mutations needed to generate the observed variations in the sequence data, which we denote N. For each of the 165 proteins we found the protein having the smallest number of amino acid differences. The sum of these differences gave N. When summing differences, if more than one protein had the minimum difference, we included all the proteins having the minimum. We then determined the number of these mutations that were associated with each of the 210 amino acids, which we denote in. Thus, in estimates the relative mutation rate of amino acid i, and these estimates account for the evolutionary history of the proteins. If all amino acids had an equal mutation rate, then we would expect the in to all be approximated by N/210. We assumed that the true relative rate of mutation was related to x according to the logistic function: \u03bc(x)\u200a=\u200a\u03b20, \u03b21, and \u03b22 are constants. To account for overdispersion among the in, which might be due to unmeasured covariates associated with the proteins, we assumed that the variation between the in could be described by the beta-binomial distribution. Under these assumptions, the log-likelihood of the model described by the set of parameters \u03b8\u200a=\u200a{\u03b20,\u03b21,\u03b22,\u03c6}, is given by:BB is the beta-binomial distribution, which describes the probability of observing n successes from N trials when, on average, successes occur with probability \u03bc and variation in this probability among replicates is described by the beta-distribution with variance \u03bc(1\u2212\u03bc)\u03c6/(1+\u03c6) To determine if \u0394x was found by applying a likelihood ratio test (LRT) comparing the fit of the full model with the model that ignored x . Let LL1 and LL0 be the maximum log-likelihood of the full model and the simpler model, respectively. Under the null hypothesis that x is not associated with mutation rate, the test statistic G\u200a=\u200a2[LL1\u2212LL0] is chi-square distributed with two degrees of freedom, as the more complex model has two additional free parameters: \u03b21 and \u03b22. A LRT was also used to test for overdispersion by comparing the fit from the full model described above with the model that assumed variation had a binomial distribution . This latter test, if significant, justifies the use of the beta-binomial distribution rather than the binomial. Confidence intervals for model parameters were estimated using the likelihood profile approach.Evidence that mutation rate was related to The genome accession numbers analysed are: NP_232242.1, NP_246094.1, NP_249343.1, NP_439118.1, NP_458435.1, NP_462369.1, NP_671249.1, NP_716257.1, NP_760245.1, NP_799172.1, NP_873260.1, NP_927748.1, YP_052151.1, YP_089126.1, YP_128534.1, YP_152459.1, YP_205663.1, YP_237645.1, YP_262678.1, YP_272974.1, YP_455981.1, YP_492074.1, YP_526229.1, YP_564189.1, YP_588978.1, YP_606222.1, YP_690711.1, YP_693743.1, YP_718344.1, YP_751967.1, YP_855526.1, YP_928876.1, YP_941848.1, YP_960806.1, YP_001048976.1, YP_001092716.1, YP_001143048.1, YP_001178491.1, YP_001189422.1, YP_001218107.1, YP_001343325.1, YP_001440391.1, YP_001443362.1, YP_001464812.1, YP_001475605.1, YP_001503357.1, YP_001675803.1, YP_001759053.1, YP_001909102.1, YP_002152521.1, YP_002228709.1, YP_002294894.1, YP_002476451.1, YP_002650381.1, YP_002801694.1, YP_002875051.1, YP_002893931.1, YP_002923696.1, YP_002986005.1, YP_003002662.1, YP_003008634.1, YP_003039145.1, YP_003074496.1, YP_003255073.1, YP_003261368.1, YP_003469961.1, YP_003532766.1, YP_003555253.1, YP_003812150.1, YP_003914673.1, YP_004117516.1, YP_004211044.1, YP_004382110.1, YP_004391469.1, YP_004419866.1, YP_004472683.1, YP_004565203.1, YP_004713013.1, YP_004821770.1, YP_005091541.1, YP_005334361.1, YP_005458526.1, YP_005817463.1, YP_006006755.1, YP_006238931.1, YP_006286710.1, YP_006326252.1, YP_006459298.1, YP_006523113.1, YP_006588319.1, ZP_00134303.1, ZP_00991497.1, ZP_01161654.1, ZP_01215522.1, ZP_01815379.1, ZP_01894180.1, ZP_01898714.1, ZP_02478644.1, ZP_02958582.1, ZP_03319669.1, ZP_03611762.1, ZP_03825776.1, ZP_04636540.1, ZP_04640765.1, ZP_04752629.1, ZP_04977551.1, ZP_05043634.1, ZP_05637197.1, ZP_05774479.1, ZP_05849758.1, ZP_05879825.1, ZP_05880998.1, ZP_05919259.1, ZP_05972068.1, ZP_05990699.1, ZP_06018230.1, ZP_06051220.1, ZP_06126446.1, ZP_06542208.1, ZP_06637662.1, ZP_07161146.1, ZP_07222409.1, ZP_07266238.1, ZP_07379670.1, ZP_07395486.1, ZP_07528968.1, ZP_07744420.1, ZP_07777878.1, ZP_07888842.1, ZP_08039455.1, ZP_08068248.1, ZP_08079426.1, ZP_08100561.1, ZP_08148040.1, ZP_08310711.1, ZP_08519301.1, ZP_08725568.1, ZP_08731411.1, ZP_08745737.1, ZP_08754750.1, ZP_09013912.1, ZP_09039716.1, ZP_09185001.1, ZP_09505069.1, ZP_09557915.1, ZP_09710329.1, ZP_09778630.1, ZP_09972449.1, ZP_10075284.1, ZP_10125383.1, ZP_10128956.1, ZP_10135899.1, ZP_10142323.1, ZP_10146384.1, ZP_10426764.1, ZP_10438900.1, ZP_10622342.1, ZP_10628430.1, ZP_10630449.1, ZP_10643899.1, ZP_10655392.1, ZP_10677933.1, ZP_10700164.1, and ZP_10763153.1.Figure S1ENM representation of CAP. Alpha helices are represented in magenta and beta sheets in yellow. Blue spheres show the positions of the C\u03b1 atoms, and the black lines display the connectivity of the Hookean springs with a cutoff of 8 \u00c5. Apo and singly bound ENMs were constructed by manually removing cAMP from the holoenzyme.(TIF)Click here for additional data file.Figure S2Validation of ENM methodology. (A) CAP B-factors are independent of coarse-grained methodology. The chart represents the B-factor plotted against amino acid number for the crystal structure, ENM, and molecular dynamics. (B) Mode frequencies are independent of methodology. The chart represents the mode frequency plotted against mode number for ENM and molecular dynamics.(TIF)Click here for additional data file.Figure S3ENM predicted residue interactions that impact on cooperativity. (A) The change in cooperativity that occurs when kR/k is varied at the indicated residue (legend) against every amino acid within the same monomer (within an 8 \u00c5 cutoff). (B) The change in cooperativity that occurs when kR/k is varied at the indicated residue (legend) against every amino acid within the opposing monomer (within an 8 \u00c5 cutoff).(TIF)Click here for additional data file.Figure S4Least-squares superposition of one representative chain of each of the seven doubly cAMP-bound crystal structures treating the two domains (dimerization/cAMP-binding domain and DNA-binding domain) as rigid bodies with a flexible linker . The transformation matrices were obtained using RAPIDO (TIF)Click here for additional data file.Figure S5Fitting of ITC data. Binding isotherm for a representative data set for the calorimetric titration of cAMP to wild-type CAP protein showing experimental data and fitted curves for two and three molecules of ligand cAMP. The inset shows the structure of CAP (green) with three bound molecules of cAMP (blue).(TIF)Click here for additional data file.Figure S6Calculated and observed values for cooperativity in CAP. (A) The ratio of the second to first dissociation constants for cAMP (K2/K1) for wild-type and mutant CAP proteins were calculated from the ENMs  or obtained by ITC (observed). The coloured lines correspond to the value for K2/K1 in the wild-type to enable comparison of the direction of change. (B) Values for K2/K1 obtained by ITC plotted against values for K2/K1 predicted by the ENM demonstrating the correlation between the extents of experimentally observed and predicted values for K2/K1. Dotted line represents the 95% confidence interval for the linear regression (R2\u200a=\u200a0.85).(TIF)Click here for additional data file.Figure S7Mapping local dynamics in CAP. (A) The effect of mutation of V140 and H160 on local dynamics over the CAP monomer. The chart represents the percentage variation in B-factor from the wild-type ENM plotted against amino acid number. Inset shows the same chart with an expansion of the y-axis. (B) The chart is identical to that shown in panel C except with the y-axis expanded.(TIF)Click here for additional data file.Figure S8The dependence of K2/K1 on the number of summed modes. The chart represents the calculated value for K2/K1 from the ENM plotted against the total number of summed modes.(TIF)Click here for additional data file.Table S1Least-squares superposition of all independent protein chains in each of the doubly cAMP-bound CAP crystal structures.(PDF)Click here for additional data file.Table S2Experimental thermodynamic parameters for CAP proteins.(PDF)Click here for additional data file.Table S3Experimental thermodynamic parameters for GlxR proteins.(PDF)Click here for additional data file.Table S4Crystallographic data collection and refinement statistics.(PDF)Click here for additional data file."}
+{"text": "Figure 2. The text should read:This General Commentary provides a corrected version of the caption for (A) Canonical grid layout stereotypically read in a \u201cZ-path.\u201d (B) Layout where a vertical panel \u201cblocks\u201d the creation of a row of panels. (C) Layout where panels are separated by a wide space. (D) Layout where panels overlap each other. (E) Layout where panels are staggered to no longer retain a contiguous gutter.http://www.visuallanguagelab.com/P/ECS_Supplement.pdfIn addition, the link to the supplementary analysis of the Steranko page has been changed to:"}
+{"text": "Clostridium acetobutylicum, a functionally unknown protein (encoded by SMB_G1518) with a hypothetical alcohol interacting domain was identified. Disruption of SMB_G1518 and/or its downstream gene SMB_G1519 resulted in increased butanol tolerance, while overexpression of SMB_G1518-1519 decreased butanol tolerance. In addition, SMB_G1518-1519 also influences the production of pyruvate:ferredoxin oxidoreductase (PFOR) and flagellar protein hag, the maintenance of cell motility. We conclude that the system of SMB_G1518-1519 protein plays a role in the butanol sensitivity/tolerance phenotype of C. acetobutylicum, and can be considered as potential targets for engineering alcohol tolerance.Solvents toxicity is a major limiting factor hampering the cost-effective biotechnological production of chemicals. In  The toxicity of organic solvents to microorganisms is a major limiting factor hampering the cost-effective biotechnological production of solvents On the other hand, alcohol can be used as an anesthetic. The anaesthetic effect was initially ascribed to the perturbation of cell membrane In various animal cells, cysteine-rich zinc finger subdomains of PKC interacting with alcohol are highly conserved . Zinc fiClostridium acetobutylicum is an important producer of solvents . Among these products, butanol is the most toxic as it reduces cell growth by 50% at a concentration of 7\u201313 g/L groESLClostridium. To test the above described hypothesis, potential candidate genes were identified in the genome from bioinformatics analysis. The functions of the candidate genes were then characterized.C. acetobutylicum. In order to identify such possible proteins, the first step is to filter the proteomic information of C. acetobutylicum through a series of criteria until potential candidate proteins are obtained. These candidate proteins are expected to share structural and sequence similarity to the regulating region of PKC and possess the alcohol binding sites.As alcohol interacting regions are highly conserved in animal cells  and thesC. acetobutylicum DSM 1731 (its whole genome sequence shares 99% similarity to that of the type strain C. acetobutylicum ATCC 824 C. acetobutylicum ATCC 824), contains Zn-finger DNA-binding domain, and the potential butanol binding sites such as Tyr, Lys and Glu also appear to be dispersed throughout the conserved region. SMB_G1518 is located in a two-gene operon together with SMB_G1519 (annotated as CAC1494 in the genome of C. acetobutylicum ATCC 824) C. acetobutylicum.PKC superfamily contains 8 types of isomers, the mechanism for PKC isomers \u03b1 and \u03b4 interacting with anesthetics has been extensively studied As SMB_G1518 contains cysteine-rich zinc finger domain putative interacting with alcohol, inactivation of SMB_G1518, SMB_G1519, and SMB_G1518-1519 is expected to make the mutants less sensitive to butanol. To test this hypothesis, we inactivated SMB_G1518 and SMB_G1519, respectively, by using the ClosTron system based on group II intron retrotransposition. The genotypes of the resulting mutants DC93 and DC94 were confirmed by sequencing PCR products and southern blot . ConstruA600 has been regarded as one of the most sensitive indicator for assessing butanol tolerance of C. acetobutylicumA600 reached 0.75\u00b10.05 , followed by measuring the subsequent growth and calculating the growth inhibition degree  Under normal condition, overexpression of SMB_G1518-1519 in DSM 1731 did not alter the growth pattern as compared to the control strain 1731(pIMP1) . HoweverThe growth pattern of DDC14(p1518-1519) is similar to overexpression strain 1731(p1518-1519) under normal condition or butanol stress, suggesting the introduction of p1518-1519 (copy number of 8) into SMB_G1518-1519 deletion mutant DDC14 made the host sensitive to butanol stress in view to the overexpression of SMB_G1518-1519 .C. acetobutylicum.To rule out the influence of fermentation products on the growth, fermentation products of deletion mutants DDC14, overexpression strain 1731(p1518-1519) and their respective controls were analyzed when 50% of growth inhibition degree was achieved by 1% butanol treatment . Under nThe observation that the function of SMB_G1518-1519 was closely related with butanol tolerance prompted an investigation of the biological mechanism on butanol tolerance. SMB_G1518-1519 encoding proteins were thought to be involved in the regulation of butanol tolerance through protein-protein interaction due to that Zn finger located in their N-terminal end PFOR catalyzes the coenzyme A (CoA)-dependent oxidative decarboxylation of pyruvate. Under normal conditions, no significant variation in expression level of PFOR was detected in the deletion mutant DDC14 and its control DSM 1731 . ButanolHag makes up the flagellum basal structure flagellin which assembles flagellum filament. Under normal condition, the deletion or overexpression of SMB_G1518-1519 had no significant effect on the expression level of Hag . While HA600 by over 70% when these mutants suffered from 1% butanol stress at the initial stage. This indicated SMB_G1518- G1519 encoding proteins may be negative regulators involved in butanol tolerance and used as ideal targets for engineering alcohol tolerance.Alcohol toxicity was regarded as one of the key problems associated with the fermentative production of alcohol Salmonella entericaserovar Typhimurium LT2 and CsrA acting as carbon storage regulator in Bacillus subtilisC. acetobutylicum. C. acetobutylicum DSM 1731 belongs to the multiple-flagellin systems because it possesses 4 flagellin genes  and encodes four flagellins approximately of 30 kDa. A typical feature of multiple-flagellin systems is that they have redundant flagellins Clostridium acetobutylicum was contradictory to traditional knowledge which attributed it to that the presence of a plasmid represents a metabolic burden and a cellular stress The variation of flagellum components especially Hag can result in the change in motility The significance of this work is the discovery of two unknown genes SMB_G1518 and SMB_G1519. Their functional identification unraveled at least part of the complex physiological mechanism of alcohol tolerance in prokaryotes. Zinc finger protein was found to be existed in many sequenced microbial strains and may have a chance to be involved in alcohol tolerance like SMB_G1518 encoding protein. If so, it can be regarded as potential target for engineering microbial alcohol tolerance.E. coli strains were grown aerobically at 37\u00b0C in LB broth. C. acetobutylicum strains were grown anaerobically at 37\u00b0C in reinforced clostridial medium (RCM) for routine growth and making competent cells, clostridial growth medium (CGM) for butanol challenge experiments A600) of appropriate dilutions with a UV/Vis 2802PC spectrophotometer . For recombinant strains, antibiotics were added into the medium at the following final concentration: 100 \u00b5g/ml for ampicillin, 30 \u00b5g/ml for chloramphenicol and 50 \u00b5g/ml for erythromycin. All C. acetobutylicum and E. coli strains were stored at \u221280\u00b0C in RCM and L broth supplemented with 15% glycerol, respectively.Plasmids and strains used in this study are listed in www.sigmaaldrich.com/TargeTron Gene Knockout) and then the intron re-targeting PCR primers for SMB_G1518 including 1518-160/161s-IBS, 1518-160/161s-EBS1d and 1518-160/161s-EBS2 were designed, the primers for retargeting SMB_G1519 were recommended from the previous study (A group II intron based system modified by Dong was adopted to disrupt SMB_G1518 and SMB_G1519 us study . [22]. Dus study .. GenbanC. acetobutylicum DSM 1731 with primers P1492 and P1495-3E (A fragment from 387 bp upstream of SMB_G1518 (which includes the promoter of SMB_G1518) to 198 bp downstream of SMB_G1519  was amplified by PCR from chromosomal DNA of P1495-3E .. After st Strand cDNA Synthesis Kit  Co., Ltd) with 1 \u00b5g of total RNA as the template. The primers Re-1493 and A2-14 used for the real-time PCR assay was designed targeting the junction between SMB_G1518 and SMB_G1519 (After cells were cultured with 1% (vol/vol) butanol for 6 h as described in butanol challenged experiment, RNA sampling and isolation were performed as previously described MB_G1519 .. The 16A600 0.75\u00b10.05, each culture was split into three 100 mL aliquots and then challenged with 0 or 1% (vol/vol) butanol, respectively. Effect of varied butanol concentrations on the growth of these strains was further measured by Unico UV-2000 Spectrophotometer. The concentration of glucose, acetate, butyrate, acetone, butanol and ethanol in broth cultures were determined followed the method described by Mao Mutant and overexpression strains and their respective control strains were grown in 500 mL flasks containing 400 mL CGM at 37\u00b0C anaerobically. When the cell density attained Cells were cultured with 1% (vol/vol) butanol for 6 h as described in butanol challenged experiment. Subsequent treatment of cells for proteomic analysis followed the methods described by Mao Two-dimensional gel electrophoresis (2-DE) was performed as described previously C. acetobutylicum strains were grown in CGM at 37\u00b0C. After the cell density reached A600 0.75\u00b10.05, 10 mL of culture was centrifuged and concentrated ten folds. 10 microliters of the concentrated cell suspension was spotted onto a CGM agar plate supplemented with 1% (vol/vol) butanol, CGM agar plate without butanol addition was used as the control. All plates were supplemented with 0.7% agar. The inoculated plates were incubated anaerobically for 48 h at 37\u00b0C. Photographs of the plates were taken with a Canon camera.Figure S1Conservancy analysis of the region interacting with butanol in protein kinase C(PKC) \u03b1, \u03b4. A) Amino acid alignment of the C1A domains of PKC. B) Amino acid alignment of the C1B domains of PKC. Mus, Mus musculus (house mouse); Ory, Oryctolagus cuniculus (rabbit); Can, Canis lupus (dog); Rat, Rattus norvegicus (rat); Hom, Homo sapiens (human); Dro, Drosophila melanogaster (fruit fly); \u03b1, PKC\u03b1.(TIF)Click here for additional data file.Figure S2Construction of SMB_G1518-1519 disruption mutants. A) Two sets of primers P1493-5, SMB_G1518-3E and Cac1494B, Pex1494E flanking the target site of SMB_G1518 and SMB_G1519 were adopted to identify insertion mutants by PCR, The results showed that about 0.9-kb intron fragments were integrated into the target site of SMB_G1518 and SMB_G1519; B) SMB_G1518-1519 and the expected disrupted SMB_G1518 and SMB_G1519 in the chromosome were schematicly shown; C) Southern blot analysis of SMB_G1518 and SMB_G1519 disruption using CAC34 probe showed that the size of the CAC34-hybridized DNA fragments of strain DC93 and DC94 was about 0.9 kb larger than that of parental strain DSM 1731; D) Southern blot analysis of SMB_G1518 and SMB_G1519 disruption using Intron probe showed that no hybridized signals were detected in the lane of DSM 1731.(TIF)Click here for additional data file.Figure S3Transcriptional analysis of SMB_G1518-1519. A) Transcriptional analysis of SMB_G1518-1519 in DSM 1731, 1731(pIMP1) and 1731(p1518-1519) by Real-Time PCR; A, DSM 1731; B, 1731(pIMP1); C, 1731(p1518-1519). B) Transcriptional analysis of SMB_G1518-1519 in 1731(pIMP1) and 1731(p1518-1519) by semi-quantitative PCR; B1, 1731(pIMP1) under normal condition; B2, 1731(pIMP1) under butanol stress; C1, 1731(p1518-1519) under normal condition; C2, 1731(p1518-1519) under butanol stress; M, marker; N, negative control without DNA template.(TIF)Click here for additional data file.Figure S4Images of all gels, DSM 1731 (left) and DDC14 (right) under normal condition. a, b and c are experimental triplicate of each strain. Differentially expressed proteins are labeled, and details about them are shown in (TIF)Click here for additional data file.Figure S5Images of all gels, DSM 1731 (left) and DDC14 (right) under 1% butanol stress. a, b and c are experimental triplicate of each strain. Differentially expressed proteins are labeled, and details about them are shown in (TIF)Click here for additional data file.Figure S6Images of all gels, 1731(pIMP1) (left) and 1731(p1518-1519) (right) under normal condition. a, b and c are experimental triplicate of each strain. Differentially expressed proteins are labeled, and details about them are shown in (TIF)Click here for additional data file.Figure S7Images of all gels, 1731(pIMP1) (left) and 1731(p1518-1519) (right) under 1% butanol stress. a, b and c are experimental triplicate of each strain. Differentially expressed proteins are labeled, and details about them are shown in (TIF)Click here for additional data file."}
+{"text": "Lactobacillus plantarum species is a good source of esterases since both lipolytic and esterase activities have been described for strains of this species. No fundamental biochemical difference exists among esterases and lipases since both share a common catalytic mechanism. L. plantarum WCFS1 possesses a protein, Lp_3561, which is 44% identical to a previously described lipase, Lp_3562. In contrast to Lp_3562, Lp_3561 was unable to degrade esters possessing a chain length higher than C4 and the triglyceride tributyrin. As in other L. plantarum esterases, the electrostatic potential surface around the active site in Lp_3561 is predicted to be basic, whereas it is essentially neutral in the Lp_3562 lipase. The fact that the genes encoding both proteins were located contiguously in the L. plantarum WCFS1 genome, suggests that they originated by tandem duplication, and therefore are paralogs as new functions have arisen during evolution. The presence of the contiguous lp_3561 and lp_3562 genes was studied among L. plantarum strains. They are located in a 8,903 bp DNA fragment that encodes proteins involved in the catabolism of sialic acid and are predicted to increase bacterial adaptability under certain growth conditions. Hydrolases constitute a class of enzymes that catalyze the hydrolysis of a wide variety of substrates. The diversity of substrate specificity has complicated hydrolase classification. However, they are typically classified to their known specificity. Among hydrolases, esterases (EC 3.1.1) hydrolyze ester bonds and are subdivided as carboxylesterases , when they catalyze the hydrolysis of small carboxylic acid ester-containing molecules at least partially soluble in water, or lipases (EC 3.1.1.3), when maximal hydrolytic activity is displayed against water-insoluble long chain triglycerides . AlthougLactobacillus plantarum is an industrially important species, which can be found in numerous fermented foods , NC8, and LPT 57/1 strains were kindly provided by M. Kleerebezem , L. Axelsson , and J. L. Ru\u00edz-Barba , respectively. Eight strains were provided by the Spanish Type Culture Collection (CECT): L. plantarum CECT 220 (ATCC 8014), CECT 221 (ATCC 14431), CECT 223, CECT 224, CECT 749 (ATCC 10241), CECT 4645 (NCFB 1193), and the type strain L. plantarum subsp. plantarum CECT 748T . Seven strains were purchased from the German Collection of Microorganisms and Cell Cultures (DSMZ): L. plantarum DSM 1055, DSM 2648, DSM 10492, DSM 12028, DSM 13273, DSM 20246, and the type strain of L. plantarum subsp. argentoratensis DSM 16365T. Eleven strains were isolated from must grape or wine of different wine-producing areas of Spain over the period from 1998 to 2001  adjusted to pH 6.5 and incubated at 30\u00b0C. Escherichia coli DH10B  was used as host strain for all DNA manipulations. E. coli BL21 (DE3) was used for heterologous expression in the pURI3-Cter vector (E. coli strains were cultured in Luria-Bertani (LB) medium at 37\u00b0C with shaking at 200 rpm. When required, ampicillin and chloramphenicol were added at a concentration of 100 or 20 \u03bcg/ml, respectively.r vector . E. coliL. plantarum strains was extracted as previously described (L. plantarum Lp_3561 and Lp_3562 esterases (lp_3561 and lp_3562) were amplified by PCR using chromosomal DNA. The lp_3561 gene (0.8 kb) was amplified by using primers 957 (5\u2032-TAACTTTAAGAAGGAGATATACATATGAGATATGAGCAATTGAGATTAA) and 958 (5\u2032-GCTATTAATGATGATGATGATGATGTTTAAAGTCCACTTGCGTCAAATC). Oligonucleotides 575 and 576 were used to amplify lp_3562 gene  in L. plantarum WCFS1 was amplified by PCR using the primers 957 and 958. Prime Star HS DNA polymerase (TaKaRa) was used for PCR amplification. The 837-bp purified PCR product was inserted into the pURI3-Cter vector using a restriction enzyme- and ligation-free cloning strategy  was transformed into E. coli BL21 (DE3) with pGro7 (TaKaRa), a vector overexpressing GroES/GroEL chaperones. The recombinant Lp_3561 enzyme was produced as previously described for esterase Lp_2631  as substrate (p-nitrophenyl esters of various chain lengths (Sigma\u2013Aldrich): p-nitrophenyl acetate (C2), p-nitrophenyl butyrate (C4), p-nitrophenyl caprylate (C8), p-nitrophenyl laurate (C12), p-nitrophenyl myristate (C14), and p-nitrophenyl palmitate (C16) as substrates as described previously  was added, and the reaction mixture was incubated at 37\u00b0C. The experiments were performed in triplicate.Esterase activity was assayed in the pH range from 3.0 to 9.0, and at temperatures of 5, 20, 30, 37, 40, 45, 55, and 65\u00b0C as described previously . Enzyme 1 . However, among the L. plantarum WCFS1 proteins annotated as \u201cesterase/lipase\u201d in its genome, the highest sequence identity is shown between Lp_3561 and Lp_3562 (44%). Both proteins share other additional features: they are 278 amino acid proteins, have a similar theoretical isoelectric point of 5.4 and 5.1, and molecular sizes of 31.5 and 30.9 kDa, for Lp_3561 and Lp_3562, respectively. According to the ESTHER database Lp_3561 (lacpl-LP.3561) and Lp_3562 (lacpl-LP.3562), belong to the hormone-sensitive lipase family . Moreover, both genes (lp_3561 and lp_3562) are only separated by 13 bp, which points to a process of gene tandem duplication.A protein amino acid sequence alignment of the nine esterases characterized in lp_3561 gene from L. plantarum WCFS1 was cloned into the pURI3-Cter vector (E. coli BL21 (DE3) cells. SDS-PAGE analysis of cell extracts showed a major protein band of approximately 30 kDa, present as inclusion bodies in the insoluble fraction (data not shown). To obtain Lp_3561 in a soluble form co-overexpression with molecular chaperones was considered by using the plasmid pGro7 as previously published . Lp_3561 was purified by IMAC.The r vector  and tranublished . When pUp-nitrophenyl esters possessing acyl chains with different lengths from C2 to C16. From the substrates assayed, Lp_3561 showed preference for pNP-acetate, being unable to degrade esters with chain lengths higher than C4 . This result was also confirmed when the esterase activity was assayed on a library composed of 40 different esters . From the esters assayed, only phenyl acetate, a short acyl chain ester, was efficiently hydrolyzed by Lp_3561.Purified Lp_3561 protein was biochemically characterized. Esterase activity was determined using Figure 5). Lp_3561 retained 40% of its maximal activity at 55 and 65\u00b0C after prolonged incubation time. Activity was greatly increased by the addition of MnCl2 and inhibited by ZnCl2, urea and SDS.Some physicochemical properties of Lp_3561 were also analyzed. Esterase Lp_3561 showed maximal activity at pH 6.5 and 40\u00b0C (L. plantarum WCFS1 (PDB entry: 4bzw) that we have determined recently , which are known to affect to the esterase/lipase character of the enzymes  both genes are absent. In order to determine the extent of the presence of both paralog genes among strains belonging to the L. plantarum group, their presence was studied in 28 L. plantarum strains isolated from different sources. To determine the presence of both genes, chromosomal DNA was extracted and PCR amplified using oligonucleotides designed on the basis of the L. plantarum WCFS1 sequence. Apart from L. plantarum WCFS1, ten additional strains possessed lp_3561 and lp_3562 genes . Interestingly, all the strains which possess one of these genes possessed also the other gene. This observation was also noticed on the L. plantarum strains whose complete genome is available. Moreover, in these strains both genes are contiguous. For example, on L. plantarum ZJ316 strain, lp_3561 is located on the zj316_0167 locus and lp_3562 is located contiguously on the zj316_0168 locus. A more detailed analysis of the L. plantarum strains whose genomes are available identified a 8,903 bp region only present in the strains possessing both esterase genes . The publically available genomes of seven L. plantarum strains revealed that this region is only absent on L. plantarum B21 strain. This allowed for identification of insertion point at the 8,903 region on the intergenic region within the SH83_RS14770 locus  and SH83-RS14775 locus (encoding a N-acetylmannosamine-6-phosphate 2-epimerase). Strains JDM1 and CMPG5300 possessed identical organization, while WCFS1 strain possessed an additional 854 bp region encoding two putative transposases (Lp_3569 and Lp_3570). In several L. plantarum strains , the insertion of this 8,903 bp region has been accompanied with the deletion of seven genes .The genomes of several lp_3561 and lp_3562 in WCFS1 strain), the 8,903 pb region encoded proteins involved in the catabolism of N-acetyl-D-neuraminic acid, a sialic acid (Lp_3566 to Lp_3568).In addition to the two esterase genes . Apart from these two motifs, these proteins exhibited low sequence similarity. The nine esterases characterized in L. plantarum WCFS1 showed that only a 10\u201333% identity is found among them, being the highest sequence identity found between Lp_3561 and Lp_3562 (44%). These two proteins also shared additional features that may suggest that both genes could be originated by tandem duplication . This contrasts with the results obtained for Lp_3562, which was active against all the substrates assayed, from C2 to C16  exhibited also similar biochemical properties, the biochemical activity of Lp_3561 was determined and compared to Lp_3562 which exhibited lipase activity . The lp_2 to C16 . By usin2 to C16 . Tributy2 to C16 . LipasesComparison of amino acid sequences and 3D-structures of lipases and esterases suggested that they can be distinguished by a pH-dependent electrostatic \u201csignature\u201d ; the actAs indicated above, interesting information can be inferred from the analysis of the main features of the electrostatic potential surfaces of these proteins around the putative active sites. Notably, these features are more similar between Lp_3561 and Cest-2923 than between Lp_3562 and Cest-2923, which is consistent with the fact that the former two proteins share essentially the same substrate specificity profiles, namely, both are esterases. In particular, in both proteins it is observed a basic region close to the active site  together with a neighboring acidic crevice, which is basic in Lp_3562. Whereas the residues of Lp_3561 contributing to the basic character of the above region around the active site (distances <13 \u00c5) are Arg52, Lys232, Arg244, and Glu249, those from Lp_3562 are Arg52, Arg244, and Glu249 . Conversely, the acidic residues present in the crevice of Lp_3561 are Glu15, Glu51, Glu55, and Glu72, whereas those from Lp_3562 are Glu55, Lys72, and Glu78 (in Cest-2923 the residues are Glu53 and Asp78). Since these electrostatic features may relevant in conferring the esterase character to Lp_3561 in contrast to the lipase character of Lp_3562, the role of these differential residues are currently being explored, in particular Lys72 in Lp_3562 which breaks the basic character of the crevice. In this regard, we would like to remark that despite the above molecular characteristics are derived from theoretical, structural models we believe that there are two aspects that further support our conclusions. First, the \u03b1/\u03b2 hydrolase fold is a highly conserved protein architecture of the lipase/esterase superfamily . Memberslp_3561 and lp_3562 genes in L. plantarum strains revealed that these genes are frequently absent on strains from this bacterial species. Generally, these genes are inserted as a 8903 bp region contiguous to the genes nanAKE  that encode proteins involved in the catabolism of N-acetyl-D-neuraminic acid, the most abundant and widely studied sialic acid  is still unknown. A plausible hypothesis explaining their presence next to the cluster of the nanAKE genes is that they would play the role of NagA, which is not present in the L. plantarum strains that acquired the nanAKE genes by horizontal transfer. As expected from this hypothesis, we observe that Lp_3561 and Lp_3562 hydrolyze ester bonds with acetyl groups in the acidic part similarly to NagA.In this context, the biological relevance of the genes coding for both esterases ; acquisition, analysis or interpretation of data for the work: gene cloning, protein production and substrate range studies (ME), protein biochemical characterization (LS), PCR experiments (IR), and protein modeling (JM). Drafting the work or revising it critically for important intellectual content . Final approval of the version to be published . Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest."}
+{"text": "DNA barcoding has been an effective tool for species identification in several animal groups. Here, we used DNA barcoding to discriminate between 47 morphologically distinct species of Brazilian sand flies. DNA barcodes correctly identified approximately 90% of the sampled taxa  using clustering based on neighbor-joining distance, of which four species showed comparatively higher maximum values of divergence (range 4.23\u201319.04%), indicating cryptic diversity. The DNA barcodes also corroborated the resurrection of two species within the shannoni complex and provided an efficient tool to differentiate between morphologically indistinguishable females of closely related species. Taken together, our results validate the effectiveness of DNA barcoding for species identification and the discovery of cryptic diversity in sand flies from Brazil. Leishmania Ross parasites, the etiologic agents of the leishmaniases. These insects are also vectors of other pathogens, including Bartonella Strong et al. and Phleboviruses  was split in two groups, as indicated using one white dash  was grouped with Evandromyia lenti (Mangabeira) as indicated with two thin black dashes in the NJ tree , Evandromyia edwardsi (Mangabeira), Pintomyia monticola (Costa Lima), and Sciopemyia microps (Mangabeira), showed deep intraspecific variation, forming two or more intraspecific barcode groups with mean divergence greater than 2.15%  and Evandromyia spp. (Pa. bigeniculata (PS1 and PS2) sequences to the sequences from Pa. limai (Fonseca). Psathyromyia limai showed 41 fixed differences in relation to both provisional species of Pa. bigeniculata (PS1 and PS2), while Pa. bigeniculata PS1 showed nine fixed differences in relation to both Pa. bigeniculata PS2 and Pa. limai. Likewise, Pa. bigeniculata PS2 showed 13 fixed differences in relation to both Pa. bigeniculata PS1 and Pa. limai. These three species still showed one fixed difference among each other that can be used as diagnostic site (421st position of the COI sequence)  showed 36 fixed (Brumptomyia genus showed seven fixed differences that can be used as diagnostic sites (Evandromyia tupynambai showed 27 fixed differences in relation to Evandromyia spp. (We found fixed differences within yia spp. . Becauseequence) . The thr and PS2 . The two36 fixed . The thric sites . Evandroyia spp. .2 = -0.0265, p = 0.587)    and maxi= 0.861)  were not= 0.861) .COI DNA barcoding has been useful for species recognition and the discovery of different taxa of insect vectors of parasites  were identified and all the females were identified as Brumptomyia sp. The specimens of Br. cunhai were collected from a sand fly survey from Pancas, Esp\u00edrito Santo and females from this locality were not analyzed. Unfortunately, this species was not included in the sand fly survey from that area because, at that time, Br. cunhai specimens were misidentified as Br. nitzulescui [Br. nitzulescui (one from Pancas and others from others areas). Indeed, we find that the group from Pancas was composed of specimens of Br. cunhai. This result highlights the importance of integrative approaches based on morphology and molecular biology to reach accurate results with sand fly identification. In our study, Br. cunhai showed 46 fixed differences in relation to Br. ortizi and Br. nitzulescui, including seven fixed differences that can be used as diagnostic sites among these three species. Besides the seven diagnostic sites, Br. ortizi showed 26 fixed differences in relation to Br. cunhai and Br. nitzulescui  or Evandromyia costalimai (Mangabeira) the females of these three species cannot be distinguished. We did not analyze the males of Ev. callipyga or Ev. costalimai; however our data suggest that the barcode gene can discriminate among these species since the females identified as Evandromyia sp. were clustered in two different groups with high sequence divergence and fixed differences among them   . This reollected .In conclusion, except in cases of species that have undergone introgressive hybridization, DNA barcoding allowed discrimination of sand fly species from Brazil and the discovery of species within putative sand fly species complex. This method also reliably detected sand fly species misidentification and allowed the association between males and females among species that are morphologically similar. Finally, this study highlights the importance of utilizing integrative approaches for sand fly species identification in order to achieve accurate and reliable results.S1 FigBichromomyia flaviscutellata; Br_nit = Brumptomyia nitzulescui; Br_cun = Brumptomyia cunhai; Br_ort = Brumptomyia ortizi; Br_sp = Brumptomyia spp.; Ev_car = Evandromyia carmelinoi; Ev_edw = Evandromyia edwardsi; Ev_len = Evandromyia lenti; Ev_sp = Evandromyia spp.; Ev_ter = Evandromyia termitophila; Ev_tup = Evandromyia tupynambai; Ex_fir = Expapillata firmatoi; Lu_ale = Lutzomyia alencari; Lu_cru = Lutzomyia cruzi; Lu_dis = Lutzomyia dispar; Lu_ren = Lutzomyia renei; Lu_sp = Lutzomyia sp.; Mi_cap = Micropygomyia capixaba; Mi_ech = Micropygomyia echinatopharynx; Mi_fer = Micropygomyia ferreirana; Mi_per = Micropygomyia peresi; Mi_qui = Micropygomyia quinquefer; Mi_sch = Micropygomyia schreiberi; Mg_mig = Migonemyia migonei; Ny_int = Nyssomyia intermedia; Ny_whi = Nyssomyia whitmani; Ny_yui = Nyssomyia yuilli yuilli; Pi_bia = Pintomyia bianchigalatiae; Pi_fis = Pintomyia fischeri; Pi_mis = Pintomyia misionensis; Pi_mon = Pintomyia monticola; Pr_cho = Pressatia choti; Pr_sp = Pressatia spp.; Pa_big = Psathyromyia bigeniculata; Pa_lim = Psathyromyia limai; Pa_lut = Psathyromyia lutziana; Pa_pas = Psathyromyia pascalei; Pa_pel = Psathyromyia pelloni; Ps_ayr = Psychodopygus ayrozai; Ps_dav = Psychodopygus davisi; Ps_hir = Psychodopygus hirsutus; Ps_mat = Psychodopygus matosi; Sc_mic = Sciopemyia microps; Sc_sor = Sciopemyia sordellii; Sc_sp = Sciopemyia spp.; Th_via = Trichophoromyia viannamartinsi.Each tip label in the tree contains the sand fly species name abbreviation (five words), the sample ID (number) and sex of the specimen . The sand fly species names were abbreviated as follows: Bi_fla = (PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 FigPsathyromyia bigeniculata (PS1 and PS2); B) Evandromyia edwardsi ; C) Pintomyia monticola (PS1 and PS2); D) Brumtomyia genus ; and E) Evandromyia tupynambai and Evandromyia spp. .A) (PDF)Click here for additional data file.S4 Fig(PDF)Click here for additional data file.S1 TableMaximum and mean intraspecific values of genetic divergence (Kimura 2-parameter pairwise distances) are shown. As implemented by the Barcode of Life Database (BOLD), values are given only to species represented by three or more individuals and showing at least one nucleotide substitution. Nominal species marked with one asterisk showed distinct intraspecific lineages suggesting cryptic species complexes. Classification follows ,16 and t(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 TableAll the distances were estimated in kilometers. .(PDF)Click here for additional data file."}
+{"text": "Cytochrome c oxidase I (COI) is a powerful marker for DNA barcoding of animals, with good taxonomic resolution and a large reference database. However, when used for DNA metabarcoding, estimation of taxa abundances and species detection are limited due to primer bias caused by highly variable primer binding sites across the COI gene. Therefore, we explored the ability of the 16S ribosomal DNA gene as an alternative metabarcoding marker for species level assessments. Ten bulk samples, each containing equal amounts of tissue from 52 freshwater invertebrate taxa, were sequenced with the Illumina NextSeq 500 system. The 16S primers amplified three more insect species than the Folmer COI primers and amplified more equally, probably due to decreased primer bias. Estimation of biomass might be less biased with 16S than with COI, although variation in read abundances of two orders of magnitudes is still observed. According to these results, the marker choice depends on the scientific question. If the goal is to obtain a taxonomic identification at the species level, then COI is more appropriate due to established reference databases and known taxonomic resolution of this marker, knowing that a greater proportion of insects will be missed using COI Folmer primers. If the goal is to obtain a more comprehensive survey the 16S marker, which requires building a local reference database, or optimised degenerated COI primers could be more appropriate. They also tested an insect mock sample containing DNA from 14 species with COI primers detecting the same amount or less taxa than with 16S. However, the performance of 16S metabarcoding primers with aquatic invertebrate communities has not been extensively tested.DNA metabarcoding is a novel and powerful method to assess biodiversity in ecosystems . Well-deIn this study, we evaluated the performance of an insect primer pair targeting a short 16S region as compared to the standard COI Folmer marker  for metaThe same DNA aliquots as in We used 16S markers ins_F/ins_R to amplify a \u223c157 bp of the mitochondrial 16S gene. This marker was developed as part of this project using the ecoPrimers program  and reprThe same one-step PCR and library preparation conditions as in Paired-end Illumina sequencing was performed on a NextSeq 500 system using the mid output kit v2 kit with 300 cycles (150 bp PE sequencing) at the Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany.https://github.com/VascoElbrecht/PrimerMiner) and are available together with the generated reference sequences on BOLDsystems (TMIX Vasco). An annealing temperature of 52\u00b0C was used for all primer combinations using HotMaster Taq  for amplification.Due to the limited availability of 16S reference sequences on GenBank (NCBI), we constructed a reference library for the 52 morphotaxa used in this study, if tissue was still available. Standard DNA salt extraction, PCR, PCR clean-up, and Sanger sequencing were conducted as described in The ten samples were dereplicated using derep_fulllength, but singletons were included in the data set. Sequences of each sample were matched against the OTUs with a minimum match of 97% using usearch_global. The hit tables were imported and the sequence numbers were normalised to the total sequence abundance and tissue weight for the various taxa. Only OTUs with a read abundance above 0.003% in at least one replicate were considered in downstream analysis.Due to the exponential nature of PCR, statistical tests on weight adjusted relative read abundances were carried out on decadic logarithm. Expected relative abundance was calculated by dividing 100% by the number of morphospecies detected with each marker.SRR2217415). Cluster density was 177 K/mm2 and read quality good with Q30 \u2265 85.3%. Read abundance was 17% higher when sequencing started with the P5_Ins_F primers  and were thus included in further analysis. Taxonomy could be assigned for most OTUs based on available reference data and our own reference sequences. Reference data for the 16S marker could be generated for 42 of the 52 morphotaxa by Sanger sequencing. Together with 16S sequences from NCBI  we were The taxonomic assignment was straigthforward for the COI marker, due to the availability of reliable reference databases, which was not the case for the 16S marker. Forty-one out of 42 insect species were detected by the 16S. The Sanger sequence generated for the Tipulidae present in our mock samples showed 3 mismatches within the first bases at the 3\u2032 end of both the forward and reverse 16S primers and was not detected in the data set.Sericostoma personatum, Rhyacophyla). The 16S primers worked very effectively for insect taxa, specifically in the indicator taxa Ephemeroptera, Plecoptera and Trichoptera . Additionally, the COI primers showed more dropouts of a few specimens per taxa , no insect taxon was only detected by COI, while 16S detected three more taxa . Up to 10 samples can be uniquely tagged from forward and reverse direction and pooled in one NextSeq run. The bases used for shifting on Ins_F and Ins_R can be used to uniquely tag samples (inline barcodes). It is recommended that all 10 primer pairs are used in the following combination to maximize sequence diversity and reduce effects of tag switching by uniquely tagging samples from both sides: P5_Ins_R0+P7_Ins_F4, P5_Ins_R1+P7_Ins_F3, P5_Ins_R2+P7_Ins_F2, P5_Ins_R3+P7_Ins_F1, P5_Ins_R4+P7_Ins_F0, P5_Ins_F0+P7_Ins_R4, P5_Ins_F1+P7_Ins_R3, P5_Ins_F2+P7_Ins_R2, P5_Ins_F3+P7_Ins_R1, P5_Ins_F4+P7_Ins_R0.Click here for additional data file.10.7717/peerj.1966/supp-2Figure S2Number of sequences obtained per sample after library demultiplexing (A) and percentage of sequences excluded in different bioinformatic analysis steps (B). (A) Library demultiplexing; Numbers above bars indicate the relative contribution (in percent) to the total number of sequences obtained for each sample. Sequencing started with Ins_F (white) or Ins_R (black) is indicated by bar color. (B) Number of reads excluded in data processing steps. Mean percentage of sequence abundance in each processing step is written in brackets. Ins_F/Ins_R primer bias was tested with a t-test.Click here for additional data file.10.7717/peerj.1966/supp-3Table S1Click here for additional data file.10.7717/peerj.1966/supp-4Table S2Click here for additional data file.10.7717/peerj.1966/supp-5Table S3Click here for additional data file.10.7717/peerj.1966/supp-6Supplemental Information 1Click here for additional data file."}
+{"text": "Pandoraea was first proposed in 2000 following the isolation from the sputum of cystic fibrosis patients . This bacterium was grown aerobically in LB broth at 37\u00b0C. Isolation and purification of bacterial genomic DNA was performed with MasterPure DNA Purification Kit  according to the manufacturer's instruction. The purity, quality and quantity of purified genomic DNA were assessed using NanoDrop 2000 UV-Vis spectrophotometer  and Qubit 2.0 fluorometer , respectively.P. pnomenusa DSM 16536T was prepared following the \u201cProcedure a Checklist-20kb Template Preparation using BluePippinTM Size Selection\u201d protocol, in which size selection of the constructed SMRTbell templates was performed with a cutoff length of 7kb. Purified and size-selected SMRTbell library was sequenced on four SMRT cells using P5C3 chemistry on Pacific Biosciences (PacBio) Single Molecule, Real-Time (SMRT) RS II instrument. Sub-reads which were generated from the raw sequencing reads after adapter removal were further filtered and mapped prior to de novo assembly using Hierarchical Genome Assembly Process (HGAP) version 3. The polished assembly generated was examined for circularity based on the presence of overlapping sequences at both ends of the contig. Location of the overlapping sequence were determined using Gepard dotplot program  , in which the genome sequences data are available in FASTA, annotated GenBank flat file, graphical and ASN.1 formats, functional annotation results of this genome are also accessible through KEGG resources.The genome of http://www.genome.jp/kegg/catalog/org_list.html) with the organism prefix of ppnm. Further, through the ppnm hyperlink, cross-reference information in the form of protein, and small-molecules interaction network maps (http://www.genome.jp/kegg-bin/show_organism?menu_type=pathway_maps&org=ppnm), BRITE biological systems hierarchical classifications , KEGG modules (http://www.genome.jp/kegg-bin/show_organism?menu_type=pathway_modules&org=ppnm), and whole genome map which can be visualized using genome map browser (http://www.genome.jp/kegg-bin/show_genomemap_top?org_id=ppnm) can be accessed through the subdirectory panel. Further insights to the potential pathogenicity of this clinical strain can also be gained from its genomic information through KEGG Pathogen resource (http://www.genome.jp/kegg/disease/pathogen.html).The overview of this genome record can be attained from the complete genome directory of KEGG ORGANISMS database  and other pathogens such as Listeria monocytogens, Actinobacillus pleuropneumoniae, Neisseria meningitis, and Shigella flexneri , Victors and PATRIC curated virulence database (PATRIC_VF). A total of 16 virulence factors were identified, namely: Phosphoribosylformylglycinamidine cyclo-ligase (LV28_09200), RNA polymerase sigma factor RpoE (LV28_09850), phosphoenolpyruvate synthase (LV28_01060), RecA protein (LV28_10630), aminase component of anthranilate synthase (LV28_11620), imidazole glycerol phosphate synthase cyclase subunit (LV28_12510), translation elongation factor Tu , argininosuccinate synthase (LV28_16050), endonuclease III (LV28_23310), 3-isopropylmalate dehydrogenase (LV28_23930), 3-isopropylmalate dehydrogenase (LV28_23930), RNA-binding protein Hfq (LV28_24345), chorismate synthase (LV28_04475), acetolactate synthase large and small subunit (LV28_04625 and LV28_04620), and chemotaxis protein CheY (LV28_14025). These identified virulence factors are well-characterized virulence determinants in the Pseudomonas aeruginosa and Escherichia coli ; KEGG database (entry number of T03411) and JGI portal with GOLD ID of Gp0107448 and IMG taxon ID of 2606217238.The assembled and annotated genome of YL, RE, DY, CY, GA, WY perform the experiments and collected the data. KC conceived the idea, obtained the funding and WY managed the finance of the project. YL, RE, DY, CY, GA, WY, and KT prepared the draft, and KC proofread the final draft. All authors approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest."}
+{"text": "Porphyromonas gingivalis has been implicated as a major pathogen in the development and progression of chronic periodontitis. P. gingivalis biofilm formation in the subgingival crevice plays an important role in the ability of the bacteria to tolerate stress signals outside the cytoplasmic membrane. Some bacteria use a distinct subfamily of sigma factors to regulate their extracytoplasmic functions (the ECF subfamily). The objective of this study was to determine if P. gingivalis ECF sigma factors affect P. gingivalis biofilm formation.P. gingivalis, chromosomal mutants carrying a disruption of each ECF sigma factor-encoding gene were constructed. Bacterial growth curves were measured by determining the turbidity of bacterial cultures. The quantity of biofilm growing on plates was evaluated by crystal violet staining.To elucidate the role of ECF sigma factors in P. gingivalis strain 33277 and the ECF mutants indicated that the growth rate of the mutants was slightly lower than that of the wild-type strain. The PGN_0274- and PGN_1740-defective mutants had increased biofilm formation compared with the wild-type (p\u2009<\u20090.001); however, the other ECF sigma factor mutants or the complemented strains did not enhance biofilm formation.Comparison of the growth curves of wild-type P. gingivalis.These results suggest that PGN_0274 and PGN_1740 play a key role in biofilm formation by The online version of this article (doi:10.1186/1472-6831-15-4) contains supplementary material, which is available to authorized users. Porphyromonas gingivalis is considered one of the important etiological agents of periodontal disease [P. gingivalis must be capable of sensing and responding to the prevailing environmental conditions, including variations in temperature, oxygen tension, pH, nutrient availability and the presence of other bacterial cells. P. gingivalis possesses transcriptional regulators that have been implicated in protection against heat shock stress or oxidative stress, such as RprY [The anaerobic Gram-negative bacterium  disease . To colo as RprY , 3 and O as RprY . In addi as RprY . Dental  as RprY . Oral baP. gingivalis 33277 genome encodes six ECF sigma factors  [Extracytoplasmic function (ECF) sigma factors serve as bacterial transcriptional regulators in the response to various stresses. The wild-type P. gingivalis biofilm formation, disruption of the ECF sigma factors, except PGN_1108, was performed. The PGN_1108-defective mutant may have a mutator phenotype, and we therefore excluded it from our experiments in this study [In this study, to analyze the role of ECF sigma factors in P. gingivalis cells were grown anaerobically  in enriched brain heart infusion (BHI) broth and on enriched tryptic soy (TS) agar [P. gingivalis strains, the following antibiotics were added to the medium: 15\u00a0\u03bcg/ml erythromycin (Em), and 0.7\u00a0\u03bcg/ml tetracycline (Tc).All bacterial strains and plasmids used in the present study are listed in Table\u00a0TS) agar . For bloP. gingivalis 33277 using Takara Ex Taq  and the gene-specific primers listed in Table\u00a0BamHI-SacI fragment (BglII-SacI fragment for PGN_0970) containing the 3\u2032 end of each sigma factor gene was extracted from the resulting plasmid and ligated into the BamHI-SacI site (BglII-SacI fragment for PGN_0970) of the plasmid containing the 5\u2032 end of the corresponding ECF gene. The ermF-ermAM cassette of pKD355 [BamHI site within PGN_0274 of pKD817, PGN_0319 of pKD818, PGN_0450 of pKD814 and PGN_1740 of pKD821, or the BglII site within PGN_0970 of pKD819 to yield pKD822, pKD823, pKD824, pKD827 and pKD825, respectively. These plasmids were linearized by NotI digestion and introduced into P. gingivalis 33277 cells by electroporation as described previously [ermF ermAM), KDP315 (PGN_0319:: ermF ermAM), KDP316 (PGN_0450:: ermF ermAM), KDP317 (PGN_0970:: ermF ermAM) and KDP319 (PGN_1740:: ermF ermAM). Correct gene replacement of these strains, which had been generated by double crossover recombination events, was verified by PCR and Southern blot analysis (data not shown).To disrupt the ECF sigma factor genes, PGN_0274-, PGN_0319-, PGN_0450-, PGN_0970- and PGN_1740-encoding genes were PCR-amplified from the chromosomal DNA of f pKD355  was inseeviously , resultiSphI-BamHI fragment of PGN_0274 or the BamHI fragment of PGN_1740 were extracted from the resulting plasmid and ligated into the SphI-BamHI or BamHI site of pT-COW [For complementation of PGN_0274 and PGN_1740, the whole ECF sigma factor gene region with its upstream and downstream flanking regions (0.5\u00a0kb) was PCR-amplified from the chromosomal DNA using Takara Ex Taq with the upper and lower primers  and cultured anaerobically at 37\u00b0C for 2 d. The culture medium was then removed from each well and 0.5\u00a0ml of 0.1% crystal violet solution was added. After 15\u00a0min, the wells were rinsed three times with PBS and air-dried. The crystal violet remaining in the biofilm was solubilized with 0.5\u00a0ml of 1% SDS and absorbance was measured at A600 using a microplate reader . Biofilm mass was determined by crystal violet staining and adjusted for growth (A600 units per OD660 unit).Biofilm formation was examined by the modified protocol of Saito . In briep\u2009<\u20090.05.The one-way ANOVA Test/Dunnett\u2019s Multiple Comparison Test was used to compare the differences between 33277 and ECF mutants using GraphPad Prism version 6.0 for Windows . Data were considered significant if All five ECF mutants grew more slowly than the wild-type strain in exponential phase, and final yields of the ECF mutants were less than that of the wild-type following a 48-h incubation under anaerobic conditions Figure\u00a0. The PGNP. gingivalis biofilm formation.To determine if the enhanced biofilm mass was caused by the deletion of PGN_0274 and PGN_1740, we constructed strains where the PGN_0274 and PGN_1740 were restored. The PGN_0274 and PGN_1740 complemented strains were constructed by introduction of the pT-COW containing the wild-type PGN_0274 and PGN_1740 into each of the mutants. This complementation restored the biofilm formation ability to the wild-type levels Figure\u00a0. These rMethylobacterium extorquens, PhyR, has been identified and determined to combine domains of both systems [Bacteria sometimes encounter an environment unfavorable to their survival. The human oral microbiota is also often influenced by various stresses; hence, it must possess the ability to defend itself. Two principal regulatory mechanisms interact with cytoplasmic and extracytoplasmic regions via alternative ECF sigma factors and phosphorylation-dependent response regulators  , 17. ECF systems . Taken tP. gingivalis ECF sigma factors have been previously described. Nevertheless, there is no information on the ECF sigma factors that may operate in this bacterium in response to biofilm formation. In Bacillus subtilis and Pseudomonas aeruginosa, ECF sigma factors are involved in regulating biofilm development [P. gingivalis is regulated by ECF sigma factors. This study demonstrated that PGN_0274 and PGN_1740 mutants yielded higher biofilm formation than that obtained with the wild-type or the other ECF sigma factor mutants. The inactivation of PGN_1740 also increased the expression of fimS at the transcriptional level [fimS was examined using RT-PCR, which showed the fimS expression was downregulated Additional file 1:Biofilm formation by homotypicP. gingivalis33277 or ECF sigma factor mutants using non-coated microplate.(PPTX 80 KB)Additional file 2:The RNA expression of\u2009fimS\u2009inP. gingivalis33277, PGN_1740 mutant and complemented mutant strain.(PPTX 101 KB)Additional file 3:Protein profile on an SDS-PAGE gel.(PPTX 343 KB)Additional file 4:"}
+{"text": "Mycobacterium smegmatis contains three hemerythrin-like proteins, MSMEG_3312, MSMEG_2415 and MSMEG_6212. In this study, we have systematically analyzed all three hemerythrin-like proteins in M. smegmatis and our results identified and characterized two functional classes: MSMEG_2415 plays an important role in H2O2 susceptibility, and MSMEG_3312 and MSMEG_6212 are associated with erythromycin susceptibility. Phylogenetic analysis indicated that these three proteins have different evolutionary origins, possibly explaining their different physiological functions. Here, combined with biological and phylogenetic analyses, our results provide new insights into the evolutionary divergence of the hemerythrin-like proteins in M. smegmatis.Hemerythrin-like proteins are oxygen-carrying non-heme di-iron binding proteins and their functions have effect on oxidation-reduction regulation and antibiotic resistance. Recent studies using bioinformatic analyses suggest that multiple hemerythrin-like protein coding sequences might have been acquired by lateral gene transfer and the number of hemerythrin-like proteins varies amongst different species.  Bioinformatic evidence indicates that prokaryotic genomes collectively encode hundreds of hemerythrin-like proteins12Methylococcus capsulatus functions as an oxygen-carrierCampylobacter jejuni acts to protect iron-sulfur cluster enzymes from oxidative damageVibrio choleraeHemerythrin-like proteins are non-heme, di-iron and Oin vivo)The number of hemerythrin-like proteins differs from strain to strain and this variation is predicted to be related to differences in the oxygen concentration of the environmentMycobacterium is comprised of a number of Gram-positive bacteria, including both pathogens, such as Mycobacterium tuberculosis and Mycobacterium leprae, and nonpathogens, such as the soil microorganism Mycobacterium smegmatis, which is commonly used in laboratory experiments as a model organism for M. tuberculosisMycobacterium is capable to survive under environmental stresses, such as oxidative stress, hypoxia and exposure to multiple antimicrobial agents8M. tuberculosis and response to antibiotic exposure in mycobacteria10M. tuberculosis (NC_000962.3) has been predicted to contain three hemerythrin-like proteins. Five genes have been predicted to encode hemerythrin-like proteins in Mycobacterium avium (NC_008595). M. smegmatis possesses three hemerythrin-like proteins, MSMEG_3312, MSMEG_2415 and MSMEG_6212. In this study, we sequentially overexpressed and deleted each of the three genes encoding hemerythrin-like proteins in M. smegmatis. We showed that MSMEG_6212 and MSMEG_3312 modulated erythromycin susceptibility and that the resistance of msmeg_3312 and the msmeg_6212 double-knockout strain, mc2155:\u03943312-6212, was similar to single-knockout strains mc2155:\u03943312 and mc2155:\u03946212. MSMEG_2415 plays a major role in H2O2 susceptibility but not in erythromycin susceptibility. MSMEG_3312 exhibited only a mild H2O2 response in mc2155:\u03942415.The genus 2O2 susceptibility; overexpression of msmeg_6212 in both mc2155:\u03942415 and the mc2155:\u03942415-3312 double-knockout strain did not influence H2O2 susceptibility relative to the corresponding parental strains. Phylogenetic analysis of bacterial hemerythrin-like proteins showed that three mycobacterial hemerythrin-like proteins are likely derived from different lineages, possibly explaining their different biological functions. Here, combined with analyses of biological function and phylogenetic analyses our results provide new insights into the evolutionary divergence of the hemerythrin-like proteins in M. smegmatis.In addition, MSMEG_6212 was not associated with HM. smegmatis hemerythrin-like proteins, MSMEG_3312, MSMEG_2415 and MSMEG_6212, have distinct or overlapping functions, we used a series of strains overexpression individual genes and knockout mutants. The specialized transduction strategy for the sequential deletion of the three genes encoding hemerythrin-like proteins in M. smegmatis, and the overexpression of individual genes encoding hemerythrin-like proteins is shown in 2O2 pathway and that MSMEG_3312 is associated with erythromycin susceptibility12msmeg_6212 knockout strain (mc2155:\u03946212). Knockout mutants were confirmed by PCR analysis (msmeg_6212 gene fragment was not amplified and no msmeg_6212 mRNA was detected in an assay of its mRNA expression (data not shown). The msmeg_6212 mutant, mc2155:\u03946212, was complemented with a single integrated copy using pMV361-6212. The constructed M. smegmatis mutant strain mc2155:\u03946212 was initially tested for growth in rich 7H9 medium and defined Sauton medium. Growth of mc2155:\u03946212 appeared to have no discernable phenotypic difference from the wild type strain mc2155, in either rich  of eleven antibiotic drugs, and H2O2 in the msmeg_6212 knockout strain mc2155:\u03946212 and wild type strain mc2155  erythromycin. As shown in 2155:\u03946212 was greater than that of wild type mc2155, whereas the complemented strain pMV361-6212/mc2155:\u03946212 did not grow well and its survival was partially reversed to that of the wild-type. As overexpression of MSMEG_6212 increased susceptibility to erythromycin, we used 15.6\u2009mg/L (5 \u00d7 MIC) to perform the killing experiment: overexpression of msmeg_6212 caused greater susceptibility to erythromycin and lower survival than in wild type mc2155 under the same treatment . This result suggests that WhiB7 responds to erythromycin independent of MSMEG_3312, and MSMEG_6212. In contrast, knockout of msmeg_3312 led to a 2.19\u2009\u00b1\u20090.07 fold increase in the mRNA level of mtrA relative to wild type mc2155, and a 1.94\u2009\u00b1\u20090.05 fold increase in mtrA mRNA in mc2155:\u03946212  and msmeg_0637 (encoding iron-sulfur binding oxidoreductase)2155, mc2155:\u03946212, mc2155:\u03943312 and complemented strains pMV361-3312/mc2155:\u03943312 and pMV361-6212/mc2155:\u03946212. The level of msmeg_1875 and msmeg_0637 mRNA increased in mc2155 in response to erythromycin, but induction of msmeg_1875 and msmeg_0637 was abrogated in both mc2155:\u03946212 and mc2155:\u03943312 in response to erythromycin  and msmeg_4753 (encoding antioxidant) in mc2155:\u03946212 and mc2155:\u03943312 relative to that in mc2155. Consistent with results for H2O2 survival assays, there were no significant changes in the levels of sigF, msmeg_1782 and msmeg_4753 mRNA  or the msmeg_3312 knockout strain harboring pMV261-2415 (pMV261-2415/ mc2155:\u03943312-6212) for 3h and then spotted the cells on 7H10 media. We did not detect any difference in growth between the pMV261/ mc2155:\u03943312-6212 strain and the pMV261-2415/ mc2155:\u03943312-6212 strain under drug treatment (msmeg_3312 knockout strain (mc2155:\u03943312) and the msmeg_3312 and msmeg_2415 double-knockout strain (mc2155:\u03943312-2415) when treated with erythromycin  (mtrA mRNA and that of its regulon (msmeg_1854 and msmeg_0637) in the triple mutant were comparable to that in mc2155:\u03943312 (2O2 susceptibility of mc2155:\u03943312-6212-2415 was comparable to that of mutant mc2155:\u03942415 (sigF and SigF regulon (msmeg_4753 and msmeg_1782) mRNA in \u03943312-6212-2415 was comparable to that in mc2155:\u03942415  . The lev55:\u03943312 . As expe55:\u03942415 . The lev55:\u03942415 .2O2 susceptibility, while MSMEG_2415 has a major effect on H2O2 susceptibility, and MSMEG_6212 has a role in erythromycin susceptibility. Phylogenetic analysis can help to explain the protein evolution of physiological adaptationsM. smegmatis hemerythrin-like proteins. We first retrieved the sequences of the closest neighbors (top 100BLAST hits) of M. smegmatis MSMEG_6212, MSMEG_2415 and MSMEG_3312 from the UniRef 90 dataset of UniProtMycobacterium, while those in the MSMEG_3312 cluster were derived from Mycobacterium, Rhodococcus and Sciscionellas. A large portion of the hemerythrin-like proteins belonging to the Streptomyces, Saccharomonospora and Amycolatopsis were present in the MSMEG_6212 cluster, suggesting that msmeg_6212 may have an independent origin. Taken together, this phylogenetic data suggests that the origins and evolution of MSMEG_3312, MSMEG_2415 and MSMEG_6212 are different. Differences in origins may explain the differences in their physiological functions.The above results demonstrated that the three hemerythrin-like proteins have different roles: MSMEG_3312 is associated with both erythromycin and H2O2 susceptibility in M. smegmatis. This study is the first to analyze the function and relationship between multiple hemerythrin-like proteins within one organism.In this study, we have systematically evaluated the roles of multiple hemerythrin-like proteins  on erythromycin and HM. tuberculosis proliferation by binding to the dnaA promoter23151617We showed that MSMEG_6212 is associated with erythromycin susceptibility but not susceptibility to the other drugs tested, including isoniazid (INH), ciprofloxacin (CIP) and rifampin (RFP) . Erythro21M. tuberculosis contains five resuscitation-promoting factor (Rpf)-like proteins and ten universal stress proteins (USPs)2627usp genes, suggesting that USP proteins in M. tuberculosis have redundant functionsM. tuberculosis rpf genes indicate that they have redundant rolesM. smegmatis possesses three hemerythrin-like proteins, MSMEG_3312, MSMEG_2415 and MSMEG_62122155:\u03943312-6212 showed comparable erythromycin resistance to that of the single-knockout mutant mc2155:\u03943312  might have a selective advantage facilitating its survival. This independent phylogenetic clade may explain the different roles of MSMEG_2415 (in H2O2 susceptibility), MSMEG_6212 (in erythromycin susceptibility) and MSMEG_3312 (in both erythromycin and H2O2 susceptibility). Further work to identify the functions of M. tuberculosis hemerythrin-like proteins and a comparison of their hemerythrin-like protein with those in M. smegmatis would provide insight into the evolution and selection of virulence and antibiotic susceptibility.Phylogenetic analysis of respiratory hemerythrin-like proteins and hemocyanins shows that the distribution of analyzed respiratory proteins may partially explain physiological adaptionst clades . InteresM. smegmatis and our results indicated that the three members of this protein family possess overlapping and distinct functions: MSMEG_2415 plays an important role in H2O2 susceptibility, and MSMEG_3312 and MSMEG_6212 can modulate erythromycin resistance. Phylogenetic analysis indicated that these three proteins have different evolutionary origins, possibly explaining their different physiological functions. The functional and phylogenetic analyses of hemerythrin-like proteins in M. smegmatis would provide insight into the evolutionary selection of antimicrobial resistant traits.In summary, we have systematically analyzed all three hemerythrin-like proteins in M. smegmatis strains consisted of Middlebrook 7H9 medium  supplemented with 10% ADS (5% (w/v) bovine serum albumin fraction V, 2% (w/v) dextrose and 8.1% (w/v) NaCl), 0.5% (v/v) glycerol, and 0.05% (v/v) Tween80. 7H10 media containing Middlebrook 7H10 medium , 10% ADS, and 0.5% (v/v) glycerol was used for solid culture to examine growth status. Hygromycin (Hyg) was purchased from GenView, erythromycin (EM) from Merck, hydrogen peroxide (H2O2) and kanamycin (Kan) from Sigma. Restriction enzymes such as Van91I, AlwNI, MfeI, and PacI were purchased from Fermentas. T4 DNA ligase and Q5 DNA polymerase were purchased from New England Biolabs.As previously describedmsmeg_6212 gene were amplified from M. smegmatis genomic DNA using the following PCR conditions: 98\u2009\u00b0C for 3\u2009min, 32 cycles of 98\u2009\u00b0C for 30\u2009s, 60\u2009\u00b0C for 30\u2009s, and 72\u2009\u00b0C for 30\u2009s, and 72\u2009\u00b0C for 10\u2009min. The primers for msmeg_6212 knockout are listed in 6212 were then digested with PacI and ligated using T4 DNA ligase to create a shuttle plasmid. MaxPlax packaging extract  was used for phage packaging and transformed into E. coli HB101 cells according to the manufacturer\u2019s instructions. Successful phasmids were transduced into M. smegmatis strain mc2155 at 30\u2009\u00b0C, which allowed replication and amplified high titer phages. Transduction into M. smegmatis was then performed at 37\u2009\u00b0C with the high titer lysate at an MOI of 10:1. Gene knockout was confirmed by PCR screening using primers outside the upstream and downstream flanking regions and the corresponding vector primers. Complemented strain of msmeg_6212 was constructed by cloning the full-length genes into the integrating vector pMV361 to yield pMV361-6212/ mc2155:\u03946212.Mycobacteriophage-based specialized transduction was used to generate hemerythrin-like gene knockout strains13msmeg_3312 and msmeg_6212 double knockout strain, we unmarked the mc2155:\u03943312 strain used in previous study2155:\u03943312 strain by electroporation and plated onto 7H10 media containing 25\u2009mg/L kanamycin. Kanamycin resistant colonies were screened by a pick-and-patch method for hygromycin sensitivity, streaked on 7H10 media alone and on 7H10 media with 50\u2009mg/L hygromycin. The hygromycin-sensitive colonies were then plated onto 7H10 media with 5% sucrose. The selected colonies were spread on 7H10 media supplemented with 5% sucrose to obtain kanShygS colonies. The unmarked mc2155:\u03943312 strain was used for construction of the msmeg_6212 knockout strain, yielding the mc2155:\u03943312-6212 double-knockout strain. The triple mutant mc2155:\u03943312-6212-2415 was generated from the double-knockout progenitor mc2155:\u03943312-6212.To obtain the msmeg_3312, msmeg_2415 and msmeg_6212, were sub-cloned into pMV261 to yield pMV261-3312, pMV261-2415 and pMV261-6212 for transformation into the corresponding M. smegmatis strains  were treated with erythromycin at the indicated concentrations, aliquots (~50\u2009\u03bcl) were removed at the indicated times and spread onto 7H10 medium. Colony Forming Units (CFUs) were counted after 3 days of incubation. Experiments were repeated at least 3 times.The killing curve under erythromycin treatment was determined as indicated. Logarithmic phase cultures (OD2O2 treatment was determined as indicated. Logarithmic phase cultures (OD600\u2009~\u20090.3) were treated with erythromycin (15.6\u2009mg/L) or H2O2 (5\u2009mM) at the indicated concentrations for 3 h, serially diluted (1:10) and spotted (3\u2009\u03bcl) onto 7H10 medium. Photographs were taken after three days of incubation at 37\u2009\u00b0C. Experiments were repeated at least 3 times.Survival under erythromycin and H600\u2009~\u20090.3)cultures of the corresponding M. smegmatis strains treated with 0 or 3.125\u2009mg/L erythromycin for 30\u2009min were collected. After resuspending in TRIzol (Invitrogen), RNA was purified according to the manufacturer\u2019s instructions. The SuperScriptTM III First-Strand Synthesis System (Invitrogen) was used to synthesize the corresponding cDNA. qRT-PCR was performed on a Bio-Rad iCycler. M. smegmatis rpoD (the coding sequencing of RNA polymerase sigma factor SigA) was used to normalize gene expression. The relative ratio was calculated using the 2\u2212\u0394\u0394CT methodLogarithmic phase ."}
+{"text": "Numerous inflammation-related pathways have been shown to play important roles in atherogenesis. Rapid and efficient assessment of the relative influence of each of those pathways is a challenge in the era of \u201comics\u201d data generation. The aim of the present work was to develop a network model of inflammation-related molecular pathways underlying vascular disease to assess the degree of translatability of preclinical molecular data to the human clinical setting.We constructed and evaluated the Vascular Inflammatory Processes Network (V-IPN), a model representing a collection of vascular processes modulated by inflammatory stimuli that lead to the development of atherosclerosis.-/- mice (78\u00a0weeks) and human coronary arteries with advanced atherosclerotic lesions identified significant commonalities in the two species, as well as several mechanisms specific to human arteries that are consistent with the development of unstable atherosclerotic plaques.Utilizing the V-IPN as a platform for biological discovery, we have identified key vascular processes and mechanisms captured by gene expression profiling data from four independent datasets from human endothelial cells (ECs) and human and murine intact vessels. Primary ECs in culture from multiple donors revealed a richer mapping of mechanisms identified by the V-IPN compared to an immortalized EC line. Furthermore, an evaluation of gene expression datasets from aortas of old ApoEWe have generated a new biological network model of atherogenic processes that demonstrates the power of network analysis to advance integrative, systems biology-based knowledge of cross-species translatability, plaque development and potential mechanisms leading to plaque instability. The addition of these data-derived HYPs to the literature-based framework generated the integrated model [[Hs_athCA_vs_ctIMA])[[Mm_Ao_16w_ApoE_CS_vs_sham]), were analysed by RCR. RCR results (HYPs) were then used to evaluate network performance by determining HYP-level coverage and odds ratios (OR) across the six subnetworks constituting the V-IPN. Names describing the species and experimental settings for each dataset were created and they were used throughout the results and discussion section to facilitate comparative analyses.Four transcriptomics datasets from isolated human ECs (GSE13139 _vs_ct]) and athe_ctIMA]), as well[Hs_NHBE_CDKinh_rel_vs_blk_8h]), human cardiac and lung microvascular ECs (MVEC-L and MVEC-C) (GSE11341 [Hs_JurkT_ars_vs_ct])[[Hs_JurkT_ars_vs_ct])[Three datasets from normal human bronchial epithelial (NHBE) cells (E-MTAB-1272 _vs_ct]) and JurkEvaluating the RCR results for each dataset in the context of each of the six V-IPN subnetworks was estimated by calculating coverage and odds ratio (OR). Figure\u00a0[Mm_Ao_16w_ApoE_CS_vs_sham] transcriptomics dataset was generated from aortas displaying evidence of atherosclerotic plaques in ApoE-/- mice exposed to cigarette smoke (CS). All animal experimental procedures and CS exposure were approved by an Institutional Animal Care and Use Committee (IUCAC) and are described in detail in the Additional file-/- mice were conducted according to methods detailed in the Addditional fileWe set up a study in ApoE-deficient mice in which we investigated the effects of CS on cardiovascular endpoints including plasma lipid profiles, and transcriptomics of aortas. The E-MTAB-1696 Endothelial Cell Activation, Endothelial Cell-Monocyte Interaction, Foam Cell Formation, Platelet Activation, Smooth Muscle Cell Activation and Plaque Destabilization .To capture the diverse array of biological processes involved in the development of atherosclerotic plaques, the V-IPN network model was constructed using a modular approach that represents key processes related to vascular inflammation and atherogenesis. The biology modelled started with a literature-derived network scaffold followed by a RCR analysis of two murine and one human transcriptomics datasets  exhibiting the highest degree of coverage (24%) and the highest OR (2.47)  and an immortalized EC line (Hs_EC_GFP_oxLDL_vs_ct) showed the lowest HYP coverage (9%)  exhibited a lower coverage and OR compared to datasets from intact murine  and human (Hs_athCA_vs_ctIMA) vascular tissues. (Mm_Ao_78w_ApoE_vs_wt), a model construction dataset, is included here only as a reference. Within the EC datasets, primary cells treated with Ox-PAPC (Hs_EC_oxPAP_vs_ct) showed the largest coverage compared to both Ox-LDL-treated HAEC datasets  and MVECs . Within the HAEC study, a larger HYP coverage was shown by LOX-1-transfected ECs (Hs_EC_LOX1_oxLDL_vs_ct) compared to cells transfected with GFP (Hs_EC_GFP_oxLDL_vs_ct), suggesting that vascular inflammatory processes are indeed initiated by overexpressing LOX-1.Figure\u00a0Hs_athCA_vs_ctIMA exhibited statistically significant ORs for all of the six subnetworks and the highest coverage of all datasets  in four of the six subnetworks: Plaque Destabilization, Platelet Activation, EC-Monocyte Interaction and Foam Cell formation. This remarkable finding underlines the value of contrasting gene expression datasets from atherosclerotic arteries (e.g. coronary arteries) with normal vessels from the same subjects  as performed for this dataset) are activated in response to TF-VIIa-Xa, a ternary complex that is also linked to inflammation within plaques prone to rupture[-/- mice[The human advanced coronary lesion dataset echanism. In agre rupture. Plaque -/- mice.Other significant HYPs predicted by the datasets included NOS3 and ANGPT1, both of which regulate vascular tone and permeability, as well as blood vessel maturation and stability. Intra-plaque neovascularization may lead to hemorrhage, fissure development and plaque rupture. The neovascularization process that occurs in advanced lesions indicateRCR-based models do not operate with integrated feedbacks and non-linear elements that contribute to regulating a dynamic output. In order to make accurate predictions, mathematical models incorporate feedback elements tuned to match phenotypic constrains . In RCR, all biological feedbacks are implicitly integrated in the datasets. RCR-based models do not dynamically model the regulatory processes controlling biological pathways. RCR-based models are tools to extract biological processes embedded in large sets of molecular data driven by specific experimental perturbations, and to contextualize those findings within a body of knowledge.In summary, we have demonstrated that RCR analysis of large gene expression datasets coupled with HYP mapping to the V-IPN was able to discern the mechanistic variability underpinning the development of atherosclerotic lesions in a variety of experimental and species contexts. The mapping of predicted HYPs to the V-IPN was able to successfully distinguish between early and advanced murine lesions, as well as advanced murine and human atherosclerosis, thus pointing to a distinct subset of mechanisms that are translatable to the human condition. Importantly, our computational model proved to be a powerful tool to further our pathophysiological understanding of vascular inflammation, atherogenesis and plaque destabilization. The dynamic nature of the model\u2019s structure allows for further refinement as additional datasets become available and represents a useful tool for the interrogation of cross-species translatability in the context of cardiovascular disease.Reverse Causal Reasoning (RCR): A computational methodology for identifying potential upstream controllers leading to differential molecular profiles.Selventa Knowledgebase (SK): A network representing a working set of knowledge fit for a specified use. The SK is used as a substrate for RCR. It encodes prior scientific knowledge as a network of nodes that are connected by edges.Biological Expression Language (BEL): The knowledge representation language used to build the SK.Node: A biological entity or process in the SK.Edge A causal relationship  connecting two nodes in the knowledgebase.State Change (SC): A differential measurement across a sample group  that is converted to a discrete value of increase, decrease, or no change, based on two statistical metrics: richness and concordance.Hypothesis (HYP): A small, directed causal network containing an upstream node representing a biological entity or process connected by a causal increase, decrease or ambiguous edges to downstream nodes representing measured entities.HYP upstream node: A controller of downstream nodes in a HYP and a potential explanation for state changes (SC) mapped to the downstream nodes.HYP downstream nodes: Nodes in a HYP mapped to quantities measured in the dataset.HYP causal edges: The causal relationships  connecting the HYP upstream node to each downstream node.Richness: A measure of the relevance of a HYP to the changes observed in an experimental dataset.Concordance: A measure of the consistency of the direction of the changes observed in an experimental dataset.Coverage (sensitivity): An estimate of the fraction of possible HYPs in a subnetwork that are significant in a dataset. Coverage is a measure of HYP enrichment.Odds ratio (OR): The probability of having significant dataset HYPs within a network. The higher the OR, the better the network encompasses the biology embedded in a given dataset.X: Protein abundance of Xtaof(X): Transcriptional activity of Xexp(X): RNA expression of Xgtpof(X): GTP-bound activity of Xkaof(X): Kinase activity of Xpaof(X): Phosphatase activity of Xcatof(X): Catalytic activity of XX P@Y: Abundance of X phosphorylated at YBEL: Biological expression language; HYP: Hypothesis; OR: Odds ratio; RCR: Reverse causal reasoning; SC: State change; SK: Selventa knowledgebase.The authors declare no competing interests. H\u00e9ctor De Le\u00f3n, St\u00e9phanie Bou\u00e9, Walter K Schlage, Stephan Gebel, Marja Talikka, Emilija Veljkovic, Michael J Peck, Carole Mathis, Carine Poussin, Katrin Stolle, Julia Hoeng, Manuel C Peitsch are employees of Philip Morris International R&D. Natalia Boukharov, Jurjen W Westra, Aaron VanHooser, R Brett Fields, Vy Hoang, and Renee Deehan are employees of Selventa.HDL, SB and MT contributed to dataset model mapping and data interpretation. RBF and NB contributed to data interpretation. HDL, SB and WKS participated in data processing and analysis, and in data interpretation. HDL, RBF and NB drafted and revised the manuscript. SB, WKS, NB, SG, MT, EV, MJP, CM, CP, and KS participated in model conception and design, and in literature curation and vetting. JWW, AVH, VH, and RD participated in model conception and design, and in network construction. JH and MCP contributed to model conception and design. NB and SG participated in network construction. SB, WKS, NB, JH, and MCP revised the manuscript. All authors read and approved the final manuscript.Vascular Biology Keywords.Click here for fileSupplementary Methods.Click here for fileSix .XGMML and six .XLS files of all V-IPN subnetworks. The network architecture may be viewed from the XGMML files using freely available network visualization software such as Cytoscape (http://www.cytoscape.org/).Click here for fileFrequency rate of nodes and HYPs across the six subnetworks. As the number of events increases, the frequency of those occurrences in all networks decreases. Twenty five HYPs were present in all subnetworks at a simultaneous event rate of 4, whereas a single HYP, Ox-LDL, was present once in all networks.Click here for fileNode overlap between subnetworks. Table A shows the number of overlapping nodes between all six of the individual subnetworks. Table B shows, as a percentage, the degree of node overlap between the six subnetworks. Colored cells reflect the degree of overlap from low (dark blue) to high degrees of overlap (dark red).Click here for file(Hs_NHBE_CDKinh_rel_vs_blk_8h) used as a negative control. (\u2191) predicted increased, (\u2193) predicted decreased.Common HYP coverage of V-IPN and cell proliferation subnetworks by a dataset from NHBE cells Click here for fileDatasets_Analysis_Dashboards.Click here for fileA. The HYP with the upstream node macrophage activation scored for the Mm_Ao_78w_ApoE_vs_wt dataset. This HYP contains 23 measured RNA abundance nodes, represented as circles colored by differential expression . A total of 18 differentially expressed RNAs mapped to the HYP network, including 15 supporting increased mechanism activity (solid arrows) and three supporting decreased activity (dotted lines). B. The HYP with the upstream node Ccl5 scored for the Mm_Ao_78w_ApoE_vs_wt dataset. This HYP contains 41 measured RNA abundance nodes, represented as circles colored by differential expression . A total of 24 differentially expressed RNAs mapped to the HYP network, including 19 supporting increased mechanism activity (solid arrows) and five supporting decreased activity (dotted lines). C. The HYP with the upstream node monocyte adherence, scored for the Mm_Ao_78w_ApoE_vs_wt dataset. This HYP contains 87 measured RNA abundance nodes, represented as circles colored by differential expression . A total of 36 differentially expressed RNAs mapped to the HYP network, including 33 supporting increased mechanism activity (solid arrows) and three supporting decreased activity (dotted lines). D. The HYP with the upstream node kaof(Chuk), scored for the Mm_Ao_78w_ApoE_vs_wt dataset. This HYP contains 44 measured RNA abundance nodes, represented as circles colored by differential expression . A total of 25 differentially expressed RNAs mapped to the HYP network, including 23 supporting increased mechanism activity (solid arrows) and two supporting decreased activity (dotted lines).Click here for fileHs_athCA_vs_ctIMA (GSE40231) across other network models. Subnetworks with less than 10 HYPs were not included. IPN: Inflammatory Process Network, TRAG: Tissue Repair and Angiogenesis. DACS: DNA damage, Autophagy, Cell death (apoptosis and necroptosis), and Senescence.Coverage and OR of the dataset Click here for fileTranscriptomics-based evaluation of the effects of oxidative stimuli on primary HAEC cultures vs. immortalized HAECs.Click here for fileMm_Ao_78w_ApoE_vs_wt and Hs_athCA_vs_ctIMA. (\u2191) predicted increased in both datasets; (\u2193) predicted decreased in both datasets.DAVID functional clustering of common HYPs between Click here for fileSummary of findings and insights provided by each dataset evaluated by RCR and the V-IPN.Click here for fileHs_athCA_vs_ctIMA (GSE40231). PCA plot A illustrates the principal components of the gene expression profiles of 37 pairs of samples from the atherosclerotic coronary arteries and control internal mammary arteries from the STAGE study. Although the separation between atherosclerotic tissue and control mammary artery is relatively clear, the pairing of the samples (each pair from one patient) empowers the downstream analysis as illustrated when looking at the distance between the pairs of samples. PCA plot B highlights the relationships between the paired samples, demonstrating that for the samples that may look at the borderline between the two groups (ATHERO and CTRL), the difference between the atherosclerotic vessel and its control artery is still very clear, and in the same direction as for the other pairs.Principal component analysis (PCA) of samples from Click here for file"}
+{"text": "Purpose. We tried to establish clinically relevant human myeloma cell lines that can contribute to the understanding of multiple myeloma (MM). Materials and Methods. Mononuclear cells obtained from MM patient's bone marrow were injected via tail vein in an NRG/SCID mouse. Fourteen weeks after the injection, tumor developed at subcutis of the mouse. The engraftment of MM cells into mouse bone marrow (BM) was also observed. We separated and cultured cells from subcutis and BM. Results. After the separation and culture of cells from subcutis and BM, we established two cell lines originating from a single patient (SNU_MM1393_BM and SNU_MM1393_SC). Karyotype of the two newly established MM cell lines showed tetraploidy which is different from the karyotype of the patient (diploidy) indicating clonal evolution. In contrast to SNU_MM1393_BM, cell proliferation of SNU_MM1393_SC was IL-6 independent. SNU_MM1393_BM and SNU_MM1393_SC showed high degree of resistance against bortezomib compared to U266 cell line. SNU_MM1393_BM had the greater lethality compared to SNU_MM1393_SC. Conclusion. Two cell lines harboring different site tropisms established from a single patient showed differences in cytokine response and lethality. Our newly established cell lines could be used as a tool to understand the biology of multiple myeloma. Multiple genetic and microenvironmental changes  lead to https://www.atcc.org/).By the way, multiple myeloma has correlation with plasmacytoma, which is a mass of plasma cells found outside of bone marrow  that neeMany clinicians are curious about the adequate treatment strategy of plasmacytoma . And theFor this aspect, we present, in this study, the establishment of two human multiple myeloma cell lines, called SNU_MM1393_BM and SNU_MM1393_SC from a patient with aggressive multiple myeloma using an animal model. SNU_MM1393_BM cell line is derived from bone marrow of a mouse, and SNU_MM1393_SC is derived from a subcutaneous plasmacytoma. Here, we characterized phenotypic, genetic, and functional properties of these cell lines. Also, we further investigated the response to cytokines and chemotherapeutic agents of these cell lines. RB1 deletion, trisomy 1q, IgH rearrangement, and IgH 3 copies. His myeloma cell secreted monoclonal protein of immunoglobulin A, kappa chain. He received radiotherapy for vertebral plasmacytoma. After radiotherapy, four cycles of thalidomide/dexamethasone chemotherapy were given to the patient, which yielded in partial response (PR). Stem cell collection was performed in preparation for autologous stem cell transplantation after PR to thalidomide/dexamethasone chemotherapy. We used bone marrow cells at the time of stem cell collection for this experiment. The karyotype was sustained to be normal by the time of this stem cell collection. He is still in PR after autologous stem cell transplantation with high-dose melphalan conditioning with progression free survival time of 8.3 months. He did not receive either bortezomib or panobinostat.In February 2012, a 63-year-old male patient visited Seoul National University Hospital for back pain and tingling sense on the trunk below nipple. He was diagnosed as multiple myeloma with spinal cord compression due to osseous plasmacytoma on the third thoracic vertebrae. His disease stage was 3 by Durie-Salmon staging and 2 by International Staging System. Karyotype of this patient was normal, but fluorescent in situ hybridization (FISH) revealed that the disease had trisomy 9,6 mononuclear cells suspended in a total volume of 300-microliter PBS. After 14 weeks, bone marrow specimens were obtained from mice, and isolated bone marrow cells were cultured in RPMI-1640 medium  supplemented with 10% heat-inactivated fetal bovine serum, penicillin (100\u2009U/mL), and streptomycin (100\u2009g/mL) . They were cultured in a highly humidified atmosphere of 5% CO2 and 95% air at 37\u00b0C. The medium was exchanged every 3-4 days depending on the rate of cell growth.Bone marrow specimens were obtained from a patient diagnosed with multiple myeloma under a protocol approved by the Seoul National University Hospital Institutional Review Board. Mononuclear cells were separated by Ficoll-Hypaque density sedimentation. Eight-week-old NRG/SCID mice were injected intravenously via the dorsal tail vein (i.v.) with 1 \u00d7 10Histological sections of bone marrow from NOD/SCID mice were prepared and stained with haematoxylin and eosin (H&E) using standard methods. Cell morphology was examined using light microscopy.Metaphase chromosome spreads from peripheral blood and the cell line were prepared and G-banded according to standard procedures. The karyotype was described according to the International System for Human Cytogenetic Nomenclature (ISCN) (2009) .Cell proliferation assay was performed using Cell Counting Kit-8  according to the manufacturer's instructions. Means and standard deviations were generated from three independent experiments. Absorbance values were normalized to the values obtained from control group to determine the value for % of survival.\u03bcg of whole cell protein extracts was boiled for 5\u2009min, and the proteins were resolved in 10% SDS-polyacrylamide gel electrophoresis and electrotransferred to polyvinylidene difluoride membranes . The membranes were blocked in Tris-buffered saline containing 0.05% Tween 20 and 5% nonfat dry milk for 1\u2009h at room temperature and incubated with the appropriate primary antibody for 2\u2009h. Immunoreactive proteins were detected using horseradish peroxidase-conjugated secondary antibody  and enhanced chemiluminescence reagents .The cells were treated with indicated reagents for the indicated time periods, washed once in ice-cold phosphate buffered saline (PBS), and resuspended in lysis buffer (20\u2009mM MOPS (pH 7.0), 2\u2009mM EGTA, 5\u2009mM EDTA, 30\u2009mM sodium fluoride, 60\u2009mM b-glycerophosphate (pH 7.2), 20\u2009mM sodium pyrophosphate, 1\u2009mM sodium, orthovanadate, 1% Triton X-100, 1\u2009mM PMSF, aprotinin, leupeptin, and pepstatin 1\u2009mg/mL). The protein concentration of lysate was measured, 30\u20096\u2009cells/mL) were incubated with 20\u2009\u03bcL of phycoerythrin-conjugated anti-CD138 and anti-CD45, respectively  for 30\u2009min at 4\u00b0C, washed, and then fixed with 2% paraformaldehyde. Then, the samples were analyzed by FACSCalibur flow cytometer (Becton Dickinson) and inbuilt software.Cells  as a control experiment in our study. The used cell lines include U266, U266_SC, IM9, and RPMI8226 cell lines.We used commercially available cell lines which were bought from DSMZ  . Myelomaraploidy . Hence, As shown in When we evaluated the cytokine response in these cell lines, differential response was noted between the two cell lines. We used cytokine interleukin-6 (IL-6) and soluble IL-6 receptor (sIL-6R), which is well known to regulate the biologic behaviors of myeloma cells in the progression of multiple myeloma. It is well known that sIL-6R potentiates the IL-6 mediated signaling . Based oIn myeloma cells established from bone marrow of a mouse (SNU_MM1393_BM), phosphorylated form of Erk (p-ERK) was increased when treated with IL-6 only and combination of IL-6 and sIL-6R, but phosphorylated form of Akt (p-AKT) was not. On the other hand, in myeloma cells established from subcutaneous plasmacytoma (SNU_MM1393_SC), the induction of p-ERK was not as evident as in SNU_MM1393_BM. Increase in p-AKT was not evident in SNU_MM1393_SC either .Since IL-6 acts in an autocrine manner , we measP < 0.05). Meanwhile, SNU_MM1393_BM seemed to be more sensitive to panobinostat compared to SNU_MM1393_SC (P > 0.05). However, when compared to well-known myeloma cell lines (U266 and U266_SC), SNU_MM1393_SC and SNU_MM1393_BM were more resistant to bortezomib  (We tested the cytotoxic effect of proteasome inhibitor bortezomib and pan-HDAC inhibitor panobinostat in these two cell lines. Both SNU_MM1393_SC and SNU_MM1393_BM were more sensitive to panobinostat when compared to U266 (e <0.05) . ex vivo cultured cells via tail vein into NRG/SCID mouse. We used three kinds of cells: (1) ex vivo cultured myeloma cells of a patient's bone marrow, (2) cells from SNU_MM1393_BM, and (3) cells from SNU_MM1393_SC. When this was performed, we found tumor growth at both bone marrow and subcutis with ex vivo cultured myeloma cells. However, when cells from SNU_MM1393_BM were injected into a mouse, growth at subcutis was not noted. Only engraftment at bone marrow was observed. Lastly, when SNU_MM1393_SC cells were injected into a mouse, a mouse died in 7 weeks after injection. Tumor at subcutis was not noted at the time of death.To determine the tumorigenicity of these cells, we reinjected theseIt is known that the establishment of multiple myeloma cell line is notoriously difficult . Here, w in vivo and ex vivo. As an in vivo report, Yuan et al. [ ex vivo data, Balsas et al. [We observed several interesting findings during the establishment of these cell lines. First, there was a difference between patient's original myeloma cells and established cell lines. While patient's karyotype was a diploidy, karyotype of SNU_MM1393 was tetraploidy. This reflects a clonal evolution of a myeloma cell during cell line establishment. In fact, there are reports regarding the clonal evolution to near-tetraploidy bothn et al.  reporteds et al.  reporteds et al. . In a cos et al. . We thinFrom the above, we conjecture that aggressive myeloma cells might have been selected through cell line establishment using animal model. And, as expected, SNU_MM1393_SC had greater lethality than original myeloma cells of the patient. Mouse injected with SNU_MM1393_SC died within 7 weeks, while mouse injected with the original myeloma cells survived more than 14 weeks. One interesting finding in this experiment (regarding the tumorigenicity of established cell line) is that SNU_MM1393_BM was not as lethal as SNU_MM1393_SC. This coincides with the results that SNU_MM1393_SC showed more prominent resistance to bortezomib and SNU_MM1393_SC had more chromosome 13 loss which is a well-known negative prognostic marker in multiple myeloma . VariousSecond, there were differences between SNU_MM1393_BM and SNU_MM1393_SC in various aspects . In an eLastly, interesting findings were when drug response was investigated in these cell lines. Most importantly, newly established cell lines showed relative resistance to bortezomib compared to U266. This is an unexpected phenomenon, because the myeloma cell of our patient is bortezomib na\u00efve. Considering relative indolent clinical course of our patient, it is unusual that a myeloma clone of our patient is resistant to bortezomib. When used with dexamethasone, response rate of bortezomib in treatment-na\u00efve myeloma patients is 88%. Hence, we conjecture that this resistance to bortezomib shown in our newly established cell line would originate from clonal evolution during cell line establishment. We suggest that biologic behaviors of myeloma cells could be altered in the course of clonal expansion and this alteration would contribute to the chemotherapeutic resistance.Whether the above findings could be generalized to myeloma cells in bone marrow and plasmacytoma is questionable. Not many researches have been performed regarding the comparison of bone marrow myeloma cells and plasmacytoma, and we think our study results should be recognized as exploratory ones. Comparison with future researches focusing on plasmacytoma biology is necessary to adapt our findings to clinical practices. We are also willing to supply our established cell lines to other researchers for further studies.Two cell lines harboring different site tropisms established from a single patient showed differences in cytokine response. Our newly established cell lines could be used as a tool to understand the biology of multiple myeloma and its chemotherapeutic responses."}
+{"text": "Human decision making is rarely conducted in temporal isolation. It is often biased and affected by environmental variables, particularly prior selections. In this study, we used a task that simulates a real gambling process to explore the effect of the risky features of a previous selection on subsequent decision making. Compared with decision making after an advantageous risk-taking situation (Risk_Adv), that after a disadvantageous risk-taking situation (Risk_Disadv) is associated with a longer response time  and higher brain activations in the caudate and the dorsolateral prefrontal cortex (DLPFC). Compared with decisions after Risk_Adv, those after Risk_Disadv in loss trials are associated with higher brain activations in the left superior temporal gyrus (STG) and the precuneus. Brain activity and relevant RTs significantly correlated. Overall, people who experience disadvantageous risk-taking selections tend to focus on current decision making and engage cognitive endeavors in value evaluation and in the regulation of their risk-taking behaviors during decision making. Decision making requires the ability to select from competing actions that are associated with varying levels of risk and reward. Human decision making is rarely conducted in temporal isolation. Current choices are always affected by environmental variables and often evaluated depending on the outcomes preceded by choices  of prior selections. For example, participants who lose in a gamble are more risky than those who win  participated in this study. They provided written informed consent, which was approved by The Human Investigations Committee at Zhejiang Normal University. None of them reported current Axis I disorders as assessed, using structured psychiatric interviews (M.I.N.I.)  were presented on the computer screen  decision making after advantageous risk-taking trials (Risk_Adv); (2) decision making after disadvantageous risk-taking trials (Risk_DisAdv). Second, we further divided each of these conditions into two different ones, according to the outcomes of their previous selections: (1) decision making after advantageous/disadvantageous risk-taking and win trials (Risk_Adv/DisAdv_Win); (2) decision making after advantageous/disadvantageous risk-taking and loss trials (Risk_Adv/DisAdv_Loss).Participants who chose the same selections for more than 50 percent of all trials (they might have selection bias) or chose the same selections for more than 10 times (they might be lack of motivation to perform properly) were excluded from further analysis. Participants who had less than 10 trials in one of these four conditions were excluded from further analysis to keep the statistical power. In this study, we only focused on how the previous selections and its outcomes would affect current decision-making process.TR = 1700 ms, echo time (TE) = 3.93 ms, slice thickness = 1.0 mm, skip = 0 mm, flip angle = 15\u00b0, inversion time = 1100 ms, field of view (FOV) = 240 \u00d7 240 mm, in-plane resolution = 256 \u00d7 256). Functional MRI was performed on a 3T scanner (Siemens Trio) with a gradient-echo EPI T2 sensitive pulse sequence in 33 slices . Stimuli were presented via Invivo synchronous system  through a screen in the head coil, enabling participants to view the stimuli. A total of 630 volumes were acquired for each participant during the 1260 s of task performance.The image acquisition parameters have been described previously  as described previously  was applied to identify blood oxygen level dependence (BOLD) activation in relation to separating event types. The six head-movement parameters derived from the realignment stage were included as covariates of no interest. In addition, reward history , and response history (stay/switch to previous selections) were included as parameters in the model to eliminate their potential influence to the results. For these conditions, the duration is 4000 ms. There are 11 predictors in the model . Further analysis includes 4 interested conditions: decision making after Risk_DisAdv Win/lose; decision making after Risk_Adv Win/lose, and 9 other predictors as described above. All valid trials were included in the analysis. GLM was independently applied to each voxel to identify voxels that were significantly activated for the different events of each condition.p < 0.01, as also used for displaying purposes in the figures. We then tested these clusters for cluster-level FWE correction p < 0.01 and the AlphaSim estimation indicated that clusters with 42 contiguous voxels would achieve an effective FWE threshold p < 0.01. The smoothing kernel used during simulating false-positive (noise) maps with AlphaSim was 6.0 mm, and was estimated from the residual fields of the contrast maps entered into the one-sample t-test. The formula used to compute the smoothness was that used in FSL .Second level analysis treated inter-subject variability as a random effect. Primarily, we determined to take voxels to show a main effect in different conditions. Second, we tested for voxels that showed higher or lower activity in all contrasts of interest. We first identified clusters of contiguously significant voxels at an uncorrected threshold We first compared the brain activations between \u201cRisk_DisAdv\u201d and \u201cRisk_Adv\u201d and then took the surviving clusters as ROIs for further analyses. For each ROI, a representative beta value was obtained by averaging the signal of all the voxels within the ROI . We calculated correlations to support our hypothesis: correlations between the brain activity  in caudate in Risk_DisAdv/Risk_DisAdv_Win and relevant response time (RT); correlation between brain activity  in precuneus in Risk_DisAdv_Lose and relevant RT.t(21) = 2.530, p = 0.019, d = 0.73 than that after Risk_Adv]  = 7.076, p = 0.016, F = 2.474, p = 0.135, F = 0.243, p = 0.622]. The repeating rates (subjects selected the same risky feature as their previous selections) in Risk_DisAdv (0.35 \u00b1 0.13) were significantly lower than that in Risk_Adv (0.62 \u00b1 0.17) . The stay/switch rates after advantageous and win decisions are 78%: 22% [F = 6.86, p < 0.001, F = 4.69, p < 0.001, F = 1.22, p > 0.05, F = 5.04, p < 0.001, The decision-making after Risk_DisAdv showed significant longer response time   Figure . Furthert Figure . No inteCompared with the Risk_Adv, the Risk_DisAdv showed higher brain activation in the right caudate and right DLPFC Table . SignifiThe comparison between Risk_Adv_Win and Risk_DisAdv_Win showed great similarity to the comparison between Risk_Adv and Risk_DisAdv. During the process of Risk_DisAdv_Win, relative to Risk_Adv_Win, greater BOLD signal was observed in caudate, and right DLPFC Table . MarginaThe Risk_DisAdv_Lose, relative to Risk_Adv_Lose, showed increased BOLD signal in the left superior temporal gyrus, and right precuneus Table . SignifiUsing a task that simulates real-life gambling, we found that the risky features of previous selections can affect current decision making. These effects can be observed in behavioral and brain activities.The comparison between Risk_DisAdv and Risk_Adv shows that the risky features of previous decisions can affect current decision making. Neuroimaging results show that Risk_DisAdv is associated with high brain activation in the right caudate and the right DLPFC, which supports our hypothesis. Neuroimaging and anatomical studies show that the caudate is fundamental to the selection of behaviors on the basis of the changing values of goals and knowledge of which actions lead to what outcomes  and the precuneus. The STG is involved in the perception of negative emotions (Bigler et al., The right precuneus is another brain area that survived after the comparison between Risk_DisAdv_Loss and Risk_Adv_Loss. Precuneus activities reflect increased visual attention due to difficult task demands (Barber and Carter, We therefore conclude that people engage greater attention in their current decisions after Risk_DisAdv_Loss than after Risk_Adv_Loss.People engage greater attention in current decision making after Risk_DisAdv than after Risk_Adv. Moreover, people engage more cognitive endeavors in value evaluation and in the regulation of their risk-taking behaviors after Risk_DisAdv than after Risk_Adv.GD designed the research and wrote the manuscript, YZ and XL contributed in data collecting, data analyzing and figure preparing. JX contributed in manuscript preparing. XD contributed in data collecting and preprocessing.The funders had no role in study design, data collection, and analysis, decision to publish, or preparation of the manuscript. The contents of the manuscript do not necessarily reflect the views of the funding agencies.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest."}
+{"text": "Chromatin immunoprecipitation (ChIP) followed by next-generation sequencing (ChIP-Seq) has been widely used to identify genomic loci of transcription factor (TF) binding and histone modifications. ChIP-Seq data analysis involves multiple steps from read mapping and peak calling to data integration and interpretation. It remains challenging and time-consuming to process large amounts of ChIP-Seq data derived from different antibodies or experimental designs using the same approach. To address this challenge, there is a need for a comprehensive analysis pipeline with flexible settings to accelerate the utilization of this powerful technology in epigenetics research.de novo motif finding from transcription factor (TF) binding sites and functional annotation of peak associated genes.We have developed a highly integrative pipeline, termed HiChIP for systematic analysis of ChIP-Seq data. HiChIP incorporates several open source software packages selected based on internal assessments and published comparisons. It also includes a set of tools developed in-house. This workflow enables the analysis of both paired-end and single-end ChIP-Seq reads, with or without replicates for the characterization and annotation of both punctate and diffuse binding sites. The main functionality of HiChIP includes: (a) read quality checking; (b) read mapping and filtering; (c) peak calling and peak consistency analysis; and (d) result visualization. In addition, this pipeline contains modules for generating binding profiles over selected genomic features, HiChIP is a comprehensive analysis pipeline that can be configured to analyze ChIP-Seq data derived from varying antibodies and experiment designs. Using public ChIP-Seq data we demonstrate that HiChIP is a fast and reliable pipeline for processing large amounts of ChIP-Seq data.The online version of this article (doi:10.1186/1471-2105-15-280) contains supplementary material, which is available to authorized users. Chromatin immunoprecipitation (ChIP) coupled with next-generation sequencing (ChIP-Seq) represents a powerful approach to identify genome-wide occupancy of transcription factors (TFs) and histone tail modifications . The ENCChIP-Seq data processing starts with the mapping of short reads to a genome reference. The mapped reads  are then used to generate signal tracks in a variety of formats  for data visualization. They are further used to identify regions showing significant enrichment over a control library like an IgG control generated using a non-specific IgG antibody, or an input control without using an antibody . ChIP-SeThere are over thirty publicly available programs for peak calling . Most ofThe ENCODE consortium recommends that ChIP-Seq experiments have two biological replicates in order to assess data reliability. Based on the previous guideline, a ChIP-Seq experiment is considered to be reproducible if at least 75% of the peaks overlap between replicates; or top 40% of the peaks show >80% overlap . A methoSeveral packages have been developed for downstream analysis of identified peaks. The most common analyses include the assignment of peaks to gene bodies or gene regulatory domains ; the genA few pipelines have been developed to analyze ChIP-Seq data , 16\u201318. To address this shortfall, the Highly Integrative Chromatin Immunoprecipitation (HiChIP) pipeline provides comprehensive analysis of ChIP-Seq data. HiChIP has the following features: (a) the analysis of both paired-end and single-end data; (b) filtering of mapped reads based on duplicate level, mapping quality score, genomic uniqueness, insertion size and orientation (for paired-end reads only); (c) the selection of an appropriate peak finder based on binding profile, with MACS  for puncThis ChIP-Seq analysis pipeline has been developed by integrating public packages with internally developed tools. It is intended for research purposes only. The core functions include: (a) read quality assessment and read mapping; (b) filtering of mapped reads and estimation of library complexity; (c) peak calling and identification of highly consistent peaks between replicates; (d) signal intensity estimation, normalization and visualization; and (e) annotation of peaks and binding profiles Figure\u00a0.Figure 1http://bioinformaticstools.mayo.edu/. Website containing license agreements for each of the public tools is also provided in the user manual.Since researchers may not always have immediate access to cluster resources, this pipeline allows either parallel processing of a large number of samples in a cluster or serial processing of multiple samples on a single machine. Detailed instructions about how to run HiChIP pipeline and how to use individual tools are described in the user manual available at: To test HiChIP performance, we used five public datasets in human, including single-end ChIP-Seq datasets targeting TFs NFKB and ER and histone mark H3K27me3; a paired-end ChIP-Seq dataset targeting TF RUNX1; and an ER chip-chip dataset. Each of the ChIP-Seq datasets includes both IP and control.http://hgdownload.cse.ucsc.edu/goldenPath/hg18/encodeDCC/wgEncodeYaleChIPseq.The NFKB datasets are from cell lines GM12878 and GM12891; each with two replicates for both IP and control. The FASTQ sequence files were downloaded from: The ER ChIP-Seq datasets include 18 libraries from five cell lines . Each cell line had 2\u20133 replicates for IP and a single control. We downloaded the BWA aligned BAM files from National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) under accession GSE32222 .The RUNX1 dataset is from an acute myeloid leukemia patient with the t translocation . The FAShttp://hgdownload.cse.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeBroadHistone and these for MCF-7 were from: http://hgdownload.cse.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeSydhHistone.The H3K27me3 datasets are from cell lines GM12878, HeLa S3 and MCF-7. GM12878 had two replicates in IP and one control library, while the other two cell lines each had two replicates for both IP and control. The FASTQ sequence files for GM12878 and HeLa S3 were downloaded from: http://research4.dfci.harvard.edu/brownlab//datasets/index.php?dir=ER_MCF7_whole_human_genome/.The ER chip-chip data is generated from the MCF-7 cell line using the Affymetrix human tiling microarray. The dataset was downloaded from: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). For each sample, FastQC reports the distribution of average per-base and per-read quality, as well as the level of duplication and possible sources of contaminations. If there is indication of abnormality in mapping results, such as low mapping rate, user can review read quality in the FastQC reports and try to improve the mapping rate by trimming low-quality bases or adaptor sequences in the reads.FastQC is a fast and flexible package for checking overall sequence quality (http://www.novocraft.com/main/index.php) is slower than BWA but is known to have higher sensitivity [Several mapping software packages have been developed to map short reads to the reference genome . BWA is sitivity . To deciAfter initial alignment, the mapped reads need to be further processed in order to improve peak calling sensitivity and specificity. The post-processing steps below address the issues of poorly mapped reads, duplicate reads and reads mapping to multiple locations.It is a common practice to remove reads with low mapping quality. For single-end reads, HiChIP uses samtools  to filteFor ChIP experiments, the sequencing library is mostly generated from a much smaller amount of DNA compared to standard DNA or RNA sequencing. Duplicate reads that map to the same genomic location and strand are frequently present in ChIP-Seq datasets. For many applications, duplicate reads are removed as they are considered likely represent experimental artifacts. However, in the context of a ChIP-Seq experiment duplicate reads can also occur during the sequencing of identical DNA fragments in peak regions. In this case, duplicate reads contribute to peak identification and should not be removed.et al. reported that duplicate removal could improve the specificity of MACS peak calling [http://picard.sourceforge.net/) is included in HiChIP to remove duplicate reads by default. A user can specify whether to remove duplicate reads. To reflect the level of duplicate reads, HiChIP uses a custom script to measure library complexity as the ratio between number of duplicate-filtered reads and the total number of uniquely mapped reads. As a guideline, library complexity needs to reach ~0.8 at a sequencing depth of 10 million mapped reads [Chen  calling . Since ted reads . Low libhttp://hgdownload.cse.ucsc.edu/goldenPath/hg19/encodeDCC/), between 16% and 28% of the mapped reads have multiple matches in the genome. It has also been shown that some TF binding sites are located in regions with poor mappability [In ChIP-Seq analysis, reads mapping to multiple genomic locations are often discarded . Dependipability . In suchHiChIP allows the user to specify whether to filter out reads matching multiple locations. For single-end reads, only uniquely mapped reads are kept by default, with the option to include one random match for reads mapping to multiple locations. For paired-end reads, we developed an in-house script to filter out undesired pairs. Only mapped pairs with appropriate insertion sizes and correct orientation are kept. Depending on the user\u2019s specification, these reads are further processed to retain pairs belonging to one of the three types: (a) only uniquely mapped pairs; (b) pairs with at least one uniquely mapped end; or (c) pairs with at least one uniquely mapped end, plus a random match if both ends align to multiple locations. No currently available public tool provides equivalent flexibility in the filtering of mapped paired-end ChIP-Seq reads.There are two major ChIP-Seq binding profiles: punctate binding and diffuse binding. For punctate binding sites, peak calling identifies locations with maximum read density. For diffuse binding sites, the main goal is to define the boundary of individual binding domains. Therefore, different peak callers need to be used to take into account the differences in binding profile.We used MACS to identify punctuate binding sites because of its high specificity and sensitivity , 5, 10. et al. [The consistency of identified peaks can be assessed for punctate binding sites where replicates are available. In HiChIP, we implemented the IDR method to measure the consistency of peaks between replicates . To prepet al. . The meret al. [As suggested by Landt et al. , if a ChTo identify diffuse binding sites, HiChIP leverages a widely used program called SICER . SICER uAfter peak calling, potential cis-regulated genes associated with peaks are identified, which is based on the maximum distance of peaks to the transcriptional start sites (TSSs) or translational end sites (TESs). By default, this distance is set at 10 kilobases.To enable visual inspection of discovered binding sites and their association with annotated genes or other genomic features, HiChIP generates files that can be visualized in a genome browser like the Integrative Genomics Viewer (IGV) . CEAS neTo generate the bedGraph file, filtered reads in BAM format are first processed into bed format as follows. Single-end reads are extended by the average fragment length of the library (default 200\u00a0bp). For paired-end reads, the HiChIP pipeline keeps the first end and extends by the fragment length estimated from mapping positions of the two ends, rather than by the average fragment length of the library. Given the variability of fragment lengths across a complex genome like human genome, the use of actual coordinates of mapped pairs is expected to achieve better resolution in signal visualization. The bed file is then used to generate a bedGraph file by the genomeCoverageBed command from BEDTools .http://www.broadinstitute.org/software/igv/igvtools). The normalized coverage in TDF format and identified peaks in bed format can be visualized by uploading files to IGV, or by opening the provided igv_session.xml file in IGV.The Wig file is generated from the bedGraph file, using an in-house script that computes the extended read coverage at a user-defined step size (default: 20\u00a0bp). The extended read coverage is normalized to a library size of one million mapped reads, and converted into the TDF format using the toTDF command from the igvtools package  (for MACS peaks) or \u2013log10 (FDR) cutoff (for SICER peaks). Since the detection of binding motif(s) using MEME is dependent upon the set of DNA sequences provided, attention needs to be paid to the cutoff for peak selection. By default the top 10% of peaks with the largest \u2013log10  will be used. The HiChIP pipeline also allows the user to select a certain number of top peaks for motif discovery. CEAS uses normalized Wig files and peak files (bed format) as inputs, and performs binomial test for enrichment of binding over genomic regions such as gene promoters, gene bodies, exons and introns.HiChIP selects top peaks as input for CEAS and MEME. Peaks used by CEAS are selected based on the pre-defined \u2013loghttp://bejerano.stanford.edu/help/display/GREAT/Genes). For each gene annotated with at least one ontology term, the HiChIP pipeline first defines its regulatory domain as the region from upstream (U\u2009+\u2009UE) bp to downstream (D\u2009+\u2009DE) bp around the TSS, where the region from upstream U bp (default 5000) to downstream D bp (default 1000) represents the proximal regulatory domain, and UE and DE denote the maximum upstream and downstream extension, respectively. Binomial tests are then performed to identify a list of GO terms that are enriched in genes associated with peaks.An in-house method is implemented to identify GO terms that are enriched in peak-associated genes. This method uses a similar approach as GREAT  that couWe tested the pipeline performance on a Linux platform with an 8-core GenuineIntel CPU at 2.66\u00a0GHz. For a typical ChIP-Seq dataset containing a single IP and control library, each with 20\u201350 million pairs of reads, HiChIP takes 6\u201314 hours to complete at ~5-8 Gb memory usage.The summary report provides links to the FastQC output files and an igv_session.xml file for data visualization. It also contains an html document that covers sample information, mapping summary, library complexity . In addition, CEAS provides a report summarizing the percentage of peaks located in different regions such as promoters, gene upstream and downstream, UTRs, and provides plots showing binding profiles over selected genomic features  that had the most extra peaks  had a duplication rate between 42.8% to 57%, containing roughly half of the total duplicates  and largely associated with transcription repression. FASTQ files for six single-end H3K27me3 ChIP libraries and five control libraries were downloaded from the ENCODE project, mapped by BWA, and duplicates filtered by Picard  in MACS peak calling. When plotting the number of reproducible peaks over different IDR values, a clear transition was observed from highly reproducible peaks to poorly reproducible peaks  and a single control  Tables\u00a0 and 5. T and IP_2HiChIP is a comprehensive ChIP-Seq data analysis pipeline with more than 10 functions Programming language: Shell, Perl and ROther requirements:JAVA version 1.6.0_17 or higherPerl version 5.10.0 or higherPython version 2.7 or higherPython-devCython and Numpy python modulesR version 2.14.0 or higherFastQC version 0.10 or higherBWA version 0.5.9 or higherMACS version 2.0.10 or higherSICER version 1.1IGVTools version 2.3.16Samtools version 0.1.19MEME version 4.8.1CEAS version 1.0.2Picard version 1.97BEDTools version 2.17.0Table S2. Number of consistent peaks at different IDR values. ER ChIP-Seq datasets from MCF-7 cell line were used. Table S3. Summary of IDR analysis of ER ChIP-Seq data. Table S4. A snapshot of output from GO enrichment analysis. ER ChIP-Seq library IP_1  was used. Table S5. Duplicate level in six H3K27me3 ChIP-Seq libraries. (XLS 68 KB)Additional file 1: Table S1: Complexity of NFKB ChIP-Seq IP and control libraries. Snapshot of output from CEAS analysis. Reads mapping to chromosome 1 from libraries IP_1 and input were used [Additional file 2:ere used . Top panBWA versus Novoalign in mapping single-end reads. The 28-bp ChIP-Seq reads from eight libraries of TF NFKB were downloaded from UCSC . Reads were mapped to the human genome reference hg19 using BWA and Novoalign. BWA parameters are: bwa aln -o 1 -l 32 -t 4 -k 2 and bwa samse -n 10 -f; novoalign parameters are: Novoalign -r Random --hdrhd off -c 1 -d reference.nix -F STDFQ -f end1.fastq -o SAM. BWA mapping: libraries 1, 3, 5, 7, 9, 11, 13, and 15; Novoalign mapping: libraries 2, 4, 6, 8, 10, 12, 14, and 16. Numbers in parentheses represented number (in million) of total raw reads and uniquely mapped reads, respectively. For six of the eight libraries, BWA increased uniquely mapped reads by 3.2-4.8%. 1: GM12878_Input_IgG_rep1  2: GM12878_Input_IgG_rep1 . 3: GM12878_Input_IgG_rep2  4: GM12878_Input_IgG_rep2 . 5: GM12878_NFKB_IP_rep1  6: GM12878_NFKB_IP_rep1 . 7: GM12878_NFKB_IP_rep2  8: GM12878_NFKB_IP_rep2 . 9: GM12891_Input_IgG_rep1  10: GM12891_Input_IgG_rep1 . 11: GM12891_Input_IgG_rep2  12: GM12891_Input_IgG_rep2 . 13: GM12891_NFKB_IP_rep1  14: GM12891_NFKB_IP_rep1 . 15: GM12891_NFKB_IP_rep2  16: GM12891_NFKB_IP_rep2 . (PPT 152 KB)Additional file 3:"}
+{"text": "Xenopus, and contains two evolutionarily conserved sequences in the transmembrane domains (TMs) and the C-terminal region, named region A and region B, respectively. To elucidate the molecular nature of Nemp1, we analyzed its interacting proteins through those conserved regions. First, we found that Nemp1 interacts with itself and lamin through the TMs and region A, respectively. Colocalization of Nemp1 and lamin at the INM suggests that the interaction with lamin participates in the INM localization of Nemp1. Secondly, through yeast two-hybrid screening using region B as bait, we identified the small GTPase Ran as a probable Nemp1-binding partner. GST pulldown and co-immunoprecipitation assays using region B and Ran mutants revealed that region B binds directly to the GTP-bound Ran through its effector domain. Immunostaining experiments using transfected COS-7 cells revealed that full-length Nemp1 recruits Ran near the nuclear envelope, suggesting a role for Nemp1 in the accumulation of RanGTP at the nuclear periphery. At the neurula-to-tailbud stages of Xenopus embryos, nemp1 expression overlapped with ran in several regions including the eye vesicles. Co-knockdown using antisense morpholino oligos for nemp1 and ran caused reduction of cell densities and severe eye defects more strongly than either single knockdown alone, suggesting their functional interaction. Finally we show that Arabidopsis thaliana Nemp1-orthologous proteins interact with A. thaliana Ran, suggesting their evolutionally conserved physical and functional interactions possibly in basic cellular functions including nuclear transportation. Taken together, we conclude that Nemp1 represents a new type of RanGTP-binding protein.The inner nuclear membrane (INM) protein Nemp1/TMEM194A has previously been suggested to be involved in eye development in  The nuclear envelope (NE) is not only the boundary that separates the nuclear and cytoplasmic compartments of eukaryotic cells but it also plays regulatory roles in chromatin organization and gene expression through its nucleoplasmic surface . The NE INM proteins such as Emerin and MAN1 have been shown to bind to lamins and hence reside on the INM . In termNPCs mediate the bidirectional transport of proteins and RNAs across the NE. Nuclear transport proteins, such as importin \u03b2/karyopherin \u03b2, exportin 1/Crm1, and the small GTPase Ran facilitate the transport of proteins through NPCs . Ran exiXenopus . To. To10]. The INM proteins MAN1 and Emerin have been shown to be associated with each other in vitro . TherefoXenopus embryos [Xenopus embryo and also in vitro, we performed co-IP and GST pulldown assays using HA- or Myc-tagged proteins. In parallel, we also tested whether Nemp1 binds to either RanGTP or RanGDP using the GTP- and GDP-bound mutant forms RanQ69L and RanT24N, respectively. For co-IP analysis, Xenopus embryos were coinjected with mRNAs encoding for Mm_Bt-HA and Myc-Mm_Ran constructs. As shown in Because the previous study has shown that region B faces the nucleoplasm and is required for the eye-reducing activity of Nemp1 in  embryos , we searWe then examined the region of RanGTP that binds to region B. RanGTP is known to bind to both importin \u03b2 and RanBP1. These interactions are disrupted in Mm_RanT42A, which has a point mutation in its effector domain . In addiTo determine a minimal Ran-binding region within the Bt region, we next performed co-IP experiments using HA-tagged deletion constructs of Mm_Bt, which were stabilized by fusing to EGFP. Interactions with Myc-Mm_Ran were detected with Bt and B but not with Ba, Bb, or Bt2 constructs . FurtherTo assess the interaction between Nemp1 and Ran, we used full-length Nemp1 to perform co-IP and confocal microscopic analyses. As shown in Xenopus development when cells are actively divided. Western blotting analysis of embryonic lysates containing the phosphatase inhibitor NaF revealed that shifted bands were strongly detected at the blastula stage (stage 9), and the intensity of these bands was reduced at neurula-to-tailbud stages , suggesting that the modification of Nemp1 occurs in proliferating cells , nemp1MOs [ranMO. We designed ranMO to be complementary to the sequence encompassing the translation start sites of both X. laevis homoeologs of ran. We confirmed that ranMO specifically inhibited protein synthesis from Xl_Ran-Myc mRNA containing the MO target sequence but not from Myc-Xl_Ran mRNA without the target  (nemp1MOs (10 or 20 ng/embryo) but not stdMO or ranMO significantly reduced cell densities  . Injectiensities  and was ensities . Co-injeockdowns . The redmp1 mRNA  and the an mRNAs . These densities , suggestantibody . Thus, iNematostella vectensis (sea anemone) but also in a plant, Arabidopsis thaliana . As shown in Database search and phylogenetic analysis revealed that Nemp proteins exist not only in eumetazoans including thaliana . To examA previous study has shown that the signal peptide and TMs are necessary and sufficient for Nemp1 to localize at the NE . The locWe have found that Nemp1 specifically interacts with RanGTP via region B Figs  and 4. RC. elegans and Arabidopsis that endogenous Ran localizes at the NE during interphase [What is the function of Nemp1? We have shown that the coexpression of Mm_Nemp1 promotes the accumulation of Ran at the nuclear envelope (NE) in COS-7 cells . Based oterphase ,30. The Some nuclear lamina proteins (lamins and INM proteins) are reported to be phosphorylated in the prophase of mitosis, resulting in their dysfunction during the NE breakdown. For example, lamin filaments are depolymerized upon phosphorylation of lamin B proteins ,32, and nemp1 and ran elicits reduction of cell density and eye defects more significantly than the individual knockdown for nemp1, supporting their functional interaction.A previous study revealed that Nemp1 interacts with BAF through the BBS and that the BBS is required for the eye-reducing activity of overexpressed Nemp1 . Recentlnemp1 reduced the expression of early eye marker genes, rax and pax6 [nemp1 caused the reduction of cell densities  is a deletion mutant of KRa. Lower panels, subcellular localization of GST-mRFP fusion constructs. COS-7 cells were transfected with HA-tagged GST-mRFP fusion constructs as indicated, fixed, and stained with anti-HA antibody (red) and SYTOX Green for DNA. Scale bars, 5 \u03bcm. (B) Subcellular localization of Mm_Bt and its GST-mRFP- HA construct. COS-7 cells were transfected with the HA-tagged mouse Bt construct (Mm_Bt-HA) or GST-mRFP-Mm_Bt-HA, fixed, and stained with anti-HA antibody (red) and SYTOX Green for DNA. GST-mRFP-Mm_Bt-HA exhibited cytoplasmic localization, but also nuclear localization in some cases. Scale bars, 5 \u03bcm. We have previously shown that Myc-tagged Xl_Ct and KR constructs but not Xl_Bt  fusion did not, indicating that both KRa and KRb sequences function as NLSs  and that the first Arg residue of the RKIKXKRAK (X is R or L) motif is required for this activity. We also analyzed a short sequence from Mm_Nemp1, whose position corresponds to that of KR in Xl_Nemp1, named KRm, though KRm does not contain a canonical NLS sequence. As expected, KRm did not elicit NLS function . However, although Mm_Bt does not have a canonical NLS sequence either, HA-tagged Mm_Bt exhibited nuclear localization . Therefore, we analyzed NLS function of Mm_Bt suing GST-mRFP-HA, and observed that GST-mRFP-Mm_Bt-HA exhibited weak nuclear localization , and, in a few cases, it was exclusively localized to the nucleus (lower panels), suggesting that Mm_Bt could have NLS function. Thus, we conclude that the C terminal region of Nemp1 proteins  exhibits NLS function.(A) Subcellular localization of GST-mRFP fusion constructs for the l_Bt see  is local(TIF)Click here for additional data file.S2 FigThese deletion constructs were used in (TIF)Click here for additional data file.S3 FigXenopus embryos with mRNA for a Myc-tagged construct of Mm_Ran (WT) or its mutants . Injected embryos were collected at the mid blastula stage (stages 8\u20138.5) and lysed with lysis buffer B for Co-IP. Black arrowheads, modified forms of Nemp1-HA. Note that WT Nemp1 has two major modified bands (lane 2) and co-expression with Ran enhanced these modifications (lane 3). Also note that the upper modified band disappeared in 5SA and 5SE constructs , suggesting that all or some of these five serine residues are involved in modification (phosphorylation) by functioning as either phosphorylation sites or recognition sites or both, and that there are other phosphorylation sites besides there five serine residues.This is the original data for Figs (TIF)Click here for additional data file.S4 FigArabidopsis thaliana; Dm, Drosophila melanogaster; Mb, Monosiga brevicollis (choanoflagellate); Mm, Mus musculus; Xl, Xenopus laevis.Only region A (red box) and region B (green box) were aligned for Mm_Nemp1, Mm_Nemp2, Xl_Nemp1b, Dm_Nemp, At_Nemp-A, At_Nemp-B, At_Nemp-C, and Mb_Nemp. Dots, identical amino acid residues; hyphens, gaps; dashed line, DUF2215 domain. The KR sequence and BAF binding sites are colored in yellow as indicated. Blue boxes indicates phosphorylation sites in Mm_Nemp1 and the corresponding serine residues in other species. The serine residues corresponding to Ser-366, Ser-376, and Ser380  in Mm_Nemp1 are conserved in Xl_Nemp1. BAF binding sites containing Ser380 are conserved in vertebrate Nemp1 and Nemp2, but not in others. At, (TIF)Click here for additional data file.S5 Figran-a, b mRNAs around the initiation codon (underlined), and ranMO (upper panel). Western blot analysis of Myc-tagged Xl_Ran fusion protein (lower panel). ranMO or stdMO (60 ng) was injected into both blastomeres of two cell stage embryos, and followed by injection with either 200 pg of Xl_Ran-Myc or Myc-Xl_Ran mRNA.-, embryos injected with mRNA alone.Nucleotide sequences of Xl_(TIF)Click here for additional data file.S6 FigP<0.05; ***, P <0.005; error bars, standard deviation.Combinations of injected MOs and mRNAs as well as amounts of MO (ng/embryo) and mRNA (pg/embryo) are as indicated. Experiment conditions are the same as in (TIF)Click here for additional data file.S7 FigArabidopsis Nemp homologs  serve as outgroups. Note that Nemp is evolutionary conserved from metazoans to choanoflagellates to plants, mainly in the terminal part of region A , Florida lancelet Branchiostoma floridae (Bf), nematode Caenorhabditis elegans (Ce), ascidian Ciona intestinalis (Ci), Drosophila melanogaster (Dm), zebrafish Danio rerio (Dr), chick Gallus gallus (Gg), human Homo sapiens (Hs), choanoflagellate Monosiga brevicollis (Mb), mouse Mus musculus (Mm), sea anemone Nematostella vectensis (Nv), African clawed frog Xenopus laevis (Xl), and western clawed frog Xenopus tropicalis (Xt). Accession numbers of amino acid sequences: Hs_Nemp1, O14524; Mm_Nemp1, Q6ZQE4; Gg_Nemp1, XM_001232566; Xl_Nemp1a, NP_001090391; Xl_Nemp1b, NP_001091224; Xt_Nemp1, NP_001034832; Dr_Nemp1, XP_683418; Hs_Nemp2, A6NFY4; Mm_Nemp2, Q8CB65; Gg_Nemp2, Q5ZJY9; Dr_Nemp2, XP_693037; Bf_Nemp, XP_002585718; Ci_Nemp, AK116477; Sk_Nemp, XP_002741981; Sp_Nemp, XP_001196379; Dm_Nemp, NP_573142; Ce_Nemp, NP_497202; Nv_Nemp, XP_001640959; At_Nemp-A, NM_102639; At_Nemp-B, NM_001037091; At_Nemp-C, NM_114844; Mb_Nemp, XP_001742508. B. Conserved synteny of vertebrate nemp2 genes. A boat-shape object represents a gene with a direction, in which the tip of boat corresponds to the 3\u2019 end of the gene. Genes indicated with a same color mean orthologous genes, in which white boats indicates unrelated genes. Black boats indicate nemp2. Black circles indicate the ends of chromosomes or scaffolds. These maps are drawn based on JGI Metazome data, with some manual editing and corrections. The corresponding synteny maps of X. laevis (ver. 7.1) and X. tropicalis (ver. 7.1) suggest that Xenopus species do not have nemp2 orthologs. In addition, EST databases for X. laevis and X. tropicalis do not contain nemp2-like sequences. C. Diagram of Arabidopsis Nemp-A,-B, and-C proteins. According to the Arabidopsis genome sequence, typical signal peptide (SP) sequences were not detected in At_Nemp-B and At_Nemp-C. At_Nemp-C is predicted to contain six TMs, but the last two TMs may be a single TM. Colored boxes: blue, signal peptides (SP); magenta, transmembrane domains (TMs); yellow, region B.A. Phylogenetic analysis. A phylogenetic tree was constructed by the Maximum Likelihood (ML) method using Treefinder with the protein matrix LG after amino acid sequences of the DUF2215 domain in various organisms were aligned using the ClustalW alignment tool with the Gonnet series protein weight matrix see  and trimon A see . In vert(TIF)Click here for additional data file.S1 Table(TIF)Click here for additional data file.S2 Table(TIF)Click here for additional data file."}
+{"text": "High-throughput sequencing of PCR-amplified taxonomic markers (like the 16S rRNA gene) has enabled a new level of analysis of complex bacterial communities known as microbiomes. Many tools exist to quantify and compare abundance levels or OTU composition of communities in different conditions. The sequencing reads have to be denoised and assigned to the closest taxa from a reference database. Common approaches use a notion of 97% similarity and normalize the data by subsampling to equalize library sizes. In this paper, we show that statistical models allow more accurate abundance estimates. By providing a complete workflow in R, we enable the user to do sophisticated downstream statistical analyses, whether parametric or nonparametric. We provide examples of using the R packages dada2, phyloseq, DESeq2, ggplot2 and vegan to filter, visualize and test microbiome data. We also provide examples of supervised analyses using random forests and nonparametric testing using community networks and the ggnetwork package. Bacteria can now be identified through the use of next generation sequencing applied at several levels. Shotgun sequencing of all bacteria in a sample delivers knowledge of all the genes present. Here we will only be interested in the identification and quantification of individual taxa (or species) through a \u2018fingerprint gene\u2019 called 16s rRNA which is present in all bacteria. This gene presents several variable regions which can be used to identify the different taxa.The3. These approaches do not incorporate all the data, in particular sequence quality information and statistical information available on the reads were not incorporated into the assignments.Previous standard workflows depended on clustering all 16s rRNA sequences (generated by next generation amplicon sequencing) that occur within a 97% radius of similarity and then assigning these to \u2018OTUs\u2019 from reference treesde novo read counts used here will be constructed through the incorporation of both the quality scores and sequence frequencies in a probabilistic noise model for nucleotide transitions. For more details on the algorithmic implementation of this step seeIn contrast, thephangorn.After filtering the sequences and removing the chimer\u00e6, the data are compared to a standard database of bacteria and labeled. In this workflow, we have used the labeled sequences to build a de novo phylogenetic with theThe key step in the sequence analysis is the manner in which reads are denoised and assembled into groups we have chosen to call RSVs  instead of the traditional OTUs .This article describes a computational workflow for performing denoising, filtering, data transformations, visualization, supervised learning analyses, community network tests, hierarchical testing and linear models. We provide all the code and give several examples of different types of analyses and use-cases. There are often many different objectives in experiments involving microbiome data and we will only give a flavor for what could be possible once the data has been imported into R.In addition, the code can be easily adapted to accommodate batch effects, covariates and multiple experimental factors.5. We provide all steps necessary from the denoising and identification of the reads input as raw sequences infastq files to the comparative testing and multivariate analyses of the samples and analyses of the abundances according to multiple available covariates.The workflow is based on software packages from the open-source Bioconductor projectThis section demonstrates the \u201cfull stack\u201d of amplicon bioinformatics: construction of the sample-by-sequence feature table from the raw reads, assignment of taxonomy, and creation of a phylogenetic tree relating the sample sequences.First we load the necessary packages.library                        (                        \"knitr\"                        )                        library                        (                        \"BiocStyle\"                        )                        opts_chunk                        $                        set                                                read_chunk                        )                                            .cran_packages                         <- c                        .bioc_packages                         <- c                                                                    .inst                         <-                         .cran_packages                         %in%                         installed.packages                                                if                        (                        any                        (                        !                        .inst)) {                           install.packages                        (.cran_packages[                        !                        .inst])}                                            .inst                         <-                         .bioc_packages                         %in%                         installed.packages                                                if                        (                        any                        (                        !                        .inst)) {                           source                        (                        \"http://bioconductor.org/biocLite.R\"                        )                           biocLite                        }                                            # Load packages into session, and print package version                        sapply                        , require,                         character.only                         =                         TRUE                        )                                            set.seed                        (                        100                        )                                            6. These 360 fecal samples were collected from 12 mice longitudinally over the first year of life, to investigate the development and stabilization of the murine microbiome7. These data are downloaded from the following location:http://www.mothur.org/MiSeqDevelopmentData/StabilityNoMetaG.tar.The data we will analyze here are highly-overlapping Illumina Miseq 2\u00d7250 amplicon sequences from the V4 region of the 16S genemiseq_path                         <- file.path                        filt_path                         <- file.path                                                                    if) {                          dir.create                        (miseq_path)                          download.file                        )                          system                        ,                                         \" -C \"                        , miseq_path,                         \"/\"                        ))}                                            fns                         <- sort                        )fnFs                         <-                         fns[                        grepl                        ]fnRs                         <-                         fns[                        grepl                        ]                                            We begin by filtering out low-quality sequencing reads and trimming the reads to a consistent length. While generally recommended filtering and trimming parameters serve as a starting point, no two datasets are identical and therefore it is always worth inspecting the quality of the data before proceeding.ii                         <- sample                        (                        length                        (fnFs),                         3                        )                        for                        (i                         in                         ii) {                         print                         +                         ggtitle                        (                        \"Fwd\"                        )) }                        for                        (i                         in                         ii) {                         print                         +                         ggtitle                        (                        \"Rev\"                        )) }                                            Most Illumina sequencing data shows a trend of decreasing average quality towards the end of sequencing reads.Here, the forward reads maintain high quality throughout, while the quality of the reverse reads drops significantly at about position 160. Therefore, we choose to truncate the forward reads at position 245, and the reverse reads at position 160. We also choose to trim the first 10 nucleotides of each read based on empirical observations across many Illumina datasets that these base positions are particularly likely to contain pathological errors.8. Trimming and filtering is performed on paired reads jointly, i.e. both reads must pass the filter for the pair to pass.We combine these trimming parameters with standard filtering parameters, the most important being the enforcement of a maximum of 2 expected errors per-readif)                         dir.create                        (filt_path)                        filtFs                         <- file.path                        )filtRs                         <- file.path                        )                        for                        ) {                          fastqPairedFilter                        ,\t\t                              c                        ,                                              trimLeft                        =                        10                        ,                         truncLen                        =                        c                        ,                                              maxN                        =                        0                        ,                         maxEE                        =                        2                        ,                         truncQ                        =                        2                        ,                                              compress                        =                        TRUE                        )}                                            4.After filtering, the typical amplicon bioinformatics workflow clusters sequencing reads into operational taxonomic units (OTUs): groups of sequencing reads that differ by less than a fixed dissimilarity threshhold. Here we instead use the high-resolution DADA2 method to infer ribosomal sequence variants (RSVs) exactly, without imposing any arbitrary threshhold, and thereby resolving variants that differ by as little as one nucleotidederep-class objects by their sample name.The sequence data is imported into R from demultiplexed fastq files (i.e. one fastq for each sample) and simultaneously dereplicated to remove redundancy. We name the resultingderepFs                         <- derepFastq                        (filtsFs)derepRs                         <- derepFastq                        (filtsRs)sam.names                         <- sapply                        (                        strsplit                        (                        basename                        (filtsFs),                         \"_\"                        ), `)                                            Notably, chimeras have not yet been removed. The error model in the sequence inference algorithm does not include a chimera component, and therefore we expect this sequence table to include many chimeric sequences. We now remove chimeric sequences by comparing each inferred sequence to the others in the table, and removing those that can be reproduced by stitching together two more abundant sequences.seqtab                         <- removeBimeraDenovo                                                                    Although exact numbers vary substantially by experimental condition, it is typical that chimeras comprise a substantial fraction of inferred sequence variants, but only a small fraction of all reads. That is what is observed here: 1503 of 1892 sequence variants were chimeric, but these only represented 10% of all reads.dada2 package implements the naive Bayesian classifier method for this purpose9. This classifier compares sequence variants to a training set of classified sequences, and here we use the RDP v14 training set10.One of the benefits of using well-classified marker loci like the 16S rRNA gene is the ability to taxonomically classify the sequence variants. Theref_fasta                         <-                         \"data/rdp_train_set_14.fa.gz\"                        taxtab                         <- assignTaxonomy                                                colnames                        (taxtab)                         <- c                                                                    fasta files formatted for theassignTaxonomy function are also available for download athttps://www.dropbox.com/sh/mfcivbudmc21cqt/AAB1l-AUM5uKvjrR33ct-cTXa?dl=0.GreenGenes and Silva training setDECIPHER R package11.Phylogenetic relatedness is commonly used to inform downstream analyses, especially the calculation of phylogeny-aware distances between microbial communities. The DADA2 sequence inference method is reference-free, so we must construct the phylogenetic tree relating the inferred sequence variants de novo. We begin by performing a multiple-alignment using theseqs                         <- getSequences                        (seqtab)                        names                        (seqs)                         <-                         seqs                         # This propagates to the tip labels of the tree                        alignment                         <- AlignSeqs                        (                        DNAStringSet                        (seqs)                        ,                         anchor=                        NA                        )                                            ## Determining distance matrix based on shared 5-mers:#### Clustering into groups by similarity:#### Aligning Sequences:#### Determining distance matrix based on alignment:#### Reclustering into groups by similarity:#### Realigning Sequences:#### Refining the alignment:                                            phangorn R package is then used to construct a phylogenetic tree. Here we first construct a neighbor-joining tree, and then fit a GTR+G+I  maximum likelihood tree using the neighbor-joining tree as a starting point.Thephang.align                         <- phyDat                        ,                         type                        =                        \"DNA\"                        )                        dm                         <- dist.ml                        treeNJ                         <- NJ                        (dm)                         # Note, tip order != sequence order                        fit                         = pml                                                ## negative edges length changed to 0!                        fitGTR                         <- update                        fitGTR                         <- optim.pml                        )                        detach                                                                    phyloseq package organizes and synthesizes the different data types from a typical amplicon sequencing experiment into a single data object that can be easily manipulated. The last bit of information needed is the sample data contained in a.csv file.Themimarks_path                         <-                         \"data/MIMARKS_Data_combined.csv\"                        samdf                         <- read.csv                        samdf                        $                        SampleID                         <- paste0                        ,                         \"D\"                        , samdf                        $                        age-                        21                        )samdf                         <-                         samdf                         # Remove dupicate entries for reverse reads                        rownames                        (seqtab)                         <- gsub                        )                         # Fixing an odd discrepancy                        all                        (                        rownames                        (seqtab)                         %in%                         samdf                        $                        SampleID)                         # TRUE                        ## [1] TRUE                        rownames                        (samdf)                         <-                         samdf                        $                        SampleIDkeep.cols                         <- c                                                 samdf                         <-                         samdf                                            The full suite of data for this study \u2013 the sample-by-sequence feature table, the sample metadata, the sequence taxonomies, and the phylogenetic tree \u2013 can now be combined into a single object.ps                         <- phyloseq                        (                        tax_table                        (taxtab),                         sample_data                        (samdf),                                         otu_table                        ,                        phy_tree                        (fitGTR                        $                        tree))                                            phyloseq12 is an R package to import, store, analyze, and graphically display complex phylogenetic sequencing data that has already been clustered into Operational Taxonomic Units (OTUs) or more appropriately denoised, and it is most useful when there is also associated sample data, phylogeny, and/or taxonomic assignment of each taxa.phyloseq leverages and builds upon many of the tools available in R for ecology and phylogenetic analysis , while also using advanced/flexible graphic systems (ggplot216) to easily produce publication-quality graphics of complex phylogenetic data. Thephyloseq package uses a specialized system of S4 data classes to store all related phylogenetic sequencing data as a single, self-consistent, self-describing experiment-level object, making it easier to share data and reproduce analyses. In general, phyloseq seeks to facilitate the use of R for efficient interactive and reproducible analysis of amplicon count data jointly with important sample covariates.phyloseq home page is a good place to begin browsing additional phyloseq documentation, as are the three vignettes included within the package, and linked directly atthe phyloseq release page on Bioconductor.This tutorial shows a useful example workflow, but many more analyses are available to you in phyloseq, and R in general, than can fit in a single workflow. Theimport_biom function to read recent QIIME format files, older files can still be imported withimport_qiime. More complete details can be found on thephyloseq FAQ page.Many use cases result in the need to import and combine different data into a phyloseq class object, this can be done using thedada2 sequence processing were organized into a phyloseq object. This object was also saved in R-native serialized RDS format. We will re-load this here for completeness as the initial objectp0.In the previous section the results oflibrary                        (                        \"phyloseq\"                        )                        library                        (                        \"gridExtra\"                        )ps                         = readRDS(                        \"data/ps.rds\"                        )ps## phyloseq-class experiment-level object## otu_table   OTU Table:         [ 389 taxa and 360 samples ]## sample_data Sample Data:       [ 360 samples by 14 sample variables ]## tax_table   Taxonomy Table:    [ 389 taxa by 6 taxonomic ranks ]## phy_tree    Phylogenetic Tree: [ 389 tips and 387 internal nodes ]                                            Shiny-phyloseq17 is an interactive web application that provides a graphical user interface to the phyloseq package. The object just loaded into the R session in this workflow is suitable for this graphical interaction with Shiny-phyloseq.It can be beneficial to start the data exploration process interactively, this often saves time in detecting outliers and specific features of the data.phyloseq provides useful tools for filtering, subsetting, and agglomerating taxa \u2013 a task that is often appropriate or even necessary for effective analysis of microbiome count data. In this subsection, we graphically explore the prevalence of taxa in the example dataset, and demonstrate how this can be used as a filtering criteria. One of the reasons to filter in this way is to avoid spending much time analyzing taxa that were seen only rarely among samples. This also turns out to be a useful filter of noise , a step that should probably be considered essential for datasets constructed via heuristic OTU-clustering methods, which are notoriously prone to generating spurious taxa.In many biological settings, the set of all organisms from all samples are well-represented in the available taxonomic reference database. When (and only when) this is the case, it is reasonable or even advisable to filter taxonomic features for which a high-rank taxonomy could not be assigned. Such ambiguous features in this setting are almost always sequence artifacts that don\u2019t exist in nature. It should be obvious that such a filter is not appropriate for samples from poorly characterized or novel specimens, at least until the possibility of taxonomic novelty can be satisfactorily rejected. Phylum is a useful taxonomic rank to consider using for this purpose, but others may work effectively for your data.To begin, create a table of read counts for each Phylum present in the dataset.# Show available ranks in the dataset                        rank_names                        (ps)                                            ## [1] \"Kingdom\" \"Phylum\" \"Class\" \"Order\" \"Family\" \"Genus\"                                            # Create table, number of features for each phyla                        table                        (                        tax_table                        (ps),                         exclude                         =                         NULL                        )                                            ##                        ##              Actinobacteria              Bacteroidetes                        ##                          13                         23                        ## Candidatus_Saccharibacteria  Cyanobacteria/Chloroplast                        ##                           1                          4                        ##         Deinococcus-Thermus                 Firmicutes                        ##                           1                        327                        ##                Fusobacteria             Proteobacteria                        ##                           1                         11                        ##                 Tenericutes            Verrucomicrobia                        ##                           1                          1                        ##                        <NA>                        ##                           6                                            This shows a few phyla for which only one feature was observed. Those may be worth filtering, and we\u2019ll check that next. First, notice that in this case, six features were annotated with a Phylum of NA. These features are probably artifacts in a dataset like this, and should be removed.The following ensures that features with ambiguous phylum annotation are also removed. Note the flexibility in defining strings that should be considered ambiguous annotation.ps0                         <-                         subset_taxa                                                 & !                        Phylum                         %in%                         c                        )                                            prevalence in the dataset, which we will define here as the number of samples in which a taxa appears at least once.A useful next step is to explore feature# Compute prevalence of each feature, store as data.frame                        prevdf                         =                         apply                        (                        X                         =                         otu_table                        (ps0),                                         MARGIN                         =                         ifelse                        (                        taxa_are_rows                        (ps0),                         yes                         =                         1                        ,                         no                         =                         2                        ),                                         FUN =                         function                        (                        x                        ){                        sum                        (x >                         0                        )})                        # Add taxonomy and total read counts to this data.frame                        prevdf                         = data.frame                        ,                                              tax_table                        (ps0))                                            Are there phyla that are comprised of mostly low-prevalence features? Compute the total and average prevalences of the features in each phylum.plyr::                        ddply                        {                        cbind                        ,                        sum                        )})                                            ##                          Phylum       1      2## 1                Actinobacteria   120.2   1562## 2                 Bacteroidetes   265.5   6107## 3   Candidatus_Saccharibacteria   280.0    280## 4     Cyanobacteria/Chloroplast    64.2    257## 5           Deinococcus-Thermus    52.0     52## 6                    Firmicutes   179.2  58614## 7                  Fusobacteria     2.0      2## 8                Proteobacteria    59.1    650## 9                   Tenericutes   234.0    234## 10              Verrucomicrobia   104.0    104                                            Deinococcus-Thermus appeared in just over one percent of samples, and Fusobacteria appeared in just 2 samples total. In some cases it might be worthwhile to explore these two phyla in more detail despite this (though probably not Fusobacteria\u2019s two samples). For the purposes of this example, though, they will be filtered from the dataset.# Define phyla to filter                        filterPhyla                         = c                                                # Filter entries with unidentified Phylum.                        ps1                         = subset_taxa                        ps1## phyloseq-class experiment-level object## otu_table   OTU Table:         [ 381 taxa and 360 samples ]## sample_data Sample Data:       [ 360 samples by 14 sample variables ]## tax_table   Taxonomy Table:    [ 381 taxa by 6 taxonomic ranks ]## phy_tree    Phylogenetic Tree: [ 381 tips and 379 internal nodes ]                                            supervised, because they relied on prior information that is external to this experiment (a taxonomic reference database). This next filtering step is completelyunsupervised, relying only on the data in this experiment, and a parameter that we will choose after exploring the data. Thus, this filtering step can be applied even in settings where taxonomic annotation is unavailable or unreliable.The previous filtering steps are consideredFirst, explore the relationship of prevalence and total read count for each feature. Sometimes this reveals outliers that should probably be removed, and also provides insight into the ranges of either feature that might be useful. This aspect depends quite a lot on the experimental design and goals of the downstream inference, so keep these in mind. It may even be the case that different types of downstream inference require different choices here. There is no reason to expect ahead of time that a single filtering workflow is appropriate for all analysis.# Subset to the remaining phyla                        prevdf1                         =                         subset                        )                        ggplot                        ,                        color                        =Phylum)) +                          # Include a guess for parameter                          geom_hline                         +                         geom_point                         +                          scale_x_log10                         +                          xlab                         +                         ylab                         +                          facet_wrap                        (~Phylum) +                         theme                        (                        legend.position                        =                        \"none\"                        )                                            Sometimes a natural separation in the dataset reveals itself, or at least, a conservative choice that is in a stable region for which small changes to the choice would have minor or no effect on the biological interpreation (stability). Here no natural separation is immediately evident, but it looks like we might reasonably define a prevalence threshold in a range of zero to 10 percent or so. Take care that this choice does not introduce bias into a downstream analysis of association of differential abundance.The following uses five percent of all samples as the prevalence threshold.#  Define prevalence threshold as 5% of total samples                        prevalenceThreshold                         =                         0.05                         *                         nsamples                        (ps0)prevalenceThreshold## [1] 18                        # Execute prevalence filter, using `prune_taxa` function                        keepTaxa                         = rownames                        (prevdf1)                        ps2                         = prune_taxa                                                                    merge_taxa function in phyloseq. That kind of exquisite functional data is usually not available, and different pairs of microbes will have different sets of overlapping functions, complicating the matter of defining appropriate grouping criteria.When there is known to be a lot of species or sub-species functional redundancy in a microbial community, it might be useful to agglomerate the data features corresponding to closely related taxa. Ideally we would know the functional redundancies perfectly ahead of time, in which case we would agglomerate taxa using those defined relationships and theWhile not necessarily the most useful or functionally-accurate criteria for grouping microbial features (sometimes far from accurate), taxonomic agglomeration has the advantage of being much easier to define ahead of time. This is because taxonomies are usually defined with a comparatively simple tree-like graph structure that has a fixed number of internal nodes, called \u201cranks\u201d. This structure is simple enough for the phyloseq package to represent taxonomies as table of taxonomy labels. Taxonomic agglomeration groups all the \u201cleaves\u201d in the hierarchy that descend from the user-prescribed agglomerating rank, this is sometimes called \u2018glomming\u2019.The following example code shows how one would combine all features that descend from the same genus.# How many genera would be present after filtering?                        length                        )## [1] 49ps3                         = tax_glom                                                                    If taxonomy is not available or not reliable, tree-based agglomeration is a \"taxonomy-free\" alternative to combine data features corresponding to closely-related taxa. In this case, rather than taxonomic rank, the user specifies a tree height corresponding to the phylogenetic distance between features that should define their grouping. This is very similar to \u201cOTU Clustering\u201d, except that in many OTU Clustering algorithms the sequence distance being used does not have the same (or any) evolutionary definition.h1                         =                         0.4                        ps4                         = tip_glom                                                                    plot_tree function compare the original unfiltered data, the tree after taxonoic agglomeration, and the tree after phylogenetic agglomeration. These are stored as separate plot objects, then rendered together in one combined graphic usinggridExtra::grid.arrange.Here phyloseq\u2019smultiPlotTitleTextSize                         =                         8                        p2tree                         = plot_tree                                                 +                          theme                        (                        plot.title                         =                         element_text                        (                        size                         = multiPlotTitleTextSize))p3tree                         = plot_tree                                                 +                          theme                        (                        plot.title                         =                         element_text                        (                        size                         = multiPlotTitleTextSize))p4tree                         = plot_tree                                                 +                          theme                        (                        plot.title                         =                         element_text                        (                        size                         = multiPlotTitleTextSize))                        # group plots together                        grid.arrange                                                                    transform_sample_counts function. The first argument to this function is the phyloseq object you want to transform, and the second argument is an R function that defines the transformation. The R function is applied sample-wise, expecting that the first unnamed argument is a vector of taxa counts in the same order as the phyloseq object. Additional arguments are passed on to the function specified in the second argument, providing an explicit means to include pre-computed values, previously defined parameters/thresholds, or any other object that might be appropriate for computing the transformed values of interest.It is usually necessary to transform microbiome count data to account for differences in library size, variance, scale, etc. The phyloseq package provides a flexible interface for defining new functions to accomplish these transformations of the abundance values via theplot_abundance, that uses phyloseq\u2019spsmelt function to define a relative abundance graphic. We will use this to compare differences in scale and distribution of the abundance values in our phyloseq object before and after transformation.This example begins by defining a custom plot function,plot_abundance                         =                         function                        {                          # Arbitrary subset, based on Phylum, for plotting                          p1f                         = subset_taxa                        )                          mphyseq                         = psmelt                        (p1f)  mphyseq                         <- subset                                                  ggplot                        )                         +                            geom_violin                        (                        fill                         =                         NA                        )                         +                            geom_point                        )                         +                            facet_wrap                        (                        facets                         = Facet)                         +                         scale_y_log10                                                +                            theme                        (                        legend.position                        =                        \"none\"                        )}                                            proportions orrelative abundances. This function is so simple that it is easiest to define it within the function call totransform_sample_counts.The transformation in this case converts the counts from each sample into their frequencies, often referred to as# Transform to relative abundance. Save as new object.                        ps3ra                         = transform_sample_counts                        {x /                         sum                        (x)})                                            Now plot the abundance values before and after transformation.plotBefore                         = plot_abundance                        plotAfter                         = plot_abundance                                                # Combine each plot into one graphic.                        grid.arrange                                                                    Lactobacillales appears to be a taxonomic Order with bimodal abundance profile in the data. We can check for a taxonomic explanation of this pattern by plotting just that taxonomic subset of the data. For this, we subset with thesubset_taxa function, and then specify a more precise taxonomic rank to theFacet argument of theplot_abundance function that we defined above.Notice on the previous plot thatpsOrd                         = subset_taxa                                                plot_abundance                                                                    At this stage in the workflow, after converting raw reads to interpretable species abundances, and after filtering and transforming these abundances to focus attention on scientifically meaningful quantities, we are in a position to consider more careful statistical analysis. R is an ideal environment for performing these analyses, as it has an active community of package developers building simple interfaces to sophisticated techniques. As a variety of methods are available, there is no need to commit to any rigid analysis strategy a priori. Further, the ability to easily call packages without reimplementing methods frees researchers to iterate rapidly through alternative analysis ideas. The advantage of performing this full workflow in R is that this transition from bioinformatics to statistics is effortless.We back these claims by illustrating several analyses on the mouse data prepared above. We experiment with several flavors of exploratory ordination before shifting to more formal testing and modeling, explaining the settings in which the different points of view are most appropriate. Finally, we provide example analyses of multitable data, using a study in which both metabolomic and microbial abundance measurements were collected on the same samples, to demonstrate that the general workflow presented here can be adapted to the multitable setting..cran_packages                         <- c                                                .github_packages                         <- c                        (                        \"jfukuyama/phyloseqGraphTest\"                        )                        .bioc_packages                         <- c                                                # Install CRAN packages                         .inst                         <-                         .cran_packages                         %in%                         installed.packages                                                if                         (                        any                        (!.                        inst)){                          install.packages                                                }                        .inst                         <-                         .github_packages                         %in%                         installed.packages                                                if                         (                        any                        (                        !                        .inst)){                          devtools                        ::                        install_github                        (.github_packages[                        !                        .inst])                        }                        .inst                         <-                         .bioc_packages                         %in%                         installed.packages                                                if                        (                        any                        (                        !                        .inst)){                          source                        (                        \"http://bioconductor.org/biocLite.R\"                        )                          biocLite                        (.bioc_packages[                        !                        .inst])                        }                        Before doing the multivariate projections, we will add a few columns to our sample data, which can then be used to annotate plots. Fromqplot                        (                        sample_data                        (ps)                        $                        age,                         geom                         =                         \"histogram\"                        )                         +                         xlab                        (                        \"age\"                        )                        qplot                        (                        log10                        (                        rowSums                        (                        otu_table                        (ps))))                         +                          xlab                        (                        \"Logged counts-per-sample\"                        )                                            For a first pass, we look at principal coordinates analysis (PCoA) with either the Bray-Curtis dissimilarity on the weighted Unifrac distance. We see immediately that there are six outliers. These turn out to be the samples from females 5 and 6 on day 165 and the samples from males 3, 4, 5, and 6 on day 175. We will take them out, since we are mainly interested in the relationships between the non-outlier points.pslog                         <- transform_sample_counts                                                 log                        (                        1                         +                         x))                        sample_data                        (pslog)                        $                        age_binned                         <- cut                        (                        sample_data                        (pslog)                        $                        age,  \t\t\t\t                                  breaks                         =                         c                        )out.wuf.log                         <- ordinate                                                                    evals                         <-                         out.wuf.log                        $                        values                        $                        Eigenvalues                        plot_ordination                                                 +                          labs                        (                        col                         =                         \"Binned Age\"                        )                         +                          coord_fixed                        )                                            Before we continue, we should check the two female outliers \u2013 they have been taken over by the same OTU/RSV, which has a relative abundance of over 90% in each of them. This is the only time in the entire data set that this RSV has such a high relative abundance \u2013 the rest of the time it is below 20%. In particular, its diversity is by far the lowest of all the samples.rel_abund                         <- t                        (                        apply                        (                        otu_table                        (ps),                         1                        ,                         function                        (                        x                        ) x /                         sum                        (x)))                        qplot                                                 +                          xlab                        (                        \"Relative abundance\"                        )                                            In the ordination plots inThe reason for this is that as we are trying to represent the distances between samples as faithfully as possible; we have to take into account that the second eigenvalue is always smaller than the first, sometimes considerably so, thus we normalize the axis norm ratios to the relevant eigenvalue ratios.phyloseq, which provides many distances and ordination methods.As we have seen, an important first step in analyzing microbiome data is to do unsupervised, exploratory analysis. This is simple to do inAfter documenting the outliers, we are going to compute ordinations with these outliers removed and more carefully study the output. We see that there is a fairly substantial age effect that is consistent between all the mice, male and female, and from different litters. We\u2019ll first perform a PCoA using Bray-Curtis dissimilarity.The first plot shows the ordination of the samples, and we see that the second axis corresponds to an age effect, with the samples from the younger and older mice separating fairly well. The first axis correlates fairly well with library size (this is not shown). The first axis explains about twice the variability than the first, this translates into the elongated form of the ordination plot.setup_example                        )out.bc.log                         <- ordinate                                                                    evals                         <-                         out.dpcoa.log                        $                        eig                        plot_ordination                         +                          coord_fixed                        )                         +                          labs                                                                    evals                         <-                         out.bc.log                        $                        values                        $                        Eigenvalues                        plot_ordination                                                 +                          coord_fixed                        )                         +                          labs                        (                        col                         =                         \"Binned Age\"                        )                                            20, which is a phylogenetic ordination method and that provides a biplot representation of both samples and taxonomic categories. We see again that the second axis corresponds to young vs. old mice, and the biplot suggests an interpretation of the second axis: samples that have larger scores on the second axis have more taxa from Bacteroidetes and one subset of Firmicutes.Next we look at double principal coordinates analysis (DPCoA)out.dpcoa.log                         <- ordinate                                                                    Finally, we can look at the results of PCoA with weighted Unifrac. As before, we find that the second axis is associated with an age effect, which is fairly similar to DPCoA. This is not surprising, because both are phylogenetic ordination methods taking abundance into account. However, when we compare biplots, we see that the DPCoA gave a much cleaner interpretation of the second axis, compared to weighted Unifrac.out.wuf.log                         <- ordinate                                                                    Microbial abundance data is often heavy-tailed, and sometimes it can be hard to identify a transformation that brings the data to normality. In these cases, it can be safer to ignore the raw abundances altogether, and work instead with ranks. We demonstrate this idea using a rank-transformed version of the data to perform PCA. First, we create a new matrix, representing the abundances by their ranks, where the microbe with the smallest in a sample gets mapped to rank 1, second smallest rank 2, etc.plot_ordination                                                 +                          coord_fixed                        )                                            evals                         <-                         out.wuf.log                        $                        values                        $                        Eigenvalues                        plot_ordination                         +                          coord_fixed                        ) +                          labs                                                                    plot_ordination                                                 +                          coord_fixed                        )                                            abund                         <- otu_table                        (pslog)                        abund_ranks                         <- t                        )                                            21 for minimally abundant taxa. To avoid this, all those microbes with rank below some threshold are set to be tied at 1. The ranks for the other microbes are shifted down, so there is no large gap between ranks. This transformation is illustrated inNaively using these ranks could make differences between pairs of low and high abundance microbes comparable. In the case where many bacteria are absent or present at trace amounts, an artificially large difference in rank could occurabund_ranks                         <-                         abund_ranks -                         329                        abund_ranks[abund_ranks                         <                         1                        ]                         <-                         1                                            We can now perform PCA and study the resulting biplot, given inranks_pca                         <- dudi.pca                        row_scores                         <- data.frame                        )col_scores                         <- data.frame                        )tax                         <- tax_table                        (ps)                        @                        .Data                         %>%                          data.frame                        (                        stringsAsFactors                         =                         FALSE                        )tax                        $                        seq                         <- rownames                        (tax)                        main_orders                         <- c                        tax                        $                        Order[                        !                        (tax                        $                        Order                         %in%                         main_orders)]                         <-                         \"Other\"                        tax                        $                        Order                         <- factor                        )tax                        $                        otu_id                         <- seq_len                        (                        ncol                        (                        otu_table                        (ps)))row_scores                         <-                         row_scores                         %>%                          left_join                        (                        sample_data                        (pslog))col_scores                         <-                         col_scores                         %>%                          left_join                        (tax)                                            The results are similar to the PCoA analyses computed without applying a truncated-ranking transformation, reinforcing our confidence in the analysis on the original data.abund_df                         <-                         melt                                                 %>%                          left_join                        )                        colnames                        (abund_df)                         <-                         c                        abund_df                         <-                         melt                                                 %>%                          left_join                        )                        colnames                        (abund_df)                         <-                         c                        sample_ix                         <-                         sample                        (                        1                        :                        nrow                        (abund_df),                         8                        )                        ggplot                        (abund_df                         %>%                                  filter                        (sample                         %in%                         abund_df                        $                        sample[sample_ix]))                         +                          geom_point                        ,                                      position                         =                         position_jitter                        (                        width                         =                         0.2                        ),                         size                         =                         .7                        )                         +                          labs                                                 +                          scale_color_brewer                                                                    Canonical Correspondence Analysis (CCpnA) is an approach to ordination of a species by sample table that incorporates supplemental information about the samples. As before, the purpose of creating biplots is to determine which types of bacterial communities are most prominent in different mouse sample types. It can be easier to interpret these biplots when the ordering between samples reflects sample characteristics \u2013 variations in age or litter status in the mouse data, for example \u2013 and this central to the design of CCpnA.The function allows to create biplots where the positions of samples are determined by similarity in both species signatures and environmental characteristics; in contrast, principal components analysis or correspondence analysis only look at species signatures. More formally, it ensures that the resulting CCpnA directions lie in the span of the environmental variables; thorough treatments are available inordinate inphyloseq. In order to use supplemental sample data, it is necessary to provide an extra argument, specifying which of the features to consider \u2013 otherwise,phyloseq defaults to using allsample_data measurements when producing the ordination.Like PCoA and DPCoA, this method can be run usingps_ccpna                         <- ordinate                                                                    scores function in thevegan. Further, to facilitate figure annotation, we also join the site scores with the environmental data in thesample_data slot. Of the 23 total taxonomic orders, we only explicitly annotate the four most abundant \u2013 this makes the biplot easier to read.To access the positions for the biplot, we can use theps_scores                         <-                         vegan                        ::                        scores                        (ps_ccpna)sites                         <- data.frame                        (ps_scores                        $                        sites)sites                        $                        SampleID                         <- rownames                        (sites)sites                         <-                         sites                         %>%                          left_join                        (                        sample_data                        (ps))species                         <- data.frame                        (ps_scores                        $                        species)species                        $                        otu_id                         <- seq_along                        (                        colnames                        (                        otu_table                        (ps)))species                         <-                         species                         %>%                          left_join                        (tax)                                            evals_prop                         <-                         100                         *                         (ranks_pca                        $                        eig /                         sum                        (ranks_pca                        $                        eig))                        ggplot                                                 +                          geom_point                        ,                         shape                         =                         2                        )                         +                          geom_point                        ,                                      size                         =                         .3                        ,                         alpha                         =                         0.6                        )                         +                          scale_color_brewer                                                 +                          facet_grid                        (~ age_binned)                         +                          guides                        (                        col                         =                         guide_legend                        (                        override.aes                         =                         list                        (                        size                         =                         3                        )))                         +                          labs                        ),                                y                         =                         sprintf                        ))                         +                          coord_fixed                        (                        sqrt                        (ranks_pca                        $                        eig[                        2                        ] / ranks_pca                        $                        eig[                        1                        ]))                         +                          theme                        ))                                            Evidently, the first CCpnA direction distinguishes between mice in the two main age bins. Circles on the left and right of the biplot represent microbes that are characteristic of younger and older mice, respectively. The second CCpnA direction splits off the few mice in the oldest age group; it also partially distinguishes between the two litters. These samples low in the second CCpnA direction have more of the outlier microbes than the others.This CCpnA analysis supports our conclusions from the earlier ordinations \u2013 the main difference between the microbiome communities of the different mice lies along the age axis. However, in situations where the influence of environmental variables is not so strong, CCA can have more power in detecting such associations. In general, it can be applied whenever it is desirable to incorporate supplemental data, but in a way that (1) is less aggressive than supervised methods, and (2) can use several environmental variables at once.evals_prop                         <-                         100                         *                         ps_ccpna                        $                        CCA                        $                        eig[                        1                        :                        2                        ] /                         sum                        (ps_ccpna                        $                        CA                        $                        eig)                        ggplot                                                 +                          geom_point                        ,                         shape                         =                         2                        ,                         alpha                         =                         0.5                        )                         +                          geom_point                        ,                         size                         =                         0.5                        )                         +                          geom_text_repel                        (                        data                         = species                         %>%                         filter                        (CCA2                         < -                        2                        ),\t\t                            aes                        ,\t\t                            size                         =                         1.5                        ,                         segment.size                         =                         0.1                        )                         +                          facet_grid                        (. ~ age_binned)                         +                          guides                        (                        col                         =                         guide_legend                        (                        override.aes                         =                         list                        (                        size                         =                         3                        )))                         +                          labs                        ),                                y                         =                         sprintf                        ))                         +                          scale_color_brewer                                                 +                          coord_fixed                        (                        sqrt                        (ps_ccpna                        $                        CCA                        $                        eig[                        2                        ] / ps_ccpna                        $                        CCA                        $                        eig[                        1                        ])                        *                        0.33                        )                         +                          theme                        ))                                            ggplot                                                 +                          geom_point                        ,                         shape                         =                         2                        ,                         alpha                         =                         0.5                        )                         +                          geom_point                        ,                         size                         =                         0.5                        )                         +                          geom_text_repel                        (                        data                         = species                         %>%                         filter                        (CCA2                         < -                        2                        ),                                            aes                        ,\t\t                            size                         =                         1.5                        ,                         segment.size                         =                         0.1                        )                         +                          facet_grid                        (. ~ family_relationship)                         +                          guides                        (                        col                         =                         guide_legend                        (                        override.aes                         =                         list                        (                        size                         =                         3                        )))                         +                          labs                        ),                                y                         =                         sprintf                        ))                         +                          scale_color_brewer                                                 +                          coord_fixed                        (                        sqrt                        (ps_ccpna                        $                        CCA                        $                        eig[                        2                        ] / ps_ccpna                        $                        CCA                        $                        eig[                        1                        ])                        *                        0.45                           )                         +                          theme                        ))                                            caret package wraps many prediction algorithms available in R and performs parameter tuning automatically. Since we saw that microbiome signatures change with age, we\u2019ll apply supervised techniques to try to predict age from microbiome composition.Here we illustrate some supervised learning methods that can be easily run in R. The24. The first step is to divide the data into training and test sets, with assignments done by mouse, rather than by sample, to ensure that the test set realistically simulates the collection of new data. Once we split the data, we can use thetrain function to fit the PLS model.We\u2019ll first look at Partial Least Squares (PLS)setup_example                        )                        sample_data                        (pslog)                        $                        age2                         <- cut                        (                        sample_data                        (pslog)                        $                        age,                         c                        )dataMatrix                         <- data.frame                        (                        age                         =                         sample_data                        (pslog)                        $                        age2,                         otu_table                        (pslog))                        # take 8 mice at random to be the training set, and the remaining 4 the test set                        trainingMice                         <- sample                        (                        unique                        (                        sample_data                        (pslog)                        $                        host_subject_id),                         size                         =                         8                        )inTrain                         <- which                        (                        sample_data                        (pslog)                        $                        host_subject_id                         %in%                         trainingMice)training                         <-                         dataMatrixtesting                         <-                         dataMatrixplsFit                         <- train                                                                    predict function and compare to the truth. We see that the method does an excellent job of predicting age.Next we can predict class labels on the test set using theplsClasses                         <- predict                                                table                        #### plsClasses rfClasses                         <- predict                                                table                        #### rfClasses ,\t\t                             \"scores\"                         =                         scores                        )                        class                        (pls_biplot                        $                        scores)                         <-                         \"matrix\"                        pls_biplot                        $                        scores                         <- data.frame                        (                        sample_data                        (pslog),\t\t\t\tpls_biplot                        $                        scores)                                            tax                         <- tax_table                        (ps)                        @                        .Data                         %>%                          data.frame                        (                        stringsAsFactors                         =                         FALSE                        )main_orders                         <- c                        tax                        $                        Order[                        !                        (tax                        $                        Order                         %in%                         main_orders)]                         <-                         \"Other\"                        tax                        $                        Order                         <- factor                        )                        class                        (pls_biplot                        $                        loadings)                         <-                         \"matrix\"                        pls_biplot                        $                        loadings                         <- data.frame                                                                    The resulting biplot is displayed inggplot                                                 +                          geom_point                        ,                         shape                         =                         2                        )                         +                          geom_point                        ,\t                              size                         =                         0.3                        ,                         alpha                         =                         0.6                        )                         +                          scale_color_brewer                                                 +                          labs                                                 +                          guides                        (                        col                         =                         guide_legend                        (                        override.aes                         =                         list                        (                        size                         =                         3                        )))                         +                          facet_grid                        (                         ~                         age2)                         +                          theme                        ))                                            A random forest proximity plot is displayed inrf_prox                         <- cmdscale                                                 %>%                          data.frame                        (                        sample_data                        (pslog))                                            ggplot                        (rf_prox)                         +                          geom_point                        ,                                      size                         =                         .4                        ,                         alpha                         =                         0.6                        )                         +                          scale_color_manual                        )                         +                          guides                        (                        col                         =                         guide_legend                        (                        override.aes                         =                         list                        (                        size                         =                         3                        )))                         +                          labs                                                                    Lachnospiraceae and genusRoseburia.To further understand the fitted random forest model, we identify the microbe with the most influence in the random forest prediction. This turns out to be a microbe in familyas.vector                        (                        tax_table                        (ps))## [1] \"Lachnospiraceae\" NAimpOtu                         <- as.vector                        (                        otu_table                        (pslog))maxImpDF                         <- data.frame                        (                        sample_data                        (pslog),                         abund                         = impOtu)                        ggplot                        (maxImpDF)                         +                         geom_histogram                        (                        aes                        (                        x                         = abund))                         +                          facet_grid                        (age2                         ~                         .)                         +                          labs                                                                    ggnetwork. This package overloads the ggplot syntax, so you can use the function ggplot on an igraph object and addgeom_edges andgeom_nodes geoms to plot the network. To be able to color the nodes or edges a certain way, we need to add these attributes to the igraph object. Below we create a network by thresholding the Jaccard dissimilarity (the default distance for the functionmake_network) at .35, and then we add an attribute to the vertices indicating which mouse the sample came from and which litter the mouse was in. Then we can plot the network with the coloring by mouse and shape by litter. We see the resulting network inPhyloseq has functionality for creating graphs based on thresholding a distance matrix, and the resulting networks can be plotting using thesetup_example                        )                                            net                         <- make_network                        sampledata                         <- data.frame                        (                        sample_data                        (ps))                        V                        (net)$                        id                         <-                         sampledata                        V                        (net)$                        litter                         <-                         sampledata                                            ggplot                        ,                         layout                         =                         \"fruchtermanreingold\"                        )                         +                          geom_edges                        (                        color                         =                         \"darkgray\")                         +                          geom_nodes)                         +                          theme                        )                         +                          guides                        (                        col                         =                         guide_legend                        (                        override.aes                         =                         list                        (                        size                         =                         .25                        )))                                            25 as a generalization of the Wald-Wolfowitz runs test. They proposed the use of a minimum spanning tree (MST) based on the distances between the samples, and then counting the number of edges on the tree that were between samples in different groups. It is not necessary to use a minimum spanning tree (MST), the graph made by linking nearest neighbors26 or distance thresholding can also be used as the input graph. No matter what graph we build between the samples, we can approximate a null distribution by permuting the labels of the nodes of the graph.Graph-based two-sample tests were introduced by Friedman and Rafskyfamily_relationship) come from the same distribution. Since there is a grouping in the data by individual (host_subject_id), we can\u2019t simply permute all the labels, we need to maintain this nested structure \u2013 this is what thegrouping argument does. Here we permute thefamily_relationship labels but keep thehost_subject_id structure intact.We first perform a test using an MST with Jaccard dissimilarity. We want to know whether the two litters gt                        $                        pval                                            ## [1] 0.01                                            plotNet1                        =plot_test_network                        (gt)                         +                         theme                        (                        legend.text                         =                         element_text                        (                        size                         =                         8                        ),                                 legend.title                         =                         element_text                        (                        size                         =                         9                        ))                        plotPerm1                        =plot_permutations                        (gt)                        grid.arrange                                                                    k-nearest neighbors graph is obtained by putting an edge between two samples whenever one of them is in the set ofk-nearest neighbors of the other. We see fromThegt                         <- graph_perm_test                                                                    plotNet2                        =plot_test_network                        (gt)                         +                         theme                        (                        legend.text                         =                         element_text                        (                        size                         =                         8                        ),                                 legend.title                         =                         element_text                        (                        size                         =                         9                        ))                        plotPerm2                        =plot_permutations                        (gt)                        grid.arrange                                                                    We can also compute the analogous test with two-nearest neighbors and the Bray-Curtis dissimilarity. The results are not shown, but the code is given below.gt                         <- graph_perm_test                                                                    27. The testing function lets the user supply an absolute distance threshold; alternatively, it can find a distance threshold such that there are a prespecified number of edges in the graph. Below we use a distance threshold so that there are 720 edges in the graph, or twice as many edges as there are samples. Heuristically, the graph we obtain isn\u2019t as good, because there are many singletons. This reduces power, and so if the thresholded graph has this many singletons it is better to either modify the threshold or consider a MST or k-nearest neighbors graph.Another way of making a graph between samples is to threshold the distance matrix, this is called a geometric graphgt                         <- graph_perm_test                                                                    plotNet3                        = plot_test_network                        (gt)                         +                         theme                        (                        legend.text                         =                         element_text                        (                        size                         =                         8                        ),\t                         legend.title                         =                         element_text                        (                        size                         =                         9                        ))                        plotPerm3                        =plot_permutations                        (gt)                        grid.arrange                                                                    Then we can try a similar procedure with an increased number of edges to see what happens (code given below but output not shown).gt                         <- graph_perm_test                                                                    It is often of interest to evaluate the degree to which microbial community diversity reflects characteristics of the environment from which it was sampled. Unlike ordination, the purpose of this analysis is not to develop a representation of many bacteria with respect to sample characteristics; rather, it is to describe how a single measure of overall community structure  is associated with sample characteristics. This is a somewhat simpler statistical goal, and can be addressed through linear modeling, for which there are a range of approaches in R. As an example, we will used a mixed-effects model to study the relationship between mouse microbial community diversity and the age and litter variables that have been our focus so far. This choice was motivated by the observation that younger mice have noticeably lower Shannon diversities, but that different mice have different baseline diversities. The mixed-effects model is a starting point for formalizing this observation.We first compute the Shannon diversity associated with each sample and join it with sample annotation.setup_example                        )ps_alpha_div                         <- estimate_richness                        ps_alpha_div                        $                        SampleID                         <- rownames                                                 %>%                          as.factor                        ps_samp                         <- sample_data                        (ps)                         %>%                          unclass                                                 %>%                          data.frame                                                 %>%                          left_join                                                 %>%                          melt                                                # reorder's facet from lowest to highest diversity                        diversity_means                         <-                         ps_samp                         %>%                          group_by                        (host_subject_id)                         %>%                          summarise                        )                         %>%                          arrange                        (mean_div)ps_samp                        $                        host_subject_id                         <- factor                                                                    nlme to estimate coefficients for this mixed-effects model.We use thealpha_div_model                         <- lme                                                                    To interpret the results, we compute the prediction intervals for each mouse by age bin combination. These are displayed innew_data                         <- expand.grid                        (                        host_subject_id                         =                         levels                        (ps_samp                        $                        host_subject_id),\t\t\t                           age_binned                         =                         levels                        (ps_samp                        $                        age_binned))new_data$                        pred                         <- predict                        X                         <- model.matrix                        [-                        2                        ]),\t\t  new_data[-                        ncol                        (new_data)])pred_var_fixed                         <- diag                        )new_data                        $                        pred_var                         <-                         pred_var_fixed                         +                         alpha_div_model                        $                        sigma                         ^                         2                                            # fitted values, with error bars                        ggplot                        (ps_samp                         %>%                         left_join                        (new_data))                         +                          geom_errorbar                        ,\t\t                              ymax                         = pred                         +                         2                         *                         sqrt                        (pred_var)),\t\t                          col                         =                         \"#858585\"                        ,                         size                         =                         .1                        )                         +                          geom_point                        ,                         size                         =                         0.8                        )                         +                          facet_wrap                        (                        ~                        host_subject_id)                         +                          scale_y_continuous                        ,                         breaks                         =                         seq                        )                         +                          scale_color_brewer                                                 +                          labs                                                 +                          guides                        (                        col                         =                         guide_legend                        (                        override.aes                         =                         list                        (                        size                         =                         4                        )))                         +                          theme                        ),\t                         axis.text.x                         =                         element_text                        ,\t                         axis.text.y                         =                         element_text                        (                        size                         =                         6                        ))                                            p-values to ensure a False Discovery Rate upper bound. This can be accomplished through the Benjamini-Hochberg procedure, for example28. However, this procedure does not exploit any structure among the tested hypotheses \u2013 for example, it is likely that if one Ruminococcus species is strongly associated with age, then others are as well. To integrate this information30, proposed a hierarchical testing procedure, where taxonomic groups are only tested if higher levels are found to be be associated. In the case where many related species have a slight signal, this pooling of information can increase power.Hypothesis testing can be used to identify individual microbes whose abundance relates to sample variables of interest. A standard approach is to compute a test statistic for each bacteria individually, measuring its association with sample characteristics, and then jointly adjustWe apply this method to test the association between microbial abundance and age. This provides a complementary view of the earlier analyses, identifying individual bacteria that are responsible for the differences between young and old mice.DESeq2 package. The two transformations yield similar sets of significant microbes. One difference is that, after accounting for size factors, the histogram of row sums for DESeq is more spread out in the lower values, refer toDESeq2\u2019s variance stabilizing transformation on aphyloseq object.We digress briefly from hierarchical testing to describe an alternative form of count normalization. Rather than working with the logged data as in our earlier analysis, we consider a variance stabilizing transformation introduced bysetup_example                        )ps_dds                         <- phyloseq_to_deseq2                                                varianceStabilizingTransformation                                                                    ## class: DESeqTransform## dim: 389 344## metadata(1): version## assays(1): ''## rownames(389):##   GCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGCAGGCGGAAGATCAAGTCAGCGGTAAAATTGAGAGGCTCAACCTCTTCGAGCCGTTGAAACTGGTTTTC##   GCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGCAGGCGGACTCTCAAGTCAGCGGTCAAATCGCGGGGCTCAACCCCGTTCCGCCGTTGAAACTGGGAGCC##   ...##   GCTAGCGTTGTTCGGAATTACTGGGCGTAAAGCGCGTGTAGGCGGTTTGCCAAGTTGGGTGTGAAAGCCTTGAGCTCAACTCAAGAAATGCACTCAGTACTGG##   GCAAGCGTTACTCGGAATCACTGGGCGTAAAGAGCGCGTAGGCGGGATAGTCAGTCAGGTGTGAAATCCTATGGCTTAACCATAGAACTGCATTTGAAACTAC## rowData names(5): baseMean baseVar allZero dispGeneEst dispFit## colnames(344): F3D0 F3D1 ... M6D8 M6D9## colData names(17): collection_date biome ... age_binned sizeFactor                                            ps_dds                         <- estimateSizeFactors                        (ps_dds)ps_dds                         <- estimateDispersions                        (ps_dds)abund                         <- getVarianceStabilizedData                        (ps_dds)                                            structSSI to perform the hierarchical testing33. For more convenient printing, we first shorten the names of each microbe.We use theshort_names                         <- substr                        (                        rownames                        (abund),                         1                        ,                         5                        )                        %>%                          make.names                        (                        unique                         =                         TRUE                        )                        rownames                        (abund)                         <-                         short_names                                            treePValues, is available for this; it expects an edgelist encoding parent-child relationships, with the first row specifying the root node.Unlike standard multiple hypothesis testing, the hierarchical testing procedure needs univariate tests for each higher-level taxonomic group, not just every bacteria. A helper function,el                         <- phy_tree                        (pslog)                        $                        edgeel0                         <-                         elel0                         <-                         el0el_names                         <- c                                                $                        Nnode))el                         <-                         el_names]el                         <-                         el_names[                        as.numeric                        ]unadj_p                         <- treePValues                                                $                        age_binned)                                            p-value using the hierarchical testing procedure. The test results are guaranteed to control several variants of FDR control, but at different levels; we defer details toWe can now correcthfdr_res                         <- hFDR.adjust                                                summary                        (hfdr_res)                                            ## Number of hypotheses: 776## Number of tree discoveries: 461## Estimated tree FDR: 1## Number of tip discoveries: 219## Estimated tips FDR: 1####  hFDR adjusted p-values:##             unadjp       adjp adj.significance## GCAAG.71  1.01e-67   2.02e-67              ***## GCAAG.96  1.33e-67   2.65e-67              ***## GCAAG.190 1.10e-58   2.21e-58              ***## GCAAG.254 2.01e-48   4.03e-48              ***## GCAAG.150 4.90e-46   9.80e-46              ***## GCGAG.2   5.28e-38   1.06e-37              ***## GCAAG.170 6.54e-38   1.31e-37              ***## GCAAG.1   1.16e-35   2.32e-35              ***## GCAAG.146 4.83e-33   9.66e-33              ***## GCGAG.21  1.40e-28   2.79e-28              ***                                            abund_sums                         <- rbind                        (                        data.frame                        (                        sum                         =                         colSums                        (abund),\t\t\t                                  sample                         =                         colnames                        (abund),\t\t\t                                  type                         =                         \"DESeq2\"                        ),\t\t                              data.frame                        (                        sum                         =                         rowSums                        (                        otu_table                        (pslog)),\t\t\t                                  sample                         =                         rownames                        (                        otu_table                        (pslog)),\t\t\t                                  type                         =                         \"log(1 + x)\"                        ))                        ggplot                        (abund_sums)                         +                          geom_histogram                        (                        aes                        (                        x                         = sum),                         binwidth                         =                         20                        )                         +                          facet_grid                        (type                         ~                         .)                         +                          xlab                                                                    ## [only 10 most significant hypotheses shown]## ---## Signif. codes: 0 '***' 0.015 '**' 0.15 '*' 0.75 '.' 1.5 '-' 1                                            plot                                                 # opens in a browser                                            p-values, from blue to orange, representing the strongest to weakest associations. Grey nodes were never tested, to focus power on more promising subtrees. Scanning the full tree, it becomes clear that the association between age group and bacterial abundance is present in only a few isolated taxonomic groups, but that it is quite strong in those groups. To give context to these results, we can retrieve the taxonomic identity of the rejected hypotheses.The plot opens in a new browser \u2013 a static screenshot of a subtree is displayed inoptions                        (                        width                        =                        100                        )                        tax                         <- tax_table                        (pslog)                         %>%                          data.frame                        tax                        $                        seq                         <-                         short_names                                            hfdr_res                        @                        p.vals                        $                        seq                         <- rownames                        tax                         %>%                          left_join                                                 %>%                          arrange                        (adjp)                         %>%                         head                        (                        10                        )                          ## \t          Family            Genus        seq     unadjp      adjp  adj.significance## 1     Lachnospiraceae        Roseburia   GCAAG.71   1.01e-67  2.02e-67               ***## 2     Lachnospiraceae             <NA>   GCAAG.96   1.33e-67  2.65e-67               ***## 3     Lachnospiraceae Clostridium_XlVa  GCAAG.190   1.10e-58  2.21e-58               ***## 4     Lachnospiraceae             <NA>  GCAAG.254   2.01e-48  4.03e-48               ***## 5     Lachnospiraceae Clostridium_XlVa  GCAAG.150   4.90e-46  9.80e-46               ***## 6  Porphyromonadaceae             <NA>    GCGAG.2   5.28e-38  1.06e-37               ***## 7     Lachnospiraceae Clostridium_XlVa  GCAAG.170   6.54e-38  1.31e-37               ***## 8                <NA>             <NA>    GCAAG.1   1.16e-35  2.32e-35               ***## 9     Lachnospiraceae             <NA>  GCAAG.146   4.83e-33  9.66e-33               ***## 10 Porphyromonadaceae             <NA>   GCGAG.21   1.40e-28  2.79e-28               ***                                            Lachnospiraceae, which is consistent with the random forest results in Section.It seems that the most strongly associated bacteria all belong to familyMany microbiome studies attempt to quantify variation in the microbial, genomic, and metabolic measurements across different experimental conditions. As a result, it is common to perform multiple assays on the same biological samples and ask what features \u2013 bacteria, genes, or metabolites, for example \u2013 are associated with different sample conditions. There are many ways to approach these questions, which to apply depends on the study\u2019s focus.PMA34.Here, we will focus on one specific workflow that uses sparse Canonical Correlation Analysis (sparse CCA), a method well-suited to both exploratory comparisons between samples and the identification of features with interesting variation. We will use an implementation from the35. There are two tables here, one for bacteria and another with metabolites. 12 samples were obtained, each with measurements at 637 m/z values and 20,609 OTUs; however, about 96% of the entries of the microbial abundance table are exactly zero. The code below retrieves this data.Since the mouse data used above included only a single table, we use a new data set, collected by the studysetup_example                        )                                            metab_path                         <-                         \"data/metabolites.csv\"                        microbe_path                         <-                         \"data/microbe.rda\"                        metab                         <- read.csv                                                metab                         <- as.matrix                        (metab)                        microbe                         <- get                        (                        load                        (microbe_path))                                            Our preprocessing mirrors that done for the mouse data. We first filter down to microbes and metabolites of interest, removing those that are zero across many samples. Then, we transform them to weaken the heavy tails.keep_ix                         <- rowSums                        (metab                         ==                         0                        )                         <=                         3                        metab                         <-                         metab                        microbe                         <- prune_taxa                        (                        taxa_sums                        (microbe)                         >                         4                        , microbe)                        microbe                         <- filter_taxa                        ),                         TRUE                        )                                            metab                         <- log                                                X                         <- otu_table                        (microbe)                        @                        .Data                        X[X                         >                         50                        ]                         <-                         50                                                                    We can now apply sparse CCA. This method compares sets of features across high-dimensional data tables, where there may be more measured features than samples. In the process, it chooses a subset of available features that capture the most covariance \u2013 these are the features that reflect signals present across multiple tables. We then apply PCA to this selected subset of features. In this sense, we use sparse CCA as a screening procedure, rather than as an ordination method.penaltyx andpenaltyz are sparsity penalties. Larger values ofpenaltyx will result in fewer selected microbes, similarlypenaltyz modulates the number of selected metabolites. We tune them manually to facilitate subsequent interpretation \u2013 we generally prefer more sparsity than the default parameters would provide.Our implementation is below. The parameterscca_res                         <- CCA                        (                        t                        (X),                         t                        (metab),                          penaltyx                         =                         .15                        ,                         penaltyz                         =                         .15                        )## 123456789101112131415cca_res## Call: CCA(x = t(X), z = t(metab), penaltyx = 0.15, penaltyz = 0.15)###### Num non-zeros u's: 5## Num non-zeros v's: 15## Type of x: standard## Type of z: standard## Penalty for x: L1 bound is 0.15## Penalty for z: L1 bound is 0.15## Cor: 0.974                                            With these parameters, 5 microbes and 15 metabolites have been selected, based on their ability to explain covariation between tables. Further, these 20 features result in a correlation of 0.974 between the two tables. We interpret this to mean that the microbial and metabolomic data reflect similar underlying signals, and that these signals can be approximated well by the 20 selected features. Be wary of the correlation value, however, since the scores are far from the usual bivariate normal cloud. Further, note that it is possible that other subsets of features could explain the data just as well \u2013 sparse CCA has minimized redundancy across features, but makes no guarantee that these are the \u201ctrue\u201d features in any sense.Nonetheless, we can still use these 20 features to compress information from the two tables without much loss. To relate the recovered metabolites and OTUs to characteristics of the samples on which they were measured, we use them as input to an ordinary PCA.combined                         <- cbind                                                ,\t\t                            t                        )                        pca_res                         <- dudi.pca                                                                    # annotation                        genotype                         <- substr                        (                        rownames                        (pca_res                        $                        li),                         1                        ,                         2                        )                        sample_type                         <- substr                        (                        rownames                        (pca_res                        $                        l1                        ),                         3                        ,                         4                        )                        feature_type                         <- grepl                        )                        feature_type                         <- ifelse                                                sample_info                         <- data.frame                                                feature_info                         <- data.frame                        ,                         1                        ,                         6                        ))                                            triplot, where we show different types of samples and the multidomain features (Metabolites and OTUs). This allows comparison across the measured samples \u2013 triangles for Knockout and circles for wild type \u2013 and characterizes the influence the different features \u2013 diamonds with text labels. For example, we see that the main variation in the data is across PD and ST samples, which correspond to the different diets. Further, large values of 15 of the features are associated with ST status, while small values for 5 of them indicate PD status. The advantage of the sparse CCA screening is now clear \u2013 we can display most of the variation across samples using a relatively simple plot, and can avoid plotting the hundreds of additional points that would be needed to display all of the features.ggplot                                                 +                         geom_point                        ,                         size                         =                         3                        )                         +                          geom_label_repel                        ,                                             size                         =                         2                        ,                         segment.size                         =                         0.3                        ,                                             label.padding                         =                         unit                        ,                         label.size                         =                         0                        )                         +                          geom_point                        ,\t                              size                         =                         1                        ,                         shape                         =                         23                        ,                         col                         =                         \"#383838\"                        )                         +                          scale_color_brewer                                                 +                          scale_fill_manual                        )                         +                          guides                        ))                         +                          coord_fixed                        (                        sqrt                        (pca_res                        $                        eig[                        2                        ]                         /                         pca_res                        $                        eig[                        2                        ]))                         +                          labs                        ,                         2                        )),                               y                         =                         sprintf                        ,                         2                        )),                               fill                         =                         \"Feature Type\"                        ,                         col                         =                         \"Sample Type\"                        )                                            3.3 ofR and version3.3 ofBioconductor.The programs and source for this article can be run using versionR is now available to denoise, identify and normalize next generation amplicon sequencing reads using probabilistic models with parameters fit using the data at hand.We have shown how a complete workflow inR environment. Multivariate projections using the phylogenetic tree as the relevant distance between OTUs/RSVs can be done using weighted unifrac or double principal coordinate analyses using thephyloseq package. Biplots provide the user with an interpretation key. These biplots have been extended to triplots in the case of multidomain data incorporating genetic, metabolic and taxa abundances. We illustrate the use of network based analyses, whether the community graph is provided from other sources or from a taxa co-occurrence computation using a Jaccard distance.We have provided a brief overview of all the analyses that become possible once the data has been imported into theWe have briefly covered a small example of using three supervised learning functions  to predict a response variable,ggplot2 andggnetwork allow for the layering of information in the output into plots that combine graphs, multivariate information and maps of the relationships between covariates and taxa abundances. The layering concept allows the user to provide reproducible publication level figures with multiple heterogeneous sources of information. Our main goal in providing these tools has been to enhance the statistical power of the analyses by enabling the user to combine frequencies, quality scores and covariate information into complete and testable projections.The main challenges in tackling microbiome data come from the many different levels of heterogeneity both at the input and output levels. These are easily accommodated through R\u2019s capacity to combine data into S4 classes. We are able to include layers of information, trees, sample data description matrices, contingency table in the phyloseq data sctructures. The plotting facilities ofC wrapped within the Bioconductor packagedada2 to enable the different steps to be undertaken on a laptop.This illustration of possible workflows for microbiome data combining trees, networks, normalized read counts and sample information showcases the capabilities and reproducibility of an R based system for analysing bacterial communities. We have implemented key components inphangorn. The sequences are then assembled into a phyloseq object containing all the sample covariates, the phylogenetic tree and the sample-taxa contingency table.Once the sequences have been filtered and tagged they can be assembled into a phylogenetic tree directly in R using the maximum likelihood tree estimation available inThese data can then be visualized interactively with Shiny-phyloseq, plotted with one line wrappers in phyloseq and filtered or transformed very easily.The last part of the paper shows more complex analyses that require direct plotting and advanced statistical analyses.Multivariate ordination methods allow useful lower dimensional projections in the presence of phylogenetic information or multidomain data as shown in an example combining metabolites, OTU abundances,Supervised learning methods provide lists of the most relevant taxa in discriminating between groups.25 tests for microbiome data which have not been published previously.Bacterial communities can be represented as co-occurrence graphs using network based plotting procedures available in R. We have also provided examples where these graphs can be used to test community structure through non parametric permutation resampling. This provides implementations of the Friedman RafskyThe data referenced by this article are under copyright with the following copyright statement: Copyright: \u00a9 2016 Callahan BJ et al.https://github.com/spholmes/F1000_workflow and at the Stanford digital repository permanent url for this paper:http://purl.stanford.edu/wh250nn9648. All other data have been previously published and the links are included in the paper.Intermediary data for the analyses are made available both on GitHub athttps://www.bioconductor.org/. CRAN packages athttps://cran.r-project.org/.Bioconductor packages athttps://purl.stanford.edu/wh250nn9648Permanent repository for the data and program source of this paper:https://github.com/spholmes/F1000_workflowLatest source code as at the time of publication:10.5281/zenodo.5454436Archived source as at the time of publication: Zenodo: F1000_workflow: MicrobiomeWorkflowv0.9, doi: The title and abstract appropriately summarize the contents, and the text is fluent to read. The manuscript aims to cover a vast area, and does good job in summarizing the relevant key aspects in a single workflow. The study design, methods and analysis and their suitability are properly described with appropriate references. The work describes a recommendation for a workflow based on the author's comprehensive experience in this field. It does not provide thorough comparison or benchmarking of the methods but relevant research is cited and key comparisons are already available in the literature. The contribution in this work is to combine the individual elements into a coherent workflow that describes very typical steps and recommended choices in standard taxonomic analysis. * Major comments: The main shortcoming is that the workflow is not provided in a readily reproducible format. I cloned the github repository, and could run the analysis rnw files  individually, as well as the main.rnw. These result in .text file but using pdflatex or latex could not be readily applied to convert these into the final PDF format. The github site does not mention how the rnw files should actually be converted into PDF. This is not evident as there are many ways to do this and the success will depend on the overall setup. The authors could include for instance a simple shell script or README in the github main directory, showing what steps are taken to get from the original R/Rnw files to the final PDF reports. This would greatly increase the utility and reproducibility of this work. * Minor comment: - Something is missing from Conclusions. There is paragraph that in its entirety reads as follows: \"We have briefly covered a small example of using three supervised learning functions  to predict a response variable,\" - it misses text in parentheses and ends with a comma. - Could you cite or discuss in more detail based on your experience whether Friedman Rafsky method outperforms alternative or at least closely related methods in the pairwise comparison task ? Not required but would be interesting to know. - The phyloseq package might serve its purpose better if split in smaller and more compact packages. The class structure is really useful and valuable, and would deserve its own package. This would better serve the overall microbiome data analytics community which can build on this and expand phyloseq capabilities in separate packages, in the same way as certain microarray data structures became a norm with the RMA and limma packages, with subsequent explosion in analysis methodologies. I am here just repeating my comment from the first review. Not required for this manuscript, however.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. This article is a valuable resource for the metagenomics field. The thorough examples of several statistical analyses of metagenomic data will help both the novice and expert in analyzing their own data. Additionally, this paper sets a standard in the field for documenting analyses. Both DADA2 and PhyloSeq have much to offer. DADA2 identifies OTUs, which are termed in this paper \u2018Ribosomal Sequence Variants,\u2019 reflecting the extra granularity with which DADA2 is capable of resolving OTUs. The RSVs identified by DADA2 offer the ability to conduct higher resolution analyses on 16S data. PhyloSeq is comprised of numerous capabilities to analyze metagenomic data, making it quite easy for a user to load and analyze their data. Below I make a few suggestions for clarification purposes. I enjoyed reading this article and have already benefited greatly from using DADA2 and PhyloSeq in my own work.A very attractive feature of DADA2 is its ability to resolve RSVs. I wonder if the authors could expand more on the findings they have made with the higher resolution OTUs found by DADA2. This would highlight why DADA2 is such a powerful tool.et al. 2013 and Schlosset al. 2012 find in these data sets? Were DADA2 and PhyloSeq used to analyze the data in these two papers? If not, are the findings different? I enjoyed reading about the different metagenomic properties of mice of different ages. More description along these lines in the introduction would make it motivating to understand why the various preprocessing steps are done and an overview of what is to come.I wonder if the examples that the authors provide could be more biologically motivated. For example, could the authors explain the mouse data set in greater depth in the introduction? What did KozichPage 4 \u2013 it could be helpful to illustrate some of the properties of the software with numbers and data. For example, DADA2 has the ability to infer OTUs from pooled or unpooled data. Could the authors illustrate the number of RSVs found in the two scenarios?Figure 2 -- Could the authors explain on Page 4 what sequencing error rates are being inferred (i.e. transition and transversion errors)? Which parameters are inferred to come up with the solid black line? An explicit reference to Figure 2 in the text could help. Additionally, headers indicating Forward and Reverse reads in Figure 2 could help to distinguish the plots.Page 6 \u2013 Is the multiple sequence alignment feature capable of multiple methods? If so, do you advocate for using ClustalW for metageonomic data? Why?Page 6 -- Could the authors define what a GTR+G+I model is?I wonder if the authors could give some more guidance on how to construct the PhyloSeq object from scratch without relying import functions. For example, I tried making a PhyloSeq object using Metaphlan2 output. Unfortunately I could not figure out how to merge Metaphlan2 biom files for each sample, and so I had to fiddle with Phyloseq for sometime to manually create the OTU, sample, and taxa tables for multiple samples. Minor critiques and suggestions:I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Thanks for your comments and suggestions. We made several improvements to the revised manuscript in response: We added an explicit reference to Figure 2 in the text. The error rates being estimated in each plot are indicated in the text just above each plot. A2C (A to C) is shorthand for an A being converted to a C by errors in the amplicon sequencing process. We changed the multiple-sequence alignment method in the workflow to that implemented by the DECIPHER package, largely because of its improved computational performance. We added a brief text description of GTR+G+I . We did not expand our evaluation of RSVs vs. OTUs or pooled vs. unpooled inference.\u00a0Performing such evaluations well is a significant undertaking and would take significant space to explain, and our primary purpose here is to demonstrate the many features of an R/Bioconductor amplicon analysis workflow. For evaluation of DADA2, our manuscript introducing the method examines differences between the output of DADA2 and OTU methods and we are writing another manuscript that looks at performance on datasets with many samples. On the issue of pooled vs. unpooled results, the short answer is that we find both approaches work well. If just counting the number of output OTU sequences, pooled inference generally finds more because of its higher sensitivity to sequences that are found in many samples but are rare in each. Of note, we generally find these pooled-only sequences to be very highly enriched for contaminants (eg. kit contaminants), which are expected to distributed in just this way. We also did not expand much on the biological findings from this dataset in the initial paper , as they were quite limited, essentially boiling down to the observation that gut sample early in life differed by more on average than samples from later in life. However, the dataset has been used in a number of studies as an example dataset for testing new methods  and that is the way in which we are using it here. There is a growing push in the computational sciences for adopting software practices that promote replicability and provide methodological transparency. In the field of microbiome research these practices should minimize the standard culprits of error-creep such as file proliferation, and incompatible formats; they should provide sound default choices for the core computational steps of sequence clustering and taxonomic assignment; and they should facilitate reproducible statistical analyses of the resulting data. By providing a step-by-step analysis of a microbiome dataset that can be completed entirely from within the R statistical computing environment, this workflow does an admirable job of bringing these best-practices to the world of microbiome science. The article takes a reader through the steps of processing raw sequence data and loading the data into R. It then demonstrates how to use basic exploratory data analysis to get a sense of the data and finally introduces the use of various statistical packages to search-for and validate patterns. The majority of the article focuses on the application of statistical concepts \u00a0to microbiome data and this is where scientists would like to be spending their time. However, this allocation of ink-space is only possible because the recent release of the DADA2 package allows the authors (and subsequent users) to condense all the read-processing portion of the tutorial into a few short steps. \u00a0DADA2 provides a new and arguably superior method for clustering raw amplicon reads and, by processing the reads and assigning taxonomy, it fills in the computational gap required to work completely within R. \u00a0The benefits of this workflow are fairly self-evident in the amount of space in their workflow devoted to data processing versus exploration, however, there are other benefits as well, of which I will name two. First, by using packages hosted on CRAN or Bioconductor, the authors can leverage the Bioconductor build system and ensure a fully working environment, a non-trivial prerequisite in a field with myriad tools. Second, by providing an integrated set of tools there are few, if any, intermediate files required to analyze a dataset. In addition to reducing the cognitive burden of a newcomer, this generally reduces the footprint for errors. This article is an excellent introduction on how to process and analyze a 16S amplicon dataset. Because of the relative ease of working entirely within a single environment, and for the sound design principles used by the core R packages in this analysis, I predict this workflow will become a useful resource, if not a direct template, for many microbiome scientists learning to process their data.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. This work reports a standard R/Bioconductor open source workflow for the analysis of microbial community profiling data based on (Illumina MiSeq) 16S rRNA amplicon sequencing. The main contribution of the paper is to present a compact overview of a typical microbiome analysis workflow in R, and to integrate accumulated knowledge by the authors regarding best practices in microbiome bioinformatics based on the R statistical programming environment. The workflow covers key steps from raw sequencing data prepreprocessing to standard statistical testing, data integration, and visualization. The methodologies are rigorous, and represent a straightforward combination of previously published R tools that are among the state-of-the-art in the field. Reliance on Bioconductor packages provides further guarantees for high quality of the software components. All data and code underlying the paper are openly available, and I was also able replicate the complete workflow after some initial setups. I examined about half of the examples in more detail, and could reproduce manuscript figures in all cases that I tested. No new methods are introduced, and the main contribution of the work is to showcase good statistical practice based on existing software components, some of which have been previously published by the authors of this manuscript. Appropriate references are provided throughout the text. Such overview papers are useful, however, as they can provide benchmarks and recommendations on complete workflows, where the different analysis steps are not independent in any real study and deserve analysis in their own right. The analysis steps are explained in clear language and with sufficient detail. The work is technically sound. The main drawback is that the manuscript is somewhat scattered as it aims to cover a large and versatile set of tools in a single paper. The quality of the analysis is high, and the overview is useful, and the paper could be accepted after taking into account my comments below.Major commentsThe work is somewhat scattered due to the wide coverage. The paper could benefit from having less figures and and increasing focus on key aspects. For instance the number of biplots and network figures could be reduced. The data integration part (CCA etc.) is useful but very brief and probably difficult to comprehend by readers who are new to those approaches. I would recommend either cutting or expanding this part and also otherwise checking if the manuscript can be made more compact by removing some examples .The examples with DADA2 and the hierarchical testing procedure are particularly useful; these recently published methods would deserve to become more widely used. Sufficient details have been given for this work.Instructions on how to exactly use the source files provided in Github are missing. The rnw files are missing latex headers so I could not readily generate final readable reports from the rnw files. The code itself was clear, and after some setups I could replicate all analyses after changing some path definitions and running the code interactively on R command line. But this relied on my earlier good knowledge on R and automated document generationsystems. Users who are less experienced with these tools would benefit from improved instructions on how to run the workflow. The\u00a0 README.md file in Github should give more detailed instructions (or link to instructions) on how to exactly reproduce the complete example workflow and generate the final reports.In the \"Infer sequence variants\" section it is mentioned that \"Sequence inference removed nearly all substitution and indel errors from the data\". How this was quantified to reach this conclusion?Minor commentsThe phyloseq R package has been published earlier and represents an extremely useful class structure for microbiome profiling data that has high potential of becoming a popular standard in R. These tools, and their (online) documentation form essential background material for this manuscript. Better separaration of the data structures and tools in this manuscript, the R packages (in particular phyloseq) and their documentation. This would make it easier for the wider R community to build on this work and contribute further tools that take advantage of the phyloseq data structure. This is not required for this manuscript but a suggestion for improvement.I had to investigate the code a while to see that the file http://www.mothur.org/MiSeqDevelopmentData/StabilityNoMetaG.tar has to be stored in a data/MiSeq_SOP/ directory after download and extraction. Not a big deal but it would be even more handy to have a download script (R or shell) available in the F1000_workflow/data/ directory, and the instructions would then give clear advice on how to automate the complete analysis workflow. To streamline the workflow example, consider providing some example data sets as R data packages.At the github repo README.md the command knit(\"PartIIphyloseq.Rnw\") should be knit(\"PartIIphyloseq.rnw\")In PartIIIanalysis.rnw the script gets stuck at: options . Therefore I skipped this row in my tests. Please fix.Is it intentional that figures 8, 10, 11, 12, 13, 14 and some other figures have an unbalanced width/height ratio? The figures might seem more clear if the width/height ratio was more balanced.The plot_abundance function could be readily provided in the phyloseq package?Quality of Figure 1 is relatively poor and could be improved.Figure 31: in title: fix \"muliple\" into \"multiple\"I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard."}
+{"text": "Klebsiella pneumoniae has emerged worldwide as a cause of pyogenic liver abscess (PLA) often complicated by meningitis and endophthalmitis. Early detection of this infectious disease will improve its clinical outcome. Therefore, we tried to isolate immunodominant proteins secreted by K. pneumoniae strains causing PLA.K. pneumoniae PLA. A ~30-kDa immunodominant protein was then identified. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) revealed an open reading frame (KP1_p307) located on the pK2044 plasmid and bioinformatic analysis identified this protein as a signal peptide of unknown function. The KP1_p307 gene was more prevalent in PLA strains and capsular type K1/K2 strains, but disruption of this gene in NTUH-K2044 strain did not decrease virulence in mice. Ten of fourteen (71%) sera from patients with K. pneumoniae PLA were immunoreactive with the recombinant KP1_p307 protein. Seroconversion demonstrated by a rise in serum titer in serial serum samples confirmed that antibodies against the KP1_p307 protein were elicited after infection.The secreted proteins of the NTUH-K2044 strain were separated by two-dimensional electrophoresis and then immunoblotted using convalescent sera from patients with K. pneumoniae PLA, particularly in K1/K2 PLA strains.The KP1_p307 protein could be used as an antigen for early serodiagnosis of  Klebsiella pneumoniae is a common Gram-negative enteric bacterium that causes hospital-acquired urinary tract infection, septicemia, and pneumonia as well as community-acquired pneumonia. Recently, community-acquired pyogenic liver abscess (PLA) caused by K. pneumoniae complicated with metastatic meningitis and endophthalmitis has emerged globally especially in Asia . K.. K.Klebsustralia . The morK. pneumoniae liver abscess  to the placZ deletion mutant strain [white colony]).Female 5-week-old BALB/cByl mice were inoculated with escribed . Four miescribed . The amoThe purified recombinant His-tag KP1_p307 protein was separated on 12% SDS-PAGE (~5 ng per lane) and then transferred onto a membrane. Patients\u2019 sera (1:2000 dilution) were screened simultaneously using Mini-PROTEAN II Multiscreen Apparatus . Those collected first were screened first, and immunoblotting with serial sera was performed when the first collected serum was antibody negative.K. pneumoniae strains were determined by wzc genotyping [Capsular types of the notyping . The seqK. pneumoniae NTUH-K2044 strain causing pyogenic liver abscess (PLA), the secreted proteins were isolated as described above and analyzed by immunoblotting using sera from one healthy subject and two patients in the convalescent phase of PLA caused by K. pneumoniae  and 53% (95/180) identity to a hypothetical protein of Enterococcus dispar (WP_016173221.1). Bioinformatic analysis revealed that this protein contained a signal peptide located at N-terminal residues 1 to 24 but had no known function.The protein spots on the two-dimensional gels were excised, digested by trypsin, and analyzed by LC-MS/MS. The obtained peptide sequences were compared with the whole genome sequences of NTUH-K2044 strain (chromosome and a large plasmid: AP006725.1 and AP006726.1). A 921-bp open reading frame (KP1_p307) located on the large plasmid pK2044 was confirmed. The sequences of peptides obtained from LC-MS/MS represented 93.1% of KP1_p307 protein sequences. The theoretical pI and molecular weight of KP1_p307 protein  matched those of the immunoreactive protein. The amino acid sequences of KP1_p307 revealed 47% (107/229) identity to a hypothetical protein of E. coli to confirm its immunoreactivity. Sera from another four patients with PLA and three healthy subjects were used to immunoblot the recombinant GST-KP1_p307 protein. The GST-KP1_p307 protein was immunoreactive with sera from patients but not from healthy subjects . K1 and K2 were the most prevalent capsular types causing PLA and were strongly associated with virulence. Thirty-seven of the 42 PLA strains were K1/K2, whereas 4 of the 32 non\u2013tissue-invasive strains were found to be K1/K2. The prevalence of K1/K2 strains with KP1_p307 (39 out of 41) was significantly higher than that of non-K1/K2 strains with KP1_p307 (6 out of 33) . Because this gene might be associated with bacterial virulence, a KP1_p307 knockout mutant of the NTUH-K2044 strain was generated and its virulence was tested in mice. The survival of mice intraperitoneally infected with 1\u2009\u00d7\u2009103 cfu of wild type or KP1_p307 knockout mutant was not significantly different  of NTUH-K2044 strain given intraperitoneally. Moreover, the in vivo competition between the wildtype and KP1_p307 knockout mutant strains was also not significantly affected . These results confirm that the antibodies against the KP1_p307 protein were elicited after infections. Therefore, the KP1_p307 protein could be used to serodiagnose K. pneumoniae PLA.Antibodies against the KP1_p307 could be detected in the first serum sample (collected 1\u201326 days after admission) from eight of the ten patients with positive reactions. Four of these eight patients had positive serological reactions to KP1_p307 protein on the first day after hospital admission Table\u00a0 on the 5K. pneumoniae infections other than PLA was further examined  have KP1_p307. Four of the 8 sera (50%) collected from patients infected with KP1_p307-positive K. pneumoniae strains when they visited the hospital were immunoreactive with the recombinant KP1_p307 protein.The diagnostic application of KP1_p307 protein in K. pneumoniae PLA strain by a proteomic approach and evaluated its application to the serodiagnosis of K. pneumoniae PLA. The sensitivity of K. pneumoniae PLA detection was 71.4% (10/14) and four out of six sera (66.7%) collected on the first day of hospitalization for K. pneumoniae PLA were positive for anti-KP1_p307 antibodies. Our previous study indicated that 80% of patients with PLA were caused by K. pneumoniae infection [K. pneumoniae PLA was relatively few. We included serum samples from three patients with LA not caused by K. pneumoniae and sera from ten healthy subjects to confirm the specificity. The results indicated that the specificity was 100% (13/13). The detection of antibodies against KP1_p307 protein could be performed directly using sera and did not require the cultivation of bacteria. Although the sensitivity and specificity of this test still need to be confirmed by using a larger number of clinical samples, we suggest that detection of anti-KP1_p307 antibody was considered to be an easy and rapid method for early screening of K. pneumoniae PLA. As shown in Figure\u00a0th day), therefore antibody titers could persisted for months after successful treatment. If the antibodies persist, the KP1_p307 antigen could not be used in the diagnosis of K. pneumoniae re-infection. However, because we did not have adequate follow-up sera, how long the anti-KP1_p370 antibodies persisted could not be determined.In this study, we identified an immunodominant antigen in a nfection . TherefoL. rossiae and a hypothetical protein of E. dispar. Hence, the cross-reactivity of antibodies against L. rossiae and E. dispar to KP1_p307 protein may occur. However, the infections caused by L. rossiae and E. dispar were extremely rare. There were neither L. rossiae nor E. dispar isolates in the bacteria collection of NTUH, Taipei VGH and Chang Gung Memorial Hospital, thus we have difficulties to examine the crossreactivity. But, we expected that the possible crossreactivity would not interfere in the application of KP1_p307 to the serodiagnosis of K. pneumoniae PLA. Moreover, the function of KP1_p307 protein remains unknown and difficult to study. Although the presence of this protein was correlated with K. pneumoniae PLA, knock out of KP1_p307 did not decrease the virulence of K. pneumoniae NTUH-K2044 strain in mice even its in vivo ability to compete with wild type strain. Our preliminary result demonstrated that the anti-KP1_p307 antibodies could not block the infection of K. pneumoniae NTUH-K2044 strain in mice (data not shown). Further investigation of the functions of KP1_p307 protein and anti-KP1_p307 antibody is still needed.The KP1_p307 protein was predicted to have a signal peptide similar to that of the KP1_p307 protein present in the fraction secreted from bacteria. The amino acid sequences of KP1_p307 revealed approximately 50% identity to a hypothetical protein of K. pneumoniae K1 strains with the presence of aerobactin [rmpA gene (for regulator of mucoid phenotype A) was associated with K. pneumoniae PLA [iuc region (which encodes aerobactin) or p-rmpA did not decrease its virulence in mice [K. pneumoniae PLA, but knockout of this gene had no influence on virulence. Because these genes  are all located on the large plasmid and associated with bacterial virulence, we propose that other virulence factors might be present in the large plasmid of K. pneumoniae. These genes could serve as markers of bacterial virulence due to their co-inheritance together with virulence genes carried by this plasmid.Previous studies have demonstrated the correlation of the virulence o f robactin ,26. Our niae PLA . However in mice ,28. HereK. pneumoniae strains with capsular types K1 and K2 are more virulent than those with other capsular types [K. pneumoniae PLA but also possibly other infections caused by capsular type K1/K2 strains. The majority of K. pneumoniae PLA strains were capsular type K1 and K2 [K. pneumoniae strains isolated from these 14 PLA patients in this study  of K. pneumoniae which carry a virulent plasmid.As shown in Table\u00a0ar types ,29. Ther1 and K2 . But we K. pneumoniae, such as OmpA and OmpK36 etc., have been reported previously [K. pneumoniae PLA patients were not immuno-reactive to the recombinant OmpK36 protein (data not shown). We speculated that OmpK36 antigen located on the outer membrane might be masked by capsule and might not be exposed, because the strains causing PLA were observed to be heavily encapsulated [K. pneumoniae PLA infections. Moreover, OmpK36 protein is commonly distributed among K. pneumoniae strains and has sequence similarity to that of E. coli. These results indicated that KP1_p307 would be a better antigen than OmpK36 to serodiagnose K. pneumoniae PLA.There are several immunogenic antigens of eviously . The immpsulated . The antK. pneumoniae strain causing PLA. The KP1_p307 gene was located on the large plasmid and more prevalent in PLA strains and capsular type K1/K2 strains, but deletion of KP1_p307 did not decrease the virulence of this strain in mice. The high sensitivity and specificity demonstrated here indicates that the Kp1_p307 protein could be used to serodiagnose K. pneumoniae PLA or other infections.In conclusion, an immunodominant antigen, KP1_p307, was identified in a"}
+{"text": "Lactobacillus plantarum constitutes a well-recognized food-grade system for the expression of recombinant proteins in the field of industrial and medical biotechnology. For applications in vivo or in biotechnological processes, the level of expression of e.g. antigens or enzymes is often critical, as expression levels should be of a certain effectiveness, yet, without putting too much strain to the overall system. The key factors that control gene expression are promoter strength, gene copy number and translation efficiency. In order to estimate the impact of these adjusting screws in L. plantarum CD033, we have tested several constitutive promoters in combination with high and low copy number plasmid backbones and varying space between the Shine-Dalgarno sequence and the start-codon.L. plantarum CD033 and L. buchneri CD034, a synthetic promoter, originally derived from L. plantarum WCSF1 and a heterologous promoter derived from L. buchneri CD034 with a high and a low copy number origin of replication we demonstrated various expression levels of the model protein mCherry. All promoters were feasible for protein expression and in all cases, the high copy number origin of replication increased expression twofold. We found that the optimal spacer between the Shine-Dalgarno sequence and the start codon in L. plantarum consists of 8 nucleotides and elongation as well as shortening this sequence gradually down-regulates gene expression.By combining strong promoters, such as transcription elongation factor promoters, isolated from L. plantarum CD033. We have thus, provided potential expression vectors useful for constitutive protein expression in lactic acid bacteria ranging from moderate to strong production levels.We have evaluated the effects of a set of gene regulatory tools to fine tune recombinant gene expression in  Lactococcus lactis [Lactobacillus sakei which was shown to drive high-level gene expression in L. sakei and Lactobacillus plantarum [L. plantarum [L. plantarum WCFS1 [Lactic acid bacteria (LAB) are responsible for various fermentation processes leading to food and feed preservation and improvement in flavour and texture of the fermented substrate . Furthers lactis -15. Indus lactis . Anotherlantarum . Anotherlantarum ), which lantarum . Induciblantarum , for thelantarum ,7,21 or lantarum . For theum WCFS1 . It was um WCFS1 ,24. TherLactobacillus buchneri strain, and its origin of replication was shown to support plasmid maintenance in L. plantarum [Besides promoter activity, also plasmid copy numbers have a major impact on recombinant protein expression. Most of the commonly used plasmid backbones are based on low copy number origins of replication p256)  or high 6  or higlantarum . The rellantarum . Besideslantarum ,29.L. plantarum with the purpose to provide suitable constitutive systems for applications in e.g. feed silage, food fermentation or in vivo drug delivery. Therefore, we compared different autologous and heterologous promoters, the impact of high and low copy number plasmid backbones and the influence of the distance between the Shine-Dalgarno sequence and the translation start signal. Our expression host was L. plantarum CD033. This strain has been isolated from a grass silage in Austria and may be used as an efficient starter culture for this purpose. In addition, L. plantarum CD033 was previously described to be feasible for highly efficient transformation with non-methylated DNA, allowing direct transfer of a ligation mix or assembled PCR fragments [L. lactis or E. coli for high yield plasmid production are no longer required, which allows us very fast plasmid construction and manipulation, ideal for testing a large set of genetic elements.The goal of this study was to identify and evaluate simple tools and measures to fine-tune recombinant protein expression in ragments . TherefoE. coli was required, all plasmids were designed without any additional E. coli specific origin of replication or selection markers. In the first experiment, we included four different constitutive promoters and tested for cytoplasmic expression of the reporter gene mCherry. The strong P11 promoter, a synthetic sequence based on an rRNA promoter from L. plantarum WCSF1 [L. plantarum as its transcriptional activity was comparable to the inducible pSIP-based expression system [L. sakei [tuf) from L. plantarum CD033 (Ptuf33) and from L. buchneri CD034 .Since no amplification of shuttle vectors in um WCSF1  was prevn system . AnotherL. sakei . FurtherL. plantarum CD033 cells carrying the pCD256\u0394Ec-based constructs using a Tecan\u2122 reader. Cells were cultivated and measurements were performed for 23\u00a0h , indicating that its low activity is a species specific effect, and in the context of L. plantarum this promoter is not feasible for further experiments.Pretesting of the promotor activities was accomplished by monitoring fluorescence signals of L. plantarum strains have copy numbers between one and five. Yet for the pSIP411-based expression system also the high copy number origin of replication derived from pSH71 is used [L. buchneri CD034 [\u2122 platform.It has been shown for several plasmid based expression systems, that gene copy numbers have a strong influence on overall expression of a heterologous protein. While normally an increase leads to higher expression rates, sometimes too high replication rates can be detrimental to cell growth . Most of is used . In orderi CD034  resultintuf33_mCherry and pCDLbu-1\u0394Ec_Ptuf34_mCherry, both containing the high copy number origin of replication, produce less biomass during fermentation. This might be because, due to overproduction of mCherry, the overall metabolic load hampers the growth rate. Alternatively, the high number of Ptuf-promoter copies may capture essential sigma factors, and the cell is unable to proceed with translation of homologous genes at a normal rate. When comparing the overall transcriptional activities . Figure\u00a0charides ,7. FinalLactobacillus plantarum is widely spread in nature. It is used as a highly effective silage additive, has probiotic properties and serves as a cell factory to produce recombinant proteins. Here we have tested several constitutive promoters in combination with high and low copy number plasmid backbones in L. plantarum CD033. Thereby, we confirmed the previously described promoter P11 [tuf33 and Ptuf34, which now are available as additional candidates to drive constitutive expression in L. plantarum as well as in L. buchneri. The impact of different origins of replication was investigated, demonstrating twofold higher product yields for the pCDLbu-1\u0394Ec-based constructs containing the high copy number origin of replication derived from the L. buchneri CD034 plasmid pCD034-1 [L. plantarum CD033 there was a direct correlation between these two parameters, reaching the highest expression levels when the spacer spanned 8 nucleotides. While the performance and behavior of expression regulatory elements might differ in dependence of the target gene, predictions about their impact will facilitate vector design strategies and experimental set-ups in the future. Overall, we believe that the silage strain L. plantarum CD033 as well as the L. plantarum species in general is a highly versatile tool for improving nutrition quality, human health and biomass based energy production.oter P11  to be fepCD034-1 . BesidesLactobacillus plantarum strain CD033 was grown in de Man-Rogosa-Sharpe (MRS) medium [\u22121) if required. The transformation of plasmids into L. plantarum CD033 was accomplished according to the electroporation protocol described earlier [The ) medium  at 30\u00b0C  earlier .All Enzymes were purchased from New England Biolabs . DNA fragments were amplified using the Phusion High-Fidelity DNA Polymerase according to the manufacturer\u2019s recommendations. All resulting clones were colony screened using OneTaq DNA Polymerase as recommended by the producer. All PCRs were carried out with a C1000 Thermal Cycler . Restriction digests were performed following the manufacturer\u2019s instructions. PCR products were purified using the NucleoSpin Gel and PCR Clean-up Kit . Ligations were performed using T4-ligase. All primers are listed in Table\u00a0L. plantarum WCFS1 using the webtool JCat (http://www.jcat.de/), encoding the red fluorescent protein mCherry was synthesized as a gBlock  and amplified using the primers mCherry_F (NdeI)/mCherry_R (BamHI). Promotor P11 was also amplified from a gBlock using the primers P11_F /P11_R (NdeI). The two PCR products were digested with NdeI and ligated one with each other to gain the DNA-fragment P11_mCherry. The ligation product was again amplified using the primers P11_F /mCherry_R (BamHI).A gene, codon optimized for For construction of the theta-replicating expression vectors the plasmid pCD256\u0394EC_hTTF1  was ampltuf33, Ptuf_CD034_F (KpnI)/Ptuf34_R (BsrGI) for promoter Ptuf34 and efp-sense_F /Pefp_R (BsrGI) for the efp-promoter Pefp. The PCR products were KpnI/BsrGI digested and each promotor was ligated with the vector backbone described above. The resulting constructs were pCD256\u0394Ec_Ptuf33_mCherry, pCD256\u0394Ec_Ptuf34_mCherry and pCD256\u0394Ec_Pefp_mCherry. All constructs were introduced into L. plantarum CD033 by electroporation. The pCD256\u0394Ec constructs were colony screened using the primers Cat_seq2_back and efp_screen_back. All constructs were confirmed by sequencing using the same primers .To get constructs with the other promoters, pCD256 \u0394EC _P11_mcherry was amplified using the primers mCherry_F (BsrGI)/sCAT_R (KpnI). The promoters were amplified using the primers Ptuf_CD033_F (KpnI)/Ptuf33_R (BsrGI) for promoter PE.coli-specific sequences were removed by PCR using the primers Cat_F (NheI)/M13_R (NheI). After NheI digestion the amplicon was recircularized by selfligation and transformed directly into L. plantarum CD033. The resulting vector was designated pCDLbu-1\u0394EC and was subsequently amplified using the primers sCAT_R (KpnI)/Tldh_F (BamHI) and digested with KpnI/PstI. The already finished pCD256\u0394Ec vectors served as template for insert amplification. Therefore, forward primers P11_F (SacI/KpnI), efp-sense_F , Ptuf_CD033_F (KpnI), Ptuf_CD034_F (KpnI) and the reverse primer Tldh_amp_R (PstI) were used to obtain the expression cassettes P11_mCherry_Tldh, Pefp_mCherry_Tldh, Ptuf33_mCherry_Tldh and Ptuf34_mCherry_Tldh. After a KpnI/PstI digest the inserts were ligated with the pCDLbu-1\u0394Ec backbone to gain the constructs pCDLbu-1\u0394Ec _P11_mCherry, pCDLbu-1\u0394Ec_Pefp_mCherry, pCDLbu-1\u0394Ec_Ptuf33_mCherry and pCDLbu-1\u0394Ec_Ptuf34_mCherry which were used to transform L. plantarum CD033 by electroporation. The pCDLbu1\u0394Ec constructs were colony screened using the primer pair Cat_seq2_back /4_6_n2_R. All constructs were confirmed by sequencing using the same primers .For the RCR-constructs plasmid pCDLbu-1  served aThe general vector designs are shown in Figure\u00a0L. plantarum CD033.Plasmids pCDLbu1\u0394Ec_mCherry and pCD256\u0394Ec_mCherry lacking a promoter upstream of the mCherry gene served as negative controls. Therefore, plasmids pCDLbu1\u0394Ec _P11_mCherry and the pCD256\u0394Ec _P11_mCherry were amplified using the primers P11_control_R (KpnI)/sCAT_R (KpnI). After a KpnI-digest the PCR products were self-ligated and used to transform L. plantarum CD033.Plasmid pCD256_P11_mCherry was used as PCR template for this experiment. Constructs were amplified using the forward primers SDOPT_5_F (XbaI), SDOPT_6_F (XbaI), SDOPT_7_F (XbaI), SDOPT_8_F (XbaI) SDOPT_9_F (XbaI), SDOPT_10_F (XbaI), SDOPT_11_F (XbaI) and SDOPT_12_F (XbaI) and the reverse primer SDOPT_R (XbaI). Restriction digests with XbaI were performed. The DNA-fragments were self-ligated and used to transform Colonies resistant to chloramphenicol were screened by PCR using the primers Cat_seq2_back/EFP_screen_back and correctness of the constructs was confirmed by sequencing of the obtained PCR-products .600 value of 0.1. 200\u00a0\u03bcL of each sample was pipetted into a 96 well clear bottom plate . The mCherry fluorescence at 587\u00a0nm was measured at 30\u00b0C every 30\u00a0minutes over 23\u00a0h. A gain of 140 was used for fluorescence measurments. Immediately prior to fluorescence measurment, samples were shaken for 15\u00a0seconds. Samples were analyzed in quadruplicate.The Infinite M1000 Tecan\u2122 reader connected to the Tecan i-control 1.6 software  was used for pretesting. Overnight cultures were diluted to an OD600 value of 0.1 and subsequently 800\u00a0\u03bcl of each sample were pipetted into a MTP-48 FlowerPlate\u2122 . Fluorescence was determined using the E-OP-119 LED module for mCherry at 580\u00a0nm and a gain of 80. Measurement was executed every 15\u00a0minutes, cells were cultivated at 30\u00b0C for 24\u00a0hours under constant shaking at 1,000\u00a0rpm. Negative controls were analyzed in duplicate, samples were analyzed in triplicate. For biomass analysis a calibration curve was generated. The OD600 values of a L. plantarum CD033 o/n cultures were measured undiluted, 1:1.3, 1:2, 1:3, 1:4, 1:5, 1:7, 1:10 and 1:20 diluted in an Implen Nano Photometer  and correlated with the scattered light data at 620\u00a0nm and a gain of 20 measured using the BioLector\u2122 system. The linear equation of the standard curve was y = 0.0312x \u2013 0.6465 with a correlation coefficient R = 0.9991.mCherry measurements were accomplished using the BioLector\u2122 Basic device (m2p-labs Germany). Data were analyzed using the BioLection 2.3.13 software . Overnight cultures were diluted to an OD"}
+{"text": "Puccinia striiformis f. sp. tritici, is a costly global disease that burdens farmers with yield loss and high fungicide expenses. This sophisticated biotrophic parasite infiltrates wheat leaves and develops infection structures inside host cells, appropriating nutrients while suppressing the plant defense response. Development in most eukaryotes is regulated by small RNA molecules, and the success of host-induced gene silencing technology in Puccinia spp. implies the existence of a functional RNAi system. However, some fungi lack this capability, and small RNAs have not yet been reported in rust fungi. The objective of this study was to determine whether P. striiformis carries an endogenous small RNA repertoire.Wheat stripe rust, caused by P. striiformis draft genome and removing reads present in uninfected control libraries. Sequencing and bioinformatics results were verified by RT-PCR. Like other RNAi-equipped fungi, P. striiformis produces large numbers of 20\u201322\u00a0nt sequences with a preference for uracil at the 5\u2032 position. Precise post-transcriptional processing and high accumulation of specific sRNA sequences were observed. Some predicted sRNA precursors possess a microRNA-like stem-loop secondary structure; others originate from much longer inverted repeats containing gene sequences. Finally, sRNA-target prediction algorithms were used to obtain a list of putative gene targets in both organisms. Predicted fungal target genes were enriched for kinases and small secreted proteins, while the list of wheat targets included homologs of known plant resistance genes.We extracted small RNA from rust-infected wheat flag leaves and performed high-throughput sequencing. Two wheat cultivars were analyzed: one is susceptible; the other displays partial high-temperature adult plant resistance. Fungal-specific reads were identified by mapping to the This work provides an inventory of small RNAs endogenous to an important plant pathogen, enabling further exploration of gene regulation on both sides of the host/parasite interaction. We conclude that small RNAs are likely to play a role in regulating the complex developmental processes involved in stripe rust pathogenicity.The online version of this article (doi:10.1186/s12864-015-1895-4) contains supplementary material, which is available to authorized users. Puccinia striiformis f. sp. tritici (Pst) is a Basidiomycete fungus that causes wheat stripe rust disease. Repeated outbreaks in Northern Africa, the Middle East, and Central Asia have contributed to economic hardship and food insecurity in these regions [ regions . Stripe  regions . Transna regions . Althoug regions .P. striiformis is an obligate biotroph; the fungus produces specialized infection structures called haustoria inside living host cells [Pst draft genome and transcriptome, genes coding for proteins with effector-like amino acid sequences were identified for further analysis [st cells . The haust cells . In othest cells . In turnst cells . Using tanalysis , 10.The complex two-way interaction between pathogens and their hosts can be partly decoded via patterns of gene expression and regulation. Dual RNA-sequencing of both pathogen and host is an elegant means to explore both sides of this interaction , 12. TheNeurospora [Saccharomyces cerevisiae and the plant pathogen Ustilago maydis, were found to have lost their RNAi capability [Botrytis cineria function as virulence factors by silencing plant defense genes [Pst, which maintains an intimate relationship with its host both physically and evolutionarily, might be particularly adapted to employ sRNA-based effectors [Small RNA from many fungal species have been surveyed since the first discovery of RNAi in urospora . Severalpability \u201322. Howepability \u201325. Smalse genes , 27. A bffectors .P. triticina and stem rust fungus P. graminis using this technology [Puccinia species, whether endogenous or HIGS-induced. Much remains unknown about the fungal gene silencing machinery in general; some evidence suggests there are sRNA biogenesis pathways found only in fungi [Pst.Fundamental research on post-transcriptional gene silencing in parasitic fungi has led to a tantalizing prospect for molecular genetic control of pathogen virulence via host-induced gene silencing (HIGS) . HIGS wochnology , 33. Howin fungi . The goaMagnaporthe or Botrytis, it is currently not feasible to raise axenic cultures of P. striiformis in the laboratory. Thus, obtaining samples during development must involve extracting RNA from infected plant tissue, and then removing contaminating wheat sequences [Unlike many other pathogenic fungi, such as equences . In thisTwo soft white spring wheat cultivars, \u2018Penawawa\u2019 and \u2018Louise\u2019, were chosen as host plants. Penawawa is susceptible to strain PST-100, whereas Louise possesses partial high temperature adult plant (HTAP) resistance, largely controlled by a locus on chromosome 2BS . We specFully-emerged flag leaves on 6\u00a0week-old wheat plants were inoculated with either PST-100 spores mixed with talcum powder, or mock-inoculated with talcum powder only. There were 4 treatment groups: Infected Penawawa (IP), Infected Louise (IL), Uninfected Penawawa (UP), and Uninfected Louise (UL). Three biological replicates  were in each treatment group; there were 12 samples total. Flag leaf tissue was collected for RNA extraction at four days post-inoculation (dpi). This time point corresponds to a high rate of haustorium growth , and falPst race 130 predicted several genes required for small RNA-mediated gene silencing, including Dicer-like (RNAse III) and Argonaute genes [PSTG_15713) and Argonaute (PSTG_06326) are also present in a different draft genome: PST-78 [PSTG_03098.1, PSTG_15184.1) are highly similar to an RNA-dependent RNA polymerase necessary for the quelling of transgenes in Neurospora crassa (QDE-1).Prior genome analysis of te genes . BLAST s: PST-78 . Also, aTo determine whether these genes are expressed during stripe rust infection, reverse transcription followed by PCR (RT-PCR) was performed on the total RNA extracts. Fragments of all four genes were successfully amplified from infected Penawawa plants, and were not observed in the uninfected Penawawa control Fig.\u00a0. The expSmall RNA sequencing generated over 50 million total reads between 18\u201340\u00a0nt in length Table\u00a0. Not couP. striiformis PST-78 draft genome. Approximately 1.3\u00a0% of all nonredundant sequences in the infected Louise treatment mapped with zero mismatches to the Pst genome  were never found in the corresponding uninfected replicates of that variety. For example, 50,909 mapped reads were found in Infected Louise, but never in Uninfected Louise  are processed in a Dicer-dependent manner. Under the null hypothesis, nonspecific RNA degradation would be the primary source of sRNA reads, and particular sequences with fixed lengths would not accumulate. However, the size distribution clearly deviated from the random or flat distribution expected in the absence of sRNA biogenesis  position. A majority (75\u00a0%) of 20\u201322\u00a0nt Pst-sRNA sequences began with uracil, whereas guanine and cytosine were suppressed  were found in both IP and IL. After Empirical Analysis of Differential Gene Expression (EDGE) at an FDR-adjusted p-value of 0.05, no sRNA sequences in this length class were found to be differentially expressed. On the other hand, some longer sRNAs (\u226530\u00a0nt in length) were both abundant and unique to either infected Louise or infected Penawawa  [Pst carries milRNA, we used the ShortStack software package [MicroRNAs originate from a single-stranded RNA precursor, which folds into a characteristic stem-loop secondary structure when transcribed. Dicer activity then cleaves the precursor into a miRNA/miRNA* duplex with two-nucleotide 3\u2032 overhangs . Some fu(milRNA) , 43. To  package , which hPSTG_12821 and PSTG_12822) and near, but not overlapping, multiple Harbinger and Copia transposable elements. All reads in this region mapped to a single strand of the DNA sequence. If transcribed, this region would assume a stem-loop secondary structure with two clusters of 22\u00a0nt mapped sRNA reads . However, none of these additional loci had the depth or pattern of mapped sRNA reads that would indicate multiple members of the pst-mil-163 family.A minor criterion for miRNA annotation is conservation among related species, but BLAST searches with the pst-mil-163 precursor sequence against the Another locus identified by Maple as miRNA-like is located on Supercontig 1.61. Interestingly, two adjacent pairs of milRNA sequences were predicted on a single long precursor Fig.\u00a0. Pst-milPst-sRNA loci.ShortStack outputs an annotation file with the genomic coordinates of all clusters of mapped reads above a user-defined threshold. Using the default threshold of 20 overlapping reads, 138 clusters were detected in IL and 112 clusters in IP  described previously in fungi [The two genes in the pst-sir-9 locus are closely related, with high sequence homology throughout the coding region Fig.\u00a0. In contin fungi . HoweverP. striiformis employs small RNA to regulate endogenous fungal gene expression, then the sRNA sequences described in this study will share regions of complementarity with protein-coding sequences. Likewise, recent discoveries in Botrytis [P. striiformis and T. aestivum.If Botrytis  providedArabidopsis and non-model plants [In general, target prediction programs first align a given sRNA sequence to more or less complementary regions in a database of target transcripts. Likelihood scores are calculated via criteria from empirically-validated sRNA-target pairs, or by predicting the binding affinity of the sRNA, given the native secondary structure of the target. If the score meets a user-defined cutoff, then the program outputs the sRNA sequence paired with its predicted target gene accession. To date, no software has been designed specifically to predict small RNA targets in fungi. Therefore, three different target prediction tools were run and compared: psRNATarget , TAPIR Fl plants .Pst-sRNA sequences that were 20\u201330\u00a0nt in length and with at least one read in every replicate of IL and/or IP. This equalized inputs to the three programs (psRNATarget discards sRNA sequences >30\u00a0nt in length), and avoided spending computing resources on the least-abundant Pst-sRNAs. TargetFinder, TAPIR, and psRNATarget were used to predict targets in both Pst and wheat transcripts. The sRNA-target pairs output by each program were counted and compared  is five times larger than for Pst (2.3x107 bp). The overlapping regions of Fig.\u00a0We selected red Fig.\u00a0. Approxider Fig.\u00a0. In contmis Fig.\u00a0. This prst 2.3x10 bp. The Puccinia Group database [Target genes that were predicted by two or more programs were screened to determine interesting candidates for functional analysis. Predicted fungal target genes were searched via the BROAD Institute\u2019s database , and werPst genome overall . The kinase genes included members of the FunK1 family of fungal protein kinases, an atypical phosphatidyl inositol 3\u2032 kinase-related protein kinase (PIKK), and an ULK/ULK serine/threonine protein kinase. Genes related to vesicle-mediated intracellular transport, G protein signaling, and protein turnover were also prevalent  were assigned the molecular function \u201ckinase activity\u201d (GO: 0016301). This term was found with greater frequency in the target list than in the \u00a0% were aEffector protein expression is an important step in the development of biotrophic pathogens. Prediction of effectors has focused on small, cysteine rich proteins that contain a secretion signal and a known effector motif or nuclear localization signal . In our As with the fungal targets, wheat target genes predicted by at least two out of three programs were annotated using Blast2GO software. BLAST hits were compiled for 429 genes that matched sequences in related species or wheat progenitors; 359 of these were assigned at least one GO term. The top molecular function terms assigned were ATP binding , zinc ion binding , and DNA binding . Interestingly, BLAST searches of the wheat target list revealed many genes carrying the features of known resistance genes  after planting, when expanded flag leaves showed visible ligules, but before heading (Feekes Growth Stage 9).A sample of the isolate PSTv37 (PST-100) was obtained courtesy of Dr. Xianming Chen . Urediniospores were increased on Penawawa seedlings prior to the experiment. Spores were stored at 4\u00a0\u00b0CC with calcium sulfate desiccant until just before use. Spores were diluted by a factor of 10 (w:w) with talcum powder. This mixture was applied liberally to both sides of flag leaves using gloved fingers. Half of the plants in each variety were spore-inoculated; the other half were mock-inoculated with pure talcum powder and subjected to identical conditions. Three biological replicates  were inoculated in each treatment group.2.After inoculation, plants were misted lightly with distilled water. Plastic sleeves were placed around the mock-inoculated pots to prevent contamination. Plants were placed in a sealed dew chamber at 10\u00a0\u00b0C with 95\u00a0% relative humidity. After 24\u00a0h, they were removed from the dew chamber and placed in a climate-controlled chamber for an additional 3\u00a0days , totaling 4\u00a0days post-inoculation. Whole flag leaves were harvested just above the ligule with scissors and placed in a sealed 15\u00a0mL Falcon tube, then immediately frozen in liquid NFrozen tissue was ground in liquid nitrogen using a mortar and pestle. After grinding, each sample was divided, and two parallel RNA extractions were performed: one for total RNA, and the other for the small RNA fraction only (\u2264200\u00a0nt). The mirVana RNA isolation kit  was used for both extractions. RNA was quantified with a NanoDrop 1000  and with a Bioanalyzer 2100  to check RNA integrity. The sRNA fraction was used for cDNA library preparation using the Ion Torrent Total RNA-seq Kit Version 2 . Barcoded sequencing adapters enabled multiplexed sequencing of all 12 sample libraries. High-throughput sequencing was performed using the Ion Proton platform  at the WSU Molecular Biology and Genomics Core.P. striiformis actin were used as controls. Fungal-specific primer pairs were designed with NCBI Primer BLAST to avoid amplification of wheat genes  and reverse transcribed using SuperScript III . PCR was performed using AmpliTaq Gold polymerase . Samples were pre-heated for 8\u00a0min at 95\u00a0\u00b0C, followed by 35\u00a0cycles of PCR with the following conditions: 15\u00a0s at 95\u00a0\u00b0C; 30\u00a0s at 52\u00a0\u00b0C; 60\u00a0s at 72\u00a0\u00b0C. Wheat GAPDH and P. striiformis PST-78 draft genome were accepted. BAM mapping files from Butter were imported into CLC Genomics Workbench 7 , where sequences were tabulated and counted using the small RNA analysis toolkit. Mapped sequences that were present in both infected and uninfected libraries were removed from the infected libraries to enrich the library for fungal sequences. The resulting sequence list was mapped to the Washington Wheat Transcriptome [Triticum aestivum. Size distribution and 5\u2032 nucleotide bias were performed in CLC Genomics Workbench 7. Empirical Analysis of Differential Gene Expression was also performed in CLC, using the edgeR method described in [Ion Torrent software  was used to trim adapter and barcode sequences, assign reads to each library based on barcode, and filter out low-quality reads (average PHRED <15). Mapping of 18\u201340\u00a0nt reads was performed using Butter 0.3.2, a variant of Bowtie optimized for small RNA and included in the ShortStack package . Butter ribed in .A portion of the original size-selected sRNA extract was used to validate RNA-seq results via endpoint RT-PCR, as described in . Small Rhttp://github.com/MikeAxtell/ShortStack) and installed on a Linux workstation running Perl 5.14. Trimmed sRNA reads and the PST-78 genome were input into ShortStack; the program was run using the following options: \u2013mismatches 0, \u2013mindepth 20, \u2013pad 200, \u2013dicermin 18, \u2013dicermax 35, \u2013miRType \u2018plant\u2019, \u2013phasesize \u2018all\u2019. Resulting GFF3 annotation files, carrying the genomic coordinates of ShortStack-determined sRNA loci, were imported into CLC Genomics Workbench as tracks on the genome. Maple (miRNA discovery) was run using default settings. Scores generated by Maple fall between 0 (poor) and 1 (excellent), plus an overall verdict (PASS/FAIL) for each putative miRNA cluster. Loci receiving a PASS verdict were automatically output to RNAfold to graphically display secondary structure (http://www.tbi.univie.ac.at/).The ShortStack package was obtained from the Axtell Lab (http://www.girinst.org/repbase/). RepeatMasker 4.0.5 was run on the stripe rust genome using the following options: \u2212nolow, \u2212no_is, \u2212gff. Next, tRNAScan-SE 1.3.1 was run on the whole genome sequence using default parameters. A Perl script was used to convert the output of tRNAScan-SE to GFF. Current Rfam and gene annotations were downloaded from the Broad Institute Puccinia group as GTF files (http://www.broadinstitute.org/annotation/genome/puccinia_group/MultiHome.html). Annotation files were imported into CLC Genomics Workbench 7. A track list was constructed over the PST-78 genomic sequence that included ShortStack loci and all annotations mentioned above. Then, the tool \u201cAnnotate with Overlap Information\u201d was used to find the number of ShortStack loci with boundaries that overlapped each annotation feature . The tool \u201cExtract Reads Based on Overlap\u201d was used to obtain the RNA-seq reads corresponding to each annotation feature.Repeat elements specific to fungi  were downloaded from RepBase 20.01 (P. striiformis gene sequences were downloaded from the Broad Institute in FASTA format [https://github.com/carringtonlab/TargetFinder). By default, TargetFinder searches a single sRNA sequence against a target gene database. A Perl script was written to loop TargetFinder for a list of many sRNA sequences, and output the results as comma-separated text. TargetFinder was run using default settings and a score cutoff\u2009\u2264\u20093.5. The psRNATarget program is available as a browser-based tool (http://plantgrn.noble.org/psRNATarget/). Default settings were used with a score cutoff\u2009\u2264\u20092.5. TAPIR 1.2 is a Perl program obtained from the Van de Peer lab (http://bioinformatics.psb.ugent.be/webtools/tapir/). TAPIR was run in FASTA mode using default settings and a score cutoff\u2009\u2264\u20093.5. Output from each program was limited to 25 hits for each small RNA.A format . Wheat sA format . TargetFOutput from all three programs was manipulated into the text format \u201csRNA_accession;TargetGene_accession\\n\u201d to create comparable lists of sRNA-target pairs. Lists were compared using the browser-based BioVenn tool and visualized as area-proportional Venn diagrams .P. striiformis genome were downloaded from supplemental files in [Pst-sRNAs was BLASTed against the NR protein database .Small RNA-target pairs predicted by two or more software programs were extracted from BioVenn output; FASTA files with the nucleotide sequences of these target genes were imported into Blast2GO 3.0 . BLASTx was run on fungal target gene sequences against the nonredundant (NR) protein database  at NCBI. InterProScan 5.9 was run on BLAST results, and GO terms were assigned to target gene sequences. This process was repeated for the entire list of predicted PST-78 genes . The numfiles in . Similarwww.inkscape.org).Figures\u00a0http://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?study=SRP060546The data set supporting the results of this article is available in the NCBI Sequence Read Archive, accession SRP060546, BioProject PRJNA289147."}
+{"text": "Dynamic predictors of fluid responsiveness (FR) perform poorly in ICU patients receiving partial ventilatory assistance. Because these modes of partial support are increasingly used, FR dynamic indexes are applicable only in a few ICU patients . To overWe hypothesize that during Pressure Support (PS) a brief variation in intrathoracic pressure, such as that produced by a deeper inflation lasting some seconds, would differently affect PP in fluid responder and non-responders.\u00ae. The ventilator was set adding to PS a time cycled (4 seconds) pressure-targeted  sigh breath. The three preset levels of sigh pressure  were applied in random order according to a pre-generated sequence. The PP variation (\u0394PP) was calculated considering the average PP value in the 20 heartbeats preceding sigh application (baseline), and 10 (\u0394PP_10) and (\u0394PP_20) following the sigh. The lowest PP value obtained during the 20 heartbeats (\u0394PP_Nadir) was compared with the baseline value  and \u0394PP_20  (Figure In hemodynamically unstable patients undergoing PS, \u0394PP_Nadir determined adding SIGH_35, while not SIGH_15 and SIGH_25, allows assessment of FR."}
+{"text": "The hop gene is of eukaryotic origin. Likewise, the chloroplast elongation factor G (cEF-G) catalyzes the translocation step in chloroplast protein synthesis. The chl-fus gene, which encodes the cEF-G protein, is of plastid origin. Both proteins, Hop and cEF-G, derived from domain duplications. It was demonstrated that the nuclear chl-fus gene locates in opposite orientation to a hop gene in Glycine max. We explored 53 available plant genomes from Chlorophyta to higher plants, to determine whether the chl-fus gene was transferred directly downstream of the primordial hop in the proto-eukaryote host cell. Since both genes came from exon/module duplication events, we wanted to explore the involvement of introns in the early origin and the ensuing evolutionary changes in gene structure.The co-chaperone Hop [fus gene for the future.We reconstructed the evolutionary history of the two convergent plant genes, on the basis of their gene structure, microsynteny and microcolinearity, from 53 plant nuclear genomes. Despite a high degree (72\u00a0%) of microcolinearity among vascular plants, our results demonstrate that their adjacency was a product of chromosomal rearrangements. Based on predicted exon\u2009\u2212\u2009intron structures, we inferred the molecular events giving rise to the current form of genes. Therefore, we propose a simple model of exon/module shuffling by intronic recombinations in which phase-0 introns were essential for domain duplication, and a phase-1 intron for transit peptide recruiting. Finally, we demonstrate a natural susceptibility of the intergenic region to recombine or delete, seriously threatening the integrity of the chl-fus gene was transferred from the chloroplast to a chromosome different from that of hop, in the primitive photosynthetic eukaryote, and much later before the appearance of angiosperms, it was recombined downstream of hop. Exon/module shuffling mediated by symmetric intron phases  was essential for gene evolution. The intergenic region is prone to recombine, risking the integrity of both genes.Our results are consistent with the interpretation that the chl-The online version of this article (doi:10.1186/s12864-015-1780-1) contains supplementary material, which is available to authorized users. We examined the gene microsynteny and microcolinearity of the pair hop (nuclear origin) \u2013 chl-fus (chloroplast origin) from 53 plant nuclear genomes, describe their phylogenetic relationships, and discuss the influence of intron phase distribution on the evolution of both genes by exon shuffling. Predicted recombination events, in higher plants, support the hypothesis that the chromosomal region downstream of the hop gene is prone to recombine, having favored the shuffling of the chloroplast chl-fus gene adjacently to hop, in an opposite orientation.Conserved synteny is the degree to which genes remain on corresponding chromosomes , 2. The heat shock protein (HSP) organizing protein] has been shown to bind both Hsp70 and Hsp90 into supercomplexes that act as an adaptor for protein folding and maturation  with A. thaliana Hop proteins. In this alignment, a perfect match is obvious between the two proteins, excluding the extra 71\u00a0N-terminal amino acids of Micromonas sp. (bordered by a rounded rectangle). In Additional file Micromonas sp. and predicted C. reinhardtii hop genes. We propose that nucleotides in bold belong to a phase-0 intron (Ih), which is in frame with the first and second exons. Conveniently, the exon\u2013intron boundaries conserve the canonical splice consensus sequences AG:GT and CAG:GC  , cDNA2 [GenBank:AK228637] and cDNA3 [GenBank:NM_104952] for hop gene and cDNA1 [GenBank:NM_104951], cDNA2 [GenBank:AK221774] and cDNA3 [GenBank:AY142646] for chl-fus gene. Sequence alignments were performed using ClustalW (EBI) under default parameters [The  X71439]  was used X71439] . Picea aax cEF-G  and humaax cEF-G  were useCAG:GT\u2014 intron\u2014 AG:GTC ACG3\u2032. Phase-1 introns split the ORF between the first and second nucleotides of a codon, e.g., 5\u2032CCA G:GT\u2014intron\u2014AG:GT CAC3\u2032. Phase-2 introns interrupt the ORF between the second and third nucleotides of a codon, e.g., 5\u2032GGC AG:GT\u2014intron\u2014AG:G TCA3\u2032. Recombinable modules are defined as a set of exons flanked by introns of the same phase, typically phase-0 [Intron phase was assigned as stated by Patty . Phase-0 phase-0 .A. thaliana mEF-G [GenBank:NC_003070] were used as outgroups. Additional EF-G sequences were: A. caulinodans ORS 571 [GenBank:YP_001525473], A. fabrum str. C58 [GenBank:NP_354925], F. alni ACN14a [GenBank:YP_711337], K. radiotolerans [GenBank: SRS30216YP_001360437], R. prowazekii str. Madrid E [GenBank: NP_220524], Synechococcus sp. [GenBank: P18667] and S. coelicolor [GenBank: NP_628821].Maximum Likelihood phylogenetic trees were constructed using RaxML program version 7.3.0 . All othO. lucimarinus [GenBank:NC_009360], Micromonas sp. RCC299 [GenBank:NC_013040], C. reinhardtii [GenBank:NW_001843572], L. alabamica [GenBank:ASXC01000179], A. arabicum [GenBank:ASZG1007785] and human [GenBank:NC_000011].Through the HCA method , we circ"}
+{"text": "Drosophila melanogaster and their execution and testing on multiple Graphics Processing Units (GPUs). Neurokernel provides a programming model that capitalizes upon the structural organization of the fly brain into a fixed number of functional modules to distinguish between these modules\u2019 local information processing capabilities and the connectivity patterns that link them. By defining mandatory communication interfaces that specify how data is transmitted between models of each of these modules regardless of their internal design, Neurokernel explicitly enables multiple researchers to collaboratively model the fruit fly\u2019s entire brain by integration of their independently developed models of its constituent processing units. We demonstrate the power of Neurokernel\u2019s model integration by combining independently developed models of the retina and lamina neuropils in the fly\u2019s visual system and by demonstrating their neuroinformation processing capability. We also illustrate Neurokernel\u2019s ability to take advantage of direct GPU-to-GPU data transfers with benchmarks that demonstrate scaling of Neurokernel\u2019s communication performance both over the number of interface ports exposed by an emulation\u2019s constituent modules and the total number of modules comprised by an emulation.We have developed an open software platform called Neurokernel for collaborative development of comprehensive models of the brain of the fruit fly  Reverse engineering the information processing functions of the brain is an engineering grand challenge of immense interest that has the potential to drive important advances in computer architecture, artificial intelligence, and medicine. The human brain is an obvious and tantalizing target of this effort; however, its structural and architectural complexity place severe limitations upon the extent to which models built and executed with currently available computational technology can relate its biological structure to its information processing capabilities. Successful development of human brain models must therefore be preceded by an increased understanding of the structural/ architectural complexity of the more tractable brains of simpler organisms and how they implement specific information processing functions and govern behavior .Drosophila melanogaster possesses a range of features that recommend it as a model organism of choice for relating brain structure to function. Despite the obvious differences in size and complexity between the mammalian and fruit fly brains, researchers dating back to Cajal have observed common design principles in the structure of their sensory subsystems [self.in_gpot_ports]))# Log input spike data:self.log_info (\u2018input spike port data: \u2018+\\ str (self.pm[\u2018spike\u2019][self.in_spike_ports]))# Output random graded potential data:out_gpot_data = \\ gpuarray.to_gpu(np.random.rand(len(self.out_gpot_ports)))self.pm[\u2018gpot\u2019][self.out_gpot_ports] = out_gpot_dataself.log_info (\u2018output gpot port data: \u2018+str (out_gpot_data))# Output spikes to randomly selected output ports:out_spike_data = \\ gpuarray.to_gpu))self.pm[\u2018spike\u2019][self.out_spike_ports] = out_spike_dataself.log_info(\u2018output spike port data: \u2018+str(out_spike_data))Next, we create a subclass of GPUPortMapper class stored in the self.pm attribute. Updated data associated with output ports is propagated to the relevant destination LPUs by Neurokernel before the next iteration of the emulation\u2019s execution.The data arrays associated with an LPU\u2019s ports may be accessed using their path-like identifiers via two instances of the Selector is a convenience class that provides methods and overloaded operators for combining and manipulating sets of validated port identifiers. For example, Selector(\u2018/a/in/gpot[0:2]\u2019) corresponds to the set of two input graded potential port identifiers /a/in/gpot[0] and /a/in/gpot[1]. Additional methods for manipulating port identifiers are provided by the SelectorMethods class.# Define input graded potential, output graded potential,# input spiking, and output spiking ports for LPUS \u2018a\u2019 and \u2018b\u2019:m1_sel_in_gpot = Selector(\u2018/a/in/gpot[0:2]\u2019)m1_sel_out_gpot = Selector(\u2018/a/out/gpot[0:2]\u2019)m1_sel_in_spike = Selector(\u2018/a/in/spike[0:2]\u2019)m1_sel_out_spike = Selector(\u2018/a/out/spike[0:2]\u2019)m2_sel_in_gpot = Selector(\u2018/b/in/gpot[0:2]\u2019)m2_sel_out_gpot = Selector(\u2018/b/out/gpot[0:2]\u2019)m2_sel_in_spike = Selector(\u2018/b/in/spike[0:2]\u2019)m2_sel_out_spike = Selector(\u2018/b/out/spike[0:2]\u2019)# Combine selectors to obtain sets of all input, output,# graded potential, and spiking ports for the two LPUs:m1_sel = m1_sel_in_gpot+m1_sel_out_gpot+\\ m1_sel_in_spike+m1_sel_out_spikem1_sel_in = m1_sel_in_gpot+m1_sel_in_spikem1_sel_out = m1_sel_out_gpot+m1_sel_out_spikem1_sel_gpot = m1_sel_in_gpot+m1_sel_out_gpotm1_sel_spike = m1_sel_in_spike+m1_sel_out_spikem2_sel = m2_sel_in_gpot+m2_sel_out_gpot +\\ m2_sel_in_spike+m2_sel_out_spikem2_sel_in = m2_sel_in_gpot+m2_sel_in_spikem2_sel_out = m2_sel_out_gpot+m2_sel_out_spikem2_sel_gpot = m2_sel_in_gpot+m2_sel_out_gpotm2_sel_spike = m2_sel_in_spike+m2_sel_out_spike# Count the number of graded potential and# spiking ports exposed by each LPU:N1_gpot = SelectorMethods.count_ports(m1_sel_gpot)N1_spike = SelectorMethods.count_ports(m1_sel_spike)N2_gpot = SelectorMethods.count_ports(m2_sel_gpot)N2_spike = SelectorMethods.count_ports(m2_sel_spike)To connect two LPUs, we specify the ports to be exposed by each LPU using path-like selectors. The example below describes the interfaces for two LPUs that each expose two graded potential input ports, two graded potential output ports, two spiking input ports, and two spiking output ports. Pattern class, setting its port input/output and transmission types, and populating it with connections:# Initialize connectivity pattern that can link# ports in m1_sel with ports in m2_sel:pat12 = Pattern# Set the input/output and transmission type attributes of each port in the pattern\u2019s two interfaces:pat12.interface[m1_sel_out_gpot] = pat12.interface[m1_sel_in_gpot] = pat12.interface[m1_sel_out_spike] = pat12.interface[m1_sel_in_spike] = pat12.interface[m2_sel_in_gpot] = pat12.interface[m2_sel_out_gpot] = pat12.interface[m2_sel_in_spike] = pat12.interface[m2_sel_out_spike] = # Create the connections between ports:pat12[\u2018/a/out/gpot[0]\u2019, \u2018/b/in/gpot[0]\u2019] = 1pat12[\u2018/a/out/gpot[1]\u2019, \u2018/b/in/gpot[1]\u2019] = 1pat12[\u2018/b/out/gpot[0]\u2019, \u2018/a/in/gpot[0]\u2019] = 1pat12[\u2018/b/out/gpot[1]\u2019, \u2018/a/in/gpot[1]\u2019] = 1pat12[\u2018/a/out/spike[0]\u2019, \u2018/b/in/spike[0]\u2019] = 1pat12[\u2018/a/out/spike[1]\u2019, \u2018/b/in/spike[1]\u2019] = 1pat12[\u2018/b/out/spike[0]\u2019, \u2018/a/in/spike[0]\u2019] = 1pat12[\u2018/b/out/spike[1]\u2019, \u2018/a/in/spike[1]\u2019] = 1Using the above LPU interface data, we construct an inter-LPU connectivity pattern by instantiating the Manager class instance that connects them together with the above pattern. The setup_logger function may be used to enable output of log messages generated during execution:logger = setup_loggerman = Managerm1_id = \u2018m1\u2019man.add, np.zeros, device = 0)m2_id = \u2018m2\u2019man.add, np.zeros, device = 1)man.connectWe can then pass the defined LPU class and the parameters to be used during instantiation to a # Compute number of execution steps given emulation duration# and time step (both in seconds):duration = 10.0dt = 1e-2steps = int(duration/dt)man.spawnman.start(steps)man.waitAfter all LPUs and connectivity patterns are provided to the manager, the emulation may be executed for a specified number of steps as follows. Neurokernel uses the dynamic process creation feature of MPI-2 supported by OpenMPI to automatically spawn as many MPI processes are needed to run the emulation:To evaluate Neurokernel\u2019s ability to facilitate interfacing of functional brain modules that can be executed on GPUs, we employed Neurokernel\u2019s programming model to interconnect independently developed LPUs in the fruit fly\u2019s early visual system to provide insights into the representation and processing of the visual field by the cascaded LPUs. We also evaluated Neurokernel\u2019s scaling of communication performance in simple configurations of the architecture parameterized by numbers of ports and LPUs.http://neurokernel.github.io/docs.html.The scope of the effort to reverse engineer the fly brain and the need to support the revision of brain models in light of new data requires a structured means of advancing and documenting the evolution of those models and the framework required to support them. To this end, the Neurokernel project employs Requests for Comments documents (RFCs) as a tool for advancing the designs of both Neurokernel\u2019s architecture and the LPU models built to use it. IPython notebooks and RFCs , 41 conthttp://github.com/neurokernel/retina-laminaThe integrated early visual system model we considered consists of models of the fruit fly\u2019s retina and lamina. The retina model comprises a hexagonal array of 721 ommatidia, each of which contains 6 photoreceptor neurons. The photoreceptor model employs a stochastic model of how light input (photons) produce a membrane potential output. Each photoreceptor consists of 30,000 microvilli modeled by 15 equations per microvillus, a photon absorption model, and a model of how the aggregate microvilli contributions produce the photoreceptor\u2019s membrane potential ; the entThe combined retina and lamina models were executed on up to 4 Tesla K20Xm NVIDIA GPUs with an 8 second natural video scene provided as input to the retinal model\u2019s photoreceptors. The computed membrane potentials of specific photoreceptors in each retinal ommatidium and of select neurons in each cartridge of the lamina were recorded ; videos We compared the performance of emulations in which port data stored in GPU memory is copied to and from host memory for traditional network-based transmission by OpenMPI to that of emulations in which port data stored in GPU memory is directly passed to OpenMPI\u2019s communication functions. The latter functions enabled OpenMPI to use NVIDIA\u2019s GPUDirect Peer-to-Peer technology to perform accelerated transmission of data between GPUs whose hardware supports the technology by bypassing the host system\u2019s CPU and memory . All tesrun_step method (see the Application Programming Interface section) and measured (1) the average time taken per execution step to synchronize the data exposed by the output ports in each of two connected LPUs with their respective destination input ports; (2) the average throughput per execution step (in terms of number of port data elements transmitted per second) of the synchronization, where each port is stored either as a 32-bit integer or double-precision floating point number (both of which occupy 8 bytes).To evaluate how well inter-LPU communication scales over the number of ports exposed by an LPU on a multi-GPU machine, we constructed and ran emulations comprising multiple connected instances of an LPU class with an empty We initially examined how the above performance metrics scaled over the number of output ports exposed by each LPU in a 2-LPU emulation and over the number of LPUs in an emulation where each LPU is connected to every other LPU and the total number of output ports exposed by each LPU is fixed. We compared the performance for scenarios where data in GPU memory is directly exposed to OpenMPI to that for scenarios where the data is copied to the host memory prior to transmission; the former scenarios enabled OpenMPI to accelerate data transmission between GPUs using NVIDIA\u2019s GPUDirect Peer-to-Peer technology. The metrics for each set of parameters were averaged over 3 trials; the emulation was executed for 500 steps during each trial.The scaling of performance over number of ports depicted in Current research on the fruit fly brain is mainly focused on LPUs in the fly\u2019s central complex and olfactory and vision systems. Since the interplay between these systems will be key to increasing understanding of multisensory integration and how sensory data might inform behavior mediated by the central complex, we examined how well Neurokernel\u2019s communication mechanism performs in scenarios where LPUs from these three systems are successively added to a multi-LPU emulation. Starting with the pair of LPUs with the largest number of inter-LPU connections, we sorted the 19 LPUs in the above three systems in decreasing order of the number of connections contributed with the addition of each successive LPU and measured the average speedup in synchronization time per execution step due to direct GPU-to-GPU data. The number of connections for each LPU was based upon estimates from a mesoscopic reconstruction of the fruit fly connectome; these numbers appear in Document S2 of the supplement of . The LPUCaenorhabditis elegans and the full reconstruction of its connectome [In light of their low costs and rapidly increasing power and availability, there is growing interest in leveraging the power of multiple GPUs to support neural simulations with increasingly high computational demands \u201346. Whennnectome  to develnnectome  that usennectome , 17 for Currently available neural simulation software affords researchers with a range of ways of constructing neural circuit models. These include tools that enable models to be explicitly expressed as systems of differential equations , structuExisting technologies for interfacing neural models currently provide no native support for the use of GPUs and none of the aforementioned services required to scale over multiple GPU resources. Neurokernel aims to address the problem of model incompatibility in the context of fly brain modeling by ensuring that GPU-based LPU model implementations and inter-LPU connectivity patterns that comply with its APIs are interoperable regardless of their internal implementations.Despite the impressive performance GPU-based spiking neural network software can currently achieve for simulations comprising increasingly large numbers of neurons and synapses, enabling increasingly detailed fruit fly brain models to efficiently scale over multiple GPUs will require resource allocation and management features that are not yet provided by currently available neural simulation packages. By explicitly providing services and APIs for management of GPU resources, Neurokernel will enable fly brain emulations to benefit from the near-term advantages of scaling over multiple GPUs while leaving the door open to anticipated improvements in GPU technology that can further accelerate the performance of fly brain models.The challenges of reverse engineering neural systems have spurred a growing number of projects specifically designed to encourage collaborative neuroscience research endeavors. These include technologies for model sharing , 57, 58,Neuromorphic platforms whose design is directly inspired by the brain have the potential to execute large-scale neural circuit models at speeds that significantly exceed those achievable with traditional von Neumann computer architectures \u201365. IncrAlthough the Neurokernel project is specifically focused upon reverse engineering the fruit fly brain, the framework\u2019s ability to capitalize upon the structural modularity of the brain and facilitate collaborative modeling stand to benefit efforts to reverse engineer the brains of other model organisms. To this end, we have already used Neurokernel to successfully scale up the retinal model described in the Integration of Independently Developed LPU Models section to emulate the retina of the house fly, which comprises almost 10 times as many differential equations (18.8 billion) as that of the fruit fly (1.95 billion). Further development of Neurokernel\u2019s support for multiple GPUs and\u2014eventually\u2014neuromorphic hardware will hopefully open the doors to collaborative modeling of the brains of even more complex organisms such as the zebra fish and mouse.5 spiking neurons and \u223c107 synapses using single GPUs [Efforts at reverse engineering the brain must ultimately confront the need to validate hypotheses regarding neural information processing against actual biological systems. In order to achieve biological validation of the Neurokernel, the computational modeling of the fruit fly brain must be tightly integrated with increasingly precise electrophysiological techniques and the recorded data evaluated with novel system identification methods , 66\u201370. gle GPUs , 33, 71,gle GPUs , 62\u201365, Although Neurokernel currently permits brain models to make use of multiple GPUs, it requires programmers to explicitly manage the GPU resources used by a model\u2019s implementation. Having implemented the API for building and interconnecting LPUs described in the Application Programming Interface section within Neurokernel\u2019s application plane, our next major goal is to implement a prototype GPU resource allocation mechanism within the control plane to automate selection and management of available GPUs used to execute a fly brain model. Direct access to GPUs will also be restricted to modeling components implemented by LPU developers and added to Neurokernel\u2019s compute plane; models implemented or defined in the application plane will instantiate and invoke these components. These developments will permit experimentation with different resource allocation policies as LPU models become more complex. Restricting parallel hardware access to modeling components exposed by the compute plane will also facilitate development of future support for other parallel computing technologies such as non-NVIDIA GPUs or neuromorphic hardware.Neurokernel is a fundamental component of the collaborative workflow needed to accelerate the process of fruit fly brain model development, execution, and refinement by multiple researchers. This workflow, however, also requires a means of efficiently constructing brain models and modifying their structure and parameters in light of output discrepancies observed during validation or to incorporate new experimental data. As noted in the Application Programming Interface section, Neurokernel currently can execute LPU models declaratively specified as GEXF files that each describe an individual LPU\u2019s design as a graph of currently supported neuron and synapse model instances and separately specified inter-LPU connectivity patterns. Since this model representation must either be manually constructed or generated by ad hoc processing of connectome data, modification of LPUs is currently time consuming and significantly slows down the improvement of brain models. LPUs explicitly implemented in Python that do not use supported neuron or synapse models are even less easy to update because of the need to explicitly modify their implementations.To address these limitations and enable rapid updating and reevaluation of fly brain models, we are building a system based upon graph databases called NeuroArch for the specification and sophisticated manipulation of structural data associated with LPU models and inter-LPU connectivity . NeuroArhttps://neurokernel.github.io/), online code repository (https://github.com/neurokernel/neurokernel), and development mailing list (https://lists.columbia.edu/mailman/listinfo/neurokernel-dev).Despite the fruit fly brain\u2019s relative numerical tractability, its successful emulation is an ambitious goal that will require the joint efforts of multiple researchers from different disciplines. Neurokernel\u2019s open design, support for widely available commodity parallel computing technology, and ability to integrate independently developed models of the brain\u2019s functional subsystems all facilitate this joining of forces. The framework\u2019s first release is a step in this direction; we expect and anticipate that aspects of the current design such as connectivity structure and module interfaces will be superseded by newer designs informed by the growing body of knowledge regarding the structure and function of the fly brain. We invite the research community to join this effort on Neurokernel\u2019s website  of select photoreceptors (R1) in retina and neurons  in the lamina.(ZIP)Click here for additional data file."}
+{"text": "Mycoplasma hyopneumoniae is a genome-reduced pathogen of swine that lacks the genetic repertoire to synthesize amino acids and relies on the host for availability of amino acids for growth. M. hyopneumoniae recruits plasmin(ogen) onto its cell surface via the P97 and P102 adhesins and the glutamyl aminopeptidase MHJ_0125. Plasmin plays an important role in regulating the inflammatory response in the lungs of pigs infected with M. hyopneumoniae. We show that recombinant MHJ_0461 (rMHJ_0461) functions as a leucine aminopeptidase (LAP) with broad substrate specificity for leucine, alanine, phenylalanine, methionine and arginine and that MHJ_0461 resides on the surface of M. hyopneumoniae. rMHJ_0461 also binds heparin, plasminogen and foreign DNA. Plasminogen bound to rMHJ_0461 was readily converted to plasmin in the presence of tPA. Computational modelling identified putative DNA and heparin-binding motifs on solvent-exposed sites around a large pore on the LAP hexamer. We conclude that MHJ_0461 is a LAP that moonlights as a multifunctional adhesin on the cell surface of M. hyopneumoniae.Aminopeptidases are part of the arsenal of virulence factors produced by bacterial pathogens that inactivate host immune peptides.  The genome of M. hyopneumoniae is small (893\u2013920 kb) and lacks the genetic repertoire to construct a cell wall or perform oxidative phosphorylation via the TCA cycle, and is reliant on swine for the availability of macromolecular building blocks to assemble proteins, nucleic acids and lipid membranes for growth [M. hyopneumoniae is armed with enzymes that degrade nucleic acids and proteins and membrane-associated transporters that facilitate uptake of the products of these degradative processes [globally . Like otall 893\u201390 kb and all 893\u201390 kb and rocesses .M. hyopneumoniae adheres tightly to cilia on the mucosal epithelial lining of trachea, bronchi and bronchioles of the upper respiratory tract causing ciliostasis and epithelial cell death, but the mechanism(s) deployed to destroy mucociliary function are poorly understood. Adherence is largely mediated via interactions between members of the P97 and P102 adhesin families and P159 with extracellular matrix components, glycosaminoglycans (GAGs) and fibronectin that decorate the surface of eukaryotic cells [M. hyopneumoniae. Cleavage fragments derived from these adhesins bind heparin [ heparin  and plas heparin ,7,11,12. heparin , in P159 heparin  and in P heparin ,15.7T-L-L\u2193L\u2193A\u2193T\u2193A\u2193A\u2193A-I-I-G-S-T-V-F-G-T-V-V-G-L-A-S30 [10, A11, T12, A13 and A14 are indicative of aminopeptidase activity. Aminopeptidase activity was observed at a number of putative endoproteolytic cleavage sites in Mhp493 (P216) suggesting that M. hyopneumoniae expresses several aminopeptidases on its cell surface [M. hyopneumoniae cells and on the cell surface of M. hyopneumoniae [M. hyopneumoniae [M. hyopneumoniae is able to proliferate in the porcine respiratory tract during an inflammatory response. Plasmin is known to initiate a proteolytic cascade by activating matrix metalloproteases that cleave extracellular matrix and other circulatory host molecules, generating neo-N-terminal substrates for extracellular aminopeptidases [Recently, we showed that a dominant cleavage event occurred within a putative transmembrane domain in the N-terminus of P216 Mhp493) with sequence -L-A-S30 . Consecu surface . Recentl93 with seumoniae ,18. We oeumoniae . NotablyM. hyopneumoniae. Like MHJ_0125, MHJ_0461 lacks evidence of a signal sequence or stretches of hydrophobic amino acids sufficient to traverse the cell membrane, and both are predicted by PSORTb to reside in the cytosol of M. hyopneumoniae. LAPs are proteases with broad substrate specificity but preferentially cleave N-terminal leucine residues [Escherichia coli [Genome-reduced pathogens that rely heavily on their host for the supply of essential metabolic precursors are likely to benefit from increased plasmin activity at the site of infection. Our proteome studies identified a putative leucine aminopeptidase  to be exposed on the cell surface of residues . In addihia coli , regulathia coli , activathia coli  and conthia coli . Here, w3.3.1.Aminomethylcoumarin (AMC)-coupled amino acid substrates were purchased from both Bachem (UK) and Peptide Institute, Inc. (Japan). Amastatin, bestatin, ethylenediaminetetraacetic acid (EDTA), tributylphosphine (TBP), insulin, heparin, substance P, bovine serum albumin (BSA), streptavidin-peroxidase and 3, 3-diaminobenzidine were purchased from Sigma . MS grade trypsin was purchased from Promega (USA). Acrylamide was purchased from Bio-Rad (USA). Pre-cast gels, buffers, molecular weight markers and all standard molecular biology reagents were purchased from Life Technologies , unless otherwise noted.3.2.M. hyopneumoniae cells were grown in modified Friis media [g for 15 min and stored at \u221280\u00b0C until use.is media  for 48 h3.3.mhj_0461 gene was synthesized and cloned into the expression vector PS100030 by Blue Heron Biotech (USA) removing in frame TGA codons. In mycoplasmas, the TGA codon encodes for tryptophan, which results in truncated proteins when expressing Mycoplasma genes in E. coli [2+-charged resin (Bio-Rad) as per the manufacturer's instructions. Briefly, a cleared BL21 cell lysate was mixed with Ni2+ resin overnight at 4\u00b0C, loaded into a 10 ml column and washed twice with 4 ml wash buffer . Bound proteins were eluted in elution buffer , dialysed against PBS in 10 K MWCO dialysis tubing and stored at either 4\u00b0C or \u221220\u00b0C.The  E. coli . In fram3.4.Protein samples were prepared by adding 10 mM TBP and boiling at 99\u00b0C in SDS sample buffer  for 10 min. Proteins were separated by SDS-PAGE, stained overnight in Coomassie blue G250 and destained in 1% acetic acid as described previously .3.5.4HCO3 and then reduced and alkylated in 5 mM TBP, 20 mM acrylamide and 10 mM NH4HCO3. Each gel piece was incubated overnight at 37\u00b0C with 12.5 ng \u00b5l\u22121 Trypsin Gold MS grade (Promega) and the tryptic peptides were solubilized with 2% formic acid (v/v). Peptide samples were analysed using a TEMPO nanoLC system  coupled to a QSTAR Elite Quadrupole TOF MS (Applied Biosystems/MDS Sciex). Intelligent Data Acquisition was performed to analyse charged ions (2+ to 5+) that were detected at greater than 30 counts per scan. MS/MS data files were searched usingMascotDaemon (v. 2.3.02) against the LudwigNR database using the following parameters: fixed modifications: none; variable modifications: propionamide, oxidized methionine, deamidated asparagine and glutamine; enzyme: semitrypsin; number of allowed missed cleavages: 3; peptide mass tolerance: 100 ppm; MS/MS mass tolerance: 0.2 Da; charge state: 2+, 3+ and 4+. The results of the search were then filtered by including only protein hits with at least one unique peptide and excluding proteins identified by a single peptide hit with a p-value > 0.05.Details regarding peptide preparation for MS analysis have been described previously . Briefly3.6.M. hyopneumoniae cells were added to 50 \u00b5M AMC-coupled substrates in combination with 5 mM metal cofactors  and a range of pH conditions (50 mM of either sodium acetate (pH 4\u20135.5), Tris-HCl (pH 6\u20138.8) or sodium borate (pH 10)). For inhibition studies, prior to the addition of substrate, rMHJ_0461 or M. hyopneumoniae cells were incubated with 1 mM bestatin or 1 mM amastatin for 20 min. For all assays, fluorescence was measured using a 96-well ELISA plate with a Synergy HT multi-mode microplate reader  linked to gen5 v. 1.08 software (BioTek). Reactions were mixed for 2 s immediately prior to fluorometric analysis. Assays were every 60 s for 1 h at a wavelength of 360 nm and 460/40 nm at 37\u00b0C.To determine the specificities of N-terminal amino acid cleavage, 30 nM rMHJ_0461 or freshly cultured 3.7.modeller [modeller. Catalytic sites were deduced from sequence searches by NCBI Sequence Viewer v. 2.21 and alignments to both prokaryotes and eukaryotes using EMBL-EBI Clustal Omega. Solvent accessibility of MHJ_0461 lysine residues were predicted using PHDacc [modeller. DNA-binding motifs were searched using GYM 2.0 [Comparative molecular modelling of MHJ_0461 was performed using modeller . The mosg PHDacc . Consensg PHDacc  and inpu GYM 2.0  and DNA- GYM 2.0 . Final L GYM 2.0 . Cell lo GYM 2.0 , TMpred  GYM 2.0  and Sign GYM 2.0 , respect3.8.Antisera against rMHJ_0461 were generated using New Zealand White rabbits following a protocol described previously .3.9.M. hyopneumoniae strain J culture was centrifuged at 10 000\u00d7g for 10 min and washed three times with 1 ml sterile PBS. A 1 in 100 dilution of cells was made in PBS and added to glass coverslips and allowed to settle for 15 min at room temperature. Paraformaldehyde (4%) was added and incubated at room temperature for 30 min. Non-specific binding sites were blocked using 2% BSA in PBS overnight at 4\u00b0C. Cells were incubated with either a 1 in 100 dilution of rMHJ_0461 antisera or control rabbit sera for 1 h at room temperature, followed by 1 h incubation at room temperature with 1 in 1000 dilution of goat anti-rabbit antibodies conjugated to Alexa Fluor 488 (Life Technologies). Control sera were collected from rabbits prior to immunization with rMHJ_0461. Coverslips were mounted in VECTASHIELD onto microscope slides and imaged using an Olympus BX51 Upright Epi Fluorescence microscope. Images were captured using an Olympus DP97 Digital Microscope Camera coupled with Olympus DP Controller software.Microscopy was performed following the same protocol as in . Briefly3.10.M. hyopneumoniae surfaceome analysis was performed using both cell surface biotinylation and enzymatic cell surface shaving with trypsin as previously described [escribed ,12.3.11.M. hyopneumoniae whole cell lysates were run through 1 ml HiTrap Heparin HP columns  and fractions collected and separated into low and high affinity interactions in accordance to an elution profile based on an increasing salt gradient. These fractions were then separated by SDS-PAGE and proteins were identified by LC-MS/MS.Heparin affinity chromatography was performed using Waters 2690 Alliance LC separations modules as described previously . BrieflyFor microscale thermophoresis (MST) analysis of binding kinetics between fluorescently labelled heparin and rMHJ_0461, samples were prepared as per manufacturer's instructions. Briefly, 20 \u03bcl of rMHJ_0461 at a concentration of 5 \u03bcM was added to a reaction tube. Ten microlitres of PBS with 0.05% (v/v) Tween 20 was then added to an additional 15 reaction tubes. Serial dilutions were made by transferring 10 \u03bcl from the first tube to the next, discarding 10 \u03bcl from the last tube after transfer. Ten microlitres of 2 \u03bcM heparin labelled with red fluorescent dye NT-647 (NanoTemper) was added to each tube and incubated at room temperature for 1 h. Samples were then loaded into hydrophilic capillaries (NanoTemper) and MST was executed on a NanoTemper Mononlith NT.115 using the following parameters at 24\u00b0C: LED power set at 50%, MST power at 40, 60 and 80% with fluorescence measurements taken after 30 s. All experiments were performed in triplicate.3.12.\u22121) [The ability of rMHJ_0461 to influence the conversion of plasminogen to plasmin in the presence of tPA was determined using a method described previously . Purifie\u22121)  was incu3.13.\u025b-aminocaproic acid. The remaining membrane was used as a control and incubated in PBS only. All three membranes were then probed with streptavidin-peroxidase (1 in 3000 dilution) for 60 min and developed using 3, 3-diaminobenzidine peroxidase substrate.Ligand blotting was used to determine whether rMHJ_0461 binds plasminogen. Serially diluted rMHJ_0461 was spotted on three PBS soaked nitrocellulose Hybond C-Super membranes assembled in a Bio-Dot Microfiltration apparatus and the wells were washed three times with 50 \u03bcl of PBS under gravity filtration. Membranes were blocked with 1% skim milk powder in PBS Tween 20 (0.1% v/v). Two membranes were then incubated with a 1 in 1000 dilution of biotinylated plasminogen prepared as described previously [3.14.\u22121. DNA fragments (800 bp) of salmon sperm DNA were generated by sonicating 130 \u03bcl of 1 mg ml\u22121 DNA solution in a Covaris M220 for 80 s . Protein\u2013DNA binding preparations were set up as described previously [\u22121 of DNA with approximately 1 mg ml\u22121 of rMHJ_0461. All assays were completed in DNA-binding buffer  for 30 min at 37\u00b0C. DNA : protein dilutions 1 : 1, 1 : 10, 1 : 20, 1 : 50, 1 : 100, BSA and DNA controls were run on a Bioanalyzer 2100 High Sensitivity chip in the Agilent 2100 Bioanalyzer as per the manufacturer's instructions. Briefly, gel\u2013dye mix was prepared to allow high sensitivity DNA dye concentrate (blue) and high sensitivity DNA gel matrix (red) to equilibrate to room temperature for 30 min. Fifteen microlitres of high sensitivity DNA dye concentrate (blue) was added to a high sensitivity DNA gel matrix vial (red), vortexed, pulse spun for 5 s and transferred to a spin filter to be centrifuged at 2240\u00d7g \u00b1 20% for 10 min. Using a new high sensitivity DNA chip on the chip priming station, 9.0 \u00b5l of gel\u2013dye mix was added in wells marked \u2018G\u2019 and 5 \u00b5l of marker (green) was added into all sample and ladder wells. One microlitre of high sensitivity DNA ladder (yellow) was used. The chip was placed horizontally in the adapter and vortexed for 1 min at 2400 r.p.m. The chip was then run in the Agilent 2100 Bioanalyzer using standard high sensitivity settings.Salmon sperm DNA (Sigma D1626) was dissolved in nuclease-free water to make a solution at 2 mg mleviously , incubat4.4.1.M. hyopneumoniae cells showed tryptic peptides mapping to MHJ_0461 (a) were detected by LC-MS/MS, indicating that MHJ_0461 resides on the cell surface. To confirm the surface accessibility of MHJ_0461, freshly cultured M. hyopneumoniae cells were briefly labelled with biotin and biotinylated proteins were recovered by avidin chromatography, separated by SDS-PAGE and subjected to LC-MS/MS. Tryptic peptides that mapped to MHJ_0461 were detected using this approach, confirming that MHJ_0461 resides on the cell surface of M. hyopneumoniae (a). These techniques have been used extensively to reliably determine the cell surface location of other proteins in M. hyopneumoniae [M. hyopneumoniae by immunofluorescence microscopy using mono-specific polyclonal antibodies raised against rMHJ_0461. Bound antibodies were detected on the surface of freshly grown M. hyopneumoniae cells with anti-rabbit antibodies conjugated with Alexa Fluor 488. In control experiments, antibodies in rabbit antiserum collected from the same rabbit but prior to immunization did not bind to the surface of M. hyopneumoniae cells (data not shown). All M. hyopneumoniae cells that stained with the nuclear stain 4\u2032, 6-diamidino-2-phenylindole (DAPI) displayed MHJ_0461 on the cell surface (examples seen in figure 1b).MHJ_0461 has a predicted mass and pI of 51.4 kDa and 8.85, respectively. Cell surface shaving experiments using viable, freshly cultured MHJ_0461 a were deeumoniae a. These eumoniae . We furt4.2.a). All five migratory forms of the protein were digested with trypsin and confirmed to be rMHJ_0461 by LC-MS/MS. Tryptic peptide coverage to MHJ_0461 ranged from 52 to 68% (a) and our analyses did not find any tryptic peptides that mapped to contaminating E. coli proteins. rMHJ_0461 resolves during Blue Native PAGE as two complexes of approximately 500 and 800 kDa, suggesting that MHJ_0461 forms multimers comprising more than 10 subunits. Each complex generated tryptic peptides spanning 62% and 69% of MHJ_0461 sequence, respectively (b). Multimeric forms of MHJ_0461 were also evident on blots of whole cell lysates of M. hyopneumoniae probed with rabbit anti-rMHJ_0461 sera (d).rMHJ_0461 resolved as five bands during SDS-PAGE and the different forms of rMHJ_0461 were unaffected by conditions used for reduction and alkylation a. All fi2 to 68% a and ourectively b. Multim461 sera d.Figure2+ in the order of leucine > methionine > phenylalanine > arginine > alanine > isoleucine > glycine > valine. Cleavage at N-terminal residues was also greatly increased in the presence of Co2+ and Mg2+, and to a lesser extent in the presence of Zn2+ (a). The binding of different metal ions can alter protein structure, referred to as a metalloform. This explains not only why activity is altered in the presence of different cofactors, but also why inhibitors of one protease metalloform may not inhibit the same protease in another metalloform [The specificity of rMHJ_0461 for N-terminal amino acids was determined using AMC-coupled amino acids in the presence of various divalent cations using a standard kinetic assay. At pH 7.2, rMHJ_0461 demonstrated greatest aminopeptidase activity against leucine-coupled substrates but also exhibited strong activity against other amino acids with hydrophobic side chains including methionine, phenylalanine and alanine and positively charged arginine. Weaker activity was observed with isoleucine-, glycine- and valine-coupled substrates but no activity was observed for proline and negatively charged aspartic and glutamic acids. In the absence of divalent cations, rMHJ_0461 displayed limited substrate hydrolysis. Aminopeptidase activity was strongest in the presence of Mn of Zn2+ a. The bialloform .Figure\u00a02+ was tested using a range of buffered substrates from pH 4 to pH 10. For each substrate, activity was not apparent in acidic conditions. Aminopeptidase activity was first observed at pH 7.2, peaked at pH 8.8 and remained detectable at pH 10 (b).LAPs are generally most active across neutral and basic pH levels but exceptions have been reported . Activitat pH 10 b.2+ was reduced by 99% after incubation with 1 mM bestatin, 69% by 1 mM amastatin and 58% by 10 mM EDTA. Copper and calcium divalent ions at 5 mM were also found to inhibit activity by 99% and 60%, respectively (c). Metal inhibitory agents were explored as one current drug development strategy involves the use of metal complexes that bind to the active site of a protease, leading to competitive inhibition [Aminopeptidase activity against leucine-AMC substrate in the presence of Mnectively c. Metal hibition .M. hyopneumoniae, freshly cultured M. hyopneumoniae cells were incubated with AMC-coupled substrates. With the exception of alanine-AMC, aminopeptidase activity was analogous with the rMHJ_0461 substrate specificity profile described above (d). Leucine-AMC activity on the cell surface was inhibited by 1 mM amastatin by 66%, a level comparable to the 69% activity reduction seen in rMHJ_0461 (e).To determine if aminopeptidase activity is present on the surface of ed above d. LeucinMHJ_0461 e.4.3.modeller. LAP from E. coli  was identified as the most suitable template for modelling M. hyopneumoniae MHJ_0461 with a GA341 score of 1.00 indicating more than 95% probability of having the correct fold [Comparative molecular modelling of MHJ_0461 and its catalytic sites was performed using the program ect fold , and a dect fold .M. hyopneumoniae presented as a homohexamer is shown in figure 4a. ProSA assigned a Z-score of 8.34 with all residues falling within the midrange of available nuclear magnetic resonance and X-ray crystallography structures currently available (figure 4b).LAPs typically display hexameric tertiary structures ,44. The 2+ cations coordinated in trigonal prismatic geometry to ligands Lys231, Asp236, Asp254, Asp313 and Glu315. A water molecule also takes part in the nucleophilic cleavage of a substrate (a). MHJ_0461 from M. hyopneumoniae strain J shares high sequence identity with putative LAPs from different strains of M. hyopneumoniae (98\u2013100%). Sequence identity is considerably lower when MHJ_0461 is aligned with putative LAPs from other Mycoplasma sp. including Mycoplasma fermentans (43%) and Mycoplasma bovis (42%) and orthologues from phylogenetically related bacteria including Clostridium perfringens (42%) and Bacillus cereus (38%). A low sequence identity was observed throughout the entire molecule when aligned against a variety of organisms ranging from 31% for Bacillus anthracis to 20% for Fasciola hepatica. However, the metal and substrate binding sites and the NTDEAGRL motif characteristic to M17 family proteases [b).The model catalytic site for MHJ_0461 displays two Mnubstrate a. MHJ_044.4.M. hyopneumoniae were identified by heparin-agarose chromatography. Soluble proteins retained during heparin-agarose chromatography were eluted with a salt gradient from 300 to 2000 mM, separated by SDS-PAGE and identified by LC-MS/MS. Peptides matching MHJ_0461 were detected in three gel slices (a), suggesting that different forms of the molecule retain the ability to bind heparin. MST studies showed rMHJ_0461 bound heparin with a dissociation constant (Kd) of 6.89 nM (b). We examined the sequence of MHJ_0461 for motifs that may play a role in binding heparin. The motif XBXBBX  located at amino acid residues 64\u201369 has been implicated in binding heparan sulfate [modeller, rendered over the predicted ribbon structure and shown to be surface accessible around a large pore. An additional cluster of repetitively spaced basic residues with the sequence XBXXBXXBX was identified between amino acids 71 and 79 located around the same pore (c).Putative heparin-binding proteins of l slices a, sugges 6.89 nM b. We exa sulfate . The biname pore c.Figure4.5.a). In the presence of a lysine analogue, \u025b-aminocaproic acid, binding of porcine plasminogen to rMHJ_0461 was diminished, suggesting surface-exposed lysines are critical in binding interactions . In the presence of tPA, there was a distinct increase in plasmin activity (figure 7b) at all molar ratios of rMHJ_0461 to plasminogen, ranging from 0.5 : 1 to 8 : 1. In control experiments using plasminogen and tPA, maximum plasmin activity was reached by 75 min. The same level of activity was reached with an incubation time of 60 min when rMHJ_0461 was present at a 0.5 : 1 molar ratio, by 35 min at 1 : 1 and 2 : 1 molar ratios and by 20 min at molar excess ratios 4 : 1 and 8 : 1. To examine the role of molecular crowding, we performed control experiments by measuring plasmin activity when plasminogen was incubated with substance P and insulin. In both instances, plasmin activity was lower and did not reach the same level of absorbance by 90 min in the presence of substance P and insulin at 1 : 1 molar ratios. No plasmin activity was observed when rMHJ_0461 was incubated with plasminogen in the absence of tPA, indicating that MHJ_0461 is incapable of directly cleaving plasminogen to release plasmin.Several bacterial species manipulate host defences by commandeering host plasminogen . Many cefigure 74.6.E. coli (PDB 1GYT) was selected as the most statistically suitable template to model a proposed three-dimensional structure for MHJ_0461. E. coli LAP is known to bind DNA, and the interaction with DNA is suggested to be structural as there are no known DNA-binding motifs in the sequence [a,b). In control experiments where DNA was incubated with BSA, no change in DNA concentration was observed as expected (data not shown). Bioinformatic analysis of rMHJ_0461 identified one helix-turn-helix (HTH) motif common in DNA-binding proteins spanning amino acids 66\u201388. Additionally, 18 DNA-binding residues were identified using the BindN algorithm [c).LAP from sequence . To invelgorithm , 65% of 5.M. hyopneumoniae, an observation confirmed by immunofluorescence microscopy using anti-MHJ_0461 antibodies. MHJ_0461 also displays additional moonlighting functions by binding heparin, plasminogen and DNA, indicating that MHJ_0461 plays an important role in survival of M. hyopneumoniae in the host and in pathogenesis more broadly.Aminopeptidases have long been known to play an important role in nutrient acquisition and cell homeostasis. However, it is also becoming apparent that aminopeptidases are multifunctional proteins with secondary functions (i) as viral or toxin receptors, (ii) as site-specific recombination factors, (iii) as transcriptional repressors and (iv) in vesicular trafficking . These fM. hyopneumoniae cells (d). With the exception of alanine, the activity profiles were comparable with the substrate specificity profile of rMHJ_0461 for the panel of amino acids tested. The additional alanine activity could be attributed to surface-exposed glutamyl aminopeptidase (MHJ_0125) previously shown to have high activity against alanine-AMC [M. hyopneumoniae. LAP activity has been described on the surface of Mycoplasma bovirhinis, Mycoplasma dispar and M. bovis [M. bovirhinis and M. dispar are not available; however, three strains of M. bovis have been sequenced revealing five predicted cytosolic aminopeptidases. Like MHJ_0461, none of these putative aminopeptidases possess signal peptides. These data suggest that active aminopeptidases from pathogenic mycoplasmas are transported to the cell surface albeit by an unknown mechanism.Activity against a range of fluorogenic substrates was detected in freshly cultured ae cells d. With tM. bovis . Genome Pseudomonas aeruginosa, Salmonella enterica serotype Typhimurium, E. coli and Vibrio proteolyticus [Streptomyces septatus and V. proteolyticus has 23% and 18% activity, respectively [Geobacillus thermoleovorans [Bacillus subtilis [M. hyopneumoniae with a source of this important amino acid for protein synthesis. We recently showed that the glutamyl aminopeptidase MHJ_0125 is presented on the cell surface of M. hyopneumoniae and was able to cleave glutamic acid, alanine and leucine but poorly hydrolysed aspartic acid, arginine, proline, valine and phenylalanine [M. hyopneumoniae with the capacity to cleave a wide range of amino acids from neo-N-termini.rMHJ_0461 showed the greatest activity against leucine-AMC but also efficiently cleaved large hydrophobic residues such as methionine and phenylalanine. rMHJ_0461 substrate specificity was leucine > methionine > phenylalanine, which is comparable to LAPs from olyticus . rMHJ_04ectively ,51. Receectively . rMHJ_04eovorans  and Bacisubtilis . Notablysubtilis . rMHJ_04MHJ_0461 can be classified as belonging to the M17 family of proteases by the presence of the NTDAEGRL motif and a C-terminal catalytic domain with highly conserved metal binding sites. These binding residues are coordinated to two divalent ions which act as positively charged electrophilic catalysts that complex an oxygen atom at a scissile peptide bond, thus facilitating the nucleophilic attack of an additionally coordinated water molecule leading to peptide bond cleavage . Our datM. hyopneumoniae to mucosal cells and respiratory cilia constitutes the first crucial stage of infection and is facilitated by surface-exposed adhesins that, in part, bind to extracellular matrix components such as fibronectin and GAGs [M. hyopneumoniae is pre-incubated with the GAG heparin [M. hyopneumoniae infections [M. hyopneumoniae has been isolated from other organs, including the liver, spleen and kidney [M. hyopneumoniae to proliferate in tissue sites distal to the respiratory tract.Adherence of and GAGs \u20137,37. Bi heparin ,55. Many heparin \u201312,14,37 heparin , we propfections . GAG binfections  and hostfections . Despited kidney  as well d kidney . GAG binM. hyopneumoniae is very adept at recruiting plasmin(ogen) onto its cell surface and facilitating activation to plasmin. Endoproteolytic fragments of members of the P97 and P102 adhesin families bind plasminogen and promote conversion of plasminogen to plasmin in the presence of tPA [M. hyopneumoniae consistently showed greater plasmin activity compared with BAL fluid recovered from the same animals prior to experimental challenge. These observations suggest that elevated plasmin levels in BAL fluid are a consequence of M. hyopneumoniae infection [M. hyopneumoniae. Here, we show that rMHJ_0461, like rMHJ_0125, binds plasminogen. Plasminogen bound to rMHJ_0461 was efficiently converted to plasmin in the presence of tPA. rMHJ_0461 enhanced plasmin activity at all molar ratios tested. The increase in the rate of activation was greater than that observed for rMHJ_0125 which induced plasmin conversion only in protein to plasminogen molar ratios greater than 4 : 1. The presence of control proteins substance P and insulin lowered the rate of plasmin conversion. These data suggest that the increase in activity seen in rMHJ_0461 was not due to molecular crowding and that the presence of non-binding proteins at 1 : 1 molar ratios inhibits the conversion of plasminogen to plasmin. The observation that rMHJ_0461 in the absence of tPA did not produce plasmin activity shows that the protease is unable to directly cleave the plasminogen R561\u2013V562 bond to form plasmin consistent with it functioning as an aminopeptidase. Interactions between rMHJ_0461 and plasminogen were abolished in the presence of a lysine analogue, \u03b5-aminocaproic acid, indicating that rMHJ_0461 relies on surface-exposed lysine residues for plasminogen binding.e of tPA ,7,11,12.nfection ,18. Seconfection , stimulanfection \u201365. Thirnfection ,66. Lastnfection , which anfection . This obM. hyopneumoniae would benefit from the pool of free amino acids generated by both these aminopeptidases and is consistent with a model we proposed earlier [Mycoplasma pneumoniae, an organism also reliant on the host to provide a pool of free amino acids for growth, suggested that 354 amino acids per second must be imported into each cell during exponential growth [Plasmin is an endoprotease with broad substrate specificity. Plasmin cleaves extracellular matrix proteins and plays a key role in the processing and activation of matrix metalloproteases . Collect earlier . Metaboll growth . These sE. coli have DNA-binding capabilities [M. hyopneumoniae is the first example of a cell surface aminopeptidase that binds DNA. Few extracellular DNA-binding proteins have been identified to date; examples include proteins involved in preventing adaptive immune responses [M. hyopneumoniae will be the subject of future studies.rMHJ_0461 was shown to bind double-stranded DNA. While further studies are required to determine the biological significance of this interaction, a putative DNA-binding motif and putative DNA-binding residues within MHJ_0461 reside within the N-terminal 100 amino acids. This observation is consistent with reports that the N-terminus of some LAPs plays an important role in binding DNA . LAPs frbilities ,69 whichbilities . Howeveresponses  and thosesponses . The rol"}
+{"text": "Here, we describe a large, diverse set of sequencing data for seven human genomes; five are current or candidate NIST Reference Materials. The pilot genome, NA12878, has been released as NIST RM 8398. We also describe data from two Personal Genome Project trios, one of Ashkenazim Jewish ancestry and one of Chinese ancestry. The data come from 12 technologies: BioNano Genomics, Complete Genomics paired-end and LFR, Ion Proton exome, Oxford Nanopore, Pacific Biosciences, SOLiD, 10X Genomics GemCode WGS, and Illumina exome and WGS paired-end, mate-pair, and synthetic long reads. Cell lines, DNA, and data from these individuals are publicly available. Therefore, we expect these data to be useful for revealing novel information about the human genome and improving sequencing technologies, SNP, indel, and structural variant calling, and  Developing Reference Materials is a unique measurement science task, where significant resources can be expended to deeply characterize a small number of samples. Reference Materials act to calibrate, benchmark, or validate a measurement process. These samples are often the source of the scales on which we report our results , and they can be a physical realization of the SI units. Our ability to compare results between laboratories in most applications depends on Reference Materials.1. New technologies are used, rigorous experimental designs are employed, and exotic methods applied3. In a virtuous cycle, existing methods are optimized and new methods are developed using reference materials as the benchmarks. Regulated applications depend on reference materials for quantitative, objective oversight; this opens new applications for a measurement technology, with great quality-of-life social benefit. With sequencing technologies and bioinformatics changing rapidly, whole genome reference materials and diverse data types like those presented here are a valuable resource for developing, improving, and assessing performance of these methods.There is a tradition of innovation in measurement science to characterize these high-impact samples4, as well as large deletions and insertions5. We plan to use similar methods as well as new methods to characterize these genomes using the data described in this work.The National Institute of Standards and Technology (NIST)-hosted Genome in a Bottle Consortium is developing reference materials from well-characterized genomic DNA from 5 individuals . These rThe NIST Reference Material DNA has been characterized to an unprecedented degree. We have collected a large diverse set of data from 12 sequencing technologies and library preparation methods . These dThe pilot genome (NIST RM 8398) is an oft-used genome of Caucasian ancestry: NA12878 from the CEPH Utah Reference Collection. In addition, genomes from two family trios (both Mother-Father-Son) have been selected from the Personal Genomes Project (PGP), one of Ashkenazi Jewish (AJ) ancestry and the other of Han Chinese ancestry. These genomes are available as cells or extracted DNA from the Coriell Institute for Medical Research and are or will be available as DNA as NIST Reference Materials. The NIST Reference Materials are extracted DNA from large, homogenized batches of cells prepared specially by Coriell to control for any batch effects. The samples from PGP are consented more broadly for many applications, including commercial redistribution. There are already three commercial products from the same cell lines from which the NIST Reference Material DNA is prepared: AcroMetrix Oncology Hotspot Control from Thermo Fisher Scientific, GIAB HDx Reference Standards from Horizon Diagnostics, and cell line DNA with synthetic DNA spike-ins from SeraCare Life Sciences.For the AJ trio, Chinese son, and NA12878, libraries were prepared from 6 vials of the NIST Reference Material DNA for each individual. For the Chinese parents, a single library was prepared from genomic DNA from the Coriell Institute for Medical Research. For each Reference Material, 12 (or 14 for NA12878) libraries were prepared in parallel using the Illumina TruSeq (LT) DNA PCR-Free Sample Prep Kits (FC-121\u20133001). Two (or 3 for NA12878) libraries each were made from the first and last tubes in the lot, two libraries each were prepared from four samples pulled randomly from each quarter of the lot. This library design is intended for homogeneity analyses not presented here.DNA concentrations were measured using a Qubit 2.0\u2009fluorometer (Life Technologies). Genomic DNA (1.5\u2009ug) was fragmented using a Covaris S2 focused ultrasonicator in micro TUBE AFA Fiber Pre-Slit Snap-Cap 6\u00d716\u2009mm micro tubes and the Covaris MicroTUBE holder  under the following conditions for a target insert size of 550 base pairs. Duty cycle: 10%; Intensity: 2.0; Cycles Per Burst: 200; Duration: 45\u2009s; Mode: Frequency Sweeping; Displayed Power: 9\u2009W; Temperature: 5.5\u00b0 to 6\u2009\u00b0C. After Fragmentation, DNA was cleaned up using illumina Sample Purification Beads. End Repair was performed in 0.2\u2009ml PCR tubes on an MJ research PTC-200 thermal cycler. The optional end repair control was not used. Size selection was done using a 96-well 0.8\u2009ml plate (Fisher Scientific Part # AB-0859), a magnetic stand-96 (Ambion part # AM10027) and the Illumina sample purification beads according to the 550\u2009bp insert protocol.Adenylation of 3\u2032 ends was done in 0.2\u2009ml PCR tubes on an MJ Research PTC-200 thermal cycler. The optional A-Tailing control was not used. Ligation of indexed paired-end adapters was done in 0.2\u2009ml PCR tubes using the DNA adapter tubes included in the Illumina TruSeq (LT) DNA PCR-Free Sample Prep Kit on an MJ Research PTC-200 thermal cycler. The optional ligation control was not used. The libraries were cleaned up in a 96-well 0.8\u2009ml plate (Fisher Scientific Part # AB-0859) and a magnetic stand-96 (Ambion part # AM10027) using the Illumina sample purification beads. The final libraries were run on an Agilent 2100 Bioanalyzer HS-DNA chip to verify fragment size distribution. Final library concentration was measured via qPCR using the KAPA library quantification kit for Illumina sequencing platforms (KAPA part # KK4835). Libraries were then pooled based to the qPCR quantification data. The pool was intentionally made uneven so as to acquire greater sequence depth from the libraries made from the first and last tubes in each lot. The pools were adjusted between sequencing runs based on index balance.For the Chinese son, DNA libraries were prepared in the same manner as they were for the Ashkenazim trio. The initial pool was made based on quantification measurements made using an Agilent 2100 Bioanalyzer, qPCR was not performed. This initial pool was sequenced on an Illumina MiSeq. The index balance obtained from the MiSeq run was used to adjust the pool for Sequencing on an Illumina HiSeq. The pool was intentionally made uneven so as to acquire greater sequence depth from the libraries made from the first and last tubes in each lot. The pools were adjusted between sequencing runs based on index balance.For NA12878, the AJ Trio, and the Chinese parents, the pooled TruSeq libraries were run on an Illumina HiSeq 2500 in Rapid mode (v1) with 2\u00d7148 paired end reads. Pooled Libraries were initially loaded at a concentration of 10\u2009pM. loading concentration was adjusted accordingly on subsequent runs to balance the libraries as well as possible.For the Chinese son, the libraries were sequenced on an Illumina HiSeq 2500 in rapid mode (v2) with 2\u00d7250 paired end reads. Pooled Libraries were initially loaded at a concentration based on the information from the MiSeq run. Loading concentration was adjusted accordingly on subsequent runs to optimize cluster density.The runs were designed to get approximately 300x total coverage of each of NA12878, the AJ Trio, and the Chinese son, and 100x coverage of each of the Chinese parents.Mate Pair libraries were generated for the AJ Trio and Chinese Trio using Nextera Mate Pair Sample Preparation Kit . Briefly, 4\u2009\u03bcg of high molecular weight genomic DNA from the NIST Reference Materials (or from Coriell for the Chinese parents) was fragmented to about 7\u2009kb in a 400\u2009ml tagmentation reaction containing 12\u2009\u03bcl of Tagment Enzyme at 55\u2009\u00b0C for 30\u2009min. The tagmented DNA fragments were purified with Zymo Genomic DNA Clean & Concentrator Kit . The gap in the tagmented DNA was filled with a Strand Displacement Polymerase in a 200\u2009\u03bcl strand displacement reaction at 20\u2009\u00b0C for 30\u2009min. DNA was then purified with AMPure XP Beads  and size-selected by 0.6% agarose gel electrophoresis in 0.5x TBE buffer. The 6\u20139\u2009kb fragments were excised from gel and DNA was recovered using a ZymocleanTM Large Fragment DNA Recovery Kit . Up to 600\u2009\u03bcg of DNA was then circulated overnight at 30\u2009\u00b0C with Circularization Ligase in a 300\u2009\u03bcl reaction.After overnight circularization, the uncirculated linear DNA was removed by Exonuclease digestion. Both DNA Ligase and Exonuclease were inactivated by heat treatment and the addition of Stop Ligation Buffer. Circularized DNA was then sheared to smaller sized fragments (300\u20131000\u2009bp) using Covaris S2 with T6 (6\u00d732\u2009mm) glass tube  under these conditions: Intensity of 8, Duty Cycle of 20%, Cycles Per Burst of 200, Time of 40\u2009s, Temperature of 6\u20138\u2009\u00b0C.The sheared DNA fragments that contain the biotinylated junction adapter are mate pair fragments. These fragments were isolated by binding to Dynabeads M-280 Streptavidin Magnetic Beads  in Bead Bind Buffer. The unbiotinylated molecules in solution are unwanted genomic fragments that are removed through a series of washes. All downstream reactions were carried out on bead and beads were washed between successive reactions. The sheared DNA was first end-repaired to generate blunt ends followed by an A-Tailing reaction to add a single \u2018A\u2019 nucleotide to the 3\u2032 ends of the blunt fragments. Then the Illumina T-tailed indexing adapters were ligated to the A-tailed fragments.The adapter-ligated fragments were PCR amplified  to generate the final library. The amplified library was purified using AMPure XP Beads (0.67x vol) and eluted in Resuspension Buffer. The size distribution of the library was determined by running a sample on an Agilent Technologies 2100 Bioanalyzer. Library concentration was measured by the Qubit dsDNA HS Assay Kit .Pooled Mate-Pair libraries were sequenced on an Illumina HiSeq 2500 in Rapid mode (v1) with 2\u00d7101\u2009bp paired-end reads. The loading concentration was 9.5\u2009pM. This Initial run was for library QC purposes prior to running high throughput.The Mate-Pair libraries were also sequenced on an Illumina HiSeq 2500 in high output mode (v4) with 2\u00d7125\u2009bp paired-end reads. Libraries were sequenced on individual lanes (not pooled). The template loading concentration for each lane was adjusted based on the cluster density from the QC run. Two replicate flowcells were sequenced simultaneously, each with 6 lanes of mate-pair libraries.Synthetic long-read libraries were generated for the AJ Trio and Chinese Trio using the TruSeq Synthetic Long-Read DNA Library Prep Kit . 500\u2009ng of DNA from the NIST Reference Materials (or from Coriell for the Chinese parents) was sheared, end-repaired, A-tailed, and adapters ligated before size-selecting 9\u201311\u2009kb fragments according to the manufacturer\u2019s protocol (Illumina Part # 15047264 Rev. B). Each resulting library was then diluted and aliquoted across a 384-well plate to limit the number of molecules to be amplified by PCR in each well. Amplified products were then tagmented and indexed by a second round of PCR (see referenced protocol for conditions) before pooling and concentrating all 384 wells for final product size selection and validation, again according to manufacturer\u2019s instructions.The synthetic long-read libraries for each genome were pooled and sequenced on an Illumina HiSeq 2500 in high output mode (v4) with 2\u00d7125\u2009bp paired-end reads. Pooled libraries from each genome were loaded on individual lanes, with two lanes of each genome sequenced. The loading concentration for each lane was adjusted based on the cluster density of a previous (failed) run.Captured exome DNA libraries for the Ashkenazim Jewish trio and the Chinese son were prepared from 4 vials of NIST Reference Material DNA. The library preparation was performed using Agilent SureSelect Automated Library Prep and Capture System Protocol and the Agilent SureSelect Target Enrichment System kit for 96 reactions (Agilent Technologies).The DNA concentration for each sample was measured by Qubit 2.0\u2009fluorometer (Life Technologies). The DNA samples were normalised with Low TE buffer to 4\u2009ng/ul. 50\u2009ul of each sample was fragmented in a 130\u2009ul 96 MicroTUBE Plate with 96 microTUBE Foil Seal (part numbers 520078 and 520073 respectively) using the Covaris E-Series E220 focused ultrasonicator. The samples were sheared to an average of 250\u2009bp, using the following instrument shearing settings: Average fragment size: 250\u2009bp; Acousty Duty Factor: 10%; PeakIncidence Power, W: 140; Cycles Per Burst: 200; Treatment Time: 160\u2009s; Temperature: 6\u2009\u00b0C.After fragmentation, the sheared DNA was purified using Agencourt AMPure XP purification beads, and the fragment size was controlled using the TapeStation 2200 with D1000 Reagents and ScreenTape. Following purification, the sample ends were modified for SureSelect target enrichment, through end-repair, 3\u2032 end adenylation, and adaptor ligation. The prepared DNA was purified after each each modification step. Sample purification, GA end-repair, A-tailing, and adaptor ligation were performed on the Bravo Agilent NGS Workstation robot in Nunc DeepWell plates. The adapter-ligated DNA fragments were captured and amplified in a 0.2\u2009ml ABGENE 96-well PCR plate using the Applied Biosystems Veriti 96-well thermocycler. The thermocycler was programmed with the following settings: 98\u2009\u00b0C/3\u2009min, 10 cycles of , 72\u2009\u00b0C/10\u2009min, and 4\u2009\u00b0C/hold.The libraries were cleaned up in a Nunc DeepWell plate using Agencourt AMPure XP beads on the Agilent NGS Workstation. To verify the DNA fragments has a size distribution between 200 and 400\u2009bp, the libraries were measured on the TapeStation 2200 with D1000 Reagents and ScreenTape. The library concentration was measured using the Quant-iT dsDNA High-Sensitivity Assay Kit with the Victor3 1420 Multilabel counter. 750\u2009ng of each prepped library sample was aliquoted for hybridization to the SureSelect Capture Library. Hybridization of the DNA libraries and the SureSelct Capture libraries was performed in a Nunc DeepWell plate on the Agilent NGS Workstation, and in a 0.2\u2009ml ABGENE 96-well PCR plate on the Applied Biosystems Veriti 96-well thermocycler. The thermocycler was programmed with a 95\u2009\u00b0C hybridization step for 5\u2009min followed by a 65\u2009\u00b0C hold step. The hybridized libraries were captured with SureSelect Binding Buffer and purified using Dynabeads MyOne Streptavidin T1 bead suspension.Addition of indexing tags to the SureSelect enriched captured libraries was performed through PCR based amplification, using the Agilent NGS Workstation and Applied Biosystems Veriti 96-well thermocycler. The thermocycler was programmed with the following settings: 98\u2009\u00b0C/2\u2009min, 10 cycles of , 72\u2009\u00b0C/10\u2009min, and 4\u2009\u00b0C/hold.The amplified and indexed DNA libraries were purified with Agencourt AMPure XP beads. Quality control of the amplified captured libraries was done using the TapeStation 2200 High Sensitivity D1000 Reagents and ScreenTape to ensure a fragment size of 300\u2013400\u2009bp and a concentration of 10\u2009nM. To estimate the cluster density and achive the final library concentration, qPCR was performed for each library using the KAPA library quantification kit for Illumina sequencing platforms. All four samples were prepared equally.The libraries were pooled and diluted to a 2\u2009nM pool , based on the qPCR measures. The library pool was sequenced on the Illumina HiSeq 2500 sequencing platform in high output run mode with 2\u00d7125\u2009bp paired-end reads.6 against b37 human decoy reference genome. The alignments were sorted and PCR duplicates were marked by Picard (http://picard.sourceforge.net). For AJ trios, a joint variant calling was performed by GATK7 HaplotypeCaller on all three samples. For the Chinese son, both single sample variant calling (a VCF file) and the first step in cohort analysis (a gVCF file) were performed by GATK HaplotypeCaller. All variants in VCF files were quality filtered by standard GATK SNP variant quality score recalibration and indel hard filtration according to GATK Best Practices recommendations9.The sequencing data were aligned by bwa mem6 cells per extraction were pelleted and washed with PBS at RT. The Proteinase K and RNaseA digestion was incubated for 30\u2009min at 25\u2009\u00b0C. Genomic DNA was purified using MagAttract Suspension G with Buffer MB, washed twice with Buffer MW1, and twice with Buffer PE. Finally the beads were rinsed twice with nuclease-free water for exactly 60\u2009s. DNA was eluted with Buffer AE and quantified using the Qubit dsDNA HS Assay Kit .Genomic DNA was purified using a modified version of the MagAttract HMW DNA Kit  from GM12878, GM24149, GM24143 and GM24385 cells . Briefly, 1\u00d710Sample indexed and partition barcoded libraries were prepared using the GemCode kit . 1.2\u2009ng of DNA was used for GEM reactions where DNA fragments were massively partitioned into molecular reactors to extend the DNA and introduce specific 14-bp partition barcodes. GEM reactions were thermal cycled  and purified using the GemCode protocol. Purified DNA was sheared to 800-bp . Peak incident power: 75.0\u2009W; duty factor: 5.0%; cycles per burst: 200; treatment time; 50 (s), temperature: 20.0\u2009\u00b0C; sample volume 50\u2009ul. End repair, Adenylation tailing of 3\u2032 ends, universal adapter ligation and sample indexing were performed according to the manufacture\u2019s recommendations. Whole genome GemCode libraries were quantified by qPCR . The NA12878 library was pooled with the NA24149 library and run on an Illumina HiSeq 2500 in Rapid mode (v1) with paired end 2\u00d798-bp, 14-bp I5 and 8-bp I7 reads. For analysis the demultiplexed results from three flow cells were combined for a total of approximately 1.25 billion and approximately 810 million reads for NA12878 and NA24149, respectively. The NA24385 and NA24143 libraries were each run individually on a single high output mode (v4) lane for approximately 958 and approximately 900 million reads, respectively. Sequencing results were analyzed using the GemCode Long Ranger Software Suite.10, but with a two adapter library protocol (library version 2). Briefly, sequencing substrates were generated by means of genomic DNA fragmentation to a median fragment length of about 450 base pairs and recursive directional adapter insertion with an intermediate type IIS restriction enzyme digestion. The resulting circles were then replicated with Phi29 polymerase (RCR)11 by synchronized synthesis to obtain hundreds of tandem copies of the sequencing substrate, referred to as DNA nanoballs (DNBs) which were adsorbed to silicon substrates with grid-patterned arrays to produce DNA nanoarrays.Except for the Chinese parents, the NIST reference material was used as input. In the case of the Chinese trio, the full trio was sequenced from cells purchased from Coriell, and the son (GM24631) was also sequenced from the NIST reference material. Library prep followed the basic approach detailed previouslyHigh-accuracy cPAL sequencing chemistry (Version 2 sequencing) was used on automated sequencing machines to independently read up to 19 bases adjacent to each of the four anchor insertion sites, resulting in a total of 29-base mate-paired reads (58 bases per DNB). DNB intensity information is interpreted with the following steps: 1) background removal, 2) image registration, 3) intensity extraction. The intensity data from each field were then subjected to base calling, which involved four major steps: 1) crosstalk correction, 2) normalization, 3) base calling, and 4) raw base score computation.The LFR libraries were constructed as described in ref. Sequencing was performed as described above for regular Complete Genomics WGS, with the additional step of sequencing the well ID barcodes.Exome libraries for 4 NIST Reference Materials, the AJ trio and Chinese son, were prepared using Ion AmpliSeq Exome RDY Kit, with a mean insert size of 215\u2009bp. Each sample was assigned a distinct barcode: IonXpress_020 for NA24385, IonXpress_022 for NA24149, IonXpress_024 for NA24143, and IonXpress_026 for NA24631. Each barcode library is diluted to 100\u2009pM. The libraries were emulsion-amplified individually and enriched using Ion OneTouch 2 System and Ion PI Template OT2 200 Kit v4. Outputs from 4 OneTouch runs for each sample were pooled together.Each sample was sequenced on 4 Ion Proton instruments using Ion PI Sequencing 200 Kit v4. BaseCalling and alignment were performed on a Torrent Suite v4.2 server.DNA sequencing on a Life Technologies 5500\u00d7l Wildfire was performed according to manufacturers protocols with noted modifications for each genome. A semi-automated library preparation process was first performed for the Chinese son Reference Material DNA. A modified manual library preparation was performed for the AJ son Reference Material DNA in an attempt to obtain smaller libraries for AJ son to maximize efficiency of colony formation on the 5500\u2009W. The two procedures used for each genome are detailed below.A semi-automated library preparation using the AB Library Builder System was used to prepare Chinese son libraries for sequencing on a 5500\u00d7l Wildfire. The workflow to produce 5500\u2009W DNA fragment libraries from Chinese son human genomic DNA (gDNA) was as follows also in :Shearing of gDNA was performed using the Covaris g-Tube (PN 520079) in conjunction with the Covaris S2 Focused Ultrasonicator. To obtain a uniform intermediate size distribution of approximately 10\u2009kb, 2.0\u2009ug of gDNA was initially \u2018pre-sheared\u2019 using a Covaris g-Tube in an Eppendorf 5424 centrifuge. The g-tubes were centrifuged twice at 4200\u2009rpm for 60\u2009s in each direction. Shearing was completed using the Covaris S2 per the User Guide \u2018Fragment Library Preparation Using the AB Library Builder System: 5500 Series SOLiD Systems\u2019 (PN 4460965 Rev. A). Approximately 1.5\u2009ug of \u2018pre-sheared\u2019 gDNA was further sheared using the Covaris S2. Shearing was assessed on an Agilent 2100 Bioanalyzer High Sensitivity DNA Chip (PN 5067\u20134626) which showed a broad distribution of sheared material with peak at approximately 175\u2009bp.The Life Technologies AB Library Builder System was used to partially automate the library preparation process. End Repair, Size Selection, PolyA Tailing and Adaptor Ligation were performed on the AB Library Builder System to generate 5500 DNA fragment libraries. The Life Technologies Library Builder Fragment Core Kit for 5500 Genetic Analysis Systems (PN 4463763) and Beckman Coulter Agencourt AMPure XP Reagent (PN A263800) were used to prepare 5500 libraries on the AB Library Builder System. Adaptor amounts were calculated, per the Library Builder User Guide, based on input mass for a given sample. Library Preparation input mass ranged from 1.0\u20131.5\u2009ug of sheared DNA depending on the given sample.The AB Library Builder 5500 libraries then underwent manual nick translation and Wildfire library conversion to prepare libraries compatible for sequencing on a Life Technologies 5500\u00d7l Wildfire. Wildfire conversion was performed per the Quick Reference \u20185500\u2009W Series Genetic Analysis Systems: Conversion of 5500 Library to 5500\u2009W Library\u2019 (PN 4477188 Rev. B). Six cycles of amplification were performed in the conversion process.Following an AmPure XP Reagent Cleanup the final 5500\u2009W DNA fragment libraries were run on the Sage Science BluePippin automated DNA size selection and collection system to further narrow the size distribution of the final libraries. A BluePippin DNA 2% Dye-Free Agarose gel cassette with V1 Marker (PN BDF2010) was used to capture DNA in a target range of 200\u2013300\u2009bps. All 5500\u2009W library for a given sample was loaded into the assigned well on cassette and run per the BluePippin 2% Agarose Gel Cassette Quick Guide. Upon completion of size selection 40\u201360\u2009ul of size selected library was removed from the elution well and cleaned and concentrated using a 1.8X Agencourt AMPure XP (PN A263800) cleanup. Cleaned-size selected DNA was eluted in 32\u2009ul of TE buffer. Size selection assessed using a Bioanalyzer High Sensitivity DNA Chip and showed the final Chinese son 5500\u2009W libraries with a size distribution of approximately 200\u2013350\u2009bps with peak at approximately 285\u2009bps.A modified manual library preparation process for the AJ son was used to obtain appropriately sized libraries for sequencing on a 5500\u00d7l Wildfire. The workflow to produce 5500\u2009W DNA fragment libraries from AJ son human genomic DNA (gDNA) was as follows also in :Shearing of gDNA was performed using the Covaris g-Tube (PN 520079) in conjunction with the Covaris S2 Focused Ultrasonicator. To obtain a uniform intermediate size distribution of approximately 10\u2009kbp, 2.5\u2009ug of gDNA was initially sheared using a Covaris g-Tube in an Eppendorf 5424 centrifuge. The g-tubes were centrifuged twice at 4200\u2009rpm for 60\u2009s in each direction. Shearing was completed using the Covaris S2 per the User Guide for \u2018Fragment Library Preparation: 5500 Series SOLiD Systems\u2019 (PN 4460960 Rev. B). Approximately 2.0\u2009ug of \u2018pre-sheared\u2019 gDNA was sheared using the Covaris S2.DNA fragment library preparation was performed using the total mass of sheared DNA (approximately 2.0\u2009ug). Following the aforementioned 5500 Fragment Library Preparation guide, the 5500 SOLiD Fragment Library Core Kit (PN 4464412) was used to prepare 5500 libraries. The ends of the DNA fragments were repaired and DNA was cleaned and concentrated. Prior to size selection, the fragmented end-repaired DNA was assessed on an Agilent 2100 Bioanalyzer High Sensitivity DNA Chip (PN 5067\u20134626). Shearing resulted in a broad distribution with peak at approximately 175\u2009bps.To obtain a narrow fragment size distribution of DNA for AJ son 5500 library preparation, the DNA was run on the Sage Science BluePippin automated DNA size selection and collection system. A BluePippin DNA 3% Dye-Free Agarose gel cassette with Q2 Marker (PN BDF310) was used to capture DNA in a target range of 100\u2013150\u2009bps. Approximately 1.0\u20131.5\u2009ug of end-repaired DNA was loaded into an appropriate well on the cassette and run per the BluePippin 3% Agarose Gel Cassette Quick Guide. Upon completion of size selection 40\u201360\u2009ul of size selected DNA was removed from the elution well and cleaned and concentrated using a 1.8X Agencourt AMPure XP (PN A263800) cleanup. Cleaned size-selected DNA was eluted in 32\u2009ul of TE buffer. Size selection was again assessed using a Bioanalyzer High Sensitivity DNA Chip and showed a peak at approximately 128\u2009bps.Using reagents provided in the 5500 SOLiD Fragment Library Core Kit, dA Tailing, adaptor ligation and nick translation were performed per the 5500 Fragment Library Preparation Guide. Adaptor volumes were calculated using the mass calculated from the Bioanalyzer High Sensitivity Chip following the cleanup of the size-selected DNA. Two rounds of cleanup using Agencourt AMPure XP reagent were performed per the User Guide.The completed 5500 libraries were then converted to 5500\u2009W libraries compatible for sequencing on a Life Technologies 5500\u00d7l Wildfire. Wildfire conversion was performed per the Quick Reference \u20185500\u2009W Series Genetic Analysis Systems: Conversion of 5500 Library to 5500\u2009W Library\u2019 (PN 4477188 Rev. B) utilizing reagents provided in the Life Technologies 5500\u2009W Conversion Primer Kit (PN 4478020) and Platinum PCR SuperMix (PN 11306\u2013081). Six cycles of amplification were performed in the conversion process. Following completion of the conversion process, cleaned and concentrated libraries were assessed using a Bioanalyzer High Sensitivity DNA Chip and showed a peak at approximately 270\u2009bps with a distribution from approximately 150\u2013400\u2009bps.A second round of BluePippin size selection was performed to tighten the size distribution of the final 5500\u2009W library. The 5500\u2009W libraries were run on a DNA 2% Dye-Free Agarose gel cassette with V1 Marker to capture DNA in a target range of 200\u2013300\u2009bps. All DNA for a given sample was loaded into the assigned well on the cassette and run per the BluePippin 2% Agarose Gel Cassette Quick Guide. Upon completion of size selection 40\u201360\u2009ul of size selected DNA was removed from the elution well and cleaned and concentrated using a 1.8X Agencourt AMPure XP cleanup. Cleaned-size selected 5500\u2009W libraries were eluted in 32\u2009ul of TE buffer. Size selection was assessed using a Bioanalyzer High Sensitivity DNA Chip and showed the final AJ son 5500\u2009W libraries with a peak at approximately 275\u2009bps and a distribution from approximately 240\u2013320\u2009bps.A Life Technologies 5500\u00d7l Wildfire  was used to sequence 5500\u2009W AJ son and Chinese son libraries using ICS software version 2.1. The User Guide \u20185500\u2009W Series Genetic Analysis System (Americas)\u2019 (PN 4481746 Rev. B) was followed and used to prepare the samples and load a 5500\u2009W v2 FlowChip (PN 4475661). The Wildfire Template Amplification Protocol v6.1, located in the User Guide, was followed for template amplification. The 5500\u2009W FlowChip Prep Enzyme Kit (PN 4481058) and 5500\u2009W Template Amplification Reagents v2 (PN 4475663) were used to prepare FlowChips for \u2018on-instrument\u2019 template amplification following template hybridization. 5500\u2009W library molar concentrations were calculated from the Bioanalyzer High Sensitivity chip following the final size selection of the 5500\u2009W libraries. These concentrations were used in calculation of FlowChip loading concentrations. Libraries were deposited into individual lanes at final concentrations of 100 to 250\u2009pM. The library concentrations vary due to adjustments in subsequent instrument runs to increase colony density for a given library on the FlowChip.5500\u2009W fragment libraries were sequencing with single-end 75\u2009bp reads using the 5500\u2009W Forward SR 75 Reagent (PN 4475685). Two libraries were prepared and sequenced for each genome for a total of 24 lanes (4 FlowChips) per genome. This sequencing yielded approximately 72x coverage/genome.Lymphoid-cell lines from the AJ Trio cell cultures obtained from Coriell Cell Repositories  were pelleted and washed with Life Technologies PBS  at 1X concentration; the final cell pellet was re-suspended in cell suspension buffer using the Bio-Rad CHEF Mammalian Genomic DNA Plug Kit. Cells were then embedded in Bio-Rad CleanCut low melt Agarose and spread into a thin layer on a custom support in development. Cells were lysed using BioNano Genomics IrysPrep Lysis Buffer, protease treated with QIAGEN Puregene Proteinase K, followed by brief washing in Tris with 50\u2009mM EDTA and then washing in Tris with 1\u2009mM EDTA before RNase treatment with Qiagen Puregene RNase. DNA was then equilibrated in Tris with 50\u2009mM EDTA and incubated overnight at 4\u2009\u00b0C before extensive washing in Tris with 0.1\u2009mM EDTA followed by equilibration in New England BioLabs NEBuffer 3 at 1X concentration. Purified DNA in the thin layer agarose was labeled following the BioNano Genomics IrysPrep Reagent Kit protocol with adaptations for labeling in agarose. Briefly, 1.25\u2009ug of DNA was digested with 0.7 units of New England BioLabs Nt.BspQI nicking endonuclease per \u03bcl of reaction volume in New England BioLabs NEBuffer 3 for 130\u2009min at 37\u2009\u00b0C, then washed with Affymetrix TE Low EDTA Buffer, pH 8.0, followed by equilibration with New England BioLabs 1x ThermoPol Reaction Buffer. Nick-digested DNA was then incubated for 70\u2009min at 50\u2009\u00b0C using BioNano Genomics IrysPrep Labeling mix and New England BioLabs Taq DNA Polymerase at a final concentration of 0.4\u2009U/\u03bcl. Nick-labeled DNA was then incubated for 40\u2009min at 37\u2009\u00b0C using BioNano Genomics IrysPrep Repair mix and New England BioLabs Taq DNA Ligase at a final concentration of 1\u2009U/\u03bcl. Labeled-repaired DNA was then recovered from the thin layer agarose by digesting with GELaseand counterstained with BioNano Genomics IrysPrep DNA Stain prior to data collection on the Irys system.DNA was isolated from a lymphoid-cell cell culture of the Chinese son (GM24631) using the Bio-Rad CHEF Mammalian Genomic DNA Plug Kit protocol and lysed using BioNano Genomics IrysPrep Lysis Buffer and digested with QIAGEN Puregene Proteinase K. DNA was solubilized using GELase Agarose Gel-Digesting Preparation and drop-dialyzed before labeling using standard IrysPrep Reagent Kit protocols.Labeled and stained DNA samples were loaded into BioNano Genomics IrysChips and run on the BioNano Genomics Irys System imaging instrument. Data was collected for each sample until desired fold coverage of long molecules (>150\u2009kb) was achieved. BioNano Genomics IrysView visualization and analysis software application was used to detect individual linearized DNA molecules using the Life Technologies YOYO-1 Iodide in DMSO and determine the localization of labeled nick sites along each DNA molecule. BioNano Genomics IrysSolve analytical and assembly pipeline compiled the sets of single-molecule maps for each sample and were then used to build a full genome assembly.DNA library preparation and sequencing was performed according to the manufacturer\u2019s instructions with noted modifications. Following the Pacific Biosciences Protocol, \u201820-kb Template Preparation Using Blue Pippin Size-Selection System\u2019, library preparation was performed using the Pacific Biosciences SMRTbell Template Prep Kit 1.0 (PN # 100-259-100). In short, 10\u2009\u03bcg of extracted, high-quality, genomic DNA from the NIST Reference Material DNA for the AJ trio, were used for library preparation. Genomic DNA extracts were verified with the Life Technologies Qubit 2.0 Fluorometer using the High Sensitivity dsDNA assay (PN# Q32851) to quantify the mass of double-stranded DNA present. After quantification, each sample was diluted to 150\u2009\u03bcl, using kit provided EB, yielding a concentration of approximately 66\u2009ng/\u03bcl. The 150\u2009\u03bcl aliquots were individually pipetted into the top chambers of Covaris G-tube (PN# 520079) spin columns and sheared for 60\u2009s at 4500\u2009rpm using an Eppendorf 5424 benchtop centrifuge. Once complete, the spin columns were flipped after verifying that all DNA was now in the lower chamber. The columns were spun for another 60\u2009s at 4500\u2009rpm to further shear the DNA and place the aliquot back into the upper chamber. In some cases G-tubes were centrifuged 2\u20133 times, in both directions to ensure all volume had passed into the appropriate chamber. Shearing resulted in a approximately 20,000\u2009bp DNA fragments verified using an Agilent Bioanlyzer DNA 12000 gel chip (PN# 5067\u20131508). The sheared DNA isolates were then purified using a 0.5X AMPure PB magnetic bead purification step . This AMPure purification step assures removal of any small fragment and/or biological contaminant. The sheared DNA concentration was then measured using the Qubit High Sensitivity dsDNA assay. These values were used to calculate actual input mass for library preparation following shearing and purification.After purification, approximately 8 to 9\u2009\u03bcg of each purified sheared sample went through the following library preparation process per this protocol also in :de novo assembly possible. Without selection, smaller 2000\u201310,000\u2009bp molecules dominate the zero-mode waveguide loading distribution, decreasing the sub-read length. Size-selection was confirmed using pre and post size selected DNA using an Agilent DNA 12000 chip. Final library mass was measured using the Qubit High Sensitivity dsDNA Assay. Approximately 15\u201320% of the initial gDNA input mass resulted after elution from the agarose cassette, which was enough yield to proceed to primer annealing and DNA sequencing on the PacBio RSII instrument. This entire library preparation and selection strategy was conducted 7, 2 and 2 times across AJ son, AJ father, and AJ mother respectively, to provide enough library for the duration of this project.All library preparation reaction volumes were scaled to accommodate input mass for a given sample. Library size selection was performed using the Sage Science BluePippin 0.75% Agarose, Dye Free, PacBio approximately 20\u2009kb templates, S1 cassette (PN# PAC20KB). Size selections were run overnight to maximize recovered mass. Approximately 2\u20135\u2009mg of prepared libraries were size selected using a 10\u2009kb start and 50\u2009kb end in \u2018Range\u2019 mode. This selection is necessary to narrow the library distribution and maximize the SMRTbell sub-read length for the best Sequencing reflects the P6-C4 sequencing enzyme and chemistry, respectively. (Note that 10.3% of the data was collected using the P5-C3 enzyme/chemistry prior to the release of the P6-C4 enzyme and chemistry.) Primer was annealed to the size-selected SMRTbell with the full-length libraries (80\u2009\u00b0C for 2\u2009min 30 followed by decreasing the temperature by 0.1\u00b0/s to 25C\u00b0). To prepare the polymerase-template complex, the SMRTbell template complex was then bound to the P6 enzyme using the Pacific Biosciences DNA Polymerase Binding Kit P6 v2 (PN# 100-372-700). A ratio of 10:1, polymerase to SMRTbell at 0.5\u2009nM, was prepared and incubated for 4\u2009h at 30\u2009\u00b0C and then held at 4\u2009\u00b0C until ready for magbead loading prior to sequencing. The Magnetic bead-loading step was conducted using the Pacific Biosciences MagBead Kit (PN# 100-133-600) at 4\u2009\u00b0C for 60-minutes per manufacturer\u2019s guidelines. The magbead-loaded, polymerase-bound, SMRTbell libraries were placed onto the RSII instrument at a sequencing concentration of 100 to 40\u2009pM to optimize loading across various SMRTcells. Sequencing was performed using the C4 chemistry provided in the Pacific Biosciences DNA Sequence Bundle 4.0 (PN# 100-356-400). The RSII was then configured for at least 240-minute continuous sequencing runs.The genomic DNA library preparation consists of the ligation of a hairpin adapter to dsDNA molecules  such that the template, then adapter, then complement can be passed through the pore sequentially. This library design produces a current time-series dataset with three distinct sections, of which the template and complement can be isolated from the adapter region. After base-calling is performed, the template and complement are aligned to produce two-direction, or 2D, reads. If the quality of one or both sequences is limited, a 2D read may not be produced, though a1D read is made available.Genomic DNA from the Ashkenazi Jewish (AJ) son was prepared for sequencing via the Oxford Nanopore Technologies MinION single molecule sequencing instrument. Two libraries were generated, one with the \u2018SQK-MAP-004 genomic DNA\u2019 kit and one with the \u2018SQK-MAP- 006 genomic DNA\u2019 kit provided as part of the MAP. Library preparation and sequencing was done according to manufacturer\u2019s instructions with all optional steps executed. Both libraries were prepared with 1\u2009\u03bcg HMW-gDNA of the HG-002 RM. DNA concentration was measured using Life Technologies Qubit dsDNA BR assay (PN# Q32850). DNA quality was measured with the Agilent 2200 Tapestation Genomic DNA Analysis assay (PN# 5067\u20135365). Shearing was done with Covaris G-tubes (PN#520079) and an Eppendorf 5424R centrifuge (PN# 5404000413). Prior to library preparation, the optional New England BioLlabs preCR repair (PN# M0309S) step was taken for the SQK-MAP-004 library and the corresponding optional NEB FFPE (PN# M6630S) repair step was taken for the SQK-MAP-006 library. The RM was never stored via FFPE but the protocol distributed by ONT suggests using this particular reagent prior to library preparation to repair potential DNA damage in the interest of producing the highest quality signal during sequencing.Both AJ Son gDNA libraries were sequenced via single 48\u2009h runs on the MinION instrument. A MinION version 7.3 flow cell was used for the SQK-MAP-004 library and a MinION MkI for the SQK-MAP-006 library, each being the most current version at the time of sequencing. Flowcells were received along with library preparation kits as part of the MAP. Flowcells were primed twice at 10\u2009min intervals prior to loading the library, as described in each respective library preparation protocol. Sequencing runs were controlled using default versions of MinKNOW protocols \u2018MAP_48Hr_Sequencing_Run.py\u2019 (SQK-MAP-004 library) and \u2018MAP_48Hr_Sequnecing_Run_SQK_MAP006.py\u2019 (SQK-MAP-006 library).Methods to generate 2D reads from the Oxford Nanopore MinION from two different libraries are described in ref. The genomes sequenced in this work see  and theiApproximately 300x 148\u2009bp\u00d7148\u2009bp Illumina paired end WGS data from NA12878 (HG001) is in the NCBI SRA SRX1049768 to SRX1049855 [Data Citation 1].148\u00d7148\u2009bp HiSeq sequencing and analyses of two 40x to 50x runs from each member of the Ashkenazim trio  using the BWA-GATK pipeline on Basespace. Raw data is available in the SRA: SRX847862 to SRX848317 [Data Citation 2]. The BAM, VCF, and fastq files have been uploaded to:ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/HG002_NA24385_son/NIST_HiSeq_HG002_Homogeneity-10953946/ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/HG003_NA24149_father/NIST_HiSeq_HG003_Homogeneity-12389378/ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/HG004_NA24143_mother/NIST_HiSeq_HG004_Homogeneity-14572558/Data from the Chinese trio is in the NCBI SRA SRX1388368 to SRX1388459 [Data Citation 3]. Fastq files for 300x sequencing of Chinese son (HG005), as well as approximately 45x bam files generated from each flow cells are located here:ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/ChineseTrio/HG005_NA24631_son/HG005_NA24631_son_HiSeq_300xFastq files for 100x sequencing of the Chinese parents (GM24694 and GM24695), as well as approximately 100x bam files generated for each genome are located here:ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/ChineseTrio/HG006_NA24694-huCA017E_father/NA24694_Father_HiSeq100xftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/ChineseTrio/HG007_NA24695-hu38168_mother/NA24695_Mother_HiSeq100xIllumina mate-pair data are available at the NCBI SRA SRX1388732 to SRX1388743 [Data Citation 4], and as bam files in the NIST_Stanford_Illumina_6\u2009kb_matepair directory for each genome :ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/HG002_NA24385_son/NIST_Stanford_Illumina_6kb_matepair/ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/HG003_NA24149_father/NIST_Stanford_Illumina_6kb_matepair/ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/HG004_NA24143_mother/NIST_Stanford_Illumina_6kb_matepair/ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/ChineseTrio/HG005_NA24631_son/NIST_Stanford_Illumina_6kb_matepair/ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/ChineseTrio/HG006_NA24694-huCA017E_father/NIST_Stanford_Illumina_6kb_matepair/ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/ChineseTrio/HG007_NA24695-hu38168_mother/NIST_Stanford_Illumina_6kb_matepair/Illumina read cloud data are available as fastq\u2019s (for the AJ trio and Chinese trio) and as bam files (currently only for the AJ son and father) in the NIST_Stanford_Moleculo directory for each genome genome :ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/HG002_NA24385_son/NIST_Stanford_Moleculo/ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/HG003_NA24149_father/NIST_Stanford_Moleculo/ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/HG004_NA24143_mother/NIST_Stanford_Moleculo/ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/ChineseTrio/HG005_NA24631_son/NIST_Stanford_Moleculo/ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/ChineseTrio/HG006_NA24694-huCA017E_father/NIST_Stanford_Moleculo/ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/ChineseTrio/HG007_NA24695-hu38168_mother/NIST_Stanford_Moleculo/Illumina paired-end WES data for AJ Trio  and the Chinese son (HG005) are available. Raw data (fastq files) are available in the SRA: SRP047086 [Data Citation 5]. The BAM and VCF files have been uploaded to:BAM files and their index files:ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/HG002_NA24385_son/OsloUniversityHospital_Exomeftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/HG003_NA24149_father/OsloUniversityHospital_Exomeftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/HG004_NA24143_mother/OsloUniversityHospital_Exomeftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/ChineseTrio/HG005_NA24631_son/OsloUniversityHospital_ExomeJoint Variant Calling file for AJ trio:ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/analysis/OsloUniversityHospital_Exome_GATK_jointVC_11242015Single sample variant calling file for the Chinese son:ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/ChineseTrio/analysis/OsloUniversityHospital_Exome_GATK_jointVC_11242015gVCF for the Chinese son:ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/ChineseTrio/analysis/OsloUniversityHospital_Exome_GATK_jointVC_11242015http://software.10xgenomics.com/ for detailed information on file formats. 10X Genomics data are available at http://software.10xgenomics.com/giab201510X Genomics data was generated with cell lines acquired from Coriell . Aligned reads with barcode and phasing information are provided in BAM format for each sample. VCF files with small variants are also provided for each sample, and SV calls are provided for NA12878 and the AJ son. See The same data are available at the NCBI SRA SRX1392293 to SRX1392296 [Data Citation 6] and on the GIAB FTP:ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/NA12878/10XGenomicsftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/HG002_NA24385_son/10XGenomicsftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/HG003_NA24149_father/10XGenomicsftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/HG004_NA24143_mother/10XGenomicsThe Complete Genomics files are available on the NCBI SRA [Data Citation 7] and [Data Citation 8], and on the GIAB FTP site as summarized in http://www.completegenomics.com/documents/DataFileFormats_Standard_Pipeline_2.5.pdf.Directory structures and file formats for the \u2018Full package\u2019, as well as the other supplementary files discussed below, are described in http://cgatools.sourceforge.net/docs/1.8.0/cgatools-user-guide.pdf.For both the Chinese and Askenazi trios, a multisample VCF including only small variants was generated from masterVar files using the CGA tools mkvcf program described in All other VCF files contain small variants, CNVs, SVs and MEIs. Note that for CNVs and SVs, more complete information is available in the ASM/CNV and ASM/SV directories of the full package. For CNVs, VCF files contain the information sourced from the cvnDetails files but do not provide information on any segmentation of the genome into ploidy or coverage levels. For SVs, VCF files contain information sourced from the allJunctionsBeta and highConfidenceJunctionsBeta, but information from the allSvEventsBeta and highConfidenceSvEventsBeta files is not included.de novo assembly (LDN) stage of genome assembly, reads can be re-mapped, added or removed from the assembly within the region undergoing LDN. The reads and the mappings that support variant calls after LDN is complete are provided in the evidence files. EvidenceDnbs* bam files are generated with our evidence2sam tool from CGA Tools (http://cgatools.sourceforge.net/docs/1.8.0/cgatools-user-guide.pdf). A detailed description of the data file can be found in the, \u2018Representation of the Complete Genomics Data in SAM Output Format\u2019 appendix of the CGA Tools User Guide (http://cgatools.sourceforge.net/docs/1.8.0/cgatools-user-guide.pdf). They contain the reads and mappings that support one of the called alleles by at least 2\u2009dB over the other called allele. This means they will not contain reads and mappings that do not support either of the called alleles. The evidence BAMs do not contain reads and mappings for loci that were ultimately no-called or called homozygous ref, unless those regions were selected for de novo assembly because they were suspected to contain a variation. Every read that is found in the evidence files will also be present in the initial mappings, but the mapping positions may be different. In this case, where a read is found in both the *_mapping_sorted_header.bam and evidenceDnbs* files, the mapping in the evidence files is preferred.BAM files are provided in order to provide evidence of variants called. However, it is not appropriate to remap and recall variants based on these BAM files as proper re-mapping of reads should take into account the gapped read structure. The *_mapping_sorted_header.bam files include the initial mappings of all reads. They were generated with the map2sam program from CGA Tools with the --mate-sv-candidates and --add-unmapped-mate-info parameters. Inconsistent mappings are normally converted as single arm mappings with no mate information provided, but with the --mate-sv-candidates option map2sam will mate unique single arm mappings in SAM including those on different stands and chromosomes. The tag \u2018XS:i:1\u2019 is used to distinguish these \u2018artificially\u2019 mated records. The MAPQ provided for these records is a single arm mapping weight. The --add-unmapped-mate-info parameter generates mate sequences and score tags for inconsistent mappings. In the subsequent local http://www.completegenomics.com/documents/DataFileFormats_Standard_Pipeline_2.5.pdf for details on file formats. In addition, summary files are included with assembly statistics.The Complete Genomics LFR data was sequenced from cells sourced from Coriell . VCF and var formats files that include small variant calls are available for GM12878 and the Ashkenazi trio as indicated in The VCF and var files also include two additional FORMAT fields: MEWC and SWC. MEWC, minimum exclusive well count, indicates the number of LFR wells that support the REF or ALT allele (whichever is fewer) exclusively, and not the other allele. SWC, shared well count, indicates the number of LFR wells that support both the REF and ALT allele. High confidence variant calls should have a high MEWC  and a low  SWC. Note that small variant sensitivity is somewhat lower for the LFR process compared to standard Complete Genomics sequencing, so the standard sequencing should be deferred to for unphased variants.The files generated by Thermo Fisher Scientific describe genomic variants called from Ion Torrent sequencing data with AmpilSeq exomes. The variants are represented in VCF files, each accompanied by an effective region BED file describing the corresponding genomic scope of called variants.Four GIAB samples with were sequenced using AmpliSeq exome and sequenced on the Ion Proton .AmpliseqExome.20141120.16runs.vcf.zip -- 16 VCF files produced by Torrent Variant Caller v4.4 on 16 AmpliSeqExome runs, 4 of each samples picked by Genome in a Bottle consortium .AmpliseqExome.20141120.NA24143.vcf\u2014HG004 variants called on 4 runs combined, above a quality score of 25;AmpliseqExome.20141120.NA24149.vcf\u2014HG003 variants called on 4 runs combined, above a quality score of 25;AmpliseqExome.20141120.NA24385.vcf\u2014HG002 variants called on 4 runs combined, above a quality score of 25;AmpliseqExome.20141120.NA24631.vcf\u2014HG005 variants called on 4 runs combined, above a quality score of 25;AmpliseqExome.20141120_effective_regions.bed -- Genomic scope of AmpliSeqExome variant calls. This file describes the region in which variants are called with Torrent Suite v4.4 and later, and the region on which curation has been performed.High_Confidence_Variants_NA24385.bed -- A list of inspected HG002 variants based on Ion Torrent, Complete Genomics, 23andme and manual curationHigh_Confidence_Variants_NA24385_effective_regions.bed -- Genomic scope of inspected NA24385 variants.The Ion Exome data are available on the NCBI SRA [Data Citation 9], [Data Citation 10], and [Data Citation 11], and on the GIAB FTP site at:ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/HG002_NA24385_son/ion_exome/ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/HG003_NA24149_father/ion_exome/ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/HG004_NA24143_mother/ion_exome/ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/ChineseTrio/HG005_NA24631_son/ion_exome/ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/NA12878/ion_exome/The SOLiD 5500\u00d7l Wildfire WGS data for the AJ Son (HG002) and Chinese Son (HG005) are currently available as xsq files on the GIAB ftp site because this is the native format for SOLiD, as well as bam files mapped with lifescope and read groups for each lane. These data are available on the NCBI SRA [Data Citation 12] and [Data Citation 13], and on the GIAB FTP site at:ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/HG002_NA24385_son/NIST_SOLiD5500Wftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/ChineseTrio/HG005_NA24631_son/NIST_SOLiD5500Wall.bnx is the raw data after image processing and filtering for molecules >150\u2009kbde novo assembly consensus genome map setEXP_REFINEFINAL1.cmap is the The following files result from the alignment of genome maps to hg19:EXP_REFINEFINAL1.xmap is the alignment file with match group informationde novo genome maps that align to hg19 EXP_REFINEFINAL1_q.cmap is the EXP_REFINEFINAL1_r.cmap is an in silico map of hg19 BioNano data for the AJ Trio and Chinese Son  are available atftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/HG002_NA24385_son/BioNano/ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/HG003_NA24149_father/BioNano/ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/HG004_NA24143_mother/BioNano/ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/ChineseTrio/HG005_NA24631_son/BioNano/The PacBio data for the AJ Trio  are available on the NCBI SRA [Data Citation 14] and on the GIAB FTP site at:ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/HG002_NA24385_son/PacBio_MtSinai_NIST/ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/HG003_NA24149_father/PacBio_MtSinai_NIST/ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/HG004_NA24143_mother/PacBio_MtSinai_NIST/The file/directory naming convention is defined as follows: [SampleName]/[WellName]_[CollectionNumber].[UUID].tar.gz Note that SampleName may contain other genomes in the name since this is hardcoded by the run name, but the data directories only contain run data from AJ son, AJ father, and AJ mother. For example, for SampleName of HG002new_O1_BP_P6_021815_MB_105\u2009pM, WellName of A01, and CollectionNumber of 3, you will see a tar.gz file in HG002new_O1_BP_P6_021815_MB_105\u2009pM directory with name A01_3.[UUID].tar.gz The UUID is currently used for only hashing purpose. The tar.gz file contains the raw SMRTPortal data including following contents:tar.gz | [movie name].1.xfer.xml | [movie name].2.xfer.xml | [movie name].3.xfer.xml | [movie name].mcd.h5 | [movie name].metadata.xml\\---Analysis_Results \u2004 | [movie name].1.bax.h5 \u2004 | [movie name].1.log \u2004 | [movie name].1.subreads.fasta \u2004 | [movie name].1.subreads.fastq \u2004 | [movie name].2.bax.h5 \u2004 | [movie name].2.log \u2004 | [movie name].2.subreads.fasta \u2004 | [movie name].2.subreads.fastq \u2004 | [movie name].3.bax.h5 \u2004 | [movie name].3.log \u2004 | [movie name].3.subreads.fasta \u2004 | [movie name].3.subreads.fastq \u2004 | [movie name].bas.h5 \u2004 | [movie name].sts.csv \u2004 | [movie name].sts.xmlhttp://files.pacb.com/software/instrument/2.0.0/bas.h5%20Reference%20Guide.pdfThe metadata.xml contains all the metadata of this particular sample in the xml format; for example, in the TemplatePrep field you might see \u2018DNA Template Prep Kit 2.0 (3\u201310\u2009Kb),\u2019 and in the BindingKit field you might see \u2018DNA/Polymerase Binding Kit P6,\u2019 etc. For information about bas.h5/bax.h5 files, please see: https://speakerdeck.com/pacbio/track-1-de-novo-assemblyFor information about subreads, please see: The Oxford Nanopore raw reads and 2D reads for the AJ Son (HG002) are available at:ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/HG002_NA24385_son/CORNELL_Oxford_Nanopore/ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp). Preliminary data will be placed in the technical directory and analyses and finalized data will be placed under each trio or genome in the data directory. The directories under the analysis directory for each family contains the institution, data set, type(s) of variants, analysis tool, and date.We expect to continue to accrue public data for these genomes as new methods become available. These data will be placed in the NCBI SRA when possible, linked to the GIAB BioProject PRJNA200694 and the appropriate BioSample listed in Several statistics were calculated for each flow cell using the Illumina BaseSpace Isaac Whole Genome Sequencing v3 analysis pipeline .6 with default settings, and duplicates were marked using samblaster14. Statistics are summarized in To assess duplication rate, coverage, and insert size of the mate-pair libraries, reads were stripped of adapter sequences. Read pairs were removed if the sequence of one or both mates was less than 20\u2009bp after adapter stripping, or if the adapter sequence was at the beginning rather the end of a read (indicating the read inserts were likely to be in inward-facing F/R orientation rather than the expected outward-facing R/F orientation). Reads were then mapped to the hg19 reference genome using \u2018bwa mem\u2019The high rate of PCR duplicates (close to 50% in some libraries) resulted in lower than expected sequence coverage . A more relevant metric for mate-pair data is the physical coverage, which measures the number of inferred fragments that cover a particular genomic position (including both the sequenced ends as well as the unsequenced genomic region between the ends). Because the empirical insert size average was between 6\u20137\u2009kb per individual, the physical coverage of the genome was quite high . BAMs were stripped of duplicate reads to reduce file size, but the full data are available in fastq format.Several statistics were generated to assess the Illumina read clouds : the reaTo assess duplication rate, coverage, and insert size of the libraries, picard CollectAlignmentSummaryMetrics, CollectInsertSizeMetrics and CalculateHsMetrics were performed on each sample BAM file. Statistics are summarized in Statistics for the AJ trio and Chinese son were generated using the 10X GemCode Long Ranger software .http://www.completegenomics.com/documents/DataFileFormats_Standard_Pipeline_2.5.pdf). The gapped read pairs were aligned to the NCBI Build 37 reference gnome using an index lookup based fast algorithm. At locations where the mapping results suggest the presence of a variant, mapped reads were refined, expanded and then assembled into a best-fit, diploid sequence with a custom software suite employing both Bayesian and de Bruijn graph techniques13. This process yielded diploid reference, variant or no-call at each genomic location with associated variant quality scores.Sequencing results in mate-paired reads with a 2\u20134 base overlap between adjacent contiguous sequences, as described in the \u2018Read Data Format\u2019 section of the Data File Formats documentation  Various measures of coverage for tiled 2 and 100\u2009kb windows across the genome are determined. This is provided in the cnvDetails* and depthOfCoverage files. 2) The genome is segmented into called ploidy levels  or coverage levels  using diploid and non-diploid HMM-based algorithms. The segmentation patterns called are provided in the cnvSegments* files. 3) The lesser allele fraction (LAF) is calculated for 100\u2009kb windows across the genome\u2014the LAF calculations are included in the cnvDetailsNondiploid and cnvSegmentsNondiploid files (and not in the diploid files because of the 100\u2009kb window size restriction). To identify SVs, DNB mappings found during the standard assembly process are analyzed to find clusters of DNBs in which each arm maps uniquely to the reference genome, but with an unexpected mate pair length or anomalous orientation. SVs are encoded in the junctions and highConfidenceJunctions files where the latter file contains a high-confidence filtered subset of the data in former file. In addition to calling structural variant junctions, junctions are rationalized into structural variation events using the CGA Tools junctions2events algorithm. These data are provided in the svEvents and highConfidenceSvEvents files. Additional information on the CNV, SV and MEI algorithms is available here: Assembly metrics are summarized in 12. LFR assemblies include only small variants and their associated haplotypes and well counts.Genomic assembly was performed as described above for Regular Complete Genomics WGS, with the added assembly step of haplotype generation using well informationAssembly metrics are summarized in Sequencing reads with a mean read length of 190\u2009bp were mapped to human genome version hg19. Mean coverage across AmpliSeq Exome target regions is 256x per run, with raw read accuracy at 99%. Additional summary statistics are in As recommended by the manufacturer, the statistics reported from the instrument from each run of the SOLiD 5500\u00d7l Wildfire WGS were examined to ensure consistency in quality. These statistics included \u2018Quality Value\u2019 and \u2018Fraction of good+best\u2019, which are a function of the quality of the signal at each ligation. Reads were mapped using Lifescope, which generated the summary statistics given in De novo assembly of single molecules is accomplished using BioNano Genomics IrysSolve, a proprietary assembler software application, based on an overlap-layout-consensus paradigm15\u201317. Molecules longer than 150\u2009kb were the input for a pairwise comparison to find all overlaps; then a draft consensus map (BioNano Genomics CMAP) was constructed based on these overlaps. The draft BioNano Genomics CMAP was refined by mapping single molecules to it and iteratively recalculating the label positions. Next, the draft BioNano Genomics CMAP (consensus genome maps) were extended by aligning overhanging molecules to the consensus maps and calculating a consensus in the extended regions. Finally, the consensus maps were compared and merged iteratively five times where the patterns matched and then the final label position calculation was made. Summary statistics for each sample are presented in Assuming a 3.2\u2009Gb human genome, sequencing was conducted to approximately 69X, 32X, and 30X coverage for AJ son (HG002), AJ father (HG003), and AJ mother (HG004) across 292, 139, and 132 SMRT cells, respectively. 27.4, 13.2, and 12.4\u2009M subreads were generated resulting in 220.0, 101.6, and 94.9\u2009Gb of sequence data with sub-read length N50 values of 11,087, 10,728, and 10,629 basepairs. The coverage distribution for each genome is also depicted in https://github.com/arq5x/poretools) was used to extract sequence information from these directories as fasta or fastq files (poretools fasta -- type 2D./). The ensuing 2D reads were mapped to the human hg19 reference using BWA  and GraphMap . CIGAR strings in the generated BAM files were updated to the X/= format using the SamFixCigar module of the jvarkit package (https://github.com/lindenb/jvarkit/wiki/SamFixCigar). Error rates were calculated according to the GIGAR strings using a modified version of count-errors.py (https://github.com/arq5x/nanopore-scripts), modified code provided as The base-calling tool provided by Oxford Nanopore Technologies, Metrichor, is a cloud-based service that provides the user with a choice of 1D or 2D base-calling, the latter being more accurate. Raw current was processed during sequencing of the SQK-MAP-004 library via Metrichor version 2.26 protocol \u20182D Basecalling\u2019 and during sequencing of SQK-MAP-006 library via Metrichor version 2.34.3 protocol \u20182D Basecalling for SQK-MAP-006.\u2019 These protocols segregate output into \u2018pass\u2019 and \u2018fail\u2019 directories, corresponding to the success of alignment of complement and template sections. Basecalls and relevant information are stored in fast5 files in each directory. Poretools  .http://tinyurl.com/giabpilot). The NIST RM 8398 was prepared by Coriell from a large growth of cells, and the DNA was extracted and mixed to produce about 8300 10\u2009ug vials of DNA. The remaining genomes are from the Personal Genome Project. These genomes are also available as EBV-immortalized B lymphoblastoid cell lines and as extracted DNA from Coriell, and 4 of them will be available as NIST RMs, planned for release in mid 2016. The AJ Son will be distributed as RM 8391, the AJ Trio will be distributed as RM 8392, and the Chinese Son will be distributed as RM 8393 (note that the Chinese parents are only available from Coriell). Similar to the pilot genome, the other candidate NIST RMs are extracted DNA from a large batch of cells. Except for technologies that optimally start with cells , all data in this work are collected from the NIST RM DNA. It is possible that small differences may exist between the NIST RM DNA and the DNA from Coriell because they come from different passages of cells and may contain different new mutations.The genomes sequenced in this work  and theiftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp. To facilitate data analysis in cloud, all the data have been mirrored to the Amazon Web Services Public Datasets repository with \u2018s3://giab\u2019 as bucket name. In addition, data that were submitted to SRA can also be accessed through NCBI BioProject (http://www.ncbi.nlm.nih.gov/bioproject/200694). The Genome in a Bottle Consortium has formed an Analysis Team to coordinate analyses by groups that are interested in analyzing these data. The primary goal of this group is to establish high-confidence phased variant calls of all sizes for these genomes, so that anyone can benchmark accuracy of their calls for these genomes. The Analysis Team has several sub-groups working on assembly, small variant calling, structural variant calling, and phasing. The intermediate analysis results from these sub-groups are being organized in subdirectory under \u2018analysis\u2019  with the name describing analyzer\u2019s name who performed the analysis, technology for dataset(s) that has been used, type of variant being characterized, analysis tool or algorithm being utilized, and the submission date (MMDDYYYY format) serving as version for better understanding what the datasets were about. The integrated high-confidence calls for the trio samples will be available at ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/release/, and the subdirectory with name \u2018latest\u2019 will always contain the latest results published by the Genome in a Bottle Consortium. To make it easier to visualize these data in a web-based genome browser, the GeT-RM browser at NCBI is also hosting some of the vcf and bam files from NA12878 and is currently working on hosting additional data from NA12878 as well as the other GIAB genomes.All data from Genome in a Bottle project are available without embargo, and the primary location for data access is https://github.com/genome-in-a-bottle) has also been developed for the Genome in a Bottle project. The idea behind this site is to help the users navigate the main ftp site more easily, and find as much answers as possible by themselves related to the Genome in a Bottle project and its data. Several repositories have been established within the genome-in-a-bottle GitHub site. The repository of \u2018about_GIAB\u2019 describes the objectives and overview of the Genome in a Bottle project, while the repository of \u2018giab_data_indexes\u2019 lists and organizes the index files for the raw sequences and/or alignments by the sequencing platforms for each of the individuals or trio families, therefore, users could use the specific index file to guide their downloading for the desired dataset from the ftp site. The repositories of \u2018giab_data_analysis\u2019 and \u2018giab_latest_release\u2019 provide easy links to the specific ftp locations regarding ongoing analysis results performed by individual analysis groups and the latest high-confidence sets released by the Genome in a Bottle Consortium. The tools and methods that have been used by the analysis groups have been documented in \u2018giab_tools_methods\u2019 repository, while the scientific publications are listed in \u2018giab_publications\u2019 repository. The \u2018giab_FAQ\u2019 repository provides short answers for the frequently asked questions regarding how to effectively access and appropriately utilize the data from the Genome in a Bottle project.In order to improve the accessibility and usability of the Genome in a Bottle project data, a GitHub site ."}
+{"text": "Caulobacter crescentus is cell cycle regulated and we unearth a bacterial transglutaminase homolog, HvyA, as restriction factor that prevents capsulation in G1-phase cells. This capsule protects cells from infection by a generalized transducing Caulobacter phage (\u03c6Cr30), and the loss of HvyA confers insensitivity towards \u03c6Cr30. Control of capsulation during the cell cycle could serve as a simple means to prevent steric hindrance of flagellar motility or to ensure that phage-mediated genetic exchange happens before the onset of DNA replication. Moreover, the multi-layered regulatory circuitry directing HvyA expression to G1-phase is conserved during evolution, and HvyA orthologues from related Sinorhizobia can prevent capsulation in Caulobacter, indicating that alpha-proteobacteria have retained HvyA activity.Despite the crucial role of bacterial capsules in pathogenesis, it is still unknown if systemic cues such as the cell cycle can control capsule biogenesis. In this study, we show that the capsule of the synchronizable model bacterium DOI:http://dx.doi.org/10.7554/eLife.03587.001 Many bacteria have a tough outer coating known as capsule that protects them from untoward environmental conditions. This capsule also prevents viruses called bacteriophages from invading the bacterial cells, and it shields those bacteria that can infect humans from attack by our immune system. External conditions\u2014such as a lack of nutrients and physical stresses\u2014are known to trigger capsule formation. However, almost nothing is known about the signals from within the bacteria that control the formation of a capsule.Caulobacter crescentus to show that capsule formation is regulated by the bacterial cell cycle. This cycle is a series of events and checkpoints that happen every time a cell divides to form two new cells. Ardissone et al. revealed that capsule cannot form during the first phase of the cell cycle. The bacterium only forms its capsule as this phase ends and before it copies its DNA and later divides in two.Now, Ardissone et al. have used the capsulated bacterium called Ardissone et al. discovered that an enzyme called HvyA, which is only produced during the first phase of the cell cycle, prevents the capsule from forming. Inactivating the HvyA enzyme was also shown to make the bacteria impervious to infection by a bacteriophage. Furthermore, Ardissone et al. dissected the complicated steps involved in regulating the production of the HvyA enzyme and showed that such regulatory steps are also used by other species of bacteria.Without their capsules, bacteria can take up new genetic material from a number of sources that might help them adapt to a changing environment. Ardissone et al.'s findings suggest that by only exchanging genetic material during the first phase of the cell cycle, bacteria ensure that any useful DNA is taken up and copied along with their own DNA later in the cell cycle.Antibiotic resistance spreads between bacteria via the exchange of genetic material, making it increasingly difficult to treat bacterial infections. Interfering with the formation of the capsule during an infection could help overcome this problem by making the bacteria more vulnerable to attack either by our own immune system or by bacteriophages that can be used to treat bacterial infections. By investigating how genetic exchange and capsule formation are linked and regulated, the findings of Ardissone et al. might now open up new strategies to help combat bacterial infections.DOI:http://dx.doi.org/10.7554/eLife.03587.002 Genetic exchange is both fundamental to the adaptation of bacterial cells faced with ever-changing environmental conditions and the cause of the alarming dissemination of antibiotic resistance determinants among the bacterial pathogens. The underlying mechanisms include direct uptake of naked DNA (transformation) by bacterial cells as well as cell- or bacteriophage-based delivery systems  . Thus, uMicrobial polysaccharidic capsules can also restrict bacteriophage-mediated genetic exchange. Typically, they mask bacteriophage receptor sites that are on or near the cell surface . MoreoveCaulobacter crescentus (henceforth Caulobacter) is the pre-eminent model system for cell cycle studies (Caulobacter this asymmetric cell division yields a motile and piliated swarmer (SW) cell that is in a G1-arrested state and a sessile stalked (ST) cell that resides in S-phase (Caulobacter populations (by density gradient centrifugation) . CtrA activates transcription of many late S- and G1-phase genes that are repressed by the transcriptional regulators SciP or the MucR1/2 paralogs, respectively 7-TTAA-3\u2032 motif (CtrA box) located in many Caulobacter and Sinorhizobium promoters (Caulobacter origin of replication (Cori)   for celllobacter  and in tmeliloti , a plantectively . In addiromoters  and the n (Cori) .10.7554/Binding of CtrA to its target sites is stimulated 100-fold by phosphorylation of aspartate at position 51   through Caulobacter capsule as determinant of the buoyancy trait and we identify HvyA, a member of the poorly characterized bacterial transglutaminase-like cysteine protease (BTLCP) family, as a PleC-dependent negative regulator that is restricted to G1-phase to prevent capsulation at this time in the cell cycle. As the capsule protects Caulobacter cells from infection by the generalized transducing Caulophage \u03c6Cr30 and no CRISPR/Cas -based adaptive immunity system to protect cells from invading genetic material is encoded in the Caulobacter genome  Caulobacter cells is cell cycle-regulated, but the underlying regulatory mechanism is elusive. We used a developmental mutant (\u0394pleC) as an entry point to identify the genetic determinants conferring the change in buoyancy. Density gradient centrifugation of a WT culture yields ST and PD cells with the characteristic high buoyancy  and SW cells with the characteristic low buoyancy  insertions that render \u0394pleC cells \u2018heavy\u2019 , a Wzc-like chain length regulator/tyrosine kinase (CCNA_00163), a putative O-antigen polymerase/ligase (CCNA_00164), a putative Wzb-like metallophosphatase (CCNA_00167), and a Wza-like outer membrane translocon (CCNA_00168), all commonly associated with capsular export systems. No Tn insertions were found in the other two genes within this cluster, CCNA_00166 and pssY. For the latter, this could be explained by a functional redundancy of pssY with the orthologs encoded by pssZ and hfsE, all encoding polyisoprenylphosphate hexose-1-phosphotransferases  and we describe below that an in-frame deletion of CCNA_00166  cells  on a medium\u2013copy number plasmid  cells . Moreove (pMT335 ) restorefunction .10.7554/mobile genetic element (MGE) that has previously been implicated in buoyancy (E. coli that is involved in O-antigen (O26) synthesis (CCNA_03998), a polysaccharide polymerase (CCNA_00470) and a GDP-L-fucose synthase (CCNA_00471). The three genes are near other coding sequences for polysaccharides biosynthesis proteins, including two other putative glycosyltransferases (CCNA_00466 and CCNA_00469), a Wzx-like polysaccharide flippase/translocase (CCNA_00467) and a sugar mutase homolog (CCNA_00465)  in WT or \u0394pleC mutant cells and found the resulting single or double mutants to be \u2018heavy\u2019  . ConsistA_00465) , we wereynthesis . To conf \u2018heavy\u2019 .CCNA_03998 to the predicted N-acetyl-L-fucosamine transferase WbuB (C. crescentus (WT or \u2018light\u2019 mutants (\u0394pleC) had a mucoid (\u2018smooth\u2019) colony appearance , \u0394CCNA_00166 , and \u0394hvyA \u0394CCNA_00163  cells. As the Caulobacter capsule is primarily composed of neutral monosaccharides including fucose, mannose, galactose, and glucose , whereas those from the \u2018heavy\u2019 mutants (i.e. the \u0394CCNA_00163 single mutant and the \u0394hvyA \u0394CCNA_00163 double mutant) contained far less fucose, galactose, and mannose . We also observed a significant reduction in galacturonic acid in the preparations from the \u2018heavy\u2019 mutants vs WT or \u2018light\u2019 cells, raising the possibility that this saccharide is also a constituent of the NA1000 capsule  G1-phase SW progeny and one capsulated (\u2018light\u2019) S-phase ST cell  , but lacking discernible hydrophobic sequences or a lipidation signal for retention in the membrane, suggesting that it is periplasmic. The C-terminal part of HvyA features a BTLCP domain. This domain is thought to introduce intra- or inter-molecular crosslinks by transamidation, forming \u03b3-glutamyl-\u03b5-lysine isopeptide bonds between Gln and Lys residues, to hydrolyse amide bonds by the reverse protease reaction and/or to execute deamidation/esterification reactions of glutamine residues (hvyA (\u0394hvyA) phenocopied the buoyancy defect of \u0394pleC cells  reversed the buoyancy defect of \u0394hvyA and \u0394pleC cells, analogous plasmids encoding the predicted catalytic mutants  were unable to do so, although all the HvyA variants accumulated to comparable steady-state levels as the WT protein on immunoblots  on PYE sucrose plates (van- or a Pxyl-plasmid) in WT, \u0394pleC, or \u0394hvyA cells also renders cells \u2018heavy\u2019 and \u2018rough\u2019  . The hvy protein  harbouriresidues . Cysteinresidues  for the residues . As an iunoblots . Thus ex \u2018heavy\u2019 , we rease plates . Importa \u2018rough\u2019 . On the  \u2018rough\u2019 .10.7554/hvyA, \u2018light\u2019 mucoid) and non-capsulated  cells  microscopy was comparable (hvyA cells and by its absence from \u0394CCNA_00163 cells. Atomic force microscopy (AFM) (CCNA_00163 and \u0394hvyA strains (from two independent preparations for each). As opposed to \u0394CCNA_00163 cells, which were readily imaged without apparent cell surface damage (hvyA cells (CCNA_00163 cell was smooth (surface roughness on 0.06 \u00b5m2 areas of \u223c0.9 nm) and featureless, \u0394hvyA cells were rougher (roughness of \u223c2.2 nm) and showed streaks in the scanning direction, suggesting that soft, loosely bound material was pushed away by the tip. In light of earlier AFM studies  cells . Akin toeumoniae , the zonmparable . The incpy (AFM)  providedpy (AFM) . Figure e damage , the suryA cells  was stroyA cells . While t studies , we noteCCNA_00163 cells are devoid of capsule, while a soft layer of capsular polysaccharides covers \u0394hvyA cells. The results above raised the possibility that HvyA normally prevents capsulation in G1-phase cells, and FITC-dextran staining of an NA1000 culture expressing a ST cell marker (SpmX-mCherry) indeed revealed that G1-phase cells (i.e. SW cells isolated on density gradient) did not exclude the polymer and are thus non-capsulated, whereas ST/PD cells (\u2018light\u2019 cells on density gradient) show a much bigger area of FITC-dextran exclusion , we found that PhvyA is indeed positively dependent on PleC  and that this binding is strongly diminished in \u0394pleC cells  that directly bind PhvyA  of CtrA  is expressed from a promoter that is temporally confined during the cell cycle via activation and repression by PleC/CtrA\u223cP and MucR1/2, respectively.Next, we explored if the G1-specific regulation of HvyA is due to transcriptional regulation by PleC. Using a eC cells . Moreove WT CtrA . By contnd PhvyA  and thatnd PhvyA ). Thus, vely see . Consist of CtrA . Thus, hhvyA by MucR is conserved in S. meliloti. We found that PhvyA-lacZ was strongly de-repressed (1683 \u00b1 130% of WT activity) in a mucR::Tn mutant derivative (Rm101) of the S. meliloti WT strain , is regulated by MucR in both these alpha-proteobacteria. The promoter probe plasmid PSMc00998-lacZ indicated that the SMc00998 promoter is strongly de-repressed in S. meliloti mucR::Tn (314 \u00b1 25% of WT activity) and the Caulobacter \u0394mucR1/2 mutant . This prediction was based on the strong de-repression of PhvyA in the absence of MucR1/2 observed above and our result that overexpression of HvyA renders Caulobacter cells \u2018heavy\u2019. To our surprise, we found that \u0394mucR1/2 cells are in fact \u2018light\u2019  induced the \u2018heavy\u2019 (non-capsulated) phenotype in \u0394mucR1/2 cells  can partially complement the Caulobacter \u0394mucR1/2 mutant. Interestingly, in the presence of a heterologous MucR, increased translational activity of PhvyA-hvyA::lacZ was always accompanied with a commensurate repression of the PhvyA-lacZ transcriptional reporter ] or its antagonist gene sciP [sciP(T24I) or sciP(T65A)]  pattern with \u2018heavy\u2019 and \u2018light\u2019 cells, while PhvyA was still de-repressed (WT SciP from pMT335 cripples PhvyA-hvyA::lacZ translation (33.9 \u00b1 4.7% of WT activity), while over-expression of SciP(T65A) only has negligible effects ]  are episepressed . In supphvyA in Caulobacter cells and showed that these two functions can be genetically uncoupled. This regulatory complexity highlights the requirement of proper buoyancy and capsulation control during the cell cycle. Since SciP is the negative regulator of S-phase genes , while MucR1/2 negatively regulate G1-specific genes , the coordinated transcriptional and translational control of hvyA by CtrA, MucR1/2, and SciP suggests that cells prepare themselves for the impending capsule-less SW cell phase by setting the stage for rapid translation of HvyA once the transcript is synthesized at compartmentalization.Importantly, we identified mechanisms for both transcriptional and translational regulation of hvyA quantitatively by genome-wide Tn mutagenesis followed by deep-sequencing (Tn-Seq) of Caulobacter cells challenged or not with bacteriophage \u03c6Cr30. This analysis revealed that hvyA is one of the two major \u03c6Cr30-resistance determinants, along with the rsaA locus encoding the RsaA subunit of the S-layer and CCNA_01057 that is predicted to function in S-layer assembly  compared to the complete absence of HvyA due to Tn insertions in hvyA.As it has been suggested that the MGE and the buoyancy phenotype can influence the resistance profile to the S-layer specific Caulophage \u03c6Cr30 , we evalassembly   indicating that these genes have the opposite fitness effect, as would be predicted from our epistasis experiments on capsulation  where the phage had been spotted  were unable to do so (hvyA or \u0394pleC strains harbouring \u0394CCNA_00163 or \u0394CCNA_00470 mutations exhibited clearing zones (lysis) akin to the WT  tag  due to overexpression of HvyA-TAP, serial enrichment of the mutant pool by density gradient centrifugation led to recovery of \u2018light\u2019 mutants , which is known to destabilize the outer membrane (Caulobacter (CCNA_02223) along with WT or mutant HvyA-TAP variants, but not cytoplasmic proteins such as CtrA  expressing a HvyA-derivative with a C-terminal TAP (ion) tag  under th mutants . Followi mutants , we reco mutants . While tmembrane . Such tr as CtrA .Caulobacter BTLCP, we asked if heterologous BTLCPs support HvyA function in Caulobacter. While this was not the case for the BTLCP from A. tumefaciens (Atu0252) or two HvyA paralogs from S. fredii NGR234 (NGR_c19800 and NGR_c36180), the BTLCP homologs SMc00998 and NGR_c12490 from S. meliloti and S. fredii NGR234, respectively, restored \u03c6Cr30-sensitivity and partially compensated for the buoyancy defect of \u0394hvyA cells  in the Caulobacter the T4-like phage \u03c6Cr30 is obstructed by capsulation. As the G1-specific inhibition of capsulation by HvyA is released with the removal of HvyA during the G1\u2192S transition, the nascent S-phase cells are protected from infection by \u03c6Cr30. As \u03c6Cr30 is a generalized transducing bacteriophage, this temporal confinement of capsulation also has the consequence of limiting \u03c6Cr30-mediated horizontal exchange of genetic material to SW cells.In addition to offering protection from immune cells, capsulation is a known resistance mechanism to bacteriophage adsorption . Our dathvyA cells compared to WT or the \u0394hvyA \u0394CCNA_00167 double mutant , so they probably affect also phenotypes unrelated to surface polysaccharides, mucoidy, and/or buoyancy. However, BTLCPs are predicted to have signal sequences for export into the periplasm and are often encoded in the vicinity of trans-envelope transport systems or enzymes predicted to act in the periplasm, suggesting that they act on periplasmic or extra-cytoplasmic targets. Consistent with an enzymatic activity, mutations in predicted catalytic residues of HvyA abrogate function and we were able to demonstrate limited promiscuity among HvyA, NGR_c12490, and SMc00998 in vivo, at least under conditions of over-expression. Such protein crosstalk is not unexpected and has also been documented between non-cognate histidine kinases and response regulators for example. The crosstalk among the three transglutaminase/protease-like enzymes HvyA, NGR_c12490, and SMc00998 indicates similar targets in Caulobacter and Sinorhizobia. Unlike HvyA, NGR_c12490, and SMc00998, coding sequences are not embedded within genes encoding known surface polysaccharides export proteins. However, over-expression of HvyA did not alter the abundance or migration of the CPS export proteins CCNA_00162, CCNA_00163, CCNA_00164, CCNA_00167, and CCNA_00168 by SDS-PAGE immunoblotting, or the amount of assembled RsaA S-layer subunit  abundance during the alpha-proteobacterial cell cycle represents the major regulatory mechanism to constrain BTLCP activity temporally.BTLCPs are particularly widespread in alpha-proteobacterial genomes and are also found in certain gamma-proteobacterial clades such as  subunit , indicat genomes . While tin vitro . LapG isin vitro . Moreoveorescens . ResidueCaulobacter crescentus NA1000 , and Rosetta(DE3)pLysS  were grown at 37\u00b0C in LB. The E. coli mutator strain XL-1 Red  was grown at 30\u00b0C in LB. Motility assays, swarmer cells isolation, electroporations, biparental matings, and bacteriophage \u03c6Cr30-mediated generalized transductions were performed as described (S. meliloti), 20, 1 (10 for E. coli and S. meliloti), and 1 (10 for E. coli and S. meliloti) \u03bcg/ml, respectively. Plasmids for \u03b2-galactosidase assays were introduced into S. meliloti by bi-parental mating and into C. crescentus by electroporation.s NA1000  and derii NGR234  was growivatives  were gro7-1 \u03bbpir , EC100D,escribed . NalidixpleC and \u0394hvyA strains was done by intergeneric conjugation from E. coli S17-1 \u03bbpir harbouring the himar1-derivative pHPV414 as previously described , CCNA_00163(101-300)-His6, His6-SUMO-CCNA_00164(481-620), His6-HvyA(26-272), His6-CCNA_00167(1-108), His6-SUMO-CCNA_00168(41-198), and CCNA_02223(22-289)-His6 were expressed in E. coli Rosetta (DE3)pLysS cells and the recombinant proteins were purified using Ni-NTA agarose . His6-SUMO-CCNA_00168(41-198) and CCNA_02223(22-289)-His6 were purified in the soluble fraction and directly used to immunize rabbits . Purified His6-SUMO-CCNA_00162(51-422), CCNA_00163(101-300)-His6, His6-SUMO-CCNA_00164(481-620), His6-HvyA(26-272), and His6-CCNA_00167(1-108) were excised from 12.5% SDS polyacrylamide gels and used to immunize rabbits. For immunoblots, protein samples were separated on SDS polyacrylamide gel, transferred to polyvinylidene difluoride (PVDF) Immobilon-P membranes (Merck Millipore), and blocked in PBS , 0.1% Tween20, and 5% dry milk. The anti-sera were used at the following dilutions: anti-CtrA   , anti-Pi:10,000) , anti-Cc:10,000) , anti-mC:10,000) , anti-CC600nm \u223c 0.6), centrifuged and washed twice with HEPES 10 mM pH 7.2. Cells were re-suspended in HEPES 10 mM pH 7.5 containing 10 mM EGTA (ethylene glycol tetraacetic acid) and incubated at room temperature for 10 min. Cells were then pelleted by centrifugation and 20 \u03bcl of supernatant loaded on a 7.5% SDS polyacrylamide gel, followed by Coomassie Blue staining.Cultures (8 ml) were grown to exponential phase  before adding chloramphenicol (2 \u03bcg/ml). CtrA or mCh-HvyA levels were monitored by immunoblotting of samples taken at different time points after addition of chloramphenicol.C. crescentus cells  were grown in PYE to exponential phase (OD600nm \u223c 0.6), pelleted by centrifugation, and re-suspended in 20 mM Tris, pH 7.5, 100 mM NaCl. The susceptibility of surface proteins to proteolysis was determined by treating whole cells with 0.5 mg/ml proteinase K; after incubation at 37\u00b0C , 1\u00d7 protease inhibitors  were added. The cells were washed four times with 20 mM Tris (pH 7.5), 100 mM NaCl, 1\u00d7 protease inhibitors, re-suspended in SDS-PAGE loading buffer and boiled. Protein samples were analysed by immunoblotting using antibodies to CCNA_00168.hvyA alleles mutated in the predicted catalytic residues, the hvyA ORF was sub-cloned into pOK12  were obtained following random mutagenesis of pUG52, which was passed through the 660nm = 0.1\u20130.5 were lysed with chloroform and mixed with Z buffer  to a final volume of 800 \u03bcl. 200 \u03bcl of ONPG  were added and the reaction timed. When a medium-yellow colour developed, the reaction was stopped by adding 400 \u03bcl of 1M Na2CO3. The OD420nm of the supernatant was determined and the Miller units (U) were calculated as follows: U = (OD420nm * 1000)/(OD660nm * time [in min] * volume of culture used [in ml]). Error was computed as standard deviation (SD).\u03b2-galactosidase assays were performed at 30\u00b0C. Cells (50\u2013200 \u03bcl) at ODC. crescentus cells grown in 2L PYE were pelleted by centrifugation, washed twice with phosphate saline buffer , and lyophilized. Lyophilized cells were used for the purification of capsular polysaccharides, which was performed using a modification of the method described by O-trimethylsilyl (TMS) derivatives of the monosaccharide methyl glycosides produced by acidic methanolysis. Briefly, after treatment of cellular lysates with 95% ethanol for polysaccharide enrichment, contaminants such as DNA, RNA, and proteins were removed by successive digestion with DNase I, RNase A, and proteinase K. Every enzymatic digestion step was followed by dialysis against distilled deionized water. Samples were then subjected to ultracentrifugation  to pellet lipopolysaccharide (LPS). The supernatant containing capsular polysaccharides was freeze-dried and used for glycosyl composition analysis. Inositol (20 \u03bcg) was added, as internal standard, to 500 \u03bcg of each sample. Polysaccharides were first hydrolysed with 2 M trifluoroacetic acid (TFA) at 120\u00b0C for 2 hr. Methyl glycosides were prepared from the dry samples by mild acid treatment (methanolysis in 1 M HCl in methanol at 80\u00b0C for 16 hr) followed by re-acetylation with pyridine and acetic anhydride in methanol (for detection of amino-sugars). The samples were then per-O-trimethylsilylated by treatment with Tri-Sil reagent  at 80\u00b0C for 30 min , washed once with PBS, and resuspended in 30 \u03bcl of PBS. 10 \u03bcl of bacterial suspension was mixed with 2 \u03bcl of FITC-dextran (10 mg/ml in water), applied onto a microscope slide, and firmly covered with a coverslip. Cells expressing SpmX-mCherry were grown in PYE supplemented with 1% sucrose to a final OD600nm = 0.6. SW and ST/PD cells were separated by centrifugation on density gradient, then washed with PBS, and incubated with FITC-dextran as described above. The samples were imaged as described for fluorescence and DIC images  was used to assess capsule thickness as previously described . BrieflyC images . Images C images . StatistCaulobacter cells grown overnight in liquid PYE were rinsed in PBS buffer and resuspended in 4% paraformaldehyde (Sigma-Aldrich) solution for 1 hr at room temperature for fixation. Cells were then rinsed in PBS buffer and filtered through polycarbonate porous membrane . AFM imaging was performed using a Nanoscope VIII Multimode  and oxide-sharpened microfabricated Si3N4 cantilevers with a nominal spring constant of \u223c0.01 N/m . After filtering the cell culture, the filter was gently rinsed with the buffer, carefully cut (1 cm \u00d7 1 cm), attached to a steel sample puck using a small piece of double face adhesive tape, and the mounted sample was transferred into the AFM liquid cell while avoiding dewetting. Images were taken in PBS buffer in contact mode under minimal applied force. Images were analysed using Nanoscope 8.10 software . Rms (root mean square) roughness values were calculated on 250 \u00d7 250 nm2 areas of the high magnification height images subjected to second order filtering.hvyA by a mCherry-hvyA N-terminal fusion (strain SA1737) were created using pNPTS138 derivatives constructed as follows:_\u039400162: PCR was used to amplify two DNA fragments flanking the CCNA_00162 ORF, by using primers 162_ko1/162_ko2 and 162_ko3/162_ko4. The PCR fragments were digested with HindIII/BamHI and BamHI/EcoRI, respectively, then ligated into pNPTS138, restricted with HindIII and EcoRI.pNPTS_\u039400163: PCR was used to amplify two DNA fragments flanking the CCNA_00163 ORF, by using primers 163_ko1/163_ko2 and 163_ko3/163_ko4. The PCR fragments were digested with HindIII/BamHI and BamHI/EcoRI, respectively, then ligated into pNPTS138, restricted with HindIII and EcoRI.pNPTS_\u039400164: PCR was used to amplify two DNA fragments flanking the CCNA_00164 ORF, by using primers 164_ko1/164_ko2 and 164_ko3/164_ko4. The PCR fragments were digested with HindIII/BamHI and BamHI/EcoRI, respectively, then ligated into pNPTS138, restricted with HindIII and EcoRI.pNPTS_\u0394hvyA: PCR was used to amplify two DNA fragments flanking the hvyA ORF, by using primers hvyA_ko1/hvyA_ko2 and hvyA_ko3/hvyA_ko4. The PCR fragments were digested with EcoRI/BamHI and BamHI/HindIII, respectively, then ligated into pNPTS138, restricted with HindIII and EcoRI.pNPTShvyA-mCh::hvyA: primers hvyA_up_H and hvyA_up_B were used to amplify a 921-bp fragment encompassing the region upstream of hvyA and the first 78 bp of the hvyA ORF . Primers mCh_B and mCh_X were used to amplify the mCherry coding sequence (without ATG and stop codon). Primers hvyA_down_X and hvyA_down_E were used to amplify a 719-bp fragment of hvyA ORF. The three PCR fragments were digested with HindIII/BamHI, BamHI/XbaI, and XbaI/EcoRI, respectively, and ligated into pNPTS138, restricted with HindIII and EcoRI.pNPTS_P_\u039400167: PCR was used to amplify two DNA fragments flanking the CCNA_00167 ORF, by using primers 167_ko1/167_ko2 and 167_ko3/167_ko4. The PCR fragments were digested with EcoRI/BamHI and BamHI/HindIII, respectively, then ligated into pNPTS138, restricted with HindIII and EcoRI.pNPTS_\u039400167hvyA). The PCR fragment was digested with EcoRI/BglII and ligated into pNPTS_\u039400167, restricted with BamHI and EcoRI.pNPTS_\u039403998: PCR was used to amplify two DNA fragments flanking the CCNA_03998 ORF, by using primers 3998_ko1/3998_ko2 and 3998_ko3/3998_ko4. The PCR fragments were digested with EcoRI/BamHI and BamHI/HindIII, respectively, then ligated into pNPTS138, restricted with HindIII and EcoRI.pNPTS00466: PCR was used to amplify two DNA fragments flanking the CCNA_00466 ORF, by using primers 466_ko1/466_ko2 and 466_ko3/466_ko4. The PCR fragments were digested with MunI/BamHI and BamHI/HindIII, respectively, then ligated into pNPTS138, restricted with HindIII and EcoRI.pNPTS_\u039400467: PCR was used to amplify two DNA fragments flanking the CCNA_00467 ORF, by using primers 467_ko1/467_ko2 and 467_ko3/467_ko4. The PCR fragments were digested with HindIII/BamHI and BamHI/EcoRI, respectively, then ligated into pNPTS138, restricted with HindIII and EcoRI.pNPTS_\u039400470: PCR was used to amplify two DNA fragments flanking the CCNA_00470 ORF, by using primers 470_ko1/470_ko2 and 470_ko3/470_ko4. The PCR fragments were digested with EcoRI/BamHI and BamHI/HindIII, respectively, then ligated into pNPTS138, restricted with HindIII and EcoRI.pNPTS_\u0394C. crescentus strains. Double recombination was selected by plating bacteria onto PYE plates containing 3% sucrose. Putative mutants were confirmed by PCR using primers external to the DNA fragments used for the pNPTS138 constructs.Bi-parental matings were used to transfer the resulting pNPTS138 derivatives into rsaA gene in the \u0394hvyA, \u0394CCNA_00163, or \u0394hvyA \u0394CCNA_00163 mutant strains, plasmid pNPTS138_\u0394rsaA was introduced into the strains by bi-parental mating. Clones that had undergone a single recombination event were selected on PYE plates containing kanamycin and verified by PCR.To inactivate the mucR1\u0394mucR2 with hvyA replaced by N-terminal mCherry-tagged hvyA), a 719-bp fragment was amplified by PCR using primers hvyA_in_B and hvyA_E. The PCR fragment was digested with BamHI/EcoRI and ligated into pGS18T, restricted with the same enzymes. The resulting plasmid (pSA480) was integrated into the hvyA locus in SA1737 (strain SA1951), and the mCh-hvyA fusion was transduced into the \u0394mucR1\u0394mucR2 strain by \u03c6Cr30-mediated transduction and selection on PYE kanamycin plates.To created strain SA1984 , a 531-bp DNA fragment, encompassing 513-bp upstream of hvyA and the first six codon of hvyA ORF, was amplified by PCR with primers PhvyA_B/PhvyA_P, digested with BglII and PstI, and ligated into pJC327 , the fragment corresponding to the hvyA promoter region was excised from pSA184 with BglII and PstI, and ligated into pRKlac290 , a 542-bp DNA fragment was amplified by PCR with primers Sm998_B/Sm998_P from S. meliloti genomic DNA, digested with BamHI and PstI, and ligated into pRKlac290, cut with the same enzymes.To create the PS. meliloti Rm2011 and Rm101 by bi-parental mating.Plasmids for \u03b2-galactosidase assays were introduced into hvyA mutation, the hvyA ORF was amplified by PCR with primers hvyA_N and hvyA_E. The resulting PCR product was digested with NdeI and EcoRI and ligated into pMT335  or pMT375 , restricted with the same enzymes.To complement the \u0394r pMT375  , the hvyA ORF was amplified by PCR with primers hvyA_N and hvyA_CTIF (without stop codon). The PCR fragment was digested with NdeI and EcoRI and cloned into a pMT335 derivative harbouring the TAP epitope cloned as EcoRI/XbaI fragment (To create the Pfragment .In-frame deletions and replacement of van) were constructed as follows:CCNA_00162 ORF was amplified by PCR with primers 162_N (with NdeI site overlapping the start codon) and 162_M (with MunI site flanking the stop codon) and cloned into pMT335, restricted with NdeI and EcoRI.pSA362: CCNA_00163 ORF was amplified by PCR with primers 163_N (with NdeI site overlapping the start codon) and 163_E (with EcoRI site flanking the stop codon) and cloned into pMT335, restricted with NdeI and EcoRI.pSA361: CCNA_00164 ORF was amplified by PCR with primers 164_N (with NdeI site overlapping the start codon) and 164_E (with EcoRI site flanking the stop codon) and cloned into pMT335, restricted with NdeI and EcoRI.pSA401: CCNA_00167 ORF was amplified by PCR with primers 167_N (with NdeI site overlapping the start codon) and 167_E (with EcoRI site flanking the stop codon) and cloned into pMT335, restricted with NdeI and EcoRI.pSA62: CCNA_00168 ORF was amplified by PCR with primers 168_N (with NdeI site overlapping the start codon) and 168_E (with EcoRI site flanking the stop codon) and cloned into pMT335, restricted with NdeI and EcoRI.pSA324: CCNA_03998 ORF was amplified by PCR with primers 3998_N (with NdeI site overlapping the start codon) and 3998_E (with EcoRI site flanking the stop codon) and cloned into pMT335, restricted with NdeI and EcoRI.pUG35: CCNA_00466 ORF was amplified by PCR with primers 466_N (with NdeI site overlapping the start codon) and 466_M (with MunI site flanking the stop codon) and cloned into pMT335, restricted with NdeI and EcoRI.pUG28: CCNA_00470 ORF was amplified by PCR with primers 470_N (with NdeI site overlapping the start codon) and 470_E (with EcoRI site flanking the stop codon) and cloned into pMT335, restricted with NdeI and EcoRI.pSA102: SMc00998 ORF was amplified by PCR from S. meliloti genomic DNA with primers Sm998_N (with NdeI site overlapping the start codon) and Sm998_E (with EcoRI site flanking the stop codon) and cloned into pMT335, restricted with NdeI and EcoRI.pSA264: NGR_c12490 ORF was amplified by PCR from S. fredii NGR234 genomic DNA with primers Sf12490_N (with NdeI site overlapping the start codon) and Sf12490_M (with MunI site flanking the stop codon) and cloned into pMT335, restricted with NdeI and EcoRI.pSA142: NGR_c19800 ORF was amplified by PCR from S. fredii NGR234 genomic DNA with primers Sf19800_N (with NdeI site overlapping the start codon) and Sf19800_E (with EcoRI site flanking the stop codon) and cloned into pMT335, restricted with NdeI and EcoRI.pSA141: NGR_c36180 ORF was amplified by PCR from S. fredii NGR234 genomic DNA with primers Sf36180_N (with NdeI site overlapping the start codon) and Sf36180_E (with EcoRI site flanking the stop codon) and cloned into pMT335, restricted with NdeI and EcoRI.pSA147: Atu0252 ORF was amplified by PCR from A. tumefaciens genomic DNA with primers At252_N (with NdeI site overlapping the start codon) and At252_E (with EcoRI site flanking the stop codon) and cloned into pMT335, restricted with NdeI and EcoRI.pSA309: Bh_MucR: a synthetic fragment encoding the mucR homolog from Bartonella henselae (PRJBM_00467) was ligated into pMT335 (using NdeI/EcoRI).pMT335-Plasmids to complement the in-frame deletion mutants and for over-expression (from PPRJBM_00467 (codon optimized for C. crescentus) (5\u2032\u20133\u2032):Synthetic DNA fragment (Integrated DNA Technologies) encoding CATATGGAGCACCGACCGGTGCTGGAAACCGAGTCGAATCTGGTCATCACCCTCGTCGCCGACATCGTCGCCGCGTATGTGTCGAACAACTCCATCCGTCCCACCGAGGTCCCCAGCCTCATCGCGGACGTCCATGCGGCTTTCCGCAAGGCCGGCAACGCCGACTTGACGGAAGTTGAGGTGGAGAAGCAGCGCCCTGCGGTCAACCCGAAGCGCAGCATCTTCCCGGACTACCTTATCTGCCTGGAAGATGGCAAGAAGTTCAAGAGCCTGAAGCGCCACCTGATGACGCACTATGGCATGCTGCCGGAAGAGTATCGCGAGAAGTGGCAGCTGGACTCTTCGTACCCCATGGTGGCCCCGAACTACGCGAAGGCCCGGTCGGCCCTGGCCAAAGAGATGGGCCTGGGGCGGAAGTCCAAGCGGAAAAAGACCAAGTGAATTC6-SUMO-CCNA_00162(51-422) under the control of the T7 promoter. To construct pSA354, a fragment encoding residues 51-422 of CCNA_00162 was amplified by PCR with primers 162_in_N and 162_in_S, digested with NdeI and SacI, and cloned into pCWR547, restricted with the same enzymes.Plasmid pSA354 is a derivative of pCWR547  expressi(101-300)-His6 under the control of the T7 promoter. To construct pCWR508, a fragment encoding residues 101-300 of CCNA_00163 was amplified by PCR with primers 163_in_N and 163_His_E , digested with NdeI and EcoRI, and cloned into pET47b, restricted with the same enzymes.Plasmid pCWR508 is a derivative of pET-47b (Novagen) expressing CCNA_001636-SUMO-CCNA_00164(481-620) under control of the T7 promoter. To construct pSA352, a fragment encoding residues 481-620 of CCNA_00164 was amplified by PCR with primers 164_in_N and 164_in_S, digested with NdeI and SacI, and cloned into pCWR547, restricted with the same enzymes.Plasmid pSA352 is a derivative of pCWR547 expressing HishvyA is a derivative of pET-28a (Novagen) expressing His6-HvyA(26-272) under the control of the T7 promoter. To construct pET-hvyA, a fragment encoding residues 26-272 of HvyA was amplified by PCR with primers hvyA_short and hvyA_E, digested with NdeI and EcoRI and cloned into pET-28, restricted with the same enzymes.Plasmid pET-00167 is a derivative of pET-28a expressing His6-CCNA_00167(1-108) under the control of the T7 promoter. To construct pET-00167, a fragment encoding residues 1-208 of CCNA_00167 was amplified by PCR with primers 167_N and 167_in_E, digested with NdeI and EcoRI, and cloned into pET-28, restricted with the same enzymes.Plasmid pET-6-SUMO-CCNA_00168(41-198) under the control of the T7 promoter. To construct pSA342, a fragment encoding residues 41-198 of CCNA_00168 was amplified by PCR with primers 168_short and 168_E, digested with NdeI and EcoRI, and cloned into pET-28. The CCNA_00168 fragment was then sub-cloned into pCWR547 using NdeI/SacI.Plasmid pSA342 is a derivative of pCWR547 expressing His(22-289)-His6 under control of the T7 promoter. To construct pCWR496, a fragment encoding residues 22-289 of CCNA_02223 (\u03b2-lactamase) was amplified by PCR with primers bla_N and bla_His_E , digested with NdeI and EcoRI, and cloned into pET47b, restricted with the same enzymes.Plasmid pCWR496 is a derivative of pET-47b expressing CCNA_02223 review process). Similarly, the author response typically shows only responses to the major concerns raised by the reviewers.eLife posts the editorial decision letter and author response on a selection of the published articles . An edited version of the letter sent to the authors after peer review is shown, indicating the substantive concerns or comments; minor concerns are not usually shown. Reviewers have the opportunity to discuss the decision before the letter is sent , a Reviewing editor, and 3 reviewers, one of whom, Ry Young, has agreed to reveal his identity (the other 2 remain anonymous).Thank you for sending your work entitled \u201cCell cycle constraints on capsulation and genetic exchange\u201d for consideration at The Reviewing editor and the other reviewers had a vigorous discussion of the implications of your findings before reaching the decision to consider your manuscript for publication. The Reviewing editor has assembled the following comments to help you prepare a revised submission.In this paper, Ardissone et al. investigate the molecular basis of the cell cycle switch in cellular buoyancy in Caulobacter crescentus and doing so, demonstrate that it is linked to cell capsulation and its control during the cell cycle. Specifically, the authors show that the expression of HvyA, a newly identified negative regulator of capsulation is both under tight transcriptional and translational cell cycle control to become cleared when the cells enter the S-phase, allowing the capsulation of a particular cell type. This ensures a cell-specific protection against phage infections and potentially other environmental insults. The regulatory mechanism has been genetically dissected and shown to involve known cell cycle regulators MucR1/2, SciP, CtrA and a still elusive factor X. This regulation or at least the MucR part may be conserved in other alphas. HvyA inhibits capsule formation, but its mode of action, which appears to be conserved in other alpha proteobacteria, remains unknown. Overall, the experiments are high quality and the conclusions are justified.However, all reviewers agree the following modifications are critical:1) The text needs to be condensed and revised to make the paper easier to read. In addition, the discussion that capsule was selected for phage depredation should be eliminated. Instead, the authors should discuss other possible benefits of the capsule and the observed regulation.2) Since \u201cseeing is believing\u201d, the authors should image the capsule at the surface of the stalk (possibly by EM) in both wild type and mutant strains. The revised manuscript features the following editorial and experimental amendments.1) We streamlined the text, with a shortened Introduction and Discussion as recommended.2) We included cytological experiments providing compelling evidence that Caulobacter S-phase cells are encased in a polysaccharidic capsule.hvyA (capsulated) mutant versus the delta-CCNA00163 (non-capsulated) mutant in which a component of the export machinery has been inactivated. These fluorescence images and quantification are show in the new 2a) First, negative staining fluorescence microscopy revealed a thick layer that excludes FITC-labeled dextran from the delta-CCNA00163 cells compared to delta-hvyA cells (new 2b) Second, atomic force microscopy of the same strains revealed a smoother surface on delta-ells new . The dif2c) Third, we used live cell fluorescence microscopy of cells expressing a different component of the capsule export machinery  as full-length translational fusion to mCherry to find that the mCherry-derived fluorescence emanates through the cell, suggesting that the export machinery (and thus the exported capsule) is not localized to specific subcellular site. These images are shown in Figure 4\u2013figure supplement 1).2d) Lastly, negative stain fluorescence microscopy of Caulobacter (NA1000) WT cells using FITC-dextran revealed that S-phase  cells exclude the dye. Cells that do not exclude the dye are stalkless and non-constricted (and thus in G1-phase). This important result that reinforces our conclusion that G1-cells are non-capsulated is also shown in the new"}
+{"text": "Mycobacteria antibiotics resistance are crucial for better targets to combat the ever-increasing drug resistant strains. Mycobacterium tuberculosis Rv1152, a novel GntR family transcriptional regulator and a promising vancomycin adjuvant target, was firstly characterized in our study. Overexpression of Rv1152 in Mycobacterium smegmatis decreased bacterial susceptibility to vancomycin. Moreover, a deficiency in MSMEG_5174, an Rv1152 homolog made M. smegmatis more sensitive to vancomycin, which was reverted by complementing the MSMEG_5174 deficiency with Rv1152 of M. tuberculosis. Rv1152 negatively regulated four vancomycin responsive genes, namely genes encoding the ribosome binding protein Hsp, small unit of sulfate adenylyltransferase CysD, L-lysine-epsilon aminotransferase Lat, and protease HtpX. Taken together, Rv1152 controls the expression of genes required for the susceptibility to vancomycin. This is the first report that links the GntR family transcriptional factor with vancomycin susceptibility. Inhibitors of Rv1152 might be ideal vancomycin adjuvants for controlling multi-drug resistant Mycobacterial infections.Novel factors involved in  Mycobacterium tuberculosis (M. tuberculosis) infection, remains the second highest pandemic disease with formidable rate of morbidity and mortality worldwideM. tuberculosis45Mycobacterium includes both pathogenic and saprophytic species that are able to survive under environmental stresses, including oxidative and genotoxic stress, hypoxia, nutrient starvation and multiple antibiotics7Bacillus subtilis transcription regulator GntR, the first characterized transcriptional GntR-type repressor required for gluconate metabolism9913161718M. tuberculosisM. smegmatisTuberculosis, caused by M. tuberculosis GntR family regulator, Rv1152, which can alter cell wall permeability of M. smegmatis to acid and surface stress and play an important in vancomycin loss of susceptibility through negatively regulating the genes responsive to vancomycin. In brief, M. smegmatis overexpressed M. tuberculosis Rv1152 (MS_Rv1152) was more resistant to vancomycin than M. smegmatis harboring the vector only (MS_Vec), while the MSMEG_5174 (the homologous gene of Rv1152 in M. smegmatis) deletion mutant (\u25b3MSMEG_5174) was more sensitive to vancomycin than the wild type M. smegmatis. More importantly, the susceptibility phenotype of \u25b3MSMEG_5174 to vancomycin can be complemented by the Rv1152 (\u25b3MSMEG_5174::Rv1152). Several vancomycin responsive genes were down regulated in M. smegmatis overexpressed Rv1152 strain, while the expression of the same set of vancomycin responsive genes was up regulated in homologous gene MSMEG_5174 knock out strains. The genes regulated by Rv1152 are responsible for the sensitivity of M. smegmatis to vancomycin. These data suggest that Rv1152 involved in the loss of susceptibility to vancomycin through negatively regulating the expression of vancomycin responsive genes.In this study, we identified a novel M. smegmatis mc2155 strains were preserved by the Institute of Modern Biopharmaceuticals. Escherichia coli DH5\u03b1 strain used for gene clone was grown at 37\u2009\u00b0C in Luria-Bertani (LB) broth or on LB agar with appropriate antibiotics. M. smegmatis was grown at 37\u2009\u00b0C in Middlebrook (MB) 7H9 liquid medium or on MB 7H10 agar supplemented with 0.2% (w/v) glucose, 0.5% (v/v) glycerol and 0.05% (v/v) Tween 80. Hygromycin (100\u2009\u03bcg/ml) was added when required. All strains were stored with sterile 20% glycerol at \u221280\u2009\u00b0C for further use. The genomic DNA of M. tuberculosis H37Rv was provided by Chongqing Pulmonary Hospital. The bacterial strains and plasmids used in this study are described in M. tuberculosis H37Rv genome DNA using the specific gene primer pairs (BamHI and ClaI to generate the recombinant pALACE-Rv1152. For the vancomycin responsive genes in M. tuberculosis and their homologous genes in M. smegmatis: including Rv0251c (MSMEG_0424), Rv1285 (MSMEG_4979), Rv0563 (MSMEG_1134) and Rv3290c (MSMEG_1764), the PCR products were ligated into the plasmid pALACE digested by BamH I and NdeI. All plasmids were electroporated into M. smegmatis, a non-pathogenic, fast-growing mycobacterium that, serves as a surrogate model organism to study genes functions of the virulent M. tuberculosis22M. smegmatis strains were plated on Middlebrook (MB) 7H10 agar containing 50\u2009\u03bcg/ml hygromycin after in vitro growth in MB 7H9 liquid medium for 3\u2009hour. The positive strains were further verified by Western blot.The full length of Rv1152 gene and several vancomycin responsive genes was amplified from er pairs . For Rv1M. smegmatis GroEL2, which contains a string of endogenous histidinesGenerally, the acetamide-induced recombinant MS_Rv1152 and MS_Vec were sonicated. The whole lysates were centrifuged at the speed of 3,000\u2009\u00d7\u2009g for 5\u2009min at 4\u2009\u00b0C to remove un-lysed cells and cell debris. The supernatants were ultra-centrifuged at the speed of 27,000\u2009\u00d7\u2009g for 40\u2009min at 4\u2009\u00b0C. After ultra-centrifugation, the pellets were considered the cell wall fraction, and the supernatants were supposed to be cell membrane and cytosol fractions. The pellets were further suspended in 1\u2009\u00d7\u2009PBS. Equal amounts of protein from pellet and supernatant fractions were subjected to Western blotting for analyzing the expression of Rv1152. Native M. tuberculosis Rv1152), a GntR-family response regulator of M. smegmatis was constructed using Xer site-specific recombinationM. tuberculosis using specific primers .An in-frame deletion of the gene MSMEG_5174  were constructed by integrating M. tuberculosis homolog Rv1152 into the chromosomes of the respective deletion strains. Briefly, the Rv1152 gene was first cloned into a pALACE vector, and the recombinant plasmid pALACE_Rv1152 was transformed into the respective M. smegmatis mutant strains. The complementation strain was selected on 7H10 medium (complemented with 0.2% glycerol) containing 50\u2009\u03bcg/mL hygromycin and the hygromycin-resistant strains were selected and further confirmed by using Western blot.For the complementation strains, the Rv1152 complemented M. smegmatis was grown overnight in Middlebrook 7H9 medium (complemented with 0.05% Tween 80 and 0.2% glycerol). Recombinant MS_Vec and MS_Rv1152 were grown in presence of surface stress and acidic stress. For surface stress, acetamide-induced MS_Vec and MS_Rv1152 were treated with 0.05% SDS for 1, 2, 3 and 4\u2009h. For acidic stress, HCl was added into the 7H9 medium and adjusted to pH\u2009=\u20094. MS_Rv1152 and MS_Vec were exposed for duration of 3, 6 and 9\u2009h, respectively. After SDS and acidic treatment, the recombinant strains were diluted and plated onto MB 7H10 agar containing hygromycin, the bacteria were counted after 3 days of incubation.Growth patterns of the two recombinant mycobacteria were examined according to previously described proceduresM. smegmatis strain (WT), the gene deletion mutant (\u25b3MSMEG_5174), and complementation strains (\u25b3MSMEG_5174::Rv1152), the overexpression strain (MS_Rv1152) and the control strain (MS_Vec) were measured according to the procedures described previously with minor modification23M. smegmatis strains were grown in replicates in 7H9 medium to an OD600 of 0.8, 1% of original bacteria was inoculated to 100\u2009\u03bcl of the prepared culture with or without antibiotics. MIC values of each antibiotic were determined as drug concentration that inhibited bacterial growth by at least 99%.Seven antibiotics including vancomycin (Van), norfloxacin (Nor), ciprofloxacin (Cip), ofloxacin (OFL), erythromycin (Ery), isoniazid (INH), and rifampicin (Rif) were used in this study. Growth patterns of the wild type M. smegmatis strains including WT, MS_Vec, MS_Rv1152, \u25b3MSMEG_5174, and \u25b3MSMEG_5174::Rv1152 were grown overnight in Middlebrook 7H9 broth (supplemented with 0.05% Tween80 and 0.2% glycerol). Hygromycin was not added in the 7H9 medium when assaying antibiotics resistance of all strains. When cells entered a stationary growth phase (OD600 between 0.8\u20131.0), each culture was 100-fold diluted in 100\u2009\u03bcl of fresh 7H9 broth containing the indicated concentration of each antibiotic. The cultures were then allowed to grow further at 37\u2009\u00b0C with shaking at 110\u2009rpm. After 24\u2009h treatment with these antibiotics with different concentration, the bacteria were diluted by 10-fold and plated into 7H10 agar medium. The bacterial numbers were counted after 3 days culture. The medium without any antibiotics serves as the control to make sure the normal growth of bacteria.To determine mycobacterial growth curves and the effect of antibiotics, M. smegmatis harboring pALACE (MS_Vec) and Rv1152 overexpression M. smegmatis strains (MS_Rv1152). RT-PCR was used to compare the transcriptional levels of genes expression using gene specific primers and the real-time PCR analysis was subsequently carried out according to previously described proceduressigA gene transcription. Average relative expression levels and standard deviations were determined from three independent experiments. All the gene specific primers used for RT-PCR were listed in the Isolation of mRNA and cDNA preparation were performed from wild type strains (WT), MSMEG_5174 deletion mutants (\u25b3MSMEG_5174), Rv1152 complementation strains (\u25b3MSMEG_5174::Rv1152), The experiments were performed in triplicate. Differences between groups were analyzed by using Prism 6 and Student\u2019s t test. ***P\u2009<\u20090.001, **P\u2009<\u20090.01, *P\u2009<\u20090.05, means\u2009\u00b1\u2009SEM from at least three biological replicates.M. smegmatis harboring pALACE_Rv1152 (MS_Rv1152) and vector only (MS_Vec). Rv1152 gene was successfully amplified from the M. tuberculosis genome by using gene specific primers M. tuberculosisM. smegmatis to vancomycin, as the MIC of MS_Rv1152 for vancomycin is 80\u2009\u03bcg/ml while MS_Vec is 20\u2009\u03bcg/ml, there is no significant difference in MIC when using Cip, OFL, Nor, Ery, INH, and Rif (5 (CFU/ml) of MS_Rv1152 survived while no colony of MS_Vec can be detected when 80\u2009\u03bcg/ml vancomycin was used (M. smegmatis contributes to reduced susceptibility to vancomycin.Vancomycin (Van), the last-resort antibiotics against infections caused by meticillin-resistant  and Rif . In comp and Rif . In addiwas used . These dM. smegmatis, MSMEG_5174, was deleted (\u25b3MSMEG_5174). As shown in BglII restriction sites was found in the correct position, replacing the 247 central nucleotides of the target gene  from M. tuberculosis and their homologous genes  from M. smegmatis decreased susceptibility of host bacteria to vancomycin  was constructed, MSMEG_5174, the M. smegmatis homolog of Rv1152, bearing the signature of the YtrA subfamily memberM. smegmatis . \u25b3MSMEG_5174 is more susceptible to vancomycin than the parental M. smegmatis, suggesting the involvement of MSMEG_5174 in the tolerance to vancomycin. In addition, the response of Rv1152 complemented \u25b3MSMEG_5174 strain to vancomycin was restored to the level of parental M. smegmatis. Taken together, our data indicate that Rv1152 plays an important role in vancomycin loss of susceptibility.M. tuberculosis transcriptome alteration in response to vancomycinM. smegmatis, including ribosome binding protein (MSMEG_0424), small unit of sulfate adenylyltransferase (MSMEG_4979), L-lysine-epsilon aminotransferase (MSMEG_1764), and protease HtpX (MSMEG_1134), respectively. Their expression was also increased in response to vancomycin treatment. The expression of these four genes was increased in \u25b3MSMEG_5174, and the complementary strains \u25b3MSMEG_5174::Rv1152 showed the restored levels of transcription for these four genes. We further found these four genes are responsible for susceptibility to vancomycin. In summary, our data demonstrate that M. tuberculosis Rv1152 plays a role in vancomycin loss of susceptibility via negatively regulating the expression of genes responsive to vancomycin. Vancomycin is a robust glycopeptide antibiotic against multiple drug resistant clinical strains. This is the first report of Mycobacteria GntR family transcriptional factor involved in vancomycin loss of susceptibility. Further discovery of inhibitors against Rv1152 may provide good adjuvants for vancomycin or other antibiotics targeting the cell wall biosynthesis.How to cite this article: Zeng, J. et al. Mycobacterium tuberculosis Rv1152 is a Novel GntR Family Transcriptional Regulator Involved in Intrinsic Vancomycin Resistance and is a Potential Vancomycin Adjuvant Target. Sci. Rep.6, 28002; doi: 10.1038/srep28002 (2016)."}
+{"text": "The use of next generation sequencing (NGS) to identify novel viral sequences from eukaryotic tissue samples is challenging. Issues can include the low proportion and copy number of viral reads and the high number of contigs (post-assembly), making subsequent viral analysis difficult. Comparison of assembly algorithms with pre-assembly host-mapping subtraction using a short-read mapping tool, a k-mer frequency based filter and a low complexity filter, has been validated for viral discovery with Illumina data derived from naturally infected liver tissue and simulated data. Assembled contig numbers were significantly reduced (up to 99.97%) by the application of these pre-assembly filtering methods. This approach provides a validated method for maximizing viral contig size as well as reducing the total number of assembled contigs that require down-stream analysis as putative viral nucleic acids. The decDespite its success, the use of NGS for pathogen discovery is not straightforward. Any biological sample from a patient or animal tissue will inevitably consist predominantly of host-derived sequences. In some cases, the greater proportion of host genetic material will all but drown out any pathogen derived sequences leaving far too many single reads or assembled contigs to analyse.de novo assembly. However, reported validation/testing studies of this methodology are few or, where they do exist are limited, such as SURPI [Mapping subtraction to remove host reads from putative pathogen reads is often the first computational step used in many studies and pipelines \u20139 thoughDe novo assembly of complete or subtraction-reduced read datasets into contigs can dramatically reduce the number of sequences that need to be put through homology search programs. The increased length of contigs over primary sequence reads allows greater certainty that homology hits are accurate. Additionally, when searching for novel pathogens, the generation of large contigs from sequence reads that do not possess significant similarity to any nucleic acid sequence in the reference databases may also indicate the presence of a novel, highly divergent pathogen. A potential problem with this method is the copy number of the viral pathogen itself. Despite the depth of sequence information from current NGS platforms, optimal coverage may be limited due to the sub-optimal quality of rare clinical samples or naturally low level of viral nucleic acids. For example, high quality total RNA from multiple biopsy sections of liver tissue naturally infected by Hepatitis C Virus (HCV), when Illumina sequenced, revealed only four viral reads per million [de novo assembly algorithms, a vital step in novel pathogen discovery. The use of de novo assembly is further complicated by the number of assembly programs available as well as the choice of settings to use within them. Unfortunately, direct comparisons of de novo assembly algorithms in the literature are often sub-optimally validated for these specific requirements or are unreported. More rigorous algorithm comparisons have been focused on bacterial genome characterization, many times larger and less variable than many viral genomes [et al [de novo assembly as a first step followed by a BLAST search of existing databases with no subtraction step. Additionally, assembly parameters varied from 100% similarity over 18 nt to 85% similarity over 25 nt and the BLASTn e-value parameter varied from 10 to 10\u20135. Others use a +ve or-ve subtraction process in the first instance. It is essential for this field to define optimal methodologies in order to circumvent both time and computationally inefficient tests but also to provide the researcher with high certainty that subtraction methodologies do not unnecessarily remove pathogen sequence just for the sake of speed and that available de novo assemblers are compared and optimized. Here we have used NGS data sets derived from naturally virally infected human liver biopsies, an artificial viral pathogen \u2018metagenomics\u2019 dataset modified using a profile based read emulator [ million . The low genomes \u201320. An as [et al  have higs [et al , 22\u201340 memulator  in the cFive Illumina Hi-Seq datasets derived from human liver tissue were used in this study, representing a cross-section of viral genome coverage and viral classes see .A database of 62 human virus chromatids from 35 distinct viruses were used to generate simulated Illumina read pairs (10x coverage depth) with the use of pIRS methods). These paired reads  were combined with 16.7 million Illumina paired sequence reads from a \u2018clean\u2019 healthy liver sample (methods). Using the mapping algorithms CLC, BWA and Bowtie (methods) we investigated the percentage subtraction of host and viral sequence reads following mapping to human reference sequence data sets including the human genome, the human mitochondrial genome and a human rRNA sequence set .We compared a range of assembly algorithms and word size settings using four real Illumina datasets: two from HCV infected liver  and two from HBV infected liver . The reads were assembled using four algorithms: Velvet , MetaCorde novo assembled viral contigs [With the optimal word size parameters for each assembly algorithm determined, we investigated the effect of host mapping subtraction and k-mer filtering on resulting  contigs . Pre-assOur primary objective was not only to determine and validate the optimal assembler algorithm, assembly parameters and the effects of read filtering methods but to assess whether the bulk read subtraction processes would consequently reduce the number of contigs assembled thus resulting in fewer contigs to analyse for putative virus. Assembled contigs less than, or equal to, the largest trimmed Illumina sequence read were discarded. For all assemblers tested, k-mer filtering reduced the number of contigs assembled by 98.7\u201399.6% and host-mapping subtraction reduced the number of contigs assembled by 98.5\u201399.6% . There wWe investigated the effect of filtering the NGS reads with a low complexity filter  see met prior tode novo assembly at optimal word size (as before). Contigs with greater than 90% homology to a viral reference genome were extracted from the assembled contigs and sorted by viral reference and percentage reference coverage. The mean reference coverage of the single largest contigs to each viral reference following assembly only was 86.3% \u00b1 19.7% (arithmetic mean \u00b1 SD). Assembly following host-mapping subtraction only, resulted in mean reference coverage of the largest contigs of 91.4% \u00b1 15.5%. Assembly following k-mer filtration only, resulted in mean reference coverage of the largest contigs of 79.7% \u00b1 25.5%. Assembly following k-mer filtration and hostmapping subtraction together, resulted in mean reference coverage of the largest contigs of 80.6% \u00b1 25.9% [To assess the effects of our pre-assembly filtering methodologies across a broader range of human viruses we used the artificial viral Illumina read set embedded in Illumina reads derived from healthy liver total RNA (described in methods). We applied the hostmapping subtraction and k-mer filtering (as before), prior to CLC v6.5  \u00b1 25.9% . N25-N90 \u00b1 25.9% . Mean N9 \u00b1 25.9% , 853,000de novo assembly yielded contig numbers again commensurate with the previously tested control samples with an approximate decrease in contigs following the application of the short read mapper subtraction of ~100-fold for both idiopathic samples tested. Secondary application of the k-mer filter decreased the contig numbers further for samples 1 and 2 by 90% (620 contigs assembled) and 83% respectively (8027 contigs assembled) and shown in Two human hepatitis liver samples clinically defined as idiopathic (explanted liver from the transplant setting) and confirmed as negative for hepatitis associated viruses prior to transplant were processed for total RNA and SISPA processed for Illumina NGS as described in materials and methods. Sample 1 (121 million 100nt reads) and sample 2 (105 million reads 100nt reads) were filtered by the application of a short read mapper, with and without the application of the k-mer filter (Kontaminant). Read subtraction following these processes is shown in Using the SURPI pipeline in comprehensive mode  we ran tFor the two HCV datasets, viral reads were subtracted at the pre-processing step but not at the alignment to human DB step with a total subtraction of HCV reads of 4% and 5.3% for the 9x and 0.7x coverage HCV datasets respectively. This compares to 0% subtraction of reads using our trimming and human read short read alignment process as described. For the Artificial metagenomics viral dataset, SURPI pre-processing removed 0.43% of the viral reads and no further reads were subtracted by SNAP to human DB. This compares to 0.2% subtraction using our trimming and human read short read alignment process as described.De novo assembled contig numbers were comparable between SURPI and our short read mapping subtraction followed by assembly for the three sets tested: Artificial/HCV 9x coverage/ HCV 0.7x coverage. For SURPI the contig numbers were 7563 / 42900 / 41957 respectively and for our described processes the assembled contig numbers were ~ 4700 / 26700 / 37400.The SURPI pipeline uses ABySS + Minimo to assemble reads negatively selected by SNAP to pathogens together with viral SNAP aligned reads. The assembled contigs generated were aligned by us to the reference sequences to ascertain the largest contigs and the total reference coverage of all the assembled contigs and compared to contigs generated by our processes as described .Our mapping and assembly process using the artificial viral metagenomics dataset  shows a Analysis of our HCV sets allows some consideration of low (9x) coverage and very low (0.7x) coverage with datasets derived from naturally virally infected human tissue . For thede novo assembly of unfiltered data sets restricts downstream analysis. We have explored and validated a range of computational filtration / subtraction methods using a combination of Illumina NGS data sets derived from viral infected liver tissues covering a range of viral coverage depths, together with an Illumina read simulated data set containing a broad range of viral sequence reads embedded with a large dataset of non-synthetic human liver reads. In the context of viral discovery, we have characterized the effects of these pre-assembly methods according to changes in post-assembly viral contig size, viral genome coverage of all contigs and the total numbers of contigs assembled  as well as back to back comparisons of several popular de novo assemblers.The identification of viral sequences by NGS, in eukaryotic cells and tissues is problematic despite the development of enrichment methodologies , 48. Prode novo assembly settings were then applied to our five data sets [Figs de novo assembly is still very high and remains computationally expensive and time consuming to screen by homology to a complete nucleotides database particularly at the amino acid level. To attempt to further subtract the host reads with a view to further reducing the number of contigs subsequently assembled we employed the contaminant removal software (Kontaminant). Host read subtraction using Kontaminant was greater than subtraction using the short read mapping tools tested but with the consequence that, post-assembly, the maximum viral contig sizes as a percentage of the viral references were generally reduced relative to identical sets that were not filtered using Kontaminant. However, considering all five of our read sets together, the increase/ reduction in largest viral contig size, following the application of Kontaminant (pre-assembly) ranged from a 66.7% reduction up to an increase of 30.4%. The overall mean showed a reduction of 6.53% with an SD of 20.59%. We believe that this negative effect would, in general, not compromise viral characterization particularly when viewed in its proper context, as an initial first-pass approach to viral discovery in short read NGS data derived from eukaryotic tissues. In this context, the primary requisites are confidence that the assembler chosen and the k-mer size choice is appropriate to maximize viral contig size and that the total number of assembled contigs is reduced without removing putative pathogen sequence, facilitating the speed and ease of post-assembly analysis. The use of Kontaminant might therefore be considered as a 2nd step process after the optimized first step use of short read aligners . The further reduced (post-assembly) contig set from step 2 can then be used to extract the longer, more complete contigs assembled after step 1 only.We first characterized NGS read reduction potential using two distinct approaches Figs \u20134 togethets Figs \u201311 and tets Figs . Post-asWe found no clear advantage in the application of a low-complexity filter (DUST) to our Illumina read sets pre-assembly, alone or in combination Figs \u20139 which st step processes. Viral reads lost at the SURPI pre-processing step may be due to the inclusion of the dust module and may be relatively more important were viral coverage is very low. The poor performance of the de-novo assembly component of the SURPI pipeline again highlights the need for rigorous optimization to increase viral contig size if one is to maximize the potential for identifying highly divergent viral species rather than ultimately relying on high identity with viral sequences in the public domain with short Illumina reads.The use of the PIRS software will not introduce the same level of sequence bias that may be seen with randomly amplified nucleic acids and this potentially remains an issue with the use of artificial datasets. However, the application of the optimized mapping subtraction protocol and k-mer filter (Kontaminant) to our idiopathic hepatitis liver samples  shows post-assembly data that is consistent with our viral control and artificial datasets . Comparide novo assembled contigs in a search for putative viral sequence from Illumina NGS data can be made more manageable by removing the majority of host nucleic acid derived sequences prior to assembly by the application of mapping tool subtraction sequences and host k-mer frequency based filtering to host references, separately in a two step process. The first step consists of subtraction using standard aligning tools  which can be validated using a broad range of viral sequences modified by an emulator to mirror genuine host read data in which they are embedded. Stringencies defined to maximize the subtraction of host whilst minimizing subtraction of viral sequences can then be applied to the novel sequence datasets to provide a reduced set that significantly improves the subsequent assembly of viral contigs whilst ensuring that the assembled contig number is dramatically reduced for subsequent direct analyses if required. Step 2 entails the use of a k-mer frequency filtering system (Kontaminant) in order to remove most of the remaining host reads with the effect that the post-assembly contig number is further reduced whilst likely retaining all representatives of the viral contigs which can be used to extract the larger assembled contigs from step one. The overall dramatically reduced set is small enough to directly use BLASTn and even tBLASTx to a complete NCBInt database due to the contig numbers typically being less than 103 (+/-50%) determined by us using over 50x idiopathic liver sample  Illumina sequenced with a read depth average of 160 million placing this \u2018high certainty\u2019 viral discovery approach in the hands of the small laboratory with standard desktop equipment.Taken together, the commonly encountered difficulty of analyzing many thousands of de novo assembly algorithms and a single specific metagenomics assembler, demonstrating striking differences between them and highlighting the need to compare and optimise the use of assembly algorithms in the context of viral discovery from human tissue derived NGS datasets. In theory, metagenomic assembly tools should be well suited to low coverage pathogen sequences in the context of tissue derived host sequences and many additional assemblers are worth comparing as has been recently undertaken together with an ensemble strategy [We compared three strategy .Human liver samples were acquired from the Institute of Liver Studies, Kings College Hospital, London, University of London, UK. Samples were obtained with patient written consent. This work forms part of a broader project with ethical approval provided by the UK National Research Ethics Service, Cambridge 3 Research Ethics Committee, Cambridge CB21 5XB  and Kings College Hospital Research Ethics Committee, London SE5 9RS (REC reference number 04/Q0703/27).Two liver samples naturally infected with HCV and HBV viruses together with an uninfected \u2018healthy\u2019 liver sample were used in this study. Total extracted RNA and cytosolic viral particle enriched fractions (for each sample) were prepared using the SISPA protocol and sequenced using the Illumina platform .http://www.ncbi.nlm.nih.gov/sra/ with the accession numbers ERX180664, ERX180665, ERX180666, ERX180667, ERX286289.The five controlled test sets used in this study have been previously reported . They inPrior to sequence assembly and / or host sequence read removal, we trimmed all Illumina paired read data sets using CLC Bio v5.5 Trimmer . SISPA PCR primers used for random priming and amplification  were remThe HCV and HBV sets  were used in the first instance to ascertain optimal word size values with four assembler algorithms. Velvet 1.1.04 , MetaCor62 full-length viral chromatids from 35 distinct human viral genomes were collated. The viruses were chosen to reflect a molecular genetic and life-style spread by choosing them from all Baltimore viral classification groups. Names, accession numbers, and viral groups chosen include:Alphapapillomavirus_7, group I, NC_001357.1. Human_papillomavirus_type_16, group I, NC_001526.2. Human_herpesvirus_1, group I, NC_001806.1. Vaccinia_ virus, group I, NC_006998.1. Human_herpesvirus_8, group I, NC_009333.1. Merkel_cell_polyomavirus, group I, NC_010277.1. Human_adenovirus_54, group I, NC_012959.1. Adeno-associated_virus_1, group II, NC_002077.1. Human_ parvovirus_B19, group II, NC_000883.2. Torque_teno_virus group II, NC_015783.1. Colorado_tick_fever_virus, group III, NC_004181.1, NC_004182.1, NC_004183.1, NC_004184.1, NC_004185.1, NC_004186.1, NC_004187.1, NC_004188.1, NC_004180.1, NC_004189.1, NC_004191.1, NC_004190.1. Rotavirus_ A, group III, NC_011500.2NC_011501.2, NC_011502.2NC_011503.2, NC_011504.2, NC_011505.2, NC_011506.2, NC_011507.2, NC_011508.2, NC_011509.2, NC_011510.2. Hepatitis_E_virus, group IV, NC_001434.1. Hepatitis_ A_virus, group IV, NC_001489.1. Rubella_virus, group IV, NC_001545.2. Human_ astrovirus, group IV, NC_001943.1. Norwalk_virus, group IV, NC_001959.2. Hepatitis_C_virus, group IV, NC_004102.1. Human_coronavirus_HKU1, group IV, NC_006577.2. Rabies_virus, group V, NC_001542.1. Vesicular_ stomatitis_Indiana_virus, group V, NC_001560.1. Borna_disease_virus, group V, NC_001607.1. Marburg_marburgvirus, group V, NC_001608.3. Mumps_virus, group V, NC_002200.1. Ebola_virus_Mayinga_Zaire_1976, group V, NC_002549.1. Human_metapneumovirus, group V, NC_004148.2. Lassa_virus, group V, NC_004297.1. Influenza_A_virus__A_New_York_392_2004_H3N2, group V, NC_007366.1, NC_007367.1, NC_007368.1, NC_007369.1, NC_007370.1, NC_007371.1, NC_007372.1, NC_007373.1. Rift_Valley_fever_virus, group V, NC_014397.1, NC_001653.2. Human_T-lymphotropic_virus_1, group VI, NC_001436.1. Human_immunodeficiency_virus_1, group VI, NC_001802.1. Human_ immunodeficiency_virus_2, group VI, NC_001722.1. Hepatitis_B_virus, groupVII, NC_003977.1. Hepatitis_delta_virus, group NA, NC_001653.2.ftp://ftp.genomics.org.cn/pub/pIRS/). The healthy  human liver sample total RNA Illumina Hi-Seq FASTQ data set (above) was used to train the software to modify the viral genome sequences. The modifications included percentage GC content profile, error and quality distribution, read size and pair distance mirroring. The viral read sequences were pIRS selected to match the clean liver reads including the mean paired distance at 327.96nt with an SD of 77.1 with the read lengths universally set at 100nt as with the clean liver reads. Additionally, pIRS was used to generate different depths of viral sequence coverage . The different viral coverage data sets were then separately embedded into ~17,200,000 paired-end reads from the healthy liver FASTQ set used to \u2018train\u2019 the pIRS software. Optimal word size assembly was determined with the use of CLC Bio assembler v6.1,  to assemble contigs across a range of word sizes [Illumina paired end reads (100nt in length) were simulated using the freely available software, pIRS  , Bowtie2 (version 2.1.0), CLC Bio v6.5 .The CLC mapper was tested by fixing the gap and mismatch penalty at 3 and 2 respectively, and altering the proportion of the sequence read aligned (70\u2013100%) and the homology to the reference sequence of the aligned portion of the sequence read (70\u2013100%). Bowtie and BWA were run in paired-end mode with the scoring settings adjusted to reflect CLC parameters. Mismatch penalty was set to 2, gap open penalty was set to 3 and gap extension was set to 1. All other parameters were set as default. To compare the percentage of read and identity therein, BWA and Bowtie mapped reads were extracted from each Sequence Alignment/Map (SAM) file and a custom Perl script was used to parse the CIGAR and MD tags of each mapped read and subsequently calculate the proportion of the sequence reads aligned and the homology to the reference sequence of the aligned portion of the sequence read.Homo sapiens mitochondrion, NC_012920.1; and c) a Homo sapiens ribosomal RNA set . NB: the mitochondrial consensus is not included in the GRC human build 37 and the four cytoplasmic rRNA molecules (non-MT encoded) are included separately due to the presence of the spacer DNA in the genomic sequence.At \u226580% of the read with \u226590% homology to a human reference sequence, the percentage of viral sequence reads subtracted was 0.016\u20130.02% depending on the algorithm tested. This setting was used for all subsequent host-mapping subtraction experiments Figs  and 12. http://www.tgac.ac.uk/kontaminant/ or https://github.com/TGAC/kontaminant [k over the reference, base-by-base, creating k-mers as it moves. Reads are then filtered by scanning FASTQ files through Kontaminant and comparing k-mers in reads with the k-mers in the reference. A read is filtered (discarded) if the number of shared k-mers with the reference is greater than or equal to a threshold value . A k-mer size of 21 was used. Observations have shown that k-mers of this size tend to be unique amongst different species and should be more than large enough to differentiate between viral and human genomes [K-mer filtering was carried out using the freely available Kontaminant tool developed at The Genome Analysis Centre (TGAC), taminant . To use  genomes \u201351. Multde novo assembly, Illumina FASTQ data was converted to FASTA as input to mdust. Low complexity regions were masked as ambiguous and these regions were subsequently trimmed as described.Low complexity filtering was performed using mdust , a standContig length and total reference coverage of viral contigs were determined for all sets and experiments by the same method irrespective of the read filtering method used pre-assembly: 1) Assembled consensus contigs were extracted in FASTA format unless otherwise stated. 2) These contigs were aligned to a database containing the viral references and the human reference using BLAST+ 2.2.25. The blastn program was used with default settings. To ascertain if there were any chimeric contigs, all BLAST hits were examined for each contig. If a contig partially aligned with 75% identity to both the human reference and a viral reference, it was flagged as chimeric. No such contigs were found for any of the assemblers. 3) Non-chimeric contigs matching the viral references were extracted as a FASTA file and mapped to the references. This process allowed common reference contigs to be overlaid on their respective reference sequences to ascertain a) the total reference coverage, b) the coverage of the largest contig, c) to observe the degree of consensus identity to the reference and to discount contig terminal end mismatching from the coverage and contig size estimations. The nucleotide sequences of all the resulting viral contigs deviated from the reference by less than 1%. Standard N25-N90 values used . \u221280. Unique query hits were then selected by highest hit length. Contig queries were retained if viral taxonomic descriptors matched for all the above criteria . Matching query sequences from each sample were collated and mapped to the best-hit reference to determine percentage coverage of each contig and total coverage of the reference [Two idiopathic hepatitis liver samples  were pre-assembly filtered with a short read mapper and a K-mer filter as previously described .. Remaineference .. Each oDe novo assembly files were taken from the default output directory and pre-processing and subtraction step information was extracted from the human.snap.unmatched / preprocessed and cutadapt.cropped.dusted.bad FASTQ default output files.As described above, the two HCV infected liver datasets with 0.7x and 9x mean depth of viral genome coverage and the metagenomics viral dataset embedded in Illumina 100nt paired-end clean liver reads were used to compare the SURPI (comprehensive mode) pipeline [http://chiulab.ucsf.edu/surpi/) was installed as an Amazon EC2 cloud-computing instance using default parameters and all dependencies installed.SURPI ("}
+{"text": "Porphyromonas gingivalis this reaction is not catalyzed by PGN_1171, previously annotated as butyryl-CoA:acetate CoA transferase, but by three distinct CoA transferases, PGN_0725, PGN_1341, and PGN_1888. Gas chromatography/mass spectrometry (GC-MS) and spectrophotometric analyses were performed using crude enzyme extracts from deletion mutant strains and purified recombinant proteins. The experiments revealed that, in the presence of acetate, PGN_0725 preferentially utilized butyryl-CoA rather than propionyl-CoA. By contrast, this preference was reversed in PGN_1888. The only butyryl-CoA:acetate CoA transferase activity was observed in PGN_1341. Double reciprocal plots revealed that all the reactions catalyzed by these enzymes follow a ternary-complex mechanism, in contrast to previously characterized CoA transferases. GC-MS analysis to determine the concentrations of short chain fatty acids (SCFAs) in culture supernatants of P. gingivalis wild type and mutant strains revealed that PGN_0725 and PGN_1888 play a major role in the production of butyrate and propionate, respectively. Interestingly, a triple deletion mutant lacking PGN_0725, PGN_1341, and PGN_1888 produced low levels of SCFAs, suggesting that the microorganism contains CoA transferase(s) in addition to these three enzymes. Growth rates of the mutant strains were mostly slower than that of the wild type, indicating that many carbon compounds produced in the SCFA synthesis appear to be important for the biological activity of this microorganism.Butyryl-CoA:acetate CoA transferase, which produces butyrate and acetyl-CoA from butyryl-CoA and acetate, is responsible for the final step of butyrate production in bacteria. This study demonstrates that in the periodontopathogenic bacterium  Porphyromonas gingivalis is the best-studied periodontal pathogen.Periodontal diseases are a group of inflammatory conditions that lead to the destruction of tooth-supporting tissues and appear to be associated with serious systemic conditions . Among tPorphyromonas gingivalis, which is a Gram-negative, black-pigmented, asaccharolytic anaerobe, is implicated in the initiation and progression of periodontal diseases . In this study, we first demonstrate that PGN_1171 is not involved in the reaction of butyrate production from butyryl-CoA, and, instead, we identify three candidate CoA transferases using a homology search with CoA transferase in Roseburia hominis, which is an anaerobic intestinal bacterium  in their supernatants were quantified and analyzed. In addition, the genes encoding the putative enzymes were cloned and expressed in Escherichia coli, and the recombinant proteins were purified and then enzymatically characterized.We recently reported the identification and characterization of two reductases that produce succinate semialdehyde and 4-hydroxybutyrate, both of which are intermediates of the butyrate synthetic pathway of ngivalis , 2016. Wacterium . To undePorphyromonas gingivalis strains used in this study are listed in Table 1, and were grown anaerobically at 37\u00b0C in a modified GAM broth  or on Brucella HK agar plates , supplemented with 5% rabbit blood. The following antibiotic concentrations were used, as appropriate: 20 \u03bcg/ml erythromycin, 0.5 \u03bcg/ml tetracycline, and/or 10 \u03bcg/ml ampicillin. E. coli DH5\u03b1 and BL21 (DE3) strains were grown aerobically at 37\u00b0C in 2\u00d7 YT medium  with 100 \u03bcg/ml ampicillin, 200 \u03bcg/ml erythromycin, or 10 \u03bcg/ml tetracycline.Porphyromonas gingivalis deletion mutants lacking PGN_1171, PGN_0725, PGN_1341, and/or PGN_1888 gene were constructed by replacing each gene with ermF and ermB (erm cassette), as previously described , PGAGU104 (PGN_0725::erm), PGAGU108 (PGN_1888::erm), and PGAGU109 (PGN_1341::erm). To create double mutants, tetracycline resistance gene tetQ, amplified from plasmid pT-COW , PGAGU114 (PGN_1341::erm PGN_1888::tetQ), or PGAGU115 (PGN_0725::tetQ PGN_1341::erm). A triple mutant strain lacking PGN_0725, PGN_1341, and PGN_1888 was also constructed, by replacing PGN_1888 in PGAGU115 with the ampicillin resistance gene cepA. The cepA gene was amplified from plasmid pCEPA, kindly provided by Dr. Naito . The resultant mutant strain was designated PGAGU118 (PGN_0725::tetQ PGN_1341::erm PGN_1888::cepA). The integration of ligated PCR products at the expected chromosomal location(s) was confirmed by PCR amplification of the specific products across the upstream and downstream insertion boundaries using primers that were designed based on the flanking sequences extraneous to those used for gene targeting.escribed . Approxi pVA2198 . The amp pVA2198  or the Id pT-COW , was useP. gingivalis ATCC 33277 and its derivatives grown anaerobically in modified GAM broth to log phase. Harvested cells were washed two times with phosphate-buffered saline (PBS), suspended in PBS containing 0.1 mM N-\u03b1-tosyl-L-lysine chloromethyl ketone, 0.2 mM phenylmethylsulfonyl fluoride, and 0.1 mM leupeptin (as protease inhibitors), and then lysed by ultrasonication on ice. After ultracentrifugation at 30,000 \u00d7 g to remove insoluble material, crude enzyme concentrations were determined using the Pierce BCA Protein Assay Kit .Crude enzyme extracts were obtained from P. gingivalis ATCC 33277 using primers listed in Supplementary Table Table 1. Protein concentrations were determined, as described previously , as described previously . Coding eviously , and proA412). Acetyl-CoA concentrations were calculated using a standard curve. In addition to the reaction conditions described above, varying concentrations of sodium acetate (20\u2013250 mM) and either butyryl-CoA (0.5\u20135.0 mM) or propionyl-CoA (0.2\u20133.0 mM) were used to determine the kinetic parameters of each recombinant protein. These parameters were computed from Lineweaver\u2013Burk transformation (V-1 vs. S-1) of the Michaelis\u2013Menten equation. The kcat values were calculated from Vmax and molecular weights of the proteins. Data were obtained from three independent experiments.CoA transferase activity in crude enzyme extracts or purified recombinant proteins was measured by determining the concentrations of acetyl-CoA, a reaction byproduct, with citrate synthase assay . Reactio2, and 1 \u03bcg/ml recombinant protein . After incubation for 60 min at 37\u00b0C, the concentration of unconsumed NADH was determined spectrophotometrically at A340, using a standard curve. The concentration of unconsumed acetyl-CoA was determined using a citrate synthase assay, as described above.Recombinant protein CoA transferase activity with 4-hydroxybutyrate and acetyl-CoA was determined using PGN_0724, a succinate semialdehyde reductase producing 4-hydroxybutyrate from succinate semialdehyde , since 4600) = 0.375 \u00b1 0.025 (1.5 \u00d7 105 CFU/\u03bcl) to normalize cell concentration. Diluted bacterial cultures were immediately centrifuged to remove bacterial cells. Aliquots (100 \u03bcl) of enzyme reaction mixtures or supernatants were added to 500 \u03bcl of acetone. After incubation at -20\u00b0C for 2 h, the samples were centrifuged to remove proteins.SCFAs in the reaction mixtures and culture supernatants were evaluated by gas chromatography/mass spectrometry (GC-MS) with a ZB-FFAP column , as described previously . BrieflyP < 0.01.Differences between groups were analyzed by one-way analysis of variance, followed by the Student\u2013Newman\u2013Keuls multiple-comparisons test. Differences were considered significant when P. gingivalis ATCC 33277 . Integration of the resistance genes at the expected chromosomal locations in the resulting mutant strains was verified by PCR . Butyryl-CoA:acetate CoA transferase activity of crude enzyme extracts was determined by measuring the production of acetyl-CoA from butyryl-CoA and sodium acetate. The initial velocity of acetyl-CoA production by PGAGU101 (PGN_1171::erm) crude enzyme extracts was not significantly different from that of the wild type , indicating that PGN_1171 has no detectable butyryl-CoA:acetate CoA transferase activity. By contrast, disruption of PGN_0725, PGN_1341, or PGN_1888 resulted in a significant decrease in the reaction velocity, suggesting that all the encoded proteins possess the butyryl-CoA:acetate CoA transferase activity.Butyrate and acetyl-CoA are produced from butyryl-CoA and acetate in the last step of butyrate production . The abilities of crude enzyme extracts of PGAGU101, PGAGU109 (PGN_1341::erm), and the wild type strain to form acetyl-CoA from propionyl-CoA and acetate were not significantly different . The initial velocities of PGAGU104 (PGN_0725::erm) and PGAGU108 (PGN_1888::erm) crude enzyme extracts were significantly lower than that of the wild type. These findings demonstrated that PGN_0725 and PGN_1888, but not PGN_1171 and PGN_1341, possess the propionyl-CoA:acetate CoA transferase activity.Likewise, propionate and acetyl-CoA were produced from propionyl-CoA and acetate .To characterize PGN_0725, PGN_1341, PGN_1888, and PGN_1171 protein functions, each corresponding gene was expressed in Figure 4). Propionate was detected in reaction mixtures containing propionyl-CoA, sodium acetate, and either recombinant PGN_0725 or PGN_1888, but not PGN_1341. As expected, incubation of the recombinant PGN_1171 protein with butyryl-CoA or propionyl-CoA in the presence of sodium acetate resulted in no detectable production of butyrate or propionate, respectively .GC-MS analysis demonstrated that incubation of butyryl-CoA and sodium acetate with any of the recombinant PGN_0725, PGN_1341, and PGN_1888 proteins led to the production of butyrate . Line series intersecting to the left of y-axes (V-1) suggested that PGN_0725, PGN_1341, and PGN_1888 proteins act via a ternary-complex kinetic mechanism. Km and Vmax values were estimated from secondary plots . In addition, kcat values were calculated from enzyme concentrations in the reaction mixtures. Kinetic properties of the recombinant PGN_0725 and PGN_1888 proteins were also determined by analyzing acetyl-CoA production from sodium acetate and propionyl-CoA (Table 3). These reactions also follow the ternary-complex mechanism .We also investigated the kinetic parameters of recombinant PGN_0725, PGN_1341, and PGN_1888 using a colorimetric assay. Initial velocities were determined at either fixed sodium acetate concentrations at different butyryl-CoA concentrations, or fixed butyryl-CoA concentrations at different sodium acetate concentrations. Lineweaver\u2013Burk  plots were then constructed  were evaluated as substrates, in addition to butyryl-CoA and propionyl-CoA  were tested as enzyme substrates in the presence of propionyl-CoA and butyryl-CoA, respectively. GC-MS analysis revealed that the incubation of a sodium butyrate/propionyl-CoA pair or sodium propionate/butyryl-CoA pair with PGN_0725, PGN_1341, or PGN_1888 resulted in no detectable production of propionate or butyrate (data not shown). These findings suggested that the three CoA transferases utilize no SCFA substrates other than acetate.Figure 1), we examined the 4-hydroxybutyrate CoA transferase activity of recombinant PGN_0725 . By contrast, the growth rates of other mutant strains were obviously slower than that of the wild type, as were culture turbidities in the stationary phase. This suggested that the intermediates and end products of the butyrate, propionate, and related molecule biosynthetic pathways might be important for the metabolism of this microorganism.PGAGU108 grew as fast as the wild type, as determined by OD600 of each culture was adjusted to 0.375 \u00b1 0.025 to normalize cell concentrations. This conversion allowed us to compare the production of SCFAs per cell number in the wild type and mutant strains . Butyrate concentrations in culture media of all the mutant strains were significantly lower than that of the wild type (P < 0.01). Butyrate concentrations in the culture supernatants of PGAGU104, PGAGU111 (PGN_0725::erm PGN_1888::tetQ), PGAGU115 (PGN_0725::tetQ PGN_1341::erm), and PGAGU118 (PGN_0725::tetQ PGN_1341::erm PGN_1888::cepA), all of which lacked the PGN_0725 gene, were significantly lower, not only than that of the wild type, but also than those of other mutants. Likewise, propionate concentrations in culture supernatants of PGAGU108, PGAGU111, PGAGU114 (PGN_1341::erm PGN_1888::tetQ), and PGAGU118, all of which lacked the PGN_1888 gene, were significantly lower than those of other strains. Deletion of PGN_0725, PGN_1341, or PGN_1888 resulted in a drastic decrease in isobutyrate and isovalerate concentrations in culture supernatants . Interestingly, the triple mutant, PGAGU118, still released butyrate, propionate, isobutyrate, and isovalerate into the culture supernatant .To evaluate the contribution of PGN_0725, PGN_1341, and PGN_1888 to SCFA production, SCFA concentrations in stationary phase bacterial cultures were quantified using GC-MS. The ODR. hominis . The deduced amino acid sequence of PGN_1171 shared 46 and 24% identity with PcaJ from Pseudomonas putida . Notably, butyrate concentrations in culture supernatants of these mutants, all of which lacked PGN_0725 (Table 1), were significantly lower, not only than that of the wild type, but also other mutants. Similarly, butyryl-CoA:acetate CoA transferase activities in crude enzyme extracts of PGN_0725-lacking strains were lower than those of other strains . Furthermore, of the recombinant proteins tested, PGN_0725 displayed the highest butyryl-CoA:acetate CoA transferase activity (Table 2). Taken together, the PGN_0725 protein contributes the most to butyrate production in P. gingivalis. Likewise, the findings summarized in Figures 2 and 8, and Table 3, demonstrate that PGN_1888 is a CoA transferase contributing the most to the production of propionate. Deletion of PGN_0725, PGN_1341, or PGN_1888 resulted in low concentrations of isobutyrate and isovalerate in culture supernatants . These observations suggested that the isobutyrate and isovalerate synthetic pathways might share their intermediates with the butyrate synthetic pathway in P. gingivalis. Synthetic pathways of those molecules remain to be elucidated. It is also of interest that the CoA transferase activity in crude enzyme extracts of the triple mutant strain (PGAGU118) was \u223c20% that of the wild type, suggesting that P. gingivalis harbors additional CoA transferase(s) other than PGN_0725, PGN_1341, and PGN_1888. CoA transferase(s) with relatively broad substrate specificity may be partially involved in the formation of butyrate and propionate from butyryl-CoA and propionyl-CoA, respectively. Further studies are necessary to precisely understand the production of butyrate and propionate in bacteria.Several mutant strains were constructed to examine the role of PGN_0725, PGN_1341, and PGN_1888 . However, considering that the butyryl-CoA:acetate CoA transferase activity of PGN_1341 is much lower than that of PGN_0725, the primary role of PGN_1341 in P. gingivalis remains unknown. Although PGN_1341 is not responsible for propionate production from propionyl-CoA in this bacterium, replacement of its gene with the erm cassette resulted in a reduction of propionate levels in culture supernatants . These findings suggest that PGN_1341 might primarily function as a CoA transferase involved in the butyrate and propionate biosynthetic pathways.PGN_0725 was annotated as a 4-hydroxybutyrate CoA transferase . Indeed,nsferase , was hignsferase . Howevernsferase , similarkcat values of R. hominis RHOM_13820  were higher than that of PGN_1341, but lower than those of PGN_0725 and PGN_1888. By contrast, kcat values of RHOM_13820 and AbfT for propionyl-CoA  were also much lower than those of PGN_0725 and PGN_1888 proteins. PGN_0725, PGN_1341, and PGN_1888 proteins catalyzed transferase reactions via a ternary-complex kinetic mechanism, whereas RHOM_13820 protein was reported to catalyze a transferase reaction via a ping-pong bi-bi mechanism . Further studies of molecules associated with the butyrate production pathway are currently underway in our laboratory.This study addressed the function of three genes, and the proteins that they encode, as CoA transferases associated with SCFAs production in Conception and design of the experiments: YY. Acquisition of the data: MS, YY. Analysis of the data: MS, YY, KN, YH. Interpretation of data: MS, YY, JT, FY.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest."}
+{"text": "Streptococcus mitis frequently causes invasive infections in neutropenic cancer patients, with a subset of patients developing viridans group streptococcal (VGS) shock syndrome. We report here the first complete genome sequence of S.\u00a0mitis strain SVGS_061, which caused VGS shock syndrome, to help elucidate the pathogenesis of severe VGS infection. Streptococcus mitis, which is closely related to Streptococcus pneumoniae, is the most frequent cause of bacteremia in neutropenic cancer patients , patients . The cliesistant , which iesistant . DespiteS.\u00a0mitis strain SVGS_061, which was isolated from the bloodstream of a neutropenic-acute myelogenous leukemia patient with VGS-shock syndrome. SVGS_061 was resistant to moxifloxacin  and tetracycline  and had intermediate resistance to penicillin . The SVGS_061 genome was determined using the PacBio SMRT technology , SPRITE (n = 18), and BOX (n = 81) repeats that are typically present in S. pneumoniae genomes in high density and likely regulate gene expression (5253-like integrative and conjugative element (ICESVGS_061) was identified and was most similar to ICESpn22664 from S. pneumoniae (99% identity over 48% nucleotide overlap). ICESVGS_061 contained several hallmark proteins, including site-specific integrases, type IV secretion, conjugation protein homologs, and Tn5252 and Tn916 open reading frames (ORFs) (mef (locusID_AXK38_05275), tetM (locusID_AXK38_05320), and cat (locusID_AXK38_05440) genes that confer macrolide, tetracycline, and chloramphenicol resistance, respectively. Furthermore, combined CARD (\u2013mrA (locusID_AXK38_03855) efflux gene, which is associated with fluoroquinolone resistance , and amidase (LytC), were identified via the VFDB database (S. pneumoniae. Capsular proteins of SVGS_061 are most closely related to serotype 4F and are most similar to the capsular proteins of S. pneumoniae TIGR4 (96% identity over 56% nucleotide overlap). OrthoMCL analysis (S.\u00a0pneumoniae TIGR4 genome. Exotoxins similar to those causing toxic shock in staphylococci or \u03b2-hemolytic streptococci were not identified in SVGS_061. The availability of the complete genome sequence of SVGS_061 should help facilitate a better understanding of the VGS shock syndrome resulting from S.\u00a0mitis invasive infection.Homologs of a number of database  search. analysis  identifiCP014326. The version described in this paper is the first version.The complete genome sequence has been deposited at DDBJ/EMBL/GenBank under the accession no."}
+{"text": "Spounavirinae viruses have received an increasing interest as tools for the control of harmful bacteria due to their relatively broad host range and strictly virulent phenotype.Spounavirinae subfamily of the Myoviridae. A set of comparative analyses identified a distinct, recently proposed Bastille-like phage group within the Spounavirinae. More importantly, type 1 thymidylate synthase (TS1) and dihydrofolate reductase (DHFR) genes were shown to be unique for the members of the proposed Bastille-like phage group, and are suitable as molecular markers. We also show that the members of this group encode beta-lactamase and/or sporulation-related SpoIIIE homologs, possibly questioning their suitability as biocontrol agents.In this study, we collected and analyzed the complete genome sequences of 61 published phages, either ICTV-classified or candidate members of the Spounavirinae, and propose that the presence of TS1- and DHFR-encoding genes could serve as signatures for the new Bastille-like group. In addition, the presence of metallo-beta-lactamase and/or SpoIIIE homologs in all members of Bastille-like group phages makes questionable their suitability for use in biocontrol.We confirm the creation of a new genus\u2014the \u201cBastille-like group\u201d\u2014in The online version of this article (doi:10.1186/s12864-015-1757-0) contains supplementary material, which is available to authorized users. Spounavirinae is a subfamily of the Myoviridae, and its members possess a large isometric head (75\u2013100\u00a0nm) with a long contractile tail (140\u2013220\u00a0nm) , Spock [NC_022763], B4 [JN790865], Riley [NC_024788], Troll [NC_022088], BigBertha [NC_022769], Hoody T [NC_024205], Evoli [NC_024207], CAM003 [NC_024216], W.Ph. [NC_016453], BPS13 [NC_018857], BPS10C [NC_023501], Megatron [NC_024211], Hakuna [NC_024213], JBP901 [KJ676859.1], Bcp1 [NC_024137], BCP78 [NC_018860], BCU4 [JN797798], phiNIT1 [NC_021856], Mater [KM236245], Moonbeam [KM236246], phiAGATE [NC_020081], Bobb [NC_024792], SP10 [NC_019487], CampHawk [NC_022761], Shanette [KC595513], and JL [KC595512].In addition, 27 complete genome sequences of candidate Bacillus phages  available in NCBI database whose genome is bigger than 127\u00a0kb. However, they were excluded from the analysis because of either the lack of information (the family of phage Grass is not specified) or significantly bigger genome size .As of the date of manuscript submission, there are three more complete genome sequences of Lactobacillus phage Lb338-1 was included in all the analysis since it has been reported as an SPO1-like phage [Furthermore, ke phage .CLuster Analysis of Sequences (CLANS) software package [P-values of high scoring segment pairs (HSPs) [The  package  was used package . It usess (HSPs) . Dot plos (HSPs) . We alsos (HSPs) . Bootstrs (HSPs) .\u221250 or greater than 32.5\u00a0% identity with at least one other protein [The Phamerator database was created as described previously . It uses protein , 27. Con protein . Pham ciThe dataset supporting the results of this article is included within the additional files (Additional file"}
+{"text": "Weissella hellenica 4\u20137, was previously characterized but its full amino acid sequence remain unknown. The draft genome sequencing analysis of Weissella hellenica 4\u20137 was performed and the open reading frame (ORF) encoding the weissellicin L was identified and clarified.Weissellicin L, a novel bacteriocin produced by The obtained results indicated that the mature bacteriocin consists of 29 amino acid residues with a molecular weight of approximately 3205.64\u00a0Da. A conserved processing site of two glycine residues in positions -1 and -2 was observed in the leader peptides. The possibility that bacteriocin secretion depended on ATP-binding cassette (ABC) transporter was therefore suggested. Furthermore, primers were designed from 5\u2019 and 3\u2019 flanking sequences of the weissellicin L structural gene. PCR presented a single product and was useful to detect weissellicin L structural gene.To our knowledge, this is the first report describing the full amino acid sequence of Weissellicin L. A rapid method to detect weissellicin L structural gene was also reported in this study. Weissella hellenica 4\u20137, isolated from the traditional Taiwanese fermented food sian-sianzih (fermented clams), is capable of producing a novel bacteriocin, termed weissellicin L  strains produce proteinaceous antibacterial compounds, termed as bacteriocins. Many bacteriocins show great inhibitory ability against food pathogens and therefore attract special interest  to clarify the full amino acid sequence of weissellicin L, and 2) to rapidly detect weissellicin L structural gene by using PCR amplification method.Several characteristics of weissellicin L, such as sensitivities to enzymes and heat, inhibition spectra, and partial amino acid sequences, have been previously reported  medium under the same conditions previously described by Leong et al. . Search for similarity between sequences was performed using NCBI BLAST (http://blast.ncbi.nlm.nih.gov/).Illumina GA IIx genome analyzer  was applied to reveal the genome sequence of http://www.ncbi.nlm.nih.gov/tools/primer-blast/)  and W. hellenica 203 previously isolated from fermented zoned cerith  was used as the size standard.Besides Weissella hellenica 4\u20137 but only partial N-terminal amino acid sequence was observed and the complete sequence remained unknown . The previously identified partial N-terminal amino acid sequence of weissellicin L, NH2-KGFLSWASKATSWLVGP, was applied to search against the draft genome of W. hellenica 4\u20137. An open reading frame was detected to match the partial sequence of weissellicin L completely.In our previous study, a new bacteriocin termed weissellicin L was identified in http://web.expasy.org/compute_pi/). This calculated molecular weight corresponded to the previously determined molecular weight of 3205.64\u00a0Da using MALDI-TOF MS  transporter is required for the secretion of many class II bacteriocin in Gram-positive bacteria (Michiels et al. W. hellenica 4\u20137 (Figure\u00a0W. hellenica strains BCRC 80264T and 203 (Figure\u00a0W. hellenica 4\u20137 was sequenced and the nucleotide sequences encoding the target gene was confirmed to be identical as reported (Figure\u00a0W. hellenica strains.Primers specific for the weissellicin L gene were designed to perform PCR amplification. A single 457-bp fragment was amplified from the genomic DNA of 7 Figure\u00a0. Howeverd Figure\u00a0. The resW. hellenica 4\u20137 are necessary to understand more bacteriocin related information and other characteristics of LAB.Our results report the full amino acid sequences of weissellicin L and the nucleotide sequences encoding the weissellicin L gene. In addition, this study provides a quick method to screening the weissellicin L-producing strain. Further analyses on the genome sequences of"}
+{"text": "Background. Previously we established two cell lines (SNU_MM1393_BM and SNU_MM1393_SC) from different tissues (bone marrow and subcutis) of mice which were injected with single patient's myeloma sample. We tried to define genetic changes specific for each cell line using whole exome sequencing (WES). Materials and Methods. We extracted DNA from SNU_MM1393_BM and SNU_MM1393_SC and performed WES. For single nucleotide variants (SNV) calling, we used Varscan2. Annotation of mutation was performed using ANNOVAR. Results. When calling of somatic mutations was performed, 68 genes were nonsynonymously mutated only in SNU_MM1393_SC, while 136 genes were nonsynonymously mutated only in SNU_MM1393_BM. KIAA1199, FRY, AP3B2, and OPTC were representative genes specifically mutated in SNU_MM1393_SC. When comparison analysis was performed using TCGA data, mutational pattern of SNU_MM1393_SC resembled that of melanoma mostly. Pathway analysis using KEGG database showed that mutated genes specific of SNU_MM1393_BM were related to differentiation, while those of SNU_MM1393_SC were related to tumorigenesis. Conclusion. We found out genetic changes that underlie tropism of myeloma cells using WES. Genetic signature of cutaneous plasmacytoma shares that of melanoma implying common mechanism for skin tropism. KIAA1199, FRY, AP3B2, and OPTC are candidate genes for skin tropism of cancers. BrieflyFrom genetic perspective, we believe that unique somatic mutations may allow tumor cells to adapt and survive in tumor microenvironment. In other words, there may be specific genetic changes that contribute to tumor tropism. Hence, in this study, we tried to characterize genomic profile specific for SNU_MM1393_BM and SNU_MM1393_SC to understand genetic background of difference in these cell lines. We were particularly interested in genetic changes specific for SNU_MM1393_SC, because we thought these SNU_MM1393_SC specific genetic changes would reveal genetic background for plasmacytoma which exhibit tropism for extramedullary space. To find these genetic changes, we performed whole exome sequencing (WES) using DNA of both cell lines. As it is well known, WES allows comprehensive characterization of genomic changes in individual tumors [DNA was extracted from two cell lines (SNU_MM1393_BM and SNU_MM1393_SC). QuickGene DNA whole blood kit S  was used to extract DNA according to the manufacturer's recommendations. For WES, we sequenced exome using the Solexa sequencing technology platform  following the manufacturer's instructions. We randomly sheared 3\u2009ug of genomic DNA using Covaris System to generate about 150\u2009bp inserts. The fragmented DNA was end-repaired using T4 DNA polymerase and Klenow polymerase, and Illumina paired-end adaptor-oligonucleotides were ligated to the sticky ends. We analyzed the ligation mixture by electrophoresis on an agarose gel, sliced and purified fragments with 200\u2013250\u2009bp sizes. Purified DNA library was hybridized with SureSelect Human All Exon V4 probes set  to capture 50Mb targeted exons following manufacturer's instruction. We prepared the HiSeq2000 paired-end flow cell to the manufacturer's protocol using captured exome library. Clusters of PCR colonies were then sequenced on the HiSeq2000 platform using recommended protocols from the manufacturer.FASTQ files were aligned to human reference (human_g1k_v 37.fasta) by using the Burrows-Wheeler aligner (BWA-0.7.5)  to make http://genome.wustl.edu/) [p value below 0.05 other option value set as default values. To select unique mutation, we performed comparison between two calling results. For functional annotation and prediction of variant effect, we used ANNOVAR [For single nucleotide variant (SNV) and small indel calling, we used Varscan2 (tl.edu/) . Because ANNOVAR  with Pol ANNOVAR  databasehttps://tcga-data.nci.nih.gov/tcga/tcgaHome2.jsp), cBioPortal for Cancer Genomics , and KEGG database for pathway analysis (http://david.abcc.ncifcrf.gov/).For comparing public data with results in this study, we used datasets from TCGA  were covered sufficiently to pass our thresholds for calling variants (MAPQ > 20 by NGS QC Toolkitv2.3). MAPQ distribution following that above 30 was 98.2% (164088367), above 20 was 0.8% (1395476), and below 20 of MAPQ was under 10%. For SNU_1393MM_SC, MAPQ distribution following that above 30 was 98.1% (159871347), above 20 was 0.8% (154084), and below 20 of MAPQ was around 10%.When SNV calling was performed using Varscan, a total of 18573 SNVs were found in SNU_MM1393_SC. Their distribution according to the functional consequences was as follows: 8595 (46.2%) nonsynonymous, 9575 (51.5%) synonymous, 68 (0.003%) stop-gain, and 6 (0.0003%) stop-loss. In SNU_MM1393_BM, a total of 18781 SNVs were found and their distribution was as follows: 8694 (46.2%) nonsynonymous, 9667 (51.5%) synonymous, 75 (0.004%) stop-gain, and 5 (0.0003%) stop-loss. As for nonsynonymous SNVs, we found 8595 nonsynonymous SNVs in 4901 genes for SNU_MM1393_SC, while 8694 nonsynonymous SNVs in 4969 genes were found in SNU_MM1393_BM. There was overlapping of 8344 nonsynonymous SNVs, and 251 nonsynonymous SNVs and 350 nonsynonymous SNVs were unique for SNU_MM1393_SC and SNU_MM1393_BM, respectively Figures .The rate of transversion and transition in the coding region was different between the two cell lines. While transversion was dominant event in SNU_MM1393_BM cell line, transition was dominant event in SNU_MM1393_SC. Absolute transversion rate was much higher in SNU_MM1393_BM (65.5%) than SNU_MM1393_SC (34.0%) .http://www.cBioportal.org). Around half of SNVs found in our cell lines were found with low frequency (0.5\u20132%) in open source database of multiple myeloma   using cB myeloma .http://www.cBioportal.org). When this analysis was performed, SNVs which are unique for SNU_MM1393_SC were frequently detected in melanoma (52.8%) and uterine cancer (49.0%). On the other hand, SNVs those which are unique for SNU_MM1393_BM were frequently found in ovarian cancer (74.7%) and bladder cancer (72.8%)  .p = 0.14), while it was 1.1 for SNU_MM1393_SC (p = 0.07). Hence, SNV distribution in both cell lines was random with cut-off p value of 0.05. Our results indicated that unique nonsynonymous mutations of SNU_MM1393_SC seemed biologically more neutral than those of SNU_MM1393_BM although they were statistically insignificant.Using Monte Carlo Simulation (Ratio of dNs/dS), we examined distribution statistics of SNVs found in two cell lines, respectively. It is believed that nonrandomness of SNV distribution is related to the functional importance of those SNVs. When this analysis was performed, ratio for SNU_MM1393_BM was 2.8  overlapped between the cell lines, suggesting their common originality from a single patient. And the most coding sequence SNVs in both SNU_MM1393_BM and SNU_MM1393_SC were neutral with respect to adaptation and cancer cell growth. This is also supported by the outcome of Monte Carlo Simulation, where distribution of SNVs did not show significant nonrandomness. It has been suggested that bone marrow microenvironment strictly regulates the growth of cell via dynamic interplay among hematopoietic cells . And our KIAA1199, FRY, AP3B2, and OPTC may be the very gene related to skin tropism of cancers. These 4 are the genes that are mutated in SNU_MM1393_SC and are frequently found in melanoma samples. In fact, it has been known that there are common genetics between melanoma and plasmacytoma such as CDKN2A germline mutation in prenext generation sequencing (NGS) era [The most interesting finding in our data is related to the genetic changes unique for SNU_MM1393_SC. We conjectured in the planning of this study that genetic change unique for SNU_MM1393_SC would be related to skin tropism and formation of plasmacytoma. And, as expected, genomic signature that is unique to SNU_MM1393_SC had more than 50% of overlapping with melanoma which is a primary skin tumor. This finding highly coincides with our conjecture and SNVs such asNGS) era .One more noticeable finding in our study is that the number of SNVs was higher in SNU_MM1393_BM than in SNU_MM1393_SC and transversion rate was higher in SNU_MM1393_BM than in SNU_MM1393_SC. Also dynamic gene to gene interaction in SNU_MM1393_BM was more complex than that in SNU_MM1393_SC. Along with this phenomenon and from the previous report that the number and pattern of somatic SNVs determine pathway underling cancer , we thinIn fact, we had difficulties in the analysis of WES data due to the lack of germline reference DNA in this study. Because we used public germline database as reference in the calling of SNVs, the number of SNVs was very large compared to the previous multiple myeloma genomic studies. Moreover, it is well known that the majority of mutations observed in cancer sequencing studies are believed to be passenger mutations having little impact on the cancer cell . To over"}
+{"text": "Cuscuta is a group of parasitic plants that are distributed world-wide. The process of parasitization starts with a Cuscuta plant coiling around the host stem. The parasite\u2019s haustorial organs then establish a vascular connection allowing for access to the phloem content. The host and the parasite form new cellular connections, suggesting coordination of developmental and biochemical processes. Simultaneous monitoring of gene expression in the parasite\u2019s and host\u2019s tissues may shed light on the complex events occurring between the parasitic and host cells and may help to overcome experimental limitations . A novel approach is to use bioinformatic analysis to classify sequencing reads as either belonging to the host or to the parasite and to characterize the expression patterns. Owing to the lack of a comprehensive genomic dataset from Cuscuta spp., such a classification has not been performed previously.The genus Cuscuta japonica and the non-model host plant Impatiens balsamina. Without established reference sequences, we classified reads as originating from either of the plants by stepwise similarity search against de novo assembled transcript sets of C. japonica and I. balsamina, unigene sets of the same genus, and cDNA sequences of the same family. We then assembled de novo transcriptomes from the classified read sets. We assessed the quality of the classification by mapping reads to contigs of both plants, achieving a misclassification rate low enough (0.22-0.39%) to be used reliably for differential gene expression analysis. Finally, we applied our read classification method to RNA-Seq data from the interface between the non-model parasitic plant C. japonica and the model host plant Glycine max. Analysis of gene expression profiles at 5 parasitizing stages revealed differentially expressed genes from both C. japonica and G. max, and uncovered the coordination of cellular processes between the two plants.We first classified RNA-Seq reads from an interface region between the non-model parasitic plant We demonstrated that reliable identification of differentially expressed transcripts in undissected interface region of the parasite-host association is feasible and informative with respect to differential-expression patterns.The online version of this article (doi:10.1186/s13007-015-0066-6) contains supplementary material, which is available to authorized users. In angiosperms, approximately 4000 species are parasitic to some extent . ParasitCuscuta is a prominent group of parasitic plants. It consists of 150\u2013200 species that are distributed world-wide . Sequence files of Cj_contigs_ci1 and Ib_contigs are available as Additional file Assembly of  1.2.10)  and, sub 1.2.10) . The k-mde novo transcriptome assembly using Cj_Gmif_reads1 and Cj_Gmif_reads2, the same assembly procedure using Velvet/Oases as described above was used. The resulting transcript sets are referred to as Cj_contigs_cg1 and Cj_contigs_cg2, respectively. Sequence file of Cj_contigs_cg1 is available as Additional file In the de novo transcriptome assembly of C. reflexa, all read pairs from C. reflexa that survived the filtering pipeline described above were used for de novo transcriptome assembly using Trinity (version r20140717 with default parameters and \u2013jaccard_clip option) [In the  option) .e value <1e-5) using BLASTX [Arabidopsis thaliana genes (ftp://ftp.arabidopsis.org/home/tair/Genes/TAIR10_genome_release/). The contig sets were further matched against unigenes of C. pentagona [C. suaveolens [C. reflexa (SRA: SRP038020), Tryphysaria versicolor (TrVeBC1 and TrVeBC2), Striga hermonthica (StHeBC1 and StHeBC2), and Orobanche aegyptiaca (OrAeBC4) (Parasitic Plant Genome Project http://ppgp.huck.psu.edu/) using BLASTN with e value <1e-5. Ib_contigs were used for BLASTX search against the plant protein database of refseqplant . According to the similarity to Arabidopsis genes, a GO annotation was obtained as described in Mochizuki et al. [http://www.plant.osakafu-u.ac.jp/~ogata/downloadgo.html. Prediction of ORFs was performed with the OrfPredictor software [C. reflexa contigs were checked for ORFs using an in-house Python script (https://github.com/cschu/fortuna). Contigs with an ORF of at least 200\u00a0bp were then searched against the plant protein database of refseqplant using BLASTX. GO annotation was then attempted for all contigs that matched against refseqplant using the same data sets described above. C. reflexa BLASTX runs were performed with the following parameters: e value <1e-5, \u226575% query coverage, >40% identity (identities\u2009+\u2009positives).Cj_contigs_ci1, Cj_contigs_cg1 and Cj_contigs_cg2 were searched against the plant protein database of refseqplant . Reads per kilobase per million mapped reads (RPKM) were calculated separately. Library size normalization and differential gene expression analysis were performed using the DESeq  and R soe value <1e-20, 1 mismatch and 1 gap insertion allowed). Cj_nc_reads that were mapped to Ib_contigs, and Ib_nc_reads that were mapped to Cj_contigs_ci1 were regarded as misclassified. To evaluate the rate of assignment of C. japonica reads to I. balsamina, or vice versa, Cj_nc_reads and Ib_nc_reads, respectively, were mapped to a merged transcript set consisting of Cj_nc_contigs and Ib_nc_contigs using BLASTN as described above. Cj_nc_reads that were mapped to Ib_nc_contigs as well as Ib_nc_reads mapped to Cj_nc_contigs were regarded as misclassified. For the binary classification of C. japonica reads and I. balsamina reads, the following 4 outcomes are possible. We defined a C. japonica read as a true positive (TP) if it was mapped to a C. japonica contig, and as a false negative (FN) if it was mapped to an I. balsamina contig. An I. balsamina read that was mapped to a C. japonica contig was defined as a false positive (FP). Finally, an I. balsamina read that was mapped to an I. balsamina contig was defined as a true negative (TN). For the nomenclature of I. balsamina reads, switch the term \u201cC. japonica\u201d and \u201cI. balsamina\u201d in the definition above. The true positive rate (TPR) was defined as TP / (TP\u2009+\u2009FN) and false positive rate (FPR) as FP / (TN\u2009+\u2009FP). The ROC AUC was calculated using the R package ROCR [C. japonica and G. max.To evaluate the degree of misclassification of reads with respect to their source organism, Cj_nc_reads and Ib_nc_reads were mapped to a merged transcript set consisting of Cj_contigs_ci1 and Ib_contigs using BLASTN . Fixed samples were sliced into 80 \u2013 100 micrometer-thick sections with the Vibratome . Histochemical staining of sections was performed using a 0.5% (w/v) solution of Toluindine Blue O  in distilled water. Stained slices were observed and photographs were taken by using the Biological Microscope BX51  with the CCD camera, VB-7010 ."}
+{"text": "Eucalyptus genus, studies on genome composition and transposable elements (TEs) are particularly scarce. Nearly half of the recently released Eucalyptus grandis genome is composed by retrotransposons and this data provides an important opportunity to understand TE dynamics in Eucalyptus genome and transcriptome.In Copia and Gypsy superfamilies in Eucalyptus grandis genome and we depicted genomic distribution and copy number in two Eucalyptus species. We also evaluated genomic polymorphism and transcriptional profile in three organs of five Eucalyptus species. We observed contrasting genomic and transcriptional behavior in the same family among different species. RLC_egMax_1 was the most prevalent family and RLC_egAngela_1 was the family with the lowest copy number. Most families of both superfamilies have their insertions occurring <3 million years, except one Copia family, RLC_egBianca_1. Protein theoretical models suggest different properties between Copia and Gypsy domains. IRAP and REMAP markers suggested genomic polymorphisms among Eucalyptus species. Using EST analysis and qRT-PCRs, we observed transcriptional activity in several tissues and in all evaluated species. In some families, osmotic stress increases transcript values.We characterized nine families of transcriptionally active LTR retrotransposons from Eucalyptus, and each family has a particular genomic and transcriptional pattern. Overall, our results show that retrotransposon activity have differentially affected genome and transcriptome among Eucalyptus species.Our strategy was successful in isolating transcriptionally active retrotransposons in The online version of this article (doi:10.1186/s12870-015-0550-1) contains supplementary material, which is available to authorized users. For primers with 100\u00a0% efficiency, the fold equals 2.qPCR reactions were conducted in a Step One Plus Real Time PCR System (Applied Biosystems) and analyzed in Step One 2.1 software (Applied Biosystems).Each qPCR reaction was performed in 5\u00a0\u03bcl of GoTaq\u00ae qPCR Master Mix (Promega), with 1.0\u00a0ng of each primer and 3.7\u00a0\u03bcl of ultra-pure water. The cycling conditions were as follows: 5\u00a0min at 95\u00a0\u00b0C, followed by 45\u00a0cycles each of 15\u00a0s at 95\u00a0\u00b0C, 60\u00a0s at 60\u00a0\u00b0C. In order to confirm the reproducibility of our results, reactions were done in technical triplicates in three independent experiments using 0.125, 0.25 and 0.5\u00a0ng of genomic DNA.Copia and Gypsy-like LTR-RTEs families were calculated according to Kraitshtein et al.  and [(AG)10\u2009T], probably sampling LTR-RTEs located in pericentromeric regions, which are gene-poor and enriched in repetitive sequences, especially retrotransposons [Eucalyptus species, as observed in Diospyros sp. and Medicago sativa [The average size of REMAP fragments was probably the result of proximity between LTR and SSR regions than LTR-RTEs in tandem insertions. The pattern of REMAP fragments per nsposons . The higo sativa , 86.Eucalyptus, and genomic polymorphism suggests differential activity among RTEs within subgenus Symphyomyrtus. Those species occupy the same clade within subgenus Symphyomyrtus [Eucalyptus species was not completely supported by bootstrap analyses. Distribution of Eucalyptus species in dendrogram has some differences comparing to molecular analyses based on DArT markers [E. tereticornis and E. urophylla were the most close in RTE-based tree differently from the close relation usually present between E. grandis and E. urophylla, also observed for E. brassiana and E. tereticornis using other nuclear markers. On the other hand, our analysis shows a small distance between E. saligna and E. grandis, in agreement with a previous report using a nuclear gene [This is the first report using IRAP and REMAP markers for genetic diversity in yomyrtus  but it i markers . E. tereear gene .Eucalyptus genomes. RLC_egAle_1 was the family with the largest number of ESTs. RLC_egBianca_1 was the most ubiquitous element, with EST in the six mined Eucalyptus species. Detailed information of ESTs matching LTR-RTEs is available in Supporting Information Additional file The annotation of ESTs related to LTR-RTEs was an initial assessment of transcriptional activity of these elements in Eucalyptus spp. tissues and species  and with more genomic copies, RLC_egIvana_1, RLC_egAle_1 and RLC_egAle_2, had transcriptional activity modification, except RLC_egMax_1.This is the first work that LTR-RTEs were evaluated in roots submitted to PEG osmotic stress. egAle_1, RLC_egAngela_1, RLC_egIvana_1 e RLG_egTekay_1 families had a peak after 6\u00a0h of osmotic stress by PEG followed by a decrease in expression level in RLC_egIvana_1 and RLG_egTat_1 families. This observation suggests that both families have their transcription triggered by similar stress conditions, a common feature among TEs [Eucalyptus.The transcriptional activity from RLC_mong TEs . Future Copia and Gypsy families\u00a0in Eucalyptus  have a different amplification pattern. Particularly in E. grandis and E. urophylla, that have diverged from a common ancestor\u2009~\u200920 Mya ago, we observed lower copy number in most LTR-RTEs at E. urophylla compared to E. grandis. These differences warrant further investigation to determine if recombination, nucleotide divergence or a specific burst of amplification can explain this pattern. Despite conservation to LTR-RTEs between species, IRAP and REMAP markers analyses based on transcriptionally active LTR-RTEs suggest different level of transpositional activity within Eucalyptus genus. This hypothesis is reinforced taking account that transcriptional activity is not the same among Eucalyptus species. Future studies can address if LTR-RTEs are specifically modulated by other stresses beside osmotic shock. Another important issue is to address if Eucalyptus LTR-RTEs families characterized here are in expansion in Eucalyptus genus, or even if they are conserved across other families rather than Myrtaceae, which may indicate horizontal transfer and/or purifying selection.This study demonstrated that each The data sets supporting the results of this article are available in the Dryad repository, doi:10.5061/dryad.h2t57."}
+{"text": "A novel class of transcripts, long non-coding RNAs (lncRNAs), has recently emerged as a key player in several biological processes, and important roles for these molecules have been reported in a number of complex human diseases, such as autoimmune diseases, neurological disorders, and various cancers. However, the aberrant lncRNAs implicated in myasthenia gravis (MG) remain unknown. The aim of the present study was to explore the abnormal expression of lncRNAs in peripheral blood mononuclear cells (PBMCs) and examine mRNA regulatory relationship networks among MG patients with or without thymoma.Microarray assays were performed, and the outstanding differences between lncRNAs or mRNA expression were verified through RT-PCR. The lncRNAs functions were annotated for the target genes using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) biological pathway. The potential regulatory relationships between the lncRNAs and target genes were analyzed using the \u2018cis\u2019 and \u2018trans\u2019 model. Outstanding lncRNAs were organized to generate a TF-lncRNA-gene network using Cytoscape software.The lncRNA and mRNA expression profile analysis revealed subsets of differentially expressed genes in MG patients with or without thymoma. A total of 12 outstanding dysregulated expression lncRNAs, such as lncRNA oebiotech_11933, were verified through real-time PCR. Several GO terms including the cellular response to interferon-\u03b3, platelet degranulation, chemokine receptor binding and cytokine interactions were very important in MG pathogenesis. The chromosome locations of some lncRNAs and associated co-expression genes were demonstrated using \u2018cis\u2019 analysis. The results of the \u2018trans\u2019 analysis revealed that some TFs  regulate lncRNA and gene expression. The outstanding lncRNAs in each group were implicated in the regulation of the TF-lncRNA-target gene network.The results of the present study provide a perspective on lncRNA expression in MG. We identify a subset of aberrant lncRNAs and mRNAs as potential biomarkers for the diagnosis of MG. The GO and KEGG pathway analysis provides an annotation to determine the functions of these lncRNAs. The results of the \u2018cis\u2019 and \u2018trans\u2019 analyses provide information concerning the modular regulation of lncRNAs.The online version of this article (doi:10.1186/s12920-015-0087-z) contains supplementary material, which is available to authorized users. Myasthenia gravis (MG) is a T and B cell-mediated autoimmune disease of the neuromuscular junction; cytokines and chemokines may play a crucial role in the pathogenesis and perpetuation of this disease -3. MusclLong non-coding RNAs (lncRNAs) are a group of RNA transcripts that are more than 200 nucleotides in length and lack significant open reading frames (ORFs) -15. lncRIn the present study, we performed an array of lncRNA chip assays on PBMCs of MG patients. The outstanding lncRNAs functions were annotated based on co-expression genes and the GO biological process. The relationships among the lncRNAs were revealed through \u2018cis\u2019 and \u2018trans\u2019 analyses. These results provide information for further studies on MG.To investigate the expression levels of lncRNAs associated with MG with or without thymoma, lncRNA and mRNA microarray analyses were performed on the PBMCs of MG patients. After separating the signal from noise and performing a t-test, significant differences in lncRNAs and mRNA expression of up to two-fold (P\u2009<\u20090.05 and FDR\u2009<\u20090.05) were observed. These results are summarized in Table\u00a0To further explore the function of lncRNAs in MG, the results of the lncRNA and mRNA chip analyses were subjected to Pearson\u2019s correlation coefficient analysis, in which co-expression was considered at P\u2009>\u20090.8. To determine the level of lncRNA and mRNA co-expression, we divided the different lncRNAs into two subsets: the upregulated lncRNAs and downregulated lncRNAs. When the subset contained more than 100 different lncRNAs, we selected the top 100 most distinguished lncRNAs, and when the subset contained less than 100 lncRNAs, all lncRNAs in the subset were used. Because the file is extremely large, the information pertaining to lncRNA oebiotech_11933 is shown as a representative result. The results for the other lncRNAs are provided in Additional file http://david.abcc.ncifcrf.gov/ gene2gene.jsp). The lncRNA oebiotech_11933 function was annotated using GO and KEGG pathway analyses. Selecting the reliability prediction terms, a total of 20 enrichment GO terms were obtained. Table\u00a0lncRNA oebiotech_11933 exhibited the highest upregulation of lncRNA expression among MG patients with thymoma versus healthy controls. A total of 552 genes relative to lncRNA oebiotech_11933 exhibited P\u2009>\u200990%  for co-expressed and aberrant lncRNA genes, respectively  or regulatory T cells (Treg) through the expression of cytokines and chemokines. Because of the responses to chemokines and the interactions of these molecules with other cells, cytokines and chemokines are likely important in the pathogenesis of MG. Cufi et al [Precisely annotating the functions of lncRNAs remains complex. Here, we interpret the lncRNA functions based on co-expression gene GO and pathway analyses. As shown in Table\u00a0fi et al  conductefi et al ,29 also fi et al . Uzawa efi et al  measuredfi et al  reportedThe most enriched GO terms in the predicted target genes of the lncRNAs were \u2018cell response to interferon-\u03b3\u2019, \u2018platelet degranulation\u2019, \u2018chemokine receptor binding\u2019 and \u2018cytokine-cytokine interaction\u2019 in the pathogenesis between MG patients with thymoma and MG patients without thymoma Figure\u00a0. Many stThe molecular regulation through lncRNAs remains unknown because the functions of lncRNAs vary . Indeed,In the present study, we identified a subset of aberrant lncRNAs to distinguish MG patients with or without thymoma compared with healthy individuals. The function and biological processes of lncRNAs in the pathogenesis of MG were determined according to co-expression gene GO and pathway annotations. The results of the \u2018cis\u2019 and \u2018trans\u2019 analyses provide information for future studies of aberrantly expressed lncRNAs in MG. These results provide support for future investigations of the pathogenesis of MG.7 cells were resuspended in Trizol\u00ae (Invitrogen) and stored at -80\u00b0C.This study was approved by the ethical review committees of Xiangya Hospital.A total of 34 MG patients examined at the Neurology Department of Xiangya Hospital and 13 healthy donors were recruited from May 2010 to March 2012. MG was diagnosed based on a combination of fluctuating muscle weakness with a positive neostigmine test or abnormal single-fiber EMG test. The MG patients were divided into two groups: patients with or without thymoma. The Myasthenia Gravis Foundation of America (MGFA) clinical classification was also used to identify MG subgroups. All MG patients did not receive immunomodulatory or immunosuppressive treatment and were not treated with thymectomy. Information pertaining to sex, age at onset, disease duration, MGFA clinical classification upon first visit to the hospital, additional autoimmune diseases, including thyroid disorders, systemic lupus erythematosus (SLE), rheumatoid arthritis, and Sjogren syndrome, and previous treatments, including acetylcholinesterase inhibitors, immunosuppressive drugs, prednisone, plasma exchange, IVIg and thymectomy, is summarized in Additional file Total RNA was extracted from PBMCs using Trizol\u00ae reagent (Invitrogen). Approximately 200 ng of total RNA from each sample was used for the lncRNA microarray analysis. lncRNA expression was analyzed from May 2012 to August 2013 using OE_Biotech Human lncRNA chip software V2.0(4*180K),containing 46,506 lncRNAs and 30,656 mRNAs collected from eight authoritative databases, including Agilent_ncRNA, lncRNAdb, Gencode V13, H-invDB, NONCODE v3.0, RefSeq, UCR and UCSC_lncRNAs Transcripts. The lncRNA chip experiments were conducted at the OEbiotech Corporation in Shanghai, P.R. China.Raw data from each array were first normalized using GeneSpring software (version 12.5) and subsequently analyzed using an unpaired t-test, with a P-value cut-off of 0.05 and a fold-change cut-off of 2.0.MG patients with or without thymoma were compared with each other and healthy controls. The common characteristic elements between the three groups were determined using Venn analysis.Different lncRNAs and mRNAs were analyzed using Cluster 3.0 software, and the data were used to examine a series of parameters, such as log transform data, normalized genes and arrays, and hierarchical parameters of genes and arrays. The results were further analyzed using Tree View software. Green-yellow indicates low expression, and red indicates high expression.The expression of different lncRNAs and mRNAs was analyzed using Pearson\u2019s correlation coefficient. The absolute value of 0.8 was considered relevant, a value less than 0.8 represented a negative correlation, and a value greater than 0.8 represented a positive correlation. A P-value of less than 0.05 was considered statistically significant. The expression of genes encoding each differentially expressed lncRNA, the ontology classification of the co-expression genes based on gene annotation and summary information are available through DAVID . The predicted target genes were assigned to functional groups based on molecular function, biological processes and specific pathways. The lncRNA gene function was predicted based on the co-expression gene GO functional annotation, selecting  the top 100 GO and 200 function reliability prediction terms. Statistical function annotation generated additional GO terms, and the most enriched terms might reflect potential lncRNA functions.The gene location for different lncRNAs on the chromosome was determined. Subsequently, the common lncRNA co-expression genes were intersected to identify the genes 300 kbp upstream or downstream of the lncRNAs as potential \u2018cis\u2019 genes. The schematic shows the chromosome location of the lncRNAs and cis genes.lncRNA sequences were mapped to the genome in the Sanger database. Jemboss software was used to examine the alignment of lncRNA and putative transcription factor binding sequences. The genome browser database was used to build the network describing the relationships between transcription factors and lncRNAs. An adjacency matrix was implemented in Java according to the binding of lncRNA and transcription factors. The core transcription factor is the most important center in the network, with the highest degree of expression ,54. PearThe TF-lncRNA-gene network was constructed based on the interactions of lncRNAs and target co-expression genes as previously described . The lnc\u0394\u0394ct method against GAPDH for normalization. The data represent the means of three experiments.Total RNA was extracted using Trizol\u00ae reagent (Invitrogen). The primers for RT-PCR were designed based on the lncRNA sequences from the UCSC. The primers were synthesized and purified at Invitrogen . The RT reactions were performed using a cDNA synthesis kit . Real-time PCR was performed using the ABI StepOne Plus\u2122 Multicolor. The qPCR cycle was 98\u00b0C for 2 min, followed by 40 cycles of 95\u00b0C for 15 sec and 60\u00b0C for 30 sec. A final melt-curve analysis (60\u201395\u00b0C) was included. The standard curve was produced with slopes at approximately -3.32 (~100% efficiency). The lncRNA PCR results were quantified using the 2oebiotech_11933-F: GAAACGGTCCAGGAGCTGAToebiotech_11933-R: CTTGCTTCTGGAGAGCCTGTA_24_P927716-F: TCTGCCCTTCACCTGCTCCTA_24_P927716-R: TTGTAGTAGTTGTCAAAAATA_21_P0010030-F: GCAATGTCAAGGCCTCATCTA_21_P0010030-R: CGCTATGGCAAGTCACAAGAA_21_P0002844-F: GGAAGGCAATGAATGAGAAGA_21_P0002844-R: AGATCAGTAGGAAGTGGTAToebiotech_03926-F: GCGTGGTGGATCACTTCTGToebiotech_03926-R: ACATGGCTTTCATGCTAAAToebiotech_02627-F: GGGATCTCAAACCTGGAACAoebiotech_02627-R: TTCGGCATCTCGTTAGCTCToebiotech_22482-F: AACCATAACCAGCCAACCAAoebiotech_22482-R: CCTGGGCAACAGAGCAAGACA_19_P00315959-F: GACGGAACCACATGGAGACTA_19_P00315959-R: GCAGCTATTGTCTGCCTTCCoebiotech_13222-F: TTCAGAATAACCCGCCAGTCoebiotech_13222-R: ATTCACCAAGCATGCAAACAoebiotech_22652-F: ATGCCAGCCACTGTGATAGAoebiotech_22652-R: ACTGACATTCATCTAAACAGoebiotech_16223-F: CTCAGCAAAAATGCCCAAAToebiotech_16223-R: GGGAGGTGTAGCTGAAGCAGGAPDH-F: GAGTCAACGGATTTGGTCGTGAPDH-R: TTGATTTTGGAGGGATCTCGReal-time PCR primers:"}
+{"text": "Coprothermobacter proteolyticus DSM 5265, isolated from a thermophilic digester fermenting tannery wastes and cattle manure.Here we present the complete 1,424,912-bp genome sequence of  Coprothermobacter proteolyticus is a nonmotile, non-spore-forming, rod-shaped, Gram-negative anaerobic bacterium isolated from a thermophilic consortium fermenting tannery wastes and cattle manure  to sequence the genomes of representatives of the seven phyla of bacteria that at the time had cultured representatives but no available genome sequence.e manure . C.\u00a0protdditives . It was olyticus . C.\u00a0protC.\u00a0proteolyticus DSM 5265 was grown in DSM medium 481, and DNA was extracted using standard techniques. Sanger sequencing and genome assembly were performed as previously described for genomes sequenced by TIGR  often found near the origin in prokaryotic genomes and G+C nucleotide skew (G\u00b7C/G+C) analysis  were predicted as previously described . All preCP001145. The version described in this paper is version CP001145.1.This genome sequence has been deposited at DDBJ/EMBL/GenBank under the accession no."}
+{"text": "Drosophila, cells compare their fitness via several isoforms of the transmembrane protein Flower [Drosophila in which mechanical puncture activates regenerative neurogenesis based on damage-responsive stem cells [Darwinian-like cell selection has been studied during development and cancer . Cell sen Flower . Here, wn Flower , whereasn Flower . We reasem cells . We foun \u2022Brain injury modulates neuronal fitness fingerprints in the adult brain\u2022DrosophilaComparison of neuronal fitness drives brain tissue replacement in \u2022De novo-generated cells are favored over damage-affected neurons Drosophila model of brain regeneration to show that impaired neurons are eliminated next to newly generated cells because they carry low-fitness marks. Such interactions between old and new tissues may be relevant for other regenerative processes.After injury, less functional neurons are contained within the brain. Here, Moreno et\u00a0al. use a  In many clinically relevant injuries, such as stroke or traumatic brain injury, impaired cells remain within an organ. In order to study how damaged brain tissue interacts and may be replaced by newly generated cells after injury, we subjected adult flies to penetrating traumatic brain injury, by lesioning the optic lobe (OL) unilaterally with a thin metal filament A and 1B.Traumatic brain injuries typically cause a variable extent of tissue damage. Neurons can persist in vulnerable states due to axon stretching and tearing, activating secondary injury processes , which are poorly understood . To studDrosophila to mark newly generated tissue [We have previously shown that neuronal apoptosis is detectable within the first hours after damage (AD) as a direct consequence of the mechanical impact . Extended tissue . Three dd tissue .The newly formed tissue observed 6\u00a0days after brain damage consisted mainly of newborn neurons I 20], w, w20], wMost apoptotic cells were found close (within three cell diameters) to newly generated cells 3\u00a0days and 6\u00a0days AD  J. In conThus, we have identified a burst of delayed cell death in injury-exposed brain tissue that is not caused by the primary mechanical insult but is associated with the onset of regenerative neurogenesis.TRE-gfp, a sensitive JNK pathway reporter 34].Moreover, Darwinian-like cell selection could play a role during liver regeneration in mice. An initial study reported a striking increase in apoptosis of host hepatocytes immediately adjacent to transplanted progenitor cells, which can repopulate the liver . It willWe therefore propose that comparison of cellular fitness between damaged and intact tissue may be a common mechanism during regeneration and relevant for stem cell-based replacement therapies after injury.fweReporter(yfp_gfp_rfp) [fweReporter (myc_HA_flag) [GMR-Gal4, fweReporter (yfp_gfp_rfp)/Cyo; MKRS/TM6b; elav-Gal4; Gal80ts; UASbskDN; UASpuc; TRE::gfp [UASlacz; 10xStat92E-DGFP [exp-lacz; Diap-lacz (a gift from B. Thompson), UAS-fweLoseB [UAS-fweubi [UAS-fweLoseA [RNAi flower (KK); RNAi flowerLoseA/B [w1118;\u00a0+; rnGal4, UASeiger, tubGal80ts [The following fly stocks were used: gfp_rfp) , fweRepoHA_flag)   [Flies were immobilized on a COe Tools) . Flies wubi (1:30) [Brains were prepared as described previously . The foli (1:30)  (in combUASlacz) and were corrected for multiple testing with Holm\u2019s method [First, an ANOVA model was fitted to log-transformed values of the apoptotic cell counts and validated via Tukey-Anscombe plot and QQ plot of the residuals. Subsequently, p values were calculated by comparison of experimental genotypes with the control genotype (s method .E.M. and C.R. conceived and performed the experiments. P.M. and Y.-F.M. repeated some of the experiments and controls. E.M. and C.R. wrote the paper."}
+{"text": "Streptococcus tigurinus is responsible for severe invasive infections such as infective endocarditis, spondylodiscitis and meningitis. As described, S. tigurinus isolates AZ_3aT and AZ_14 were highly virulent (HV phenotype) in an experimental model of infective endocarditis and showed enhanced adherence and invasion of human endothelial cells when compared to low virulent S. tigurinus isolate AZ_8 (LV phenotype). Here, we sought whether genetic determinants could explain the higher virulence of AZ_3aT and AZ_14 isolates. Several genetic determinants specific to the HV strains were identified through extensive comparative genomics amongst which some were thought to be highly relevant for the observed HV phenotype. These included i) an iron uptake and metabolism operon, ii) an ascorbate assimilation operon, iii) a newly acquired PI-2-like pilus islets described for the first time in S. tigurinus, iv) a hyaluronate metabolism operon, v) an Entner-Doudoroff pathway of carbohydrates metabolism, and vi) an alternate pathways for indole biosynthesis. We believe that the identified genomic features could largely explain the phenotype of high infectivity of the two HV S. tigurinus strains. Indeed, these features include determinants that could be involved at different stages of the disease such as survival of S. tigurinus in blood (iron uptake and ascorbate metabolism operons), initial attachment of bacterial pathogen to the damaged cardiac tissue and/or vegetation that formed on site (PI-2-like pilus islets), tissue invasion  and regulation of pathogenicity (indole biosynthesis pathway). Streptococcus tigurinus is a recently identified species belonging to the Streptococcus mitis group. It is a commensal of the human oral cavity [S. tigurinus might currently be underreported due to limited identification in a clinical routine laboratory [S. tigurinus because of the morphological resemblance to its closest related species, i.e., S. mitis, Streptococcus oralis, Streptococcus pneumoniae, Streptococcus pseudopneumoniae and Streptococcus infantis. Analysis of the 5\u2019-end of the 16S rRNA gene is mandatory for accurate identification of S. tigurinus since a significant sequence demarcation was demonstrated [l cavity , 2 and cl cavity \u20139. Compaboratory . Indeed,nstrated .S. tigurinus was found to be highly virulent in our rat model of experimental endocarditis [Staphylococcus aureus or enterococci but important variability was observed in the virulence capacity of different S. tigurinus strains [T and AZ_14 induced infective endocarditis in \u226580% of the rats, compared to the low virulent (LV) S. tigurinus isolate AZ_8 which produced infective endocarditis in only 56% of the animals [S. tigurinus AZ_8 in the experimental infective endocarditis model was previously classified as S. mitis and turned out as S. tigurinus after 16S rRNA gene analysis [carditis . Its cap strains . When in animals . Moreove animals . A well-analysis .S. tigurinus strains and performed comparative genomics analyses including additional previously sequenced S. tigurinus strains to seek whether genetic differences could potentially explain differences observed in strain\u2019s virulence phenotypes.In the present study, we performed de novo whole-genome sequencing of four S. tigurinus isolates AZ_3aT , AZ_8 (CCOS 678) and AZ_14 (CCOS 689) are clinical strains recovered from blood samples from patients diagnosed with infective endocarditis [S. tigurinus strain 859 was isolated from the nasopharynx of a children from South Africa [S. tigurinus strain by comparative genomics, the S. tigurinus strain ATCC15914 originally isolated from human throat [S. tigurinus strains 1366 , 2425 and 2426  were previously sequenced and were originally isolated from a patient with prosthetic joint infection [2 for 24 h.h Africa . In ordenfection . BacteriS. tigurinus strains AZ_8, AZ_14, ATCC15914 and 859 were purified using a Wizard Genomic DNA purification kit . Genomic DNA libraries from AZ_8, AZ_14, and 859 were prepared using 1\u03bcg of the purified genomic DNA and the TruSeq DNA LT Sample Preparation Kit according to the manufacturer\u2019s protocol . The resulting libraries were pooled into a single library for paired-end sequencing of 2x100-bp on the Illumina HiSeq 2500 using TruSeq PE Cluster Kit v3 (Cat. No. PE-401-3001) and TruSeq SBS Kit v3 (Cat. No. FC-401-3001). Data were processed using the Illumina Pipeline Software package v1.82 and aligned using Eland v2e. Assembly of paired-end reads was done with Edena [T, 1366, 2425, 2426 or full genome of Streptococcus oralis strain Uo5 (closest relative of S. tigurinus) and full annotations were obtained from NCBI . Full genome of Streptococcus oralis strain Uo5, which is the closest relative of S. tigurinus, was obtained from NCBI (accession number 331265438).Genomic DNA from th Edena . ATCC159S. tigurinus strains de novo sequenced in this study have been deposited in the DDBJ/EMBL/GenBank database under the following accession numbers: S. tigurinus AZ_8, LNVF00000000; S. tigurinus AZ_14, LNVG00000000; S. tigurinus 859, LNVH00000000; and S. tigurinus ATCC15914, PRJNA302887.The genome sequences of the four S. tigurinus strains and S. oralis Uo5, were identified by blasting each single protein sequence of a given strain to each single protein sequence of the eight other strains using BlastP with e-value threshold of 10\u221210. Corresponding nucleotide sequences of genes coding for core proteins present in single copies in all genomes were further used to build a multiple alignment with MAFFT-7.187 [http://doua.prabi.fr/software/njplot) [S. oralis Uo5 as outgroup. This tree was named Tree_9strains. A similar approach was used to generate a tree for the three strains of interest only, namely AZ_3aT, AZ_14 and AZ_8. In this case, the tree was rooted using AZ_8 as outgroup and was named Tree_3 strains.Fasta files of Open Reading Frames (ORFs) (both nucleotide and amino-acids sequences) from each genome were downloaded from NCBI and used by two in-house perl scripts (available upon request) to build a phylogenetic tree. Briefly, core proteins, i.e. proteins encoded on the genome of the eight FT-7.187 . Newick FT-7.187 . Finally/njplot)  to visuahttp://phobius.sbc.su.se) and Clustal Omega was used to perform alignment of gene products .The core proteome comparison of all strains was performed using the described script \u201cget_homologues.pl\u201d . In thisde novo sequenced in this study and no trimming was therefore needed. Reads assembled into 35, 38, and 14 contigs for strains AZ_8, AZ_14, and 859 respectively .More than 12 millions high quality 100-bp paired-end reads were obtained for each of the four strains that were ectively . PacBio ectively . Genome ectively . %GC conS. tigurinus strains and S. oralis Uo5 is presented in S. oralis Uo5 (S. tigurinus most closely relative) used as outgroup readily diverged from the S. tigurinus strains. Of note, clustering of strains 2425 and 2426 together with strain 1366 supported accuracy of the phylogenetic tree. Indeed, strains 2425 and 2426 were previously described as two highly similar small-colony variants derived from the parental wild-type strain 1366 [T and AZ_14 clustered together  to 98.8% . Similar to the observation made in the phylogenetic tree (see above), high homology between strains 1366, 2425, 2426 , the corresponding core proteomes were compared. As represented in After having identified some general trends specific to the HV strains through phylogenetic analysis using core genes and pairwise comparison of core proteomes we decided to focus on and investigate further genetic determinants acquired and/or maintained in both HV strains but absent from the LV strain. Indeed we thought that such determinants could represent good candidates to explain, at least in part, the observed phenotype of increased infectivity of HV strains in the experimental infective endocarditis model . CompariHyaluronate utilization: A cluster of 13 genes involved in hyaluronate utilization was identified in the two HV strains , unsaturated glucuronyl hydrolase (ugl), a deshydrogenase (kduD), an isomerase (kduI), hyaluronate-oligosaccharide-specific phosphotransferase system components (PTSa-d) and a repressor of the PTS system (regR) were present in this gene cluster. A very similar cluster was found in S. pneumoniae TIGR4  and kdgK  encoding an aldolase and a kinase involved in the Entner-Doudoroff pathway, respectively  coding for structural components of a ferric iron ABC transporter and two genes reg_RR and reg_SK coding for a response regulator and a sensor kinase of a two-component system associated with the transporter, respectively, were acquired by both HV strains . Of note, no further genes were found between both genes in AZ_8 as well as S. mitis NTCC 12261  was found in both HV strains but not in the LV strain  was truncated at N-terminal part in AZ_3aT removing a predicted VWA_2 domain, see below. The full length ORF could be retrieved from RAST annotation (862 Aa encoded by AZ3a_RAST_1017) . Of note, despite pitA is a shared gene name for both the present adhesin and AZ3a_ORF_18880 of the gene cluster involved in iron uptake and metabolism (see above), both proteins are indeed distinct with different functions.The first ORF with gene name ST_1017) . AZ3a_RAST_1017) . PhobiusST_1017) . Homologectively . MoreoveAZ3a_ORF_11620 and by AZ14_ORF_04300 in AZ_3aT and AZ_14, respectively. Both homologs shared 99% identity and 100% coverage. Further homologs harboring 99% identity and 100% coverage were also found in many strains of S. pneumoniae (not shown).The second ORF, annotated as S26 family signal peptidase, was encoded by AZ3a_ORF_11625 and by AZ14_ORF_04305 in AZ_3aT and AZ_14, respectively. Gene product homologs harbored 59% identity over 100% coverage  and one sodium-dependent transporter (data not shown), six genes involved in tryptophan synthesis from chorismate (described below) were lost in AZ_8.trpEB and a truncated version of trpB\u2014of a cluster encoding for enzymes involved in tryptophan synthesis from chorismate -glycerol phosphate which is a precursor of both indole and tryptophan through two different reactions catalyzed by the same enzymatic complex formed by TrpEa and TrpEb [Presence of nd TrpEb . Therefond TrpEb . It has nd TrpEb . AccordiS. tigurinus with a relevant alternative carbon source possibly enhancing survival in blood in which ascorbic acid is available as a powerful antioxidant [Our analysis identified an ascorbate metabolism pathway in both HV strains. To the best of our knowledge such pathway has not previously been linked to enhanced bacterial virulence. However, we believe that it could provide ioxidant . Indeed,S. aureus for isd genes [piuABCD present in all strains, the presence of pitACD encoding for an additional ferric ABC transporter allows HV strains to efficiently capture free iron in blood. It is known that concentration of free iron in blood usually accessible for bacteria is below the concentration required for bacterial survival [In the same manner and as described in sd genes , we hyposurvival . Howeversurvival \u201338, undesurvival . TherefoAZ3a_ORF_0110920 to AZ3a_ORF_11635 and AZ14_ORF_04295 to AZ14_ORF_04305 in AZ_3aT and AZ_14, respectively correspond to a PI-2-like pilus islets previously described in several streptococcal species among which S. pneumoniae, S. mitis and S. sanguinis [S. tigurinus AZ_3aT, AZ_14, ATCC15914 and 859 is very similar to the previously described PI-2 pilus islets. The gene product encoded by the first ORF harbor a VWA_2 domain at the N-terminus and a sortase processing LPXTG-like motif (VPETG) at C-terminus. This protein could therefore well represent a PitA homolog since PitA has been shown to harbor such domains in several S. pneumoniae strains [AZ3a_RAST_1017 and AZ14_ORF_04295 encode for a PitA homolog with adhesive function. Similarly the third ORF  encode for proteins harboring a non-canonical LPXTG-like motif (VTPTG). Regarding genomic localization and since VTPTG motifs have been reported in pilus backbone proteins PitB from several other streptococci [S. pyogenes [S. suis SipA homolog was found to be highly upregulated when the bacterium interacted with brain microvascular endothelial cells, suggesting a role in the infection process of this pathogen [S. tigurinus PI-2 pilus islets. Sortases are classified within families of classes A\u2013F [S. pneumoniae to eukaryotic cells [S. mitis [S. tigurinus.We believe that the gene clusters spanning anguinis . Indeed, strains . Moreove strains \u201344 and L strains . Therefoptococci , 44, it pyogenes  and verypathogen . Finallysses A\u2013F . These esses A\u2013F . Sortasesses A\u2013F  and sortsses A\u2013F , 52. Whisses A\u2013F . Taken tS. mitis . To the S. tigurinus HV strains AZ-3aT and AZ_14 but not present in the S. tigurinus LV strain AZ_8. We believe that the identified features could largely explain the phenotype of high virulence previously observed for both HV strains in an experimental model of infective endocarditis in rats. Indeed, these features include determinants that could be involved at different stages of the disease. First of all, determinants involved in iron uptake and ascorbate metabolism could directly promote survival of S. tigurinus in blood. Second, specific adhesins encoded on new PI-2-like islets could mediate initial attachment of bacterial pathogen to the damaged cardiac tissue and/or vegetation that formed on site. Third, newly acquired tissue-destroying enzymes , enzymes involved in carbohydrates metabolism (Entner-Doudoroff pathway) and associated transporters could act in concert to increase tissue invasion. In addition, some of these steps could well be coordinated within the pathogenic population through a specific indole based QS. Obviously, these hypotheses need experimental validations, but our list of genes potentially responsible for the increased infectivity of HV strains represents relevant genetic determinants to explore further. For instance, deletion mutants could be evaluated in our model of infective endocarditis. Moreover, if correlation between our findings and experimental results is demonstrated, new PCR assays targeting HV specific genes aiming at predicting the infectivity potential of any S. tigurinus isolate could well be developed.Using comparative genomics approaches, we were able to identify several genetic features acquired only by the two S1 FigT and AZ_14 but absent in the low virulent (LV) strain AZ_8. A similar cluster is found on S. pneumoniae TIGR4. Genes are represented by colored arrows pointing in the direction of transcription. Gene names are indicated.This cluster is found in the highly virulent (HV) strains AZ_3a(TIFF)Click here for additional data file.S2 FigT and AZ_14 but absent in the low virulent (LV) strain AZ_8. A similar gene cluster was found in S. mitis NCTC 12261 and S. pneumoniae TIGR 4. Genes are represented by colored arrows pointing in the direction of transcription. Gene names are indicated. The cluster is localized between a gene coding for a hypothetical protein (red arrow) and a tRNA leucine synthase.This cluster is found in highly virulent (HV) strains AZ_3a(TIFF)Click here for additional data file.S3 FigT and AZ_14 but absent in all other strains included in the present study. A similar gene cluster was found in S. pneumoniae TIGR 4. Genes are represented by colored arrows pointing in the direction of transcription. Gene names are indicated. The cluster is localized between a gene coding for sakacin (red arrow) and a transketolase (purple arrow).This cluster is found in highly virulent (HV) strains AZ_3a(TIFF)Click here for additional data file.S4 FigThe LPXTG-like sortase processing motifs (VPETG) are highlighted in bold and underscored. The 3 conserved residues (DTD) found in the MIDAS feature of the identified N-terminal vWA_2 domain (pfam 13529) are highlighted in bold. Transmembrane domains identified by Phobius are highlighted in bold and in italic at C-terminus.(TIFF)Click here for additional data file.S5 FigThe non-canonical LPXTG-like sortase processing motifs (VTPTG) are highlighted in bold and underscored. Transmembrane domains identified by Phobius are highlighted in bold and in italic at C-terminus.(TIFF)Click here for additional data file.S6 FigT and AZ_14 but absent in the low virulent (LV) strain AZ_8. A similar cluster is found in S. gordonii strain Challis substr. CH1. Genes are represented by colored arrows pointing in the direction of transcription. Gene names are indicated. The cluster is localized between msba coding for a lipid A permease and comC coding for a processing protease involved in competence. In AZ_8 only a single copy of trpEb and a truncated version of trpB are present.This cluster is found in highly virulent (HV) strains AZ_3a(TIFF)Click here for additional data file.S1 TableThis list has been obtained using the Family History by Dollo Parsimony tool of Count Software. Genomic regions of particular interest further investigated in the present study are highlighted in light grey.(DOCX)Click here for additional data file."}
+{"text": "Clostridium difficile contains many integrated and extrachromosomal genetic elements. In this study, we determined, annotated, and analyzed the complete genome of the C.\u00a0difficile bacteriophage phiCDIF1296T using single-molecule real-time sequencing technology. To our knowledge, this represents the largest genome (131\u00a0kb) of a temperate C.\u00a0difficile phage recognized so far.  The extrachromosomal phage genome sequence was detected during a whole-genome sequencing approach with the Clostridium difficile strain DSM 1296T.In this study, we determined the complete genome sequence of the bacteriophage phiCDIF1296T RSII . Assembly was performed using the RS_HGAP_Assembly.3 protocol included in SMRT Portal version 2.2.0 and yielded the complete chromosome of phage phiCDIF1296T. This sequence was circularized and adjusted to the putative small subunit of the terminase gene as position 1 (CDIF1296T_phi001) of the phage chromosome. A final genome quality of QV60 was determined during resequencing using the RS_BridgeMapper.1 protocol in SMRT Portal. Automated genome annotation was performed using Prokka , integrase (CDIF1296T_phi052), two putative transposases (CDIF1296T_phi054/148), repressors (CDIF1296T_phi048/051/154), antirepressors (CDIF1296T_phi022/129), and a large number of 12 putative transcriptional regulators (phiCDIF1296T_020/047/048/051/131/133/138/153/155/167/171/172).N-acetylmuramoyl-l-alanine amidase (CDIF1296T_phi042), glucosaminidase (CDIF1296T_phi043) and two putative holin-like proteins (CDIF1296T_phi044/055) with one and two transmembrane domains (class II holin), respectively (The gene cluster for host cell lysis consisted of one gene for an Genes for DNA replication encoded a putative Holliday junction resolvase (CDIF1296T_phi057), a DNA primase (CDIF1296T_phi071), a helicase (CDIF1296T_phi072), a nuclease (CDIF1296T_phi062), an Erf-like protein (CDIF1296T_phi074), a DNA ligase (CDIF1296T_phi115), and a DNA polymerase (CDIF1296T_phi084). Additionally, phage phiCDIF1296 harbored three genes coding for methylases that are probably involved in DNA modification (CDIF1296T_phi060/061/087). Furthermore, we identified genes that encode proteins for DNA metabolism, such as guanylate kinase (CDIF1296T_phi058) and dUTPase (CDIF1296T_phi056).Finally, we determined several additional interesting genes, e.g., for a putative anaerobic ribonucleoside-triphosphate reductase that might play a role in nucleotide metabolism (CDIF1296T_phi113), a putative antirestriction protein that might protect the phage genome against the host restriction system (CDIF1296T_phi166), and a Doc-like protein that might act as a prophage maintenance system killer protein (CDIF1296T_phi145) (CP011970. The version described in this paper is version CP011970.1.The complete genome is deposited at GenBank under the accession no."}
+{"text": "Clostridium difficile type strain DSM 1296T. A combination of single-molecule real-time (SMRT) and Illumina sequencing technology revealed the presence of one chromosome and two extrachromosomal elements, the bacteriophage phiCDIF1296T and a putative plasmid-like structure harboring genes of another bacteriophage.In this study, we sequenced the complete genome of the  Clostridium difficile infections have conspicuously increased in recent decades and are among the most prevalent and costly infectious disease worldwide. The bacterium was first described in 1935 as part of the intestinal flora of neonates (de novo the complete genome sequence of the C.\u00a0difficile type strain DSM 1296T (=ATCC 9689T) using a combination of single-molecule real-time (SMRT) and Illumina sequencing technology.neonates . In thisC.\u00a0difficile strain DSM 1296T was cultivated anaerobically in Wilkins-Chalgren Anaerobe Broth  at 37\u00b0C. Genomic DNA was extracted using the Genomic-tip 100/G kit , according to the instructions of the manufacturer, with the following modification: directly after 1\u00a0h of lysis with lysozyme, 0.5 M EDTA (pH\u00a08.0) was added to a final concentration of 20\u00a0mM, followed by incubation with proteinase K overnight.T was carried out on the PacBio RSII  using P5 chemistry. Genome assembly was performed with the RS_HGAP_Assembly.3 protocol included in SMRT Portal version 2.2.0, utilizing 56,696 postfiltered reads, with an average read length of 9,898\u00a0bp. Three contigs were obtained that represented three different replicons. Each contig was trimmed and circularized. In parallel, genome sequencing was carried out on a GAIIx Genome Analyzer  in a 112-bp paired-end single-indexed run, resulting in 3.8 million paired-end reads. Quality improvement of the final consensus sequence was performed with the Burrows-Wheeler Aligner (BWA) (Genome sequencing of strain DSM 1296er (BWA) .C.\u00a0difficile DSM 1296T has a size of 4,109,692\u00a0bp and contains 3,596 predicted coding sequences, 35 rRNAs, and 90 tRNAs. The G+C content is 28.8%. Further analysis of the chromosome revealed the presence of genes encoding the complete pathogenicity locus associated with toxin production (CDIF1296T_00820 to CDIF1296T_00825). In addition, the genes encoding a Wood-Ljungdahl pathway cluster (CDIF1296T_00886 to CDIF1296T_00900), a carbon monoxide dehydrogenase (CODH) cluster (CDIF1296T_00296 to CDIF1296T_00298), a ferredoxin:NAD+-oxidoreductase (RNF) complex (CDIF1296T_01211 to CDIF1296T_01216), formate dehydrogenases (CDIF1296T_00938/CDIF1296T_02282/CDIF1296T_03435), and hydrogenases (CDIF1296T_01059/CDIF1296T_01060/CDIF1296T_03372/CDIF1296T_03512 to CDIF1296T_03514) were detected (The chromosome of C.\u00a0difficile-infecting bacteriophage phiCDIF1296T (As part of the genome, two additional replicons were identified, including the recently published novel temperate DIF1296T  and a puCP011968 and CP011969. The versions described in this paper are versions CP011968.1 and CP011969.1.The nucleotide sequence has been deposited at GenBank under the accession numbers"}
+{"text": "A previously published Rasch-built activity and participation scale specifically designed for patients with myotonic dystrophy type 1 (DM1) was criticized for having been constructed in a relatively small cohort of patients and containing items too broadly phrased for DM1 patients, thus hampering its clinical use.C) through Rasch analyses using an expanded questionnaire containing 146 more simply phrased activity and participation inquiries completed by 340 patients with DM1.We report the results of the reconstructed Rasch-built DM1 activity and participation scale for clinical use (DM1-ActivC consisting of 25 items that showed good Rasch model fit, including construct convergent validity, items\u2019 weights and persons\u2019 locations reliability, and unidimensionality.Through stepwise investigation including data quality control, model fit, response category ordering, local dependency and item bias, we succeeded in reconstructing the DM1-ActivC scale has been reconstructed and fulfills all modern clinimetric requirements. Its use is recommended in future longitudinal trials in patients with DM1 to determine its responsiveness.The DM1-Activ At current stage, treatment of patients with myotonic dystrophy type 1 (DM1) is almost exclusively limited to symptomatic therapies with no therapeutic interventions to reverse of slow down the progression of the illness. However, since genetically based therapeutic treatments are emerging, solid outcome measures are needed to capture possible relevant clinical changes in patients with DM1 being exposed to these new therapies .Previously, we have described the first Rasch-built activity and participation measure specifically designed for patients with DM1 (DM1-Activ) , 4. The C), using a larger sample of patients with DM1 and a much broader pool of items representing the Activities and Participation component of the International Classification of Functioning, Disability and Health (ICF 2001) [Therefore, we have reconstructed the DM1-Activ for clinical use (DM1-ActivCF 2001) .Patients older than 18 years with genetically proven DM1 were recruited through the Dutch neuromuscular patients\u2019 association. The protocol was approved by the Medical Ethics Committee of the Maastricht University Medical Center. Written informed consent was obtained from all participants. A total of 340 patients with DM1 were included, fulfilling optimal sample size requirements for scale construction .C scale contained 146 items that provide information about activities and participation, phrased in a simplistic, short and unambiguous way. Response options were: unable to perform (0), able to perform, but with difficulty (1), and able to perform, without any difficulty (2) and were based on the previously determined discriminative ability of DM1 patients [The questionnaire was constructed as previously described . In addipatients . An itemC was completed by 340 patients. A random sample of 223 patients completed the questionnaire ~4 weeks later for test-retest reliability studies. The original 20-item DM1-Activ scale was also completed separately to provide convergent evidence for construct validity.The preliminary DM1-ActivC data were subjected to Rasch analyses to determine whether model expectations were met. Several aspects were addressed including fit statistics, ordered thresholds, local independency, differential item functioning (item bias: DIF) and unidimensionality. Item bias was checked on personal factors: age , gender, diagnosis phenotype , and degree of education  as was previously applied [Obtained preliminary DM1-Activ applied , 7, 8.C scale was determined by calculating the Person Separation Index (PSI). Moreover, test-retest reliability studies were performed to investigate consistency of item difficulty hierarchy and patient ability locations. Reliability was quantified by calculation of the intraclass correlation coefficient using ANOVA for group comparison and expressed as R2: the fraction of variance [C scale was tested by correlations with the original 20-item DM1-Activ scale, using the obtained Logits scores from both scales [Internal consistency reliability of the final DM1-Activvariance , 10. Theh scales .Further analyses were undertaken using Stata Statistical Software version 12.0 with Bonferroni corrections if needed and SigmaPlot Software .The basic characteristics of the 340 patients are presented in C showed overall misfit. The mean residual for items was -0.085 (SD 1.213) and for persons -0.278 (SD 1.361), indicating reasonable model fit. However, the item-trait chi-square probability was significant  420), indicating lack of invariance. No disordered thresholds were seen. A proportion of 0.14 (95% CI 0.11\u20130.17) of the t-tests performed fell outside the \u00b11.96 range, indicating multidimensionality.The preliminary 105-items DM1-ActivThe individual item fit statistics of 32 items demonstrated model misfit  and were removed one by one (73 items remaining).Local dependency was seen between many items\u2019 residuals (defined as Spearman\u2019s correlation coefficient \u22650.3). Starting with the highest residual correlations , the item of each residual correlating set showing the least face validity according to experts\u2019 opinion  or having over- or underdiscrimination on its category probability curve, was removed .One item showed item bias (uniform DIF) on age and diagnosis phenotype and two items had non-uniform DIF on diagnosis phenotype. All 3 items were removed. Hence, at this stage 25 items were kept. However, the item \u201cable to run\u201d demonstrated uniform DIF on age. This item was one of the most difficult activities to perform. Since we aimed to obtain a wide range of measurement with a proper targeting of patient locations by the items\u2019 thresholds, we decided to keep this item in the model. However, before splitting this item and creating 2 subsets (able to run for age <30 years versus able to run for ages \u226530 years), a test for unidimensionality was performed, since RUMM2030 software does not provide the opportunity to do this after splitting an item.C met all Rasch model\u2019s expectations  was the easiest item while \u201cable to run\u201d for patients \u226530 years (3.904 logits) was the most difficult item , thus providing a better targeting of patients. This was also reflected in a lower percentage of ceiling effect in DM1 patients (7.4% versus 8.6%) [C represent the whole spectrum of abilities of the preliminary 146 items. Item \u201cable to eat soup\u201d was initially among the 9 easiest items, whereas \u201cable to run\u201d was initially one of the most difficult items to perform of all 146 items. Therefore, we believe that the final 25 items of the DM1-ActivC represent a wide range of daily and social activities. Finally, for the construction of DM1-ActivC a larger number of patients was examined having elementary school as their highest educational degree . This implies that the final DM1-ActivC model findings represents more appropriately patients with DM1 having all levels of study degree , since the final 25-items did not show any item bias on personal factor educational level.Compared to the previously published 20-item DM1-Activ scale , the curus 8.6%) . In addiC and the original DM1-Activ, further studies are currently performed investigating the impact of DM1 from a more holistic point of view: from person factors that might influence scoring , amount of family support or number of family members affected, duration of illness) to the impact of impairments such as e.g. fatigue, sleep disturbances, pain, apathy, cognitive dysfunction, depression, cardiac involvement, and having a pacemaker or a defibrillator implanted or not, that might influence daily/social functioning as assessed with DM1-ActivC. These efforts are currently being performed at our center through an initiated national registry and follow up study, and as part of the ongoing international collaborative OPTIMISTIC longitudinal trial including examining the responsiveness of DM1-ActivC [C scoring needs to be determined in future studies. Third, a total of 52% of the original DM1-Activ findings could be explained by muscle weakness, suggesting that other factors contribute to activity limitations and participation restrictions. Of particular interest would also be to study the discriminatory ability of the DM1-ActivC using the generally applied muscular impairment rating scale staging [C. However, these studies were not part of the scope of the current paper. In the current study, the most practical available factors have been examined. Fourth, while daily and social activities are not primarily dependent on someone\u2019s health status , the difficulty to execute a specific daily task (i.e. the corresponding weight of an item) is certainly disease specific. Therefore, it is not surprising that the difficulty estimates of DM1-ActivC items differ from similar items in other neuromuscular Rasch-built measures like the ACTIVLIM (Rasch-built measure of activity limitations in children and adults with neuromuscular disorders), I-RODS  or R-Pact  [C [C. Finally, other factors like duration of complaints might be of influence in completing the DM1-ActivC items. In the current study, \u201cduration\u201d as a person factor could not be used, since the data were incomplete on this factor. However, in the original paper, no item bias on duration was seen [There are some methodological issues that should be addressed. First, despite the strong correlation between the DM1-Activ1-ActivC \u201317. Seco staging . We beliy scale) \u201321. Simiy scale) . Effortsy scale) \u201325. Fiftcale) [C . Throughwas seen .C scale fulfills all modern clinimetric requirements, and its use is recommended in future longitudinal trials in patients with DM1 to determine its responsiveness.In conclusion, the DM1-ActivS1 FigFor further instructions to translate the obtained ordinal scores to an interval (Logits) scores, please contact the authors.(PDF)Click here for additional data file.S1 AppendixC : r_ods001 = bend and pickup; r_ods002 = stand <15 minutes; r_ods003 = stand for hours; r_ods004 = stand on one leg; r_ods005 = stand up from sitting; r_ods006 = stand up from lying; r_ods007 = get out of bed; r_ods008 = stand up from squatting position; r_ods009 = kneel down; r_ods010 = sit down from standing; r_ods011 = lie down from standing; r_ods012 = walk indoor; r_ods013 = walk up 1 stair; r_ods014 = walk up 2 stairs; r_ods015 = walk up 3 stairs; r_ods016 = walk upstairs with a bag; r_ods017 = jump; r_ods018 = run; r_ods019 = walk outdoor <100 meters; r_ods020 = walk outdoor <1 kilometers; r_ods021 = walk outdoor >1 kilometers; r_ods022 = walk uphill; r_ods023 = walk in the woods; r_ods024 = walk in the dunes; r_ods025 = walk downstairs; r_ods026 = walk on even ground; r_ods027 = walk on uneven ground; r_ods028 = walk avoiding obstacles; r_ods029 = dance; r_ods030 = travel by public transport; r_ods031 = travel by train; r_ods038 = open/close the door; r_ods039 = turn a key in a lock; r_ods040 = open a door with a key; r_ods041 = open an upper window; r_ods042 = open a low window; r_ods043 = carry a tray; r_ods044 = serve coffee/tea on a tray; r_ods047 = put down a mug/glass; r_ods051 = hang up a coat; r_ods052 = lift heavy object (10 kilograms); r_ods053 = carry/put down a heavy object; r_ods054 = move a table; r_ods055 = move a chair; r_ods056 = pick up a small object; r_ods057 = handle small objects (e.g. a coin); r_ods058 = catch an object ; r_ods059 = throw an object ; r_ods060 = wash your upper body; r_ods061 = wash your lower body; r_ods062 = wash your face; r_ods063 = wash your entire body; r_ods064 = take a shower; r_ods066 = dry your body; r_ods067 = brush your teeth; r_ods068 = care your hair/body; r_ods069 = go to the toilet; r_ods071 = clip your finger nails; r_ods072 = clip your toe nails; r_ods076 = dress your upper body; r_ods077 = dress your lower body; r_ods078 = put on a sweater; r_ods079 = put on a T-shirt; r_ods080 = button shirt/blouse; r_ods081 = zip trousers; r_ods082 = put on a coat; r_ods083 = put on gloves; r_ods084 = put on shoes; r_ods085 = tie laces; r_ods086 = eat; r_ods087 = drink out of a mug/glass; r_ods088 = use a knife/fork(spoon); r_ods089 = eat soup; r_ods090 = peel a banana; r_ods091 = peel an apple/orange; r_ods092 = mop the floor; r_ods094 = vacuum; r_ods099 = clean the toilet; r_ods102 = put away the dishes; r_ods104 = put the laundry in the washing machine; r_ods108 = empty the dustbin; r_ods109 = put rubbish outside; r_ods110 = use a dustpan and brush; r_ods111 = do dusting; r_ods113 = make up the bed; r_ods114 = change sheets; r_ods115 = do the cooking; r_ods116 = make a sandwich; r_ods117 = slice vegetables; r_ods118 = make coffee/tea; r_ods119 = go to the supermarket; r_ods120 = do shopping; r_ods121 = carry shopping; r_ods124 = go to the general practitioner; r_ods125 = go to the hospital; r_ods128 = get money from the cashpoint; r_ods129 = fill in a form; r_ods136 = go to a party; r_ods137 = go out for dinner; r_ods139 = visit family/friend; r_ods140 = visit neighbors; r_ods141 = work on a computer; r_ods142 = read a book; r_ods143 = read a newspaper; r_ods144 = make a telephone call. Empty cells are missing data.Legend to data: Studynr = study number; gender ; agecat = age category ; diagnosis = diagnosis type ; studydegr = study degree . Description items preliminary DM1-Activ(XLS)Click here for additional data file."}
+{"text": "Current sequencing technology enables taxonomic profiling of microbial ecosystems at high resolution and depth by using the 16S rRNA gene as a phylogenetic marker. Taxonomic assignation of newly acquired data is based on sequence comparisons with comprehensive reference databases to find consensus taxonomy for representative sequences. Nevertheless, even with well-characterised ecosystems like the human intestinal microbiota it is challenging to assign genus and species level taxonomy to 16S rRNA amplicon reads. A part of the explanation may lie in the sheer size of the search space where competition from a multitude of highly similar sequences may not allow reliable assignation at low taxonomic levels. However, when studying a particular environment such as the human intestine, it can be argued that a reference database comprising only sequences that are native to the environment would be sufficient, effectively reducing the search space.We constructed a 16S rRNA gene database based on high-quality sequences specific for human intestinal microbiota, resulting in curated data set consisting of 2473 unique prokaryotic species-like groups and their taxonomic lineages, and compared its performance against the Greengenes and Silva databases. The results showed that regardless of used assignment algorithm, our database improved taxonomic assignation of 16S rRNA sequencing data by enabling significantly higher species and genus level assignation rate while preserving taxonomic diversity and demanding less computational resources.The curated human intestinal 16S rRNA gene taxonomic database of about 2500 species-like groups described here provides a practical solution for significantly improved taxonomic assignment for phylogenetic studies of the human intestinal microbiota.The online version of this article (doi:10.1186/s12864-015-2265-y) contains supplementary material, which is available to authorized users. As the most genetically diverse and functionally complex microbial ecosystem of the human body the intestinal microbiota has become one of the major areas of interest in microbial ecology . In partThe bacterial and archaeal 16S small subunit ribosomal RNA (16S rRNA) gene has been established as the most widely used phylogenetic marker due to its conserved and variable regions and universal presence in prokaryotes. By sequencing the pool of 16S rRNA genes, community composition can be investigated in a comprehensive and rapid manner by high-throughput sequencing platforms harbouring the capacity for millions of reads per single run , 12. As While being highly successful at gathering data, the high-throughput technologies also present challenges for data analysis by requiring sophisticated computational methods not only in correcting technical artifacts but also for organizing the output and extraction of biologically meaningful features. A crucial step in deciphering 16S rRNA reads data is the taxonomic annotation of the discovered sequences. This holds true especially because current sequencing technologies typically cover only a part of the 16S rRNA gene, the large number of reference sequences and limited resolution at genus and species levels , 18. TaxWe hypothesized that by reducing the size of the reference database to encompass only the sequences innate to the environment under study would lead to improved taxonomic assignations at lower taxonomic levels due to less competition among targets. In this respect, the human intestinal microbiota presents an advantageous model system because it is already well characterized by sequencing , 26 whilde novo OTUs. In total the database contained 2473 species-like clusters  datasets like in this study. For example, the heuristic OTU clustering algorithm Uclust applied here to construct HITdb is slightly less robust than the UPGMA method ) AND (\u201cbacteria\u201d[porgn] OR \u201carchaea\u201d[porgn]) AND 1000:2000[SLEN]. The extracted sequences were matched against the Greengenes 13_5 [usearch_global. The matched sequences were extracted from both databases and subjected to chimeric sequence removal by UCHIME v. 7.0.1001  using the 16S reference database available at http://drive5.com/uchime/gold.fa [de novo. At minimum two sequences were required for each de novo OTU. The OTU clustering was performed at 97\u00a0% identity threshold in Qiime v. 1.8.0 [pick_open_reference_otus.py with parameters suppress_taxonomy_assignment, min_otu_size\u2009=\u20092, prefilter_percent_id\u2009=\u20090.0, percent_subsample\u2009=\u20090.1 and suppress_align_and_tree. Next, the representative sequences of OTU clusters were matched back to the reference species\u2019 sequences using Usearch v. 7.0.1001 command usearch_global with parameters id\u2009=\u20090.5 and maxhits\u2009=\u20091. OTUs having a match over 97\u00a0% similar to any of the cultivable species were removed (i.e. collapsed with the corresponding species). Furthermore, for each OTU, the nearest cultivable species was determined from the sequence match results. The final sequence content of HITdb consisted of representative sequences of the processed de novo OTUs and cultivable species.To obtain a comprehensive set of near full-length 16S sequences originating from human intestinal microbiota a search was performed against the NCBI Genbank nucleotide database using the command nes 13_5  and Silvnes 13_5  .The HITdb bacterial and archaeal sequences were separately aligned using Muscle v. 3.8.31 with default settings . The aliFastTree . The treThe 16S sequences of 953 cultivable human intestinal bacterial species were aligned to 518R  and 338Rassign_taxonomy.py, make_otu_table.py, summarize_taxa_through_plots.py) [Both biological and synthetic 16S reads were taxonomically assigned using in-built functions of Qiime v. 1.8.0 (lots.py)  with deflots.py)  also HITlots.py)  and Mothlots.py)  were useTwo sets of fecal samples obtained from children were sequenced for evaluating the HITdb performance with real data. Sample collection and DNA extraction were performed as described before , 49. Foruchime_ref; default parameters) with 16S reference database available at http://drive5.com/uchime/gold.fa. The non-chimeric reads were length filtered to exclude reads shorter than 500\u00a0nt. Thereafter the read numbers were rarefied by randomly sampling the lowest common read number (4246) from each sample using the Biostrings library [fastq_mergepairs [fastq_filter with parameters fastq_truncqual\u2009=\u200910 and fastq_maxee\u2009=\u20090.75. The merged and filtered Illumina reads were rarefied to 13,303 reads per sample.The raw pyrosequencing reads were subjected to reference-based chimera filtering using UCHIME v. 7.0.1001  (command library . The reargepairs  with parhttp://sra.dnanexus.com/). The SRA sample ID codes are given in Additional file 16S data of 192 Human Microbiome Project fecal samples were obtained from Sequence Read Archive (http://www.hmpdacc.org/HMSMCP/). The samples with the same SRS codes as in 16S HMP data were selected for taxonomic comparison. For HITdb, OTUs were excluded from comparative analysis at species level to make comparisons equal.Metagenomic, taxonomically profiled data from HMP were obtained from HMSMCP - Shotgun MetaPHlAn Community Profiling  to lower numbers at a decrement of 10,000, and the mean number of OTUs over 5000 draws was calculated for each sampling. Quantiles for OTU numbers in each sample of 5000 draws was calculated for probabilities 0.95 and 0.05.To estimate the sampling distribution of numbers of assigned taxa in synthetic reads data, the results of taxonomic assignment were bootstrapped 1000 times for each taxonomic level and employed database/assignment algorithm combination at that level. The proportion of present vs. absent taxa was calculated for each bootstrap sampling iteration.To compare absolute and relative numbers of assigned taxa between databases in 454 and Illumina sequencing data sets, paired two-way Wilcoxon signed rank test was performed. All analyses were performed in R software v. 3.1.1 .https://github.com/microbiome/HITdb.git.HITdb is available in GitHub at https://github.com/microbiome/HITdb/archive/master.zip.For direct download, use The contained README file provides instructions and other information."}
+{"text": "The channel\u2013blue hybrid also provides an excellent model to investigate molecular mechanisms of environment-dependent heterosis. However, transcriptome and methylome studies suffered from low alignment rates to the channel catfish genome due to divergence, and the genome resources for blue catfish are not publicly available.The blue catfish is of great value in aquaculture and recreational fisheries. The FThe blue catfish genome assembly is 841.86 Mbp in length with excellent continuity  and completeness (98.6% Eukaryota and 97.0% Actinopterygii BUSCO). A total of 30,971 protein-coding genes were predicted, of which 21,781 were supported by RNA sequencing evidence. Phylogenomic analyses revealed that it diverged from channel catfish approximately 9 million years ago with 15.7 million fixed nucleotide differences. The within-species single-nucleotide polymorphism (SNP) density is 0.32% between the most aquaculturally important blue catfish strains (D&B and Rio Grande). Gene family analysis discovered significant expansion of immune-related families in the blue catfish lineage, which may contribute to disease resistance in blue catfish.We reported the first high-quality, chromosome-level assembly of the blue catfish genome, which provides the necessary genomic tool kit for transcriptome and methylome analysis, SNP discovery and marker-assisted selection, gene editing and genome engineering, and reproductive enhancement of the blue catfish and hybrid catfish. Ictalurus furcatus , is an important aquaculture species in the United States, which is native to the Mississippi River basin and along the Atlantic and Gulf coast slopes [Ictaluruspunctatus \u2640 \u00d7 I. furcatus \u2642 (C \u00d7 B), constitutes more than 50% of the total harvest [Catfish is the largest segment of US aquaculture , and catt slopes . The hyb harvest , 5.1 hybrid (C \u00d7 B) is superior in many production traits, including faster growth rate [US catfish production peaked in 2003 at around 300 million kg. However, it has been declining since then due to increased feed and energy cost, low catfish market price, and competition from imports, notably Asian catfish , 7. Intewth rate , improvewth rate , 11, morwth rate , better wth rate , diseasewth rate , and enhwth rate . Collectwth rate , 17. In wth rate , and yet3 or less [1 hybrids [1 hybrids to determine how gene regulation in hybrid catfish was altered. However, only \u223c60% of the blue catfish reads can be aligned to the channel catfish genome [In channel\u2013blue catfish hybrids, only C \u00d7 B hybrids demonstrated heterobeltiosis characteristics , and the or less . The asy or less . Transgr hybrids , may con hybrids . To undeh genome  due to sEdwardsiella ictaluri, the pathogen for enteric septicemia of catfish [Aeromonas disease than C \u00d7 B hybrids, whose mortality (32%) [Aeromonas hydrophila. Understanding the blue catfish genome will facilitate the selection of the disease-resistant alleles from blue catfish for superior hybrid catfish breeds. As a cost-effective approach, marker-assisted selection (MAS) has been applied to select superior breeders for traits of interest, which relies on the selection of the best representative single-nucleotide polymorphisms (SNPs) from genome-wide association study (GWAS) peaks. Public genome assembly and annotation are already available for channel catfish [Using channel catfish \u00d7 blue catfish hybrid crosses and backcrosses, previous research identified major genetic loci responsible for the resistance of 3 economically important catfish diseases . For cer catfish , 29, whe catfish . Blue caty 32%) , 31 was % , 31 waty (32%) ) under t catfish , 34, and catfish , 36. A h1 hybrids have identical nuclear genome configurations (29 chromosomes from the blue catfish and 29 from the channel catfish) [in vitro [In addition to transcriptomic and genomic analyses, a blue catfish reference genome will also enable epigenomic investigations, which is critical for studying the heterosis and reproductive biology of the hybrid catfish. To explain the phenotypic differences between B \u00d7 C and C \u00d7 B hybrids, an epigenetic component must be considered because the Fcatfish) . As a kecatfish) , DNA metin vitro . Unlike in vitro , 40. Sinin vitro , but subin vitro . Thus, ain vitro , and it in vitro . To deteI. furcatus) using PacBio long-read sequencing. This high-quality, high-continuity assembly will allow researchers to better investigate the genomic underpinnings of production phenotypes. The annotation of the blue catfish genome makes it possible to conduct comprehensive functional genomics studies. The blue catfish genome resource will provide a solid molecular basis for investigating the mechanism of heterosis in the C \u00d7 B hybrids, as well as improving the genetic potential for commercial production by genetic enhancement programs.To fill the gap in the catfish genomic toolkit, in this study, we reported the first genome assembly of blue catfish  were obtained from the brood stock ponds at the Auburn University Fish Genetics Research Unit , including 2 blue catfish  from the D&B strain and 2 blue catfish  from Rio Grande strain. All the fish were anesthetized using 100\u00a0mg/L buffered MS-222 , and blood samples were collected using a syringe from the caudal vasculature and immediately put into the lithium heparin\u2013containing blood collection tubes . After blood collection, the 4 fish were temporarily reared in an indoor tank with dissolved oxygen level >8\u00a0mg/L, water temperature 21\u201322.5\u00b0C, and pH 6.8\u20137.0, for them to recover for a few days before releasing back into the research pond. All experimental animal protocols, including animal care and tissue sample collections, were approved by the Auburn University Institutional Animal Care and Use Committee (IACUC) under PRN# 2019\u20133520.\u00ae HMW DNA Extraction Kit for Cells & Blood kit  following the manufacturer's protocol. A total of 20 \u03bcL blood sample was used as input for each extraction. The concentration of gDNA was determined by a Qubit 3.0 Fluorometer instrument . The gDNA integrity and size distribution were checked by an Agilent TapeStation 4200 .High molecular weight (HMW) genomic DNA (gDNA) was extracted from 1 female D&B, 1 male D&B, 1 female Rio Grande, and 1 male Rio Grande blue catfish blood sample  using thRRID:SCR_017990) at the HudsonAlpha Genome Sequencing Center .PacBio CCS (circular consensus sequencing) library was constructed on 10\u00a0\u03bcg female D&B blue catfish HMW gDNA sheared into 20-kb fragments, using the SMRTbell Template Prep Kit v2 following the CCS HiFi library protocols . The PacBio library was prepared and sequenced on a PacBio Sequel II System \u00a0 at Novogene .Four 10\u00d7 Genomics linked-read libraries  [k-mer counting using Jellyfish version 2.3.0 \u00a0 [RRID:SCR_017014) [k-mer frequency distributions.Illumina sequencing reads were used to estimate the genome size of _011848) , with th_005491)  with par_017014)  based onRRID:SCR_021069) [De novo assemblies of the 10\u00d7 Genomics linked reads were performed using Supernova version 2.1.1 \u00a0 with default parameters [RRID:SCR_016965) [After removing the PacBio sequencing adapters and primers, the CCS HiFi reads were assembled to blue catfish contigs using hifiasm version 0.13   with deframeters . The quirameters  was usedrameters , and nonRRID:SCR_014731) [Based on previous cytogenetic studies, blue catfish have the same number of chromosomes as the channel catfish (2\u00a0N = 58), and the blue and channel catfish chromosomes cannot be distinguished in the karyotyping results . To asse_014731) . Potenti_014731) , and no RRID:SCR_015008) [I. punctatus) reference genome [Pangasianodon hypophthalmus, also known as striped catfish) genome assembled recently [The final genome assembly statistics Table\u00a0 were det_015008)  and compe genome , 67 and recently . Ortholorecently , and theRRID:SCR_014583) [RRID:SCR_010910) [To assess the quality of the blue catfish reference genome assembly, we compared blue catfish transcriptome and DNA methylome data alignment rate to channel catfish reference genome versus this blue catfish assembly. For RNA-seq, adult liver transcriptome data from our previous research were used with NCBI GEO (Gene Expression Omnibus) databases, accession number GSE186603 . For DNA_014583)  and trim_014583) , with th_010910) , with paRRID:SCR_015027) [I. punctatus) genome assembly [P. hypophthalmus) genome [RRID:SCR_012954) [To compare the quality of blue catfish genome with related species genome, RepeatModeler version 2.0.1 \u00a0  was perfassembly , and tra) genome . The int_012954) .RRID:SCR_018550) [De novo SNP calling was performed in the LongRanger pipeline v2.1.6 using UnifiedGenotyper in GATK version 3.6  [De novo SNP calling was performed in the LongRanger pipeline using GATK version 3.4 with the default parameters [RRID:SCR_005227) [A total of 18.6 million PacBio CCS HiFi reads generated from the blue catfish D&B strain were aligned to the channel catfish reference genome version 1.2  using Mi_018550) , 77. De _001876)  with \u201c-s_001876) . De novorameters . The ins_005227) . To obtaab initio, RNA-seq-based, and homology-based approaches for gene prediction in the repeat-masked assembly. Trimmed RNA-seq reads were mapped to the blue catfish genome assembly with Tophat version 2.1.1 [RRID:SCR_014597) [de novo transcript contig assembly was performed using Trinity version 2.4.0 \u00a0 [RRID:SCR_005309) [ab initio gene prediction algorithms with protein and transcriptome evidence by EST2GENOME and PROTEIN2GENOME procedures in MAKER. The RNA-seq GFF3 file and transcript assembly were provided as expressed sequence tag (EST) evidence, and annotated protein sequences of teleost species in the OrthoDB database version 9.1 were utilized as homology evidence [RRID:SCR_002127) [RRID:SCR_008417) [RRID:SCR_017646, version 1.8) [RRID:SCR_011919) [To annotate the protein-coding genes in the blue catfish genome, we exploited on 2.1.1 , and tra_014597) . In addi_013048) . The blu_005309) . Gene moevidence . The ini_002127)  and the _008417) , 87 gene_008417) , and bluion 1.8) , based o_011919) .RRID:SCR_007891)/INFERNAL version 1.1.4 \u00a0 [RRID:SCR_010835) implemented in the MAKER pipeline [Noncoding RNA genes were predicted by the Rfam  . The trapipeline . For 28SIn silico PCR hits less than or greater than 71\u00a0bp or with indels in them were excluded. Channel catfish and blue catfish alleles were determined for the SNP position in each probe. Density patterns of channel\u2013blue SNPs covered by the catfish 690\u00a0K array were plotted across each chromosome using the CMplot package in R [The widely applied catfish 690\u00a0K SNP array using Affymetrix Axiom technology  was evalage in R .RRID:SCR_010519) [RRID:SCR_017650) [RRID:SCR_011798) [To compare the genome assembly in blue catfish and channel catfish, the chromosome ideogram was drawn according to a previous karyotyping study . The blu_010519) . Unique _017650) , a Pytho_011798) .RRID:SCR_011980) [I. punctatus), Atlantic herring (Clupea harengus), zebrafish (Danio rerio), northern pike (Esox lucius), large yellow croaker (Larimichthys crocea), spotted gar (Lepisosteus oculatus), Nile tilapia (Oreochromis niloticus), guppy (Poecilia reticulata), greater amberjack (Seriola dumerili), and pufferfish (Takifugu rubripes). A total of 5,269 single-copy 1:1 orthologs among these species were identified. Protein sequences were aligned using MAFFT version 7.407  [RRID:SCR_017254) [RRID:SCR_008515) [To investigate the phylogenetic relationship between blue catfish and other Actinopterygii fish, 10 species were selected from 52 Actinopterygii species in OrthoDB version 10.1  , includi_011811)  and conc_017254)  was used_008515) .RRID:SCR_009457) [\u20135. Gene family clusters were identified using OrthoMCL (version 2.0.9) [RRID:SCR_021161) [I. punctatus and Danio rerio, 142 million years ago) and 2 constraining time points  from the TimeTree  [RRID:SCR_005983) [Pvalue less than 0.05. The genes in significantly expanded gene families were annotated by orthology in other species from OrthoDB version 10.1 or the eggNOG-mapper version 2.1.7  [The gene models from the blue catfish and 10 other Actinopterygii species in the phylogenomics analysis were obtained from this assembly and OrthoDB version 10.1 , respect_009457) , with ann 2.0.9) . The div_021161) . The pen_021162) . Based o_005983)  was used_021165) .I. furcatus is 839,021,413\u00a0bp, based on the k-mer distribution and depth in the Illumina short-read data generated in this study, and the inferred overall rate of heterozygosity is 0.597%. A total of 316.9 Gb PacBio raw reads were generated from the blood DNA of a single female blue catfish (D&B strain). Genome assembly was performed using 21.3 Gb PacBio CCS HiFi reads, and a total of 563 contigs were assembled, which was much fewer than the long-read assemblies of channel catfish and tra catfish genomes (Table\u00a01 backcrosses in 2016 [n = 11), the channel\u2013blue linkage markers were sufficient for the chromosomal assembly. The final assembly consisted of 29 chromosomes, a circularized mitochondrial genome, and 241 unplaced contigs. The final genome size was 841,864,377\u00a0bp, with a scaffold N50 of 28.24 Mb . The completeness score against Actinopterygii BUSCO was 97.0%, which was slightly higher than the channel catfish (95.0%) and tra catfish . Homology-based GeMoMa algorithm identified 20,460 protein-coding genes based on channel catfish gene models in Ensembl version 99. These gene models were merged based on BLAT results to the blue catfish genome assembly, resulting in 33,677 predicted protein-coding genes. Among these gene models, 30,971 (92.0%) are complete with a start codon and a stop codon. The average coding region length was 1,195\u00a0bp, and the mean number of coding exons per gene model was 7.2. There were a total of 21,781 genes with RNA-seq reads aligned to the gene region , and 21,A total of 6,192 tRNAs were identified using tRNA-scan Table\u00a0. Among tTeleost species underwent a fish-specific ancient whole-genome duplication (WGD) event during evolution . To inveComparative genomic analysis was performed using the whole-genome alignment tool Mauve , and blue\u2013channel pairwise chromosomal alignment revealed large syntenic blocks between homologous chromosomes Fig.\u00a0. For exade novo SNP calling using GATK, we identified a total of 1,433,465 SNP positions between the D&B and the Rio Grande strains. Among these, 607,059 were fixed differences between D&B and Rio Grande, and 826,406 were segregating within the Rio Grande strain. We estimated that the intraspecific SNP density between these 2 strains was 0.0032. The assembled and circularized D&B mitochondrial (MT) genome is 16,499\u00a0bp in length, which is the same length as a previously assembled blue catfish MT genome (NCBI GenBank accession number NC_028\u00a0151). We identified 1 fixed nucleotide difference between our D&B MT genome assembly and NC_028\u00a0151 (C12307T), which is a synonymous substitution at the third position in a codon encoding Phe. We assembled the first MT genome in the Rio Grande strain, and the circularized genome size is also 16,499\u00a0bp. There are 53 substitutions between D&B and Rio Grande , resulting in a sequence divergence of 0.0032 in the entire genome and 0.0026 in the genic region . The following classes of repeat accounted for more than 1% of the blue catfish genome: LINEs (3.0%), Gypsy/DIRS1 elements (2.3%), L2/CR1/Rex clade (1.9%), and simple repeats (3.3%).Repetitive sequence annotation identified that 47% (395.5 Mb) of the blue catfish genome belongs to repetitive regions . The phylogenomic analysis provides highly supported internal branches with 100 bootstrap values  and channel catfish (I. punctatus) occurred approximately 9 million years ago, according to 4,698 single-copy ortholog genes in these 11 species , such as reverse transcriptases and transposases, are the second-largest category of rapidly expanded families.To reveal the gene family evolution among blue catfish and 10 other fish species, divergence times and gene family expansion/contraction were determined for each species (see Methods). Phylogenomic analysis indicated that the divergence of blue catfish  and 10\u00d7 Genomics linked-read technologies. The final assembly is chromosome-level, telomere-to-telomere for many chromosomes, with scaffold N50 of 28.2 Mbp and contig N50 of 8.6 Mbp. The BUSCO completeness score is 98.4% when assessed using Eukaryota BUSCO genes and 97.0% using Actinopterygii BUSCOs. The assembly statistics data indicate that our assembly is excellent in terms of both genome continuity and completeness.Previously, the channel catfish genome was used as the reference for blue catfish sperm RNA-seq and DNA methylome analyses, which had significantly lower mapping rates, and many critical blue catfish genes were not covered due to higher divergence. Our novel blue catfish assembly dramatically improved the sequencing alignment rate for RNA-seq, Methyl-seq, and genome resequencing data in blue catfish. Using computational prediction and RNA-seq evidence-based approaches, we identified a total of 33,686 protein-coding genes. Of these, over 20,000 genes are with RNA-seq and blue catfish EST support. The number of gene models is comparable to the channel catfish annotation , which iThe phenomenon of heterosis was first reported by Charles Darwin  and has Hemibagrus wyckioides) [Tachysurus fulvidraco) [Bagarius yarrelli) in Sisoridae [Ictalurus punctatus) and black bullhead catfish  in the family of Ictaluridae. Our blue catfish genome will provide an invaluable resource to investigate molecular phylogeny and comparative analysis in Siluriformes.In the present study, we estimated that blue catfish diverged from channel catfish approximately 9 million years ago, based on 1:1 single-copy orthologs. The estimate is significantly more recent than a previous prediction (16.6 million years) based on the divergence of the cytochrome b gene alone , which mkioides)  and yellvidraco)  in the fisoridae , 12 Pangisoridae , 3 Clariisoridae , and chaAlthough blue catfish and channel catfish have morphologically indistinguishable chromosomes with essentially identical Giemsa banding patterns , 95, in Interestingly, we identified blue catfish lineage-specific gene family expansions. It is not surprising that transposases and reverse transcriptases are among the rapidly expanding family because of the active TE (transposable elements) turnover. Genes with immune-related functions account for 40% of the known expanded families, including T-cell receptor delta, lectin, complement control proteins, glucocorticoid receptor, chemokine interleukin 8, CD225/Dispanin, and others. This is consistent with our previous findings that the blue catfish had the highest immune activity compared to the channel and hybrid catfish at 10 months of age , and the1 hybrid catfish can reduce loss from 40% to 20% by carrying disease-resistant alleles from the blue catfish genome. Our research team, along with other researchers, has identified the genetic loci responsible for the resistance of 3 major catfish diseases, Enteric Septicemia of Catfish (ESC), columnaris, and Aeromonas diseases [2 and F1 backcrosses would be an ideal and effective approach to select superior breeders for traits of interest. Choosing the best representative blue\u2013channel SNPs from the GWAS peaks requires a high-quality blue catfish genome due to the following reasons. First, equal PCR amplification efficiency or probe affinity is ideal for the SNP typing assays, and sequence information is needed from both channel and blue genomes for proper primer design. Second, the presence of paralogous sequences will result in spurious SNP calls, and the blue catfish genome is needed to exclude these positions. Last but not least, historical whole-genome duplications in fish genomes further complicate accurate SNP genotyping [Pathogenic infection disease is the number one cause of catfish production loss. Paternal genetic contributions from blue catfish are essential for improving industry-relevant traits , and F1 diseases , and in notyping . Our repAdditional genetic enhancement of the hybrid catfish is essential for better profitability and sustainability. To further improve disease resistance through genomic techniques, we must understand the blue catfish genome in single-base pair resolution. With the new blue catfish assembly, we identified 15 million fixed differences between blue and channel catfish, with a density of 18.7 SNPs per Kb. This is higher than the previous estimation from the blue EST database (13 to 15 SNPs per Kb) , which iThe D&B blue catfish strain was widely used in commercial aquaculture, which was obtained originally from rivers in Arkansas, Mississippi, and Texas . D&B wasIctalurus furcatus has been deposited at NCBI under Assembly accession number JAJOLW000000000 and project ID PRJNA785621. Pacbio raw sequencing data have been deposited at NCBI SRA (Sequence Read Archive) under accession number SRR18963096. Illumina sequencing data of the 10\u00d7 Genomics libraries have been deposited at NCBI SRA under accession numbers SRR18966193, SRR18966194, SRR18966195, and SRR18966196. RNA-seq data were deposited at NCBI with accession numbers SRR16609847, SRR16609846, SRR18989496, and SRR18989495. The mitochondrial genome of the blue catfish D&B strain is submitted to NCBI GenBank under accession number ON022108. The mitochondrial genome of the blue catfish Rio Grande strain is submitted to NCBI GenBank under accession number ON022107. All additional supporting data and materials are available in the\u00a0GigaScience\u00a0GigaDB database [The draft genome assembly of database .Supplementary Fig. S1. Circos plot showing paralogous gene pairs in the blue catfish genome.Supplementary Fig. S2. Synteny alignments of blue catfish and channel catfish chromosomes based on DNA sequence similarity.Supplementary Table S1. Summary of PacBio and Illumina (10\u00d7 Genomics) sequencing data generated for blue catfish genome assembly.Supplementary Table S2. Chromosomal locations of telomeric regions in the blue catfish genome.Supplementary Table S3. Chromosomal locations of telomeric regions in the channel catfish genome.Supplementary Table S4. RNA sequencing data yield, quality control, and alignment statistics to channel catfish and blue catfish genomes.Supplementary Table S5. Summary nucleotide substitutions in the mitochondrial genome between blue catfish D&B and Rio Grande strains.Supplementary Table S6. Summary nucleotide substitutions in the mitochondrial genome between blue catfish and channel catfish.Supplementary Table S7. Summary of gene family expansion and contraction results.Supplementary Table S8. List of gene families underwent rapid expansion in blue catfish.Supplementary Data S1. Locations of 1,800 linkage markers of Ictalurus punctatus \u00d7 Ictalurus furcatus crosses in the channel catfish and blue catfish genomes.Supplementary Data S2. Annotation of tRNA genes in blue catfish genome.Supplementary Data S3. Annotation of rRNA gene clusters and 5S rRNA genes in blue catfish genome.Supplementary Data S4. Annotation of snRNA genes in blue catfish genome.Supplementary Data S5. Annotation of snoRNA genes in blue catfish genome.Supplementary Data S6. Annotation of miRNA genes in blue catfish genome.Supplementary Data S7. List of genes of significant expansion gene family in blue catfish and their annotation from orthoDB and eggNOG-mapper.giac070_GIGA-D-22-00096_Original_SubmissionClick here for additional data file.giac070_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giac070_Reviewer_1_Report_Original_SubmissionMikhail Ozerov -- 5/23/2022 ReviewedClick here for additional data file.giac070_Reviewer_2_Report_Original_SubmissionJie Mei -- 5/23/2022 ReviewedClick here for additional data file.giac070_Reviewer_2_Report_Revision_1Jie Mei -- 6/2/2022 ReviewedClick here for additional data file.giac070_Supplemental_FilesClick here for additional data file.This project is supported by the USDA National Institute of Food and Agriculture Hatch project 1018100 and an Alabama Agriculture Experiment Station (AAES) Agriculture Research Enhancement, Exploration, and Development (AgR-SEED) award. X.W. is supported by the National Science Foundation EPSCoR RII Track-4 award (OIA1928770) and a laboratory startup fund from Auburn University College Veterinary Medicine. H.W. is supported by the Auburn University Presidential Graduate Research Fellowship, College of Veterinary Medicine Dean's Fellowship, and the China Scholarship Council.The authors declare no competing interests.BLAST: Basic Local Alignment Search Tool; bp: base pair; BUSCO: Benchmarking Universal Single-Copy Orthologs; CCS: circular consensus sequencing; DNA: deoxyribonucleic acid; EM-seq: Enzymatic Methyl-Seq; ESC: Enteric Septicemia of Catfish; EST: Expressed sequence tag; GATK: Genome Analysis Tool Kit; Gb: gigabase pairs; GBS: Genotyping-by-sequencing; gDNA: genomic DNA; GEO: Gene Expression Omnibus; GWAS: Genome-Wide Association Study; HiFi: high fidelity; HMW: High molecular weight; IACUC: Institutional Animal Care and Use Committee; Kb: kilobase pairs; LINE: Long Interspersed Nuclear Element; MAS: Marker-Assisted Selection; Mb: megabase pairs; miRNA: microRNA; ML: maximum likelihood ; MT: mitochondria; NCBI: National Center for Biotechnology Information; PacBio: Pacific Biosciences; PCR: polymerase chain reaction; PL: penalized likelihood; QTL: Quantitative trait locus; RNA: Ribonucleic acid; RNA-seq: RNA sequencing; rRNA: Ribosomal RNA; snoRNA: Small nucleolar RNA; SNP: single-nucleotide polymorphism; snRNA: Small nuclear RNA; TRIP: Telomeric Repeats Identification Pipeline; TRMs: telomeric repeat motifs; tRNA: Transfer RNA; UCSC: University of California, Santa Cruz; VCF: Variant Call Format; WGD: whole-genome duplication."}
+{"text": "Uloborus diversus. The assembly reinforces evidence of an ancient arachnid genome duplication and identifies complete open reading frames for every class of spidroin gene, which encode the proteins that are the key structural components of spider silks. We identified the 2 X chromosomes for U. diversus and identify candidate sex-determining loci. This chromosome-level assembly will be a valuable resource for evolutionary research into the origins of orb-weaving, spidroin evolution, chromosomal rearrangement, and chromosomal sex determination in spiders.The orb web is a remarkable example of animal architecture that is observed in families of spiders that diverged over 200 million years ago. While several genomes exist for araneid orb-weavers, none exist for other orb-weaving families, hampering efforts to investigate the genetic basis of this complex behavior. Here we present a chromosome-level genome assembly for the cribellate orb-weaving spider  Spiders are among the most successful and diverse terrestrial predators on Earth. Almost 400 million years of evolution has produced more than 50,000 extant spider species representing 128 families that are distributed over every continent except Antarctica . SpidersRemarkably, the orb web is not restricted to a single monophyletic group but is observed in 2 lineages that diverged 250 million years ago, leading to considerable debate about its evolutionary origins , 7\u20139 Fi. AraneoiUloborus diversus genome, we provide the first chromosome-scale assembly for a member of the UDOH clade. In addition, to date, only 1 genome represents a member of the cribellate retrolateral tibial apophysis (RTA) clade, which is the sister clade to the UDOH clade  , as well as a complementary transcriptome assembly and gene annotations. This genome enabled the identification of full >10-kb spidroin genes, as well as the identification of sex chromosomes for this species. This chromosome-level assembly will be a valuable resource for evolutionary research into the origins of orb-weaving, spidroin evolution, chromosomal rearrangement, and chromosomal sex determination in spiders.09) Fig.\u00a0, of the U. diversus using a hybrid approach that leveraged the complementary benefits of multiple technologies. The genome of U. diversus contains long regions of a low-complexity sequence, which hinders assembly using short reads alone, as well as extremely long protein-coding genes, which makes long reads necessary for a reference-quality assembly . We. We44]. U. diversus from the ancestral lands of the Ramaytush, in Half Moon Bay, California, USA. We collected colony founders from a single greenhouse during several trips between 2016 and 2019 and transported them to custom-fabricated habitats in an on-campus greenhouse at Johns Hopkins University. We later transferred experimental animals from the on-site greenhouse to custom-fabricated habitats in the laboratory until required for experiments. We fed all animals alternately Drosophila melanogaster or Drosophila virilis once per week.We collected spiders of the species We soaked embryos soaked in Grace's insect medium  containing 0.1% colchicine for 2 hours. We then added an equal volume of hypotonic solution. After 15\u00a0minutes, we transferred the embryos to a 3:1 ethanol/acetic acid solution for 1 hour. After fixing, we transferred embryos to gelatin-coated microscope slides and dissociated them in a drop of 45% acetic acid. We used siliconized coverslips to squash the dissociated tissue and briefly froze them in liquid nitrogen. After removing the slides from LN2, we immediately removed the coverslips with a razor blade and transferred the slides as quickly as possible to 95% ethanol. We then performed a step-down series from 95% ethanol to 70%, 35%, and finally to Grace's insect medium to return the tissue to an aqueous solution. We then transferred the slides to a 1-\u00b5g/mL DAPI solution. After a 10-minute incubation, we transferred the slides to deionized water to rinse and mounted coverslips with a drop of Vectamount .We extracted total RNA from multiple samples: a whole adult female, a whole adult male, adult female prosoma and opisthosoma, adult male prosoma and opisthosoma, pooled legs from both the adult female and adult male dissections, a fourth instar female, and approximately 30 pooled second instars. We used the Qiagen RNeasy Mini Kit  to extract total RNA, following the manufacturer's protocol. We estimated the quality and quantity of total RNA using a NanoDrop One Microvolume UV-Vis Spectrophotometer . Before library preparation, we also measured the quality, quantity, and fragment length of our total RNA using a TapeStation 4200 System with RNA ScreenTape and reagents . We prepared barcoded, directional, paired-end RNA-seq libraries with the NEBNext Ultra II Directional RNA Library Prep Kit for Illumina  using the NEBNext Poly(A) mRNA Magnetic Isolation Module. We submitted the resulting libraries to the Johns Hopkins Genomics Core Resources Facility to be sequenced on an Illumina HiSeq 2500 Sequencing System with 150-bp paired-end chemistry.2 and crushed them with a pellet pestle  in a Protein LoBind tube  containing 220 \u03bcL Buffer ATL. We then added 20 \u03bcL Proteinase K and briefly vortexed the sample. We next incubated the sample overnight at 56\u00b0C with 900\u00a0rpm shaking on a ThermoMixer C . After the overnight incubation, we then briefly centrifuged the sample to spin down condensate on the tube. We next transferred 200 \u03bcL lysate to a fresh 2-mL sample tube and followed the manufacturer's protocol for manual purification of HMW DNA from fresh or frozen tissue. We estimated DNA quality using a NanoDrop One Microvolume UV-Vis Spectrophotometer and quantified DNA using a Qubit 4 Fluorometer (ThermoFisher) with a Quant-iT dsDNA HS Assay Kit. We also measured DNA quality, quantity, and fragment length distributions using the Agilent TapeStation 4200 System with Genomic DNA ScreenTape and reagents before proceeding to library preparation. A typical preparation from a 20-mg spider yielded 8.5 \u03bcg DNA with a major integrated area percentage peaks accounting for an average of 86.7% of the total mass centered around an average fragment length of 20\u00a0kbp.Prior to extraction of DNA, we withdrew food for 3 days to minimize the potential contribution of contaminating DNA from dietary sources. We extracted high molecular weight (HMW) DNA using the QIAgen MagAttract HMW DNA kit. Prior to HMW purification, we followed the manufacturer's protocol for disruption/lysis of tissue. We avoided fast pipetting and prolonged vortexing to minimize shearing of DNA. We flash-froze adult spiders in liquid NRRID:SCR_016387) with 150-bp paired-end chemistry.For Illumina sequencing, we extracted genomic DNA from a single, whole, unmated penultimate stage female to minimize the potential contribution of extraneous haplotypes from stored sperm after mating events. We submitted the HMW genomic DNA to the Johns Hopkins Genetic Resources Core Facility, where they prepared a PCR-free library of approximately 400-bp DNA insert size using the Illumina TruSeq PCR-Free High Throughput Library Prep Kit, according to the manufacturer's protocol. They then sequenced the prepared library on an Illumina NovaSeq 6000 Sequencing System (RRID:SCR_017987) sequencing platform. We then used ONT's Albacore basecalling software v.2.0.1\u00a0(RRID:SCR_015897) to basecall the raw fast5 data.For ONT sequencing, we extracted HMW genomic DNA from 3 adult females. We prepared sequencing libraries using the Ligation Sequencing Kit (SQK-LSK109) , according to the manufacturer's protocols. Third-party reagents we used during library preparation included New England Biolabs  NEBNext End Repair/dA-Tailing Module (E7546), NEBNext FFPE DNA Repair Mix (M6630), and NEB Quick Ligation Module (E6056). We then sequenced the libraries, using ONT R.9.4.1 flowcells (FLO-PRO002), on an ONT PromethION . The suspension was spun again at 16,000 \u00d7 g at 4\u00b0C for 2\u00a0minutes and the supernatant discarded. The spider tissue pellet was combined with 20 \u03bcL Proteinase K and 150 \u03bcL Buffer PL1 and resuspended by pipetting with a P200 wide-bore pipette tip. The tissue was incubated on a ThermoMixer at 55\u00b0C with 900\u00a0rpm mixing for 1 hour. After lysis, 20 \u03bcL RNaseA was added, and the lysate was mixed by pipetting with a P200 wide-bore pipette tip. The lysate was incubated at room temperature for 3\u00a0minutes. After RNaseA incubation, 25 \u03bcL Buffer SB was added, and the lysate was vortexed 5 \u00d7 1-second pulses and then centrifuged at 16,000 \u00d7 g at 4\u00b0C for 5\u00a0minutes. The supernatant (\u223c200 \u03bcL) was transferred to a 70-\u03bcM filter  set in a new 1.5-mL Protein LoBind tube . The tube with the 70-\u03bcM filter was spun on a mini-centrifuge  for 1\u00a0second and then the filter was discarded. Then, 50 \u03bcL Buffer BL3 was added to the cleared lysate and the tube was inversion mixed 10\u00d7. The tube was then incubated on a ThermoMixer at 55\u00b0C with 900\u00a0rpm mixing for 5\u00a0minutes. After incubation, the tube was allowed to come to RT, which took about 2\u00a0minutes. The tube was spun for 1\u00a0second on a mini-centrifuge to spin down condensate from the lid. One 5-mm Nanobind disk was added to the tube followed by 250 \u03bcL isopropanol and then the tube was inversion mixed 5\u00d7. The tube was then rocked on a platform rocker  at RT and maximum speed for 30\u00a0minutes. The DNA-bound Nanobind disk was washed according to handbook directions with one 500-\u03bcL CW1 wash and one 500-\u03bcL CW2 wash. The tube with the disk was tap spun for 2 \u00d7 1\u00a0second to dry the disk. The DNA was eluted with 50 \u03bcL Buffer EB and incubated at RT overnight. The next day, the eluate was pipette mixed with a standard bore pipette tip 5\u00d7 and then quantitated with Nanodrop and Qubit dsDNA BR assay and then sized by pulsed-field gel electrophoresis.For PacBio sequencing, HMW DNA were extracted from a single adult female spider provided to Circulomics . They extracted DNA using a modified protocol with the Nanobind Tissue Kit . Briefly, they froze and crushed a single, adult female spider with a pellet pestle  in a Protein LoBind tube  containing 200 \u03bcL Buffer CT. The crushed spider was centrifuged at 16,000 \u00d7 RRID:SCR_017990) 8\u00a0M SMRT Cell using a 30-hour HiFi run mode and processed using SMRT Link v.9.0 software.We then submitted the DNA sample to the University of Maryland School of Medicine Genomics Core Facility for PacBio HiFi sequencing. There, they size-selected the DNA using a Safe Science BluePippin with a 9-kb high-pass cutoff. They prepared the sequencing library using the Express v2 kit, according to the standard protocol for preparing HiFi sequencing libraries. They then sequenced the library on a PacBio Sequel II  for Chicago and Hi-C library preparation as previously described [RRID:SCR_016385) on 1 flowcell.To further improve the escribed . They prRRID:SCR_014583). For DNA-seq data, we determined that, due to the high quality of reads and the absence of adapter sequences, no further processing would be required and proceeded to assembly with raw read data. For RNA-seq data, we used TrimGalore [RRID:SCR_011847) to apply quality filtering and remove adapter sequences from the FASTQ files. We performed additional filtering for quality with Trimmomatic [RRID:SCR_011848). For ONT, reads shorter than 3\u00a0kbp were discarded. The length-filtered ONT long reads were used in downstream assembly.For Illumina, we examined read quality using FastQC  v.0.11.9mmomatic  v.0.33 (RRID:SCR_005491) to count the frequency of canonical 21-mers in our Illumina sequencing data. We used the resulting sorted k-mer frequencies versus counts histogram as input to GenomeScope [RRID:SCR_017014) to estimate genome size, heterozygosity, and repetitiveness.Prior to assembly, we used Jellyfish  v.2.2.4 omeScope ,147 v.2.RRID:SCR_017335) to generate a complete circularized mitochondrial sequence using raw Illumina read data. The mitochondrial sequences of several spider species were used to provide seed sequences (RRID:SCR_011779) was used to visualize the annotated mitogenome.We used Novoplasty  v.4.2 (Requences . The resRRID:SCR_010691). We used default settings, including the default CABOG contigging module, in lieu of the Flye assembler. The resulting genome assembly is referred to as U. diversus v.1.0.Illumina reads were assembled into contigs, and the resulting contigs were scaffolded with ONT long reads using the MaSuRCA assembly pipeline , 53 v.3.Rascaf [RRID:SCR_022014) to scaffold with Illumina RNA-seq read data. The resulting genome assembly is referred to as U. diversus v.1.1. To reduce redundancy in the assembly due to the presence of alternative haplotigs, we used Pseudohaploid with default settings. The resulting genome assembly is referred to as U. diversus v.1.2To improve the assembly, we used Rascaf  v.2016\u20131RRID:SCR_021966) with default settings, specifying a genome size of 1.9 Gbp, to assemble the HiFi reads. The resulting genome assembly is referred to as U. diversus v.2.0.We used PacBio's IPA HiFi Genome Assembler v.1.3.2  using the arachnida_odb10 database [RRID:SCR_001228).For each assembly, completeness was estimated with BUSCO  v.5.2.1 database . Contigudatabase  v.5.0.2 HISAT2 [RRID:SCR_015530). We then used the Trinity assembler v.2.12.0 (RRID:SCR_013048) to produce a genome-guided transcriptome assembly (\u2013CPU 60 \u2013max_memory 200\u00a0G \u2013genome_guided_max_intron 20000 \u2013SS_lib_type RF \u2013include_supertranscripts \u2013verbose). We used TransDecoder [RRID:SCR_017647) with default settings, including homology searches using both BlastP [RRID:SCR_001010) against a SwissProt UniProt database [RRID:SCR_021164), as well as the Pfam database [RRID:SCR_004726), as ORF retention criteria.Cleaned and trimmed Illumina RNA-seq reads were aligned to the genome using HISAT2  v.2.2.1 h BlastP , 62  with default parameters. We used RepeatMasker [RRID:SCR_012954) to screen and mask repeat and low-complexity regions of the genome with the Dfam consensus [RRID:SCR_021168) and RepBase RepeatMasker Edition [RRID:SCR_021169) repeat libraries.To characterize the repeat elements in the tModeler  v.2.0.2 atMasker  v.4.1.2 onsensus  v.3.4 (R Edition  v.2018\u20131RRID:SCR_018964) with RNA-seq evidence and protein homology evidence based on a custom library of spider sequences obtained from NCBI. BRAKER2 uses RNA-seq data to produce intron hints for training the ab initio gene prediction program AUGUSTUS (RRID:SCR_008417) [We performed gene annotation using the BRAKER2 pipeline , 158\u2013168_008417) , 161 on RRID:SCR_010835) with default settings to predict tRNAs. We then used Barrnap [RRID:SCR_015995) with default settings to predict rRNAs.We used tRNAscan-SE , 69 v.2. Barrnap  v.0.9 (RRRID:SCR_015647) to translate and trim the coding sequences. Once translated and trimmed, we used the BLAST+ v.2.10.1+ Blastp tool to search against the UniProt SwissProt database with an e-value cutoff of 1e-10. We used InterProScan [RRID:SCR_005829) to predict motifs, domains, and gene ontology (GO) [RRID:SCR_002811), as well as MetaCyc [RRID:SCR_007778) and Reactome [RRID:SCR_003485) pathways, using the following analyses: CDD [RRID:SCR_002077), Coils v.2.2.1 (RRID:SCR_008440), Gene3D [RRID:SCR_007672), Hamap [RRID:SCR_007701), MobiDBLite [RRID:SCR_014542), PANTHER [RRID:SCR_004869), Pfam [RRID:SCR_003352) databases, PRINTS [RRID:SCR_003412), the ProSite (RRID:SCR_003457) ProSitePatterns [RRID:SCR_001375), SMART [RRID:SCR_005026), SUPERFAMILY [RRID:SCR_007952), and TIGRFAMS [RRID:SCR_005493).We started the annotation of predicted genes used the BLAST+ BLASTP algorithm. First, we obtained the longest coding sequence for each gene predicted by BRAKER2. We then used the EMBOSS  v.6.6.0.rProScan , 67 (RRIogy (GO) ,171 term MetaCyc , 173 , Hamap  v.2020\u20130biDBLite  v.2.0 (R PANTHER  v.15.0 (9), Pfam  v.34.0, 9), Pfam  v.3.10 a9), Pfam  v.2021\u20130, PRINTS  v.42.0 , SMART , 188 v.7ERFAMILY , 190 v.1TIGRFAMS  v.15.0 (U. diversus by conducting BLAST [RRID:SCR_001004) to visualize mapping of Illumina RNA-seq data and PacBio HiFi reads to the assembled genome. RNA-seq reads were mapped to the genome with HISAT2 [RRID:SCR_018550) was used to map PacBio HiFi reads. Samtools [RRID:SCR_012880) and inspected for ORFs as well as the presence of repetitive motifs characteristic of spidroins. Predicted splice sites were compared with RNA-seq data. Unsupported splice sites, either by lack of evidence in the mapping of RNA-seq reads or by the obvious presence of spidroin repeat motifs within the predicted intronic region, were removed from the annotations. Spidroins sequences were called based upon the preponderance of available evidence, which in some cases conflicted with the structure predicted by BRAKER2.We identified ng BLAST , 62 searng BLAST   to predict the secondary structure of each sequence. The sequences were often too long and necessitated judicious segmentation into reasonable sequences that were short enough for analysis. In such cases, we selected natural breaks in the sequence structure, such as separating the N-terminal region from the repetitive regions. We used SignalP [RRID:SCR_015644) to predict the presence of signal peptides and signal peptidase cleavage sites in the N-terminal regions.We used the ExPASy web server tool ProtScale to find the amino acid composition of each sequence, as well as to estimate the hydrophobicity using the Kyte\u2013Doolittle method ,199. We  SignalP  v.6.0 . A.G. acknowledges funding from NIH (R35GM124883). A.V.Z. acknowledges funding from the USDA National Institute of Food and Agriculture (2018\u201367015\u201328199), NSF (IOS-1744309), and NIH (R01-HG006677 and R35-GM130151).J.M., A.Z., and A.G. designed the research study. J.M. performed DNA purification and sample preparation for Illumina and Oxford Nanopore sequencing. J.M. performed all computational analyses, except for HiRise scaffolding (performed by Dovetail), MaSuRCA, and SAMBA. A.Z. performed MaSuRCA assembly and merging with SAMBA. J.M. and A.G. analyzed the data and wrote the paper.giad002_Spidroin_sequencesClick here for additional data file.giad002_Figure_Supplemental_1Click here for additional data file.giad002_Table_S1Click here for additional data file.giad002_Table_S2Click here for additional data file.giad002_Table_S3Click here for additional data file.giad002_Table_S4Click here for additional data file.giad002_Table_S5Click here for additional data file.giad002_Table_S6Click here for additional data file.giad002_GIGA-D-22-00169_Original_SubmissionClick here for additional data file.giad002_GIGA-D-22-00169_Revision_1Click here for additional data file.giad002_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giad002_Reviewer_1_Report_Original_SubmissionJonathan Coddington -- 8/10/2022Click here for additional data file.giad002_Reviewer_1_Report_Revision_1Hui Xiang, PH.D -- 11/24/2022Click here for additional data file.giad002_Reviewer_2_Report_Original_SubmissionHui Xiang, PH.D -- 8/13/2022Click here for additional data file.giad002_Reviewer_3_Report_Original_SubmissionZhisheng Zhang, Ph.D -- 8/20/2022Click here for additional data file."}
+{"text": "The overall survival (OS) of stage I operable lung cancer is relatively low, and not all patients can benefit from adjuvant chemotherapy. This study aimed to develop and validate a radiomic signature (RS) for prediction of OS and adjuvant chemotherapy candidates in stage I lung adenocarcinoma.A total of 474 patients from 2 centers were divided into 1 training (n\u2009=\u2009287), 1 internal validation (n\u2009=\u2009122), and 1 external validation (n\u2009=\u200965) cohorts. We extracted 1218 radiomic features from preoperative CT images and constructed RS. We further investigated the prognostic value of the RS in survival analysis. Interaction between treatment and RS was assessed to evaluate its predictive value. Propensity score matching (PSM) was conducted.P\u2009=\u20090.020). Within the stratified analysis, good chemotherapy efficacy was only observed for patients with stage IB disease (interaction P\u2009<\u20090.001).Overall, 474 eligible patients with stage I lung adenocarcinoma  were identified. The RS was significantly associated with OS in the training and two validation cohorts (hazard ratios [HRs] \u2009>\u2009\u2009=\u20093.22). In multivariable analysis, the RS remained an independent prognostic factor adjusting for clinicopathologic variables (adjusted HRs\u2009>\u2009\u2009=\u20092.63). The prognostic value of RS was also confirmed in PSM analysis. In stage I patients, the interaction between RS status and adjuvant chemotherapy was significant (interaction Our results suggested that the radiomic signature was associated with overall survival in patients with stage I lung adenocarcinoma and might predict adjuvant chemotherapy benefit, especially in stage IB patients. The potential of radiomic signature as a noninvasive predictor needed to be confirmed in future studies.The online version contains supplementary material available at 10.1186/s12967-022-03547-9. Surgery with curative intent is the foundation for management of early-stage non-small-cell lung cancer (NSCLC). However, 10\u201320% of stage IA and 30% of stage IB patients will die within 5\u00a0years of surgery [P\u2009<\u20090.001) and adjuvant chemotherapy benefits identification for patient with resected stage I lung adenocarcinoma (P\u2009=\u20090.040) [Computed tomography (CT) is routinely used in lung cancer diagnosis and could provide the possibility of calculating prognostic and predictive biomarkers for patients\u2019 management. CT-based radiomics has become an attractive method for predicting gene mutations, treatment sensitivity, and prognosis in NSCLC \u20136. Recen=\u20090.040) . HoweverThe goal of cancer treatment is to improve patient survival. Overall survival (OS) is an optimal end point which indicates patient benefit. In the present study, we developed and validated a radiomic signature (RS) to predict OS in lobectomized stage I lung adenocarcinoma, and then set out to explore if the RS could be a biomarker for identification patients who can benefit from adjuvant chemotherapy.This was a retrospective multicenter study including three independent cohorts  cancer staging manual were identified [The demographic and clinicopathological characteristics were collected from medical records databases in two hospitals. Platinum-based doublet chemotherapy was used as the basic regimen. The platinum drugs included cisplatin and carboplatin, and other drugs included vinorelbine, paclitaxel, gemcitabine or pemetrexed.Ethics committee approval was obtained from the institutional review boards at Shanghai Chest Hospital  and Xinhua Hospital . Informed consent was waived because data were deidentified.The primary endpoint was OS, which was defined as the time from surgery to death from any cause. All patients were postoperatively followed every 3\u00a0months during the first 2\u00a0years and then every 6\u00a0months annually thereafter. The clinical evaluations included physical examination, blood tests, chest CT, abdominal CT, or ultrasound. Whole-body bone scans and a cranial CT or magnetic resonance imaging were performed annually.Chest CT scans were performed using the following 4 scanners: Discovery CT750HD CT scanner , 256-detector row scanner , 64-detector row scanner , and a 16-detector row scanner . Patients were scanned at the end of inspiration during a single breath hold in the supine position. The HRCTs were performed with collimation of 0.625\u20131.25\u00a0mm, pitch of 0.64, section thickness of 0.625\u20131.25\u00a0mm without overlap, matrix of 512\u2009\u00d7\u2009512 or 1,024\u2009\u00d7\u20091,024, field of view (FOV) of 350\u2013400\u00a0mm, 120 kVp, and 220\u2013300\u00a0mA. All imaging data were reconstructed using the standard algorithm. One radiologist  identified manually at the voxel level the areas of interest for the included nodules based on CT scans using 3D Slicer . Then, the VOI was confirmed by another radiologist .Radiomics features were extracted by Image Biomarker Standardization Initiative (IBSI) compliant AK software . Totally, 1218 radiomic features were extracted from CT images, including first order statistical features, morphological features, gray-level co-occurrence features matrix-based features, gray-level run length matrix-based features, gray-level size zone matrix-based features, gray-level dependence matrix-based features, and the transform features of wavelet and Laplace changes.P\u2009<\u20090.05 were used for further analysis. The Spearman correlation was applied to eliminate the redundancy of the feature set (coefficient of chosen here | r |>\u20090.8). Finally, the least absolute shrinkage and selection operator (LASSO) method to select the most valuable prognostic features from the training cohort. The optimal cutoff value for RS was determined using X-tile software version 3.6.1  in the training cohort [The RS was calculated with chest CT based on the training cohort. Univariate Cox analysis was firstly used to detect the associations between each feature and the patients\u2019 OS. The features with g cohort . The samg cohort . NearestU test was used to examine the difference between the two groups. Categorical data were compared using the \u03c72-test or Fisher\u2019s exact test, as appropriate. OS was calculated using the Kaplan\u2013Meier method and log-rank test. The univariate and multivariate Cox proportional hazards model was utilized to estimate the HR and 95% CI for the outcome. Interaction between the RS and adjuvant chemotherapy was assessed by means of the Cox model.Mann\u2013Whitney We established a clinicopathologic model and a radiomic nomogram to determine whether the RS added incremental value for predicting OS. Model performance was assessed by Harrell\u2019s concordance index (C-index), calibration curves and decision curve analysis.P\u2009<\u20090.05 was considered statistically significant in all 2-tailed tests. The statistical analyses were performed using R version 3.6.1  and SPSS version 23.0 .For all analyses, Table The calculation formula for RS was 0.456 * log_sigma_5_0_mm_3D_firstorder_Maximum\u2009+\u20090.355 * original_firstorder_Minimum\u2009+\u20090.236 * log_sigma_2_0_mm_3D_gldm_Large Dependence High Gray Level Emphasis\u2009+\u20090.218 * original_gldm_Large Dependence High Gray Level Emphasis\u2009+\u20090.149 * log_sigma_4_0_mm_3D_glszm_Small Area Low Gray Level Emphasis\u2009+\u20090.015 * wavelet_HLH_firstorder_Mean\u20140.072 * log_sigma_5_0_mm_3D_firstorder_Skewness. To determine the optimal cutoff value of RS, X-tile was used. The optimal cutoff value was 0.98  which showed the most significant prognostic effect in predicting OS in the training cohort. Therefore, RS of 1 was used as the cutoff point in the following analyses. The Kaplan\u2013Meier survival curves confirmed a significant difference in OS (HRs\u2009>\u2009\u2009=\u20093.22) between the high and low risk groups  who either received or did not receive adjuvant chemotherapy. In stage I patients, the interaction between RS status and adjuvant chemotherapy was significant  [P\u2009<\u20090.001), with an absolute increase in OS of 4% at five years [STK11 alterations, were enriched in certain CT-based radiomic clusters [In a previous study, Xie and colleagues found that RS could predict RFS and identify the patients benefit from adjuvant chemotherapy in stage I adenocarcinoma patients (=\u20090.040) . Most st=\u20090.040) \u201313. In ave years . In addive years . Thus, wve years . When PSclusters . TherefoIn patients with stage IA, high RS was associated with worse OS. However, adjuvant chemotherapy could not improve survival in these high risk patients. Therefore, clinicians should formulate a detailed follow-up plan in order to detect local or metastatic relapse. Molecular residual disease (MRD) detection could precisely predict the recurrence in patients with NSCLC after definitive surgery . It willPoorly differentiated, lymphovascular invasion, visceral pleural invasion, incomplete lymph node sampling, or wedge resection were defined as high risk factors in stage IB by the current NCCN guideline . SeveralEGFR mutation-positive NSCLC [Osimertinib has been recommended to use in patients with stage IB ve NSCLC . In addive NSCLC . TherefoThere were some limitations in our study. First, as a retrospective study, potential selection bias may hamper the reproducibility and comparability of the results. Thus, we included three independent cohorts from two medical centers to validate our findings. Second, genetic data was not included because gene detection was not a routine practice. Third, the role of radiomic signature was only assessed in Chinese patients. The performance of RS in other ethnic patients was still unknown. Finally, it would be interesting to perform experiments with cell lines and animal models to reveal the underlying mechanism of radiomic signature.In summary, our results showed that radiomic signature might be a promising biomarker to predict OS and benefit of adjuvant chemotherapy in resected stage I lung adenocarcinoma. Further prospective studies are warranted to validate these results.Additional file 1: Figure S1. The process of patient selection in the training and two validation cohorts. Figure S2. Kaplan-Meier overall survival curves according to the radiomic signature among stage I lung adenocarcinoma patient subgroups. The training cohort , The internal validation cohort , and external validation cohort . P values were calculated using two-sided log-rank test. Figure S3. Kaplan-Meier overall survival curve according to the radiomic signature after propensity score matching. Table S1. Multivariate Cox Regression analyses for overall survival in the training and validation cohorts. Table S2. Demographic and clinicopathological characteristics of patients by radiomic signature level before and after propensity score matching. Table S3. The performances of the different models in the training and validation cohorts."}
+{"text": "This study investigates the expression and effect of hsa_circ_0004099 in acute ischemic stroke (AIS). We conducted a case-controlled study that included 40 patients with AIS within 24 hours and 40 healthy subjects during the same period as a control group. Differentially expressed circular RNAs (circRNAs) were obtained using GEO2R, and the expression of hsa_circ_0004099 was verified using RT-PCR. Correlation analysis of the National Institutes of Health Stroke Scale (NIHSS) disease severity score and ischemic time with hsa_circ_0004099 expression levels was also performed. The receiver operating characteristic (ROC) curve of hsa_circ_0004099 was constructed, and bioinformatic analysis of hsa_circ_0004099 was performed. NIHSS scores negatively correlated with hsa_circ_0004099 levels , whereas infarct time was negatively correlated with hsa_circ_0004099 levels ; hsa_circ_0004099 could benefit clinical diagnosis . Kyoto encyclopedia of genes and genomes (KEGG) analysis showed that hsa_circ_0004099 was enriched in several cancer pathways, which were collectively enriched in four genes namely TCF7L2, NRAS, CTNNB1, and KRAS. Eight core proteins were screened using a protein-protein interaction (PPI) network namely SMAD4, HIF1A, CTNNB1, CDKN1B, CDK6, FOXO3, KRAS, and NRAS. hsa_circ_0004099 is a potential clinical diagnostic marker. In addition, the possible role of hsa_circ_0004099 in the pathogenesis of AIS was analyzed using bioinformatics, which provided a new potential molecular target for AIS treatment. Eighty-five percent of stroke survivors experience an acute ischemic stroke (AIS) caused by intracranial artery occlusion or extracranial carotid artery occlusion, leading to brain tissue death, focal neurological deficits, and disability, thereby causing a huge burden to patients and society , 2. MechCircular RNAs (circRNAs) are a class of noncoding RNAs widely present in eukaryotic cells . Unlike The host gene of hsa_circ_0004099 is differentially expressed in normal and neoplastic cells (DENN) domain-containing 5A (DENND5A) gene. The DENN domain is an evolutionarily ancient enzymatic module that confers guanine nucleotide exchange factor activity to proteins and is a key regulator of cell membrane trafficking, mainly in neuronal tissues. Knockdown of DENND5A results in marked alterations in neuronal development . In thisThis was a case-controlled study. Forty AIS patients were recruited from the emergency department of the Second Affiliated Hospital of Guangxi Medical University from April 2021 to August 2021. In addition, 40 healthy subjects matched for gender and age with patients with AIS were recruited in the same hospital and physical examination center during the same period. This study was approved by the Ethics Committee of the Second Affiliated Hospital of Guangxi Medical University (NO:2021 [KY-011]) and all subjects provided written informed consent. Patients with MRI- and CT-confirmed AIS were included in the study. The inclusion and exclusion criteria were as follows: Inclusion criteria: 1) Aged 55\u201375 years old; 2) AIS occurred for the first time; 3) Patients admitted within 24 hours. Exclusion criteria: 1) AIS complicated with other cerebrovascular diseases; 2) patients with severe infection or taking anticoagulant drugs at the time of admission; 3) patients who received any AIS intervention therapy. Whole blood was collected from all subjects in two 5 ml EDTA anticoagulation tubes.g for plasma extraction. Each sample consumed 250 \u03bcl plasma for total RNA extraction. The concentration of total RNA in each sample was greater than 100 ng/\u03bcl, and the ratio of A260/280 was between 1.8\u20132.0. cDNA was reverse transcribed into cDNA using the HiScript Ill RT SuperMix for gPCR (genomic DNA [gDNA] wiper) reverse transcription kit . The reaction conditions were as follows: 42\u00b0C for 2 min \u2192 4\u00b0C hold ; 37\u00b0C for 15 min \u2192 85\u00b0C for 5 s \u2192 4\u00b0C (reverse transcription). A ChamQ Universal SYBR qPCR Master Mix  was used to quantify the expression levels of hsa_circ_0004099. The reaction conditions were as follows: pre-denaturation at 95\u00b0C for 30 s \u2192 40 cycles of 95\u00b0C for 10 s, and 60\u00b0C for 30 s. Primer design and synthesis were performed using the Takara Bio software  disease severity score was recorded within 24 hour of admission for AIS. Furthermore, patients were classified according to the cerebral infarction etiological classification  criteria.https://www. ncbi. nlm. nih. gov/), and platform files as well as a series of matrix files were downloaded. circRNA definition conditions were as follows: |log2FC|\u22652, p <0.05.The human plasma circRNA gene assay was selected from the GEO database (https://circinteractome.nia.nih.gov/) and circBank (http://www.circbank.cn/). Next, the intersection of miRNAs was obtained. TargetScan (http://www.targetscan.org/vert_72/), miRDB (http://mirdb.org/index.html), and miRTarbase (https://mirtarbase.cuhk.edu.cn/) predicted the downstream target genes of intersecting miRNAs, and the intersecting results of these three software programs were considered co-regulated mRNAs. Finally, a Venn diagram of the intersection of miRNAs and mRNAs was constructed using a free online platform for data analysis and visualization (http://www.bioinformatics.com.cn).The microRNAs (miRNAs) bound by circRNAs were predicted using the circular RNA Interactom (https://david.ncifcrf.gov/).Visual analysis of circRNA-miRNA-mRNA networks was performed using Cytoscape 3.7.2. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis of hsa_circ_0004099 downstream target genes were performed using DAVID (http://www.bioinformatics.com.cn). The target genes were obtained in TSV format using STRING (https://string-db.org/) and imported into Cytoscape. Finally, a protein-protein interaction (PPI) network map was obtained using the NetworkAnalyzer degree algorithm and the combined score.Furthermore, a bubble map and KEGG signaling pathway map were obtained using an online platform for data analysis and visualization  test or Steel-Dwass test. In the correlation analysis, Pearson linear correlation was used for measurement data, Spearman rank correlation was used for grade data, and a receiver operating characteristic (ROC) curve was drawn to demonstrate the diagnostic ability of hsa_circ_0004099. A P-value of < 0.05 was considered statistically significant.Statistical data were collected using GraphPad Prism 8.0.1 and SPSS 23. The measurement data are expressed as This study strictly followed the above process .All participants completed the study. The expression level of hsa_circ_0004099 in patients with AIS (0.28 [0.17\u20130.40]) was lower than that in normal individuals (1.43 [0.99\u20132.08]), as determined using RT-PCR verification , consistSpearman rank correlation analysis showed that the severity of AIS was highly negatively correlated with the expression level of hsa_circ_0004099  . The expDue to the skewed distribution of the data, the Kruskal-Wallis H-test was used to compare the hsa_circ_0004099 expression levels in patients with the three infarct types, LAA, SOA, and CE, with those of healthy subjects, and pairwise analysis was performed using the Steel-Dwass test. The results showed that the hsa_circ_0004099 expression levels of patients with LAA (0.36[0.17\u20130.57]), SAO (0.28[0.17\u20130.38]), and CE (0.20[0.16\u20130.28]) were all significantly different from those of healthy subjects (1.43[0.99\u20132.08]) (P <0.001), with the expression level being lower than that of healthy subjects. There was no statistically significant difference in the expression levels of hsa_circ_0004099 among the LAA, SAO, and CE groups .ROC curve analysis revealed that the expression levels of hsa_circ_0004099 had a good diagnostic value for AIS . In addition, the AUC reached a maximum when the cut-off was 0.85, and the sensitivity and specificity of the optimal cut-off point were 0.95 and 0.90, respectively .http://gb.whu.edu.cn/CSCD/). The binding fractions of hsa_circ_0004099 and of the 10 miRNAs were obtained using the circular RNA Interactom (Ten miRNAs were found at the intersection of hsa_circ_0004099  with 616teractom .Cytoscape was used to construct the competing endogenous RNA (ceRNA) network for hsa_circ_0004099 .GO and KEGG enrichment analyses of target genes regulated by hsa_circ_0004099 were performed. The GO analysis results showed that the target genes were: 1) mainly enriched  in the bIn the PPI network, 152 nodes and 367 correlation pairs were identified. In addition, eight core proteins were present in the PPI network: SMAD4, HIF1A, CTNNB1, CDKN1B, CDK6, FOXO3, KRAS, and NRAS .The expression of circRNAs in brain tissue is closely associated with cerebral ischemia; these circRNAs can be repeatedly detected in human peripheral blood samples . The hosThe NIHSS total score ranges from 0 to 42, with higher scores associated with poor long-term prognoses . In thisIn this study, the Kruskal-Wallis H rank-sum test was used to analyze the expression of hsa_circ_0004099 in patients with LAA, SOA, and CE infarction types and compare it with that in normal plasma. The expression levels of hsa_circ_0004099 in patients with LAA, SAO, and CE were significantly higher than those in normal subjects (P<0.001). In addition, most patients had LAA (17 cases), followed by SOA (15 cases) and CE (8 cases). In 2702 patients with AIS, the most common infarction subtype was LAA (39%), followed by SVO (23%) and CE (22%); LAA was the most common subtype of posterior circulation infarction . These rThe ROC curve analysis in this study indicated that the AUC of hsa_circ_0004099 to distinguish AIS patients from normal subjects was 92.3%. In addition, when the critical value of hsa_circ_0004099 was 0.85, AUC reached the maximum value, and the sensitivity and specificity of the optimal cut-off point were 0.95 and 0.90, respectively, indicating high diagnostic efficiency. In addition, Zuo et al.  investighttps://www.genecards.org/cgi-bin/carddis.pl?gene=MIR217#localization). Shi et al. [in vitro experiments showed that miR-217 promotes the accumulation of histone deacetylase 5 (HDAC5) in the nucleus by targeting MEF2D, resulting in decreased expression of IL-10, thereby aggravating cognitive dysfunction after cerebral ischemia. Rao [In the biological information section, 10 miRNAs in the downstream intersection of hsa_circ_0004099  were jointly predicted using the circRNA interatom and circBank. Some studys found that miR-217 and miR-665 were associated with cerebral ischemia \u201325. miR-i et al.  found thmia. Rao  found thmia. Rao . Yang [2KEGG enrichment analysis showed the enrichment of many cancer pathways, such as colorectal, endometrial, and prostate cancer pathways. Jiang et al.  found thIn the PPI network, the following eight core proteins were identified: SMAD4, HIF-1A, CDKN1B, CDK6, FOXO3, CTNNB1, KRAS, and NRAS. SMAD4 is a member of the SMAD family of encoded signal transduction proteins that are essential for maintaining tissue homeostasis and cell cycle regulation , 36. AmiCTNNB1, KRAS, and NRAS are also three intersecting genes in the cancer pathway in this study, indicating that they may be key target genes regulating the development of AIS.This study has some limitations. Since this is a hospital-based case-controlled study, not population-based, it might have been affected by selection bias. Furthermore, the specific mechanism of hsa_circ_0004099 regulating AIS is not clear, and animal and cell experiments were not conducted to explore this specific mechanism. Therefore, the mechanism by which hsa_circ_0004099 regulates AIS remains to be determined.In conclusion, this study demonstrated that patients with AIS have low expression of hsa_circ_0004099. The NIHSS score and infarct time were negatively correlated with the expression levels of hsa_circ_0004099, suggesting that hsa_circ_0004099 may be a potential therapeutic target for improving AIS outcomes. Further research showed that the AUC of hsa_circ_0004099 is 92.3%, indicating a good predictive value for AIS. Hence, hsa_circ_0004099 is a potential tool for the diagnosis and prognosis of AIS with significant clinical implication.S1 Fig(PDF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file."}
+{"text": "Objective: Circular RNAs (circRNAs) have been demonstrated to be closely involved in colorectal cancer (CRC) pathogenesis and metastasis. More potential biomarkers are needed to be searched for colorectal cancer (CRC) diagnosis and treatment. The objective of this study is to seek differentially expressed circRNAs (DEcircRNAs), test their roles in CRC and construct a potential competing endogenous RNA (ceRNA) network.Methods: CircRNA microarrays were obtained from Gene Expression Omnibus, and differential expression was analyzed by R software. The relative expressions of DEcircRNAs were confirmed in CRC tissues and cell lines by qRT-PCR. MTs and Transwell experiments were performed for detecting the roles of circRNAs on CRC cell proliferation and migration, respectively. Targeted miRNAs of circRNAs and targeted mRNAs of miRNAs were predicted and screened by bioinformatics methods. A ceRNA network of DEcircRNAs was constructed by Cytoscape. To further verify the potential ceRNA network, the expressions of miRNAs and mRNAs in knockdown of DEcircRNAs CRC cells were detected by qRT-PCR.Results: Two DEcircRNAs (hsa_circ_0040809 and hsa_circ_0000467) were identified and validated in CRC tissues and cell lines. The results of MTs and Transwell experiments showed that hsa_circ_0040809 and hsa_circ_0000467 promoted CRC proliferation and migration. Bioinformatics analysis screened 3 miRNAs  and 2 mRNAs (FADS1 and RUNX1), and a ceRNA network was constructed. In knockdown of hsa_circ_0040809 HCT-116 cells, the expression of miR-330-3p was significantly upregulated, while RUNX1 was significantly downregulated. In knockdown of hsa_circ_0000467 HCT-116 cells, the expressions of miR-326 and miR-330-3p were upregulated, while FADS1was downregulated.Conclusion: We found that hsa_circ_0040809 and hsa_circ_0000467 were upregulated in CRC tissues and cell lines, and promoted CRC cell progression. A circRNA-miRNA-mRNA network based on hsa_circ_0040809 and hsa_circ_0000467 was constructed. Colorectal cancer (CRC) is the third most common cancer and the second leading cause of cancer-related death in the world . SurgicaCircular RNA (circRNA) is a novel class of non-coding RNA, more stable than linear RNAs due to the lack of 5\u2032 end caps and 3\u2032 poly (A) tails. CircRNAs have been found differentially expressed in various malignant tumors, and therefore been a focus of research as potential biomarkers for cancer. Increasing evidences have shown that circRNAs may function as novel diagnostic and prognostic biomarkers in cancers including CRC , gastricA number of circRNAs have been reported to be differentially expressed in CRC, and play key roles in cancer progression. For example, circDENND4C  and circIn this study, we aimed to seek differentially expressed circRNAs (DEcircRNAs) in CRC, explore their role for CRC and construct a possible competing endogenous RNA (ceRNA) network. We obtained the DEcircRNAs by analyzing the circRNA microarray from Gene Expression Omnibus (GEO). The DEcircRNAs were validated in CRC cell lines and tissues. The roles of DEcircRNAs were detected in CRC cell lines. A potential ceRNA network was constructed by a series of bioinformatics analysis.https://www.ncbi.nlm.nih.gov/geo/). We searched circRNA expression profiles for human colorectal carcinoma and adjacent normal tissues using the following search terms: colorectal cancer (All Fields) and [circRNA (All Fields) or circular RNA (All Fields)]. Microarrays with sample counts of less than five were excluded. Datasets from CRC cell lines were excluded. Finally, we selected GSE126094, GSE138589, and GSE142837, which were based on the same platform (GPL19978).CircRNA microarrays of CRC and adjacent normal tissues were collected from GEO datasets .via the STR method. FHC cells were cultured in DMEM cell medium, while HT29 and HCT 116 were cultured in 1,640 cell medium. The methods of cells culture were described previously . All cell lines were validated eviously .The siRNAs to knockdown has_circ_0000467 and has_circ_0040809 were provided by GenePharma . The experimental procedure was conducted according to the manufacturer\u2019s instructions. The knockdown efficiency of has_circ_0000467 and has_circ_0040809 was detected by qRT-PCR.\u2212\u0394\u0394Ct method. The sequences of primers for circRNAs, miRNAs, mRNAs and GAPDH mRNA are shown in CRC and para-carcinoma tissues were ground before RNA extraction. Total RNAs were extracted from cell lines and CRC tiusses by TRIzol reagent  according to the manufacturer\u2019s instructions. The purity and concentration of the extracted RNAs were measured using a NanoDrop spectrophotometer . The relative expressions of DEcircRNAs were detected by qRT-PCR, described in our previous study . The rel6/ml, and 200\u00a0\u00b5L of the cell suspension was placed onto the upper chamber of each well on 24-well-Transwell plates. The lower chamber contained medium with 30% fatal bovine serum (FBS). After incubation at 37\u00b0C for 24\u00a0h, the cells in the upper chamber were wiped off with cotton swabs, and the cells in the other side of chamber membrane were fixed with methanol for 15\u00a0min, dried, stained with 0.1% crystal violet, and randomly pictured under inverted microscope . The cells were counted with ImageJ program.Cell concentration was adjusted to 1\u00d710Cells were plated into 96-well-plates . After culturing 0\u20133\u00a0days, 20\u00a0\u00b5l of MTs  were added to each well-followed by incubation at 37\u00b0C for 2\u00a0h. Cell medium only with serum was used as background control. The absorption of the palates at 490\u00a0nm were read on a Bio-Rad  plate reader.https://portal.gdc.cancer.gov/). The differentially expressed miRNAs and mRNAs  from TCGA were analyzed by R Limma package. |log 2 [FC]| \u22651.5 and p < 0.05 was used to indicate significance.The miRNA and mRNA data of expressions in CRC were obtained from TCGA database (https://circinteractome.irp.nia.nih.gov/)  . The intih.gov/)  and Targih.gov/) . Hub genp < 0.05 was considered statistically significant. Figures were drawn by GraphPad Prism 7 .All statistical analyses were performed using IBM SPSS version 23.0 . Student\u2019s t test was performed for statistical analysis. A Three circRNA microarrays , were selected for further analysis in our study. In the GSE126094 dataset, 1,775 DEcircRNAs were identified, including 11 downregulated and 1,764 upregulated circRNAs. In the GSE138589 dataset, 30 downregulated circRNAs and 156 upregulated circRNAs were identified. A total of 43 DEcircRNAs, including nine downregulated and 34 upregulated circRNAs, were identified in the GSE142837 dataset. Venn diagram analysis was performed to identify overlapping DEcircRNAs among the three datasets. The results revealed four up-regulated circRNAs  in all three datasets. The details of the four up-regulated circRNAs in CRC are presented in We detected the expression of the above four DEcircRNAs in the HT29, HCT116, and FHC cell lines by qRT-PCR. The results revealed that the expressions of hsa_circ_0084615, hsa_circ_0040809, and hsa_circ_0000467 were up-regulated in HT29 cells . In HCT1Hsa_circ_0040809 and hsa_circ_0000467 are upregulated in CRC cells and tissues, suggested that hsa_circ_0040809 and hsa_circ_0000467 may promote CRC progression. To test this hypothesis, we designed siRNAs to silence hsa_circ_0040809 and hsa_circ_0000467, which were transfected into HCT116 and HT29. After siRNA transfection to HCT116 cell line, the expressions of hsa_circ_0040809 and hsa_circ_0000467 declined about 70% and 60%, respectively . DownregIn HT29 cells, the expressions of hsa_circ_0040809 and hsa_circ_0000467 decreased about 60% and 40%, respectively. Meanwhile, we detected the host genes of hsa_circ_0040809 and hsa_circ_0000467 (BANP and SKA3), and there was no difference between siRNA and NC groups . We detehttp://gb.whu.edu.cn/CSCD/). And we found that hsa_circ_0040809 and hsa_circ_0000467 were exon-circRNAs. That suggested that hsa_circ_0040809 and hsa_circ_0000467 may play a role as a sponge of miRNAs.To investigate a possible molecular mechanism of promoting CRC progression, we firstly gained the information of hsa_circ_0040809 and hsa_circ_0000467 in CSCD database  were downregulated. The expression of the miRNAs in CRC is shown in Potential targeted miRNAs of hsa_circ_0040809 and hsa_circ_0000467 were predicted Potential targeted mRNAs of the miRNAs were predicted by TargetScan and miRDB. A total of 780 potential targeted mRNAs were gained. And 86 of 780 mRNA were upregulated in the TCGA database.via STRING database. There were 86 nodes and 40 edges in this PPI network (The protein-protein interaction (PPI) network of the 86 mRNAs was revealed  network . Hub genFADS1 and RUNX1) of the 41 hub genes were negatively associated with overall survival  , 3 miRNAs  and 2 mRNAs (d RUNX1) .FADS1, and RUNX1 in siRNA-hsa_circ_0040809 or siRNA-hsa_circ_0000467 HCT-116 cells. In siRNA-hsa_circ_0040809 HCT-116 cells, the expression of miR-330-3p was significantly upregulated, while RUNX1 was significantly downregulated .Acting as ceRNA is a mechanism for regulation of gene expression and a main function of exonic circRNAs in diseases . CircRNAAccording to ENCORI database, the expressions of three miRNAs  were lower in CRC compared with adjacent tissues. They have been reported to suppress cancer development in previous studies. Hsa-miR-326 suppressed cancer development in non-small cell lung cancer  and prosFADS1 and RUNX1 were linked to poor prognosis in CRC by GEPIA database. These genes were associated with the progression of cancers, including CRC. GO enrichment analysis showed that FADS1 and RUNX1 were associated with nucleus, positive regulation of transcription from the RNA polymerase II promoter, DNA-templated, peripheral nervous system neuron development, and the nucleoplasm. RUNX1 was reported to be associated with proliferation, inhibition of apoptosis, and tumor metastasis and invasion (invasion .FADS1, and RUNX1 in siRNA-hsa_circ_0040809 or siRNA-hsa_circ_0000467 HCT-116 cells. And the results were accord with the expressions of cicRNAs, miRNAs and mRNAs in ceRNA network. There is no doubt that the results cannot confirm ceRNA mechanism in this study. More efforts will be required to elucidate the role of the identified axes in CRC through in vitro and in vivo experiments. Nevertheless, the results of this study provide a potential mechanism on that hsa_ circ_0040809 and hsa_circ_0000467 promote CRC proliferation and migration.To determine the association of between expressions of miRNAs, mRNAs and circRNAs in the ceRNA network, we detected the expressions of miR-326, miR-330-5p, miR-330-3p, FADS1, hsa_circ_0000467/miR-326/FADS1, and hsa_circ_0040809/miR-330-3p/RUNX1 axes is constructed. These axes might be associated with tumorigenesis and prognosis in CRC. Potential novel circRNA biomarkers for CRC were identified and the results provide insights into the underlying mechanisms of CRC pathogenesis. We are furtherly exploring the mechanism of these axes in CRC.Hsa_ circ_0040809 and hsa_circ_0000467 are upregulated in CRC and promote CRC proliferation and migration. A ceRNA network including hsa_circ_0000467/miR-330-5p/"}
+{"text": "To explore various immune cell-related causal pathways for primary sclerosing cholangitis (PSC).  Immune cell-related pathway association study was conducted via integrative analysis of PSC GWAS summary and five immune cell-related eQTL datasets. The GWAS summary data of PSC was driven from 4,796 PSC cases and 19,955 healthy controls. The eQTL datasets of CD4+ T cells, CD8+ T cells, B cells, natural killer cells (NK), monocytes, and peripheral blood cells (PB) were collected from recently eQTL study. The PSC GWAS summary dataset was first aligned with eQTL datasets of six blood cells to obtain the GWAS summary data at overlapped eQTL loci, separately. For each type of cell, the obtained PSC GWAS summary dataset of eQTLs was subjected to pathway enrichment analysis. 853 biological pathways from Kyoto Encyclopedia of Genes and Genomes, BioCarta, and Reactome pathway databases were analyzed. P values <5.0 \u00d7 10\u22125). Comparing the pathway analysis results detected 25 pathways shared by five immune cells, such as KEGG_CELL_ADHESION_MOLECULES_CAMS  and REACTOME_MHC_CLASS_II_ANTIGEN_ PRESENTATION . Several cell-specific pathways were also identified, including BIOCARTA_INFLAM_PATHWAY  for B cell.  We identified 36 pathways for B cells, 33 pathways for CD4+ T cells, 28 pathways for CD8+ T cells, 33 pathways for monocytes (MN), 35 pathways for NK cells, and 33 for PB cells (all empirical  Our study holds potential to identify novel candidate causal pathways and provides clues for revealing the complex genetic mechanism of PSC. Primary sclerosing cholangitis (PSC) is a chronic cholestatic liver disease, characterized by widespread inflammation and fibrosis of bile ducts . PSC is Both environmental and genetic factors contribute to the development of PSC \u201310. GenoIt is well known that the gene expression is under genetic control . ExtensiOver the past decades, GWAS has identified a large number of susceptibility genes related to complex traits and diseases. However, the genetic risks of human diseases, especially complex diseases, are usually determined by the joint effects of multiple susceptibility loci. Furthermore, most of risk alleles identified by GWAS have weak genetic effects. The power of GWAS for detecting these loci was limited. The statistical power can be significantly increased if functionally relevant genetic loci  could be grouped together for testing. To address this issue, GWAS-based pathway enrichment analysis was proposed and successfully applied in the genetic studies of complex diseases . In thisP values <1.0 \u00d7 10\u22126 in controls (excluding those in the HLA region) or call\u2009rate < 80% were removed. The samples genotyped by the Affy6 and Omni2.5 arrays were phased and imputed, separately. Prephasing was performed using the SHAPEIT2, and imputation was conducted using IMPUTE2 [A recent large-scale GWAS summary data of PSC was used here . Briefly IMPUTE2 , 35. A c IMPUTE2 . Details IMPUTE2 .The eQTL datasets of CD4+ T cells, CD8+ T cells, B cells, NK cells, monocytes, and PB cells were driven from Ishigaki et al.'s study . BrieflyGenome-wide SNP genotyping for eQTL analysis was conducted using Infinium Omni Express Exome Bead Chips (Illumina). Quality control (QC) of the genotyping data was performed using PLINK 1.90 . After qhttp://software.broadinstitute.org/gsea/msigdb/index.jsp) [P value and false discovery rate (FDR) of each pathway for each cell. 20,000 permutations were conducted. Detailed analyzing procedures have been detailed in our previous studies [P value <5 \u00d7 10\u22125.The PSC GWAS summary dataset was first aligned with the eQTL datasets of six blood cells to obtain the GWAS summary data at the overlapped eQTL SNP loci, separately. For each type of blood cell, the obtained PSC GWAS summary dataset of eQTLs was subjected to pathway enrichment analysis , 43. Thedex.jsp) . The appdex.jsp) , 45.853  studies . For thi+ T cells, 37,222 eQTL variants for CD8+ T cells, 51,798 eQTL variants for B cells, 44,886 eQTL variants for NK cells, 62,315 eQTL variants for MN cells, and 54,980 eQTL variants for PB cells. Pathway enrichment analysis identified 36 pathways for B cells, 33 pathways for CD4+ T cells, 28 pathways for CD8+ T cells, 33 pathways for monocytes, 35 pathways for NK cells, and 33 pathways for PB cells .After aligning the PSC GWAS summary and eQTL datasets, we used 37,080 eQTL variants for CD4P value <5 \u00d7 10\u22125) and REACTOME_MHC_CLASS_II_ANTIGEN_ PRESENTATION   was highly enriched only in the eQTLs of B cell. The REACTOME_RNA_POL_I_RNA_POL_III_AND_ MITOCHONDRIAL_TRANSCRIPTION  and REACTOME_ MEIOTIC_SYNAPSIS  were only detected for NK cells. REACTOME_MEIOSIS was found to be enriched in the eQTLs of B cells  and NK cells .Further comparing the list of identified pathways, we detected 25 pathways significantly enriched in the eQTLs of five immune cells, such as KEGG_CELL_ADHESION_MOLECULES_CAMS ( \u00d7 10\u22125) . Additio \u00d7 10\u22125) . For insIntegrative analysis of GWAS and eQTL datasets is capable of providing new insights into the genetic mechanism of complex diseases . In this\u2212 T cells accumulated in livers of patients with PSC. Moreover, activated CD28\u2212 T cells released high levels of TNF and IFN proinflammatory cytokines and induced upregulation of intercellular cell adhesion molecule-1(CAM-1), HLA-DR, and CD40 by primary epithelial cells [This study detected multiple pathways with common causal effects across the five immune cells. For instance, KEGG_CELL_ADHESION_MOLECULES_CAMS was shared by all five immune cells. Cell adhesion molecules (CAMs) are proteins expressed on the cell surface and play a critical role in a variety of biologic processes, including the immune response and inflammation , 48. Preal cells . Anotheral cells , 52.REACTOME_MHC_CLASS_II_ANTIGEN_PRESENTATION was another common pathway shared by the five immune cells. MHC class II molecules are a class of major histocompatibility complex (MHC) molecules normally found only on antigen-presenting cells, which serve a critical role in immune response, such as some endothelial cells and B cells. The MHC class II protein complex is encoded by the human leukocyte antigen gene complex (HLA) in human. PSC is strongly associated with the HLA complex, for instance, HLA-DRB1. Varies of HLA-DRB1 genes has been proved to be associated with PSC, including risk alleles and protective alleles , 54. REA\u03b21 has previously been shown to be associated with hepatic fibrosis [\u03b21 expression to slow the progression of hepatic fibrosis into cirrhosis in PSC patients [We found that some biological pathways were associated with PSC in certain immune cells. For instance, the BIOCARTA_INFLAM_PATHWAY was significantly enriched in the eQTLs of B cells. BIOCARTA_INFLAM_PATHWAY consists of 29 genes, functionally involved in cytokines and inflammatory response. Some genes of BIOCARTA_INFLAM_PATHWAY have been demonstrated to contribute to the development of PSC. For instance, IL-8 in bile and serum has been identified as an important indicator of disease severity and prognosis for primary sclerosing cholangitis . TGF-\u03b21 fibrosis . Anotherpatients . Recent patients . HoweverOne advantage of this study is that we focused on the gene expression rather than genetic variations or SNPs. Although a dramatic increase in the number of reported SNPs implicating in a wide variety of diseases has been reported recently, the majority of them were located in the noncoding chromosomal regions. Compared with GWAS-based pathway association analysis, eQTL-based pathway analysis can help to identify novel causal genes and pathways implicated in PSC pathogenesis through gene expression regulation. Furthermore, the discovery of identified eQTL pathways varied across cell types demonstrated that different immune cells may play different roles in the pathogenesis of PSC, providing novel clues for revealing the complex genetic basis of PSC and it might be a new therapeutic target for PSC.There is an issue in our study that should be noted. The population of PSC GWAS summary dataset was from UK, US, Scandinavia, and Germany other than Chinese, which lowers the priority of our work in some extent. Further biological studies in Chinese population are needed.In conclusion, we conducted an immune cell-related eQTL-based pathway association study of PSC through integrating PSC GWAS and eQTL datasets. We identified a group of PSC-related biological pathways with common effects or cell specific effects across various immune cells. Our study holds potential to identify novel candidate causal pathways and provides novel clues for revealing the complex genetic mechanism of PSC."}
+{"text": "Trichodesmium spp. obtained near Station ALOHA in the North Pacific Ocean. HetDA_MAG_SS10, an alphaproteobacterium in the order Micavibrionales, is proposed to be photoheterotrophic via rhodopsin and has the potential for dimethylsulfoniopropionate (DMSP) demethylation.Here, we present HetDA_MAG_SS10, a metagenome-assembled genome (MAG) from an enrichment of a heterocystous diazotroph originally living in association with  Trichodesmium spp. were collected from the top 10 m with a 130-\u03bcm plankton net, and colonies were hand-picked and incubated in sterile YBC-II medium at 24\u00b0C with a 12-h light (100\u2009\u03bcmol photons\u2009m\u22122\u2009s\u22121)/12-h dark cycle. The enrichment was grown under the aforementioned conditions for 5\u2009years prior to sequencing and was transferred to fresh medium every month. A 500-mL subsample of the enrichment was gravity filtered onto a 5.0-\u03bcm polycarbonate filter, and DNA was extracted using the Qiagen DNeasy PowerSoil kit. The DNA was sent to the University of Southern California Epigenome Center and sequenced on an Illumina MiSeq sequencer  using the MiSeq reagent kit v2 with 300 cycles. Reads were trimmed using Trimmomatic v0.38   . The MinNLEN:50) , BinSaniNLEN:50)  using deMicavibrionales strain TMED2 (GenBank assembly accession number GCA_002168225.1), and HetDA_MAG_SS10 was determined to be novel via GTDB-tk  value of 77.19%, was  GTDB-tk -generateg CheckM , and falbd complex (KEGG Orthology codes K00425 and K00426), which is associated with microaerobic respiration (TIGR03753), and rhodopsin (PFAM accession number PF01036), this organism is predicted to be capable of photoheterotrophy. HetDA_MAG_SS10 may also play a role in sulfur cycling, because it contains the dmdA gene for dimethylsulfoniopropionate (DMSP) demethylase, which is involved in DMSP degradation (HetDA_MAG_SS10 contains most of the genes required for glycolysis and the tricarboxylic acid (TCA) cycle and lacks ribulose-1,5-bisphosphate carboxylase-oxygenase (RuBisCo). It contains the cytochrome piration . Based oradation .In summary, our MAG is predicted to be photoheterotrophic and to have roles in sulfur cycling, with its nearest relatives being obligatory parasites.PRJNA719568. Raw reads are available in the SRA under accession number SRR14140256. The genome is available under BioSample accession number SAMN18613316.Raw sequences and MAGs are available under BioProject accession number"}
+{"text": "Previous studies have reported that the tumor heterogeneity and complex oncogenic mechanisms of proximal and distal colon cancer (CRC) are divergent. Therefore, we aim to analyze the differences between left-sided CRC (L_cancer) and right-sided CRC (R_cancer), as well as constructing respective nomograms.We enrolled 335 colon cancer patients (146 L_cancer patients and 189 R_cancer patients) from The Cancer Genome Atlas (TCGA) data sets, and 102 pairs of color cancer tissue and adjacent normal tissue (51 L_cancer patients and 51 R_cancer patients) from our hospital. Firstly, we analyzed the differences between the L_cancer patients and R_cancer patients, and then established the L_cancer and R_cancer prognostic models using LASSO Cox.R_cancer patients had lower survival than L_cancer patients. R_cancer patients had higher ESTIMATE and immune scores and lower tumor purity. These patterns of expression of immune checkpoint-related genes and TMB level were higher in R_cancer than in L_cancer patients. Finally, we using Lasso Cox regression analyses established a prognostic model for L_cancer patients and a prognostic model for R_cancer patients. The AUC values of the risk score for OS in L_cancer were 0.862 in the training set and 0.914 in the testing set, while those in R_cancer were 0.835 in the training set and 0.857 in the testing set. The AUC values in fivefold cross-validation were between 0.727 and 0.978, proving that the two prognostic models have great stability. The nomogram of L_cancer included prognostic genes, age, pathological M, pathological stage, and gender, the AUC values of which were 0.800 in the training set and 0.905 in the testing set. Meanwhile, the nomogram of R_cancer comprised prognostic genes, pathological N, pathological T, and age, the AUC values of which were 0.836 in the training set and 0.850 in the testing set. In the R_cancer patients, high-risk patients had a lower proportion of \u2018B cells memory\u2019, \u2018Dendritic cells resting\u2019, immune score, ESTIMATE score, immune checkpoint-related genes, and HLA-family genes, and a higher proportion of \u2018T cells follicular helper\u2019, \u2018Dendritic cells activated\u2019, and \u2018Mast cells activated\u2019.We found significant differences between L_cancer and R_cancer patients and established a clinical predictive nomogram for L_cancer patients and a nomogram for R_cancer patients. Additionally, R_cancer patients in low-risk groups may be more beneficial from immunotherapy.The online version contains supplementary material available at 10.1186/s12876-022-02585-3. Colon cancer (CRC) is one of the most common cancers and cause of cancer death globally, seriously endangering the health of patients . In receNomograms are widely used for prognosis in CRC patients. However, few previous studies have separately built predictive models to predict patient prognosis with respect to location. In this study, we separately build predictive models for L_cancer and R_cancer, identifying potential prognostic biomarkers for left and right CRC. Age, sex, histological classification, and so forth, are also important factors that can influence clinical outcomes and can improve the accuracy of models. Therefore, we also aimed to analyze the differences between L_cancer and R_cancer and construct respective nomograms for L_cancer and R_cancer, containing prognostic gene signatures and clinical prognostic factors, which are expected to allow for more accurate predictions in the prognosis of CRC, facilitating accurate diagnosis and treatment.https://portal.gdc.cancer.gov/), which includes transcriptome data for 332 CRC patients (146 L_cancer patients and 189 R_cancer patients) and somatic mutation data for 329 CRC patients (142 L_cancer patients and 187 R_cancer patients).The transcriptome data, somatic mutation data, and clinical information of CRC patients were downloaded from The Cancer Genome Atlas  adenocarcinoma arising from the caecum, ascending colon, or hepatic flexure. Any tumor that arises in the splenic flexure, descending colon or sigmoid colon was referred to as L_cancer.Using Kaplan\u2013Meier survival analysis, we evaluated the differences in survival between patients with different clinicopathological characteristics, between high-risk and low-risk groups and between the L_cancer and R_cancer groups in the data sets mentioned above. The \u2018survival\u2019 package in R was used to perform a two\u2010sided log\u2010rank test and univariate and multivariate Cox regression analyses .P\u2009<\u20090.05 were set as thresholds. To investigate the possible biological processes, cellular components, and molecular functions of DEGs, GO enrichment and KEGG pathway analyses were performed by using the R software package \u201cclusterProfiler\u201d [By using the \"edgeR\" package in R, we identified differentially expressed genes (DEGs) between L_cancer and R_cancer, L_cancer and L_normal, R_cancer and R_normal based on differential expression analysis. To screen for DEGs, |log2 FC (fold-change)|>\u20091 and rofiler\u201d \u201312.By using the \"GSVA\" package in R, we evaluated the t-scores and assigned pathway activity conditions to L_cancer and R_cancer patients to reveal pathway enrichment. The \"limma\" package in R was also used to show differences in pathway activation between L_cancer and R_cancer patients \u201315.https://cibersort.stanford.edu/download.php), is also needed in R. ESTIMATE was used to calculate immune, stromal, and ESTIMATE scores, as well as tumor purity, based on Yoshihara et al. [In each cancer sample, the relative proportions of 22 immune cell types were calculated using the CIBERSORT software . A file a et al. .P-values.The TMB was defined as: TMB\u2009=\u2009/(the whole length of exons). In a waterfall plot, the mutation profiles of two groups were compared using the maftools package . AfterwaLASSO Cox regression analysis with the R package glmnet was then used to identify hub genes associated with the prognosis of L_cancer or R_cancer, and a Risk Score was calculated for each sample using the screened hub genes following the following formula :\\documenP-value, HR, and 95% CI for each variable, using R's 'forest plot' package. Based on independent prognostic factors, the nomograms were generated in R using the rms, nomogramEx, and ggDCA packages. In the next step, Using calibration curves, we determined whether the predicted survival outcome matched the actual outcome. Moreover, training set decision curve analysis (DCA) and internal validation set DCA, which is a statistical method for assessing and comparing predictive models, was used to determine the clinical suitability of our established nomograms.To construct the nomograms, we used univariate and multivariate Cox regression analyses. Forest plots were used to display the For total RNA isolation, the TRIzol reagent by Invitrogen was used, and for complementary DNA synthesis, the PrimeScript RT reagent kit by Takara was used. RT-PCR was carried out using SYBR Premix Ex Taq I. GAPDH served as an internal control. Relative RNA abundances were calculated by using the standard 2-\u0394Ct method.A two-sided significance level of 0.05 was used to determine statistical significance in all analyses using R software (version 3.6.3). All significance levels were two-sided.P\u2009<\u20090.05). It is noteworthy that we observed lower survival after R_cancer versus L_cancer  and HLA family-related genes levels, which are considered biomarkers for predicting the efficacy of immunotherapy, between L_cancer and R_cancer patients and found that the expression levels of immune checkpoint-related genes and HLA family-related genes were significantly higher in R_cancer patients -related genes\u2019 mutation in each group, which showed that the L_cancer patient had MSI Fig.\u00a0D, E.By comparing the transcriptome data of L_cancer and L_normal groups, we identified 4788 up-regulated DEGs and 4062 down-regulated DEGs Fig.\u00a0A. The toLikewise, the DEGs between R_cancer and R_normal identified 6261 up-regulated DEGs and 4501 down-regulated DEGs  values of risk scores predicted in the testing set for 1-year, 3-year, 5-year, 7-year, and all-time OS were 0.597, 0.696, 0.722, 0.723, and 0.914, respectively , pathological N, pathological T, and age, which can be used to predict the survival rate; meanwhile, the R_cancer nomogram comprises prognostic genes , age, pathological M, pathological T, pathological stage, and gender, which can also be used to predict the survival rate.Numerous studies have confirmed that the right- and left-sided colons are distinct due to their embryological origins. The right-side colon originate from the midgut, whereas the left-side colon originate from the hindgut. In this study, we confirmed that there exist significant differences in the TMB and immune microenvironment between right- and left-sided CRC patients. Furthermore, right-sided CRC tend to have worse prognosis than left-sided CRC patients. The difference between right- and left-sided CRC patients' survival rates is might be caused by the higher frequency of mutations in addition to changes in the tumor microenvironment associated with tumor purity. According to recent research, mutation prevalence differs depending on side and location. RAS mutations declined from 70% in patients with right-sided CRC to 43% in those with left-sided CRC, while the number of BRAFV600 mutations increased from 10 to 22% between the same locations. Sigmoid and rectal tumors with left-sided mutations were more likely to harbor TP53 mutations than PIK3CA, BRAF, or CTNNB1 mutations [The tumor microenvironment (TME) refers to the physical environment around a tumor, including the immune cells, neurons, blood vessels, extracellular matrix, and other cellular functions related to tumor progression and therapy effects. We also confirmed that the immune microenvironment affects the prognosis of patients with CRC. Aggressively growing tumors create a highly immunosuppressive TME that depletes antitumor responses and promotes tumor progression , 20.Based on the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data approach, immune score and tumor purity can reveal information about the tumor's immune status. Low immune scores and high tumor purity have been associated with better prognoses in several studies \u201323. BaseGiven this, this study independently assessed the effect of the tumor microenvironment in L_cancer and R_cancer of high- and low-risk patients from two aspects (TMB and immune microenvironment), leading us to speculate that R_cancer\u2014especially low-risk R_cancer\u2014patients may benefit more from immunotherapy , 28. ValThe hub genes in the signature have previously been shown to be potential biomarkers. Relevant research has reported that PTGS2-driven inflammatory responses can induce tumor expression of microRNA-21, which can increase the level of the inflammatory mediator prostaglandin E2 (PGE2) by down-regulating PGE2-metabolizing enzymes, contributing to colorectal cancer development \u201332. PLEKThis study had some limitations. The signatures and nomograms constructed in this study using vast datasets from TCGA and our patient database were robust, but the study was still a retrospective one. Second, we explored the TMB and immune microenvironment landscape between right- and left-sided CRC patients and between patients in different risk groups; however, the study lacked experimental verification. Third, as previously noted, obtaining risk scores requires knowledge of ten genes expressed in tumor tissues, thereby increasing the difficulty of applying the nomograms. It appears that many molecular diagnostic or prognostic models have the same problem. Researchers and clinicians need to figure out how to simplify the application of these models in clinical settings. In the future, molecular detection technology may solve this dilemma. The constructed nomograms may be used routinely.We found significant differences between L_cancer and R_cancer patients, including clinical features, transcriptome, TMB, immune microenvironment landscape, suggesting that colon cancer can be classified and analyzed into different clinical types with respect to their differences in anatomical location and gene expression, thus aiding in the early diagnosis and prognosis of colon cancer. We established two clinical predictive nomograms in combination with clinical features to provide a basis for the personalized and precise treatment of L_cancer and R_cancer. These hub genes may become promising biomarkers for the diagnosis, treatment, and prognosis of colon cancer. Moreover, The findings support previous studies suggesting that proximal and distal CRC can be classified differently in terms of epidemiology, pathology, and genetics.Additional file 1: Fig. S1. (A) Kaplan-Meier survival analysis of ten hub genes  in L_cancer patients between high-expression and low-expression groups. (B) Kaplan-Meier survival analysis of ten hub genes  in R_cancer patients between high-expression and low-expression groups.Additional file 2: Fig. S2. (A) The comparison of immune infiltration levels between high-risk and low-risk groups in L_cancer patients, based on CIBERSORT. (B) The Stromal Score difference, Immune Score difference, ESTIMATE Score difference, and tumor purity difference between high-risk and low-risk groups in L_cancer patients. (C) The immune checkpoint-related gene expression levels in high-risk and low-risk groups in L_cancer patients. (D) The tumor mutation burden difference between high-risk and low-risk groups in L_cancer patients. (E) HLA-related gene expression level between high-risk and low-risk groups in L_cancer patients. (Notes: ns P>0.05).Additional file 3: Fig. S3. Show the correlation of L_cancer RiskScore and L_cancer hub genes expression with immune infiltration level in L_cancer patients."}
+{"text": "The human amnion is an intrauterine tissue which is involved in the initiation of parturition. In-depth understanding of gene expression signatures of individual cell types in the amnion with respect to membrane rupture at parturition may help identify crucial initiators of parturition for the development of specific strategies to prevent preterm birth, a leading cause of perinatal mortality.Six major cell types were revealed in human amnion including epithelial cells, fibroblasts and immunocytes as well as three other cell types expressing dual cell markers including epithelial/fibroblast, immune/epithelial and immune/fibroblast markers. The existence of cell types expressing these dual cell markers indicates the presence of epithelial-mesenchymal (EMT), epithelial-immune (EIT) and mesenchymal-immune (MIT) transitions in amnion at parturition. We found that the rupture zone of amnion exhibited some specific increases in subcluster proportions of immune and EMT cells related to extracellular\u00a0matrix remodeling and inflammation in labor. The non-rupture zone exhibited some common changes in subcluster compositions of epithelial and fibroblast cells with the rupture zone in labor, particularly those related to oxidative stress and apoptosis in epithelial cells and zinc ion transport in fibroblasts. Moreover, we identified that C\u2013C motif chemokine ligand 20 (CCL20) was among the top up-regulated genes in amnion epithelial cells, fibroblasts and immunocytes in the rupture zone at parturition. Studies in pregnant mice showed that administration of CCL20 induced immunocytes infiltration to tissues at the maternal\u2013fetal interface and led to preterm birth.Apart from the conventional epithelial, fibroblast and immunocytes, human amnion cells may undergo EMT, EIT and FIT in preparation for parturition. Intense inflammation and ECM remodeling are present in the rupture zone, while enhanced apoptosis and oxidative stress in epithelial cells and zinc ion transport in fibroblasts are present in amnion regardless of the rupture zones at parturition. CCL20 derived from the major cell types of the amnion participates in labor onset.The online version contains supplementary material available at 10.1186/s13578-022-00797-4. Preterm birth is the leading cause of perinatal morbidity and mortality \u20133. Our iThe human fetal membranes are composed of two layers of tissue, the amnion and chorion, which overlie the inner side of both the uterus body and the cervix . It is bAlthough the anatomical layers of fetal membranes may vary across different species, the inner layer is consistently the amnion membrane. Accumulating evidence indicates that the amnion is not only the most tensile layer of the membranes, but is also the source of crucial signals which initiate labor and parturition \u201315. In tTo address the issues described above, we performed scRNA-seq on human amnion cells isolated from both ZAM and non-ZAM regions in labor, and generated a novel comprehensive transcriptomic profile of single cell signatures. Six major cell types were identified in the amnion including conventional epithelial, fibroblast and immune cells as well as cell types expressing dual cell markers including epithelial/fibroblast, epithelial/immune and fibroblast/immune markers. Although the non-ZAM region manifested some changes, the ZAM region exhibited the most pronounced changes in the proportions of cell subclusters with unique signatures of gene expression in labor. Among these signatures, the chemokine C\u2013C motif chemokine ligand 20 (CCL20) was identified as one of the top up-regulated genes in all the major cell types at parturition. The crucial role of CCL20 in labor initiation was further validated in both human and mouse studies.KRT6A, KRT14, etc.)/fibroblast , epithelial /immune  or fibroblast /immune  cell markers  or term elective c section without labor . Paired ZAM and non-ZAM tissues were sampled from the amnion of each woman with spontaneous labor, and designated as term labor-proximal  and term labor-distal  respectively. In the case of elective c section without labor, tissue was sampled only from the artificial rupture site over the cervix, which was designated as term non-labor-proximal . Total amnion cells were isolated from the tissues sampled above for subsequent scRNA-seq using a commercial 10\u2009\u00d7\u2009Genomics platform  was among those highly expressed genes, which is known related to alleviation of oxidative stress and apoptosis  N025). Pregnancies with complications including preeclampsia, gestational diabetes, fetal growth restriction, and chorioamnionitis were excluded from this study. For scRNA-seq, amnion tissue samples were collected from four women with spontaneous labor at term  and four women undergoing elective c section without labor at term . In the case of TL, paired amnion tissues were sampled from the ZAM [designated as term labor-proximal (TL_P)] over the cervix and non-ZAM [designated as term labor-distal (TL_D)], 10\u00a0cm distal to the ZAM. In the case of TNL, amnion tissue was sampled at the artificial rupture site over the cervix, which was designated as term non-labor-proximal (TNL_P). For validation of the changes of crucial gene expression in amnion in parturition observed with scRNA-seq, another twenty-eight pregnant women were recruited into TL and TNL groups for analysis with qRT-PCR and ELISA. The number of pregnant women was given in the figure legend of each of the studies. The clinical information for all subjects is given in Additional file Fresh amnion was minced into small pieces and digested twice with 0.125% trypsin  followed by further digestion with 0.1% collagenase . All digestions were combined and centrifuged for collection of amnion cells. To avoid any cell aggregates from the single-cell suspension, the cell suspension was filtered through a 40-\u03bcm nylon mesh. Collected cells were washed and resuspended in calcium/magnesium-free phosphate-buffered saline (PBS) containing 0.1% bovine serum albumin. Cell viability was assessed by trypan blue  staining. Cell viability\u2009\u2265\u200990% was used for library generation on 10\u2009\u00d7\u2009Genomics system. Briefly, the cells were partitioned for genetically engineered model (GEM) generation and barcoded cDNA library construction using 10\u2009\u00d7\u2009Chromium Single-cell kits following the manufacturer\u2019s protocol. Then all libraries were subjected to quality tests on a Fragment Analyzer 2100  and sequenced on the Illumina sequencing platform .cellranger mkfastq. Subsequent read alignments and transcript counting were performed individually for each sample using cellranger counts with standard parameters. Then, unique molecular identifier (UMI) count matrix was imported into the Seurat R package (version 3.2.0) [Raw sequencing data were processed using the Cell Ranger software pipeline (version 3.1.0) provided by 10\u2009\u00d7\u2009Genomics. Raw binary base call files were demultiplexed using the n 3.2.0) . Qualityn 3.2.0) .After quality standardization, the Seurat R package was applied to analyze the scRNA-seq data . FirstlyFor subcluster analysis, cell types with cell number large enough were extracted via the \u201cSubsetData\u201d function following primary annotation. \u201cFindClusters\u201d and \u201cFindAllMarkers\u201d functions were applied, and the selected cells were reclustered by tSNE and annotated by the dominant cell markers.To confirm the existence of cell types expressing dual cell markers observed with scRNA-seq, immunofluorescence co-staining of the amnion tissue was carried out. Amnion tissue was collected from elective c section without labor at term and fixed with 10% neutral buffered formalin at room temperature for 24\u00a0h. The tissue was then transferred to 70% ethanol, followed by paraffin embedding. Paraffin-embedded amnion tissue was sectioned at 5\u00a0\u03bcm thickness. After deparaffination, the section was permeabilized with 0.4% Triton X-100. After blocking with normal serum, the section was incubated with the combination of two different primary antibodies representing different cell types overnight at 4\u00a0\u00b0C, followed by incubation with respective Alexa Fluor 488- and 594-conjugated secondary antibodies (Life Technologies) at 37\u00a0\u00b0C for 2\u00a0h. Primary antibodies used for immunofluorescence co-staining are as follows: immune cell markers: rabbit anti-CD45 ; mesenchymal cell markers: mouse anti-vimentin , rabbit anti-vimentin , mouse anti-N-cadherin ; epithelial cell markers: rabbit anti-E-cadherin , mouse anti-E-cadherin , mouse anti-cytokeratin 14 (KRT14) . Nuclei were counterstained with 4\u2019,6-diamidino-2-phenylindole . The slides were examined under a fluorescence microscope .DDX3Y, USP9Y EIFAY, etc.) encoded in the Y chromosome of the male fetus. Gene expression matrices were used to perform Seurat alignment and t-SNE clustering as described above. Detection of any expression of the Y chromosome\u2013encoded genes indicated fetal origin, otherwise an indication of maternal origin.To analyze the origin of immune cells detected in the amnion with scRNA-seq, samples from only male fetuses were analyzed to take the advantage of the unique genes  in Seurat package . P valuehttps://github.com/aertslab/SCENIC.The active TFs in the major cell types of human amnion in different groups were analyzed with SCENIC analysis utilizing the motifs database for RcisTarget and GRNboost . BrieflyTo confirm the changes of the signature molecule CCL20 observed with scRNA-seq in amnion at the rupture site with labor, qRT-PCR, ELISA and immumohistochemical staining were conducted.CCL20 mRNA measurement, total RNA was extracted from the homogenized ground tissue using total RNA isolation kit . After examination of RNA quality, reverse transcription was carried out using a Prime-Script RT Master Mix Perfect Real Time Kit . The amount of CCL20 mRNA was determined with qRT-PCR using the above reverse-transcribed cDNA and power SYBR\u00ae Premix Ex Taq\u2122 (TaKaRa) following a previously described protocol [\u2206\u2206Ct method. Primer sequences for PCR were as follows: GAPDH, forward, 5\u2019-CCCCTCTGCTGATGCCCCCA-3\u2019 and reverse, 5\u2019-TGACCTTGGCCAGGGGTGCT-3\u2019; CCL20, forward, 5\u2019- TCCTGGCTGCTTTGATGTCA-3\u2019 and reverse 5\u2019- CAAAGTTGCTTGCTGCTTCTGA-3\u2019. For CCL20 protein measurement, grounded tissue was homogenized and lysed in ice-cold RIPA lysis buffer containing a protease inhibitor cocktail. After centrifugation, the supernatant was collected for measurement of CCL20 protein with an ELISA kit  following the protocol provided by the manufacturer.For qRT-PCR and ELISA, RNA and protein were extracted from amnion tissue obtained from twenty-eight pregnant women with and without labor. Briefly, amnion tissue was cut within or 10\u00a0cm distal to the spontaneous rupture site with labor and from the artificial rupture site without labor, and then grounded in liquid nitrogen. For protocol . Houseke2O2 following deparaffination. After blocking with normal serum, the section was incubated with a primary antibody against CCL20  at 1:100 dilution or non-immune serum for negative control overnight at 4\u25e6C. After washing with PBS, the section was incubated with a biotinylated secondary antibody for 2\u00a0h. After washing, the avidin\u2013biotin complex reagent conjugated with horseradish peroxidase  was applied to react with the secondary antibody. The substrate 3-amino-9-ethyl carbazole (Vector Laboratories) was then added to develop peroxidase activity as a red color. The slide was counterstained with hematoxylin and mounted for examination under a microscope (Zeiss).For immumohistochemical staining, the amnion tissue collected from deliveries with and without labor at term was used. The endogenous peroxidase activity of the tissue section was quenched with 0.3% HC57BL/6 mice  were used following accepted standards for animal care, which was approved by the Institutional Review Board of Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University. Mice aging from 10 to 13\u00a0weeks were mated. When a vaginal plug was present, it was counted as 0.5\u00a0days post-coitus (dpc). To observe whether injection of CCL20 could induce preterm birth, recombinant CCL20 (10\u00a0\u03bcg/kg BW) or equivalent maximal amount of LPS (1\u00a0ng/kg BW) remained in the preparation of recombinant CCL20 was injected intraperitoneally on 17 dpc. Some of the mice were allowed to deliver spontaneously for observation of delivery time and fetal demise, and some were sacrificed 12\u00a0h after injection for collection of fetal membranes, uterus, decidua and placentae to examine the infiltration of leukocytes with immunohistochemical staining of CD45 as described above with a CD45 antibody  at 1:100 dilution.Paired or unpaired Student\u2019s t-tests where appropriate was used to assess the difference between TL_P and TL_D groups or between TL_P and TNL_P groups in terms of subcluster proportion and CCL20 expression. Unpaired Student\u2019s t-test was performed to compare the delivery time of pregnant mice with or without CCL20 injection. Quantitative data are presented as mean\u2009\u00b1\u2009SEM. Significance was set at P\u2009<\u20090.05.Additional file 1: Fig. S1. Information of individual human amnion sample. Fig. S2. Expression of established markers in six cell types of the human amnion. Fig. S3. Feature plots of immune cell marker expression in immunocytes of the human amnion. Fig. S4. GO analysis of highly-expressed genes in subclusters 1 and 4 of immunocytes of the human amnion. Fig. S5. Top DEGs in individual subclusters of EpC_FB of the human amnion. Fig. S6. Immunohistochemical staining of CCL20 in the human amnion in TNL_P, TL_D and TL_P groups. Fig. S7. Negative control of immunohistochemical staining of CD45 in mouse intrauterine tissues. Table S1. Information of each individual sample. Table S2. Cell number and proportion of each cell type. Table S3. Demographic and clinical characteristics of recruited pregnant women.Additional file 2: Dataset 1. DEGs in epithelial cells between TL_P and TNL_P groups. Dataset 2. DEGs in epithelial cells between TL_P and TL_D groups. Dataset 3. DEGs in fibroblast cells between TL_P and TNL_P groups. Dataset 4. DEGs in fibroblast cells between TL_P and TL_D groups. Dataset 5. DEGs in immunocytes between TL_P and TNL_P groups. Dataset 6. DEGs in immunocytes between TL_P and TL_D groups. Dataset 7. Intersection of DEGs between TL_P and TNL_P groups with DEGs between TL_P and TL_D groups in epithelial cells. Dataset 8. Intersection of DEGs between TL_P and TNL_P groups with DEGs between TL_P and TL_D groups in fibroblast cells. Dataset 9. Intersection of DEGs between TL_P and TNL_P groups with DEGs between TL_P and TL_D groups in immunocytes. Dataset 10. Specific DEGs in epithelial cells between TL_P and TL_D groups, TL_P and TNL_P groups. Dataset 11. Specific DEGs in fibroblast cells between TL_P and TL_D groups, TL_P and TNL_P groups. Dataset 12. Specific DEGs in immunocytes between TL_P and TL_D groups, TL_P and TNL_P groups."}
+{"text": "Tenacibaculum sp. strains that were isolated from Cyclopterus lumpus (lumpfish) were investigated to elucidate possible routes of transmission between Salmo salar  and lumpfish.Draft genome sequences of 23  Lepeophtheirus salmonis) is the most common parasite in Norwegian salmon farming at sea and constitutes a serious welfare threat to the Atlantic salmon itself and to wild salmonid populations , nonmotile rods (by phase-contrast microscopy) were subcultured and cryopreserved at \u221280\u00b0C. DNA from revived cultures on marine agar was extracted on a QIAcube (Qiagen) utilizing a QIAamp DNA QIAcube minikit, following the manufacturer\u2019s recommendations. Twenty-three sequencing libraries were generated with a Nextera DNA Flex library preparation kit (Illumina), following the manufacturer\u2019s standard protocol. Each library was sequenced on a MiSeq system (Illumina) with a v3 flow cell and 300-bp paired-end chemistry.The salmon louse (ulations . Cleanerlum spp. , and it GCA_001483385.1 and GCA_900239185.1 were used as references for Tenacibaculum finnmarkense genomovar finnmarkense, GenBank accession numbers GCA_900239485.1 and GCA_900239495.1 for Tenacibaculum finnmarkense genomovar ulcerans, and GenBank accession numbers GCA_900239455.1 and GCA_900239305.1 for Tenacibaculum dicentrarchi (The resulting numbers of reads per sample are listed in ntrarchi .PRJNA777885, with accession numbers for each assembly as shown in This whole-genome shotgun project has been deposited in DDBJ/ENA/GenBank as BioProject"}
+{"text": "Klebsiella pneumoniae is one of the primary bacterial pathogens that pose a significant threat to global public health because of the lack of available therapeutic options. Phage therapy shows promise as a potential alternative to current antimicrobial chemotherapies. In this study, we isolated a new Siphoviridae phage vB_KpnS_SXFY507 against KPC-producing K. pneumoniae from hospital sewage. It had a short latent period of 20\u2009min and a large burst size of 246 phages/cell. The host range of phage vB_KpnS_SXFY507 was relatively broad. It has a wide range of pH tolerance and high thermal stability. The genome of phage vB_KpnS_SXFY507 was 53,122\u2009bp in length with a G\u2009+\u2009C content of 49.1%. A total of 81 open-reading frames (ORFs) and no virulence or antibiotic resistance related genes were involved in the phage vB_KpnS_SXFY507 genome. Phage vB_KpnS_SXFY507 showed significant antibacterial activity in vitro. The survival rate of Galleria mellonella larvae inoculated with K. pneumoniae SXFY507 was 20%. The survival rate of K. pneumonia-infected G. mellonella larvae was increased from 20 to 60% within 72\u2009h upon treatment with phage vB_KpnS_SXFY507. In conclusion, these findings indicate that phage vB_KpnS_SXFY507 has the potential to be used as an antimicrobial agent for the control of K. pneumoniae.Carbapenem-resistant  Klebsiella pneumoniae is a gram-negative bacterium that belongs to the Enterobacteriaceae family. It is a part of the healthy microbiome of individuals and colonized in gastrointestinal tract, respiratory tract, urogenital tract, skin, and, nasopharynx  appeared  broth at 37\u00b0C with shaking at 200\u2009rpm and stored at \u221280\u00b0C in 30% glycerol (v/v).Carbapenem-resistant K. pneumoniae-specific khe gene  of the filtered lysate was prepared in SM buffer and then mixed with K. pneumoniae SXFY507 followed by incubating at room temperature for 5\u2009min. The mixture was added to 5\u2009ml of top agar (BHI with 0.75% agar), and then poured onto a plate (1.5% agar), and incubated overnight at 37\u00b0C to form phage plaques. For phage purification, a single plaque was picked and then resuspended in the SM buffer. Double-layer agar method was performed for forming and screening the phage plaques. The experiment of phage purification was repeated three more times to obtain the purified phage. The purified phage preparation was stored at 4\u00b0C for further studies.The untreated sewage sample was collected from a public hospital in Taiyuan, China. Phage isolation and purification were performed as previously described . Briefly16\u2009PFU/ml) was spotted onto a carbon-coated copper grid and then negatively stained with 2% phosphotungstic acid for 5\u2009min. The morphology of phage vB_KpnS_SXFY507 was examined by transmission electron microscopy  at an acceleration voltage of 80\u2009kV.The purified phage  followed by incubation at 37\u00b0C 160\u2009rpm for 5\u2009h. The mixture was centrifuged to remove bacterial cells, and the supernatant was filtered by a 0.22\u2009\u03bcm filter. The titer of the phage was determined by the double-layer agar method. The proportion that generated the highest phage titer was considered as the optimal MOI.The multiplicity of infection (MOI) determination was carried out as previously described . Phage vK. pneumoniae SXFY507 at an MOI of 0.001. After adsorption at room temperature for 5\u2009min, the mixture was centrifuged at 5,000\u2009\u00d7\u2009g for 10\u2009min at 4\u00b0C to remove the unabsorbed phage in the supernatant. The centrifuged precipitation was then resuspended in BHI broth followed by incubation at 37\u00b0C 160\u2009rpm. Samples were taken at 10\u2009min intervals within 120\u2009min and then centrifuged the samples to obtain the supernatant. The supernatant was filtered through a 0.22\u2009\u03bcm filter and then titrated by the double-layer agar method. The experiment was repeated three times.The one-step growth curve was determined to measure the incubation period and the burst size of the phage . Phage vvia the double-layer agar method.To investigate the effect of different thermals and pH on the activity of phage vB_KpnS_SXFY507, the phage was treated with different temperatures and pH values. Briefly, phage vB_KpnS_SXFY507 was incubated at different temperatures  and pH values (2.0\u201312.0) for 60\u2009min, respectively. Then the titers of the treated phage were determined K. pneumoniae strains  was spotted onto the double-layered plate, and then quiescence the plate at room temperature until the phage suspension was absorbed. The plates were incubated overnight at 37\u00b0C to allow the formation of plaques.The host range of phage vB_KpnS_SXFY507 was determined by a spot test using 27 The genomic DNA of phage vB_KpnS_SXFY507 was extracted using the protease K/SDS method as previously described . The NEBhttps://www.ncbi.nlm.nih.gov/refseq/, Accessed on 8 July 2022) databases. The putative transfer RNA (tRNA)-encoding genes were searched using tRNA scan-SE (Open-reading frames (ORFs) were predicted using RAST (Rapid Annotation using Subsystem Technology) 2.0  combined scan-SE . The pre scan-SE , and the scan-SE  and ResF scan-SE . Multipl scan-SE  and BLASThe amino acid sequences of the terminase large subunit (TerL) of indicative phages were aligned using MUSCLE 3.8.31 . Unroote600\u2009=\u20090.6) K. pneumoniae SXFY507 at MOI of 1 and 0.001, respectively. The mixture was incubated at 37\u00b0C 160\u2009rpm for 5\u2009h. The OD600 and bacterial CFU of the mixture were detected every 1\u2009h within 5\u2009h. The experiment was repeated three times.Phage vB_KpnS_SXFY507 was mixed with the log-phase  was injected into the last right proleg of larvae by a microsample syringe, and an hour after infection, 10\u2009\u03bcl of phage vB_KpnS_SXFY507 was injected into the last left proleg of larvae for treatment at MOI of 100, 10, 1, and 0.001. The other three groups were injected with PBS (10\u2009\u03bcl/each), phage vB_KpnS_SXFY507 , and K. pneumoniae SXFY507  on the right last proleg of larvae, respectively. The number of surviving G. mellonella larvae was observed and recorded every 8\u2009h for 72\u2009h. The survival curves were plotted using the Kaplan\u2013Meier method, and differences in survival rates between groups were calculated using the log-rank test. Statistical analysis was performed with SPSS statistics software (version 27.0).eumoniae . A totalKlebsiella phage vB_KpnS_SXFY507 genome was submitted to GenBank under accession number ON045001.The complete sequence of K. pneumoniae-specific khe gene and demonstrated class A carbapenemase activity. The PCR screening assay of carbapenemase and extended-spectrum beta-lactamases genes detected blaKPC, blaCTX-M-9G, and blaSHV (but none of the other bla genes tested) in K. pneumoniae SXFY507.Strain SXFY507 harbored K. pneumoniae SXFY507 as the indicator bacterium. Phage vB_KpnS_SXFY507 formed large, clear plaques on the double-layer plate , morphogenesis (16 ORFs), lysis (two ORFs), DNA-packing protein (two ORFs), and, hypothetical protein 52 ORFs; . In addiKlebsiella phage KL3 (GenBank accession number OK019720), followed by the Klebsiella phage KMI8 (GenBank accession number MN101222) shared 93% coverage and 86.2% identity. The average nucleotide identity (ANI) values between phage vB_KpnS_SXFY507 and phage KL3, phage KMI8 were 96 and 86.33%, respectively. The result indicated that phage vB_KpnS_SXFY507 and phage KL3 but not KMI8 belong to the same group  with the me group . Furtherme group .Klebsiella phage KL3 . Both the group of larvae inoculated with phage and the PBS control group were all survived , exonuclease, DNA primase, and DNA helicase, respectively. SSBs bind with high affinity to single-stranded DNA, and DNA exonuclease is a multifunctional hydrolase, which plays a role in DNA replication, DNA mismatch repair, and DNA double-strand break repair . DNA priPhage vB_KpnS_SXFY507 has 16 ORFs encoded proteins that might engage in phage morphogenesis. ORF3 encoded portal protein, which acted as DNA sensors that facilitate packaging and release of the genome . MeanwhiORF42 and ORF43 encoded holin and lysin were involved in the lysis module. The DNA-packing proteins in phage vB_KpnS_SXFY507 were terminase small subunit (TerS) encoded by ORF1 and terminase large subunit (TerL) encoded by ORF2. When the TerS recognizes the concatemeric viral DNA, the step of packing phage DNA into procapsids is initiated. Then, the TerL assembles onto the TerS:DNA complex. TerL possesses ATPase and nuclease activity, therefore the TerL can cut the DNA and package it in an ATP-dependent process .K. pneumoniae. Comparative genomic analysis and phylogenetic tree showed that phage vB_KpnS_SXFY507 has a close evolutionary relationship with KL3, and the two phages belong to a new group.The remaining 52 ORFs encoded hypothetical proteins, and further study is required. Moreover, no antibiotic resistance or virulence related genes were detected in the genome of phage vB_KpnS_SXFY507, suggesting that phage vB_KpnS_SXFY507 might be theoretically safe for the control of In vitro, phage vB_KpnS_SXFY507 showed significant antibacterial activity at MOI of 1 and 0.001, suggesting that it could be used as a biocontrol agent to control the spread of K. pneumoniae SXFY507 in vitro. Galleria mellonella is a flexible and rapid tool to assess phage efficacy  of phage vB_KpnS_SXFY507 were used for the treatment experiments. The survival rates were increased to 50 and 60% upon treatment with phage vB_KpnS_SXFY507 at the MOI of 10 and 100. The result was consistent with the previous study that the higher doses of phage led to higher survival rates of G. mellonella larvae  can be found at: Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.JF, LY, and CW conceived the study. JF, FL, LS, LD, LG, and HW performed the experiment and computational analysis. JF, FL, LY, and CW wrote the article. All authors contributed to the article and approved the submitted version.This work was supported by the National Natural Science Foundation of China (82002207) and Program of Graduate Innovation Research of Shanxi Province (2021Y122).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher."}
+{"text": "High hsa_circ_0003570 expression was an independent prognostic factor for overall survival (hazard ratio (HR), 0.541; 95% confidence interval (CI), 0.327\u20130.894; p = 0.017) and progression-free survival . Hsa_circ_0003570 is a potential prognostic biomarker in patients with HCC, and further validation of hsa_circ_0003570 is needed.Circular RNAs (circRNAs) are potential biomarkers owing to their stability, tissue specificity, and abundance. This study aimed to evaluate the clinical significance of hsa_circ_0003570 expression and to investigate its potential as a biomarker in hepatocellular carcinoma (HCC). We evaluated hsa_circ_0003570 expression in 121 HCC tissue samples, its association with clinicopathological characteristics, and overall and progression-free survival. Hsa_circ_0003570 expression was downregulated in HCC tissues. Low hsa_circ_0003570 expression was more common in tumors larger than 5 cm (odds ratio (OR), 6.369; 95% confidence interval (CI), 2.725\u201314.706;  Circular RNAs (circRNAs) are single-stranded, covalently closed RNA molecules produced from pre-mRNAs through backsplicing. Advances in RNA sequencing and bioinformatics tools have enabled the discovery of various circRNAs and their functions . CircRNAIn addition to being cancer hallmarks, circRNAs have also been shown to be highly stable and can be found in exosomes, saliva, urine, and plasma ,9. Thus,Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and the most common cause of death in people with cirrhosis . The proFor investigating circRNAs as a biomarker in HCC, hsa_circ_00033570 was previously reported and analyzed in HCC tissues . A previn = 30) and those lost to follow-up (n = 9). Two patients were not checked for target circRNAs due to a shortage of tissue samples. Finally, 121 patients were included in the analysis. The median follow-up period was 24.5 months, ranging from 0.7 to 69.8 months.This study included 162 patients with HCC who underwent a diagnostic biopsy or surgical resection at a single center between March 2015 and August 2016, and who have previously been studied . We exclThe patients were monitored every three months using liver dynamic computed tomography (CT) or gadoxetic acid disodium\u2013enhanced liver magnetic resonance imaging. HCC recurrence was recognized if a tumor exceeded 1 cm and showed contrast enhancement in the arterial phase and washout in the portal or delayed phase. Response Evaluation Criteria in Solid Tumors (version 1.1) was used to evaluate tumor response. We defined overall survival as the time between the date of initial HCC diagnosis, and either the date of death from any cause or the date of last contact with the patient. Progression-free survival was defined as the time between the initial date of HCC diagnosis, and either the first event of recurrence or progression or until death from any cause.The tissue specimens for tumors and adjacent nontumor tissues were immediately stored at 4 \u00b0C for 24 h in RNAlater reagent  and then stored at \u221280 \u00b0C. We collected patients\u2019 clinicopathological data, including age, sex, etiology of liver disease, Child\u2013Tucotte\u2013Pugh (CTP) class, laboratory findings, \u03b1-fetoprotein (AFP) level, tumor size and number, presence of macrovascular invasion, and tumor stage. The tumor-node metastasis (TNM) stage, based on the criteria of the American Joint Committee on Cancer, 8th edition, and Barcelona Clinic Liver Cancer (BCLC) stage, was adopted. This study was approved by the institutional review board (KNUH-2014-04-056-001) and was conducted in accordance with the ethical guidelines of the 1975 Declaration of Helsinki. Written informed consent was obtained from all patients prior to sample collection.Total RNA was extracted from the frozen tissues using QIAzol Lysis Reagent  according to the manufacturer\u2019s instructions. Total RNA samples were verified for concentration and purity using NanoPhotometer N60 . Synthesis of cDNA was reverse transcribed using a High-Capacity cDNA Reverse Transcription Kit  following the manufacturer\u2019s instructions.\u2212\u0394\u0394Ct method.Quantitative real-time polymerase chain reaction (qRT-PCR) was performed with the SYBR Green PCR Master Mix  following the manufacturer\u2019s instructions. For circular RNA expression analysis, the primers of hsa_circ_0003570 were designed, including a gap junction of circular RNA. The primers\u2019 sequences of hsa_circ_0003570 were 5\u2032- CAA GAT GGC ACA GCA GCA CAC GC -3\u2032 (forward) and 5\u2032- ATG CTG GTG CTC GGT TGG TC -3\u2032. The primers\u2019 sequences of lyceraldehyde 3-phosphate dehydrogenase (GAPDH), as a normalizer, were 5\u2032- GGA AGG TGA AGG TCG GAG TC -3\u2032 (forward) and 5\u2032- GTT GAG GTC AAT GAA GGG GTC -3\u2032. All of the primers were synthesized by Bionics . qRT-PCR was performed in triplicate and amplification of hsa_circ_0003570 was confirmed via melt curve analysis. The relative expression results from the qRT-PCR were calculated with the 2t-test was used. To compare the clinicopathological characteristics between the two groups according to the hsa_circ_0003570 expression, we used the chi-square or Fisher\u2019s exact probability test. Using the Kaplan\u2013Meier method and log-rank test, we analyzed patient survival and compared survival between the groups. To identify the predictors of survival, we performed a logistic regression based on the Cox proportional hazards model. The statistical significance was set at p < 0.05. We conducted all the analyses using R statistical software 3.6.3 , and the GraphPad Prism 6 program for Windows  was used to generate figures. Categorical data are expressed as numbers (%), and numerical data are expressed as the mean and standard deviation for normally distributed data. For non-normally distributed data, the data are expressed as medians with interquartile ranges. To analyze the differences in hsa_circ_0003570 expression between the tumor and adjacent nontumor tissues, a paired p = 0.001). p < 0.001); vessel invasion ; advanced TNM stage ; higher BCLC stage ; and higher AFP .The differences in patient characteristics based on the expression levels of hsa_circ_0003570 are shown in The overall survival of the patients differed significantly according to the hsa_circ_0003570 expression a. The cup = 0.003); multiple tumors ; AFP level > 200 ng/mL ; poor CTP class ; chronic hepatitis B ; and curative treatment .p = 0.017); poor CTP class ; and curative treatment  as independent prognostic factors for overall survival.Multivariate analysis identified high hsa_circ_0003570 expression ; multiple tumors ; AFP level >200 ng/mL ; poor CTP class ; and curative treatment .p = 0.048); poor CTP class ; and curative treatment  were independent prognostic factors for progression-free survival.Multivariate analysis identified that high hsa_circ_0003570 expression (HR, 0.633; 95% CI, 0.402\u20130.997; CircRNAs are well-known potential biomarkers in cancer research because of their stability , tissue We also discovered that hsa_circ_0003570 was downregulated in HCC compared to noncancerous human liver tissue. Its expression level was associated with various clinicopathological characteristics, particularly tumor size, vessel invasion, TNM stage, BCLC stage, and AFP level. Low expression of hsa_circ_0003570 was associated with poor overall and progression-free survival, among other clinical variables. Our results are consistent with those of a previous study . PreviouThe difference between the two studies is that we enrolled patients with advanced-stage HCC and considered the survival and progression of HCC according to the treatment modalities. To the best of our knowledge, this study is the first to discover an association between hsa_circ_0003570 and survival and progression in HCC patients. This raises the possibility of using hsa_circ_0003570 as a prognostic biomarker for HCC.However, this study had some limitations. First, the retrospective nature of the study introduces a selection bias. We excluded 11 patients with HCC who were missing medical records due to loss to follow-up. Second, we could not acquire further pathological information, such as microvascular invasion or cell differentiation, owing to a needle biopsy performed in the advanced stage of HCC. Moreover, hsa_circ_0003570 cannot reflect the tumor heterogeneity of HCC. Therefore, it is necessary to recruit a larger number of HCC patients and validate hsa_circ_0003570 as a noninvasive biomarker using patient serum or urine. Third, we did not investigate the underlying mechanism of hsa_circ_0003570. Based on these results, we can only hypothesize that these circRNAs may act as a tumor suppressor.In conclusion, we explored the clinical significance of hsa_circ_0003570 in patients with HCC. It was associated not only with the clinicopathological characteristics of HCC, but also with the survival and progression of HCC patients. Hsa_circ_0003570 can be a potential prognostic biomarker in patients with HCC, but further validation of hsa_circ_0003570 is needed in a future study."}
+{"text": "A chimeric fusion of T7 RNAP and deaminase edits the DNA under the T7 promoter in plant cells. It directs the continuous synthetic evolution of OsALS to produce variants with herbicide resistance. Nicotiana benthamiana transient assays to target a transgene expressing GFP under the control of the T7 promoter and observed C-to-T conversions. We then targeted the T7 promoter-driven acetolactate synthase sequence that had been stably integrated in the rice genome and generated C-to-T and G-to-A transitions. We used herbicide treatment as selection pressure for the evolution of the acetolactate synthase sequence, resulting in the enrichment of herbicide-responsive residues. We then validated these herbicide-responsive regions in the transgenic rice plants. Thus, our system could be used for the continuous synthetic evolution of gene functions to produce variants with improved herbicide resistance.Synthetic directed evolution via localized sequence diversification and the simultaneous application of selection pressure is a promising method for producing new, beneficial alleles that affect traits of interest in diverse species; however, this technique has rarely been applied in plants. Here, we designed, built, and tested a chimeric fusion of T7 RNA Polymerase (RNAP) and deaminase to enable the localized sequence diversification of a target sequence of interest. We tested our T7 RNAP\u2013DNA base editor in  In crop plants, natural evolution and artificial selection  relied on naturally occurring genetic variation. Increasing genetic variation in crop plants provides a basis for breeding and developing new traits of value. Genetic variation generated through random mutagenesis provides a basis for creating protein variants and useful alleles for breeding, and for evolution to develop new traits . Random Synthetic directed evolution relies on generating targeted or random genetic variability, followed by screening under selection pressure to identify beneficial mutations within a gene or pathway of interest . ConventEscherichia coli strain XL1-red  and adenine base editors (ABEs), are powerful tools for introducing point mutations for genome engineering and synthetic evolution. Various naturally occurring cytosine deaminases, such as activation-induced cytidine deaminase (AID), rAPOBEC1, and pmCDA1 convert cytosine to uracil . The U\u2022GOsALS were used to produce variants tolerant to the herbicide bispyribac sodium (BS)  . Howeverium (BS) . Using COryza sativa) callus with CRISPR/Cas9 along with a pool of sgRNAs targeting the splicing factor locus OsSF3B1. Mutants were produced due to non-homologous end joining (NHEJ) under selection pressure imposed by the splicing inhibitor GEX1A. The recovered SF3B1 variants showed different levels of tolerance of GEX1A (CRISPR-directed evolution (CDE) was recently performed by transforming rice  . The tarGFP sequence and achieved localized sequence diversification at high efficiency. Most of the transitions were C-to-T substitutions. Single-vector and two-vector approaches for the evolution of OsALS allowed us to recover gene variants conferring herbicide resistance, a trait of interest in rice. We identified herbicide-responsive residues in the resulting acetolactate synthase (ALS) protein, and generated ALS variants conferring herbicide resistance. This technique opens up myriad possibilities for synthetic evolution in plants, including the development of crops with increased resistance to changing climate conditions, resistance to pests and pathogens, and improved productivity.Here, we established a T7 RNAP deaminase editor system for targeted mutagenesis for the first time in plant cells. In transient experiments, we targeted the Bacteriophage T7 RNAP transcribes DNA sequences under the control of the T7 promoter. A fusion protein of T7 RNAP with a cytidine deaminase (Editor) could continuously edit the DNA bases downstream of the T7 promoter (Target) . Once th35S promoter. The sequence encoding the cytidine deaminase AID was removed for the mock control experiments   in the periments .N. benthamiana leaves using GFP as a target. GFP was expressed under the control of the T7 promoter as a binding target of T7 RNAP, the pTarget. For continuous expression of GFP, the constitutive CaMV 35S promoter was inserted upstream of the T7 promoter (Agrobacterium tumefaciens via electroporation and co-infiltrated into N. benthamiana leaves for transient expression (We tested this editing system by performing transient assays in promoter . The binpression . We assepression .GFP sequence (nd position to isoleucine (ATT), whereas the second C > T substitution is a silent mutation of asparagine (AAC to AAT) at the 198th position  showed C > T substitutions of the sequence . The firposition . ImportaGFP sequence downstream of the T7 promoter were amplified . To this end, we cloned OsALS under the control of the T7 promoter in various rice expression vectors as a pTarget  and histidine 415 to tyrosine (CAC to TAC) (GC to GAC). The other mutation in plant #2 was a silent mutation at proline 248 (CCA to CCG) (GCA to ACA) and glycine 485 to aspartic acid (GCG to ACG) (G to CAA) and serine 132 (TCC to TCT)  plants showed at least one read with a base substitution, whereas in the two-vector experiment, three of eight (37.5%) plants contained at least one read with base substitutions. For the single-vector approach, 2 of 10 reads in plant #1 contained C > T substitutions. These substitutions convert alanine 334 to valine (G to TAC) . Plant # to CCG) . For the to ACG) . Plant # to TCT) .To perform detailed analysis of the substitution rate and to calculate the mutation percent frequency, we performed amplicon deep sequencing using the PacBio platform. We analyzed the data using the offline version of CRISPR-sub and subtracted p35S-pT7-ALS reads to calculate the substitution efficiency. Similar to the Sanger sequencing results, the substitution frequency was 1.06% and 0.53% for the single- and two-vector systems, respectively . We alsoOsALS for evolution of herbicide resistance. For this purpose, we performed Agrobacterium-mediated stable transformation of rice callus using our single-vector and two-vector systems harboring OsALS as the target locus. After co-cultivation, the callus was transferred to selection medium supplemented with 0.5 or 0.75 \u03bcM BS to inhibit the growth of wild-type callus and exert selection pressure to generate OsALS variants. For the mock control, the callus was grown on selection medium without BS. After 2 wk of selection, five independent actively growing callus pieces were pooled and used to amplify the OsALS sequence in the transgene  for targeted mutagenesis of genes controlled by the T7 promoter. We achieved high C > T editing efficiency in N. benthamiana transient assays, establishing the utility of the system in plants.We previously demonstrated that directed evolution is possible in plants using our newly established CDE platform. A library of sgRNAs was synthesized to tile all of the available PAM sites of the spliceosomal component SF3B1 in rice , 2021. TN. benthamiana, we observed not only C > T but also G > A transitions in these stable rice lines. We took advantage of this system to improve the herbicide resistance trait in rice. We applied selection pressure using an herbicide and identified the herbicide-responsive residues in the OsALS gene sequence via a comparison with mock treatment. Ideally, such modifications could be introduced into the rice genome via HDR-based technologies such as prime editing, RNA-templated DNA repair, or Cas9-VirD2 fusion-mediated repair -Hyperactive AID-T7 RNA Polymerase-UGI-T2A-td Tamato  using pN. benthamiana experiments, the 3xFLAG_AID_T7_UGI fragment was cloned into pK2GW7 via LR reaction under the control of the 35S promoter to generate pEditor p35S_3xFLAG_AID_T7_UGI _pK2GW7. To prepare p35S_3xFLAG_T7_UGI_pK2GW7 without AID, the p35S_3xFLAG_AID_T7_UGI _pK2GW7 vector was digested with XmaI and re-ligated. For GFP expression (pTarget), pLSLGFP was used  were designed to amplify the 35S promoter. The 35S promoter was cloned upstream of the T7 promoter using HindIII and PstI to generate the final plasmid p35S_pT7_GFP_NOSterm.For the e-51494; ), and SIList of sequences used in this studyTable S1 OsUbiquitin promoter to generate pEditor pUBI_3xFLAG_AID_T7_UGI_pRGEB32. Oligos for the T7-promoter_RBS_MCS_T7-terminator_CaMV poly(A) signal sequence were annealed and ligated with HindIII-SbfI overhangs and cloned into the rice pEditor vector. The 35S promoter was cloned upstream of the T7 promoter in the rice pEditor vector using only HindIII. The OsALS sequence (LOC_Os02g30630) was amplified with oligos ApaI_AvrII_ALS_F and XbaI_KpnI_ALS_R (1.963 Kb) and cloned under the control of pT7 or p35S-pT7 in rice pEditor vectors via digestion with ApaI and XbaI.For the single-vector system in rice, the fragment 3xFLAG_AID_T7_UGI was cloned into pRGEB32 via LR reaction under the control of the N. benthamiana pEditor p35S_3xFLAG_AID_T7_UGI _pK2GW7 was used as pEditor for rice. The AID_T7pol_UGI fragment was removed from p35S_pT7_ALS_AID_T7pol_UGI_pRGEB32 by BsrGI digestion to prepare p35S_pT7_ALS_pRGEB32 as pTarget. All the primers sequences used in this study are given in Table S1.In rice for the two-vector system, pEditor and pTarget were transformed into rice as separate vectors. Agrobacterium tumefaciens strain GV3101. Single colonies grown for two nights in selective medium were centrifuged, resuspended in infiltration medium , and incubated at ambient temperature for 2 h. For infiltration into wild-type plants, the cultures were mixed at an OD600 ratio of 0.2:0.4:0.4  and infiltrated into 3- to 4-wk-old leaves of N. benthamiana plants with a 1-ml needleless syringe. GFP expression was observed at 3, 5, and 7 dpi using a hand-held UV light. Photographs were taken with a Nikon camera under UV light.Constructs harboring pEditor (p35S_3xFLAG_AID_T7_UGI_pK2GW7), pEditor without AID (p35S_3xFLAG_T7_UGI_pK2GW7), pTarget (p35S_pT7_GFP_NOSterm_T7term), and P19 were individually electroporated into A. tumefaciens strain EHA105. Agrobacterium-mediated rice transformation was performed as described previously  and cloned using a CloneJET PCR Cloning Kit (K1231). The same fragment was sequenced with three to four overlapping primers to cover the entire coding sequence. At least 10 colonies were subjected to Sanger sequencing to analyze the mutation.After 1 wk of growth, when plants were established in soil, DNA was extracted from a leaf sample. To genotype pTarget GFP, two regions were amplified with the primers GFP_F7 + GFP_R1 (402 bp) and PGD_GFP_F+ GFP_R2 (280 bp). Purified PCR products were used to prepare Illumina TruSeq DNA Nano libraries according to the manufacturer\u2019s instructions. The libraries were analyzed on the NovaSeq platform. The online tool CRISPR-Sub was used for data analysis . The purified PCR products were sequenced using PacBio Sequel I technology. The data were analyzed using the offline version of CRISPR-Sub. To avoid the factor of sequencing errors and somaclonal variations we subtracted the mock-treated sample (without AID) reads from the experimental sample (with AID) reads to calculate editing efficiency.For amplicon deep sequencing of rice callus samples, five independent callus pieces per treatment were collected after 2 wk of culture on medium without BS, with 0.5 \u03bcM BS, or with 0.75 \u03bcM BS. Equal amounts of samples from the same treatment groups were pooled together and used for DNA extraction. The Data from the 0.5 \u03bcM BS- and 0.75 \u03bcM BS-treated samples were compared with data from untreated samples to identify the BS-responsive substitutions.Geneious Prime software was used to calculate the mutation percent frequency of each type of base editing for the NovaSeq and PacBio data. The results of the data analysis are available as Additional_File_2 for Mutation rate GFP and ALS, Additional_File_3 for Substitution rate for ALS and Additional_File_4 for BS-responsive regions at the ALS.The herbicide-responsive point mutations were introduced into the ALS sequence of the vector pTarget, p35S_pT7_ALS_pRGEB32, via site-directed mutagenesis (GenScript). The vectors with ALS mutations were transformed into wild-type rice callus and selected on medium containing 50 mg/l hygromycin B. The seeds of the progeny plants were used for herbicide resistance analysis.For seedling analysis, the seeds were de-husked, sterilized, and grown vertically on square plates containing \u00bd MS media (with 50 mg/l hygromycin B). Seeds of wild-type and pTarget, p35S_pT7_ALS_pRGEB32, without ALS mutations were used as controls. After 3 d, seedlings with similar root growth were transferred to \u00bd MS plates supplemented with BS. The root tips were marked to observe growth. The seedlings were grown vertically for another 14-d and then imaged.For germination analysis, the seeds were de-husked, sterilized, and grown vertically on square plates containing \u00bd MS media containing different concentrations of BS. Seeds of wild-type and pTarget, p35S_pT7_ALS_pRGEB32, without ALS mutations were used as controls. The seeds were grown vertically for 14-d and then imaged."}
+{"text": "Vibrio parahaemolyticus, new and effective strategies were needed to control the emergence of vibriosis. Lytic bacteriophages come up as a promising way to resist the pathogenic population in various applications. In this study, a V. parahaemolyticus specific phage vB_VpS_PG28 was isolated from sewage in the seafood market. Results showed vB_VpS_PG28, is strictly a lytic bacteriophage and has a relatively large burst size of 103 plaque-forming units per infected cell. Comparative genomic and bioinformatic analyses proved that vB_VpS_PG28 is a new bacteriophage that had a homologous relation with Vibrio phages of family Siphoviridae, especially with phage VH2_2019, but transmission electron microscopy of vB_VpS_PG28 morphology characterized its morphology is similar to that of Myoviridae family. In silico analysis indicated that the vB_VpS_PG28 genome consists of 82712 bp (48.08% GC content) encoding 114 putative ORFs without tRNA,and any gene associated with resistance or virulence factors has not been found. The bacteriophage in the present study has shown significant outcomes in order to control bacterial growth under in vitro conditions. Thus, we are suggesting a beneficiary agent against foodborne pathogens. Further, to ensure the safe usage of phage oral toxicity testing is recommended.Foodborne diseases have become a serious havoc, where antimicrobial resistance is throwing significant challenges on daily basis. With the increase of drug-resistant bacteria and food-borne infection associated with  Vibrio parahaemolyticus (V. parahaemolyticus) is a gram-negative motile  bacterium residing in marine  and estuarine environments all over the world [V. parahaemolyticus in aquaculture production. However, frequent use of antibiotics leads towards multiple-antibiotic resistances, mostly against Ampicillin and Streptomycin [V. parahaemolyticus are non-pathogenic strains. However, clinical strains yield thermostable direct hemolysin (TDH), and other virulence factors [V. parahaemolyticus infections.With the passage of time morbidity and mortality rate has increased due to foodborne diseases all around the world . Moreovehe world \u20137. Most he world . Contamiptomycin . Environ factors , 11. TheBacteriophages (phages) are viruses that can specifically infect and kill the bacteria . They arBacteriophages having lytic life cycle are potential candidates for biocontrol. It\u2019s not only the morphology but also the genetic makeup which helps to determine the potential of these phages against pathogenic bacteria because of their virulent genes . TherefoV. parahaemolyticus. Detailed genomic and biological properties of phage vB_VpS_PG28 were analyzed. The outcome may result to get potent biocontrol phages thathelp to combat with pathogenic bacteria.This study aims to isolate and characterize the novel polyvalent biocontrol agent (vB_VpS_PG28) with a broad-spectrum activity against an MDR (multidrug resistance) strains of 5\u2019-AGAGTTTGATCCTGGCTCAG-3\u2019 1492R:5\u2019-GGTTACCTTGTTACGACTT-3\u2019. Then, the Sanger sequencing was performed to obtain the sequence of the PCR product. The resulting sequence was compared against the GenBank database using BLASTn and the best match result calculated by BLAST was used to determine the identification of the bacteria. 30% glycerol was used to store the strain at \u221280\u00b0C and routinely grown in liquid 2216E medium  at 37\u00b0C overnight.The host bacteria were isolated from diseased shrimp in Qingdao, China and initially cultured on 2216E medium . The isolated strain was confirmed by amplifying a 1463-bp fragment of the 16S rRNA gene with the universal primer pair27F:V. parahaemolyticus isolated from diseased shrimp was used as host bacteria for phage isolation. Firstly, the sewage from the seafood market was centrifuged and the supernatant was filtered through a 0.22\u03bcm syringe filter. Then, it was enriched, mixed with the cultured bacterial liquid and incubated at 37\u00b0C for 6h. After high-speed centrifugation, the supernatant was filtered through a 0.22\u03bcm syringe filter. 100 \u03bcL filtrate was mixed with 500\u03bcL bacterial liquid, and the mixture was presented on the double layer agar plate. Specific phages were isolated by double agar plate method [Phage vB_VpS_PG28 was isolated from sewage at the seafood market in Qingdao. The e method , and purThe phage and host bacteria were mixed according to MOI of 1, 0.1, 0.01, 0.001 and 0.0001, respectively, and cultured at 37\u00b0C for 6h. After high-speed centrifugation (12000\u00d7g for 2 mins), supernatant was filtered through a 0.22\u03bcm syringe filter to obtain phages. Phage titer was determined using the double agar layer method. The experiment was performed in triplicates.The thermal and pH stabilities of vB_VpS_PG28 were evaluated under the optimal MOI conditions as previously described with some modifications . For theThe purified phage sample was obtained by centrifugation at 12000\u00d7g for 2 mins. The supernatant was filtered through a 0.22\u03bcm filter. Ten microliters of purified phage solution were poured onto a copper grid and rested it for 1 min. Then, for staining the samples, 2% phosphotungstic acid was used and extra solution was washed out. Then waiting until grids were air dried and observed with a TalosL120C transmission electron microscope  set at 120 kV to obtain the phage morphology.Vibrio species. Double agar plate method was used to check the host specificity of phage by spotting a 10\u03bcL drop of phage lysate on the plate surface, followed by overnight incubation at 37\u00b0C. Subsequently, phage activity was examined visually by clearance zones represented bacterial cell lysis.Host range analysis was performed by testing phage vB_VpS_PG28 against 14 strains of Phage latent period and phage burst size were determined as described in , 31 withPhage genomic DNA was extracted by the phenol-chloroform method . For DNAThe product of genome extraction was used to construct a 600 bp insert length library using the NEBNext\u00ae Ultra\u2122 II DNA Library Prep Kit for Illumina. Illumina Miseq  was used for high-throughput sequencing. De novo assembly with 392,358 trimmed reads (97.88% of raw reads) was performed using SPAdes v3.13.0.http://rast.nmpdr.org/) online annotation server was used to annotate the whole genome of phage. Blastp (http://www.ncbi.nlm.nih.gov/BLAST) against non-redundant protein database was used to further predict the functions of annotated proteins. The virulence determinants and the genes involved in antibiotic resistance were determined using the Virulence Factor Database (VFDB) [https://cge.cbs.dtu.dk/services/ResFinder) respectively. Genes encoding tRNAs were predicted by the tRNAscan-SE program [http://lowelab.ucsc.edu/tRNAsc an-SE/). Circular genome mapping was performed using an in-house python script. VIRIDIC (http://rhea.icbm.uni-oldenburg.de/VIRIDIC/) was used in order to determine the intergenomic similarity and differences of phage vB_VpS_PG28 with other phages [https://www.ncbi.nlm.nih.gov). Two proteins, terminase large subunit (ORF19) and DNA polymerase (ORF31) were used to construct the phylogenetic trees to infer the evolutionary history of proteins along with target phage vB_VpS_PG28. The homologous sequences of these two proteins were downloaded from NCBI. The Neighbour-Joining (NJ) method in MEGA v7.0 [Packaging mechanism was determined using PhageTerm tool . RAST (he (VFDB)  and ResFe (VFDB)   off-white translucent colonies . The bacterial strain used as a host strain in this study was isolated from diseased shrimp in Qingdao, China. 16s rRNA gene sequencing was done to confirm the bacterial host strain as a V.parahaemolyticus.The bacterial strain V. parahaemolyticus 6A . Plaques formed by phage vB_VpS_PG28 were 1.5 to 2.0 mm in diameter with well-defined boundaries against the V. parahaemolyticus bacterial host strain. Morphology and plaque size may differ in their measurements according to growth conditions, but it was observed that typical virulent phagesproduce clear plaques. Oppositely, phages those have ability to lysogenize form turbid plaques, substantiated that vB_VpS_PG28 may be initially assessed as a virulent phage. Morphological characteristics of phage vB_VpS_PG28 under the transmission electron microscopy (TEM) indicated that it possessed isometric, icosahedral capsids (approximately 56\u00b13 nm in diameter) and contractile tails . Bacteriophages are classified according to the morphology of their virion characteristics.Variable tail morphology was observed in the vB_VpS_PG28 sample, which is similar to Myoviridae phage.Phage vB_VpS_PG28 was isolated from sewage at the seafood market in Qingdao. Phage produces clear plaques on double agar 2216E plates after co-culturing with Vibrio hosts by using spot test. Results showed that phage vB_VpS_PG28 form clear plaques against host bacteria suggesting that phage vB_VpS_PG28is more specific. In addition, it was only susceptible to its host bacteria and V. parahaemolyticus 2294 within the test range.Itcould not be able to form plaques against other vibrio strains .The host range of the phage was assessed against 6 phages with 108 cells . The one-step growth experiment was performed to investigate the growth parameters by observing phage growth cycle. The result exhibited that the latent period and burst period of phage vB_VpS_PG28 were 60 mins and 210 mins, respectively, and an average burst size was about 103 plaque forming units (PFUs)/infected cell . Thermal and pH tolerance represented the range of application for the phage. The thermal stability test showed that vB_VpS_PG28 was stable below 60\u00b0C and the stability decreased gradually at 70\u00b0C . In addition, phage vB_VpS_PG28 remained active over a wide pH range (pH 4\u201311), which suggests that phage vB_VpS_PG28 could be applied in harsh environment .MOI results indicate that the optimal multiplicity of infection is 0.01, when mixing10Fig 4. The complete genome sequence of phage vB_VpS_PG28 has a GC content of 48.08%. No tRNA genes were detected in the genome of phage vB_VpS_PG28, indicating that vB_VpS_PG28 depends on the translation machinery of the host.To understand more about phage biology, the phage genome was sequenced. Finally, 749606 raw readswith average length of 300 bp was obtained and one contig of 82712bp was assembled after trimming. The average depth of phage contig is 148 after assembly. The termini of the phage genome were predicted by using PhageTerm v1.0.12.1, suggesting that the phage genome has a fixed terminus for packaging and that the other termini may be generated randomly by a headful packaging mechanism. The complete circular genome map of phage vB_VpS_PG28is shown in , with ATG (110/114), TTG (1/114), and GTG (3/114) serving as start codons and TAA (71/114), TAG (22/114), and TGA (21/114) serving as stop codons. Coding density of open reading frames is 93.828%, covering a total of 77607bp.The genome annotation analysis predicted 114 open reading frames in the complete vB_VpS_PG28 genome , resulting that vB_VpS_PG28 has the maximum similarity with Vibrio phage VH2_2019 (80.4%), and the homology with other phages were less than 10%. These two phages are sufficient to be classified at the level of a new genus.To determine the intergenomic similarity between vB_VpS_PG28 and other phages, a heatmap was generated using VIRDIC . Total number of phages used for this analysis were divided into Podoviridae, Siphoviridae and Myoviridae according to genome data available in the NCBI database. Interestingly, vB_VpS_PG28 was similar to be the members of Myoviridae family by its morphological characteristics.In the phylogenetic trees based on terminase large subunit and DNA polymerase, vB_VpS_PG28 was clustered with Siphoviridae phages and distant from Myoviridae phages Vibrio phage vB_VpS_PG28 showed high sequence identity (88.81% over 93% query cover) to the genome of Vibrio phage VH2_2019(MN794238.1). The genetic kinships between these phages could be related to similarity in their biological properties because the conserved core genes involved in the replication and morphogenesis modules of each genome. Interestingly, these bacteriophages give efficacious results in controlling the infections caused by Vibrio, suggesting that phagevB_VpS_PG28 may prove a biological control agent. Complex evolutionary relationships can be predicted between these two phages as both phages were isolated from different territories of the world [The Bacterial and Archaeal Viruses Subcommittee (BAVS) of the ICTV describes that all species should differ from each other at least 5% of their genome sequence according to a BLASTn search. BLASTn analysis showed that the genome of he world .Fig 7 displays the comparative analysis betweenthese two phage genomes. Both of them have a lot of hypothetical proteins, indicating that their genomes are newly discovered.Genes conservation between these two genomes may demonstrate that the phages possessed ancestral structural genes to sustain their infective capacity to establish infective cycle on bacterial hosts. Oppositely, the tail protein encoded by phage vB_VpS_PG28 show a greater divergence. To confer the host specificity of a phage, tail proteins are involved in host recognition. Moreover, even these two phages share high DNA sequence homology but could show different host specificities. The possible reason behind this, small differences in tail fiber proteins that frequently related to remarkable differences in host ranges and other biological properties. Thephage vB_VpS_PG28 genome has a high gene density 1.38 genes per kilobase. Genomic analysis of the phage vB_VpS_PG28 suggests that it is strictly lytic (no lysogenic genes were detected) and does not encode any gene associated with virulence determinants or any immunereactive allergens in their genomes. Therefore, this is the more desirable feature of any phage to use it as a biocontrol agent. However, further testing related to oral toxicity is required to ensure the safe usage of phage.The genome of phage vB_VpS_PG28 has a comprehensive organization of gene structure that is commonly seen in tailed bacteriophages and every specific structure in this consists of number of genes that have role in similar metabolic pathways including packaging of DNA, morphogenesis structure, replication modules, DNA metabolism and cell lysis.Structural module of phage vB_VpS_PG28 is located in the central position of the gene sequence. This module mainly included major capsid, head completion, tail, tail tape measure and neck protein.Vibrio phage vB_VhaS-VHB1. The presumed product of ORF90 showed similarity (36.62% identity) with the head completion protein of Vibrio phage1.215.A._10N.222.54.F7.Comparative analysis revealed that major capsid protein encoded by ORF40 showed close relation (74% similarity) with head proteins of Vibrio phage.The module for the head structural components involved ORF88 and ORF90 based on a comparison with other phage head proteins in the NCBI database. Blastp analysis predicted that ORF88 encoded a major capsid protein and exhibited 74% identity to that of Vibrio phage vB_Vals_PJ32. One putative major tail protein (ORF93) and one putative tail tape measure protein (ORF97) have been predicted in the phage vB_VpS_PG28, which exhibited 61.98% identity to that of Vibrio phage vB_Vcas_HC and 42.84% identity to that of Vibrio phage vB_Vhas_VHB1, respectively. Furthermore, ORF91 showed 60.78% similarity with Vibrio phage vB_Vcas_HC.Phage neck plays a role in the association of the virion head and tail after packaging of viral DNA within the head [The tail of phage vB_VpS_PG28 was composed entirely of three proteins  including the tail completion protein, major tail protein, and tail tape measure protein. The product of ORF92 showed 49.30% homology with the tail protein of the head . TogetheVibrio phage 1.215.A._10N.222.54.F7. It is responsible for DNA splicing and packaging. Terminase large subunit may bind and cut specifically near the initiation packaging site [Vibrio phage 1.215.A._10N.222.54.F7. It is an important protein in all aspects of bacteriophages including packaging, maturation process and maintain a conserved function. Owing their dynamic role, portal proteins are found variable, and their conformations alters at every assembly stage [DNA packaging module was also identified in the genome of vB_VpS_PG28 which includes terminase large subunit and portal protein. ORF19 encoded the terminase large subunit which showed 64% identity to ing site . Anothering site . ORF 32 ly stage . As the The replication module of phage vB_VpS_PG28 is scattered throughout in its genome. It mainly includes DNA helicase(ORF2), DNA binding protein(ORF3), RecA(ORF5), endodeoxyribonuclease(ORF8), DNA polymerase I(ORF31), RNaseH(ORF60) and DNA primase(ORF114).DNA primase encoded by ORF114 showed 35% similarity with Rhizobium phage (accession no. QIG76855.1). DNA replication has a semi-discontinuous nature, that is why primases are needed for the initiation and lagging strand replication . FurtherProteins that bind to DNA are ubiquitous in biology. The ability of these proteins to bind to specific DNA sequences with high affinity is often central to their function, and it is not uncommon for a single mutation to affect the protein ability to bind to the DNA .Vibrio phage VH2_2019. Lysis protein can destroy the cell wall peptidoglycan structure which suggests that the lytic mechanism of phage vB_VpS_PG28is predicted to be accomplished by this protein.The Lytic gene (ORF11) is adjacent to the replication module. ORF11 was predicted as a lysis protein which shared 95% identity to the N-acetyl-alpha-D-glucosaminyl L-malate deacetylase 1 of Bacteria protect themselves from the attacks of bacteriophages or any other foreign DNA by using Restriction-modification systems (R-M). Nuclease and methyltransferase enzymes are considered to be R-M systems . The orfCurrent study presented, the biological and genomic characteristics of phage vB_VpS_PG28. The results showed that phage vB_VpS_PG28 acts as a promising phage inphage therapy and/or food protection. Various futures were observed advantageous: (I) apparently absence of virulent genes; (II) Clear plaque formation and absence of genes related to lysogenization, representing the virulence-only type of development; (III) efficient adsorption to host cells; (IV) effective lytic development; and (V) relatively more resistant to different environmental factors (pH & temperature). Overall, based on these properties we propose that further research on vB_VpS_PG28 may provide an avenue to drive its application in food protection on an industrial level.Complete genome sequence of phage vB_VpS_PG28 and 16s were submitted to GenBank under the accession numbers MT735630 and MZ226961, respectively. Raw reads were submitted to NCBI under the SRA accession number SRR14274268.S1 Table(DOCX)Click here for additional data file.S1 Raw image(TIF)Click here for additional data file."}
+{"text": "Yet we warrant the use of hsa_circRNA_0040462 as an onco-therapeutic target given its double-edged roles on breast cancer progression, i.e., suppressive on the growth and promotive on the migrative ability of triple negative breast cancer cells. Our study for the first time focused on markers prognostic of CAP's efficacy and tumors' sensitivity to CAP treatment under a certain parameter configuration, and reported hsa_circRNA_0040462 as a sensor of cells' response to CAP treatment. Also, the uncovered dual roles of hsa_circRNA_0040462 further advanced our knowledge on the complex yet critical regulatory functionalities of circular RNAs in cancer progression.Cold atmospheric plasma (CAP) represents a novel onco-therapeutic approach that has demonstrated its efficacy in many types of tumors. The efficacy of CAP is dose-dependent that determines the panel of tumors feasible for receiving CAP treatment under a certain parameter configuration. Identifying markers for easy and fast prognosis of tumors' sensitivity in response to CAP exposure is of critical value towards optimized therapeutic outcome, the lack of which has largely limited the translation of CAP into clinics. Circular RNAs represent a novel type of biomarkers for disease diagnosis that is featured by easy detection and stability. Through whole transcriptome sequencing, followed by  Circular RNAs (circRNAs), a novel type of RNA prevalently present in the eukaryotic transcriptome, form a covalently closed continuous loop 2O2, O3, O, O2-, NO, OONO-, ONOOH), has demonstrated its efficacy in killing many types of cancer cells such as melanoma Cold atmospheric plasma (CAP), being the fourth state of matter and a cocktail of reactive oxygen and nitrogen species . The applied voltage ranged from 11V to 12V. The flow rate of He gas was 1 L/min and the distance between the plasma injection and the medium surface was from 0.6 cm to 1cm. The exposure duration was 4 min/well in a 24-well plate setup where each well contained 2 mL CAP-activated medium (PAM). During the experiment, cancer cells were pre-washed using PBS twice before being cultured with PAM .The device consists of a power controller, helium (He) gas cylinder, rotor flow meter, oscilloscope and plasma jet . The SUM159PT cell line was cultured using Ham's F-12 medium  supplemented with 5% fetal bovine serum (FBS) , 0.00325% (1IU) insulin, 1% (1M) HEPES , 138 \u03bcL (10 Mm/mL) hydrocortisone and 1% Penicillin Streptomycin . The MCF7 cell line was cultured in DMEM/HIGH GLUCOSE medium  containing 10% FBS and 1% Penicillin Streptomycin.6/well for 48 h. The 96-well plate was refreshed with new media containing 1% thiazolyl blue tetrazolium bromide solution (MTT)  and cultured in an incubator at 37 \u00b0C for 4 h. The media was replaced with 0.1 mL dimethyl sulfoxide (DMSO)  and subjected to slight shaking for 10 min. OD value was measured by ELIASA at 590 nm.Cells were grown in 96-well plates at a density of approximately 8\u00d7108/well cells were grown in 6-well plates overnight, where two parallel lines were drawn at the bottom of the plates. Two vertical lines were scratched on cell surface using tips. The plates were washed by phosphate buffered saline (PBS) for 3 times and then cells were cultured using serum-free media. Cell migration was monitored at different time points with images taken using inverted phase contrast microscope (ZEISS). Image J software was used to measure the width of the scratch to calculate the migration rate.Over 5\u00d710Cells were cultured in 12-well plates where each well had round coverslip on the bottom. Cells were dyed by ROS Fluorescent Probe-Dihydroethidium (DHE)  at 37 \u00b0C in the darkness for 1 h. The nucleus was dyed using DAPI. The slides were covered using round coverslips. Fluorescence intensity was recorded using fluorescence microscope . Software Image J was used to measure the mean fluorescence intensity to reflect the concentration of ROS.2O were mixed and centrifuged. The mixture was placed in Gradient thermal cycler , pre-denatured at 95 \u00b0C for 10 min, and run with '95 \u00b0C for 10 sec, 60 \u00b0C for 1 min, 72 \u00b0C for 20 sec' for 40 cycles.Total RNA was collected from the cells using Ultrapure RNA Kit  and reverse transcribed to cDNA by PrimeScript\u2122 RT reagent Kit with gDNA Eraser following the manufacturer's protocol . 4 \u03bcL cDNA samples, 5 \u03bcL 2\u00d7UltraSYBR MixTure , 0.2 \u03bcL forward and 0.2 \u03bcL backward primers, 0.6 \u03bcL ddHCells were cultured in 6-well plates, washed twice by pre-chilled PBS, supplemented with lysis buffer (RIPA: protease inhibitor: phosphatase inhibitors = 100:1:1), placed on ice for 20 min, and centrifuged at 12,000 g for 20 min to collect the supernatants. BCA Proteib analysis kit  was used to estimate and quantify protein concentration. Proteins were separated by 10% SDS-PAGE gel at 125 V for 75 min and transferred to a PVDF membrane at 200 mA for 120 min. The membrane was blocked with 5% skim milk in TBST for 1 h followed by incubation with the primary antibody at 4 \u00b0C overnight. The membrane was washed 3 times, each for 5 min, and incubated with the secondary antibody for 1 h at the room temperature. The membrane was washed again and added with high sensitivity ECL chemiluminescence detection kit  before being visualized in the darkness using Automatic chemiluminescence image analysis system . The signals of bands were measured and quantified by Image J software.Antibodies used in this study are listed in Table SUM159PT and MCF7 cells at the time points of 0 h, 1 h and 8 h post-CAP exposure were collected, each with three replicates. Total RNA of these 18 samples were extracted and sequenced using HiSeqX10 (Illumina) by Vazyme Company .2(fold change)| \u22651 and corrected p value <0.05.Raw reads were pre-processed by Vazyme using their in-house pipeline. Differentially expressed transcripts were identified as those with |loghttp://starbase.sysu.edu.cn/) The mRNAs positively correlated with hsa_circRNA_0040462 were identified from our whole transcriptome dataset, namely 'mRNA_set1', by calculating the Pearson correlation score, with the absolute value of the correlation score being over 0.8 and p < 0.05 being used as the selection threshold. The miRNAs regulated by mRNAs were defined as 'miRNA_set1' and predicted through starBase (http://www.miranda.org/) http://www.mirdb.org/) The miRNAs regulated by hsa_circRNA_0040462 (defined as 'miRNA_set2') were predicted using Miranda  enrichment analysis were conducted for the union of mRNA_set1 and mRNA_set2, and for mRNA_set2, respectively, using the R package 'clusterProfiler' to assess the functional significance of genes regulated by hsa_circRNA_0040462 regardless of CAP perturbation and cell response, respectively. Differentially identified KEGG pathway and GO terms were considered to be involved in cells' response to CAP treatment. Fisher's exact test was used to assess the statistical significance. The p-values were adjusted using Benjamini-Hochberg false discovery rate (FDR), with the adjusted p<0.01 being considered as the cutoff threshold.The intersection between miRNA_set1 and miRNA_set2 was taken and considered as the set of miRNAs regulated by hsa_circRNA_0040462 and regulating experimentally identified mRNAs from mRNA_set1 (denoted as 'miRNA module') in response to CAP exposure. The miRNA module, mRNAs in 'mRNA_set2' and hsa_circRNA_0040462 were subjected to circRNA-miRNA-mRNA network construction using Cytoscape Figure ). The migration rates have been reduced to 20% and 40%, respectively, for SUM159PT and MCF7 cells, each with a significance of 4.28E-2 and 1.47E-2 .CAP could significantly halt the growth and migrative ability of both SUM159PT and MCF7 cells. Specifically, CAP selectively reduced the growth of SUM159PT cells to approximately 1/3 of its untreated peers and suppressed that of MCF7 to around 3/5 of its control, both with p<1E-4 . In particular, hsa_circRNA_0040462 increased to approximately 1.5 folds in both SUM159PT and MCF7 cells . This has been experimentally validated in vitro, where hsa_circRNA_0040462 expression increased 27 folds  in SUM159PT cells and enhanced in MCF7 cells, though slightly, with statistical significance 1 h post-CAP exposure . In addition, relapsed cell proliferation on CAP exposure was observed with statistical significance (p=0.028) in SUM159PT cells when hsa_circRNA_0040462 was silenced, consolidating the role of hsa_circRNA_0040462 in being a sensor of cells' response to CAP treatment . These results collectively suggested the high sensitivity of hsa_circRNA_0040462 in response to CAP treatment especially in SUM159PT cells.Through differentially expressed gene analysis, we identified one circRNA, hsa_circRNA_0040462, highly expressed in both SUM159PT and MCF7 cells 1 h post-CAP exposure but not in MCF7 cells . Specifically, GLG1 showed approximately half expression in SUM159PT cells as compared with that in MCF7 cells before and after CAP exposure . These are suggestive of the tumor suppressive role of GLG1, which has been confirmed by its clinical association. In particular, 10-year relapse-free survival of protein expression data from 126 patients was conducted (Liu_2014) using Kaplan-Meier Plotter  Figure ).The level of the host gene (GLG1) of hsa_circRNA_0040462 was higher in MCF7 than in SUM159PT cells, which decreased on CAP exposure and decreased with the exposure duration (Supplementary Table 1), and regulated by 276 miRNAs ('miRNA_set1') on CAP exposure (Supplementary Table 2). On the other hand, we found that hsa_circRNA_0040462 regulated the expression of 8 miRNAs  and 3394 mRNAs  without CAP perturbation.We obtained 22 mRNAs ('mRNA_set1') whose transcription levels were highly and positively correlated with hsa_circRNA_0040462 (clec7a) that was regulated by hsa-miRNA-32-3p. These 4 miRNAs and the one mRNA were considered to be the downstream players of hsa_circRNA_0040462 regardless of CAP exposure.The intersection of miRNA_set1 and miRNA_set2 included 4 miRNAs , and that of mRNA_set1 and mRNA_set2 contained only one gene , suggestive of its pivotal role in hsa_circRNA_0040462-triggered signal relay.The circRNA-miRNA-mRNA network constructed using hsa_circRNA_0040462, the 4 miRNAs and mRNA_set1 showed that most edges were connected via hsa-miRNA-1-3p , which are all canonical pathways responsible for cell proliferation . Ras signaling was revealed from both KEGG and GO analyses using both '_set1/2' and '_set2' datasets , which is the upstream player of PIK3/AKT and MAPK pathways . 'Signaling pathway regulating pluripotency of stem cells' was shown available in the KEGG analysis of the '_set2' dataset but vanished in that of the '_set1/2' dataset . GO terms enriched from both '_set1/2' and '_set2' datasets were identical . We examined the effect of hsa_circRNA_0040462 on the level of total and phosphorylated AKT as the PI3K/AKT signaling was ranked first from the enriched KEGG pathways , and found that phosphorylated PI3K, phosphorylated AKT and total AKT levels were all substantially elevated by silencing hsa_circRNA_0040462 .Define KEGG pathways and GO terms obtained by taking the union of mRNA_set1 and mRNA_set2 as 'KEGG_set1/2' and 'GO_set1/2', and those obtained from mRNA_set2 as 'KEGG_set2' and 'GO_set2'. KEGG pathways identified using mRNA_set1/2 was a subset of those enriched using mRNA_set2. PI3K/AKT and MAPK signalings were the top pathways enriched in both KEGG_set1/2 and KEGG_set2 , we reduced the expression of this circRNA to almost half of its basal level with statistical significance . Cells with reduced hsa_circRNA_0040462 expression exhibited significantly enhanced growth rate in SUM159PT cells . Interestingly, reduced hsa_circRNA_0040462 level led to suppressed mobility of SUM159PT cells .We next examined the functionalities of hsa_circRNA_0040462 in cancer progression Figure ). By examining the protein expression of KI67 . However, suppressed hsa_circRNA_0040462 expression reduced the activity of p65 to approximately half of the control  without substantially affecting its total protein level , suggestive of inhibited migrative ability of cancer cells.Through computational analysis, the prominent role of PI3K/AKT, MAPK and RAS signaling in hsa_circRNA_0040462-assisted cell proliferation, and the importance of players contributing to cell migration, cell stemness and anti-oxidant were revealed . It is important to notice that knocking down hsa_circRNA_0040462 suppressed p-p65 to half of its control, yet CAP reduced it to 20% of its original level , suggesting that the effect of CAP on p-p65 overrides that of hsa_circRNA_0040462 despite its promotive role on hsa_circRNA_0040462 expression.Contrary to knocking down hsa_circRNA_0040462, CAP significantly suppressed FOXO1 (p=1E-2) and KI67 (p=1.04E-6), where KI67 expression reduced to 40% of its original level, suggesting the consistent regulatory direction of CAP and hsa_circRNA_0040462 in cancer cell proliferation. However, CAP also dramatically reduced both the levels of p65 and its phosphorylated levels , hsa_circRNA_0040462 suppressed TNBC cell growth but promoted cells' migrative ability . Further investigations on primary signaling molecules using western blotting indicated the opposite roles of CAP and hsa_circRNA_0040462 on the activated form of p65 , a molecule primarily responsible for cell migration. The fact that hsa_circRNA_0040462 activated p65 but suppressed cell proliferation and antioxidant ability suggested its double-edged roles in cancer progression given its numerous downstream targets and the complexity of the regulatory network it was involved in. CAP, on the other hand, could systematically adjust the network signaling and thus exhibited a stronger suppressive role on cell migration to override the effect of hsa_circRNA_0040462 on p-p65 . Thus, CAP could elevate the redox level of cells and target their anti-oxidant ability towards halted cancer cell progression that cannot be explained by any single pathway or hub gene; and hsa_circRNA_0040462, a marker identified with high sensitivity to CAP treatment, may only be able to function as a sensor of CAP efficacy but not a therapeutic target.Though CAP showed selectivity against breast cancer cell proliferation and migration, and substantially elevated the expression of hsa_circRNA_0040462 in these malignant cells (clec7a) as important downstream players of hsa_circRNA_0040462, with hsa-miRNA-1-3p being the hub of the constructed network. These molecules have all been associated with cancer. For instance, dysregulation of hsa-miRNA-1-3p has recently been associated with breast cancer clec7a that encodes a glycoprotein with a distinct role in innate immunity regulation was up-regulated after CAP treatment clec7a over-expression conferred a prior drug resistance in leukemic cells We identified 4 miRNAs  and one gene , each representing a distinct type of breast cancer subtype, in this study to keep consistent with the number of cell lines used in the whole transcriptome sequencing. The findings should be carried over to other breast cancer cell lines for additional validations.Through a series of computational analyses, we unveiled the importance of PI3K/AKT and MAPK signaling in relaying hsa_circRNA_0040462 mediated cell growth. Yet no specific pathway responsible for cell migration was popped up during bioinformatics prediction. We examined a panel of canonical markers characterizing cancer cell proliferation , migration (p65), stemness and anti-oxidant ability , yet these do not cover all possible mechanisms underlying the observed opposite roles of hsa_circRNA_0040462 on cell growth and migration that requires additional explorations. Further in-depth investigations on the functionalities of hsa_circRNA_0040462 during cancer progression and its phenotypic manifestations are necessary and left over for further studies.We report in this study hsa_circRNA_0040462 as a sensor of CAP treatment response that can be used to adjust the dose of CAP towards optimized therapeutic response and as a prognostic marker of tumors feasible for receiving CAP treatment. However, given the complexity of the regulatory network of a circular RNA, hsa_circRNA_0040462 is not a feasible therapeutic target as evidenced by its opposite functionalities on cancer cell proliferation and migration.Supplementary figures and tables.Click here for additional data file."}
+{"text": "Cognitive impairment has been suggested to be associated with coronary artery disease [CAD]; however, the underlying mechanism is not fully understood. Our current study aimed to explore the brain activity in CAD patients compared to healthy controls [HCs].Twenty-two CAD patients and 23 HCs were enrolled in our study. A low-frequency oscillation at the voxel level in all participants based on the amplitude of low-frequency fluctuations [ALFF] was measured using resting-state functional magnetic resonance imaging. All participants underwent neuropsychological examinations  and visual acuity examination.P < 0.05] in the right precuneus gyrus [Precuneus_R], left supramarginal gyrus [Supramarginal_L], left angular gyrus [Angular_L], and left middle cingulum gyrus [Cingulum_Mid_L] than healthy controls. Lower MoCA scores in CAD patients significantly correlated with lower Supramarginal_L [P = 0.001] and Cingulate_Mid_L [P = 0.004] ALFF values. Reduced visual acuity significantly correlated with lower Precuneus_R [P = 0.019] and Cingulate_Mid_L [P = 0.011] ALFF values in CAD patients.CAD patients showed significantly lower ALFF values [These findings may provide further insight into the underlying neuropathophysiology of CAD with cognitive impairment. CoronarCAD has been reported to cause embolic stroke and chronic cerebral hypoperfusion, which may lead to cognitive impairment -5. ReporResting-state functional magnetic resonance imaging [rs-MRI] is an important imaging modality that allows researchers and clinicians to explore the neuropathophysiology of a disease. Rs-MRI is relatively easy to perform since it simply requires patients to remain still with their eyes closed. Therefore, the technique has a wide range of potential applications in clinical studies , 9. QuanALFF analysis has been used to explore the functional modulations and showed the pathophysiological characteristics in the resting state of patients with cognitive impairment . Given t2This observational cross-sectional study was done at the First Affiliated Hospital of Zhejiang University School of Medicine. The inclusion criteria for CAD patients were as follows: 1). age between 35 and 80 years; 2). diagnosed with CAD; 3). could cooperate during magnetic resonance imaging.The control group involved individuals who attended our hospital for annual health check-ups and had no history of neurologic or cardiovascular diseases.All participants were evaluated for cardiovascular risk factors, medical history, and medication use and had a comprehensive cardiovascular physical examination by a cardiologist. The study was approved by the Ethics Committee of First Affiliated Hospital of Zhejiang University School of Medicine. Participants recruited provided written informed consent before enrolling in the study.2.1All participants underwent a Montreal Cognitive Assessment, MoCA, and Mini-Mental State Examination, MMSE, which are examinations to screen for cognitive decline. These examinations have a total score of 30, and a score lower than 26 indicates worse cognition in MoCA, while a score lower than 24 indicates worse cognition in MMSE.Visual acuity examination for both eyes was done for all participants. Visual acuity for both eyes was later converted to the minimum angle of resolution [LogMAR] for data analysis.2.2Whole-brain MRI data were acquired at the Center for Brain Imaging Science and Technology, First Affiliated Hospital of Zhejiang University School, on a Siemens MAGNETOM Prisma 3T scanner . All participants were placed in the machine with foam padding around the head to reduce motion; they were asked to keep still with their eyes closed during imaging.2. Anatomical T1-weighted whole brain magnetization-prepared rapid gradient echo images were obtained using the following parameters: 160 sagittal slices, slice thickness/gap = 1.2/0 mm, in-plane resolution = 512 x 512, TR = 5000 ms, TE = 2.9 ms, inversion time [TI] = 700 ms, flip angle = 4\u00b0 and FOV = 256 x 256 mm2.An echo-planar imaging sequence was used to acquire the functional images with the following parameters: 60 axial slices, thickness/gap = 2.0/0mm, in-plane resolution = 64 x 64, repetition time [TR] = 2000ms, echo time [TE] = 34 ms, flip angle = 62\u00b0 and field of view [FOV] = 220 x 220 mm2.3http://www.fil.ion.ucl.ac.uk/spm] was used to implement pre-processing of all fMRI data while data processing was done with Data Processing Assistant for Resting-State fMRI [http://www.restfrmi.net]. The initial 10 volumes of the functional images were discarded to remove initial transient effects and to allow the participant to adjust to the scanner noise before pre-processing. The rest of the fMRI images were acquired with slice timing for the acquisition delay between slices and correction of head motion. All participants who were under imaging had less than 1.5 mm maximum displacement in x, y, or z and 1.5\u00b0 angular motion during imaging. Spatial normalization and resampling to 3 mm voxels were used to acquire realigned images, while a Gaussian filter [6 mm FWHM] was used to spatially smoothen the images. Smoothened images were filtered using a typical temporal bandpass [0.01 \u2013 0.08 Hz] to reduce low-frequency drift, and physiological high-frequency respiratory and cardiac noise. Linear trends were removed within each time series. Lastly, spurious variances from several sources were removed by linear regression, including six head motion parameters, along with average signals from cerebrospinal fluid and white matter.SPM8 [2.4http://www.restfmri.net] was used to calculate the ALFF. The preprocessed time series were first converted to a frequency domain with a fast Fourier transform, and the power spectrum was obtained. The square root of the power spectrum was calculated for each frequency of the power spectrum, and the averaged square root was obtained across 0.01 \u2013 0.08 Hz at each voxel [REST software [ch voxel . The ave2.5http://warwick.ac.uk/snpm] to confirm the results between the two groups. AlphaSim, a Monte Carlo cluster-wise simulation program implemented in AFNI [http://afni.nimh.nih.gov], was used to correct for multiple comparisons . Importantly, CAD patients showed significantly lower  MMSE and MoCA scores than healthy controls.Twenty-two CAD patients  and 23 healthy controls  were included in our data analysis. Out of the 22 CAD patients, 15 [68.18%] had hypertension, 2 [9.09%] had diabetes and 4 [18.18%] had dyslipidemia. There was no significant difference in the years of education between CAD and healthy controls , left supramarginal gyrus [Supramarginal_L], left angular gyrus [Angular_L], and left middle cingulum gyrus [Cingulum_Mid_L] than healthy controls.CAD patients showed significantly lower ALFF values 3.2P = 0.001, Table 3] and Cingulate_Mid_L  ALFF values. Reduced visual acuity significantly correlated with lower Precuneus_R  and Cingulate_Mid_L  ALFF values in CAD patients.Lower MoCA scores in CAD patients significantly correlated with lower Supramarginal_L , left supramarginal gyrus [Supramarginal_L], left angular gyrus [Angular_L], and left middle cingulum gyrus [Cingulate_Mid_L]. Importantly, we showed that the lower ALFF values in some regions of the brain significantly correlated with their clinical insinuations.Cerebral functional impairment [decreased ALFF values] predominantly occurred in the frontal, temporal and parietal lobes. Previous cerebral microstructural reports showed reduced volume in some gyri of the parietal, frontal and temporal lobe in CAD patients compared to healthy controls , 14. BesThe association between lower MoCA scores and reduced ALFF values in the left supramarginal gyrus and left middle cingulate gyrus in CAD patients is in line with previous neuroimaging reports , 16, 28.Existing studies -33 have Our study has several limitations. As with most imaging tools, participant cooperation is necessary. Patient movement can diminish the quality of images and 3 participants were excluded from the study because of movement during MR imaging. LFO amplitude methodology is often studied in larger populations; larger study sample sizes are needed to confirm the importance of our results. The clinical importance of the MRI procedure was evaluated with visual acuity and cognitive tools; further studies are warranted to assess its value in treatment response and scoring systems for coronary angiography. Performing cognitive assessment could be challenging during the acute phase of CAD, as some patients could be prone to acute confusional states. Nonetheless, our study excluded patients with confusional states and delirium. Further studies may include such patients to provide a clearer view of the structural changes in the brain and their association with cognitive tools. Importantly, our cognitive assessment was limited to MoCA and MMSE; further studies with extensive neuropsychological evaluation may be needed.In conclusion, we used the ALFF approach derived from rs-MRI to assess the low-frequency oscillations at the voxel in CAD patients compared to healthy controls. Our report showed that CAD had reduced ALFF values in the right precuneus gyrus, left supramarginal gyrus, left angular gyrus, and left middle cingulum gyrus than healthy controls. We also showed that the lower ALFF values in CAD patients correlated with their lower MoCA scores and reduced visual acuity. These findings may provide further insight into the underlying neuropathophysiology of CAD patients."}
+{"text": "Drosophila rhabdomeric terminal photoreceptor differentiation is an extended process taking several days to complete. Following ommatidial patterning by the morphogenetic furrow, photoreceptors are sequentially recruited and specified, and terminal differentiation begins. Key events of terminal differentiation include the establishment of apical and basolateral domains, rhabdomere and stalk formation, inter-rhabdomeral space formation, and expression of phototransduction machinery. While many key regulators of these processes have been identified, the complete network of transcription factors to downstream effector molecules necessary for regulating each of these major events remains incomplete. Here, we report an RNAi screen to identify additional molecules and cellular pathways required for photoreceptor terminal differentiation. First, we tested several eye-specific GAL4 drivers for correct spatial and temporal specificity and identified Pph13-GAL4 as the most appropriate GAL4 line for our screen. We screened lines available through the Transgenic RNAi Project and isolated lines that when combined with Pph13-GAL4 resulted in the loss of the deep pseudopupil, as a readout for abnormal differentiation. In the end, we screened 6,189 lines, representing 3,971 genes, and have identified 64 genes, illuminating potential new regulatory molecules and cellular pathways for the differentiation and organization of Drosophila rhabdomeric photoreceptors. Drosophila retinas are organized with \u223c800 ommatidia, each containing a cluster of eight photoreceptors, and their associated retinal accessory cells, pigment, and cone cells, in a stereotyped pattern. After the passage of the morphogenetic furrow in the third larval instar eye disc, the photoreceptors are sequentially recruited, and this is followed by terminal differentiation which occurs throughout pupariation. The determination of retinal tissue and initial recruitment and specification of the eight photoreceptors is well defined =ey-FLP.N}2, w[*], P{w[+mC]=tubP-GAL80}LL1P{ry[+t7.2]=neoFRT}19A (RRID: BDSC 42717).Mutations were introduced to Ten to 20 eyes were dissected from \u223c2- to 5-day-old females. Ommatidia were dissociated as previously described . In someP-values.Images were acquired via tile scan on the Nikon Ni-E FLM and quantified manually in ImageJ with help from the \u201cCell Counter\u201d feature. Dorsal rim ommatidia (DRA) were ignored and R7s coexpressing Rh3 and Rh4 were counted as Rh4 expressing for statistical purposes  for RNAi lines against transcription factor/DNA binding genes identified using Gene List Annotation for Drosophila . We identified 1,841 RNAi lines targeting 1,172 genes with predicted DNA binding activity  using Pph13-GAL4. We found a similar apical domain defect associated with both EcR-RNAi and EcR-DN expression  has long and short isoforms called ttk88 and ttk69, respectively . Ttk88 smmatidia . The supLztr1 is a leucine zipper-like transcription factor that is a negative regulator of Ras signaling. Lztr1 regulates Ras through ubiquitination .Drosophila mamo encodes a zinc finger BTB transcription factor that is required for meiosis  Rh5 expression in R8 photoreceptors. In contrast, we observed a relatively small but significant increase in the proportion Rh3 vs Rh4 expressing R7 photoreceptors in mamo-RNAi flies vs controls   . Furthermamo function, we generated several mutant lines of mamo by crossing an available guide RNA line from the Weizmann Knockout Project  terms  and testnorpA and rdgB mutations  knockdown photoreceptors  and resulted in disrupted rhabdomere formation, and rhabdomere fusions , we obtained an identical phenotype as observed with RNAi against eys and EYS protein is not detected. Subsequent analysis demonstrated the shRNA has 14\u2009bp homology with the fifth coding exon of EYS. While it appears both these lines are outliers within the TRiP collection, it demonstrates the need for researchers to perform quality assessments of lines for hits obtained from RNAi screens and conducting appropriate follow-up experiments to confirm the results.Interestingly, phenotypes associated with at least two of the hits from our screen were found to be due to off-target effects, In future work, we will confirm and characterize the role of the genes that we have identified in this screen in photoreceptor differentiation, and we will further explore the new potential cellular processes and avenues this screen has revealed.jkac257_Supplementary_Figure_S1Click here for additional data file.jkac257_Supplementary_Figure_S2Click here for additional data file.jkac257_Supplementary_Figure_S3Click here for additional data file.jkac257_Supplementary_Figure_S4Click here for additional data file.jkac257_Supplementary_Figure_S5Click here for additional data file.jkac257_Supplementary_Figure_S6Click here for additional data file.jkac257_Supplemental_Figure_LegendsClick here for additional data file.jkac257_Supplementary_Table_S1Click here for additional data file.jkac257_Supplementary_Table_S2Click here for additional data file.jkac257_Supplementary_Table_S3Click here for additional data file.jkac257_Supplementary_Table_S4Click here for additional data file."}
+{"text": "The \u201cwriters\u201d mainly act as intermediaries between these modifications and associated biological processes. However, little is known about the interactions and potential functions of these RM writers in hepatocellular carcinoma (HCC).RNA methylation (RM) is a crucial post-translational modification (PTM) that directs epigenetic regulation. It mostly consists of NThe expression properties and genetic alterations of 38 RM writers were assessed in HCC samples from five bioinformatic datasets. Two patterns associated with RM writers were identified using consensus clustering. Then, utilizing differentially expressed genes (DEGs) from different RM subtypes, we built a risk model called RM_Score. Additionally, we investigated the correlation of RM_Score with clinical characteristics, tumor microenvironment (TME) infiltration, molecular subtypes, therapeutic response, immunotherapy effectiveness, and competing endogenous RNA (ceRNA) network.RM writers were correlated with TME cell infiltration and prognosis. Cluster_1/2 and gene.cluster_A/B were shown to be capable of distinguishing the HCC patients with poor prognosis after consensus and unsupervised clustering of RNA methylation writers. Additionally, we constructed RNA modification pattern-specific risk model and subdivided the cases into RM_Score high and RM_Score low subgroups. In individual cohorts or merged datasets, the high RM_Score was related to a worse overall survival of HCC patients. RM_Score also exhibited correlations with immune and proliferation related pathways. In response to anti-cancer treatments, the RM_Score had a negative correlation (drug sensitive) with drugs that focused on the MAPK/ERK and metabolism signaling, and a positive correlation (drug resistant) with compounds targeting RKT and PI3K/mTOR signaling pathway. Notably, the RM_Score was connected to the therapeutic effectiveness of PD-L1 blockage, implying that RM writers may be the target of immunotherapy to optimize clinical outcomes. Additionally, a ceRNA network was generated including 2 lncRNAs, 4 miRNAs, and 7 mRNAs that was connected to RM writers.We thoroughly investigated the potential functions of RNA methylation writers and established an RM_patterns-based risk model for HCC patients. This study emphasized the critical functions of RM modification in TME infiltration, targeted therapy, and immunotherapy, providing potential targets for HCC.The online version contains supplementary material available at 10.1186/s40001-023-01016-7. Hepatocellular carcinoma (HCC), accounting for approximately 90% of liver cancer, is the major histologic type of liver cancer. Annually, more than a million individuals are newly diagnosed with liver cancer, raising concerns on the world\u2019s health . As an i1-methyladenosine (m1A), 5-methylcytosine (m5C), N3-methylcytidine (m3C), N6-methyladenosine (m6A), and 2\u2032-O-methylation (Nm), is described as a crucial PTM in governing RNA maturation, splicing, stability, and translation. Among all these RNA methylation modifications, N6-methylation of adenosines is the most common and abundant RNA methylation modification that occurs at stop codons and within 3\u2032 UTRs. The m1A mutation is likewise found at the first position of the adenine base. Recently, m1A is also reported enriched at translation start sites (5\u2032 UTRs). Nm, which is found on the 2\u2032 hydroxyl ribose moiety of ribonucleosides, has been found in all major eukaryotic RNA . We performed Spearman correlation analysis to calculate the correlation between drug sensitivity and RM_Score. |Rs|>\u20090.3 and FDR\u2009<\u20090.05 (Benjamini and Hochberg adjusted) were considered as significant correlation.The transcription profiles for about 1000 cancer cell lines, drug response measurements as AUC for antitumor drugs in cancer cell lines, and targets/pathways of drugs are downloaded from Genomics of Drug Sensitivity in Cancer , TargetScan (http://www.targetscan.org/vert_72/) and miRDB (http://mirdb.org/). miRNA\u2013mRNA interactions overlapped in the three databases were utilized for further analysis. lncRNA\u2013miRNA interactions were predicted by TargetScan. Cytoscape were performed to generate mRNA\u2013miRNA\u2013lncRNA network.miRNA\u2013mRNA interactions were retrieved from the miRTarBase and R Bioconductor packages. Receiver operating characteristic (ROC) curve, Kaplan\u2013Meier method and univariate/multivariate Cox regression model were used to verify the validity of the model. RCircos package was used to present the distribution of the RM writers in the chromosome. 1A writers, 8 m5C writers, 9 m6A writers, 4 m3C writers, and 12 Nm writers. 69 of the 377 liver cancer samples tested positive for the mutations of writers mentioned above. Among them, KIAA1429 displayed relatively higher mutation frequency . It was speculated that the writers' genetic alterations may have contributed to the development of HCC.38 \u201cwriters\u201d of the five most prevalent RNA methylation modifications were analyzed in this study, including 5 m5C writer NSUN6 displayed downregulated expression levels in HCC tissues  was conducted using hallmark gene sets. As shown in Fig.\u00a0ues Fig.\u00a0E. In ordues Fig.\u00a0: Fig. S2As shown in Fig.\u00a0p\u2009<\u20090.0001). Subsequently, we conducted\u00a0GSVA enrichment analysis to explore the biological implications of these different RM patterns  and T cells CD4 memory resting (p\u2009=\u20099.03\u2009\u00d7\u200910\u2212 3) was higher in Cluster_1. In line with this, M0 macrophage and T cell CD4 memory resting marker gene expression was considerably elevated in Cluster_1 . We then developed an RM_Score algorithm to characterize the RNA modification profile of individual HCC patients based on these RM-related DEGs. We found that RM_Score of Cluster_2 and gene.cluster_B were significantly higher than Cluster_1 and gene.cluster_A , together with stage, were independent prognostic biomarkers. Consistently, RM_Score  and stage were also defined as independent markers in TCGA cohort.HCC can be divided into 3 molecular subtypes, named iCluster1\u20133 with distinct molecular features. Alluvial diagrams were plotted to display the association between different classifiers and the subtypes Fig.\u00a0C, D, impThen, we initially compared the two RM_Score subgroups-related pathways in the combined cohorts. DNA replication, cell cycle, immune checkpoints, DNA repair, the Wnt pathway, and DNA replication were enriched in the RM_Score-high group, whereas angiogenesis and antigen processing were involved in the RM_Score-low group Fig.\u00a0A. In TCGp\u2009=\u20090.00022). Additionally, the 348 individuals in the IMvigor210 cohort responded to anti-PD-L1 blockers with varying degrees . In the GDSC database, we found 28 strongly associated pairs between RM_Score and drug sensitivity using the Spearman correlation analysis , BCL2 inhibitor , mTOR inhibitor  and ALK inhibitor . 19 pairs showed drug sensitivity, e.g., MEK inhibitor  and ERK inhibitor . Further, we analyzed the signaling pathways of the genes targeted by these drugs. We discovered that compounds that were sensitive to high RM_Score primarily targeted the signaling pathways for MAPK/ERK and metabolism. In contrast, low RM_Score associated drugs targeted RKT and PI3K/mTOR signaling pathway , 165 differentially expressed miRNA  and 302 differentially expressed lncRNA  were identified between high RM_Score and low RM_Score samples  cell line dataset by ridge regression and validated using tenfold crossover. For each sample, subsequently, IC50 values were calculated and the discrepancies were compared. We identified that the high score group in the discovery cohort was more likely to be responsive to sorafenib  sorafenib was approved by the USA FDA in 2007 for the treatment of advanced HCC. Increasing oral multi-targeted TKIs were subsequently awarded permission for use in treating HCC worldwide , 32. Fur7G and A-I modification. In addition, larger and multi-center clinical cohorts should be employed to assess the accuracy of RNA methylation-based classifier and the RM_Score prognostic model.As previously stated, the findings presented here demonstrate that these RM networks enable connections between clinical and RNA modification by stratifying patients' prognosis and therapy responses. However, this study had some limitations as well. Though the promising conclusions were based on the integrative bioinformatics in multiple levels, future functional and mechanistic research on these RNA writers will reveal clinical phenotypes that are caused by RNA methylation writers. To fully illustrate the network of RNA methylation modifications, future studies should also include \u201creaders\u201d and \u201cerasers\u201d and other RNA methylation like mIn conclusion, we have demonstrated that RNA methylation modification patterns may serve crucial role in the progression of liver\u00a0malignancy and immune dysfunction. Additionally, the subgroup classification based on RNA methylation writers may distinguish individuals with poor survival and forecast the efficacy of immunotherapy or targeted therapy. Moreover, RNA methylation modification regulators are also highlighted as robust biomarkers or potential targets for HCC.Additional file 1: Figure S1. The survival status in patients with or without RM writer mutation. A, B The OS and DFS in HCC patients at mutation type and non-mutation type in TCGA-LIHC cohort. C1-C38, The OS in HCC patients with or without RM writers mutations in TCGA-LIHC cohort. Figure S2. The distribution of correlation coefficient between writer expression and CNV in HCC. The mRNA expression of the 38 RM writers in Normal, CNV_loss, None_CNV, and CNV_gain groups. Figure S3. The prognostic analysis of RM writers and correlation with TME cells. A The Univariate cox analysis to evaluate the correlation of RM writers with overall survival of HCC patients. B Heatmap showed the positive (red) and the negative (blue) correlation between TME infiltration and RM writers in HCC. Figure S4. The biological functions and pathways underlying the RM phenotype-related DEGs. A GO enrichment of the 62 RM phenotype-related DEGs. B KEGG enrichment analysis of the 62 RM phenotype-related DEGs. Figure S5. The correlation of RM_Score with TME infiltration. Heatmap shows the differences in TME infiltration between RM_Score-high and -low groups in the combined cohorts.Additional file 2: Table S1. Information of HCC cohorts. Table S2. RNA Modification Writers. Table S3. Samples clustering in 5 HCC cohorts. Table S4. GSVA_KEGG analysis of the DEGs between the Cluster 1/2. Table S5. GSVA_HALLMARK analysis of the DEGs between the Cluster1/2. Table S6. \u00a0Difference of TME infiltration characteristics between Cluster_1 /2. Table S7. Gene clustering in the pooled HCC cohorts. Table S8. RM_ScoreGroup stratification of the pooled cohort. Table S9. Sample information of the three computational methods of classification in 5 HCC cohorts. Table S10. Association of subtype with three computational methods of classification in TCGA LIHC cohorts. Table S11. The immune-related score of the samples in 5 HCC cohort. Table S12. Clinical information of IMvigor210 cohort. Table S13. The RM_Score of the samples in IMvigor210 cohort. Table S14. The RM_Score related ceRNA network."}
+{"text": "Glioma is one of the most common primary tumors in the central nervous system. Circular RNAs (circRNAs) may serve as novel biomarkers of various cancers. The purpose of this study is to reveal the diagnostic value of hsa_circ_0004214 for glioma and to predict its molecular interaction network. The expression of hsa_circ_0004214 was evaluated by RT-qPCR. The vector and siRNAs changed the expression of hsa_circ_0004214 to judge its influence on the migration degree of glioma cells. hsa_circ_0004214 can be stably expressed at a high level in high-grade glioma tissue (WHO III/IV). The area under the ROC curve of hsa_circ_0000745 in glioma tissue was 0.88, suggesting good diagnostic value. While used to distinguish high-grade glioma, AUC value can be increased to 0.931. The multi-factor correlation analysis found that the expression of hsa_circ_0004214 was correlated with GFAP (+) and Ki67 (+) in immunohistochemistry. In addition, the migration capacity of U87 was enhanced by overexpression of hsa_circ_0004214. Through miRNA microarray analysis and database screening, we finally identified 4 miRNAs and 9 RBPs that were most likely to interact with hsa_circ_0004214 and regulate the biological functions of glioma. Hsa_circ_0004 214 plays an important role in glioma, its expression level is a promising diagnostic marker for this malignancy. Glioma, the most common primary cancer in the central nervous system, is an aggressive and highly lethal disease , accountEndogenous noncoding RNAs include microRNAs (miRNAs), long noncoding RNAs (lncRNAs), and circular RNAs (circRNAs) . AlthougRecently, this study found an interesting circRNA, hsa_circ_0004214, which independently testing and assessing their expression in tissues will more accurately determine the presence of gliomas and the WHO classification of gliomas. Moreover, in this study, a novel circular RNA (hsa_circ_0004214) was found to be important for migration of tumors. By expanding the number of human glioma and control samples used in this study, we were able to analyze the expression of circRNAs that were independently detected in tissue samples from glioma patients. We analyzed the diagnostic value of hsa_circ_0004214 in different grades of glioma and evaluated the correlation between various pathological factors and hsa_circ_0004214. In addition, we preliminarily verified the value of hsa_circ_0004214 in judging the migration capacity of glioma cells in vitro, and constructed the molecular network that hsa_circ_0004214 regulates the occurrence and development of tumors.A total of 30 pairs of glioma tissue samples were collected, including 20 pairs of glioma samples matched with paracancerous tissue. In addition, 10 pairs of glioma samples matched with control cortex (non-glioma tissue as control group) were collected. All specimens came from surgery. In order to accurately collect glioma and paracancerous tissues, experienced doctors separated the tissues according to the intraoperative situation or navigation guidance. We tried to collect the glioma core tissue after excluding necrotic tissue, and the paracancerous tissue should be at least 1 cm away from the glioma on a safe basis. Non-glioma control brain tissue (10 cases) is usually collected with non-functional regions next to lesions of non-glioma patients. Fresh tissues were taken from the Neurosurgery Department of the Second People\u2019s Hospital of Wuxi from 2019 to 2022. After the tissues were taken, the blood on the surface of the tissues was washed with normal saline, and stored in liquid nitrogen for more than 10 min. These actions were completed within 15 min at most. Finally, the samples were quickly transferred to a \u221280 \u00b0C freezer for long-term storage. The patient\u2019s diagnosis was independently re-reviewed by 2 pathologists and classified according to WHO criteria. Written informed consent was obtained from all patients for this research. Tumor volumes were measured using preoperative MRI scans that were acquired on the day of or prior to surgery. All of these samples were obtained at the initial diagnosis. Each patient\u2019s age, gender and clinical stage were recorded. The study was approved by the Ethics Committee of Wuxi Second People\u2019s Hospital [Y-166].2. Use Lipofectamine 3000  to transfect cells with designated nucleotides or plasmids, according to the manufacturer\u2019s instructions. We constructed the overexpression vector of hsa_circ_0004214. We also constructed siRNA1 and siRNA2 of hsa_circ_0004214. The transfection process used Opti-MEM . Plasmid and siRNA details are in the Glioma cell line (U87) was purchased from the Stem Cell Bank, the Chinese Academy of Sciences. U87 cells were cultivated with DMEM high glucose  supplemented with 10% fetal bovine serum . U87 were incubated at 37 \u00b0C in 5% COTotal RNA was isolated from tissues by TRIzol reagent  according to the kit instructions. The cDNA was synthesized with random primers using the Hifair II 1st strand cDNA synthesis kit . RT-qPCR uses Bio-Rad CFX96TM real-time PCR system, hieff qPCR SYBR Green Master Mix . Primers were synthesized by general biology . Data were normalized to GAPDH. The relative expression of RNA was analyzed by 2-\u0394\u0394Ct or log2 method.The location of hsa_circ_0004214 in U87 was detected by FISH assay using fluorescence probes. Reagents included, 4% paraformaldehyde , 1% Triton x-100 , wet box, pre-hybridization solution , oligonucleotide probe diluent , DAPI staining solution (Beyotime C1005), anti-fluorescence quenching mounting medium . The Fish probe sequence of hsa_circ_0004214 is (5\u2032 to 3\u2032): GTTCTTGGCGTGCTGACTGG. The probe concentration was diluted to 500 nM, and denatured at 85 \u00b0C for 5 min. See the 4 cells were mixed with serum-free medium and added into the upper chamber of the insert. Then, 800 \u03bcL complete medium was added to the bottom chamber. The U87 in the chamber were mixed in 6% paraformaldehyde for 10 min and stained with crystal violet. After 30 min, we removed the chamber and gently wiped the dye on the surface of the upper chamber. U87 in the lower chamber were preserved and the number of cells on the bottom surface was observed and analyzed under a microscope.Transwell migration assay Transwell  with a multipolar (8.0 \u03bcm) polycarbonate membrane was utilized to conducted cell migration experiments. 5 \u00d7 10http://www.circbase.org) (accessed on 10 April 2022). Databases of circBANK (http://www.circbank.cn) (accessed on 14 May 2022), CSCD (https://gb.whu.edu.cn/CSCD/) (accessed on 11 May 2022), ENCORI (https://starbase.sysu.edu.cn/) (accessed on 17 June 2022) and miRDB (http://mirdb.org/) (accessed on 11 May 2022) were crossed to forecast miRNA and RBP. The profiles of miRNA for human samples derived from patients with glioma were obtained from the GEO database (https://www.ncbi.nlm.nih.gov/geo/) (accessed on 28 May 2022). The GSE13030 miRNA microarray was selected based on preset criteria: (1) The expression profiles of miRNA in human samples were derived from glioma patients and matched normal tissues; (2) Candidate microarrays enrolled at least three pairs of samples. The downstream target gene interaction relationship of miRNAs comes from PubMed (https://pubmed.ncbi.nlm.nih.gov/) (accessed on 21 August 2022) and NCDB  (accessed on 12 August 2022).The biological analysis of hsa_circ_0004214 comes from circbase  curve was established to evaluate the diagnostic value. The cut-off value of hsa_circ_0004214 was analyzed with SPSSUA.All experimental data were analyzed using prism8.0 (GraphPad) and image J software. The expression level of every circRNA was represented as the 2-\u0394\u0394Ct method or log2 transformation. Differences of circRNA levels between glioma and peritumoral tissues, were calculated by Characterization of hsa_circ_0004214 in glioma according to the human reference genome (GRCh37/hg19) acquired, we found that the genomic length of the hsa_circ_0003258 is 922 bp and the spliced length is 922 bp from the circBase database and NCBI genome database. hsa_circ_0004214 was formed by the back-splicing of exon 3 of linear gene angiomotin like 1 (AMOTL1). AMOTL1 is a member of the Motin protein family . Meanwhip < 0.05). After that, we divided 20 independent tissue samples from different patients into two groups, including a non-glioma control group (n = 10) and a glioma group (n = 10), and detected the expression of hsa_circ_0004214 in each tissue, respectively. The results showed that the expression level of the hsa_circ_0004214 was higher in the glioma group than in the non-glioma control group (p < 0.01). The sensitivity and specificity of hsa_circ_0004214 differential expression in gliomas and paracancerous tissues in the same patient, were significantly steadier than those between non-glioma control group and glioma group collected from different patients (p > 0.05). Relatively, there was no difference in the expression of hsa_circ_0004214 in central glioma tissues between the two groups (p > 0.05). These results indicated that hsa_circ_0004214 was highly expressed in glioma.According to previous work, we have identified several circRNAs that were potentially differentially expressed in glioma core tissue. In this article, we focused on the expression level and diagnostic potential of hsa_circ_0004214 in glioma. Here, we investigated the expression of hsa_circ_0004214 in 20 pairs of glioma core tissues and corresponding paracancerous normal brain tissues by RT-qPCR. Considering the clear relationship between the experimental group and the paracancerous group, we defined \u2206\u2206CP < \u22120.5 as high expression when counting the CP value of RT-qPCR. We found that the expression level of hsa_circ_0004214 in glioma core tissue was steadily increased compared to that in tumor peripheral tissue of the same glioma patient (patients . The expn = 10), WHO II (n = 5), WHO III (n = 10), and WHO IV (n = 15). The expression levels of hsa_circ_0004214 were analyzed and compared, respectively, among each group. Normality testing studies whether quantitative data  analysis had normal distribution properties. Shapiro-Wilk test is recommended for small samples (n < 30). Evaluating specifically at the expression level of hsa_circ_0004214 (p = 0.398), indicating there was no statistical evidence of bias at the significance level 0.05. In other words, the hypothesis was accepted , and hsa_circ_0004214 had normality characteristics. Accordingly, the 95% confidence interval (CI) for calculating the expression level of hsa_circ_0004214 was 15.64(LL)\u201318.81(UL) . We considered the non-one-to-one relationship between the experimental group and the control group and took 95% CI \u00b1 1 as the expression range of hsa_circ_0004214 in non-glioma normal brain tissue. The results showed that hsa_circ_0004214 was highly expressed in high-grade gliomas (WHO III/IV) compared with non-glioma controls and low-grade (WHO II) gliomas . However, there was no statistical difference in hsa_circ_0004214 expression levels between WHO II glioma and non-glioma control group (p > 0.05). Meanwhile, there was no difference in the expression level of hsa_circ_0004214 between WHO III and WHO IV (p > 0.05). The experimental results showed that the high expression of hsa_circ_0004214 may be correlated with the glioma high WHO grade. Compared with the non-glioma controls and low-grade (WHO II) gliomas, the expression of hsa_circ_0004214 was significantly higher in WHO III/IV gliomas.To investigate whether hsa_circ_0004214 expression varied in different WHO grades, we detected hsa_circ_0004214 expression in 30 gliomas and 10 non-glioma controls. The glioma specimens were verified and classified according to the WHO grading standard of glioma in 2021 by two experienced clinical pathologists. The samples were divided into four groups, including the non-glioma control group (https://spssau.com/index.html) (accessed on 11 September 2022). AUC is the area under the ROC curve. Its value ranges from 0 to 1. The closer the AUC is to 1, the better the diagnostic effect will be. The judgment standard of AUC is as follows: 0.5 or less does not meet the actual situation, 0.5 indicates no diagnostic value at all, 0.5\u20130.7 indicates low diagnostic value, 0.7\u20130.9 indicates certain diagnostic value, and above 0.9 indicates high diagnostic value. The AUC value of hsa_circ_0004214 expression was 0.880 (95% CI: 77.27%~98.73%), which indicated that hsa_circ_0004214 expression had certain diagnostic value for glioma . However, the results showed that there was no significant difference between the two ROC regions  curves were drawn using the SPSSAU system  and edema belt (p < 0.01). Further, the hsa_circ_0004214 expression is used as an independent variable, and the edema bandwidth is used as a dependent variable for linear regression analysis. Edema bandwidth = 1.669 + 0.001 hsa_circ_0004214 expression, and the R square value of the model was 0.001, which indicated that the circRNA expression could explain the 0.1% variation in edema bandwidth. When the F test was performed on the model, it was found that the model did not pass the F test , which means that the expression of hsa_circ_0004214 does not affect the bandwidth of edema. Therefore, it is impossible to specifically prove the relationship between the independent variable and the impact relationship. At the same time, we collected the immunohistochemical and genetic detection results of glioma patient samples, including Vimentin, S-100, GFAP, SYN, EMA, CD34, Ki67, IDH mutation, MEMG methylation and 1p/19q lost and other information (p < 0.005) and GFAP (p < 0.005).The clinical data of 30 glioma patients were collected, including age, gender, height, body mass index (BMI), WHO grade, edema belt, position, pathological type and blood pressure. We analyzed whether the differential expression of hsa_circ_0004214 was related to the above factors . The resormation . The resp < 0.05), the transwell assay indicated that the migration capacity of the transfected U87 cells was increased (p < 0.05), transwell assay showed that the migration capacity of U87 cells with low expression of hsa_circ_0004214 was decreased  are small non-coding RNA molecules, usually 21\u201325 nucleotides in length, that negatively regulate protein expression . They arThe occurrence and development of glioma constitute a complex biological process. Molecular alterations of a large number of genes are involved in cancer progression to metastasis . AlthougOne of the most extensive studies is circRNAs terminating the regulation of miRNA to its target gene by binding to the miRNA as a competing endogenous RNA (ceRNA) through the base complementary pairing principle . AccordiStudies have reported that circRNAs can be used as a diagnostic marker for tumors. For example, circNHSL1 was confirmed to be a highly stable circRNA and an appropriate diagnostic and prognostic marker for gastric cancer . CompareTo assess whether hsa_circ_0004214 is involved in the regulation of glioma cell behavior. The hsa_circ_0004214 over-expression vector and siRNA were constructed and transfected into U87, respectively. Transwell showed that compared with the control group, U87 overexpressing hsa_circ_0004214 had a stronger migration capacity. On the contrary, the migration capacity of U87 with reduced expression of hsa_circ_0004214 was suppressed. It is suggested that hsa_circ_0004214 can affect the migration capacity of glioma and has the potential as a prognostic factor. According to the results of transwell, the high expression of hsa_circ_0004214 in high-grade glioma reflects its high activity in the migration process of high-grade glioma. It is well known that miRNAs can inhibit translation or reduce mRNA stability by directly targeting gene 3\u2032-UTR, thereby acting as a post-transcriptional regulator. We predicted a ceRNA network based on hsa_circ_0004214 covering 4 miRNAs and 9 RBPs. We further established a prognosis- associated ceRNA subnet consisting of 1 circRNAs, 4 miRNAs, 9 RBPs and 11 mRNAs. The relationship between all miRNAs and downstream target genes has been experimentally determined. The mechanism of miRNA and RBP interaction with hsa_circ_0004214 remains to be further verified. In summary, our study revealed that intraoperative detection of circular RNA hsa_circ_0004214 may serve as a diagnostic marker for high-grade glioma, supplementing to the immunohistochemical diagnosis of glioma.This study comprehensively discussed the expression of hsa_circ_0004214 between the control group and different groups of glioma, and counted the expression of the adjacent tissues of glioma and the non-glioma control group of non-glioma patients. The possibility of hsa_circ_0004214 as a glioma marker was evaluated and the diagnostic success and failure ratios were calculated according to different perspectives. Regretfully, according to the results of this study, it can only be found that hsa_circ_0004214 is abnormally expressed in high-grade glioma tissue. Although this can be used as auxiliary evidence for immunohistochemistry, surgery is still required to remove the tumor tissue. Accurately diagnosing glioma without surgery has always been our pursuit, which at least requires markers that can be secreted in cerebrospinal fluid . Even beIn our study, we found that hsa_circ_0004214 has a high diagnostic value in judging high-grade glioma, and hsa_circ_0004214 can promote the migration process of glioma cells in vitro. At the same time, we successfully constructed a hsa_circ_0004214 centered circ RNA regulatory network."}
+{"text": "The forward head posture of visual display terminal (VDT) users induces various physical and cognitive clinical symptoms. However, few studies have been conducted to identify and solve problems associated with VDT posture. This study aimed to examine the adverse effects of VDT posture and the positive effects of traction-combined workstations by measuring postural alignment, muscle properties, blood velocity, preference, and working memory. Thirty-four healthy VDT users  participated in the experiment at three workstations, including conventional (VDT_C), head support (VDT_S), and upright (VDT_U) workstations. They conducted 2-back working memory task. The craniovertebral angle (CVA), muscle tone and stiffness, blood velocity and visual analogue discomfort scale (VADS) were measured to examine the influence of workstations. VDT_C showed increased muscle tone or stiffness in the levator scapulae (LS), suboccipital muscle (SM), and sternocleidomastoid muscle (SCM) and an increased reaction time (RT) in working memory. However, VDT_S showed decreased stiffness and tone of SM and improved comfort. In addition, VDT_U showed decreased stiffness or tone of the LS and SCM and improved blood velocity and RT. In conclusion, maintaining neutral alignment significantly improved working memory performance, muscle properties, and blood velocity. As non-contact online services are growing, work with mobile devices, including computers, has become an essential competency for everyone, and the incidence of symptoms or diseases related to the usage of computers and mobile devices is substantially increasing owing to the increased use time and number of users. Visual display terminal (VDT) syndrome is a representative clinical symptom caused by the long-term operation of display terminals that causes serious health problems. In particular, VDT users, due to prolonged sedentary behavior, are at an increased risk of chronic diseases, such as cardiovascular disease and diabetes ,2. They Furthermore, poor posture can lead to impaired blood circulation around the neck and shoulders. For example, abnormal cervical lordosis reduces blood flow in the vertebral artery . It is aVDT users want to maintain an upright posture. However, it is still difficult to maintain neutral alignment during VDT work because their shoulders gradually slouch forward in the process of VDT work to view small display screens. More than 70% of VDT users work with slouched shoulders and a forward-bending posture . TherefoTherefore, we propose a new method that can be effectively applied to VDT users for a long time by designing a VDT workstation using a traction device. In this study, two VDT workstations were designed. One aimed to reduce the head weight and the other to maintain an upright posture. In head weight support VDT workstation (VDT_S), one of the risk factors for VDT posture is musculoskeletal load by head weight. The forward head posture, such as the VDT posture, increases the twofold compression force, increases the fourfold anterior shear force in the cervical spine, and induces abnormal muscle hyper- and hypo-tonicity . In addiWith new VDT workstations using a traction device as a novel solution to prevent the VDT posture, this study therefore aims to examine the adverse effects of VDT posture and advantages of VDT_S and VDT_U in physical, physiological, and cognitive aspects by measuring postural alignment, muscle properties, blood velocity, preferences, and working memory performance. The first hypothesis of this study is that the conventional VDT posture (VDT_C) affects muscle properties, working memory performance, and blood velocity. Second, VDT_S and VDT_U are effective in reducing the problems of VDT_C. Consequently, the present study aims to provide practical and sustainable solutions for VDT users.This study examined 34 right-handed visual display terminal (VDT) users  who had used a computer for more than 5 years and for at least 4 h a day . All parParticipants were explained about three different VDT workstations and practiced on the 2-back working memory task for more than 5 min to familiarize themselves with the task and the workstation. We performed experiments using VDT_C, VDT_S, and VDT_U in a random order to eliminate the order effect. The participants had an adaptation time of 5 min at the first workstation  and then performed a 2-back task for 5 min, providing the reaction time (RT) and false rate. Immediately after completion of the 2-back task, the visual analogue discomfort scale (VADS) on the workstation was assessed. Then, the CVA, muscle properties and blood velocity were measured at the same workstation. The participants were asked to maintain their experimental posture until the end of the measurements. After all measurements were completed, the next workstation proceeded. Experiments in the next workstation were conducted after a 5 min break to wash out the effects of the previous workstation. The above procedure was equally applied in each workstation . Three VDT workstations were used in this study. VDT_S and VDT_U were created using a TM300 traction system . The traction device was positioned vertically at the center of the subject\u2019s head to effectively support the head weight. In the VDT_C, participants were asked to perform their ordinary posture, which was the habituated forward head posture in VDT users. The VDT works induce a forward bending posture toward the monitor. The forward head and trunk flexion become postural habit during VDT works, causing to lower cervical flexion, upper cervical extension, shoulder elevation and kyphotic thoracic posture ,29,30. IThe VDT_S was used to reduce the head weight with a traction device in the VDT_C posture. The head weight was assumed to be 7.3% of the subjects\u2019 total body weight . To ensuA 2-back working memory task was performed to assess the concentration and working memory according to VDT workstations. A 2-back task is to distinguish positive (target) or negative (non-target) stimuli with the series of numbers for 5 min. For 2-back stimulation, one of nine different single digits (1\u20139) was randomly displayed for the 2-back stimulation in white on the middle of a black background and in 30 points font size. The digits were presented for 500 ms, followed by a cross mark for 1500 ms. The \u201cM\u201d key of keyboard was set as a positive response and \u201cN\u201d key was a negative response. In the 2-back task, a positive stimulus was defined as the one with a 2-steps back and any other digit as a negative stimulus. During the 2-back task, participants were asked to press \u201cM\u201d or \u201cN\u201d key according to the positive or negative stimulus. A total of 150 responses were recorded during 5 min of the 2-back task. The 2-back task was performed on each of the VDT_C, VDT_S and VDT_U workstations. Therefore, the three different 2-back tasks used in this study were programmed using DMDX software 2017 . Participants were asked to respond as quickly and accurately as possible and were trained sufficiently before the experiment. Changes in the cervical angle were evaluated with CVA, which can be measured with the intersection angle between the horizontal line and the line connecting the C7 vertebra and the tragus of the ear. CVA is a representative method for distinguishing forward head posture. The markers were attached to the anatomical landmarks of the C7 vertebra and tragus and photographed using a camera placed 1.5 m away at the height of the acromion ,34. The Superficial skeletal muscle properties, such as stiffness (N/m) and muscle tone (Hz), were measured with a handheld myotonometer . Both sides of the suboccipital muscles (SM), upper trapezius (UT), levator scapulae (LS), and sternocleidomastoid (SCM) muscles were measured. Muscle stiffness is the magnitude of the force against the displacement of the fascia tissue and indicates the level of resistance of the myofascial tissue to external forces. Muscle tone is the intrinsic tension of a muscle in its passive or resting state without any voluntary contraction . The meaThe measurement points of SM, UT and SCM muscles were as follows. SM was marked between the middle of the C2 spinous process and the occiput. The point of SM was placed above the atlanto-occipital joint and included the rectus capitis posterior ,36,37. TThe systolic peak velocity (PSV) and end-diastolic blood flow velocity (EDV) of both common carotid arteries (CCA) were measured using color Doppler ultrasound  using a 4 cm linear transducer . An experienced ultrasonographer conducted all velocity measurements. A 60\u00b0 angle correction was used to obtain Doppler velocities. The mean of the three beats was used as PSV and EDV . p < 0.001) [The Visual Analogue Discomfort Scale (VADS) was used to assess the discomfort of each VDT workstation ,40. The < 0.001) . Discomfp < 0.05) was applied to all analyses. The false rate in the 2-back task was expressed as a percentage of the number of incorrect responses divided by the total number of responses. The mean RTs were calculated from a total of 150 RTs measured at each workstation. Post hoc pairwise comparisons were conducted using Tukey\u2019s honest significant difference test. The tables only show statistically significant outcomes.Statistical analysis was performed using Jamovi 2.2.5 . One facF = 5.32; p = 0.007). The VDT_U showed the fastest RT of 640.45 ms, which was significantly faster than VDT_C by 33.48 ms (4.97%)  and VDT_S by 42.75 ms (6.26%)  .The traction workstation significantly changed the RT during the 2-back task (= 0.044) . HoweverF = 115.30; p < 0.001). CVA improved in both VDT_S and VDT_U compared to VDT_C, and the greatest improvement was observed in VDT_U. By comparing the mean difference (MD) of CVA, VDT_U was improved by 12.90\u00b0 , and VDT_S was significantly improved by 2.50\u00b0 compared to VDT_C  (The traction workstation had a significant effect on the CVA improvement during the 2-back task (= 0.026) . t = 3.91; p = 0.001), and left tone in LS (Tone_L_LS) was significantly decreased in VDT_U than in VDT_S and VDT_C . Furthermore, only the left stiffness in LS (Stiffness_L_LS) showed a significant difference. Stiffness_L_LS was significantly higher in VDT_S than that in VDT_C and VDT_U   of VDT_U was significantly decreased by 1.15 Hz than VDT_S (< 0.001) .p < 0.001; VDT_C\u2013VDT_S in Tone_L_SM, MD = 0.81, p = 0.017). The stiffness of right and left SM  showed significant differences by the traction workstation , and both sides of SM stiffness were significantly decreased in VDT_S compared to VDT_C and VDT_U. Stiffness_R_SM of VDT_S was significantly decreased by 28.71 N/m compared to VDT_C (p = 0.002), and 26.15 N/m than VDT_U (p = 0.016). Stiffness_L_SM was significantly decreased by 25.41 N/m and 21.59 N/m compared to VDT_C (p = 0.012) and VDT_U (p = 0.016), respectively. In SCM, which plays the role of rotation and flexion of the head, stiffness was significantly decreased in VDT_U. The stiffness in right SCM (Stiffness_R_SCM) was significantly decreased by 11.38 N/m in VDT_U compared to VDT_C   was significantly lower in VDT_S than in VDT_C  . HoweverF = 4.80, p = 0.012). The average R_PSVs for VDT_U, VDT_S, and VDT_C were 116.40 cm/s, 109.82 cm/s, and 113.57 cm/s, respectively. Among them, the VDT_U significantly increased 6.58 cm/s compared to VDT_S (p = 0.029) (The traction workstation had a significant effect on the right CCA PSV (R_PSV) (= 0.029) . HoweverF = 4.48; p = 0.015). The average VDT_S had the lowest VADS score , and when compared to VDT_C, a significant improvement of comfort was observed in VDT_S  (The VADS was significantly affected by the traction workstation (= 0.007) .We examined the adverse effects of VDT_C on physical, physiological, and cognitive function and further investigated the benefits of traction combined with VDT workstations (VDT_S and VDT_U). In this study, significant improvements in postural alignment, muscle properties, blood velocity, preference, and working memory performance were observed in the VDT_S and VDT_U groups. To the best of our knowledge, this is the first evidence that a forward-bent VDT posture is associated with poor performance on working memory tasks. We observed a significant improvement in RT in VDT_U compared with VDT_C and VDT_S , probablIn this study, the level of forward posture was evaluated by CVA, and VDT_U had a significant effect on maintaining an upright posture during VDT work . The CVAThe upright posture using VDT_U significantly affected the mechanical properties of the muscle. Tone_R_LS, Tone_L_LS, Stiffness_L_LS, and Stiffness_R_SCM were significantly decreased in VDT_U compared to VDT_C or VDT_S , indicatTone_R_SM, Tone_L_SM, Stiffness_R_SM, and Stiffness_L_SM significantly decreased in VDT_S . The SM On the contrary, there were no significant differences in the UT mechanical properties. Similar to previous studies, the tone and stiffness of the UT did not change in forward head posture ,49. ThisA significant increase in the right CCA PSV in VDT_U was observed , suggestThe greatest discomfort was observed in VDT_C . AlthougThis study investigated the effect of three different workstations, but it was still limited to explain the long-term effect because the intervention was a single trial and the application time of each workstation was slightly short. In addition, because we recruited small, painless samples, it was difficult to emphasize specifically whether the three workstations were effective for all VDT users or patients with musculoskeletal disorders. Additionally, since the study evaluated only the SM, UT, LS and SCM, the changes in other muscles were not clear. Furthermore, the effect of head support was difficult to apply every VDT user because it was applied only to the forward head posture. Therefore, we still need to find out optimal method of applying head support. In further studies, application of head supports to various VDT postures like backward lean or upright posture should be conducted and long-term effects should be analyzed in the prolonged workstation environment. In addition, it needs to be expanded to clinical applications, including the patients with musculoskeletal disorders or disc degeneration. Finally, the use of traction device workstation should be investigated to prevent or relieve pain. Therefore, the proposed method can help prevent cumulative trauma disorders such as disc degeneration and myalgia, which are commonly induced by a long-term bad posture in VDT users, thereby providing high-quality and healthy work life. This study demonstrated the cognitive and physical problems caused by the conventional VDT posture and examined the effects of the traction device workstation, proving its effectiveness.Forward head posture in VDT_C increased musculoskeletal load, induced high stiffness or tone in the LS, SM, and SCM, and responded late during working memory tasks. In VDT_S, however, the benefits of head weight reduction provided better comfort and reduced SM tone and stiffness, but there was no difference in working memory performance compared with VDT_C. In VDT_U, the traction device stably maintained upright alignment during VDT work in the normal CVA range for a few minutes. The tone and stiffness of the LS and SCM significantly decreased, but the R_PSV increased. In particular, the working memory task performance is improved (faster RT).In conclusion, VDT_S influenced head weight reduction, but maintaining the neutral alignment of VDT_U was more effective in improving working memory performance than simply reducing the musculoskeletal load. Therefore, being in an upright posture during work helps to improve the musculoskeletal safety and/or work productivity, and the traction device has positive effects on being upright, muscle tension relief, blood circulation and comfort. Therefore, we expect it to be a practical solution for large-scale VDT users."}
+{"text": "Listeria monocytogenes is a foodborne pathogen and the causative agent of listeriosis, a disease associated with high fatality (20\u201330%) and hospitalization rates (>95%). ATP-Binding Cassette (ABC) transporters have been demonstrated to be involved in the general stress response. In previous studies, in-frame deletion mutants of the ABC transporter genes, LMOf2365_1875 and LMOf2365_1877, were constructed and analyzed; however, additional work is needed to investigate the virulence potential of these deletion mutants. In this study, two in vitro methods and one in vivo model were used to investigate the virulence potential of in-frame deletion mutants of ABC transporter genes. First, the invasion efficiency in host cells was measured using the HT-29 human cell line. Second, cell-to-cell spread activity was measured using a plaque forming assay. Lastly, virulence potential of the mutants was tested in the Galleria mellonella wax moth model. Our results demonstrated that the deletion mutant, \u22bfLMOf2365_1875, displayed decreased invasion and cell-to-cell spread efficiency in comparison to the wild-type, LMOf2365, indicating that LMOf2365_1875 may be required for virulence. Furthermore, the reduced virulence of these mutants was confirmed using the Galleria mellonella wax moth model. In addition, the expression levels of 15 virulence and stress-related genes were analyzed by RT-PCR assays using stationary phase cells. Our results showed that virulence-related gene expression levels from the deletion mutants were elevated (15/15 genes from \u22bfLMOf2365_1877 and 7/15 genes from \u22bfLMOf2365_1875) compared to the wild type LMOf2365, suggesting that ABC transporters may negatively regulate virulence gene expression under specific conditions. The expression level of the stress-related gene, clpE, also was increased in both deletion mutants, indicating the involvement of ABC transporters in the stress response. Taken together, our findings suggest that ABC transporters may be used as potential targets to develop new therapeutic strategies to control L. monocytogenes. Listeria monocytogenes, a Gram-positive foodborne pathogen, is an important public health concern since it can cause listeriosis associated with a mortality rate of approximately 20 to 30% in animals and humans [L. monocytogenes is also commonly found in the environment, and it is difficult to eliminate this pathogen from food processing facilities since it is able to survive under harsh conditions such as low pH and high salt [d humans . Listerid humans . L. monoigh salt .L. monocytogenes virulence involves adhesion and invasion to host cells, escape from vacuoles, intracellular growth, and cell-to-cell spread [prfA gene encodes a transcriptional regulator that turns on transcription of several virulence genes, including hly, plcA, plcB, and inlA [sigB gene positively regulates transcription of stress-related genes, including clpC and clpE [actA, ami, fbpA, and flaA, and internalin A and B (inlA and inlB) that facilitate invasion into mammalian cells [hly gene encodes for listeriolysin O, a pore-forming toxin, which in combination with the action of two phospholipases, plcA and plcB, is responsible for escape of L. monocytogenes from vacuoles. Intracellular motility and cell-to-cell spread involve the action of the actA and iap genes [Listeriosis occurs primarily in immunocompromised individuals, including pregnant women, the newborn, and the elderly . L. monol spread . The actl spread , and manl spread ,9. The pand inlA ,11. A trand clpE . Severalan cells . The hlyap genes .Galleria mellonella, has been shown to be a promising model to assess virulence of numerous human pathogens, including L. monocytogenes [L. monocytogenes [G. mellonella resembles that of mammals, with enzymes, reactive oxygen species, and antimicrobial peptides necessary against protection from bacterial infection [G. mellonella model has also been successfully utilized to explore cadmium resistance in L. monocytogenes [In recent years, an infection model using larvae of the greater wax moth, ytogenes \u201316. Advaytogenes . Most imnfection . In addiytogenes , as wellytogenes .L. monocytogenes genome [Salmonella [ATP-Binding Cassette (ABC) transporters serve as major transport systems in bacteria . More ths genome . Typicals genome . ABC tras genome . In addilmonella .LMOf2365_1875 , LMOf2365_1876 , and LMOf2365_1877  were highly induced in milk at 4\u00b0C; however, this ABC transporter operon was inhibited in RTE meats [L. monocytogenes strains [L. monocytogenes transcriptional regulators such as SigB. Previous studies have shown that the in-frame deletion mutants \u22bfLMOf2365_1875 and \u22bfLMOf2365_1877 had no overall growth defects in Brain Heart Infusion (BHI) medium, but were sensitive to salt, acid, and nisin, indicating that LMOf2365_1875 and LMOf2365_1877 may be involved in the general stress response [LMOf2365_1875 and \u22bfLMOf2365_1877 to gain insight into the possible role of the ABC transporter during infection of the human host.Liu and Ream  showed tTE meats . Magnesi strains ,28. To oresponse . HoweverSalmonella enterica serovar Typhimurium in macrophages and for virulence in vivo [LMOf2365_1875 encodes for a putative ABC transporter, manganese-binding protein, we hypothesized that manganese transport may be blocked in \u22bfLMOf2365_1875; therefore, virulence was reduced in L. monocytogenes. While this hypothesis needs further testing, it is supported by the following lines of evidence. Manganese also plays an important role in streptococcal virulence [Streptococcus pyogenes was related to virulence since a deletion mutant resulted in attenuated virulence [Agrobacterium tumefaciens mutant with a manganese transport deficiency had attenuated virulence in plants [L. monocytogenes since an ABC transporter mutant impaired in heme uptake displayed decreased virulence [Manganese is involved in bacterial virulence ,29. For  in vivo . Since Lirulence . An ABC irulence . MtsA shn plants . Similarirulence .L. monocytogenes strain F2365 (isolated from Mexican-style soft cheese) [L. monocytogenes F2365, L. monocytogenes Scott A, L. innocua, and two isogenic deletion mutants (\u22bfLMOf2365_1875 and \u22bfLMOf2365_1877) of the parent strain LMOf2365 ( cheese)  was used cheese) . L. monoLMOf2365  stored aListeria strains [L. monocytogenes strains  and L. innocua were used for the invasion assays performed as described previously [L. monocytogenes  and L. innocua were grown to log-phase (OD600nm ~0.3) at 37\u00b0C. HT-29 cell monolayers incubated in medium without antibiotics for 24 h were infected for 1 h at 37\u00b0C with 107 CFU bacterial cells in 300 \u03bcl BHI medium . The cell monolayers were washed with DMEM and incubated in DMEM containing gentamicin (100 \u03bcg/ml) for 1.5 h at 37\u00b0C. HT-29 cell monolayers were gently washed three times with phosphate buffered saline (pH 7.4) and then disrupted with 1 ml cold sterile water (4\u00b0C). Viable bacteria were counted after plating serial dilutions onto TSA. The results were expressed as the percentage of CFU recovered after 2 h relative to the number of bacterial cells deposited per well. Three independent experiments were performed for each strain.HT-29 cells (ATCC HTN-38) were used to determine the virulence of the  strains . L. monoL. monocytogenes  and L. innocua were used for PFA assays, which were performed using HT-29 cells as described previously [Listeria cells (described above) were used to infect HT-29 cell monolayers with a dilution series of 102 to 107 CFU/ml cells per well, and then incubated for 2 h at 37\u00b0C. After removing the bacterial suspensions, cell monolayers were washed with DMEM and incubated in DMEM containing 100 \u03bcg/ml of gentamicin for 1.5 h. Each well was then covered with DMEM with 0.5% agarose containing 10 \u03bcg/ml gentamicin. After solidification, 400 \u03bcl of the same liquid medium were added to the top of the agar to prevent starvation. Tissue culture plates were incubated for 3 days at 37\u00b0C under 5% CO2 (v/v). Enumeration of formed plaques was performed using an inverted microscope. The results were expressed as log numbers of plaques per 107 CFU/ml deposited per well. Experiments were performed in duplicate and repeated twice for each strain.Strains of eviously . In brieL. monocytogenes strains in this study was conducted using the G. mellonella larvae model, described in our previous work [L. monocytogenes strains were grown overnight at 37\u02daC in BHI broth and on BHI agar plates. The overnight liquid cultures in BHI broth were washed twice and serially diluted with phosphate buffered saline (PBS). Appropriate dilutions were plated onto BHI agar and incubated for 24 h at 37\u02daC to obtain the CFU count. Colony counts were used to calculate the bacterial inoculum for Galleria infection. A set of 20 Galleria larvae of the similar size (approximately 200\u2013300 mg), light-colored, with a good motility, were inoculated with appropriate dilutions of L. monocytogenes in the PBS (Fisher BioReagents), for final concentrations of 106 and 105CFU/larva. Inoculated larvae were incubated at 37\u00b0C and monitored for mortality and phenotypic changes, including changes in color, motility, dryness, and pupation for a period of seven days. For each treatment, the number of dead larvae was recorded daily for up to seven days. From these data, percent mortality was calculated. Each trial included one set of ten uninoculated larvae and one set of ten larvae inoculated with sterile 0.85% saline solution. One group of uninoculated larvae served as a control for adaptation of Galleria larvae to 37\u00b0C, while a second group served as a \u201cmanipulation\u201d control. Experiments were conducted with three independent trials.The assessment of virulence of ous work . L. monoL. monocytogenes    were inoescribed . Primers control . cDNA syData collected from this study were analyzed using the Student\u2019s t test of the Statistical Analysis Software  for paired comparison with P < 0.05 considered significant.L. monocytogenes that were defective in host infection [L. monocytogenes to human host cells. To understand if LMOf2365_1875 and LMOf2365_1877 are involved in causing host infection, cell invasion and plaque forming in vitro assays using HT-29 cell monolayers were employed to test the virulence potential of each deletion mutant. As shown in L. monocytogenes Scott A expressed the highest invasion (1.6 log10 CFU/well), and L. monocytogenes F2365 (LMOf2365) also had a high invasion efficiency (0.4 log10 CFU/well). Both L. monocytogenes F2365 and L. monocytogenes Scott A belong to serotype 4b strains, which is the serotype most often associated with outbreaks of listeriosis. The adhesion and invasion efficiency of LMOf2365 was lower compared to the LM Scott A strain , which is consistent with the fact that LMOf2365 has a truncated inlB gene that is involved in adhesion and invasion [L. innocua, used as a negative control, showed no invasion. The deletion mutant strain \u22bfLMOf2365_1875 showed a deficiency in invasion (0.1 log10 CFU/well) , whereas \u22bfLMOf2365_1877 had a slightly higher invasion efficiency (0.7 log10 CFU/well)  compared to the wild type strain (LMOf2365).Previous studies identified mutants of an ABC transporter responsible for oligopeptide transport in nfection . Since Ainvasion . Non-patin vitro assay for Listeria virulence was based on the ability of strains to form plaques on HT-29 monolayers. L. monocytogenes F2365 formed a higher number of plaques (approximately 3.9 log10 pfu/well) in comparison to the two mutants but a lower number compared to Scott A  (L. innocua strain. \u22bfLMOf2365_1875 formed 71% lower number of plaques (2.8 log10 pfu/well) compared to the wild type  whereas there was a smaller difference in plaque forming ability between \u22bfLMOf2365_1877 (3.4 log10 pfu/well) and the wild type LMOf2365 (3.9 log10 pfu/well) . Examining all of the data, results from the invasion and plaque forming virulence assays demonstrated that the deletion mutant \u22bfLMOf2365_1875 displayed some weakness in invasion and intracellular cell-to-cell spread in HT-29 monolayers, suggesting that LMOf2365_1875 may be required for L. monocytogenes virulence.The other of 0.05) . In contGalleria mellonella insect larvae model has been successfully utilized to assess virulence properties of various L. monocytogenes isogeneic mutants [LMOf2365_1875 and \u22bfLMOf2365_1877 and compared them with the parent strain LMOf2365. Our results (6 or 105 CFU/larva), exhibited reduced mortality, hence lower virulence potential, compared to the parent strain LMOf2365. At the dose of 106 CFU/larva  in \u22bfLMOf2365_1875 compared to the wild type parental strain LMOf2365, expression levels of the genes  regulated by pfrA were not up-regulated in \u22bfLMOf2365_1875. On the other hand, the expression levels of other virulence-related genes  were up-regulated. Our previous studies indicated that stationary phase cells of the deletion mutants (\u22bfLMOf2365_1875 and \u22bfLMOf2365_1875) were more resistant to multiple stress conditions [sigB, clpC, and clpE) were tested using RT-PCR assays. As shown in clpC were moderately elevated (6.8 and 10.1-fold) in \u22bfLMOf2365_1875 and \u22bfLMOf2365_1877, respectively. The increased levels of stress-related gene, clpC, expression confirmed our previous observation that these deletion mutants may contribute to general stress. In addition, the expression levels of clpE and sigB were also elevated  in \u22bfLMOf2365_1877.To determine the gene expression levels in \u22bfresponse  were cho5 strain , indicaty higher .4-fold iLMOf2365_1875 and \u22bfLMOf2365_1877.In this study, the deletion mutants showed reduced virulence in terms of invasion and cell-to-cell spreading ability; however, a number of virulence genes showed increase expression under stationary-phase growth. This seems to be contradictory, but the gene expression experiments were not performed under conditions that would occur during infection. It is likely that the virulence gene expression levels were repressed due to catabolite repression under stationary-phase growth conditions; however, these genes were de-repressed in \u22bfListeria, whether by plasmid or by an integration vector, do not exactly mimic the wild type situation because of the difference in genetic machinery involved in complementation and the topology of the complemented gene.We did not perform the complementation experiments for the deleted genes because these deletions are in frame (21), and which by design assures non-interference of other genes at the transcription level. A complementation experiment may not provide any additional information. In addition, gene complementation in \u0394LMOf2365_1875 and \u0394LMOf2365_1877, was assessed using both in vitro (invasion and plaque forming ability) and in vivo (G. mellonella insect model) assays. Our study showed for the first time that LMOf2365_1875 encoding for a manganese-binding protein of an ABC transporter might be required for virulence. In the G. mellonella model, decreased mortality of the deletion mutant \u22bfLMOf2365_1877 also indicates a possible role in the virulence potential of L. monocytogenes. In addition, the gene expression levels of L. monocytogenes virulence and stress-related genes were elevated in \u0394LMOf2365_1877 under normal laboratory growth conditions. Targeting virulence factors could be a promising approach to develop new strategies against resistant microorganisms.The virulence potential of the deletion mutants,"}
+{"text": "Vibrio parahaemolyticus, a widespread marine bacterium, is responsible for a variety of diseases in marine organisms. Consumption of raw or undercooked seafood contaminated with V. parahaemolyticus is also known to cause acute gastroenteritis in humans. While numerous dsDNA vibriophages have been isolated so far, there have been few studies of vibriophages belonging to the ssDNA Microviridae family. In this study, a novel ssDNA phage, vB_VpaM_PG19 infecting V. parahaemolyticus, with a 5,572\u2009bp ssDNA genome with a G+C content of 41.31% and encoded eight open reading frames, was isolated. Genome-wide phylogenetic analysis of the total phage isolates in the GenBank database revealed that vB_VpaM_PG19 was only related to the recently deposited vibriophage vB_VpP_WS1. The genome-wide average nucleotide homology of the two phages was 89.67%. The phylogenetic tree and network analysis showed that vB_VpaM_PG19 was different from other members of the Microviridae family and might represent a novel viral genus, together with vibriophage vB_VpP_WS1, named Vimicrovirus. One-step growth curves showed that vB_VpaM_PG19 has a short incubation period, suggesting its potential as an antimicrobial agent for pathogenic V. parahaemolyticus.IMPORTANCE Vibriophage vB_VpaM_PG19 was distant from other isolated microviruses in the phylogenetic tree and network analysis and represents a novel microviral genus, named Vimicrovirus. Our report describes the genomic and phylogenetic features of vB_VpaM_PG19 and provides a potential antimicrobial candidate for pathogenic V. parahaemolyticus. Vibrio parahaemolyticus is a short, rod-shaped halophilic Gram-negative bacterium that is widely distributed in seas and estuaries. It is known to infect shrimp, resulting in acute hepatopancreatic necrosis disease (AHPND), which can lead to the early death of shrimp, with consequent huge economic losses  phages, and there have been few single-stranded DNA (ssDNA) phage isolates. The few that have been isolated are mostly in the e family . Microvies ICTV; ). Here, Microviridae family. The host cross-infection experiment showed that phage vB_VpaM_PG19 has a narrow host range. Of the 13 strains tested, it was found to only infect three strains of Vibrio parahaemolyticus ATCC 17802, Vibrio tasmaniensis LMG 21574, and Vibrio hangzhouensis CN83  images show that vB_VpaM_PG19 has an isometric capsid with T\u2009=\u20091 icosahedral symmetry, with a diameter of 22\u2009\u00b1\u20090.5\u2009nm and without a tail , conformsis CN83 . A one-ssis CN83 . Phage vsis CN83 . To dete.1, or 1 .Phage vB_VpaM_PG19 contained a 5,572\u2009bp ssDNA genome with a G+C content of 41.31%. No tRNA genes were predicted . The ORF18\u2013Microviridae contains two major subfamilies: Bullavirinae and Gokushovirinae. These subfamilies differ from their hosts\u2019 genome structure and virion composition. Members of Gokushoviridae were originally thought to occupy a unique ecological niche, infecting obligate intracellular bacteria. However, genome analysis has shown that this population also infects free-living hosts   in 2020, ng hosts . Membersbacteria . In rece thought \u201328. The phiX174) , whereasl capsid , 30, 31.assembly \u201334, so thttps://www.genome.jp/viptree/)   Fig.\u00a03)https://wpaM_PG19 . This meThe result of average nucleotide identity (ANI) between vB_VpaM_PG19 and vB_VpP_WS1, calculated by OrthoANI, was 89.67%. However, OrthoANI is not functional when the ANI is less than 70%. Therefore, the sequence similarity based on average amino acid identity (AAI) was confirmed. The all-versus-all AAI analysis among vB_VpaM_PG19, vB_VpP_WS1, ID52, and two contigs from the IMG/VR database was calculated by the AAI calculator . vB_VpaMThe Bacterial and Archaeal Viruses Subcommittee (BAVS) of the International Committee on the Taxonomy of Viruses (ICTV) considers phages sharing\u2009\u226550% ANI as members of the same genus . The higBullavirinae and Gokushovirinae subfamily reference sequences from the NCBI virus database were selected, with vB_VpaM_PG19 and vB_VpP_WS1 together, to establish the whole-genome phylogenetic tree. Among them, vB_VpaM_PG19 and vB_VpP_WS1 originated from the tree root and formed a separate clade  in April 2021 and stored at 4\u00b0C.Vibrio parahaemolyticus ATCC 17802, which was a pathogenic strain , and then filtered onto a 0.22 \u03bcm PES Millipore filter. After the filtrate was gradually diluted, the above procedure was repeated. The infection step was repeated at least three times to ensure that the phage solution was completely purified. The purified phage solution was cultured to 500\u2009mL and treated overnight with 10% (wt/vol) PEG6000 at 4\u00b0C to precipitate the phages. The next day, the sample was centrifuged at 4,500 g at 4\u00b0C for 30\u2009min, and then the supernatant was gently poured off without disturbing the precipitate. The precipitate was resuspended in 5\u2009mL SM Buffer. The purified phage solution was stored in SM Buffer at 4\u00b0C until processing (The sewage sample was filtered through a 0.22 \u03bcm Polyethersulfone (PES) Millipore filter, to remove the bacteria and phytoplankton. The phages in the sample were then separated by the double agar layer method. In summary, 200\u2009\u03bcL of sewage sample filtrate and 200\u2009\u03bcL of logarithmic growth phase bacterial were mixed into a suspension in a cryopreservation tube and left to stand at 25\u00b0C for 30 min. The mixture was then injected into 4.5 mL of semisolid medium melted at 50\u00b0C and poured onto the surface of the solid medium. After culturing the agar plate in an incubator at 25\u00b0C for 24\u2009h, the formation of plaques was observed. If there was plaque on the plate, it was picked out and placed in 1\u2009mL of SM Buffer  phosphotungstic acid (pH 7.0). Their morphology was examined on a transmission electron microscopy  at 80\u2009kV to identify the structural features . Each grhttps://rast.nmpdr.org/rast.cgi), and ORFfinder (https://www.ncbi.nlm.nih.gov/orffinder/). Gene functions were predicted by finding homologs in the nonredundant database (http://blast.ncbi.nlm.nih.gov/) (http://pfam.xfam.org/).Viral nucleic acid was extracted using an OMEGA viral DNA kit according to the manufacturer's instructions. Purified phage genomic DNA was sequenced by the Illumina NovaSeq 2\u2009\u00d7\u2009150 bp paired-end sequence method. Gaps between remaining contigs were closed via the Gap Closer v1.12. Open reading frames (ORFs) were analyzed by GeneMarkS , GeneMarih.gov/) , 45 and Vibrio. To expand the phage vB_VpaM_PG19 group, all sequences annotated as Microviridae in the IMG/VR v3 database , gpD , and gpF (major capsid protein) homologous to vB_VpaM_PG19  were screened using BLASTp, and their gpA, gpD, and gpF were connected in series to establish a multigene maximum-likelihood phylogenetic tree. The phylogenetic tree was generated using MAFFT V7 and visualized with iTOL v4  and average amino acid identity , perhaps because these are the only two isolated microviruses that infect database  were que iTOL v4 .Bullavirinae and Gokushovirinae subfamily reference sequences from the NCBI virus database; vB_VpaM_PG19 and vB_VpP_WS1 were used as queries to construct the whole-genome phylogenetic tree. vB_VpaM_PG19, vB_VpP_WS1, two contigs (GCA_003371205 and DTR_607544) from the IMG/VR database that were closest to vB_VpaM_PG19 in the results from the multigene maximum-likelihood phylogenetic tree, and Escherichia phage ID52, which has the highest AAI with vB_VpaM_PG19 among the NCBI virus reference sequences, were selected to perform the multigenomic alignments.A proteomic tree based on the similarities of the whole genome was generated using ViPTree. The phylogenetic tree was generated using 32 classified Microviridae in the IMG/VR database were placed into a single set together with vB_VpaM_PG19. All-to-all BLASTn was performed for the 15,276 Microviridae genomes to remove duplications with the nondefault parameters: E value 1e-10, max_hsps 1, max_target_seqs 100,000, perc_ident 99, qcovs_per_hsp 100. In total, 3,051 duplicate genomes were found and removed from the original set, resulting in 12,225 nonredundant genome sets. The genome of vB_VpaM_PG19 was subjected to tBLASTx against the nonredundant genome set with the nondefault parameters ; 87 vB_VpaM_PG19-associated microviral genomes were retrieved. vB_VpaM_PG19 and 87 vB_VpaM_PG19-associated genomes were combined and subjected to tBLASTx against the nonredundant genome set with the same parameters; 64 extra PG19 indirectly associated microviral genomes were retrieved. The intergenomic relationships were analyzed based on the set containing vB_VpaM_PG19 and 151 vB_VpaM_PG19-associated genomes. vB_VpaM_PG19 and all vB_VpaM_PG19-associated genomes were subjected to BLAST to perform all-to-all tBLASTx, with the parameters: E value 1e-10, max_target_seqs 100,000; 135,972 high-scoring pairs (hsps) were generated. The bit scores from reciprocal hit pairs were averaged to eliminate the scoring bias between queries and subjects. To eliminate the impact resulting from differing genome lengths of these microviral genomes, bit scores of hsps were summed for each query\u2013subject pair and normalized by the total length of the two genomes. Normalized bit scores of query\u2013subject pairs as the weight of edges were used to build the binary profile of the genomic relationship network. The layout of the network was calculated from the binary profile based on the Force Atlas algorithm. The modularization of the intergenomic relationship network was calculated with the default resolution.Sequences from all microviruses in the NCBI virus database and all contigs annotated as Microviridae in the NCBI virus database were larger than 4,000\u2009bp in length, sequences with genome length less than 4,000 bp were deleted from the heatmap. The proposed viral clusters (VCs) were identified in accordance with the specific presence or absence of some PCs in modules, which were manually corrected to determine the distinguishing criteria and boundaries among different proposed VCs.vB_VpaM_PG19 and 151 vB_VpaM_PG19-associated genomes were subjected to BLAST to perform all-to-all tBLASTx with the parameters E value 1e-10, max_target_seqs 100,000; 135,972 high scored pairs were generated. The bit scores from reciprocal hit pairs were averaged to eliminate the scoring bias between queries and subjects. To eliminate the impact resulting from genome length differences of these microviral genomes, bit scores of hsps were summed for each query\u2013subject pair and normalized by the total length of two genomes. Normalized bit scores of query\u2013subject pairs were used as the weight of edges to build the binary profile of genomic relationship network. The layout of the network was calculated from the binary profile based on the Force Atlas algorithm. The modularization of the intergenomic relationship network was calculated with the default resolution. The gene calling of vB_VpaM_PG19 and 151 vB_VpaM_PG19-associated genomes was performed using Prodigal  with theThis article does not contain any studies with animals or human participants performed by any of the authors.OM055665. The 16S rRNA sequence of the host also has been deposited in NCBI under accession number GU460378.1.The genome sequence of vibriophage vB_VpaM_PG19 has been deposited in GenBank under accession number"}
+{"text": "Rhizopogon roseolus, Mariannaea elegans, Myrothecium verrucaria, and Sphaerostilbella broomeana and the identification of biosynthetic gene clusters for fungal peptide natural products, G3 Genes|Genomes|Genetics, Volume 12, Issue 7, July 2022, https://doi.org/10.1093/g3journal/jkac095This is a correction to: Genome sequences of Data availability. The final sentence of the first paragraph should read: \u201cThe genome assemblies, predicted gene features and sequences, and antiSMASH annotations are archived on Zenodo with the DOI https://doi.org/10.5281/zenodo.7032226.\u201d instead of: \u201cThe antiSMASH annotations are available on github under https://microbiologyethz.github.io/vogt_fungal_genomes_2022/.\u201dAn emendation is made from the originally published version of this manuscript within section This has now been emended within the article."}
+{"text": "Then, the downstream miRNA and mRNA of specific circRNAs were identified. Compared to healthy subjects, 35 circRNAs were upregulated and 9 circRNAs were downregulated in sepsis patients. The top 10 differentially expressed circRNAs were selected for validation and has_circ_0003091 was selected. The ALI mice presented significantly elevated has_circ_0003091 (mmu_circ_0015268). The functional analysis revealed that mmu_circ_0015268 contributed to the pulmonary injury, cell apoptosis, inflammatory responses, and endothelial activation in the ALI murine model. On the other hand, silencing mmu_circ_0015268 showed protective effects in LPS-treated mice and PMVECs. Furthermore, mmu_circ_0015268 sponged miR-149 to upregulate the expression of its target Smad2. In summary, we demonstrated that has_circ_0003091 might be a novel target for the management and treatment of sepsis-induced ALI.Sepsis-induced acute lung injury (ALI) is a severe cause of death. Increasing evidence has identified circular RNAs (circRNAs) acting as critical regulators of human diseases. However, their expression pattern and underlying mechanisms in ALI remain unclear. Herein, we screened the circRNAs of ALI patients and constructed a lung injury murine model using lipopolysaccharides (LPS) induction. Functional analyses of targeted circRNA were performed  Sepsis is caused by pathogen infections and is considered a serious and life-threatening disease with a high death rate. Acute lung injury (ALI) induced by sepsis is an acute disorder with respiratory failure . During Circular RNAs (circRNAs) are non-coding RNAs generated from protein-coding regions by back splicing . Increasin vitro and in vivo models.Therefore, in the present study, we for the first time characterized the profile of circRNAs in sepsis patients. Then, we functionally investigated their regulatory effects and underlying mechanisms using First, we collected clinical blood samples from sepsis patients (n=3) and healthy controls (n=2) and performed transcriptomic profiling to identify deregulated circRNAs. A total of 44 circRNAs were differentially expressed (35 upregulated and 9 downregulated) between sepsis patients and healthy subjects , 1B.Further, we found that upregulated circRNAs were associated with metabolic processes and catalytic activity in sepsis patients, while downregulated circRNAs were related to cellular processes . The KEGConsistent with the sequencing analysis, the qRT-PCR results revealed that, compared to healthy controls, 7 circRNAs were upregulated and 3 were downregulated in both peripheral blood and bronchoalveolar lavage fluid from sepsis patients , 1F. ParTo explore the effects of has_circ_0003091 (mmu_circ_0015268) in endothelial cell dysfunction and pulmonary vascular injury, its expression was determined in the lung and lung EC tissues from LPS-induced ALI mice. The histological analysis showed that sepsis led to pulmonary injury , 2B. AddIncreased expression of has_circ_0003091 was associated with increased production of inflammatory cytokines in lung tissues, including IL-1\u03b2, IL-6, TNF-\u03b1 , and BAFTo verify the role of mmu_circ_0015268 on endothelial dysfunction, we transfected PMVECs with mmu_circ_0015268 overexpressing plasmids or mmu_circ_0015268 shRNA. Increased levels of ICAM1, VCAM1, and E-selectin were observed after has_circ_0003091 overexpression, suggesting aggravation of LPS-caused endothelial activation. Consistently, compared to LPS-treated control PMVECs, the LPS-treated PMVECs with has_circ_0003091 overexpression presented significantly reduced levels of AJs proteins p120-catenin, \u03b2-catenin, and E-cadherin, which was tightly associated with increased endothelial activation. On the other hand, mmu_circ_0015268 knockdown alleviated these damages . The quaAfter bioinformatics analyses, miR-149 was identified as a potential target of has_circ_0003091 . CompareThe putative binding site between miR-149 and Smad2 was found using the TargetScan database and indicated that has_circ_0003091 exerted its regulatory function by sponging miR-149 and subsequently affecting the Smad2 expression in sepsis-induced ALI . AdditioThe expression of Smad2 was significantly enhanced by has_circ_0003091 overexpression and impaired by miR-149 overexpression in LPS-stimulated PMVECs. Moreover, the elevated Smad2 expression was suppressed by miR-149 mimics , suggestAcute respiratory distress syndrome (ARDS) or ALI is a devastating disease characterized by dysregulated immune responses, endothelial activation, and microvascular thrombosis . RecentlThrough miRNA sponging, circRNAs can regulate the expression of target mRNAs . Previouin vivo and in vitro. The injection of mmu_circ_0015268-expressing adenoviral vector aggravated the inflammatory response, cell apoptosis, and endothelial activation in sepsis-induced ALI. Consistently, downregulation of mmu_circ_0015268 suppressed the elevated cell apoptosis and inflammatory cytokines production caused by LPA stimulation, suggesting the promotive effect of mmu_circ_0015268 on cell apoptosis and inflammation. Moreover, mmu_circ_0015268 over-expression aggravated LPS-induced endothelial activation, while its knockdown led to decreased expression of endothelial adhesion molecules and pro-apoptotic markers. In the current study, we explored the role of circ_0015268 on endothelial cell activation by using pulmonary microvascular endothelial cells to establish an ALI model. Nevertheless, the pathogenesis of ALI/ARDS also involves big damage to alveolar epithelial cells, which we will explore in the future. Altogether, these results demonstrated that mmu_circ_0015268 silencing might be a novel candidate for the treatment of sepsis-induced ALI.Herein, we observed increased expression of mmu_circ_0015268 after LPS stimulation both in vitro, suggesting the critical role of Smad2 in sepsis-induced ALI.Furthermore, it has been demonstrated that circRNAs can regulate gene expression by sponging miRNAs . ConsistIn summary, we demonstrated that has_circ_0003091 (mmu_circ_0015268) expression was upregulated in sepsis-induced ALI mice. Additionally, mmu_circ_0015268 silencing ameliorated sepsis-induced the production of proinflammatory cytokines, cell apoptosis, and endothelial activation via the miR-149/Smad2 axis. These findings indicated that has_circ_0003091 might be a novel target for sepsis-induced ALI treatment.Blood samples were collected from three sepsis patients and two healthy subjects from the China-Japan Friendship Hospital following the Helsinki Declaration. p-value (p < 0.05) thresholds. The genes related to the circRNAs screened were analyzed using GO and KEGG enrichment.The PAXgene Blood RNA Kit (QIAGEN) was used to extract total RNA from clinical blood samples. A Nanodrop spectrophotometer (Thermo Fisher) was used to determine RNA concentration. Total RNA extraction and library construction were performed by the Annoroad Gene Technology Co., Ltd. . The R software was used for subsequent data processing. The expression profile of circRNAs was screened based on the fold-change (FC) (FC > 2.0) and First, C57BL/6 mice  were randomly divided into 6 groups: Sham, Model, Model + empty vector (EV), Model + mmu_circ_0015268 overexpressing vector (OV), Model + negative control (NC), Model + mmu_circ_0015268 shRNA (shRNA). All mice were housed under a 12:12 h light/dark photocycle. LPS (0.5 mg/kg body weight) (Sigma) was used to induce sepsis via intravenous (i.v.) injection. An equal volume of sterile normal saline was used as vehicle control. Adenoviral vectors containing mmu_circ_0015268 cDNA or shRNA were packed into adenovirus particles, and i.v. injected one week before ALI induction. The animal study protocols were approved by the Institutional Animal Care and Use Committee of our hospital.After digestion with a mouse lung dissociation kit (Miltenyi Biotec), CD45+ immune cells were removed using the obtained CD45 single-cells conjugated microbeads. Then, CD31 conjugated microbeads were used to select endothelial cells. Finally, a suspension with CD45-negative and CD31-positive was selected.PMVECs were cultured using DMEM and transfected with mmu_circ_0015268 or mmu_circ_0015268 shRNA adenoviral vectors. After 24 h of transfection, cells were treated with LPS (100 ng/mL). Gene analysis was carried out 6 h after transfection.Tumor necrosis factor-\u03b1 (TNF-\u03b1), interleukin-6 (IL-6), and interleukin-1\u03b2 (IL-1\u03b2) were quantified using ELISA kits (R&D Systems). After sample addition and 2 h of incubation with detection antibody, plates were incubated with a substrate solution and the optical absorbance at 450 nm was measured using a plate reader .First, Total RNA was isolated from lung tissues or cells and reversely transcribed into cDNA. The expression of screened circRNAs was validated using qRT-PCR. Primers used in this study are shown in Lysis Buffer and BCA Protein Assay Kit from Thermo Fisher were employed to extract total protein and determine their concentrations, respectively. After separation using 10% SDS-PAGE, protein bands were transferred to the PVDF membrane. After 1 h of blocking with 5% non-fat milk, samples were incubated overnight with primary antibodies against BAX , BCL2 , Caspase3 , cleaved PARP1 , E-selectin , ICAM1 , VCAM1 , P120-catenin , \u03b2-catenin , and E-cadherin  at 4\u00b0 C. Finally, samples were incubated for 1 h with HRP-conjugated secondary antibodies and the protein bands were analyzed.Changes in lung tissue morphology were determined using H&E staining. Briefly, lung tissue sections were stained with H&E following a routine protocol, and results were observed and recorded at 200 x magnification.The TUNEL assay  was used to determine cell apoptosis. Apoptotic cells were measured under a fluorescence microscope (200 x).Briefly, tissue sections or PMVECs were washed with PBS, fixed with 4% polyformaldehyde, and penetrated with 0.5% Triton X-100. After 30 min of blocking with 5% BSA, sections were incubated with primary antibodies against ICAM1 and VCAM1 (Abcam) at 4\u00b0 C overnight, followed by 30 min of incubation at 37\u00b0 C with Alexa Fluor 488 and Alexa Fluor 555-labeled IgG. Then, DAPI staining was conducted for cell nuclei detection, and a light microscope was used to visualize the results.Lipofectamine 2000 was used to co-transfect HEK-293T cells with the luciferase report vector containing the 3\u2019 UTR of WT-has_circ_0003091 or MUT- has_circ_0003091 and miR-149 agomir or agomir NC. After 24 h, the Luciferase Assay Reporter System (Promega) was used to evaluate the luciferase activity.p < 0.05 was defined as a significant difference between indicated groups.The data were analyzed using GraphPad Prism 8.0 and are presented as means \u00b1 standard deviations (SDs). The data were compared using Student's t-test or ANOVA. A Supplementary Figures"}
+{"text": "Background: Various circular RNA (circRNA) molecules are abnormally expressed in acute myeloid leukemia (AML), and associated with disease occurrence and development, as well as patient prognosis. The roles of circ_0059706, a circRNA derived from ID1, in AML remain largely unclear.Results: Here, we reported circ_0059706 expression in de novo AML and its association with prognosis. We found that circ_0059706 expression was significantly lower in AML patients than in controls (p < 0.001). Survival analysis of patients with AML divided into two groups according to high and low circ_0059706 expression showed that overall survival (OS) of patients with high circ_0059706 expression was significantly longer than that of those with low expression (p < 0.05). Further, female patients with AML and those aged >60\u00a0years old in the high circ_0059706 expression group had longer OS than male patients and those younger than 60\u00a0years. Multiple regression analysis showed that circ_0059706 was an independent factor-affecting prognosis of all patients with AML. To evaluate the prospects for application of circ_0059706 in machine learning predictions, we developed seven types of algorithm. The gradient boosting (GB) model exhibited higher performance in prediction of 1-year prognosis and 3-year prognosis, with AUROC 0.796 and 0.847. We analyzed the importance of variables and found that circ_0059706 expression level was the first important variables among all 26 factors included in the GB algorithm, suggesting the importance of circ_0059706 in prediction model. Further, overexpression of circ_0059706 inhibited cell growth and increased apoptosis of leukemia cells in vitro.Conclusion: These results provide evidence that high expression of circ_0059706 is propitious for patient prognosis and suggest circ_0059706 as a potential new biomarker for diagnosis and prognosis evaluation in AML, with high predictive value and good prospects for application in machine learning algorithms. Acute myeloid leukemia (AML) is one of the most common hematological malignancies and the most frequent type of acute leukemia in adults . In rececirc-VIM is significantly up-regulated in AML, and its over-expression is an independent prognostic factor associated with duration of overall and leukemia-free survival of patients with AML (Circular RNA (circRNA) is a type of non-coding RNA characterized by a closed ring structure and circRNA molecules are widely distributed in eukaryotes, where they perform complex biological functions . As circwith AML .Developments in big data and computer hardware and software technologies have led to widespread use of machine learning in medicine . CompareID1 transcript level significantly increased in AML and act as an independent risk factor in young non-M3 patients. Circ_0059706 is a circular RNA, formed by ID1 during its splicing. In this study, we investigated circ_0059706 expression in patients with AML, evaluated its clinical significance, and analyzed the predictive ability of circ_0059706 expression for AML prognosis using machine learning. The aim of the study was to explore the value of circ_0059706 as a new tumor marker for predicting AML prognosis.Our previous study has revealed This study was approved by the Ethics Committee of the Affiliated People\u2019s Hospital of Jiangsu University and included 100 patients newly-diagnosed with AML and 33 healthy controls (K-20190020-Y). All samples were from the sample bank at our hospital and all patients signed informed consent forms. AML was classified according to World Health Organization (WHO) criteria and French-American-British (FAB) classification. Mutations were detected by high-resolution melting analysis .2 humidified atmosphere. Lentiviruses over-expressing circ_0059706 were purchased from Shanghai Jikai Biological Co., Ltd.  and cell transfection performed according to the manufacturer\u2019s instructions.The K562 and THP-1 human leukemia cell line were purchased from ATCC. Cells were cultured in RPMI 1640 medium  containing 10% fetal calf serum (FCS)  and 100 U/ml penicillin/streptomycin  at 37\u00b0C in a 5% CO3 per well in 96-well plates. After culture for 0, 24, 48, and 72\u00a0h, 10\u00a0\u03bcl CCK-8  solution was added to each well. Optical density values were measured at 450\u00a0nm absorbance using a microplate reader.Cells were seeded at 3 \u00d7 105) were seeded into 6-well plates containing complete 1640 culture solution, without FCS, for 48\u00a0h. Apoptosis rate was determined using an apoptosis detection kit , and analyzed by flow cytometry on a FACSCalibur platform .Cells (5 \u00d7 10circ_0059706 were 5\u2032-TGG\u200bTAA\u200bACT\u200bCTC\u200bATT\u200bCCA\u200bCGT\u200bTC-3' (forward) and 5\u2032-CCA\u200bCTG\u200bGCG\u200bACT\u200bTTC\u200bATG\u200bAT-3' (reverse). The primers used as controls were ABL and sequences were 5\u2032- TCC\u200bTCC\u200bAGC\u200bTGT\u200bTAT\u200bCTG\u200bGAA\u200bGA -3\u2032 (forward) and 5\u2032- TCCAACGAGCGGCTTCAC -3' (reverse). The primers for miR-326 were 5\u2032-GTC\u200bGTA\u200bTCC\u200bAGT\u200bGCA\u200bGGG\u200bTCC\u200bGAG\u200bGTA\u200bTTC\u200bGCA\u200bCTG\u200bGAT\u200bACG\u200bA CCTGGAG-3' (forward) and 5\u2032- GCC\u200bGAG\u200bCCT\u200bCTG\u200bGGC\u200bCCT\u200bTC-3' (reverse). The primers for U6 were 5\u2032-CTC\u200bGCT\u200bTCG\u200bGCA\u200bGCA\u200bCA-3' (forward) and 5\u2032-AAC\u200bGCT\u200bTCA\u200bCGA\u200bATT\u200bTGC\u200bGT-3' (reverse). Relative circ_0059706 expression levels were calculated using 2\u2212\u0394\u0394CT method.Mononuclear cells were extracted from bone marrow (BM) specimens using gradient centrifugation . RNA extraction and reverse transcription were conducted based on the instructions in miScript kits . Reverse transcription and RQ-PCR were conducted as previously reported . The pricirc_0059706 expression were calculated using the 2\u2212\u0394\u0394CT method. Categorical variables were analyzed using chi square tests and/or Fisher\u2019s exact tests. The diagnostic capacity of circ_0059706 was analyzed using receiver operating characteristic (ROC) curves and area under the curve (AUC) values. Survival was analyzed using the Kaplan-Meier method. Univariate and multivariate Cox regression analyses were conducted. Differences in continuous variables between two groups were compared by Student\u2019s t-test. Differences were considered statistically significant at two-tailed p < 0.05.Data were analyzed using SPSS 20.0 software. Relative levels of circ_0059706 expression level, sex, age, white blood cell (WBC) count, hemoglobin (HB) level, platelet (PLT) count, BM blasts, diagnosis, karyotype chromosome abnormalities, chromosome risk group, blast percentage, and granulocyte count. Mutations of nine genes  were also included as variables. Five derivative variables and the methods used to generate them are shown in For machine learning, case and survival data, including 26 characteristic variables from 57 cases, were used. Twelve basic variables included in the analysis were: circ_0059706 expression in BM samples from 100 patients with AML and 33 healthy controls were detected by RQ-PCR. Median circ_0059706 expression levels in healthy controls and patients with AML were 4.581 and 0.153, respectively; Circ_0059706 expression was significantly lower in AML than in controls (p < 0.001)   . The AUC< 0.001) , indicatcirc_0059706 expression with AML clinical characteristics, the total patient group was divided into circ_0059706high and circ_0059706low groups, according to median +1/16 standard deviation of circ_0059706 expression level, using a cutoff value of 0.254 . Then, clinical parameters were compared between the high and low expression groups , patients in the circ_0059706high group had significantly longer overall survival (OS) than those in the circ_0059706low group (p = 0.047)  (circ_0059706high group was significantly longer than that of those in the circ_0059706low group who were younger than 60-years-old AML (p = 0.009)  were included in multivariate analysis, which demonstrated that circ_0059706 was an independent factor associated with poor prognosis in the total AML patients (p = 0.020) (To explore association of = 0.047) . Further= 0.037) . Compare= 0.009) . Variabl= 0.020) .First, LR, RF, GB, NNK, SVM, KNN, and GNB 7 machine learning algorithms were developed using training set data, and their performance evaluated. As shown in Circ_0059706 expression level was the first important variable among all 26 features included in the GB and RF algorithms and was among the most important in the LR algorithm   .circ_0059706, we performed additional analysis. We analyzed the miRNAs that may bind to circ_0059706 by use of the circinteratome database (https://circinteractome.nia.nih.gov/index.html). Then, the expression levels of these miRNAs in AML patients were analyzed by the GEO database (Datasets: GSE51908). Bioinformatics analysis revealed that circ_0059706 contains a binding site for miR-326 . Multivariate analysis showed that circ_0059706 low expression was an independent factor associated with poor prognosis of all patients with AML, indicating that circ_0059706 has potential for application as a new biomarker for diagnosis and prognosis evaluation of AML.poptosis . Our groCirc_0059706 level are closely related with survival, suggesting its potential as a biomarker in patients with AML. Different machine learning algorithms may be optimal for any data set; therefore, to assess the prospects for application of circ_0059706 levels in AML in machine learning algorithms, we developed seven types of machine learning algorithm, including LR, RF, GB, NNK, SVM, KNN, and GNB. The GB model had better performance in predicting 1-year prognosis and 3-year prognosis. We were unable to predict 5-year survival due to insufficient data. In recent years, scholars have established many risk assessment methods in various disease prognosis models using machine learning, which can provide guidance for the selection of treatment methods and prognosis assessment. For example, Heo applied an NNK algorithm to establish a prediction model for long-term prognosis in patients following ischemic stroke (Traditional statistics are generally used to infer relationships between variables, while machine learning models aim to make the most accurate predictions possible, and are increasingly being applied in medical prediction models. Gao et al. predicted a significant association between Luminal and HER2 breast cancer subtypes and estrogen/progesterone and HER2 receptor status, using the DeepCC method . Lee et c stroke , while Tc stroke . Moreovec stroke .Circ_0059706 expression level was the first important variable among all 26 features included in the GB and RF algorithms, and it ranked highly in the LR algorithm. It indicated that circ_0059706 has a high predictive value and a good prospect for application in machine learning, supporting the potential of this circRNA as a new biomarker for diagnosis and prognosis evaluation in AML.Variables are crucial to the prediction results generated by machine learning; hence, the key roles of variables included in the machine learning models was also a focus of our attention. We analyzed the importance of variables in the GB, LR, and RF algorithms, which had good modeling performance. circ_0059706 on cell growth and apoptosis in leukemia cells. The results showed that circ_0059706 overexpression inhibited cell growth and increased apoptosis, further supporting the hypothesis that the high expression of this circRNA is propitious for patient prognosis. To investigate the mechanisms, we analyzed miRNAs with common binding sites for circ_0059706 in the circinteratome database. The expression levels of miRNAs were analyzed by datasets GSE51908. Combined with literature reports, we focused miR-326, which was downregulated in GSE51908 datasets. P Cheng reported that expression of miR-326 was downregulated in AML patients compared with that in normal. Overexpression of miR-326 inhibited proliferation, promoted cell apoptosis and PMA-induced differentiation in AML cells (miR-326 down regulated in ALL patients and negative associated with its expression and MDR (miR-326 maybe act as a tumor suppressor miRNA in leukemia and it was upregulated in circ_0059706 over-expressed cells. miR-326 was up-regulated in circ_0059706 overexpression cells, it may be a mechanism of inhibited growth and promoted cell apoptosis. However, more experiments needed to verify, such as luciferase reporter experiment, the effect of up/down-regulation of miR-326 expression on cell biological function, etc.Furthermore, we analyzed the effect of ML cells . Moreove and MDR . These rcirc_0059706 is a frequent event and predicts poor prognosis in patients with de novo AML. Circ_0059706 showed good predictive effects in machine learning models and was among the most important variables in the developed models. In addition, circ_0059706 overexpression could inhibit cell growth and increase apoptosis. These results demonstrated that circ_0059706 might act as a potential biomarker for prognosis in de novo AML.Taken together, our results indicate that down-regulation of"}
+{"text": "Recent technological developments have made genome sequencing and assembly highly accessible and widely used. However, the presence in sequenced organisms of certain genomic features such as high heterozygosity, polyploidy, aneuploidy, heterokaryosis, or extreme compositional biases can challenge current standard assembly procedures and result in highly fragmented assemblies. Hence, we hypothesized that genome databases must contain a nonnegligible fraction of low-quality assemblies that result from such type of intrinsic genomic factors.de novo genome assembly to assess several parameters and generate informative plots to assist in the identification of nonchanonical genomic traits. Karyon includes automated de novo genome assembly and variant calling pipelines. We tested Karyon by diagnosing 35 highly fragmented publicly available assemblies from 19 different Mucorales (Fungi) species.Here we present Karyon, a Python-based toolkit that uses raw sequencing data and Our results show that 10 (28.57%) of the assemblies presented signs of unusual genomic configurations, suggesting that these are common, at least for some lineages within the Fungi. Karyon is freely available in GitHub and as a docker container , our results suggest that the number of unreported deviations in genome architecture in Fungi is considerable. This is emphasized if we consider that most researchers that have produced low-quality assemblies are unlikely to publish their data.Recent developments in high-throughput sequencing and bioinformatic tools have made the process of sequencing the genome of a new organism a routine task for many laboratories, especially those working on groups with small compact genomes . The success of a genome assembly is limited by technical aspects as well as by intrinsic properties of the sequenced genome. A successful assembly depends on the quality, design, and depth of the sequencing libraries, which must typically adapt to budget limitations. Naturally, if the sequencing methodology or the computational approaches are inappropriate, the resulting assembly will be poor . However, additional difficulties might arise independently of the methodology employed, due to intrinsic properties of the genome that interfere with genome assembly algorithms.The main intrinsic factors that compromise the success of a genome assembly are the genome size, the sequence heterozygosity, the abundance of low-complexity regions , and the presence of high or uneven ploidy, contaminating sequences or extreme nucleotide compositions Fig.\u00a0.Genome size impacts computational costs, as many assembly algorithms scale nonlinearly . HeterozSimilarly, ploidy deviations can greatly affect genome assembly. The first possible ploidy deviation is polyploidy Fig.\u00a0, which iIn syncytial organisms, such as filamentous fungi or slime molds, there is the possibility of coexistence of genetically different populations of nuclei within a cytoplasmic continuum, a condition known as heterokaryosis Fig.\u00a0 20\u201322]..20\u201322]. k-mer estimations. Highly diverse contaminations  introduce sequences with highly variable level of coverage, heterozygosity, and composition. On the other hand, highly abundant contaminants  are typically more homogeneous in all these parameters but might still form chimeric contigs and would indirectly reduce the depth of coverage in the main genome. Contaminations reducing the signal of the main genome are particularly problematic for single-cell sequencing projects [The presence of sequence contamination Fig.\u00a0 can greaprojects , 30. Thik-mers composed of the favored nucleotide pair will appear at higher frequencies. GC% has a well-documented effect on some sequencing technologies, most notably on the quality of Illumina reads [Finally, genomes with extreme compositions, typically very high or low GC content (GC%), can be difficult to assemble Fig.\u00a0. For thena reads , 32. Forna reads , 34. Lowna reads . DespiteIf the presence of the factors outlined above is anticipated, specific technical approaches\u2014both experimental and computational\u2014 can be used. Contaminating DNA can be identified easily because sequencing coverage, nucleotide composition, and phylogenetic signal are usually different from the main genome, and several programs have been developed to identify contamination . Ploidy Thus, biological factors affecting genome assembly quality increase the overall costs of a project and require expertise that might not be available. Given the difficulty of performing analyses on low-quality assemblies, it is likely that published genomes are biased in favor of organisms with genomic characteristics that make them easier to work with. In contrast, genomic projects that choose organisms with nonstandard genomic architectures are more likely to suffer methodological obstacles that delay or even prevent analyses. Our inability to work around nonstandard genomic architectures distorts our perception of biological phenomena, relegating them to mere oddities.https://github.com/Gabaldonlab/karyon.To aid in the identification of these noncanonical genomic architectures, we developed Karyon, a Python-based toolkit that assesses several parameters of sequencing data and their derived assemblies that are common indicators of different intrinsic genomic features leading to poor assemblies. Karyon comprises different modules that can be used independently or sequentially. Karyon is written in Python 3 and freely available to download as a Docker build or as a standalone project in de novo assembly using SPAdes v3.9.0 [de novo assembly to generate a reduced assembly using Redundans [Karyon integrates Trimmomatic  as an ops v3.9.0 , dipSPAds v3.9.0 , Platanus v3.9.0 , or SOAPs v3.9.0 . Karyon edundans . Redundaedundans  and geneedundans . A battek-mer  spectrum analysis as part of its report. From this analysis, it produces frequency histograms representing coverage versus k-mer counts. These plots inform on ploidy and heterozygosity of a genome. In a haploid genome, k-mers of enough size will appear either 1 or 0 times, with unique k-mers having an average coverage roughly equal to the average global depth of coverage. Deviations from these patterns suggest alternative architectures. For instance, the presence of 2 peaks in the k-mer plot typically indicates a genome that is totally or partially a nonhomozygous diploid. To complement these analyses and provide further information on the features of the genome, Karyon assesses scaffold length distributions, relationships between scaffold length and coverage, sliding-window analysis of coverage, and genetic variation, as well as allele frequency distributions per scaffold   to proviKaryon generates a series of original plots that aim to provide valuable information regarding the architecture of the problem assembly:Scaffold length plots  presented very low levels of heterozygosity and a relatively homogeneous coverage across the genome, suggesting that those strains are haploid or, if presenting higher ploidy, extremely homozygous. Fragmentation in these cases might be caused by insufficient coverage, presence of repetitive regions, or some other methodological constraints. However, our pipeline uncovered cases that produced anomalous results in the different Karyon tests. Many zygomycetous fungi exhibit low or very low GC%. In our dataset, 5 species showed GC% below our threshold of 35%, with several others approximating that value. Additionally, some of the analyzed genomes show signs of ploidy anomalies or contamination. Below we describe these cases and propose a plausible scenario to explain each of the obtained results based on the data obtained from the Karyon pipeline.Rhizopus microsporus strains. Interestingly, 3 of them presented a genome size estimated around 25 Mbp, 4 had a genome size close to 50 Mbp, and 1 presented a genome size of 75 Mbp. Only the 3 strains with a genome size of 25 Mbp had sufficiently good assemblies considering they were based on short reads, with a scaffold number below 1,000, and thus were not selected for further analyses. Additionally, the raw libraries for one of the strains presenting a 50-Mbp genome assembly size (R. microsporus var. chinensis CCTCC M201021) were not publicly available and thus could not be part of the survey. For the remaining 3 strains with genome size close to 50 Mbp , our de novo assembly pipeline recovered a genome size of approximately 40 Mb, which is smaller than the assemblies deposited in NCBI , despite presenting a 3-fold increase in genome size as compared to other strains of the same species  and the one reconstructed here (71 Mbp). The genome of  Mbp and Mucor racemosus B9645 depicted a genome with a dual behavior. The distribution of heterozygosity and coverage showed 2 peaks with very low heterozygosity but with different coverage \u2060 of those species in the Mucorales with a highly fragmented assembly , which included at least 1 paired-end Illumina library larger than 1 Gb after quality filtering . At this moment, the pipeline assumes the use of at least 1 Illumina paired-end sequencing library. Because of this, we recommend the use of other genome assemblers if other sequencing technologies  are to be used, and the same goes for variant calling protocols.We provided a practical example of the usage of Karyon on a publicly available set of fungal genomes from the order Mucorales. While most analyzed assemblies show no sign of any of the considered biological conditions, we were able to effectively find underlying nonstandard genomic architectures that had been previously unnoticed in these assemblies. These results suggest that many authors do not take into consideration these kinds of genomic accidents, which in turn greatly hampers the results that might be obtained from them.How common are these nonstandard genomic architectures? Our results suggest that they might be quite abundant, although so far they are restricted to a limited selection of species within a narrow clade of Fungi. As such, these genomic anomalies might, or might not, be common in other lineages. However, we consider that there are 3 important arguments in favor for considering our dataset an underestimation of the abundance of unorthodox fungal genomes, even within the limited taxonomic range we have selected. The first one is the fact that fungal biomass used for DNA extraction and subsequent sequencing typically comes from cultures. This implies an important ecological step in which the fungus grows at optimal speed and in the absence of most stressors. Aneuploidies, polyploidies, and other similar genomic rearrangements are common in the presence of stressors , 52, 65 Even if we consider these possible biases as negligible, our results recover a significant fraction of publicly available genomes with unorthodox genomic configurations. These have been correlated in many fungal groups with adaptation to novel environments , resistaProject name: Karyonhttps://github.com/Gabaldonlab/karyonProject homepage: Operating system(s): e.g., Linux, any with the Docker imageProgramming language: Python, BashOther requirements: Check the installationLicense: GNU GPL v3RRID: SCR_022544biotools ID: karyonGigaScience database GigaDB [All data used for this study were downloaded from NCBI SRA. Table\u00a0e GigaDB .BUSCO: Benchmarking Universal Single-Copy Orthologs; NCBI: The National Center for Biotechnology Information; SNP: single-nucleotide polymorphism; SRA: Short Read Archive.The authors state that they have no conflicts of interests.Supported by the Spanish Ministry of Science and Innovation (grant PGC2018-099921-B-I00), cofounded by the European Regional Development Fund (ERDF); the Catalan Research Agency (AGAUR), SGR423; the European Union's Horizon 2020 research and innovation program (ERC-2016\u2013724173); the Gordon and Betty Moore Foundation (grant GBMF9742); and the Instituto de Salud Carlos III (IMPACT grant IMP/00019 and CIBERINFEC CB21/13/00061-ISCIII-SGEFI/ERDF).giac088_GIGA-D-21-00155_Original_SubmissionClick here for additional data file.giac088_GIGA-D-21-00155_Revision_1Click here for additional data file.giac088_GIGA-D-21-00155_Revision_2Click here for additional data file.giac088_GIGA-D-21-00155_Revision_3Click here for additional data file.giac088_GIGA-D-21-00155_Revision_4Click here for additional data file.giac088_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giac088_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giac088_Response_to_Reviewer_Comments_Revision_2Click here for additional data file.giac088_Response_to_Reviewer_Comments_Revision_3Click here for additional data file.giac088_Reviewer_1_Report_Original_SubmissionZhong Wang -- 6/29/2021 ReviewedClick here for additional data file.giac088_Reviewer_1_Report_Revision_1Zhong Wang -- 12/2/2021 ReviewedClick here for additional data file.giac088_Reviewer_2_Report_Original_SubmissionMichael F. Seidl -- 7/4/2021 ReviewedClick here for additional data file.giac088_Reviewer_2_Report_Revision_1Michael F. Seidl -- 11/29/2021 ReviewedClick here for additional data file.giac088_Reviewer_3_Report_Original_SubmissionKamil S. Jaron -- 7/10/2021 ReviewedClick here for additional data file.giac088_Reviewer_3_Report_Revision_1Kamil S. Jaron -- 1/10/202 ReviewedClick here for additional data file.giac088_Reviewer_3_Report_Revision_2Kamil S. Jaron -- 5/17/2022 ReviewedClick here for additional data file."}
+{"text": "TCONS_00049507 and TCONS_00049510 competitively interacted with mtr_miR169l-5p, which upregulated the expression of NUCLEAR FACTOR-Y (NF-Y) transcription factor subfamily NF-YA genes MtNF-YA2 and MtNF-YA3 to regulate their downstream drought-response genes. Our results emphasized the importance of SNF plants affecting drought tolerance. In conclusion, our work provides insight into ceRNA involvement in rhizobium symbiosis contributing to drought tolerance and provides molecular evidence for future study.Drought, bringing the risks of agricultural production losses, is becoming a globally environmental stress. Previous results suggested that legumes with nodules exhibited superior drought tolerance compared with the non-nodule group. To investigate the molecular mechanism of rhizobium symbiosis impacting drought tolerance, transcriptome and sRNAome sequencing were performed to identify the potential mRNA\u2013miRNA\u2013ncRNA dynamic network. Our results revealed that seedlings with active nodules exhibited enhanced drought tolerance by reserving energy, synthesizing N-glycans, and medicating systemic acquired resistance due to the early effects of symbiotic nitrogen fixation (SNF) triggered in contrast to the drought susceptible with inactive nodules. The improved drought tolerance might be involved in the decreased expression levels of miRNA such as mtr_miR169l-5p, mtr_miR398b, and mtr_miR398c and its target genes in seedlings with active nodules. Based on the negative expression pattern between miRNA and its target genes, we constructed an mRNA\u2013miR169l\u2013ncRNA ceRNA network. During severe drought stress, the lncRNA alternative splicings  SPL13 through accumulation of osmoprotective compounds proline, ABA, and antioxidants in alfalfa [Drought has become a global issue resulting in a decrease of crop production and enormous economic loss  . Drought alfalfa . In toma2 into nitrate nitrogen (NO3\u2212-N) and ammonium nitrogen (NH4+-N) [Symbiotic nitrogen fixation (SNF) is a nitrogen-fixation system based on legume-rhizobia symbiosis, and can convert atmospheric N(NH4+-N) .The SNF (NH4+-N) . Our ear(NH4+-N) . Moreove(NH4+-N) . The dro(NH4+-N) . PreviouNon-coding RNAs were involved in many biological processes in plants, such as reproductive development , positivSPL2-like/SPL33\u2013miR156a\u2013MLNC3.2/MLNC4.6 network was involved in the regulations of apple  fruit pigment [MLNC3.2/MLNC4.6 as miR156a sponge under white or blue light, anthocyanin was accumulated in apple fruit by improving the expression of mRNA SPL2-like/SPL33 [NBS-LRR\u2013miR482a\u2013lncRNA15492, a ceRNA network, responded to biotic stress in plants [NBS-LRR, which positively regulated tomato (Solanum lycopersicum) resistance to Phytophthora infestans, was cleaved by miR482a, whereas lncRNA15492 inhibited precursor miR482a expression through antisense strands of lncRNAs [TCONS_00021861 could be competitively combined with miR528-3p, which released YUCCA7, the miRNA target gene, to active IAA biosynthetic pathway and confer resistance to drought stress [The mRNA, lncRNA, and circRNA were named as competing endogenous RNAs (ceRNAs) as they competitively combine with the same miRNA . For ins pigment . With moke/SPL33 . Furthern plants . The mRN lncRNAs . In ricet stress . HoweverMedicago truncatula, with the characteristics of a short lifecycle, a small genome size , and self-pollination, is a model leguminous plant for the study of nodule nitrogen fixation, especially the drought tolerance associated with rhizobium symbiosis. Previous study showed that legumes with nodules exhibited superior drought tolerance compared with the control (without nodules) treatment. However, the ceRNA regulatory network of nodules contributing to the drought tolerance of M. truncatula is still unclear. To investigate the regulatory mechanism of ceRNA, transcriptome and sRNAome sequencing were performed to identify the potential mRNA\u2013miRNA\u2013ncRNA dynamic network. Our result showed that as the water deficiency continues, M. truncatula in active nodule (AN) treatment medicated systemic acquired resistance due to early effects triggered by SNF. The mRNA\u2013miR169l\u2013ncRNA network contributed to the drought tolerance by its target genes competitively combined with mtr_miR169l-5p.M. truncatula inoculated with rhizobium formed nodules in the root. During our early research, the active nodules were pink with nitrogen fixation ability, while the inactive nodules were white without (or barely with) nitrogen fixation ability [ ability . With diUnder different treatment, 27 samples were sequencing of transcriptome and sRNAome to investigate the molecular regulation mechanism of nodules impacting drought tolerance . On averMTR_7g013820 gene putatively encoding NINJA family protein AFP3 was the only upregulated DEG detected in D0_IN vs. D0_NN, D0_AN vs. D0_NN, and D0_AN vs. D0_IN. It showed that AFP3 might be upregulated in IN and have the highest expression level in AN plants. Under the D1 condition, the DEGs of D1_IN vs. D1_NN, D1_AN vs. D1_NN, D1_AN vs. D1_IN were 768 (16 upregulated and 752 downregulated), 988 (24 upregulated and 964 downregulated), and 12 (7 upregulated and 5 downregulated), respectively. Most of the DEGs shared between D1_IN vs. D1_NN, and D1_AN vs. D1_NN were downregulated in the mild drought stress  . While ct stress B. It indIn the severe drought stress (D2) condition, there were 38 DEGs (downregulated) of D2_IN vs. D2_NN, 710 DEGs (290 upregulated and 420 downregulated) of D2_AN vs. D2_NN, and 2158 DEGs (1087 upregulated and 1071 downregulated) of D2_AN vs. D2_IN. DEGs shared between D2_AN vs. D2_NN, and D2_AN vs. D2_IN showed that AN plants had specific genes to respond long term drought tolerance C. It seeTo describe the function of DEGs, gene ontology (GO) enrichment analysis was used to classify and annotate DEGs To comprehend how nodules affected the pathway regulation of plants, we introduced KEGG (Kyoto Encyclopedia of Genes and Genomes) to investigate pathways triggered by nodulation during the regulation of abiotic stress . In the https://bioconductor.org/packages/TCseq/, accessed on 20 August 2021) was used to identify the DEGs expression patterns. All the DEGs were divided into 10 expression clusters  were performed to identify the expression pattern between nodulation and drought. The R package TCseq , all the differentially expressed lncRNAs (DElncRNAs) detected in AN plants were upregulated compared with NN and IN plants. On the contrary, in D1 treatment DElncRNAs of IN and AN plants were downregulated except TCONS_00002192. TCONS_00002192 was the unique gene upregulating in AN plants contrasted with IN or NN plants. Under D2 treatment, only a few downregulated DElncRNAs were identified in AN and IN plants compared with NN plants. Considering AN vs. IN, 57 DElncRNAs (15 upregulated and 42 downregulated) were identified . Under D2 treatment, eight DEmiRNAs, of which miR159b, miR169l-5p, miR397-5p, miR5747, and miR5291b were negatively regulated with their target DEGs, were predicted to interact with target DEGs . Within D1 treatment, mtr-miR2608 expression was negatively contrary with MTR_0011s0020 and eight lncRNAs . Under D2 treatment, mtr_miR159b, and mtr_miR169l-5p, constructed mRNA\u2013miRNA\u2013lncRNA(\u2013circRNA) networks, respectively. Intriguingly, mtr_miR159b, mtr_miR398b, mtr_miR398c, and mtr_miR169l-5p had a negative regulatory relationship with TCONS_00049510 and TCONS_00049507. These two lncRNAs belonged to splice variants of DElncRNA genes. Meanwhile, these indicated that both TCONS_00049510 and TCONS_00049507 comprised disparate MREs, which interacted with different miRNAs. These could account for the similar expression pattern of mtr_miR169l-5p, mtr_miR398b, and mtr_miR398c. Besides mRNA MTR_7g106450 and MTR_2g041090, and mtr_miR169l-5p were negatively regulated by 2 circRNAs, mtr_circ_0000090, and mtr_circ_0000202. Strikingly, MTR_7g106450 (MtNF-YA2) and MTR_2g041090 (MtNF-YF3) were presumed to be nuclear transcription factor Y subunit A (NF-YA) family members, which were consistent with previous study acting as target genes of miR169 [Based on the negative relationship between miRNA and target genes, a ceRNA regulation network was established . At D0, f miR169 ,31,32,33mtr_circ_0000090, and mtr_circ_0000202 MREs indicated that both of these circRNAs had diverse miRNA MRE sites of miRNA mtr_miR398b and mtr_miR398c. Mtr_miR159b took part in negative regulation of MTR_0005s0200, whereas mtr_miR397-5p was participant in negative regulation of MTR_1g047800 in D2. In consideration of the interaction between mtr_miR397-5p and its negative regulation lncRNAs under D0 condition, mtr_miR397-5p was possibly suppressed by lncRNA in D0 treatment and inhibited expression of MTR_1g047800 in the drought stress.Different M. truncatula of different nodule treatments.Our results showed that AN and IN plants had improved drought tolerance than NN. Especially, AN plants exhibited the optimal drought-resistant properties via SNF-triggered pathway. Based on the RNA-seq analysis, we clarified the potential molecular mechanism of drought tolerance in With D0 treatment, the DEGs in IN were enriched in proline biosynthetic and positive regulation of transcription. Plants accumulated proline in response to abiotic stress of drought , heat 3, cold 3, and salM. truncatula. The AN plants specifically showed negative regulation in valine, leucine, and isoleucine degradation; nitrogen metabolism metabolic pathways; and positive adjustment in N-glycan biosynthesis and systemic acquired resistance. In exchange for the reduced nitrogen from the bacteria, the host plant provided the rhizobium with carbon as energy in exchange for the nitrogen from the nodulation [M. truncatula in SNF was decreased to reserve energies in response to water deprivation. Both abiotic stress and biotic stress with pathogenic or symbiotic bacteria were able to trigger unfolded protein response (UPR) [More defense-responsive genes were expressed to improve the drought tolerance of AN plants in D2 conditions. Severe drought stress-triggered different dynamic regulatory networks in dulation . Due to dulation , carbon se (UPR) ,44. As ase (UPR) . Lack ofse (UPR) . AltogetRegulation of miRNAs was found to be involved in biotic and abiotic stress . In our Arabidopsis provoked a decrease in lignin content, and was more sensitive to salt stress [PeLAC10 in Arabidopsis led to an increase in the lignin content and improvement of drought tolerance [LAC4 modulating secondary cell-wall biosynthesis in Citrus sinensis [Verticillium dahliae infection, the ghr-miR397-knockdowned plants exhibited improvement in G-lignin biosynthesis [GhLAC4 involved defense-induced lignin biosynthesis. Our early study suggested that rhizobium symbiosis could increase the lignin content in alfalfa [Pythium ultimum infection [Monochasma savatieri [SNF might trigger the downregulation of mtr_miR397 and upregulation of its target genes facilitating lignin biosynthesis. Previous studies showed that miR397 was participated in defense response to pathogen infection . Targetet stress . Overexpolerance . Moreovesinensis ,52. Withynthesis . It indi alfalfa . Moreovenfection . Furtheravatieri . Laccaseavatieri . TherefoCSD, APX, and CAT, which were reported as the target genes of sly-miR398b, were involved in SOD, APX, and CAT, respectively [Bamboo mosaic virus and accompanying upregulation of miR398 [CSD, APX, and CAT enhancing detoxification of ROS in drought stress.The miR398 induced by SNF mediated drought tolerance through the ROS metabolism network. The ectively . As the f miR398 . It suggf miR398 . When suf miR398 . Thus, SArabidopsis [M. truncatula root with SNF [Arabidopsis/Nicotiana interfamilial heterograft [Our result showed that mtr_miR169l-5p, mtr_miR398b, and mtr_miR398c, had a similar expression pattern. This might be owing to the circRNA and lncRNAs, which had different MREs, absorbing these three miRNAs simultaneously. The miR169 was first demonstrated to target specific NF-YA family members in response to abiotic stress in bidopsis . It was with SNF . With SNerograft . We assuerograft , subsequerograft ,62.MtNF-YA2 and MtNF-YA3) and lncRNA (TCONS_00049507 and TCONS_00049510), as the target genes, were suppressed because of the high expression level of mtr_miR169l-5p. During D1 treatment, sharply increased expression levels of TCONS_00049507 and TCONS_00049510 competitively combined with mtr_miR169l-5p led to the upregulation of MtNF-YA2 in plants. The mtr_miR169l-5p was competitively combined with the lncRNAs in IN and AN plants, while its target genes  were upregulated. Even though mtr_miR169l-5p was competitively combined by the increasing lncRNA, MtNF-YA3 was still downregulated by the suppression of the active mtr_miR169l-5p in NN plants. Under D2, due to the competitive interaction between TCONS_00049507/TCONS_00049510 and mtr_miR169l-5p, plants were able to continuously upregulated MtNF-YA2 and MtNF-YA3 in AN plants.In this study, we constructed an mRNA\u2013miR169l\u2013lncRNA dynamic ceRNA network to explain the SNF-triggered plants with improved drought tolerance . We founGmNF-YA5, NF-YA8, and GmNFYA13, enhanced the drought tolerance of plants [NF-YA2, NF-YB3, and DPB3-1 could form a transcriptional complex to activate the promoter of the heat stress-inducible gene in Arabidopsis [MtNF-YA2 and MtNF-YA3 were accumulated through SNF-induced downregulation of mtr_miR169l-5p, and the combination between mtr_miR169l-5p and TCONS_00049507, which were rapidly upregulated.It was reported that NF\u2013YA families participated in drought response by mediating the expression of several drought stress-responsive genes in an ABA-dependent manner ,64. Overf plants ,65,66. Mbidopsis . TherefoM. truncatula seeds were disinfected with 75% alcohol for 10 min and then vernalized at 4 \u00b0C for 48 h in petri dishes with wet filter paper in the darkness. We irrigated the sands with diluted NaClO (1000 mg/L) before planting seedlings to kill the potential rhizobia in sand or on the pots. And the results showed that seedlings inoculated with rhizobia developed active nodules, whereas the uninoculated seedlings did not develop any nodules. After germinating in the plant growth chamber for 3 days, the 1.5\u20132.5 cm seedlings were transplanted into 9 cm plots with sterilized sand (100 mesh) in greenhouse of Northwest A&F University, Yangling, Shaanxi, China . The reformative 1/2 Hoagland solution [solution  was irri3\u2212) 1/2 Hoagland solution. After 60 days since inoculation, each group  was subjected to different degrees of drought stress. Sand was carefully removed from the roots. Seedlings were transferred to pots, which were filled with dry sterilized sand for drought treatments. Plants before drought treatment were set as control (D0). Seedlings planted in dry sand for 3 and 8 h were defined as mild (D1) and severe (D2) drought treatments, respectively. Thus, nine treatments were obtained, D0_NN, D0_IN, D0_AN, D1_NN, D1_IN, D1_AN, D2_NN, D2_IN, D2_AN  no nodules  group without rhizobia inoculated, (2) inactive nodules (IN) group with \u2018Duomeng\u2019 rhizobia inoculant  inoculated but irrigated with full N 1/2 Hoagland solution to inactivated nodules, and (3) activate nodules (AN) group with rhizobia and irrigated with low N . 10 \u03bcg extracted RNA was removed rRNA and generated mRNA, lncRNA, and circRNA libraries with Ribo-off rRNA Depletion Kit . The small miRNA libraries were generated by Multiplex Small RNA Library Prep Kit for Illumina . The prepared libraries were sequenced on novaseq of Illumina.http://www.bioinformatics.babraham.ac.uk/projects/fastqc/, accessed on 27 July 2021). HISAT2 [M. truntacula genome . Bowtie2 [The raw sequencing data were evaluated by FAST-QC  < 0.05 [M. truncatula genome and novel miRNAs that were mapped to module species or predicted by biological software. EBSeq [Bam documents obtained from mapping to reference genome were transcript reconstructed by StringTie . After f) < 0.05 . The anae. EBSeq  algorith\u03c72 test were used to classify the GO category, and the FDR was calculated to correct the p-value. The smaller the FDR, the small the error in judging the p-value.Gene Ontology (GO) enrichment analysis was applied to analyze the main function of the differential expression genes according to the gene ontology, which is the key functional classification of NCBI . Generalhttp://www.genome.jp/kegg/, accessed on 30 July 2021). The fisher exact test was used to find the significant enrichment pathway [p values were adjusted using the BH FDR algorithm with FDR < 0.05.Pathway analysis was used to find out the significant pathway of the differential genes. Pathway annotations of Microarray genes were downloaded from KEGG  package was used to preform series cluster analysis of DEGs, DEmiRNAs, DElncRNAs, and DEcircRNAs. WGCNA [Following different signal density change tendencies of genes under different situations, we identified a set of unique model expression tendencies. TCseq  Co., Ltd., AG11711, Changsha, Hunan, China). Reverse transcriptional cDNA was subsequently mixed with SYBR\u00ae Green Premix Pro Taq HS qPCR Kit (Accurate Biotechnology (Hunan) Co., Ltd., AG11701). The primers used were listed in M. truncatula actin gene and U6 gene were used to normalize the expression levels of select mRNA, lncRNAs, and miRNA, respectively. The relative expression was then analyzed via the 2\u2212\u0394\u0394CT method [Total RNA was extracted by EasyPure miRNA Kit , and reverse transcribed by T method . Data anM. truncatula-forming nodules exhibited improved drought tolerance compared with the NN group. The decrease of mtr_miR169l-5p, mtr_miR398b, and mtr_miR398c, provided an opportunity for the improvement expression levels of lncRNAs and mRNAs. As the water deficiency became severe, plants reserved energy and medicated systemic acquired resistance due to early effects of SNF-triggered to improve the drought tolerance. We constructed a ceRNA competing network that the downstream drought-response genes were upregulated continuously in AN plants, while TCONS_00049507 and TCONS_00049510 competitively interacted with mtr_miR169l-5p. Liberated MtNF-YA2 and MtNF-YA3 subsequently participated in the downstream response to drought. Our results explained that the SNF impacted drought tolerance with molecular pathways, and the mRNA\u2013miR169l\u2013ncRNA ceRNA network was constructed to promote the genetic research and provide agronomic character improvement theoretical grounding in legumes.Taken together, SNF-triggered plants response to stress might trigger the improvement of tolerance to water deficiency."}
+{"text": "A virus-infected cell triggers a signalling cascade, resulting in the secretion of interferons (IFNs), which in turn induces the upregulation of the IFN-stimulated genes (ISGs) that play a role in antipathogen host defence. Here, we conducted analyses on large-scale data relating to evolutionary gene expression, sequence composition, and network properties to elucidate factors associated with the stimulation of human genes in response to IFN-\u03b1.We find that ISGs are less evolutionary conserved than genes that are not significantly stimulated in IFN experiments (non-ISGs). ISGs show obvious depletion of GC content in the coding region. This influences the representation of some compositions following the translation process. IFN-repressed human genes (IRGs), downregulated genes in IFN experiments, can have similar properties to the ISGs. Additionally, we design a machine learning framework integrating the support vector machine and novel feature selection algorithm that achieves an area under the receiver operating characteristic curve (AUC) of 0.7455 for ISG prediction. Its application in other IFN systems suggests the similarity between the ISGs triggered by type I and III IFNs.http://isgpre.cvr.gla.ac.uk/. The docker image at https://hub.docker.com/r/hchai01/isgpre can be downloaded to reproduce the prediction.ISGs have some unique properties that make them different from the non-ISGs. The representation of some properties has a strong correlation with gene expression following IFN-\u03b1 stimulation, which can be used as a predictive feature in machine learning. Our model predicts several genes as putative ISGs that so far have shown no significant differential expression when stimulated with IFN-\u03b1 in the cell/tissue types in the available databases. A web server implementing our method is accessible at  Interferons (IFNs) are a family of cytokines defined for their capacity to interfere with viral replication. They are secreted from host cells after an infection by pathogens such as bacteria or viruses to trigger the innate immune response with the aim of inhibiting viral spread by \u201cwarning\u201d uninfected cells . The resAll 3 types of IFNs are capable of activating the Janus kinase/signal transducer and activator of transcription (JAK-STAT) pathway and inducing the transcriptional upregulation of approximately 10% of human genes that prime cells for stronger pathogen detections and defence , 14, 15.Most research on ISGs has focused on elucidating their role in antiviral activities or discovering new ISGs within or across species , 23, 24.in vivo and in vitro gene expression profiles in the context of IFN stimulation ). The highest prediction score within the non-ISGs was found on ubiquitin conjugating enzyme E2 R2 . It contains many features similar to the ISGs but was not differentially expressed in the presence of IFN-\u03b1 in the human fibroblast cells  to assess the selection on protein sequences and mutational processes affecting the human genome [From the perspective of evolution, we used the number of transcripts, ORFs, and count of exons used for coding to quantify the alternative splicing process. Genes with more transcripts and ORFs have higher alternative splicing diversity to produce proteins with similar or different biological functions , 91, 92.n genome .From the perspective of nucleotide composition, we calculated the percentage of adenine, thymine, cytosine, guanine, and their 4-category combinations in the coding region of the canonical transcript. The first category measured the proportion of 2 different nitrogenous bases out of the implied 4 bases . The second category also focused on the combination of 2 nucleotides but added the impact of phosphodiester bonds along the 5\u2032 to 3\u2032 direction  . The thiFrom the perspective of amino acid composition, we calculated the percentage of 20 standard amino acids and their combinations based on their physicochemical properties . PatternRRID:SCR_014651) [To infer network properties for the gene products, we constructed a human PPI network based on 332,698 experimentally verified interactions (confidence score >0.63) from HIPPIE (_014651) . Nodes a_014651) . Cluster_014651)  while th_014651) .P < 0.05). SLAAPs were constructed with 3 to 4 fixed amino acids separated by putative gaps. The gap could be occupied by at most 1 random amino acid, producing 1,312,000 alternative choices. Likewise, binary features were prepared for SLAAPs showing significant enrichment in the ISG products than in the non-ISG products (Pearson's chi-squared test: P < 0.05). Since there were lots of results rejecting the null hypothesis, we adopted the Benjamini\u2013Hochberg correction procedure to avoid type I error [In this study, categorical features were used to check the occurrence of short linear sequence patterns in the genome and proteome. SLNPs constructed in this study contained 3 to 5 random nucleotides, producing 708,540 alternative choices. SLNPs with no restrictions on their first or last position were not taken into consideration as their patterns could be expressed in a more concise way. A SLNP was picked out to encode a binary feature when its occurrence level in the coding region of the canonical ISG transcripts was significantly higher than that for the non-ISGs (Pearson's chi-squared test:  I error . Additio I error .2(fold change) ranging from 0 to 12.6, which meant they were upregulated after IFN-\u03b1 treatments in the human fibroblast cells. In order to measure the average level of feature representation (AREP) for genes with similar expression during IFN stimulations, we introduced a 0.1-length sliding window to divide the data into 126 bins with different log2(fold change). Here, PCC was introduced to test the association between the representation of discrete features and IFN-\u03b1\u2013triggered stimulation (log2(fold change) >0). It can be formulated asn is the number of divided parts that equals 126 in this study; 2(fold change) and AREP in the ith part; 2(fold change), which are set as 6.4 and 3.7, respectively, in this study; and P-value calculated by the Student t-test was lower than 0.05.We obtained 8,619 human genes with expression data from the OCISG database . In totaWe designed a machine learning framework for the prediction of ISGs. First, all features were encoded and normalised based on their major representations Eq.\u00a0. Then weIn this study, the prediction results were evaluated with 3 threshold-dependent criteria, including sensitivity, specificity, and MCC , and 2 tProject name: ISGPREhttp://isgpre.cvr.gla.ac.uk/Project homepage: Operating system: Platform independentProgramming language: JavaOther requirements: Docker or JDK 8+https://hub.docker.com/repository/docker/hchai01/isgpreDocker image: https://bio.tools/isgpreBiotools repository: Research Resource Identification Initiative ID: SCR_022730https://github.com/HChai01/ISGPREDocumentation and tutorials: License: GNU GPL v3.0https://isgpre.cvr.gla.ac.uk/ and [GigaScience GigaDB repository [The implemented web server and all reproduceable data are freely accessible at .uk/ and . Code snpository .Supplementary Data S1. Basic information and usage of our compiled 10,836 human genes.Supplementary Data S2. The result of Mann\u2013Whitney U tests for discrete features.Supplementary Data S3. Association between feature representations and IFN-\u03b1 stimulations.Supplementary Data S4. The result of Pearson's chi-squared tests for sequence motifs.Supplementary Data S5. Features and their individual performance in machine learning.giac103_GIGA-D-22-00042_Original_SubmissionClick here for additional data file.giac103_GIGA-D-22-00042_Revision_1Click here for additional data file.giac103_GIGA-D-22-00042_Revision_2Click here for additional data file.giac103_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giac103_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giac103_Reviewer_1_Report_Original_SubmissionMilton Pividori, Ph.D. -- 3/29/2022 ReviewedClick here for additional data file.giac103_Reviewer_1_Report_Revision_1Milton Pividori, Ph.D. -- 9/10/2022 ReviewedClick here for additional data file.giac103_Reviewer_2_Report_Original_SubmissionMuthukumaran Venkatachalapathy -- 6/1/2022 ReviewedClick here for additional data file.giac103_Supplemental_FilesClick here for additional data file.APC: anaphase promoting complex; AREP: average level of feature representation; ASI: AUC-driven subtractive iteration algorithm; AUC: area under the receiver operating characteristic curve; cDNA: complementary DNA; dN: nonsynonymous substitutions; dS: synonymous substitutions; ELGs: human genes with limited expression in the IFN-\u03b1 experiments; FDR: false discovery rate; FFS: forward feature selection; GAF: IFN-\u03b3 activation factor; GAS: gamma-activated sequence promoter elements; gBGC: GC-biased gene conversion; HIPPIE: Human Integrated Protein\u2013Protein Interaction rEference; IDRs: intrinsically disordered regions; IFNAR: interferon-\u03b1 receptor; IFNGR: IFN-\u03b3 receptor; IFNLR1: IFN-\u03bb receptor 1; IFNs: interferons; IL-10R2: interleukin-10 receptor 2; IRF9: interferon regulatory factor 9; IRG: interferon repressed (downregulated) human genes; ISGF3: interferon stimulated gene factor 3 complex; ISGs: interferon-stimulated (upregulated) human genes; ISRE: interferon-stimulated response elements; JAK1: Janus kinase 1; KNN: k-nearest neighbours; MCC: Matthews correlation coefficient; non-ISGs, human genes not significantly upregulated by interferons; OCISG: Orthologous Clusters of Interferon-Stimulated Genes; ORF: open reading frame; PCC: Pearson's correlation coefficient; PPI: protein\u2013protein interaction; RefSeq: Reference Sequence; RF: random forest; SLAAP: short linear amino acid pattern; SLNP: short linear nucleotide pattern; SN_496: sensitivity of samples with the top 496 prediction scores; STAT: signal transducer and activator of transcription; SVM: support vector machine.The authors have declared no conflict of interest.H.C. is supported by the China Scholarship Council (201706620069). J.H., Q.G., and D.L.R. are supported by the Medical Research Council (MC_UU_1201412). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Conceptualization: all authors; data curation: H.C.; formal analysis: H.C.; funding acquisition: D.L.R.; investigation: H.C.; methodology: H.C.; project administration: D.L.R., J.H.; resources: Q.G., J.H., D.L.R.; web server: H.C.; software: H.C.; supervision: Q.G., J.H., D.L.R.; validation: all authors; visualization: H.C.; writing original draft: H.C.; writing review and editing: all authors."}
+{"text": "A 71-year-old man was referred to our department with a post-esophagectomy anastomotic atresia. Nine months previously, he had undergone transhiatal esophagectomy for esophageal intramucosal squamous carcinoma. Frustratingly, he developed a serious post-esophagectomy stenosis. Several esophageal bouginage and stent procedures failed to stop the progressive stenosis. Finally, a jejunal nutrition tube was placed for feeding, piercing the epigastric skin.After the large amount of retained fluid had been suctioned, the upper esophagus appeared to be blocked and a guidewire could not be advanced under esophagogastroduodenoscopy guidance . EndoscVideo\u20061\u2002Re-establishment of the digestive lumen in a post-esophagectomy anastomotic atresia. Up to three monitors including endoscopic ultrasound, digital subtraction angiography, and reverse-direction transnasal gastroscopy from the jejunal fistula were used to supervise and guide the whole procedure.Anastomotic atresia developing from severe post-esophagectomy stricture has not been reported previouslyEndoscopy_UCTN_Code_TTT_1AS_2AB"}
+{"text": "The growing volume and heterogeneity of next-generation sequencing (NGS) data complicate the further optimization of identifying DNA variation, especially considering that curated high-confidence variant call sets frequently used to validate these methods are generally developed from the analysis of comparatively small and homogeneous sample sets.We have developed xAtlas, a single-sample variant caller for single-nucleotide variants (SNVs) and small insertions and deletions (indels) in NGS data. xAtlas features rapid runtimes, support for CRAM and gVCF file formats, and retraining capabilities. xAtlas reports SNVs with 99.11% recall and 98.43% precision across a reference HG002 sample at 60\u00d7 whole-genome coverage in less than 2 CPU hours. Applying xAtlas to 3,202 samples at 30\u00d7 whole-genome coverage from the 1000 Genomes Project achieves an average runtime of 1.7 hours per sample and a clear separation of the individual populations in principal component analysis across called SNVs.https://github.com/jfarek/xatlas.xAtlas is a fast, lightweight, and accurate SNV and small indel calling method. Source code for xAtlas is available under a BSD 3-clause license at  Over the past 20 years, multiple methods and approaches have surfaced, which aim to identify small variants across next-generation sequencing (NGS) short read data . The impRuntime efficiency and speed are especially important when analyzing large datasets at the cohort or population level. The recent release of 3,202 samples across diverse populations at 30\u00d7 whole-genome coverage by the 1000 Genomes Project (1KGP), for example, is an important step toward improving variant calling methods  has sequHere, we describe xAtlas, a lightweight and accurate single-sample SNV and small indel variant caller. xAtlas includes features that allow it to easily scale to population-scale sample sets, including support for CRAM  and gVCFFig.\u00a0While collecting candidate variants to evaluate, xAtlas also records values for a number of sequence and alignment features associated with each candidate variant, including mean base quality value, mean alignment quality for reads covering the candidate variant's genomic position, and coverage counts for reads supporting the candidate variant allele. These values are provided either to a SNV or to an indel logistic regression model to assign a score of the candidate variant's likelihood of being a real variant. This likelihood score, along with separate cutoffs for low alignment quality and other filtering criteria, determines whether the variant is called and whether a called variant is filtered. A variant's likelihood score also provides a proxy measure of confidence of the variant's called genotype, as xAtlas does not calculate phred-scaled genotype quality (GQ) scores directly.xAtlas allows the user to reconfigure the logistic regression intercepts and variable coefficients of these models. After assigning variant confidence scores from the logistic regression models and applying VCF filters, xAtlas genotypes and reports the candidate variant in the output VCF. SNVs and indels are written to separate output VCF files.xAtlas' variant evaluation model excludes some techniques that are employed by other single-sample variant callers. Notably, xAtlas does not perform local realignment around candidate variant regions, which contributes in part to reduced application runtimes. xAtlas also attempts to call no more than 1 SNV or indel variant allele at a given genomic locus. Despite these model limitations, xAtlas nonetheless achieves a high degree of variant sensitivity and precision.xAtlas is implemented as a command-line application written in C++ that employs HTSlib  to handlxAtlas variant calling accuracy was assessed by measuring the concordance between xAtlas variant calls on reference alignments and corresponding benchmark call sets from release 4.2.1 of the NIST GIAB . Table\u00a01Next, we assessed xAtlas performance in terms of speed and accuracy across another GIAB benchmark sample, NA12878 . xAtlas,To assess the performance of xAtlas on a large-scale NGS dataset, xAtlas was run on a dataset of 3,202 samples from the 1KGP , with a First we assessed the concordance between xAtlas and published SNP calls that were curated over the entire 3,202 samples . For thiThe transition vs. transversion (Ti/Tv) ratios of passing SNVs called in autosomal chromosomes for the 1KGP dataset had a mean of 1.928 with a standard deviation of 0.0094. Within the Ashkenazi trio, when limited to GIAB high-confidence variant calling regions determined for the trio, the average Ti/Tv ratio for passing variants in the Ashkenazi trio was 2.08. This is in agreement with the expectation of a Ti/Tv ratio of 2.07 to 2.10 for whole-genome sequencing in humans . GIAB hiPrincipal component analysis (PCA) of SNVs called across these samples supports variant call accuracy across diverse populations. Fig.\u00a0To summarize, xAtlas has demonstrated a combination of computational efficiency and variant call accuracy. Sensitivity and precision rates for both SNVs and indels called by xAtlas rank among those of other variant calling methods that have been used in practice. xAtlas has permitted fast and cost-effective variant analysis across multiple projects at the BCM-HGSC consisting of tens of thousands of whole-genome samples. For small- or large-scale variant analysis, xAtlas can be scaled and run in compute environments ranging from a single laptop to large HPC clusters or arrays of cloud instances. With the ability to generate VCFs and gVCF-formatted variant call sets in terms of minutes or hours per sample, development of new variant analysis methods can also be carried out with rapid turnaround rates.xAtlas is a command-line C++ application that employs HTSlib  to handlxAtlas variant calling is performed in the following high-level stages: preliminary read filtering, collecting candidates for variant calls from the alignment file, evaluating each candidate variant, and reporting candidate variants Fig.\u00a0.Preliminary read filtering is performed to filter out uninformative reads. As reads are scanned from the input alignment, reads marked as unmapped, as duplicate reads, or having a mapping quality score below a minimum threshold, with a default of 1, are filtered out from further evaluation.Candidate sequence variations are then collected from the unfiltered reads and grouped for variant calls. To aggregate candidates, sequence variations are identified within each read by locating the coordinates at which sequences differ from the provided reference genome. The SAM format's CIGAR string, which defines the edit operations between the read's sequence and the reference sequence at its mapped position, is used to determine variant coordinates. SNVs are defined as point differences between reference and aligned sample sequences within the spans of CIGAR match operators. Variant alleles are assigned reference coordinates that correspond to its parsimonious representation within the alignment, as defined by Tan et al. .While collecting candidate variants to evaluate, xAtlas also records a number of values associated with sequence and alignment features, such as average base quality score across supporting reads, for each candidate variant. These values are then fed to 1 of 2 logistic regression models, for either SNVs or indels, to calculate the probability that the candidate variant is a real variant based on the assessed features. Table\u00a0After assigning confidence scores to candidate variants and applying filters, xAtlas then determines the most likely genotype and reports the candidate variant in the VCF. A variant call is reported in the VCF only if the candidate's logistic regression value is greater than an adjustable cutoff, with a default value of 0.25. If multiple variants may be reported at the same position, xAtlas reports only the variant at that position with the greatest number of reads supporting the variant sequence. For SNVs, if there are still multiple candidates tied for the greatest number of supporting reads, the candidate variant with the highest logistic regression value is then selected. xAtlas assigns the genotype 1/1, 0/1, or 0/0 to called variants. For indels, genotypes are assigned based on cutoffs for the ratio of reads supporting the variant allele to the total number of reads overlapping the indel. For SNVs, each SNV is assigned the genotype with the highest genotype likelihood as determined by xAtlas.The logistic regression model retraining performed as part of this study was performed by building sets of positive and negative examples of variant sites from pairs of sample alignments and using subsets of these variant sites in logistic regression model fitting. The set of all possible candidate variant sites and the values that xAtlas supplies to the SNV and indel logistic regression models were compiled for each sample. Subsets of positive and negative variant site examples were then derived from this set based on variant site overlaps with a truth set of high-confidence variants and with high-confidence variant regions. Positive variant sites were selected from variant sites present in both technical replicates, overlapping the NIST high-confidence variants, and restricted to the NIST high-confidence regions. Two types of negative variant sample sites were compiled, where variant sites are either present in both technical replicates or present in only 1 of the 2 replicates, with both types restricted to the NIST high-confidence regions but not overlapping the NIST high-confidence variants. Each of these comprised half of the negative example variant sites in assembled training and testing sets. Training and testing sets were compiled as nonoverlapping sets of 10,000 randomly sampled positive and negative variant site examples, with a 1:1 ratio of positive vs. negative examples in each set. Logistic regression model fitting using these training and testing sets was performed using the LogisticRegression classifier from scikit-learn .All GIAB and 1000 Genomes Project sample alignments used as input for xAtlas were in CRAM format and aligned to the GRCh38 human reference genome. xAtlas runs were performed on a Linux HPC cluster at the BCM-HGSC, with runtimes measured using system time utilities on the HPC cluster. xAtlas command-line invocations included the \u201c\u2013gvcf\u201d and \u201c\u2013bgzf\u201d output options.xAtlas variant call precision and sensitivity measurements were calculated by the vcfeval function in RTG-tools  version RRID:SCR_001757) [Principal component analysis was carried out by constructing a project-level VCF (pVCF) file of SNVs called by xAtlas on 3,202 samples from the 1000 Genomes Project (1000GP) sample set and estimating principal components based on the called genotype of passing variants across the sample set at each genetic locus. Principal component analysis on the final pVCF was performed by PLINK (_001757)  version We compared the publicly available 1000 Genomes catalog to xAtlas using RTG-tools  version The project-level VCF for the 3,202 1000GP samples was constructed in a few phases. First, the union of all genetic loci to be considered for principal component estimation was determined. This was defined to be all genetic loci for which xAtlas called a passing SNV on at least 1 sample with the 3,202-sample set and for which the MAF of the called variant within the 3,202-sample set was at least 0.05. Next, a pVCF file was constructed by combining the xAtlas-called genotypes for the 3,202 samples into a single file in VCF format. Each VCF record in the pVCF file represents a genetic locus from the previously determined set of all xAtlas-called genetic loci across the sample set. Each of the 3,202 samples is represented in the pVCF by a sample-level column containing xAtlas-called genotypes from the sample-level VCF across all genetic loci recorded in the pVCF. While this pVCF was constructed using purpose-built scripts, other utilities that can also be used to construct pVCF files include GLnexus , which ihttps://github.com/jfarek/xatlas. HTSlib (https://github.com/samtools/htslib) is required for building xAtlas.xAtlas source code and instructions may be downloaded from Project name: xAtlashttps://github.com/jfarek/xatlasProject homepage: Operating system(s): Platform independentProgramming language: C++Other requirements: HTSlib 1.3 or higherLicense: BSD 3-ClauseRRID: SCR_022987GigaScience GigaDB database [NIST GIAB data were obtained from . 1000 Gedatabase .1KGP: 1000 Genomes Project; GIAB: Genome In the Bottle Project; GQ: genotype quality; gVCF: genome VCF; MAF: minor allele frequency; NGS: next-generation sequencing; PCA: principal component analysis; pVCF: project-level VCF; SNV: single-nucleotide variant; Ti/Tv: transition vs. transversion.This work has been supported by NHGRI Centers for Common Disease Genomics grant 5UM1HG008898-02.The authors declare no conflicts of interest.Application development and code implementation: J.F. and D.H. Sample analysis: J.F., Y.Z., A.P., A.M., O.K., and A.E. Project coordination: W.S., R.G., Z.K., and F.S. All authors contributed to the manuscript writing.giac125_GIGA-D-21-00249_Original_SubmissionClick here for additional data file.giac125_GIGA-D-21-00249_Revision_1Click here for additional data file.giac125_GIGA-D-21-00249_Revision_2Click here for additional data file.giac125_GIGA-D-21-00249_Revision_3Click here for additional data file.giac125_GIGA-D-21-00249_Revision_4Click here for additional data file.giac125_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giac125_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giac125_Response_to_Reviewer_Comments_Revision_2Click here for additional data file.giac125_Response_to_Reviewer_Comments_Revision_3Click here for additional data file.giac125_Reviewer_1_Report_Original_SubmissionRuibang Luo -- 9/19/2021 ReviewedClick here for additional data file.giac125_Reviewer_2_Report_Original_SubmissionJorge Duitama -- 10/4/2021 ReviewedClick here for additional data file.giac125_Reviewer_2_Report_Revision_1Jorge Duitama -- 3/7/2022 ReviewedClick here for additional data file.giac125_Reviewer_4_Report_Revision_1Lachlan Coin -- 8/28/2022 ReviewedClick here for additional data file."}
+{"text": "Cellular senescence is a tumor suppressive response in which the cell cycle is in a state of permanent arrest and can inhibit tumor cell proliferation. In recent years, induction of cellular senescence has been shown to be important for antitumor therapy, and the link between cellular senescence and clinical prognosis and immunotherapy of hepatocellular carcinoma is still unknown.We performed enrichment analysis of genes in three cellular senescence gene sets, screened for gene sets significantly enriched in hepatocellular carcinoma and extracted genes from them. Signature were constructed using senescence-related genes, and their expression was verified at the protein and RNA levels. Survival, clinical staging and grading, immune infiltration, immunotherapy, and drug sensitivity were also analyzed between risk groups.The q-PCR and immunohistochemistry results revealed significant differences in the expression of the signature genes between normal and tumor tissues. Significant differences in clinicopathological features, prognosis and immune infiltration were observed between risk groups. In the low-risk group, better OS and lower TMB scores were demonstrated, while the high-risk group had higher immune checkpoint expression, as well as lower risk of immune escape. In addition, we found that the High-risk group was more sensitive to sorafenib.In summary, the signature constructed using aging-related genes can reliably predict patient prognosis and immunotherapy efficacy, providing a new idea for immune system therapy of hepatocellular carcinoma.The online version contains supplementary material available at 10.1186/s12575-022-00187-7. Hepatocellular carcinoma (HCC) is currently the sixth most common tumor worldwide, accounting for approximately 5% of all cancers . It has Cellular senescence is a marker of biological and temporal aging, and a potential indicator of pathological tissue status . CellulaCellular senescence is considered to be the response of proliferating somatic cells to exogenous and endogenous stress and injury. It is characterized by a permanent blockage of the cell cycle . CellulaTherefore, in this study, we constructed a signature for predicting HCC prognosis by targeting cellular senescence-related genes, providing new insights into the prognosis and immunotherapeutic targets of HCC.First, we entered the Gene Set Enrichment Analysis (GSEA) database, then entered the MsigDB section and click Search, and search for cellular senescence gene sets. We screened three gene sets: \u201cGOBP_CELL_AGING\u201d, \u201cGOBP_REGULATION_OF_CELL_AGING\u201d and \u201cREACTOME_CELLULAR_SENESCENCE\u201d.342 HCC patients  were obtained from the TCGA database. 231 HCC patients were obtained from the ICGC database (ICGC-LIRI-JP) (metastatic hepatocellular carcinoma and patients with missing data were excluded). Variance analysis was performed using the \"limma\" R package, with FDR values set to less than 0.05 and logFCfilter set to greater than 0.5.Screening of senescence-related genes using Weighted Gene Co-expression Network Analysis (WGCNA) algorithm. The minimum number of module genes was set to 30, and the gene hierarchy clustering dendrogram was pruned using the shear dynamic function to generate co-expression modules. The differences in module signature genes (ME) were calculated using the module signature gene function, and the modules with the highest correlation coefficients were extracted for further study.The differential expressed genes (DEGs), prognostic genes and the highest correlated modular genes from WGCNA in TCGA were taken as the intersection set, and the obtained genes were used for signature construction. The 342 HCC patients were divided into a training set (set1) and a Test set (set2) (1:1 randomized assignment), with the overall TCGA dataset as the validation set (set3) and external validation using the ICGC dataset (set4). Risk signature were constructed from the training set (RiskScore\u2009=\u2009expression level of mRNA\u2009\u2217\u2009The regression coefficient).The validation groups  were divided into high and low risk groups, followed by survival analysis and plotting of ROC curves . The age, sex, Stage staging, A nomogram based on senescence-related genetic features was constructed using \"rms\" and \"regplot\" (R package) to combine rank staging and risk scores. The accuracy and reliability of the nomogram is judged from the calibration curve. Subsequently, the signature was further validated in three validation cohorts . In addition, a comparative analysis with other HCC signatures was performed to further determine the accuracy of our signature \u201319.After obtaining informed consent from patients, we collected 30 pairs of HCC tissues and paraneoplastic tissues , while one normal hepatocyte line (7702) and four HCC cell lines  were cultured (cells were from the Shanghai Institute of Cell Biology). All cell lines were cultured in high glucose DMEM  supplemented with 10% fetal bovine serum , 100\u00a0\u00b5g/ml streptomycin and 100 U/mL penicillin at 37\u00a0\u00b0C, and in a 5% CO2 humidified incubator.Tissues and cells were extracted for total RNA. total RNA was extracted according to the instructions of Trizol kit (Invitrogen). cDNA was synthesized using reverse transcription kit (Takara). qRT-PCR was used to detect mRNA expression levels of the characterized genes. The primer sequences of the signature genes are shown in Table HCC cell lines and tissue samples were extracted with RIPA lysate for total protein, and protein concentrations were determined by the BCA method. Each group of proteins was sampled and subjected to SDS-PAGE electrophoresis, electrotransferred to PVDF membrane, closed with 5% skimmed milk for 2\u00a0h, incubated with primary antibody overnight at 4\u00a0\u00b0C, washed 3 times with TBST for 10\u00a0min each time, and then the corresponding secondary antibody was added and incubated for 2\u00a0h at room temperature, washed 3 times with TBST for 10\u00a0min each time for fluorescent color development.HCC tissues were paraffin-embedded, sectioned, dewaxed and hydrated, incubated with anti-trait gene antibodies overnight at room temperature, then labeled with secondary antibodies for 30\u00a0min, stained and photographed.The specific primary antibodies were purchased from the following resource:GAPDH), CBX2, IHC(1:100)), CDKN2B, IHC(1:100)), ETS2, IHC(1:150)), HMGA1, IHC(1:100)), UBE2S, IHC(1:100)).The content of 22 human immune cell subpopulations in TCGA-LIHC was assessed using the CIBERSORT algorithm, followed by visual analysis of differences in immune cells between risk groups.In order to further verify the relationship between the constructed signature and tumor microenvironment and immunotherapy, the \"ggplot\" R package was used to analyze the degree of Tumor mutational burden (TMB) and Microsatellite Instability (MSI) among different risk groups. Then, the samples were divided into high TMB(H-TMB) and low TMB(L-TMB) according to the median value of TMB to compare whether there was difference in survival between the two groups. At the same time, in order to further reflect the survival difference between high and low risk groups, we also analyzed the survival difference between high TMB and low TMB among different risk groups.The immune escape relationship was compared between the risk groups by the TIDE algorithm. In addition, the difference in IC50 of sorafenib between the two groups and the correlation with the risk score were compared using the \"pRRophetic\" (R package).P value\u2009<\u20090.05 indicates statistical significance.The R language (version 4.1.2) and GraphPad Prism 8.0 were used for statistical analysis. the Chi-square test was used for correlation analysis of categorical data, Based on TCGA dataset, we performed enrichment analysis of \"GOBP_CELL_AGING\", \"GOBP_REGULATION_OF_CELL_AGING\" and \"REACTOME_CELLULAR_SENESCENCE\" and found that senescence-related genomes were significantly activated in HCC Table S Fig.\u00a01AA1A-C.FigAfter the three gene sets were opened in text form, the genes in the gene set were extracted, and we got 299 genes after removing duplicate genes. Then, we extracted these genes from TCGA-LIHC for differential expression analysis, and obtained 126 genes with significant differences (52 down-regulated and 74 up-regulated) , prognostic genes and turquoise module genes and obtained 33 intersecting genes. Using these 33 genes to construct a signature of senescence-related genes. First, 342 samples were randomly divided 1:1 into training set and Test set. These 33 intersecting genes were analyzed by univariate Cox regression to derive genes with prognostic features in the training set for the next step of analysis. Then, the signature genes and their regression coefficients were obtained by Lasso and multivariate cox regression analysis   Fig.\u00a0A, and by05) Fig.\u00a0B. As the05) Fig.\u00a0C. The AU05) Fig.\u00a0D.Fig. 4PFirst, the expression levels of the signature genes in cells  and tissues (hepatocellular carcinoma tissues vs paraneoplastic tissues) were verified by q-PCR assay Fig.\u00a0A-J.Fig. Then, we examined the differences in expression of the signature genes in eight pairs of hepatocellular carcinoma and paraneoplastic tissues by Western blot Fig.\u00a0A. At theFinally, we carried out an immunohistochemical experiment. We showed representative images of five genes, and then compared the expression differences between HCC tissues and paraneoplastic tissues using relative optical density scores Fig.\u00a0.Fig. 7StMolecular Interaction Networks of Signature Genes and Signature Validation.http://genemania.org/) online website to analyze the molecular interaction network between the five signature genes and found that the functions of these five genes and interacting genes  were mainly related to regulation of cellular senescence, cyclin-dependent protein serine/threonine kinase regulator activity, regulation of G1/S transition of mitotic cell cycle, ubiquitin ligase complex, negative regulation of cell cycle phase transition, protein kinase inhibitor activity and nuclear ubiquitin ligase complex and CDKN2B seems to be enriched with even more features . The AUC values at 1, 2 and 3\u00a0years were 0.745, 0.734 and 0.719, respectively , survival was worse in the high-risk group (p\u2009<\u20090.05). The AUC values at 1, 2, and 3\u00a0years were 0.810, 0.748, and 0.719, respectively , to validate the extent of classification between risk groups, we used t-SNE and PCA downscaling, and we found that the samples between risk groups could be well differentiated between HCC patients Figure SA-B. Agai, to valiThe signature was next tested using ICGC data, and we also used t-distributed Stochastic Neighbor Embedding (t-SNE) and Principal Component Analysis (PCA) downscaling analysis, and found that the samples between risk groups also distinguished HCC patients well Figure SC-D, In aIn addition, to verify the accuracy and reliability of our signature, our signature was compared with four signatures from previous studies, and it was found that the consistency index (C-index) of our signature was higher. Meanwhile, we constructed signatures with our data using the signature genes of four other studies, and then obtained ROC curves for 1, 2, and 3\u00a0years, and found that the predictive power of our signature has higher accuracy Figure S.P\u2009<\u20090.05)  Fig.\u00a0A, B. For05) Fig.\u00a0C, D.Fig.P\u2009<\u20090.05)   Fig.\u00a0A  Fig.\u00a0. We pres05) Fig.\u00a0. Also, w05) Fig.\u00a0B-E.Fig. GO enrichment analysis suggested that the high risk group was mainly associated with HUMORAL_IMMUNE_RESPONSE_MEDIATED_BY_CIRCULATING_IMMUNE, PHAGOCYTOSIS_RECOGNITION RECOGNITION and IMMUNOGLOBULIN_COMPLEX. The low-risk group is mainly associated with ALPHA_AMINO_ACID_CATABOLIC_PROCESS, CELLULAR_AMINO_ACID_CATABOLIC_PROCESS and FATTY_ACID_BETA_OXIDATION Fig.\u00a0A,B.Fig. KEGG enrichment analysis suggested that the high-risk group was mainly associated with CELL_CYCLE, DNA_REPLICATION and ECM_RECEPTOR_INTERACTION. The low-risk group is mainly associated with COMPLEMENT_AND_COAGULATION_CASCADES, DRUG_METABOLISM_CYTOCHROME_P450 and FATTY_ACID_METABOLISM Fig.\u00a0C,D.P\u2009<\u20090.05). However, the level of resting T-cell CD4 memory, monocytes, macrophage M1 and mast cells were lower (P\u2009<\u20090.05) , T-cell CD4 memory activation, and macrophage M0, B-cell memory  Table SA. Also, ) Table SB. We als) Table S.Fig. 11IP\u2009<\u20090.05)    Fig.\u00a0C, D. The05) Fig.\u00a0E. Micros05) Fig.\u00a0F.P\u2009<\u20090.05)  Fig.\u00a0D.Fig. 12Meanwhile, In order to analyse the differences in immunotherapy between the high and low risk groups, we used the \"pRRophetic\" R package to predict the gene expression and drug sensitivity of the cell lines. The analysis of riskScore and drug sensitivity showed that the higher the risk score, the higher the sensitivity to sorafenib and the better the treatment outcome is likely to be Fig.\u00a0E. And thIn this study, we established the first signature constructed from cellular senescence genes in hepatocellular carcinoma, which provides promising new molecular markers and predictors of immunotherapy and chemotherapy through the study of cellular senescence and provides new insights for individualized treatment of hepatocellular carcinoma.Senescent cells are characterized by persistent growth arrest and activation of damage-sensing signaling pathways, resulting in the expression of a large number of senescence-related substances . HoweverTumor suppression by cellular senescence is one of the most widely known cell-intrinsic mechanisms to prevent tumor transformation . Mice wiIn our present study, a signature constructed from senescence-related genes could accurately determine the prognosis of patients with HCC. And our experiments have confirmed that these five signature genes are significantly differentially expressed in HCC tissues and normal liver tissues. CDKN2B has been shown to be associated with the development of colorectal and gastric cancers , 26. RecWe showed significantly higher expression of the common 14 immune checkpoints and a better response to immune checkpoint inhibitors in the high-risk group. Targeted therapy is now crucial in the treatment of HCC, and sorafenib is an effective first-line therapy in advanced HCC . The preIn summary, the senescence-related gene signature can well predict the prognosis of HCC patients, and the signature provides a new idea to improve the immunotherapy of hepatocellular carcinoma.Additional file 1: Figure S1. Weighted gene coexpression network analysis.Additional file 2: Figure S2. Molecular interaction networks of signature genes.Additional file 3: Figure S3. Riskscore validation.Additional file 4: Figure S4. t-SNE and PCA between the high- and low-risk groups in TCGA and ICGC.Additional file 5: Figure S5. Comparison with other signatures.Additional file 6: Figure S6. The differences of Grade, Stage and T stages between high-risk group and low-risk group.Additional file 7: Figure S7. Relationship between risk scores and clinical characteristics.Additional file 8: Figure S8. Correlation between immune cells and 5 senescence-related genes.Additional file 9: Table S1. Senescence-Related Genes in three gene sets.Additional file 10: Table S2. Difference analysis between normal and tumor groups of LIHC.Additional file 11: Table S3. Univariate cox regression for senescence-related genes.Additional file 12: Table S4. The difference of 22 kinds of immune cells between high and low risk groups."}
+{"text": "Streptococcus agalactiae is a leading cause of infections in neonates. This opportunistic pathogen colonizes the vagina, where it has to cope with acidic pH and hydrogen peroxide produced by lactobacilli. Thus, in the host, this bacterium possesses numerous adaptation mechanisms in which the pleiotropic regulators play a major role. The transcriptional regulator CcpA (catabolite control protein A) has previously been shown to be the major regulator involved in carbon catabolite repression in Gram-positive bacteria but is also involved in other functions. By transcriptomic analysis, we characterized the CcpA-dependent gene regulation in S. agalactiae. Approximately 13.5% of the genome of S. agalactiae depends on CcpA for regulation and comprises genes involved in sugar uptake and fermentation, confirming the role of CcpA in carbon metabolism. We confirmed by electrophoretic mobility shift assays (EMSAs) that the DNA binding site called cis-acting catabolite responsive element (cre) determined for other streptococci was effective in S. agalactiae. We also showed that CcpA is of capital importance for survival under acidic and oxidative stresses and is implicated in macrophage survival by regulating several genes putatively or already described as involved in stress response. Among them, we focused our study on SAK_1689, which codes a putative UspA protein. We demonstrated that SAK_1689, highly downregulated by CcpA, is overexpressed under oxidative stress conditions, this overexpression being harmful for the bacterium in a \u0394ccpA mutant.IMPORTANCEStreptococcus agalactiae is a major cause of disease burden leading to morbidity and mortality in neonates worldwide. Deciphering its adaptation mechanisms is essential to understand how this bacterium manages to colonize its host. Here, we determined the regulon of the pleiotropic regulator CcpA in S. agalactiae. Our findings reveal that CcpA is not only involved in carbon catabolite repression, but is also important for acidic and oxidative stress resistance and survival in macrophages. Streptococcus agalactiae, also referred as group B Streptococcus, is a Gram-positive bacterium with a broad spectrum of hosts. First discovered as an agent of bovine mastitis   . During ach year .S. agalactiae has to adapt to variations in physicochemical conditions. As an example, when S. agalactiae colonizes the vagina, it is confronted with oxidative stress and acidic pH (pH\u2009<\u20094.5). Indeed, lactobacilli, which are part of the vaginal flora, produce hydrogen peroxide and organic acids that lower the vaginal pH  that play an important role in carbon catabolite repression (CCR) attest to this adaptability  DNA sequence enabling the regulation of approximatively 10 to 20% of the genome . Among the CcpA regulated genes, up to 12 genes were potentially involved in the stress response. We showed that CcpA contributed to S. agalactiae survival under acidic and oxidative conditions and for surviving inside macrophages. The role of two putative universal stress proteins (UspA), SAK_1689, whose encoding gene is strongly regulated by CcpA in the transcriptome, and SAK_1741, was assessed under stress conditions.In this study, we determined the regulon of CcpA in ccpA in the mid-exponential phase of growth in a chemically defined medium (CDM) supplemented with glucose. The mutant strain A909\u0394ccpA exhibited a growth delay which was reversed in the complemented A909\u0394ccpA::ccpA strain  of 0.45, and the mutant strain A909\u0394ccpA reached an OD600 of 0.3. To determine the appropriate glucose concentrations for the transcriptome, we first performed growth curves with glucose concentrations ranging from 0.1 to 5%. As shown in Fig. S1 in the supplemental material, the A909WT and \u0394ccpA strains showed major growth defects with 0.1% and 5% glucose. In addition, the stationary phase of the \u0394ccpA mutant with 2% glucose in the medium had a significantly decreased OD. Thus, for RNA sequencing (RNA-seq) experiments, we decided to supplement the chemically defined medium with 0.25% or 1% glucose, which were both the extreme concentrations without any phenotypic variation in both strains\u2019 growth.In order to identify the CcpA regulon and to determine the global impact of glucose on gene expression, we performed transcriptome analysis of the strains A909WT and A909\u0394A strain . Hence, SAK_0087 and SAK_1651 coding two alcohol dehydrogenases, SAK_0170 and SAK_0171 belonging to a ribose operon, SAK_0528 and SAK_0529 belonging to a galactitol PTS operon, and SAK_0257 coding a trehalose PTS. In order to demonstrate a glucose effect in S. agalactiae, the A909WT strain was cultured in chemically defined medium supplemented with different carbon sources  alone or together. At the mid-exponential phase of growth, RNAs were extracted and reverse transcriptase quantitative PCR (RT-qPCR) was performed on four genes  known to be regulated by glucose in other Gram-positive bacteria. Overall, there was no transcriptional difference between all tested conditions, making it impossible to show a glucose effect on the gene regulation of S. agalactiae  under our conditions. Despite the apparent lack of glucose effect, 274 and 192 genes were differentially regulated by CcpA under 0.25% and 1% glucose, respectively, when comparing the A909\u0394ccpA mutant and the parental strain  outnumbered the upregulated genes  by a factor of two to one. Genes differentially transcribed were mostly genes involved in carbon metabolism . Instead, upregulation of most genes by CcpA probably occurs indirectly.We next searched for the presence of the in silico cre site, EMSAs were performed using S. agalactiae CcpA and DNA fragments spanning the region containing the putative cre site of three genes regulated by CcpA  under the transcriptome conditions. Proteins encoded by rbsR and SAK_0257 are both involved in carbon metabolism. The gene typA codes for a translational regulator involved in the expression of virulence and pathogenicity factors under carbon starvation in Escherichia coli , 7 were inhibited by CcpA through direct interaction with a cre site. CcpA inhibited genes coding PTS of rapidly metabolizable sugars such as ptsG, coding the enzyme EIIABC of the glucose PTS system, and genes coding PTS of known secondary sugars such as the SAK_0528-SAK_0530 genes, encoding a potential galactitol PTS transporter carried by the insertion sequence IS1381. Three operons encoding ABC transporters involved in carbon metabolism were downregulated by CcpA: SAK_0532-0539, SAK_0166-SAK_0171, and SAK_1475-SAK_1477 coding the transporters of N-acetylglucosamine, ribose, and cyclodextrin, respectively, for which a cre site was present.Among the genes coding the 17 putative PTS predicted in Firmicutes  have been identified in S. agalactiae, but none of them was differentially expressed in the A909\u0394ccpA strain under the test conditions. Thus, although most of the pyruvate is converted to lactic acid in S. agalactiae, CcpA was not involved in the regulation of lactate dehydrogenases under our conditions. The second identified fermentation pathway results in the formation of butane-2,3-diol. Among the different enzymes involved in this fermentation pathway, only acetoin reductase, encoded by the gene SAK_0674, was significantly inhibited by CcpA in the presence of glucose. This gene has a cre site. The third fermentation pathway is characterized by the production of acetate from pyruvate derived from glycolysis via the acetyl phosphate intermediate. Phosphotransacetylase and acetate kinase, respectively, encoded by the pta and ackA genes that possess cre sites, were downregulated by CcpA. Finally, glucose fermentation in S. agalactiae can also lead to the production of formate. The transcriptomic analysis revealed that the genes SAK_1001-1002, SAK_1003, and SAK_1004, encoding the enzymes involved in the formation of formate from pyruvate, were downregulated by CcpA with the gene SAK_1001 presenting a cre site  and formate (fold change of 1.87 compared to strain A909WT) by the ccpA mutant . These results were in agreement with our transcriptome data since CcpA does not regulate lactate dehydrogenases but represses genes involved in acetate and formate production.To confirm the impact of CcpA regulation on several fermentation processes, we analyzed by nuclear magnetic resonance (NMR) spectroscopy the end products of fermentation in the culture supernatants of strain A909WT, the S. agalactiae strains A909, H36B, and 515, the LambdaSa04 prophage found in S. agalactiae strains A909 and CJB111  or downregulated (37 out of 48 genes), respectively. Out of 20 genes from a mobile genetic element containing the IS1381, 11 were downregulated by CcpA and code proteins involved in carbon metabolism.Genes belonging to three mobile genetic elements were regulated by CcpA: the LambdaSa03 prophage found in d CJB111 , and thecre site in their regulatory region. In addition, four other genes involved in stress responses  and regulated by CcpA were located elsewhere in the transcriptome because of their other physiological functions. For instance, cidA-B (SAK_1233-1234) was upregulated; lrgA-B (SAK_0250-0251) and lytR (SAK_0247-0249 operon), which code a two-component system transcriptional regulator known to regulate lrgA-B, were downregulated by CcpA thanks to the presence of a cre site in the regulatory region of each operon. These genes have already been shown to affect stress responses and pathogenicity in S. mutans  with Listeria monocytogenes Lmo0866, could play a role in ethanol stress resistance . Among them, eight had a . mutans , 49\u201351. e stress ; SAK_090sistance ; and SAKed below .ccpA, and A909\u0394ccpA::ccpA in Todd Hewitt (TH) broth (2O2 or in acidic TH (pH\u2009=\u20094) to assess the role of CcpA in stress conditions. We observed that the percent survival of strain A909\u0394ccpA was impaired 270- and 152-fold under these two conditions, respectively, compared to the wild-type strain  broth , until me strain  and B. Tstresses .ccpA mutant strain, we searched for its direct targets putatively involved in stress responses and regulated in our transcriptomic analysis. Our transcriptome analysis indicates that SAK_1689 was downregulated by CcpA with a log2 fold change of 4.91 (Table S3). To the best of our knowledge, the function of the resulting protein has not yet been characterized in S. agalactiae. This gene presents a cre site which spans the \u221235 box. It codes a putative universal stress protein A (UspA) with 52% sequence identity and 70% sequence similarity with Lmo1580 of Listeria monocytogenes EGDe . The latter is involved in acidic and oxidative stresses and survival in macrophages (E. coli (SAK_1689 is overexpressed under nutrient stress (extended stationary phase) in S. agalactiae  or TH containing 2.5\u2009mM hydrogen peroxide (=MIC) was evaluated by RT-qPCR. No overexpression was observed in acidic pH (2 fold change of 1.91) in the wild-type strain . Thus, SAK_1689 was significantly more transcribed in the \u0394ccpA mutant than in the wild-type (WT) strain under oxidative conditions. Therefore, we hypothesize that an unknown activator upregulates SAK_1689 under oxidative conditions and that CcpA limits this overexpression.The expression of cidic pH , though e strain . To dete of 3.96 . The expSAK_1689 is overexpressed under oxidative stress, a deletion mutant and its complemented strain were first constructed. After growth in the TH medium   under stress conditions. Usp-containing organisms are, indeed, usually equipped with several usp genes. Interestingly, in silico analysis of the S. agalactiae genome permits the detection of a paralog of SAK_1689, SAK_1741, encoding a protein that has 67% sequence identity with SAK_1689 . The SAK_1741 gene is not significantly regulated by CcpA, nor does it have cre site. Thus, we hypothesized that there might be functional redundancy between SAK_1689 and SAK_1741 under stress conditions. An RT-qPCR experiment that analyzed SAK_1741 expression in A909WT, A909\u0394ccpA, and A909\u0394SAK_1689 strains showed that this gene was weakly expressed in all the strains, particularly in A909\u0394ccpA. No difference in SAK_1741 gene expression was found, except under acidic stress conditions, where the transcription of SAK_1741 was higher in the WT and A909\u0394SAK_1689 strains   involved in stress responses and (ii) functionally redundant even if the expression of SAK_1741 was not differentially affected under stress conditions in the \u0394SAK_1689 mutant. The growth of the three strains was first determined in TH medium and highlighted a growth delay for A909-SAK_1741Y5STOP and A909\u0394SAK_1689 SAK_1741Y5STOP strains  . However9 mutant . To explctively)  and D. W strains . Howeverimpacted  and B, dSAK_1689 under the oxidative stress conditions and prevents the overproduction of SAK_1689 that could be harmful to the bacteria. Indeed, in E. coli, Nystr\u00f6m et al. . The survival of the A909\u0394SAK_1689 mutant was weakly but not significantly impaired (fold change of 1.88)  . Thus, CS. agalactiae is essential to understand how this bacterium adjusts to changes in its environment. The variability of the sites colonized by S. agalactiae attests to its great capacity for adaptation: the ability to acquire nutrients and but also to cope with physicochemical variations such as an acidic pH or oxidative stress in the vagina.A better understanding of the mechanisms involved in the physiology of ccpA gene in strain A909 to create a ccpA mutant which displayed a growth delay. However, Hooven et al., showed that ccpA was an essential gene using a transposon insertion sequencing (Tn-seq) system  gene highly downregulated by CcpA, which presents a cre site and has been previously shown to be important in an in vitro model of multiple phagosomal biochemical/oxidant stressors or in macrophages , whose role was described by Lechardeur et al.  system . We usedisappear . We showrmicutes \u201334. Moremicutes \u2013\u201334, 59 btabolism \u201364, phosabolism \u2013, stress abolism \u2013, 65, spoabolism \u2013, colonizabolism \u2013, biofilmabolism \u2013, and virabolism \u2013, 68, 69.rophages . Furtherr et al.  and thatalactiae , 49\u201351. response , 65, vere stress , 71.2O2, depending on the substrate used as an energy source , clonal complex 7 (CC7), serotype IA clinical isolate from a human case of bacteremia. All strains used in this study are listed in Table S1. Escherichia coli strains were grown in Lysogeny broth (LB) medium  at 37\u00b0C with agitation or on an LB agar plate. S. agalactiae strains were routinely grown in Todd Hewitt (TH) broth  at 37\u00b0C without agitation or on TH-agar plates. When necessary, strains were grown in filter-sterilized chemically defined medium (CDM) (The reference wild-type (WT) um (CDM)  supplemeS. agalactiae statically cultured overnight in TH broth was purified by the phenol-chloroform method (E. coli plasmids were purified with a NucleoSpin plasmid kit (Macherey-Nagel) according to the manufacturer\u2019s instructions.Chromosomal DNA of m method . E. coliPCRs were performed with a SimpliAmp thermal cycler (Thermo Fisher Scientific) using Platinum SuperFi high-fidelity DNA polymerase (Thermo Fisher Scientific). The resulting PCR fragments were purified with a NucleoSpin gel and PCR cleanup kit (Macherey-Nagel) according to the manufacturer\u2019s instructions.PCR products purified with the NucleoSEQ kit (Macherey-Nagel) were sequenced on both strands using the BigDye Terminator (version 3.1)\u2009cycle sequencing kit from Applied Biosystems and the ABI Prism 310 genetic analyzer.S. agalactiae strain A909WT . Pelleted bacteria were suspended in 500\u2009\u03bcL of Z-buffer for the WT and A909\u0394ccpA::ccpA strains and 350\u2009\u03bcL for A909\u0394ccpA and lysed mechanically with glass beads in a FastPrep-24 instrument, and cell debris was eliminated by centrifugation  (595) and the absorbance after addition of o-nitrophenyl-\u03b2-d-galactopyranoside at 420\u2009nm (A420) were measured as described by Patron et al. (Cmg/mL) was deduced from A595. \u03b2-Galactosidase activity was calculated in arbitrary units per milligram of protein using the following formula: /(V2 \u00d7 t \u00d7 Cmg/mL), with V1 being the volume of the sample that was added to the reaction mixture for \u03b2-galactosidase in milliliters, V2 being the volume of the sample that was added to the reaction mixture in milliliters, and t being the reaction time in minutes. The experiments were performed over four independent biological replicates.The promoter regions of TCV-lacZ  vector tn et al. . Briefly000\u2009\u00d7\u2009g) . Supernan et al. . Proteint\u2009=\u20090) and after 24\u2009h, samples were taken from each culture, serially diluted with physiological saline (milli-Q water\u2009plus\u20090.85% NaCl), and plated on TH agar plates. Assays were performed over at least three independent biological replicates.For survival experiments under acidic stress, bacterial strains were grown in TH to the mid-exponential phase, and then 10\u2009mL of the culture was pelleted and resuspended in 10\u2009mL of TH adjusted to pH\u20094 with HCl. At time point zero (2O2 was added. At time point zero (t\u2009=\u20090) and after 30 min, samples were taken from each culture, serially diluted with physiological saline (milli-Q water\u2009plus\u20090.85% NaCl), and plated on TH agar plates. Assays were performed over at least three independent biological replicates.For survival experiments under oxidative stress, bacterial strains were grown in TH broth to the mid-exponential phase and 20 mM HSAK_1689 and SAK_1741 under stress conditions, overnight cultures were subcultured  in TH liquid medium. At the mid-exponential phase, bacterial pellets were resuspended in TH at pH\u20094, or 2.5 mM H2O2 (=MIC) was added in the culture. After 20 min at 37\u00b0C, cells were harvested by centrifugation at 7,000\u2009\u00d7\u2009g for 10 min, and total RNAs were extracted for RT-qPCR with the primers listed in Table S2. The expression levels of the tested genes were normalized with the recA gene. Each assay was performed at least in technical duplicate and repeated with at least three biological independent RNA samples.For transcriptional study of 5 cells/well 24 h before the assay. Bacterial strains were grown in TH until they reached the mid-exponential phase. Cultures were washed in RAW medium  and adjusted to the desired inoculum in RAW medium, and CFU counts were verified by plating serial dilutions onto TH agar plates. Macrophages were infected with S. agalactiae strains at a multiplicity of infection of ~15 in RAW medium at 37\u00b0C with 5% CO2 for 1\u2009h to allow bacterial phagocytosis. Cells were washed twice in RAW medium and then incubated in RAW medium-gentamicin (500\u2009\u03bcg/mL) (Gibco) for 2 h. Gentamicin (50\u2009\u03bcg/mL) was added in the RAW medium until the T24 h. Time zero (T0) of the assay was determined as the time after incubation with antibiotic. Infected macrophages at T0 and T24 h were washed three times with phosphate-buffered saline (PBS) and then lysed with 1\u2009mL ice-cold Milli-Q water for 30\u2009min. CFU counts were determined by plating serial dilutions onto TH agar plates. Assays were performed over four technical replicates and repeated over at least two independent biological replicates.Survival experiments in RAW 264.7 macrophages (ATCC TIB-71) were performed in 24-well plates. Cells were dispensed to 24-well plates at 4.10gyrB primers to check for DNA contamination (Table S2).Bacterial pellets were resuspended in a buffer  and lysed mechanically with glass beads in a FastPrep-24 instrument, and total RNAs were extracted using a phenol/TRIzol-based purification method . Total Rhttp://bioinfo.ut.ee/primer3-0.4.0/) (mT) of \u224860\u00b0C and to amplify \u2248100-bp amplicons (Table S2). qPCRs were performed with 50\u2009ng of cDNA, 0.33 \u03bcM gene-specific primers, and 1\u00d7 LightCycler 480 SYBR green I master mix (Roche). PCR amplification, detection, and analysis were performed as described by Moulin et al.  was used according to the manufacturer\u2019s instructions to synthesize cDNA. Primers were designed with Primer3 software (-0.4.0/)  in ordern et al. . The folT method . For eacRNA integrity was verified with the Agilent Bioanalyzer 2100. mRNA enrichment using the MICROBExpress kit (Ambion) and preparation of strand-specific RNA-seq libraries using the Illumina primer ligation method were performed as previously described  on threeS. agalactiae A909 genome sequence (NC_007432.1) was used as a reference sequence to map trimmed reads using Bowtie (version 0.12.7)   as previ 0.12.7) . RNA-seq 0.12.7) . For dif 0.12.7) ; P valuerocedure .S. agalactiae A909 genome sequence (NC_007432.1) was completed using the MicroScope platform (https://www.genoscope.cns.fr/agc/microscope) (https://pfam.xfam.org/)  (https://www.kegg.jp/kegg/) (Annotation of unknown function coding genes of the am.org/) . S. agalp/kegg/)  in orderrbsR (SAK_0171), ptsG (SAK_1920), glpK (SAK_0345), pyk (SAK_1037), ptsK (SAK_0862), ptsI (SAK_0946), budB (SAK_1279), pfkA (SAK_1036), ldhA (SAK_0821), adhP (SAK_0087), SAK_0674, covR (SAK_1639), pflB (SAK_1735), and ahpF (SAK_1854) genes with the primers listed in Table S2, using RNA extracted under the same conditions as for the RNA-seq. The numbers of transcripts of each gene were normalized against transcript levels of two housekeeping genes (gyrB and rpoB). Three independent biological replicates with three technical replicates for each were performed.To confirm the data obtained in the RNA-seq, RT-qPCRs were performed on S. agalactiae genome, the positional weight matrix (PWM)-based model of the streptococcal cre site proposed by RegPrecise (https://regprecise.lbl.gov/) was used. The motif WWGWAARCGNTTWCWW  in the leader regions from position \u2212500 to +500\u2009bp relative to the translational start site, allowing no more than 2 mismatches, was searched in the entire genome of S. agalactiae A909 using the Virtual Footprint web server.For the prediction of CcpA binding sites on the cre site of rbsR (SAK_0171), SAK_0257, and typA (SAK_0575) genes were amplified by PCR using the primers listed in Table S2. The probes containing SAK_1068 and SAK_0473 coding regions were used as negative controls. The amplified DNA fragments were end-labeled with digoxigenin-11-ddUTP  in CDM supplemented with 0.25% glucose. After 24\u2009h, cultures were centrifuged at 7,000\u2009\u00d7\u2009g for 10 min. The culture supernatants were collected and filtered. Then, 150\u2009\u03bcL of culture supernatants was added with 50\u2009\u03bcL of 0.2 M potassium phosphate buffer in 99% deuterium oxide (D2O) at pH\u20097.4. Samples were spiked with 10\u2009\u03bcL of 3-trimethylsilylpropionic acid (3.2\u2009mM in D2O) as an internal reference (Ref), and then samples were transferred to conventional 3-mm NMR tubes. 1H-NMR spectra were obtained with an AVANCE III HD 600 spectrometer equipped with a TCI cryoprobe (Bruker). Standard water-suppressed 1H-NMR spectra were acquired at 298 K using a \u201cnoesypr1d\u201d pulse sequence with a relaxation delay of 20 s and 64 scans. Spectra were processed using Topspin software (Bruker). 1H-NMR spectra were automatically reduced to ASCII files using the AMIX software package . Spectral intensities were scaled to the internal reference intensity, and then concentrations were calculated using the equationOvernight cultures of strains A909WT, A909\u0394Nbr H Ref, number of hydrogen of the internal reference; nbr H compound, number of hydrogen of the compound. The experiments were performed over three independent biological replicates with three technical replicates for each.t test, analysis of variance (ANOVA) test, unpaired t test, or nonparametric Wilcoxon test. A probability value of less than 0.05 was considered statistically significant.Analyses were performed using the one-sample http://www.ebi.ac.uk/arrayexpress) under accession number E-MTAB-11639.RNA-seq data are available in the ArrayExpress database ("}
+{"text": "Emerging studies indicated that circular RNA hsa_circ_ 0023404 and its target miR-217/MARK1 axis play a critical role in cancer progression such as non-small cell lung cancer and cervical cancer. However, the role of hsa_circ_0023404/miR-217/MARK1 involved in endometrial cancer (EC) was not investigated yet. The aim of this study is to investigate the functions of hsa_circ_0023404 in endometrial cancer (EC) and the potential molecular mechanism.We used RT-qPCR and Western blot approach to detect the expressed levels of related genes in EC cell lines. Transfected siRNAs were applied to knockdown the level of related mRNA in cells. Cell proliferation by CCK-8 assay and colony formation assay were applied to detect cell proliferation. Transwell migration and invasion assay was for detecting the migration and invasion of the cells.RT-qPCR showed that the levels of hsa_circ_0023404 and MARK1 mRNA were upregulated, but mirR-217 was decreased in three endometrial cancer cell lines. Knockdown of hsa_circ_0023404 by siRNA markedly increased the level of miR-217 and reduced the proliferation of the Ishikawa cells. It also inhibited the cell migration and invasion. Anti-miR-217 can reverse the promoted proliferation, migrations and invasion of Ishikawa cells mediated by si-circ_0023404. si-MARK1 restored the inhibited cell proliferation, migration and invasion of the co-transfected Ishikawa cells with si- circ_0023404 and anti-miR-217.hsa_circ_0023404 exerts a tumor-promoting role in endometrial cancer by regulating miR-217/MARK1 axis. hsa_circ_0023404 inhibit miR-217 as sponge which inhibit endometrial cancer cell growth and metastasis. MARK1 is downstream target of miR217 and upregulated by hsa_circ_ 0023404/miR-217 axis and involved in the endometrial cancer progression.The online version contains supplementary material available at 10.1186/s40001-022-00866-x. Endometrial cancer (EC) is one of the most common types of gynecological cancer and the fourth most common cancer among women. Morbidity and mortality rates among patients with EC remain high globally . Each yeRecent large-scale genomic studies have shown that a large number of non-coding RNAs (such as microRNAs and long non-coding RNAs) are associated with the occurrence of gynecological diseases , 4.CircuCircRNA hsa_circ_0023404 (chr11: 71668272\u201371671937) is derived from mRNA of ring finger protein 121 . Increasing evidence supported that hsa_circ_0023404 play a critical role in cancer progression. For example, it showed that hsa_circ_0023404 can promote the proliferation, migration and invasion of non-small cell lung cancer (NSCLC) by regulating miR-217/ZEB1 axis . RecentlThese indicated that hsa_circ_0023404 and its target miR-217/MARK1 axis play a critical role in cancer progression such as non-small cell lung cancer and cervical cancer, but the role of hsa_circ_0023404/miR-217/MARK1 involved in endometrial cancer was not investigated yet. In this study, we investigated the role of hsa_circ_0023404 in promoting endometrial cancer cells associated with miR-217/MAPK1 axis.2.Human endometrial endothelial cell (HEEC) and human endometrial cancer cells  were purchased from the American Type Culture Collection  or National Infrastructure of Cell Line Resource . Cells were incubated in DMEM  contained 10% fetal bovine serum  and 1% penicillin/streptomycin  at 37\u00a0\u00b0C and 5% COTotal RNA was extracted using Neurozol reagent  and cDNA was generated using reverse transcription reagent kit . Real-time PCR was performed using SYBR Green PCR kit . U6 and GAPDH are internal controls. The qPCR analysis was then performed on an ABI 7500 Real-time PCR System  according to the instructions supplied by the manufacturer. The relative expression levels of the genes were calculated by comparing to U6 or GAPDH using 2\u2009\u2212\u2009\u0394\u0394CT method. The primers were used as follows:miR-217 FORWARD: CGCGTACTGCATCAGGAACTG;miR-217 REVERSE: AGTGCAGGGTCCGAGGTATT;miR-217-5p RT (anti-miR-217) Primer: GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACTCCAAT; U6 FORWARD: CTCGCTTCGGCAGCACA;U6 REVERSE: AACGCTTCACGAATTTGCGT;circ_0023404 FORWARD: ACCGTGGCCATGAAGCTATG;circ_0023404REVERSE: GGTCACCATATTGTAGGAGCGT;GAPDH FORWARD: AGAAGGCTGGGGCTCATTTG;GAPDH REVERSE: AGGGGCCATCCACAGTCTTC;MAPK1 FORWARD: CAGTTCTTGACCCCTGGTCC;MAPK1 REVERSE: GTACATACTGCCGCAGGTCA.2 for 48\u201372\u00a0h.si-NC (negative control) sequence: UUCUCCGAACGUGUCACGUTT, si-circRNA . They were transfected in Ishikawa cells using Lipofectamine 3000 Reagent  and then culture in at 37\u00a0\u00b0C and 5% CO2 incubator at 37\u00a0\u00b0C. The absorbance of each well at 450\u00a0nm was read in GloMax\u2122 96 MICROPLATE .Ishikawa cells were plated at 2\u2009\u00d7\u200910E3 cells/well in 96-well plates and grown in medium containing 10% FBS for 24\u00a0h. After transfection with siRNA, 10\u00a0\u03bcl of cell count kit-8  was added into each well and cells were incubated for 2\u00a0h in a 5% COIshikawa cells were transfected with siRNA for 48\u00a0h and trypsinized and dispensed into 6-well plates with a density of 800 cells/well. When the number of cells in a colony is more than 50, 10% formaldehyde was employed to fix colonies for 10\u00a0min and 0.5% crystal violet was adopted to stain colonies for 5\u00a0min. Images were photographed and the number of colonies was calculated by ImageJ.For migration assay, transfected Ishikawa cells (1\u2009\u00d7\u200910E5 cells) were suspended in 200 ul serum-free medium and then seeded on the top chamber. Medium contained 10% FBS was added into the lower chamber. After 24\u00a0h of incubation, cells on the lower surface of the lower chamber were fixed with 4% PFA and stained with 0.1% crystal. Cells were counted from five randomly selected microscopic fields. For invasion assay, Transwell inserts  were coated with Matrigel . After 24\u00a0h incubation, cells on the upper surface of the Transwell membrane were gently removed, and cells on the lower surface of the Transwell membrane were fixed and stained with crystal violet, counted from five randomly selected microscopic fields.Cells were collected and lysed with RIPA buffer . Equal amount of protein was separated on SDS-PAGE and transferred to PVDF . Then, the membranes were incubated with the primary antibodies anti-MARK1  and anti-actin . ECL substrates were used to visualize protein bands .p\u2009<\u20090.05; ** means p\u2009<\u20090.01; *** means p\u2009<\u20090.001.All experiments were replicated thrice and all data were expressed as mean\u2009\u00b1\u2009standard deviation (SD). The software GraphPad 8.0 were used to carry out all statistical analyzes. Student's t-test and one-way ANOVA followed by Bonferroni 's post hoc test were utilized to analyze 2 or multiple groups, respectively. * means To examine the role of hsa_circ_0023404 and its target miR-217/MARK1 axis in endometrial cancer cell lines, The RT-qPCR was applied to determine the level of hsa_circ_0023404, miR-217 and MARK1 mRNA in human endometrial endothelial cell (HEEC) and three human endometrial cancer cells . It was shown that hsa_circ_0023404  analyzed by RT-qPCR Fig.\u00a0D. Among To investigate the role of miR-217 in endometrial cancer cells, Ishikawa cells were transfected with mimic NC and miR-217 mimic. The expression of transfected miR-217 mimic was confirmed by RT-qPCR . Co-transfection showed that si-circ_0023404 attenuated the expression level of hsa_circ_0023404 while anti-miR-217 increased hsa_circ_0023404  for miRNAs, lncRNAs and mRNAs, thus impacting along their axis , 18, 19.Dysregulated miRNA expression was involved in malignancies and miRNAs may serve as tumor suppressor or oncogene to participate in human cancer progression. As a miRNA, miR\u2011217 is closely linked to tumor progression and poor prognosis . PreviouThe MAPK pathway is effectively involved in the regulation of cancer cell proliferation, invasion and survival by activating target genes such as transcriptional factor ELK1, C-Fos and the ErbB, VEGF, which contributes to the progression of tumors , 15. PreWith the advancement of RNA sequencing technology and the rapid development of bioinformatics, a large number of circRNAs were discovered widely involved in a variety of cancer-related pathogenesis and drug resistance and in the diagnostic and prognostic biomarker and the therapeutic target in human cancer . The powIn the current study, we demonstrated the molecular mechanism of hsa_circ_0023404/miR-217/MAPK involved in the endometrial cancer progression. However, there are several limitation. First, it was limit to draw the conclusion completely only dependent in vitro experiments, therefore, we would further carry out in vivo experiments on hsa_circ_0023404/miR-217/MAPK axis involved in the endometrial cancer. Second, the application of hsa_circ_0023404 on liquid biopsy was not perform on patients with endometrial cancer. We will collect the patients to investigate the level of hsa_circ_0023404/miR-217/MAPK in their blood sample and examine their potential as novel biomarker for endometrial cancer.In this study, our data demonstrated that hsa_circ_0023404 exerts a tumor-promoting role in endometrial cancer by regulating miR-217/MARK1 axis. The hsa_circ_0023404 act as sponge for and inhibit miR-217 which inhibit endometrial cancer cell growth and metastasis. MARK1 is downstream target of miR217 and the induced MARK1 by hsa_circ_0023404 through miR217 inhibition contribute to the endometrial cancer progression Fig.\u00a0D. TargetAdditional file 1: Figure S1. Uncropped Western blot images."}
+{"text": "In this study, we identified an unexpected role of Mycobacterium smegmatis GntR family transcriptional regulator MSMEG_5174 and its homologous gene Mycobacterium tuberculosis Rv1152 in aminoglycoside antibiotic resistance. Deficiency of MSMEG_5174 rendered Mycobacterium smegmatis highly resistant to aminoglycoside antibiotic treatment, and ectopic expression of Rv1152 in MSMEG_5174 mutants restored antibiotic-induced bacterial killing. We further demonstrated that MSMEG_5174 negatively regulates the expression of purine metabolism-related genes and the accumulation of purine metabolites. Moreover, overexpression of xanthine dehydrogenase MSMEG_0871 or xanthine treatment elicited a significant decrease in aminoglycoside antibiotic lethality for Mycobacterium smegmatis. Together, our findings revealed MSMEG_5174 as a metabolic regulator and hint toward unexplored crosstalk between purine metabolism and antibiotic resistance.The increasing incidence of drug-resistant tuberculosis is still an emergency for global public health and a major obstacle to tuberculosis treatment. Therefore, deciphering the novel mechanisms of  Mycobacterium tuberculosis (M. tuberculosis), is the leading cause of death worldwide. In 2020, an estimated 10 million people developed active TB and approximately 1.5 million individuals died from TB (Tuberculosis (TB), an infectious disease caused by  from TB . The eme from TB . General from TB . In turn from TB . Interes from TB . TherefoBacillus subtilis (B. subtilis), are named after a gluconate operon repressor (Mycobacterium smegmatis (M. smegmatis) with the signatures of the YtrA subfamily  was purchased from Solarbio Life Science, Beijing, China. Middlebrook 7H9 Broth (271310) was purchased from BD/Difco, Franklin Lakes, NJ, United States. Amikacin (A602232), kanamycin (A100408), gentamicin (A100304), streptomycin (A100382), chloramphenicol (A100230), ciprofloxacin (A600310), rifampicin (A600812), and isoniazid (A600544) were purchased from Sangon Biotech, Shanghai, China. The Anti-His antibody (#9991) was purchased from Cell Signaling Technology, Danvers, MA, United States.Mycobacterium smegmatis mc2 155 WT, MSMEG_5174 gene knock-out strain (\u0394MSMEG_5174), and gene complementary strain (\u0394MSMEG_5174 + pRv1152) were kindly gifts provided by Prof. Jianping Xie . The full length of the xanthine dehydrogenase encoding gene MSMEG_0871 was amplified from M. smegmatis mc2 155 genomic DNA using gene-specific primers pALACE-MSMEG_0871 (F and R) listed in M. smegmatis. The resulting strains harboring pALACE and pALACE-MSMEG_0871 were named MS_Vec and MS_MSMEG_0871, respectively. The expression of MSMEG_0871 was detected by Western blotting using an anti-His antibody. All the strains were grown in 7H9 broth or 7H9 agar supplemented with 0.5% glycerol, 0.05% Tween 80, and 0.2% glucose  analysis according to the previously described method with minor modification . GeneralLogarithmic phase bacterial samples were prepared according to the previously described with minor modifications . BrieflyAminoglycoside antibiotics including gentamicin, streptomycin, kanamycin, and amikacin were used in this study. The minimum inhibitory concentration (MIC) of these antibiotics for WT and \u0394MSMEG_5174 were measured according to the method described previously . The dilM. smegmatis strains, including MS_Vec, MS_MSMEG_0871, WT, \u0394MSMEG_5174, and \u0394MSMEG_5174 + pRv1152, were grown into logarithmic phase. The bacterial cells were collected and diluted in a 7H9 medium to an OD600 of 0.1. The final concentrations of all indicated antibiotics were as follows: gentamicin , kanamycin , streptomycin , amikacin , ciprofloxacin , chloramphenicol , rifampicin , and isoniazid . For metabolite supplementation experiments, bacterial cells were grown in a 7H9 medium supplied with 1 mM xanthine and subjected to an antibiotic killing assay. After indicated antibiotic treatment, 100 \u03bcl aliquot samples were removed for 10-fold serial dilution, and the diluted bacterial cells were plated 10 \u03bcl into 7H9 agar plates. All plates were incubated at 37\u00b0C for 3 days and the colony-forming units (c.f.u) were counted. Percent survival was calculated by dividing the c.f.u of treated groups by that of the control group.Antibiotic lethality assay was performed as previously described . M. smegSRR19667998 and SRR19667999, respectively.Logarithmic phase WT and \u0394MSMEG_5174 strains were subjected to transcriptome analysis. The bacteria pellets were collected and total RNA was isolated using the RNeasy mini kit . Libraries were constructed and subjected to sequencing using the Illumina HiSeq 2500  at Shanghai Biotechnology Corporation. The raw reads were preprocessed and low-quality reads were filtered out. The fold change of each gene was estimated according to the FPKM value generated by Cufflinks v2.1.1 after genome mapping. The Cuffdiff and false discovery rate (FDR) were used for dysregulated gene identification and multiple testing correction, respectively. The dysregulated genes were selected and filtered by FDR \u2264 0.05 and fold-change \u2265 2. The raw data were deposited to NCBI and the accession numbers for WT and \u0394MSMEG_5174 are sigA . cDNA was synthetized from 1 \u03bcg of total RNA using the RevertAid First Strand cDNA Synthesis kit  with random primers. Quantitative real-time PCR was performed by using the iQ SYBR Green Supermix in the CFX96 Touch System  under the following thermocycling parameters: 95\u00b0C for 5 min and 40 cycles at 95\u00b0C for 30 s, 60\u00b0C for 30 s and 72\u00b0C for 30 s. Gene expression was normalized to sigA  and genep-value). The metabolites with a significant difference were determined and filtered by the VIP values \u2265 1.0 and p-value \u2264 0.1. The dysregulated metabolites were subjected to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis.Sample preparation and analysis were performed as previously described with minor modifications . WT, \u0394MSThe accumulation of ethidium bromide (EB) was measured as previously described . Logaritt-test was used to determine the statistical significance between different groups. \u2217\u2217\u2217P < 0.001, \u2217\u2217P < 0.01, \u2217P < 0.05, means \u00b1 SD from at least three biological replicates. Data are representative of at least three independent experiments.All statistics were calculated using GraphPad Prism 8 software and Student\u2019s M. smegmatis (WT) and MSMEG_5174 mutants (\u0394MSMEG_5174) were monitored. No significant difference was detected in morphology between WT and \u0394MSMEG_5174 strains grown in 7H9 agar   , indicatreatment . In contreatment . These dreatment . To furtibiotics . These dL-lysine) and five carbon sources  were decreased in MSMEG_5174 mutants compared to WT  can be found below: WD, ZZ, SG, and HZ conceived and designed the experiments and wrote the manuscript. WD, ZZ, and YC performed most experiments with assistance from MY, JY, WL, and JZ. WD, ZZ, and JZ analyzed the data. JX modified the manuscript. All authors have read and approved the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher."}
+{"text": "Identifying structural variants (SVs) is critical in health and disease, however, detecting them remains a challenge. Several linked-read sequencing technologies, including 10X Genomics, TELL-Seq and single tube long fragment read (stLFR), have been recently developed as cost-effective approaches to reconstruct multi-megabase haplotypes (phase blocks) from sequence data of a single sample. These technologies provide an optimal sequencing platform to characterize SVs, though few computational algorithms can utilize them. Thus, we developed Aquila_stLFR, an approach that resolves SVs through haplotype-based assembly of stLFR linked-reads.Aquila_stLFR first partitions long fragment reads into two haplotype-specific blocks with the assistance of the high-quality reference genome, by taking advantage of the potential phasing ability of the linked-read itself. Each haplotype is then assembled independently, to achieve a complete diploid assembly to finally reconstruct the genome-wide SVs. We benchmarked Aquila_stLFR on a well-studied sample, NA24385, and showed Aquila_stLFR can detect medium to large size deletions (50\u2009bp\u201310\u2009kb) with high sensitivity and medium-size insertions (50\u2009bp\u20131\u2009kb) with high specificity.https://github.com/maiziex/Aquila_stLFR.Source code and documentation are available on Bioinformatics Advances online. Recently developed sequencing technologies, including 10X Genomics, TELL-Seq and single tube long fragment reads (stLFR) offer promise for large-scale \u2018perfect genome\u2019 assembly . Additionally, the \u2018Aquila_hybrid\u2019 mode applies an analogous concept to reconstruct long DNA fragments and generate the same data structure for long DNA fragments of both 10X and stLFR data  from the input VCF file to annotate each LFR with heterozygous SNPs and then relies on all pairs of heterozygous SNPs supported by different clusters of LFRs to apply a recursive clustering algorithm to finally partition LFRs into haplotype-specific blocks  and paftLFR data . Aquila_ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/HG002_NA24385_son/stLFR/), since we can evaluate SV (\u226550\u2009bp) detection by comparing Aquila_stLFR\u2019s calls against the GiaB NA24385 SV benchmark callsets. This stLFR library has approximately 48X Illumina sequencing coverage, and the average inferred (reconstructed) DNA fragment (LFR) length is around 30\u2009kb. To demonstrate the performance of SV calling for Aquila_stLFR, we have compared it against two other linked-reads SV callers: GROC-SVs and NAIBR  to compare SV calls with the benchmark callset from three different SV size range thresholds. In In this study, we used the stLFR sequencing library for NA24385 from the Genome in A Bottle (GiAB) website (nd NAIBR . After gThis research was supported by Vanderbilt University Development Funds (FF_300033), the Joint Initiative for Metrology in Biology  and Research Grant Council Early Career Scheme (HKBU 22201419).Conflict of Interest: There is NO Competing Interest.vbab007_Supplementary_DataClick here for additional data file."}
+{"text": "CDKN1A) mRNA and stabilization of this complex and leads to cell cycle arrest at the G1/S checkpoint, inhibiting cell proliferation of vascular smooth muscle cells in vitro and NIH in vivo. Importantly, hsa_circ_0000280 reduced neointimal thickness and smooth muscle cell proliferation in vivo. Taken together, these findings reveal a novel pathway in which hsa_circ_0000280 facilitates the regulation of ELAVL1 on CDKN1A mRNA to inhibit NIH. Therefore, measuring and modulating their expression might represent a potential diagnostic or therapeutic strategy for CHD.The pathological proliferation of cells in vascular smooth muscle underlies neointimal hyperplasia (NIH) development during atherosclerosis. Circular RNAs (circRNAs), which represent novel functional biomarkers and RNA-binding proteins, contribute to multiple cardiovascular diseases; however, their roles in regulating the vascular smooth muscle cell cycle remain unknown. Thus, we aimed to identify the roles of circRNAs in vascular smooth muscle during coronary heart disease (CHD). Through circRNA sequencing of CHD samples and human antigen R (ELAVL1) immunoprecipitation, we identified circRNAs that are associated with CHD and interact with ELAVL1. Our results suggested that the hsa_circ_0000280 associated with CHD inhibits cell proliferation and induces ELAVL1-dependent cell cycle arrest. Gain/loss-of-function experiments and assays in vivo indicated that hsa_circ_0000280 facilitates interactions between ELAVL1 and cyclin-dependent kinase suppressor 1 (The online version contains supplementary material available at 10.1007/s00018-022-04602-w. Neointimal hyperplasia (NIH) is defined as the migration and proliferation of smooth muscle cells in the tunica intima, leading to thickened arterial walls and reduced luminal space. NIH plays an integral role in the development of coronary heart disease (CHD) . A cruciCircular ribonucleic acids (circRNAs) are highly conserved, long non-coding RNAs (lncRNAs) that can form stable closed loops lacking 5\u02b9-end caps and 3\u02b9-end poly (A) tails . Some ciHuman antigen R , a member of the embryonic lethal abnormal vision (ELAV)/Hu family, is an RNA-binding protein that regulates RNA metabolism and is involved in neuronal development, proliferation, and migration. It binds U-rich sequences or AU-rich elements (ARE) in the 3\u02b9-untranslated regions (UTRs) of target mRNAs \u201313. MoreAGO2) gene, is part of a miRNA-induced silencing complex involved in tumor progression that binds and activates the ELAVL1 protein [Additionally, circAGO2, derived from the Argonaute 2 . Patients with\u2009>\u200950% and\u2009<\u200950% coronary stenosis were respectively assigned to the CHD and control groups . PeripheWe acquired coronary artery samples from five donors with brain death (DBD) at the Organ Transplantation and Donation Department at Qilu Hospital. Family members provided written informed consent post-mortem. Samples were stored in cold HypoThermosol FRS Preservation Solution  at 4\u00a0\u00b0C within 30\u00a0min. The coronary arteries were immediately dissected using a stereomicroscope into 5\u20136\u00a0mm arterial rings with or without plaques, and placed in liquid nitrogen for long-term storage.The present study proceeded according to the principles enshrined in the Declaration of Helsinki (2013 amendment). The Medical Institutional Ethics Committee of Qilu Hospital approved the collection and use of human blood and vessel samples (Prot. KYLL-2019-080).Detailed protocols can be found in the Supplementary Tables S2 and S3 show details of the study protocols.t tests, and \u03c72 tests, respectively, and the results of multiple comparisons were assessed using one-way ANOVA. Data are presented as means\u2009\u00b1\u2009SD unless otherwise indicated. All data were analyzed using SPSS 25.0  or GraphPad Prism 8 . Values with p\u2009<\u20090.05 were considered statistically significant.All experiments comprised at least three independent replicates and groups contained five animals each. Differences between groups of continuous and data categorical were determined using Student We sequenced 30 non-CHD and 70 CHD samples of PBMCs from the patients to identify potential circRNA biomarkers associated with CHD. Compared with controls, 86 circRNAs were significantly upregulated; whereas 2,283 were downregulated, including hsa_circ_0000280 Fig.\u00a0C and fouHPS5 gene. The length of hsa_circ_0000280 was found to be 656 nt  and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) . These results suggest that hsa_circ_0000280 can bind to ELAVL1 protein and CDKN1A mRNA, providing potential evidence for the function study of hsa_circ_0000280.We reconfirmed the RIP-sequencing results using RIP-agarose gel electrophoresis and RNA pull-down-western blotting in HASMCs . When hsa_circ_0000280 was pulled down, ELAVL1 protein was specifically enriched but the antisense probe did not bind to it Fig.\u00a0A and B. RIP Fig.\u00a0C, D. We RIP Fig.\u00a0E, F. FISFurthermore, using the catRAPID and Vienna RNA algorithm for RNA\u2013protein interactions and circRNA secondary structure analyses, the binding of hsa_circ_0000280-ELAVL1 was predicted to occur between three major RNA regions , and five ELAVL1 protein domains . Moreover, knocking down hsa_circ_0000280 decreased the expression of VSMC differentiation markers, including actin and transgelin, in differentiated HASMCs that have less migratory and proliferative capacity [We transfected siRNA to decrease hsa_circ_0000280 levels . Downregulated endogenous hsa_circ_0000280 promoted HASMC proliferation by 55.1%  and increased their migratory capacity . Cell cycle analysis further revealed that decreased hsa_circ_0000280 expression diminished HASMC accumulation at the G1/S phase with more cells remaining in the S phase . Reduced hsa_circ_0000280 also decreased mRNA levels of capacity   than with other target mRNAs after hsa_circ_0000280 overexpression  and circ_280(del501-602), which were similarly expressed . Downregulated CDKN1A resulted in suppressing the influence of exogenous hsa_circ_0000280 on cell proliferation . Hsa_circ_0000280 did not affect neointimal areas in ELAVL1ice Fig.\u00a0A and B. 280 Fig.\u00a0D and E. ice Fig.\u00a0F and G. 280 Fig.\u00a0H and I.Overall, these findings showed that hsa_circ_0000280 regulates the biological activities of SMCs in vascular pathologies and that the effect of hsa_circ_0000280 on vascular diseases is dependent on ELAVL1.As emphasis on circRNAs associated with cardiovascular diseases has increased, several circRNAs have been identified as important factors in vascular biology . In VSMCOur findings revealed a novel circRNA that regulates the SMC cell cycle and NIH in atherosclerosis. The stable circRNA hsa_circ_0000280 is downregulated in PBMCs of patients with CHD and in human atherosclerotic vessels. Moreover, this circRNA can be accurately quantified using PBMCs isolated from collected blood samples, which is far less invasive than collecting coronary artery tissues. Thus, hsa_circ_0000280 has considerable potential for the diagnosis of CHD.The protein ELAVL1 plays an essential role in several biological processes such as proliferation, cell cycle, and cancer growth. Specifically, ELAVL1 regulates cell division and checkpoint reactions via diverse mechanisms, including modulating the translation and stability of key regulators of the cell cycle, such as cyclins , 27, 35.Our loss/gain-of-function studies have shown that hsa_circ_0000280 inhibits SMC proliferation and cell cycle progression, as shown by less SMCs in the S phase and G1/S phase arrest after the delivery of hsa_circ_0000280. The primary checkpoints of G1/S, which have been demonstrated to interact with ELAVL1 are CCND, CCNE, and CDK2. However, hsa_circ_0000280 delivery elicited no significant changes in levels of CCND or CCNE, whereas those of CDK2 were downregulated. The level of CDKN1A was also increased, which might have induced G1/S phase arrest , 38. We SMKO mice are consistent with these findings.Most studies on ELAVL1 in SMCs and vascular pathologies have found that ELAVL1 cooperates in the regulation of VSMC homeostasis , 26, witFurthermore, ELAVL1 localization also regulates the cell cycle , 41. In HPS5 gene from which hsa_circ_0000280 originates has not been investigated from the viewpoint of atherosclerosis. Although hsa_circ_0000280 regulation did not impact the mRNA level of HPS5, whether hsa_circ_0000280 affects homeostatic progression via HPS5 is difficult to determine. A CDKN1A knock down assay in vivo in a future study would be another way to confirm the effects of hsa_circ_0000280. In addition, hsa_circ_0000280 appears to be downregulated in PBMCs from patients. The function of hsa_circ_0000280 in PBMCs is also unknown. This might be associated with atherosclerosis caused by chronic systemic inflammation. However, additional investigations are required to confirm this postulation.The In conclusion, this study revealed the roles of a novel circRNA, hsa_circ_0000280, in regulating ELAVL1 activity in SMC and NIH. This information provides scientific evidence and guidance for future studies on circRNAs as potential diagnostic biomarkers and therapeutic targets against atherosclerosis.Supplementary file1 (XLSX 261 kb)Supplementary file2 (XLSX 61 kb)Supplementary file3 (DOCX 5594 kb)Below is the link to the electronic supplementary material."}
+{"text": "Previous research studies have shown that the elevation of circular RNA (circRNA), hsa_circRNA_002178, was associated with the poor prognosis of breast cancer and colorectal cancer, while its molecular mechanisms underlying the effects on hepatocellular carcinoma (HCC) are still elusive. in vivo.  The microarray dataset GSE97332 was obtained from the Gene Expression Omnibus (GEO) database and calculated by using the GEO2R tool to identify differentially expressed circRNAs. Differentially expressed hsa_circRNA_002178, in 7 HCC tissue samples and paracancerous tissues, as well as in HCC cell lines and normal hepatocytes, was checked by RT-qPCR. Cell proliferation, invasion, migration, and epithelial-to-mesenchymal transition (EMT)-related proteins were tested in hsa_circRNA_002178-overexpressed or hsa_circRNA_002178-knocked down HCC cells. Subsequently, we identified whether hsa_circRNA_002178 binds to serine- and arginine-rich splicing factor 1 (SRSF1) and then analyzed their function in regulating HCC cell behavior. The effect on HCC cell xenograft tumor growth was observed by the knockdown of hsa_circRNA_002178  GEO2R-based analysis displayed that hsa_circRNA_002178 was upregulated in HCC tissues. Overexpression or knockdown of hsa_circRNA_002178 encouraged or impeded HCC cell proliferation, migration, invasion, and EMT program. Mechanically, hsa_circRNA_002178 bound to SRSF1 3\u2032-untranslated region (UTR) and stabilized its expression. SRSF1 weakening eliminated the effects of pcDNA-hsa_circRNA_002178 on cell malignant behavior. Finally, the knockdown of hsa_circRNA_002178 was confirmed to prevent xenograft tumor growth.  hsa_circRNA_002178 overexpression encouraged the stability of SRSF1 mRNA expression, and it may serve as an upstream factor of SRSF1 for the diagnosis of HCC. Primary liver cancer is one of the most common malignancies worldwide, among which hepatocellular carcinoma (HCC) is the most common, accounting for approximately 75\u201385% . Althougin vitro to examine the effects of hsa_circRNA_002178 and its downstream on the malignant behavior. In this study, we evaluated the expression levels of hsa_circRNA_002178 in HCC patient tissues and cells, and the effects on the malignant cell behavior. The possible potential downstream genes were predicted and validated by the analysis website as well as luciferase reporter gene and RIP assays.circRNAs are a kind of noncoding RNAs newly discovered in recent years, which widely exist in organisms. Unlike linear RNAs in general, circRNAs do not have a common 5\u2032cap and 3\u2032 adenosine tail structure. Circular RNAs have been shown to be more stable than linear RNAs . CurrentBased on the above reports, this study systematically explored the roles of hsa_circRNA_002178 in the biological functions of HCC cells, aiming to provide useful assistance for HCC clinical diagnosis.A total of 50 cases were selected and included at the Zibo Central Hospital attended patients with HCC. The pathological diagnosis of HCC was carried out according to the standards of the World Health Organization. This study was conducted by Zibo Central Hospital, and ethics approval was obtained by the ethics committee. Inclusion criteria were as follows: the first diagnosis of HCC between 2015 and 2018 , no previous diagnosis of cancer, and no direct evidence of disease within 1 month after the first surgery. All patients underwent routine physical examinations every 3\u20136 months for the first 5 years of follow-up visits and annually thereafter. Overall survival was defined as the number of months from surgical treatment to death. The seizure-free survival time is defined as the time from surgical treatment to the clinical medical detection of seizures (in months as the enterprise).\u03bcg/mL streptomycin (in which SNU-1 cell culture medium does not contain sodium pyruvate).Huh7, Hep3B, HepG2, HCCLM3, SK-hep1, and L02 cell lines were obtained from Shanghai Huiying Biotech Co., Ltd, Shanghai, China. Subsequently, cells were subjected to mycoplasma testing and isoenzyme testing, and cell viability assays were performed by the biological company . All cells were cultured in the DMEM containing 10% fetal bovine serum, 100\u2009U/mL penicillin, and 100\u2009https://www.ncbi.nlm.nih.gov/geo/). Dataset GSE97332 was based on the platform GPL19978. GSE97332 data included 7 hepatocellular carcinoma primary tumor tissues and 7 adjacent nontumor tissues. Differentially expressed genes between hepatocellular carcinoma primary tumor tissues and paired nontumor tissues were accepted when |log2 fold change|\u2009\u2265\u20091.5 and p < 0.01. Gene expression profiling interaction analysis  was used to analyze the expression of SRSF1 in hepatocellular tissues and adjacent normal tissues and the 60-month survival rate of HCC patients with high or low expression of hsa_circRNA_002178. Starbase was used to predict the targeting site between hsa_circRNA_002178 and SRSF1 mRNA 3\u2032-untranslated region (UTR).Microarray data are available with accession number GSE97332 from Gene Expression Omnibus database  and SRSF1 (sh-SRSF1). Confluent cells were diluted in the DMEM, and the cells were observed to grow to about 70% confluence when the cell monolayer was covered with the serum-free DMEM. In brief, before transfection, cells were digested with 1% trypsin treatment. After being counted in a blood counting chamber, the cells were plated onto six\u2010well culture plates for 24 hours and then transfected at 40%\u201360% confluence. Lipofectamine\u00ae 3000 reagent was diluted by using Opti-MEM medium (2 tubes) and mix well sufficiently. DNA master mix was then prepared by diluting the DNA by using Opti-MEM medium, followed by the addition of p3000\u2122 reagent. Lipofectamine\u00ae 3000 had been diluted in each tube reagent with diluted DNA (1\u2009:\u20091 ratio). DNA-liposome complexes were added to the cells after 5-min incubation at room temperature. All cells in each group were collected for subsequent experiments after incubating in an incubator at 37\u00b0C with 5% CO\u2212\u0394\u0394CT method. The sequences of primers were as follows (designed on website https://www.PrimerBank.com): hsa_circRNA_002178 forward, 5\u2032- CTG GTA TCC CTG CAA GTT AAG TC-3\u2032; and reverse, 5\u2032-TGC TCC CGT GGC TGG TCT AAC GCA AA-3\u2032; GAPDH forward, 5\u2032-TGC AGT GGC AAA GTG GAG ATT-3\u2032; and reverse, 5\u2032-TCG CTC CTG GAA GAT GGT GAT-3\u2032.Cells from each group were collected, TRIzol  was added to extract total cellular RNA, and total cellular RNA was reverse transcribed to cDNA using a reverse transcription kit. Using cDNA as a template, RT-qPCR was performed, and the PCR products were detected with the StepOnePlus real-time PCR system , with three replicate wells set for each group, and GAPDH as the internal reference. The relative expression levels were calculated by using the 2\u03bcg protein samples were subjected to SDS-PAGE and transferred onto PVDF membranes. The membrane was blocked with blocking solution (5% nonfat dry milk) for 2\u2009h and subsequently washed three times using TBST. Specific primary and secondary antibodies were next added separately, followed by incubation on a shaker. ImageJ software was applied to detect and analyze the gray values of protein bands on the membrane. The primary antibodies are as follows: rabbit polyclonal anti-\u03b2-actin antibody , rabbit monoclonal anti-SRSF1 antibody , rabbit monoclonal anti-E-cadherin antibody , and rabbit monoclonal anti-Vimentin antibody .Total cell protein was extracted with RIPA lysate, and the protein concentration was determined using a BCA protein assay kit  in a microplate reader. After denaturation for 10\u2009min with the addition of loading buffer, 50\u20093, and 5 replicate wells were set 0069\u2009n each group. Transfection was performed after incubation at 37\u00b0C in 5% CO2 until the cells became adherent. Then, the cells were incubated at 37\u00b0C in a 5% CO2 incubator for 0, 24, 48, 72, 96, and 120\u2009h after which the supernatant was discarded and 100\u2009\u03bcl of complete medium and 10\u2009\u03bcL of CCK-8 . After incubation at 37\u00b0C in 5% CO2 for 1\u2009h, the optical density (OD) value at 450\u2009nm was measured using a microplate reader .The cells were seeded in 96-well culture plates at a cell number per well of 3\u2009\u00d7\u200910\u03bcm chamber plates. The upper surface of the Transwell filter we used was coated with Matrigel . Firstly, cells were planted into the 8.0-\u03bcm chamber plates, then 300\u2009\u03bcL of serum-free DMEM medium was added to the upper compartment of the chamber, and then, 500\u2009\u03bcL of DMEM medium supplemented with 10% FBS was added to the lower chamber for 48-h incubations. Then, the noninvasive cells on the upper side of the chamber were suspended with a cotton swab, and then, the invasive cells were fixed in 4% paraformaldehyde and stained with a crystal violet solution. We stained infiltrating cells by using an Olympus IX70 inverted microscope  and randomly selected the best six fields of view, and each experiment was repeated three times.The cell invasion ability was detected by treatment with 8.0-5 cells/well and then scratched with the tip of a pipette 24\u2009h later. Subsequently, the cell fluid was replaced with a complete medium. Photographs were taken every 24\u2009h. The cells were observed once with an inverted microscope to detect their migration ability.Cells from each group were seeded in 6-well plates at a concentration of 10Full-length DNA coding sequence of SRSF1 was inserted into pGL3-basic vector, and then, two vectors containing different inserts cloned from hsa_circRNA_002178 were constructed downstream SRSF1. With pGL3-SRSF1-hsa_circRNA_002178 reporter vector transfection of Huh7 cells, roots' luciferase activity of individual groupings was assessed using the dual luciferase reporter system .Protease inhibitor EDTA as well as RNase inhibitor  was added to IP lysis buffer to lyse cells. After the addition of magnetic beads preclearing for 30\u2009min, IgG antibody and SRSF1 antibody  were added, and then, the added magnetic beads were rotated to mix for 2\u2009h at room temperature. Aspirate supernatant on magnetic stand and wash beads with IP lysis buffer. After the removal of proteins by the addition of proteinase K, TRIzol LS was added to extract RNA and finally subjected to subsequent analysis.The relationship between hsa_circRNA_002178 and SRSF1 was predicted by Starbase and verified by RNA pull-down assay. Biotinylated hsa_circRNA_002178 was synthesized and transfected into Huh7 cells, and the negative control (NC) was set to demonstrate the specificity of the response. After 48 hours, the cells were washed and collected, and then, the lysate was incubated with avidin-anchored magnetic beads at 4\u00b0C for 3\u2009h. After washing the beads sufficiently, the RNA-protein complex was eluted, and the RNA pull-down product was subjected to RT-qPCR.5 Huh7 cells were injected subcutaneously into the abdominal cavity of the mice. For the hsa_circRNA_002178 knocking-down group, 5\u2009\u00d7\u2009105 Huh7 cells transfected with hsa_circRNA_002178 shRNA were injected subcutaneously into the abdominal cavity of the mice. Tumor length and width were calculated with vernier calipers every 3 days. After 36 days, the mice were humanely sacrificed, and the subcutaneous tumors were excised and removed.Animal experiments were approved and supervised by the Animal Ethics Committee of Shandong University. Five-week-old male athymic BALB/c nude mice were obtained from the Experimental Animal Center of Shandong University  and subsequently randomly divided into 2 groups, including control and hsa_circRNA_002178 knocking-down. First, we transfected hsa_circRNA_002178 shRNA into Huh7 cells to knock down hsa_circRNA_002178. For the control group, 5\u2009\u00d7\u200910t-test was performed for the comparison between two groups. The means of the different groups were compared using one-way or two-way analysis of variance (ANOVA) following Tukey's post hoc test. P < 0.05 was considered statistically significant. All experiments were repeated 3 times (n\u2009=\u20093).SPSS 22.0 and GraphPad Prism 7.0 were used for data analysis and mapping. The pairwise comparisons were analyzed using the chi-square test. The measurement data were represented as mean\u2009\u00b1\u2009SEM with normal distribution and homogeneity of variance. Student's Several studies have elucidated that circRNAs display molecular functions in HCC. Here, the analysis of the HCC dataset GSE97332 downloaded from the GEO database repository revealed that differentially expressed circRNA, hsa_circRNA_002178, was prominently increased in HCC tissues Figures . Subsequp < 0.01). Additionally, we further evaluated the effects of hsa_circRNA_002178 overexpression on the invasion and migration abilities of HCC. As shown in Figures To further explore whether hsa_circRNA_002178 is involved in HCC cell malignant behavior, we established two stable hsa_circRNA_002178-overexpressing cell lines. The overexpression efficiency of pcDNA-hsa_circRNA_002178 in Huh7 and sk-hep1 cells is displayed in \u03bcg or 2\u2009\u03bcg hsa_circRNA_002178 shRNA and its negative control for 24\u2009h, respectively. And the resulting cell lines exhibited memorably reduced the hsa_circRNA_002178 level in a dose-dependent manner (p < 0.01). The CCK-8 assay displayed that hsa_circRNA_002178 knockdown inhibited SK-hep1 and Huh7 cell proliferation . Furthermore, we evaluated the effect of hsa_circRNA_002178 interference on cell invasion and migration. hsa_circRNA_002178 interference markedly inhibited the cell invasion and migration, in which the high-dose group of hsa_circRNA_002178 shRNA had prominently higher inhibitory effects on cell proliferation and invasion its low-dose transfection group .Subsequently, SK-hep1 and Huh7 cells were transfected with 1\u2009https://jaspar.genereg.net/) to predict targets that may be regulated by hsa_circRNA_002178. Figure A displays the SRSF1 motifs and its binding sites to hsa_circRNA_002178. To further verify whether hsa_circRNA_002178 could directly target with SRSF1, the RNA pull-down and RIP assays were performed. The RIP assay has displayed that a higher hsa_circRNA_002178 level was detected in anti-SRSF1immuno-precipitates relative to control IgG immune precipitates (p < 0.01). Then, the RNA pull-down results are shown in p < 0.01). Moreover, we discovered that hsa_circRNA_002178 overexpression could increase the SRSF1 mRNA level after treating with actinomycin D , while hsa_circRNA_002178 inhibition showed the opposite result. This finding revealed that SRSF1 mRNA stability was added in hsa_circRNA_002178-upregulated cells and declined in hsa_circRNA_002178-depleted cells (p < 0.05). Subsequently, we found that the SRSF1 level was significantly higher in HCC tissues than in normal tissues . Additionally, the effects of hsa_circRNA_002178 on cell proliferation and invasion implied that EMT may mediate the roles of hsa_circRNA_002178 in HCC. The results displayed that hsa_circRNA_002178 overexpression reduced the E-cadherin level and raised the Vimentin level, suggesting that hsa_circRNA_002178 overexpression induced EMT of HCC cells, which were memorably reversed by SRSF1 inhibition .Next, we further evaluated whether hsa_circRNA_002178 and SRSF1 are functionally associated, and rescue experiments were performed by co-transfectingpcDNA-hsa_circRNA_002178 with SRSF1 shRNA plasmids. Figure A and B display the transfection efficiency as SRSF1 shRNA. From the cellular behavior, knockdown of SRSF1 prominently reversed the promoting of hsa_circRNA_002178 upregulation on the Huh7 cell functions, which was reflected by cell proliferation, invasion, and migration  in cells subjected to hsa_circRNA_002178 interference, as well as memorably increased vimentin level , cell invasion (p < 0.01), and cell migration (p < 0.01). In summary, SRSF1 upregulation could reverse the effects of hsa_circRNA_002178 restrain on HCC cell EMT program, invasion, and migration.Subsequently, hsa_circRNA_002178 shRNA was transfected alone or together with pcDNA-SRSF1 into Huh7 cells. The efficiency of pcDNA-SRSF1 on its expression level in Huh7 cells is shown in Figures p < 0.01). Furthermore, compared to the control group, the SRSF1 mRNA level was prominently decreased in the hsa_circRNA_002178 knocking-down group, which was consistent with our previous in vitro findings. In conclusion, the knockdown of hsa_circRNA_002178 functions as a tumor suppressor by restraining tumor growth in vivo and in vitro.To confirm our in vitro results that hsa_circRNA_002178 inhibition could restrain HCC growth, the effects of hsa_circRNA_002178 knockdown in a xenograft murine model were next evaluated. Nude mice were subcutaneously injected with Huh7 cells stably transfected with hsa_circRNA_002178 shRNA plasmid to further evaluate the effect of hsa_circRNA_002178 on HCC cell growth in vivo. The tumor weight and volume were prominently decreased in the hsa_circRNA_002178 knockdown group  is a representative member of the alternative splicing factor family, which exerts the function of alternative splicing by recognizing and binding to corresponding splice sites . SRSF1 iOur findings demonstrated that hsa_circRNA_002178 knockdown inhibited the malignant behavior of HCC cells. Besides, the effect of hsa_circRNA_002178 interference was proved in the HCC mouse xenograft model. With the development of the theory of precision medicine, the diagnosis and treatment of HCC have risen to the molecular level, and the expression and mutation detection of related genes have gradually become the basis of their clinical treatment. The present study identified the roles of hsa_circRNA_002178 in HCC cells, which may provide potential therapeutic targets and schemes for HCC treatment."}
+{"text": "Introduction: CircRNAs are engaged in the tumorigenesis and progression of oral squamous cancer cells (OSCC). However, the function and underlying mechanism of circRNAs on tumor-associated immunity escape are largely unknown.in situ hybridization was applied to detect subcellular location of circRNA. A luciferase activity assay was used to detect the interaction of has_circ_0069313 and miR-325-3p and its downstream target, Foxp3. Exosomes were collected to detect the exosomal circRNAs and co-culture assays were performed to detect the function of exosomal circRNAs on Tregs.Materials and methods: We analyzed the expression pattern of has_circ_0069313 in our in-house database and its correlation with OSCC prognosis. Immunohistochemistry was applied to detected PDL1 expression. RNA fluorescence Results: has_circ_0069313 was upregulated in OSCC tissues and predicts poor prognosis. has_circ_0069313 promotes immunity escape through inhibiting miR-325-3p-induced Foxp3 degradation. has_circ_0069313 is an exosomal circRNA and the transfer of has_circ_0069313 to Treg cells promotes the Treg function through maintaining Foxp3 levels.Conclusion: Our results indicate that has_circ_0069313 induces OSCC immunity escape via the miR-325-3p-Foxp3 axis in both OSCC cells and Treg cells. Oral cancer is the sixth most frequent malignancy worldwide and the oral squamous cell cancer (OSCC) subtype accounts for 90% of cases . At presCircular RNAs are generally reported as non-coding RNAs which play vital roles in the progression of physiology and pathology , 6. CircIn the present study, we analyzed the expression level of has_circ_0069313 in OSCC patients and reveal its\u2019 biological functions and potential mechanism in OSCC cell lines. Our results showed that for the first time has_circ_0069313 promotes the tumor immunity escape and suggested it as the potential therapeutic target.***p < 0.001). We next divided the whole cohort into two groups, \u2018high has_circ_0069313\u2019 and \u2018low has_circ_0069313\u2019, taking the mean level as the cut-off. Patients with has_circ_0069313 higher than the mean value were identified as \u2018high\u2019, otherwise as \u2018low\u2019. Overall survival analysis was applied. Patients with high levels of has_circ_0069313 have shorter middle survival time (***p < 0.001). We next detected has_circ_0069313 levels in OSCC cell lines and normal epithelium cells. has_circ_0069313 was higher in OSCC cells compared with normal cells (***p < 0.001).To detect the expression pattern of has_circ_0069313 in OSCC patients, we designed the junction-specific primers shown in We collected has_circ_0069313 high and has_circ_0069313 low samples and subjected them to immunohistochemistry (IHC) assay. Staining with CD8 antibody was used to detect the effector T cell infiltration. The representative images were shown in ***p < 0.001). We next applied the RNA pull-down assay using the circRNA specific junction probe and found that miR-325-3p was detectable in the complex (***p < 0.001). As the interaction between circRNAs and miRNAs is carried with the help of Ago2, we applied RNA immunoprecipitation (RIP) assay using Ago2 and has_circ_0069313 and miR-325-3p were detected. The results showed that has_circ_0069313 and miR-325-3p were both detectable in the RIP complex (***p < 0.001). Fluorescence in situ hybridization (FISH) was applied using the junction-specific probe and a miR-325-3p probe. The results also confirmed the interaction (***p < 0.001). For further evidence, we thus established a has_circ_0069313 WT and a MUT allele (***p < 0.001). RNA pull-down also confirmed this result (***p < 0.001). We next detected the relative RNA level of miR-325-3p and analyzed the correlation between has_circ_0069313 and miR-325-3p. The results showed that miR-325-3p was downregulated in OSCC samples and was in a negative correlation with has_circ_0069313 (***p < 0.001).CircRNAs exert their function mostly through acting as competing endogenous RNAs. After searching CircNET, RNAhybrid, and miRanda tools and we identified miR-325-3p as the candidate target miRNA. We first detected the expression of miR-325-3p in stable cell lines, and results indicated that miR-325-3p is upregulated in has_circ_0069313 knockdown cell lines but decreased in has_circ_0069313 overexpression cells . We established Foxp3 WT and MUT luciferase activity reporter assays and transfected them into OSCC cell lines and the relative luciferase activity was detected (***p < 0.001). The results indicated that the Foxp3 luciferase increased with the MUT allele (***p < 0.001). We then transfected with has_circ_0069313, WT/MUT miR-325-3p mimic/inhibitor alone or in combination, relative luciferase activity was detected and the results suggested that has_circ_0069313 inhibited miR-325-3p induced Foxp3 degradation through binding to the 3\u2032UTR of Foxp3 (***p < 0.001). We next transfected a miR-325-3p inhibitor into has_circ_0069313 knockdown cells and a miR-325-3p mimic in has_circ_0069313 overexpressing cells. Foxp3 and PDL1 were detected. The results indicated that Foxp3 and PDL1 levels were completely restored with the introduction of the inhibitor/mimic (***p < 0.001).miRNAs prefer to bind to the untranslated region of target genes mRNA and promote its degradation and eventually the decrease of the target genes levels. Using Targetscan we identified Foxp3 as the potential downstream target of miR-325-3p. We next detected the RNA level and protein level of Foxp3 in different cell lines. Foxp3 decreased in has_circ_0069313 knockdown cells but increased in has_circ_0069313 overexpressing cells (***p < 0.001). Exosomes markers CD9, CD54, and GM130 were detected as loading controls [***p < 0.001). We next transfected has_circ_0069313 specific shRNA into the cells and then treated with tumor exosome. The intracellular has_circ_0069313 was detected and shows an increased level as well (***p < 0.001). This result excluded the influence of epigenetic regulation and attributed the increase of the intracellular has_circ_0069313 to the exosome has_circ_0069313.CircRNAs were reported to exert paracrine functions through exosomes . We nextcontrols  . We thus hypothesized that OSCC has_circ_0069313 levels were associated with Treg function. We have previously indicated that has_circ_0069313 may exert its activity via a paracrine function. We next co-culture OSCC cells with Treg and treated Treg cells with OSCC derived exosomes (***p < 0.001).We applied the IHC assay using the Treg cell marker CD25 and analyzed the correlation with has_circ_0069313. The results showed that has_circ_0069313 negatively correlates with CD25 (exosomes . has_cir***p < 0.001). IHC was then applied to detected PDL1 and CD25 levels to measure the immunity status. The results showed that has_circ_0069313 knockdown cells developed impaired tumor growth and Treg cell infiltration. PDL1 and CD25 also decreased in has_circ_0069313 knockdown cells but increased in has_circ_0069313 overexpressing cells (***p < 0.001). taken together, Our results indicate that has_circ_0069313 induces OSCC immunity escape via the miR-325-3p-Foxp3 axis in both OSCC cells and Treg cells . Written and informed consent was obtained from patients. This study was conducted in accordance with the Declaration of Helsinki, the information of patients was shown in Human immortalized HaCaT and oral SCC Cal-27 cells were maintained in DMEM (Sigma) medium supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin, while other cell lines were maintained in complete DMEM-F12 medium. PcDNA3.1 vector containing has_circ_0069313 cDNA  were transfected into OSCC cells with a low has_circ_0069313 level using Lipofectamine 3000 . All treated cells were selected with G418 . Lentiviruses containing has_circ_0069313-targeting shRNAs (Genechem) were used to stably infect OSCC cells, which have a high has_circ_0069313 level. The sequences of shRNAs were as following, NC: TTGGCGCGTATGCAAC, ShRNA-1: CTTGTACATGCAATTGCGCGG, ShRNA-2: AAACTTGTACATGCAATTGCG.Equal protein was run on SDS-PAGE gel and transferred onto PVDF membranes. After blocking, the membranes were then incubated with primary antibodies overnight at 4\u00b0C, washed with TBST three times, and then incubated with appropriate secondary antibodies for 1 h at room temperature. Target proteins were detected with ECL (Millipore) reagent. The antibodies involved in the manuscript were as below: PDL1 , \u03b2-actin , CD8 , CD25 , Foxp3 , GM130 , CD54 , CD9 .Briefly, paraffin-embedded tissues were cut at a 6\u201310 \u03bcM thickness and were deparaffinized in xylene and then rehydrated. Antigens were restored and blocked with goat serum dilution buffer for 1 h at room temperature. The tumor sections were incubated with primary antibodies in a wet chamber overnight at 4\u00b0C and then secondary antibodies were added for incubation at room temperature for 1 h. The tumor sections were subsequently visualized by Diaminobenzidine (DAB) reagent and then counterstained with hematoxylin for detection. Representative images were from at least three independent experiments.The coverslips seeded with cells were incubated in the incubator and fluorescently labeled junction probe for 12 hours. Then the cells were rinsed 3 times and incubated at room temperature overnight. Images were taken using Confocal microscopy and representative images were then picked. The sequence of the circRNA detection probe was as follows: 5\u2032 cy3-TAGAAGCCTGGACCTTCTTGGG 3\u2032.Total RNA was isolated with a PureLink RNA mini kit  according to manufacturer\u2019s instruction. RNA was reverse-transcribed into cDNA and then subjected to RT\u2013PCR analysis with SYBR Select Master Mix (Thermo Fisher Scientific) in a StepOne Plus real-time PCR system (Applied Biosystems). \u03b2-actin was used as internal control. The key primers are listed below: has_circ_0069313-F: CCAGAGGACAGTTCCTGGAC; has_circ_0069313-R: AGATGGCATGAGGGATATCG.The Renilla luciferase (Rluc) and firefly luciferase (Luc) sequences were cloned into the reporting plasmid, The Foxp3 sequence along with its 3\u2032UTR was amplified and inserted between Rluc and Luc. Relative activity was calculated by determining the ration of Rluc/Luc.Magna RIP Kit (Millipore) was used for RNA immunoprecipitation (RIP) assay. Briefly, the cells were lysed with RIP lysis buffer and then incubated with magnetic beads. Afterwards, proteinase K was added for purification of RNA. The enriched RNA was analyzed by qRT\u2013PCR further analysis.Exosomes derived from OSCC were performed through differential ultracentrifugation. Briefly, cells cultures were centrifuged at 4\u00b0C to obtain supernatant and then centrifuged at 10,000 \u00d7 g for 20 min, 70 min and 60 min respectively.Human Treg (CD4+ CD25hi) were purified from PBMCs from healthy donors, after staining with the following antibodies at 1:100 dilution: FITC anti-human CD4 , PE anti-human CD25 . Treg cells were treated with Detach reagent (Invitrogen) to remove antibody. Treg cells were thus harvested and subjected to the following experiments.6 cells were subcutaneously injected into the right flanks for establishment of xenograft model. The tumor samples were harvested and further subjected to IHC staining. Representative images were from at least three independent experiments.The C57BL6/n nude mice  were obtained from the Laboratory Animal Center of Sun Yat-sen University (L102012020086). All animals were maintained under the guidance of the Committee on Animals of Sun Yat-sen University. A total of 5 \u00d7 10t-test. OS was analyzed by Kaplan-Meier methods, and P less than 0.05 was considered to be a statistically significant difference from the control.Statistical analyses were analyzed using SPSS 20.0 statistical version. The significance between two groups were compared by Student\u2019s The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request."}
+{"text": "A glioma is a tumor originating from glial cells in the central nervous system. Although significant progress has been made in diagnosis and treatment, most high-grade glioma patients are prone to recurrence. Therefore, molecular targeted therapy may become a new direction for adjuvant therapy in glioma. In recent years, many studies have revealed that circular RNA (circRNA) may play an important role in the occurrence and development of many tumors including gliomas. Our previous study found that the expression of hsa_circ_0008922 was up-regulated in glioma tissues upon RNA sequencing. The biological mechanism of circ_0008922 is still unreported in gliomas. Therefore, in this study, we preliminarily outlined the expression of hsa_circ_0008922 in glioma and explored its biological functions.The expression of hsa_circ_0008922 in forty glioma tissues and four glioma cell lines  was detected by quantitative real-time polymerase chain reaction (qRT-PCR). The correlation between hsa_circ_0008922 expression and clinicopathological features of glioma patients was evaluated by Fisher\u2019s exact test. To understand the potential function of hsa_circ_0008922 in glioma, we constructed small interfering RNA (siRNA) to hsa_circ_0008922 to downregulate its expression in glioma cell lines A172 and U251. With these hsa_circ_0008922 downregulated cells, a series of assays were carried out as follows. Cell proliferation was detected by CCK8 assay, migration and invasion were determined by wound healing assay and transwell assay, respectively. Colony formation ability was evaluated by plate clonogenic assay. Moreover, flow cytometry combined with Western blot was performed to analyze apoptosis status and the expression of apoptotic related proteins (caspase 3 and caspase 9). Finally, the possible biological pathways and potential miRNA targets of hsa_circ_0008922 were predicted by bioinformatics.We found that the expression of hsa_circ_0008922 in glioma tissues was 3.4 times higher than that in normal tissues. The expression of has_circ_0008922 was correlated with WHO tumor grade. After down-regulating the expression of hsa_circ_0008922, malignant biological behavior of glioma cells was inhibited, such as cell proliferation, colony formation, migration, and invasion. At the same time, it also induced apoptosis of glioma cells. Predicted analysis by bioinformatics demonstrated that hsa_circ_0008922 may be involved in tumor-related pathways by acting as a molecular sponge for multiple miRNAs . Finally, we integrated our observation to build a circRNA-miRNA-mRNA predictive network. Glioma is one of the most common primary malignant tumors in the central nervous system. Glioblastoma multiforme (GBM) is the highest grade (IV grade) based on the WHO classification and has high invasiveness and lethality . DespitecircRNA is a new class of non-coding RNAs with covalently closed-loop structures , which iin vitro can weaken the malignant behavior of glioma cells, such as decreasing cell proliferation, migration, invasion and inducing apoptosis. Additionally, bioinformatic analysis demonstrated that hsa_circ_0008922 may exert a molecular sponge role for some miRNAs in glioma. Therefore, these results provide a basis for further study of hsa_circ_0008922 in glioma in future.With RNA sequencing of a panel of glioma tissues and normal brain tissues we found that hsa_circ_0008922 was highly expressed in glioma tissues compared with normal brain tissues. As far as we know, there is only a report for hsa_circ_0008922 showing its abundant expression in hypopharyngeal squamous cell carcinoma and correlation with worse outcome of patients . HoweverFrom May 2018 to November 2021, glioma tissues were obtained from 40 patients with glioma who underwent surgical resection in the Department of Neurosurgery, the First Affiliated Hospital of Guangxi Medical University. All patients were confirmed to be glioma by postoperative pathology without preoperative radiotherapy or chemotherapy. At the same time, 10 non-glioma surgical brain tissue samples were collected. The brain tissue was obtained from the brain tissue needed to be removed due to the surgical approach and the focal edema tissue , and the histological characteristics were confirmed by the pathologist. All surgically resected specimens were frozen in liquid nitrogen. The specimen collection was examined and authorized by Medical Ethics Committee of Guangxi Medical University (2018087). We received written informed consent from participants of our study.n = 5) and GBM . Total RNA was extracted from glioma tissues using TRIzol reagent  according to the manufacturer\u2019s protocol and sequenced by Aksomics Biology Technology Co. Ltd . The RNA library was constructed by using KAPA Stranded RNA-Seq Library Prep Kit . The raw sequencing data (FASTQ files generated by the Illumina sequencer) is subjected to quality control to assess whether the sequencing data can be used for subsequent analysis. The expression of circRNA was quantified by calculating the Backsplice junction reads through CIRCexplorer2. Differential expression analysis was conducted using Ballgown . The circRNA with a P-value \u2264 0.05 and fold change \u2265 or \u22641.5 was considered significantly differentially expressed. The statistical power of this experimental design, calculated in RNASeqPower using the web page at https://rodrigo-arcoverde.shinyapps.io/rnaseq_power_calc/ is 0.8083.The tissues were collected as previously described in 2 incubator.The human GM cell lines  were purchased from the Chinese Academy of Sciences . Cells were cultured in complete medium ; 1% penicillin streptomycin ; DMEM medium ) and placed in a 37 \u00b0C, 5% COBased on the hsa_circ_0008922 sequence, two siRNAs (s1-hsa_circ_0008922 and s2-hsa_circ_0008922) and random siRNA (NC) fragments were ordered from Bioengineering Co., Ltd. . According to the instructions for siRNA usage, Lipofectamine 3000  was used to transfect siRNA into cells. The sequences of siRNA hsa_circ_0008922 were as follows: s 1_hsa_circ_0008922: (sense) 5\u2032-AAG AUA AGU AAC GAU GAC U-3\u2032, (antisense) 5\u2032-AGU CAU CGU UAC UUA UCU U-3\u2032; s2_hsa_circ_0008922: (sense) 5\u2032-UAA CGA UGA CUU GAA AGU A-3\u2032, (antisense) 5\u2032-UAC UUU CAA GUC AUC GUU A-3\u2032.\u2212\u0394\u0394Ct.Total RNA was extracted by Vazyme Kit (RC101-01) . RNA was reverse transcribed into cDNA by HiScript III RT SuperMix 100 for qPCR (+gDNAwiper) . ChamQSYBR qPCR Master Mix  was used for polymerase chain reaction in StepOne Real-time PCR System . The relative level was calculated with 2According to the instructions in RNase R endonuclease digestion kit , the total RNA was divided into digestion group (RNase R treatment) and control group, with 5 \u03bcg RNA (1 \u03bcg RNA required 8 U RNase R endonuclease digestion) in each group. the RNase R was not added in the control group. The above groups were incubated at 37 \u00b0C for 25 min. Then 1 \u03bcg RNA was reversed and detected by qRT-PCR. The qRT-PCR products amplified by the divergent primer were transcribed to Sangon Biotech  Co., Ltd. for TA cloning sequencing to determine the full length of PCR products. The PCR products of gDNA and cDNA were further detected by 1.3% agarose gel electrophoresis. PCR products were separated by 110 V electrophoresis for 35 min and detected by UV. GL DNA Marker 100  was used as a marker of DNA size. The sequences of primers were as follows: hsa_circ_0008922 Divergent Primer: (sense) 5\u2032-TCC ATC AGG ACC CCA GAT GTC-3\u2032, (antisense) 5\u2032-ACT GCA CAT GCA GAC TGT CAC-3\u2032; hsa_circ_0008922 convergent Primer: (sense) 5\u2032-GGG CAT CCT TCA CCC ATC TG-3\u2032, (antisense) 5\u2032-ATC TTG GTG TCA CAC AGG GC-3\u2032.4 cells were lifted and seeded into the single hole of ibidi plug-in and cultured for 22 h. After the cells were full of ibidi plug-in, the plug-in was removed. Then the DMEM complete medium was replaced with appropriate low serum DMEM complete medium (2% FBS). Photographs were taken at 0, 6, 12 h and 0, 12, 24 h, respectively.After transfection with siRNA to hsa_circ_0008922 for 48 h, 100 \u03bcL of 2 \u00d7 103 cells were inoculated in 96-well plates, five wells in each group. A total of 10 \u03bcL of CCK8 solution was added to each well and cells were incubated at 5% CO2 37 \u00b0C for 2 h. The optical density (OD) values at 450 nm were measured at 0, 24, 48, 72 and 96 h, respectively.CCK8 detection kit  was used to detect the proliferation of glioma cells treated with siRNA. A total of 100 \u03bcL of 4 \u00d7 104 cells) was inoculated in the upper chamber of transwell chamber, and DMEM (500 \u03bcL) containing 20% FBS was added in the lower chamber of transwell chamber. They were incubated at 37 \u00b0C for 24 h. Then, 4% methanol was applied to fix the cells for 30 min followed by staining with 0.1% Crystal Violet Stain solution  for 30 min. Stained cells were photographed by inverted microscope  and counted manually.The migration and invasion ability of glioma cells were evaluated using a Transwell chamber  with or without matrix glue (Absin). Forty-eight hours after transfection, a 200 \u03bcL cell suspension . After glioma cells were transfected, the cells were completely digested with EDTA-free trypsin and washed twice with PBS buffering at 4 \u00b0C. The cells were dyed with 5 \u03bcL FITC Annexin V and 5 \u03bcL propidium iodide (PI) for 15 min at room temperature in the dark and then observed by flow cytometry . FlowjoV 1.8.1  was used to analyze the results.Western Blot was used to detect apoptosis-related proteins in glioma cells. After transfecting siRNA into cells and culturing cells for 48 h, the cells were harvested and lysed with protease inhibitor and phosphatase inhibitor to extract proteins. Then, the protein was separated by SDS-PAGE and transferred to PVDF membrane. 1:1,000 diluted primary antibodies  were incubated overnight at 4 \u00b0C. After completing incubation of primary antibodies, the secondary antibodies in 1:5,000 dilution were incubated at room temperature .The clone formation rate indicated the proliferation capacity and cell population dependency .3 of siRNA treated cells were inoculated in the 6-well plates at 37 \u00b0C in 5 % CO2 for 11 days. After culture, cells were fixed with 4% formaldehyde for 30 min and stained with 0.1% Crystal Violet Stain solution for 12 min. The photograph was recorded with a stereomicroscope .3 \u00d7 10https://starbase.sysu.edu.cn/agoClipRNA.php?source=circRNA) was used to predict hsa_circ_0008922 downstream miRNA. OECloud tools (https://cloud.oebiotech.cn/task/detail/array_miranda_plot/?version=old) was used to predict binding fraction and free energy. miRDB (http://mirdb.org/index.html), miRWalk , Linkedomics (http://linkedomics.org/login.php) were used to predict miRNA downstream mRNA, respectively. Cytoscape was used to draw the interaction network of hsa_circ_0008922 with its downstream miRNA, and the interaction network of miRNA with its downstream mRNA (https://cloud.oebiotech.cn/task/detail/enrichment-oehw/?id=57).ENCORI ,with a junction of 161 nt (http://www.circbank.cn/). We designed divergent primers for hsa_circ_0008922 and convergent primers for MATR3 mRNA. As shown in In order to seek circRNA aberrantly expressed in glioma, we performed RNA sequencing of glioma tissues. Here, we selected a novel circRNA, hsa_circ_0008922, from circRNA identified above because there was no report in glioma except only one study in hypopharyngeal squamous cell carcinoma . As showhttp://yang-laboratory.com/circpedia/; circRNADb, circatlas.biols.ac.cn; TSCD, http://gb.whu.edu.cn/TSCD/; MiOncoCirc, https://mioncocirc.github.io/; and circRNADisease, http://cgga.org.cn:9091/circRNADisease/). Therefore, we tested our clinical samples as a primary study for hsa_circ_0008922 expression. As shown in n = 40) and normal tissues (n = 10) was detected by qRT-PCR. We found that the relative expression of hsa_circ_0008922 was significantly higher in gliomas than in normal tissues (P = 0.027). It was found that in low-grade tumor and high-grade tumor the expression of hsa_circ_0008922 was significantly higher than normal tissues, respectively . As shown in in vitro. We first detected the relative expression of hsa_circ_0008922 in a panel of glioma cells  by qRT-PCR. Considering up-regulation of hsa_circ_0008922 correlated with some of clinicopathological parameters of glioma patients, we further conducted loss-of-function experiments to explore the biological function of has_circ_0008922 in vitro. Transfecting cells with two siRNA fragments, s1-hsa_circ_0008922 and s2-hsa_circ_0008922, respectively, it was found that s2-hsa_circ_0008922 had the best effect of -hsa_circ_0008922 down-regulation in the time point of forty-eight hours   \u20137C. Furt < 0.05)  and 7E a < 0.05)  and 7F thttps://cloud.oebiotech.com/task/detail/array_miranda_plot/ . With the combined analysis of three databases  we gained mRNAs that were all related to glioma. As shown in Generally, circRNA regulates gene transcription and expression by serving as a sponge of miRNA. circRNA-miRNA-mRNA regulatory axis has been widely accepted to illustrate the biological functions of circRNA . To undeBased on the ceRNA hypothesis , namelyGlioma is a very common and aggressive intracranial tumor, which leads to brain dysfunction in many patients and poses a serious threat to the health of patients. At present, the traditional treatment of glioma is the comprehensive treatment in surgery, radiotherapy, and chemotherapy, but the prognosis is still poor, and the postoperative recurrence rate is as high as 90% . HoweverIn early studies, circRNA was considered as \u201cnoise\u201d and had no biological function. However, in recent years, it was found that circRNAs mostly distributed in the brain and had significant biological functions, especially in cancer . In 2019Through RNA sequencing, we found that hsa_circ_0008922 highly expressed in glioma tissues. hsa_circ_0008922 is derived from exon 3 and exon 4 of Matrin3 (MATR3). MATR3 is a host gene of hsa_circ_0008922 and it is derived from human chromosome 5 and highly expresses in the brain . MATR3 pPrevious studies have shown that circRNA is often used as a miRNA sponge to regulate the expression of miRNA in various cancers, forming a circRNA-miRNA-mRNA network to regulate the expression of downstream target genes and affect the development of cancer . For exaIt was interesting that a gene called DNA polymerase lambda (POLL) may be regulated by above mentioned three miRNAs , simultaneously, and its expression may be negatively correlated with the expression of these three miRNAs. Now, there is a report that POLL is differentially expressed in IDH (lactate dehydrogenase) mutant and wild type carriers, relating to the survival rate of patients with LGG . In futue.g., microRNAs in cancer signaling pathway, glioma signaling pathway. It was also included in cell cycle signaling pathway, Ras signaling pathway which is a pathway closely related to glioma, activating RAS mutation to increase gliogenesis  resulting from removing 5\u2032 and 3\u2032 splice sequences and filtering out excessively short fragments \u226420 bp. Mapped: reads are mapped to the reference genome. mtRNAs: Ratio of the number of fragments aligned to the mitochondrial genome to the number of Pairs. rRNAs: Ratio of the number of fragments matched to the rRNA to the number of Pairs. Unmapped: reads are not mapped into the reference genome.Click here for additional data file.10.7717/peerj.14552/supp-4Supplemental Information 4Click here for additional data file.10.7717/peerj.14552/supp-5Supplemental Information 5PCR products amplified by divergent primers were used for TA cloning and sequencing to confirm that hsa_circ_0008922 had the reverse splicing siteClick here for additional data file.10.7717/peerj.14552/supp-6Supplemental Information 6Raw data for Click here for additional data file.10.7717/peerj.14552/supp-7Supplemental Information 7Raw data for Click here for additional data file.10.7717/peerj.14552/supp-8Supplemental Information 8Click here for additional data file.10.7717/peerj.14552/supp-9Supplemental Information 9Click here for additional data file."}
+{"text": "Nature Cell Biology 10.1038/s41556-022-00976-y, published online 12 September 2022.Correction to: The authors noticed a mistake in Supplementary Table 1 after publication. The sequences listed in rows 44\u201346 for oligos sgRagC_5\u2032_rev, sgRagC_3\u2032_for and sgRagC_3\u2032_rev were not correct, due to copy\u2013paste errors introduced during file preparation. The correct sequences for these oligos are now presented in rows 44\u201346 in the corrected Supplementary Table 1 online. All other oligo sequences were double-checked and remain accurate. The respective plasmid constructs used to knock out RagC as well as the resulting cell lines are also correct. The authors sincerely apologize for this error."}
+{"text": "Cervical cancer is the fourth common cancer among women. Its prognosis needs our more attention. Our purpose was to identity new prognostic gene sets to help other researchers develop more effective treatment for cervical cancer patients and improve the prognosis of patients.  We used gene set variation analysis (GSVA) to calculate the enrichment scores of gene sets and identified three subtypes of cervical cancer through the Cox regression model, k-means clustering algorithm, and nonnegative matrix factorization method (NMF). Chi-square test was utilized to test whether a certain clinical characteristic is different among divided subtypes. We further screened the prognostic gene sets using differential analysis, univariate Cox regression analysis, and least absolute shrinkage and selection operator (LASSO) regression. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to analyze which pathways and function the genes from screened gene sets enriched. Search Tool for the Retrieval of Interacting Genes (STRING) was used to draw the protein-protein interaction network, and Cytoscape was used to visualize the hub genes of protein-protein interaction network. \u2217\u20092.617 + N_HALLMARK_ANGIOGENESIS\u2217\u20094.860. Survival analysis presented that in these two gene sets, high enrichment scores were all significantly related to worse overall survival. The hub genes from T gene set included CXCL1, CXCL2, CXCL8, ALDOA, TALDO1, LDHA, CCL4, FCAR, FCER1G, SAMSN1, LILRB1, SH3PXD2B, PPM1N, PKM, and FKBP4. As for N gene sets, the hub genes included ITGAV, PTK2, SPP1, THBD, and APOH.  We identified three novel subtypes of cervical cancer in The Cancer Genome Atlas (TCGA) samples and validated in Gene Expression Omnibus (GEO) samples. There were significant variations between the three subtypes in histological type, T stage, M stage, and N stage. T_GSE36888_UNTREATED_VS_IL2_TREATED_STAT5_AB_KNOCKIN_TCELL_2H_UP and N_HALLMARK_ANGIOGENESIS were screened prognostic gene sets. The prognostic model was as follows: riskScore = T_GSE36888_UNTREATED_VS_IL2_TREATED_STAT5_AB_KNOCKIN_TCELL_2H_UP Three novel subtypes and two prognostic gene sets were identified. 15 hub genes for T gene set and 5 hub genes for N gene set were discovered. Based on these findings, we can develop more and more effective treatments for cervical cancer patients. Based on the gene enriched pathways, we can development specific drugs targeting the pathways. Globally, cervical cancer recently has been ranked as the fourth most common cancer against women, with estimated 570,000 cases and 311,000 deaths in 2018 , 5. TherFortunately, as it has been observed in many studies in recent years, genetic factors can be closely related with the susceptibility and tumorigenesis of cervical cancer , 7, and The gene set variation analysis (GSVA) is a nonparametric and unsupervised gene set enrichment method, assaying the variation of gene set enrichment over sample population, thus condensing gene expression profiles into gene set or pathway summary . Using GIn this study, we selected the sequencing data of cervical cancer patients from several cohort in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases for prognostic analysis. The cervical cancer was automatically classified into 3 subtypes, and they revealed different features in some clinical traits like tumor T, N, and M stages. Moreover, the data were screened to identify the prognostic relevant pathways and gene sets in tumor and nontumor tissues by differential analysis, univariate Cox regression analysis, and the least absolute shrinkage and selection operator (LASSO) regression. As a result, one tumor (T gene set) and one nontumor (N gene set) gene sets were found, and high enrichment scores of them were all strongly associated with poor overall survival. Functional enrichment analysis and protein-protein network analysis for the hub genes were conducted to investigate the possible regulatory mechanisms. The results indicated that the genes in T gene set were connected with immune activity and metabolic process, while the genes in N gene set were related to angiogenesis and protein regulation. Our findings may be helpful for uncovering the biomarkers of effective prognosis and potential therapeutic targets for precise treatment of cervical cancer patients.http://portal.gdc.cancer.gov). The clinical data of cervical cancer in TCGA database [http://xena.ucsc.edu/). The cervical cancer datasets GSE44001 were downloaded from Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) [The transcriptome data from TCGA-CESC (HT-Sequence-FPKM) were downloaded from TCGA website (database  were dowov/geo/) . The datov/geo/)  for GSVAGSVA can detect the slight pathway activity changes within large number of gene sets . It tranThe CancerSubtypes package  in R wasFirst of all, we used the Cox regression model to do the biological feature selection through CancerSubtypes package in R by \u201cFSbyCox\u201d function. Then, NbClust package was utilized to discover the optimal number of clusters and visualize it. The nonnegative matrix factorization (NMF) was used to reduce the dimensions of complex data and provide a powerful assistance for clustering . Based oThe cluster heatmap was drawn on combined clinicopathological data and enrichment scores of gene sets through pheatmap package. The cluster heatmap presented the correlation between clinical characteristics and divided subtypes. Chi-square test was utilized to test whether a certain clinical characteristic is different among divided subtypes.The differential analysis of gene sets was performed with limma package and visualized through VennDiagram package. Then, based on the number of differential gene sets we found, the heatmap of represented gene sets in divided subtypes was drawn through pheatmap package in R.p < 0.05) within TCGA cohort. Then, combined with survival data in GEO cohort, we further explored central parts of the screened prognosis-related gene sets using LASSO regression [i=1nCoefi\u2217xi. Coef is the coefficient of the Cox regression analysis.First of all, the univariable Cox regression analysis and log-rank test were exerted to screen potentially prognosis-related gene sets  enrichment analysis  was usedWe applied the Search Tool for the Retrieval of Interacting Genes (STRING) database to help us construct the protein-protein interaction (PPI) networks for genes from the specific gene sets in obtained risk model . Genes fp is lower 0.05.All the analyses were conducted using R (version 4.1.2). The classified data were summarized in the form of counts (percentage). The results were considered to be significant when http://portal.gdc.cancer.gov) and then got 4299 gene set expression data. The enrichment scores of 4299 immune-related gene sets were calculated by GSVA and were shown in the following heatmap (We first downloaded the transcriptome data (N = 3 and T = 306) of cervical cancer from TCGA website ( heatmap . In normk = 3), and the result is exhibited in Therefore, we paid attention to classify cervical cancer patients into different subtypes. There is one patient who was removed for lack of prognostic information in clinical data (307 remained). Through \u201cavereps\u201d function in limma package, a microarray data object was condensed, and there were 304 gene set expression samples left. Based on the enrichment scores calculated by GSVA and the prognostic information, we used the Cox regression to do the feature selection through CancerSubtypes package. We got the optimal number of clusters   . Finally < 0.05) .As displayed in p < 0.05), including N_HALLMARK_ANGIOGENESIS, N_HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION, and T_GSE36888_UNTREATED_VS_IL2_TREATED_STAT5_AB_KNOCKIN_TCELL_2H_UP , protein-protein interaction (PPI) networks were constructed for the two prognostic gene sets: T and N gene sets, respectively , a rare and aggressive subtype of cervical cancer , decreasThen, two prognosis-related gene sets, T gene set: T_GSE36888_UNTREATED_VS_IL2_TREATED_STAT5_AB_KNOCKIN_TCELL_2H_UP and N gene set: N_HALLMARK_ANGIOGENESIS, were identified through LASSO method. Moreover, in these two gene sets, high enrichment scores were all significantly related to worse overall survival time. N gene set contains genes upregulated during formation of blood vessels . TherefoOur study also found that the genes from T gene set were enriched in cytokine-cytokine receptor interaction, IL-17 signaling pathway, and viral protein interaction with cytokine and cytokine receptor pathways. These pathways are all related to metabolism and immune activities. Genes from T gene set also participate in response to lipopolysaccharide, response to molecule of bacterial origin, cytokine receptor binding, receptor ligand activity, signaling receptor activator, and neutrophil activation. For nontumor genes, they participate in glycosaminoglycan binding, heparin binding, and sulfur compound binding and are enriched in focal adhesion, PI3K-Akt signaling pathway, and proteoglycans in cancer.The hub genes from T gene set included CXCL1, CXCL2, CXCL8, ALDOA, TALDO1, LDHA, CCL4, FCAR, FCER1G, SAMSN1, LILRB1, SH3PXD2B, PPM1N, PKM, and FKBP4. The hub genes of the clusters were mainly linked to immune reaction and metabolic process. It is reported that CXCL1, CXCL2, and CXCL8 are related to the tumor growth in cervical cancer , 44. ALDAs for N gene sets, the hub genes included ITGAV, PTK2, SPP1, THBD, and APOH. These hub genes were mostly related to protein regulation. PTK2  SPP1 5050 were aThere were some limitations in our study. First of all, there were only 3 adjacent nontumor samples in TCGA cohort, and the others were tumor samples. Therefore, these results need to be validated after recruiting more normal people. Second, the two prognostic gene sets were not validated within clinical samples. Further work needs to be done to focus on investigating the clinical value of these gene sets. As a result, more researches should be done to verify these results.In conclusion, our study discovered three novel subtypes of cervical cancer and identified two prognostic gene sets of cervical cancer. The hub genes of the two prognostic gene sets were also identified. Our study presented a theoretical foundation for other researchers to find better therapy strategies for cervical cancer patients. Based on these findings, we can develop more and more effective treatments for cervical cancer patients. Based on the gene-enriched pathways, we can develop specific drugs targeting the pathways."}
+{"text": "Accumulating evidence has demonstrated the roles of circular RNAs (circRNAs) in hepatocellular carcinoma (HCC); however, their roles in HCC need to be further studied. Through high-throughput human circRNA microarray analysis of HCC and adjacent normal tissues, we identified hsa_circ_0051040 as a novel candidate circRNA for the diagnosis and treatment of HCC. In this study, we found that hsa_circ_0051040 was overexpressed in HCC tissues and cell lines and that its expression was correlated with poor prognosis. Knockdown of hsa_circ_0051040 inhibited the migration, invasion, and proliferation of HCC cells in vitro and in vivo, whereas overexpression of hsa_circ_0051040 had the opposite effects. Moreover, our data demonstrated that hsa_circ_0051040 acted as a sponge for miR-569 to regulate ITGAV expression and induce EMT progression. Our findings indicated that hsa_circ_0051040 promotes HCC development and progression by sponging miR-569 to increase ITGAV expression. Thus, hsa_circ_0051040 is a good candidate as a therapeutic target. Hepatocellular carcinoma (HCC) is one of the most common primary liver malignancies and the second leading cause of cancer-related death worldwide . Many paCircular RNAs (circRNAs), noncoding RNAs, are covalently linked to form a closed circular structure without a 5\u2032 cap and 3\u2032 poly(A) tail . CircRNAIn our study, we analyzed the expression profile of circRNAs in HCC tissues through microarrays and identified a novel dysregulated circRNA, hsa_circ_0051040, whose expression was increased in HCC tissues. Additionally, the expression of hsa_circ_0051040 was closely related to HCC patient prognosis. We further demonstrated that hsa_circ_0051040 acted as a sponge of miR-569 to upregulate the expression of integrin alpha V (ITGAV), induce epithelial\u2013mesenchymal transition (EMT) and consequently promote the proliferation, invasion and migration of HCC cells. Thus, hsa_circ_0051040 may be a novel therapeutic target for HCC.Fresh HCC tissues and adjacent nontumor sites were obtained from patients who were diagnosed with primary liver cancer prior to any therapy at Affiliated Nantong Hospital 3 of Nantong University between 2016 and 2020. The plasma and tissue samples were not obtained from the same set of individuals. Plasma samples of 186 HCC patients, 24 benign liver disease patients, and 122 healthy people were also obtained from Nantong Third People\u2019s Hospital . The stu2 at 37 \u00b0C.The human HCC cell lines PLC/PRF/5, SK-HEP-1, HuH-7, Li-7, Hep3B2.1-7 and the normal human liver LO2 cell lines were purchased from the Chinese Academy of Sciences Cell Bank . PLC/PRF/5, SK-HEP-1 and HuH-7 cells were cultured in MEM ; Li-7 and Hep3B2.1-7 cells were cultured in RPMI 1640 medium  supplemented with 10% fetal bovine serum  in a humidified atmosphere of 5% COCircRNA microarray analysis of five paired tissue samples  was performed with a CapitalBio Technology Human CircRNA Array v2. The CapitalBio Technology Human CircRNA Array v2 was designed with four identical arrays per slide (4 \u00d7 180 K format), with each array containing probes for approximately 170,340 human circRNAs. Each circRNA was simultaneously detected by a long probe and a short probe.Cy3-labeled probe sequences targeting hsa_circ_0051040 and FAM-labeled probes targeting miR-569 were constructed by GenePharma . Nuclei were stained with DAPI. The probe signals were detected by a Fluorescent in Situ Hybridization Kit  according to the manufacturer\u2019s protocol. Images were acquired using fluorescence microscopy. Sequences of the FISH probes are listed in The EdU incorporation assay was carried out with an EdU detection Kit  to assess cell proliferation viability according to the manufacturer\u2019s protocol. All images were acquired with an Olympus IX73-FL-PH fluorescence microscope . The experiments were performed at least three times independently with triplicate samples.\u2212\u0394\u0394Ct method. Sequences of the qRT-PCR primers are listed in Total RNA was isolated using TRIzol reagent  according to the manufacturer\u2019s protocol. The concentration of the purified total RNA was detected using a NanoDrop spectrophotometer . For mRNAs and circRNAs, cDNA was synthesized using a HiScript II 1st Strand cDNA Synthesis Kit . For RNase R treatment, 1 \u03bcg of total RNA was incubated with 0.1 \u03bcL RNase R (20 U/\u03bcL) and 1 \u03bcL 10\u00d7 RNase R Reaction Buffer at 37 \u00b0C for 15 min. For miRNA, cDNA was synthesized using a miRNA reverse transcription PCR kit . Genomic DNA (gDNA) was isolated with a QIAamp DNA Mini Kit . qRT-PCR was performed in a Bio\u2013Rad CFX Real-Time PCR System. GAPDH was used as an endogenous control for circRNA and mRNA. U6 and 18S rRNA were used as endogenous controls for tissue and plasma miRNA, respectively. The relative RNA expression levels were calculated using the 2Proteins were extracted in RIPA Lysis buffer  with protease and phosphatase inhibitors. Proteins were separated on 10% SDS-PAGE gels and then transferred onto nitrocellulose membranes . After blocking in nonfat milk, membranes were incubated overnight at 4 \u00b0C with a primary antibody and subsequently with a secondary antibody for 1 h at room temperature. The antibodies used in Western blotting were as follows: horseradish peroxidase (HRP)-conjugated anti-\u03b2-actin , anti-ITGAV , anti-E-cadherin , anti-Vimentin , anti-Snail , and HRP-conjugated goat anti-rabbit IgG . The bands were visualized by an enhanced chemiluminescence detection system , analyzed by ImageJ software and normalized to the internal control \u03b2-actin.For the wound-healing assay, transfected cells at a density of 100% were seeded into 6-well plates with serum-free medium. Streaks were created across the monolayer using a sterile 10 \u03bcL pipette tip. Images of cell migration were acquired 0 and 24 h after wounding using a microscope . The experiments were performed at least three times independently with triplicate samples.Transfected cells were plated in 6 cm dishes and fixed in 1% paraformaldehyde after incubation for 7 days. After staining with 0.1% crystal violet, cell colonies were counted and analyzed under a light microscope.SK-HEP-1 and PLC/PRF/5 cells were seeded into 24-well plates and transfected with luciferase reporter vector  and the miR-569 mimic or miR-NC using Lipofectamine 3000 reagent. After 48 h of incubation, firefly and Renilla luciferase activities were quantified with a dual luciferase reporter assay (Promega) according to the manufacturer\u2019s protocol. Firefly luciferase activity was normalized to Renilla luciferase activity.To construct the hsa_circ_0051040 overexpression plasmids, human hsa_circ_0051040 cDNA was synthesized and cloned into the pcDNA3.1 vector GenePharma . Empty vector was used as the negative control. SiRNAs targeting hsa_circ_0051040  and the corresponding negative control siRNA were synthesized by GenePharma . Hsa_circ_0051040 knockdown lentiviral vectors pGLV3-H1-GFP-Puro (LV-circ0051040) and the corresponding negative controls (LV-NC) were designed and synthesized by GenePharma . Mimics and inhibitor of miR-569 were synthesized by RiboBio .Biotin-labeled hsa_circ_0051040 and a negative control probe were designed and synthesized by GenePharma . Briefly, hsa_circ_0051040-high-expressing PLC/PRF/5 and SK-HEP-1 cells were fixed with 1% formaldehyde for 10 min, and the cells were then lysed. After centrifugation, 50 \u03bcL of the supernatant was retained as input, and the rest was incubated with streptavidin-coated magnetic beads  conjugated with biotin-labeled probe overnight at 4 \u00b0C. The pull-down product was washed with wash buffer, and total RNA was then extracted to detect the expression of hsa_circ_0051040 and miRNAs by qRT-PCR. The experiments were performed at least three times independently with triplicate samples.5 cells. Cells were resuspended in 200 \u03bcL of serum-free MEM medium and seeded into the upper chambers  with or without a Matrigel-coated membrane . MEM (600 \u03bcL) containing 20% FBS was placed into the bottom chambers as the attractant. After incubation for 48 h, the cells that migrated or invaded across the membrane were fixed with 4% paraformaldehyde, stained with 0.1% crystal violet solution for 30 min, and visualized under a microscope . Cells were counted in five randomly selected microscopic fields. The experiments were performed at least three times independently with triplicate samples. The experiments were performed at least three times independently with triplicate samples.Transwell assays were used to evaluate the invasion and migration abilities of cells in vitro. Briefly, transfected SK-HEP-1 or PLC/PRF/5 cells were plated in 24-well plates at a density of 1 \u00d7 10Paraffin sections (5 \u03bcm) of tissue samples were subjected to H&E and IHC staining. For IHC staining, paraffin sections were incubated with primary antibodies against Ki67 or ITGAV (1:100) overnight at 4 \u00b0C. After washing in PBS, the sections were incubated with anti-mouse HRP-conjugated secondary antibody for 1 h at room temperature. The paraffin sections were stained with DAB and hematoxylin and covered with coverslips for microscopic observation.7) with stable knockdown of hsa_circ_0051040 or control cells resuspended in 100 \u03bcL of PBS were subcutaneously injected into the flanks of 6-week-old male BALB/c nude mice . Tumor growth was monitored every week by measuring the tumor width and length with calipers. Mice were sacrificed, and the tumors were excised, fixed with 4% paraformaldehyde, and processed for H&E and IHC staining. The liver metastasis model was established by spleen injection with two transfection groups of PLC/PRF/5 cells. After 30 days, we sacrificed the mice and evaluated the liver metastasis ability. H&E staining was performed to analyze the formation of metastasis. All procedures were approved by the Animal Care Committee of Nantong University.PLC/PRF/5 cells . p values <0.05 were considered significant.GraphPad Prism 7.0  and SPSS version 17.0 software were used to conduct statistical analyses. Student\u2019s p < 0.05; (2) signal value greater than 1000; and (3) upregulated in each pair. Among these significantly upregulated circRNAs, hsa_circ_0051040 was selected as a candidate for further experiments. We examined the expression level of hsa_circ_0051040 in 91 HCC and paired adjacent normal tissues using qRT-PCR with divergent primers. The results showed that the hsa_circ_0051040 expression level in HCC tissues was significantly higher than that in paired adjacent normal tissues  to predict the potential miRNAs that bind to hsa_circ_0051040 and found seven miRNAs  from the intersection of the two databases A. Next, https://cancergenome.nih.gov/, accessed on 7 February 2022) database. The results revealed that the ITGAV expression level was significantly higher in HCC patients than in normal controls  were used for gene expression profiling to analyze the differentially expressed mRNAs. We then chose the 11 most downregulated genes for further target gene selection. We found that ITGAV was the target gene of miR-569, which was most significantly downregulated by hsa_circ_0051040 knockdown in HCC A. To detcontrols B. The pacontrols C. Moreovcontrols D. Since controls E. The ducontrols F,G. To fcontrols H,I and dcontrols J. MiR-56controls K. Moreovcontrols L. EMT plcontrols M. These To verify whether hsa_circ_0051040 exerts its promotive effect on HCC by sponging miR-569, we further performed rescue assays to examine the functional interaction between hsa_circ_0051040 and miR-569. The results showed that the miR-569 inhibitor significantly reversed the inhibition of cell migration, invasion and proliferation induced by hsa_circ_0051040 knockdown A\u2013C. ThesTo investigate the effect of hsa_circ_0051040 on tumor growth in vivo, PLC/PRF/5 cells transduced with NC lentiviral vectors (LV-NC) or hsa_circ_0051040 knockdown lentiviral vectors (LV-circ0051040) were subcutaneously injected into nude mice. The results revealed that the tumor volume in the LV-circ0051040 group was significantly reduced A. qRT-PCTaken together, these findings provide evidence that hsa_circ_0051040 regulates ITGAV expression through miR-569, contributing to the proliferation and metastasis of HCC J.Recently, studies concerning the functions of circRNAs have attracted increasing attention. Many studies have revealed a close correlation between circRNA expression and multiple diseases and pathological processes, especially tumorigenesis ,17,18. HTaken together, our findings showed that hsa_circ_0051040 functions as an oncogene and plays a notable role in the progression of HCC. Our study further revealed that the hsa_circ_0051040/miR-569/ITGAV axis is a new ceRNA regulatory network and promotes EMT progression in HCC. However, the role of the hsa_circ_0051040/miR-569/ITGAV axis in other cancers needs to be further investigated. In summary, our data demonstrated that hsa_circ_0051040 can sponge miR-569 to increase the expression of ITGAV and thereby promote EMT in HCC. These findings suggest that hsa_circ_0051040 might be a novel diagnostic and therapeutic target for HCC."}
+{"text": "Mesocricetus auratus) has been suggested as a useful mammalian model for a variety of diseases and infections, including infection with respiratory viruses such as SARS-CoV-2. The MesAur1.0 genome assembly was generated in 2013 using whole-genome shotgun sequencing with short-read sequence data. Current more advanced sequencing technologies and assembly methods now permit the generation of near-complete genome assemblies with higher quality and greater continuity.The Syrian hamster (M. auratus genome (BCM_Maur_2.0) using Oxford Nanopore Technologies long-read sequencing to produce a chromosome-scale assembly. The total length of the new assembly is 2.46 Gb, similar to the 2.50-Gb length of a previous assembly of this genome, MesAur1.0. BCM_Maur_2.0 exhibits significantly improved continuity, with a scaffold N50 that is 6.7 times greater than MesAur1.0. Furthermore, 21,616 protein-coding genes and 10,459 noncoding genes are annotated in BCM_Maur_2.0 compared to 20,495 protein-coding genes and 4,168 noncoding genes in MesAur1.0. This new assembly also improves the unresolved regions as measured by nucleotide ambiguities, where \u223c17.11% of bases in MesAur1.0 were unresolved compared to BCM_Maur_2.0, in which the number of unresolved bases is reduced to 3.00%.Here, we report an improved assembly of the Access to a more complete reference genome with improved accuracy and continuity will facilitate more detailed, comprehensive, and meaningful research results for a wide variety of future studies using Syrian hamsters as models. Mesocricetus auratus, NCBI:txid10036) has been used in biomedical research for decades because it is a good model for studies of cancer [The Syrian hamster -dependent type I (IFN-I) and type III interferon (IFN-III) signaling . IFN-I sectivity . Because349665.1) is typical of those produced at that time, containing 237,699 separate contigs with contig N50 of 22,512\u00a0bp. The quality and research potential of the existing Syrian hamster genome is limited by the technology that was available at the time of its development; e.g., the cluster of IFN-I genes was not resolvable with this technology. In this Data Note, we report the production of a new Syrian hamster reference genome that was sequenced using long-read methods on the Oxford Nanopore Technologies (ONT) PromethION platform and assembled into highly contiguous chromosomes using a combination of Flye [The currently available reference genome sequence for the Syrian hamster was produced in 2013 using a whole-genome shotgun sequencing approach implementing short-read sequencing technology. The resulting MesAur1.0 reference sequence  that was purchased from Charles River, Inc. . All procedures were performed in accordance with the guidelines set by the Institutional Animal Care and Use Committee at the University of Wisconsin-Madison. The protocol was approved by the Institutional Animal Care and Use Committee at the University of Wisconsin-Madison (protocol No. V00806). Data from this individual are available in NCBI BioProject PRJNA705675, BioSamples SAMN18096087, and SAMN18096088. Qiagen AllPrep DNA/RNA Mini kits were used to extract DNA from frozen liver tissue samples while Qiagen Blood and Cell Culture DNA Midi Kits were used for extractions from frozen kidney tissue samples. Ultra-high molecular weight DNA for optical mapping was purified from frozen liver using an Animal Tissue DNA Isolation Kit from Bionano Genomics, Inc. .RRID:SCR_017987) using neuronal network\u2013based software .We prepared 3 separate genomic DNA isolates from the same Syrian hamster (BioSample SAMN18096087). These aliquots were sheared to distinct target fragment lengths  to assess the effect of fragment size on flow cell yield and improve efficiency. The 2 shorter fragment libraries were sheared using Covaris gTube and the 30-kb\u00a0targeted size library was fragmented with Diagnode Megarupter 3, all following manufacturer's recommendations. The Oxford Nanopore sequencing libraries were prepared using the ONT 1D sequencing by ligation kit (SQK-LSK109). Briefly, 1\u20131.5 \u03bcg of fragmented DNA was repaired with the NEB FFPE repair kit , followed by end repair and A-tailing with the NEB Ultra II end-prep kit. After a clean-up step using AMPure beads, the prepared fragments were ligated to ONT-specific adapters via the NEB blunt/TA master mix kit. Each library underwent a final clean-up and was loaded onto a PromethION flow cell per manufacturer's instructions. One library was sequenced per flow cell with standard parameters for 72\u00a0hr. Base-calling was done onboard the PromethION instrument  was used to generate standard PCR-free Illumina paired-end sequencing libraries. Libraries were prepared using KAPA Hyper PCR-free library reagents  in Beckman robotic workstations (Biomek FX and FXp models). Total genomic DNA was sheared into fragments of \u223c200\u2013600\u00a0bp in a Covaris E220 system (96-well format) followed by purification of the fragmented DNA using AMPure XP beads. A double size selection step was used, with different ratios of AMPure XP beads, to select a narrow size band of sheared DNA molecules for library preparation. DNA end-repair and 3\u2032-adenylation were then performed in the same reaction, followed by ligation of the barcoded adaptors to create PCR-free libraries. The resulting libraries were evaluated using the Fragment Analyzer  to assess library size and presence of remaining adaptor dimers. This was followed by qPCR assay using KAPA Library Quantification Kit and their SYBR FAST qPCR Master Mix to estimate the size and quantify fragment yield.RRID:SCR_016387, San Diego, CA) using the S4 reagent kit (300 cycles) to generate 2 \u00d7 150\u00a0bp\u00a0paired-end reads. The final concentration of the libraries loaded on flow cells was 400\u2013450 pM. Briefly, the libraries were diluted in an elution buffer and denatured in sodium hydroxide. The denatured libraries were loaded into each lane of the S4 flow cell using the NovaSeq Xp Flow Cell Dock. Each lane included \u223c1% of a PhiX control library for run quality control.Sequencing was performed on the NovaSeq 6000 Sequencing System  [de novo genome assembly. Given the potential sequence error rate of PromethION reads, it is advisable to use higher quality Illumina short reads mapped to an assembly to correct sequence errors in initial contigs. Consequently, we used Pilon software v. 1.23  [We generated 221 Gb of sequence data using the ONT PromethION platform . This represents an anticipated 88\u00d7 coverage of the expected 2.5-Gb Syrian hamster genome. The raw sequencing reads exhibited an N50 length of 15,730\u00a0bp. We used the Flye assembler v2.8.1   to gener_014731)  with defUltra-high molecular weight (UHMW) DNA was extracted following manufacturer's guidelines (Bionano Prep SP Tissue and Tumor DNA Isolation protocol) from frozen liver tissues obtained from the same animal used for ONT PromethION sequencing (SAMN18096087). Briefly, a total of 15\u201320 mg\u00a0of liver tissue was homogenized in cell buffer and digested with Proteinase K. DNA was precipitated with isopropanol and bound with nanobind magnetic disk . Bound UHMW DNA was resuspended in the elution buffer and quantified with Qubit dsDNA assay kits . DNA labeling was performed following manufacturer's protocols (Bionano Prep Direct Label and Stain protocol). Direct Labeling Enzyme 1 (DLE-1) reactions were carried out using 750\u00a0ng of purified UHMW DNA. Labeled DNA was loaded on Saphyr chips for imaging. The fluorescently labeled DNA molecules were imaged sequentially across nanochannel arrays (Saphyr chip) on a Saphyr instrument . Effective genome coverage of >100\u00d7 was achieved for all samples. All samples also met the following quality control metrics: labelling density of \u223c15/100\u00a0kb; filtered (>15 kb)\u00a0N50\u00a0> 230\u00a0kb; map rate >\u00a070%.https://bionanogenomics.com/wp-content/uploads/2018/04/30142-Bionano-Access-Software-User-Guide.pdf). Hybrid scaffolding was performed using Bionano's custom software program implementing the following steps: (i) generate in silico maps for sequence assembly; (ii) align in silico sequence maps against Bionano genome maps to identify and resolve potential conflicts in either dataset; (iii) merge the non-conflicting maps into hybrid scaffolds; (iv) align sequence maps to the hybrid scaffolds; and (v) generate AGP and FASTA files for the scaffolds. Pairwise comparisons of all DNA molecules were made to generate the initial consensus genome maps (*.cmap). Genome maps were further refined and extended with best matching molecules. Optical map statistics were generated using Bionano software, producing the Bionano Molecule Quality Report.Genome analysis of the resulting data was performed using software solutions provided by Bionano Genomics. Briefly, automated optical genome mapping specific pipelines consisting of Bionano Access v1.4.3 and Bionano Solve v. 3.6.1 were used for data processing  was 0.2341 Mb, and the mean label density (scaffolds \u2265150\u00a0kb) was 17.40/100\u00a0kb. This yielded an effective molecule coverage with optical mapping information of 125.38\u00d7. The optical mapping analysis identified 84 conflicts with the prior Flye/Pilon scaffolds, and these initial scaffolds were broken at those 84 sites. The completed assembly was submitted to NCBI and is available under accession GCA_017639785.1.NCBI performed gene annotation using RNA-Seq data from multiple tissues, including lung, trachea, brain, olfactory bulb, and small intestine, that are targets for SARS-CoV-2 infection (NCBI BioProject PRJNA675865) .RRID:SCR_001228) [RRID:SCR_018171) [RRID:SCR_010910) [To assess the quality of our assembly compared to the previous MesAur1.0 we used Quast v5.0.2   together_018171) . These t_010910) . Quast wRRID:SCR_015008) [RRID:SCR_011980) release consisting of 12,692 genes shared across the superorder Euarchontoglires [We next used the software BUSCO v5.2.2   to assestoglires , the appRRID:SCR_005189) [In addition, FRCbam   was usedThe initial Flye assembly consisted of 2.38 Gb of sequence across 6,741 scaffolds with a scaffold N50 of 10.56 Mb , and EST (n = 558) data to BCM_Maur_2.0 show \u226599.44% mean identity and \u226598.88% mean coverage. Alignments of these same transcript datasets to MesAur1.0 show \u226599.13% mean identity and \u226593.49% mean coverage. Alignments of RefSeq transcripts showed a similar mean percentage of indels in the BCM_Maur_2.0 (0.10%) and MesAur1.0 (0.11%) assemblies. Protein alignments of Syrian hamster RefSeq (n = 261) and Genbank (n = 485) data to BCM_Maur_2.0 show \u226580.95% mean identity and \u226589.18% mean coverage. Alignments of these same protein datasets to MesAur1.0 show \u226580.57% mean identity and \u226584.87% mean coverage.NCBI annotation of BCM_Maur_2.0  with SyrNCBI annotated 21,616 protein-coding genes and 10,459 noncoding genes in BCM_Maur_2.0 compared to 20,495 protein-coding genes and 4,168 noncoding genes in MesAur1.0 . Only 7%Ifnb1) and IFN-\u03f5 (Ifne) genes. Although neither of these genes are present on the MesAur1.0 scaffold NW_004801649.1, this assembly does contain an Ifne gene on a short 2,408-bp contig that is predicted to code for a protein of 192 amino acids. These observations emphasize the need for an improved genomic assembly for Syrian hamsters given that the IFN-\u03b1 gene cluster includes more than a dozen tightly linked functional genes plus multiple pseudogenes in a wide variety of species including mice and humans.Given the importance of IFN-I responses during SARS-CoV-2 infection, we next compared the IFN-I\u03b1 gene cluster in the BCM_Maur_2.0 assembly relative to this genomic region in the original MesAur1.0 assembly. The MesAur1.0 scaffold NW_004801649.1 includes annotations for 4 IFN-I\u03b1 loci, but this genomic sequence is riddled with numerous gaps. Of these 4 candidate loci, only LOC101824534 appears to contain a complete IFN-\u03b1-12\u2013like coding sequence with the ability to encode a predicted protein (XP_005074343.1). The LOC101824794 gene sequence can only encode a 162 amino acid protein owing to a 5\u2032 truncation. The remaining pair of candidate genes (LOC101836618 and LOC101836898) appear to have aberrant transcript models that have fused putative exons from neighboring loci. In mice and humans, the IFN-\u03b1 gene cluster is flanked by single-copy IFN-\u03b21 . This Ifnz gene family seems to be absent in Syrian hamsters because the closest matches to predicted hamster protein sequences are only 28% identical at the amino acid level. The IFN-I\u03b1 gene cluster in the BCM_Maur_2.0 assembly lies within >12 Mb of contiguous genomic sequence, with the nearest flanking gaps located 2.66 Mb proximal and 9.07 Mb distal to the Ifne and Ifnb1 genes, respectively. The availability of a contiguous hamster genomic sequence and associated transcriptional regulatory elements for this complex immune gene region may be helpful for investigators who are interested in unravelling mechanisms that control interferon expression during infections with SARS-CoV-2, as well as challenges with other viral pathogens.In the BCM_Maur_2.0 assembly, the IFN-I\u03b1 gene cluster is contained on the NW_024429197.1 super-scaffold that spans nearly 75 Mb. Figure\u00a0-12\u2013like . The remThe improved Syrian hamster assembly and annotation described here will facilitate research into this important animal model for COVID-19. Specifically, reagents for studying immune responses in hamsters have lagged behind those available for laboratory mice. BCM_Maur_2.0 will facilitate the identification of cross-reactive reagents originally developed to study immunity in other species. Additionally, a more accurate genome assembly will improve the analyses of host responses to infection by enabling more accurate interpretation of RNA-seq experiments.Relative to other recent assemblies that use a combination of long-read sequencing and short-read polishing, this genome assembly and annotation compares very favorably. The scaffold N50 of >85\u00a0Mb is quite consistent with other long-read assemblies. The contig N50 and total number of scaffolds or contigs are likewise reasonable and consistent with other similar mammalian reference genomes. The number of protein-coding genes identified is within the expected range, although additional attention will likely be needed to resolve duplicated, repetitive gene loci, potentially leveraging recent advances in ultralong-read sequencing.What additional genomic resources would be needed to make hamsters a better model for COVID-19? Deep long-read transcriptome analysis of multiple tissues and ages would be the best next step, to define not just the genes expressed but the alternative splicing of genes across tissues and developmental stages. Also, long-read RNA-seq of tissues following experimental challenge with SARS-CoV-2 and other viruses would facilitate improvements in the quality of antiviral gene models.The availability of higher accuracy sequences should lead to the development of specific reagents for monitoring immune responses. For example, epitopes that are shared between hamsters and other rodents can be used to identify monoclonal antibody reagents for flow cytometry that are predicted to be cross-reactive. Additional reagent development will be enabled by creating synthetic versions of hamster proteins that can be used as immunogens to make hamster-specific antibodies.One surprising motivation for this study is that Syrian hamsters, which were quickly identified as a high-value model for COVID-19, did not have a higher quality reference genome at the start of the pandemic. While we worked quickly to generate this genome and make it available to the scientific community, better preparedness will be critical for future unexpected epidemics. To this end, we would encourage investment in continued refinement and improvement of reference genomes for all of the rodent, bat, and nonhuman primate models that are commonly used to study viruses to prevent this situation from recurring. Such an investment would also yield improved genomic resources that would provide broad benefit to the entire scientific community.GCA_000349665.1). The new BCM_Maur_2.0 genome assembly is available in the NCBI data repository under BioProject PRJNA705675 (GenBank accession GCA_017639785.1). Oxford Nanopore (SRX11206953) and Illumina (SRX10928323) sequencing data are available through the NCBI SRA. The Bionano data are available from the BioProject page as NCBI accession SUPPF_0000004259. The Illumina RNA-Seq data from multiple tissues including lung, trachea, brain, olfactory bulb, and small intestine are available under NCBI BioProject PRJNA675865. All supporting data and materials are available in the GigaScience GigaDB database [The MesAur1.0 genome assembly is available in the NCBI database under BioProject PRJNA77669 (GenBank accession database .Supplementary Table S1. Predicted genes in the IFN-I\u03b1 cluster of the BCM_Maur_2.0 assembly.Supplementary Fig. S1. FRC_align.giac039_GIGA-D-21-00197_Original_SubmissionClick here for additional data file.giac039_GIGA-D-21-00197_Revision_1Click here for additional data file.giac039_GIGA-D-21-00197_Revision_2Click here for additional data file.giac039_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giac039_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giac039_Reviewer_1_Report_Original_SubmissionDerek Bickhart -- 8/16/2021 ReviewedClick here for additional data file.giac039_Reviewer_1_Report_Revision_1Derek Bickhart -- 11/12/2021 ReviewedClick here for additional data file.giac039_Reviewer_1_Report_Revision_2Derek Bickhart -- 2/18/2022 ReviewedClick here for additional data file.giac039_Reviewer_2_Report_Original_SubmissionYang Zhou -- 8/25/2021 ReviewedClick here for additional data file.giac039_Reviewer_2_Report_Revision_1Yang Zhou -- 11/8/2021 ReviewedClick here for additional data file.giac039_Supplemental_FilesClick here for additional data file.ACE2: angiotensin-converting enzyme 2; BCM: Baylor College of Medicine; bp: base pairs; BUSCO: Benchmarking Universal Single-Copy Orthologs; BWA: Burrows-Wheeler Aligner; COVID-19: coronavirus disease 2019; EST: expressed sequence tag; FFPE: formalin-fixed, paraffin-embedded; Gb: gigabase pairs; IFN: interferon; kb: kilobase pairs; Mb: megabase pairs; NCBI: National Center for Biotechnology Information; NEB: New England BioLabs; ONT: Oxford Nanopore Technologies; RNA-Seq: RNA-sequencing; SARS-CoV-2: severe acute respiratory syndrome coronavirus 2; SRA: Sequence Read Archive; STAT2: signal transducer and activator of transcription factor 2; TMPRSS2: transmembrane protease serine 2; UHMW: ultra-high molecular weight.The authors declare that they have no competing interests.This research was supported by contract HHSN272201600007C awarded to D.H.O. from the National Institute of Allergy andInfectious Diseases of the National Institutes of Health. The content of this publication is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.R.A.H. performed genome assembly and quality assessment, data and metadata submission, and contributed to manuscript preparation. F.J.S. and M.M. performed assembly assessment and comparison analyses. T.M.P. and R.W.W. performed transcript and annotation comparisons. D.H.O. managed experimental design and oversight and coordinated manuscript preparation. H.D., Q.M., and Y.H. developed, optimized, and implemented protocols for ONT PromethION sequencing. M.R., D.M., J.A.K., and J.R. performed project and/or data management. R.A.H., D.H.O., D.T.L., T.M.P., R.W.W., M.M., F.J.S., and J.R. wrote the manuscript. All authors approved the manuscript."}
+{"text": "Providencia rettgeri isolate PROV_UAMS_01, which was recovered in 2021 from a urine sample from a hospitalized patient in Arkansas, USA. The genome sequence of P. rettgeri isolate PROV_UAMS_01 comprises a single chromosomal replicon with a G+C content of 40.51% and a total of 3,887 genes.Here, we report the complete genome sequence of  Providencia rettgeri clinical isolate with a carbapenem-resistant antibiogram that is also resistant to ampicillin, aztreonam, cefazolin, nitrofurantoin, piperacillin and tazobactam, and tetracycline. The isolate was collected from a urine sample from a hospitalized patient in Faulkner County, AK. Culturing of the urine sample on a blood agar plate yielded P. rettgeri, which was confirmed by matrix-assisted laser desorption ionization\u2013time of flight (MALDI-TOF) mass spectrometry . Antimicrobial susceptibility testing was performed using the Vitek 2 system (bioM\u00e9rieux) with the AST-GN card. Detection of carbapenemase production was negative based on the modified carbapenem inactivation method (mCIM)  , followiP. rettgeri subcultured for 24\u2009h on blood agar plates. The colonies were resuspended into a DNA/RNA Shield collection and lysis tube. Then, genomic DNA was extracted using the Quick-DNA fungal/bacterial kit  and further purified using AMPure XP beads (Beckman Coulter). The DNA concentration was quantified and quality controlled using a NanoDrop spectrophotometer, the Agilent 2200 TapeStation system, and a Qubit 3.0 fluorometer (Thermo Fisher Scientific). The purified DNA was aliquoted into two tubes for MinION and Illumina sequencing.Genomic DNA was extracted, purified, and sequenced as described in references An Oxford Nanopore Technologies (ONT) sequencing library was prepared using a PCR-free method of multiplexing samples with the rapid barcoding kit (SQK-RAD004); the library was sequenced using a FLO-MIN106 (R9.4) flow cell for 48\u2009h. The short-read sequencing library was sequenced using the DNBSEQ-G400 platform at BGI Genomics , where they followed their standard protocol to construct DNA libraries of 2\u2009\u00d7\u2009150-bp paired-end reads. Reads with adapter contamination and low-quality reads with a base quality score of <Q20 were filtered out using SOAPnuke software (https://github.com/rrwick/Porechop). Nanopore read quality control was performed using NanoFilt v2.3 and NanoStat v1.5.0 from NanoPack  v5.3 (P. rettgeri strains. Assignment of PROV_UAMS_01 to the species P. rettgeri was further confirmed as in reference Providencia genomes available in GenBank (220 genomes as of 13 March 2021). A summary of the whole-genome sequencing data and the main genomic features of isolate PROV_UAMS_01 are listed in The complete circular chromosome was rotated with Unicycler, using the AP) v5.3 . A BLASTP. rettgeri isolate PROV_UAMS_01 was deposited in DDBJ/ENA/GenBank under the accession number CP090005.1. The long and short reads are available in the NCBI SRA database under the accession numbers SRR17269189 and SRR17269190, respectively.The complete genome assembly of"}
+{"text": "The dysregulation of circular RNAs (circRNAs) participates in the malignant progression of multiple cancers, including osteosarcoma (OS). However, the role of circ_0097271 in OS development remains unclear. We thus aimed at unveiling the functional role and mechanism of circ_0097271 in OS. in vivo was assayed by establishing animal models. The predicted binding relationship between miR-640 and circ_0097271 or MCAM was verified by dual-luciferase reporter or RIP assay.  The expressions of circ_0097271, miR-640, and MCAM in OS were analyzed by qPCR. Cell proliferation and migration were inspected by CCK-8 assay, colony formation assay, and Transwell assay. Circ_0097271's role in vivo. Circ_0097271 targeted miR-640 and negatively modulated miR-640 expression. MiR-640 was poorly expressed in OS, and its depletion recovered OS cell growth and migration that were repressed by circ_0097271 knockdown. MiR-640 bound to MCAM 3'UTR and thus suppressed MCAM expression. MCAM knockdown repressed OS cell growth and migration, while additional miR-640 depletion partially abolished the anticancer effects of MCAM knockdown in OS cells.  Circ_0097271's expression was enhanced in OS samples and cells. The knockdown of circ_0097271 restrained OS cell growth and migration, and its downregulation also blocked solid tumor growth  Circ_0097271 is an oncogenic driver and contributes to OS development via targeting the miR-640/MCAM pathway, which provides a potential opinion for OS treatment. Osteosarcoma (OS) is the most common primary malignant bone tumor, most often occurring in adolescence . Due to 2+ transporting 2 (ATP2A2) genes by back-splicing. In view of the blank of circ_0097271' role in OS, we focus on circ_0097271 and investigate its functional role and mechanism in OS progression.CircRNAs are a new class of regulatory factors that play specific roles in tumor progression and are classified as noncoding RNAs. CircRNAs originate from precursor mRNA via a \u201cback-splicing\u201d mechanism and featured by covalently closed structure and high stability in eukaryotic organisms , 8. GrowThe bioinformatics tool circInteractome presents that miR-640 was a putative target of circ_0097271 because circ_0097271 sequence fragment contains miR-640 binding sites . MiR-640Our present work for the first time investigated the expression pattern and functional role of circ_0097271 in OS development. In addition, we validated the binding relationship between miR-640 and circ_0097271 or MCAM in OS development to unveil the regulatory mechanism of circ_0097271. Our aim was to further understand OS pathogenesis from the insight of a novel circRNA, circ_0097271.Human OS samples and matched noncancer normal control tissues were removed from 38 OS cases at our hospital. Before biopsy, all patients were firstly diagnosed with OS and had never received therapeutic measures against OS, such as chemotherapy and radiotherapy. Patients with other bone diseases or systemic diseases were excluded. The informed consent was acquired from each case. The procedures of human sample use were approved by our hospital.2).Human OS cells, Saos-2 (RRID: CVCL_0548) , SW1353 (RRID: CVCL_0543) (Bena), SOSP-9607 (RRID: CVCL_4V80) , and HOS (RRID: CVCL_0312)  were cultured in 90% DMEM  added with 10% FBS (Sigma-Aldrich) in an incubator  and their matched control (si-NC) from FenghBio. MiR-640 mimic, miR-NC, miR-640 inhibitor, and inhibitor-NC were directly bought from Ribobio . When 70-80% cells were fused, the experimental cells were transfected with different oligonucleotides using Lipofectamine\u2122 2000 . 24 hours later, cells were collected and used for transfection efficiency detection. The sequence of siRNA is shown in Supplementary Table \u0394\u0394Ct\u2212 method. We displayed primer sequences in Total RNA was acquired from samples using the TRIzol reagent . Next, two commercial reverse transcription kits, including First-Strand cDNA Synthesis Kit  and miRNA First-Strand cDNA Synthesis Kit , were utilized for cDNA synthesis. The diluted cDNA was reacted with SYBR Mixture  for qPCR detection through a LightCycler 96 thermocycler . The expression of circ_0097271 and MCAM was normalized by GAPDH, and miR-640's expression was normalized by U6. The expression level was evaluated using the 2Total RNA was acquired and experienced with RNase R  for 15 minutes at 37\u00b0C. After treatment, total RNA was reverse-transcribed and used for qPCR as abovementioned.For circRNA subcellular location analysis, we briefly isolated cytoplasmic RNA and nuclear RNA from OS cells using the commercial PARIS kit  as the protocol suggested. The abundance of circ_0097271 in different locations was analyzed by qPCR assay, using GAPDH as an internal reference in the cytoplasm and U6 as an internal reference in the nucleus.\u03bcL culture medium. Every 24\u2009h interval , CCK-8  was added into each corresponding test well with 10\u2009\u03bcL per well, and cells were next incubated for additional 4 hours in incubators. The optical density (OD) was evaluated by measuring the 450\u2009nm absorbance using a microplate reader .We prepared the transfected cells into 96-well plates at a density of 1500 cells/well in 100\u2009We prepared the transfected cells into cell culture dishes at a density of 300 cells/well. Cells in complete culture medium were cultured in incubators for 12 days. The medium was refreshed every two days to induce colony formation. At last, cell debris was removed by PBS washing, and cell colonies (over 50 cells) after methanol fixation and 0.1% crystal violet  staining were observed by light microscopy .5 cells were placed into the upper chamber  and maintained in 500\u2009\u03bcL serum-free medium. The bottom of chambers was supplemented with matched culture medium (800\u2009\u03bcL) containing 20% FBS. After 24\u2009h incubation, cells migrated to the low surface were subjected to methanol fixation and 0.1% crystal violet staining. Representative pictures were captured by light microscopy in random 5 fields.For migration analysis, 5 \u00d7 106 cells per mouse) were hypodermically injected into the groin of nude mice, with 5 mice in each group. Tumor volumes (length \u00d7 width2 \u00d7 0.5) were weekly recorded by a vernier caliper. After 5 weeks, all mice were euthanized by intraperitoneal injection with pentobarbital. Then, tumor tissues were excised from animal body and used for further analysis. The procedures of animal use were approved by our hospital.Nude mice  used in this study were bought from Vital River  and then acclimated for one week in pathogen-free animal room. Circ_0097271-specific short-hairpin-RNA (sh-circ) and its matched control (sh-NC) were synthesized by Geneseed and packaged into lentiviral vectors. The lentiviral particles of sh-circ or sh-NC were used to infect Saos-2 cells to mediate circ_0097271 stable downregulation. To construct transplanted tumor models, the infected Saos-2 cells , mutation at binding site 1 (Mut1), mutation at binding site 2 (Mut2), and common mutation (Co-Mut), were all constructed into pmirGLO vector to verify these putative binding sites. Similarly, the WT and Mut reporter vectors of MCAM were also constructed. The WT/MUT reporter vector was transfected with miR-640 mimic or mimic-NC into Saos-2 and SW1353 cells using Lipofectamine\u2122 2000.48 hours later, we examined luciferase activities in line with the protocol of a dual luciferase reporter assay system .Using a commercial RIP assay kit from Merck Millipore , RIP assay was implemented to ensure the involvement of circ_0097271 with miR-640. In brief, Saos-2 and SW1353 cells were lysed, and cell lysates were incubated with magnetic beads coated with Ago2 antibody (Anti-Ago2) or IgG antibody (Anti-IgG). RNA complexes could be captured by antibody-coated beads and then eluted for qPCR analysis.\u03bcg per lane) were separated by 10% SDS-PAGE and electron transferred to nitrocellulose membranes (Invitrogen). After blocking with 5% skim milk at room temperature for 1 hour, the membranes were probed with the primary antibody against MCAM  or GAPDH  overnight at 4\u00b0C. After the incubation with the matched secondary antibody  for 2 hours at room temperature, the membranes were exposed to the ECL reagent  to display protein bands.Utilizing the RIPA Lysis Buffer , total proteins were acquired from samples. Then, proteins were quantified using the BCA Protein Assay Kit . After denaturing by boiling for 10 minutes, equal amounts of protein lysates  was used for figure drawing and statistical analysis of collected data from three independent experiments. Measurement data were displayed as mean\u2009\u00b1\u2009standard deviation. Student's To realize circ_0097271's expression pattern in OS, we conducted qPCR analysis and monitored that circ_0097271's expression was greatly reinforced in OS tumor samples in contrast to normal controls . DiagnosWe ensured that circ_0097271 expression was remarkably declined in si-circ-transfected Saos-2 and SW1353 cells in contrast to si-NC-transfected cells . The OD4We constructed the transplanted tumor models by injecting Saos-2 cells (with sh-circ or sh-NC infection) into nude mice. Then, we observed that the infection of Saos-2 cells infected with sh-circ resulted in tumor tissues with smaller tumor size, while the infection of Saos-2 cells infected with sh-NC resulted in tumor tissues with bigger tumor size . The detWe exploited the downstream miRNAs targeted by circ_0097271, aiming to unveil circ_0097271's functional mechanism. Circ_0097271 was observed to have binding sites with miR-640 by circInteractome tool . Then, wIn view of the binding between circ_0097271 and miR-640, we further reduced miR-640 expression in circ_0097271-depleted OS cell to observe functional effects. At first, we ensured that miR-640 expression was greatly strengthened in Saos-2 and SW1353 cells transfected with si-circ but greatly declined in OS cells transfected with miR-640 inhibitor; In comparison to alone si-circ transfection, si-circ+inhibitor cotransfection partly impaired the expression of miR-640 . In termThere are numerous potential functional genes targeted by miR-640. We thus utilized TargetScan tool to predict miR-640's target genes, and miR-640 was shown to possess binding site on MCAM 3\u2032UTR . MiR-640We then performed rescue experiments to test the interactions between miR-640 and MCAM. At first, we observed that MCAM protein level was greatly reduced in Saos-2 and SW1353 cells transfected with si-MCAM but notably strengthened in OS cells transfected with miR-640 inhibitor; in comparison to alone si-MCAM transfection, si-MCAM+inhibitor cotransfection partially recovered MCAM expression . In funcOur study mainly discovered that circ_0097271's expression was greatly reinforced in OS samples and cells. Knockdown of circ_0097271 impeded OS cell growth and migration, and also repressed tumorigenesis in animal models. We further found that miR-640 was targeted by circ_0097271, and MCAM was a downstream target of circ_0097271/miR-640. Accordingly, we proposed that circ_0097271 accelerated OS malignant progression at least in part by modulating the miR-640/MCAM pathway .CircRNAs exerting diverse functional effects in OS have been widely stated. For instance, circ_0001721 was overexpressed in OS, and circ_0001721 downregulation restrained OS development via repressing glycolysis metabolism, cell growth, migration, and invasion . Circ_00\u03b2-catenin oncogenic pathway [Regarding the regulatory mechanism of circRNAs, it is widely reported that circRNAs serve as competing endogenous RNAs (ceRNAs) to thus modulate the miRNA/mRNA networks . For ins pathway . Zhai et pathway . Moreove pathway , whereasWe further validated that MCAM was a target gene of miR-640. MCAM, widely known as CD146, has been regarded as an oncogenic biomarker and therapeutic target for multiple cancers , 24. ForThere are limitations in our present work. For example, the role of circ_0097271 on energy metabolism, cell cycle, and other cell behaviors is unclear, and more efforts should be done to address the detailed role of circ_0097271. Besides, the clinical practice and implication of circ_0097271 should be further summarized to enrich its potency as a biomarker for OS. These issues will be addressed in future work.Circ_0097271 was overexpressed in OS and exerted oncogenic effects to enhance OS cell growth and migration. Circ_0097271 drove its oncogenic role in OS development at least in part by controlling the miR-640/MCAM axis. The management of circ_0097271-governed miR-640/MCAM axis might be a promising strategy for OS treatment."}
+{"text": "Circular RNAs (circRNAs) play critical regulatory roles in cancer biological processes. Nevertheless, the contributions and underlying mechanisms of circRNAs to pancreatic ductal adenocarcinoma (PDAC) remain largely unexplored. Dysregulated circRNAs between cancerous tissues and matched adjacent normal tissues were identified by circRNA microarray in PDAC. The biological effect of hsa_circ_007367 both in vitro and in vivo was demonstrated by gain- and loss-of-function experiments. Further, dual-luciferase reporter and RNA pull-down assays were performed to confirm the interaction among hsa_circ_007367, miR-6820-3p, and Yes-associated protein 1 (YAP1). The expression of hsa_circ_007367 and YAP1 were detected by in situ hybridization (ISH) and immunohistochemistry (IHC) using tissue microarray (TMA) in 128 PDAC samples. We first identified that a novel circRNA, hsa_circ_0007367, was markedly upregulated in PDAC tissues and cells. Functionally, in vivo and in vitro data indicated that hsa_circ_0007367 promotes the proliferation and metastasis of PDAC. Mechanistically, we confirmed that hsa_circ_0007367 could facilitate the expression of YAP1, a well-known oncogene, by sponging miR-6820-3p, which function as a tumor suppresser in PDAC cells. The results of ISH and IHC demonstrated that hsa_circ_0007367 and YAP1 were upregulated in PDAC tissues. Furthermore, clinical data showed that higher hsa_circ_0007367 expression was correlated with advanced histological grade and lymph node metastasis in PDAC patients. In conclusion, our findings reveal that hsa_circ_0007367 acts as an oncogene via modulating miR-6820-3p/YAP1 axis to promote the progression of PDAC, and suggest that hsa_circ_0007367 may serve as a potential therapeutic target for treatment of PDAC. Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers with the 5-year survival rate of 10% . The higCircular RNAs (circRNAs) are a type of long non-coding RNA (lncRNA) with a covalently closed loop, and originate from pre-mRNA transcripts through the back-splicing . CompareYes-associated protein 1 (YAP1) is the major downstream effector of the highly conserved Hippo signaling pathway . Hippo sIn this study, we identified an oncogenic circRNA, hsa_circ_0007367, through circRNA microarray, which was derived from UBAP2 gene. We first proved its remarkably elevated expression in PDAC tissues and cells. Meanwhile, hsa_circ_0007367 facilitated proliferation, migration, and invasion in vitro, induced tumorigenesis in vivo. Furthermore, hsa_circ_0007367 functioned as a sponge for hsa-miR-6820-3p (miR-6820-3p) to enhance the level of YAP1. Simultaneously, the anti-tumor effects of miR-6820-3p could partly reversed by co-transfection with hsa_circ_0007367. The overexpression of both hsa_circ_0007367 and YAP1 were positively correlated with poorer prognosis of PDAC patients. Our findings indicated hsa_circ_0007367 could be a potential biomarker and provided a new insight into the diagnosis and treatment of PDAC.Two independent cohorts were performed in the study: (1) 25 pairs of fresh frozen pancreatic cancer and adjacent nontumorous tissues were acquired the pathology department mentioned above. (2) Patient paraffin samples were collected from the pathology department of Harbin Medical University Cancer Hospital  from January 2016 to December 2018. Tissue microarray was made of a total of 158 cases were involved 128 cases of PDAC and 30 cases of adjacent normal pancreatic tissues. None of the patients had undergone chemotherapy, radiotherapy, or immunotherapy before surgery. All clinicopathological diagnoses were confirmed by two pathologists. This study was approved by the Ethics Committee of Harbin Medical University (IRB3002720).2.The human PDAC cell lines AsPC-1, BxPC-3, Capan-1, PANC-1, and SW1990 cells were purchased from Chinese Academy of Sciences , and human pancreatic ductal epithelial (HPDE) cells were purchased from Zeye Biotechnology . PDAC-1 and BxPC-3 cells were cultured in DMEM , AsPC-1 and HPDE cells were cultured in RPMI-1640 , Capan-1 cells were cultured in IMDM  and SW1990 cells were cultured in L-15 . The cells authenticated by short tandem repeat (STR) profiling, and tested free from mycoplasma. All of the above complete mediums were supplemented with 10% fetal bovine serum (FBS)  and 1% penicillin, streptomycin and amphotericin B. All the cells except SW1990 were cultured in a humidified incubator at 37\u2009\u00b0C with 100% atmosphere were in a humidified incubator at 37\u2009\u00b0C with 5% COAll microRNA oligonucleotides including miR-6820-3p mimics or inhibitor and their corresponding controls were purchased from General Biosystem Company . Small interfering RNA targeting hsa_circ_0007367 (si-hsa_circ_0007367) and the forced expression vector of hsa_circ_0007367 (hsa_circ_0007367) were designed synthesized by RiboBio Biotechnology . Transient transfections were conducted using Lipofectamine 3000 .\u2212\u25b3\u25b3CT method, U6 and \u03b2-actin were used as internal references.Total RNA was extracted from PDAC cell lines and tissues by using Trizol reagent . Next, extracted RNA was utilized for reverse transcription to obtain the single-stranded cDNA using the Prime Script RT reagent Kit  on T100 Thermal Cycler . Finally, according to the manufacturer\u2019s instructions, quantitative PCR analyses were performed using a SYBR Green Mix kit  to reveal the expression levels of related RNAs, and the process was conducted on the ABI 7500 Real-Time PCR System . The relative fold change of miRNAs circRNAs and mRNAs was calculated using the 2Three pairs of fresh frozen human PDAC tissues and adjacent nontumorous tissues were analyzed by using the Arraystar Human circRNA Array v2  . Total RNA was digested with RNase R  to remove linear RNAs and enrich circular RNAs. Concerned microarray analyses were performed by Aksomic Corporation .The viability of PDAC cells (PANC-1 and AsPC-1) was assessed by using Cell Counting Kit-8 Kit (CCK-8) assay  following the manufacturer\u2019s protocol. After transfection, cells were seeded in 96-well plates cultured for 0, 24, 48, and 72\u2009h. And then absorbance was measured at 450\u2009nm using a microplate reader  after 2\u2009h of incubation at 37\u2009\u00b0C.The proliferative function of PANC-1 and AsPC-1 cell lines was examined using the EdU assay kit  as the manufacturer\u2019s instructions. The cells used for the experiments were seeded in 24-well plates and incubation with EdU for 2\u2009h, then fixed with 4% paraformaldehyde. The proliferated cells and cell nucleus were stained by using Apollo Dye Solution and the Hoechst33342, respectively. Slices were removed from the 24-well plates, observed and photographed under a fluorescence microscope .Cells were cultured and transfected in the 6-well plates with serum-free medium. Next, the changes of wound area between 0 and 24\u2009h were recorded by microscope  at \u00d7100 magnification after using a 10\u2009\u03bcl-pipette tip to make.For the transwell invasion assay, the transfected cells were suspended in serum-free medium and 200\u2009\u03bcl of the cell suspension was placed in the upper chamber , containing an 8\u2009\u03bcm polycarbonate filter coated with 100\u2009\u03bcl of Matrigel . 500\u2009\u03bcl culture medium with 10%FBS were added in the lower chamber. 24\u2009h later, the invaded cells on the surfaces of lower chambers were fixed with cold methanol and stained with crystal violet, followed by the counts of cells to invade at \u00d7100 magnification with a Zeiss Axio Scope A1 microscope .IHC staining was performed on paraffin-embedded TMA sections. Anti-YAP1  polyclonal antibody at dilution 1:100 was used as primary antibody for IHC staining. Paraffin-embedded tissues were sectioned, dewaxed, hydrated, heated in EDTA (pH 7.2) for antigen retrieval, and inhibited with 3% hydrogen peroxide. The sections were incubated with primary antibodies overnight at 4\u2009\u00b0C and the antigen\u2013antibody reactions was developed with a horseradish peroxidase\u2013diaminobenzidine substrate kit . Images were observed under multifunction microscope  Scoring was performed blindly by two pathologists.Cell climbing slices were placed in 24-well plates and cells were seeded in the well before the experiment. Cell density per pore was 60\u201370% and then pre-hybridized. FISH was performed according to the manufacturer\u2019s protocol with the fluorescent in situ hybridization kit . Briefly, fluorescence-labeled specific probes for hsa_circ_0007367  were incubated with cells at 37\u2009\u00b0C overnight in the dark chamber. A confocal laser scanning microscope  was used to capture the images.Hsa_circ_0007367 expression in PDAC tissues was measured using ISH with biotin-labeled hsa_circ_0007367 by the manufacturer\u2019s instructions. In brief, the specimens on TMA were dewax, rehydrate and digest, followed by hybridizing with the specific hsa_circ_0007367 probe . The samples were incubated with Anti-Digoxin HRP-conjugate  and stained by DAB . Then the expression of hsa_circ_0007367 were quantified and imaged in TMAs from PDAC. Scoring was performed blindly by two pathologists.The biotin-labeled hsa_circ_0007367 and control probe were synthesized by Ribobio . First, beads were preprocessed and lysates from cells were collected and incubated with specific hsa_circ_0007367 probes. And then, streptavidin magnetic beads were added and incubated with hsa_circ_0007367 or control probes at 4\u2009\u00b0C for 1\u2009h with agitation to pull down the biotin-labeled RNA complex. The RNA-beads complex was treated by RNase R and purified for reverse transcription. The abundance of related microRNAs was analyzed by qRT-PCR.Wild-type (WT) and mutant (mut) fragments corresponding to the 3\u2032UTR in hsa_circ_0007367 or YAP-1 that may bind to miR-6820-3p were cloned and inserted into the psiCHECK-2 plasmid (Promega). The mimic or NC of miR-6820-3p and hsa_circ_0007367/YAP-1 WT or mut reporter genes were co-transfected into HEK293T cells. After 48\u2009h, the activities of firefly and Renilla luciferase in the cells were detected by luciferase reporter assay system .PANC-1 or AsPC-1 cells were cultured in six-well plates and total protein was extracted by RIPA lysis buffer . After the cell lysates were sonicated, and treated with the BCA Protein Analysis kit  to detect the concentration of protein by using an enzyme-labeled instrument . Proteins were used to SDS-page and transferred to nitrocellulose filter membrane , blocked with rapid blocking buffer  for 17\u2009min and incubated with primary antibody overnight at 4\u2009\u00b0C. The next day it was washed with TBST and incubated with secondary antibodies for 40\u2009min, the membranes were scanned and analyzed with Odyssey infrared imaging instrument . Antibodies used in this study include: anti-\u03b2-actin , anti-YAP1 , anti-CTGF , and secondary antibody .7 PANC-1 cells stably transfected sh-hsa_circ_0007367 or sh-NC in were resuspended in 200\u2009\u03bcl high concentrations of matrigel. Ten 4-week-old male BALB/c nude mice  were randomly divided into two groups (n\u2009=\u20095/group) and each mouse was injected subcutaneously under armpit with 200\u2009\u03bcl cell suspension of sh-hsa_circ_0007367 or sh-NC, testing the tumor volume (length\u2009\u00d7\u2009width2/2) in mice every week. Four weeks later, the mice were sacrificed by cervical vertebra detachment and tumor tissues were collected, measured, and weighed. This study was approved by the Ethics Committee of Harbin Medical University.The constructs of hsa_circ_0007367 shRNA(sh-hsa_circ_0007367) and control vector (sh-NC) packaged with lentivirus were constructed by Hanbio Biotechnology . 5\u2009\u00d7\u200910P\u2009<\u20090.05 was considered statistically significant.The statistical analyses were mainly conducted using SPSS 16.0  and GraphPad Prism 8.0 . All data are presented as means\u2009\u00b1\u2009standard deviation (SD). Researchers were blinded to the group allocation both during the experiment and when assessing the outcome. Kaplan\u2013Meier method and log-rank test were used for overall survival (OS) analysis. Correlation analysis between groups was performed by Pearson correlation coefficient. To identify key circRNAs that involved in PDAC progression, circRNA microarray data was analyzed from 3 pairs of PDAC and matched normal pancreas tissues Fig. .Fig. 1IdP-value < 0.05, 1753 differentially expressed circRNAs were identified, 988 of which were upregulated and 765 were downregulated   Fig. .Fig. 8UpCollectively, these clinical data suggested hsa_circ_0007367 might act as a potential biomarker to predicting the prognosis of PDAC patients.As novel non-coding RNAs, accumulating evidence indicates that circRNAs involved in pleiotropically modulated cellular function and played important regulatory roles in various tumors , 24. ForNumerous recent investigations have revealed that CircRNAs primarily function as miRNA sponges in many tumors, including lung adenocarcinoma , hepatocIn the present study, as we know that hsa_circ_0007367 is generated from the coding exons of UBAP2 and localized predominantly in the cytoplasm, we speculated that hsa_circ_0007367 affects downstream target genes by acting as miRNA sponge. Accordingly, we applied three databases  to predict the potential targets of hsa_circ_0007367 and identified 7 miRNAs as candidate targets. Notably, RNA pull-down assays validated that hsa_circ_0007367 interacted with miR-6820-3p. Dual-luciferase reporter assays confirmed the sponge effect and binding sites of hsa_circ_0007367 on miR-6820-3p. Statistical analysis demonstrated that the expression of miR-6820-3p was negatively correlated with hsa_circ_0007367 in PDAC tissues. Abnormal expression of miRNAs is involved in progression of different cancers . Due to In human cancer, miRNAs are often dysregulated and circRNA\u2013microRNA code interferes the regulation of gene expression to impact the progression of disease . We furtHowever, our study has several limitations. First, whether hsa_circ_0007367 influences PDAC development via other biological processes, such as interacting with proteins or encoding polypeptides. Second, we validated the expression of hsa_circ_0007367 in PDAC tissues and cells, additionally, it needs to be detected in a wider range of clinical samples, including blood, saliva, and urine, to enhance its diagnostic and therapeutic value. Thus, mechanistic research should be further performed, and more clinical samples to be collected in future study.In conclusion, we proved hsa_circ_0007367 was highly expressed in PDAC tissues and cells, and promoted cell proliferation, migration, and invasion in PDAC cells by serving as a sponge for miR-6820-3p. MiR-6820-3p effected as a tumor suppressor to decrease YAP1 expression in PDAC. Clinically, hsa_circ_0007367 expression was positively associated with aggressive clinicopathological characteristics in PDAC patients. Therefore, our findings demonstrated that hsa_circ_0007367 upregulated YAP1 expression via sponging miR-6820-3p to promote PDAC progression, and indicated hsa_circ_0007367 could be a potential biomarker to provide a novel therapeutic strategy in PDAC.Supplemental Figure legendSupplementary Fig. S1Supplementary Fig. S2Supplemental table S1Supplemental table S2Supplemental table S3Original Data Fileaj-checklist"}
+{"text": "The effective features were selected by the least absolute shrinkage and selection operator to construct the radiomics score (Rad_score). And the radiomics model (R_model) was constructed based on Rad_score for prediction of recurrence. Then, univariate and multivariate analyses were used to screen out the independent clinical risk parameters and construct the clinical model (C_model). A combined model (RC_model) was developed based on the Rad_score and independent clinical risk parameters and presented as radiomics nomogram. The performance of the above three models was assessed by the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA).Eighty-four high-risk neuroblastoma patients were retrospectively enrolled and divided into training and test sets according to the ratio of 3:2. [Seven radiomics features were selected for building the R_model. The AUCs of the C_model in training and test sets were 0.744  and 0.750 , respectively. The R_model yielded AUCs of 0.813  and 0.869  in the training and test sets, respectively. The RC_model demonstrated the largest AUCs of 0.889  and 0.892  in the training and test sets, respectively. DCA demonstrated that RC_model added more net benefits than either the C_model or the R_model for predicting recurrence in high-risk pediatric neuroblastoma.The combined model performed well for predicting recurrence in high-risk pediatric neuroblastoma, which can facilitate disease follow-up and management in clinical practice. Neuroblastoma is one of the most common pediatric cancers, accounting for approximately 10% of all childhood malignant diseases . High-ri18F]FDG PET/CT shows tumor glucose metabolic activity in the majority of neuroblastoma, though [123I]-metaiodobenzylguanidine (MIBG) scintigraphy remains the dominant disease-specific imaging method for this disease [18F]FDG PET/CT is selectively applied in some countries or centers because of MIBG availability, and [18F]FDG PET/CT is used for non-MIBG-avid neuroblastoma [18F]FDG PET/CT shows high per-patient sensitivity for lesion diagnosis in high-risk neuroblastoma patients [[ disease . Currentblastoma . 18F]FDFFD18F]FD18F]FDG PET/CT in predicting recurrence has been demonstrated in previous studies [18F]FDG PET/CT in predicting recurrence in neuroblastoma, especially in the high-risk subgroups, has not been reported previously. Therefore, the present study investigated the value of radiomics based on [18F]FDG PET/CT in the prediction of recurrence of high-risk neuroblastoma patients. The final goal of the research aimed to develop and validate a combined model based [18F]FDG PET/CT radiomics and clinical parameters, which can predict the recurrence with good performance in high-risk neuroblastoma patients.Radiomics is a new quantitative imaging method that allows a thorough analysis of medical images data and attracted more and more attention in the field of medicine in recent years. Radiomics extracts numerous and quantitative information from medical images, including CT, MRI and PET, with high throughput to facilitate clinical decision-making . The goa studies \u201315. HoweThe Institutional Review Board of Beijing Friendship Hospital, Capital Medical University approved this retrospective study and waived the requirement for written informed consent .18F]FDG PET/CT imaging data; (4) complete clinical information; (5) without cancer-related therapy before PET/CT imaging; (6) complete laboratory and genetic data. High-risk neuroblastoma was defined according to (1) age older than 18 months and stage IV disease according to the International Neuroblastoma Staging System; or (2) any age and stage II\u2013IV disease with MYCN amplification. All patients had received multidisciplinary treatment and then started maintenance treatment. These patients were monitored and evaluated throughout maintenance treatment, with follow-up ending on 31 October 2021. These patients were randomly divided into training and test sets with a ratio of 3:2.A total of 84 high-risk neuroblastoma patients were recruited between March 2018 and November 2019. The inclusion criteria of neuroblastoma were as follows: (1) pathologically confirmed neuroblastoma; (2) age\u2009\u2264\u200918 years at diagnosis; (3) complete [123I]MIBG scanning, ultrasound, and computed tomography. Quantification of serum tumor markers, urine VMA and HVA, microscopic examination of bone marrow, and imaging tests were performed every 3 months, and [123I]MIBG scanning was performed every 6 months  and urine vanillylmandelic acid (VMA) and homovanillic acid (HVA) were quantified. After multidisciplinary treatment, the therapeutic response was determined by quantification of serum tumor markers, urine VMA and HVA, microscopic examination of bone marrow, 23IMIBG s18F]FDG was injected intravenously 40\u201360\u00a0min before the PET/CT scan. Firstly, the low-dose CT scan was performed for anatomical reference and attenuation correction, with 120\u00a0keV tube voltage and automatic modulated tube current. The CT image parameters were as follows: resolution 0.586 mm\u00a0\u00d7 0.586\u00a0mm, 2\u00a0mm slice thickness, and matrix size 512\u2009\u00d7\u2009512. The PET scan was carried out with 2\u00a0min per bed position immediately after the whole-body CT scan. PET images were reconstructed using the ordered subsets-expectation maximization algorithm with time-of-flight. Attenuation corrections were applied during the reconstruction and a gaussian filter of 5\u00a0mm in full width at half-maximum was applied to the PET images. The PET image parameters were as follows: resolution 4.07 mm\u00a0\u00d7 4.07\u00a0mm, 3\u00a0mm slice thickness, and matrix size 200\u2009\u00d7\u2009200.All patients underwent PET/CT examinations on Siemens Biograph mCT-64 PET/CT following European Association of Nuclear Medicine guidelines , 17. TheThe regions of interest (ROI) of the primary tumor were semi-automatically segmented using 3D Slicer (version 4.10.1), which was delineated along the edge of neuroblastoma on CT images, including the entire tumor. The tumor size in our study was 1.7\u00a0cm\u201320\u00a0cm. The process may be variable and cause bias in the evaluation of derived radiomics features . TherefoP\u2009<\u20090.05 were retained. Finally, the least absolute shrinkage and selection operator (LASSO) regression was applied for features selection and regularization in the training set.Radiomics features from CT and PET images were extracted separately using pyradiomics , an openThe radiomics score (Rad_score) for each patient was calculated by using a linear combination of the selected features weighted by their respective coefficients, and the R_model was constructed based on Rad_score. The clinical parameters for predicting recurrence in high-risk neuroblastoma were screened by univariate and multivariate analysis and were selected as parameters to construct the C_model. RC_model was constructed by combining the Rad_score and the predictors in the C_model. All models were built and trained in the training set, and the prediction performance was evaluated in the training and test sets. The performance of each model was assessed by the area under the receiver operating characteristic (ROC) curve (AUC). The calibration of the RC_model was assessed with calibration curves. Decision curve analysis (DCA) was used to estimate the clinical utility of the RC_model, R_model, and C_model.www.python.org) and R . Univariate analysis was used to compare differences in the clinical parameters between the training and test sets, using the independent t-test or Mann-Whitney U test for quantitative data, and the\u00a0chi-squared test for categorical variables. The Python package of \u201csklearn\u201d was used for LASSO regression and the ROC curve. The nomogram and calibration curve were depicted using the \u201crms (R)\u201d package. DCA was performed using the \u201crmda (R)\u201d package. Two-sided P\u2009<\u20090.05 indicated statistical significance.Statistical analyses were performed with Python  were used to build the R_model and calculate the Rad_score. Among these features, six were from PET images and one from CT images. The Rad_score for each patient was calculated by the following formula:Rad_score = \u22120.97572 \u2212 0.00042 \u00d7 PET_original_gldm_LargeDependenceHighGrayLevelEmphasis + 20.38846 \u00d7 PET_wavelet-HLL_glcm_Imc1 + 34.07874 \u00d7 PET_wavelet-LHL_firstorder_Median + 59.78913 \u00d7 PET_wavelet-HHH_glcm_Contrast \u2212 15.62000 \u00d7 PET_wavelet-LLH_gldm_LowGrayLevelEmphasis \u2212 13.32169 \u00d7 PET_wavelet-LLL_glcm_MCC \u2212 6.07790 \u00d7 CT_wavelet-HLL_glszm_SmallAreaEmphasisAll clinical parameters were compared between recurrence and non-recurrence groups Table\u00a0. Three cThe performance, including sensitivity, specificity, accuracy, and AUCs of different models were shown in Table\u00a0Although there are highly effective salvage therapies for patients with low-risk and intermediate-risk diseases who have a local recurrence, recurrence disease in high-risk neuroblastoma patients remains a clinical challenge. Therefore, accurate, non-invasive, and early recognition of high-risk neuroblastoma patients who had the possibility of recurrence is very important for clinical management. The present study demonstrated that the C_model and R_model had moderate power for predicting recurrence in the training and test sets. The RC_model showed a strong power for predicting recurrence in high-risk pediatric neuroblastoma, with sensitivity, specificity, accuracy, AUCs of 0.806, 0.800, 0.804, and 0.889 in the training set and 0.900, 0.769, 0.848, and 0.892 in the test set. The predictive ability of the RC_model was better than the C_model and R_model.Among the clinical parameters, three clinical parameters were used to construct a prediction model, including age, urine VMA and HVA. Age is an important risk stratification or prognostic index in neuroblastoma. International Neuroblastoma Pathology Classification is defined by multiple factors, including age at diagnosis . The ris18F]FDG PET/CT in high-risk neuroblastoma patients has been investigated previously. [18F]FDG PET/CT and bone marrow sampling may suffice for disease monitoring in high-risk neuroblastoma [18F]FDG PET/CT based on visual analysis has significant implications for prognostic assessment in these patients [18F]FDG uptake may assist in the identification of patients with poor prognoses [18F]FDG PET/CT were analyzed visually and semi-quantitatively in above these studies. These analytical methods were influenced by many factors, including physician experience, interval time between [18F]FDG injection and scan, blood glucose serum, and so on. Therefore, more objective and accurate analytical methods of [18F]FDG PET/CT images were needed in neuroblastoma patients, especially in high-risk sub-group patients. Radiomics transforms medical images into quantitative indexes through high-throughput extraction by data-assessment algorithms for predicting important clinical outcomes [The value of [blastoma . 18F]FDFFD18F]FDrognoses . Howeveroutcomes . After toutcomes , 29. In outcomes .In the present study, to achieve a more holistic model, we incorporated clinical parameters with radiomics features, leading to a significantly improved prediction efficacy of the RC_model than the C_model and R_model for predicting recurrence in high-risk neuroblastoma patients. This finding is noteworthy because the predictive efficacy of the C_model and R_model is moderate. To minimize potential limitations of a R_model including overfitting and poor reliability, we applied the standardized image prepossessing and multi-step feature selection to build the R_model with nonredundant, reliable, and informative features. Additionally, DCA demonstrated the RC_model added more net benefits for predicting recurrence in high-risk pediatric neuroblastoma than either the R_model or the C_model.The potential clinical significance of the present study included: (1) RC_model based on radiomics features and clinical parameters provides an accurate method in a non-invasive way for predicting recurrence of high-risk neuroblastoma; (2) The early prediction of recurrence in high-risk neuroblastoma can help clinicians following-up these patients, guiding further management in these sub-group patients.This study had several limitations. Firstly, this was a retrospective study with relatively small sample size, particularly in the test set, which might have selection bias and influence the robustness and generalizability of our predictive model. And we randomly divided the patients into training and test sets in the ratio of 3:2, which may lead to the poor stability of our results. We will try other methods, such as using the k-fold cross-validation strategy to divide the training and test sets to improve the stability of our results in future studies. Secondly, to ensure better accuracy, we semi-automatically delineated the primary tumor, however, it was labor-intensive, time-consuming, and subject to inter-and intra-observer variability. Moreover, it produced poorly reproducible results. Therefore, in future studies, we will try to overcome these issues using automatic segmentation methods to improve the reproducibility of radiomics studies. And we segmented the primary tumor on the CT images only, anatomical information often does not match metabolic information at all though we resampled PET images based on B-spline interpolation to ensure that they had the same voxel spacing as the CT images. Therefore, we will try to use a proper method to segment the ROI on PET images in future studies, and segment both CT and PET images to make our results more accurate. Finally, we only performed internal validation. Therefore, it is necessary to validate the results in large, multicentric cohorts and improve the reliability of models for predicting the recurrence of high-risk neuroblastoma.18F]FDG PET/CT images could predict recurrence in high-risk neuroblastoma with high discriminatory ability. The predictive model could serve as a potential decision support tool for both clinicians and radiologists, and help guide appropriate management for high-risk neuroblastoma patients.Our combined model integrating clinical parameters with the Rad_score from ["}
+{"text": "Mycobacterium tuberculosis and influence disease presentation and outcome. Strongyloides stercoralis is common among populations with high tuberculosis prevalence. Our aim was to determine whether S. stercoralis coinfection influenced clinical presentation, cerebrospinal fluid (CSF) inflammation, and outcome from tuberculous meningitis (TBM).Helminth infections may modulate the inflammatory response to S. stercoralis testing by serology, stool microscopy, and/or stool polymerase chain reaction. Comparisons of pretreatment TBM severity, CSF inflammation (including cytokines), and 3-month clinical end points were performed in groups with or without active S. stercoralis infection.From June 2017 to December 2019, 668 Vietnamese adults with TBM, enrolled in the ACT HIV or LAST ACT trials (NCT03092817 and NCT03100786), underwent pretreatment S. stercoralis. Active S. stercoralis infection was significantly associated with reduced pretreatment CSF neutrophil counts , and with reduced CSF interferon \u0263, interleukin 2, and tumor necrosis factor \u03b1 concentrations , compared with uninfected participants. Neurological complications by 3 months were significantly reduced in participants with active S. stercoralis infection compared with uninfected participants .Overall, 9.4% participants (63 of 668) tested positive for S. stercoralis coinfection may modulate the intracerebral inflammatory response to M. tuberculosis and improve TBM clinical outcomes. Strongyloides stercoralis coinfection was significantly associated with reduced intracerebral inflammation and reduced neurological complications by 3 months, compared with S. stercoralis\u2013uninfected participants. S. stercoralis coinfection may modulate neuroinflammatory response, and improve outcome, in TBM.In Vietnamese adults with tuberculous meningitis (TBM), active  Strongyloides stercoralis causes strongyloidiasis, a neglected chronic parasitic disease of humans. Found throughout tropical and subtropical regions of the world, S. stercoralis infects an estimated 30\u2013100 million individuals globally ) was 39 (31\u201350) years, and 67.5% of the participants (451 of 668) were male. The MRC TBM severity grade  vs 10% [5%\u201327%]; both P = .04). In addition, in participants with active S. stercoralis infection, compared with uninfected participants, trends were seen toward reduced grade 3 disease , reduced total CSF white blood cell (WBC) count , reduced CSF protein , and elevated CSF/blood glucose ratio .A comparison of baseline TBM severity and routine CSF parameters between primary analysis populations is shown in S. stercoralis group versus the S. stercoralis\u2013uninfected group . In addition, there was reduced positivity with the GeneXpert MTB/RIF assay in the active S. stercoralis compared with the S. stercoralis\u2013uninfected group . CSF/blood glucose ratios were significantly higher, and CSF protein levels significantly lower, in the active S. stercoralis versus the S. stercoralis\u2013uninfected group, in a HIV-coinfected subgroup .S. stercoralis infection, CSF concentrations of the proinflammatory cytokines IFN-\u0263, IL-2, and TNF-\u03b1 would be reduced and CSF concentrations of the regulatory cytokines IL-4, IL-5, IL-10, and IL-13 would be increased, compared with S. stercoralis\u2013uninfected participants. These cytokines, in addition to IL-1\u03b2, IL-6, and IL-12p70 , were measured in CSF and compared between primary analysis populations.We hypothesized that in participants with active S. stercoralis infection (n = 25), compared with uninfected participants (n = 105)  , compared with uninfected participants .CSF cytokine testing populations are shown in  = .01]) . In addiS. stercoralis infection compared with S. stercoralis\u2013uninfected participants   and CSF concentrations of IL-5 were increased . Median cytokine concentrations for S. stercoralis\u2013uninfected, past infection, and active infection groups, together with ratio of change and statistical comparison between groups, are shown in S. stercoralis infection compared with uninfected participants. CSF concentrations of TNF-\u03b1, IFN-\u03b3, IL-1\u03b2, IL-2, IL-4, IL-6, and IL-10 were significantly reduced in participants with active S. stercoralis infection, compared with uninfected participants, in an HIV-coinfected subgroup; however, these significant differences were not seen in an HIV-uninfected subgroup  . Seventysubgroup .S. stercoralis infection (3.8% [1 of 26]), compared with uninfected participants . Neurological complications are listed in S. stercoralis infection, and 78.8% (26 of 33) in S. stercoralis\u2013uninfected participants. In a multivariate logistic regression, active S. stercoralis infection was significantly and independently associated with reduced neurological events by 3 months (P = .01) . Neurological complications by 3 months remained significantly reduced in participants with active S. stercoralis infection versus uninfected participants in HIV uninfected individuals but not in those who were HIV coinfected (P = .01) . Death binfected .S. stercoralis results, are shown P = .12). For participants in whom both S. stercoralis serology and stool microscopy were performed, neither neurological events by 3 months nor death by 3 months differed significantly between participants with positive results of both S. stercoralis serology and stool microscopy, positive results of S. stercoralis serology but negative results of stool microscopy, or negative results of S. stercoralis serology  and elevated proinflammatory CSF cytokine concentrations , 13, 27.M. tuberculosis) and positive GeneXpert MTB/RIF assay results in active S. stercoralis infection suggest reduced mycobacterial burden in these participants. We speculate that these findings may reflect better host immunological control of TBM disease in the context of S. stercoralis infection.Significantly reduced \u201cdefinite\u201d TBM cases  [S. stercoralis coinfection will still be missed. S. stercoralis serological tests are affected by reduced sensitivity in advanced immunosuppression [This study has limitations. The rate of true  is low <% [30] oww (<30%) , yet sompression , 33 or ppression . S. stercoralis serology, serological results were less likely to be positive in HIV coinfection, possibly reflecting false-negative results in this group. Follow-up in our study was limited to 3 months; longer-term impact on neurological complications or death therefore cannot be assessed. In addition, repeated CSF cytokine analysis, to assess immune responses after S. stercoralis eradication, was not performed. Finally, the study drug allocation (dexamethasone or placebo) of the trial participants remains unknown. This will not influence baseline phenotype or pretreatment CSF analyses; all of which represent data or sampling before study drug administration. Given the randomized study drug allocation (1:1), dexamethasone and placebo are expected to be evenly distributed within each individual analysis population.In our subpopulation in which all participants underwent S. stercoralis coinfection. It is part of 2 clinical trials with precise treatment protocols and standardized testing and data collection procedures. In this study of TBM, CSF is used for routine parameter and cytokine measurement, allowing a study of inflammation at the site of the disease instead of using blood inflammatory changes to assess intracerebral inflammation.The strengths of the current study are that it is large and prospective, with careful clinical characterization of TBM and S. stercoralis coinfection in TBM was associated with reduced intracerebral inflammation and reduced neurological events. Further understanding of these immunomodulatory processes may aid the development of novel host-directed therapies to manage excessive and damaging inflammation of TBM.In conclusion, in our study active The Journal of Infectious Diseases online. Consisting of data provided by the authors to benefit the reader, the posted materials are not copyedited and are the sole responsibility of the authors, so questions or comments should be addressed to the corresponding author.Supplementary materials are available at jiaa672_suppl_Supplementary_Table_1Click here for additional data file.jiaa672_suppl_Supplementary_Table_2Click here for additional data file.jiaa672_suppl_Supplementary_Table_3Click here for additional data file.jiaa672_suppl_Supplementary_Table_4Click here for additional data file.jiaa672_suppl_Supplementary_Table_5Click here for additional data file.jiaa672_suppl_Supplementary_Table_6Click here for additional data file.jiaa672_suppl_Supplementary_Table_7Click here for additional data file.jiaa672_suppl_Supplementary_Table_8Click here for additional data file.jiaa672_suppl_Supplementary_Table_9Click here for additional data file.jiaa672_suppl_Supplementary_Table_10Click here for additional data file.jiaa672_suppl_Supplementary_Table_11Click here for additional data file.jiaa672_suppl_Supplementary_Materials_1Click here for additional data file."}
+{"text": "Xanthomonas is an important genus of plant-pathogenic bacteria that affects agronomic and economically important crops, causing serious economic losses. In fact, several Xanthomonas species are considered regulated quarantine pests. Due to the lack of effective control measures to treat plant-pathogenic bacteria, innovative control tools are needed to carry out integrated disease management. In this regard, bacteriophages (phages), viruses of bacteria, constitute a promising biocontrol tool. In this work, we report the isolation and characterization of 11 novel Xanthomonas arboricola pv. juglandis phages belonging to different families and genera of the class Caudoviricetes. Infectivity matrix in more than 60 isolates of different xanthomonads and other phytopathogenic bacteria suggests that these phages are specific to the Xanthomonas genus, with different host ranges depending on the isolates tested. Interestingly, some of these phages showed relevant features to be used as biocontrol tools to combat pathogenic Xanthomonas spp. as important as X. oryzae or X. citri.IMPORTANCE Phytopathogenic bacteria represent serious losses worldwide. The lack of current treatments has focused the spotlight on phages, viruses of bacteria, as very promising biocontrol tools. Phages are very specific and can help to control bacterial infections in crops, as is the case of xanthomonads-associated diseases. The discovery of new environmental phages with lytic capacity that can help to combat these pathogens is of special relevance, and it is necessary to implement phage isolation and characterization techniques to determine their host range and their genomic properties. The establishment of phage collections worldwide will allow their use as preventive, diagnostic, or therapeutic tools. Although there is still a long way to go, this work is a step forward in the implementation of new ecofriendly techniques to combat key pathogens in the field. Plant diseases are a major cause of production losses worldwide, with important economic, social, and ecological consequences. In particular, plant-pathogenic bacteria are difficult to manage due to their recurrence, rapid bacterial dissemination in crops, and lack of effective control methods , 2. The In this context, the use of bacteriophages, bacterial viruses, is one of the most promising ecofriendly alternatives to combat phytopathogenic bacteria. Phages have important advantages over chemical compounds. Their natural presence in all ecosystems does not pose a danger to the environment since their use is considered a minor intervention in the native microbiota of the plant . In factXanthomonas stands out for containing a large number of species (more than 35) pathogenic to more than 400 plant species  and have been shown to successfully control pathogens that cause tomato and pepper spot disease and citrus canker disease. Despite this, there is great unexploited potential for the use of phages to replace or reduce the number of agrochemicals used for control of most plant-pathogenic bacteria.The first use of phages as biocontrol agents against  century . Since t century , belongieviridae . These loviridae , 14. Recas citri , 16, Xanmpestris  and Xants oryzae . To dateX. arboricola pv. juglandis from environmental samples in Valencia (Spain). All phages were morphological and genomically characterized and showed high diversity. Host range analysis on a large collection of pathogenic Xanthomonas spp. and other phytopathogenic bacteria showed that the isolated phages were highly specific against Xanthomonas spp. and had strong lytic activity against some quarantine bacterial strains of species that constitute major threats for agriculture. This work provides an excellent starting point for investigating the potential use of these phages in biocontrol programs to combat xanthomonads worldwide.In the present work, we have isolated and characterized 11 novel bacteriophages against the pathogen X. arboricola pv. juglandis phages against the host IVIA 1317-1a, named vB_Xar_IVIA-DoCa1 to vB_Xar_IVIA-DoCa11, were isolated from sewage using the double-layer agar technique. To isolate each phage, a triple plaque-to-plaque transfer was performed. Subsequently, each isolated plaque was used to infect log-phase bacterial cultures, and the supernatant was titered by the standard plaque assay. Under these conditions, the viral titer of the isolated phages lysates ranged from 109 to 1010 PFU/mL on lawns of IVIA 1317-1a. All phages formed clear plaques, with sizes ranging from 0.5 to 4.0\u2009mm in diameter and well-defined boundaries.A total of 11 Transmission electron micrographs from high-titer phage-lysed cultures revealed three main phage morphologies . Phages To assess the lytic activity of the isolated phages in liquid medium, log-phase IVIA 1317-1a cultures were inoculated with each phage in parallel with noninfected cultures grown as controls . In the X. arboricola pv. juglandis phages, spot tests in soft agar plates were performed for a battery of 63 bacterial isolates, including 46 Xanthomonas species isolates and 19 distant phytopathogenic bacterial species  that were used to perform the  v3.13.1 . One lon v3.13.1 .Caudoviricetes; vB_Xar_IVIA-DoCa2, vB_Xar_IVIA-DoCa4, vB_Xar_IVIA-DoCa7, and vB_Xar_IVIA-DoCa9 showed similarities with phages from the Autographiviridae family, while vB_Xar_IVIA-DoCa1, vB_Xar_IVIA-DoCa3, vB_Xar_IVIA-DoCa5, vB_Xar_IVIA-DoCa6, vB_Xar_IVIA-DoCa8, vB_Xar_IVIA-DoCa10, and vB_Xar_IVIA-DoCa11 were more similar to phages from the Septimatrevirus, Nipunavirus, and Bosavirus genera. Specifically, the closest relatives were of the genus Pradovirus for vB_Xar_IVIA-DoCa2, vB_Xar_IVIA-DoCa4, and vB_Xar_IVIA-DoCa9, Septimatrevirus for vB_Xar_IVIA-DoCa1, vB_Xar_IVIA-DoCa8, and vB_Xar_IVIA-DoCa11, Nipunavirus for vB_Xar_IVIA-DoCa3, and Bosavirus for vB_Xar_IVIA-DoCa5, vB_Xar_IVIA-DoCa6, and vB_Xar_IVIA-DoCa10.Comparison against the nucleotide database using BLAST  showed aAutographiviridae family and Septimatrevirus genus, while more than 70 ORFs were found in all phages belonging to the Nipunavirus and Bosavirus genera. BLAST , vB_Xar_IVIA-DoCa2, vB_Xar_IVIA-DoCa4, and vB_Xar_IVIA-DoCa9 are phiKMV-like phages with genomes consisting of three functional modules, with a gene encoding a single-subunit RNA polymerase at the end of the second cluster formed by genes involved in DNA metabolism  (Pseudomonas phages vB_PaeS_PAJD-1 (accession number MW835180) and Pamx25 (accession number NC_041953), NP1-like phages belonging to the Nipunavirus genus , and Stenotrophomonas maltophilia phage DLP4 (accession number MG018224). VB_Xar_IVIA-DoCa5 was more different than VB_Xar_IVIA-DoCa6 and VB_Xar_IVIA-DoCa10 (and to phages previously described) but had a modular arrangement of genes similar to those and Xanthomonas phage Bosa , was detected.All phages showed a genomic organization previously described in phages from the same genus . Similartabolism . In the Q307387) . vB_Xar_us genus , 28. In age DLP4 . InteresAutographiviridae and phages belonging to the subfamily Bradleyvirinae and the genera Pradovirus, Nipunavirus, and Septimatrevirus were selected and used to perform a single-gene phylogenetic analysis and a whole-genome comparison.To determine the relationship between the isolated phages and those previously described, unclassified virus belonging to the family Septimatrevirus genus, and VB_Xar_IVIA-DoCa3 was closer to phages belonging to the Nipunavirus genus. VB_Xar_IVIA-DoCa5, VB_Xar_IVIA-DoCa6, and VB_Xar_IVIA-DoCa10 fell within phages belonging to the Bradleyvirinae subfamily, even though VB_Xar_IVIA-DoCa6 and VB_Xar_IVIA-DoCa10 were more closely related, and to other phages of the Bosavirus genus. On the other hand, VB_Xar_IVIA-DoCa4, VB_Xar_IVIA-DoCa2, VB_Xar_IVIA-DoCa7, and VB_Xar_IVIA-DoCa9 were grouped with phages of the Autographiviridae family, although VB_Xar_IVIA-DoCa2, VB_Xar_IVIA-DoCa4, and VB_Xar_IVIA-DoCa9 were closer to each other, and to other phages belonging to the Pradovirus genus. As previously shown by other methods, VB_Xar_IVIA-DoCa7 appeared to be the most distant phage.First, a maximum likelihood phylogeny was performed with the sequences of the DNA polymerase protein using the VT substitution model, grouping the phages into different taxonomic ranks . On one Second, fixing a threshold of 95% for the demarcation of viruses into species and 70% into genera, as suggested by the International Committee on Taxonomy of Viruses (ICTV), whole-genome sequencing comparison was done by VIRIDIC. Nine of the 11 phages isolated clustered with previously described phages (at least at the genus level), similar to the results from single-gene phylogeny. However, vB_Xar_IVIADoCa5 and vB_Xar_IVIA-DoCa7 remained unclustered . These rBosavirus genus and the detection of a gene very similar to a putative integrase gene predicted in the genome of Xanthomonas phage Bosa, lifestyle prediction by PhageAI , and an isolate X. arboricola pv. juglandis was used as the primary host. After purification of the phages that showed different plaque morphology or from different sampling times, in depth characterization of the phages was performed, including phenotypic and genomic characterization. vB_Xar_IVIA-DoCa7 was the most divergent phage, showing only 4% similarity with previously reported viruses. The closest phage, within the Pradovirus genus, included interesting phages capable of lysing the major pathogen Xylella fastidiosa  belong to the Autographiviridae family. So far, these phages represent the first phages isolated against X. arboricola pv. juglandis belonging to a different family. Moreover, all of these phages are highly specific, since they have not been able to infect any of the 19 isolates of different phytopathogenic bacteria tested, which makes them good candidates for use in biocontrol of walnut blight disease.Previous work isolating phages against vailable . All of was done . Again, Phage biocontrol requires the use of lytic phages to combat pathogenic bacteria without disturbing the microbiome of the plant and reducing secondary effects in the plant and the ecosystem . LysogenXanthomonas spp. In fact, high genome plasticity has been demonstrated in several xanthomonads, pointing to mechanisms of adaptation and selection  in 1993, was used as a primary host to isolate phages. Additionally, 46 bacterial isolates belonging to several species of Xanthomonas and 19 isolates of other phytopathogenic bacterial species were used . All bacteria were routinely grown at 25 to 28\u00b0C  in Luria-Bertani (LB) broth under shaking conditions.g for 10\u2009min at room temperature and filtered twice through 0.45- and 0.22-\u03bcm syringe filters to remove large particles, including bacterial cells and cellular debris. Then, 1-mL aliquots of the filtered samples were added to 100 \u03bcL of overnight cultures of the strain IVIA 1317-1a in stationary phase, and the mixture was poured onto petri dishes using the soft agar overlay method. Plates were incubated at 25\u00b0C for 24 h to allow phage plaques to develop. Isolated plaques were picked by micropipette aspiration using filter tips, and a triple plaque-to-plaque transfer was performed to generate pure phage isolates. Cleaned phage stocks were kept at 4\u00b0C.Sewage samples from two wastewater treatment plants from the Valencia metropolitan area (Spain), belonging to the Entidad P\u00fablica de Saneamiento de Aguas Residuales (EPSAR) from the Valencian region, were used for phage hunting using strain IVIA 1317-1a as a primary host. Wastewater samples were centrifuged at 3,000\u2009\u00d7\u2009600) of 0.2 were mixed with 10 \u03bcL of the cleaned phage stock to amplify it, and the mix was incubated for up to 24 h at 25\u00b0C and 250\u2009rpm. Then, cultures with phages were centrifuged , and the supernatants were filtered (0.22-\u03bcm filter). Lysates were serially diluted and titrated to verify the amplification. To obtain high-titer lysates, amplified phages were filtered through 0.22-\u03bcm filters and concentrated by high-speed centrifugation . For each phage, the pellet was resuspended in 200\u2009\u03bcL of SM buffer  and kept at \u221270\u00b0C for further analysis.Amplification of the phages was performed in liquid cultures of the bacteria. Briefly, 500 \u03bcL of IVIA 1317-1a culture at an optical density at 600 nm  was applied over carbon-coated copper grids, and grids were immediately placed on top of sample drops for 10\u2009min. After two brief washes in distilled water, samples were contrasted with 2% uranyl acetate for 5\u2009min. Excess fluid was removed and allowed to dry before examination with a transmission electron FEI Tecnai G2 Spirit microscope . All images were acquired using Radius software (version 2.1) with a digital camera Xarosa .9\u2009PFU/mL was spotted onto bacterial lawns, followed by overnight incubation at the optimal growth temperature for each bacterial isolate. Plates were visually examined for lysis-cleared zones after 24 to 48 h, and the presence of a clear zone in the spot area was considered evidence of bacterial susceptibility to the tested phages. Three replicates were performed to ensure reproducibility of the results.The spot assay method on the surface of double-layer agar plates was used to determine the host range of the isolated phages with 63 isolates encompassing different phytopathogenic bacterial species. Challenged bacteria in the exponential growth phase (100 \u03bcL) were mixed with 3.5\u2009mL of soft LB agar (0.7%) and poured on top of a bottom layer containing 1.5% agar. A drop of 1\u2009\u03bcL of each amplified phage at 10600 was adjusted to 0.2  using fresh LB broth. A volume of 20 \u03bcL of the refreshed culture was inoculated into 178\u2009\u03bcL of LB broth with 2 \u03bcL of each phage at 109\u2009PFU/mL . Control bacterial cultures in the absence of phage were generated in parallel. Growth curves were recorded in 96-well plates with Bioscreen C Pro  equipment at 25\u00b0C, and OD600 measurements were acquired every 30\u2009min across 112\u2009h. All experiments were done in triplicate.Growth curves of the strain IVIA 1317-1a in the presence and absence of each phage in liquid cultures were performed to determine the ability of the phages to lyse host bacteria. The strain IVIA 1317-1a was grown in LB broth overnight, and the ODk-mer coverage per sample. Read coverage of the final contig was obtained by mapping reads using bbmap.sh , and DNA was quantified using Qubit. DNA was tagged for library preparation using a Nextera XT DNA library preparation kit, and samples were sequenced using the MiSeq Illumina platform (250 paired-end reads). Quality assessment of the reads was performed using the fastp program  . The resbbmap.sh . BLAST (bbmap.sh  against Because of the transposon-based library preparation, the packaging strategy of the phages could not be predicted. Alternatively, and due to the absence of a close relative common to all the phages, the large terminase subunit, a conserved gene through all the phages, was searched by an initial annotation and fixed as the start point to reorder the genomes. The reorganized genomes were checked for assembly errors with Pilon  and reanTo better understand the relatedness of our isolated phages between each other and previously described viruses, phages phylogenetically related to the closest relatives of isolated phages by NCBI Taxonomy were selected. To perform a single-gene phylogenetic analysis, amino acid sequences of the DNA polymerases were aligned using ClustalW . The besDue to the similarity and phylogenetic relatedness of some of our isolated phages to temperate phages, lifestyle prediction was performed by using PhageAI  and BacpON911538, ON911539, ON911540, ON932078, ON932079, ON932080, ON932081, ON932082, ON932083, ON932084, and ON932085, respectively.The sequences of the isolated phages vB_Xar_IVIA-DoCa1 to vB_Xar_IVIA-DoCa1-11 are available in GenBank under the accession numbers"}
+{"text": "Enterococcus faecium.A 56-year-old man with liver, peritoneal, and lymph node metastases after previous transthoracic esophagectomy was diagnosed with acute calculous cholecystitis. Transduodenal endoscopic ultrasound-guided gallbladder drainage (EUS-GBD) was performed using a 10\u200a\u00d7\u200a10\u200amm cautery-enhanced lumen-apposing metal stent (LAMS). Purulent fluid was aspirated and submitted for culture, which identifiedAfter EUS-GBD the patient initially recovered well, but 3 weeks later fever and abdominal pain recurred. A computed tomography (CT) scan revealed a persistent hydropic gallbladder suggesting stent dysfunction. An upper endoscopy was then performed, which showed obstruction of the LAMS with necrotic tissue . Using Video\u20061\u2002Endoscopic removal of necrotic gallbladder tissue through a lumen-apposing metal stent.EUS-GBD using LAMS is an innovative technique for patients with acute cholecystitis who are unfit for surgeryIn conclusion, to the best of our knowledge, we present the first report of endoscopic drainage and subsequent endoscopic necrosectomy of gangrenous cholecystitis.Endoscopy_UCTN_Code_TTT_1AS_2AD"}
+{"text": "Circular RNA (circRNA) is considered to be an essential regulator of multiple human malignancies. However, the role and molecular mechanism of circ_0061140 in lung adenocarcinoma ((LUAD) remain elusive. The levels of circ_0061140, microRNA (miR)-653 and hexokinase 2 (HK2) were examined by RT-qPCR. Downstream targets of circ_0061140 were predicted by circinteractome website and verified by luciferase reporter and RIP assays. HK2 protein level was assessed via Western blotting. The migratory and invasive abilities of LUAD cells were assessed via wound healing and transwell assays. It was uncovered that circ_0061140 level was elevated in LUAD samples, and the high level of circ_0061140 was related to poor survival rate of LUAD patients. Circ_0061140 deletion inhibited glycolysis, migration and invasion of hypoxia-treated LUAD cells. Moreover, circ_0061140 could modulate HK2 level by absorbing miR-653. Furthermore, miR-653 silence or HK2 addition neutralized the effects of circ_0061140 knockdown on LUAD progression under hypoxia. This study elaborated that circ_0061140 accelerated hypoxia-triggered glycolysis, migration and invasion in LUAD cells via downregulating miR-653 and increasing HK2 expression. Lung cancer is one of the most common malignancies and has the highest mortality rate in the world . Lung adCircular RNAs (circRNAs) are a class of non-coding RNAs (ncRNAs) that harbor covalently closed-loop structures . RecentlMicroRNAs (MiRNAs) are a group of non-coding RNAs containing ~ 22 nts, with a role in regulating gene expression . DysreguThis work aimed to explore the expression and underlying mechanism of circ_0061140 in LUAD. It was hypothesized that circ_0061140 was upregulated in LUAD and promoted migration, invasion, and glycolysis in hypoxia-induced LUAD cells via the miR-653/HK2 axis.38 pairs of LUAD tissues and matched normal lung tissues were acquired from the Second Affiliated Hospital of Zhejiang University School of Medicine. All specimens were immediately conserved at \u221280\u00b0C for use. Patients did not receive systemic treatment of chemotherapy or radiotherapy before surgery and written informed consents from patients were gained. This study was permitted by the Ethics Committee of the Second Affiliated Hospital of Zhejiang University School of Medicine.2 at 37\u00b0C. For hypoxia stimulation, A549 and NCI-H23 cells were grown in a hypoxia chamber with 1% O2 for different time points .Human LAUD cell lines  and a normal human bronchial epithelial cell line (HBE) were gained from ATCC  and cultured in RPMI-1640 medium (Invitrogen) with 10%  against circ_0061140 (si-circ_0061140), negative control (si-NC), miR-653 mimics, NC mimics, miR-653 inhibitor and NC inhibitor were purchased from Ribobio . HK2 sequence was cloned into pcDNA3.1 to construct pcDNA3.1/HK2. The transfection was done with Lipofectamine 2000 (Invitrogen).\u2013\u0394\u0394CT method was used for the calculation of gene expression. GAPDH or U6 were regarded as controls [Total RNAs were extracted using TRIzol Kit (Invitrogen). RNA was reverse-transcribed into cDNA using a PrimeScript RT reagent Kit (Takara). Then, RT-qPCR was conducted using SYBR-Green PCR kit (Applied Biosystems). The 2controls .Briefly, A549 and NCI-H23 cells were seeded in 6-well plates. Then, the scratches were made with a pipette tip. Cells were starved and cultured for 24\u00a0h. Cell migration was analyzed using an inverted microscope (Olympus Corporation) .A549 and NCI-H23 cells were added into 24-well plates and stimulated by hypoxia for 24\u00a0h. Glucose consumption and lactate production were examined using a Glucose Uptake Colorimetric Assay Kit and Lactate Colorimetric Assay Kit  .Total protein was isolated using RIPA buffer (Beyotime), exposed to 10% SDS-PAGE, and transferred onto PVDF membranes (Millipore). Next, the membranes were blocked in 5% skim milk, and then interacted with primary antibodies containing anti-HK2 , and anti-GAPDH overnight at 4\u00a0C. Following the incubation of secondary antibody for 1\u00a0h. The protein signals were analyzed by the ECL system kit  .The invasive ability was detected via transwell invasion assay using transwell chambers (pore size 8\u00a0\u03bcm) coated with Matrigel (Sigma). Briefly, transfected cells were seeded into the apical chamber with free-serum medium. The bottom chambers were filled with complete RPMI-1640 medium. After 24\u00a0h, cells in the bottom chamber were dyed with crystal violet (Sangon). Cell numbers were counted by a microscope  .The sequences of circ_0061140 and HK2 3\u2019-UTR containing wild-type or mutated miR-653 binding sites were cloned into pmirGLO reporter vectors (Promega), namely circ_0061140-WT, circ_0061140-Mut, HK2-WT, or HK2-Mut. The miR-653 mimics or NC mimics and the above vectors were co-transfected into cells. After 48\u2009h, the luciferase activity was detected using the dual-luciferase detection system (Promega).RIP assay was performed using the EZ-Magna RIP kit (Millipore). A549 and NCI-H23 cells transfected with miR-653 mimics or NC mimics were lysed in RIP lysis buffer, cell lysates were treated with magnetic beads combined with anti-Ago2 or anti-IgG. The abundances of circ_0061140, miR-653, and HK2 were measured by RT-qPCR .Data analyses were assessed using SPSS 21.0 (IBM) and exhibited as the mean \u00b1 SD. The comparison was performed with Student\u2019s t-test or one-way ANOVA. Survival curves were generated by Kaplan-Meier method. Pearson correlation analysis assessed the interaction between genes. P <\u00a00.05 had statistical significance.In this study, we investigated the biological role and molecular mechanism of circ_0061140 in LAUD. Functional assays revealed that knockdown of circ_0061140 inhibited hypoxia-induced glycolysis, migration, and invasion in LAUD via sponging miR-653 and modulating HK2 expression.Firstly, we detected the abundance of circ_0061140 in LAUD tissues by RT-qPCR. As exhibited in Then, the regulatory role of circ_0061140 in LAUD progression under hypoxia was explored. It was manifested that circ_0061140 level was decreased by circ_0061140 deletion in hypoxia-treated LAUD cells . Moreovehttps://circinteractome.nia.nih.gov/). The binding sites of circ_0061140 and miR-653 were displayed in The downstream regulatory mechanism of circ_0061140 in LAUD was elucidated, and the targets of circ_0061140 were predicted by Circular RNA Interactome , and we uncovered that HK2 was a potential target of miR-653 . MoreoveTo study whether circ_0061140 modulated HK2 level, the relationship among circ_0061140, miR-653, and HK2 was assessed. We identified that miR-653 level was inversely correlated with circ_0061140 or HK2 . HoweverTo investigate whether miR-653 and HK2 participated in circ_0061140-mediated glycolysis, migration, and invasion of LAUD under hypoxia, A549 and NCI-H23 cells were transfected with si-NC, si-circ_0061140, si-circ_0061140+\u00a0miR-NC inhibitor, si-circ_0061140+\u00a0miR-653 inhibitor, si-circ_0061140+\u00a0pcDNA3.1 and si-circ_0061140+\u00a0pcDNA3.1/HK2. We discovered that miR-653 silence or HK2 supplementation rescued the suppressive effects of circ_0061140 silence on glucose consumption, lactate production, HK2 expression in A549 and NCI-H23 cells under hypoxia . MeanwhiAccumulating studies illuminated the crucial role of circRNAs in the biological processes of diverse malignancies. For example, deficiency of circ_0013958 restrained cell viability and metastasis, but facilitated cell apoptotic in ovarian cancer . circFBLCircRNAs exhibited their biological roles by acting as ceRNAs of miRNAs in human cancers . It was It is well known that the biological effects of miRNAs are achieved by regulating mRNA level, so we used bioinformatics websites to predict the potential target gene of miR-653, HK2 was selected as the target of miR-653 because of its tumor-promoting impact. Previous studies have shown that HK2 is essential for glucose metabolism in various cancers. Wang et al exhibited that miR-202 impaired pancreatic cancer glycolysis and repressed cell proliferation by increasing HK2 . BesidesOur study demonstrated that circ_0061140 was upregulated in LAUD under hypoxia. Moreover, circ_0061140 deficiency mitigated hypoxia-induced glycolysis, migration and invasion in LAUD via sponging miR-653 and modulating HK2, suggesting that circ_0061140 might be a novel therapeutic and diagnostic target for LAUD."}
+{"text": "Cryogenic Propellant management is a critical roadblock to enable long term space missions. Commonly used propellants (liquid hydrogen and methane) undergo constant vaporization but there is limited knowledge on the phase change rate and its implications on long term storage stability. This is, in part, due to the inability to image the liquid-vapor mixture inside opaque metallic containers at cryogenic temperatures. Here, neutron imaging is used as a visualization technique to track the liquid-vapor interface inside Al 6061 and SS 316 test cells. The data contains first known images of steady evaporation/condensation in cryogenic propellants. The experiments were conducted at the NIST Center for Neutron Research using the BT-2 Neutron Imaging facility. The test cells were instrumented with temperature sensors and inserted into a 70-mm liquid helium cryostat before being placed into the neutron beam. Temperatures and pressures were altered to achieve condensation/evaporation and Neutron images were captured during the entire phase change process. Phase change rates were obtained through image processing. The data contains raw images and processed phase change rates along with experimental temperature and pressure. The one-of-a-kind data could be used for model validation, correlation development or serve as a benchmark for future experiments. Specifications TableValue of the Data\u2022The data contains first-of-its-kind neutron image data during steady evaporation/condensation of cryogenic propellants (liquid hydrogen and methane)\u2022The data is beneficial to cryogenics and space science research communities.\u2022The data serves as a proof-of-concept and a benchmark for future experiments.\u2022The data enables model validation for cryo-storage stability in long term space missions.\u2022The data allows for correlation development for future mission design.1The data repository \u2022Jan\u25ca hydrogen data\u25cbjan_tcX_runY\u25a0jan_tcX_runY_exp_jpg\u25ca neutron images\u25a0\u25ca daq datajan_tcX_runY_exp_daq.csv \u25a0\u25ca tabulated data for each imagejan_tcX_runY_exp_img.csv \u25a0\u25ca evaporation/condensation rate(s)jan_tcX_runY_exp_img_rate.csv \u25a0\u25ca plot of pressure and heater powerjan_tcX_runY_exp_press_heat.png \u25a0\u25ca plot of temperaturejan_tcX_runY_exp_temp.png \u25a0\u25ca plot of liquid volumejan_tcX_runY_exp_vol.png \u25a0\u25ca plot of film thicknessjan_tcX_runY_exp_thick.png \u2022Sept\u25ca methane data\u25cbsept_tc1_runY\u25a0sept_tc1_runY_exp_jpg\u25ca neutron images\u25a0\u25ca daq datasept_tc1_runY_exp_daq.csv \u25a0\u25ca tabulated data for each imagesept_tc1_runY_exp_img.csv \u25a0\u25ca evaporation/condensation rate(s)sept_tc1_runY_exp_img_rate.csv \u25a0\u25ca plot of pressure and heater powersept_tc1_runY_exp_press_heat.png \u25a0\u25ca plot of temperaturesept_tc1_runY_exp_temp.png \u25a0\u25ca plot of liquid volumesept_tc1_runY_exp_vol.png \u25a0\u25ca plot of film thicknesssept_tc1_runY_exp_thick.png The corresponding data is organized into a folder structure, as follows:X represents test cell # and Y represents test run #. . Each test contains a folder of neutron images, 3 csv files and 4 png files. These are detailed below:\u2022*_jpg folder contain an image #, elapsed time and average pressure and outer wall temperature measured during the image integration time of 10s. The images are median filtered, adjusted for contrast and embedded with test condition data. Unprocessed neutron images are available on request.The images in the \u2022*daq.csv files contain the data measured by the DAQ: 3 outer wall temperatures (s2-s4), helium vapor temperature (s1), heater temperature (htr), sample holder temperature (stick), pressure (p_kpa) and heater power (htr_pow) as a function of time. Temperatures are in K, time in s, pressure in kPa and heater power is a % where 100%\u00a0=\u00a05W.The \u2022*img.csv files tabulate the avg temperatures , vapor pressure (p_kpa), heater power (htr), volume (vol_*), film thickness (thickness) and location of apex (apex). Temperatures are in K, time in s, pressure in kPa, volume in mm3, thickness in \u00b5m, apex in pixel location value and heater power is a % where 100%\u00a0=\u00a05W. Most files have volume computed from both interface tracking (vol_it) and optical density (vol_od). Some files also contain additional information such as the attenuation coefficient (\u00b5) and beam hardening factor (\u03b2) for either the liquid or the vapor.The \u2022*img_rate.csv files contain data on the evaporation/condensation rates computed from the vol vs time data. The image window for which the rate was computed, the mean and standard deviation in the pressure and temperature during the image window are also provided in addition to the computed rate.The \u2022*.png plots utilize data from the csv files described above and are provided only for quick, convenient visualization. The user is encouraged create custom high-resolution visuals from the csv data provided.The Here, 2The high neutron cross section of hydrogen in comparison with metals allows for the ability to visualize a hydrogenated fluid inside an opaque metallic container. In this study, we use the BT-2 Neutron Imaging Facility at NIST Center for Neutron Research in Gaithersburg, MD, USA to visualize the evaporation/condensation process in hydrogen and methane at temperatures as low as 18K and 100K respectively. The BT-2 beam line uses thermalized neutrons with an energy of 25 meV. The low energy does not significantly alter evaporation/condensation and the increase in temperature of the fluid is estimated to be < 1 \u00b5K. Temperatures, pressures, and neutron images are recorded during the phase change process. The images are then processed to calculate the phase change rates and film thicknesses using a combination of ImageJ and MATLAB. The motivation behind the experiments 2.1Cylindrical test cells of various sizes (5 \u2013 30 mm) are fabricated from SS 316 and Al 6061 stock material. A universal lid is fabricated from SS 316 such that it could be used with all test cells. The lid is attached to the test cell using an indium seal. Several Lakeshore DT-640 Si-diode temperature sensors (S1-S4) are mounted on the test cell at various locations 2.2The cryostat with inserted sample holder is placed in the beamline of the BT-2 Neutron Imaging Facility such that the center of the beam passes through the test cell . Thermal2.3The cryostat, test cell and vapor lines are purged with helium gas and evacuated to ensure purity prior to introducing the propellants. A small amount of helium gas is added to the cryostat sample well to aid in thermal transport after the last evacuation. Cryogen vapor is introduced at a constant preset pressure and the heater temperature is decreased below the saturation condition to initiate condensation. After sufficient rise in the liquid fill level, the temperature is increased above saturation to initiate evaporation. During the entire process images are captured every 10 s while the temperatures  and pressures (manifold and sample) are captured every second. 2.4To correlate the imaging data with DAQ data (temperatures and pressures), the avg temperatures and pressures for the 10 s duration of each image is extracted and embedded directly into the image along with the corresponding time stamp. Background radiation noise is removed using a median filter and enhanced for better contrast. The evaporation/condensation rates are obtained by picking a imaging window and then using edge detection based interface tracking and optical density transformation (detailed in a separate manuscript The work described here does not involve human subjects, animal experiments or social media platforms. All funding sources and stakeholders are acknowledged, and the data is not published elsewhere.Kishan Bellur: Investigation, Methodology, Formal Analysis, Writing \u2013 original draft; Ezequiel F. Medici: Investigation, Methodology; Daniel S. Hussey: Methodology, Resources, Software, Visualization, Writing \u2013 review & editing; David L. Jacobson: Resources, Software; Jacob LaManna: Resources; Juscelino B. Leao: Resources; Julia Scherschligt: Resources; James C Hermanson: Conceptualization, Writing \u2013 review & editing; Chang Kyoung Choi: Conceptualization, Supervision; Jeffrey S. Allen: Conceptualization, Supervision, Project administration, Writing \u2013 review & editing.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper."}
+{"text": "MYCN positive NB is poor, and there is no targeted drug for N-myc at present. This study aims to screen out hub genes closely related to MYCN, analyze the relationship between hub genes and NB microenvironment, and provide basis for molecular targeted therapy of MYCN positive NB.The prognosis of MYCN positive (n=185) and MYCN negative NB (n=951), performed WGCNA, Lasso regression and Roc analyses on the merged matrix, and obtained the hub genes related to MYCN in the training group.\u00a0We performed ssGSEA on the experimental group to calculate the infiltration level of 28 kinds of immune cells in each sample, compared the differences of immune cell infiltration between MYCN positive and MYCN negative group. The influences of hub genes on the distribution of each immune cell were also analyzed by ssGSEA.\u00a0The expression differences of the three hub genes were verified in the E-MTAB-8248 cohort (n=223), and the correlation between hub genes and prognosis of NB was calculated by Kaplan-Meier method in GSE62564 (n=498) and the validation group. We also verified the expression differences of hub genes by qRT-PCR in SK-N-BE(2), SKNDZ, Kelly and SH-SY5Y cell lines.We combined the microarray data of GSE45547 (n=649) and GSE49710 (n=498), screened the DEGs between MYCN positive\u00a0NB in the training group. Overlap of the DEGs and WGCNA networks identified four shared genes, namely, ZNF695, CHEK1, C15ORF42 and EXO1,\u00a0as candidate hub genes in MYCN positive NB.\u00a0Three core genes, ZNF695, CHEK1 and C15ORF42, were finally identified by Lasso regression and Roc analyses.\u00a0ZNF695, CHEK1 and C15ORF42 were highly expressed in MYCN positive NB tissues and cell lines. These three genes were closely related to the prognosis of children with NB.\u00a0Except that Activated CD4 T cell and Type2 T helper cell increased, the infiltration levels of the other 26 cells decreased significantly in MYCN positive NB tissues. The infiltration levels of Type2 T helper cell and Activated CD4 T cell were also significantly positively correlated with the expression levels of the three hub genes.Here were 880 DEGs including 420 upregulated and 460 downregulated genes in ZNF695, CHEK1 and C15ORF42 are highly expressed in MYCN positive NB, and their expression levels are negatively correlated with the prognosis of children with NB. The infiltration levels of Activated CD4 T cell and Type2 T helper cell increased in the microenvironment of MYCN positive NB and were significantly positively correlated with the expression levels of the three hub genes. The results of this study provide that ZNF695, CHEK1 and C15ORF42 may be potential prognostic markers and immunotherapy targets for MYCN positive NB. MYCN amplification is the most important biological factor affecting the risk staging of NB (Neuroblastoma (NB) is one of the most common solid extracranial malignancies in infants . Surgeryng of NB .MYC oncogene family, MYCN has highly conserved amino acid sequences with other members of MYC family, and these highly conserved amino acid sequences determine that members of MYC family have many identical biological properties  is a bioinformatics analysis method that is often used to effectively explore the relationship between genes and phenotypes .\u00a0The sigMYCN positive NB were screened out by GEO and ArrayExpress databases, and gene co-expression network was constructed by WGCNA, Lasso regression and Roc analyses to obtain modules and hub genes that play important biological roles in MYCN positive NB.\u00a0The immunocyte correlation analyses of MYCN positive NB, MYCN negative NB and hub genes were also carried out.\u00a0MYCN positive NB.In this study, differentially expressed genes (DEGs) of MYCN is highly expressed in SK-N-BE . Relative RNA amount was calculated by 2-\u0394\u0394Ct . Then cDNA was synthesized using HiScript@IIQ RT SuperMix  and qRT-PCR was performed with AceQ qPCR SYBR Green Master Mix (Low ROX Premixed) according to the manufacturer\u2019s instructions. The reaction conditions were: pre-denaturation at 95 C for 5\u00a0min, 40 cycles, denaturation at 95 C for 10 s, annealing at 60.0 C for 30 s, extension at 95C for 15 s, 60C for 60 s and 95 C for 15 s. qRT-qPCR was performed on a Lightcylerhttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE45547), GSE49710 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE49710), E-MTAB-8248 (https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-8248/) and GSE62564 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE62564) cohorts were selected as the objects of this research. The same patients were enrolled but different platforms were used in GSE62564 and GSE49710. All cohorts met the following criteria: 1) large sample size, 2) complete clinical information and microarray data, 3) fresh NB tissues for microarray analysis. Because of the same platform, GPL16876, GSE45547 and GSE49710 used, we integrated the data of the two cohorts into a large, fused expression matrix, after removing batch effects in order to obtain as many samples as possible to improve the statistic power. Meanwhile, E-MTAB-8248 cohort was selected as the validation group to fully verify the reliability of the results. GSE62564 was only used for survival analysis. After excluding the cases with unclear MYCN expression, 643 cases were included in GSE45547, including 93 MYCN positive cases and 550 MYCN negative cases.\u00a0GSE49710 included 493 cases, including 92 MYCN positive cases and 401 MYCN negative cases.\u00a0A total of 222 patients were included in the E-MTAB-8248 cohort, including 46 MYCN positive cases and 176 MYCN negative cases. Log2 transformation of transcriptome data was performed and probes were matched with genetic symbols according to the annotated documentation of the corresponding platform.\u00a0Finally, the gene matrix with row name as sample name and column name as gene symbol was obtained for subsequent analysis. Data from GSE45547 and GSE49710 was averaged and combined into genomicMatrix as training group. By comparing the gene expression profiles of MYCN positive NB with those of MYCN negative NB, a list of DEGs with |log2fc|> 1.2 and p-value< 0.05 was obtained in the combined cohort.Through data retrieval of GEO and ArrayExpress databases, GSE45547  according to the principle of scale-free network and finally constructed a scale-free weighted gene co-expression network. Topological overlap measurement (ToM) was used to identify highly co-expressed gene modules to reduce the sensitivity of the network to false connections. By cutting the cluster tree into branches, the genes with high absolute correlation were clustered into the same module. Only\u00a0modules with more than 60 genes could be defined as valid modules and the modules with high correlation would be merged (MEDissThres = 0.25). Each module would be assigned a different color for visual analysis. Cluster analysis of modules was based on the eigenvector value of each module.\u00a0The \u201chclust\u201d and \u201cmerge\u201d functions were used to draw hierarchical clustering trees and merge modules with high correlation respectively.\u00a0Finally, we plotted the scatter plot between Gene Significance (GS) and Module Membership (MM) within each module through the \u201cplot\u201d function to understand the significance of high-connectivity genes within the module.GO function analysis was divided into three parts: biological process (BP), cell component (CC) and molecular function (MF). We performed GO enrichment analyses of the DEGs by \u201cclusterProfiler\u201d R package and drew bubble graph of GO enrichment analysis through \u201cGGplot\u201d R package.MYCN positive group and MYCN negative group. P-value < 0.05 was selected as the cutoff value. The annotated gene set entitled \u201cimmunesigdb\u201d used in GSEA was downloaded from Molecular Signatures Database .Gene set enrichment analysis (GSEA) was performed with \u201cclusterProfiler\u201d R package to calculate the gene sets with\u00a0statistical differences between MYCN positive NB were obtained by cross-validation .\u00a0We draw receiver operator characteristic curve (Roc) for each gene by running \u201cpROC\u201d function, calculated area under the curve (AUC) and evaluated the degree of association between MYCN positive NB and the hub genes in the training group and validation group.\u00a0Survival curves of core genes were plotted by running the \u201cSURv\u201d function, and Kaplan-Meier analysis was conducted to estimate the correlation between hub genes and prognosis.The intersecting genes were obtained by overlap of DEGs  and hub genes, and the Lasso regression analysis was constructed by \u201cGlmnet\u201d function.\u00a0The minimum regularization parameter lambda (\u03bb) and genes highly associated with MYCN positive and MYCN negative samples by Wilcoxon test and\u00a0plotted the violin through the \u201cVioplot\u201d R package. The correlation between 28 infiltrating immune cells and hub genes was further analyzed by \u201ccor.test\u201d function, and the heatmap was drawn by \u201cggplot\u201d function.We performed single-sample gene set enrichment analysis (ssGSEA) on the training group using the \u201cGSVA\u201d function to calculate the infiltration level of 28 kinds of immune cells in each sample, compared the difference of immune cell infiltration between MYCN positive NB by analyzing the gene expression levels of the two groups  . Among all DEGs, the 10 up-regulated genes with highest value of log2fc were SLCO4A1, LMO3, MGC16291, ABCA12, HOXD10, TWIST1, NPW, GABRA5, NMU and DUXAP10. The 10 down-regulated genes with minimal log2fc were NTRK1, KRT19, LOC388002, IL7, RGS9, CCL19, RP13-102H20.1, SLC18A2, ECEL1 and FAM70A.A total of 1136 cases and 19806 genes were included by filtering and integrating the microarray data of GSE45547 and GSE49710 cohort. We selected 880 DEGs, including 420 highly expressed genes and 460 low expressed genes in -10), T cell differentiation , positive regulation of leukocyte cell-cell adhesion , positive regulation of T cell activation  and lymphocyte differentiation . With respect to CC, DEGs were mainly concentrated in MHC class II protein complex , chromosome-centromeric region , clathrin-coated endocytic vesicle membrane , MHC protein complex  and condensed chromosome, centromeric region . DEGs were mainly clustered in MHC class II protein complex binding , MHC protein complex binding , hormone activity , neuropeptide hormone activity  and MHC class II receptor activity  in MF category. These results suggested that a large number of MYCN related DEGs were enriched into immune-related pathways, and these DEGs might play important roles in the microenvironment of MYCN positive NB.Three subontologies of DEGs including biological process (BP), cellular component (CC) and molecular function (MF) were analyzed by GO analysis. 639, 43 and 20 pathways were enriched in each subontology respectively , GSE15750_DAY6_VS_DAY10_EFF_CD8_TCELL_UP , GSE36476_CTRL_VS_TSST_ACT_72H_MEMORY_CD4_TCELL_YOUNG_DN  and GSE39556_CD8A_DC_VS_NK_CELL_MOUSE_3H_POST_POLYIC_INJ_UP , GSE18893_TCONV_VS_TREG_24H_TNF_STIM_UP . 1904 immune-related pathways were enriched in MYCN negative NB , GSE7218_IGM_VS_IGG_SIGNAL_THGOUGH_ANTIGEN_BCELL_DN , GSE10325_LUPUS_CD4_TCELL_VS_LUPUS_BCELL_UP , GSE7509_UNSTIM_VS_FCGRIIB_STIM_DC_DN  and GSE7509_UNSTIM_VS_IFNA_STIM_IMMATURE_DC_DN . The above results fully indicated that there were significant differences in immune-related pathways enriched in MYCN positive and MYCN negative NB, especially in the expression of CD4 T, CD8T, B cell and other immune cells. MYCN and MYCN related genes might play an important role in the immune regulation of NB.To explore the potential immune regulatory mechanism in NB, we used the annotated gene set entitled \u201cimmunesigdb\u201d from MsigDB as a reference for GSEA analysis in this study.\u00a0According to the standard of adjust p-value<0.05, A total of 439 immune-related pathways were enriched in MYCN positive and 951 MYCN negative NB tissues were preprocessed, and duplicated genes and missing values were removed to obtain a combined matrix containing 19806 genes.\u00a0To ensure the accuracy of the results, we performed cluster analysis on the samples after removing the outlier samples. The sample dendrogram and trait heatmap are shown in ). Nineteen modules were initially obtained and similar modules were further combined into 15 modules as shown in the hierarchical clustering tree . We found that turquoise module contained the largest number of genes among all modules  was obtained as the object of our further study.\u00a0According to the filtering conditions of gene significance (GS)>0.5 and module membership (MM)>0.8, a total of 27 core genes were screened in turquoise module (ZNF695 (GS: 0.6229 MM: 0.8559), CHEK1 (GS: 0.5655 MM: 0.8746), C15ORF42 (GS: 0.5479 MM: 0.9063), EXO1 (GS: 0.5051 MM: 0.8569)  highly associated with MYCN positive NB were obtained by cross-validation  . Roc regression analysis was further performed to evaluate the association between the three genes and MYCN positive NB. In the training group, the AUC values of ZNF695, CHEK1 and C15ORF42 reached 0.956, 0.926 and 0.930 respectively (ZNF695 (0.954), CHEK1 (0.922) and C15ORF42 (0.921) in the validation group . Ke ZB et\u00a0al. found that ZNF695 is an important marker of radiotherapy resistance in prostate cancer , a member of the CHEK family, is a serine/threonine-specific protein kinase that mediates cell cycle arrest in response to DNA damage , a human homolog of yeast sld3 protein  and chip-seq data from Zeid R\u2019s research  can be found in the article/BJ and YF, conceptualization, methodology, writing-reviewing, and editing. JC and MS, investigation, data curation, and writing-original draft preparation. JC and CC, visualization, validation, supervision, and software. All authors contributed to the article and approved the submitted version.This study was supported by the General Project of Nanjing Medical University (NMUB20210060), National Natural Science Foundation of China (81903383), Natural Science Foundation of Jiangsu Province (BK20211009), Scientific Research Projects of Jiangsu Health Commission (ZDB2020018), China Postdoctoral Science Foundation funded project (2021M701764), Special Fund for Health Science and Technology Development in Nanjing (JQX19008), Nanjing Medical Science and Technology Development Project (YKK21149), Young Talent Support Project of Children\u2019s Hospital of Nanjing Medical University .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher."}
+{"text": "Developments in deep learning techniques have led to significant advances in automated abnormality detection in radiological images and paved the way for their potential use in computer-aided diagnosis (CAD) systems. However, the development of CAD systems for pulmonary tuberculosis (TB) diagnosis is hampered by the lack of training data that is of good visual and diagnostic quality, of sufficient size, variety, and, where relevant, containing fine region annotations. This study presents a collection of annotations/segmentations of pulmonary radiological manifestations that are consistent with TB in the publicly available and widely used Shenzhen chest X-ray (CXR) dataset made available by the U.S. National Library of Medicine and obtained via a research collaboration with No. 3. People\u2019s Hospital Shenzhen, China. The goal of releasing these annotations is to advance the state-of-the-art for image segmentation methods toward improving the performance of fine-grained segmentation of TB-consistent findings in digital Chest X-ray images. The annotation collection comprises the following: 1) annotation files in JSON (JavaScript Object Notation) format that indicate locations and shapes of 19 lung pattern abnormalities for 336 TB patients; 2) mask files saved in PNG format for each abnormality per TB patient; 3) a CSV  file that summarizes lung abnormality types and numbers per TB patient. To the best of our knowledge, this is the first collection of pixel-level annotations of TB-consistent findings in CXRs.Dataset:https://data.lhncbc.nlm.nih.gov/public/Tuberculosis-Chest-X-ray-Datasets/Shenzhen-Hospital-CXR-Set/Annotations/index.html. To the best of our knowledge, unlike other collections that provide coarse bounding-box annotations , this is2.The annotations of lung abnormalities for TB patients in the Shenzhen dataset were collected in collaboration with radiologists at the Chinese University of Hong Kong, China. The Shenzhen CXR dataset includes 662 CXRs, of which 326 are normal cases and 336 are cases with manifestations of TB . The abnOf note, since the JSON files will be publicly available, they could be used as ground truth or comparison in future studies and hackathons as was done recently with a similar set .2.1.x coordinates for all points, y coordinates for all points, and abnormality type. An annotation file in JSON format can be directly visualized by VGG Image Annotator (VIA) [An annotation file for a given image has the same name as the CXR image, except that the extension of \u201cpng\u201d is replaced with \u201cjson\u201d. It includes the following information: filename, image size, abnormality shape (polygon), or (VIA) , a web-b2.2.All mask file names follow the same template: CHNCXR_####_1_****_X.png, where CHNCXR_####_1 is the name of an original CXR PNG image with #### representing a 4-digit numerical identifier and 1 indicating an abnormal CXR image; **** is the type of abnormality, and X ranges from 1 to 19, indicating the mask ID. For a given CXR image CHNCXR_####_1.png including M abnormalities, there will be a total of M masks generated and saved separately in PNG format. Taking the CXR image CHNCXR_0329_1.png as an example, two abnormalities are found: clustered nodule (2mm-5mm apart) and calcified nodule; therefore, two masks are generated with the following names:CHNCXR_0329_1_Clustered_Nodule_(2mm-5mm_apart)_1.pngCHNCXR_0329_1_Calcified_Nodule_2.png.Within the 336 abnormal CXRs, radiological signs of TB are observed only in 330 CXRs. The six CXRs with no radiological signs are CHNCXR_0467_1.png, CHNCXR_0484_1.png, CHNCXR_0606_1.png, CHNCXR_0609_1.png, CHNCXR_0612_1.png, and CHNCXR_0624_1.png. No marks or annotations are generated for these six CXR images.2.3.The CSV file named \u201cStatistics_ShenzhenDataset.csv\u201d provides information on abnormality type and number of occurrences for each TB CXR image. It includes 20 columns, where the first column is the CXR image name, and columns 2 to 20 correspond to the 19 abnormalities. Taking the CXR image CHNCXR_0329_1.png as an example again, both columns \u201cCalcified_Nodule\u201d and \u201cClustered_Nodule_(2mm-5mm_apart)\u201d are assigned 1s, indicating that one calcified nodule and one clustered nodule (2mm-5mm apart) are found in this CXR image. 3.In this paper, we establish a collection of annotations/segmentations for lung abnormalities in the publicly available Shenzhen chest X-ray (CXR) dataset , which e"}
+{"text": "Background: Lung adenocarcinoma (LUAD) is a highly malignant cancer with a bleak prognosis. Pyroptosis is crucial in LUAD. The present study investigated the prognostic value of a pyroptosis-related signature in LUAD.Methods: LUAD\u2019s genomic data were downloaded from TCGA and GEO databases. K-means clustering was used to classify the data based on pyroptosis-related genes (PRGs). The features of tumor microenvironment were compared between the two subtypes. Differentially expressed genes (DEGs) were identified between the two subtypes, and functional enrichment and module analysis were carried out. LASSO Cox regression was used to build a prognostic model. Its prognostic value was assessed.Results: In LUAD, genetic and transcriptional changes in PRGs were found. A total of 30 PRGs were found to be differentially expressed in LUAD tissues. Based on PRGs, LUAD patients were divided into two subgroups. Subtype 1 has a higher overall survival rate than subtype 2. The tumor microenvironment characteristics of the two subtypes differed significantly. Compared to subtype 1, subtype 2 had strong immunological infiltration. Between the two groups, 719 DEGs were discovered. WGCNA used these DEGs to build a co-expression network. The network modules were analyzed. A prognostic model based on seven genes was developed, including FOSL1, KRT6A, GPR133, TMPRSS2, PRDM16, SFTPB, and SFTA3. The developed model was linked to overall survival and response to immunotherapy in patients with LUAD.Conclusion: In LUAD, a pyroptosis-related signature was developed to predict overall survival and treatment responses to immunotherapy. Lung cancer is a worldwide public health problem . The mosPyroptosis is a type of programmed cell death that results in the release of pro-inflammatory cytokines . Pyroptovia the EGFR/Akt signaling pathway. Patients with LUAD who had less GSDMD expression had a better prognosis  and responses to treatment. This study would promote the rationale use of immunotherapy in LUAD.The Cancer Genome Atlas (TCGA) genomic data for LUAD samples were obtained from the Genomic Data Commons. Gene Expression Omnibus (GEO) was used to download gene expression microarrays of LUAD samples (GSE31210) and non-small cell lung cancer (NSCLC) samples (GSE37745 and GSE50081) and lung cancer (GSE30219). The Robust Multichip Average (RMA) method and R package \u201caffy\u201d normalized GSE37745 gene expression data. Detailed information of the cohorts is presented in IMvigor210 was a single-arm phase \u2161 study that looked into an anti-PD-L1 agent (atezolizumab) in patients with metastatic urothelial carcinoma (mUCC) (NCT02108652 and NCT02951767) . The R pA total of 47 PRGs were obtained from the study of The Pathway Commons database was used to find PRG protein\u2013protein interactions. Pearson correlation was used to examine the co-expression status of PRGs . CytoscaBased on the pyroptosis genes and R package \u201cpheatmap,\u201d K-means clustering was used to determine the pyroptosis-related subtypes (subtypes 1 and 2). The Kaplan\u2013Meier survival analysis was performed to analyze patient differences between the two subtypes in conjunction with the log-rank test. The difference between two subtypes based on the PRG expression was investigated using principal component analysis (PCA).We obtained 25 cancer treatment-predicted signature sets from various publications . The R pThe range of infiltration of 22 immune cells in TCGA LUAD samples was inferred by the CIBERSORT (Cell-type Identification by Estimating Relative Subsets of RNA Transcripts) method . CIBERSOp < 0.001. A web-based program, Metascape, was used to perform the enrichment analysis on 719 DEGs using ontology sources such as KEGG Pathway, GO, Reactome, and other canonical pathways  between two subtypes with |log2FC| > 0.5 and pathways . Then, aWGCNA  is a data reduction method and an unsupervised classification method . The co-The least absolute shrinkage and selection operator (LASSO) approach and Cox regression model were employed to screen the prognostic genes in the key module. One standard error (SE) over the minimum threshold was chosen. The R package \u201cglmnet\u201d managed the entire process. Finally, a seven-gene risk score formula was developed, and multivariate Cox regression coefficients were computed using the R package \u201csurvival\u201d: Pyroptosis subtype-related risk score (PSR_score) = (exp Gene1 * coef Gene1) + (exp Gene2 * coef Gene2) + \u2026 +(exp Gene7* coef Gene7).Patients were classified based on the median of their PSR_score. The R package \u201csurvival\u201d used the log-rank test to compare the survival times of patients with high PSR_score and patients with low PSR_score. Furthermore, stratified analysis was performed to determine the protective effect of PSR_score based on the T stage, N stage, M stage, and tumor stage. Chi-square tests were used to examine the connections between the PRG score and clinical factors such as age, gender, T stage, N stage, and M stage. The data were presented using Kaplan\u2013Meier graphs .p < 0.05 was considered statistically significant.The one-sided Wilcoxon rank-sum test was used to determine the difference between the two subtypes or high- and low-PSR_score groups. R version 4.1.2 was used for all statistical studies. p < 0.1). Following that, we investigated the CNV landscape of PRGs in LUAD ( in LUAD . Copy nup < 0.05), with 23 genes showing substantial upregulation and seven showing significant downregulation in tumor samples.Furthermore, we investigated the difference in PRG expression levels between tumor and normal tissues . A totalWe built an interaction network to investigate the relationship between PRGs . The colp = 0.039, log-rank test). According to principal component analysis (PCA), LUAD patients had unique PRG expression patterns between two subtypes . Patients in subtype 2 were found to be more amenable to treatment.The therapeutic differentiation between the subtypes was investigated, and the GSVA approach was utilized to determine the score of 25 therapeutic signature sets in TCGA LUAD data . A totalp < 0.05). \u201cB cells memory,\u201d \u201cmacrophages M1,\u201d \u201cNK cells resting,\u201d, \u201cT cells CD4 memory activated,\u201d and \u201cT cells CD8\u201d showed significantly lower infiltration in subtype 1 than in subtype 2 (p < 0.05). Furthermore, we investigated the tumor purity differentiation across the subtypes, finding that the ESTIMATE score, stromal score, and immune score in subtype 1 were considerably lower than those in subtype 2 (p < 0.05). Furthermore, we investigated the distinction between the subtypes in the ability to recognize tumor cells and execute immune responses. We looked at the differential expression of five immunological checkpoints and discovered that the expression of all the five immunological checkpoints was considerably greater in subtype 2 than that in subtype 1 (p < 0.05). The result indicated that samples in subtype 2 had a higher level of immune infiltration.The differentiation of TME between two subtypes is then evaluated. According to the CIBERSORT algorithm, infiltration of \u201cB cells naive,\u201d \u201cdendritic cells activated,\u201d \u201cmast cells resting,\u201d \u201cmonocytes,\u201d and \u201cneutrophil plasma cells\u201d were higher in subtype 1 than in subtype 2 . The area under the curve (AUC) of the receiver operating characteristic (ROC) curve revealed that PSR_score correctly predicted mortality .The PSR_score of each patient in TCGA was calculated using the seven-gene-involved formula. The patients were divided into two groups using the median as the cutoff value: those with a high PSR_score and those with a low PSR_score. Patients with a high PSR_score had a substantially shorter life expectancy . We combined four GEO lung cancer cohorts into a big dataset to confirm the robustness of PSR_score. Similarly, patients with a high PSR_score had a significantly poor OS , with an AUC of 0.682 , and the results revealed that a high PSR_score indicated a poor prognosis in all GEO datasets . Furthermore, ESTIMATE score of high PRG_score samples was higher than that of low PRG_score samples (p < 0.05).Pearson correlation analysis was performed to assess the relationship between PRG_score and the number of immune cells to study the link between PRG_score and immunological infiltration. Infiltration of \u201cmacrophages M1,\u201d \u201cT cells CD4 memory activated,\u201d \u201cmacrophages M0,\u201d \u201cNK cells resting,\u201d \u201cNK cells activated,\u201d \u201cT cells CD8,\u201d and \u201cdendritic cells activated\u201d was significantly positively connected with PRG_score . In addition, we looked at the differences in immune checkpoint gene expression between high and low PRG_score groups. PD-L1 and CD47 in the high PRG_score group of the IMvigor210 cohort were significantly greater than those in the low PRG_score group (p < 0.05).To further explore if the risk score can predict patients' responses to immunotherapy, we compared OS of patients with a high PRG_score versus low PRG_score who were receiving immunotherapy. In IMvigor210 and GSE135222 cohorts, patients with a high PRG_score had a significantly worse prognosis (Increasing research has proven the role of pyroptosis in cancer progression . However+, and CD8+ T cells. Patients with LUAD have lower numbers of NK cells, CD4+, and CD8+ T cells (+ and CD8+ T cells are critical in mediating antitumor responses. Patients with higher numbers of CD4+ T cells respond better to PD-1 blockade therapy (The score of 25 therapeutic signature sets was calculated to investigate the therapeutic differentiation between the two subtypes. There were 23 therapeutic signatures that differed between the two subtypes. Patients with subtype 2 responded well to the treatment . The dif T cells . CD4+ an therapy . The samFollowing that, DEGs between subtypes 1 and 2 were identified. A total of 719 DEGs were found to be enriched in immune-related pathways and processes, such as \u201cleukocyte activation.\u201d A co-expression network was constructed by WGCNA using these DEGs, and four modules were identified. The blue module was associated with the prognosis and tumor stage .A seven-gene-involved prognosis model was created using LASSO Cox regression to investigate the prognostic value of genes in the blue module, comprising FOSL1, KRT6A, GPR133, TMPRSS2, PRDM16, SFTPB, and SFTA3. The patients were divided into two groups based on the prognostic model: those with a high PSR_score and those with a low PSR_score. Patients in the low PSR_score group have a better OS than those in the high PSR_score group in TCGA cohort. The GEO cohorts yielded comparable results . FOSL1 a+ and CD8+ T cells, and NK cells were all found to be positively linked with the PRG scores. Higher levels of immunological infection were associated with higher PRG scores and ESTIMATE scores. The infection levels of B cells, CD4+ T cells, and neutrophils have prognostic values for LUAD (Then, the correlation between PSR_score and cancer immune features was evaluated. M1 and M0 macrophages, CD4for LUAD . Furtherfor LUAD . Accordifor LUAD .Finally, we investigated the predictive value of PSR_score for immunotherapy response. Patients with a low PRG_score have a greater OS rate than those with a high PRG_score. Furthermore, in the IMvigor210 cohort, PD-L1 and CD47 were strongly expressed in the high PSR_score group . LUAD TMThe current study established a pyroptosis-related signature for predicting OS and immunotherapy responses in LUAD, which may lead to new insights into the individualized LUAD therapy."}
+{"text": "Transcriptome analyses showed that 69 differentially expressed genes between the original type and mutant fruit skin were highly correlated with anthocyanin content. DNA methylation in the promoter of five anthocyanin-associated genes was increased in the mutant compared with the original type as determined using DNA methylation profiling. Among the genetic and epigenetic factors that directly and indirectly influence anthocyanin content in the mutant apple fruit skin, the hypermethylated promoter of MdMYB10 is important. This study indicated that numerous somatic mutations accumulated at the emergence of a bud sport from a genome-wide perspective, some of which contribute to the low coloration of the bud sport.Apple bud sports offer a rich resource for clonal selection of numerous elite cultivars. The accumulation of somatic mutations as plants develop may potentially impact the emergence of bud sports. Previous studies focused on somatic mutation in the essential genes associated with bud sports. However, the rate and function of genome-wide somatic mutations that accumulate when a bud sport arises remain unclear. In this study, we identified a branch from a 10-year-old tree of the apple cultivar \u2018Oregon Spur\u00a0II\u2019 as a bud sport. The mutant branch showed reduced red coloration on fruit skin. Using this plant material, we assembled a high-quality haplotype reference genome consisting of 649.61\u00a0Mb sequences with a contig N50 value of 2.04\u00a0Mb. We then estimated the somatic mutation rate of the apple tree to be 4.56\u2009\u00d7\u200910 Published by Oxford University Press on behalf of Nanjing Agricultural University. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.</p>  <p>Received: 10 January 2022; Revised: 9 June 2022; Accepted: 2 August 2022</p> Bud sports have been widely used for selection of new cultivars in fruit trees, including grape, peach, apple, and citrus . Of variAccurate genome assembly is the basis of genome-wide gene function studies. Due to the high heterozygosity and repetitive sequences, assembling an accurate apple reference genome is a time-consuming and labor-intensive challenge . CurrentMdMYB10 is associated with the red-skinned phenotype of apple .\u20138.6\u20138].de novo approach, we identified ~380.7\u00a0Mb sequences being TEs that represented 58.57% of the \u2018Oregon Spur II\u2019 genome. Long terminal repeat (LTR) retrotransposons accounted for 42.22% of the genome as the most common type of TE. The most abundant LTR retrotransposons were the Gypsy elements (15.46%), followed by Copia elements (11.08%) (Using a (11.08%) .de novo prediction, protein-based homology detection, and RNA sequence mapping  were distributed in the intergenic regions. Only 33 SNPs were located in the coding regions. Of these, 16 were non-synonymous SNPs that may affect protein properties  and 24\u00a0kb upstream of a MATE-type anthocyanin transporter gene (OS_046435)  accounted for 88.44% of the total InDels. Between OS-R and OS-G, 118 somatic InDels were identified. Four InDels were predicted to cause frame shifts in four genes: S_043076 . Of themn factor . Moreoven factor . The del_046435) . AltogetA total of 628.62 million Illumina reads were generated from 24 libraries of OS-R and OS-G at four developmental stages with three biological replicates. Then, RNA-seq reads were cleaned and the resulting clean reads with Q20 and Q30 were mapped to the \u2018Oregon Spur II\u2019 reference genome. About 95.49% of clean reads were mapped to the reference genome . GeneralAt each developmental stage, the transcriptome comparison between OS-R and OS-G was used to identify differentially expressed genes (DEGs). This allowed 4257 DEGs to be identified (3228 non-redundant), which included 680 genes differentially expressed at multiple stages . There wr\u2009=\u2009.92, P\u2009=\u20098\u2009\u00d7\u200910\u221210), colorindex , and lightness  and moderately correlated with redness, yellowness, and hue angle and yellowness   was adopted to identify gene modules and key genes that may lead to the color change of fruit skin. A total of 3228 DEGs were used in the WGCNA analysis and clustered into 16 modules . Analysihue angle. The mod\u2009\u00d7\u200910\u22126) . MoreoveThe results of GO analysis of cluster genes in the \u2018MEpurple\u2019 module showed that the GO terms were \u2018flavonoid metabolic process\u2019, \u2018flavonoid biosynthetic process\u2019, \u2018anthocyanin-containing compound metabolic process\u2019, \u2018pigment metabolic process\u2019, \u2018regulation of flavonoid biosynthetic process\u2019, \u2018response to UV\u2019, and \u2018phenylpropanoid metabolic process\u2019 . As flavMdMYB10 (OS_027409), MdCHI3 (OS_046031), MdMYB6-like (OS_039911), MdUFGT1-1 (OS_046729), MdUFGT1-2 (OS_022154), MdANS-1 (OS_022201), MdANS-2 (OS_042975), MdCHS-1 (OS_040070), MdF3H (OS_012046), Md4Cl (OS_046747), MdDFR (OS_000222), and MdGSTF6 (OS_030227)   . Their e_030227) . Moreove (edges) . The aboOS_007139 (ERF transcription factor), OS_024777 (NB-ARC transcription factor), OS_028744 (nucleoporin and receptor-like protein), OS_008922 (TIR-NB-LRR), OS_005743 , and OS_037028 (O-fucosyltransferase) (OS_046436 (SCARECROW-LIKE) did not show a clear difference in expression pattern between OS-R and OS-G (OS_046435 (MATE-type anthocyanin transporter) showed a higher expression level in OS-R than in OS-G at the last two stages  of fruit skin development  . Of the and OS-G . OS_0464elopment . OS_0464\u2019 module .Epigenetic variations have been reported to cause stable changes in gene expression to generate sport mutants, such as DNA methylation , 13. To To show the global DNA methylation levels, the average 5mC rate of 1-Mb windows through the genome was plotted as a heat map in CG, CHG, and CHH contexts respectively . The metAn Upset plot was used to present the distribution of differentially methylated regions (DMRs) in genes and promoter regions in CG, CHG, and CHH contexts. The OS-R_vs_G comparison group comprised 5283, 2276, and 2025 DMR-associated genes (DMR_genes) as well as 4554, 4452, and 5371 DMR_promoter_genes (genes of DMR-associated promoters) in the CG, CHH, and CHG contexts, respectively . The metTo further understand the function of the DMR_genes and DMR_promoter_genes, GO analysis was performed. The DMR_promoter_genes were enriched in various processes, including response to salt stress and phenylpropanoid biosynthetic process for the CG context , flavonoMdMYB10 , MdUFGT1-1 (CG_hypermethylated_promoter), MdUFGT1-2 (CHG_hypermethylated_promoter), MdMYB6-Like (CHG_hypermethylated_promoter), MdGSTF6 (CHH_hypermethylated_promoter), and MdMYB6-Like (CHH_hypomethylated_genebody)  . On the MdMYB10 promoter were validated using the bisulfite sequencing PCR (BSP) approach. The region from \u22121289 to \u22121012 was used to detect the target 221-bp DNA fragment (\u22121254 to \u22121034) containing 29 CHH, 12 CG, and 7 CHG cytosine methylation sites , middle (2), and basal nodes (3) of shoots on the original-type branch and the mutant branch were collected in 2020 for DNA isolation using a previously described method [This study used a tree produced in 2010 by grafting d method . With thd method . These lL), redness (a), and yellowness (b), were measured using a chroma meter . The other parameters, hue angle (h\u00b0) and color composition index (CCI), were determined according to published methods [Chromatic analyses of fruit skin color were executed following the Commission International de l\u2019Eclairage (CIE) system. The parameters, including lightness  was used to annotate the repetitive sequences, construct the TE library, and filter raw TE candidates. Then the TEs were searched and identified by mapping the sequence to the EDTA library with RepeatMasker [Extensive atMasker .de novo gene prediction and homologous gene prediction along with RNA-seq-assisted prediction to annotate the genome assembly based on the MAKER annotation pipeline [de novo gene prediction was performed by using BRAKER2 with transcriptional data downloaded from NCBI [de novo gene prediction, assembled transcripts, and protein sequences . The Annotation Edit Distance (AED) score was used to filter the gene set. BUSCO was used for the evaluation of annotation completeness with eudicotyledons_odb10 [We used both pipeline . The de rom NCBI , 56 and rom NCBI , 58. Addrom NCBI , 60. Aftns_odb10 .We performed functional annotation by mapping the \u2018Oregon Spur\u00a0II\u2019 protein sequences to SwissProt and NR databases using diamond . The InterProScan (version 5.36) was used to annotate the motifs and domains with the parameter -appl ProDom,PRINTS,Pfam,smart,PANTHER . Based oM. domestica cultivars [The clean paired-end reads of six samples from \u2018Oregon Spur\u00a0II\u2019 and seven published ultivars  were mapultivars . Then thultivars . The pipultivars  was usedultivars  based onSVs were called based on the \u2018Oregon Spur II\u2019 reference genome and the Illumina whole-genome resequencing data of the six \u2018Oregon Spur\u00a0II\u2019 samples using Manta v1.6.0 , LUMPY vM. domestica varieties, PCA and kinship analysis were carried out based on the population SNPs using plink (v1.9) [To investigate the relationship between different samples of the \u2018Oregon Spur II\u2019 tree and other k (v1.9)  and KINGk (v1.9) . The phyk (v1.9) . The popTotal RNA was extracted according to a published method  and usedP\u2009<\u2009.05 [The raw reads were cleaned as described above. After that, the clean reads were mapped to the \u2018Oregon Spur II\u2019 reference genome using Hisat2 (v2.2.1) with default parameters . Then, rP\u2009<\u2009.05 . OS-R anDEGs were selected between OS-R and OS-G at each developmental stage for follow-up analysis. The highly co-expressed gene modules were inferred from the DEGs using WGCNA . A totalq-value <.05) was employed [We used clusterProfiler (v3.18.1) for statistical analysis of GO enrichment, and a significance level  at each developmental stage as described before. The details of the qRT\u2013PCR primers are given in Four BS-seq libraries were constructed using genomic DNA extracted from fruit skin tissues collected from OS-R and OS-G at 120\u00a0DAFB, including two biological replicates for each fruit type. The DNA was treated as described before for constructing the library . FinallyP\u2009<\u2009.05).The raw reads were cleaned by removing adapters and low-quality reads before being mapped to the \u2018Oregon Spur II\u2019 reference genome with Bismark (v0.23.0) and default parameters . Global MdMYB10 promoter in fruit skin of OS-R and OS-G at the four developmental stages as previously described [A BSP assay was conducted to analyze the methylation levels in the escribed . The dett-test and the correlation of two variables was evaluated using Pearson correlation analysis.Statistical analyses were performed using SPSS 20.0 . Three replicates were used to analyze variance and least significant differences. The experimental data were analyzed using Student\u2019s We are grateful to Professor Xiaofei Wang for help in revision of the manuscript. This work was financially supported by the National Key Research and Development Project , the Shaanxi Apple Industry Science and Technology Project (2020zdzx03-01-04), the Studying Abroad Personnel Science and Technology Activity Fund Project of Shaanxi Province (2020-07), the Cyrus Tang Foundation, the Tang Scholar by Cyrus Tang Foundation and Northwest A&F University, and the China Apple Research System (CARS-27).D.Z. and J.L.Y. designed the research. Y.L., N.A., C.P.Z., and X.K.Z. collected the samples. J.J.M., X.H.G., and M.Z.L. performed the experiments. Y.L., L.T., and L.B.X. analyzed the data. Y.L., D.Z., and J.L.Y. wrote the manuscript. M.M.T. revised the manuscript. All authors participated in the research and approved the final manuscript.https://ngdc.cncb.ac.cn/gsa. Genome assembly and annotation data have been deposited at GSA under accession number GWHBJED00000000 and are publicly accessible at https://ngdc.cncb.ac.cn/gwh.All the raw data, as well as whole-genome, whole-genome bisulfite, and transcriptome sequencing reads have been deposited in the Genome Sequence Archive  at the National Genomics Data Center (Nucleic Acids Res 2021), China National Center for Bioinformation/Beijing Institute of Genomics, Chinese Academy of Sciences (GSA: CRA005304) and are publicly accessible at The authors declare that they have no conflict of interest.Horticulture Research online.supp_data_uhac179Click here for additional data file."}
+{"text": "Bougainvillea is known for its specialized, large, and colorful bracts, which contrast with its tiny colorless flowers. As a plant whose bracts vary greatly in terms of coloration, the molecular mechanisms for Bougainvillea bract coloration and polychroism are largely unknown. The lack of genomic information for Bougainvillea largely hinders studies into the evolution and genetic basis of bract color variation. In this study, a pan-transcriptome of bracts obtained from 18 Bougainvillea glabra accessions was employed to investigate the global population-level germplasm kinship and the gene regulation network for bract color variation. Our results showed that the bracts of B. glabra accessions have largely differentiated International Commission on Illumination (CIE) L-a-b values. Moreover, germplasm kinship detected using principal component analysis, phylogeny, and admixture analysis showed three optimal subgroups, two of them distinctly clustered, which were not directly correlated with bract color variation at the population level. Differentially expressed genes (DEGs) between accessions of high vs. low L-a-b values revealed several considerable upregulated genes related to bract color L-a-b variation. A weighted gene co-expression network was constructed, and eight co-expressed regulation modules were identified that were highly correlated with variation in bract CIE L-a-b color values. Several candidate DEGs and co-expressed hub genes  that were tightly associated with bract color variation were eventually determined responsible for L-a-b colorations, which might be the core regulation factors contributing to the B. glabra bract color variation. This study provides valuable insights into the research on germplasm kinship, population-level pan-transcriptome expression profiles, and the molecular basis of color variation of key innovative bracts in horticultural Bougainvillea.Bracts are the metamorphic non-flower organ in angiosperm plants. The variation of the color and shape of bracts was found to be neo-functionalized , garnering research interest as a pollinator attractor.  Neoregelia punctatissima in tropical rain forests can keep water stagnant and form a mutually beneficial symbiosis with Utricularia vulgaris  has always been responsible for attracting pollinators and protecting reproductive organs , the varvulgaris . It has visitors . The bravisitors .Bougainvillea spp., Davidia involucrata, Anthurium andraeanum, Zantedeschia aethiopica, N. punctatissima, Euphorbia pulcherrima, and Euphorbia milii. Several studies focus on the mechanism of development, color formation, and surface structure of bracts, among which some are based on the metabolomics-associated transcriptome  L-a-b values using a spectrometer. The germplasm kinship within these accessions was detected at the global population level by principal component analysis (PCA), a phylogenetic tree, and admixture analysis. Moreover, an analysis of differentially expressed genes (DEGs) related to the high vs. low L-a-b value of bract coloration was conducted. In addition, the weighted gene co-expression network analysis (WGCNA) was performed between pan-transcriptome expression profiles and bract color variation of CIE L-a-b data to construct the essential regulation networks and identify the core regulation factors for B. glabra bract coloration. Finally, the pan-transcriptome profiles linked to B. glabra population-level color variation and associated regulation mechanisms were discussed.Even though there are lots of applications for riations . In thisB. glabra cultivars or accessions cultivated in the nursery of the Institute of Bougainvillea in Yuanshan, Zhangzhou, China. For each examined sample, mature bracts on several branches from a single accession were used. The information on the sampled accessions was listed in Bract samples used in this study were obtained from cuttings (3\u20135 years old) of In order to quantify the variation of bract coloration, the CIE L-a-b value  of standard color space model was used to measure the bract surface color using the AvaSpec-ULS2048CL-EVO high-speed CMOS spectrometer  . The meade novo transcriptome assembly were downloaded from the Genome Sequence Archive (GSA), National Genomics Data Center (NGDC), under Bioproject number PRJCA011746 .The RNA sequencing (RNA-seq) data and B. glabra accessions were mapped onto the de novo assembled pan-transcriptome assembled transcripts using Bowtie2 (The transcriptome reads of the 18  Bowtie2 . The var Bowtie2 . The varB. glabra accessions was constructed with filtered variant sets using the software IQ-TREE 2.1.3 (A maximum likelihood (ML) tree of the 18 EE 2.1.3 . IteratiEE 2.1.3 .To further analyze the genetic relationship among the 18 accessions, the population admixture was constructed using admixture v1.3.0 . The optThe genetic relationship matrix among the 18 accessions was calculated using the software GCTA . The eigThe expression levels of unigenes for bracts among the 18 accessions were quantified using the software RSEM  using BAP-adjust values <0.05 and two times up/down fold changes were screened. Expression data of accessions with the highest and the lowest Aa and Ab values were also processed in a similar way.In DEG analysis, expression data of three accessions with the highest AL values vs. three accessions with the lowest AL values were used for comparison in DESeq2  with mulTo determine the correlation between bract phenotypical color variation and their gene expression levels, a gene expression network based on color variant trait data and gene TPM expression data of bracts from the 18 accessions was constructed using the WGCNA pipeline . After tThe module genes with high correlation with color trait data were exported to calculate the weight nodes and screen the hub genes in the cytoHubba plug-in of Cytoscape software . MaximalP < 0.05.The selected genes from the DEG analysis and the genes in the essential modules from WGCNA were enriched based on the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) database using clusterProfiler package in R . The topB. glabra accessions.The candidate genes from DEG analysis and the hub genes in the top WGCNA modules that correlated with L-a-b values were selected for expression pattern analysis. The expression patterns of selected genes were visualized using pheatmap package in R  accordin\u2212\u25b3\u25b3CT method and normalized by 18S rRNA  was designed to verify the expression trend of candidate genes, and they were compared with transcriptome expression data . Candida18S rRNA . The reaThe color variation of bract surface among the 18 accessions was quantified and represented by L-a-b values Figure\u00a01The phylogenetic tree clustered the 18 accessions into three major subgroups: Group I, Group II, and Group III  values in box plot , cell wall (6/0.00), external encapsulating structure (6/0.00) according to GO enrichment  with the lowest Aa values and the three accessions  with the highest Aa values, 101 significantly upregulated/downregulated genes (76 upregulated/25 downregulated) were identified  with the lowest Ab values and the three accessions  with the highest Ab values, 196 significantly upregulated/downregulated genes (75 upregulated/121 downregulated) were identified  with the lowest AL value and the three accessions  with the highest L value, 119 significantly upregulated/downregulated genes (23 upregulated/96 downregulated) were identified  at 7, which passed the scale independence and mean connectivity standard for the network analysis. Gene expression was detected as an outlier based on the \u201cward.D2\u201d module with all accessions passed. A total of 40 modules were constructed in the WGCNA , MEcyan (0.61/0.007), and MEred (0.62/0.006) were selected for enrichment analysis. GO enrichment indicated that 1,621 genes in these top modules were mainly involved in oxidoreductase activity , terpene synthase activity (6/0.00), GTPase activity (5/0.00), and response to UV-B (3/0.00) , MEsaddlebrown (0.58/0.01), and MEyellowgreen (0.66/0.003) were selected for enrichment analysis. GO enrichment indicated that 473 genes in these top modules were mainly involved in catalytic activity (5/0.00), ATPase activity, coupled to transmembrane movement of substances (3/0.04), endopeptidase inhibitor activity (2/0.01), and S-adenosylmethionine-dependent methyltransferase activity (2/0.02)  were selected for enrichment analysis. GO enrichment indicated that 129 genes in this module were mainly involved in cytochrome-c oxidase activity (3/0.00), response to auxin (2/0.01), and response to stimulus (1/0.01)  gene, one (-)-germacrene D synthase-like (GERD) gene, one chalcone synthase (CHS) gene, and one S-adenosylmethionine synthase 2-like (metK) gene were selected to show the consistency of expression variation with variation in bract Aa value (AOC3) gene, one cytochrome P450 (CYP716A) gene, and one cytochrome P450 76T24-like (CYP76C) gene were selected. In accessions O_07_DP, L_21_SP, L_19_DP, and N_07_SP with the highest Ab value, these genes were upregulated considerably, while in accessions F_06_SZ, Out03_DZ, M_04_SP, and C_02_SZ with lower Ab value, genes were generally downregulated  genes, RING finger and CHY zinc finger domain-containing protein 1 (RCHY1) gene, and one GERD were selected (ABA2) genes, one metK, ditrans,polycis-polyprenyl diphosphate synthase (DHDDS) gene, and chlorophyll b reductase (NOL) gene were selected to show the consistency of expression variation with variation in bract Aa value. In module MEcyan, one ubiquitin-conjugating enzyme E2 A (UBE2A) gene, one allene oxide cyclase (AOC) gene, one molecular chaperone HtpG (HSP90A) gene, and cinnamyl-alcohol dehydrogenase (K22395) gene were selected. In the accessions F_06_SZ and Out03_DZ with the highest Aa value, these genes were generally upregulated, while in the accessions R_01_SW and C_12_DW with lower Aa value, these genes were generally expressed less  gene, one transcription factor C subunit 3 (TFC3) gene, one phospholipase C (PLC) gene, and one acylaminoacyl-peptidase (APEH) gene were selected to show the consistency of expression variation with variation in bract Ab value (ABC1) and member 3 (ABCA3), vacuolar protein sorting 34 (PIK3C3), one protein-lysine N-methyltransferase EEF2KMT (EEF2KMT) gene, one UBX domain-containing protein 7 (UBXN7) gene, one type I protein arginine methyltransferase (CARM1) gene, and one UDP-glycosyltransferase (FG3) gene were selected. In module MEsaddlebrown, one small subunit ribosomal protein S29e (RP-S29e) gene, one glutathione S-transferase T1 (GST) gene, one urate oxidase (uaZ) gene, one sphingosine kinase (SPHK) gene, and one ATP-dependent RNA helicase DHX36 (DHX36) gene were selected. In accessions O_07_DP, L_21_SP, L_19_DP, and N_07_SP with the highest Ab value, these genes were upregulated considerably, while in the accessions F_06_SZ, Out03_DZ, M_04_SP, and C_02_SZ with lower Ab value, genes were generally downregulated  gene, and one F-type H+-transporting ATPase subunit a (ATPeF0A) gene were selected to show the consistency of expression variation with variation in bract AL value (JAZ) genes, one dCTP diphosphatase (DCTPP1) gene, and one tryptophan synthase beta chain (trpB) gene were selected. In the accessions R_01_SW and C_12_DW with the highest AL value, these genes were generally upregulated, while genes were generally expressed in low levels in other accessions  upregulated in accessions of high L-a-b values was chosen for RT-qPCR validation in C_12_DW, D_31_SZ, and F_06_SZ accessions following increased Aa value  exhibited increased expression of GERD. An AOC3 gene (TRINITY_DN6496_c0_g2) upregulated in high Ab value accessions was also selected for RT-qPCR validation in F_06_SZ, L_22_SP, and O_07_DP accessions following increased Ab value  compared with F_06_SZ and an increased expression in O_07_DP  compared with F_06_SZ. A peroxidase (E1.11.1.7) gene (TRINITY_DN1078_c0_g1) upregulated in high L value accessions was chosen to perform RT-qPCR validation in F_06_SZ, O_07_DP, and C_12_DW accessions following increased AL value  compared with F_06_SZ and a reduced expression in C_12_DW  compared with F_06_SZ.Among genes screened from DEG analysis, a analysis 8A left.SGR gene (TRINITY_DN12271_c0_g2) was chosen for RT-qPCR validation in C_12_DW, D_31_SZ, and F_06_SZ accessions following increased Aa value  in F_06_SZ and D_31_SZ accessions. GST gene (TRINITY_DN671_c0_g2) was selected for RT-qPCR validation in F_06_SZ, L_22_SP, and O_07_DP accessions following increasing Ab value. The expression of GST in O_07_DP  was increased compared with F_06_SZ but no significant difference between F_06_SZ and L_22_SP (JAZ gene (TRINITY_DN16399_c0_g1) was also selected for RT-qPCR validation in F_06_SZ, O_07_DP, and R_01_SW accessions following increased AL value. The JAZ gene showed reduced expression in O_07_DP  compared with F_06_SZ but significant upregulation in R_01_SW compared with F_06_SZ   genes that were involved in the flavone, flavonol, and betalain biosynthesis  genes found in these modules that participated in the carotenoid biosynthesis might also be involved in these processes  genes participate in flavonoid transport  genes involved in oxidative phosphorylation during fruit ripening and petal senescence  as the hub genes showed high expression in brightest white accession C_12_DW. Several studies showed that COX protein accumulated in ripping fruits and petal senescence  and can be decolorized by peroxidase  were enriched in MAPK signaling pathway, with four tructure . Studieselopment . In addinescence . The CYPsynthase  might acroxidase .B. glabra at the population level. The bract color variation among the different accessions of B. glabra was quantified in continuous CIE L-a-b values. The germplasm kinship showed that B. glabra accessions clustered into three subgroups with two of them distinctly clustered but not directly related to color variation. The pan-transcriptome-level DEG analysis and co-expression network of the 18 accessions of B. glabra were achieved. Several DEG candidates and hub genes  in the co-expression network were determined that might be involved in regulating the B. glabra bract color variation of L-a-b values. Our research will provide the important foundation for the studies into the evolution and regulation mechanism of bract traits at the population level with the pan-transcriptome, as well as the application of ornamental traits in horticultural plants.In this study, a pan-transcriptome was employed to study the germplasm kinship and regulation network of bract color variation in The datasets used in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/XM and JY conceived the project and designed the experiments, HH, HJ, SJ, WL, and LG collected the samples. HH, HJ, WL, and JL conducted the experiments, HH, HJ, SJ, and XM processed and analyzed the data. SJ, XL, and LL visualized the experimental results. JL, HH, and XL conducted the RT-qPCR experiments. HH and XM wrote the manuscript. XM, HJ, and DQ revised the manuscript. All authors read and approved the final manuscript.This work has been supported by grant from The Earmarked Fund of Science and Technology Innovation for Fujian Agriculture and Forestry University to XM (Project No. KFb22112XA), The National Natural Science Foundation of China to XM (Project No. 31700199), and The Earmarked Fund for Jiangxi Agriculture Research System to HJ (Project No. JXARS-17).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher."}
+{"text": "BB genotype show higher lambing number compare with the sheep with FecB++ genotype. This research aims to compare gene expression by small RNA-seq in adrenal tissues at follicular (F) and luteal (L) phases in FecBBB (MM) and FecB++ (ww) sheep. After analysis of gene expression, significant differentially expressed microRNAs (DEMs) and corresponding target genes were identified.The adrenal gland participates in the process of sheep reproduction. MicroRNAs (miRNAs), endogenous small noncoding RNAs, regulate gene expression at the posttranscriptional level. However, the miRNA-mRNA network profile of adrenal glands relating to reproduction in sheep is still not well-studied. As sheep with FecBTDRD3, ANAPC7, CCNL2, BRD2 and MUT, were related to the transformation from the follicular phase to the luteal phase. PLAC8L1, NFAT5, DDX24 and MBD1 were related to the high fecundity of small tail Han sheep.A total of 180 miRNAs were found in this study, of which 19 DEMs were expressed in the four comparison groups . Subsequently, 354 target genes of 19 DEMs were predicted by integrated analysis. Cluster analysis was performed by K_means_cluster, and the expression patterns of these DEMs were separated into four subclusters. Functional analysis of target genes was performed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). The results indicated that the target genes were involved mainly in the Notch signaling pathway, signal transduction, cell communication, innate immune response and amino acid metabolism. Specifically, the Notch signaling pathway, biosynthetic process and metabolic process of pyrimidine nucleotide and amino acid metabolism appear to play key regulatory roles in the sheep fertility trait. Furthermore, miRNA-mRNA interaction networks were constructed by differentially expressed genes combined with our previous study of transcriptome data. The results showed that several key genes, including In this study, the miRNA transcriptome profile was identified, and miRNA-mRNA interaction networks were constructed in adrenal gland tissue of small tail Han sheep, the interaction between miR-370-3p and its targets were considered to play a major role in the reproduction regulation process. The results enriched the number of known miRNAs in adrenal glands and provided novel ideas and further information to demonstrate posttranscriptional regulation mechanisms at follicular and luteal phases in different genotypes of small tail Han sheep.The online version contains supplementary material available at 10.1186/s12863-022-01060-y. Small ruminants, particularly native breeds, play a significant role in the livelihoods of a considerable part of the human population from socioeconomic aspects . Thus, cMoreover, the epigenome comprising different mechanisms, e.g., DNA methylation, remodeling, histone tail modifications, chromatin microRNAs and long noncoding RNAs, interacts with environmental factors such as nutrition, pathogens, and climate to influence the expression profile of genes and the emergence of specific phenotypes . Multi-lIn this study, small RNA-seq was used to detect the significantly differentially expressed miRNAs in the four groups. Target genes of miRNAs were predicted by three software programs, and Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of these target genes were conducted. Several genes responsible for reproduction were screened after analysis, which may provide a further understanding of the interaction between the HPG and hypothalamus\u2013pituitary\u2013adrenal (HPA) axes. In addition, the lncRNA and circRNA data acquired from small RNA-seq were also processed and analyzed. As the different mechanism and influence among miRNA, lncRNA and circRNA, the methods and analysis were different.. Therefore, the corresponding study of different RNAs was discussed and reported independently .The small RNA-seq data of 12 samples (according to different FecB (BB and\u2009+\u2009+) genotypes and different stages at the estrus cycle divided into 4 groups and 3 individuals in each group, named MM_F_A (FecBBB at follicular phase), MM_L_A , ww_F_A (FecB++ at follicular phase) and ww_L_A , respectively) were subjected to quality control, and the Q30 values ranged from 93.14% to 96.48%. The results are shown in Table After length filtering, known miRNA analysis was performed by comparison with the specified range sequence in miRbase. The range from 3 076 452 to 5 122 424 unique sequences  was completely matched to the miRNAs of the sheep. Structural predictions of precursor sequences were conducted, and 149 known miRNAs and 106 hairpin miRNAs were identified in adrenal gland tissue. After elimination of the tRNA, rRNA, snoRNA, and other snRNA sequences, the comparison between the remaining sequences and the mature sequence of miRNAs from sheep in miRbase was then performed. As a result, 31 mature novel miRNAs and 32 novel miRNAs were predicted by the iconic hairpin structure of the precursor of miRNA Fig.\u00a0. The aveThe expression levels of the identified miRNAs in the four comparison groups were compared. Under the criteria of a Q-value\u2009<\u20090.01 and | log2(fold change) |>\u20091, a total of 19 DEMs (9 up- and 10 downregulated) were obtained from the MM_L_A vs. ww_L_A, MM_F_A vs. ww_F_A, MM_F_A vs. MM_L_A and ww_F_A vs. ww_L_A comparison groups. In particular, there were 9 DEMs (4 down- and 5 upregulated) in the MM_F_A vs. MM_L_A group and 6 DEMs (4 down- and 2 upregulated) in the MM_L_A vs. ww_L_A groupFig.\u00a0. These DTo study the function of DEMs in the four comparisons, 4 893 and 9 318 target genes of known and novel miRNAs were predicted, respectively. Subsequently, GO term enrichment of the predicted target genes was analyzed. In the biological process category, 485, 1057 and 522 enriched GO terms were obtained from the MM_F_A vs. MM_L_A, MM_L_A vs. ww_L_A and ww_F_A vs. ww_L_A comparisons, respectively. As shown in Fig.\u00a0A total of 99, 3, 118 and 27 enriched KEGG pathways were obtained from MM_F_A vs. MM_L_A, MM_F_A vs. ww_F_A, MM_L_A vs. ww_L_A and ww_F_A vs. ww_L_A comparisons, respectively  was conducted to detect the expression levels of 10 miRNA-mRNA pairs selected randomly. As shown in Fig.\u00a0MiRNAs, as inhibitors, play pivotal roles in the regulation of life at the gene expression and posttranslational levels . In manyIn this study, the results of family analysis showed 10 miRNA families with more than two members, and the expression levels of these members were abundant in small-tailed Han sheep adrenal gland tissue. The most abundant miRNA was the let-7 family, seven members of which were detected in small-tailed Han sheep adrenal glands. In particular, six members, including let-7\u00a0g, let-7f, let-7i, let-7a, let-7b, and let-7c, were expressed in high abundance in different phases of different genotypes in small-tailed Han sheep, which was consistent with the characteristic of the highly conserved seed sequence among multiple let-7 isoforms . Among tIn the miRNA-mRNA interaction networks of MM_F_A vs. MM_L_A comparison and ww_F_A vs. ww_L_A comparison, miR-370-3p targeted 58 mRNAs in the MM MM_F_A vs. MM_L_A group and 14 mRNAs in the ww_F_A vs. ww_L_A group, which showed the importance of miR-370-3p in the process from follicular phase transformation to luteal phase. Interestingly, there were 8 common targets of miR-370-3p between the MM_F_A vs. MM_L_A group and ww_F_A vs. ww_L_A group, including TDRD3, ANAPC7, CCNL2, LOC101107851, BRD2, LOC101111607, LOC101120489 and MUT. A previous study indicated that in stress granules, TDRD3 is located in the cytoplasm, and its Tudor domain which could recognize methylated motifs can promote survival upon treatment with chemotherapeutic drugs in cancer cells . A studyIn the miRNA-mRNA interaction networks of MM_F_A vs. ww_F_A and MM_L_A vs. ww_L_A groups, miR-370-3p was also the key miRNA, which targets 34 mRNAs in MM_F_A vs. ww_F_A group and 101 mRNAs in MM_L_A vs. ww_L_A group. There were 4 identical targets of miR-370-3p: PLAC8L1, NFAT5, DDX24 and MBD1. DDX24 genes affect the development of ovaries and follicles in sheep . A studyThe most enriched GO term was positive regulation of the Notch signaling pathway in both the MM_L_A vs. ww_L_A and ww_F_A vs. ww_L_A comparisons. As one of the conservative pathways, the effect of the Notch signaling pathway is involved mainly in cell proliferation, differentiation, apoptosis, and adhesion, especially in germ cell differentiation, to become involved in the process of growth, development, and decay in living organisms , 58. PTGIn the KEGG enrichment analysis of MM_F_A vs. ww_F_A comparison, several key genes were found to participate in the reproductive process, including UBE2R2 (ubiquitin conjugating enzyme E2 R2), CDC34 and UCHL1. Ubiquitin-conjugating enzymes play important roles in the cell cycle . PreviouIn conclusion, the adrenal glands play a key role in sheep female reproductive processes and affect the reproduction of sheep through the hypothalamic\u2013pituitary\u2013adrenal axis. The adrenal glands are regulated by a variety of factors from genes to hormones. These processes are achieved by regulating different signaling pathways and related genes. In this study, we screened the DEMs (21 DEMs with 10 upregulated and 11 downregulated) and their targets (351) and constructed networks of interactions between miRNAs and mRNAs. The miRNA-mRNA pairs associated with sheep fertility were enriched by GO and KEGG analysis. Taken together, our study affirms the significance of miRNA-mRNA pathways in sheep adrenal glands. The results of the study will help us to establish a greater degree of acknowledgment of the regulatory mechanisms of miRNA-mRNA pairs in sheep reproduction.All animals were authorized by the Science Research Department  of the Institute of Animal Sciences, Chinese Academy of Agricultural Sciences . In addition, ethical approval of animal survival was given by the animal ethics committee of IAS-CAAS (No. IAS2019-449). The study was carried out in compliance with the ARRIVE guidelines. All methods were carried out in accordance with relevant guidelines and regulations.Samples used in the study were collected from small-tailed Han sheep in the Luxi area of Shandong Province, P. R. China. All sheep were fed equally under the same conditions. Healthy nonpregnant sheep aged 2 to 4\u00a0years were chosen. A total of 12 sheep (six ww and six MM) were used for the experiments. Synchronized estrus was performed on all sheep by vaginal sponges  (progesterone 300\u00a0mg) for 12\u00a0days.Fifty hours after removing the vaginal sponges, three ww and three MM ewes were euthanized ) to obtain the adrenal glands , and the remaining six sheep (three in each group) were euthanized ) on the 7th day after sponge removal  to obtain the adrenal glands. All adrenal glands were immediately put into liquid nitrogen after slaughter and stored at\u2009\u2212\u200980\u00a0\u00b0C for total RNA extraction. .Total RNA was extracted according to the manufacturer\u2019s instructions using TRIzol . To detect the degradation and contamination of RNA, agarose gels with a concentration of 1% were used for electrophoresis. A NanoPhotometer\u00ae spectrophotometer  was used for RNA purity detection. Qubit\u00ae RNA Assay Kit in Qubit\u00ae 2.0 A fluorometer  was used to determine the RNA concentration. The RNA Nano 6000 Assay Kit of the Agilent Bioanalyzer 2100 system  was used for RNA integrity detection.https://www.ncbi.nlm.nih.gov/sra/?term=PRJNA729910].An RNA library was established by the input material . Sequencing libraries were generated using NEBNext\u00ae Multiplex Small RNA Library Prep Set for Illumina\u00ae  Under the manufacturer\u2019s recommendations, index codes were added to attribute sequences to each sample. Briefly, the 3\u2019 ends of miRNAs, siRNAs and piRNAs were directly and specifically ligated with the NEB 3\u2019 SR adaptor. To prevent adaptor-dimer formation, after the 3\u2019 ligation reaction, the SR RT Primer hybridized to the excess of 3\u2019 SR adaptor (which remained free after the 3\u2019 ligation reaction) and transformed the single-stranded DNA adaptor into a double-stranded DNA molecule. In addition, as dsDNAs are not substrates for ligation mediated by T4 RNA ligase 1, dsDNAs do not ligate to the 5\u2019 SR adaptor in the subsequent ligation step. First strand cDNA was synthesized by M-MuLV Reverse Transcriptase (RNase H\u2212). LongAmp Taq 2 x Master Mix, SR Primer for Illumina and index (X) primers were used for PCR amplification. An 8% polyacrylamide gel  was used for PCR product purification. The DNA fragments corresponding to 140\u2013160\u00a0bp  were recovered and subsequently dissolved in 8 \u03bcL elution buffers. Finally, DNA High Sensitivity Chips were used for library quality assessment on the Agilent Bioanalyzer 2100 system. The datasets generated during the current study are available in the SRA public database repository [Known miRNAs were acquired using the mapped small RNA tags. Potential miRNAs and secondary structures were obtained by modified software mirdeep2 and sRNA-tools-cli in the case of miRBase20.0 as the reference . Custom Novel miRNAs were predicted by the available software miREvo and mirdeep2 according to the characteristics of the hairpin of the miRNA precursor, the Dicer cleavage site and the minimum free energy of the small RNA tags unannotated in the former steps , 71. At http://www.mirbase.org/ftp.shtml) was used to look for families of known miRNAs. Rfam (http://rfam.sanger.ac.uk/search/) was used to look for families of novel miRNAs.miFam.dat  was used for differential expression analysis of the two groups. The  Q-value . A Q-valPredicting the target gene of miRNA was performed by miRanda for animals , 74. TheFor the target gene candidates of differentially expressed miRNAs (\u201ctarget gene candidates\u201d in the following), Gene Ontology (GO) enrichment analysis was conducted. To adjust the gene length bias, a GOseq-based Wallenius noncentral hypergeometric distribution  was usedhttp://www.genome.jp/kegg/) [KEGG is a database resource for understanding the high-level functions and utilities of biological systems, such as cells, organisms and ecosystems (p/kegg/) \u201381. KOBAp/kegg/) .https://cytoscape.org).To investigate the functions of DEMs and their target genes in sheep prolificacy, the miRNA-mRNA network was constructed based on the data come from miRNAs and our previous study of mRNAs . The diaP\u2009<\u20090.05 was considered to be a significant difference. The miRNA primers were designed by RiboBio Company , and the target gene primers are shown in Table To verify the expression levels of differentially expressed miRNAs and their targets, reverse transcription and qPCR were conducted. Three samples were used for biological duplication in each group. RNA samples were the same with the samples used for RNA-seq. RT reagents  were used for reverse transcription. With U6 small nuclear RNA as an endogenous control to normalize target gene expression, all experiments were performed in triplicate. qPCR was performed on a LightCycler 480II  using SYBR Premix Ex Taq II. The procedure involved 40 cycles of predenaturation at 95\u00a0\u00b0C for 10\u00a0min and denaturation at 95\u00a0\u00b0C for 2\u00a0s. After the reaction was completed, melting curve analysis was performed. The relative expression levels were determined using the 2\u2212\u25b3\u25b3Ct method . The P vAdditional file 1:\u00a0Supplementary Fig. 1.\u00a0Purity detection of the 12 extracted RNA samplesAdditional file 2: Supplementary Fig. 2.\u00a0PCR products of the ten mRNAs used in the data validationAdditional file 3:\u00a0Supplementary Table 1. Readcount and TPM of each miRNA detected in the 12 samples\u00a0Additional file 4:\u00a0Supplementary Table 2. RQ data of miR-376c-5pAdditional file 5:\u00a0Supplementary Table 3. Fold change and p-value for differentially expressed miRNAs"}
+{"text": "Mass cytometry (CyTOF) has become a method of choice for in-depth characterization of tissue heterogeneity in health and disease, and is currently implemented in multiple clinical trials, where higher quality standards must be met. Currently, preprocessing of raw files is commonly performed in independent standalone tools, which makes it difficult to reproduce. Here, we present an R pipeline based on an updated version of CATALYST that covers all preprocessing steps required for downstream mass cytometry analysis in a fully reproducible way. This new version of CATALYST is based on Bioconductor\u2019s SingleCellExperiment class and fully unit tested. The R-based pipeline includes file concatenation, bead-based normalization, single-cell deconvolution, spillover compensation and live cell gating after debris and doublet removal. Importantly, this pipeline also includes different quality checks to assess machine sensitivity and staining performance while allowing also for batch correction. This pipeline is based on open source R packages and can be easily be adapted to different study designs. It therefore has the potential to significantly facilitate the work of CyTOF users while increasing the quality and reproducibility of data generated with this technology. Currently, mass cytometry is transitioning from an exploratory research approach toward a diagnostic tool used in clinical laboratories and this transition is associated with an increased need for standardization. Various studies have already suggested improvements on the experimental workflows to increase the robustness of mass cytometry data by working with frozen antibody cocktails or by including shared reference samples in each independent experiment to enable for batch correction. Similarly, advanced downstream analyses benefit from the large number of analysis tools and algorithms implemented in R, which allow for fully reproducible analyses.Over the past decade, mass cytometry (CyTOF) has advanced our understanding of a wide range of cellular processes, particularly in the field of immunology and tumor biology. Upon data collection, the first step consists in concatenating files from sequential CyTOF acquisitions and removing events with unstable signal, which are usually caused by uneven flow rate or introduction of air in the fluidic system. As a second step, CyTOF data need to be corrected for time dependent signal drift, which is mostly due to cone contamination, mass calibration drift or loss of detector sensitivity over time. This correction is performed by acquiring metal tagged polystyrene beads together with the cell suspension, where bead signals can be used as a reference to normalize the cell signals. Another potential artefact in CyTOF data is due to signal spillover between channels. Although lower than what is usually observed in fluorescent flow cytometry, spillover in mass cytometry can still account for up to 4% of the signal in some channels and needs to be corrected using signal compensation. Sample barcoding prior to staining is a common approach used in mass cytometry to combine multiple samples in a single experiment to minimize experimental variation due to staining and CyTOF acquisition. In this case, individual cells have to be assigned to their respective sample via a process called single cell debarcoding. In large studies where samples are collected over a long period of time by different users, on different machines or at different sites, an important step is to correct for batch effects, which can be achieved by including a shared control sample in each independent batch. Finally, only live, intact single cells are relevant for the downstream analysis. Beads, doublets, debris and dead cells are excluded by gating on scatter plots.Between data generation and downstream data analysis, data preprocessing is an multi-step procedure required to convert raw FCS files into data objects that can be input to downstream statistical analysis and visualization). This approach necessitates uploading the data to different platforms and carrying out certain steps in a purely manual fashion, which makes it time-consuming and difficult to reproduce. This is particularly limiting in a clinical setting, where reproducibility and large-scale data analysis are required. Thus, we propose a semi-automated R-based preprocessing pipeline for CyTOF data that is: i) fully reproducible; ii) includes quality checks and, iii) has limited need for supervision once the original setup has been made. This pipeline is developed around an updated version ofCATALYST, an R package designed for preprocessing and differential analysis of mass cytometry data. This new version ofCATALYST is based on Bioconductor\u2019sSingleCellExperiment class, the standard for high dimensional single cell data analysis. This pipeline can easily be adapted to each CyTOF user\u2019s needs and will accelerate CyTOF data preprocessing while improving the quality of mass cytometry data generated.Each step of the preprocessing pipeline requires expert decisions to determine the best parameters to achieve an optimal signal correction and cell selection. Moreover, all the chosen parameters should be recorded for reproducibility purposes. Despite these requirements, many current preprocessing pipelines still rely on switching between platforms that include, for example, MATLAB applications and  closed source online platforms  to identify dead cells and subsequently fixed with PFA 1.6% . Samples were stored as dry pellet at\u221280\u00b0C until CyTOF measurement.The samples of interest included tumor biopsies and blood samples collected at the University Hospital Zurich in spring 2020. These samples were assessed by mass cytometry in the context of a set of references including commercially available cell lines, PBMCs from healthy donors and PHA activated PBMCs. PBMCs from patients and healthy donors were collected based on a ficoll gradient. Reference samples were selected to contain positive and negative populations for each marker included in the study\u2019s antibody panel. This design was chosen to enable for quality control and batch correction across independent experiments based on quantile scaling as described inTM Intercalator-Ir . Finally, the cell suspension was diluted 1:10 in Maxpar\u00ae Cell Acquisition Solution  and 10% of EQ Four Element Calibration Beads , and acquired on a Helios\u2122 upgraded CyTOF 2 system at a flow rate of 150 events per second.The dataset used in this study was obtained from a single CyTOF experiment, also called batch, where nine references, two blood samples and two tumor samples were barcoded with a 20-well barcoding plateThroughout this workflow, we will make use of a set of metadata for standard preprocessing steps , as well as various quality controls previously acquired over seven independent experiments. An overview of the metadata used is given inSingleCellExperiment (SCE) class from theSingleCellExperiment package. This data structure can store all single-cell related data , allowing for synchronized and thus less error-prone data manipulation.Most data used and returned throughout this workflow are kept in an object of Bioconductor\u2019sassays, where rows are features (targets) and columns are observations (cells), that store the measurement data and any data derived thereof. Metadata associated with cells are stored undercolData, feature metadata underrowData, and any experiment-wide metadata may be stored in themetadata slot. Lastly, the SCE can store an arbitrary number of dimensionality reductions underreducedDims. For a more detailed description of usage and structure of SCEs, we refer to theSingleCellExperiment package\u2019s documentation.The key component of SCEs are matrix-likeThe pipeline presented here describes all steps required to process raw mass cytometry data to a state where the user may proceed with downstream analyses . The process includes the concatenation of the individual acquisitions, the exclusion of part of the acquisition with unstable signal, the correction for time-dependent signal drift via bead normalization, the correction for signal spillover via compensation, the selection of cells of interest via automated gating, and the correction for batch effects. The workflow is exemplified on data from a single CyTOF experiment collected via three successive acquisitions  of 15 barcoded samples mixed with calibration beads.data/ subdirectory (relative to where the code is being run); otherwise, the presented file paths require modification.Throughout, raw measurement data (FCS files) as well as all metadata  are expected to be located inside aCATALYST to perform key preprocessing steps, including: concatenation, normalization, debarcoding and compensation;openCyto andflowWorkspace for gating;ggplot2,ggcyto andpatchwork for visualization;flowCore, `r CRANpkg(\u201creshape2\u201d)\u00b4 anddplyr for data accession and manipulation; andmvtnorm to compute polygonal live gates. Thus, our workflow is limited to the following dependencies:We uselibrary(CATALYST)library(dplyr)library(flowCore)library(flowWorkspace)library(ggcyto)library(ggplot2)library(mvtnorm)library(openCyto)library(patchwork)library(reshape2)n = 7) as a reference. For consistent visualization, we define a common plotting theme for boxplots that are used to compare the current to previous experiments:Besides standard preprocessing steps, we include quality control (QC) steps to assess CyTOF sensitivity, staining efficacy, and cell yield; these rely on results from previous experiments (qc_theme <- list( theme_bw(base_size = 8), theme,  axis.text = element_text(color = \"black\"),  axis.text.x = element_text))flowCore\u2019sread.FCS function, which underliesread.flowSet for reading in a set of FCS files, transforms channel intensities and removes events with extreme values. To omit this behavior, we recommend reading in files with argumentstransformation = FALSE andtruncate_max_range = FALSE; by default, files will be read in byCATALYST\u2019s prepData function with these settings.By default,CATALYST overwrites by default.As described above, the SCE class allows the keeping of multiple data transformations in a single object. Thus, when applying a transformation to arrive at expression-like data, we can store the transformed data in a separate assay without overwriting the raw ion count data. In this way, any data generated and used throughout preprocessing  can be in principal retained, and written to intermediate FCS files for backup or quality control outside of R. However, it is worth noting that this procedure could lead to a shortage of memory for large datasets, in which case overwriting the data at each step is advisable; if not specified otherwise,CATALYST\u2019s prepData function, which accepts a path to a directory with one or many FCS files, a character vector of FCS filenames, a single or list offlowFrame(s), or aflowSet (flowCore package). By default (transform = TRUE), an arcsinh-transformation withcofactor = 5 is applied to the input (count) data, and the resulting expression matrix is stored in theexprs assay slot of the output SCE:A SCE can be constructed using# construct \u2019SingleCellExperiment\u2019fcs <- list.files)## class: SingleCellExperiment## dim: 63 368152## metadata(2): experiment_info chs_by_fcs## assays(2): counts exprs## rownames(63): 75As CD15 ... 208Pb CD45## rowData names(4): channel_name marker_name marker_class use_channel## colnames: NULL## colData names(1): sample_id## reducedDimNames(0):## mainExpName: NULL## altExpNames(0):counts) and cofactor-5 arcsinhtransformed counts (assayexprs). The cofactor used for transformation is stored inside the object\u2019s internal metadata (int_metadata(sce)$cofactor), and the FCS file of origin for each cell under cell metadata columnsample_id (accessible viacolData(sce)$sample_id or, equivalently,sce$sample_id). In our dataset, FCS files correspond to acquisitions rather than biological samples. Thus, we rename the cell metadata variablesample_id tofile_id to avoid ambiguity:Initially, our SCE has two assays containing dual ion counts ))names(colData(sce))[i]  <- \"file_id\"ncol(sce): 368152). We can summarize the number of cells in each file by tabulating thefile_ids:The total number of cells across all acquisitions corresponds to the number of columns in the SCE (data.frame(   file_id = levels(sce$file_id),   n_cells = tabulate(sce$file_id))##  file_id n_cells## 1   V1  48675## 2   V2 125607## 3   V3 193870sce variable above,prepData defaults to using targets as rownames (when available). We can retrieve each feature\u2019s measurement channel using thechannels accessor, and use channel metals and masses to extract the indices of features that are relevant to different preprocessing steps. Namely, we assign channels measuring DNA to the variabledna , and channels for live gating  tolive:In both mass and flow cytometry, each feature has both a channel and target associated with it. As can be seen from printing the# store character vector or channels nameschs <- channels(sce)# store DNA & live channel indicesdna <- greplive <- grepHigh quality data generation requires a stable signal throughout the acquisition. Various issues can lead to signal change over time, including unstable flow rate, introduction of air or introduction of metal contamination in the system. These changes in signal intensity can vary in terms of duration and intensity, and can affect all or only a subsets of channels simultaneously. In order to detect regions of the acquisition affected by signal instability, we display the signal for selected channels as a function of time in a scatter plot .# plot channels of interest vs. timecoi <- chs[c$use_channel))]plotScatter, label = \"both\") +   labs(y = \"expression\") +   scale_x_continuous(     expression(\"Time (\"*10^6~\"ms)\"),     labels = function(u) u/1e6) +   theme_bw(base_size = 8) + theme,     strip.background = element_rect(fill = NA))pop = \"+\"). Vice versa, it is possible to select a region with unstable signal, and remove it from the SCE object (pop = \"-\"). For the sake of completeness, we include how a region of unstable signal could be excluded via manual gating:In this particular experiment, we do not observe time-related signal instability. In case part of the acquisition should be excluded, this could be done by manually gating on the region with stable signal, and subsequent subsetting to only retain cells that fall within the gate\u2019s boundaries gs <- GatingSet(flowSet(ff))# apply rectangular gate to exclude unstable signalmin_t <- ...max_t <- ...gs_add_gating_method,   gating_method = \"boundary\",   gating_args = sprintf,max=c\", min_t, max_t))# plot scatter of DNA vs. Timeggcyto) +   geom_hex(bins = 128) +   geom_gate(\"stable\") +   facet_null + theme_bw +   ggtitle(NULL) + theme# subset selected eventssce <- sce. These beads are in turn used to estimate and correct for the signal\u2019s time drift. When independent experiments have to be analyzed in the same context, variation due to changes in instrument performance over time combined with intervals between scheduled maintenance have to be taken into account as well. In this case, the bead signal should be normalized to a set of reference beads from an earlier experiment. This ensures that different experiments are normalized to the same level, independent of the CyTOF\u2019s sensitivity.In the case of mass cytometry, signal drift during acquisition due to a progressive loss of sensitivity must be accounted and normalized for. A widely established strategy is to mix samples with polystyrene beads embedded with metal lanthanides, allowing monitoring of instrument performance throughout data acquisitionnolanlab/bead-normalization; current R implementations are available throughCATALYST andpremessa.CATALYST provides an extension of bead-based normalization as described by Fincket al., with automated identification of bead singlets , as well as of bead-bead and cell-bead doublets (to be removed), thus eliminating the need for manual gating. This is implemented as follows:A MATLAB tool to perform normalization outside of R was available until recently at1. beads are initially identified as those events that have their highest signals in the bead channels2. cell-bead doublets are removed by applying a separation cutoff on the distance between the lowest bead and highest non-bead channel signal3. events passing all vertical gates defined by the lower bounds of bead signals are removed (these include bead-bead and bead-cell doublets)4. median \u00b1 5mad rule to events identified in step 2; the remaining bead events are used for normalizationbead-bead doublets are removed by applying a defaultnormCytof, which takes as input a SCE and a set of arguments that control the normalization parameters and output format. Here, we specifydna = 191 (Ir191) andbeads = \"dvs\", corresponding to DVS Science beads . Secondly, we provide the path to a set of reference beads (argumentnorm_to) that are used to compute baseline intensities for normalization. Lastly, we setoverwrite = FALSE to retain both raw and normalized data, andremove_beads = TRUE to exclude bead and doublet events:The above procedure is carried out by a single function,# specify path to reference beadsref_beads <- file.path# apply bead-based normalizationsystem.time)##  user system elapsed## 20.134  1.343 21.963remove_beads = TRUE (the default),normCytof will return a list of three SCEs containing filtered, bead and remove events, respectively, as well as twoggplot objects:Whennames(res)## [1] \"data\"  \"beads\"  \"removed\" \"scatter\" \"lines\"res$data) contains the filtered data with the additional assay slot\"normed\" housing normalized expressions. The remaining two SCEs are data subsets that contain any events identified as beads (slotbeads) and all removed events , respectively; thus, thebeads themselves are a subset of theremoved events. Here, we compare the number and percentage of cells contained in each subset:The first SCE ps <- sprintf*100)data.frame))##       data beads removed## # events  337525 27544  30627## % of total 91.68 7.48  8.32res$scatter . A logical vector of which channels correspond to beads is stored underrowData columnbead_ch, which we can use to subset thecounts assay to include bead channels only.In order to assess the sensitivity of the CyTOF during acquisition and identify potential issues that would have remained undetected during the tuning of the instrument, we compute the mean bead channel counts across events identified as beads (# compute mean bead channel counts for current experimentis_bead <- rowData(res$beads)$bead_ch  # get bead channelsbead_cs <- counts(res$beads) # subset countsrownames(bead_cs) <- chs[is_bead]    # use channels as names(bead_ms <- rowMeans(bead_cs))      # compute means## Ce140Di Eu151Di Eu153Di Ho165Di Lu175Di## 2842.462 2111.367 2660.618 2538.095 2323.409ref_bead_counts.csv. The resulting boxplot )# join into single tidy data.framedf <- bind_rowsdf <- melt# boxplot of reference vs. current experiment's mean bead channel countsggplot) +  geom_boxplot +  geom_point +  labs +  qc_theme + ggtitleAfter normalization, we overwrite the input dataset with the filtered subset that no longer includes bead events, or bead-bead and bead-cell doublets:sce <- res$datamultiplexing. The most widely used barcoding scheme is based on Zunderet al., and relies on binary palladium-based mass-tag cell barcoding. Here, each samplei = 1,...,n is either positive or negative for each ofm palladium isotopes, resulting in anm-choose-k barcoding scheme, wherek is the number of positive barcodes. For example, labeling of three out of six palladium isotopes will result indeconvoluted) computationally.In mass cytometry, samples are often labeled with unique sample-specific barcodes and pooled together for processing and measurement, an approach termedseparation cutoff) are left unassigned.The single cell debarcoding (SCD) algorithm first sorts each cell\u2019s barcode intensities to assign preliminary barcode IDs such that a cell is assigned to the barcode population for which its barcode intensities are highest. Next, intensities within each barcode population are scaled using the 95th expression quantiles, and thereby brought to a comparable scale. Finally, events whose separation between highest negative and lowest positive barcode intensity is below a threshold value  preliminary barcode assignment (assignPrelim); ii) automated estimation of sample-specific separation cutoffs (estCutoffs); and, iii) application of cutoffs to arrive at final barcode assignments (applyCutoffs).The SCD algorithm is implemented inempty_1-5), resulting in 15 samples . We first read the correspondingdebarcoding_scheme.csv into R:For our dataset, a 6-choose-3 = 20 barcoding scheme was used . Five ba# read in debarcoding schemefn <- file.pathbc_key <- read.csv# all barcodes are positive for exactly 3 barcoding channelsall(rowSums(bc_key) == 3)## [1] TRUEk expressed barcode channels. Here, events whose expression is highest for a combination of barcode channels that doesnot appear in the debarcoding scheme (bc_key) will be given barcode ID 0 (for \u201cunassigned\u201d). Thus, we can remove empty barcodes from thebc_key variable such that events assigned to these barcodes are left unassigned from the start. Alternatively, one could perform debarcoding using the non-subsetted key, and filter out empty barcodes downstream.During this first debarcoding step, each event is preliminarily assigned to a barcode according to its top-# remove empty barcodes from debarcoding schemeis_empty <- grepl)bc_key <- bc_keybc_ids <- rownames(bc_key)CATLAYST\u2019s assignPrelim function, providing the input data (sce) and debarcoding scheme (bc_key). If not specified otherwise,assignPrelim will default to using theexprs assay slot (argumentassay). Because we rannormCytof withoverwrite = FALSE, this assay contains arcsinh-transformedraw counts; we setassay = \"normexprs\" in order to use the normalized values instead:For preliminary barcode assignment, we use# do preliminary barcode assignmentssystem.time)##  user system elapsed## 14.290 0.347 14.770rowData) columnis_bc indicates whether or not a channel corresponds to a barcode channel:In the returned SCE, feature metadata (# view barcode channelschannels(sce)[rowData(sce)$is_bc]##   MCB1   MCB2   MCB3   MCB4   MCB5   MCB6## \"Pd102Di\" \"Pd104Di\" \"Pd105Di\" \"Pd106Di\" \"Pd108Di\" \"Pd110Di\"colData columnbc_id. After this preliminary round of assignment, 57980/337525 events (17.18%) have been left unassigned:For each event, barcode identifiers are stored in# tabulate number of (un)assigned eventstable(sce$bc_id == 0)#### FALSE  TRUE## 279545 57980scaled. Based on these scaled barcode channel intensities, a separation value is computed as the distance between highest negative and lowest positive barcode channel; separations are stored incolData columndelta.Furthermore, for each cell, the barcode channel expressions are scaled relative to the 95th expression percentiles of its respective barcode population. The scaled data is stored in assay slotTo decide on separation cutoffs, we consider yields upon debarcoding as a function of the applied cutoff . CommonlplotYieldsInstead of a single global cutoff, we estimate a sample-specific cutoff to account for barcode population yields that decline in an asynchronous fashion. To this end, we fit both a linear and a three-parameter log-logistic model to each yield function. For the linear fit, we estimate the cutoff as the value for which yields have declined to 50%. For the log-logistic fit, we compute the cutoff as the value for which there is minimal yield decline by minimizing each yield function\u2019s 1st derivative. For each barcode, the final cutoff estimate is computed as the mean of both estimates, weighted with the goodness  of each fit see.CATALYST\u2019sestCutoffs function, which takes as input a SCE as returned byassignPrelim; that is, preliminary barcode assignments are required to estimate separation cutoffs.estCutoffs will store sample-specific cutoff estimates undermetadata slotsep_cutoffs, but will leave barcode assignments unchanged.Cutoff estimation is performed bysce <- estCutoffs(sce)metadata(sce)$sep_cutoffs## CellLine_R1 CellLine_R2 CellLine_R3 CellLine_R4   PBMC_R1   PBMC_R2## 0.13829607 0.13688845 0.09161274 0.12437132 0.13039323 0.18047875##   PBMC_R3  Tumor_R1  Tumor_R2   PBMC_S1   PBMC_S2   PBMC_S3## 0.26517442 0.21014175 0.20543502 0.10439323 0.12902725 0.24858493##  Tumor_S1  Tumor_S2  Tumor_S3## 0.18442675 0.14690041 0.20818048plotYields with argumentwhich specifying the barcode ID of interest Mahalanobis distance (argumentmhl_cutoff), a metric that quantifies the distance of a given event to the expression distribution of the barcode population it has been assigned to.Besides a cutoff on the separation between positive and negative barcode populations, to trim outliers, the SCD algorithms applies an additional cutoff on the\u223c 0.15. For this data, yields are in fact similar, independent of whether we apply sample-specific cutoffs or a single global one. Nevertheless, applying sample-specific cutoffs is recommended in order to maximize cell yields while minimizing uncertainty in barcode assignments.In# store preliminary barcode IDsbc_ids0 <- sce$bc_id# apply global & sample-specific separation cutoff(s)sce_glob <- applyCutoffssce_spec <- applyCutoffs# compare cell yields for both cutoff strategiesc, specific = mean(sce_spec$bc_id == 0))##  global specific## 0.3573839 0.3584979After debarcoding, we compare the number of events assigned to each barcode population before and after applying separation cutoffs, and filter out events that have been left unassigned (barcode ID 0). As shown in# proceed with sample-specific filteringsce <- sce_spec# compute number of events per population# before vs. after applying separation cutoffsbarplot(rbind(table(bc_ids0), table(sce$bc_id)),   beside = TRUE, ylab = \"cell count\",   las = 2, cex.axis = 0.5, cex.names = 0.5)legend,   legend = c)   # remove unassigned eventssce <- sceMass cytometry utilizes heavy metals  as reporters to label antibodies. As a result, channel crosstalk originating from spectral overlap and autofluorescence is significantly less pronounced in mass cytometry compared to flow cytometry. Yet, spillover due to abundance sensitivity, isotopic impurities, and oxide formation still exists, giving rise to artefactual signal that can impede data interpretability.et al. and is implemented in theCATALYST package. In brief, compensation is achieved via the following three-step approach outlined here (see for details).A combined experimental-computational pipeline to correct for spillover in mass cytometry data has been proposed by Chevrier1. assignPrelim,estCutoffs,applyCutoffs).Identification of single positive populations via deconvolution of single-stained beads (2. computeSpillmat).Estimation of a spillover matrix (SM) from the populations identified (3. compCytof).Compensation via multiplication of measurement intensities by the SM\u2019s inverse, the compensation matrix (spillover_matrix.csv). Thus, we enter at step 3, which involves only compensating the input dataset usingCATALYST\u2019scompCytof function. By default,compCytof will reuse the cofactor stored inint_metadata(sce)$cofactor for computing arcsinh-transformed data from the compensated counts, thus applying the same transformation as during data preparation and normalization:We will apply a pre-acquired spillover matrix sm <- read.csv# apply NNLS compensationsystem.time)##  user system elapsed## 63.538  5.880 70.095To visually inspect how compensation affects signal intensities, we can generate scatter plots pre- and post-compensation; an exemplary pair of channels is shown ini <- grepp1 <- plotScatter + ggtitle(\"Uncompensated\")p2 <- plotScatter + ggtitle(\"Compensated\") + ylab(NULL)wrap_plotsMany events acquired in mass cytometry may in fact be debris, doublets or dead cells, and should be filtered out through a gating step. Here, we suggest a strategy that first applies an elliptical gate on cell events, defined as double positive for the DNA channels Ir191/Ir193. This allows the exclusion of debris and doublets. As a second step, we discard cells that are positive for the dead cell marker Pt194.openCyto R package, and the resulting gates are visualized on scatter plots of the channels subjected to gating usingggcyto. For consistent visualization, we again define a common plotting theme for scatter plots of channelschs that include the gating boundaries for the specifiedgate_id:These two steps are performed using the.scatter <- function, \"root\", \"_parent_\")) {  p <- ggcyto, subset) +    geom_hex(bins = 100) + facet_wrap +    (if (is.null(gate_id)) list else geom_gate(gate_id)) +    ggtitle(NULL) + theme_bw(base_size = 8) + theme,       axis.text = element_text(color = \"black\"),       axis.text.x = element_text)  suppressMessages, ylim = c))}flowSet with a separate frame for each sample (argumentsplit_by = \"bc_id\"). As gating should be performed on expression-like data (not ion counts), we further specifyassay = \"exprs\" to retain the arcsinh-transformed assay slot. Thirdly, since conversion from SCE toflowCore data structures requires matrix transposition , we retain only those channels that are relevant when gating of (live) cells: DNA and dead channels, whose indices are stored in variablesdna andlive.In order to apply sample-specific gates, we first convert the SCE into a# subset DNA & live channelssub <- sce# add metadata variable \u2019i\u2019 to track cell indicescolData(sub) <- DataFrame))# split SCE by samplefs <- sce2fcs# construct \u2019GatingSet\u2019gs <- GatingSet(fs)gating_method = \"flowClust.2d\") to exclude the two lowest density percentiles (quantile = 0.98). Because the input gating set contains a separate frame for each barcode, the gate will be computed separately for each sample. In case of a single DNA channel , one-dimensional gates  would be applicable instead.We apply an elliptical gate ,   gating_method = \"flowClust.2d\",   gating_args = \"K=1,quantile=0.98,target=c\")ggcyto to produce scatter plots of the DNA channels, withgeom_gate(\"cells\") to visualize the gates computed above .live_gate defines a polygonal gate comprised of a line and a bivariate standard normal density Z, such that cells pass gating when i) their expression is within theqth quantile of Z; and, ii) their expression falls below a line parameterized by intercepti and slopes. In this way, the gate is centered around the expression peak, while excluding cells whosedead channel intensities increases with DNA content.The wrapper function# define live cell gate plug-in# x = expression matrix, q = quantile, i = intercept, s = slope.live_gate <- function {  # specifying gating function  .gating_fun <- function {     # subset channels of interest     x <- exprs     # scale data for comparison w/ \u2019qnorm\u2019     x0 <- scale(x)     # set boundary level as q-th quantile of standard normal     z <- qnorm(q)     # find p(x) for that level     pd <- dmvnorm)[1]     px <- dmvnorm(x0)     # find points above boundary level     keep1 <- px > pd     # find points below line y = a + b * x     keep2 <-  > x0     # intersection of points below line & above threshold level     pts <- x     # get boundary points (convex hull)     pts <- pts     # return gate     polygonGate  }  # register gate  suppressMessages)}l containing quantilesq, interceptsi and slopess for each sample. These parameters are updated iteratively to remove dead cells while retaining cell yields as high as possible , function(u)  setNames), sampleNames(gs)))# adjust parameters for specific samplesl$i[[\"PBMC_R2\"]] <- 1.2l$i[[\"PBMC_R3\"]] <- 1.2l$i[[\"PBMC_S1\"]] <- 1.2l$s[[\"PBMC_S2\"]] <- 0.2l$i[[\"PBMC_S2\"]] <- 0.6l$i[[\"PBMC_S3\"]] <- 1.8l$s[[\"Tumor_S2\"]] <- 0.3l$i[[\"Tumor_S2\"]] <- 0.6l$s[[\"Tumor_S3\"]] <- 0.3l$i[[\"Tumor_S3\"]] <- 0.4for (i in sampleNames(gs)) {  # register & apply live gate with sample-specific parameters  .live_gate  gs_add_gating_method,     gating_method = \"liveGate\")}.scatter\"cell\" and\"live\" gates on each samples to quickly assess the cell losses occurring at the two gating steps )df <- rename# barplot of cell yields after cell/live gatingggplot) +   geom_bar +   scale_x_discrete) +   scale_y_continuous,     limits = c, expand = c) +   labs(y = \"cell yield (%)\") + qc_theme\"live\" gate applied above by applyinggh_pop_get_indices to each sample ings. Secondly, we extract the cell indices fromgs and subset the SCE to keep only cells that passed the\"live\" gate.We extract a logical vector indicating whether a given event is included in or excluded by thefs <- gs_pop_get_data # get data from \u2019GatingSet\u2019es <- lapply      # get expression matriceses <- do.call    # join into single data.framesce <- sce]      # subset retained cellsFinally, we can again visualize scatter plots of dead channels against DNA as a quality control for the retained subset of cells .sample_id containing the FCS filename each cell originates from, andbc_id containing the barcode population assignments. We secondly rename these variable to make the following quality control steps more intuitive.Having completed the standard preprocessing steps, we proceed to investigate how the current experiment compares to prior experiments in terms of the number of cells in each reference and sample, and the expression levels of each target. Large parts of the metadata generated by now may no longer be needed, and unnecessarily increases output file sizes for large-scale datasets. Therefore, we will retain only two key cell metadata variables:# drop all cell metadata except file of origin & barcode IDscolData(sce) <- colData(sce)[c]# rename cell metadata variablei <- match))names(colData(sce))[i] <- \"sample\"bc_ids. Alternatively, and especially for more complex experimental designs, this information could be stored in a separate metadata table. Such a table could then be used to match thebc_ids with the listed samples, and add arbitrary metadata information .In the debarcoding scheme used for deconvolution of the multiplexed samples Section, barcodeCellLine,PBMC orTumor), group (R for reference orS for sample of interest), and replicate number; and follow a consistent naming scheme: \u201c<type>_<group><replicate>\u201d. We can easily extract these components and store them in the SCE\u2019s cell metadata (colData):In our example, barcode identifiers include each sample\u2019s type sce$group <- gsubi <- match(unique(sce$sample), sce$sample)colData(sce)## DataFrame with 10 rows and 4 columns##   file_id   sample##   <factor> <character>## 1    V1 CellLine_R2## 2    V1  Tumor_R1## 3    V1   PBMC_R2## 4    V1 CellLine_R3## 5    V1  Tumor_S3## 6    V1  Tumor_R2## 7    V1   PBMC_S2## 8    V1   PBMC_S3## 9    V1 CellLine_R4## 10    V1   PBMC_S1##      type    group##   <character> <character>## 1   CellLine      R## 2    Tumor      R## 3     PBMC      R## 4   CellLine      R## 5    Tumor      S## 6    Tumor      R## 7     PBMC      S## 8     PBMC      S## 9   CellLine      R## 10    PBMC      SR) to those obtained from 7 previous experiments )run <- c(table(sce$sample[sce$group == \"R\"]))# join into single tidy data.framedf <- bind_rowsdf <- meltggplot) +   geom_boxplot +   geom_point +   labs +   qc_theme + ggtitleSecondly, we compare the cell counts for the 4 samples of interest  to the number of cells recorded for 14 tumor and PBMC samples each  acquired in previous experiments . This stref <- read.csv)run <- tablerun <- as.data.framerun$type <- sce$type[match]df <- bind_rowsggplot) +   geom_boxplot +   geom_point +   labs +   qc_theme + ggtitleref_marker_levels.csv ref <- read.csv# compute 98th expression quantiles# for reference samples in current experimentes <- assayes <- esrun <- rowQuantiles# join into single tidy data.framedf <- bind_rowsdf <- meltggplot) +   geom_boxplot +   geom_point +   labs +   qc_theme + ggtitleet al., we use these references as anchors to calculate a channel-specific correction factor by dividing the 98th percentile measured in the current experiment by the average 98th percentile obtained across the first seven experiments of the project. The signal observed in each channel for the samples of interest is then divided by these correction factors derived from the reference samples.Each CyTOF experiment contains the same set of references. Similar to the approach used by Schuyler# compute 98th count quantiles via back-transformation#  & average across replicatescf <- int_metadata(sce)$cofactorqs <- colMeans(sinh(ref)*cf)# initialize correction factor of 1 for all channelscfs <- setNames), rownames(sce))# compute batch correction factors for relevant channelscs <- assaycsR <- csrun <- rowQuantilescfs[colnames(ref)] <- run / qs# apply marker-specific batch correction (bc)cs <- sweepassay <- cs# apply arcsinh-transformationassay <- asinh(cs/cf)To visually assess the effect of the batch correction applied above, we compare the expression distributions before and after scaling . We addi# subset most affected channelstop <- names(rev(sort(abs(cfs-1))))[seq(6)]sub <- sce# construct table of expressions# before & after correctionas <- ces <- lapply   data.frame),     check.names = FALSE))df <- do.calldf <- meltdf$id <- factor)# compute 98th percentiles of samplesq98_df <- df %>%   group_by %>%   summarize_at# compute 98th percentiles of references (average across 7)ref_df <- data.frame(variable = colnames(ref), value = colMeans(ref))ref_df <- bind_rows)ggplot, col = id)) +   facet_wrap(~ variable) +   geom_density +   geom_vline, lty = 2) +   geom_point, size = 2) +   scale_color_manual) +   scale_x_continuous) +   labs +   qc_theme + theme)CATALYST. Our pipeline covers four standard steps: Normalization for signal time-drift using bead standards  analysis (to detect subpopulations that are differently abundant between conditions) and differential state (DS) analysis (to test for subpopulation-specific expression changes across conditions) are implemented indiffcyt.A key advantage of both using and developing Bioconductor packages is that they utilize common data structures, thereby greatly facilitating interaction between them. For example, many of the data structures used in scRNA-seq data analysis have only become established relatively recently. Meanwhile, the cytometry community has been relying on the FCS file format for data storage, andassays that can, for example, contain raw counts, expression-like data obtained upon arcsinh-transformation, as well as any intermediate data matrices obtained after normalization, compensation and batch correction. Moreover, any event (cell) and feature (marker) metadata generated in the process can be added to the object\u2019scolData/rowData, alongside an arbitrary number of dimensionality reductions. Thus, SCEs present an overall more compact and less error-prone data structure for both preprocessing and downstream analysis when compared to storing the various data matrices or metadata in separate variables, which would have to be combined for certain computations, separately subsetted and saved to independent outputs.The SCE class allows storing multipleflowCore\u2019sflowFrame andflowSet classes, or other classes derived thereof . Thus, whileCATALYST\u2019s transition to a more recent and an arguably advantageous data structure is motivated by the ability to leverage many existing and newly-developed tools, a complete dismissal of the large infrastructure that is available in the realm of cytometry data analysis is impossible at this time. To facilitate conversion between SCEs and conventional cytometry data structures,CATALYST provides thesce2fcs function, which allows the user to specify which assay data to retain, whether to drop or keep available cell metadata and dimensionality reductions, and  to split the input dataset by a non-numeric variable .There is an obvious benefit for the mass cytometry community to take advantage of these new infrastructure developments. However, it is equally important to maintain backward compatibility with well-established standards in the field. For example, it can be desirable to write out intermediate outputs (FCS files) after each proprocessing step, or make use of available tools that build aroundflowClean, an R package designed to exclude fluorescent anomalies in flow cytometry data. Given that selection of anomalies in the dataset by the user is subjective, or that they may be altogether undetectable by eye, the advantage of such an approach would be to further standardize the process while decreasing manual work.Although the current version of this pipeline constitutes a comprehensive approach to generate high-quality data for downstream analysis, further developments could be added in the future. In particular, it could be useful to implement an automated way of identifying and removing part of the data with unstable signal, similar to the approach proposed byet al.. While this approach requires a well-defined experimental procedure where references with positive and negative subsets for each marker have to be included in each experiment, it does not make any assumptions on sample compositions. Thus, since the dataset used in this pipeline was acquired on the same instrument and stained with the same frozen antibody panel as previous experiments, scaling by expression quantiles provides an efficient way to correct for batch effects.Recently, batch normalization has become of increased importance in order to enable integration of datasets acquired at different times, by different users and on different instruments. Here, we use scaling normalization where references are used as anchors to correct all samples included in the analysis in a channel specific way, similar to the strategy proposed by SchuylerCATALYST could integrate different methods that have the potential to increase batch correction efficiency. For example,CytoNorm computes quantiles for every metacluster and for every marker after aggregation of control samples from each batch. Such an approach could be more appropriate in cases where the references\u2019 expression distributions are less aligned. An alternative method,CytofRUV, exploits the concept of pseudo-replicates to remove unwanted variation (RUV) between proteins and cells. Here, cells are grouped into subpopulations usingFlowSOM clustering. Groups of cells present across all batches are considered to be pseudo-replicates, and their deviation  from the average signal across batches is used to estimate and correct for the batch effects.To increase the flexibility of batch correction in cases where the experimental variation is higher,Although various methods to correct for batch effects in both the presence and absence of references have been proposed, a systematic comparison of batch correction tools for mass cytometry data is missing. Thus, whether the approach used in this study to align batches on the basis of shared references is the most accurate remains unresolved.Our pipeline is entirely R-based and does not rely on switching between platforms. Thus, it omits the need for heavy data transfers between online cloud services, graphical user interfaces (GUI), and programming environments for different parts of preprocessing and downstream analysis. As a result, each step in the analysis is fully reproducible and any parameters used throughout can be easily modified and documented. This transition from manual, GUI-based to largely automated, programmatic data processing is indispensable for clinical and other large-scale studies, where sample throughput is high and reproducibility ever so important.CATALYST has undergone continuous maintenance and development. The most noteworthy changes include implementation of a comprehensive visualization suite based on Nowickaet al. \u2019s workflow for differential discovery; and, the transition from custom data structures to using Bioconductor\u2019sSingleCellExperiment class for differential analysis with Bioconductor v3.11, and for preprocessing with v3.12. Taken together, these developments have transformedCATALYST into a one-stop tool for cytometry data analysis, enabling both data preprocessing and in-depth downstream analysis.Since its first submission to Bioconductor in 2017,Identification of bead events. Commonly, bead events are identified by manual gating on scatter plots of DNA vs. bead channels where DNA should be low, and expression should be high across all bead channels. Instead, we propose a programmatic way to identify beads that includes detection of bead-bead and cell-bead doublets.m) matrix  and 1 (is bead), where non-bead events are positive for DNA channels only (barcode 11000. . . ), while bead events are negative for DNA and positive for all bead channels (barcode 00111. . . ):Our normalization strategy leverages the already established SCD algorithm for preliminary tagging of events as beads. In this context, the debarcoding scheme is a 2\u00d7 rule to remove low- and high-signal events from the bead population used for estimating normalization factors. Asn decreases, bead populations become more narrow and bead-bead doublets are excluded. The extent to which bead populations are trimmed can be adjusted via argumenttrim (default 5).Upon initial assignment of bead events, we apply aover-trimming does not affect normalization. It is therefore recommended to choose atrim value that is small enough to assure removal of doublets at the cost of reduced bead population sizes.Notably, slightCorrecting for signal-decrease over time. To correct for the effect of event acquisition time on signal intensity, we follow the method proposed by Fincket al.. In essence, bead intensities are smoothed using a median sliding-window with widthk (default 500 bead events). At each timepoint, the slope of a line with intercept zero is computed by minimizing the squared error between smoothed bead and mean bead intensities (= baseline). Alternatively, a reference set of beads from which to compute the baseline can be provided. Slopes for non-bead timepoints are obtained via constant interpolation of these slopes. Here, large slopes correspond to significant deviation from the baseline, and small slopes indicate that the signal is already similar to the baseline. Thus, raw bead counts are normalized by multiplication with the fitted slopes at each timepoint.Preliminary barcode assignment. The debarcoding process commences by assigning each event a preliminary barcode ID. This requires specification of a binary barcoding scheme (or debarcoding key)i = 1, ...,n is the barcode index,j = 1, ...,m a barcode channel, andn,m denote the number of unique barcodes and barcoding channels, respectively. Further, letik denote the number of positive barcoding channels for barcodel:whereik =k \u2200i = 1, ...,n , thek channels with the highest signal in a given event are considered to be positive, the remainingm \u2212 k to be negative. Theseparation \u03b4 of positive and negative events is then defined as the difference between thekth highest and (m \u2212k)th lowest scaled intensity for that event.IfSeperation cutoff estimation. When the separation between positive and negative barcoding channels is low, we cannot be confident in the barcode assignment. to the yields function withdrc\u2019sLL.R function  real andJ the observed signal. Further, letijs be the proportion of channelj signal that is due to channeli, andjw the set of channels that spill into channelj. Thenn denotes the number of samples (cells) andp the number of features (channels):J =I \u00b7SM. The real signalI can then be retrieved via:In matrix notation, measurement intensities may be viewed as the convolution of real intensities and a spillover matrixSM\u22121 is termed compensation matrix (CM).whereWhile mathematically exact, the solution to this equation will yield negative values, and does not account for the fact that ion counts are strictly non-negative. A computationally efficient way to adress this is to instead use non-negative linear least squares (NNLS), which optimizes the least squares criterion under the constraint of non-negativity:I, we apply the Lawson-Hanson algorithm for NNLS implemented in thennls package.To solve forSpillover estimation. Because any signal not in a single stain experiment\u2019s primary channelj results from channel crosstalk, each spill entryijs can be approximated by the slope of a linear regression with channelj signal as the response, and channeli signals as the predictors, wherei \u2208jw.computeSpillmat offers two alternative ways for spillover estimation ; ii) not assigned to potentionally interacting channels; and, iii) not unassigned, and subtracted from all measurements:Theijs in SM is then computed as the median spillover across all cellsc \u2208i+:Each entryijs is computed as the slope of a line through the medians (or trimmed means) of stained and unstained populations,In a population-based fashion, as done in conventional flow cytometry,M \u00b1 1 channels (abundance sensitivity),M + 16 channels (oxide formation), and channels measuring isotopes  possible interactions, wheren denotes the number of measurement parameters. Estimates falling below the threshold specified byth will be set to zero. Lastly, note that diagonal entriesiis = 1 for alli \u2208 1, ...,n, so that spill is relative to the total signal measured in a given channel.Alternatively,https://tu-pro.ch/download/catalyst.The CyTOF data as well as all metadata required to run the full pipeline presented herein are available from Figshare as well as the Tumor Profiler website athttps://doi.org/10.6084/m9.figshare.c.5063984.v1Figshare: An R-based reproducible and user-friendly preprocessing pipeline for CyTOF data.This project contains the following underlying data:CyTOF_acquisition_1-3.fcs normalization_beads.fsc ref_bead_counts.csv  obtained from 7 previous experiments (rows). \u2013 Used as a reference to assess the measurement sensitivity for the current experiment.)debarcoding_scheme.csv  and rows corresponding to barcodes  \u2013 Used for single-cell deconvolution of multiplexed of samples.)spillover_matrix.csv , the percentage of signal received by all other channels (columns). \u2013 Used for correction of spillover.)ref_cell_counts.csv . \u2013 Used to assess reference sample cell yields in the current in comparison to previous experiments.)sample_cell_counts.csv . \u2013 Used to assess sample cell yields in the current in comparison to previous experiments.)ref_marker_levels.csv (A table of the 98th expression percentiles for each target (columns) across 7 previous experiments (rows). \u2013 Used to assess the staining efficiency of the current experiment.)http://creativecommons.org/licenses/by/4.0} (CC-BY 4.0).Data are available under the terms of the [Creative Commons Attribution 4.0 International license] or the Bioconductor project (http://bioconductor.org). 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Thanks for the updated version and for assessing the suggestions and issues mentioned in the first report.\u00a0 I think this revised version is easier to follow and clearer with the updates provided. One last comment would be on the batch alignment section, the Figure 17 selected is not showing the 7 different markers distribution to compare the expression distributions before and after batch correction and I think it would be useful in the assessment of this correction.Is the rationale for developing the new method (or application) clearly explained?YesIs the description of the method technically sound?YesAre the conclusions about the method and its performance adequately supported by the findings presented in the article?YesIf any results are presented, are all the source data underlying the results available to ensure full reproducibility?YesAre sufficient details provided to allow replication of the method development and its use by others?PartlyReviewer Expertise:I work in Bioinformatics and especially in the normalization and batch correction of CyTOF datasets.I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Gating on cells: Cells are first identified using an elliptical gate to exclude the two lowest density percentiles. Firstly, this plot relies on two DNA channels (whose information is likely redundant) and wasn\u2019t directly applicable to alternative DNA stains (e.g. rhodium). Furthermore, I am wondering whether this approach might exclude for example a fraction of cycling cells or preferentially exclude cell types or states with increased chromatin accessibility and therefore higher DNA signal?Gating on live cells: The approach suggested by the authors worked well on my test data, however, it takes a while to manually adjust values for every file to fit the gates closely to the data. While I see the value of automating this step, I also think that some manual gating could simplify the process and further increase downstream data quality. Potentially, the authors could adopt an approach like the gate_draw function from the CytoRSuite library. \u00a0Compensation: The workflow includes compensation as a preprocessing step which the authors have shown in separate publications to improve data quality, but which is currently not routinely performed by many researchers working using mass cytometry. I, therefore, assume that most users of this pipeline would be relying on published spillover matrices that reflect estimates of isotope purity and oxidation. While I agree with the usefulness of this function, I believe that adding additional quality control functions could improve acceptance of and trust in this approach. For example, in flow cytometry, overcompensation is often easily spotted by the occurrence of overly negative values, however, using their NNLS approach this is not readily apparent in compensated mass cytometry data. It would be very helpful to have a quality metric that would alert users to such potential issues introduced by the compensation step. In addition, testing this pipeline on some in-house generated data, a few minor issues occurred which should be addressed:While this might only be needed in rare cases, a function to rename channels and potentially match these names across multiple fcs files could enhance the adaptability of this package. In my test case, conflicting channel names prevented the import of the files into the workflow. In other cases, it might help to match channel names between batches. The authors could look to the premessa package for inspiration.The authors have incorporated various options for DNA channels which is much appreciated. My test data had been stained with a rhodium intercalator. Specifying this worked well, only the res$scatter function seems to ignore this choice and instead seems to default to iridium DNA intercalators.Sample specific debarcoding is appreciated. Figure 6 and the plotYields function return a debarcoding percentage. I believe this percentage refers to percent of initial assignments, but it is not specifically stated. It might be helpful to get a feeling of the percentage of cells  that are assigned after refining the initial assignments.In their manuscript, Crowell & Chevrier et al. present a novel workflow to preprocess mass cytometry (CyTOF) datasets in R. The presented pipeline is a useful update on earlier publications and packages, including the authors' CATALYST package which is clearly stated. Overall, I find this to be a valuable tool that brings together different functionalities into a unified workflow that enables reproducible and comprehensive preprocessing of this data type. The different steps and approaches are well described and illustrated. Especially, the inclusion of a functionality to perform live cell gating without having to switch platforms is much appreciated, although its current implementation could be improved:Is the rationale for developing the new method (or application) clearly explained?YesIs the description of the method technically sound?YesAre the conclusions about the method and its performance adequately supported by the findings presented in the article?YesIf any results are presented, are all the source data underlying the results available to ensure full reproducibility?YesAre sufficient details provided to allow replication of the method development and its use by others?YesReviewer Expertise:Single cell proteomics, Mass Cytometry, Immunology, Stem Cell biologyWe confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Gating on cells: Cells are first identified using an elliptical gate to exclude the two lowest density percentiles. Firstly, this plot relies on two DNA channels (whose information is likely redundant) and wasn\u2019t directly applicable to alternative DNA stains (e.g. rhodium). Furthermore, I am wondering whether this approach might exclude for example a fraction of cycling cells or preferentially exclude cell types or states with increased chromatin accessibility and therefore higher DNA signal? The first gating step is indeed performed on two DNA channels which contains redundant information. However, this approach is commonly used in the mass cytometry field to exclude debris and cell doublets. By modifying the quantile and the target value defining the center of the ellipse, the user can control how many cells are excluded from the gate and ensure that most cycling cells are kept in the analysis. To gate on alternative DNA stains, a different pair of channels could be assigned to the \u201cdna\u201d variable in the corresponding code chunk. In the case of a single DNA channel, a one-dimensional gating  could be applied . We have added a comment mentioning this to the text under \u201cGating on cells\u201d.Gating on live cells: The approach suggested by the authors worked well on my test data, however, it takes a while to manually adjust values for every file to fit the gates closely to the data. While I see the value of automating this step, I also think that some manual gating could simplify the process and further increase downstream data quality. Potentially, the authors could adopt an approach like the gate_draw function from the CytoRSuite library. Indeed, the approach depicted in this paper works well in cases where a limited number of samples are included in a run and when the live/dead cell profile is well defined and consistent between samples. The process can indeed be tedious when hundreds of samples are included in a run or when the live/dead cell profile is more complex. In the latter case, including a function similar to gate_draw function from CytoRSuite could be helpful. However, we here aimed at proposing an automated pipeline; manual gating would defeat this purpose.https://dillonhammill.github.io/CytoRSuite) lists a GH repository that no-longer exists; we could find an installable version athttps://github.com/gfinak/cytoRSuite (is this the same?) but \u2018drawGate\u2019 gave an error that we have not been able to resolve; meanwhile, any of CytoRSuite, cytoRSuite and cytoSuite (from which the latter has been forked) have not been changed in 4 years. Taken together, this gave us the feeling that the tool is no longer maintained and likely to be inapplicable with current versions of R and Bioconductor. As a side note: We have attempted applying CytoRSuite, however, encountered several confusing issues that we\u2019ve been unable to resolve: The CytoRSuite site  perform manual gating and export the resulting gates into a table (gating scheme); ii) apply that scheme in an automated fashion ; and, iii) do manual adjustments to refine gates according to the current experiment.Compensation: The workflow includes compensation as a preprocessing step which the authors have shown in separate publications to improve data quality, but which is currently not routinely performed by many researchers working using mass cytometry. I, therefore, assume that most users of this pipeline would be relying on published spillover matrices that reflect estimates of isotope purity and oxidation. While I agree with the usefulness of this function, I believe that adding additional quality control functions could improve acceptance of and trust in this approach. For example, in flow cytometry, overcompensation is often easily spotted by the occurrence of overly negative values, however, using their NNLS approach this is not readily apparent in compensated mass cytometry data. It would be very helpful to have a quality metric that would alert users to such potential issues introduced by the compensation step. These are all very good points and legitimate concerns. As indicated in the original paper, the spillover matrix used to compensate mass cytometry data should be calculated based on the antibodies included in the panel. We should stress here that, based on the single-stained bead acquisition approach presented in the original paper, the experimental procedure required to generate a compensation matrix is fairly straightforward and can be achieved rapidly. Using a previously published spillover matrix is a risky strategy, which can indeed lead to inaccurate compensation. The user should instead first run the compensation in classic mode and perform a visual inspection to ensure no overcompensation can be detected before using the NNLS method. This is a valuable option to avoid this specific type of artefact. Automating this step is a good suggestion, but is out of the scope of this publication and comes with some disadvantages. The risk we see is that this process could be imperfect and potentially misleading for the user. Indeed, such an approach would only identify overcompensation in channels where a single positive population is present but not in the case of a double positive population. In other words, it will highly depend on the user\u2019s data type. Furthermore, it would not identify under-compensated signals. As a consequence, providing an approach to alert users of potential issues would likely be imperfect and could give a wrong impression that the data are correctly compensated if no alert is raised, which is not necessarily the case. Moreover, to the best of our knowledge, such an approach also doesn\u2019t exist in fluorescent flow cytometry, most likely due to the fact that ensuring accurate compensation on a fully stained data set is a challenging task. We should also mention that the spillover coefficients in mass cytometry rarely exceed 4% and therefore the consequences of a slight over or under-compensation are less important in mass cytometry than in flow cytometry. Minor comments:While this might only be needed in rare cases, a function to rename channels and potentially match these names across multiple fcs files could enhance the adaptability of this package. In my test case, conflicting channel names prevented the import of the files into the workflow. In other cases, it might help to match channel names between batches. The authors could look to the premessa package for inspiration. We very much appreciate this comment as we have encountered various discrepancies between panels, especially in long-term projects. To date, we have used a custom R script to i) read in files separately; ii) fix panels according to a reference file ; and, iii) write out a new set of FCS files with concordant panels. However, this solution is suboptimal as it leads to a duplication of files . Similarly, a GUI solution (as \u2018premessa\u2019) would defeat the purpose of providing an automated, reproducible preprocessing solution. Thus, taken together, we propose (and have now implemented) the following strategy: > \u2018prepData\u2019 now exposes additional arguments to be passed to \u2018flowCore::read.FCS\u2019 via \u2018...\u2019 > \u2018read.FCS\u2019 provides an argument \u2018channel_alias\u2019: \u201can optional \u2018data.frame\u2019 used to provide the alias of the channels to standardize and solve the discrepancy across FCS files. [...]\u201din case of any discrepancy, the newly added \u2018fix_chs\u2019 argument will be used to determine how to resolve discrepancies\u201call\u201d will keep all channels ; any missing channels will be added to the respective samples, and a channels x samples matrix is stored in the object to track which channels were present in which samples originally\u201ccommon\u201d will keep shared channels ; any other channels will be dropped from the respective files\u2018prepData\u2019 will, in any case, return a \u2018SingleCellExperiment\u2019, i.e., no altered FCS files or \u2018flowFrame\u2019s will be written out / returned > independent of whether or not this option is used, \u2018prepData\u2019 will check whether panels (FCS channel names) match between files:The authors have incorporated various options for DNA channels which is much appreciated. My test data had been stained with a rhodium intercalator. Specifying this worked well, only the res$scatter function seems to ignore this choice and instead seems to default to iridium DNA intercalators. We thank the reviewer for noticing this. Indeed, while the workflow allows for specification of the DNA channels used (via variable \u2018dna\u2019), these were fixed internally in CATALYST\u2019s \u2018normCytof\u2019 function. We have added an additional argument to allow passing custom DNA channel masses \u2019 for Ir191/3; for Rh103, the argument would be \u2018dna = 103\u2019 instead); the output scatter plot of DNA vs. bead intensities (\u2018res$scatter\u2019) is now generated based on the first matching DNA channel (see updated \u2018?normCytof\u2019 documentation).Sample specific debarcoding is appreciated. Figure 6 and the plotYields function return a debarcoding percentage. I believe this percentage refers to percent of initial assignments, but it is not specifically stated. It might be helpful to get a feeling of the percentage of cells  that are assigned after refining the initial assignments. That is correct: As in the original Finck et al. outputs (a MATLAB application), yields (left-hand y-axis) correspond to the proportion of cells that would be retained upon applying a given cutoff (x-axis). In Figure 8, we compare the absolute barcode population sizes before vs. after debarcoding. Analogously, it would be straightforward for users to generate such a barplot from cell counts obtained when applying various thresholding schemes . The manuscript is presenting an updated version of the CATALYST package for preprocessing Cytof data. It is well detailed with several examples and has been updated based on the Bioconductor SingleCellExperiment class. Every step of preprocessing is clearly stated and illustrated to guide the user on the different steps to process their data. Also, new quality checks are being reviewed to explore the quality of the data.It will be useful to define clearly what are the differences between successive acquisitions, single CyTOF run and batch.The different samples and runs listed through the different examples could be better presented with a table containing all runs and samples. In the data description, it explains that \"The dataset used in this study was obtained from a single CyTOF run containing nine references, three blood samples and three tumor samples barcoded with a 20-well barcoding plate\". However in the quality checks section; additional data is being analyzed which makes it confusing, coming from additional runs, sometimes from 7 runs or other times from 8 runs.Batch alignment: Could you provide an additional plot showing the effect of applying this correction factor? How are you assessing the performance of your batch alignment method?argument norm_to in the normCytof function: give explanations on how it being computed when giving reference beads, especially how does it compute the new baseline, does it takes into account both the beads from the reference and current by averaging both?\u00a0 Can it be used to normalize data from different batches? If so how does it deal with distinguishing times and ordering the beads and time which would be similar in separated batches?Figure 4: Could you please give more explanations on how to assess run sensitivity and how does the user decide what is acceptable and what is not? Also, you need to load the library(reshape2) to run this part.The wrap_plots function is missing here.I got an error when running the QC on reference cell counts.\u00a0 \"Error: Can't combine `1$CellLine_R1` and `2$CellLine_R1` .\" I provided below some feedback to make the manuscript clearer and some suggestions to address some issues I encountered:Minor comments:When running the code using the data provided, the directory name should be modified to \"CyTOF_acquisition_1-3.FCS/\" instead of data fcs <- list.files Also, it should be specified that the directory name containing all the data should be called \"data\" and it refers to the directory name, or an alternative is to have the local directory \".\" instead of data like in here: # specify path to reference beads ref_beads <- file.pathIntroduction:11,12 \" Add CytofRUV reference mentioned in the discussion. \"an important step is to correct for batch effects, which can be achieved by including a shared control sample in each independent batch\"In our example, barcode identifiers include each sample\u2019s type , group (R for reference or S for sample of interest), and replicate number; and follow a consistent naming scheme: We can easily extract these components and store them in the SCE\u2019s cell metadata (colData)\". The example selected is not the best one, as it not showing any differences between the 6 first row.There is a typo in the legend of some figures like figure 17: \"previously\" acquired runs.Is the rationale for developing the new method (or application) clearly explained?YesIs the description of the method technically sound?YesAre the conclusions about the method and its performance adequately supported by the findings presented in the article?YesIf any results are presented, are all the source data underlying the results available to ensure full reproducibility?YesAre sufficient details provided to allow replication of the method development and its use by others?PartlyReviewer Expertise:I work in Bioinformatics and especially in the normalization and batch correction of CyTOF datasets.I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. It will be useful to define clearly what are the differences between successive acquisitions, single CyTOF run and batch. Indeed, the meaning behind the concepts of successive acquisitions, single CyTOF run and batch was not fully clear and these terms were not used in a consistent way. A \u201cCyTOF run\u201d corresponds to an independent experiment where samples are stained and acquired simultaneously on the CyTOF. We replaced the term run with experiment to clarify the meaning. Each CyTOF experiment corresponds to one \u201cbatch\u201d and this term is used to refer to the batch correction which is performed on the different CyTOF experiments. The data from a single CyTOF experiment are usually acquired over multiple \u201csuccessive acquisitions\u201d, each leading to the generation of a single FCS file. We also made the use of these terms consistent throughout the paper.The different samples and runs listed through the different examples could be better presented with a table containing all runs and samples. In the data description, it explains that \"The dataset used in this study was obtained from a single CyTOF run containing nine references, three blood samples and three tumor samples barcoded with a 20-well barcoding plate\". However in the quality checks section; additional data is being analyzed which makes it confusing, coming from additional runs, sometimes from 7 runs or other times from 8 runs. The pipeline described in this paper was designed to preprocess CyTOF data acquired over a long period of time with a focus on ensuring data consistency over time. The aim of the workflow is to guide the readers through the preprocessing steps required to convert FCS files obtained in a given CyTOF experiment to a format suitable for downstream analysis, while presenting key quality checks to ensure the reliability of the data generated in the experiment of interest. Therefore, the whole analysis is based on a dataset obtained from a single CyTOF experiment, which is benchmarked against data acquired during a preparatory phase. For consistency reasons, we included now systematically the data from seven previous CyTOF experiments to benchmark the data of the CyTOF experiment preprocessed in this paper.Batch alignment: Could you provide an additional plot showing the effect of applying this correction factor? How are you assessing the performance of your batch alignment method? The batch alignment presented in this paper is based on a linear scaling based on a percentile, using references as anchoring points, similar to a previously published method . To assess the performance of our batch alignment method, we have now included a figure to compare the expression distributions before and after batch correction (including their 98th percentiles and those of the references). As intended, 98th percentiles align with the references\u2019 upon correction, while expression distributions remain virtually unchanged.argument norm_to in the normCytof function: give explanations on how it being computed when giving reference beads, especially how does it compute the new baseline, does it take into account both the beads from the reference and current by averaging both? Normalization using reference beads follows the methodology originally introduced in Finck et al. (2013): The baseline is computed as the mean intensity of the reference beads only, not including the current experiment. Would the average be taken over both, intensities would not be aligned between current and reference experiment. While the statement \u201c[...] We provide the path to a set of reference beads (argument `norm_to`) that are used to compute baseline intensities for normalization\u201d explains this only briefly, we believe that the method is well established and readers should refer to the original publication for more detail.Can it be used to normalize data from different batches? If so how does it deal with distinguishing times and ordering the beads and time which would be similar in separate batches? Yes, certainly. The normalization aims at correcting the signal time-drift due to progressive loss of sensitivity during acquisition. This is a technical effect that is independent of batch effects, and should be accounted for regardless of whether or not batch effects are present: these should be corrected for downstream analysis. Events from different FCS files (independent of whether these are different acquisitions of the same experiment or batches) are concatenated. How event times are dealt with depends on prepData\u2019s input arguments. When by_time = TRUE, files are ordered according to their acquisition time (stored under each flowFrame\u2019s $BTIM description field). Otherwise, they are kept in the order provided by the input metadata table (argument md).Figure 4: Could you please give more explanations on how to assess run sensitivity and how does the user decide what is acceptable and what is not? Instrument sensitivity is an important parameter that should be closely monitored. This parameter is assessed during the tuning but those data cannot be easily exported and compared between experiments. The aim was to take advantage of the beads, which are run together with the samples to report on instrument sensitivity. Figure 3 provides key information regarding how the sensitivity evolves during the run, while the point of Figure 4 is to show how the average sensitivity evolves from one experiment to another. Instrument sensitivity varies from machine to machine and deciding what is acceptable will depend on the requirements of the users. The point of this plot was to offer an option for the user to easily identify in case the sensitivity is getting low compared to previous experiments, and to make a link between the quality of the data generated in a specific experiment with the sensitivity of the instrument.Also, you need to load the library(reshape2) to run this part. Yes, thank you for catching this; we\u2019ve added reshape2 to the list of dependencies, and it is now loaded along the other required libraries.The wrap_plots function is missing here. Yes, thank you for catching this; we\u2019ve added patchwork to the list of dependencies, and it is now loaded along the other required libraries.I got an error when running the QC on reference cell counts. \"Error: Can't combine `1$CellLine_R1` and `2$CellLine_R1`.\" True, thank you; I could reproduce this with the current R and package versions. It has been fixed by converting the \u2018run\u2019 object of class \u2018table\u2019 to call \u2018integer\u2019 using c. Minor comments:When running the code using the data provided, the directory name should be modified to \"CyTOF_acquisition_1-3.FCS/\" instead of data: fcs <- list.files We are not sure we understand this comment. \u2018list.files\u2019 expects the first argument to be a directory (where the FCS files are located), not the file names themselves (\u201cxxx.FCS\u201d).Also, it should be specified that the directory name containing all the data should be called \"data\" and it refers to the directory name, or an alternative is to have the local directory \".\" instead of data like in here:# specify path to reference beads ref_beads <- file.path Thank you, yes, we forgot to mention that in the presented code all data used throughout the workflow is expected to sit inside a \u201cdata\u201d subdirectory relative to where the .Rmd file is being run. We have now added a note explaining this in the 2nd paragraph under \u201cResults\u201d.Introduction: \"an important step is to correct for batch effects, which can be achieved by including a shared control sample in each independent batch\" Add CytofRUV reference mentioned in the discussion. We updated the reference to CytofRUV to the new version of the manuscript published in eLife and added it to the introduction.\"In our example, barcode identifiers include each sample\u2019s type , group (R for reference or S for sample of interest), and replicate number; and follow a consistent naming scheme: We can easily extract these components and store them in the SCE\u2019s cell metadata (colData)\". The example selected is not the best one, as it not showing any differences between the 6 first row. True. We have modified the example to sample 10 unique \u2018sample\u2019 entries (= type_group) for which to display the \u2018colData\u2019.There is a typo in the legend of some figures like figure 17: \"previously\" acquired runs. Thanks for pointing out this typo, which was corrected in the corresponding figures."}
+{"text": "Pheochromocytoma presents various clinical manifestations and imprecise signs and symptoms. Along with other diseases, it is considered to be \u2018the great mimic\u2019. This is the case of a 61-year-old man who on arrival presented with extreme chest pain accompanied by palpitations, and with a blood pressure of 91/65\u00a0mmHg. An echocardiogram showed an ST-segment elevation in the anterior leads. The cardiac troponin was 1.62\u00a0ng/ml, 50 times the upper limit of normal. Bedside, echocardiography revealed global hypokinesia of the left ventricle, with an ejection fraction of 37%. Because ST-segment elevation myocardial infarction-complicated cardiogenic shock was suspected, an emergency coronary angiography was performed. It showed no significant coronary artery stenosis, while left ventriculography demonstrated left ventricular hypokinesia. Sixteen days after admission, the patient suddenly presented with palpitations, headache and hypertension. A contrast-enhanced abdominal CT showed a mass in the left adrenal area. Pheochromocytoma-induced takotsubo cardiomyopathy was suspected. Pheochromocytoma crisis (PCC) is defined as a sudden increase of catecholamine, resulting in hemodynamic instability to the extent of multiple organ dysfunction syndrome , 2. PCC A 61-year-old man was admitted following 5 days of fever and headache, coupled with 14\u00a0hours of aggravated chest pain. He had a medical history of hypertension and myocardial infarction, including a stent placement in the left anterior descending coronary artery at 54\u00a0years old. The patient was conscious upon arrival. His axillary temperature was 37.5\u00b0C, heart rate was 105 beats per minute (bpm), blood pressure was 91/65\u00a0mmHg and breathing was shallow at a rate of 40 breaths per minute.Admission chest radiography and computed tomography (CT) pulmonary angiography demonstrated an enlarged heart with no evidence of infection or pulmonary embolism  and 2. HOn the 16th day after admission, the patient suddenly presented with dizziness, diaphoresis, palpitation, pallor and severe fluctuation in blood pressure. We suspected pheochromocytoma and performed a series of examinations. Plasma metanephrine was normal, while plasma normetanephrine was 2327.8\u00a0ng/L . Contrast-enhanced abdominal CT showed a mass (3.7\u00a0cm\u2009\u00d7\u20093.0\u00a0cm) in the left adrenal area  and B. TPCC is a rare life-threatening condition with a high mortality 15\u201328%) [5\u201328% [1]Pheochromocytoma-induced cardiovascular complication is rare and can easily be misdiagnosed. Although hypertension is the most common complication , pheochret\u00a0al. [Catecholamine, particularly norepinephrine, is known to have a toxic effect on the myocardium. Norepinephrine released from the sympathetic nerve terminals can decrease myocyte viability through cAMP-mediated calcium overload, resulting in contraction band necrosis, a pathological hallmark of TCM . The varet\u00a0al.  demonstrWe initially suspected STEMI or TCM as the primary diagnosis. However, emergency CAG showed no significant coronary artery stenosis, while left ventriculography demonstrated left ventricular hypokinesia. Eventually, the classic triad of symptoms emerged, and enhanced abdominal CT revealed a mass in the left adrenal gland. Hence, we ruled out pheochromocytoma-induced myocardial injury as the primary cause. The following evidence and assumptions from our examination informed our clinical judgments. First was the unexplained transient hypotension in the absence of critical coronary artery stenosis. Second, elevated cardiac troponin and ST-segment elevation indicated the presence of myocardial injury. Third, progressive T-wave inversions and prolonged QT intervals could be attributed to the dynamic changes in catecholamine levels. Fourth, large regional wall motion abnormality is indicative of takotsubo syndrome. Fifth, post-operative cardiac echocardiography showed resolution of motion wall abnormalities, while follow-up ECGs noted the disappearance of QT prolongation and T-wave inversions. Based on these findings, we believe that pheochromocytoma-induced global TCM is the best explanation for our case.Left ventricular hypokinesia accompanied by transient hypotension or blood pressure fluctuation could signal the presence of a pheochromocytoma. Therefore, awareness of this rare condition is essential to avoid any delay in diagnosing such a serious but treatable disease.Supplement_1_omad011Click here for additional data file.Supplement_2_omad011Click here for additional data file.Supplement_3_omad011Click here for additional data file.Supplement_4_omad011Click here for additional data file.Supplement_5_omad011Click here for additional data file.Supplement_6_omad011Click here for additional data file.Supplement_7_omad011Click here for additional data file.Supplement_8_omad011Click here for additional data file."}
+{"text": "Pancreatic cancer (PC) is a common cause of cancer death. Although more and more evidences suggest that circular RNAs (circRNAs) are associated with the development of cancer, the biological function of circRNAs in PC has not been fully explored. Based on previous studies, Hsa_circ_0000994 was screened out, and its clinical significance, functional role, and mechanism in PC are poorly studied. In various cell lines, 50 PC tissues, and an equal number of normal tissues, RT-qPCR was used to identify expression level of Hsa_circ_0000994. The impact of Hsa_circ_0000994 on metastasis, cell proliferation, and apoptosis was detected using functional loss and functional gain tests. An animal study was also conducted. Underlying mechanisms of Hsa_circ_0000994 were revealed by luciferase reporter gene detection. Hsa_circ_0000994 was lowly expressed in PC tissues as well as various PC cell lines, and this low expression was closely related to cancer. In terms of functional testing, Hsa_circ_0000994 suppressed core ability of PC cells, including proliferation, migration, and invasion ability. Xenotransplantation studies further confirmed the effect of Hsa_circ_0000994 in promoting cell growth. Mechanically, Hsa_circ_0000994 inhibited miR-27a and miR-27b. Hsa_circ_0000994 inhibited the cancer cells through the effect on miR-27a and miR-27b. In summary, a circRNA with tumor suppressor effects on PC has been elucidated. Pancreatic cancer (PC) is a rare but highly malignant gastrointestinal tumor. Its morbidity and mortality have been rising in recent years and become a common serious cancer in the world . Though Circular RNA (circRNA) is a noncoding RNA, which has a 5\u2032-end cap and a 3\u2032-end polyA tail . BecauseWe first found the regulatory potential of Hsa_circ_0000994 on PC. At the same time, the correlation between Hsa_circ_0000994/miR-27a and Hsa_circ_0000994/miR-27b was determined, and the regulatory effect of Hsa_circ_0000994 on miR-27a or miR-27b was further verified. Our study indicated the underlying therapeutic impact of Hsa_circ_0000994 in PC patients.This study was approved by the Ethics Committee of Affiliated of Hainan Medical University. 50 pairs of fresh PCs and corresponding healthy tissues were obtained from Affiliated of Hainan Medical University. All patients obtained informed consent before the start of the study. All human PC cell lines include Capan\u20102, AsPC\u20101, BxPC3, PANC1, and SW1990. This cell line was isolated in 1978 from splenic metastasis of a stage II pancreatic cancer. The normal cell line HPDE was cultured in RPMI1640 or DMEM with additional 10% fetal bovine serum (FBS) and 1% penicillin and streptavidin, placed in a 37\u00b0C incubator with 5% carbon dioxide. In addition, we used the KM-plotter database to evaluate the prognostic information.We placed PC cells into serum-free medium and transfected it using siRNA which targeted to Hsa_circ_0000994 (si-circ). A nonspecific nucleotide served as a negative control (si-NC). In order to overexpress Hsa_circ_0000994, cloning of whole circular RNA and artificial inverted repeats into pcDNA3.1 (+) vector. The medium was changed after mingling with Lipofectamine 3000 (Invitrogen) for 6 hours. Transfected cells were then collected at a specified time point for further detection.\u03bcL enzyme-free water was used to dissolve the extracted RNA. cDNA was obtained by reverse transcription of 1\u2009\u03bcg of the extracted RNA. Finally, the SYBR Green Mix kit  was used for quantitative PCR analysis, with a total of 3 replicates and 35 cycles. U6 was utilized as internal controls for miRNAs, while GAPDH was used for mRNA, respectively. The 2\u2212\u0394\u0394Ct method was used to determine relative expression.TRIzol solution  was utilized for the extraction of total RNA. 25\u2009PC cells were cotransfected with wild or mutant Hsa_circ_0000994 plasmid, miR-27a/miR-27b mimic, or negative control. The Dual\u2010Luciferase Reporter Assay System (Promega) was used to identify the luciferase intensity in 24 hours after transfection.Cell proliferation was assayed by CCK-8  according to the instructions. Briefly, 1500 transfected PC cells were transplanted into a 96-well plate. CCK-8 solution was added from 0 to 96\u2009h with the interval of 24\u2009h. The incubation of cells was performed in dark area for 2\u2009h, and a 450\u2009nm wavelength was used to measure the absorbance.6-well plates placed in a cell culture incubator were used to culture the transfected PC cells. After 12 days of staining, visible colonies were counted and photographed.7 PANC1 cells transfected by shCtrl or sh-Hsa_circ_0000994 were subcutaneously injected into 8-week-old female BALB/C nude mice. There were 7 mice per group. Measurement of tumor weights was measured after the sacrifice in 15 days after inoculation. Afterwards, the expression of Ki67 was verified by immunohistochemistry (IHC).1\u2009\u00d7\u200910Treated PC cells were digested and collected with binding buffer. Annexin V-FITC and propidium iodide (PI) were added and incubated for 15 minutes at room temperature. Stained cells were detected on a FACScan flow cytometer (BD Biosciences).Transwell inserts were measured for invasive capacity using the Matrigel (BD Biosciences) method. The cells were suspended in the cell suspension; then, FBS (20%) in DMEM was added in the lower chamber. After 24\u2009h, cells in the upper chamber were wiped with cotton swabs. The fixation and stain of these cells were performed using 100% ethanol and 0.5% crystal violet solution, respectively. The random field is photographed to determine cell number under the microscope.P values were compared using Student's t test or ANOVA, and statistical differences were P < 0.05.Prism 8 software (GraphPad Software) was used in all statistical analyses. The results are represented as mean\u2009\u00b1\u2009SD. In PC and normal tissues, RT-qPCR analysis was used to detect the expression regularity of Hsa_circ_0000994. Tumor tissue was associated with lower expression of Hsa_circ_0000994 compared to normal tissue . Subsequ\u2217\u2217\u2217, P < 0.001, \u2217\u2217, P < 0.01, and \u2217, P < 0.05.To determine the role of Hsa_circ_0000994 in PC, knockout studies were performed in PANC1 cells. 72 hours after transfection, the si-circ_0000994 group had significant knockdown expression . Compare\u2217\u2217\u2217, P < 0.001, \u2217\u2217, P < 0.01, and \u2217, P < 0.05.Then, we elucidated the role of Hsa_circ_0000994 by overexpression. Hsa_circ_0000994 was overexpressed in SW1990 cells . Cell pr\u2217\u2217\u2217, P < 0.001, \u2217\u2217, P < 0.01, and \u2217, P < 0.05.Bioinformatics analysis predicts that miR-27a/-27b are potential targets of Hsa_circ_0000994. Potential binding sites for miR-27a/-27b are shown . Interes\u2217\u2217\u2217, P < 0.001, \u2217\u2217, P < 0.01, and \u2217, P < 0.05.To explore whether the anticancer function of Hsa_circ_0000994 in PC is related to miR-27a/-27b, we inhibited miR-27a/-27b in PANC1 cells by transfection with the specific miR\u2010inhibitor . Rescue PC is a rare but highly malignant tumor. Over the past few decades, people have made great efforts in the diagnosis and treatment of PC . CircRNACircRNAs were discovered a long time ago. However, their biological function has only been demonstrated in recent years. To identify the effect of Hsa_circ_0000994 in PC, we carried out a series of functional tests to clarify that Hsa_circ_0000994 has an important suppression effect on tumor cell. Considering that the potential correlation between PC and expression level of Hsa_circ_0000994, we detected the altered cell proliferation and metastasis characteristics of Hsa_circ_0000994 through functional loss and functional gain. In this study, the reduction of Hsa_circ_0000994 leads to downregulation of cell apoptosis and upregulation of cell proliferation. On the contrary, in SW1990, overexpression of Hsa_circ_0000994 can effectively inhibit cell proliferation and promote cell apoptosis. Further in vivo studies have also demonstrated the tumor suppressor function of Hsa_circ_0000994. We revealed that Hsa_circ_0000994 in PC can promote cell apoptosis through the caspase pathway. Although SW1990 show strong migration ability, overexpression of Hsa_circ_0000994 can significantly eliminate these cells. Transwell invasion experiments also confirmed such results.In terms of the mechanism, circRNAs can play its carcinogenic/tumor suppressive effect in malignant tumors by sponge miRNAs. For instance, downregulation of the circNFIB1 gene promoted PDAC lymphangiogenesis and lymph node metastasis. In the mechanism, circNFIB1 acted as a sponge for miR-486-5p and reversed the effect of miR-486-5p . Our stuIn summary, this study found the downregulation of Hsa_circ_0000994 in PC samples for the first time, revealing the clinical value of Hsa_circ_0000994. In addition, our data also illustrate how Hsa_circ_0000994 can suppress cancer in this deadly disease. However, the limitations of our study also deserve attention, for example, the immune promoting after upregulation of miR-27a/miR-27b expression has not been validated in vitro."}
+{"text": "Several lists of marine fish from Azores have been published in the past. Most of those publications are difficult to access on line and several were not published in peer-reviewed journals.This checklist updates all the chondrichthyan records for the Azores Exclusive Economic Zone (EEZ), according to the most recent taxonomic classification of cartilaginous fish, as well as providing information on the conservation status for all species. We also present recent literature data on rare species and recent records for Azores. This is the first comprehensive list of cartilaginous fishes from Azores to be published in the GBIF database. Chondrichthyes are commonly known as cartilaginous fishes and includes chimeras, sharks and rays. They can be found from the cold deep-sea to subtropical and tropical waters .34 and 42.976 Latitude; -35.578 and -21 Longitude.This dataset covers all the sharks, rays and chimaeras so far known to occur within the Azores' EEZ.Creative Commons Public Domain Waiver (CC-Zero)This work is licensed under a Creative Commons Attribution (CC-BY) 4.0 Licence.chondrichthyes updated checklistAzores http://ipt.gbif.pt/ipt/resource?r=azores_chondrichthyes_updated_checklist_vs_1&v=1.9http://ipt.gbif.pt/ipt/resource?r=azores_chondrichthyes_updated_checklist_vs_11chondrichthyes updated checklistAzores Darwin CoreOdontaspisferox was recently published (see This updated checklist of the chondrichthyans already identified within the Azores EEZ follows the last revised version published by shed see . The firBathytoshiacentroura (Deaniahystricosa (Dipturusintermedius (Bathyrajapallida (The following species are referred to the Azores\u2019 EEZ by some authors: entroura , Deaniahstricosa , Dipturuermedius  and Bathapallida . However90267D5A-DACF-5E92-B140-A0443022674F10.3897/BDJ.9.e62813.suppl1Supplementary material 1Species list - taxon remarks and occurrence detailsData typetaxon remarks, occurrence details, IUCN conservation statusFile: oo_510738.xlsxhttps://binary.pensoft.net/file/510738Barcelos LMD, Azevedo JMN, Barreiros JP"}
+{"text": "Extensive inflammation of endothelial cells (ECs) facilitates atherosclerotic lesion formation. Circular RNA (circRNA) participates in atherosclerosis (AS)-related inflammation responses; however, whether and how circ_0086296 regulates atherosclerotic inflammation and lesions have not been investigated. Microarray analysis, quantitative real-time polymerase chain reaction, and fluorescence in\u00a0situ hybridization assay were performed to detect the expression and location of hsa_circ_0086296 in human carotid artery plaques, aorta of atherosclerotic mice, and human umbilical vein endothelial cells (HUVECs). Sanger sequencing was used to verify the loop structure of circ_0086296. The relationship among circ_0086296, miR-576-3p, IFIT1, STAT1, and EIF4A3 was validated using bioinformatics, luciferase assay, RNA pull-down assay, and RNA immunoprecipitation. The atherosclerosis mouse model was used to evaluate the function of circ_0086296 in\u00a0vivo. circ_0086296 expression was significantly upregulated in human carotid artery plaques, oxidized low-density lipoprotein (ox-LDL)-treated HUVECs, and the aorta of atherosclerotic mice. Functional analysis indicated that circ_0086296 promotes ECs injury in\u00a0vitro and atherosclerosis progression in\u00a0vivo. The mechanism analysis indicated that circ_0086296 sponged miR-576-3p to promote IFIT1\u2013STAT1 expression. Moreover, STAT1 upregulated circ_0086296 expression, forming the circ_0086296/miR-576-3p/IFIT1/STAT1 feedback loop. Notably, inhibition of the circ_0086296/miR-576-3p/IFIT1 axis could block atherosclerotic lesion formation both in\u00a0vivo and in\u00a0vitro. Finally, circ_0086296 was overexpressed in exosomes of patients with atherosclerosis and exosomes of ox-LDL-treated ECs. Therefore, the circ_0086296/miR-576-3p/IFIT1/STAT1 feedback loop participates in atherosclerosis progression and contributes to the high circ_0086296 expression observed in the exosomes of serum of patients with atherosclerosis. This study sought to provide a deep understanding of the mechanisms underlying the aberrant EC phenotype in AS.The online version contains supplementary material available at 10.1186/s11658-022-00372-2. Atherosclerosis (AS), described as a chronic and serious inflammatory response and injury in the arterial wall, is responsible for most serious cardiovascular diseases, including myocardial infarct and stroke \u20134. AberrCircular RNAs (circRNAs) are generated through back-splicing of pre-mRNA transcripts , 11 and ISG56/interferon-induced protein with tetratricopeptide repeats 1 (IFIT1) is a member of the ISG56/IFIT1 family . IFIT1 eEukaryotic initiation factor 4A-III (EIF4A3), an RNA-binding protein (RBP), mediates exon splicing though binding with RNA and creating exon junction complexes (EJCs) . circASAInterestingly, circRNAs may be present in exosomes and carried to the surrounding cells, thereby affecting the progression of diseases involving AS , 30; exoTherefore, we aimed to determine whether circ_0086296 is involved in AS inflammatory response and lesion development. This study sought to provide a deep understanding of the mechanisms underlying the aberrant EC phenotype in AS progression.Human carotid artery plaque tissues and control samples were collected from Shanghai Tongren Hospital. This study was approved by the ethics committee of Shanghai Tongren Hospital, and informed consent was obtained from patients prior to beginning the experiments . The tissues were frozen in liquid nitrogen until use.Total RNA from human carotid artery plaque samples and control tissues was extracted and subjected to microarray hybridization. Data analyses were performed using Agilent Human lncRNA Microarray 2018  by OE Biotechnology Co., Ltd.  containing 19,247 probes for human mRNA, 15,561 probes for human lncRNAs, and 21,442 probes for human circRNA. The arrays were examined using Agilent Scanner G2505C .The primers for the Sanger sequencing assay were produced by OE Biotechnology Co., Ltd. . For the actinomycin D assay, human umbilical vein endothelial cells (HUVECs) were treated with actinomycin D for 24\u00a0h. For the RNase R assay, total RNA was incubated with RNase R, and the stability of circRNA was assessed by quantitative real-time polymerase chain reaction (qRT-PCR). Cytoplasm and nucleus in HUVECs were separated using the PARIS Kit according to the manufacturer\u2019s instructions, and the levels of circ_0086296 or UHRF2 were measured via qRT-PCR.To study the location of circ_0086296 in HUVECs and aortic tissues, a FISH kit was used . First, all the sections were fixed in 4% paraformaldehyde and dehydrated with ethanol. Then, the sections were hybridized with Cy3-labeled circ_0086296 probes at 37\u00a0\u00b0C overnight and stained with DAPI. The sections were imaged using a confocal laser scanning microscope .HUVECs were obtained from AllCells  as previously reported , 33. HUVcirc_0086296 overexpression plasmid, circ_0086296 shRNA, miR-576-3p mimics/inhibitor, IFIT1 overexpression plasmid, IFIT1 shRNA, and the corresponding controls were purchased from Genomeditech . HUVECs were transfected with the circ_0086296 overexpression plasmid, shRNA of circ_0086296, miR-576-3p mimic, or miR-576-3p inhibitor using Lipofectamine 3000  following the manufacturer\u2019s instructions. The primers are listed in Additional file Cell viability was determined using a CCK-8 kit , as previously described . The migTotal RNA was harvested by TRIzol reagent (Invitrogen). Then, the total RNA was reverse transcribed by PrimeScript RT Master Mix (for mRNA and circRNA) or miScript II RT kit (for miRNA). qRT-PCR was performed using a GoTaq qPCR Master Mix  as previously described. The primers are presented in Additional file Proteins were extracted using RIPA buffer, separated in 10% SDS-PAGE, and transferred to PVDF membranes. The blots were blocked with 5% skim milk powder and treated with primary antibodies  overnight. The membranes were then treated with HRP-linked secondary antibodies, and blots were visualized using an ECL kit .Biotin-labeled probe circ_0086296 flanking RNA sequences were obtained. Then, these flanking sequences were added into cell lysates and streptavidin magnetic beads, followed with analysis via western blotting. RIP assay was performed using the Magna RIP Kit (Millipore), as previously described.The miR-576-3p mimics and luciferase reporter plasmid were cotransfected into HEK293T cells by Lipofectamine 3000 reagent as previously described. The luciferase activity was assessed using a Dual Luciferase Assay Kit (Promega) following the manufacturer\u2019s instructions.\u2212/\u2212 mice (aged 24\u201328\u00a0days) were obtained from the Model Animal Research Center of Nanjing University and housed in specific-pathogen-free conditions. The study was approved by the animal ethics committee of Shanghai Tongren Hospital . All procedures were performed in keeping with the standards set out in the Declaration of Helsinki and Laboratory Guidelines of Research in China and the National Institutes of Health Laboratory Animal Care and Use Guidelines. The mice were fed a high-fat diet (HFD) with 0.25% cholesterol and 15% fat for 16\u00a0weeks to develop the AS mouse model [SPF-grade ApoEAorta tissue was fixed in 4% paraformaldehyde, and atherosclerotic plaques were visualized using Oil Red O staining, Masson trichrome staining, and hematoxylin\u2013eosin (HE) staining, as previously described . The areFor IHC analysis, the samples were treated with primary antibodies at room temperature for 2\u00a0h, with secondary antibodies for 30\u00a0min, and then dyed with DAB and hematoxylin. For IF analysis, the paraffin sections were treated with primary antibodies overnight at 4\u00a0\u00b0C and fluorescent-conjugated secondary antibodies. The results were then visualized with a fluorescence microscope or confocal laser scanning microscope (Leica).Exosome were obtained from HUVEC culture medium or plasma, as previously described , 35. Exot-test. Data comparisons among multiple groups were performed using one-way analysis of variance (ANOVA) followed by Tukey\u2019s post hoc test. A value of p\u2009<\u20090.05 was considered statistically significant.All data were analyzed using SPSS 21.0 software . Measurement data were expressed as mean\u2009\u00b1\u2009standard deviation. Two-group comparisons were performed using Student\u2019s p\u2009<\u20090.05; Fig.\u00a0UHRF2)  Fig.\u00a0J, K. SanF2) Fig.\u00a0L. In HUVF2) Fig.\u00a0M. We fouF2) Fig.\u00a0N. At theF2) Fig.\u00a0O. We alsF2) Fig.\u00a0P. FurtheF2) Fig.\u00a0R. CollecTo identify the biological functions of circ_0086296 in the regulation of the HUVECs phenotype, we established stable circ_0086296 knockdown (sh-circ_0086296) and overexpression (circ_0086296-OE) cell lines, along with the respective controls   were selected from the possible miR-576-3p target genes  and IFIT1 (green fluorescence) were colocalized in human coronary plaque tissues  EIF4A3-mediated circ_0086296 induces the development of atherosclerotic lesions in\u00a0vitro and in\u00a0vivo; (b) the circ_0086296/miR-576-3p/IFIT1/STAT1 feedback loop is involved in atherosclerotic lesion progression;and (c) the packaging of circ_0086296 by EVs accelerates the atherosclerotic lesion phenotype of ECs. Taken together, our results found that circ_0086296 may mediate the development of atherosclerotic lesions.Several recent studies have revealed that circRNAs act as crucial regulators in AS , myocardEIF4A3 is the key part of the exon junction complex (EJC), which is responsible for mRNA splicing, transport, and translation. Recent research has found that EIF4A3 facilitates circRNA biogenesis. For example, it was found that EIF4A3 directly combines with the MMP9 mRNA transcript, accelerates circMMP9 cyclization, and increases circMMP9 levels in glioblastoma multiforme . The cycIncreasing evidence indicates that circRNA-mediated ceRNA crosstalk plays a vital role in the pathology of cardiovascular diseases , 46. The\u2212/\u2212 mice [High IFIT1 levels have been shown to serve as a useful marker in experimental atherosclerotic animals or clinical pathological applications . Moreove\u2212/\u2212 mice . These f\u2212/\u2212 mice \u201350. ReceFurthermore, the transcription factor STAT1 could promote the transcription of UHRF2. Our data are consistent with the previous finding that transcription factors, such as STAT1 , E2F1 2, and TwiIt is broadly known that circRNAs are more stably and specifically expressed in cells. Our results showed that circ_0086296 level is overexpressed in the serum EVs of patients with AS compared with that in the serum EVs of the control donors. Our data also revealed that higher circ_0086296 expression is observed in ox-LDL-treated HUVEC-derived EVs than in untreated cells. To further explore whether circ_0086296 exerts its function via EV transmission, the EVs derived from sh-circ_0086296 or circ_0086296-OE cells were cocultured with HUVECs. We found that sh-circ_0086296 cell-derived EVs decreased the atherosclerotic lesion phenotype of HUVECs. Additionally, circ_0086296-OE cell-derived EVs promoted the atherosclerotic lesion phenotype of HUVECs. Our data suggest the potential role of circ_0086296 in AS development.In summary, we demonstrated that circ_0086296 is significantly overexpressed in human carotid artery plaques, ox-LDL-treated HUVECs, and the aortas of atherosclerotic mice. Moreover, the EIF4A3-induced circ_0086296 level and circ_0086296/miR-576-3p/IFIT1/STAT1 feedback loop aggravates the atherosclerotic lesion phenotype of HUVECs. Additionally, EVs containing circ_0086296 mediated cell communication and induced the atherosclerotic lesion phenotype of ECs Fig.\u00a0. To the Additional file 1. Additional figures S1\u2013S10.Additional file 2: Table S1. Primer sequences for RT-PCR and qPCR analysis.Additional file 3. Basic characteristics of the differently expressed circRNAs.Additional file 4. Basic characteristics of the differently expressed mRNAs.Additional file 5. The binding site of circRNAs and miRNAs.Additional file 6. The binding site of mRNAs and miRNAs."}
+{"text": "SkewC is a single-cell RNA sequencing (scRNA-seq) data quality evaluation tool. The approach is based on determining gene body coverage, and its skewness, as a quality metric for each individual cell. SkewC distinguishes between two types of single cells: typical cells with prototypical gene body coverage profiles and skewed cells with skewed gene body coverage profiles. SkewC can be used on any scRNA-seq data as it is independent from the underlying technology used to generate the data.For complete details on the use and execution of this protocol, please refer to Abugessaisa et\u00a0al. (2022).  \u2022SkewC is a data quality tool that distinguishes typical from skewed cells\u2022SkewC can assess the skewness of scRNA-seq gene body coverage\u2022Skewed cells are of poor quality and negatively impact downstream analysis\u2022SkewC can be used on any scRNA-seq data set Publisher\u2019s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. SkewC is a single-cell RNA sequencing (scRNA-seq) data quality evaluation tool. The approach is based on determining gene body coverage, and its skewness, as a quality metric for each individual cell. SkewC distinguishes between two types of single cells: typical cells with prototypical gene body coverage profiles and skewed cells with skewed gene body coverage profiles. SkewC can be used on any scRNA-seq data as it is independent from the underlying technology used to generate the data. We present a computational protocol that describes the technical details for the execution of SkewC, a quality assessment tool for scRNA-seq datasets. SkewC measures the quality of each single cell using gene body coverage and its skewness as quality metrics. The distribution of matched sequences throughout the whole gene (5\u2032 to 3\u2032 end) is used to determine the relative gene body coverage, a critical metric to assess the quality of scRNA-seq datasets.,In our previous publications1.Biological sequences aligned to a reference genome in BAM format.a.count command and usually saved under the outs folder. The barcoded BAM file consists of the aligned reads for all the individual cells (barcodes). SkewC will split the barcoded BAM into multiple BAM files (one BAM file per cell barcode). To read more about the content and specifications of the barcoded BAM files refer to 10x Genomics support portal.10x Genomics provides Cell Ranger, a fully integrated pipeline for alignment of the raw sequence reads to the reference genome and automated analysis of the datasets generated with the 10x Genomics chromium instrument. SkewC accepts the barcoded BAM file (possorted_genome_bam.bam) generated by the Cell Ranger b.For scRNA-seq datasets generated by protocols other than 10x Genomics , the set of BAM files produced by the read alignment tools should be stored in one folder and provided as an input to SkewC (one BAM file per cell). SkewC accepts both sorted and unsorted BAM file. SkewC will use the BAM file name as cell ID for analysis and annotation of individual cells.In next generation sequencing, raw sequence reads generated by high throughput sequencers are mapped to the target reference genome assembly using any of the available RNA-seq aligners  format3.a.filtered_feature_bc_matrix folder, in the output of the Cell Ranger count pipeline. The number of barcodes in barcodes.tsv should be equal to the number of barcodes in the barcoded BAM file. To read about the barcode text file specifications refer to 10x Genomics support portalTo process 10x Genomics datasets, SkewC requires the barcoded BAM file as described above and a text file with cell barcodes (barcodes.tsv) which is usually found under the b.SkewC batch command (3_filter.sh) enables the user to filter cells prior to running SkewC. The\u00a03_filter.sh command is used when the user utilizes another QC method prior to running SkewC  by SkewC.c.SkewC output formats.SkewC provides several output files:The output files from running SkewC are also provided in a convenient html file. The html page displays the plots in PDF format and enables download of the resulting SkewC annotation files as both text and R data object (.RDS). Examples of the sample outputs provided by the SkewC pipeline can be found here.Timing: 10\u201320\u00a0min1.a.$ git --versionTest the installation of Git by using the git command.b.here to install Git.In case Git is not installed, follow the instructions from Get the Git (version control system) source and version compatible with your operating system.2.a.Install docker: If you are installing SkewC to your personal computer and have admin authority, we recommend installing docker.b.Install udocker: if you want to run the pipeline in a Linux environment where you don\u2019t have any admin authority (and can\u2019t run docker).c.Install singularity: currently singularity more recommended than udocker.Install docker/udocker/singularity . We give3.Install SkewC: after completing the installation of git and any of docker/udocker/singularity, the user will be able to install SkewC using a single command in the terminal:$ git clone https://github.com/LSBDT/SkewC.gitNote: After cloning SkewC, a new folder (SkewC) will be created under the user\u2019s home directory with the structure seen in .4.To build SIF from a docker image stored in docker hub, please use this command:$ cd SkewC.\u223c/SkewC$ singularity build skewc.sif docker://moirai2/skewc:latest.Note: The skewc.sif file will be added to the SkewC work directory (irectory . Check tNote: All the subsequent batch commands are executed from the SkewC root directory as outlined in (lined in .5.a.$ cd SkewC/\u223c/SkewC$ bash 0_split10XbyBarcode.sh TestData/10xGenomics/barcoded.bam TestData/10xGenomics/barcodes.tsv.gz\u223c/SkewC$ bash 1_geneBodyCoverage.sh mm10\u223c/SkewC$ bash 2_SkewC.shTo test SkewC with the 10xGenomics (Neurons_900) dataset, use the following commands:b.$ cd SkewC/\u223c/SkewC$ bash 1_geneBodyCoverage.sh mm10\u223c/SkewC$ bash 2_SkewC.shTo test SkewC with the non10x (E-MTAB-2600) dataset, use the following commands:c.$ cd SkewC/\u223c/SkewC$ bash 2_SkewC.sh TestData/coverage.rTo test SkewC with a pre-computed gene body coverage file, use the following commands:Testing SkewC; To demonstrate the implementation of SkewC, we provide users with three types of test datasets under the directory \u223c/SkewC/TestData. Under the TestData folder there are three subfolders:To setup SkewC, the user needs to carefully follow the instructions and implement the following steps:Timing: 45\u201360\u00a0min6.Split the barcoded BAM file into a set of BAM files based on the list of barcodes provided in the (barcode.tsv) file. This is achieved using the following command:\u223c/SkewC$ bash 0_split10XbyBarcode.sh $bam $barcode $outdirArguments:$bam\u00a0\u2013 BAM file from 10Xgenomics analysis.$barcode\u00a0\u2013 barcodes.tsv.gz under 10Xgenomics outs/filtered_feature_bc_matrix/.CRITICAL: This step is required for scRNA-seq datasets generated by 10x Genomics.Note: If you don\u2019t wish to designate a specific output directory, you can omit the $outdir argument.$outdir\u00a0\u2013 directory to store split BAM files (default\u00a0= \u2019input\u2019).The bash script 0_split10XbyBarcode.sh will create multiple BAM files (one BAM file per cell barcode) under the specified output directory (default\u00a0= 'input').Time (The time depends on the number and size of the BAM files).7.Run the gene body coverage bash script:\u223c/SkewC$ bash 1_geneBodyCoverage.sh $species $indir $outdirA critical step in SkewC is the gene body coverage computation. This step will enable computation of the gene body coverage for each BAM file (cell). The bash script 1_geneBodyCoverage.sh is used for the gene body coverage computation. Another alternative to use Perl command.Arguments:$species - human 'hg38' or mouse 'mm10' (default\u00a0= 'hg38\u2032).$indir - directory where split BAM and index files are stored (default\u00a0= 'input').8.Compute gene body coverage through the command line using Perl:Perl bin/geneBodyCoverage.pl -o coverage reference/hg38_Gencode_V28.norRNAtRNA.bed input/example.TTTGTCATCTAACGGT-1.bam\u00a0>\u00a0coverage/example.TTTGTCATCTAACGGT-1.logNote: geneBodyCoverage.pl will create an index file under a reference directory (default\u00a0= 'reference') at the beginning of the first iteration. From the second iteration onwards, indexed reference files will be used to speed up calculation.Note: It\u2019s not recommended to run geneBodyCoverage.pl in parallel when it's creating an index file.Optional: Parallelization is available on SkewC by running multiple \u201c1_geneBodyCoverage.sh\u201d scripts in parallel. By calling five shell scripts, each script calculates gene body coverage of separated BAM files../1_geneBodyCoverage.sh\u00a0&./1_geneBodyCoverage.sh\u00a0&./1_geneBodyCoverage.sh\u00a0&./1_geneBodyCoverage.sh\u00a0&./1_geneBodyCoverage.sh\u00a0&$outdir - directory to store geneBodyCoverage.pl output files (default\u00a0= 'coverage').Timing: 1\u20132\u00a0min (for steps 9 and 10)9.Run bash script 2_SkewC.sh to analyze gene body coverage.\u223c/SkewC$ bash 2_SkewC.sh $prjname $indir $outdir $alphaThe final step in a typical SkewC workflow is the analysis of the gene body coverage and the preparation of the output folder.Arguments:$prjname - project name of sample (default\u00a0= 'COV'). $prjname will be printed on PDF outputs.$indir - a directory where geneBodyCoverage.pl output files are stored (default\u00a0= 'coverage').$outdir - a directory to store skewc analysis files with index HTML (default\u00a0= 'skewc').$alpha - alpha for tclust computation with three modes:(Not defined) - alpha value is decided by highest value from ctlcurves.10.a.tclust computation with auto alpha value.bash 2_SkewC.sh test input output - b.tclust computation with alpha\u00a0= 0.1.bash 2_SkewC.sh test input output 0.1 - c.tclust computation with alpha\u00a0= 0.1, 0.2, 0.3, 0.4.bash 2_SkewC.sh test input output 0.1 0.2 0.3 0.4 - Use of the alpha value in SkewC, please see the detailed description in the SkewC original publication.1.0\u20131.0 - tclust will be computed with this user specified value.Timing: 2\u20135\u00a0min11.Run the bash script 3_filter.sh as follows:\u223c/SkewC$ bash 3_filter.sh $filter $indir $matchdir $unmatchdirArguments:$filter - Filter file with list of IDs.$indir - Input directory (Default\u00a0= coverage).$matchdir - match directory with filter list (Default\u00a0= match).Optional: This step is executed only when a user needs to filter out certain cells. The user will need to prepare a text file with a list of cellIDs/barcodes that will be removed from SkewC computation. Example of a list of cell IDs .12.After filtering out unwanted cells with '3_filter.sh', run '2_SkewC.sh' again whilst specifying $indir as follows:\u223c/SkewC$ bash 2_SkewC.sh $prjname $indir $outdir $alpha$unmatchdir - unmatch directory with filter list (Default\u00a0= unmatch).here.The bash script 2_SkewC.sh utilizes four R Markdown files. These files can either be run within 2_SkewC.sh or in the RStudio environment. Here we are going to describe these R Markdowns in more details. The four R Markdown files are available from SkewC GitHub repository This R Markdown creates the coverage matrix. The input for this file is the vector of normalized values which was created by bash script 1_geneBodyCoverage.sh and stored in the coverage.r. In this file, each single cell has a vector of numerical values (n\u00a0= 100), and each cell has a cell id / barcode as identifier. The result of running the SkewC_Create_Coverage_Matrix.Rmd is a set of R data frames. After initializing some variables, the script reads the coverage.r file and converts it to the R data frame Coverage_DF. The Coverage_DF data frame consists of 101 columns with each row in the data frame representing the gene body coverage of a single cell. The Coverage_DF is used to compute the mean coverage matrix (Coverage_means_DF). The data frame name Coverage_means_DF consists of 10 columns [pmean10...pmean100] plus the cell ID/ barcode column \"Annotation\". The Coverage_means_DF data frame is processed to generate the data frame Coverage_means_DF_Clust.This R Markdown uses the R data frame Coverage_DF (output from SkewC_Create_Coverage_Matrix.Rmd) to generate two types of plots: The Full gene body coverage plot and the mean coverage plot .The R Markdown SkewC_TrimClustering.Rmd performs the trim clustering implemented in R tclust function.This R Markdown uses the output from SkewC_TrimClustering.Rmd  and the R data frame Coverage_DF to plot two plots: the gene body coverage for the typical cells and the gene body coverage of the skewed cells .here and here.Running SkewC will result html file contains all outputs. An example of SkewC html output is Note: The SkewC annotation can be added as a metadata column to R SingleCellExperiment class / R Seurat object / python anndata for further analysis or filtering of cells during an scRNA-seq data analysis workflow.As mentioned, SkewC provides visualizations of the gene body coverage for all cells , cluster\u2022We conducted performance evaluation of SkewC in.\u2022It\u2019s important to mention that the performance depends both on the BAM file and reference genome size in GB.\u2022Performance evaluation analysis of SkewC shows a linear association between the number of cells and SkewC runtime.\u2022Users of SkewC will be able to increase the number of cores to reduce the computation time, depending on the availability of resources.Gene body coverage is a computationally demanding task and users of SkewC may have concerns about the computation time that is required for scRNA-seq datasets containing thousands of cells.Failed to run git command.https://github.com/LSBDT/SkewC.git.Failed to run \u223c/git clone The user failed to run the git command to clone SkewC (related to SkewC setup). This problem occurs when git was not installed in the computing environment. Other problems related to the installation and cloning of SkewC is that either docker, udocker and singularity is not installed on the user\u2019s system.Git support to install the latest version of Git. Then follow all steps in SkewC setup.To overcome this problem, the user needs to follow the instructions Failed to open SkewC singularity image file (sif):Could not open image \u223c/SkewC/skewc.sif: failed to retrieve path for \u223c/SkewC/skewc.sif: lstat \u223c /SkewC/skewc.sif: no such file or directory\u201d.The error message \u201cThis problem related to step 4 in SkewC setup ; the above error will appear when a user tries to run the first batch command 0_split10XbyBarcode.sh after cloning SkewC.\u2022The user needs to check that the skewc.sif file was built and is located under the work directory of SkewC (step 4 in SkewC setup).\u2022If skewc.sif is missing, the user needs to build the SIF file. To build SIF from the docker image stored in the docker hub, please refer to SkewC setup.Perl: warning: Setting locale failed.The following warning messages will not stop the execution of SkewC, and the script will run as intended (related to split barcoded BAM files and compute gene body coverage).perl: warning: Setting locale failed.\u201cPerl: warning: Please check that your locale settings:LANGUAGE\u00a0= (unset),LC_ALL\u00a0= (unset),LANG\u00a0= \u201cen_US.utf-8\u201dare supported and installed on your system.Perl: warning: Falling back to the standard locale (\u201cC\u201d)\u201d.The above Perl warnings appear when running the batch command bash 0_split10XbyBarcode.sh $bam $barcode $outdir. The warning is related to the local environment settings. The warning will not impact the finalresults of the batch command.Users of SkewC need to confirm the local settings  as recommended in the warning message.Perl: Error: package or namespace load failed for 'reshape2' in dyn.load.package or namespace load failed for \u2018reshape2\u2019 in dyn.load: unable to load shared obje\u2019t '/home/imad-a/R/x86_64-pc-linux-gnu-ibrary/3.4/stringi/libs/stringi\u2019so': libicui18n.so.57: cannot open shared object file: No such file or directory.The following error message may appear \u201cExecution halted.No alpha value computation found\u201d.The above error message  and the rest of SkewC workflow.takeya.kasukawa@riken.jp).Further information and requests for source code and data processing protocols should be directed to, and will be fulfilled by, the Lead contact Takeya Kasukawa (This study did not generate new unique reagents."}
+{"text": "Metagenomic assembly using high-throughput sequencing data is a powerful method to construct microbial genomes in environmental samples without cultivation. However, metagenomic assembly, especially when only short reads are available, is a complex and challenging task because mixed genomes of multiple microorganisms constitute the metagenome. Although long read sequencing technologies have been developed and have begun to be used for metagenomic assembly, many metagenomic studies have been performed based on short reads because the generation of long reads requires higher sequencing cost than short reads.In this study, we present a new method called PLR-GEN. It creates pseudo\u2013long reads from metagenomic short reads based on given reference genome sequences by considering small sequence variations existing in individual genomes of the same or different species. When applied to a mock community data set in the Human Microbiome Project, PLR-GEN dramatically extended short reads in length of 101\u00a0bp to pseudo\u2013long reads with N50 of 33\u00a0Kbp and 0.4% error rate. The use of these pseudo\u2013long reads generated by PLR-GEN resulted in an obvious improvement of metagenomic assembly in terms of the number of sequences, assembly contiguity, and prediction of species and genes.PLR-GEN can be used to generate artificial long read sequences without spending extra sequencing cost, thus aiding various studies using metagenomes. Metagenomic sequences containing all sequenced genetic materials in environmental samples are one of the most important resources for understanding the roles of microorganisms in an environment. Metagenomic sequences have been widely used for characterizing microbial communities in various environments, such as animal organs, seawater, hydrothermal environment, plants, and soils . In studThe development of third-generation sequencing technologies aiming to increase sequence length has provided new opportunities for metagenomic assembly because longer sequences are more useful for resolving repetitive genome sequences and distinguishing sequences from different species . With rede novo assembly tool called Konnector can generate elongated pseudo\u2013long reads from paired-end tag sequencing data for a single genome assembly  funded by the Ministry of Science and ICT of Korea, a grant [2019R1F1A1042018 and 2021M3H9A2097134] funded by the Ministry of Education of Korea, and a grant [PJ01334302] funded by the Rural Development Administration of Korea.JBK conceived and designed the study. JBK, MKS, JIL, and DHL designed the PLR-GEN algorithm. MKS implemented the pseudo\u2013long read generation algorithm. MKS, SYW, and NYP performed experiments. MKS, SYW, NYP, DHK, and JBK interpreted the analysis results. MKS drafted the manuscript. JBK finalized the manuscript. All authors approved the final manuscript.giac044_GIGA-D-21-00349_Original_SubmissionClick here for additional data file.giac044_GIGA-D-21-00349_Revision_1Click here for additional data file.giac044_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giac044_Reviewer_1_Report_Original_SubmissionJin-Wu Nam -- 12/2/2021 ReviewedClick here for additional data file.giac044_Reviewer_1_Report_Revision_1Jin-Wu Nam -- 3/20/2022 ReviewedClick here for additional data file.giac044_Reviewer_2_Report_Original_SubmissionMatthew Zachariah DeMaere, Ph.D -- 12/13/2021 ReviewedClick here for additional data file.giac044_Reviewer_2_Report_Revision_1Matthew Zachariah DeMaere, Ph.D -- 3/27/2022 ReviewedClick here for additional data file.giac044_Supplemental_FilesClick here for additional data file."}
+{"text": "Based on a longitudinal analysis of mutational prevalence globally, we found an expanding repertoire of Spike protein deletions proximal to an antigenic supersite in the N-terminal domain that may be one of the key contributors to the evolution of highly transmissible variants. Finally, we generated clinically annotated SARS-CoV-2 whole genome sequences from 102 patients and identified 107 unique mutations, including 78 substitutions and 29 deletions. In five patients, we identified distinct deletions between residues 85\u201390, which reside within a linear B cell epitope. Deletions in this region arose contemporaneously on a diverse background of variants across the globe since December 2020. Overall, our findings based on genomic-epidemiology and clinical surveillance suggest that the genomic deletion of dispensable antigenic regions in SARS-CoV-2 may contribute to the evasion of immune responses and the evolution of highly transmissible variants.The emergence of highly transmissible SARS-CoV-2 variants and vaccine breakthrough infections globally mandated the characterization of the immuno-evasive features of SARS-CoV-2. Here, we systematically analyzed 2.13 million SARS-CoV-2 genomes from 188 countries/territories (up to June 2021) and performed whole-genome viral sequencing from 102 COVID-19 patients, including 43 vaccine breakthrough infections. We identified 92 Spike protein mutations that increased in prevalence during at least one surge in SARS-CoV-2 test positivity in any country over a 3-month window. Deletions in the Spike protein N-terminal domain were highly enriched for these \u2018 The continual emergence of SARS-CoV-2 variants with increased transmissibility and capacity for immune escape  threatens to prolong the pandemic and drive devastating outbreaks2. While multiple vaccines have demonstrated high effectiveness in clinical trials and real world studies5, it is known that vaccine breakthrough infections can occur, even in individuals with robust neutralizing antibody responses7. Variant classification schemes were developed by the U.S. Centers for Disease Control and Prevention (CDC)8 and the World Health Organisation (WHO)9 based on factors such as prevalence, evidence of transmissibility and disease severity, and ability to be neutralized by existing therapeutics or sera from vaccinated patients. Early and rapid detection of emerging Variants of Concern/Interest is imperative to combat and contain future outbreaks.The COVID-19 pandemic killed millions of individuals worldwide12. Recent studies also found that several neutralizing antibodies target a single antigenic supersite in the NTD of the Spike protein14. The NTD is also a hotspot for in-frame deletions in the SARS-CoV-2 genome, with four recurrent deletion regions (RDRs) identified15. Several such deletions have been experimentally demonstrated to reduce neutralization by NTD-targeting neutralizing antibodies15. Whether additional deletions are emerging in SARS-CoV-2 variants that drive case surges or vaccine breakthrough infections needs to be determined.Mapping the mutational landscape of SARS-CoV-2 in the context of natural and vaccine-induced immune responses is critical to understand the virus\u2019s molecular strategies for immune evasion. To this end, neutralizing antibodies which target the receptor-binding domain (RBD) or the N-terminal domain (NTD) of the Spike protein have been isolated from the sera of COVID-19 patients16 In this study, we uncovered that deletion mutations in the Spike protein have a high likelihood of being associated with surges in community transmission. Further, based on a global longitudinal analysis of deletions, we also highlight that the repertoire of deletion-prone regions of the Spike protein expanded during the pandemic, pointing to an evolutionary strategy of \u201cantigenic minimalism\u201d to evade immune responses. Finally, using whole genome sequencing linked to clinical annotations derived from electronic health records, we also identified an emerging hotspot of deletion mutations in SARS-CoV-2 isolated from hospitalized COVID-19 patients or vaccine-breakthrough infections. These deletions were distinct from the background set of mutations observed in the geographical region and mapped to a previously characterized linear B-cell epitope, thus representing candidates to be monitored for escape mutations.16 . These surge-associated deletions in the Spike protein occur exclusively in the N-terminal domain (NTD), which is interesting considering the recurrent deletion regions (RDRs) in the N-terminal domain15. This raises the possibility that the acquisition of deletion mutations in the NTD and substitutions in functionally important regions  may contribute to the evolution of highly transmissible variants.Further, we investigated whether a class of mutations  is enriched for association with surges. Interestingly, we found that deletions were associated with surges more frequently than expected by chance Table S1). Specifically, 18 of 45 (40%) deletions were associated with one or more surges, compared to only 74 of 599 non-deletion mutations (12%)  were not present at the time of this prior analysis. The remaining three  were consistent with previously identified recurrent deletion regions. The regions which were frequently deleted during the early months  remained prevalent subsequently, with \u039469/70 and \u0394144 present in the Alpha variant and \u0394242\u2013244 present in the Beta variant . We identified seven regions within the NTD in which deletions are observed at rates significantly higher than the background frequency . Thus, it is important to monitor circulating variants and identify new deletable regions as they emerge.The genomic-epidemiology analysis presented above based on publicly accessible data suggested that SARS-CoV-2 acquires deletion mutations to evade neutralizing antibodies and that the deletable regions are expanding. However, the genome sequences deposited in publicly accessible databases  lacked clinical and phenotypic annotations such as vaccination status and disease severity of the corresponding COVID-19 patients. To address this, we performed whole genome viral sequencing from 102 COVID-19 patients at the Mayo Clinic health system, for whom we have complete longitudinal health records and vaccination history . On the other hand, deletions in this region have emerged in the context of various lineages  across the world since December 2020  deletions are strongly associated with surges in community transmission . Thus, a concerted evolution of strategically placed deletions and substitutions appears to confer SARS-CoV-2 with improved fitness to evade immunity and achieve efficient transmission between hosts . Several of these regions overlap with the residues of the recently identified NTD antigenic supersite, and deletions within them can abrogate binding to neutralizing antibodies15. Our findings build upon this prior work by examining the deletions which have arisen in the interim, during which over 1.5 million additional sequences have been deposited. In addition to validating the previously suggested definitions of recurrent deletion regions RDR1 (\u0394H69/V70 and flanking deletions), RDR2 (\u0394Y144 and flanking deletions), and RDR3 (\u0394I210 and \u0394N211), we found that RDR4 (previously defined as positions 242\u2013248) has recently expanded to include positions 249\u2013253. These residues are indeed part of the structurally mapped supersite14, and the Lambda variant harboring the \u0394246\u2013253 deletion increased in prevalence during a test positivity surge in Chile. The \u0394F157/R158 deletion (in the Delta variant), which had expanded during the massive surge in India, marked a new recurrent deletion region which also maps to the supersite14. Experimental studies suggest that several of these mutations are associated with increased infectivity and/or reduced neutralization by antibodies from convalescent sera (Table S6). Finally, our surveillance of clinically annotated SARS-CoV-2 genomes among COVID-19 cases at the Mayo Clinic, including vaccine breakthrough infections, revealed contiguous deletions (\u039485\u201390) that are distinct from the background population and are beginning to appear in other parts of United States and the world  or relevant medical histories . Thus, while we can identify correlations between mutational prevalence and case surges, we cannot determine whether particular mutations are associated with more severe disease or are observed more frequently than expected by chance in vaccinated individuals. While the latter shortcoming is partially addressed by our independent whole genome sequencing of virus isolated from COVID-19 cases with accessible longitudinal records , this analysis was limited by the small size of the cohort (n\u2009=\u2009102) and the lack of corresponding antibody titer data.Taken together, by synthesizing insights from genomic epidemiology and clinical genomics datasets, we uncovered that SARS-CoV-2 likely employs antigenic minimalism in the Spike protein as a strategy to evade immune responses induced by infection or vaccination. These findings have important therapeutic and public health policy implications. The repertoire of deletion mutations in the N-terminal domain should be considered when developing future vaccines and biologics to counter the immuno-evasive strategies of SARS-CoV-2. From a public health standpoint, we must expand sequencing efforts around the world and encourage the transparent linking of relevant deidentified patient phenotypic data  to each deposited SARS-CoV-2 genome. While the current analysis focuses on the Spike protein, analyses focusing on other SARS-CoV-2 proteins, such as the nucleocapsid protein and RNA-directed RNA polymerase, will shed further light on how the SARS-CoV-2 proteome has evolved to improve viral fitness and facilitate immune evasion. A holistic understanding of the mutational landscape of SARS-CoV-2 is imperative to proactively predict variants that could trigger outbreaks and vaccine breakthroughs, as well as to guide the development of therapeutic strategies to defeat the COVID-19 pandemic.https://www.gisaid.org/ on 5 July 2021.) For downstream analyses we ensured that all GISAID entries corresponded to the human host and had an\u00a0exact collection date (YYYY-mm-dd) after 1\u00a0December 2019. This resulted in 2,128,574 SARS-CoV-2 genome sequences  from GISAID16 across 188 countries/territories. To filter out potential sequencing artifacts, we excluded mutations that were present in fewer than 100 sequences, resulting in 1045 unique Spike protein mutations.In total, 2,212,827 sequences were obtained from GISAID . For each country, the monthly test positivity was calculated as:Positivity data for PCR tests was obtained from the OWID resourceTo identify surge-associated mutations, we classified the monthly mutational prevalence (for each mutation) and the monthly test positivity as increasing , decreasing , or mixed over sliding 3-month intervals over the course of the pandemic. Any mutation which monotonically increased in prevalence over this interval in a country with a simultaneous monotonic increase in test positivity was defined as a \u201csurge-associated mutation.\u201d There were 89 such mutations.8. At this time, there were 4 variants of concern  and 7 variants of interest , with no variants of high consequence. From the 11 classified variants, there are 59 unique mutations (53 positions), of which 18 were found only in variants of interest, 29 were found only in variants of concern, and 12 were found in both variants of interest and concern. After identifying the surge-associated mutations as described above, we determined the fraction of mutations comprising the CDC-classified variants which were captured by this approach.In order to test the value of our method, we obtained the set of CDC variants of interest and concern as of 13\u00a0July 13 2021p value was calculated using the scipy.stats.chi2_contingency function from the scipy package (1.7.0) in Python v3.9.5. Post-hoc Fisher's tests were performed by constructing 2\u2009\u00d7\u20092 contingency tables to compare each mutation type against all others. Then, odds ratios and their corresponding 95% confidence intervals were calculated using the scipy.stats.fisher_exact function and statsmodels.stats.contingency_tables.Table2\u2009\u00d7\u20092 respectively in Python v3.9.5.After identifying the 92 surge-associated mutations, we tested whether any of the contributing mutation types  were enriched for surge-associated mutations. To do so, we constructed a 2\u2009\u00d7\u20093 table giving the number of surge-associated and non-surge-associated mutations in each category. To determine whether one or more groups showed a statistically significant enrichment, a chi-square 15.Recurrent deletion regions (RDRs) were previously defined as four sites within the NTD within which over 90% of all Spike protein deletions occurred, per the 146,795 SARS-CoV-2 sequences deposited in GISAID from 1 December 2019 to 24 October 2020R into categories  reflecting whether or not it should be considered as part of an RDR  for that month as follows .To formally identify RDRs that have emerged over the course of the pandemic, we considered the monthly distribution of deletion counts for each amino acid (i.e. number of sequences in which deletion of the given amino acid was observed in a given month) in the Spike protein. For each month, we calculated the 95th percentile of the deletion count distribution. We then bucketed each residue P in the \u201cPossible\u201d category were subjected to further analysis to convert their labels into \u201cYes\u201d or \u201cNo.\u201d Specifically, we took a stepwise approach, walking in both directions from P until the first encounter of a residue categorized as \u201cYes\u201d or \u201cNo\u201d . If a residue categorized as \u201cYes\u201d was encountered before any residue categorized as \u201cNo\u201d in either direction, then the \u201cPossible\u201d label was converted to \u201cYes.\u201d If a residue categorized as \u201cNo\u201d was encountered before any residue categorized as \u201cYes\u201d in both directions, then the \u201cPossible\u201d label was converted to \u201cNo\u201d. With each residue categorized as \u201cYes\u201d or \u201cNo\u201d, we then simply merged the residue windows with consecutive \u201cYes\u201d labels to define the updated set of Spike protein RDRs for that month.Once each residue was categorized in this way, then any residue 15. Amino acids which were included in the previously defined RDRs were indicated in the plot to distinguish them from amino acids which (1) are part of newly emerged RDRs or (2) represent contiguous expansions from a previously defined RDR.To assess the expansion of regions undergoing deletions over time, we plotted a time series tile plot indicating each month in which a given deletion was identified as part of an RDR . The residues plotted were defined based on the definition of RDRs provided above, which builds upon the regions defined previously18, was retrieved from the PDB.Structural analyses and illustrations were performed in PyMOL (version 2.3.4). The cryo-EM structure of the Spike protein characterizing the interaction with a neutralizing antibody 4A8 (PDB identifier: 7C2L), described by Chi et al.This is a retrospective study of individuals who underwent polymerase chain reaction (PCR) testing for suspected SARS-CoV-2 infection at the Mayo Clinic and hospitals affiliated to the Mayo health system.32 for SARS-CoV-2 lineage assignment; Nextclade33 for viral clade assignment, phylogenetic analysis, and S codon mutation calling, in comparison to the wild-type reference sequence of SARS-CoV-2 Wuhan-Hu-1 .SARS-CoV-2 RNA-positive upper respiratory tract swab specimens from patients with vaccine breakthrough or reinfection of COVID-19 were subjected to next-generation sequencing, using the commercially available Ion AmpliSeq SARS-CoV-2 Research Panel  based on the \"sequencing by synthesis\" method. The assay amplifies 237 sequences ranging from 125 to 275 base pairs in length, covering 99% of the SARS-CoV-2 genome. Viral RNA was first manually extracted and purified from these clinical specimens using MagMAX\u2122 Viral/Pathogen Nucleic Acid Isolation Kit (Life Technologies Corp.), followed by automated reverse transcription-PCR (RT-PCR) of viral sequences, DNA library preparation , DNA template preparation, and sequencing on the automated Genexus\u2122 Integrated Sequencer (Life Technologies Corp.) with the Genexus\u2122 Software version 6.2.1. A no-template control and a positive SARS-CoV-2 control were included in each assay run for quality control purposes. Viral sequence data were assembled using the Iterative Refinement Meta-Assembler (IRMA) application (50% base substitution frequency threshold) to generate unamended plurality consensus sequences for analysis with the latest versions of the web-based application tools: Pangolinhttps://gisaid.org/). The database identifiers are as follows: EPI_ISL_12916271, EPI_ISL_12916270, EPI_ISL_12916273, EPI_ISL_12916272, EPI_ISL_12916275, EPI_ISL_12916310, EPI_ISL_12916274, EPI_ISL_12916277, EPI_ISL_12916276, EPI_ISL_12916313, EPI_ISL_12916279, EPI_ISL_12916314, EPI_ISL_12916278, EPI_ISL_12916311, EPI_ISL_12916312, EPI_ISL_12916317, EPI_ISL_12916318, EPI_ISL_12916315, EPI_ISL_12916316, EPI_ISL_12916319, EPI_ISL_12916260, EPI_ISL_12916262, EPI_ISL_12916261, EPI_ISL_12916264, EPI_ISL_12916263, EPI_ISL_12916266, EPI_ISL_12916265, EPI_ISL_12916302, EPI_ISL_12916268, EPI_ISL_12916303, EPI_ISL_12916267, EPI_ISL_12916300, EPI_ISL_12916301, EPI_ISL_12916269, EPI_ISL_12916306, EPI_ISL_12916307, EPI_ISL_12916304, EPI_ISL_12916305, EPI_ISL_12916308, EPI_ISL_12916309, EPI_ISL_12916251, EPI_ISL_12916250, EPI_ISL_12916253, EPI_ISL_12916252, EPI_ISL_12916255, EPI_ISL_12916254, EPI_ISL_12916257, EPI_ISL_12916256, EPI_ISL_12916259, EPI_ISL_12916258, EPI_ISL_12916240, EPI_ISL_12916242, EPI_ISL_12916241, EPI_ISL_12916244, EPI_ISL_12916243, EPI_ISL_12916246, EPI_ISL_12916245, EPI_ISL_12916248, EPI_ISL_12916247, EPI_ISL_12916249, EPI_ISL_12916239, EPI_ISL_12916238, EPI_ISL_12916290, EPI_ISL_12916291, EPI_ISL_12916294, EPI_ISL_12916295, EPI_ISL_12916292, EPI_ISL_12916293, EPI_ISL_12916298, EPI_ISL_12916331, EPI_ISL_12916332, EPI_ISL_12916299, EPI_ISL_12916296, EPI_ISL_12916297, EPI_ISL_12916330, EPI_ISL_12916335, EPI_ISL_12916336, EPI_ISL_12916333, EPI_ISL_12916334, EPI_ISL_12916339, EPI_ISL_12916337, EPI_ISL_12916338, EPI_ISL_12916280, EPI_ISL_12916283, EPI_ISL_12916284, EPI_ISL_12916281, EPI_ISL_12916282, EPI_ISL_12916320, EPI_ISL_12916287, EPI_ISL_12916321, EPI_ISL_12916288, EPI_ISL_12916285, EPI_ISL_12916286, EPI_ISL_12916324, EPI_ISL_12916325, EPI_ISL_12916289, EPI_ISL_12916322, EPI_ISL_12916323, EPI_ISL_12916328, EPI_ISL_12916329, EPI_ISL_12916326, EPI_ISL_12916327.The SARS-CoV-2 sequences have been made available through the GISAID database .Supplementary Information."}
+{"text": "Stock enhancement aggressively replenishes depleted wild finfish populations. However, stock enhancement of black sea bream in Taiwan with complex genetic sources, especially when successful, maintains genetic diversity but dramatically changes the genetic structure within and among wild populations.Acanthopagrus schlegelii), a major commercial species. During 2004\u20132015, even management agencies conducted stock enhancement projects, leading to numerous private releases that have not been recorded. Stock enhancement by a private hatchery without accurate genetic records may lead to a genetic structure change in wild populations. Using allele frequencies at nine microsatellite loci, we studied the genetic effects of stock enhancement in 19 samples collected from populations in the hatcheries and the wild. In 458 individuals from nine hatchery samples, most populations showed weak but significant genetic differences and complex clusters in structure analysis, indicating dramatic stock change within and among hatcheries. The 10 wild populations (n = 773) also had a complex genetic composition and were genetically different among sampling sites and times. However, a simple and clear cluster in structure analysis was found for only one sampling site, which had no release history. Thus, stock enhancement with complex genetic sources helps maintain genetic diversity but dramatically changes the genetic structure within and among wild populations, especially when stock enhancement is successful.Stock enhancement, used for replenishing depleted wild finfish populations, is an aggressive approach. Stock enhancement projects in Taiwan involve black sea bream ( Advances in fishing technology have led to fish stocks, which are renewable fishery resources, being exhausted. Approximately half of all fish stocks have been deemed \u201cfully exploited\u201d or \u201coverexploited\u201d ,2. PolluAiming to improve fishery resources, Taiwan\u2019s government promotes a massive stock enhancement program in its coastal waters every year ,9. UnderAcanthopagrus schlegelii), an economically vital species for both fisheries and aquaculture, is widely distributed along West Pacific coasts from Japan and Korea to the East China Sea and Taiwan. In southern China, black sea bream males become sexually mature within 1 year, and 50% of them change sex by two years old. In this fish, reproduction occurs at 1\u20132 years of age in coastal waters and at river mouths ; these groups exhibited the highest intergroup variance ; ; (MT_C); (KS_C2); (XM_C); (KS_C3); and (KM_C)] was determined to have the highest CT\u03a6 and variance  with the highest e 1.20%; . In the In the STRUCTURE analysis, the best estimation of the K value (number of groups) was eight, and this corresponded to a stable representation  and wild populations . Black sea bream has been cultured for more than 30 years in Taiwan. This is the first study analyzing the genetic diversity of cultured and wild black sea bream populations in Taiwan coastal waters. Nine hatchery populations were collected, including one in northern China (as an outgroup population), QD_C (Qingdao City); one in southern China, XM_C (Xiamen city): two from Taiwan\u2019s offshore islands, KM_C (Kinmen) and MT_C (Matsu); and five from southern Taiwan, PR_C1, PR_C2, KS_C1, KS_C2, and KS_C3 (Kaohsiung City). According to the allele number (Na), observed heterozygosity (Ho), and expected heterozygosity (He) of nine microsatellite loci, the genetic diversity of the cultured populations was slightly lower than that of the wild populations, except for some such as KS_C1 (Pagrus major) in Kagoshima Bay, Japan, the released hatchery fish clearly reduced the genetic diversity of the wild population [Black sea bream, as KS_C1 . The allas KS_C1 . Geneticas KS_C1 . This inpulation . Due to Rhabdosargus sarba) presented two distinct clusters (hatchery and wild population clusters) [Nm = 10.895). Second, black sea bream is more abundant and widely distributed in Taiwan\u2019s coastal waters than silver sea bream. The stock population was initially established independently and from different areas and may have helped to maintain genetic diversity.When hatchery fish are cultured, high gene flow among hatchery populations and between wild populations cannot usually be maintained. As broodstocks are not changed each year, no random mating occurs. After several generations, hatchery populations tend to show different genetic structures to wild populations . Our stulusters) . As silvSiganus fuscescens) [STF. Notably, the hatchery populations KS_C1, PR_C1, and PR_C2 exhibited a lower pairwise STF with other wild populations than with ML_W2, PH_W, and CY_W. This indicated that the hatchery populations KS_C1, PR_C1, and PR_C2 had high gene flow with the wild populations, and changes in their genetic structure were mainly caused by fish release.We collected 10 wild populations from the following: Japan (as an outgroup population)\u2014JP_W (Nagasaki City); Taiwan\u2019s offshore islands\u2014KM_W (Kinmen) and PH_W (Penghu); northern Taiwan\u2014ML_W1, ML_W2, ML_W3 (Miaoli City), and TP_W (Taipei City); and southern Taiwan\u2014YL_W (Yunlin City), CY_W (Chiayi City), and TN_W (Tainan City). The fish from Taiwan\u2019s offshore islands, northern Taiwan, and southern Taiwan were expected to have a clear genetic structure such as one population   or two pcescens) . HoweverFST) between KS_C1 and ML_W1 is 0, the effect on the genetic structure should be minor. However, according to the present STRUCTURE analysis, KS_C1 was different from ML_W1, and stock enhancement led to evident genetic changes over three consecutive years , and their relatedness value (r) of >0.25 accurately excluded unrelated individuals. In each population, we could not find obvious inbreeding groups, and further guaranteed stock enhancement should not reduce the diversity of the wild population (Through a literature review, Araki and Schmid  summarizpulation . Howeverpulation . PairwisStock enhancement of black sea bream in Hiroshima Bay, Japan, is a successful example . GonzaleIn Taiwan, official stock enhancement and private religious release of black sea bream are conducted frequently and on a large scale. Such diverse and unpredictable fish larvae prevent the decline of overall diversity. Although determining the short-term effect of stock enhancement in Taiwan is difficult, the contribution of stock enhancement to wild populations is evidenced by changes in the genetic structure and the inconsistency of such structure."}
+{"text": "Pediatric sigmoid volvulus is a rare but emergency disease caused by abnormal twisting of the bowel along the mesenteric axisAn 8-year-old girl with a double-outlet right ventricle was admitted to our hospital because of vomiting after an injection from a gastric fistula. She had no stool or flatus passage for two days, and her facial expression was anguished. Physical examination revealed a distended tympanic abdomen with high-pitched bowel sounds. Abdominal radiography revealed a huge dilated colonic loop . ComputVideo\u20061\u2002Successful endoscopic detorsion for pediatric sigmoid volvulus by using the gel-immersion method.This case study successfully employed gel-immersion endoscopy, which may be useful in the endoscopic detorsion of a sigmoid volvulus.Endoscopy_UCTN_Code_TTT_1AQ_2AF"}
+{"text": "Notably, the package offers the possibility of incorporating weather data from field weather stations, or to retrieve global meteorological datasets from a NASA database. Daily weather data can be aggregated over specific periods of time based on naive  or phenological approaches. Different machine learning methods for genomic prediction are implemented, including gradient-boosted decision trees, random forests, stacked ensemble models, and multilayer perceptrons. These prediction models can be evaluated via a collection of cross-validation schemes that mimic typical scenarios encountered by plant breeders working with multi-environment trial experimental data in a user-friendly way. The package is published under an MIT license and accessible on GitHub.We introduce the R-package  Large amounts of data from various sources  are regularly gathered as part of multi-environment\u00a0trials (MET). The efficient exploitation of these extensive datasets has become of utmost interest for breeders to address essentially two objectives: (1) accurately predicting genotype performance in future environments; (2) untangling complex relationships between genetic markers, environmental covariables (ECs), and phenotypes to better understand the pervasive phenomenon of genotype-by-environment (G \u00d7 E) interaction. Many R packages have recently been developed that allow to implement genomic prediction models accounting for G \u00d7 E effects using mixed models: BGLR , sommer While Bayesian approaches have been successful at dramatically improving predictive ability in multi-environment breeding experiments . EnhanciIn this article, we describe the R-package learnMET and its principal functionalities. learnMET provides a pipeline to (1) facilitate environmental characterization and (2) evaluate and compare different types of machine learning approaches to predict quantitative traits based on relevant cross-validation (CV) schemes for MET datasets. The package offers flexibility by allowing to specify the sets of predictors to be used in predictions, and different methods to process genomic information to model genetic effects.To validate the predictive performance of the models, different CV schemes are covered by the package, that aim at addressing concrete plant breeding prediction problems with multi-environment field experiments. We borrow the same terminology as in previous related studies see , as follBox 1.Install learnMET> devtools::install_github(\u201ccjubin/learnMET\u201d)> library(learnMET)Dependencies are automatically installed or updated when executing the command above.Using the devtools package .Box 1.IThree toy datasets are included with the learnMET package to illustrate how input data should be provided by the user and how the different functionalities of the package can be utilized.indica, composed of 327 elite breeding lines; and japonica, composed of 320 elite breeding lines). The two populations were evaluated at a single location  across multiple years (2010\u20132012 for indica and 2009\u20132013 for japonica) and were genotyped using genotyping-by-sequencing (GBS) (The datasets were obtained from the INIA\u2019s Rice Breeding Program (Uruguay) and were used in previous studies . We usedwww.genomes2fields.org), were integrated into learnMET. Hybrid genotypic data were computed in silico based on the GBS data from inbred parental lines. For more information about the original datasets, please refer to A subset of phenotypic and genotypic datasets, collected and made available by the G2F initiative , which will undergo a quality control with the generation of an output file with flagged values. The second possibility, if the user does not have weather data available from measurements (e.g. from an in-field weather station), is the retrieval of daily weather records from the NASA\u2019s Prediction of Worldwide Energy Resources (NASA POWER) database (https://power.larc.nasa.gov/), using the package nasapower  default: use of a definite number of intervals across all environments (i.e. the window length varies according to the duration of the growing season); (2) use of day-windows of fixed length , that can be adjusted by the user; (3) use of specific day intervals according to each environment provided by the user, which should correspond to observed or assumed relevant phenological intervals; and (4) based on the estimated crop growth stage within each environment using accumulated growing degree-days in degrees Celsius.Some covariates are additionally computed, based on the daily weather data, such as vapor pressure deficit or the reference evapotranspiration using the Penman-Monteith (FAO-56) equation. The aggregation of daily information into day-interval-based values is also carried out within this function. Four methods are available and should be specified with the argument soil_variables argument. The output of create_METData is a list object of class METData, required as input for all other functionalities of the package.Box 2.Integration of input data in a METData list objectCase 1: ECs directly provided by the user> library(learnMET)> data(geno_indica)> data(map_indica)> data(pheno_indica)> data(info_environments_indica)> data(env_data_indica)> METdata_indica <- create_METDataCase 2: daily climate data automatically retrieved and ECs calculated via the package> data(geno_G2F)> data(pheno_G2F)> data(map_G2F)> data(info_environments_G2F)> data(soil_G2F)> METdata_g2f <- create_METDatahttps://cjubin.github.io/learnMET/articles/vignette_getweatherdata.htmlNote: code example to use in-field daily weather data provided at Besides weather-based information, soil characterization for each environment can also be provided given the predict_trait_MET_cv function, that is presented in the following section. In particular, the XGBoost gradient boosting library  can be seen as an additive regression model, where the final model is an ensemble of weak learners (i.e. a regression tree in this case), in which each base learner is fitted in a forward sequential manner . ConsidelearnMET, a set of prediction models, denoted xgb_reg and rf_reg, is proposed that use the XGBoost algorithm or the Random Forest algorithm, respectively, with different input variables.In contrast, in Random Forest algorithms, trees are created independently from each other, and results from each tree are only combined at the end of the process. The concept of GBDT was originally developed by X represent the input features, b the bias), and transforms the latter with a nonlinear activation function learnMET, a set of prediction models named DL_reg, are proposed that apply MLP models with different input variables.An MLP consists of one input layer, one or more hidden layers, and one output layer. Each layer, with the exception of the final output layer, includes a bias neuron  and is fully connected to the next layer. Here, the first hidden layer receives the marker genotypes and the ECs as input, computes a weighted linear summation of these inputs  on a classification or regression task. The theoretical background of this method was originally proposed by predict_trait_MET_cv , and the corresponding test set is processed using the same transformations. Performance metrics are computed on the test set, such as the Pearson correlation between predicted and observed phenotypic values , and the root mean square error. Analyses are fully reproducible given that seed and tuned hyperparameters are stored with the output of predict_trait_MET_cv. Note that, if one wants to compare models using the same CV partitions, specifying the seed and modifying the model would be sufficient.When The function applies a nested CV to obtain an unbiased generalization performance estimate. After splitting the complete dataset using an outer CV partition , an inner CV scheme is applied to the outer training dataset for optimization of hyperparameters. Subsequently, the best hyperparameters are selected and used to train the model using all training data. Model performance is then evaluated based on the predictions of the unseen test data using this trained model. This procedure is repeated for each training-test partition of the outer CV assignments. tune_bayes) or the package stacks. For models based on XGBoost, the number of boosting iterations, the learning rate, and the depth of trees represent important hyperparameters that are automatically tuned. Ranges of hyperparameter values are predefined based on expert knowledge. Bayesian optimization techniques use a surrogate model of the objective function in order to select better hyperparameter combinations based on past results > res_cv0_indica <- predict_trait_MET_cvpath_folder.Once a model has been evaluated with a CV scheme, various results can be extracted from the returned object, as shown in Box 4.met_cvExtraction of results from returned object of class # Extract predictions for each test set in the CV scheme:> pred_2010 <- res_cv0_indica$list_results_cv[[1]]$prediction_df> pred_2011 <- res_cv0_indica$list_results_cv[[2]]$prediction_df> pred_2012 <- res_cv0_indica$list_results_cv[[3]]$prediction_df# The length of the list_results_cv sub-element is equal to the number of train/test sets partitions.# Extract Pearson correlation between predicted and observed values for 2010:> cor_2010 <- res_cv0_indica$list_results_cv[[1]]$cor_pred_obs# Extract root mean square error between predicted and observed values for 2011:> rmse_2011 <- res_cv0_indica$list_results_cv[[2]]$rmse_pred_obs# Get the seed used:> seed <- res_cv0_indica$seed_usedpredict_trait_MET  as for the training set. To build an appropriate model with learning parameters, able to generalize well on new data, a hyperparameter optimization with CV is conducted on the entire training dataset when using the function predict_trait_MET.The third module in the package aims at implementing predictions for unobserved configurations of genotypic and environmental predictors using the function it_MET . The useThis function can potentially be applied to harness historical weather data and to obtain predictions across multiple years at a set of given locations # Create a training set composed of years 2014, 2015 and 2016:> METdata_G2F_training <-create_METData,],map = map_G2F,climate_variables = NULL,compute_climatic_ECs = TRUE,et0\u2009=\u2009T, # Possibility to calculate reference evapotranspiration with the package info_environments\u2009=\u2009info_environments_G2F,soil_variables = soil_G2F,path_to_save =\u201c/project1/g2f_trainingset\u201d) # path where daily weather data and plots are saved# Create a prediction set (same default method to compute ECs as above):> METdata_G2F_new <-create_METData),map = map_G2F,et0\u2009=\u2009T,climate_variables = NULL,compute_climatic_ECs = TRUE,info_environments\u2009=\u2009info_environments_G2F,soil_variables = soil_G2F,path_to_save =\u201c/project1/g2f_testset\u201d,pheno argument.as_test_set = T) # in order to provide only predictor variables (no phenotypic data for the test set available) in # Fitting the model to the training set and predicting the test set> results_list <- predict_trait_METCompared to parametric models, ML techniques are often considered as black-boxes implementations that complicate the task of understanding the importance of different factors  driving the phenotypic response. Therefore, various methods have recently been proposed to aid the understanding and interpretation of the output of ML models. Among these techniques, some are model-specific techniques , in the j as follows: Xoriginal is the original matrix of predictor variables, and Xpermuted is the matrix obtained after permuting the variable j in Xoriginal. The reason behind this approach is that, if a predictor contributes strongly to a model\u2019s predictions, shuffling its values will result in increased error estimates. On the other hand, if the variable is irrelevant for the fitted model, it should not affect the prediction error. It is recommended to repeat the permutation process to obtain a more reliable average estimate of the variable importance > variable_importance <- variable_importance_split# Model-agnostic: variable importance based on 10 permutations> variable_importance <- variable_importance_split# Model-agnostic: accumulated local effects plot> ALE_plot_splitAccumulated local effects (ALE) plots, also model agnostic, allow to examine the influence of a given predictor variable on the model prediction, conditional on the predictor value . The function summary provides a quick overview of the elements stored and collected in this first step of the pipeline (Box 7.Summary method for class METData> summary(METdata_g2f)Clustering analyses, that can help to identify groups of environments with similar climatic conditions and to identify outliers, were generated based on (a) only climate data; (b) only soil data (if available); and (c) all environmental variables together, for a range of values for K\u2009=\u20092 to 10 clusters , q being the number of ECs and W the scaled and centered matrix that contains the ECs), Phenotypic traits were predicted by the reaction norm model proposed by For additional details about the benchmark model, we refer to the original publication of learnMET were tested: (1) xgb_reg_1, which is an XGBoost model that uses a certain number of principal components (PCs) derived from the marker matrix and ECs, as features and (2) stacking_reg_3. Although computationally more expensive than parametric methods, we paid attention to reasonable computational time .Two prediction models proposed in We conducted a forward CV0 CV scheme, meaning that future years were predicted when using only past years as the training set. For the rice datasets, at least two years of data were used to introduce variation in the EC matrix characterizing the training set (only one location was tested each year). Year, location or year-location effects were not incorporated in any of the linear and machine learning models, because we focused our evaluation on how the different models could efficiently capture the effects of SNPs and ECs, and of SNP \u00d7 EC interaction effects.Results from the benchmarking approach are presented in learnMET was developed to make the integration of complex datasets, originating from various data sources, user-friendly. The package provides flexibility at various levels: (1) regarding the use of weather data, with the possibility to provide on-site weather station data, or to retrieve external weather data, or a mix of both if on-site data are only partially available; (2) regarding how time intervals for aggregation of daily weather data are defined; (3) regarding the diversity of nonlinear machine learning models proposed; (4) regarding options to provide manually specified subsets of predictor variables .To allow analyses on larger datasets, future developments of the package should include parallel processing to improve the scalability of the package and to best harness high performance computing resources. Improvements and extensions of stacked models and deep learning models are also intended, as we did not investigate in-depth the network architecture , nor other types of deep learning models that might perform better . Finally, the package could be extended to allow genotype-specific ECs, because the timing of developmental stages differs across genotypes (e.g. due to variability in earliness) and should ideally be taken into account."}
+{"text": "Drosophila larval central nervous system composed of 131,077 single cells across three developmental stages . We identify 67 distinct cell clusters based on the patterns of gene expression. These include 31 functional mature larval neuron clusters, 1 ring gland cluster, 8 glial clusters, 6 neural precursor clusters, and 13 developing immature adult neuron clusters. Some clusters are present across all stages of larval development, while others are stage specific (such as developing adult neurons). We identify genes that are differentially expressed in each cluster, as well as genes that are differentially expressed at distinct stages of larval life. These differentially expressed genes provide promising candidates for regulating the function of specific neuronal and glial types in the larval nervous system, or the specification and differentiation of adult neurons. The cell transcriptome Atlas of the Drosophila larval nervous system is a valuable resource for developmental biology and systems neuroscience and provides a basis for elucidating how genes regulate neural development and function.Molecular profiles of neurons influence neural development and function but bridging the gap between genes, circuits, and behavior has been very difficult. Here we used single cell RNAseq to generate a complete gene expression atlas of the The online version contains supplementary material available at 10.1186/s13064-022-00164-6. Making sense of any complex system involves identifying constituent elements and understanding their individual functions and interactions. Neural circuit development and function is no exception. While recent advances in connectomics \u20139 and liDrosophila larva. We did this across 3 different life stages , providing a developmental profile of gene expression. Overall, our analysis reveals 67 distinct molecularly defined classes of cells in the larval nervous systems. We annotated these clusters based on the previously known markers. These included 31 distinct functional larval mature neuron clusters, 8 glial clusters, 6 neural precursor clusters and 13 developing immature adult neuron clusters. 5 clusters showed an abundance of mixed cell type markers and were excluded from further analysis. We identified genes enriched in each cell type both across distinct life stages and separately, at each life stage.To this end, we developed a protocol to capture, sequence and transcriptionally classify the molecular cell types of the entire central nervous system of the While scRNAseq provides detailed information about the transcriptional program deployed by a cell at the time of collection, a drawback of the technique is a loss of spatial information. In proof of principle validation experiments, we therefore used a recently developed RNA fluorescent in situ hybridization (RNA-FISH) protocol to resolve the anatomical location of a molecular cell type in the whole larval brain .In summary, our gene expression Atlas for 62 distinct cell subtypes of the larval nervous system at 3 distinct developmental stages reveals a slew of candidate genes that could play a role in the development and function of these cell types. In a companion paper in the same issue, we explore in more detail the temporal patterns of gene expression across stages. Our gene expression Atlas presented in this study provides a valuable resource for the community and a basis for future investigation of molecular mechanisms underlying the development and function of the nervous system.Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Marta Zlatic (zlaticm@janelia.hhmi.org).Drosophila larvae were grown on standard fly food at 25\u00a0\u00b0C and kept in 12-h day/night light and dark cycle. Vials were timed by collecting eggs on a new food plate over the course of one hour.Drosophila lines used in this study.Please see Supplementary Table Drosophila larvae were dissected at 1\u00a0h, 24\u00a0h, 48\u00a0h, or 96\u00a0h after larval hatching (ALH). All dissections were performed in a cold adult hemolymph solution (AHS) with no calcium or magnesium at pH 7.4. Quality of single cell isolation was investigated by visual inspection with compound and confocal microscopy. Samples were placed on ice during waiting periods. Samples were isolated and run on the 10\u2009\u00d7\u2009Chromium Single Cell 3\u2019 immediately after cell dissociation.First, the complete central nervous system (CNS) was dissected from every animal. The dissected nervous systems were kept in cold AHS on ice. For those samples where the brain and the ventral nerve cord (VNC) were sequenced separately, the separation of the brain from the VNC was performed using fine-tipped forceps and MicroTools . The time from digestion (the part of the protocol most likely to induce cell stress) to on the 10\u2009\u00d7\u2009Genomic instrument was never longer than 30\u00a0min.After separation of the brain from the VNC, the desired tissue was placed in 18 \u03bcL of AHS on ice. Once all samples were prepared, 2 \u03bcL of 10\u2009\u00d7\u2009neutral protease  was added to a final volume of 20 \u03bcL. The intact brain tissue was digested for 5\u00a0min. The tissue was then transferred to a fresh drop of 20 \u03bcL of AHS.Each sample was disaggregated with a clean, thinly pulled glass electrode until no tissue was visible under a dissection microscope. All debris (pieces of nerve and undigested tissue) were removed. Samples with fluorescent markers were observed under a fluorescence microscope to approximate cell density. The samples were then loaded onto the 10\u2009\u00d7\u2009Chromium chip.Single cell capture and library construction was performed using the 10\u2009\u00d7\u2009Chromium microfluidic device and the Chromium Single Cell 3\u2019 v2 Library and Gel Bead Kit . Manufacturer\u2019s recommendations were followed for cell collection and library preparation. Samples were sequenced with an Illumina HiSeq following manufacturer\u2019s instructions.FISH probes were designed based on transcript sequences using the online Stellaris Designer and purchased from Biosearch Technologies. The probe is 18-22nt long with a 3\u2019 end amine-modified nucleotide that allows directly couple to an NHS-ester dye according to the manufacturer\u2019s instructions (Life Technologies). Dye-labeled probe was separated from the excess free dyes using the Qiagen Nucleotide Removal Columns. FISH protocol was described previously  detects fluorescence. To employ structured illumination analysis, we collect multiple images with the illumination stripe pattern shifted to tile the plane in x, and repeat the process orthogonally to tile the plane in y. The sample is then moved in z, and the imaging repeated, and so on to image the 3D volume.For confocal imaging, the tissues were mounted in DPX. Image Z-stacks were collected using an LSM880 confocal microscope fitted with an LD LCI Plan-Apochromat 25x/0.8 oil or Plan-Apochromat 63x/1.4 oil objective after the tissue cured for 24\u00a0h. For single-molecule imaging, we use a previous described Bessel beam selective plane illumination microscope (BB-SIM). Detail construction of microscope and the imaging procedure was described previously . Brieflyhttps://github.com/histonemark/Brainseq_code.Cell by count matrices for each sample were obtained with Cell Ranger software  and analyzed with the R package Seurat  in a repCell Ranger was used to perform sample demultiplexing, genome alignment, read quality filtering, and quantification. The output was a cell-by-features matrix of counts for each individually indexed and sequenced sample.In order to analyze the samples coming from different dissections and development age and remove batch effects coming from different sequencing runs we integrated the samples to a shared reduced dimensional space using the reciprocal PCA pipeline implemented in Seurat. Briefly, each sample was read as Seurat object and quality filtered retaining all cells with more than 200 genes detected and with a mitochondrial gene content below 20%. Each sample was individually lognormalized and its top 5000 variable genes selected. In order to find the matched expression states across samples 5000 features where used. Prior to integration each sample was individually scaled and its dimensionality reduced to the first 100 principal components. The anchors for integration were selected among the first 50 principal components for the 5000 features previously selected. Finally the reciprocal PCA integration was computed for the first 50 principal components.After integration, all samples were analyzed together with the \u201cstandard Seurat workflow\u201d for non conventional, non integrated samples: The expression of all genes was scaled, a principal component dimensionality reduction was computed and the first 50 components retained. Unsupervised cell classification was achieved with the Seurat FindNeighbors algorithm across the 50 principal components previously calculated. Clusters from this classification were obtained with the Seurat FindClusters algorithm on the 50 principal components and the resolution parameter fixed at 2. For cluster visualization purposes a two-dimensional reduction was calculated with UMAP on the same 50 principal components.logfc.threshold to 0.1, min.pct to 0.1, min.diff.pct to 0.1, the return.thresh to 0.0501 and retained only positive enrichments changing only.pos to TRUE. Supplementary spreadsheet To annotate the identity of the discovered clusters we calculated their differential gene expression with the Seurat command FindAllMarkers changing the default parameters Dotplots detailing the expression makeup of particular cell types were generated in main figures for the top 60 cell-type enriched genes after filtering for genes with an average log2 fold change greater than 1 and only if present in more that 19% of the cells of the category tested. For visualization purposes the cell types of interest were always positioned in the bottom of the Y-axes. The dotplots containing all genes passing the aforementioned thresholds are included as Supplementary figures.To calculate differential gene expression per cluster and stage, we iteratively applied the Seurat function FindAllMarkers to each cluster after segregating its cells by stage . The parameters were identical to the ones used in cluster annotation discussed above.To identify gene expression differences between closely related cell types, we subset the classes and re-run the FindMarkers algorithm to increase the power and detect genes that differentiate the subclasses of interest. For example, for mature neuron markers, we created a new subset containing only mature neurons and aggregated same neuron types  under the same class.Conversely all major cell-types  were aggregated before running the FindMarkers algorithm to facilitate the discovery of cell type markers at a lower level of granularity.To build a complete transcriptomic Atlas of the larval central nervous system (CNS) we captured cells at 3 distinct time points in development  and for three kinds of nervous system dissections  only, Figs. To enable the direct comparison of cellular states across stages and dissections we integrated all samples to a common reduced dimensional space with the reciprocal PCA algorithm implemented in the R package Seurat . After iClustering of all cells revealed 67 different clusters Fig.\u00a0b. For eapara), and the component of neurotransmitter vesicle release machinery  and/or para, as clusters of functional mature larval neurons . Our analysis identified further markers that were highly differentially expressed in mature larval neurons compared to other cell types in the nervous system  during embryonic development . For a fnd instar and in some cases earlier than that) they start dividing again and producing new neurons that will become part of the adult nervous system . Th. Thrut, he mouse . The isocluster) . The tra cluster . These tst instar Drosophila larval MB revealed that KCs differ based on their connectivity .Fi.FiIlp2, To validate the specificity of our scRNAseq approach for identifying AstC-R2 in IPC cells, we probed AstC-R2 mRNA in a HaloTag reporter line for the IPCs . The oveDrosophila larva with single-cell resolution across multiple life stages. Unbiased clustering of more than 131,077 cells sequenced in this study based on their patterns of gene expression revealed 67 clusters of nervous system cells. We were able to identify the majority of the clusters based on known markers for distinct cell types. These included functional larval neurons, glial cells, neuronal precursors, and developing adult neurons, as well as ring gland and hemocyte clusters. For each cluster we identified large numbers of genes that were differentially expressed in that cluster compared to all other clusters. This dataset provides a valuable resource for studying genes involved in the development and function of the nervous system.In this study we present the first full transcriptomic atlas of the entire central nervous system of For the most part, functional larval neurons segregated based on neurotransmitter identity. Thus, different clusters contained cholinergic, GABAergic, glutamatergic, octopaminergic, serotonergic, dopaminergic and peptidergic neurons. The neurons implicated in memory formation (MB KCs) and motor neurons (MNs), each formed separate clusters that were very different in terms of gene expression to all other functional larval neurons. Each of these major neuronal classes selectively expressed a specific known marker . Additionally, we identified numerous genes with previously unknown functions whose expression was highly enriched in each of these major classes, providing a rich set of candidate genes that could be required for proper function of these neuron classes  lines upstream of one marker gene and a Split-AD (or DBD) into the other could result in selective driver lines for single clusters [At this stage we were unable to link the individual neuron clusters to specific morphologically characterized known neuron types. However, the combinations of genes that were differentially expressed in each cluster could provide a basis for determining the morphology, connectivity and functional roles of many of the clusters in the future. These markers could potentially enable selective targeting of gene expression to individual neuron clusters using intersectional techniques available in clusters \u2013136. SucOur dataset also provides a resource for studying temporal changes in gene expression across larval life stages in specific types of larval neurons, glial cells, the ring gland, developing adult neurons and neural precursor cells. Genes that are differentially expressed in functional larval neurons at late stages of larval life could play roles in regulating larval molting and transitions between instar stages and in responding to hormones such as Ecdysone, juvenile hormone or prothoracicotropic hormone.Since temporal cascades of transcription factor expression in neural precursors specify the fate of progeny neurons, the data generated in this study provides a rich resource for identifying novel factors that could be involved in neural fate specification and development. We pursue this question in more depth using this dataset in a companion study in the same issue.In summary, single-cell transcriptomic atlases are the missing piece required for the combined analysis of genes, circuits and behavior. By adding a transcriptomic atlas to the atlases of neuron connectivity, neuron activity and behavior, we have set the stage for a more complete understanding of the principles that underlie the complex interplay of genes, circuits, and behavior.Additional file 1: Supplementary Spreadsheets and Figures. All spreadsheets for marker genes contain the following columns: p-value , average log2 fold-change (avg_log2FC), percent of cells in the cluster expressing the marker (pct.1), percent of cells outside the cluster expressing the marker (pct.2), the multiple test corrected p-value , the cluster number (cluster), the gene name (gene), the flybase id (Fbgn_ID), gene long name (GeneName), datestamp of flybase snapshot inclusion (datestamp) and the Flybase gene snapshot for the gene in question, when available.(gene_snapshot_text). Supplementary_spreadsheet_1_Time_and_tissue_breakdown.ods. Spreadsheet detailing the number of cells per cluster and sample of origin, a stage by\u00a0cell number breakdown and sequencing quality control metrics for each sequenced\u00a0sample. Supplementary_spreadsheet_2_Ncells_and_gene_markers_per_cluster.xlsx. Spreadsheet containing one sheet per detected cluster with all the cluster defining markers resulting from running the FindAllMarkers algorithm as detailed in the methods. An additional sheet contains the number of cells per cluster. Supplementary_spreadsheet_3_Ncells_and_gene_markers_per_cluster_and_stage.xlsx. Spreadsheet containing one sheet per detected cluster with all the cluster defining markers at each stage, ie. 1h, 24h and 48h resulting from running the FindAllMarkers algorithm as detailed in the methods for the temporal analysis. An additional sheet contains the number of cells per cluster at each stage. Supplementary_spreadsheet_4_Ncells_and_gene_markers_per_cluster_and_tissue.xlsx. Spreadsheet containing one sheet per detected cluster with all the cluster defining markers for each tissue, ie. brain, CNS and VNC, resulting from running the FindAllMarkers algorithm as detailed in the methods for the temporal analysis. An additional sheet contains the number of cells per cluster detected in each tissue dissection. Supplementary_spreadsheet_5_Differential_expression_cluster_mature_neuron_classes.ods. Spreadsheet containing one sheet per mature neuron subtype and their markers resulting from running the FindAllMarkers algorithm as detailed in the methods but restricting it to mature cell-types only: Cholinergic, Gabaergic, Glutamatergic, Kenyon Cells, Motor, Monoaminergic and Peptidergic neurons. Supplementary_spreadsheet_6_Differential_expression_cluster_big_classes.ods. Spreadsheet containing one sheet per major cell-type class and their defining markers: Immature neurons, Cholinergic neurons, Neuroprecursor cells, Gabaergic neurons, Glutamatergic neurons, Kenyon cells, Unknown neurons, Motorneurons, Glia, Hemocytes, Epithelia/trachea, Monoaminergic neurons, Peptidergic neurons and Ring Gland. Supplementary_spreadsheet_7_NPCs_markers_among.xlsx. Spreadsheet containing one sheet per Neuroprecursor cluster and their markers resulting from running the FindAllMarkers algorithm as detailed in the methods but restricting it to Neuroprecursor clusters only. Supplementary_spreadsheet_8_Immature_neuron_markers_among.xlsx. Spreadsheet containing one sheet per Immature neuron cluster and their markers resulting from running the FindAllMarkers algorithm as detailed in the methods but restricting it to Immature neuron clusters only. Supplementary_spreadsheet_9_Cholinergic_markers_among.xlsx. Spreadsheet containing one sheet per Cholinergic cluster and their markers resulting from running the FindAllMarkers algorithm as detailed in the methods but restricting it to Cholinergic clusters only. Supplementary_spreadsheet_10_Gabaergic_markers_among.xlsx. Spreadsheet containing one sheet per Gabaergic cluster and their markers resulting from running the FindAllMarkers algorithm as detailed in the methods but restricting it to Gabaergic clusters only. Supplementary_spreadsheet_11_Glutamatergic_markers_among.xlsx. Spreadsheet containing one sheet per Glutamatergic cluster and their markers resulting from running the FindAllMarkers algorithm as detailed in the methods but restricting it to Glutamatergic clusters only. Supplementary_spreadsheet_12_Octopaminergic_markers_among.xlsx. Spreadsheet containing one sheet per Octopaminergic cluster and their markers resulting from running the FindAllMarkers algorithm as detailed in the methods but restricting it to Octopaminergic clusters only. Supplementary_spreadsheet_13_Serotoninergic_markers_among.xlsx. Spreadsheet containing one sheet per Serotoninergic cluster and their markers resulting from running the FindAllMarkers algorithm as detailed in the methods but restricting it to Serotoninergic clusters only. Supplementary_spreadsheet_14_Peptidergic_markers_among.xlsx. Spreadsheet containing one sheet per Peptidergic cluster and their markers resulting from running the FindAllMarkers algorithm as detailed in the methods but restricting it to Peptidergic clusters only. Supplementary_spreadsheet_15_Kenyon-Cells_markers_among.xlsx. Spreadsheet containing one sheet per Kenyon cells cluster and their markers resulting from running the FindAllMarkers algorithm as detailed in the methods but restricting it to Kenyon cells clusters only. Supplementary_spreadsheet_16_Glia_markers_among.xlsx. Spreadsheet containing one sheet per Glia cluster and their markers resulting from running the FindAllMarkers algorithm as detailed in the methods but restricting it to Glia clusters only. Supplementary_spreadsheet_17_Enriched_markers_per_cluster_48_vs_24h.xlsx. Spreadsheet containing one sheet per big cell-type class with all the markers enriched at 48h vs 24h resulting from running the FindAllMarkers algorithm as detailed in the methods for the temporal analysis but restricting it to 48 vs 24h. Supplementary_spreadsheet_18_selective_one_per_class_075-19.xlsx. Spreadsheet containing one sheet per cluster with all markers selective for that cluster when imposing a cut-off of log2 fold-change greater than 0.75 and the requirement of being detected in more than 19% of cells. Supplementary_spreadsheet_19_Identity_markers_and_refs.ods. Spreadsheet containing the list of all markers used to identify cell classes together with literature references. Supplementary_spreadsheet_20_Brain_only_atlas_markers.xlsx. Spreadsheet containing one sheet per cluster with all markers selective for that cluster when imposing a cut-off of log2 fold-change greater than 0.75 and the requirement of being detected in more than 19% of cells. In the Brain samples and the VNC samples it can be seen that there is a drastic increase of immature neurons relative to mature neurons from 24 hrs to 48 hrs. In the Brain samples, at 24 hrs, the ratio of immature (4885) to mature neurons (8536) is 0.57; at 48 hrs the ratio of immature (12092) to mature neurons (9758) is 1.23 (2.2-fold increase). Supplementary_spreadsheet_21_VNC_only_atlas_markers.xlsx. Spreadsheet containing one sheet per cluster with all markers selective for that cluster when imposing a cut-off of log2 fold-change greater than 0.75 and the requirement of being detected in more than 19% of cells. In the Brain samples and the VNC samples it can be seen that there is a drastic increase of immature neurons relative to mature neurons from 24 hrs to 48 hrs. In the VNC samples, at 24 hrs, the ratio of immature (3146) to mature neurons (4885) is 0.64; At 48 hrs the ratio of mature (2173) to immature (3513) is 1.61 (2.5-fold increase). Supplementary_Figure_1_UMAP_plot_per_tissue.pdf. UMAP representation of the CNS cell type diversity discovered after reciprocal-PCA integration, dimensionality reduction and unsupervised clustering with Seurat and split by tissue of origin. In this 2D representation each dot represents a cell and their distribution in space is a function of their similarity in gene expression profile. Each cluster is color and number coded as depicted in the accompanying legend. Supplementary_Figure_2_Brain_independent_analysis.pdf. UMAP dimensional reduction plot with the annotated clustering resulting from the analysis of VNC samples only at 24 and 48h. Supplementary_Figure_3_VNC_independent_analysis.pdf. UMAP dimensional reduction plot with the annotated clustering resulting from the analysis of Brain samples only at 24 and 48h. Supplementary_Figure_4_endogenous-nSyb-feature_plot.pdf. Feature plot comparing the expression distribution of endogenous and UAS-GAL4 amplified expression of nSyb. Supplementary_Figure_5_feature_plot_nSyb_Repo_Notch.pdf. UMAP dimensional reduction showing the expression distribution of endogenous nSyb, repo and Notch. In this 2D representation each dot represents a cell and their distribution in space is a function of their similarity in gene expression profile. Color represents the expression of the gene for that particular cell. In each dotplot, the centered mean expression of a gene for each class is calculated and given a color ranging from blue (lowest expression) to red (highest expression), with white corresponding to 0. In this fashion different genes can be compared by their relative expression in the classes depicted irrespective of their absolute expression levels. The diameter of each dot is proportional to the number of cells expressing that gene in the class. Supplementary_Figure_6_cholinergic_markers_dotplot.pdf. Dotplot depicting Cholinergic markers showing an average log2 fold-change greater than one compared to the other clusters and present in at least more than 19% of the cells of the cluster. Supplementary_Figure_7_glutamatergic_markers_dotplot.pdf. Dotplot depicting Glutamatergic markers showing an average log2 fold-change greater than one compared to the other clusters and present in at least more than 19% of the cells of the cluster. Supplementary_Figure_8_gabaergic_markers_dotplot.pdf. Dotplot depicting Gabaergic markers showing an average log2 fold-change greater than one compared to the other clusters and present in at least more than 19% of the cells of the cluster. Supplementary_Figure_9_octopaminergic_markers_dotplot.pdf. Dotplot depicting Octopaminergic markers showing an average log2 fold-change greater than one compared to the other clusters and present in at least more than 19% of the cells of the cluster. Supplementary_Figure_10_serotoninergic_markers_dotplot.pdf. Dotplot depicting Serotoninergic markers showing an average log2 fold-change greater than one compared to the other clusters and present in at least more than 19% of the cells of the cluster. Supplementary_Figure_11_dopaminergic_markers_dotplot.pdf. Dotplot depicting Dopaminergic markers showing an average log2 fold-change greater than one compared to the other clusters and present in at least more than 19% of the cells of the cluster. Supplementary_Figure_12_peptidergic_markers_dotplot.pdf. Dotplot depicting Peptidergic markers showing an average log2 fold-change greater than one compared to the other clusters and present in at least more than 19% of the cells of the cluster. Supplementary_Figure_13_Cholinergic_among_markers_dotplot.pdf. Dotplot depicting Cholinergic markers showing an average log2 fold-change greater than one compared to the other Cholinergic clusters and present in at least more than 19% of the cells of the cluster. Supplementary_Figure_14_Glutamatergic_among_markers_dotplot.pdf. Dotplot depicting Glutamatergic markers showing an average log2 fold-change greater than one compared to the other Glutamatergic clusters and present in at least more than 19% of the cells of the cluster. Supplementary_Figure_15_cotransmitter_upset_number.pdf. Histogram with numbers and percent of cells expressing combinations of one, two, three and four fast acting neurotrasmitters compared to single neurotransmitter expressing ones. Supplementary_Figure_16_Gabaergic_among_markers_dotplot.pdf. Dotplot depicting Gabaergic markers showing an average log2 fold-change greater than one compared to the other Gabaergic clusters and present in at least more than 19% of the cells of the cluster. Supplementary_Figure_17_Octopaminergic_among_markers_dotplot.pdf. Dotplot depicting Octopaminergic markers showing an average log2 fold-change greater than one compared to the other Octopaminergic clusters and present in at least more than 19% of the cells of the cluster. Supplementary_Figure_18_Serotoninergic_among_markers_dotplot.pdf. Dotplot depicting Serotoninergic markers showing an average log2 fold-change greater than one compared to the other Serotoninergic clusters and present in at least more than 19% of the cells of the cluster. Supplementary_Figure_19_hemocytes_markers_dotplot.pdf. Dotplot depicting Hemocyte markers showing an average log2 fold-change greater than one compared to the other clusters and present in at least more than 19% of the cells of the cluster. Supplementary_Figure_20_ring-gland_markers_dotplot.pdf. Dotplot depicting Ring gland markers showing an average log2 fold-change greater than one compared to the other clusters and present in at least more than 19% of the cells of the cluster. Supplementary_Figure_21_Glia_among_markers_dotplot.pdf. Dotplot depicting Glia markers showing an average log2 fold-change greater than one compared to the other Glia clusters and present in at least more than 19% of the cells of the cluster. Supplementary_Figure_22_Immature_among_markers_dotplot.pdf. Dotplot depicting Immature neuron markers showing an average log2 fold-change greater than one compared to the other Immature clusters and present in at least more than 19% of the cells of the cluster. Supplementary_Figure_23_Npcs_among_markers_dotplot.pdf. Dotplot depicting Immature neuron markers showing an average log2 fold-change greater than one compared to the other Immature clusters and present in at least more than 19% of the cells of the cluster."}
+{"text": "Accumulating evidence shows that impaired spiral artery remodeling, placental dysfunction, and insufficient trophoblast infiltration contribute to the etiology and pathogenesis of pre-eclampsia (PE). circRNAs are a class of endogenous non-coding RNAs implicated in the pathogenesis of many diseases, including PE. This study aims to investigate the role of circRNA hsa_circ_0008726 in regulating the migration and invasion of extravillous trophoblast cells. RNase R assay was performed to confirm that circ_0008726 was a circular transcript. The expression of circ_0008726, RYBP, and miR-345-3p was examined by qRT-PCR. The functional interaction between miR-345-3p and circ_0008726 or RYBP was confirmed using dual-luciferase reporter assay and RNA immunoprecipitation (RIP). Cell migration and invasion ability was analyzed by Transwell assays. Western blot was used for the quantification of RYBP protein level. Circ_0008726 expression was significantly increased in PE placenta tissues as compared with normal placenta tissues. Circ_0008726 was resistant to RNase R digestion and was predominately located in the cytoplasm of HTR-8/SVneo cells. Silencing circ_0008726 promoted cell migration and EMT , while circ_0008726 overexpression suppressed these processes. Mechanistically, circ_0008726 sponged miR-345-3p to negatively regulate its expression, and miR-345-3p negatively modulated the expression of RYBP. In PE samples, the expression level of circ_0008726 was negatively correlated with miR-345-3p level, but was positively correlated with RYBP expression. Transfection of miR-345-3p mimic or RYBP knockdown counteracted the effects of circ_0008726 overexpression on cell migration and EMT. Our data demonstrate the upregulation of circ_0008726 in PE placenta, which inhibits the migration, invasion, and EMT of HTR-8/SVneo cells by targeting miR-345-3p/RYBP axis. These data suggest that circ_0008726 could be a potential biomarker and therapeutic target for PE. In normal pregnancy, trophoblast cells migrate through decidua and myometrium and invade maternal spiral artery to provide blood supply to the fetus. Under the condition of pre-eclampsia (PE), trophoblast cells fail to invade the myometrium, which leads to placental hypoperfusion and reduced spiral artery remodeling , 2. InsuCircRNAs are generated by the post-splicing events of precursor mRNAs and show a covalent closed-loop structure without free 3\u2032 and 5\u2032 ends. For a long time, circular transcripts have been regarded as a by-product of abnormal splicing. However, circRNAs have attracted increasing research interest because of their diverse roles in a wide spectrum of physiological and pathophysiological processes, including the regulation of microRNAs (miRNAs), gene expression, and alternative splicing. Many studies have shown that non-coding RNAs are involved in the pathogenesis of PE. For example, a recent study suggests that circ_0111277 attenuates human trophoblast cell invasion and migration by regulating miR-494/HTRA1/Notch-1 signaling axis in PE . HoweverA previous microarray analysis (GSE102897 and GSE96984) showed that hsa_circ_0008726 (circ_0008726) was highly expressed in patients with PE ), and a high salt buffer (containing 500\u00a0mM NaCl). The bound total RNA was purified using Trizol reagent  according to the manufacturer\u2019s protocol, and analyzed by qPCR.For nucleoplasm fraction experiment, the nuclear and cytoplasmic faction was extracted using NE-PER\u2122 Nuclear and Cytoplasmic Extraction Reagents , and the total RNA in each fraction was purified using Trizol reagent  according to the manufacturer\u2019s protocol. An equal number of cells were used for total cell lysate RNA extraction, which serves as the total cellular RNA level control for normalization. The extracted RNA was quantified by RT-qPCR.For circ_0008726 overexpression, the circ_0008726 sequence was cloned into the PLCDH-cir vector  , 10. The293\u00a0T cells are used to examine the effect of miR-345-3p on the luciferase reporter of circ_0008726 or RYBP. As indicated in each experiment, cells are transfected with luciferase reporters  in the presence of miRNA mimic or miR-NC by Lipofectamine 3000 (Invitrogen). After 48\u00a0h, luciferase activity was measured using the dual-luciferase reporter assay system according to the manufacturer\u2019s instructions . The firefly luciferase reporter activity was normalized to that of Renilla luciferase activity.5 cells HTR-8/SVneo cells were seeded into upper Transwell inserts  which contained RPMI-1640 medium. The lower chambers were filled with RPMI-1640 medium containing 10% FBS, and the Transwell apparatus was cultured at 37\u2103 with 5% CO2 for 48\u00a0h. The cells on the upper surface were removed by a cotton swab, fixed by 4% methanol solution for 10\u00a0min, and stained by 0.1% crystal violet for 10\u00a0min. The number of stained cells was counted under an inverted fluorescence microscope .For the invasion assay, Matrigel  was diluted 1:30 (V:V) in cold PBS and coated on the bottom of Transwell chamber . For the migration assay, Matrigel was not used. Cells were trypsinized and then resuspended in serum-free medium. 1\u2009\u00d7\u200910HTR-8/SVneo cells were lysed by RIPA lysis buffer , and the Pierce BCA Protein Assay kit  was used to measure the protein concentration. Twenty-microgram proteins were separated on 10% SDS-PAGE, and then transferred to polyvinylidene fluoride membranes , which were then blocked in 5% nonfat milk solution for 2\u00a0h. The membrane was further incubated with primary antibodies  and anti-GAPDH antibody  at 4\u2103 overnight. After three washes with TBST buffer, the membranes were incubated with secondary antibodies: goat anti-rabbit IgG H&L (HRP)  and goat anti-mouse IgG H&L (HRP)  for 1\u00a0h at room temperature. The protein bands were developed using Pierce Western Blotting ECL substrate kit  and visualized on the BandScan 5.0 system . GAPDH was used as the loading control.\u2212\u0394\u0394Ct method in qPCR analysis. Data with non-normal distribution for continuous variables were summarized as medians  and statistical significance was analyzed by the Mann\u2013Whitney U-test. Spearman\u2019s rank correlation coefficients were used to assess potential correlations between two variables. Difference between two groups were compared by Student\u2019s t tests. Difference among multiple groups were compared by one-way ANOVA with Tukey\u2019s post hoc test for pairwise comparison. P-value less than 0.05 was considered statistically significant. The statistical analysis was performed using GraphPad Prism 7.0 software.Expression levels of circ_0008726 and miR-345-3p were normalized to internal control using 2Clinical characteristics were not significantly different between the 30 PE patients and 30 healthy controls, except for gestational week, birth weight, and placental weight which were directly linked to the PE Table .Table 1Tp\u2009<\u20090.001, Fig.\u00a0In order to assess whether Hsa_circ_0008726 (circ_0008726) is related to PE, we retrieved the published microarray datasets surveying the difference in lncRNAs, mRNAs, and circRNAs in PE and normal placenta. circ_0008726 was significantly upregulated in the PE samples in both GSE96985 dataset  and GSE102897 dataset  Fig.\u00a0. We alsoWe next analyzed the functional role of circ_0008726 in trophoblasts by siRNA-mediated silencing. The knockdown efficiency in HTR-8/SVneo cells siRNA was analyzed by qRT-PCR. Transfection of si-circ_0008726#1, si-circ_0008726#2 into HTR-8/SVneo cells could effectively downregulate circ_0008726 Fig.\u00a0. Transwep\u2009<\u20090.001, Fig.\u00a0We next examined the overexpression of circ_0008726 on the migratory features of trophoblasts. The overexpression of circ_0008726 was detected in HTR-8/SVneo cells by qRT-PCR by transfecting the cells with PLCDH-circ_0008726 plasmid (http://starbase.sysu.edu.cn/). To further confirm this result, we performed dual-luciferase reporter assays by constructing wild-type (WT) and mutant (MUT) luciferase reporter vectors of circ_0008726 based on the potential binding site between miR-345-3p and circ_0008726 . To confirm the interaction of miR-345-3p and RYBP mRNA, we performed dual-luciferase reporter experiments by constructing WT or MUT RYBP binding site reporters  played a major role in successful progression of stromal cell decidualization . As a poLeavey et al. analyzed a total of 330 samples and identified RYBP gene as one of the 3663 differentially expressed genes between PE and non-PE samples . The 366It is worth mentioning that this study has a few limitations. First, the sample size of PE patients and healthy controls were relatively small. In addition, since pathogenesis of PE is thought to occur in the first trimester, collecting PE patient samples in first semester will be more clinically relevant.Second, these findings need to be validated in animal models. In addition, other potential downstream targets of circ_0008726 need to be identified through sequencing-based methods.In summary, our data show that circ_0008726 level is upregulated in PE placental tissues. circ_0008726 seems to function as a sponge for miR-345-3p in trophoblast cells to impair the migration and invasion by regulating RYBP. Our findings indicate a functional role of circ_0008726 in regulating the migration, invasion, and EMT of trophoblasts. However, PE is a complex and clinically heterogeneous disease involving multiple pathophysiological mechanisms. Future study is required to further evaluate the role of circ_0008726 in regulating trophoblast migration in animal model."}
+{"text": "Recurrent respiratory syncytial virus (RSV) infection requiring hospitalization is rare and the underlying mechanism is unknown. We aimed to determine the role of CD14-mediated immunity in the pathogenesis of recurrent RSV infection.We performed genotyping and longitudinal immunophenotyping of the first patient with a genetic CD14 deficiency who developed recurrent RSV infection. We analyzed gene expression profiles and interleukin (IL)-6 production by patient peripheral blood mononuclear cells in response to RSV pre- and post-fusion (F) protein. We generated CD14-deficient human nasal epithelial cells cultured at air-liquid interface (HNEC-ALI) of patient-derived cells and after CRISPR-based gene editing of control cells. We analyzed viral replication upon RSV infection.CD14, resulting in absence of the CD14 protein in the index patient. In vitro, viral replication was similar in wild-type and CD14\u2212/\u2212 HNEC-ALI. Loss of immune cell CD14 led to impaired cytokine and chemokine responses to RSV pre- and post-F protein, characterized by absence of IL-6 production.Sanger sequencing revealed a homozygous single-nucleotide deletion in We report an association of recurrent RSV bronchiolitis with a loss of CD14 function in immune cells. Lack of CD14 function led to defective immune responses to RSV pre- and post-F protein without a change in viral replication. Autosomal recessive CD14 deficiency is a novel genetic etiology associated with recurrent RSV bronchiolitis. Lack of CD14 function led to defective immune responses to RSV pre- and post-F protein. Respiratory syncytial virus (RSV) is one of the most important viral pathogens identified in respiratory tract infections (RTIs) in children. It causes a major global health burden, with a hospitalization rate of ~3.2 million and an estimated yearly mortality rate of 125 000 in children under 5 . DefininCD14, leading to autosomal recessive CD14 deficiency, as a novel genetic etiology associated with recurrent RSV bronchiolitis. The accidental identification of a CD14-deficient patient enabled us to investigate the role of the CD14-mediated immune response in the context of RSV infection. We aimed to confirm the role of human CD14 expressed by monocytes in response to RSV-F stimulation. Next, we studied whether RSV viral replication in vitro is affected by the absence of CD14 in airway epithelial cells.Respiratory syncytial virus F is required for infection of respiratory epithelial cells. After entering the respiratory tract, RSV is confronted with the innate antiviral responses mediated by the airway epithelium and lung resident immune cells such as alveolar macrophages . These fThe patient and infant controls were initially enrolled through a study previously performed by our group . In short, the Neon study was set up to study the phenotype and function of airway and blood-derived neutrophils of patients with severe bronchiolitis  All part\u2212/CD16+; monocytes, CD14+/CD16\u2212. 7-aminoactinomycin D was used to distinguish apoptotic cells. Generation of RSV pre-F and post-F probes and identification of RSV pre-F and post-F binding B cells were performed as described previously  4.2)  23]. Th. Th+stan.Leu66*) . This rapression . No othe+SD 24). The patient and controls showed a similar increase of CD66b and decrease of CD62L surface expression on RSV airway neutrophils compared with circulating neutrophils during severe RSV infection. In addition, oxidative burst by the patient neutrophils was similar to that of control infants (data not shown). In conclusion, local neutrophil function during RSV infection was similar in the index patient and control infants.Evaluation of patient\u2019s leukocytes by flow cytometry showed no CD14 cell surface expression (truncated or intact), and there was no intact sCD14 detected in the plasma  and d. IIL-6 mRNA response to RSV-pre-F in the index patient  and interferon (IFN)-stimulated genes  were compromised  the functionality of CD14.-dependent signaling pathways by stimulating PBMCs of the patient and (2) healthy adult controls with TLR agonists, and we measured innate immune gene expression by NanoString, a methodology for measuring messenger ribonucleic acid (mRNA) levels in the absence of amplification. The innate immune response to TLR 1/2, 2/6, 4, and 5 but not TLR 7/8 stimulation was impaired in the patient\u2019s PBMCs compared to healthy controls (HCs) . This in patient . In addipromised . SubsequRNA data . In lineRNA data  with LPS\u2212/\u2212 cells developed with CRISPR-Cas9-based gene editing (\u2212/\u2212 cells displayed a lack of CD14 expression compared with wild-type (WT) control cells , although no other CD14-deficient patient has been described thus far. Second, the amount of blood limited the number of tests we could do. For instance, we did not investigate phagocytosis or monocyte responses to viruses other than RSV. Third, for practical reasons, adult controls were used for stimulation experiments with immune cells. We found impaired TLR 1/2, 2/6, 4, and 5 responses in the index patient up to age 3, when TLR responses are at adult level in normal children [Limitations also require discussion. First, we described a single CD14-deficient patient, and no other patients could be identified because of the low allele frequency. However, there are several other loss of function mutations identified in the CD14 gene (combined allele frequency of 2.5\u2005\u00d7\u200510children , 48. Forchildren . Howeverchildren .We described a novel single-gene immunodeficiency resulting in a phenotype characterized by, but not limited to, recurrent RSV infections. This patient establishes an important role for CD14 in RSV pathogenesis. Furthermore, CD14-mediated innate immune responses are likely involved in the immune response against pathogens in the respiratory tract. We predict that other loss of function mutations in the CD14 signaling pathway may be similarly associated with recurrent or severe RSV infections.The Journal of Infectious Diseases online. Supplementary materials consist of data provided by the author that are published to benefit the reader. The posted materials are not copyedited. The contents of all supplementary data are the sole responsibility of the authors. Questions or messages regarding errors should be addressed to the author.Supplementary materials are available at jiac114_suppl_Supplementary_Figure_S1Click here for additional data file.jiac114_suppl_Supplementary_Figure_S2Click here for additional data file.jiac114_suppl_Supplementary_Figure_S3Click here for additional data file.jiac114_suppl_Supplementary_Figure_S4Click here for additional data file.jiac114_suppl_Supplementary_Figure_S5Click here for additional data file.jiac114_suppl_Supplementary_Figure_S6Click here for additional data file.jiac114_suppl_Supplementary_Figure_S7Click here for additional data file.jiac114_suppl_Supplementary_Figure_S8Click here for additional data file.jiac114_suppl_Supplementary_Figure_S9Click here for additional data file.jiac114_suppl_Supplementary_Figure_S10Click here for additional data file.jiac114_suppl_Supplementary_Figure_S11Click here for additional data file.jiac114_suppl_Supplementary_Figure_S12Click here for additional data file.jiac114_suppl_Supplementary_MaterialClick here for additional data file."}
+{"text": "Curtobacterium is a genus of Gram-positive bacteria within the order Actinomycetales. Some Curtobacterium species  are harmful pathogens of agricultural crops such as soybean, dry beans, peas, sugar beet and beetroot, which occur throughout the world. Bacteriophages  are considered to be potential curative agents to control the spread of harmful bacteria. Temperate bacteriophages integrate their genomes into bacterial chromosomes (prophages), sometimes substantially influencing bacterial lifestyle and pathogenicity. About 200 publicly available genomes of Curtobacterium species, including environmental metagenomic sequences, were inspected for the presence of sequences of possible prophage origin using bioinformatic methods. The comparison of the search results with several ubiquitous bacterial groups showed the relatively low level of the presence of prophage traces in Curtobacterium genomes. Genomic and phylogenetic analyses were undertaken for the evaluation of the evolutionary and taxonomic positioning of predicted prophages. The analyses indicated the relatedness of Curtobacterium prophage-derived sequences with temperate actinophages of siphoviral morphology. In most cases, the predicted prophages can represent novel phage taxa not described previously. One of the predicted temperate phages was induced from the Curtobacterium genome. Bioinformatic analysis of the modelled proteins encoded in prophage-derived regions led to the discovery of some 100 putative glycopolymer-degrading enzymes that contained enzymatic domains with predicted cell-wall- and cell-envelope-degrading activity; these included glycosidases and peptidases. These proteins can be considered for the experimental design of new antibacterials against Curtobacterium phytopathogens. Curtobacterium are of great interest. These actinomycetes have been found in many of Earth\u2019s microbiomes. In spite of being unable to form spores, Curtobacterium spp. are nevertheless abundant in soil [Curtobacteria demonstrate enhanced tolerance to drought, salinity, UV irradiation and metal ions, and have been suggested to play an important role in plant adaptation to stress conditions [Curtobacterium spp. have been shown to promote seed germination [Curtobacterium flaccumfaciens pv. flaccumfaciens, is an economically important plant pathogen [Curtobacterium spp. are often found in plant microbiomes relevant to bacterial diseases [Bacteria belonging to the genus  in soil ,2, marin in soil  and in t in soil . Curtobanditions ,6,7. Beimination ,9 and plmination ,11, and mination ,13. A papathogen  and, occpathogen ,16. Curtdiseases , and maydiseases .Curtobacterium sp. are not completely understood [Genetic determinants of environmental adaptation and plant pathogenicity among derstood , and mayderstood . As a rederstood ,22,23.Curtobacterium sp., to propose the origin and phylogeny of initial phages, considering recent changes in bacteriophage taxonomy, and to reveal and systematise information about cytolytic enzymes encoded in prophage sequences which may have biotechnological potential.Prophages can constitute as much as 10\u201320% of a bacterial genome and contribute to interstrain variability. Prophages may harbour virulence factors and pathogenicity islands, thereby playing an important role in the emergence of pathogens ,25. The Curtobacterium were identified in the NCBI Genome Database [C. flaccumfaciens and 28 strains were classified as pathovars of C. flaccumfaciens. Previous research [Curtobacterium and called for taxonomic re-evaluation. Thus, all 197 genomes have been used for analyses.In early summer 2022, genomes of 197 strains assigned to the genus Database . Thirty-1 score of PhiSpy [The search for genomic regions of prophage origin has been performed using the online server PHASTER  and the f PhiSpy . PHASTERf PhiSpy  and HHprf PhiSpy  searchesf PhiSpy ,34,35. Sf PhiSpy . Thus, tBacillus, Clavibacter, Clostridium, Microbacterium, Nocardia, Ralstonia, Streptomyces, Synechococcus and Xanthomonas, and species Escherichia coli, Mycobacterium tuberculosis and Pseudomonas aeruginosa. The results of PHASTER analysis indicated a smaller number of predicted prophage-derived regions in genus Curtobacterium and closely related genus Clavibacter, compared with other taxa  revealed some discrepancies in the predictions of these two tools, and possible inaccuracies in the definition of the borders of the prophage-derived genomic region. PHASTER predicted potential sites for prophage insertion for only 22 putative prophages and the prediction did not look reliable, so the borders of PDRs were suggested on the basis of the phage origin of genes and comparisons with known phage genomes. Post-processing and manual curation of the prediction were conducted in ways similar to those described in  Figure .All of the predicted regions were checked through an analysis of the gene content of predicted regions and their possible prophage origin, with a BLAST search using the NCBI and custom phage databases. The genomic content of these 64 predicted prophage regions was additionally checked through comparisons with genomes of sequenced bacteriophages using an HHpred search. Putative genes of holins and spanins were also checked by the prediction of transmembrane regions. This post-processing revealed 70 prophage-derived regions (PDRs) possessing phage structural genes. Sixty-four regions were found to contain the genes encoding major capsid protein (MCP) and terminase large subunit (TerL). They might represent recently grounded or intact prophages and have been considered for further analysis. General features of these PDRs are listed in Intergenomic comparisons of 64 predicted and curated PDRs conducted using the Virus Intergenomic Distance Calculator (VIRIDIC)  Figure  indicatePhylogenetic analysis was conducted using the major capsid protein (MCP) and large subunit of terminase (TerL) amino acid sequences encoded in the curated prophage regions and close homologous sequences found with a BLAST search using complete phage genomes available in the NCBI Genome database as of July 2022. The search did not yield the same results for these two proteins, and the topologies of the trees were not identical , even thCurtobacterium genomes used for the analyses were also checked for the presence of tail sheath protein homologues using a BLAST search, with the set of tail sheath protein sequences that were used earlier for the characterisation of the evolutionary history of proteins of this class [Bacillus subtilis, cleaving the immunity repressor [Clustering was performed using the results of MCP phylogenetic analysis. Representatives of each prophage cluster, shown in is class ; no suchis class . Interesepressor .The replication apparatus of all predicted prophages included the protein similar to the \u03bb replication protein O required for initiation of DNA replication and present in other temperate phages ,47. LargHeunggongvirae viruses, including tailed bacteriophages [Genomic regions of all predicted prophages, not trimmed by contig limits, comprised all other functional modules featuring the tailed phages, including structural and lysis modules. All predicted major capsid proteins featured HK97 fold, typical for iophages . HHpred iophages ,51, incliophages  containiiophages ,54. Mostiophages .The tail modules of analysed RDRs vary in size and complexity and can comprise up to ten or more genes, including the head\u2013tail connector complex genes. Several proteins showed structural similarity to known tail spike proteins and were predicted to contain depolymerase domains. Interestingly, some PDRs can contain the genes that can modify cell envelope components a.To define closely related phage taxonomic groups, orthoANI  and VIRICurtobacterium PDRs and known complete phage genomes. However, a small likeness of the order of 10% has been detected with some phages infecting Microbacterium bacteria, which is phylogenetically close to the genus Curtobacterium. In particular, small Microbacterium phages with a genome size under 20,000 bp [Orlajensenviridae, subfamily Pelczarvirinae, genus Paopuvirus) [Curtobacterium prophages, with a similar genome size, assigned to Group 13.Neither ANI calculations , nor a V0,000 bp , recentlpuvirus) , were shNclasvirinae and several dozen genera not assigned to any subfamily or family, including Bridgettevirus, Britbratvirus, Bronvirus, Coralvirus, Decurrovirus, Fromanvirus, Mapvirus, Timquatrovirus, etc. The related phages infect bacteria belonging to genera Arthrobacter, Attisvirus, Bifidobacterium, Corynebacterium, Gordonia, Microbacterium, Mycobacterium, Streptomyces, Propionibacterium, Rathayibacter and Rhodococcus.Phylogenetic analysis using amino acid sequences of conservative proteins can reveal more distant evolutionary and taxonomic relationships . The lisCurtobacterium genomes analysed, namely in strains Curtobacterium flaccumcaciens pv. flaccumfaciens (C. fpf) BRIP_70615, C. fpf BRIP 70615, C. fpf CFBP 3417, C. luteum JCM 1480, C. luteum ATCC 15830, C. luteum DSM 20542, C. sp. 9128 DE0339, C. sp. MCBD17_028, C. sp. MCPF17_018, C. sp. MCSS17_007 and C. sp. WW7. The total number of spacers was 72. BLAST, using the spacers\u2019 sequences, found similar regions, mainly in the genomes of actinophages of siphoviral morphology, including phages belonging to subfamilies Arquatrovirinae, Bclasvirinae, Guernseyvirinae, Mclasvirinae, Nclasvirinae, Nymbaxtervirinae, Weiservirinae, unclassified phages and different genera not assigned to subfamilies or families.CRISPR spacers represent biological records of past phage\u2013bacteria interactions, and can be used for finding related hosts and phages ,61. A CRCurtobacterium sp.  were assessed for the presence of inducible prophages through induction using different mitomycin C concentrations, as described. Bioinformatic analysis suggests the presence of prophages integrated into genomes of these strains, and strains were available. The prophage-free Curtobacterium strain CFBP 3418 was used as a control for the induction experiments.Six strains of Curtobacterium strains VKM Ac-2098, VKM Ac-2884, VKM Ac-2861, VKM Ac-1796, VKM Ac-1376 and VKM Ac-2889 that were treated with mitomycin C to a final concentration of at least 1 \u00b5g/mL resulted in the formation of a lysis zone on the bacterial lawns of all tested Curtobacterium sp. strains, except for CFBP 3418   . The indCurtobacterium sp. VKM Ac-2884 was predicted to contain two prophages. Both of them were siphoviruses, and it is impossible to distinguish between them using TEM imaging. A PCR analysis of total DNA isolated from concentrated phage particles after induction was conducted. Amplification was observed only with a set of primers constructed for the detection of phage C_sp_VKM_Ac-2884\u20162  belongs to the Britbratvirus genus not assigned to a subfamily or family. The VIRIDIC Intergenomic Distance Calculator failed to indicate any meaningful intergenomic nucleotide similarity between the induced prophage and the related phages listed above. ANI calculations using all phage sequences deposited in NCBI GenBank also failed to find closely related phages with any meaningful average nucleotide identity and coverage. Thus, the induced prophage can represent a new viral genus or a higher-ranked taxon.A phylogenetic analysis using the major capsid protein  indicateA search for peptidoglycan hydrolase (lysin) genes in the predicted prophage regions indicated the presence of homologues of phage lysins in all PDRs that were not trimmed by contig borders. Fifty-eight lysins found by the search  were cluA domainal architecture and putative functional assignments of proteins and domains have been suggested using the results of HHpred and InterProScan  searchesCluster 1 is represented by a single 444 amino acid residue (aa)-long multidomain endolysin. HHpred HMM-HMM comparisons showed the closeness of the N-terminal domain of this protein (approximately 1\u2013160 aa) to lysins belonging to the \u03b3-glutamyl D,L-endopeptidase (NlpC/P60) family [) family . This doPredicted Domain 2 (161\u2013270 aa) contains putative amino acid residues forming the substrate entrance channel groove  and is pCluster 2 include eight proteins featuring the two-domain structure with the CHAP catalytic domain putatively arranged in the N-terminal part. It is impossible to predict the catalytic function of these enzymes (amidase or endopeptidase) confidently.Endolysins assigned to Cluster 3 are similar to Cluster 2 lysins. The N-terminal catalytic domain has been proposed to function as N-acetylmuramoyl-L-alanine amidase [The two endolysins assigned to  amidase , and the amidase .Cluster 4 contains a single 372-aa-long endolysin with a predicted three-domain architecture. The N-terminal domain is similar to several lytic enzymes of Gram-positive bacteria corresponding to the lysozyme family with muramidase activity cleaving the \u03b2-1,4 glycosidic bonds in the backbone of peptidoglycan.Central and C-terminal domains can facilitate peptidoglycan binding.Cluster 5 contains endolysins putatively encoded in 11 PDRs. The N-terminal domains showed a closeness to D-Ala-D-Ala endopeptidases. Structural comparisons of C_f_VKM_Ac-1386\u20161 and Enterococcus phage IMEEF1  [on, Dali  indicatede 7D55)  , the PDRs analysed contained other genes of putative glycopolymer-degrading enzymes . These genes are regularly located downstream of the lysis module and can be part of both the lysis system and the penetration apparatus. They were present in the most common putative analysed . The resanalysed . In someanalysed . This clCluster 1 contains only one \u03b1/\u03b2-hydrolase (572 aa). The catalytic domain is similar to carboxyl esterase from the oil-degrading bacterium Oleispira antarctica  [437-His549-Asp518 can be easily detected with HHpred and structural alignment using the AF2 model. An enzymatic domain is located in the C-terminal part. The superimposition of C_albidum_DSM_20512\u20161 DPO with carboxyl esterase 3I6Y showed RMSD 2.8 \u00c5 (de 3I6Y)  and otheSD 2.8 \u00c5 .Cluster 2 includes two DPOs where an HHpred search indicated a similarity with N-acetylglucosamine-1-phosphodiester \u03b1-N-acetylglucosaminidases (NAGPA), which removed the terminal GlcNAc residues [C_luteum_NS184\u20161 (427 aa) with NAGPA 6PKU shows RMSD 2.4 \u00c5  with the alginate lyase from Defluviitalea phaphyphila (PDB code 6JP4) [de 6JP4)  shows a Cluster 4 and Cluster 5 demonstrated a structural architecture typical of tail fibre (spike) proteins [Putative depolymerase domain-containing proteins assigned to proteins ,73, inclproteins . These pproteins   [de 7BR2) . This prde 7BR2) . The topCluster 8 DPOs show a similarity with the GDSL/SGNH-like lipase/acyl hydrolase family protein from Neisseria meningitidis   . PresumaCluster 9 depolymerases share a similarity with the acyl hydrolase family protein from Parabacteroides merdae (PDB code 4Q9A) (de 4Q9A) . The domCluster 10 comprises proteins with a GDSL/SGNH hydrolase domain and versatile structural organisation. Members of this cluster have a high degree of structural similarity with a group of structurally related proteins, which belong to the SGNH-hydrolase superfamily involved in carbohydrate metabolism and polysaccharide degradation, and which can function as carbohydrate deacetylases.C_sp_MCJR17_043\u20161 (553 aa) and identical sequences from the PDRs of other Curtobacterium strains C_sp_VKM_Ac-1376\u20161 (545 aa) (C_sp_VKM_Ac-1376\u20162 (500 aa) (C_sp_MCBD17_021\u20161 (241 aa), C_sp_MCBA15_004\u20161 (347 aa), C_sp_C1\u20161 (418 aa), C_sp_VKM_Ac-2884\u20162 (228 aa) (C_sp_MCSS17_015\u20162 (366 aa), C_sp_MCBD17_021\u20161 (359 aa) and C_sp_MCBD17_030\u20162 (495 aa). Several depolymerases, including those from C_sp_PhB137\u20161 (410 aa), C_sp_VKM_Ac-1796\u20161 (411 aa) , C_sp_MCLR17_034\u20161 (534 aa) and C_sp_MCSS17_006\u20161 (560 aa), have enzymatic domains arranged in the N-terminus.Most predicted structures contain two domains, where the enzymatic domain is located in the C-terminal part of the molecule; they include the DPOs from (545 aa) , C_sp_VK(500 aa) , C_sp_MC(228 aa) , C_sp_MCC_sp_PhB137\u20161 (410 aa) and C_sp_VKM_Ac-1796\u20161 (411 aa) (C_sp_MCSS17_015\u20161 (707 aa) (C_sp_WW7\u20163 (631 aa) feature a more complicated multidomain architecture, where the catalytic domain is located after the N-terminal domain and is attached to the \u03b2-barrel subdomain, which in turn is followed by another \u03b2-barrel domain. According to the results of an HHpred search, in the case of C_sp_MCSS17_015\u20161 (707 aa), the latter domain can play the role of the additional sugar-binding domain, as in a structurally similar sugar-binding protein (PDB code 4AVS)   have sim(707 aa)  and C_spde 4AVS) .C_sp_VKM_Ac-1376\u20161 (545 aa) and C_sp_MCSS17_015\u20161 (707 aa), contain N-terminal parts composed of \u03b2-strands. Hypothetically, such domains could enhance substrate binding. In several models, such as C_sp_VKM_Ac-1376\u20162 (500 aa), the enzymatic domain is arranged between the upstream and downstream sequences, which in turn assemble a compact \u03b2-barrel structure, reminiscent of the topology of DPOs assigned to Cluster 8.Interestingly, the N-terminal domains of Cluster 10 proteins vary in size and content. Some predicted structures, such as Curtobacterium sp. is very sparse. Numerous attempts to isolate lytic phages using traditional techniques [Limestoneviruses among phages infecting potato pathogen Dickeya solani [Ficleduoviruses among phages of aquaculture pathogen Flavobacterium columnare [E. coli or Pseudomonas sp.), or needs a concerted effort from numerous researchers  [Current information on bacteriophages infecting chniques ,81 have a solani ,83, and olumnare . The accium sp.) ,86.vir mutants with reduced lysogenic ability, it is possible to improve the behaviour of phages using gene editing approaches [A complementary approach is to assess the potential of temperate phages of the target bacteria, including inducible prophages encoded in host genomes. Generally, it is advised that temperate phages are avoided in phage therapy applications. However, when appropriate lytic phages are missing, or temperate phages have unique features, using the latter can be considered . Besidesproaches , or emplproaches ,90.Curtobacterium bacteria than in most of the other analysed taxa, except for the genus Clavibacter, another member of the Microbacteriaceae family. This observation is interesting in light of the fact that relatively few Curtobacterium strains, , contain CRISPR-Cas adaptive immune system regions in the search results, while previous research studies have estimated that 50% of sequenced bacterial genomes contain CRISPR [Curtobacteria have other effective antiphage defence mechanisms. An investigation of regions related to mobile elements could provide answers to these questions. It is noteworthy that a significant part of the PHASTER prediction results related to genomic regions containing the genes of cell-envelope-modifying enzymes. The cell walls of some Curtobacterium strains were shown to contain different glycopolymers, particularly rhamnan, and cell wall hydrolysates contained rhamnose, mannose and other saccharides [Examination of the results of prophage prediction using genomes of different taxonomic groups indicated fewer predicted prophages in n CRISPR ,92. It mcharides . InteresCurtobacteria. Several prophage depolymerases have been predicted to possess hyaluronidase enzymatic activity. Such proteins have been found in various Gram-positive bacteria, playing an important role in spread and growth [An analysis of cell-envelope-degrading enzymes of prophage origin might provide insights into the phage resistance mechanisms of d growth . Some phd growth ,96.Phages use bacterial receptors to adsorb to the host cell surface. Common cell receptors of Gram-positive bacteria used by phages include murein, cell wall teichoic acids and lipoteichoic acids ,98. BactCurtobacterium high phage resistance may be associated with cell wall characteristics. This hypothesis needs further detailed study.Most predicted depolymerase could be involved in peptidoglycan (PG) or polysaccharide deacetylation  55,101,,103. O-aC_sp_MCSS17_007\u20161, was predicted to have a four-domain architecture, while another endolysin, putative GH25 family muramidase from C_sp_C1\u20161, was modelled as a three-domain structure. Interestingly, regardless of the number of domains, the catalytic domain was located in the N-terminal part of all proteins. A pronounced modular architecture of endolysins, together with a high level of accuracy of structure predictions using modern AI software, might be used for the design of chimeric proteins that are effective against Curtobacterium infections.Phage endolysins encoded in predicted prophage-derived regions were represented by several groups showing different types of enzymatic activity, but most of the predicted lysins appeared to exhibit D,D-dipeptidase activity . PredictCurtobacterium genomes were downloaded from the NCBI Genome Database [The Database  and re-aDatabase , with deDatabase  and the Database . The PhiPredicted prophage sequences were extracted using the Geneious Prime 2022.2.1 tools  and annoAverage nucleotide identity was calculated using orthoANI, with default settings . The paiCurtobacterium sp. strains VKM Ac-2098, VKM Ac-2884, VKM Ac-2861, VKM Ac-1796, VKM Ac-1376, VKM Ac-2889 and CFBP 3418 were picked from YD-agar  plates, dropped in tubes containing 10 mL YD-broth  and left to grow overnight at 27 \u00b0C in personal bioreactor RTS-1C . Overnight bacterial cultures were diluted with 25 mL of fresh YD-broth to OD600 of approximately 0,09 and then incubated at 27 \u00b0C, with shaking, at 300 rpm for 7 h to obtain a final OD600 of 0.25. Then, several aliquots of these bacterial cultures were treated with different concentrations of mitomycin C  or left without mitomycin C, as a control, and incubated under the same conditions for 22 h. After incubation, the samples were centrifuged at 7000 G for 20 min and then passed through 0.45-\u03bcm sterile membranes. The resulting filtrates were stored at 4 \u00b0C.The induction of prophages was performed as previously described , with moCurtobacterium sp. strains , using the double-layer method [Curtobacterium bacterial cultures grown in YD-broth at 27 \u00b0C (\u223c108 CFU per mL) was mixed with 3 mL of soft agar (YD-broth supplemented with 0.7% agar). The mixtures were plated onto the YD-agar. Then, 10 \u00b5L of each filtrate was spotted on the soft agar lawns and incubated at 27 \u00b0C for 18 to 24 h.The presence of induced phages in the filtrates was tested against the same r method . For thiTo obtain preparations for microscopy, 100 mL of host culture was grown and prophage was induced, as described above. The resulting lysate was then concentrated and purified, according to the protocol described by Ackermann . CentrifC_sp_VKM_Ac-2884\u20161 (280 bp product). Primers 2-119F (CGTCGCTGTCGTTCAACTTC) and 2-453R (GAAGTCGATCGTCGCCTTGA) were selected to identify the phage C_sp_VKM_Ac-2884\u20162 (335 bp product). 5\u00d7 ScreenMix  was used for PCR. Each 25 \u00b5L reaction contained 5 \u00b5L of ScreenMix, 0.3 \u00b5M of each primer and 25 ng of DNA, and the volume was adjusted using sterile Milli-Q water. Thermal cycling conditions were as follows: 94 \u00b0C for 3 min, followed by 34 cycles of melting at 94 \u00b0C for 30 s, annealing at 60 \u00b0C for 30 s, elongation at 30 \u00b0C for 30 s and finally incubation at 72 \u00b0C for 3 min. As a negative control, a reaction with the addition of an appropriate volume of water was used instead of DNA. PCR results were visualised on a 1.5% agarose gel containing ethidium bromide. For additional verification of the accuracy of determining the site in the genome, Sanger sequencing of the PCR product obtained was carried out.PCR primers were constructed with Primer3 2.3.7 , using pProtein structures were modelled using AlphaFold 2.1, AlphaFold 2.2  and RoseCurtobacterium genomes have indicated the presence of prophage-derived regions. The number of these regions appears to be smaller than in some other well-studied taxonomic groups, but the analysis and structural modelling of encoded proteins has highlighted the potential of cell-wall-degrading enzymes (CWDEs) for future use. The diversity of CWDEs may indicate the complex structure of the Curtobacterium cell envelope, and can facilitate an understanding of the mechanisms of Curtobacterium phage resistance.Due to the prospect of using phages and phage-derived antibacterials for therapy in the context of multi-drug-resistant bacterial infections, genomic studies of prophage-derived regions are of great interest. Studies of"}
+{"text": "Several traction techniques for endoscopic submucosal dissection (ESD) of gastric neoplasms have been reported to be usefulVideo\u20061\u2002Intralesional cross-traction using silicone bands to assist gastric endoscopic submucosal dissection.A 77-year-old man with a 20-mm gastric neoplasm in the lower stomach was referred to our hospital. ESD using the intralesional cross-traction method was applied. After the circumferential mucosal incision , the fiThe intralesional cross-traction method, which provides traction force over a large area and bundles the submucosal layer toward the center of the lesion, can be applied to any gastric lesion. This is one of the best assistive methods for gastric ESD.Endoscopy_UCTN_Code_TTT_1AQ_2AD"}
+{"text": "Salmon lice have plagued the salmon farming industry and have negatively impacted salmon populations in the wild. In response, researchers have generated high density genetic maps, genome assemblies, transcriptomes, and whole-genome resequencing data to better understand this parasite. In this study, we used long-read sequencing technology to update the previous genome assemblies of Atlantic Ocean salmon lice with a more contiguous assembly and a more comprehensive gene catalog of Pacific Ocean salmon lice. We were also able to further characterize genomic features previously identified from other studies by using published resequenced genomes of 25 Atlantic and 15 Pacific salmon lice. One example was further characterizing the ZW sex chromosomes. For both the Atlantic and Pacific Ocean salmon lice subspecies, we found that the female W-chromosome is only a small fraction of the Z-chromosome and that the vast majority of the W and Z-chromosome do not contain conserved regions . However, conserved orthologous protein sequences can still be identified between the W- and Z-chromosomes. Lepeophtheirus genus of parasitic copepods , reduce the number of missing or partial genes, allow standard annotation by the NCBI, and further characterize previously discovered genomic features among the populations sampled.Multiple Pacific Ocean female salmon lice were collected by members of the British Columbia Centre for Aquatic Health Sciences from an Atlantic salmon farm in March 2020 near West Vancouver Island in British Columbia and flash frozen on dry ice until they could be stored at \u221280\u00b0C. We extracted high molecular weight (HMW) DNA from the cephalothorax of several salmon lice using a modified HMW extraction protocol of the Nanobind Tissue Big DNA Kit [HMW (50\u2013400+ kb) DNA Extraction from Sea Lice homogenized with Pellet Pestle\u2014Protocol 1] (Circulomics). This protocol required the Nanobind Tissue Big DNA Kit (Circulomics) and Buffer PL1 (Circulomics). Following DNA extraction, we used the Short Read Eliminator Kit (Circulomics) to reduce the number of small DNA fragments following the manufacturer\u2019s protocol. Sequencing libraries were prepared according to the manufacturer\u2019s protocol using the Ligation Sequencing Kit (SQK-LSK109 Oxford Nanopore Technologies) and sequenced on a Flow Cell MK1 R9 of a MinION (Oxford Nanopore Technologies). Sequences were generated in FASTQ format using the Guppy Basecalling Software (version 3.4.3+f4fc735).The initial assembly was then generated using the Flye genome assembler (version 2.7b-b1528) . Pilon ]. The reNucleotide variants were called using resequenced genomes from 25 Atlantic and 15 Pacific salmon lice used in a previous study  in R R .To identify a list of W-chromosome scaffolds, missing genotypes were compared between male and female lice using a Python script (github.com/KrisChristensen/VCFStatistics) in 10\u2009kb windows . If there were twice the average number of missing genotypes between male and females, the contigs were manually checked and verified . NucleotThe chromosome-level reference genome assembly produced in this study is more contiguous and has more gene annotations than other salmon lice assemblies We observed similarly high levels of repetitive elements within the genome as previously reported  and 1.5\u2009With comparisons of 25 Atlantic and 15 Pacific salmon lice, we were able to better understand genomic features that have previously been identified. In particular, we were able demonstrate that the entire linkage group 15 (the Z-chromosome) has an uneven coverage pattern, with female read coverage half that of males . From DaAnother genomic feature that was previously observed was the lack of recombination on linkage group 12 . Low nucWhile there were more than 57 million nucleotide variants identified before filtering and \u223c14 million after filtering (\u223c2% of the genome) between resequenced genomes, many of these variants were between the Atlantic and Pacific subspecies [using IGV viewer, . The AtlIn conclusion, we have updated the salmon louse reference genome. In doing so, we have increased the known gene catalogue of the species, increased the contiguity of the genome, and we were able to further characterize genomic features. We discovered that the W-chromosome is much reduced compared to other chromosomes and that linkage group 12 may have reduced genetic diversity as well as reduced recombination that had previously been observed.https://doi.org/10.6084/m9.figshare.19026866.v1 . Python scripts are available on github.com .The genome is available in the NCBI database under the following accession number: GCF_016086655.3. The raw reads are available under: SRR12967560. Previously resequenced genomes from another study are available as: SRR1950515, SRR1950516, SRR6913704, SRR6913705, SRR6913706, SRR6913707, SRR6913708, SRR6913709, SRR6913710, SRR6913711, SRR6913712, SRR6913713, SRR6913721, SRR6913722, SRR6913723, SRR6913724, SRR6913725, SRR6913726, SRR6913727, SRR6913728, SRR6913729, SRR6913730, SRR6913737, SRR6913738, SRR6913740, SRR13076813, SRR6913714, SRR6913715, SRR6913716, SRR6913717, SRR6913718, SRR6913719, SRR6913720, SRR6913731, SRR6913732, SRR6913733, SRR6913734, SRR6913735, SRR6913736, SRR6913739. Nucleotide variants in VCF format can be found at: G3 online.jkac087_Supplemental_Figure_1Click here for additional data file.jkac087_Supplemental_Figure_2Click here for additional data file.jkac087_Supplemental_Figure_3Click here for additional data file.jkac087_Supplemental_Figure_4Click here for additional data file.jkac087_Supplemental_Figure_5Click here for additional data file.jkac087_Supplemental_Figure_6Click here for additional data file.jkac087_Supplemental_Figure_7Click here for additional data file.jkac087_Supplemental_Figure_8Click here for additional data file.jkac087_Supplemental_File_1Click here for additional data file.jkac087_Supplemental_Material_LegendsClick here for additional data file."}
+{"text": "Aldabrachelys gigantea) is one of only two giant tortoise species left in the world. The species is endemic to Aldabra Atoll in Seychelles and is listed as Vulnerable on the International Union for Conservation of Nature Red List (v2.3) due to its limited distribution and threats posed by climate change. Genomic resources for A. gigantea are lacking, hampering conservation efforts for both wild and ex situpopulations. A high-quality genome would also open avenues to investigate the genetic basis of the species\u2019 exceptionally long life span.The Aldabra giant tortoise (de novo genome assembly of A. gigantea using PacBio High-Fidelity sequencing and high-throughput chromosome conformation capture. We produced a 2.37-Gbp assembly with a scaffold N50 of 148.6 Mbp and a resolution into 26 chromosomes. RNA sequencing\u2013assisted gene model prediction identified 23,953 protein-coding genes and 1.1 Gbp of repetitive sequences. Synteny analyses among turtle genomes revealed high levels of chromosomal collinearity even among distantly related taxa. To assess the utility of the high-quality assembly for species conservation, we performed a low-coverage resequencing of 30 individuals from wild populations and two zoo individuals. Our genome-wide population structure analyses detected genetic population structure in the wild and identified the most likely origin of the zoo-housed individuals. We further identified putatively deleterious mutations to be monitored.We produced the first chromosome-level A. gigantea and one of the most complete turtle genomes available. We show that low-coverage whole-genome resequencing, for which alignment to the reference genome is a necessity, is a powerful tool to assess the population structure of the wild population and reveal the geographic origins of ex situ individuals relevant for genetic diversity management and rewilding efforts.We establish a high-quality chromosome-level reference genome for  As human activities drive our planet into its sixth mass extinction , genomicChelonoidis niger and subspecies thereof, formerly Chelonoidis niger species complex) are native to the Gal\u00e1pagos Islands in the Eastern Pacific Ocean, and taxa of this group are listed as vulnerable, endangered, or extinct according to the International Union for Conservation of Nature (IUCN) Red List (v2.3) [Aldabrachelys gigantea)  . Aldabraea) Fig.\u00a0 are endeea) Fig.\u00a0. Due to ortoises . For Aldortoises .A. gigantea (NCBI:txid167804) have been successfully used in rewilding projects on several Western Indian Ocean Islands, whose endemic giant tortoise species are now extinct [A. gigantea has been introduced to three islands belonging to Mauritius, including Ile aux Aigrettes, Round Island, and Rodrigues [A. gigantea rewilding programs require genomic information and monitoring to minimize founder effects and maximize genetic variation in newly introduced populations [Aldabrachelys lineages, as well as the number and taxonomic status of extinct lineages [ extinct . The int extinct . A. gigaodrigues . Monitorodrigues . A. gigaulations . Finallylineages , 28.A. gigantea using PacBio high-fidelity (HiFi) sequencing and chromosome conformation capture (Hi-C) sequencing for scaffolding. We assessed the utility of the reference genome by performing low-coverage whole-genome resequencing for 32 tortoises . We inferred the genetic structure of the wild population and the likely origin of zoo-housed individuals.Here, we present the first high-quality chromosome-level genome of A. gigantea (named Hermania) living in the Zurich Zoo since 1955. Because blood was subsampled during a routine veterinary blood sampling, no additional ethical approval was required. Whole blood was taken from the animal's dorsal tail vein and stored on ice in a heparin-coated blood collection tube. DNA extraction was carried out at the Genetic Diversity Center, ETH, Zurich, according to the manufacturer's instructions of the MagAttract\u00ae High Molecular Weight DNA (HMW) Kit , with a single modification: instead of using 200\u00a0\u00b5L whole blood as suggested for blood samples with nonnucleated red blood cells, a total of 50\u00a0\u00b5L whole blood was used. The purified DNA was eluted in 200\u00a0\u00b5L molecular-grade water. Subsequent steps, including genomic DNA (gDNA) quality control, PacBio HiFi library preparation, and sequencing, were carried out at the Functional Genomic Center Zurich, ETH.In December 2020, during routine veterinary blood sampling, a subsample of approximately 3\u00a0mL of whole blood was collected from a female The input HMW genomic DNA concentration was measured using a Qubit Fluorometer , and the DNA integrity was checked on a Femto Pulse Device . The HiFi library preparation started with 14\u00a0\u03bcg HMW DNA. The PacBio HiFi library was produced using the SMRTbell\u00ae Express Template Prep Kit 2.0 , according to the manufacturer's instructions. Briefly, the DNA sample was mechanically sheared to an average size of 20\u00a0Kbp using a Megaruptor 3 Device . A Femto Pulse gDNA analysis assay (Agilent) was used to assess the resulting fragment size distribution. The sheared DNA sample was DNA damage-repaired and end-repaired using polishing enzymes. PacBio sequencing adapters were ligated to the DNA template. A Blue Pippin device  was used to size-select fragments >15\u00a0Kbp. The size-selected library was quality inspected and quantified using a Femto Pulse gDNA analysis assay (Agilent) and a Qubit Fluorometer (Thermo), respectively. The SMRT\u00ae bell-Polymerase Complex was prepared using the Sequel\u00ae II Binding Kit 2.0 and Internal Control 1.0 (Pacific Biosciences) and sequenced on a PacBio Sequel II instrument using the Sequel II Sequencing Kit 2.0 (Pacific Biosciences). In total, two Sequel II SMRT Cells 8\u00a0M (Pacific Biosciences) were run, taking one movie of 30 hours per cell. This yielded 49.4 Gbp of HiFi reads with a mean read length of 22.8\u00a0Kbp, which corresponds to approximately 20.8\u00d7 coverage of the genome (NCBI SRA: SRR18672579) (Table\u00a0RRID:SCR_017332) [A. gigantea has an estimated genome size of 2.37 Gbp  (0.49 SNPs per 1\u00a0Kbp [Gorilla beringei beringei) (0.65 SNPs per 1\u00a0Kbp [Ailuropoda melanoleuca) (1.35 SNPs per 1\u00a0Kbp [Chelonoidis niger abingdonii) (0.13 SNPs per 1\u00a0Kbp [Mauremys reevesii) (0.60 SNPs per 1\u00a0Kbp [The consensus circular sequences per each Sequel II SMRT Cell (Pacific Biosciences) were filtered for adapter contamination with HiFiAdapterFilt v2.0.0 , 30 -l _017332) , 32. Our , 32. Ou2.37 Gbp  and low er 1\u00a0Kbp ), or giaer 1\u00a0Kbp ) and Reeer 1\u00a0Kbp ).RRID:SCR_015880) [RRID:SCR_021966) [RRID:SCR_021069) [RRID:SCR_001228) [The reads were then assembled with the default parameters of HiCanu v2.1.1  , 38, Imp_021966) , and Hif_021069) , 41. Add_021069) , 41 was _001228) , 43. The_001228) , 41 withRRID:SCR_018926) [RRID:SCR_016071) [RRID:SCR_001653) option. We considered a segment to be a likely contaminant based on the blast bitscore (>30), e-value (>0.0001), and the segment's GC content (>70%). None of the blastx hits passed any of these cutoffs, and hence none of them was considered a significant match and potential contaminant. Second, we assessed k-mer profiles of the most probable sources of contamination: the human genome (NCBI RefSeq: GCF_000001405.39) and the A. gigantea mitochondrial genome (NCBI RefSeq: NC_028438.1). The average k-mer frequency of each contig in the draft assembly was compared with the potential contamination source using the tool sect in the software KAT v2.4.1  [k-mer statistics indicative of potential contamination  by either source. Third, the previously published A. gigantea whole-genome resequencing dataset (NCBI SRA: SRX4741543) [RRID:SCR_001209) [Scanning for contaminant contigs in the draft assembly was performed by following three approaches. First, the draft assembly was split into 5-Kbp segments using SeqKit v0.16.1  , 45. Eac_016071) , 47, a t_016741) , 49). Le4741543) . The rea_001209) , 52. TheRRID:SCR_015008) [We assessed the completeness of the assembly based on a BUSCO analysis of single-copy orthologs v5.1.2  , 54 withRRID:SCR_020150) 150-bp paired-end run. A total of 680 million reads were produced, corresponding to approximately 85\u00d7 coverage of the genome (NCBI SRA: SRR18673000)  + 1% Triton-X solution and incubated at room temperature for 15\u00a0minutes. Then, the nuclei were collected after the mixture was spun down. The cross-linked sample was sent on dry ice to Phase Genomics  for sequencing. The Hi-C library was generated using the Phase Genomics Proximo Animal kit version 4.0. Briefly, the DNA sample was digested with DpnII and the 5\u2032-overhangs were filled while incorporating a biotinylated nucleotide. The blunt-end fragments were ligated, sheared, and the biotinylated ligation junctions captured with streptavidin beads. The resulting fragments were sequenced on a NovaSeq 6000  [A. gigantea (AldGig_1.0) has the longest contig and scaffold N50 and one of the highest BUSCO completeness scores of all available chromosome-level assembled chelonian genomes  , 56 with_017226) , 58, whi_017226) . Finally_017226) . A total_017226) , 61  [RRID:SCR_021170) [RRID:SCR_014653) [RRID:SCR_005659) [de novo, to identify and classify consensus sequences. These consensus sequences were then used to softmask the genome with RepeatMasker v4.1.0  [A. gigantea genome was found to be slightly higher than the repeat contents of the green sea turtle (Chelonia mydas) (41.67%), Goode's thornscrub tortoise (Gopherus evgoodei) (41.67%), painted turtle (Chrysemys picta belli) (42%), and red-eared slider (Trachemys scripta elegans) (45%) genomes [To identify, classify, and mask repetitive elements in the _015027) , 63. Rep_021170) , RepeatS_005659)  to detec_012954)   during routine veterinary blood sampling in the Zurich Zoo. A total of 125\u00a0\u00b5L of whole blood was immediately diluted with the same amount of water, added into TRIzol\u2122 LS Reagent , and stored on ice for <2 hours until extraction. RNA was extracted at the Genetic Diversity Center, ETH, following a combination of a TRIzol\u2122 LS (Invitrogen) RNA isolation protocol and the RNeasy Mini Kit (Qiagen). First, the sample was incubated at room temperature for 5\u00a0minutes. Then, 0.2\u00a0mL chloroform was added to the sample and the mixture was inverted for 15 seconds, followed by a 3-minute incubation at room temperature. The resulting mixture was centrifuged at 11,000\u00a0rpm for 15\u00a0minutes at 4\u00b0C. After centrifugation, the upper phase containing the RNA was collected, mixed with 1\u00d7 70% ethanol, and transferred to an RNeasy spin column. For the remaining procedure, the protocol \u201cPurification of Total RNA from Animal Tissues\u201d of the kit was followed, starting from step 6. Briefly, the RNA was bound to the spin column, washed, and eluted in 30 \u03bcL molecular grade water. The initial quality control of the RNA was done on a TapeStation (Agilent) and the concentration was measured with a Qubit Fluorometer (Thermo).The PacBio IsoSeq library for RNA sequencing (RNA-seq) was produced at the Functional Genomic Center Zurich using the SMRTbell Express Template Prep Kit 2.0 (Pacific Biosciences), according to the manufacturer's instructions. A total of 300\u00a0ng RNA was used as input for the cDNA synthesis, which was carried out using the NEBNext\u00ae Single Cell/Low Input cDNA Synthesis & Amplification Module  and Iso-Seq Express Oligo Kit (Pacific Biosciences) following instructions. To enrich for longer transcripts (>3\u00a0Kb), 82\u00a0\u00b5L ProNex Beads was used for the cleanup of the amplified DNA, as outlined in the protocol. For all subsequent quality control steps, a Bioanalyzer 2100 12-Kb DNA Chip assay (Agilent) and a Qubit Fluorometer (Thermo) were used to assess the size and concentration of the library. The SMRT bell-Polymerase Complex was prepared using the Sequel Binding Kit 3.0 (Pacific Biosciences) and sequenced on a PacBio Sequel instrument using the Sequel Sequencing Kit 3.0 (Pacific Biosciences). In total, one Sequel\u2122 SMRT\u00ae Cell 1\u00a0M v3 (Pacific Biosciences) was run with one movie of 20 hours per cell, producing \u223c1.1 Gbp of HiFi data (NCBI SRA: SRR18674283) (Table\u00a0ab initio and evidence-based prediction methods (RNA-seq and homology based) with the braker2 pipeline v2.1.5  [Gallus gallus domesticus), which is the evolutionarily closest taxon for A. gigantea available within the software. Using pretrained parameters yielded more complete annotations compared to training with extrinsic evidence  as assessed by BUSCO protein completeness analyses. The ab initio prediction was performed by utilizing the softmasked reference genome \u200b\u200b(\u2013AUGUSTUS_ab_initio \u2013softmasking). Evidence for the transcriptome-based prediction was based on combining information from A. gigantea PacBio Iso-seq and all available RNA-seq databases from chelonians in closely related genera  [RRID:SCR_018550) [RRID:SCR_011980) [G. evgoodei (NCBI RefSeq: GCF_007399415.2) and C. n. abingdonii (NCBI RefSeq: GCF_003597395.1). This dataset was aligned against the chromosome-level assembled reference genome via the ProtHint pipeline v2.6.0  [RRID:SCR_018964) run in \u2013etpmode [ab initio and evidence-based methods were integrated into a high-confidence nonredundant gene set by using TSEBRA v1.0.3 [G. evgoodei , C. n. abingdonii , and C. mydas .Gene prediction was performed using a combination of _018964) . All gen_004463) , 75 and _018550) , 77 -ax_011980)  and the _021167) , 80. RNA  and the\u2013etpmode , 81\u201385. A v1.0.3 , 87, witA v1.0.3 . OverallRRID:SCR_015008) [C. n. abingdonii  and G. evgoodei   , 54 with, 99.3%) .RRID:SCR_005829) [RRID:SCR_021164) [Functional annotation of the encoded proteins was performed using the suite of search tools included in InterProScan v5.53\u201387.0  , 90, wit_021164) . AGAT v0_021164) , 93 was RRID:SCR_011809) [RRID:SCR_007891) [Transfer RNA (tRNA), ribosomal RNA (rRNA), small nuclear RNA (snRNA), and microRNA (miRNA) were annotated using Infernal v1.1.4  , 95, whi_007891) , a databA. gigantea mitochondrial reference genome available at NCBI RefSeq with accession number NC_028438.1 [Mitochondrial reads were extracted from the PacBio HiFi dataset and assembled with Hifiasm v0.15.5 , 41 usin028438.1 .A. gigantea chromosomes with three other chromosome-level chelonian genome assemblies from three different families, including G. evgoodei , the yellow pond turtle (Mauremys mutica) , and T. s. elegans   [We investigated the collinearity of _018550) , 77 with_018550)  to ident_018550) , 103. Am_018550)  Fig.\u00a02)A. gigant_018550) , 106; se_018550) ).A. gigantea and the phylogenetically closest available chromosome-level assembled G. evgoodei (NCBI RefSeq: GCF_007399415.2) reference genomes (split time ca. 50 mya [RRID:SCR_017118) [RRID:SCR_011798) [We also performed a complementary collinearity analysis based on orthologous gene sets of . 50 mya ). We fir_017118) , 109. A _017118) , 111 to _011798) , 113  and Grande Terre   Fig.\u00a0. The colividuals . Here, wDNA extraction was performed with 3\u00a0\u00b5L of blood from Hermania and 15\u00a0mg of muscle tissue from Maleika, using the sbeadex\u2122 kit , following the manufacturer's protocol for DNA extraction from nucleated red blood cells and tissue, respectively. Genomic DNA concentrations were measured with a dsDNA Broad Range Assay Kit . More than 200\u00a0ng DNA per sample was sent to Novogene Company  for library preparation and sequencing. Briefly, the genomic DNA was randomly fragmented to a size of 350 bp, end-polished, A-tailed, and ligated with Illumina adapters for Illumina sequencing. After polymerase chain reaction (PCR) enrichment, products were purified (AMPure XP system) and checked for quality on an Agilent 2100 Bioanalyzer (Agilent). Molarity was assessed using real-time PCR. Libraries were sequenced on the Illumina Novaseq 6000 platform with paired-end runs of 150\u00a0bp read length. For each of the 32 samples, \u223c2.6 Gbp raw reads were generated (NCBI SRA: SRR18674070-101) , Table\u00a01RRID:SCR_011847) [RRID:SCR_001876) [To account for the low-coverage sequencing approach, we assessed genotype likelihoods using the Atlas Pipeline , 119. We_011847)  with def_011847)  filterin_001876) . A targeRRID:SCR_021865) [RRID:SCR_001876) [Pvalue <0.001 were retained, producing a final set of 7,131,506 variant sites.We used ANGSD v0.93  , 123 to _001876)  to infer_001876) ). Sites Our low-coverage sequencing analyses focused on revealing within- and among-island genetic differentiation within the Aldabra population, as well as assigning likely origins for zoo-housed individuals. We first assessed the global genetic structure of the samples using a principal component analysis with PCAngsd v09.85 . Based oRRID:SCR_003208) [RRID:SCR_003208) [k) between 2 and 5 and visualized the assignments with PopHelper v.1.0.10 [k = 2 clusters revealed a main split with groups formed by East Grande Terre together with East Malabar opposed to West Malabar. South and West Grande Terre individuals were assigned to both groups. At k = 4, each major sampling region was assigned to a single cluster. The zoo individual Maleika showed a genotype highly consistent with South and West Grande Terre individuals. The individual Hermania  . We perf_003208) . Pairwis_003208)  and LD p_003208)  were perv.1.0.10 , 130  [RRID:SCR_021865) [RRID:SCR_005227) [Assessing the genetic health of a species is crucial for its long-term survival, and one major aspect of genetic health is mutation load. For a first glimpse at the distribution of putatively deleterious mutations in the Aldabra giant tortoise genomes, we used SnpEff v5.1  , 132 to _021865) , 123 as _005227) , 134 appex situ populations is crucial to inform rewilding efforts and prioritize conservation efforts. Furthermore, genome-wide analyses of polymorphism can be used to assess the presence of deleterious mutations endangering the long-term health of populations and will allow high-confidence estimates of inbreeding based on runs of homozygosity. Finally, given the exceptionally long life span and large body size of A. gigantea, the high-quality genome will inform comparative genomics studies focused on the genetic underpinnings of aging and gigantism.We assembled the first high-quality, chromosome-level annotated genome for the Aldabra giant tortoise, resulting in one of the best-assembled chelonian genomes. Chromosomal collinearity analyses revealed a high degree of conservation even among distantly related tortoise species. We showed that the high-quality resources can be combined with low-coverage resequencing to gain crucial insights into the genetic structure within Aldabra, as well as to resolve the exact origin of zoo-housed individuals. Understanding levels of genomic diversity in both native and GigaScience GigaDB database [The raw sequencing data, the nuclear and mitochondrial genome assemblies, and the annotation produced in this study have been deposited in the NCBI under BioProject accession number PRJNA822095. All supporting data are available in the database .GigaScience YouTube channel: https://youtu.be/Hak1xO-H8bMA video abstract of this work is available in the Supplementary Material S1. The k-mer (k = 17) profile of the Aldabrachelys gigantea genome. Consistent with low heterozygosity, most of the k-mers form one peak centered on roughly 20\u00d7 coverage and do not form another peak centered at roughly half the coverage that would represent k-mers arising from heterozygous alleles.Supplementary Material S2. Genome contiguity statistics of the assemblies obtained from different assemblers. The column shaded in gray represents our initial assembly obtained via default parameters in Hifiasm.Supplementary Material S3. Summary of repeat annotations.Supplementary Material S4. Accession details of the short-read RNA-seq samples used in this study.Supplementary Material S5. BUSCO statistics for the protein-coding gene annotation of Aldabrachelys gigantea, Chelonoidis abingdonii, and Gopherus evgoodei.Supplementary Material S6. Summary statistics of the functionally annotated protein-coding genes.Supplementary Material S7. Circos plot showing the synteny between the Aldabrachelys gigantea Hi-C scaffolds (orange) and Gopherus evgoodei assembly pseudo-chromosomes (green).Supplementary Material S8. Details of the location of 30 low-coverage whole-genome resequencing samples.Supplementary Material S9. Principal component analysis plot of 30 wild and two zoo-housed individuals. The analysis was performed with a more stringent mapping quality filter (MQ >30). Principal components 1 and 2 account for 14.5% and 3.77% of the overall genetic variation, respectively. Wild individuals sampled in Grande Terre and Malabar are shown with circles and triangles, respectively . Two zoo-housed individuals, Hermania and Maleika, are shown with a black diamond and a light pink square, respectively.Supplementary Material S10. Numbers of annotated SNPs with no MAF filtering and MAF \u22650.05 by their impact.Supplementary Material S11. Percentage of effects by their region on the genome (A) effects of SNPs with no MAF filter and (B) MAF \u22650.05.giac090_GIGA-D-22-00112Revision_1Click here for additional data file.giac090_GIGA-D-22-00112_Original_SubmissionClick here for additional data file.giac090_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giac090_Reviewer_2_Report_Original_SubmissionV\u00c3ctor Quesada -- 6/6/2022 ReviewedClick here for additional data file.giac090_Reviewer_3_Report_Original_SubmissionMarc Tollis -- 6/13/2022 ReviewedClick here for additional data file.giac090_Reviewer_3_Report_Revision_1Marc Tollis -- 8/2/2022 ReviewedClick here for additional data file.giac090_Supplemental_FilesClick here for additional data file.k-mer analysis toolkit; LS: liquid sample; MAF: minor allele frequency; Mbp: megabase pairs; mg: milligram; miRNA: microRNA; mL: milliliter; mM: millimolar; mya: million years ago; NCBI: National Center for Biotechnology Information; NEB: New England Biolabs; ng: nanogram; NGSAdmix: Next Generation Sequencing Admixture; ngsLD: Next Generation Sequencing Linkage Disequilibrium; OrthoDB: orthologous database; PacBio: Pacific Biosciences; PBS: phosphate-buffered saline; PCA: principal component analysis; PCR: polymerase chain reaction; QUAST: Quality Assessment Tool; RefSeq: reference sequence; Rfam: RNA families; RNA-seq: RNA sequencing; rpm: revolutions per minute; rRNA: ribosomal RNA; Sauropsida_odb10: sauropsids orthologous database 10; SMRT: single molecule real time; SNP: single-nucleotide polymorphism; snRNA: small nuclear RNA; SRA: Sequence Read Archive; STAR: Spliced Transcripts Alignment to a Reference; SyRI: Synteny and Rearrangement Identifier; tRNA: transfer RNA; TSEBRA: Transcript Selector for BRAKER; UniProtKB: Universal Protein Knowledgebase; Vertebrata_odb10: vertebrate orthologous database 10.\u03bcg: microgram; \u03bcL: microliter; \u00b0C: degree Celsius; AGAT: Another Gtf/Gff Analysis Toolkit; ANGSD: Analysis of Next Generation Sequencing Data; baq: base alignment quality; bp: base pairs; BUSCO: Benchmarking Universal Single-Copy Orthologs; BWA: Burrows\u2013Wheeler Aligner; cDNA: complementary DNA; dsDNA: double-strand DNA; EAZA: European Association of Zoos and Aquaria; ETH: Swiss Federal Institute of Technology in Z\u00fcrich; GAIA: Genome-wide Alignment Including Adapter-trimming; GATK: Genome Analysis Toolkit; Gbp: gigabase pairs; GC: guanine and cytosine; GCE: Genomic Character Estimator; gDNA: genomic DNA; Hi-C: chromosome conformation capture; HiFi: high-fidelity; HMW: high molecular weight; IsoSeq: isoform sequencing; IUCN: International Union for Conservation of Nature; JBAT: Juicebox Assembly Tools; KAT: The authors declare that they have no competing interests.This study was funded through the Research Talent Development Fund of the University of Z\u00fcrich, the Swiss National Science Foundation (Project No. 31003A_182343), and University of Zurich Internal Funds, all of which were given to C.G.F.G.\u00c7. and C.G. conceived the study design. F.G.\u00c7. carried out all DNA and RNA extractions and bioinformatic analyses with guidance from D.C. and C.G. D.H. and L.D. coordinated the sampling of the zoo animals. N.B. provided administrative support for sampling on Aldabra Atoll and L.A. managed the collection, storage, and transport of samples from wild individuals. F.G.\u00c7. wrote the manuscript with guidance from C.G. and substantial input from D.C. All authors revised the manuscript."}
+{"text": "Schistosoma mansoni or Plasmodium species alter subsequent Plasmodium intensity, Plasmodium risk, and S mansoni risk. Coinfection and prior infections with S mansoni were associated with reduced Plasmodium intensity, moderated by prior Plasmodium infections, wealth, and host age. Future work should assess whether these interactions impact host health and parasite control efficacy in this vulnerable age group.Malaria\u2013schistosomiasis coinfections are common in sub-Saharan Africa but studies present equivocal results regarding the interspecific relationships between these parasites. Through mixed-model analyses of a dataset of Ugandan preschool children, we explore how current coinfection and prior infection with either  Plasmodium-Schistosoma mansonicoinfections are associated with a reduced Plasmodiumintensity in preschool-aged children. This relationship, however, is modified by prior infections, host age, and family wealth. Plasmodium species and schistosomes interact during coinfection, yet none have explicitly considered the consequences of coinfection in preschool-aged children. This exposes a crucial knowledge gap in current options for effective control in younger children with no combined control strategy currently developed ), (2) Plasmodium risk, and (3) S mansoni risk were assessed in a series of general linear (Plasmodium intensity) and generalized linear (risk models) mixed models using the ASReml-R v4 mixed modeling package . In each assessment 2 models were created, with one set incorporating all infections as presence/absence data and the other including S mansoni and Plasmodium (Ln[x\u2005+\u20051]) data as inferred intensities. These latter models allowed the investigation of potential nonlinear relationships between the parasites. Plasmodium intensity models had a Gaussian error distribution and an identity link function. Risk models used a binomial error distribution with a logit link function.All analyses were completed in R version 3.5.2 statistical software . The effect of infection history (infection at baseline) and coinfection (coinfection at the 6-month survey) on (1) S mansoni infection (the number of times a child bathed and how long they spent in water) and Plasmodium infection ; and the random terms of family identifier, the child\u2019s village, nested within lake, and age fitted with a cubic smoothing spline, as parasite-age profiles are often nonlinear [P\u2005=\u2005.05).Baseline prevalence of STH was included in all models; however, the 6-month follow-up STH prevalence was only 3.9% and therefore excluded. All starting models included a child\u2019s age, sex, family\u2019s wealth quintile, tribe, and mother\u2019s occupation and education level; behavioral variables associated with onlinear . DetailePlasmodium infection (presence/absence), in interaction with an S mansoni coinfection, was associated with a significant reduction in intensity of Plasmodium  S mansoni infection, whereas Plasmodium intensity increased with age. The age-related decline in Plasmodium intensity was steeper for those children with prior low-intensity (n\u2005=\u2005101) and high-intensity (n\u2005=\u200513) S mansoni infections. In children with moderate baseline infections, although Plasmodium intensity increased with age, it remained lower than in either infection category for the equivalent age group. Cumulative Plasmodium intensity is therefore lowest in children with moderate prior S mansoni infection  response that is largely directed against schistosome eggs [Plasmodium clearance [Plasmodium intensity, whereas we observed a reduction. Early-stage schistosome infections , however, stimulate a Th1 response [S mansoni infections may have been in the Th1 phase of immune stimulation. An early S mansoni\u2013induced Th1 response could negatively impact Plasmodium and explain the reduced intensity of Plasmodium we observe. Nevertheless, there is the possibility that the relationship between these parasite species is driven by resource competition. Plasmodium species and hookworms have been observed to compete for the same resource, blood, with hookworm coinfection reducing Plasmodium intensity [Plasmodium intensity seen in schistosome coinfections in older children [The body\u2019s response to ome eggs . As the learance , 14, it response . Of the ntensity . Blood iPlasmodium and prior S mansoni infection in respect to different schistosome intensities, a child\u2019s age was an important moderator of this relationship. Age was associated with lower Plasmodium intensity at low and high prior S mansoni intensities; however, Plasmodium intensity increased with age for moderate infections. Nonlinear effects of schistosomes on Plasmodium have been observed elsewhere, with moderate schistosome infections reducing the incidence of Plasmodium infection while high- and low-intensity infections are associated with increased incidence [S mansoni infections is a limitation of our data. In older children most studies have observed positive associations between schistosome infection and Plasmodium intensity [Plasmodium intensity trajectory we observe is positive.When we explored the relationship between ncidence . It is fntensity , 6. ThisPlasmodium species present. Three species of Plasmodium can be found within the studied communities, but P falciparum dominates (75%) [Plasmodium species [Plasmodium species.The SIMI data do not contain complete information on all es (75%) . One stu species , and furPlasmodium and S mansoni. We show that prior infections can have long-lasting effects and suggest that Plasmodium\u2013schistosome interactions are likely to be mediated by the host immune response since any infections have been removed or reduced below detection limits by chemotherapeutic interventions. Determining the order of infection was essential to understanding coinfection outcomes in this system. Our results also suggest that stage of infection (early or later-stage) may have an important influence on the relationship between Plasmodium and schistosomes, again likely mediated by the host immune response. Our work has focused on the effect parasites have on one another, but it will now be important to identify whether, and how, these interactions affect host health and the efficacy of the individual parasite control strategies, highlighting the potential need for an integrated Plasmodium\u2013schistosome control strategy for this more vulnerable age group.Assessing infections in preschool-aged children with known infection histories has enabled us to elucidate the complex relationship between The Journal of Infectious Diseases online. Supplementary materials consist of data provided by the author that are published to benefit the reader. The posted materials are not copyedited. The contents of all supplementary data are the sole responsibility of the authors. Questions or messages regarding errors should be addressed to the author.Supplementary materials are available at jiac072_suppl_Supplementary_Figure_S1Click here for additional data file.jiac072_suppl_Supplementary_Figure_S2Click here for additional data file.jiac072_suppl_Supplementary_Figure_S3Click here for additional data file.jiac072_suppl_Supplementary_Figure_S4Click here for additional data file.jiac072_suppl_Supplementary_Figure_S5Click here for additional data file.jiac072_suppl_Supplementary_Figure_S6Click here for additional data file.jiac072_suppl_Supplementary_Figure_S7Click here for additional data file.jiac072_suppl_Supplementary_Figure_S8Click here for additional data file.jiac072_suppl_Supplementary_Figure_S9Click here for additional data file.jiac072_suppl_Supplementary_Table_S1Click here for additional data file.jiac072_suppl_Supplementary_Table_S2Click here for additional data file.jiac072_suppl_Supplementary_Table_S3Click here for additional data file.jiac072_suppl_Supplementary_Table_S4Click here for additional data file.jiac072_suppl_Supplementary_Table_S5Click here for additional data file.jiac072_suppl_Supplementary_Table_S6Click here for additional data file."}
+{"text": "Circular RNAs have been proven to play a pivotal role in cervical cancer development, progression, and treatment resistance. However, it is unclear how these RNAs influence chemoresistance in cervical cancer, particularly cancer stem cell (CSC)-like properties. In this study, we found that circRNA circ_0004488 was highly expressed in CSC-enriched subsets of cervical cancer cell lines. The expression of circ_0004488 was upregulated in cervical cancer cells that were resistant to paclitaxel. When circ_0004488 expression was high, the prognosis was poor. Specifically, we discovered that knocking down circ_0004488 greatly decreased the development of cervical cancer cells in vivo by decreasing cell proliferation, invasion, and sphere formation. By blocking cir_0004488, cervical cancer cells become more sensitive to paclitaxel. In cervical cancer cells, circ_0004488 acted as a microRNA-136 (miR-136) sponge, increasing the expression of MEX3C (a direct target gene of miR-136) using dual-luciferase reporter assays. Moreover, MEX3C downregulation significantly reduced cell proliferation, invasion, sphere formation, and paclitaxel resistance. In conclusion, circ_0004488 was shown to induce CSC-like features and paclitaxel resistance through the miR-136/MEX3C axis. Therefore, circ_0004488 might be a good therapeutic target for treating cervical cancer. The self-renewal and differentiation capabilities of cancer stem cells (CSCs) are a major therapeutic challenge  since thIn cervical CSCs, PTX resistance has been demonstrated . CervicaMicroRNA (miRNA) is a kind of noncoding RNA that controls protein synthesis and post-transcriptional expression . MiRNAs Noncoding RNAs with a circular loop structure, known as circular RNAs (circRNAs), regulate gene expression via complex mechanisms . Many ciIn this research study, we observed that circ_0004488 enhanced CSC-like characteristics and PTX resistance via the miR-136/MEX3C axis. Therefore, inhibiting circ_0004488 may represent a potential therapeutic method for the treatment of cervical cancer.https://arriveguidelines.org), and all relevant guidelines and regulations. All animal studies were conducted in compliance with the China Council on Animal Care and Use Guidelines and approved by the Animal Experimentation Ethics Committee of the Second Affiliated Hospital of Nanchang University.This study was conducted using human cervical cancer tissue samples by following the ethical standards of the Helsinki Declaration of 1975 and was approved by the Ethics Committee of the Second Affiliated Hospital of Nanchang University. Written informed consent was obtained from all patients. Moreover, all experimental protocols in the animal studies were performed in compliance with the institutional ethical standards, the ARRIVE guidelines  at a density of 500 cells per well and grown in a serum-free DMEM medium supplemented with 20\u2009ng/ml epidermal growth factor , 20\u2009ng/ml basic fibroblast growth factor (R&D Systems), and B-27 supplement from Thermo Fisher Scientific, Waltham, MA, USA. The amount and size of the spheres produced were measured using a microscope after 14 days.2.HeLa, C33A, and SiHa cervical cancer cell lines and a normal Ect1/E6E7 cell line were purchased from the American Type Culture Collection  and grown in DMEM (Sigma-Aldrich) media supplemented with 10% fetal bovine serum . Paclitaxel-resistant SiHa (SiHa/PTX) cells were developed by exposing SiHa cells to gradually increasing amounts of paclitaxel (Sigma-Aldrich) . All celThe patients who received cervical cancer resection at the Second Affiliated Hospital of Nanchang University provided human cervical cancer tissues and matched normal tissues. Each patient provided written and informed permission. Preoperative chemotherapy or radiation therapy was not given to any of the patients. This research was approved by the Institute Research Ethics Committee of the Second Affiliated Hospital of Nanchang University. Snap-frozen tissue samples were kept at \u221280\u00b0C after being frozen in liquid nitrogen.https://circinteractome.nia.nih.gov/index.html). These primers span the back-splicing junction of circ_0004488. The following primers were used: Hsa_circ_0004488 forward (F): 5\u2032-ATGGTCAGAAGTGTGGCTGC-3\u2032; hsa_circ_0004488 reverse (R): 5\u2032-CGGAGCTGAAGAGCTCAATGT-3\u2032; PRPSAP2 F: 5\u2032- TGTGCAAAGCTGGTCTAACTC-3\u2032 and PRPSAP2 R: 5\u2032- GGCGCTCAGCAAAAGACTG-3\u2032; SOX2 F: 5\u2032-GCCGAGTGGAAACTTTTGTCG-3\u2032 and SOX2 R: 5\u2032- GGCAGCGTGTACTTATCCTTCT-3\u2032; NANOG F: 5\u2032-TTTGTGGGCCTGAAGAAAACT-3\u2032 and NANOG R: 5\u2032-AGGGCTGTCCTGAATAAGCAG-3\u2032; OCT4 F: 5\u2032-CTGGGTTGATCCTCGGACCT-3\u2032 and OCT4 R: 5\u2032-CCATCGGAGTTGCTCTCCA-3\u2032; KLF4 F: 5\u2032-CCCACATGAAGCGACTTCCC-3\u2032 and KLF4 R: 5\u2032-CAGGTCCAGGAGATCGTTGAA-3\u2032; CD133 F: 5\u2032-AGTCGGAAACTGGCAGATAGC-3\u2032; and CD133 R: 5\u2032-GGTAGTGTTGTACTGGGCCAAT-3\u2032; CD44 F: 5\u2032-CTGCCGCTTTGCAGGTGTA-3\u2032 and CD44 R: 5\u2032-CATTGTGGGCAAGGTGCTATT-3\u2032; GAPDH F: 5\u2032-AATCCCATCACCATCTTC-3\u2032 and GAPDH R: 5\u2032-AGGCTGTTGTCATACTTC-3\u2032; U6 F: 5\u2032-GCTTCGGCAGCACATATACTAAAAT-3\u2032 and U6 R: 5\u2032-CGCTTCACGAATTTGCGTGTCAT-3\u2032.The RNAiso Plus kit was used to extract the total RNA from cervical cancer, neighboring normal liver tissues, and cells . RNA was extracted from nuclear and cytoplasmic fractions using nuclear and cytoplasmic extraction kits from Thermo Fisher Scientific. mRNA and circRNA were reverse transcribed using Prime Script RT Master Mix . SYBR Green was used in real-time PCR testing (qRT-PCR) on cDNA (TaKaRa). Using the miRNA qRT-PCR Starter kit, miRNA expression was evaluated . Internal controls were performed using GAPDH and U6. The PCR primers used for the detection of circ_0004488 were designed using the CircInteractome database (The PARIS kit (Life Technologies) was used to extract cytoplasmic and nuclear RNAs. The qRT-PCR test was used to determine the quantity of circ_0004488, with GADPH and U6 (F: CTCGCTTCGGCAGCACA and R: AACGCTTCACGAATTTGCGT) serving as reference genes for cytoplasmic and nuclear RNA, respectively.\u03bcg/ml puromycin. Ribobio (China) supplied the miR-136 mimics, inhibitors, and their respective controls.GenePharma Biotech  created the siRNAs directed against circ_0004488 or MEX3C as well as the negative control siRNA. The siRNAs were transfected into cervical cancer cells using Lipofectamine 3000 (Thermo Fisher Scientific). According to the manufacturer's instructions, human circ_0004488 was amplified and introduced into an overexpression vector  before being transfected with Lipofectamine 3000. Following stable transfection, cervical cancer cells were selected for two weeks with 1\u2009In 96-well plates with a growth medium, cervical cancer cells were cultured. Three days after transfection, a CCK-8 solution  was added, and absorbance was measured at 450\u2009nm using a microplate reader .Transwell chambers were covered with Matrigel matrix . Cervical cancer cells were pipetted into chambers containing 0.5\u2009ml of FBS-free DMEM medium suspension solution, with the appropriate complete medium added to the bottom chamber. The upper side of the membrane was washed away 24 hours later, leaving the bottom of the membrane with invading cells. The cells were then stained for 15 minutes at room temperature with crystal violet. Finally, cell counts were determined using a microscope at 10 randomly selected sites on each membrane.In 96-well tissue culture plates, cervical cancer cells were planted. Following 24 hours of culture, the wells were supplied with a paclitaxel-containing growth medium. Wells containing drug-free growing medium were used as controls. Cell viability was subsequently assessed using CCK-8 assays after the plates had been cultured for 48 hours.\u03bcl of the supernatant was kept as input, while the remainder was incubated overnight with a biotin-labeled circ_0004488 probe-streptavidin M-280 bead combination (Thermo Fisher Scientific). To reverse the formaldehyde crosslinking, the beads-RNA combination was washed and treated with lysis solution and proteinase K (Sigma-Aldrich). Finally, the TRIzol reagent (Thermo Fisher Scientific) was added to the mixture for RNA extraction. The Magna RIP kit was used for the RNA immunoprecipitation experiment . RIP buffer containing IgG-antibody or Ago2-labeled magnetic beads was utilized, and the immunoprecipitated RNAs were then produced using the TRIzol reagent (Thermo Fisher Scientific).RiboBio (China) created a biotin-labeled version of the circ_0004488 probe. Cervical cancer cells were fixed for 10 minutes in 1 percent formaldehyde (Sigma-Aldrich) before being lysed and sonicated. Following centrifugation, 20\u2009The wild-type (WT) and mutant (MUT) circ_0004488 segments, along with the 3\u2032-UTRs of wide-type and mutant MEX3C containing miR-136 binding sites, were inserted into the pGL3 vector  and received from RiboBio (China). After overnight growth, these reporter vectors were cotransfected into cells using Lipofectamine 3000 and a miR-136 mimic or inhibitor. Using a dual-luciferase assay kit (Promega), luciferase activity was evaluated 48 hours after transfection, and firefly luciferase activity was adjusted relative to Renilla luciferase activity for comparison.\u03bcg of each protein sample was separated and then transferred to PVDF membranes (Millipore). The membranes were then incubated overnight at 4\u00b0C with the primary antibody . The next day, membranes were incubated with HRP-conjugated secondary antibodies at room temperature for one hour. Millipore chemiluminescence detection reagents were used to identify signals. In this study, \u03b2-actin was utilized as a loading control.For western blot analysis, RIPA buffer  was employed to generate cell lysates. Using a bicinchoninic acid protein assay kit, protein concentrations were measured . Using SDS-PAGE, 30\u20096) before being subcutaneously delivered to 4-week-old male BALB/c nude mice . Tumor volume was calculated using the formula volume\u2009=\u20090.5\u2009\u00d7\u2009length\u2009\u00d7\u2009width2. Tumors were weighed after animals were sacrificed.All animal research studies were approved by the Animal Experimentation Ethics Committee of the Second Affiliated Hospital of Nanchang University. Circ_0004488 was overexpressed in HeLa cells . Statistics were deemed significant at P < 0.05.Group differences were evaluated using the Student's Beginning with the HeLa cell line, sphere formation experiments were performed to establish a CSC-enriched population of spheres . As expeWe used qRT-PCR to determine whether the expression of circ_0004488 differed between paclitaxel-resistant SiHa (SiHa/PTX) cells and SiHa cells, given that CSCs are known to play a role in drug resistance in cancer cells . Circ_00The Kaplan\u2013Meier survival curves showed that patients with cervical cancer who had greater levels of circ_0004488 had a worse overall survival rate . In addiTo validate our hypothesis, siRNAs targeting circ_0004488 were transfected into SiHa cells, while a circ_0004488 expression vector was used to overexpress circ_0004488 in HeLa cells. Our qRT-PCR experiments confirmed whether circ_0004488 was downregulated or upregulated in cervical cancer cells . ProlifeGiven that circRNAs have been shown to operate as miRNA sponges and that circ_0004488 is mostly found in the cytoplasm of SiHa and HeLa cells, we speculated that circ_0004488 promotes cervical cancer chemoresistance by sponging miRNAs. In order to do this, the CircInteractome database was searched for miRNAs that may bind to circ_0004488. As a consequence, our bioinformatics prediction found a large number of miRNAs . In the We then investigated whether circ_0004488 could bind to miR-136 directly. Circ_0004488 was detected in cervical cancer cell lines using a biotin-labeled probe. Following the circ_0004488 pull-down, RNA was collected, and miR-136 levels were evaluated using qRT-PCR tests. Biotin-labeled circ_0004488 collected much more miR-136 than the control probe, as demonstrated in We hypothesized that circ_0004488-induced reduction of miR-136 is a critical mechanism underpinning CSC-like phenotypes and paclitaxel resistance based on our findings. We overexpressed or shut down miR-136 in cervical cancer cells to study its involvement and then performed cell functional studies . When miMiRNAs regulate the levels of their genes by binding to 3\u2032-UTRs . We utilTo explore the functions of MEX3C in cervical cancer, MEX3C-related genes were then investigated using gene ontology (GO) and KEGG pathways. MEX3C, a coexpressed gene, may be implicated in the control of signaling pathways governing the pluripotency of stem cells, including TGF-beta, Wnt, and Hippo signaling pathways, according to GO and KEGG pathway analysis . These fTo verify this hypothesis, we looked at MEX3C levels in the GENT database. Human cervical cancer tissues were shown to express much more MEX3C than normal tissues . QRT-PCRAccording to the findings of a western blotting study, when cervical cancer cell lines were transfected with circ_000448 siRNA or miR-136 mimics, the protein levels of MEX3C were lowered . ConversMEX3C behaves as an oncogene in colorectal cancer , bladderTo investigate circ_000448's role in cervical cancer formation in vivo, we stably transfected HeLa cells with an expression vector for circ_000448 or a control vector and implanted the cells subcutaneously into BALB/c nude mice. Tumor xenograft experiments showed that the circ_000448 vector group had much heavier and more numerous tumors compared to the control vector group Figures . Tumor tCervical cancer, a common gynecological malignancy, is a significant cause of mortality and morbidity among women worldwide . The antOur results show that circ_000448 is highly expressed in CSC-enriched populations of cervical cancer cell lines and is upregulated in paclitaxel-resistant cervical cancer cells. Furthermore, knocking down circ_000448 significantly reduced cell proliferation, invasion, sphere formation, and PTX resistance, as well as suppressing cervical cancer cell growth in vivo. When PTX-resistant tumor tissues were compared to PTX-sensitive tumor tissues, it was discovered that circ_000448 was increased in clinical samples from PTX-resistant patients. Thus, our results show that circ_000448 contributes to the maintenance of cervical cancer CSCs and that blocking circ_0004488 makes cervical cancer cells more sensitive to PTX. Our next study will need to use a larger sample size of clinical specimens to confirm these results.Numerous circRNAs are expressed differently in cervical cancer cells than in normal cells, demonstrating that these circRNAs may have potential activities and biological importance . CircRNAFurthermore, higher circ_101996 levels have been connected to worse cervical cancer patient outcomes . Circ_00It has been shown that circRNA may function as miRNA sponges, influencing the expression of miRNA target genes in various human malignancies such as cervical cancer . For insMiR-136 has been found to have a role in a wide variety of biological and clinical processes in recent years \u201315. BrinMicroRNAs (miRNAs) inhibit the levels of their targets by binding to and degrading certain mRNA targets . This stCirc_0004488 serves as an important miR-136 sponge, preventing the development of cervical cancer and PTX resistance via the miR-136/MEX3C axis."}
+{"text": "BTBD7_hsa_circ_0000563 is a novel circRNA and contains conserved binding sites with RNA-binding proteins. However, BTBD7_hsa_circ_0000563 has not been fully studied in coronary artery disease (CAD). We aimed to clarify the diagnostic value and the possible functional role of BTBD7_hsa_circ_0000563 in CAD.A total of 276 human peripheral blood mononuclear cell (PBMC) samples were employed. The circularization of BTBD7_hsa_circ_0000563 was verified via Sanger sequencing. The expression level of BTBD7_hsa_circ_0000563 in CAD samples and control individuals was analysed via qRT\u2013PCR. The diagnostic potential of BTBD7_hsa_circ_0000563 was evaluated using Spearman\u2019s analysis, univariate and multivariable logistic regression analysis, and receiver-operator characteristic (ROC) curve analysis. ChIRP-MS was performed to directly explore the proteins bound to BTBD7_hsa_circ_0000563. Bioinformatic analysis was conducted to investigate the possible functions and interactions of proteins bound to BTBD7_hsa_circ_0000563.In the present study, BTBD7_hsa_circ_0000563 was verified as a circular RNA in the PBMCs of CAD patients. The expression level of BTBD7_hsa_circ_0000563 in the CAD group was significantly lower than that in the control group. The area under the ROC curve was 0.690. ChIRP-MS found seven proteins that were directly bound to BTBD7_hsa_circ_0000563. Bioinformatic analysis of these seven proteins showed that the mitophagy and DNA repair pathways were enriched. These proteins interacted with each other to a certain extent.BTBD7_hsa_circ_0000563 may be a novel biomarker for the diagnosis of CAD and may influence the initiation and progression of CAD. These studies may reveal new possibilities for the diagnosis and treatment of CAD.The online version contains supplementary material available at 10.1186/s12014-022-09374-w. Coronary artery disease (CAD) is still the most prominent cause of mortality globally, and it leads to a substantial medical and economic burden worldwide . As a coCircular RNAs (circRNAs) are a class of endogenous RNAs with a covalently closed structure. They are generated by backsplicing and are characterized by stability and abundance . Due to Peripheral blood mononuclear cells (PBMCs) are mononuclear cells in blood and are a key part of the body\u2019s immune system . InflammChromatin isolation by RNA purification (ChIRP) is a technique that can map genome-wide RNA occupancy, and the technique depends on affinity capture of the target RNA-chromatin complex by means of tiling antisense oligos. Therefore, the complex can generate a map of genomic binding sites that has high sensitivity, low background, and a resolution of approximately several hundred bases . RecentlIn this investigation, we sought to explore the expression patterns and the RNA\u2013protein interaction profile of BTBD7_hsa_circ_0000563 in PBMCs of CAD patients and control individuals. Statistical analysis and bioinformatic analysis were conducted to evaluate the diagnostic value of BTBD7_hsa_circ_0000563 and determine possible associations between CAD and this circRNA.From October 2020 to June 2021, 238 CAD patients and non-CAD control individuals were recruited for Sanger sequencing and qRT\u2013PCR to verify the circularization and expression of BTBD7_hsa_circ_0000563. Then, from March 2021 to April 2021, 18 CAD patients and 20 non-CAD control individuals were recruited for ChIRP-MS to identify protein\u2013RNA interactions on a genomic scale. From May 2022 to June 2022, 3 CAD patients and 3 non-CAD control individuals were recruited for western blot. All subjects were enrolled at the First Affiliated Hospital of Nanjing Medical University. The enrolled subjects were all older than 18 years of age and had not undergone PCI or CABG before this hospitalization. All subjects underwent coronary artery angiography during this hospitalization. They were categorized into the CAD group  and the control group  according to the ACC/AHA classification . The genAll experimental protocols used were conducted in accordance with the Declaration of Helsinki and approved by the First Affiliated Hospital of Nanjing Medical University. Written informed consent was obtained from all subjects or their families.Nine millilitres of blood was drawn from subjects who underwent overnight fasting via venipuncture upon admission, and this blood sample was used for circularization validation and qRT\u2013PCR assay. PBMCs were isolated from the middle white monolayer using density gradient centrifugation with Lymphocyte Separation Medium . Then, after a second density gradient centrifugation step, the PBMCs were resuspended and preserved in TRIzol reagent  at -80\u00a0\u00b0C until use.Nine millilitres of overnight-fasted blood samples or ten millilitres of artery blood samples were drawn from subjects by venipuncture upon admission or artery puncture before coronary angiography, respectively, and these samples used for ChIRP. PBMCs were isolated from the middle white monolayer using density gradient centrifugation with Lymphocyte Separation Medium . Then, after a second density gradient centrifugation step, the PBMC samples were preserved at -80\u00a0\u00b0C until use.The protocols for RNA isolation and the qRT\u2013PCR assay were specifically described in our previous publication . Briefly\u2212\u0394\u0394Ct method normalized to GAPDH. All experiments were performed in triplicate. Identification of BTBD7_hsa_circ_0000563 as a circRNA was conducted via Sanger sequencing following PCR.Using the primers listed in Table\u00a0LacZ probes were used as a negative control, and GAPDH was used as an internal control. qPCR was performed to measure the recovery of target RNAs in each group above.The ChIRP assay  was condEighteen CAD samples and 20 non-CAD control individuals were mixed respectively into one CAD sample and one control sample for an independent experiment. After crosslinking with 1% formaldehyde at room temperature for 10\u00a0min, PBMCs were lysed using lysis buffer. Subsequently, chromatin from the lysed PBMCs was collected and then hybridized with different tiling probe pools of BTBD7_hsa_circ_0000563 at 37\u00a0\u00b0C for 4\u00a0h with shaking. Afterwards, complexes were incubated with beads conjugated with streptavidin at 37\u00a0\u00b0C for 30\u00a0min. Finally, the chromatin bound to the beads was eluted for LC\u2013MS/MS assays to determine the profile of the proteins bound to BTBD7_hsa_circ_0000563.The lyophilized peptide fractions were resuspended in ddH2O with 0.1% formic acid. Two-microlitre aliquots of the dissolved sample above were loaded into a nanoViper C18  trap column, after which the trapping and desalting procedure was conducted using 20 \u00b5L 100% solvent A (0.1% formic acid). The Easy nLC 1200 system (ThermoFisher) was used to carry out the online chromatography separation. Afterwards, an elution gradient of 5\u201338% solvent B  over 60\u00a0min was used on an analytical column . The tandem MS data were acquired on a ThermoFisher Q Exactive mass spectrometer  fitted with a Nano Flex ion source, of which the ion spray voltage was 1.9\u00a0kV and the interface heater temperature was 275\u00a0\u00b0C. Data-dependent acquisition (DDA) mass spectrometry techniques were used for MS scanning and tandem MS data acquisition. For the MS1 scan, the scan ranged from 350 to 2,000\u00a0m/z at a resolution of 70,000 and a maximum injection time of 100 ms. For the MS2 scan, only spectra with a charge state of 2\u20135 were selected for fragmentation by higher-energy collision dissociation with a normalized collision energy of 28. The MS2 spectra were acquired in the ion trap in rapid mode with an AGC target of 8,000 and a maximum injection time of 50 ms. Dynamic exclusion was set for 25\u00a0s.The MS/MS data were analysed for the identification and quantification of proteins bound to BTBD7_hsa_circ_0000563 using PEAKS Studio 10.6. The false discovery rate (FDR) was set\u2009<\u20090.7% after searching against the target database with a maximum of two missed cleavages. The settings of oxidation (M), acetylation (protein N-term), deamidation (NQ), pyro-glu from E, pyro-glu from Q for variable modifications and fixed carbamidomethylation of cysteine were selected. The precursor and fragment mass tolerances were set to 10 ppm and 0.05 Da, respectively.The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  partner Protein samples for western bolt were extracted from PBMC cell lysates. RIPA buffer  was used for protein extraction according to the manufacturer\u2019s instructions. The protein concentration was determined by the BCA method. The protein lysate was separated by SDS-PAGE and transferred to a PVDF membrane. The membrane was soaked in 5% milk and incubated with the primary antibody at 4\u2103 overnight. The primary antibodies against UBB (1:1000) and the control protein, \u03b2-actin (1:7500) were from proteintech and affinity, China. Horseradish peroxidase-labeled IgG (1:15000) was used as the secondary antibody . Enhanced chemiluminescence kits  were used for signal development. ImageJ software was used to analyze the grayscale values.t test and the Mann\u2013Whitney U test were used to compare the demographic and clinical pathological characteristics and the circRNA expression level between CAD patients and control individuals. The chi-square test was used to compare the categorical data between two groups. Spearman\u2019s correlation analysis was conducted to explore conventional cardiovascular risk factors and environmental factors related to BTBD7_hsa_circ_0000563. Univariate and multivariable logistic regression analyses were performed to determine whether BTBD7_hsa_circ_0000563 could be an independent factor for CAD. The receiver-operator characteristic (ROC) curve was used to evaluate the value of circRNA as a diagnostic biomarker for CAD. All data were analysed using SPSS 21 software. Two-tailed P values\u2009<\u20090.05 were considered statistically significant.Categorical data are presented as counts (percentages). Continuous variables conforming to a normal distribution are described as the mean\u2009\u00b1\u2009standard deviation, and skewed distribution variables are described as the median (25th\u201375th interquartile range). Student\u2019s https://string-db.org/). Interaction prediction scores between proteins and BTBD7_hsa_circ_0000563 were obtained from RNAct database (https://rnact.crg.eu/).Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of the proteins bound to BTBD7_hsa_circ_0000563 were performed using the clusterProfiler 4.0 package in R . ProteinTo verify that BTBD7_hsa_circ_0000563 is a circular RNA, we designed divergent primers that specifically amplified the back-spliced forms of BTBD7. The \u201chead-to-tail\u201d splicing of BTBD7_hsa_circ_0000563 in the PBMCs of 4 CAD patients was confirmed via Sanger sequencing Fig.\u00a0. MoreoveP\u2009=\u20090.002, Fig.\u00a0To verify the connection between BTBD7_hsa_circ_0000563 levels and CAD, the expression level of BTBD7_hsa_circ_0000563 in PBMCs was measured via qRT\u2013PCR in a large population . The clinical and demographical characteristics of the samples undergoing BTBD7_hsa_circ_0000563 expression validation are presented in Table\u00a0P\u2009=\u20090.158, Fig.\u00a0Given the difference in sex distribution between the CAD group and control group, we stratified the data based on sex and analysed it again. In males, the expression level of BTBD7_hsa_circ_0000563 in the CAD group was still significantly lower than that in the control group , which indicates that BTBD7_hsa_circ_0000563 might participate in the progression of CAD.To test whether the expression level of BTBD7_hsa_circ_0000563 was correlated with cardiac risk factors and conventional CAD diagnostic markers, we conducted Spearman\u2019s correlation analysis. The results Table\u00a0 showed tOR\u2009=\u20090.518, 95% CI: 0.334\u20130.802, P\u2009=\u20090.003). After adjusting for the impact of other risk factors, an independent negative correlation between BTBD7_hsa_circ_0000563 and CAD was still observed . These results identify BTBD7_hsa_circ_0000563 as an independent predictor for CAD.To explore the predictive value of BTBD7_hsa_circ_0000563 for CAD, we divided the subjects into quarters based on the interquartile range of the circRNA expression level. As presented in Table\u00a0CI: 0.613\u20130.766, P\u2009=\u20090.002, Fig.\u00a0CI: 0.659\u20130.874, P\u2009<\u20090.001, Fig.\u00a0The area under the ROC curve (AUC) for the BTBD7_hsa_circ_0000563 level in predicting CAD was 0.690   Fig.\u00a0B, indicaA total of 134 peptides were identified with FDR\u2009<\u20090.7% via PEAKS Studio after the LC\u2013MS/MS assay. The peptide-spectrum matches (PSM) score can evaluate the similarity between actual and theoretical spectra. The distribution of PSM scores  showed that all error spectra had scores below 33.5. Therefore, we set the filter criteria for peptides to have a PSM score greater than 33.5. Simultaneously, the distribution of mass accuracy  showed that the mass accuracy of the target spectra was approximately 0, which indicated high accuracy. The analyses above can guarantee the reliability of the peptide.After removing contaminating proteins, such as keratin and serum albumin, seven proteins were identified to interact with BTBD7_hsa_circ_0000563 directly through the ChIRP-MS assay . Herein, we found 4 possible key proteins including UBB, UBC, RPS27A, and UBA52. In addition, polyubiquitination is a critical modification in mitophagy and RazeP\u2009=\u20090.007, Fig.\u00a0Western blot was used to investigate the relationship between UBB and BTBD7_hsa_circ_0000563. Obvious accumulation of polyubiquitinated proteins was found in PBMCs of the control group . Thereafter, BTBD7_hsa_circ_0000563 was found in normal human tissues [Memczak et al. first reported BTBD7_hsa_circ_0000563 in animals . BTBD7_h tissues \u201327 and c tissues . Interes tissues , the exp tissues  and simi tissues . HoweverFor the difference in the diagnostic value of BTBD7_hsa_circ_0000563 between males and females, we speculate that there are two possible reasons. First, the sample size of females was insufficient , and this was a single-centre study. Second, there were significant sex differences in gene regulation . A largeFurthermore, seven proteins were identified as RBPs of BTBD7_hsa_circ_0000563, including polyubiquitin-B, ubiquitin-60\u00a0S ribosomal protein L40, ubiquitin-40\u00a0S ribosomal protein S27a, polyubiquitin-C, cathepsin D, propionyl-CoA carboxylase alpha chain mitochondrial, and arginase-1. By means of GO and KEGG enrichment analyses, we found that these proteins were mainly located in the mitochondrial outer membrane and involved in the mitophagy and DNA repair pathways.The concept of mitophagy was first proposed by Lemasters and was identified as a selective autophagy process that specifically targets damaged or dysfunctional mitochondria . As the The PPI network of the present investigation showed that the proteins bound to BTBD7_hsa_circ_0000563 cooperated with each other to a certain extent, especially the four proteins named ubiquitin-60\u00a0S ribosomal protein L40, ubiquitin-40\u00a0S ribosomal protein S27a, polyubiquitin-B, and polyubiquitin-C. Ubiquitin-60\u00a0S ribosomal protein L40 and ubiquitin-40\u00a0S ribosomal protein S27a are both ubiquitin fusion proteins . PolyubiCollectively, all the evidence above suggests that BTBD7_hsa_circ_0000563 may be involved in the initiation and progression of CAD through the mitophagy and DNA repair pathways. These results might provide new ideas for the future study of the potential mechanisms of CAD occurrence and development.The present investigation had several limitations. First, the sample size was small. The size of these groups was too small to minimize the experimental bias. Thus, for practical application in clinical diagnosis, a large-scale multicentre cohort study is still required to further validate the results obtained from the present study. Second, in addition to RNP, the ChIRP assay can also explore DNA and RNA that bind to BTBD7_hsa_circ_0000563, which is lacking in the present study. ChIRP-DNAseq and ChIRP-RNAseq assays are essential in further investigations to determine the upstream and downstream mechanisms of BTBD7_hsa_circ_0000563 in CAD. Third, only the proteins bound to BTBD7_hsa_circ_0000563 were explored, but the mechanisms in which these proteins take part were merely based on bioinformatic analysis. Therefore, for practical application as therapeutic targets in clinical treatment, in vitro and in vivo experiments of BTBD7_hsa_circ_0000563 and the 7 proteins at the cellular and animal levels should be performed to verify their function and mechanism of action in CAD.In conclusion, the present study validated the circularization of BTBD7_hsa_circ_0000563 in the PBMCs of CAD patients and confirmed that the expression level of BTBD7_hsa_circ_0000563 in the PBMCs of the CAD group was significantly lower than that in the control group. The ROC curve and univariate and multivariable logistic regression analyses revealed the value of this circRNA as an independent biomarker for CAD. By means of ChIRP-MS, seven proteins bound to BTBD7_hsa_circ_0000563 were captured. Bioinformatic analysis revealed that these proteins were mainly located on the mitochondrial outer membrane and are involved in the mitophagy and DNA repair pathways. These results indicate that BTBD7_hsa_circ_0000563 and the proteins bound to it may be novel targets for CAD. These studies may provide new ideas for the diagnosis, prevention, and treatment of CAD.Below is the link to the electronic supplementary material.Additional file 1: Figure S1 PSM score distribution. (a) Distribution of peptide score. (b) Scatterplot of peptide score versus precursor mass error. Decoy represented error spectrums and Target represented target spectrums. The vertical dotted line represented the threshold of PSM score which was set as filter criteria.Additional file 2: Table S1. Interaction analyses between BTBD7_hsa_circ_0000563 and 7 proteins Table S2. Baseline characteristics of the subjects undergoing western blot."}
+{"text": "Protein p62 (sequestosome 1) encoded by gene SQSTM1 plays a vital role in mediating protectively selective autophagy in tumor cells under stressed conditions. CircSQSTM1 (hsa_circ_0075323) is a circular transcript generated from gene SQSTM1 (chr5:179260586\u2013179260782) by back-splicing. However, the potential role of hsa_hsa_circ_0075323 in glioblastoma (GBM) remains unclear. Here, we aimed to explore the biological function of hsa_circ_0075323 in GBM and its relationship with autophagy regulation.Hsa_circ_0075323 is highly expressed in GBM cells and mainly locates in the cytoplasm. Inhibition of hsa_circ_0075323 in U87-MG and T98G cells attenuated proliferation and invasion ability significantly, while upregulation of has_ circ_0075323 enhanced proliferation and migration of U251-MG and A172 cells. Mechanistically, depletion of hsa_circ_0075323 in GBM cells resulted in impaired autophagy, as indicated by increased expression of p62 and decreased expression of LC3B.Hsa_circ_0075323 regulates p62-mediated autophagy pathway to promote GBM progression and may serve as a prognostic biomarker potentially. Every year, about 100,000 people are diagnosed with diffuse glioma worldwide . GlioblaAutophagy receptor p62 is encoded by gene SQSTM1 on chromosome 5 and participates in selective autophagy upon cellular stress . In epitCircular RNA (circRNA) is a novel class of functional non-coding RNA recognized ubiquitous in eukaryotes . Being dHere, we have identified a circular RNA generated at the SQSTM1 gene locus, termed hsa_circ_0075323. Hsa_circ_0075323 is widely expressed in A549, AG04450, BJ, HELAS3, HepG2, HUVEC, NHEK and SKNSHRA, but also specially expressed in human neuroblastoma cell . Our fur2.Normal human astrocytes (HEB) and human glioblastoma cell lines  were purchased from ATCC (American Type Culture Collection). Cells were cultured in Dulbecco\u2019s Modified Eagle\u2019s Medium (DMEM)  supplemented with 10% fetal bovine serum (Gibco), 100 U/ml penicillin and 100\u00a0\u03bcg/ml streptomycin (Gibco) in a humidified atmosphere at 37 \u2103 containing 5% CO9 TU (transfection unit)/mL before use. GBM cells were resuscitated and cultured in 6\u00a0cm cell culture dishes. The cells were subcultured in 6-well plates until the density reached 80% . Lentivirus infection was carried out in the presence of 8\u00a0\u03bcg/m polybrene. U87-MG and T98G cells were infected with lentivirus carrying si-hsa_circ_0075323, while A172 and U251-MG cells were infected with lentivirus carrying hsa_circ_0075323 sequence. The culture medium was replaced with selection medium containing 4\u00a0\u03bcg/ml puromycin after 72\u00a0h and then the cells were cultured for another 14\u00a0days. The puromycin-resistant cells were amplified in medium containing 2\u00a0\u03bcg/ml puromycin for seven days and then transferred to medium without puromycin.Recombinant lentivirus containing hsa_circ_0075323 coding sequence or specific siRNA sequence  or non-specific negative control oligos (si-NC) were constructed. To ectopic express hsa_circ_0075323, a basic sequence flanked by XhoI and Agel was synthesized. A small spacer sequence containing two restriction enzyme sites, HindIII and SalI, was added for the insertion of circRNA fragment to the plasmid vector. Lentivirus package and purification were handed over to Hanyin Co.  and the virus titer reached over 10Total RNA was extracted using TRIzol and reverse transcribed using Prime Script RT Master Mix . PCR was performed using PCR Master Mix (2\u2009\u00d7) , primer sequences for hsa_circ_0075323 were synthesized as following: forward 5\u02b9-ACATCTCCCGCCAGGAACA-3\u02b9; reverse 5\u02b9-CCTGTAGACGGGTCCACTTC-3\u02b9. To quantify the amounts of hsa_circ_0075323, real-time PCR analyses were performed using a SYBR Premix Ex TaqTM kit  with GAPDH as internal controls. Each sample was replicated three times and data were analyzed by comparing Ct values.Cy3-labeled human hsa_circ_0075323 probe was synthesized by Ribo Bio. and applied for FISH. A Fluorescent In Situ Hybridization Kit  containing pre-hybridization buffer, hybridization buffer and 4,6-diamidino-2-phenylindole (DAPI) was used and the assay was performed according to the manufacturer\u2019s instructions. The nuclei were stained with DAPI. 18S and U6 served as internal references while the former almost entirely localized in cytoplasm and the latter in nuclei. The images were photographed under fluorescence microscope .Lentivirus-infected cells were seeded into a 96-well plate at a density of 2000 cells per well and the assay was performed using the Cell Counting Kit 8  according to the manufacturer\u2019s protocol. The optical density of each well was measured at 450\u00a0nm using a microplate spectrophotometer .4) were seeded into each insert and incubated for 24\u00a0h. The cells remained in the top of the inserts were removed and migrating cells were fixed with 75% ethanol for 30\u00a0min followed by 0.1% crystal violet staining for 20\u00a0min. The cells migrating to the lower chamber were counted and photographed by microscope (Leica DMI 400B).A Boyden chamber system  was purchased for Transwell migration assay. Cells . The immunoreactive bands were detected using an ECL kit . Primary antibodies targeting the following proteins were applied: LC3B , p62  and actin . HRP-conjugated goat anti-rabbit (cat. no. SA00001-2) antibodies were used as secondary antibodies . Semi-quantitative analysis was performed by ImageJ software .t-test was used to determine differences between 2 groups. All statistical analyses were performed using SPSS software . Significance was defined as P\u2009<\u20090.05.Data of this study are presented as the mean\u2009\u00b1\u2009SD from at least 3 replicates. Student\u2019s two-tailed unpaired We examined the expression levels of hsa_circ_0075323 in GBM cells  and corresponding normal human astrocytes (HEB) by qRT-PCR. As a result, expression of hsa_circ_0075323 turned out to be extraordinarily higher in GBM cells compared with that in HEB  treatment . LC3B isThe cellular roles of circRNAs have become a focus of cancer biology. Most attention was given to its roles on protein expression from modulating transcription in the nucleus to translation in the cytoplasm. In our study, we demonstrated that hsa_circ_0075323 acts as an activator of GBM progression as shown in gain-of- and loss-of-functional assays. In additional, has_circ_0075323 regulates autophagy signaling pathway by modulating the protein expression of SQSTM1 as shown in molecular assays. Taken together, we are the first to identify tumor-promoting effect of hsa_circ_0075323 in GBM and the first to reveal underlying mechanisms in association with autophagy regulation.Several emerging experimental approaches help researchers update biological functions of circular RNA. Not only circular RNA could sponge for miRNA or proteins, form functional circRNP complexes, it could interact with mRNAs to affect their expression and even outcompete linear mRNAs for protein binding in the cytosol . ApproxiBoth LC3B and p62 are components of core autophagy machinery which is essential for autophagosome formation. There are three main types of autophagy named macroautophagy, microautophagy and chaperone-mediated autophagy (CMA) in eukaryocytes . The macIn our study, we identified a pro-cancer role for hsa_circ_0075323 in GBM cells, suggesting a potential prognostic biomarker role, but this speculation still lacks solid data support. RNA-seq of GBM clinical samples and GBM cell lines is needed to prepare, which is helpful to verify the conclusion of in vitro cell experiments in this study. Correspondingly, the correlation between the abundance of hsa_circ_0075323 in examined tissues and patient overall survival should also be studied. The outcome of these two key issues will deepen our understanding of hsa_circ_0075323 in GBM and its translation.The fact that circular RNAs differ from linear RNAs in conformation, stability and immunogenicity has prompted many attempts to develop circRNA-based technologies. These include the use of circRNA as non-coding adaptors to interfere with mRNA or proteins of core genes in intracellular processes, be a booster or inhibitor of innate immune responses, serve as templates for antisense RNA and extended translation and classically, serve as pathological targets and biomarkers. In-depth exploration the roles of hsa_circ_0075323 in autograph through in vivo experiments, combined with high-throughput sequencing, will help to uncover the application of hsa_circ_0075323 in GBM."}
+{"text": "CLas is transmitted by Diaphorina citri, the Asian citrus psyllid. Development of transmission-blocking strategies to manage huanglongbing relies on knowledge of CLas and D. citri interactions at the molecular level. Prior transcriptome analyses of D. citri point to changes in psyllid biology due to CLas infection but have been hampered by incomplete versions of the D. citri genome, proper host plant controls, and/or a lack of a uniform data analysis approach. In this work, we present lessons learned from a quantitative transcriptome analysis of excised heads, salivary glands, midguts, and bacteriomes from CLas-positive and CLas-negative D. citri using the chromosomal length D. citri genome assembly.Huanglongbing, a devastating disease of citrus, is caused by the obligate, intracellular bacterium CLas infection. Though most psyllids were infected with the bacterium, CLas-derived transcripts were not detected in all organs. By analyzing the midgut dataset using both the Diaci_v1.1 and v3.0 D. citri genomes, we showed that improved genome assembly led to significant and quantifiable differences in RNA-sequencing data interpretation.Each organ had a unique transcriptome profile and response to Our results support the hypothesis that future transcriptome studies on circulative, vector-borne pathogens should be conducted at the tissue-specific level using complete, chromosomal-length genome assemblies for the most accurate understanding of pathogen-induced changes in vector gene expression. Candidatus Liberibacter asiaticus\u201d (CLas). The Asian citrus psyllid Diaphorina citri Kuwayama (NCBI:txid121845) (Hemiptera: Liviidae) is the vector of CLas. HLB has decimated a multi-billion dollar industry in Florida and is threatening the industries in Texas and California . Dur. DurCLasl instar  but in il instar , 8. The l instar , 9, 10. e adults . Approxi psyllid , primari psyllid . The infe midgut , 12, 16.e midgut ). Specifal cells , a proceal cells . In the al cells . The movD. citri harbors 3 bacterial symbionts, \u201cCandidatus Profftella armatura,\u201d \u201cCandidatus Carsonella ruddii,\u201d and Wolbachia pipientis) or not exposed to the bacterium . Sequenced samples were pools of multiple individuals . See TaBacteriome, head, and salivary gland samples were sequenced separately from the previously published midgut samples . Raw datCLas percent infection of the D. citri populations used for dissections. Across all sample types, the percent infection was in the range 73\u201385%. Quantitative cycle (Cq) values <40 were counted as CLas (+)  salivary gland and head samples , suggesting that some AT-rich sequences were captured during poly-A enrichment. Upon closer analysis of the CLas-aligning reads from the salivary glands, when \u22653 biological replicates had a transcript with \u22651 read, 50 unique CLas messenger RNA (mRNA) transcripts were represented, with an additional six ribosomal RNA (rRNA) transcripts (three of each 16S and 23S transcripts), for a total of 56 CLas-psy62 transcripts identified. The majority of CLas reads from the salivary glands aligned to the top ten transcripts, where the total number of reads across all biological replicates of each transcript ranged from 80 to 290. Of these top ten, three were listed as \u201cprotein coding\u201d and annotated as flgB, flgC, and parB, while the rest were unlabeled/unknown (Supplementary Table S1).Using qPCR analysis of whole insects, we determined the +) Table\u00a0. In addiid in FL ) were deD. citri genome on average . The head dataset proved to be more variable as compared to the other datasets, recording the fewest raw reads and the lowest mean percent alignment. In contrast, the highest percent alignment to the D. citri genome was recorded by the bacteriome dataset, samples of which were collected from the same individual insects as the head dataset (Supplementary Table S2).Across all four datasets, we obtained an average of 27.23 million high-quality reads , and 71.3% of the reads aligned concordantly to the v3.0 D. citri dataset expression profiles was performed, where each dataset includes both CLas (+) and CLas (\u2212) biological replicates. Each organ separated from the other organs in PCA space, showing that each organ has a unique transcriptome profile. The largest source of variation (PC1 = 36%) was explained by differences in the transcriptome profiles of the midgut and bacteriome as compared to the salivary gland and head  and CLas (+) biological replicates , while midguts  showed a clear separation along PC1 between CLas (\u2212) and CLas (+) biological replicates.A principal component analysis (PCA) to examine the sources of variation among the four ead Fig.\u00a0. The secCLas infection, which is not distinct, except for the two outlier samples. The bacteriome dataset  showed some separation between CLas (+) and CLas (\u2212) biological replicates (PC2 = 15.9%) but the majority of variation was due to variance among individual biological replicates (PC1 = 16.7%). The head dataset  showed similar variation across all samples as the bacteriome dataset. This variation explained both the first and second major sources of variance  with no obvious distinctions between CLas (+) and CLas (\u2212) biological replicates.PCA plots of each organ transcriptome dataset revealed other sources of variation . The variance described by PC1 of the salivary gland dataset  was explained by two samples that were kept in the \u221280\u00b0C freezer and then sequenced a year after the other six samples, while PC2  represented the effect of CLas (+) or CLas (\u2212) replicates in addition to transcripts that were present in but differentially expressed between CLas (+) and CLas (\u2212) biological replicates using the maximum adjusted P value of 0.05 and a log2 fold change (L2FC) of >|2| were used for downstream analyses. This strict quality and DE threshold limited the number of final transcripts to a small number  (see Supplementary Tables S3\u2013S6 for the list of transcripts). A skew towards up-regulated transcripts in CLas (+) biological replicates was detected in all organs .Differentially expressed transcripts expressed in CLas. The four groups include ribosomal transcripts, immunity-related transcripts, endocytosis-related transcripts, and ubiquination-related transcripts. Each dataset varies in its magnitude of response (as measured by L2FC and the relative number of transcripts found in each of the four categories). Ubiquination genes are highly up-regulated in the salivary gland dataset  samples in all three datasets  lists from the salivary gland, bacteriome, and midgut datasets , cell structure and signaling , stress , transport , and unknown . The full list can be found in Supplementary Table S3. DE transcripts in the stress category include heat shock and cold shock protein genes, thioredoxin, and E3 ubiquitin ligase. Three heat shock proteins  are up-regulated with exposure to CLas, while the cold shock protein is down-regulated. An E3 ubiquitin ligase, a type IV collagenase, and a D. citri homologue of p53 are also up-regulated. A thioredoxin transcript and an HSP20-like chaperone transcript are down-regulated with exposure to CLas. Transport-related transcripts that are up-regulated with CLas infection include two odorant-binding protein transcripts, membrane-associated ion transporters , a vacuolar-sorting protein transcript, and an intraflagellar transport particle protein transcript, among others. Down-regulated transcripts include syntaxin, ubiquinol cytochrome-c, membrane-associated proteins and transporters, and nuclear transport factor 2.The top DE transcripts from the midgut dataset were manually sorted into five additional functional categories including biosynthesis and catabolism \u00a0 of the salivary gland DE (L2FC >|2|) transcripts can be found in Supplementary Table S4. Transcripts for 40S and 60S subunits of the eukaryotic ribosome are highly up-regulated , as well as six transcripts involved with transport which are all up-regulated . Additionally, four elongation factor (EF) transcripts are highly up-regulated , consistent with increased ribosomal activity. While ubiquination-related transcripts are present in every dataset, in the salivary gland dataset two transcripts are highly up-regulated including a ubiquitin conjugating enzyme (L2FC = 3.70) and ubiquitin-ligase E3 (L2FC = 4.69) ) of the colony. A sample was considered CLas (+) if the Cq value was <40\u00a0. The Cq data from all 20 individuals, from all three colonies , were compiled and reported in Supplementary Fig. S1. Cq values from the CLas non-exposed insects were undetected (40 PCR cycles completed without amplification).RRID:SCR_014583)  as the denominator such that positive L2FC indicates greater expression in CLas [+] replicates and negative L2FC indicates reduced expression in CLas [+] replicates relative to CLas [\u2212]). Because each dataset (except bacteriome and head) was collected and sequenced separately, normalizing the datasets to each other had too many experimental variables that were uncontrollable, so DE analysis for CLas (-/+) was performed separately for each dataset. DE results, like those of the qPCR Cq data, could be compared directly for transcripts within a dataset, while transcripts across datasets could be qualified, although no direct or quantitative comparison of expression could be made between datasets as currently analyzed. Reads that aligned to CLas in the CLas (+) samples were counted and only certain transcripts of interest were analyzed further.All four datasets comprising cleaned, PE mRNA reads were aligned to both the Diaci_v3.0 a et al.  and incl2-build) . Total c2-build) . Specifi_002105) . Once so_016323)  based on_016323)  to allow_015687) , followiCLas (\u2212) and CLas (+) replicates were removed. Individual PCA plots were also generated in R (plotPCA and ggplot) to show separation between CLas (\u2212) and CLas (+) biological replicates, using the DESeq2 rlog-transformed transcript data for each dataset individually. Following PCA analysis, R was used to generate volcano plots of the DE transcripts from each dataset individually, again using the DESeq2 rlog-transformed data. The log2 of the fold change (L2FC) of each DE transcript was plotted against the negative log of the Benjamini-Hochberg adjusted P-value for the same transcript, using ggplot.A variety of statistical methods and data visualization tools were used. A principal component analysis (PCA) of all four datasets combined was performed in R (prcomp and plot) using a large transcript count matrix combining the transcript expression count matrices from the four datasets. The count data were minimally normalized by transcript counts per million, and transcripts not present in both D. citri genome was started by choosing four transcripts present and expressed in both analyses. The two genomes presented different gene_IDs and genomic location coordinates, which was problematic for direct comparison of changes in expression or even direct comparison of transcripts. The transcript sequence from Diaci_v3.0 was analyzed using BLASTx  against the v1.1 genome to determine which v1.1 transcripts aligned to the v3.0 transcript and whether alignment was partial or full. To demonstrate differences in read distribution between the two genomes for each of the four transcripts and to show differential alignment frequencies, the v3.0 transcript sequences and associated v1.1 transcript sequences were each used as a genome and total cleaned reads were re-aligned to these sequences using HISAT2 to generate the BAM files of read alignments for each transcript. Coverage maps were generated for each transcript using an R script (BEDtools) written by Dave Tang [D. citri genomes.The comparison of expression results from the midgut dataset when aligned to either v3.0 or v1.1 of the ave Tang . The genRRID:SCR_015644) [RRID:SCR_015643) [Potential secreted effectors were determined from the list of top DE transcripts of the salivary gland dataset by running two programs\u2014SignalP-v5.0  , which a_015643) , which dRaw data have been uploaded to NCBI via BioProject accession No. PRJNA385527.\u00a0All supporting data and materials are available in the\u00a0GigaScience\u00a0GigaDB database .CLas Cq values from individuals tested from each colony used to generate RNAseq data.Supplementary Figure S1. A histogram of Diaphorina citritranscriptome datasets.Supplementary Figure S2. Principal component analysis of all four CLas transcripts identified in the Diaphorina citrisalivary gland transcriptome in three or more biological replicate samples.Supplementary Table S1. Supplementary Table S2. Metadata on RNAseq datasets and alignments.P-value<0.05. Sorted by Log2FoldChange. Aligned to v3.0 of the D. citri genome.Supplementary Table S3. All transcripts from the midgut dataset that have differential expression Log2FoldChange>|2| and adjusted P-value<0.05. Sorted by Log2FoldChange. Aligned to v3.0 of the D. citri genome.Supplementary Table S4. All transcripts from the salivary gland dataset that have differential expression Log2FoldChange>|2| and adjusted P-value<0.05. Sorted by Log2FoldChange. Aligned to v3.0 of the D. citri genome.Supplementary Table S5. All transcripts from the bacteriome dataset that have differential expression Log2FoldChange>|2| and adjusted P-value<0.05. Sorted by Log2FoldChange. Aligned to v3.0 of the D. citri genome.Supplementary Table S6. All transcripts from the head dataset that have differential expression Log2FoldChange>|2| and adjusted P-values and Log2FoldChange values for each transcript listed.Supplementary Table S7. Data used to generate Figure 3, including annotations, Diaphorina citrisalivary gland dataset that have predicted signal sequences. The four in bold text had predicted transmembrane helices.Supplementary Table S8. All transcripts from the Supplementary Table S9. All piggyBac-related genes currently annotated in the Diaci_v3.0 genome. The gene identified in our transcript analysis is in bold.giac035_GIGA-D-21-00314_Original_SubmissionClick here for additional data file.giac035_GIGA-D-21-00314_Revision_1Click here for additional data file.giac035_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giac035_Reviewer_1_Report_Original_SubmissionNabil Killiny -- 11/1/2021 ReviewedClick here for additional data file.giac035_Reviewer_2_Report_Original_SubmissionKerry Mauck -- 11/12/2021 ReviewedClick here for additional data file.giac035_Reviewer_2_Report_Revision_1Kerry Mauck -- 1/21/2022 ReviewedClick here for additional data file.CLas: Candidatus Liberibacter asiaticus; Cq: quantitative cycle; DE: differentially expressed; EF: elongation factor; HLB: huanglongbing; L2FC: log2 fold change; mRNA: messenger RNA; NCBI: National Center for Biotechnology Information; PCA: principal component analysis; PE: paired end; rRNA: ribosomal RNA; TMH: transmembrane helices; USDA: United States Department of Agriculture.ARS: Agricultural Research Service; BLAST: Basic Local Alignment Search Tool;The authors declare that they have no competing interests.This project was funded by NIFA Predoctoral Fellowship 2021\u201367011-35143 (M.M.), USDA-NIFA grants 2015\u201370016-23028 (M.H. and L.M.), 2020\u201370029-33199 (L.M.), and USDA ARS Project No. 8062\u201322410-007\u201300-D (M.H.).M.M.: Took part in, or led, all aspects including conceptualization, data curation, formal analysis, funding acquisition, investigation, methodology, validation, visualization, and writing of original draft, as well as review and editing.S.S.: Funding acquisition, conceptualization, methodology, resources, writing\u2014review and editing.J.M.C.: Visualization, writing\u2014review and editing, data curation.M.P.: Methodology, writing\u2014review and editing.K.M.: Data curation, resources.L.M.C.: Funding acquisition, project administration, resources, supervision.W.B.H.: Funding acquisition, project administration, resources, supervision, writing\u2014review and editing.L.A.M.: Funding acquisition, project administration, methodology, conceptualization resources, supervision, writing\u2014review and editing.M.H.: Took part in, or led, all aspects including conceptualization, investigation, methodology, project administration, funding acquisition, data analysis, resources, supervision, validation, visualization, writing of original draft and reviews and edits."}
+{"text": "To investigate the value of radiomics features derived from preoperative multi-sequence MR images for predicting early recurrence (ER) in patients with solitary hepatocellular carcinoma (HCC) \u22645 cm.One hundred and ninety HCC patients were enrolled and allocated to training and validation sets (n = 133:57). The clinical\u2013radiological model was established by significant clinical risk characteristics and qualitative imaging features. The radiomics model was constructed using the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm in the training set. The combined model was formed by integrating the clinical\u2013radiological risk factors and selected radiomics features. The predictive performance was assessed by the area under the receiver operating characteristic curve (AUC).Arterial peritumoral hyperenhancement, non-smooth tumor margin, satellite nodules, cirrhosis, serosal invasion, and albumin showed a significant correlation with ER. The AUC of the clinical\u2013radiological model was 0.77 (95% CI: 0.69\u20130.85) and 0.76 (95% CI: 0.64\u20130.88) in the training and validation sets, respectively. The radiomics model constructed using 12 radiomics features selected by LASSO regression had an AUC of 0.85 (95% CI: 0.79\u20130.91) and 0.84 (95% CI: 0.73\u20130.95) in the training and validation sets, respectively. The combined model further improved the prediction performance compared with the clinical\u2013radiological model, increasing AUC to 0.90 (95% CI: 0.85\u20130.95) in the training set and 0.88 (95% CI: 0.80\u20130.97) in the validation set . The calibration curve fits well with the standard curve.The predictive model incorporated the clinical\u2013radiological risk factors and radiomics features that could adequately predict the individualized ER risk in patients with solitary HCC \u22645 cm. Hepatocellular carcinoma (HCC) is the sixth most common malignancy and the third leading cause of cancer-related mortality globally . In ChinAccording to the current clinical practice guidelines, HCC recurrence is usually divided into early and late recurrence by the 2-year cutoff point \u20138. EarlyRadiomics is a process of converting digital medical images into high-throughput, innumerable quantitative features using different algorithms, which provide valuable diagnostic, prognostic, or predictive information . To datePrevious studies showed that tumor diameter greater than 5\u00a0cm was closely related to ER and high mortality \u201324. HoweThe aim of this study was to develop and validate an effective and visualized model based on multi-sequence MR images to predict ER in patients with solitary HCC \u22645 cm.This retrospective study received ethical approval, and the requirement for informed consent was waived. From January 2012 to December 2017, 712 consecutive patients underwent R0 resection in our hospital. The inclusion criteria were the following: a) histologically proven HCC with a negative resection margin, b) solitary tumor \u22645 cm, c) no preoperative history of cancer-related treatments , d) high-quality MR images performed 4 weeks preoperatively , and e) at least 2 years of follow-up. Finally, a total of 190 HCC patients (80 patients with ER and 110 patients with non-ER) were included in this retrospective study. The enrolled patients were divided into a training set (56 patients with ER and 77 patients with non-ER) and a validation set (24 patients with ER and 33 patients with non-ER) at a ratio of 7:3 Figure\u00a01The clinical and pathological variables were obtained from the electronic medical record system for all patients, including demographic characteristics, preoperative laboratory data, and postoperative pathological data.2), and dynamic contrast-enhanced (DCE) T1-weighted three-dimensional spoiled gradient echo liver acceleration volume acquisition were performed. The contrast-enhanced images were acquired at 20\u201330 s (AP), 60\u201370 s ), and 180 s (delayed phase (DP)). Gadodiamide  at a standard dose (0.2 ml/kg) was administered at a rate of 2.0 ml/s and flushed with 20\u00a0ml of 0.9% sterile saline via an automatic injector.All MR examinations were performed using 3.0 T scanners  with an 8-channel phased-array body coil. After localizer images were obtained, in-phase and opposed-phase T1-weighted imaging, fat-suppression T2-weighted imaging (T2WI/FS), diffusion-weighted imaging  reviewed all MR images. Both radiologists were blinded to any clinical and pathological information. They reached a consensus through discussion when any disagreements existed. They independently evaluated and recorded the following basic MR image features: a) maximum tumor diameter , b) liver background (cirrhosis or non-cirrhosis), c) location , d) intratumoral fat , e) DWI intensity (hyperintense or slightly hyperintense), f) capsule (complete or absent/incomplete), g) dynamic enhancement pattern , h) tumor margin (smooth or non-smooth), and i) arterial peritumoral hyperenhancement  .3 using Artificial Intelligence Kit software . Three-dimensional manual segmentation was performed by a radiologist with 3 years\u2019 MR experience using ITK-SNAP software . The volumes of interest (VOIs) were manually drawn along the boundary of the tumor on each consecutive slice for all 190 lesions. To assess the intraclass correlation coefficient (ICC), 40 VOIs were randomly chosen and performed independently by another radiologist with 6 years\u2019 experience. In total, 1,316 radiomics features were extracted from each sequence using the Artificial Intelligence Kit software based on the open-source Pyradiomics python package, which included the following parameters: first-order histogram features (n = 18), texture features  features, 16 gray-level run-length matrix (RLM) features, 24 gray-level co-occurrence matrix (GLCM) features, 14 gray-level dependence matrix features, and 5 neighboring gray-tone difference matrix features), wavelet features (n = 744), local binary pattern features (n = 279), and Laplacian of Gaussian (logSigma = 2.0/3.0) features (n = 186).T2WI/FS images and three-phase DCE-MR images were used for feature extraction. Before tumor segmentation, all preoperative MR images were resampled into a uniform voxel size of 1 \u00d7 1 \u00d7 1 mmvia the linear combination of the selected features weighted by their respective LASSO coefficients. Considering the small sample size of our datasets, this radiomics model was further verified by using 100-time bootstrap for the outer resampling loop. The whole dataset was randomly divided into the training set and validation set 100 times. The existing radiomics model was tested on the new 100 testing datasets.Features with ICC > 0.75 indicated satisfactory consistency and were retained for subsequent analysis. The least absolute shrinkage and selection operator (LASSO) logistic regression algorithm was used to identify the most predictive radiomics features, and 10-fold cross validation was used to tune the model parameter as the inner resampling loop Figure\u00a02Clinical\u2013radiological variables with p < 0.05 in the univariate analysis were included in the multivariate logistic regression analysis to confirm risk factors associated with ER, and the clinical\u2013radiological model was generated. A combined model was developed by incorporating the clinical\u2013radiological risk factors and the Rad-score. Receiver operating characteristic (ROC) curves were generated for those three models . Accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the area under the ROC curve (AUC) were calculated.Serum alpha-fetoprotein (AFP) levels and contrast-enhanced CT/MRI were performed every 3\u20136 months for 2 years after surgery. ER was defined as intrahepatic tumor relapse  and metastasis (distant metastasis or lymph node metastases) within 2 years after surgery.Categorical variables were compared by using the chi-square test or Fisher\u2019s exact test, and continuous variables were compared by using Student\u2019s t-test or the Mann\u2013Whitney U test, as appropriate. All statistical analyses were performed with SPSS software . The performance of each model was compared using the Delong test. A two-sided p < 0.05 indicated a statistical significance.Overall, 190 HCC patients  who met the inclusion criteria were included and divided into the training set  and validation set . Eighty (42.1%) of 190 patients with solitary HCC \u22645 cm experienced postoperative ER. Of the 80 patients with ER, cirrhosis was presented in 63 patients, and cirrhosis was strongly associated with ER in both the training set (p = 0.011) and validation set (p = 0.001). Except for prothrombin time (p = 0.047), no statistical difference was observed between the two sets in the clinical and radiological characteristics , as shown in Univariate analysis showed that eleven clinical and radiological characteristics including age, cirrhosis, enhancement pattern, non-smooth tumor margin, APHE, T stage, microvascular invasion (MVI), satellite nodules, serosal invasion, gamma-glutamyl transpeptidase level, and albumin level were significantly different between the ER and non-ER groups in the training set . Multivariate logistic regression analysis demonstrated that the APHE , non-smooth tumor margin , satellite nodules , cirrhosis , serosal invasion , and albumin  were independent predictors for ER in the training set, which were used to construct the clinical\u2013radiological model Table\u00a02.Among 1,316 radiomics features extracted from multi-sequence MR images, the LASSO analysis selected 12 features with non-zero coefficients to calculate the Rad-score . The following formula was used to obtain the corresponding Rad-score for each patient: Rad-score=-0.3*T2_original_shape_Sphericity-0.157*AP_wavelet_HLL_glcm_ClusterShade+0.363*DP_lbp_3D_k_glrlm_ShortRunLowGrayLevelEmphasis-0.223*AP_wavelet_LHH_glszm_HighGrayLevelZoneEmphasis+0.118*DP_wavelet_LHL_glcm_ClusterProminence-0.473*DP_wavelet_LLL_firstorder_Minimum-0.3*VP_original_glrlm_LongRunLowGrayLevelEmphasis-0.343*T2_log_sigma_2_0_mm_3D_glrlm_LongRunEmphasis-0.21*DP_wavelet_LHL_firstorder_Skewness-0.141*DP_lbp_3D_k_firstorder_10Percentile-0.31*VP_original_shape_Sphericity+0.228*DP_log_sigma_3_0_mm_3D_glcm_ClusterProminence-0.411.The AUCs of the radiomics model were 0.85 (95% CI: 0.79\u20130.91) in the training set and 0.84 (95% CI: 0.73\u20130.95) in the validation set. The outer resampling loop using the 100-time bootstrap method delivered a mean AUC of 0.85 (range from 0.70 to 0.96). Among them, 86% of AUC values were greater than 0.80, which showed good reliability of this radiomics model.The combined model was developed by incorporating the clinical\u2013radiological risk factors and the Rad-score. The AUCs of the combined model were 0.90  in the training set and 0.88  in the validation set. In the training set, the combined model displayed accuracy, sensitivity, specificity, PPV, and NPV of 81.20%, 71.83%, 91.94%, 91.01%, and 74.03%, respectively. When applied in the validation set, the combined model exhibited accuracy, sensitivity, specificity, PPV, and NPV of 84.21%, 85.71%, 83.33%, 75.00%, and 90.91%, respectively. The predictive performances of the clinical\u2013radiological model, radiomics model, and the combined model in the training and validation sets are listed in For ER prediction, the combined model outperformed both the clinical\u2013radiological model (p < 0.001) and the radiomics model (p = 0.023) in the training set. However, no significant difference was observed between the combined model and the radiomics model in the validation set (p = 0.174), although the combined model showed better performance than the clinical\u2013radiological model in the validation set (p = 0.012). ROC curves for the prediction of ER were compared among the clinical\u2013radiological, radiomics, and combined models Figure\u00a03The combined model-based nomogram is presented in In this study, we developed and validated a radiomics-based model to predict ER of HCC patients with solitary tumor \u22645 cm by incorporating clinical\u2013radiological variables and radiomics features extracted from multi-sequence MR images. The combined model achieved satisfactory predictive performance and further improved the prediction performance compared with the clinical\u2013radiological model. The combined nomogram can help the clinical doctors to identify patients at high risk of ER after R0 resection and may provide HCC patients with adequate treatment opportunities and improve their overall survival.As an emerging quantitative analysis method, radiomics plays an important role in predicting ER of HCC after hepatectomy. However, as far as we know, there were few studies to investigate the relationship between radiomics characteristics based on multi-sequence MR images and ER of single HCC \u2264 5\u00a0cm. Zhao et\u00a0al.  found thAPHE is an auxiliary diagnostic feature of malignant tumors in the liver imaging reporting and data system. Previous studies have shown that APHE was more frequently observed in the ER group than in the non-ER group and was identified as an independent predictor of ER , 13. TheHCC is rare among patients without liver disease, and hepatitis B virus (HBV)-induced cirrhosis is the main risk factor for HCC . Yao et\u00a0This study has several limitations. Firstly, selection bias was inevitable due to the retrospective nature. In order to increase the reliability, we applied the model obtained from the training set to the validation set. Secondly, our study was a single-center study from areas with a high incidence of HBV or hepatitis C virus infection, so this conclusion may not be applicable to other people with different liver diseases. Thirdly, we developed a prediction model only for ER and did not include late recurrence or long-term survival analyses because of the short postoperative follow-up time, which needs further investigation. Lastly, only patients with a single lesion \u22645 cm were recruited; therefore, this conclusion may not be extended to nodules with a maximum diameter >5\u00a0cm or multiple nodules. Thus, the results of this study need to be verified by more extensive and prospective studies in the future.In conclusion, our findings showed that the combined model integrated clinical\u2013radiological risk factors with the radiomics signature demonstrated good discriminative ability for predicting ER in HCC patients with a single nodule \u22645 cm, which may serve as a non-invasive and visualized tool in clinical decision-making. More multicenter, prospective studies will be needed to investigate the role of radiomics analysis in clinical practice in the future.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Ethics Committee of Cancer Hospital, Chinese Academy of Medical Sciences. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.Study concepts and design: LW, XM, and XZ. Literature research: LW and BF. data collection: LW, BF, ML, and DL. Image analysis: LW and BF. Data analysis: LW, SCW, and SW. Manuscript writing: LW. Manuscript review: XM and XZ. All authors read and approved the final manuscript.This work was sponsored by the PUMC Youth Fund (2017320010).Author SCW was employed by GE Healthcare.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher."}
+{"text": "Leptospira borgpetersenii serogroup Sejroe, the seroprevalence of leptospiral infections of cattle in Thailand. Two LRR recombinant proteins, rKU_Sej_LRR_2012M (2012) and rhKU_Sej_LRR_2271 (2271), containing predicted immunogenic epitopes, were investigated for their cross-protective efficacies in an acute leptospirosis model with heterologous Leptospira serovar Pomona, though, strains from serogroup Sejroe are host-adapted to bovine, leading to chronic disease. Since serovar Pomona is frequently reported as seropositive in cattle, buffaloes, pigs, and dogs in Thailand and causes acute and severe leptospirosis in cattle by incidental infection, the serogroup Sejroe LRR proteins were evaluated for their cross-protective immunity. The protective efficacies were 37.5%, 50.0%, and 75.0% based on the survival rate for the control, 2012, and 2271 groups, respectively. Sera from 2012-immunized hamsters showed weak bactericidal action compared to sera from 2271-immunized hamsters (p < 0.05). Therefore, bacterial tissue clearances, inflammatory responses, and humoral and cell-mediated immune (HMI and CMI) responses were evaluated only in 2271-immunized hamsters challenged with virulent L. interrogans serovar Pomona. The 2271 protein induced prompt humoral immune responses (p < 0.05) and leptospiral tissue clearance, reducing tissue inflammation in immunized hamsters. In addition, protein 2271 and its immunogenic peptides stimulated splenocyte lymphoproliferation and stimulated both HMI and CMI responses by activating Th1 and Th2 cytokine gene expression in vaccinated hamsters. Our data suggest that the immunogenic potential renders rhKU_Sej_LRR_2271 protein a promising candidate for the development of a novel cross-protective vaccine against animal leptospirosis.Leucine-rich repeat (LRR) proteins are advocated for being assessed in vaccine development. Leptospiral LRR proteins were identified recently in silico from the genome of  Leptospira and affects livestock in all parts of the world. More than 200 serovars of these pathogenic spirochetes have been identified ) of a single passage L. interrogans serovar Pomona (NVSL 11000-HL145A) in 1 mL sterile PBS was intraperitoneally injected into each animal . Clinical signs and mortality were monitored and recorded twice daily for 3 weeks. Hamsters with severe clinical signs of moribund , have been reported as virulence factor such as internalin J (InlJ) [Leptospira infection, it has been reported that the LRR domain-containing protein family is vital for the virulence of pathogenic Leptospira species [Proteins containing leucine-rich repeats (LRRs) have been predicted and reported to function in bacterial host-pathogen interactions, membrane anchoring, and invasions, such as proteins Internalin A, B and J, YopM, and LRR20 ,57,58,59J (InlJ) . LeucineJ (InlJ) ,60. BothJ (InlJ) ,38,39,40 species ,35. TherLeptospires per bactericidal assays. The strength of bactericidal activity exhibited by sera from 2012-vaccinated animals was less intense than the action exposed by sera from the 2271 immunized hamsters. In addition, the 2012 vaccines demonstrated only 50% protective efficacy against challenging virulent L. interrogans serovar Pomona in hamsters. The rKU_Sej_LRR_2012M protein showed poor proficiency as a leptospiral vaccine candidate under the challenging condition with virulent L. interrogans serovar Pomona. However, this report has not been performed and challenged with different pathogenic serovars and serogroups and awaits further studies.Although both LRR proteins characterized in this report exhibited rapid induction of specific humoral immune responses in immunized hamsters, only the rhKU_Sej_LRR_2271 protein induced antibody production 3 weeks after the first and the second immunization in hamsters. The high levels of IgG production against the rhKU_Sej_LRR_2271 protein from 2271 immunized hamsters also prominently promoted the complement-mediated killing of L. borgpetersenii serogroup Sejroe, the KU_Sej_R21N_2012 (NCBI accession: JN627491.1) and KU_Sej_R21C_2012 (NCBI accession: JN627492.1) genes to produce the KU_Sej_R21_2012M gene with a deletion at A346 of the gene \u201cKU_Sej_R21_2012 (NCBI accession: JN627495)\u201d [KU_Sej_R21_2012\u201d from L. borgpetersenii serogroup Sejroe genome is an orthologous gene of the LBJ_2012 gene of L. borgpetersenii serovar Hardjo-bovis str. JB197 [KU_Sej_R21_2012 (NCBI accession: JN627495)\u201d gene and L. interrogans serovar Pomona (taxid:44276), and only two genomes from L. interrogans serovar Bataviae strain 1489 and serovar Canicola strain 782 showed 74.53% identity with the KU_Sej_R21_2012 gene  protein could be detected by rabbit hyperimmune sera against L. borgpetersenii serovar Canicola, Mini, and Tarassovi in both line-blot and ELISA, but the 2012 LRR protein could not be detected by rabbit hyperimmune sera against L. interrogans serovar Pomona by both techniques [L. borgpetersenii, L. mayottensis, L. weilii, L. santarosai, and L. interrogans serovar Bataviae and serovar Canicola could present a commendable task.The rKU_Sej_LRR_2012M (2012) protein was produced from two overlapping LRR genes of 627495)\u201d . The genr. JB197 . Since t012 gene ; therefochniques . It implL. interrogans serovar Pomona was attained. In addition, sterilizing immunity was not achieved. This could be explained by the result of a BLAST search of the KU_R21_2271 gene (NCBI accession: JX522460), which yielded no significant similarity for the alignment with the genome of L. interrogans serovar Pomona (taxid:44276), whereas the KU_R21_2271 sequence similarities were 75.58% to 78.19% identity with 75 genes from other L. interrogans strains  expression systems.The patent announcement #1701001602 was advertised on 4 October 2018 regarding the expression of LRR proteins, the rKU_Sej_LRR_2012M and rhKU_Sej_LRR_2271 proteins, in"}
+{"text": "Clostridium saccharoperbutylacetonicum. Its sol operon consists of the genes encoding butyraldehyde dehydrogenase, CoA transferase, and acetoacetate decarboxylase  and the gene products are involved in butanol and acetone formation. It is important to understand its regulation to further optimize the solvent production. In this study, a new long non-coding antisense transcript complementary to the complete sol operon, now called Assolrna, was identified by transcriptomic analysis and the regulatory mechanism of Assolrna was investigated. For this purpose, the promoter-exchange strain C. saccharoperbutylacetonicum \u0394Pasr::Pasr** was constructed. Additionally, Assolrna was expressed plasmid-based under control of the native Pasr promoter and the lactose-inducible PbgaL promoter in both the wild type and the promoter-exchange strain. Solvent formation was strongly decreased for all strains based on C. saccharoperbutylacetonicum \u0394Pasr::Pasr** and growth could not be restored by plasmid-based complementation of the exchanged promoter. Interestingly, very little sol mRNA expression was detected in the strain C. saccharoperbutylacetonicum \u0394Pasr::Pasr** lacking Assolrna expression. Butanol titers were further increased for the overexpression strain C. saccharoperbutylacetonicum [pMTL83151_asr_PbgaL] compared to the wild type. These results suggest that Assolrna has a positive effect on sol operon expression. Therefore, a possible stabilization mechanism of the sol mRNA by Assolrna under physiological concentrations is proposed.Solvents such as butanol are important platform chemicals and are often produced from petrochemical sources. Production of butanol and other compounds from renewable and sustainable resources can be achieved by solventogenic bacteria, such as the hyper-butanol producer  Clostridium saccharoperbutylacetonicum was originally isolated as hyper-butanol producer and was used for industrial butanol and acetone production via fermentation in Japan (via fermentation of different substrates (sol) operon of C. saccharoperbutylacetonicum is encoding butyraldehyde dehydrogenase (bld), CoA transferase (ctfA and ctfB), and acetoacetate decarboxylase (adc) is located on the chromosome (Cspa_c56880\u2013Cspa_c56910)  . The enz_c56910) . Solvent_c56910) . The swi_c56910) . Sequenc 136\u00a0kbp . The str 136\u00a0kbp , but eff 136\u00a0kbp . Further 136\u00a0kbp .C. saccharoperbutylacetonicum , for example complementary to the complete sol operon . C. saccharoperbutylacetonicum strains were cultured at 30\u00b0C in clostridial growth medium (CGM) for transformation or pre-cultures as previously described (\u22121 thiamphenicol or 10\u00a0\u00b5g ml\u22121 clarithromycin). All strains and plasmids used are listed in B medium  or SOB mB medium  with appescribed , and optescribed . If appr\u00ae TURBO DNA-free\u2122 Kit  according to manufacturer\u2019s instructions. Remains of DNA were checked by PCR using the primers for the subsequent (q)RT-PCR.Isolation of RNA from 2-ml and 50-ml samples was carried out using TRI reagent and chloroform/isoamyl alcohol (24:1). DNA digestion of RNA from 50-ml samples was performed using DNase I . Afterwards, RNA was purified using phenol/chloroform/isoamyl alcohol (25:24:1) precipitation as described by Montoya Solano . In cont600 of 0.40) and from another 200-ml OMS culture for the solventogenic growth phase . Three technical replicates were performed by preparing RNA from three 50-ml samples from the same culture. Remaining genomic DNA was removed by digesting with TURBO DNase . The Ribo-Zero magnetic kit  was used to reduce the amount of rRNA-derived sequences. For sequencing, the strand-specific cDNA libraries were constructed with a NEBNext Ultra directional RNA library preparation kit for Illumina . To assess quality and size of the libraries, samples were run on an Agilent Bioanalyzer 2100 using an Agilent High Sensitivity DNA Kit as recommended by the manufacturer . Concentration of the libraries were determined using the Qubit\u00ae dsDNA HS Assay Kit as recommended by the manufacturer . Sequencing was performed by using the Genome Analyzer Iix maschine  for sequencing in the paired-end mode and running 2 \u00d7 75 cycles. For quality filtering and removing of remaining adaptor sequences, Trimmomatic-0.39 (C. saccharoperbutylacetonicum N1-4(HMT) (C. saccharoperbutylacetonicum N1-4(HMT). Differential expression analyses were performed with the BaySeq program (2(fold change) in expression of \u2265 2.0 or \u2264\u22122.0, a likelihood value of \u2265 0.9, and an adjusted p value of \u2264 0.05 were considered differentially expressed. The p value was corrected by the false discovery rate (FDR) based on the Benjamini-Hochberg procedure. The raw reads have been deposited in the National Center for Biotechnology Information\u2019s (NCBI) Sequence Read Archive (SRA) under accession no. SRP357609.The 50-ml samples for first transcriptome analysis were taken from one 200-ml OMS culture for the acidogenic growth phase   and unal program . Genes w600 of approximately 0.70), from after the butyrate peak , from after the acetate peak , and from the stationary growth phase . Harvested cells were suspended in 800\u00a0\u00b5l RLT buffer  with \u03b2-mercaptoethanol (10\u00a0\u00b5l ml\u22121) and cell lysis was performed using a laboratory ball mill. Subsequently, 400\u00a0\u00b5l RLT buffer  with \u03b2-mercaptoethanol (10\u00a0\u00b5l ml\u22121) and 1,200\u00a0\u00b5l 96 % (v/v) ethanol were added. For RNA isolation, the RNeasy Mini Kit  was used as recommended by the manufacturer, but instead of RW1 buffer RWT buffer  was used to isolate RNAs smaller 200\u00a0nt. To determine the RNA integrity number (RIN), the isolated RNA was run on an Agilent Bioanalyzer 2100 using an Agilent RNA 6000 Nano Kit as recommended by the manufacturer . Library preparation and sequencing was performed as described above, but with the following modifications: The Illumina Ribo-Zero plus rRNA Depletion Kit  was used to reduce the amount of rRNA-derived sequences. For preparation of strand-specific cDNA libraries, the NEBNext Ultra II directional RNA library preparation kit for Illumina and the NEBNext Multiplex Oligos for Illumina (96)  were used. Sequencing was performed on the NovaSeq 6000 instrument  using NovaSeq 6000 SP Reagent Kit v 1.5 (100 cycles) and the NovaSeq XP 2-Lane Kit v 1.5 for sequencing in the paired-end mode and running 2 \u00d7 50 cycles. The mapping against the reference genomes was performed with Salmon (v 1.5.2) (2(fold change) of expression \u2265 2.0 or \u2264 \u22122.0 and an adjusted p value of \u2264 0.05 were considered differentially expressed. The asRNA complementary to the sol operon, now called Assolrna, as well as the potential TSS corresponding to Pasr-is was identified using TraV  . As mappv 1.5.2)  using thv 1.5.2) . DeSeq2 v 1.5.2)  was usedv 1.5.2) . Genes wing TraV . In siliSAPPHIRE  and NeurSAPPHIRE . FurtherSAPPHIRE . Raw reaC. saccharoperbutylacetonicum N1-4(HMT) culture. PEX were performed according to The RNA for the primer extension (PEX) was prepared from 50-ml samples from a asr) was amplified using ReproFast proofreading polymerase  and primers Fwd_1_solregpotRNA and Rev_1_solregpotRNA, Fwd_1_solregpotRNA and Rev_2_ solregpotRNA, Fwd_1_solregpotRNA and Rev_3_solregpot RNA, Fwd_2_solregpotRNA and Rev_1_solregpotRNA, and Fwd_2_solregpotRNA and Rev_2_solregpotRNA. The yielded fragments were purified from gel using the NucleoSpin Gel and PCR Clean-up kit  and ligated into the vector pDrive using the Qiagen\u00ae PCR Cloning Kit  according to manufacturers\u2019 instructions. The ligation approach was transformed into E. coli XL1-Blue MRF\u2019. Colony PCR was carried out with primers M13F and M13R. Clones showing the expected fragment length were inoculated, plasmid was prepared using the Zyppy Plasmid Miniprep Kit , and sent for sequencing.All primers used for amplification of inserts are listed in E. coli CA434 [pMTL83251] and E. coli DH5\u03b1 [pMTL-SC7515] using the Zyppy Plasmid Miniprep Kit  according to the manufacturer\u2019s instructions. The plasmids were linearized using FseI with CutSmart Buffer by New England Biolabs GmbH  and purified using the NucleoSpin Gel and PCR Clean-up kit  according to manufacturers\u2019 instructions. Afterwards, the linearized plasmids were digested with PmeI and Buffer B  with Shrimp Alkaline Phosphatase  added to the reaction containing pMTL-SC7515 according to manufacturers\u2019 instructions. Both reactions were applied on an agarose gel and the expected fragments  were purified from gel using the NucleoSpin Gel and PCR Clean-up kit  according to manufacturer\u2019s instructions. The fragments were ligated using T4 ligase  and transformed into E. coli DH5\u03b1. Colony PCR was performed using the primers ermC-FseI_fwd and ermC-nachPmeI_rev. Positive clones were inoculated, and the plasmid prepared using the Zyppy Plasmid Miniprep Kit , and verified using control digestion. The successfully constructed plasmid was designated pMTLSC7515-Em.The vector for allelic exchange was constructed by plasmid preparation from asr through exchange by Pasr** was constructed by amplifications of the fragments LHA and RHA using the primers LHA_fwd_PromRNA, LHA_rev_ PromRNA, RHA_fwd_PromRNA, and RHA_rev_PromRNA for the PCR with ReproFast space proofreading polymerase . Afterwards, a splicing overlap extension PCR was performed using the fragments LHA and DHA as template with primers LHA_fwd_PromRNA and RHA_rev_PromRNA. The resulting fragment was purified from the gel using the Zymoclean Gel DNA Recovery kit  and ligated into the vector pDrive using the Qiagen\u00ae PCR Cloning Kit  according to manufacturers\u2019 instructions resulting in plasmid pDrive_recA. The correct clone was identified by colony PCR using primers M13F and M13R and sent for sequencing. The plasmids pDrive_recA and pMTLSC7515-Em were digested using PmeI and Buffer B . Shrimp Alkaline Phosphatase  was added to digestion of pMTLSC7515-Em. The desired fragments  were purified from gels using the Zymoclean Gel DNA Recovery kit  according to manufacturer\u2019s instructions. Ligation of the fragments was performed using the T4 ligase  and transformed into E. coli XL-1 Blue MRF\u2019. Colony PCR with primers LHA_fwd_PromRNA and RHA_rev_PromRNA was carried out to identify positive clones. The resulting plasmid pMTLSC7515-Em-recA was sent for sequencing and later renamed to pMTL-PromoterRNA.The cassette for the knock-out of P\u00ae HD Cloning Plus  according to manufacturer\u2019s instructions. Digestions were performed using Fast Digest enzymes  according to manufacturer\u2019s instructions.Ligation of complementation and overexpression plasmid was carried out using In-Fusionasr, pMTL_Komp_PasrT , and pMTL83151_asr_PbgaL were constructed as described by The plasmids pMTL_Komp_PasrT was digested using XhoI and SalI to yield the backbone of pMTL83151_asADC_PasrT. The insert was amplified from genomic DNA of C. saccharoperbutylacetonicum using the primers asADC_Pasr_fwd and asADC_Pasr_rev. Ligation was performed using In-Fusion\u00ae HD Cloning Plus  according to manufacturer\u2019s instructions. An overview on all constructed plasmids is given in The plasmid pMTL83151_Komp_PE. coli cells were prepared and transformed as previously described by C. saccharoperbutylacetonicum was electro-transformed as described by Chemically competent asr with Pasr** in C. saccharoperbutylacetonicum was performed using the allelic exchange system as described by C. saccharoperbutylacetonicum was transformed with pMTL-PromoterRNA harboring codA as a counter selection marker when 5-fluorocytosine is used. Genomic DNA of all clones was prepared using the MasterPure\u2122 Gram-Positive DNA Purification Kit  according to manufacturer\u2019s instructions. All clones were tested for genomic integration using the primers pMTL-PR_fwd and assolrna-genom_rev as well as assolrna-genom_fwd and pMTL-PR_rev. When genomic integration was verified, CGM was inoculated for plating on CGM agar containing 5-fluorocytosine (100\u00a0\u00b5g/ml\u22121). Colonies were picked, genomic DNA was isolated, and successful excision of the plasmid was tested using primers pMTL-PR_fwd and pMTL-PR_rev in a PCR. When excision was detected, the Pasr/Pasr** region was amplified using the primers assolrna-genom_fwd and assolrna-genom_rev, fragments were purified using the DNA Clean & Concentrator Kit  and sent for sequencing. The successfully constructed promoter exchange strain was designated C. saccharoperbutylacetonicum \u0394Pasr::Pasr**.Exchange of the promoter region PThe polymerases Platinum\u2122 II Hot-Start Green PCR Master Mix, Platinum\u2122 SuperFi\u2122 PCR Master Mix, Platinum\u2122 SuperFi\u2122 II PCR Master Mix , Phusion Green High-Fidelity DNA Polymerase , CloneAmp\u2122 HiFi PCR Premix , and ReproFast proofreading Polymerase  were used for amplification of fragments according to manufacturers\u2019 instructions. Sequencing reactions were performed by GATC Biotech AG  or GENEWIZ .via colony PCR and subsequently sent for sequencing. Verification of C. saccharoperbutylacetonicum strains was carried out by amplification and sequencing of 16S rDNA, Pasr/Pasr** promoter region, and transformation of genomic DNA into chemically competent E. coli with subsequent picking of colonies, purification of plasmids , and restriction digestion of purified plasmids  according to manufacturer\u2019s instructions.For verification of plasmids, the plasmids were digested using appropriate restriction enzymes or tested Glucose consumption, lactate formation, and presence of lactose was quantified using high-pressure liquid chromatography as described by Statistical analysis of the product formation of interest, i.e., butanol and acetone, was carried out using R v 4.0.3 . First, C. saccharoperbutylacetonicum aReverse transcription PCRnd SuperScript\u2122 III reverse transcriptase  according to manufacturer\u2019s instructions with primer RT_asr_fwd. The PCR was performed using 2\u00a0\u03bcl of the solution with the newly synthesized cDNA, the primers RT_asr_fwd and RT_ig670_rev), and Phusion Green High-Fidelity DNA Polymerase Reverse transcription PCR.Reverse transcription for RT-PCR was performed with DNA-free RNA of C. saccharoperbutylacetonicum genomic DNA (starting from 100\u00a0ng) with PowerUp\u2122 SYBR\u2122 Green Master Mix . The primer pairs 16SF_qPCR/16SR_qPCR, AdhEF_qPCR/AdhER_qPCR, and AdcF_qPCR/AdcR_qPCR were used for specific detection of 16S rRNA, sol mRNA, and Assolrna, respectively. Primer efficiency tests as well as quantitative PCR were performed by a CFX96 Touch\u2122 Real-Time PCR Detection System  in a PCR 96-Well TW-MT-Plate sealed with Adhesive Clear qPCR Seals, Sheets . Primer efficiency was between 0.917 and 1.000, hence they are in the appropriate range  and runs were calculated using the CFX Manager\u2122 Software Version 3.1 . CT values were normalized to the ones of 16S rRNA and relative expression levels were calculated  according to manufacturer\u2019s instructions using a 20\u00a0\u03bcM primer mix consisting of 16SR_qPCR  with eitte range . 16S rRNlculated .sol operon of C. saccharoperbutylacetonicum was characterized by Kosaka and co-workers   . This wa1-4(HMT) . Sequencory role .sol mRNA)   leading to identification of the promoter of the asRNA Assolrna (antisense to ol mRNA) . The idenscripts . Since ting TraV  and diffing TraV  were use-TCGAAT) . Reverse3,255\u00a0bp . Identifrminator . An Assoasr** .sol mRNA and Assolrna throughout a growth experiment was assessed via transcriptomic analysis of biological triplicates  and the promoter-exchange strain C. saccharoperbutylacetonicum \u0394Pasr::Pasr**. The plasmid pMTL83151_Komp_PasrT harbors a truncated Assolrna under control of the native promoter Pasr, whereas the plasmid pMTL83151_asr_PbgaL harbors a truncated Assolrna under control of the lactose-inducible promoter PbgaL. The plasmid-based transcript was truncated compared to Assolrna encoded in the wild type chromosome because its terminator overlaps with the promoter region of the sol operon, i.e., Pbld-2. Thereby, side effects from plasmid-based overexpression of the sol operon should be prevented. The plasmids as well as the vector control pMTL83151 were introduced into the wild type and the promoter-exchange strain. Growth and product formation were monitored during a growth experiment , and C. saccharoperbutylacetonicum [pMTL83151_Komp_PasrT] are comparable to each other, whereas the expression of Assolrna is lowest for the wild type and highest for the vector control strain (C. saccharoperbutylacetonicum [pMTL83151_asr_PbgaL] showed low transcription of sol mRNA and intermediate transcription of Assolrna. This is different for induced C. saccharoperbutylacetonicum [pMTL83151_asr_PbgaL]. Three samples taken during the growth experiment showed a decrease of sol mRNA transcription from 0.017 to 0.007 and stable high transcription levels of Assolrna (ranging from 0.015 to 0.011) over time  analyses were performed to test for complementation and overexpression at transcriptional basis. Maximal acetone concentrations and transcription of pression . Relativl strain . Non-indver time . Transcrectively . Transcrectively . Taking sol mRNA to Assolrna of 5:1 and larger or 1:1 and smaller. For ratios ranging between 3:1 and 2:1, acetone formation is increased to 26\u00a0mM or more, and butanol formation is decreased to 161\u00a0mM or less  or the pta-ack operon (Cspa_c13010 and Cspa_c13020) (data not shown).Similar mechanisms were previously described for FasX, RprA, RNAIII, and SolB . The smasol mRNA . This isthan 2:1 . When thutylicum . Furtheriptomics , but theiptomics . This waC. saccharoperbutylacetonicum [pMTL83151] and C. saccharoperbutylacetonicum \u0394Pasr::Pasr** [pMTL83151] showed increased acetone formation and Assolrna expression, but slightly decreased butanol formation compared to the respective parental strains  as reported in previous studies  and the proximal promoter Pbld-2 (ATAACA-20\u00a0nt.TAGAAT-9 to 10\u00a0nt-TSS) . Furthermore, it can also explain, why the ratio of sol mRNA expression and Assolrna expression differ substantially. Higher butanol formation in the strains overproducing Assolrna in the wild type strain, i.e., C. saccharoperbutylacetonicum [pMTL83151_asr_PbgaL], could be the result from incomplete destabilization and degradation of sol mRNA (mainly the bld part) by truncated plasmid-based versions of Assolrna.Furthermore, phenotypical complementation was not successful, and overexpression did not result in an increase of all solvents, but only of butanol . One rea studies . Anothere operon . The pront apart . The sol\u00a0nt-TSS) . Analysi (bld-2 . Therefo_PbgaL  as well 54-dependent promoters eg. of RpoN regulon via NR1 in E. coli or of AdhA regulation via AdhR in C. beijerinckii and other clostridia (By yet unknown mechanisms, Assolrna could also recruit proteins for DNA looping mechanisms starting from an upstream enhancer site as known for \u03c3ostridia .C. saccharoperbutylacetonicum \u0394Pasr::Pasr** lacking Assolrna transcription show that Assolrna expression is essential for sol operon expression and sufficient solvent formation to prevent acid crash. Overexpression of Assolrna in the strain C. saccharoperbutylacetonicum [pMTL83151_ asr_PbgaL] resulted in even higher butanol levels compared to C. saccharoperbutylacetonicum wild type. It is important to understand the mechanism of regulation by Assolrna to transfer it to other operons with antisense transcription and thereby to other native products such as acetate or butyrate. This could lead to construction of better industrial relevant strains for a range of products.In conclusion, the results obtained with the promoter-exchange strain"}
+{"text": "Circular RNA (circRNA), a class of RNA with a covalent closed circular structure that widely existed in serum and plasma, has been considered an ideal liquid biopsy marker in many diseases. In this study, we employed microarray and qRT-PCR to evaluate the potential circulating circRNAs with diagnostic efficacy in ovarian cancer.We used microarray to explore the circRNA expression profile in ovarian cancer patients\u2019 plasma and quantitative real-time (qRT)-PCR approach to assessing the candidate circRNA\u2019s expression. Then the receiver operating characteristic (ROC) curve was employed to analyze the diagnostic values of candidate circRNAs. The diagnostic model circCOMBO was a combination of hsa_circ_0003972 and hsa_circ_0007288 built by binary logistic regression. Then bioinformatic tools were used to predict their potential mechanisms.Hsa_circ_0003972 and hsa_circ_0007288 were downregulated in ovarian cancer patients\u2019 plasma, tissues, and cell lines, comparing with the controls. Hsa_circ_0003972 and hsa_circ_0007288 exhibited diagnostic values with the Area Under Curve (AUC) of 0.724 and 0.790, respectively. circCOMBO showed a better diagnostic utility (AUC: 0.781), while the combination of circCOMBO and carbohydrate antigen 125 (CA125) showed the highest diagnostic value (AUC: 0.923). Furthermore, the higher expression level of hsa_circ_0007288 in both plasma and ovarian cancer tissues was associated with lower lymph node metastasis potential in ovarian cancer.Our results revealed that hsa_circ_0003972 and hsa_circ_0007288 may serve as novel circulating biomarkers for ovarian cancer diagnosis.The online version contains supplementary material available at 10.1186/s13048-022-00988-0. Ovarian cancer (OC) is the most malignant female tumor and is estimated to account for approximately 21,410 new cases and 13,770 deaths in 2021 in America [At present, protein levels, such as carbohydrate antigen 125 (CA125), Human epididymis protein 4 (HE4), and the algorithms , and Risk of Ovarian Malignancy Algorithm (ROMA)) which combined CA125 and HE4, are widely applied in the diagnosis and prognosis of OC. However, the sensitivity and specificity of these approaches remain somewhat limited , is highWith the development of high-throughput sequencing and microarray technologies, increasing studies showed that circulating circRNAs had diagnostic or prognostic value in many cancers , 16. CirCurrently, the diagnostic value of circulating circRNAs in OC remains unclear. In this study, we used circRNA microarray, illustrating the circulating circRNA expression profile in plasma of OC patients to find potential diagnostic plasma circRNA biomarkers for OC.The plasma and tissue samples utilized in this study were collected between January 2020 and December 2020 from Women\u2019s Hospital of Nanjing Medical University . The clinical characteristics of the patients with plasma samples were shown in Table SPeripheral blood samples (2 ml) were obtained from preoperative patients and controls using BD Vacutainer tubes . Peripheral blood was centrifuged at 3000\u00a0rpm for 10\u00a0min. The isolated plasma was preserved in the 1.5 mL nuclease-free EP tube and stored at -80\u2103 until use.\u2212\u2206Ct method and GAPDH was used as a control.Total RNA from patients\u2019 plasma was extracted using QIAGEN miRNeasy Serum/Plasma Advanced Kit  according to the manufacturer\u2019s instructions. And total RNA from patients\u2019 tissue was extracted using GENEJET RNA purification kit  according to manufacturer\u2019s instructions. RNA was reverse transcribed into complementary DNA (cDNA) in a reaction volume of 20 \u00b5L using Revert Aid First Strand cDNA Synthesis Kit . qRT-PCR was performed using AceQ Universal SYBR qPCR Master Mix  on Applied Biosystems ABI Viia7 . The sequences of the primers used in the PCR assay were listed in Table SA total of 3\u00a0\u00b5g RNA was treated with RNase R as previously described . The conP-value\u2009<\u20090.05.The SBC Human (4\u2009\u00d7\u2009180\u00a0K) ceRNA array V1.0  was used to analyze the expression of circRNA. RNAs were extracted from the plasma of four OC patients and four uterine myoma patients as controls by using Serum/Plasma Kit . Then, NanoDrop ND-1000 instrument  and Bioanalyzer 2100  were performed to detect the purity and integrity of the RNA. Total RNAs were amplified and labeled using Low Input Quick Amp Labeling Kit, One-Color  according to the manufacturer\u2019s instructions and the labeled complementary RNAs (cRNAs) were purified by RNeasy Mini Kit . The labeled cRNAs were hybridized using Gene Expression Hybridization Kit . The completed hybridizations were scanned by an Agilent Microarray Scanner , and the Dye channel was set by the software: Green, Scan resolution\u2009=\u20093\u00a0\u03bcm, PMT 100%, 20 bit. Feature Extraction Software 10.7  was used to read the data. Finally, quantile normalization and subsequent data processing were performed using the R software package. The screening threshold was set as fold change\u2009>\u20092.0 or <\u20090.5, P-value\u2009<\u20090.05 was considered as statistically significant.SPSS 26.0  and GraphPad Prism 8.0  were used for statistical analysis. Data were compared using Student\u2019s t-tests and Man-Whitney tests, as appropriate. The Chi square test was employed to analyze associations between expression of plasma circRNAs and clinical characteristics. The circCOMBO diagnostic model was developed through binary logistic regression analyses. ROC curve analyses were used to determine optimal plasma circRNA expression cutoff values, so that diagnostic utility could be maximized after using SPSS 26.0 to generate ROC curves. The pROC package  in R StuP-value\u2009<\u20090.05). The expression of selected miRNAs was downloaded from GSE47841  (v11.0) was used to predict the association between candidate miRNAs and mRNAs. A protein-protein interaction (PPI) network was constructed using Cytoscape v3.7.0. The hub genes were selected using the Maximal Clique Centrality (MCC) method. A circRNA-miRNA-hubgene (ceRNA) network was constructed using Cytoscape v3.7.0.P-value\u2009<\u20090.05) , and 595 circRNAs were downregulated (green spots) in OC patients compared with the control (fold change\u2009>\u20092\u00a0or <0.5 and 5) Fig. S.P-value\u2009<\u20090.01, were randomly selected. The head-to-tail splicing of these circRNAs was further analyzed by RNase R treatment and Sanger sequencing. All these four circRNAs were resistant to RNase, while their liner compartments were sensitive to RNase R  with the following criteria: (1) high normalized signal (2) fold-change\u2009>\u20092\u00a0or <0.5 and n\u2009=\u200930) and low (n\u2009=\u200930) groups. Then we assessed the correlation between plasma levels of these two candidate circRNAs and the clinical characteristics of OC patients (Table\u00a0P\u2009=\u20090.02). And the plasma level of hsa_circ_0003972 was not significantly associated with all the clinical characteristics we analyzed.Then we analyzed the correlation between the expression of these two circRNAs in the benign control group (uterine fibroids versus other benign diseases) and found that the expression levels of these two candidate circRNAs showed no significant difference between uterine fibroids and other benign diseases Table S, which cts Table\u00a0. The resThen, the ROC curve analysis was used to investigate the diagnostic values of these two candidate circRNAs. As shown in Fig.\u00a0CA125 has been the most commonly used diagnostic marker of OC patients. Therefore, we further studied the combined diagnostic value of hsa_circ_0003972, hsa_circ_0007288, or circCOMBO with CA125(AUC: 0.824 (95%CI: 0.739\u20130.908)) in differentiating OC from benign controls. The results showed that the diagnostic performance of CA125\u2009+\u2009hsa_circ_0007288, CA125\u2009+\u2009hsa_circ_0003972, CA125\u2009+\u2009circCOMBO were better than hsa_circ_0007288, hsa_circ_0003972, circCOMBO or CA125 alone, and CA125\u2009+\u2009circCOMBO had the highest diagnostic value. However, there was no significant difference between CA125\u2009+\u2009hsa_circ_0007288, CA125\u2009+\u2009hsa_circ_0003972 and CA125\u2009+\u2009circCOMBO Fig.\u00a0.n\u2009=\u200941) and OC cell lines as compared with adjacent tissues (n\u2009=\u200915) , normal ovarian tissues, and normal ovarian epithelial cell line IOSE386, and found that both of these two circRNAs were downregulated in OC tissues n\u2009=\u20091 and OC As hsa_circ_0003972 and hsa_circ_0007288 were significantly downregulated in OC patients, we chose six miRNAs , hsa-miR-203, hsa-miR-421, hsa-miR-935) which harbored at least one binding site in either of these two circRNAs and were significantly upregulated in OC patients to construct the circRNA-miRNA-mRNA network. A total of 14,086 and 4239 target mRNAs of the six miRNAs were predicted by TargetScan and miRDB, respectively. Compared with normal ovarian tissues in GTEx database, 2919 mRNAs were downregulated in OC tissues in TCGA database. After taking intersections of these predicted and downregulated mRNAs, 137 mRNAs were selected as the targets of the six miRNAs for further analysis.During the construction of the circRNA-miRNA-mRNA network, hsa-miR-203 failed to link with other miRNAs and mRNAs. Therefore, two circRNAs, five miRNAs, and 137 mRNAs were seen in the ceRNA network Fig.\u00a0a. We useCircRNA in cancer mainly functions through sponging miRNAs , interacIn this study, we also confirmed that the novel circRNAs: hsa_circ_0003972 and hsa_circ_0007288 are downregulated in both the OC tissues and the plasma of OC patients as compare with the adjacent normal ovarian tissues and the plasma of patients with benign disease, indicating that they may produce from normal tissues/cells and perform as the regulator of OC progression. And their down-regulation in the plasma or tissue levels of ovarian cancer patients was possibly caused by the suppression of the oncogenic RNA binding proteins, such as in DHX9 and ADAR1 . CeRNAs in vitro and in vivo studies in the future.We selected downregulated genes in TCGA OC database to establish a circRNA-miRNA-hub gene network, while MAPK4, NCAM1, RORA, ADAMTS5, CEP85L, BEND4, ASXL3, TMOD2 were considered as the hub genes. Interestingly, RORA, acting as a potent tumor suppressor, was downregulated in several cancers, such as breast cancer and colorectal cancer, which inhibited cancers\u2019 growth through attenuating Wnt/beta-catenin signaling, inducing SEMA3F expression or stabilizing p53 to activate apoptosis \u201361. ADAMAlthough the exact mechanisms of the two identified circRNAs in OC were unknown, our results helped to underline the potential mechanisms of their pathogenesis in OC. Besides, examination of the circRNA\u2019s expression in tissues may also provide the clue to derivation of circRNAs and point out the potential systemic effect.Our research revealed that novel plasma circRNA hsa_circ_0003972 and hsa_circ_0007288, the combination of hsa_circ_0003972 and hsa_circ_0007288 (circCOMBO), the combination of circCOMBO and CA125 could serve as a novel circulating biomarker for OC diagnosis. Meanwhile, the combination of circCOMBO and CA125 showed the highest diagnostic performance. The lower plasma level of hsa_circ_0007288 could serve as a potential biomarker for OC lymph node metastasis. Besides, hsa_circ_0003972 and hsa_circ_0007288 may also become the potential therapeutic target of OC.Additional file 1:\u00a0Figure S1. CircRNA expression profiles in OC patients and benign individuals. The expression profile detected by circRNA microarray assay was shown in volcano plot. Four OC patients and four benign individuals were enrolled. Red spots indicated upregulated circRNAs and green spots indicated downregulated circRNAs.Additional file 2: Figure S2. Validation of the four candidate circRNAs. (a-d). (Left): The electrophoresis of the qRT-PCR product of circRNA and linear RNA treated with or without RNase R. (Right): Sanger sequencing of the RT-PCR product in the left, and the base in the red square represented the head-to-tail splicing sites. (e-f). qRT-PCR analysis of the expression of hsa_circ_0003972, hsa_circ_0007288 in 60 OC patients and 60 benign controls\u2019 plasma.Additional file 3: Table S1.\u00a0Clinicopathological characteristics of the enrolled patients.\u00a0Table S2. Primer sequences for real-time PCR or quantitative real-time PCR. Table S3. The miRNA target prediction software circInteratome and circbank predicted miRNAs which could bind hsa_circ_0003972.\u00a0Table S4. The miRNA target prediction software circInteratome and circbank predicted miRNAs which could bind hsa_circ_0007288.\u00a0Table S5. The expression profile of the miRNAs that were upregulated in OC tissues as compared with normal ovarian tissues downloaded from GSE47841.\u00a0Table S6. The downregulated mRNAs in OC tissues in TCGA database compared with normal ovarian tissues in GTEx database.\u00a0Table S7. The 137 candidate mRNAs expression pattern in OC tissues as compared with normal ovarian tissues.\u00a0Table S8. The microarray results of the 46 upregulated circRNAs and 595 downregulated circRNAs in OC patients compared with the benign control .\u00a0Table S9. The expression difference of candidate circRNAs between patients with uterine myoma or other benign diseases.\u00a0Table S10. Comparison of AUC in circ-0003972\u2009+\u2009CA125, circ-0007288\u2009+\u2009CA125, circCOMBO\u2009+\u2009CA125."}
+{"text": "The resulting genome collection is representative of samples around the world and contains many genomes from candidate phyla radiation (CPR) that lack monoculture. Also, it enables the discovery of new taxa such as a genus Candidatus Bgiplasma within the family Acholeplasmataceae. Large-scale metagenomic data from massive samples also allow the assembly of strains from important oral taxa such as Porphyromonas and Neisseria. The oral microbes encode genes that could potentially metabolize drugs. Apart from these findings, a strongly male-enriched Campylobacter species was identified. Oral samples would be more user-friendly collected than fecal samples and have the potential for disease diagnosis. Thus, these data lay down a genomic framework for future inquiries of the human oral microbiome.The oral cavity of each person is home to hundreds of bacterial species. While taxa for oral diseases have been studied using culture-based characterization as well as amplicon sequencing, metagenomic and genomic information remains scarce compared to the fecal microbiome. Here, using metagenomic shotgun data for 3346 oral  The human microbiome has been implicated in a growing number of diseases. The majority of microbial cells are believed to reside in the large intestine Streptococcus oralisFusobacterium nucleatumPorphyromonas gingivalisde novo assembled reference gene catalog for the oral microbiome For the oral microbiome, culture-based characterization as well as marker gene sequencing techniques has been applied in many oral bacteria-associated disease studies, such as cystic fibrosis with Recent published large-scale metagenomic assembly efforts mostly included fecal metagenomic data In this study, we present 3346 new oral metagenomic samples. A total of 56,213 medium- and high-quality metagenome-assembled genomes (MAGs) are constructed based on our collection together with previously published 808 samples. The 56,213 MAGs together with 190,309 public genomes were clustered into 3589 oral species-level clades. New taxa as well as the substantially complemented genomic content of known species are revealed. We provide a genome reference that is highly representative of metagenomic samples not used in assembly and could facilitate culturing and functional screens, as well as disease diagnosis and modulation based on the oral microbiome.We performed shotgun sequencing on 2284 saliva and 391 tongue dorsum samples from the 4D-SZ cohort  To assess the novelty of our assembled genomes, we comprehensively incorporated 190,309 existing isolate and metagenome-assembled genomes from NCBI RefSeq, eHOMD Species-level genome bins (SGBs) were computed for the 246,522 genomes following multiple steps B. In theWe next examined the ability of this species-level genome set to represent the metagenomic shotgun data. We assessed the percentage of reads that could align to cultured genomes (eHOMD) only and cultured complemented by metagenomically assembled genomes . The 152While with all genomes from the oral genome set, the median mapping rate rose to 94.36%. The remaining unmapped reads were mainly classified as homo using Kraken2 Akkermansia is the only genus from the Verrucomicrobiota phylum in the human gut and intensively pursued for its role in health and diseases, and the Verrucomicrobiota and Spirochaetota phyla take up a greater fraction in Hadza hunter gatherers compared to developed countries The taxonomic classification of 3589 representative genomes of oral SGBs was assigned using GTDB-Tk Streptococcus (12.88%), Campylobacter (7.65%), and TM7x (5.92%) , Streptococcaceae (12.88%), and Campylobacteraceae (9.51%), whereas the top 3 assigned genera were  (5.92%) C.Acholeplasma and Candidatus Phytoplasma genera (Candidatus Bgiplasma) was 0.69\u00a0\u00b1\u00a00.05 Mb, which was similar to that of Candidatus Phytoplasma (0.64\u00a0\u00b1\u00a00.14 Mb), but much smaller than that of Acholeplasma (1.50\u00a0\u00b1\u00a00.20 Mb). However, the genome of Candidatus Bgiplasma was complete according to single-copy marker genes in CheckM , but not as low as those of Acholeplasma (30.99%\u00a0\u00b1\u00a01.75%) and Candidatus Phytoplasma (25.98%\u00a0\u00b1\u00a02.68%) (Candidatus Bgiplasma genus showed two separate groups at species-level divergence (ANI\u00a0<\u00a085%)  . DespiteI\u00a0<\u00a085%) B, illustCandidatus Bgiplasma genus were annotated by eggNOG mapper Candidatus Bgiplasma, Clusters of Orthologous Groups (COG) categories of replication, recombination and repair, posttranslational modification, protein turnover, chaperones, and inorganic ion transport and metabolism are found , which was rare in the other cohorts  was five times more than the second ranking uSGB (g__Stomatobaculum_uSGB_1040) (. The AUC of g__Campylobacter_A_uSGB_1674 was 0.722 (95% CI: 0.700\u20130.744), showing the similar predict power for gender as all 3589 oral SGBs  A. The AUral SGBs B.Figure Campylobacter_A_uSGB_1674 is a conservative gender-related bacterium in saliva by integrating other existing cohort data across different populations . As shown in Campylobacter_A_uSGB_1674 was detected in oral samples from multiple regions and displayed a strongly male-enriched pattern. Moreover, the difference between genders of this uSGB was only observed in saliva, but not in dental or tongue ; the ORs of g__Campylobacter_A_uSGB_1674 for ZellerG_2014  was 1.322 (95% CI: 1.175\u20131.531), for Heintz_2016  was 1.407 (95% CI: 1.131\u20131.865), for IlanaB_2019  was 5.913 (95% CI: 2.972\u201313.610), for ZhangX_2015  was 2.115 (95% CI: 1.524\u20133.099), and for Yunnan  was 6.533 (95% CI: 4.962\u20138.884). GoltsmanDSA_2018 was excluded because it only had female samples. Virulence factor (VF) analysis showed that 15 genes of 22 identified VFs in this uSGB were flagella associated, suggesting its migration ability adapted to saliva  was 1.294 , which suggests that g__Campylobacter_A_uSGB_1674 in saliva is a risk of dental calculus. In contrast, the OR for type I diabetes  was 0.795 (95% CI: 0.649\u20130.938), for RA dental  was 0.566 (95% CI: 0.408\u20130.756), and for RA saliva  was 0.598 (95% CI: 0.419\u20130.815) (We\u00a0wondered\u00a0whether g__9\u20130.815) .e.g., Haemophilus spp. and Aggregatibacter spp., enriched in dental samples from healthy volunteers, while only a Pseudomonas SGB and an Enterococcus SGB were selected for RA samples . However, no fungus was observed from our data, probably due to a lack of such samples. Our new oral genome set enables us to identify new biomarkers for RA and CRC, but further validation would depend on more patient samples. By collecting more samples with a variety of diseases, the diversity and quality of the oral genome set will be further improved and would benefit human society by providing health management and disease prevention.Although we have reconstructed tremendous MAGs, the oral metagenomic assembly still faces many challenges. For example, the host rate is about 80% in saliva, and high-quality assembly requires further expanded sample size and sequencing depth B. Many hThe 2675 oral metagenomic samples  from the Chinese 4D-SZ cohort and 671 saliva samples from the Yunnan cohort were newly collected in this study from 2017 to 2018  containing a room temperature stabilizing reagent to preserve the metagenome DNA extraction of the stored samples within the next few months was performed using the MagPure Stool DNA KF Kit B  from 1\u00a0ml of each sample A total of 808 public oral metagenomic datasets were downloaded from NCBI Sequence Read Archive database , which came from five different studies To illustrate the representativeness of the assembled genome set for new data, additional 81 oral metagenomes from three validation cohorts were downloaded from NCBI SRA database  .https://github.com/tseemann/barrnap) with parameters \u201c--reject 0.01 --evalue 1e-3\u201d, and the tRNA sequences in the MAGs were searched by tRNAscan-SE (v2.0.3) The high-quality paried-end and single-end reads were individually assembled using the assembly module of metapi pipeline with different max kmer cutoff by different max read length of each sample applying SPAdes (v3.13.0) The 56,213 reconstructed genomes and 190,309 reference genomes were grouped into SGBs by a two-step clustering strategy as reported previously The phylogenetic trees of 3589 representative genomes of SGBs A and 76 The taxonomic classification of 3589 representative genomes of SGBs was assigned using the GTDB-Tk (v0.3.2) The mapping rates of oral metagenomic reads align to four different oral-related genome databases  which includes archaea, bacteria, fungi, protozoa, viral, and human from Loman Lab (https://lomanlab.github.io/mockcommunity/mc_databases.html) by Kraken 2.0.8-beta with default parameters.Kraken2 is a taxonomic classification system using exact The quantification of species relative abundance of oral metagenomic samples was performed with the taxonomic profiling module of metapi pipeline: 1) build the oral genome index of oral representative SGBs by Bowite2; 2) align the high-quality reads of each sample to the oral genome index using Bowtie2 with the parameters \u201c--end-to-end --very-sensitive --seed 0 --time -k 2 --no-unal --no-discordant -X 1200\u201d; 3) obtain the normalized contig depths by using jgi_summarize_bam_contig_depths; and 4) convert the normalized contig depth to the relative abundance of each SGB for each sample base on the correspondence of contigs and genome. Finally, we merged the relative abundances of all representative SGBs to generate a taxonomic profile.Principal coordinate analysis (PCoA) of human oral profile was done using the dudi.pco function in ade4 R package based on bray distance from vegan 2.5.2 R package. The mean top 10 most abundant SGBs from every study were merged  to visual in the pheatmap R package.Prevotella, Neisseria, Streptococcus, Veillonella, Porphyromonas, Fusobacterium, Pauljensenia, and Haemophilus. Then, we chose the top 10 prevalent species for each genus to do pangenome analysis. All genomes of each SGB were annotated by prokka (v1.13.7) Neisseria sp000186165_kSGB_3225, Prevotella nanceiensis_kSGB_3467, and Porphyromonas unclassified_kSGB_3273 was performed by the nonmetric multidimensional scaling analysis using the metaMDS function of R package vegan (v2.5.2).From the taxonomic profiling results of 4154 oral meta-genomic samples, the most prevalent eight genera were selected based on the ranking of average relative abundance (in descending order), occurrence frequency (in descending order), and oral genome number/SGB size (in descending order), including Campylobacter_A_uSGB_1674  by randomForest 4.6-14 R package. Receiver operating characteristic (ROC) curve was plotted with pROC R package. The most importance SGB for the gender classifier was identified to be g__SGB_1674 A. GeneraThe metagenome-wide association between 3589 SGB profiles and diseases for previously published CRC and RA studies was done using a GLM with adjustment for potential confounders such as gender, age, and BMI . BMI is The study was approved by the Institutional Review Board (IRB) of BGI-Shenzhen (Nos. BGI-IRB19121 and BGI-IRB17162) and the ethics committee of No.1 Affiliated People's Hospital of Kunming Medical University [(2017) Ethics review L No.14], China. Informed consent was obtained from each participant.https://github.com/ohmeta/metapi. The scripts of figures are available at https://github.com/ohmeta/oral-assembly.The pipeline used in this study is available at https://cngb.org/microbiome/genomecatalog/human_oral/). Oral SGB genomes and genome annotations have also been deposited in the Genome Sequence Archive PRJCA003731), and are publicly accessible at https://ngdc.cncb.ac.cn/gsa.All sequence data are available at CNGB Sequence Archive (CNSA) of China National GeneBank DataBase (CNGBdb) (CNSA: CNP0000687 for the 4D-SZ cohort and CNP0001221 for the Yunnan cohort). Oral SGB genomes and genome annotations are available at Microbiome Database of National Genomics Data Center (Jie Zhu: Conceptualization, Methodology, Software, Visualization, Writing - original draft. Liu Tian: Conceptualization, Methodology, Visualization, Writing - original draft. Peishan Chen: Investigation. Mo Han: Investigation. Liju Song: Investigation. Xin Tong: Investigation. Xiaohuan Sun: Investigation, Writing - review & editing. Fangming Yang: Investigation. Zhipeng Lin: Investigation. Xing Liu: Investigation. Chuan Liu: Investigation. Xiaohan Wang: Investigation. Yuxiang Lin: Investigation. Kaiye Cai: Investigation. Yong Hou: Supervision. Xun Xu: Supervision. Huanming Yang: Supervision. Jian Wang: Supervision. Karsten Kristiansen: Writing - review & editing. Liang Xiao: Supervision. Tao Zhang: Supervision. Huijue Jia: Conceptualization, Writing - review & editing, Supervision, Project administration. Zhuye Jie: Conceptualization, Methodology, Visualization, Writing - original draft, Project administration. All authors have read and approved the final manuscript.The authors have declared no competing interests."}
+{"text": "There are several previous studies suggesting that circular RNAs (circRNAs) are involved in tumorigenesis of non-small cell lung cancer (NSCLC). Nevertheless, the role of circRNA_0000520 (circ_0000520) in this disease has not yet been studied.  circ_0000520, microRNA (miR)-1258, and AKT serine/threonine kinase 3 (AKT3) mRNA expression levels were detected by qPCR. CCK-8, EdU, and Transwell assays were utilized to detect NSCLC cells' malignant biological behaviors. The targeted relationship between miR-1258 and AKT3 3\u2032-UTR or circ_0000520 was verified through the dual-luciferase reporter gene assay. Western blotting was utilized to measure the AKT3 expression after circ_0000520 and miR-1258 were selectively regulated.  circ_0000520 was upregulated in NSCLC. Highly expressed circ_0000520 is linked to the NSCLC patient's advanced TNM stage and lymph node metastasis. circ_0000520 overexpression facilitated NSCLC cell growth, migration, and invasion. miR-1258 was identified as the downstream target of circ_0000520. miR-1258 overexpression weakened the effect of circ_0000520 overexpression on NSCLC cells. miR-1258 targeted and inhibited AKT3. circ_0000520 positively regulated the AKT3 expression in NSCLC cells by sponging miR-1258.  circ_0000520 upregulates AKT3 by competitively binding with miR-1258 to facilitate NSCLC progression. Globally, lung carcinoma is the biggest cause of cancer-related deaths . Non-smaIn the last several decades, more and more non-coding RNAs (ncRNAs) have been discovered and investigated , 7. WithKnown as highly conserved small ncRNAs, microRNAs (miRNAs) bind to mRNA 3\u2032-UTR to result in mRNA degradation or translation inhibition, thus modulating posttranscriptional gene expression . A lot oThe present work investigated the expression pattern of circ_0000520 and subsequently explored the exact role of the circ_0000520/miR-1258/AKT serine/threonine kinase 3 (AKT3) regulatory axis in NSCLC. Our work broadens our understanding of NSCLC's pathogenesis and provides potential biomarkers for the disease.Thirty-seven pairs of tumorous tissues and para-tumorous tissues of NSCLC patients surgically resected at Xiangyang Central Hospital were collected. None of the patients received neoadjuvant therapy. This study was performed with each patient's informed consent. The procedures of the present work were approved by the Ethics Committee of Xiangyang Central Hospital. NSCLC patients' clinical features are described in \u03bcg/ml streptomycin (Invitrogen) and 10% fetal bovine serum , which was then placed in an incubator in 5% CO2 at 37\u00b0C. When the cells grew to 70\u201380% confluency, 0.25% trypsin (Roche) was used for subculture. GenePharma was the provider of circ_0000520 overexpression plasmid (pcDNA3.1-circ _0000520), negative control plasmid (pcDNA3.1-NC), miR-1258 mimics, and the control (miR-NC). Lipofectamine\u00ae 3000 (Invitrogen) was used for transfecting the abovementioned plasmids and oligonucleotides into A549 and H460 cells. 24\u2009h later, the efficiency of cell transfection was determined through quantitative real-time polymerase chain reaction (qPCR).From American Type Culture Collection, NSCLC cell lines  and immortalized bronchial epithelial cells (BEAS-2B) were bought. All of the cells were cultured in DMEM medium (HyClone) with 100\u2009U/ml penicillin and 100\u2009\u2212\u0394\u0394CT method. Check TRIzol reagent (Invitrogen) was adopted to isolate the total RNA. A PrimeScript RT kit (TaKaRa) was used for the reverse transcription. On an ABI 7900 fast real\u2010time PCR system (Applied Biosystems), qRT\u2010PCR was conducted utilizing a SYBR Green Master Mix II kit (TaKaRa). The expression of GAPDH was adopted to normalize the expression levels of mRNA and circ_0000520, and the expression of miRNA was normalized with small RNA RNU6B (U6). The relative expression of genes was quantified through the 2A PARIS\u2122 kit (ThermoFisher) was applied for carrying out the nucleocytoplasmic separation experiment. The TRIzol method was used to extract the cytoplasmic and nuclear RNA, and then, qPCR was conducted to examine the circ_0000520 expression in the nucleus and cytoplasm, respectively. U6 and GAPDH functioned as the controls of subcellular localization.3 cells/well). Then, 10\u2009\u03bcL of CCK-8 solution (MedChemExpress) was supplemented into each well at different time points, and the cells were incubated at 37\u00b0C for another 2\u2009h. After terminating the culture, the absorbance values were measured at a wavelength of 450\u2009nm.The NSCLC cells were inoculated into 96-well plates  (Olympus), and the EdU-positive cells were counted.An EdU detection kit (RiboBio) was used to detect cell proliferation. H460 and A549 cells were cultured for 24\u2009h. The cells were then treated with 50\u2009\u00b5m; 1\u2009:\u200910; BD Biosciences) was only used for invasion assay. A549 and H460 cells (5\u2009\u00d7\u2009104) were added to the top compartment of Transwell, and 10% FBS-containing DMEM was added to the bottom chamber, and the cells were cultured for 24\u2009h at 37\u00b0C. Subsequently, the cells that had failed to migrate were discarded; the cells that had migrated were fixed for 10\u2009min with 4% paraformaldehyde and subsequently stained with 0.5% crystal violet solution. Under an inverted microscope (Olympus), the cells were counted.A549 and H460 cells, after being digested with 0.25% trypsin, were centrifuged and resuspended in a serum-free medium. Matrigel (pore size: 8\u2009From Promega (Madison), all luciferase reporter vectors circ_0000520 mutant (MUT), circ_0000520 wild type (WT), AKT3 MUT, and AKT3 WT were obtained. Next, circ_0000520 WT/MUT or AKT3 WT/MUT and miR-1258 mimics or its negative control were co-transfected into H460 and A549 cells. The luciferase activity was measured at 48\u2009h after the transfection.The transfected cells were lysed by RIPA buffer (Beyotime). After centrifugation, the cell supernatant was collected. The supernatant was then heated in a 100\u00b0C water bath for 10\u2009min for denaturing the protein. Then, the protein was isolated by SDS-PAGE and transferred to the polyvinylidene fluoride (PVDF) membrane (Millipore). The membrane was blocked at room temperature with 5% skimmed milk for 1\u2009h and subsequently rinsed with Tris-buffered saline with Tween-20 (TBST) 3 times. Subsequently, the membrane and primary antibodies  and anti-GAPDH antibody ) were incubated at 4\u00b0C overnight. After TBST rinsing, the membrane and goat anti-rabbit IgG H&L  were incubated for 1\u2009h at room temperature. GAPDH acted as the internal control. The ECL chemiluminescence kit (Promega) was utilized for developing the bands.This experiment was supported by the Animal Research Ethics Review Board of Xiangyang Central Hospital. Sixteen nude mice  were randomly divided into two groups (8 mice per group). Subsequently, the transfected A549 cells were respectively inoculated into the back of the nude mice. Three weeks later, mice were sacrificed with euthanasia and the volume of the tumor in the two groups was compared.x\u2009\u00b1\u2009s\u201d was used to represent the data. t-test and one-way analysis of variance were performed to compare the means of 2 and more groups, respectively. Fisher's exact test was utilized to analyze the correlation of circ_0000520 expression with NSCLC patients' clinical parameters. Pearson correlation analysis was conducted to assess the correlation. A difference was of statistical significance when P < 0.05.Each assay was conducted in triplicates and repeated 3 times. SPSS21.0 (SPSS Inc.) was adopted to statistically analyze the data. \u201cn\u2009=\u200918) and low (n\u2009=\u200919) expression groups. It was revealed that high circ_0000520 expression was strongly associated with the NSCLC patients' lymph node metastasis and a higher TNM stage (Through the analysis of the microarray data, the dataset GSE158695, it was revealed that circ_0000520 is upregulated in NSCLC tissues . It was NM stage . AdditioNM stage .in vivo experiments suggested that high expression of circ_0000520 promoted the growth of tumor cells which were transplanted into the nude mice (To study circ_0000520s role in NSCLC, circ_0000520 overexpression plasmids were transfected into A549 and H460 cells . circ_00ude mice . The afoNext, we searched CircInteractome online website to predict circ_0000520s potential target miRNAs, and observed that there was a binding sequence between circ_0000520 and miR-1258 . OverexpNext, \u201crescue\u201d experiments were performed. It was revealed that circ_0000520 overexpression would suppress miR-1258 expression in H460 and A549 cells, whereas transfection of miR-1258 mimics would reverse this effect . AccordimiR-1258's downstream target genes were predicted utilizing the TargetScan database, and it was revealed that AKT3 has a binding site to miR-1258 . OverexpA growing amount of evidence shows that the expression characteristics of circRNAs are closely associated with the adverse clinical parameters of patients, and circRNA dysregulation often promotes different malignant behaviors , 24. ForThe role of miR-1258 in tumorigenesis has been widely reported in recent years , 28. In Known as a serine/threonine protein kinase, AKT3 is pivotal in modulating cell proliferation, differentiation, apoptosis, and migration . AKT3 isTo sum up, circ_0000520 expression in NSCLC is elevated, and it enhances the malignancy of cancer cells. In terms of mechanism, circ_0000520 increases AKT3 expression via absorbing miR-1258. These findings may provide innovative ideas for NSCLC treatment."}
+{"text": "Lappula myosotis V. Wolf 1776 is an annual or biennial plant with important medicinal value. In the present study, we report the complete chloroplast genome data of L. myosotis, which has a length of 146,668\u2009bp, including a small single-copy (SSC) region of 17,059\u2009bp, a large single-copy (LSC) region of 79,691\u2009bp, and a pair of inverted repeats (IRs) of 24,959\u2009bp. A total of 127 genes encoding tRNA and rRNA were annotated. The total CG content of the chloroplast genome was 37.7%. The maximum-likelihood\u00a0(ML) phylogenetic tree strongly supported that L. myosotis is closely related to Trigonotis peduncularis. The complete chloroplast genome of L. myosotis provides useful information on the evolution\u00a0and phylogenetic relationship among Boraginaceae plants. Lappula myosotis V. Wolf 1776 is an annual or biennial plant of the genus Lappula in the family Boraginaceae. L. myosotis has important medicinal value and can be anti-inflammatory and insecticidal  (zleztme@163.com). DNA samples were stored in the Molecular Laboratory of Heilongjiang University of Chinese Medicine .We collected samples of \u00b075\u203269\u2033) . VoucherL. myosotis by CTAB (Hamad Lithospermum erythrorhizon (NC053783) served as our reference genome. Sequencing raw data quality was assessed using FastQC v0.11.7 software  region of 79,691\u2009bp, a small single-copy (SSC) region of 17,059\u2009bp and a pair of inverted repeats (IRs) of 24,959\u2009bp  and Poales (Zea mays) as an outgroup were used to construct a ML phylogenetic tree with 1000 bootstrap replications (Best-fit model: GTR\u2009+\u2009G) (Arnebia tibetana (NC_053781), Arnebia guttata (NC_053780), Arnebia euchroma (NC_053782), Lithospermum erythrorhizon (NC_053783), Onosma fuyunensis (NC_049569)  , Borago officinalis (NC_046796)  . The folL. myosotis is the first report of a member of Lappula, which fills the gap in genome-related information. Provide data support for the subsequent classification of Boraginaceae.The complete chloroplast genome of"}
+{"text": "Actinosynnema pretiosum is a well-known producer of maytansinoid antibiotic ansamitocin P-3 (AP-3). Growth of A. pretiosum in submerged culture was characterized by the formation of complex mycelial particles strongly affecting AP-3 production. However, the genetic determinants involved in mycelial morphology are poorly understood in this genus. Herein a continuum of morphological types of a morphologically stable variant was observed during submerged cultures. Expression analysis revealed that the ssgA_6663 and ftsZ_5883 genes are involved in mycelial aggregation and entanglement. Combing morphology observation and morphology engineering, ssgA_6663 was identified to be responsible for the mycelial intertwining during liquid culture. However, down-regulation of ssgA_6663 transcription was caused by inactivation of adpA_1075, gene coding for an AdpA-like protein. Additionally, the overexpression of adpA_1075 led to an 85% increase in AP-3 production. Electrophoretic mobility shift assays (EMSA) revealed that AdpA_1075 may bind the promoter regions of asm28 gene in asm gene cluster as well as the promoter regions of ssgA_6663. These results confirm that adpA_1075 plays a positive role in AP-3 biosynthesis and morphological differentiation. A. pretiosum. Ansamitocins are of limited industrial applicability because of their low production yields. In recent decades, considerable efforts have been made to further enhance AP-3 yield to satisfy the industrial demands with medium optimiztion and genetic modifications because of its great pharmaceutical value [Ansamitocin P-3 (AP-3) exhibits antitumor activity against various cancer cell lines ,2,3. Itsal value ,10,11,12Actinobacteria remarkably exhibits complex morphology during submerged cultivation. In liquid culture, their mycelium shows filamentous growth, exhibiting dispersed mycelial form or compact mycelial network [Streptomyces hygroscopicusin producing rapamycin [Streptomyces fradiae and Streptomyces noursei [Streptomyces lividans TK21 for a hybrid antibiotic production as well [From an industrial point of view, liquid culture is favorable for large scale production of antibiotics. Actinomycetes are usually subjected to submerged fermentation. Unlike other bacteria,  network . It is w network . Three t network . The mor network . Submergapamycin . A simil noursei ,19. Howe noursei ,21. Addi as well .ssgA gene modification were employed to obtain desirable morphologies and fast growth [Therefore, in order to optimize mycelial morphology in a more targeted and flexible manner, several genetic determinants have been identified that play roles in the control of morphogenesis . The gent growth ,33.ssgA transcriptional variations are generally controlled by AdpA [S. griseus, AdpA positively controls the expression of genes involved in spore formation and aerial mycelium formation, as well as activates the transcription of various genes related to secondary metabolism [adpAsx gene in S. xiamenensis 318 had negative effects on cell division genes, such as putative ssgA, ftsZ, ftsH, and whiB. Besides, it functions as a bidirectional regulator for the biosynthesis of xiamenmycin and PTMs [Streptomyces. AdpA and its orthologs activate or down-regulate genes, including repression of its own transcription, by directly binding to operator regions containing a consensus sequence [Interestingly, morphological differentiation caused by  by AdpA ,34,35. A by AdpA ,37. In Stabolism ,39. Wherand PTMs . To dateand PTMs . As a glsequence ,42,43.A. pretiosum is being developed as a sustainable industrial production platform, the genes involved in cell division and morphological development are still poorly investigated. Gene APASM_4178 was identified as a subtilisin-like serine peptidase encoding gene responsible for mycelial fragmentation [A. pretiosum as the analogue of \u03b2-tubulin, was demonstrated to be the AP-3 binding target. Overexpression of APASM_5716 gene that encodes FtsZ resulted in AP-3 resistance and overproduction in A. pretiosum ATCC 31280 [A. pretiosum X47 strain. PhoP is the response regulator, negatively affecting morphological development and excluding its regulation on the biosynthesis of AP-3 in X47 strain [A. pretiosum has been observed to form dense pellets, while the control strain formed loose clumps. In addition, excessive mycelial fragmentation of the control strain was observed early in the fermentation. We investigated several putative genes that may contribute to cell division and pellet architecture. Gene ssgA_6663 was identified as a key genetic determinant of compact mycelial network formation during solid and liquid cultures. We also characterized the roles of AdpA_1075 in controlling the morphological differentiation and AP-3 production. AdpA_1075 was determined to positively control the biosynthesis of ansamitocin by directly regulating the expression of asm28.Although entation . FtsZ prCC 31280 . A two-c7 strain . HoweverA. pretiosum subsp. auranticum L40 was derived from A. pretiosum subsp. auranticum ATCC 31565 by atmospheric and room temperature plasma (ARTP) mutagenesis [All plasmids and strains used in this study are listed in agenesis . Strain agenesis . YMG agaThe stability of strain MD15 was tested following the method described by former study  with somCRISPR-Cas9 mediated gene inactivation. Mutants with gene ssgA_6663, adpA_1075, or asm28 disruption were performed by pCRISPR-Cas9apre with a unified construction process [ssgA_6663 deletion was described briefly. Two homologous arms  for ssgA_6663 deletion were amplified and together cloned to StuI-digested plasmid pCRISPR-Cas9apre by NEB DNA Assembly Master Mix  to give the pCRISPR-Cas9apre\u0394ssgA. The ApE software  was used to search N20 targeting sequences of sgRNAs. The sgRNA cassettes were cloned into the XmaJI/SnaBI site of pCRISPR-Cas9apre\u0394ssgA. The amplification primers used to construct pCRISPR-Cas9apre series gene knockout plasmids are shown in E. coli ET12567 (pUZ8002) was employed to introduce the resulting plasmid into L40. According to protocol described elsewhere [ process . As an elsewhere , the conConstruction of plasmids for gene overexpression. pSETK derived from pSET152 was used to prepare overexpression plasmids of ssgA_6663, adpA_1075, ftsZ_5883 or asm28. More specifically, the kasOp*-rbs fragment was introduced to XbaI/EcoRV cloning site of pSET152. Aforementioned genes were amplified from A. pretiosum L40 chromosome. The amplicons were cloned into NdeI/EcoRV site of pSETK, respectively. The obtained recombinant plasmids pSETKssgA, pSETKftsZ, pSETKadpA, pSETKasm28, pSETKftsZ:ssgA, and control plasmid pSETK were individually transferred into E. coli ET12567(pUZ8002) and then integrated into the attB site of strain MD02 by intergeneric conjugation. The verification of these recombinant strains was performed by PCR . Isolated RNA was treated by DNase I before being reverse transcribed with cDNA Synthesis Kit . The cDNA templates were amplified in triplicate for each transcription analysis using MagicSYBR Mixture  with primers listed in t method .AP-3 was extracted from the culture supernatant using a previously described method . HPLC anMycelial morphology was observed using an optical microscope . Culture broth (10 \u03bcL) was pipetted onto a standard glass slide (25 \u00d7 75 mm), dyed with crystal violet. Images were captured under an oil immersion lens .Mycelium was harvested by centrifugation, and washed with 0.1 M PBS. The mycelium was resuspended in 2.5% glutaraldehyde solution for 3 h. The fixed samples were then washed twice with 0.1 M PBS. Samples were subjected to gradient dehydration with ethanol solution . Finally, the dehydrated samples analyses were carried out on a S3400-N scanning electron microscopy .adpA_1075 was amplified with primers 28a1075-F/R. The adpA_1075 cassette was cloned in HindIII/NdeI-digested pET28a (+), generating plasmid pET-28a-adpA_1075. The plasmid was transformed into E. coli BL21(DE3) for protein overexpression. The generated strain BL21(DE3)/pET-28a-adpA_1075 was cultivated at 37 \u00b0C for 2\u20133 h in 100 mL LB medium containing 50 \u00b5g/mL kanamycin until OD600 reached about 0.6\u20130.8. Isopropyl-\u03b2-D-thiogalactoside  was added after 30 min of cooling at 4 \u00b0C and further incubated overnight at 16 \u00b0C for AdpA_1075 expression. The cells were harvested and resuspended in 50 mM phosphate buffer solution (pH 7.5). His-tagged AdpA_1075 protein was released from cells by homogenization. Ni SepharoseTM 6 Fast Flow  was applied to proteins purification with elute buffer . The purified His-tagged AdpA-1075 was analyzed by 12.5% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE).Gene EMSA was performed using a Chemiluminescent EMSA Kit  according to the manufacturer\u2019s instructions. The two complementary oligonucleotides were annealed, labeled with biotin, and incubated with recombinant proteins in the absence or presence of excess amounts of unlabeled wildtype oligonucleotides. The protein-DNA complexes were separated on 5% polyacrylamide gels and the signals were captured with a Chemiluminescence Imaging System . As a control, for each target gene, excessive unlabeled specific DNA fragments were added to the reactions, resulting in the appearance of free un-shifted probe and demonstrating that the binding was specific.ssgA_6663, ftsZ_5883, ssgB_2072, and cslA_0512, respectively, in A. pretiosum subsp. auranticum ATCC 31565 genome. To better understand the role of these genes in mutant MD15 morphological development, the transcription levels of these target genes were measured by qRT-PCR on the third day of fermentation. In mutant MD15, transcription levels of ssgA_6663 and ftsZ_5883 were 124% and 160% higher than those in strain L40, respectively   showed eectively D. The tro spores C. Negligormation . This faA. pretiosum producing AP-3 in submerged cultivation [ssgA directly activates cell division in Streptomyces [ssgA_6663 also varies with strain morphology in non-spore producing A. pretiosum  between AdpA_1075 and APASM_1021 , respectively. The results showed that the overexpression of ssgA_6663 did not improve AP-3 production, which was consistent with what was observed in S. griseus [ftsZ_5883 improved strain resistance against AP-3 [adpA_1075 increased AP-3 production by 85% without affecting dry cell weight (DCW) at the end of fermentation  was identified in S. griseus  in the upstream region of asm28  DNA binding motifs . In this species . EarlierCC 31280 . In thiseviously .asm28  also contains a TTA codon, indicating that asm28 might be an important regulatory target during ansamitocin biosynthesis. However, the function of asm28 gene and its encoded protein remain uncharacterized at present. Follow-up studies are needed to confirm this hypothesis and to elucidate the complex regulatory network in which asm28 is involved.In this study, an intergenic region containing AdpA-binding motif was identified in the upstream region of asm28 . Our finynthesis . Moreoveptomyces . GeneralssgA_6663 in mycelial development. ssgA_6663 can dominate the mycelial intertwining and pellet formation. The silencing of the ssgB_2072 gene resulted in the absence of SsgB, which may explain the fact that the strain developed into mycelium without sporulation septa.In this study, we have elucidated the function of A. pretiosum. AdpA_1075 acts as a global regulator, affecting morphological differentiation and promoting the biosynthesis of ansamitocin. Our findings provide additional useful evidence for the regulatory mechanism of ansamitocin biosynthesis.Additionally, we characterized the regulatory role of AdpA_1075 in"}
+{"text": "Recurrent gene mutations often cooperate in a predefined stepwise and synergistic manner to alter global transcription, through directly or indirectly remodeling epigenetic landscape on linear and three-dimensional (3D) scales. Here, we present a multiomics data integration approach to investigate the impact of gene mutational synergy on transcription, chromatin states, and 3D chromatin organization in a murine leukemia model. This protocol provides an executable framework to study epigenetic remodeling induced by cooperating gene mutations and to identify the critical regulatory network involved.For complete details on the use and execution of this protocol, please refer to   \u2022A computational framework for studying chromatin remodeling by mutational synergy\u2022Pipelines for linking mutation-associated chromatin elements with target genes\u2022Method for identification of critical network nodes involving chromatin remodeling Publisher\u2019s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Recurrent gene mutations often cooperate in a predefined stepwise and synergistic manner to alter global transcription, through directly or indirectly remodeling epigenetic landscape on linear and three-dimensional (3D) scales. Here, we present a multiomics data integration approach to investigate the impact of gene mutational synergy on transcription, chromatin states, and 3D chromatin organization in a murine leukemia model. This protocol provides an executable framework to study epigenetic remodeling induced by cooperating gene mutations and to identify the critical regulatory network involved. Flt3-ITD and Npm1c, both of which are present in 15%\u201320% of all AML cases. Either of the two mutations alone caused mild and pre-malignant phenotype, whereas in combination they demonstrated strong synergistic effect to induce aggressive AML. The hematopoietic stem and progenitor cells (HSPCs), a bulk cell population represented by the lineage negative (Lin-) fraction of bone marrow cells harvested from wildtype (WT), single mutant (Flt3-ITD or Npm1c), and double mutant  mice, were used to generate all the multiomics data as an exemplar in this protocol.The protocol below describes the specific steps for performing the integrative analysis of chromatin accessibility, chromatin states, DNA looping and transcriptome across four cellular states in a murine allelic series that models the two most common mutations in acute myeloid leukemia (AML): This section includes minimum requirements for computer hardware, pre-installation of software  for data processing, as well as a collection of exemplar next generation sequencing (NGS) data to be analyzed.Note: We recommend a computer system containing a minimum configuration of 16 GB local memory and 12 CPU cores.Exemplar data analysis in the protocol is performed in a computational environment with system specifications indicated in the Note: To recapitulate the procedures undertaken in our published work  and are indicated in the Algorithms and scripts required in the protocol are available in GitHub  for processing. All the experiments were performed as described .2.Multiple chromatin activation states detected by chromatin immunoprecipitation and mass parallel sequencing (ChIP-seq) on histone H3 lysine 4 mono- or trimethylation (H3K4me1 or H3K4me3) and histone H3 lysine 27 acetylation (H3K27ac).3.Promoter-anchored 3D chromatin interaction detected by Promoter capture HiC (pCHiC).4.Global gene expression profiled by RNA high-throughput sequencing (RNA-seq).Collect the bulk NGS data of each genomic profiling approach below in four cellular states  that demonstrate similar dynamic chromatin modifications. Afterwards, the specific clusters of CREs with characteristic gain or loss of enhancer signatures are annotated to target genes, using either linear or spatial proximity information. Differential mRNA expression is further analyzed for these genes along with their associated functional network. The relevant biological information of the data used and their functional interpretation are discussed in great detail in .Timing: 2\u20133\u00a0daysFASTQ files of pCHiC are converted into readable promoter-associated DNA interaction files. The data processing steps are described in great detail below.1.a..FASTQ files.> get_data.sh -g [GENOTYPE] -m [OUTPUT_FOLDER] -i [INPUT_FASTQ] -x mm10Note: QC analysis is carried out with FastQC package, and raw reads are mapped to Mus musculus (house mouse) genome assembly GRCm38 (mm10) using Bowtie2, with parameters allowing to keep reads for at most 2 alignment and 1 mismatch in the seed (20\u00a0bp default).Perform QC and reads mapping by running custom scripts (\u201cget_data.sh\u201d) on the input b.> process_aligned_reads.sh -g [GENOTYPE] -m [OUTPUT_FOLDER] -x mm10Note: This process utilizes Picard tools with the \u201cMarkDuplicates\u201d function for data filtering, and generates sorted .BAM files.Filter the mapped reads by removing duplicate reads with custom scripts  as below.c..BAM files with a pre-defined p value at 1e-20.> macs2 callpeak -t [INPUT_BAM] -g mm -f BAM -n [OUTPUT_FILE_NAME] -p 1e-20 --nomodel --nolambda --bdgNote: The parameter --nomodel here is specified for single-read ATAC-seq data (the exemplar data), without modeling the fragment size and by default extends the reads for 200\u00a0bp. This may not accurately reflect the actual length of nucleosome-free regions.Identify significant ATAC-seq peaks by running MACS2 callpeak on filtered Process raw reads in ATAC-seq and ChIP-seq data for each genotype.2.a..FASTQ files (r_1 and r_2) for each genotype.Note: This process covers QC analysis using FastQC, then reads mapping and uniquely mappable\u00a0reads extraction using STAR package which allows at most 2 mismatches, and subsequently\u00a0read counts computation for all annotated genes using a python package HTSeq.> runRNA_STAR_paired.pl [INPUT_r_1_FASTQ] [INPUT_r_2_FASTQ] [GENOTYPE] mm10 STAR-GENOMES-mm10.gencode.vM7.comprehensive gencode.vM7.comprehensive.annotation.gtf [exons y/n]Process RNA-seq data by running custom scripts (\u201crunRNA_STAR_paired.pl\u201d) on paired b.Npm1c, Flt3-ITD, or DM) and WT counterpart by running custom scripts  on .HTSEQ.COUNTS files generated in step 2a.> Rscript RNAseq_differential_analysis.RNote: Bioconductor package DESeq2 is the core analytical tool utilized in this step. The output files are in .CSV format .Analyze pairwise differential gene expression between any mutant condition . To execute HiCUP, the input HindIII_digestion_file needs to be generated using hicup_digester (included in the hicup software) using the first code above.Process pCHiC raw data  using HiCUP pipeline to map and filter the data and eventually output valid HiC fragments (termed di-tags) stored in b.i.BAM files generated by HiCUP into the CHiCAGO input data format, .CHINPUT.> bam2chicago.sh [INPUT_BAM] CHiC.mm10.baitmap Digest.mm10.rmap [OUTPUT_FILE] nodeleteNote: The availability of the shell script, as well as the description and preparation of input files can be referred to CHiCAGO online instruction (https://bitbucket.org/chicagoTeam/chicago/src/master/chicagoTools/). The rmap file (.RMAP) and baitmap file (.BAITMAP) are tab-separated files describing the restriction digestion fragments and the coordinates of the baited/captured restriction fragments, respectively, all with numeric IDs. Both files can be generated by a CHiCAGO script (\u201ccreate_baitmap_rmap.pl\u201d) which is accessible via clicking the link above.Convert filtered read pairs in .ii..CHINPUT files from genotype replicates to generate a list of significant promoter-associated DNA interactions.> Rscript runChicago.R --design-dir [DESIGN_FILES_PATH]  [OUTPUT_FILE] WT.CHiC.R1.chinput,WT.CHiC.R2.chinput WT.CHiC.R1-2Note: Significant interactions are called when CHiCAGO scores are \u22655. The format of CHiCAGO input files is described in the CHiCAGO pipeline documentation (https://bitbucket.org/chicagoTeam/chicago/src/master/chicagoTools/).CRITICAL: Data processing by HiCUP and CHiCAGO are heavy computation tasks which favor usage of multiple CPU cores and large memory. The running time can be reduced to a reasonable duration in a computational environment with at least 24 threads and 48 GB RAM.Further statistical analysis is performed on Transform valid HiC di-tags into statistically significant chromatin interactions associated with all mouse promoters using Bioconductor package CHiCAGO.Process the promoter-associated chromatin interaction data profiled by pCHiC assays in each cellular condition.In this section, the raw data from different genomic approaches are processed in a stepwise manner and are transformed into a format compatible with the subsequent integrative analysis. In brief, a QC step is applied to check the ChIP-seq and ATAC-seq data quality prior to reads mapping to mouse genome, followed by the removal of duplicated reads. Subsequently, genotype-specific open chromatin states are identified by calling significant peaks on ATAC-seq in each cellular condition. Next, transcriptome data profiled by RNA-seq are processed in a similar fashion but with different tools. In addition, the RNA-seq read counts are extracted for all annotated genes and differential expression of protein-coding genes between single or double mutant cells and wildtype cells is analyzed. Finally, chromatin interaction data stored in raw .Timing: 4\u20136 hNpm1c, Flt3-ITD and DM) at these potential CREs are computed to build a data matrix for further clustering analysis. Subsequently, the data matrix is processed in a similar way as for single-cell RNA-sequencing with the Seurat package, treating all CREs  as separate data points across all 16 assay conditions . This allows dimensionality reduction to classify and visualize clusters of CREs with similar patterns across wildtype and mutant cells. Meanwhile, specific clusters of chromatin regions showing leukemia-specific alterations of chromatin activation marks are identified for downstream gene network analysis.4..SAF required for read counts extraction using featureCounts.a..BAM files) and peak files (created by MACS2), using the layout below (row 3\u20136 are examples).Make a sample list (\u201csamplesheet_ATAC.csv\u201d) indicating which ATAC-seq samples to be processed and the path to the storage of filtered reads (in b.> Rscript ATAC_consensus_peakmax.RNote: This step is performed by running DiffBind within our custom scripts.By running custom scripts (\u201cATAC_consensus_peakmax.R\u201d) on the sample list (\u201csamplesheet_ATAC.csv\u201d) generated in step 4a, a list of consensus peak sets (\u201cATAC_consensus_peaks.bed\u201d) is computed on ATAC-seq peaks from all genotypes including all their replicates. Then supplement this list with the information of which sample has maximal ATAC-seq signal at each peak (\u201cATAC_consensus_peakmax.bed\u201d).c.> awk '{print $1\"\\t\"$2\"\\t\"$3\"\\t\"$5\"\\t\"\"[SAMPLE]\"}' [PEAK_FILE]\u00a0>\u00a0[SAMPLE_PEAK_SUMMIT_BED]> cat [ALL_PEAK_SUMMIT_BED] | sort -k1,1 -k2,2n\u00a0>\u00a0ATAC_all_summit.bed> bedtools intersect -a ATAC_consensus_peakmax.bed -b ATAC_all_summit.bed -wa -wb\u00a0>\u00a0ATAC_consensus_peakmax_intersect_summit.bed> sort -k4,4 -k9,9rn ATAC_consensus_peakmax_intersect_summit.bed | sort -uk4,4 | awk '{print $6\"\\t\"$7\"\\t\"$8\"\\t\"$4}' | sort -k1,1 -k2,2n\u00a0>\u00a0ATAC_consensus_peak_summit.bedSAFfile# \u201cATAC_consensus_peak_summit.bed\u201d is the input file for subsequent conversion to .> Rscript ATAC_peaksummit_to_saf.RIdentify the peak summit of each consensus peak sets  and convert this information to featureCounts input file (\u201cATAC_consensus_summit2kb_adj.saf\u201d) by creating genome coordinates of 2-kb bins surrounding ATAC-seq consensus peak summits (\u00b11\u2009kb from peak summit), with the help of running custom scripts (\u201cATAC_peaksummit_to_saf.R\u201d).Create a catalog of ATAC-seq consensus peak sets across four cellular states and convert it into a data table listing 2-kilo base (kb) bins at these consensus peaks (\u00b11\u2009kb from peak summit) in a format of 5..BAM files  using featureCounts.Extract the read counts for each genomic approach  in each cellular condition at the 2-kb bins of ATAC-seq consensus peaks from the corresponding > featureCounts -a ATAC_consensus_summit2kb_adj.saf -F SAF -t exon -g GeneID --largestOverlap -o ATAC_consensus_summit2kb_adj_counts.txt [ALL_BAM_FILES]6.Perform integrative analysis on multilayered chromatin profiling data of all four genotype samples by running custom scripts  to identify clusters of CREs (accessible chromatin regions) with similar dynamic chromatin states across WT and mutant conditions.> Rscript Multiomics_Seurat_analysis_v2022.R# input files \u201cATAC_consensus_summit2kb_adj_counts.txt\u201d and \u201cATAC_consensus_summit2kb_adj_counts.txt.summary\u201d were generated in step 5 by featureCountsNote: A prerequisite to dimensionality reduction analysis is a data matrix containing CREs as column  and samples as rows , filling with normalized read counts (CPM) on merged replicates of each condition,\u00a0in a layout format as listed below. An exemplar data matrix (\u201cATAC_consensus_summit2kb_adj_cpm_merge_transpose.txt\u201d) is provided in the Note: By analyzing our exemplar data, this step generates three plots as shown in Note: To link a set of CREs (tSNE clusters) with mutation condition, by qualitatively analyzing the dynamic pattern of chromatin profiles associated with mutation alone or in combination in the heatmap  exert a strong synergy to induce a marked gain of H3K27ac, elevated levels of H3K4me1 and ATAC-seq, indicating the acquisition of enhancer signals by leukemia induction. Using the promoter-associated chromatin interaction data from pCHiC assays, Cluster-6 CREs are assigned to target genes when the CREs overlap with bait promoters or interaction fragments revealed by the pCHiC data. These target genes are then examined for differential expression analysis between mutant and WT samples, by checking them in the global analysis from step 2b. Since Cluster-6 CREs represent leukemia-specific gain of chromatin activity, the mutation-induced up-regulated genes linked to Cluster-6 CREs are further selected for gene ontology analysis, leading to the identification of leukemia-specific gene network related to chromatin alteration at 3D level.7.a..RDS files (created by \u201crunChicago.R\u201d in step 3b) which contain promoter-associated DNA interactions generated by CHiCAGO, using the format below.Note: For each genotype, interaction data of two replicates are merged by running runChicago.R as input samples.Make a sample list (\u201cmakematrixsample.txt\u201d) indicating the genotypes and the correspondent b.> Rscript makePeakMatrix.R --twopass ./makematrixsample.txt pCHiC_matrix\u00a0>\u00a0pCHiC_matrix.logGenerate a consensus matrix of significant chromatin interactions (CHiCAGO score \u22655) detected in at least one genotype by running makePeakMatrix.R in CHiCAGO package (output file: \u201cpCHiC_matrix.txt\u201d).Prepare the CRE annotation file using chromatin interaction information indicated by pCHiC data.8.Identify specific target genes associated with Cluster-6 CREs which were identified in step 6 by utilizing chromatin interaction information.> Rscript Cluster_CREs_genes_diffexp.R# Annotation input files \u201cDigest.mm10.rmap\u201d and \u201cpCHiC_fragID_Gene.txt\u201d are provided in the KRT, while \u201cpCHiC_matrix.txt\u201d was generated in step 7b. Differential gene expression input file \u201cWT.DM.PC.diffExp.csv\u201d was generated in step 2b.Note: Annotation is achieved by the exploration of pCHiC data (\u201cpCHiC_matrix.txt\u201d from\u00a0step 7b), which include genomic coordinates of gene promoters (as \u201cbait\u201d fragment) and their interacting regions (as other end \u201coe\u201d fragment). Next, by intersecting CREs with\u00a0either \u201cbait\u201d or \u201coe\u201d fragments, the target genes associated with specific CREs can\u00a0be identified. These genes are further analyzed for altered expression by combined mutations (DM leukemia) in comparison to WT (9.http://bioinformatics.sdstate.edu/go/) for gene network or pathway analysis.Select the upregulated genes from previous step (the file \u201cCluster-6_DMvsWT_upgenes.txt\u201d from step 8) to load into web server ShinyGO v0.76  and setting 10 pathways to show. The output plot  which are clearly separated from each other and indicate specific patterns of chromatin dynamics across WT and mutant conditions as shown in adjP \u22640.05 being considered statistically significant. Other statistical computation involves ATAC-seq peak calling , identification of significant chromatin interaction profiled by pCHiC (CHiCAGO score \u22655).Statistical analysis in the protocol is specified in detail in each step if relevant. Statistical calculations for differential gene expression analysis are performed with DESeq2, generating two-tailed and multiple testing corrected P , with The current protocol has been established and validated for the multiomics data analysis on active chromatin modifications  at open chromatin regions. However, repressive chromatin marks  may be not a suitable source data for this approach without any optimization. Instead, consensus peak sets pooled from all profiled repressive chromatin marks can be computed to generate a compendium of repressive chromatin regions using a similar design. Therefore, though not yet tested, this multilayered analytical approach may serve other type of chromatin analysis.Some software and algorithms used in this protocol were installed and tested on their old versions, leaving potential issues on the compatibility and reproducibility when running on the latest versions.We commented on this issue in the Note of the The peak calling for ATAC-seq data using MACS2 was rather simplified and may not reflect the actual size of nucleosome-free regions, although this might be acceptable for downstream integrative analysis.We commented on this issue in step 1c. As our exemplar ATAC-seq data are single-end reads and the fragment size was not determined, we chose to run MACS2 without modeling the fragment length by adding the parameter --nomodel, which, by default, extends all the reads for 200\u00a0bp. Ideally, this may be less problematic when processing paired-end ATAC-seq data.The making of the 2-kb bins at ATAC-seq consensus peak sets across all samples requires a complex processing which is possibly problematic if peak summit is not properly identified (step 4).The key to identify the consensus peaks summit requires the preparation of two lists: 1) a list indicating which sample has maximal ATAC-seq signal at each peak; 2) a list of all peak summits of individual samples. Overlapping these two lists will output the exact peak summit for each consensus peak.Annotation of specific CREs to target genes using chromatin interaction information requires serial steps to process pCHiC data. Improper preparation of relevant annotation files (step 8) may occur and cause problems to identify the right genes.First of all, instead of using the individual interaction profile of each sample, we use the consensus profiles to represent all interactions across all four cellular conditions. Second, we separately intersect the CREs with bait promoter fragments as well as other-end (oe) interaction fragments of pCHiC (being HindIII digestion fragments), and these overlapped oe fragment were further linked to their associated bait promoters using the interaction data.All the exemplar data sets include two biological replicates, to reduce the experiment bias and increase the statistical confidence of the analysis performed with this protocol. However, this protocol did not test data sets either with only a single run or with more replicates of experiment settings, leaving a potential issue on processing variable number of replicates.In the protocol we processed the biological replicates at different stages for different purpose. For example, for differential expression analysis, the replicates were treated separately for statistical calling, whereas for the construction of chromatin interaction matrix, replicates were merged to maximize the power of identifying chromatin interactions. Therefore, the way of dealing with replicate should be individually considered for each experiment stage and analysis purpose.bjph2@cam.ac.uk).Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Dr. Brian J. P. Huntly (This protocol does not require any newly generated materials associated with this protocol."}
+{"text": "Cyclobacteriaceae. It was found in association with a HetDA cyanobiont isolated from a Station ALOHA Trichodesmium colony. Annotation suggests that HetDA_MAG_SS2 is a chemoorganoheterotroph with the potential for lithoheterotrophy, containing genes for aerobic respiration, mixed acid fermentation, dissimilatory nitrate reduction to ammonium, and sulfide oxidation.Here, we describe the metagenome-assembled genome (MAG) HetDA_MAG_SS2, in the family  Trichodesmium colonies were picked from the oligotrophic North Pacific Ocean near Station ALOHA, as detailed by Momper et al. (\u22122\u2009s\u22121)/12-h dark cycle. The enrichment was grown for 5\u2009years prior to sequencing and was transferred to fresh medium every month. A 50-mL subsample was gravity filtered onto a 5.0-\u03bcm polycarbonate filter, and DNA was extracted using the Qiagen DNeasy PowerSoil kit. The DNA was sent to the University of Southern California Epigenome Center and sequenced on an Illumina MiSeq sequencer  using the MiSeq reagent kit v2 with 300 cycles. Reads were trimmed using Trimmomatic v0.38   . The MinNLEN:50) , BinSaniNLEN:50)  using deNLEN:50) , PhyloSaN50 value of 2,360 bp, a GC content of 42.8%, and a coding density of 92.35%. HetDA_MAG_SS2 is a member of the phylum Bacteroidota, falling into the family Cyclobacteriaceae. The closest genome representative was UBA7330 (GenBank assembly accession number GCA_002471275.1), which was only 85.53% similar, based on the average nucleotide identity (ANI) derived from GTDB-tk.The assembled genome of HetDA_MAG_SS2 was identified as novel using GTDB-tk-calculated relative evolutionary divergence (RED) scores  and was cbb3-type cytochrome c oxidase and the cytochrome bd complex, which are necessary for respiration in microaerobic environments (Using KEGG  annotatironments . This finosZ gene (KEGG Orthology code K00376) for nitrous oxide reduction to N2 (\u2013HetDA_MAG_SS2 contains the genes for sulfide oxidation (KEGG Orthology codes K17218 and K17229) and sulfur assimilation , which suggests that it may can use sulfur compounds for both energy and biosynthesis (on to N2 \u201321.In summary, HetDA_MAG_SS2 is a chemoheterotroph with potential roles in both sulfur and nitrogen cycling.PRJNA719568. Raw reads are available in the SRA under accession number SRR14140256. The genome is available under BioSample accession number SAMN18613317.Raw sequences and MAGs are available under BioProject accession number"}
+{"text": "The levels of hsa_circRNA_000121, hsa_circRNA_004183, hsa-miR-4763, hsa-miR-6775, sarcoma gene (SRC), and MMP-14 were detected with real-time polymerase chain reaction. Receiver-operating characteristic (ROC) analyzed the diagnostic value of hsa_circRNA_000121 and hsa_circRNA_004183. Binary logistic regression analysis evaluated the relationship of gene expression with PTMC invasiveness. In PTMC tissue samples, compared with the metastasis group, the expression of hsa_circRNA_000121, hsa_circRNA_004183, SRC, and MMP-14 in the nonmetastasis group decreased, while the expression of hsa-miR-4763 and hsa-miR-6775 increased. In peripheral blood, compared with the metastasis group, the expression of hsa_circ_000121 and hsa_circRNA_004183 in the nonmetastasis group decreased. Both hsa_circRNA_000121 and hsa_circRNA_004183 had good sensitivity and specificity for diagnosing PTMC lymph node metastasis, with a cut-off value of 0.796 and 0.938, respectively. However, the gene expressions were not significantly associated with PTMC lymph node metastasis. Hsa_circRNA_000121 may upregulate SRC expression through hsa-miR-4763, while hsa_circRNA 000121 may upregulate MMP-14 expression through hsa-miR-6775, thereby promoting the aggressiveness of PTMC and ultimately leading to cervical lymph node metastasis. hsa_circRNA_000121 and hsa_circRNA_004183 may become potential biomarkers of PTMC aggressiveness.We detected the expressions of hsa_circRNA_000121 and hsa_circRNA_ 004183 in papillary thyroid microcarcinoma (PTMC) and explored their relationship with the invasiveness of PTMC. PTMC patients with ( More thCircular RNA (circRNA) is a highly conserved noncoding RNA ,11. TheySRC), and MMP-14 in PTMC tissues. The diagnostic value of hsa_circRNA_000121 and hsa_circRNA_004183 was assessed with the ROC analysis. The relationship of these genes with PTMC invasiveness was further evaluated. Our data may help understand the mechanism of PTMC invasiveness and may provide potential biological markers of PTMC invasiveness.Herein, we aim to identify biomarkers related to PTMC invasiveness. PTMC patients without central lymph node metastasis were included in the nonmetastasis group, and PTMC patients with central lymph node metastasis were included in the metastasis group. We collected general clinical data, peripheral blood, and tissue specimens of all enrolled patients. Then, we performed real-time polymerase chain reaction (PCR) to detect expression levels of hsa_circRNA_000121 and hsa_circRNA_004183 in PTMC tissues and peripheral blood. Meanwhile, we detected the levels of hsa_circRNA_000121, hsa-miR-4763, hsa-miR-6775, sarcoma gene  and those without lymph node  metastasis  who were hospitalized in the Thyroid Surgery Department of the First Affiliated Hospital of Xinjiang Medical University from December 2019 to December 2021. Inclusion criteria were as follows: (1) patients with PTMC of lesion diameter less than 1\u2009cm confirmed by surgery and pathology; (2) patients older than 18\u2009years; and (3) patients received surgical treatment for the first time; Exclusion criteria were as follows: (1) patients with a clear history of thyroid disease; (2) patients with a history of thyroid surgery; (3) patients who had taken oral drugs for thyroid therapy before surgery; (4) pregnant women; (5) patients with abnormal liver and kidney function; and (6) patients with other malignant tumors. Peripheral blood was obtained from each patient. PTMC tumor tissues were collected from 15 patients of each group.We included PTMC patients with lymph node  metastasis .2.2SRC, and MMP-14 in PTMC tissues were tested by real-time PCR. The primers were synthesized by Sangon Biotech , and the primer sequences are presented in 2O 7.0\u2009\u03bcL, SYBR Green qPCR Master Mix (2\u00d7) 10.0\u2009\u03bcL, forward primer (10\u2009\u03bcM) 1.0\u2009\u03bcL, reverse primer/unified reverse primers (URP) (10\u2009\u03bcM) 1.0\u2009\u03bcL, and cDNA 1.0\u2009\u03bcL. The PCR amplification conditions were 94\u00b0C for 10\u2009min, and 40 cycles of 94\u00b0C for 20\u2009s, 55\u00b0C for 20\u2009s, and 72\u00b0C for 20\u2009s. The 2\u2212\u25b3\u25b3CT method was used to calculate the relative expression of the genes in each group.Total RNA was extracted from peripheral blood and PTMC tumor tissues, respectively, using Trizol reagent  and then reverse transcribed into cDNA with a reverse transcription kit . The levels of hsa_circRNA_000121 and hsa_circRNA_004183 in the peripheral blood and the expressions of hsa_circRNA_000121, hsa_circRNA_004183, hsa-miR-4763, hsa-miR-6775, 2.3t test was used for comparison between groups. The measurement data with nonnormal distribution are represented by the median and interquartile range and compared with the rank-sum test. Enumeration data are expressed as the absolute number of cases and percentages and were analyzed with \u03c72 test. ROC was used to evaluate the diagnostic value of hsa_circ_000121 and hsa_circRNA_004183. Binary logistic regression analysis was used to evaluate the relationship between the expression levels of hsa_circRNA_000121 and hsa_circRNA_004183 in the peripheral blood and the expression levels of hsa_circRNA_000121, hsa-miR-4763, hsa-miR-6775, SRC, and MMP-14 in PTMC tissues with PTMC invasiveness. P < 0.05 indicates that the difference is statistically significant.All data are analyzed by SPSS20.0 software . The measurement data with normal distribution are expressed as mean \u00b1 SD. Then, 33.1t = 1.528, P = 0.132). The metastasis group included 13 males (43.33%) and 17 females (56.67%). The nonmetastasis group included 6 males (20%) and 24 females (80%). There was no significant difference in the sex ratio between the two groups .In this study, the peripheral blood was obtained from 30 cases of PTMC patients with metastasis (metastasis group) and 30 cases of PTMC patients without metastasis (nonmetastasis group). The average age of patients in the metastasis group was 41.07 \u00b1 11.67\u2009years, and the average age of the nonmetastasis group was 45.10 \u00b1 8.54\u2009years . There were five males (33.33%) and 10 females (66.67%) in the metastasis group and seven males (46.67%) and eight females (53.33%) in the nonmetastasis group .In addition, PTMC tumor tissues were collected from another 15 cases of PTMC patients with metastasis (metastasis group) and 15 cases of PTMC patients without metastasis (nonmetastasis group). For their clinical data, the average age of the metastasis group was 46.40 \u00b1 9.72\u2009years and that of the nonmetastasis group was 46.13 \u00b1 9.74\u2009years (3.2SRC (P < 0.05). However, the expression of hsa-miR-4763  was 1.000, the cut-off value (Youden index) was 0.796, the sensitivity was 100%, and the specificity was 100% . In addi3.4SRC, and MMP-14 in PTMC tumor tissues (Binary logistic regression analysis showed that the expression levels of circRNA-000121 and hsa_circRNA_004183 in the peripheral blood  as well  tissues  were not4circRNAs are rich in miRNA response elements and act as miRNA sponges in cells . They caSRC, was reduced in PTMC, suggesting that miRNA-4763 may play a role in the aggressiveness of PTMC via regulating the expression of SRC.miRNA is a kind of noncoding miRNA with a length of 21\u221223 nt. It can downregulate gene expression by causing mRNA degradation or inhibiting protein translation, thereby participating in regulating the development, proliferation, and differentiation of tumor cells ,29. SomeIt has been  shown thSRC is a member of the SRC family of tyrosine kinases and is closely related to the occurrence and development of tumors. As one of the earliest identified oncogenes, SRC is involved in cell proliferation, adhesion, migration, apoptosis, and angiogenesis, as well as cell invasion and metastasis of malignant tumors [t tumors . By intet tumors ,37. SRC t tumors . SRC is t tumors . In moust tumors . In addit tumors . The comt tumors . Comparet tumors . Currentt tumors . In thisMMP is a structurally zinc-dependent polypeptide endonuclease that hydrolyzes extracellular matrix protein components . The MMPIn addition, we found that expressions of hsa_circRNA_000121 and hsa_circRNA_004183 in the peripheral blood of PTMC patients with lymph node metastasis in the central area of the neck were significantly higher than those of PTMC patients without cervical lymph node metastasis. ROC analysis showed that the cut-off values of hsa_circRNA_000121 and hsa_circRNA_004183 in PTMC patients with cervical lymph node metastasis were 0.796 and 0.938, respectively. Thus, when the expression of hsa_circRNA_000121 in the peripheral blood of PTMC patients is higher than 0.796 or the hsa_circRNA_004183 expression in peripheral blood of PTMC patients is higher than 0.938, it is considered that there may be cervical lymph node metastasis.Binary logistic regression analysis showed that the expressions of circRNA-000121, hsa-miR-6775, hsa-miR-4763, MMP-14, and SRC in PTMC tissues were not significantly related to lymph node metastasis. The expression of hsa_circRNA_000121 and hsa_circRNA_004183 in the peripheral blood was also not significantly related to lymph node metastasis. These results may be related to deficiencies in the experimental design. Due to the nonrigorous diagnostic test in this study, the included patients were not selected at random, resulting in greater selection bias.5In this study, we analyzed the expression of circRNA-000121, hsa-miR-6775, hsa-miR-4763, MMP-14, and SRC in PTMC tumor tissues and compared the levels of hsa_ircRNA_000121 and hsa_circRNA_004183 in the peripheral blood of patients. The results showed that in the peripheral blood, the expression levels of hsa_circ_000121 and hsa_circRNA_004183 in the nonmetastasis group were lower than those in the metastasis group. Besides, the expression of hsa_circ_000121 greater than 0.80 or hsa_circRNA_004183 greater than 0.94 in the peripheral blood suggests that PTMC may be more aggressive. These results imply that hsa_circ_0000121 and hsa_circRNA_004183 might become potential biomarkers of PTMC aggressiveness. However, further experimental validation is needed."}
+{"text": "Klebsiella pneumoniae bacteriophage (vB_KpnM_IME346) was isolated from a hospital sewage sample. This bacteriophage specifically infects a clinical K. pneumoniae strain with a K63 capsular polysaccharide structure. The phage genome was evaluated by next-generation sequencing, which revealed a linear double-stranded DNA genome consisting of 49,482 base pairs with a G+C content of 49.1%. The latent period of vB_KpnM_IME346 was shown to be 20\u00a0min, and the burst size was 25\u201330 pfu (plaque-forming units)/infected cell. Transmission electron microscopy and phylogenetic analysis showed that the JD001-like phage belongs to the genus Jedunavirus of the family Myoviridae. The newly isolated vB_KpnM_IME346 shows infectivity in the clinical host K. pneumoniae KP576 strain, indicating that it is a promising alternative to antibacterial agents for removing K. pneumoniae from patients.A The online version contains supplementary material available at 10.1007/s00284-022-02834-4. Klebsiella pneumoniae is a Gram-negative opportunistic pathogenic bacterium and common cause of hospital- and community-acquired urinary tract infections, sepsis, and pneumonia [K. pneumoniae and complicated with metastatic meningitis and endophthalmitis have emerged worldwide, particularly in Asia [K. pneumoniae isolates to antibacterial agents cause multidrug resistance among the strains [K. pneumoniae infections, alternative treatments are urgently needed because of increasing rates of antibiotic resistance. Infections due to carbapenemase-producing K. pneumoniae (CPKp) have been recognized as an emerging challenge worldwide [K. pneumoniae infections without risking drug-resistant strain development is to exploit the ability of lytic bacteriophages to target pathogenic bacteria.neumonia . In rece in Asia \u20134, and t strains . Althougorldwide . One posPseudomonas aeruginosa demonstrated the effectiveness of topical administration of phage therapy. This was the first clinical trial of phage therapy performed in compliance with both good manufacturing practices and good clinical practices [K. pneumoniae are a polysaccharide capsule (CPS). Bacteriophage-encoded polysaccharide-degrading enzymes are considered as effective tools for controlling bacteria covered with polysaccharide capsules. Majkowska-Skrobek et al. [Bacteriophages, also known as phages, are viruses that specifically recognize their bacterial hosts. Since their discovery 100\u00a0years ago phage research has changed basic biology and medicine. With increases in antibiotic resistance, phage therapy has provided a new perspective for treating infections . Recentlractices . Thus, pk et al.  and VoloK. pneumoniae clinical KP576 strain, a K63 capsular type [In the present study, the previously unidentified bacteriophage vB_KpnM_IME346, which infects the lar type , and itsKlebsiella pneumoniae KP576 strain was isolated from a patient at the Fifth Medical Center of the Chinese General Hospital of Chinese People\u2019s Liberation Army in Beijing, China . We previously identified K. pneumoniae strain KP576 as belonging to the K63 capsular type, as described by Pan [d by Pan . The strg for 10\u00a0min. The supernatant was filtered through a 0.22-\u03bcm microporous membrane. A 3-mL aliquot of the filtrate was mixed with 500 \u03bcL of an exponentially growing K. pneumoniae KP576 LB culture (OD600\u2009=\u20090.6) and 3\u00a0mL 3\u2009\u00d7\u2009LB. This mixture was incubated overnight at 37\u00a0\u00b0C with shaking, followed by filtration through a 0.22-\u03bcm membrane. The double-layer agar procedure of phage isolation was repeated four times.The virulent phage vB_KpnM_IME346 was isolated using KP576 as the indicator strain from sewage collected at the Fifth Medical Center of PLA. The phage was isolated according to previously described procedures  with min7:109 number of phage/number of bacteria ratio), and the phage was allowed to adsorb to the bacterial cells for 1\u00a0min at 37\u00a0\u00b0C. The mixture was washed with LB medium to remove unabsorbed phages and avoid secondary adsorption. The culture was incubated at 37\u00a0\u00b0C with shaking, and samples were collected at 5- and 10-min intervals. The phage titers were then determined using the double-layer agar method. Phage morphology was further visualized and characterized by transmission electron microscopy  at an accelerating voltage of 100\u00a0kV.A one-step growth experiment was performed to determine the lysis curve and phage burst size, as described previously . BrieflyK. pneumoniae (including capsular type K63 (3/12), K47 (4/12), KN3 (1/12), K64 (2/12), and K81 (2/12)) strains were used for host range evaluation. First, the spotting method was used to evaluate the susceptibility of the bacterial strain to the phage and then the efficiency of plating was determined [K. pneumoniae KP576). This experiment was repeated three times.Bacterial strain susceptibility levels were determined as previously described . A totaltermined  by the dg for 10\u00a0min, to remove the debris. Then, the supernatant was mixed with isoamyl alcohol kept at 20\u00a0\u00b0C overnight. The air-dried precipitate was washed three times with cold 75% ethanol, and the phages\u2019 genomic DNA was finally dissolved in TE buffer .The genomic DNA of the phage was extracted using a standard phenol\u2013chloroform protocol . Brieflyhttp://www.rast.nmpdr.org) [http://binf.gmu.edu:8080/CoreGenes3.5/). The DNA sequence of the polymerase-encoding gene of phage vB_KpnM_IME346 and other homologous sequences, obtained from the International Committee on Taxonomy of Viruses , was used to construct a phylogenetic tree using MEGA 6.0 [High-throughput sequencing of the phage genomic DNA was performed using the Illumina MiSeq platform . The whole-genome sequence was assembled using Newbler V3.0 software , and annpdr.org) . Antimicpdr.org) , Comprehpdr.org) , Antibiopdr.org) , and Respdr.org) . Transfepdr.org) . To idenMEGA 6.0  and neigK. pneumoniae KP576 as an indicator strain, a previously unidentified phage was isolated and designated as vB_KpnM_IME346. Electron micrography showed that the phage had a typical icosahedral structure and contractile tail, with a head diameter of approximately 53\u2009\u00b1\u20091\u00a0nm and tail length of approximately 83\u2009\u00b1\u20092\u00a0nm, which are characteristic features of phages into the family Myoviridae , K47 (4/12), KN3 (1/12), K64 (2/12), and K81 (2/12)) strains were used for phage lysis assays to determine the lytic host range of phage vB_KpnM_IME346. Notably, phage vB_KpnM_IME346 lysed 3 K63 strains but had no effect on strains with capsular-type K47, KN3, K64, and K81 . The Phage termini of vB_KpnM_IME346 were identified using our proposed \u201cterminus analysis theory\u201d method, without identification of any fixed termini . TherefoN-acetyltransferase (ORF72), which showed low identity to the corresponding phage sequences in other phages , DNA helicase (ORF66), and DNA polymerase (ORF80), which play important roles in phage replication. The metabolism cassette module of the genome contains five ORFs; BLASTn analysis of these regions revealed putative glycosyltransferase , transketolase protein (ORF57), site-specific DNA-methyltransferase (ORF65), and Klebsiella phage JD001, with an identity of 415/477 (87%) and E-value of 0. The tape measure protein dictates the tail length and facilitates DNA transit to the cell cytoplasm during infection [Lactococcus lactis phages were highly thermoresistant and these results indicate that the tape measure protein contributes to heat stability [Pectobacterium phage PEAT2 (MG432137.1) with an identity of 97% (245/253), which mainly mediates phage head assembly.Among the 80 predicted ORFs in the phage vB_KpnM_IME346 genome, we identified two putative ORFs encoding proteins associated with lysis function, including endolysin (ORF5) and lytic transglycosylase (ORF18), with a lysozyme-like domain involved in the hydrolysis of beta-1,4-linked polysaccharides. Proteins involved in DNA packaging were encoded by genes in the phage vB_KpnM_IME346 genome, including phage large subunit terminase (ORF53) and a phage small subunit terminase (ORF54), showing 478/482 (99%) and 151/152 (99%) identity to vB_KpnM_KpV79 (NC_042041.1) and JD001 (JX866719.1), respectively. These genes code for the subunits of the terminase protein which is involved in packaging the DNA into the head shell , 26. Fivnfection . Interestability . ORF48 eAdditionally, 49 genes of unknown function were identified. Furthermore, no toxin-, virulence factor-, or antibiotic resistance-related genes were found, indicating that vB_KpnM_IME346 is a virulent and potential candidate for phage therapy. The genome of phage vB_KpnM_IME346 contains numerous hypothesis proteins of unknown function; therefore, further comprehensive functional analysis is required to determine the safety of using these phages in therapeutic applications.Klebsiella phage vB_KpnM_KpV52 (NC_041900.1). CoreGenes 3.5 analysis revealed that 55 genes were shared by vB_KpnM_IME346 and vB_KpnM_KpV52, whereas 24 genes were unique to vB_KpnM_IME346. Among the unique genes, the main functional proteins were glycosyltransferase (ORF58), site-specific DNA-methyltransferase (ORF65), N-acetyltransferase (ORF72), and large subunit terminase (ORF53).BLAST alignment (megablast) indicated that the genome of phage vB_KpnM_IME346 showed the greatest nucleotide sequence identity  with the genome sequence of Jedunavirus. The results obtained from the phylogenetic analysis that phage vB_KpnM_IME346 belongs to a JD001-like virus of unclassified genus Jedunavirus in the family Myoviridae Below is the link to the electronic supplementary material."}
+{"text": "Testosterone-related steroid hormones are associated with various types of diseases, including prostate cancer and androgenetic alopecia (AGA). The testosterone or dihydroxy testosterone (DHT) circulates through the blood, binds to the androgen receptor (AR) in the cytoplasm, and finally enters the nucleus to activate downstream target genes. We previously found that immortalized dermal papilla cells (DPCs) lost AR expression, which may be explained by the repeated cell passages of DPCs. To compensate for the AR expression, DPCs that express AR exogenously were established. In this study, we performed an RNA-Seq analysis of the AR-expressing and non-AR-expressing DPCs in the presence or absence of DHT to identify the downstream target genes regulated by AR signalling. Furthermore, we treated DPCs with minoxidil sulphate, which has the potential to treat AGA. This is the first comprehensive analysis to identify the downstream genes involved in testosterone signalling in DPCs. Our manuscript provides high-priority data for the discovery of molecular targets for prostate cancer and AGA.  This metabolic process is predominantly mediated by 5-alpha-reductase (5\u03b1-R)2. DHT has a much stronger affinity for the androgen receptor (AR) than the precursor form of testosterone. Due to the high affinity of DHT, understanding the pathogenesis of prostate cancer or androgenetic alopecia (AGA) may be important. However, the gene networks controlled by testosterone, especially in dermal papilla cells (DPCs) are not fully understood. Interestingly, we accidentally found that the expression of AR is strongly suppressed even in the early passage of human DPCs3. The suppression of AR gene expression under the cultured cell conditions of DPCs has been previously reported in rat-derived DPCs, which is consistent with our finding4. Therefore, our established immortalized DPCs are almost completely negative for AR expression. These situations led us to build the hypothesis that if we exogenously introduce AR via retrovirus, re-constitution of the AR signalling pathway might be possible in immortalized DPCs. We previously carried out the comparison between AR positive or negative DPCs with RNA-Seq, and identified that expression of caveolin-1 is suppressed in AR-expressing DPCs5. However, the downregulated caveolin-1 recovered after the DHT treatment, and this response was observed regardless of whether AR is expressed or not. Based on our previous experience trying to identify the downstream genes of AR, in this study, we set up the experimental design again to cover the gene expression change after DHT stimulation. We set up the AR negative and positive cells of DPCs, and no treated and treated cells with DHT. Furthermore, we classified AR downstream genes which are commonly upregulated or downregulated in AR-expressing cells and DHT-treated cells with AR expression. We then narrowed down the AR downstream genes that were not affected after DHT treatment under the absence of AR expression. Our high quality data would help scientists study the effect of AR signaling.The metabolic form of testosterone, dihydroxy testosterone (DHT) has greater androgenic activity than the parental form of androgen3. Based on the characteristics of the introduced genes , we named this immortalized cell line HFDPC_K4DT. Furthermore, we introduced AR-expressing retrovirus into immortalized DPCs as described in our previous papers5.We obtained human primary DPCs from Promocell  through the local distributor, Takara Bio . The primary cells were maintained in a Follicle Dermal Papilla Cell Growth Medium (Promocell) as per the manufacturer\u2019s instructions. Cells immortalized by the expression of R24C-mutant CDK4, cyclin D1, and TERT have been described in our previous paperWe set up 15 experimental groups to cover all aspects of the biological response to AR signalling activation. Each experimental group contains triplicate samples to ensure reproducibility.Sample 1, HFDPC_K4DT_rep1, K4DT, intact immortalized DPCs.Sample 2, HFDPC_K4DT_rep2, K4DT, intact immortalized DPCs.Sample 3, HFDPC_K4DT_rep3, K4DT, intact immortalized DPCs.Sample 4, HFDPC_K4DT_DHT_rep1, DHT, immortalized DPCs treated with 50\u2009nM dihydroxy testosterone (DHT). No AR expression.Sample 5, HFDPC_K4DT_DHT_rep2, DHT, immortalized DPCs treated with 50\u2009nM dihydroxy testosterone (DHT). No AR expression.Sample 6, HFDPC_K4DT_DHT_rep3, DHT, immortalized DPCs treated with 50\u2009nM dihydroxy testosterone (DHT). No AR expression.Sample 7, HFDPC_K4DT_AR_rep1, AR, immortalized DPCs with retroviral AR expression, No ligand treatment.Sample 8, HFDPC_K4DT_AR_rep2, AR, immortalized DPCs with retroviral AR expression, No ligand treatment.Sample 9, HFDPC_K4DT_AR_rep3, AR, immortalized DPCs with retroviral AR expression, No ligand treatment.Sample 10, HFDPC_K4DT_AR_DHT_rep1, ARDHT, immortalized DPCs with retroviral AR expression, treated with 50\u2009nM DHT.Sample 11, HFDPC_K4DT_AR_DHT_rep2, ARDHT, immortalized DPCs with retroviral AR expression, treated with 50\u2009nM of DHT.Sample 12, HFDPC_K4DT_AR_DHT_rep3, ARDHT, immortalized DPCs with retroviral AR expression, treated with 50\u2009nM of DHT.Sample 13, HFDPC_K4DT_AR_DHT_mino_rep1, mino, immortalized DPCs with retroviral AR expression, treated with 50\u2009nM of DHT and 30\u2009nM of Minoxidil sulphate (MXS)Sample 14, HFDPC_K4DT_AR_DHT_mino_rep2, mino, immortalized DPCs with retroviral AR expression, treated with 50\u2009nM of DHT and 30\u2009nM of MXSSample 15, HFDPC_K4DT_AR_DHT_mino_rep3, mino, immortalized DPCs with retroviral AR expression, treated with 50\u2009nM of DHT and 30\u2009nM of MXSThe cells were treated with DHT or MXS for 24\u2009h at approximately 70% confluence in 35\u2009mm diameter cell culture dish. The total cellular RNA was extracted using the Nucleopsin RNA kit (Takara Bio), according to the manufacturer\u2019s instructions. DNase I treatment was performed with the enzyme included in the RNA extraction kit. The quality of Total RNA was assessed using tapestation, and cDNA libraries were prepared using the NEBNext Ultra II Directional RNA Library Prep Kit for Illumina (New England BioLabs). We confirmed that the RIN value of extracted RNA from all samples was 10.0.6. The low-quality reads were removed with the FASTP program7. The filtered reads were mapped to the NCBI reference human genome sequence (CRCh38) using the STAR program8. The mapped data were processed into the expression count data using featureCounts9 included in the Subread package. To extract differentially expressed genes (DE genes), we first performed a pairwise comparison with iDep10. The DE genes were detected with DESeq211 with FDR cut off of 0.050 and a minimum fold change of 3. We also processed the data using TCC-GUI12 for Principal component analysis (PCA), heatmap analysis, and bar plot analysis  was used to obtain pair-end 150\u2009bp sequencing of poly-A tailed RNA. All sequencing reads were processed with PEAT to remove the adaptor sequencesis Fig.\u00a0.Fig. 1Th13. The number of sample reads is summarised in Fig.\u00a014. The mapping ratio to the reference genome was approximately 95 to 96.3%, which is sufficient for quantifying gene expression15. We compared the expression pattern to the correlation matrix, as shown in Fig.\u00a016. Furthermore, the difference in expression profiling between Samples 10\u201312: HFDPC-K4DT with AR expression, treated with 50\u2009nM DHT and Samples 13\u201315: HFDPC-K4DT with AR expression, treated with 50\u2009nM DHT and 30\u2009nM MXS was also very limited, indicating that MXS does not significantly influence the signal activation of AR.The quality score of reads is shown in Figures\u00a0We performed a pairwise comparison of Samples 1\u20133: HFDPC-K4DT and Samples 7\u20139: HFDPC-K4DT with AR expression with no DHT treatment with Dseq2 using iDep. As shown in the left panel of Fig.\u00a017, experimental groups were primarily classified into two clusters: cell cluster with DHT treatment and cell cluster without DHT treatment (Fig.\u00a0We performed PCA on gene expression profiling. Due to software limitation, the top 25,000 genes were the maximum number of genes involved in the analysis. As demonstrated by the results of 3D-PCA in Fig.\u00a0As shown in Fig.\u00a0Furthermore, we analysed the 130 upregulated genes. As shown in Fig.\u00a0The bar plots of the downregulated 27 candidates are listed in Figures\u00a0Non-coding RNAs such as lncRNA was reported to control the transcriptional activity of promoter region and gene expression. To elucidate the differentially expressed lncRNA in our study, we identified differentially expressed lnc RNAs. As shown in Fig.\u00a018. The RIN value of all the RNA was 10.0, indicating that the sample we obtained was in near-perfect condition.The RNA quality data was submitted to FigshareTo evaluate the reproducibility of the data, triple biological replicates were established. The setup of experimental groups is described in the method section. We established AR negative cells and positive cells. Similarly, we established DHT ligand-treated cells and untreated cells. In addition to these groups, immortalized DPCs without AR expression and treated by DHT ligands were established. The upregulated or downregulated genes in these groups suggest that detected gene expression change is mediated by a pathway other than the AR pathway.Fig_S1_S24"}
+{"text": "Manihot esculenta) is an important food crop with high production of starch in storage roots. Little was however known about cassava GATA domain-containing genes (MeGATAs). Thirty-six MeGATAs, MeGATA1 to MeGATA36, were found in this study. Some MeGATAs showed a collinear relationship with orthologous genes of Arabidopsis, poplar and potato, rice, maize and sorghum. Eight MeGATA-encoded proteins (MeGATAs) analysed were all localized in the nucleus. Some MeGATAs had potentials of binding ligands and/or enzyme activity. One pair of tandem-duplicated MeGATA17\u2013MeGATA18 and 30 pairs of whole genome-duplicated MeGATAs were found. Fourteen MeGATAs showed low or no expression in the tissues. Nine analysed MeGATAs showed expression responses to abiotic stresses and exogenous phytohormones. Three groups of MeGATA protein interactions were found. Fifty-three miRNAs which can target 18 MeGATAs were identified. Eight MeGATAs were found to target other 292 cassava genes, which were directed to radial pattern formation and phyllome development by gene ontology enrichment, and autophagy by Kyoto Encyclopaedia of Genes and Genomes enrichment. These data suggest that MeGATAs are functional generalists in interactions between cassava growth and development, abiotic stresses and starch metabolism.The proteins with DNA-binding preference to the consensus DNA sequence (A/T) GATA (A/G) belong to a GATA transcription factor family, with a wide array of biological processes in plants. Cassava ( MeGATAsplay multiple roles in growth and development, abiotic stresses and starch metabolism of cassava.Cassava  Populus trichocarpa  GATA (A/G) belong to GATA transcription factors (TFs), have evolutionarily blossomed into a GATA family , and havchocarpa . In planchocarpa . In fact signals . The exp signals . The C-td barley . In pland barley .Manihot esculenta) is an important food crop with high production of starch in storage roots in Africa, Asia, Latin America and the Caribbean  by using the hidden Markov model (HMM) sequence (PF00320) of the GATA family proteins and the HMMER3 tool under 1E-value of <0.01 (Arabidopsis GATA proteins and rice GATA proteins from the uniprot database (https://www.uniprot.org/) under 1E-value of <0.01. Finally, candidate GATA proteins of cassava were further confirmed by using the CDD tool under a threshold value of 0.01 and maximum hits of 500 (https://pfam.xfam.org/).The first step to identify putative GATA motif-containing proteins of cassava was to search the protein data set of cassava in v9.0 phytozome database (of <0.01 . In ordehttp://www.expasy.org/tools/). Multiple homology alignment of the proteins was conducted by using the Clustal X 2.0 tool (www.megasoftware.net) under 1000 bootstrap replications. The subcellular localization of the proteins was predicted by using the online cello tool (http://www.ebi.ac.uk/Tools/pfa/iprscan/)  under built-in default values . The motif sequences of MeGATAs were identified by using the MEME tools (https://meme-suite.org/meme/tools/meme) and annotated through the InterProScan database (http://www.ebi.ac.uk/Tools/pfa/iprscan/).The MeGATAs was conducted by using the Circos tool . The non-synonymous substitution rate (Ka) and synonymous substitution rate (Ks) of MeGATAs in the gene collinearity were calculated by using the ParaAT tool .The candidate promoter region was assumed to be localized in 1500-bp genomic DNA segments upstream of the start codons of https://string-db.org/). Briefly, in the \u2018Search\u2019 window, the \u2018Multiple proteins\u2019 followed by the ID number of MeGATAs such as cassava 4.1_033370m were selected. In the \u2018Basic Setting\u2019 window, the \u2018Network type\u2019 selected was \u2018full STRING network\u2019, and all items under \u2018active interaction sources\u2019 were selected. A low confidence (0.15) was used as the \u2018minimum required interaction score\u2019.The protein interactions were based on the Search Tool for the Retrieval of Interacting Genes (http://plantregmap.gao-lab.org/network.php) , FunTFBS (TF binding sites) method, TF (retrieve targets) mode, and cassava MeGATA ID. The downstream target genes by MeGATAs were identified and retrieved when the correlation test was significant (P \u20ac\u2266 0.05) with a correlation score higher than 0.5.In this study, this tool was operated under the specified inputs of species P. adjust value of <0.05. The KEGG analysis was conducted to analyse potential metabolic pathways of the related genes at a P-value of <0.05 in the online Omicshare tool (https://www.omicshare.com/tools/Home/Soft/getsoft).The GO analysis was conducted to analyse potential functions of the related genes at a MeGATA regulatory networks were predicted according to the previous methods described by MeGATA-targeting miRNAs were predicted with MeGATAs\u2019 coding sequences (CDS) by using the psRNATarget server (http://plantgrn.noble.org/psRNATarget/home) under default parameters except that a maximum expectation was 5.0. The miRNA-targeted sites were those highly complementary to MeGATAs\u2019 CDSs. The interaction networks were created by using the Cytoscape V3.8.2 software (https://cytoscape.org/download.html).The (miRNA)\u2013MeGATAs was based on the transcriptome data sets in the RNA-seq read archives of cassava , China]. Then, the synthesized cDNA product was diluted 10 times with RNA-free water for further use.The total RNA was isolated from 100 mg of cassava leaves by using the OmniPlant RNA Kit (DNase I) . For isolated RNA, the quality was controlled through agarose gel electrophoresis and by using the NanoDrop 2000 , and the concentration was determined by using the NanoDrop 2000. The first-strand cDNA synthesis was conducted with 1 \u03bcg of quality-controlled RNA by using the PrimeScriptMeGATAs was synthesized by PCR with the first-strand cDNA as template and sequence-specific primers [see] by using the 2\u00d7 PrimeSTAR Max Premix kit . In brief, DNA of CDS (without stop code) of MeGATAs was ligated into plasmid pCambia2300-35S-eGFP collected by our laboratory to generate pCambia2300-35S-MeGATAs-GFP. The primers used for construction were listed in MeGATAs-GFP and nuclear localization marker plasmid pA7-Ghd7-mCherry  field-grown for 180 d were planted into pots containing perlite which was saturated with water before potting and then grew for 42 d in the growth chamber with 16-h light/8-h dark and 55% air humidity. The cassava plantlets were then treated by high temperature at 42 \u00b0C while other plantlets were cultured under normal temperature at 27 \u00b0C as the control. The cassava plantlets were treated by natural drought without watering while other plantlets were normally irrigated with tap water at 27 \u00b0C as the control. The plants were with 200 mM NaCl while other plantlets were normally irrigated with tap water at 27 \u00b0C as the control. The roots of cassava plantlets were soaked in 100 \u00b5M abscisic acid (ABA), 100 \u00b5M indole 3-acetic acid (IAA) and 100 \u00b5M salicylic acid (SA), respectively, while the leaves were sprayed with 100 \u00b5M of ABA, IAA or SA. The plantlets concurrently soaked in and sprayed with tap water at 27 \u00b0C were used as corresponding controls, respectively.[see] by using the ChamQ\u2122 Universal SYBR qPCR Master Mix kit . The internal control gene was Cassava4.1_006776 (https://www.ncbi.nlm.nih.gov/tools/primer-blast/index.cgi?LINK_LOC=BlastHome) under the default parameters. The relative expression level of MeGATAs between treatment and control was calculated following the 2\u2212\u0394\u0394CT method (MeGATA \u2013 CtCassava4.1_006776) under treatment] \u2013 [(CtMeGATA \u2013 CtCassava4.1_006776) under control]. The three biological replicates for each gene were conducted with leaves of three individual plants. Differential expression of the genes between the treatments was defined at a significance level of P < 0.05 based on Duncan\u2019s multiple range test.The RT-qPCR was performed by using the StepOne\u2122 Real-Time PCR System  and conducted in a 20-\u00b5L reaction system containing the 10 fold diluted first-strand cDNA solution and sequence-specific primers T method , where \u0394P < 0.05.The statistical package for SPSS 18 program was used for statistical analysis. One-way ANOVA was performed to evaluate significant differences between data at [see]. Some MeGATAs were predicted to have potential ligand binding or enzyme activity, or both  had a collinearity only with orthologous genes of monocots  and phyllome development (GO:0048827). The KEGG enrichment-based analysis indicated that they were directed to the path of autophagy (KO04136).Although all 36 MeGATAs were analysed, only six MeGATAs  were found to target and potentially regulate the expression of the other 292 cassava genes MeGATAs , group 2 involving six MeGATAs , group 4 involving MeGATA15 and MeGATA36, group 3 involving MeGATA29, group 5 involving MeGATA25, and group 6 involving MeGATA26.A total of 53 putative miRNAs were identified to have the potential for targeting and regulating 18 networks : group 1The GFP\u2013MeGATAs fusion expression indicated that eight MeGATAs  that were randomly selected were localized in the nucleus , being cMeGATAs  were expressed at low levels or not expressed, not only in roots, stems and leaves of Arg7 and W14 [see] but also in roots of both Arg7 and KU50 at different developmental stages [see].As a whole, 14 MeGATAs  that were randomly selected was analysed under treatments involving stress and exogenous hormones. The results showed that their expression was responsive to drought [see], salt [see], high temperature [see], ABA [see], IAA [see] and SA [see], roughly echoing the existence of the cis-acting elements in the promoter [see]. At first glance, the expression peaked in the middle and late stage of treatments although early responses were found in the few MeGATAs [see\u2013].The expression of nine MeGATAs) were found in cassava, which could be divided into four evolutionary groups . These results strongly suggest again that GATA gene-based mechanisms of growth, development and stress tolerance likely vary with plant species  [see]. No definite salt-responsive elements were found in the MeGATAs [see], and three MeGATAs  were always expressed at low levels, and another six MeGATAs were highly expressed under salt [see]. Three MeGATA, , were highly expressed, and the other six genes were expressed at relatively low levels under high temperature [see]; however, only MeGATA3 had a low temperature responsiveness (LTR) element [see]. MeGATA6 had three elements responding to hormones of ABA, MeJA and ethylene [see], but, its expression was always low under treatment with ABA [see], IAA [see], and SA [see]. Most of the MeGATAs that were expressed at low levels or not expressed in tissues and organs under normal conditions [see] had the characteristics of high expression under stress  with low expression or without expression [see] happened to be miRNA targets (MeGATA12 in group 1 was predicted to be targeted by mes-miR319h ([see] hints that the expression of mes-miR319h may be repressed by other genes in group 1 (MeGATAs in evolution.It has been demonstrated that non-coding RNAs (ncRNAs) including miRNAs and long ncRNAs are important transcriptional regulators . Long ncMeGATAs  are like 2 and 4  might ha targets , suggest-miR319h , the hig group 1 . Anyway,MeGATAs also showed either whole genome duplication or tandem duplication [see]. These results indicate that duplications are likely one of the driving forces for the formation of new gene members or novel functions of the GATA gene family depending on plant species. The collinearity of some MeGATAs with GATA genes in other plants , or acquire new functions  or maintain ancestral functions  during evolution . Like otunctions .Polypeptide ligands and their cognate receptors have co-evolved . The proMeGATA7 and MeGATA24, also showed strong responses to abiotic stresses [see\u2013] and exogenous hormones [see\u2013]. Coincidentally, three corresponding genes, MeGATA7, MeGATA16 and MeGATA24, showed very low or no expression in cassava roots in the absence of stress [see]. These results together indicate that MeGATA7, MeGATA16 and MeGATA24 are the linkers in the interactions between starch degradation, stress tolerance and hormones in cassava roots.TFs can participate in metabolic activities either by indirectly regulating the activities of enzymes such as MYBs  or throuArabidopsis AtSDP1, has emerged as a key enzyme in lipid turnover in higher plants .Table S2. Primers used in cloning of MeGATAs.Table S3. Primers used in construction of gene fusion expression vector.Table S4. Primers used in RT-qPCR analysis of MeGATA expression.Table S5. The amino acid sequences of conserved motifs of MeGATAs.Table S6. The potential cis-acting elements in the promoter region of MeGATAs.Table S7. The ID number of GATA genes in Arabidopsis and rice.Table S8. The duplication events of MeGATAs.Table S9. The GATA genes in collinearity.Table S10. The cassava genes potentially targeted by MeGATAs.Table S11. Information on miRNAs and miRNAs-targeted MeGATAs.Figure S1. Expression profiles of MeGATAs in roots, stems and leaves (A) and in roots at different developmental stages (B) of cassava.Figure S2. Expression profiles of MeGATAs in the leaves of cassava SC124 under drought.Figure S3. Expression profiles of MeGATAs in the leaves of cassava SC124 under salt.Figure S4. Expression profiles of MeGATAs in the leaves of cassava SC124 under high temperature of 42 \u00b0C.Figure S5. Expression profiles of MeGATAs in the leaves of cassava SC124 under exogenous ABA.Figure S6. Expression profiles of MeGATAs in the leaves of cassava SC124 under exogenous IAA.Figure S7. Expression profiles of MeGATAs in the leaves of cassava SC124 under exogenous SA.plac057_suppl_Supplementary_Figure_S1Click here for additional data file.plac057_suppl_Supplementary_Figure_S2Click here for additional data file.plac057_suppl_Supplementary_Figure_S3Click here for additional data file.plac057_suppl_Supplementary_Figure_S4Click here for additional data file.plac057_suppl_Supplementary_Figure_S5Click here for additional data file.plac057_suppl_Supplementary_Figure_S6Click here for additional data file.plac057_suppl_Supplementary_Figure_S7Click here for additional data file.plac057_suppl_Supplementary_Table_S1Click here for additional data file.plac057_suppl_Supplementary_Table_S2Click here for additional data file.plac057_suppl_Supplementary_Table_S3Click here for additional data file.plac057_suppl_Supplementary_Table_S4Click here for additional data file.plac057_suppl_Supplementary_Table_S5Click here for additional data file.plac057_suppl_Supplementary_Table_S6Click here for additional data file.plac057_suppl_Supplementary_Table_S7Click here for additional data file.plac057_suppl_Supplementary_Table_S8Click here for additional data file.plac057_suppl_Supplementary_Table_S9Click here for additional data file.plac057_suppl_Supplementary_Table_S10Click here for additional data file.plac057_suppl_Supplementary_Table_S11Click here for additional data file."}
+{"text": "This study investigates the mechanism of hsa_circ_0001429 adsorbing miR-205 and regulating the expression of KDM4A to promote breast cancer metastasis and its mechanism. Mammary epithelial cells MCF-10A and human breast cancer cell lines BT474, SKBr-3, ZR-75-30, and MCF7 are cultured, and the mRNA expressions of hsa_circ_000 1429, miR-205, and KDM4A are detected by qRT-PCR; hsa_circ_000 1429 binds to miR-205, and miR-205 targets KDM4A. RIP verifies that hsa_circ_000 1429 binds to AGO2; RNA pull down results prove that hsa_circ_000 1429 binds to miR-205; MTT detects cell proliferation; transwell assay detects cell migration and invasion ability; flow cytometry detects cell apoptosis rate. The expressions of KDM4A, migration, and invasion-related factors, N-cadherin and MMP-9 protein, are detected by blot. hsa_circ_000 1429 may upregulate the KDM4A gene by adsorbing miR-205. Therefore, it will promote the proliferation, migration, and invasion of breast cancer cells and inhibit their apoptosis. Breast cancer is one of the most common malignant tumors in women in my country, and its morbidity and mortality are on the rise \u20134. In reA biological prediction website is used to analyze the binding sites of miR-205, and it is verified that miR-205 can bind hsa_circ_000 1429 and KDM4A. The artificially synthesized hsa_circ_0001429 and KDM4A 3'UTR gene fragments are constructed into pMIR-REPORT. The complementary sequence mutation site of the seed sequence is designed and constructed into the pMIR-REPORT reporter plasmid. The correctly sequenced luciferase reporter plasmids WT and MUT are cotransfected with miR-205 into SKBr-3 cells . 48\u2009h after transfection, the luciferase activity is detected using the dual-luciferase reporter assay system .3 cells per well are seeded into 96-well plates and cultured in an incubator. At 24\u2009h, 48\u2009h, and 72\u2009h, 20\u2009\u03bcL of 5\u2009mg/mL MTT solution is added to each well, and the culture is terminated after incubation at 37\u00b0C for 2\u2009h. The culture supernatant in the wells is aspirated and discarded, and 150\u2009\u03bcL of DMSO is added to each well. The absorbance value of each well is read at 570\u2009nm  value.Later, SKBr-3 cells in each group are transfected and cultured for 48 hours. The cells in each group are collected and counted as follows: 5\u2009\u00d7\u200910The remainder of this study is organized as follows. \u03bcL of magnetic beads are ished and resuspended in 100\u2009\u03bcL of RIP Ish buffer, and 5\u2009\u03bcg of antibody AGO2  and IgG  is incubated. Magnetic bead-antibody complexes are ished and resuspended in 900\u2009\u03bcL RIP Ish buffer; add 100\u2009\u03bcL of cell extract and incubate overnight at 4\u00b0C. The samples are digested with proteinase K to extract RNA, and the expression level of hsa_circ_000 1429 is detected by qRT-PCR.The breast cancer cells are collected and lysed, and the cell extract is incubated with the antibody for coprecipitation. 50\u2009WT biotinylated and MUT biotinylated miR-205 breast cancer cells are transfected, cell lysates  are lysed, and the lysates are mixed with precoated RNase-free, and yeast tRNA M-280 streptavidin magnetic beads  are incubated at 4\u00b0C for 3\u2009h, ished twice with cold lysis buffer, and ished with low-salt buffer and high-salt buffer, respectively. After ishing 3 times and 1 time, the bound RNA is purified by TRIzol, and the expression level of hsa_circ_000 1429 is detected by qRT-PCR.Cells are collected and lysed with RIPA lysis buffer , and then, the protein concentration is determined with the BCA protein quantification kit . Proteins are dissolved in 2\u00d7 SDS, separated by 10% SDS-PAGE, transferred to PVDF membrane, blocked with 5% nonfat milk powder at room temperature for 1\u2009h, and ished with PBS for 2\u2009min; the PVDF membrane is mixed with diluted primary antibodies KDM4A , N-cadherin , MMP-9 , GAPDH  overnight at 4\u00b0C, ished 3 times with TBST for 5\u2009min each, and incubated with 1\u2009:\u2009100 diluted HRP-labeled secondary antibody goat anti-mouse IgG antibody  for 1\u2009h. It takes an ECL fluorescence detection kit  with equal amount of solution A and solution B, mixes them in a dark room, drops them on the membrane, and puts them into a gel imager for exposure imaging. Photographs are taken with a BioRad image analysis system . GAPDH is used as an internal reference, and quantity is used as an internal reference.Total RNA is extracted using the TRIzol kit , according to the PrimeScript RT reagent kit . It is reverse-transcribed from total RNA into cDNA according to the instructions, and the Fast SYBR Green PCR kit  and ABI PRISM 7300 RT-PCR system  were used for qRT-PCR detection. Each experiment is repeated 3 times. Primer designs are given in t-test is used for comparison between two groups. One-way ANOVA is used to compare data among multiple groups. According to Tukey's post hoc test, P < 0.05 indicates that the difference is statistically significant.The SPSS version 21.0  is used for statistical analysis. Measurement data are expressed as mean\u2009\u00b1\u2009standard error P < 0.05). It is selected as the cell verified by subsequent cytological experiments. The protein expression of KDM4A is detected by Western blot. Compared with mammary epithelial cells MCF-10A, hsa_circ_000 1429, KDM4A mRNA, and protein expressions are all elevated in human breast cancer cell lines BT474, SKBr-3, ZR-75-30, MCF7, and miR-205. The expression of hsa_circ_000 1429 in SKBr-3 is the highest (P < 0.05), while the luciferase activity of hsa_circ_000 1429 mutant and KDM4A mutant has no significant change (P > 0.05), indicating that miR-205 could bind to hsa_circ_000 1429 and KDM4A, respectively. Compared with the MUT-miR-205 and bio-NC groups, the expression of hsa_circ_000 1429 bound by WT-miR-205 is significantly increased (P < 0.05), indicating that miR-205 can be directly combined with hsa_circ_000 1429. In SKBr-3 cells, anti-AGO2 antibody could precipitate hsa_circ_000 1429, indicating that hsa_circ_000 1429 could form a complex with AGO2, thereby competitively binding to miR-205. The above results indicated that hsa_circ_000 1429 may regulate the expression of KDM4A gene by adsorbing miR-205 in breast cancer, as shown in Compared with the NC mimic group, the luciferase activity of hsa_circ_000 1429 wild type and KDM4A wild type in the miR-205 mimic group specifically bound to miR-205. Both are inhibited (P < 0.05). Compared with mimics NC, the proliferation ability of breast cancer cells SKBr-3 in the miR-205 mimics group is significantly inhibited at 48\u2009h and 72\u2009h (both P < 0.05). The proliferation ability of breast cancer cells in the SKBr-3 inhibitor group and the shRNA-hsa_circ_000 1429\u2009+\u2009pcDNA-KDM4A group is significantly increased (both P < 0.05). These results indicate that silencing hsa_circ_000 1429 or overexpressing miR-205 inhibited breast cancer cell proliferation. In breast cancer cells SKBr-3, hsa_circ_000 1429 is silenced or miR-205 is overexpressed or miR-205 is silenced or KDM4A gene is overexpressed in hsa_circ_000 1429-silenced cells. Therefore, the study finds that hsa_circ_000 1429 may be activated by adsorption of miR-205, which regulates the effect of KDM4A on the biological activity of breast cancer cells. The proliferation of cells in each group is detected by MTT. Compared with the shRNA NC group, the proliferation ability of SKBr-3 breast cancer cells in the shRNA-hsa_circ_000 1429 group decreased at 48\u2009h and 72\u2009h (P < 0.05). Compared with the shRNA-hsa_circ_000 1429 group, the migration and invasion numbers of breast cancer cells in the SKBr-3 inhibitor group and the shRNA-hsa_circ_000 1429\u2009+\u2009pcDNA-KDM4A group significantly increased (P < 0.05). These results indicate that silencing hsa_circ_000 1429 or overexpressing miR-205 inhibited breast cancer cell migration and invasion. The transwell assay to detect cell migration and invasion ability is shown in Transwell assay is used to detect the migration and invasion ability of cells in each group. Compared with the shRNA NC group, the migration and invasion numbers of SKBr-3 breast cancer cells in the shRNA-hsa_circ_000 1429 group decreased (P < 0.05). Compared with the shRNA-hsa_circ_000 1429 group, the apoptosis rate of SKBr-3 breast cancer cells in the inhibitor group and shRNA-hsa_circ_000 1429\u2009+\u2009pcDNA-KDM4A group significantly decreased. These results suggest that silencing hsa_circ_000 1429 or overexpressing miR-205 promotes breast cancer cell apoptosis. Flow cytometry is used to detect cell apoptosis in each group. Compared with the shRNA NC group, the apoptotic rate of SKBr-3 breast cancer cells in the shRNA-hsa_circ_000 1429 group increased . Compared to miR-205, there is no significant change in the expression of hsa_circ_000 1429 in breast cancer cells in the mimics group (P > 0.05). The expression of miR-205 significantly increases, the mRNA and protein expressions of KDM4A significantly decrease, and the protein expressions of N-cadherin and MMP-9 significantly increase . Compared with the shRNA-hsa_circ_000 1429 group, the expression of hsa_circ_000 1429 in the breast cancer cells of the shRNA-hsa_circ_000 1429\u2009+\u2009miR-205 inhibitors group has no significant change (P > 0.05). The expression of miR-205 is significantly decreased. There is no significant difference between miR-205 and miR-205 (both) (P > 0.05), KDM4A mRNA and protein expressions significantly increased, and N-cadherin and MMP-9 protein expressions significantly decreased (P < 0.05). The above results indicate that hsa_circ_000 1429 negatively regulates the KDM4A gene by adsorbing miR-205, thereby promoting the migration and invasion of breast cancer cells. The mRNA and protein expressions of hsa_circ_000 1429 and KDM4A in breast cancer cells of the hsa_circ_000 1429 group significantly decreased. The expression of miR-205 and the protein expressions of migration and invasion-related factors N-cadherin and MMP-9 significantly increased (all Breast cancer is a malignant tumor that occurs in the mammary epithelium or ductal epithelium, and its incidence is relatively high. Improvement of diagnosis and treatment methods can significantly reduce the mortality rate of the disease. Elucidating the pathogenesis of breast cancer has a very critical role in the treatment of breast cancer. More and more studies have shown that abundant circRNAs in mammals are closely related to neurological diseases, cardiovascular diseases, orthopedic diseases, and various cancers. Unlike linear RNAs, circRNAs form a closed continuous loop structure without 5'-3' polar or polyadenylated ends. CircRNAs are widely expressed in human cells and play an important role in the regulation of gene expression at the posttranscriptional level. There is only one literature showing that hsa_circ_000 1429 can promote breast cancer proliferation, migration, and invasion. However, there is no research report on the regulatory mechanism of hsa_circ_000 1429 in breast cancer. In this study, we first detect the expression of hsa_circ_000 1429 in normal mammary epithelial cells and breast cancer cell lines by culturing mammary epithelial cells MCF-10A and human breast cancer cell lines BT474, SKBr-3, ZR-75-30, and MCF7. The results show that the expression of hsa_circ_000 1429 is upregulated in breast cancer cells. hsa_circ_000 1429 can promote breast cancer cell proliferation, migration, invasion, and cell apoptosis.CircRNAs play a key regulatory role in cancer, and most studies have shown that circRNAs have tissue and developmental stage-specific expression and act as microRNA-sponge RNAs to sequester microRNAs, thereby affecting the stability of target mRNAs and dynamically regulating mRNA translation. The paper passed the Circular RNA Interactome screening and revealed a binding site between hsa_circ_000 1429 and miR-205. The regulatory role of miR-205 in cancer is not clear. Some studies have shown that miR-205 plays a tumor suppressor role in tumors, inhibiting cancer proliferation, migration, and invasion. However, other studies have shown that miR-205 plays a role in promoting cancer. The study also finds that hsa_circ_000 1429 could act as a sponge RNA to competitively bind to miR-205. miR-205 overexpression inhibited the proliferation, migration, and invasion of breast cancer cells and promoted apoptosis. Our study confirms that miR-205 silencing can block the promotion of hsa_circ_000 1429 on breast cancer cell proliferation, migration, and invasion. It suggests that miR-205 may act as a tumor suppressor in breast cancer.In order to further explore the regulatory mechanism of miR-205 in breast cancer, the study screens the possible target genes downstream of miR-205 through the bioinformatics website. The study finds that there is a binding site between KDM4A and miR-205, and the dual-luciferase reporter assay also confirms the targeted regulatory relationship between miR-205 and KDM4A. On further study, as a member of the JmjC family, KDM4A is found to be highly expressed in many types of cancers.It has been shown that KDM4A involved in important biological processes such as gene transcription, cell cycle regulation, cellular senescence, DNA damage repair, and chromatin remodeling. Our study also shows that KDM4A overexpression blocked the promotion of hsa_circ_000 1429 on breast cancer cell proliferation, migration, and invasion.In summary, the study finds that hsa_circ_000 1429 is upregulated in breast cancer cells and could upregulate the expression of miR-205 target gene KDM4A by adsorbing miR-205, which will promote breast cancer cell proliferation, migration, and invasion and promote breast cancer cell proliferation, migration, and invasion. This study explores the regulatory mechanism of hsa_circ_000 1429 in breast cancer, further improves the pathogenesis of breast cancer, provides a new research direction for exploring the pathogenesis of breast cancer, and provides new theoretical guidance for clinical treatment. However, the study does not conduct animal experiments yet and cannot further confirm the regulatory mechanism of hsa_circ_000 1429 in vivo. More experiments are needed to explore whether it is suitable for clinical treatment."}
+{"text": "As a common complication of epithelial ovarian cancer (EOC), malignant ascites contributes to the peritoneal metastasis of EOC. CircRNAs play essential roles in tumor metastasis. However, no circRNAs have been reported to be involved in EOC peritoneal metastasis via ascites.Total of 22 samples from 9 EOC patients containing primary lesions (T), tumor cells from ascites (ASC), and metastatic lesions (M) were included for RNA sequencing to identify differentially expressed circRNAs and mRNAs among different tumors. Bioinformatic analyses, including single-sample Gene Set Enrichment Analysis and soft cluster analysis, were performed to find circRNAs potentially correlated with ascitic metastasis. Wound healing and transwell analysis were performed to evaluate tumor cells metastasis in vitro. Quantitative real-time PCR and western-blot were used for gene expression evaluation.According to transcriptomic analysis, ASC showed mesenchymal phenotype while T and M showed epithelial phenotype. 10 circRNAs were differentially expressed among ASC, T, and M. Among them, hsa_circ_0000497 and hsa_circ_0000918 were significantly up-regulated in ASC. Functional analysis showed that both hsa_circ_0000497 and hsa_circ_0000918 promoted metastasis of EOC via epithelial-mesenchymal transition (EMT) in vitro. The regulatory network construction identified 8 miRNAs and 19 mRNAs, and 7 miRNAs and 17 mRNAs as potential downstream target genes of hsa_circ_0000497 and hsa_circ_0000918, respectively, which may play pivotal roles in EOC ascitic metastasis.circRNAs (hsa_circ_0000497 and hsa_circ_0000918) contribute to metastasis of EOC via ascites by regulating EMT. These circRNAs may serve as novel potential therapeutic targets or prognostic biomarkers for EOC peritoneal metastasis.The online version contains supplementary material available at 10.1186/s12967-022-03404-9. Epithelial ovarian cancer (EOC) is the most lethal gynecological malignancy. Five years survival of EOC is only 29% due to its high invasive and metastatic feature . ApproxiAscites contains tumorigenic cytokines, proteinases, chemokines, and growth factors and function as a metastatic tumor microenvironment , which pCircular RNA (circRNA), a novel class of endogenous RNAs, was found to be involved in multiple biological processes, including tumor development and metastasis. Increasing evidence showed that circRNAs promoted cancer cell metastasis by regulating EMT , 17. TraIn the present study, we found two circRNAs, hsa_circ_0000497 and hsa_circ_0000918, significantly up-regulated in ascites tumor cells from EOC patients via transcriptomic analysis. Expression of hsa_circ_0000497 and hsa_circ_0000918 were up-regulated in ascites tumor cells compared with primary and metastatic tissues. The overexpression of these circRNAs promoted tumor cell metastasis via EMT in vitro. In addition, we constructed a regulatory network including miRNAs and mRNAs for each circRNA and identified the potential downstream targets of these circRNAs, which may contribute to the promotional role of circRNAs in tumor cell metastasis. These findings indicated that hsa_circ_0000497 and hsa_circ_0000918 contribute to peritoneal metastasis of EOC via ascites by regulating EMT of tumor cells, which can be used as novel therapeutic targets or prognostic biomarkers of EOC peritoneal metastasis.4 sets of paired samples, including primary tumor lesions (T), ascites tumor cells (ASC), and metastatic lesions (M), and an additional 5 pairs of T and M were obtained from 9 EOC patients for RNA-sequencing. And 8 sets of paired samples, including T, ASC and M were included for validation. All EOC patients were informed at the Department of Gynecology and Obstetrics of Tenth People`s Hospital of Shanghai. And the histopathological diagnosis of EOC was based on the World Health Organization criteria .This stu2 and at least 95% humidity in RPMI1640  supplemented with 10% FBS . Both cell lines were qualified by STR  before further analysis.EOC cell lines SKOV3 and OVCAR3 were cultured under a 37\u00a0\u00b0C with 5% CO\u2212\u25b3\u25b3Ct method with GAPDH reference control. All primer sequences were listed in Additional file Total RNA of samples was extracted using RNAiso Plus  according to the manufacturer's instructions. The concentration and purity of all RNA samples were subsequently measured by NanoDrop2000 . 1\u00a0ug of total RNA was used for cDNA (Complementary DNA) synthesis using PrimeScript RTMaster Mix . Quantitative real-time PCR (qRT-PCR) was performed on a QuantStudio Dx  using SYBR Premix ExTaq kit . The relative expression was calculated by the 2The protein extraction was completed with RIPA buffer  with 1% General Protease Inhibitor Cocktail  on ice and centrifuged at 4\u00a0\u00b0C, 12,000\u00a0rpm/15\u00a0min. The concentration of the supernatant was evaluated using the BCA Protein assay kit , and then the extracted proteins with 6\u2009\u00d7\u2009SDS loading buffer  were denatured by heating (95\u00a0\u00b0C) for 10\u00a0min. The sample was transferred to the PVDF membrane after SDS-PAGE electrophoresis, blocked with 5% skim milk solution for 100\u00a0min, and incubated with primary antibodies at 4\u00a0\u00b0C overnight. Then, the membranes were incubated with the secondary antibody at room temperature for 1\u00a0h. Immobilon ECL substrate  and Amersham Imager 600  were used for signals detection and image acquisition. The antibodies used in this study were listed in Additional file The pLCDH-ciR  was used to create hsa_circ_0000497 and hsa_circ_0000918 overexpressing plasmids. Small interference RNAs (siRNAs) targeting the junction sequence of hsa_circ_0000497 or hsa_circ_0000918 and control siRNA , and the relative migration rate was calculated according to the formula below [2.SKOV3 cells were seeded in 6-well plates with 1\u2009\u00d7\u200910la below .\\documenCell invasion and migration assays were performed using 24-well transwell plates  coated with or without Matrigel\u2122 Matrix . 24\u00a0h after transfection, tumor cells (1\u2009\u00d7\u2009105) for invasion assay and (5\u2009\u00d7\u2009104) for migration assay were trypsinized and washed twice with PBS, suspended in 200\u00a0\u03bcL serum-free RPMI1640, slowly dripped into the pre-coated insert, and incubated in a 24-well plate containing 600\u00a0\u03bcL RPMI1640 with 20% FBS per well for 48\u00a0h. The matrigel and cells on the upper surface of the membrane were then wiped off. The invasive cells that migrated through the membrane and adhered to the lower surface, and were fixed in 4% paraformaldehyde for 18\u00a0min and stained with DAPI . The number of invasive cells was photographed and counted using an inverted microscope imaging quantification field system .4 cells per well. 48\u00a0h after transfection, EdU was applied at 20\u00a0\u03bcM. The cells were then fixed with 4% paraformaldehyde and stained with Alexa Fluor 555 and DAPI. The cell proliferation was photographed and counted using an inverted microscope imaging quantification field system .The cell proliferation was detected using BeyoClickTM EdU Cell Proliferation Kit with Alexa Fluor 555 . SKOV3 cells were seeded in 24-well plates with 7\u2009\u00d7\u2009102 transformations were performed on normalized read counts. To avoid the log of zeroes, all read counts were increased by 1 before taking the log transformation. The circRNAs were detected and identified by the find_circ algorithm with default parameters [The next-generation RNA-sequencing was performed in . Briefly, total RNA was isolated with the RNAiso Plus . The quality was checked using Agilent Bioanalyzer . RNA integrity number (RIN) for all samples was more than 7. The Illumina TruSeq Stranded RNA Sample Preparation kit  for library preparation. Then the libraries were sequenced using Illumina NovaSeq 6000  with paired-end  was utilized to emphasize variation and similarity among the 22 samples using R packages DESeq2 [grepel, ggplot, and pheatmap packages. The venn diagram was employed to take the intersection of DECs and DEGs.Differentially expressed circRNA (DECs) and differentially expressed genes (DEGs) were identified using R packages DESeq2  with folhttp://geneontology.org/)  was applied to evaluate the enrichment score of EMT gene signature in each sample. Signatures of epithelial n\u2009=\u200931) and mesenchymal (n\u2009=\u200954), used for ssGSEA, were connected from previous literature and GO databases  was applied to perform soft clustering analysis of circRNA and mRNA from T, ASC, M expression matrices [The matrices . RNAs wehttp://www.miranda.org/) was used to identify binding miRNAs of the circRNA candidates. Interactions between miRNA (microRNA) and mRNA were analyzed based on the TargetScan (http://www.targetscan.org) and miRDB databases (miRNA target prediction database) (http://mirdb.org). DEGs recognized in both databases were considered as candidate mRNAs of circRNA-miRNA regulation elements. The circRNA\u2013miRNA\u2013mRNA regulatory network was constructed through combination analysis of circRNA\u2013miRNA pairs and miRNA\u2013mRNA pairs and visualized using Cytoscape 3.8.2 [Cancer-Specific CircRNAs Database (CSCD) is utilized for visualizing the structure of circRNAs. The circBase . p\u2009<\u20090.05 was considered to be statistically significant. * indicates p\u2009<\u20090.05, ** indicates p\u2009<\u20090.01, *** indicates p\u2009<\u20090.001 and **** indicates p\u2009<\u20090.0001.http://geneontology.org/) to conduct ssGSEA analysis [To investigate the dynamic states of epithelial-mesenchymal transition (EMT) during epithelial ovarian cancer (EOC) ascitic metastasis, we performed a comprehensive analysis of gene expression profiles of 22 samples, including 9 primary tumor lesions (T), 4 ascites (ASC), and 9 metastatic lesions (M) from 9 EOC patients. Principal component analysis (PCA) was performed to analyze the pattern of mRNA expression profile. Our results showed that ASC metastasis was distinguished remarkably from T and M, while T basically overlapped with M, indicating that the common essence showed between T and M as solid tumor tissue  and 71 circRNAs (including 57 up-regulated and 14 down-regulated) were found differentially expressed in T-ASC group and ASC-M group, respectively . Hsa_circ_0000497 consists of 5\u201313 exons of its host genes SLAIN1 with the length of 788 nucleotides (Nt) and locates at chr13:78,293,666\u201378,327,493 , respectively. After validation of the overexpression Fig.\u00a0a, tumor http://www.circbase.org/). 85 and 23 miRNAs were found to potentially bind with hsa_circ_0000497 and hsa_circ_0000918, respectively  and TargetScan (http://www.targetscan.org/vert_72/) were used to identify targeting genes of candidate miRNAs. Total of 5875 mRNAs for hsa_circ_0000497 targeting miRNAs and 4897 mRNAs for hsa_circ_0000918 targeting miRNAs were obtained from both databases. Among these mRNAs, 36 genes were found differentially expressed in ASC based on our transcriptomic analysis compared with T and M . So they may also function via encoding proteins since circRNAs containing initial AUG followed by ORF can directly encode polypeptides [Serval mechanisms underlying the regulatory function of circRNAs had been reported previously . CircRNApeptides . HoweverWe found up-regulation of hsa_circ_0000497 and hsa_circ_0000918 in ASC and identified their role in the promotion of EOC metastasis. There are still some limitations to our study. First, the sample size should be further expanded to verify the expression of hsa_circ_0000497 and hsa_circ_0000918 in ascites, primary and metastatic lesions of ovarian cancer. Second, only tumor cell lines were included for function analysis. Whether these circRNAs promote metastasis in vivo remains unclear. Xenograft mice models including patient-derived tumor xenograft (PDX), will be applied for further research in vivo in our future study. As for the underlying mechanism, only a regulatory network was constructed based on transcriptomic data. Whether or not are these downstream genes regulated by circRNAs need to be assessed via gain or loss of function assays in vitro and in vivo models. In addition, the capability of these circRNAs used as a therapeutic target or prognostic biomarkers and so on also needs more investigation.In summary, we found two circRNAs, hsa_circ_0000497 and hsa_circ_0000918, specifically up-regulated in ASC from EOC patients. Overexpression of these circRNAs enhanced the metastatic capability of EOC tumor cells via EMT. We also constructed the downstream regulatory network of these circRNAs and found out the miRNAs and differently expressed mRNAs potentially contributed to the role of circRNAs in EOC metastasis. These findings indicated that hsa_circ_0000497 and hsa_circ_0000918 contribute to intraperitoneal metastasis of EOC via ascites by regulating EMT of tumor cells. And they should be used as novel therapeutic targets or prognostic biomarkers of EOC intraperitoneal metastasis.Additional file 1:Table S1. The sequences of primers and siRNAs used for experiments in this study. Table S2. Antibodies used in this study. Table S3. Detailed information for EMT related gene sets. Table S4. Predicted miRNA of candidate circRNAs. Table S5. ORF of candidate circRNAs. Figure S1. CircRNAs differentially expressed in ascitic metastasis of ovarian cancer. Figure S2. Silencing hsa_circ_0000497 and hsa_circ_0000918 inhibit the cell invasion and migration of OVCAR3 cells. Figure S3. mRNAs differentially expressed in ascitic metastasis of ovarian cancer. Figure S4. over-expressing or silencing hsa_circ_0000497 and hsa_circ_0000918 promote or inhibit the cell proliferation ovarian cancer cells"}
+{"text": "Homology-based search is commonly used to uncover mobile genetic elements (MGEs) from metagenomes, but it heavily relies on reference genomes in the database. Here we introduce a protocol to extract CRISPR-targeted sequences from the assembled human gut metagenomic sequences without using a reference database. We describe the assembling of metagenome contigs, the extraction of CRISPR direct repeats and spacers, the discovery of protospacers, and the extraction of protospacer-enriched regions using the graph-based approach. This protocol could extract numerous characterized/uncharacterized MGEs.For complete details on the use and execution of this protocol, please refer to   \u2022Protocol to extract CRISPR-targeted sequences from the assembled metagenomic sequences\u2022Extraction of sequences from the human gut metagenome without using a reference database\u2022Extraction of sequences with various kinds of MGEs including unclassified ones Publisher\u2019s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Homology-based search is commonly used to uncover mobile genetic elements (MGEs) from metagenomes, but it heavily relies on reference genomes in the database. Here we introduce a protocol to extract CRISPR-targeted sequences from the assembled human gut metagenomic sequences without using a reference database. We describe the assembling of metagenome contigs, the extraction of CRISPR direct repeats and spacers, the discovery of protospacers, and the extraction of protospacer-enriched regions using the graph-based approach. This protocol could extract numerous characterized/uncharacterized MGEs. A typical human gut metagenome library ranges in size from a few GB to hundreds of GB. Therefore, assembling a large library might require more than 128 GB of memory, which is likely unavailable for a standard desktop machine. In this case, consider using high-capacity servers in a data center.This section describes the hardware specifications, operating system, and interpreters required to execute the protocol.The software and python packages listed in the https://www.ncbi.nlm.nih.gov/sra), Integrative Human Microbiome Projects , etc. In this article, we assume that the sequences are stored in paired short-read FASTQ files generated from whole metagenome shotgun sequencing.Note: We suggest that at least 10 independent samples from an environment are required to collect a sufficient number of spacers and candidate protospacers to build a versatile spacer co-occurrence network.Metagenomic raw reads can be downloaded from numerous sources, such as Sequence Read Archive  and require modifications to specify the file paths in their codes. The parts that require changes are noted with comments in the Bash scripts (see README files for details). Conversely, the python scripts used in this protocol do not require any change.Alternatives: We also provide FASTA and BED-formatted files, which contain spacers, protospacers, CRISPR direct repeats, and CRISPR-targeted terminally redundant sequences extracted from approximately ten thousand human gut metagenome libraries in the previous study. This dataset includes approximately two million unique spacers, ten thousand unique CRISPR direct repeats, and approximately ten thousand CRISPR-targeted unique sequences. The protocol described in this article does not require these files. However, for the case of human gut metagenome analysis, this previously extracted dataset could be used to skip the processes and reduce the analysis time.Timing: 1\u20132 hAlternatives: A pipeline script for preprocessing, assembling, and spacer collection is provided in the repository (pipeline_scripts/assembly_pipeline.sh).1.Trim poor-quality bases, adapters, and PhiX spikes.$ bbduk.sh -Xmx100g t=8 in1=r1.fastq.gz in2=r2.fastq.gz out=trimmed.fastq.gz ftm=5 qtrim=rl trimq=20 ftl=15 ref=adapters,phix ktrim=r k=23\u00a0mink=11 hdist=1 tpe tbo maq=202.Remove human-derived reads.$ bbmap.sh -Xmx100g t=8 ref={path to the human reference FASTA file} in=trimmed.fastq.gz outu=decontaminated.fastq.gz interleaved=t minratio=0.9 maxindel=3 bwr=0.16 bw=12 fast=t minhits=2 qtrim=r trimq=10 untrim=t idtag=t printunmappedcount=t kfilter=25 maxsites=1 k=14Note: We used the human genome reference FASTA file posted by the author of BBtools.3.Correct errors in the reads.$ tadpole.sh threads=8 -Xmx100g interleaved=t in=decontaminated.fastq.gz out=ecc.fastq.gz mode=correctCRITICAL: This output FASTQ file is analysis-ready and used for assembly and spacer extraction.This process is quality control of the input sequences.Timing: 1\u201312\u00a0h (Varies strongly depending on library)4.Assemble preprocessed FASTQ file using SPAdes.$ spades.py -t 8 -m 100 --meta --only-assembler --12 ecc.fastq.gz -o {path to spades output directory}Note: We skipped the error-correction step in the SPAdes program to avoid the occasional endlessly running problem. Please refer to the 5.Assign unique identifiers to all assembled contigs.$ cd {path to spades output directory}$ id=$(openssl rand -hex 12)awk -v id=${id} '/\u02c6>/ {n++; print \">contig_\"id\"_\"n;} !/\u02c6>/{print}' scaffolds.fasta\u00a0>\u00a0scaffolds.renamed.fastaCRITICAL: Assigning unique identifiers is important to avoid conflicts in the later analyses. Here, we used randomly generated strings; however, any unique readable identifier, such as sample IDs, could be used instead.Optional: We recommend removing contigs shorter than 1k bases to reduce the file size.Note: The pre and assembling processes are independently conducted for each paired FASTQ file.Optional: We advise that the system has sufficient disk space to store all preprocessed FASTQ and assembled FASTA files. The rest of the files, including intermediate and temporary files, can be deleted to save the disk space.Assemble metagenome contigs from the preprocessed FASTQ files.Timing: 1\u20132 h6.Run CRISPRDetect to discover consensus CRISPR direct repeats.$ CRISPRDetect.pl -q 0 -f scaffolds.renamed.fasta -o crispr_detect_output -array_quality_score_cutoff 27.From the output gff file, extract direct repeats, and convert them into a FASTA file.$ grep 'repeat_region' crispr_detect_output.gff | cut -f 9 | tr ';' '\u2216n' | egrep '\u02c6Note=' | cut -f 2 -d '=' | awk '{n++; printf}'\u00a0>\u00a0crispr_dr.fastaNote: Again, these direct repeat extractions are independently conducted for each assembled FASTA file.8.After discovering all direct repeats from each assembled FASTA file, we merge them into a single file, assign unique identifiers, and remove redundancy.$ cat {all crispr dr fasta files}\u00a0>\u00a0merged_crispr_dr.fasta$ awk '/\u02c6>/ {n++; print \">dr_\"n; } !/\u02c6>/ {print}' merged_crispr_dr.fasta\u00a0>\u00a0merged_crispr_dr.renamed.fasta #giving new unique identifiers$ cd-hit-est -S 1 -c 1 -i merged_crispr_dr.renamed.fasta -o merged_crispr_dr.repr.fastaOptional: We provided the direct repeat sequences extracted from the human gut metagenomes in the previous study. The sequences are stored in a FASTA formatted file located in the supplementary folder .From the assembled contigs, discover CRISPR consensus direct repeats.Timing: 1 hAlternatives: A pipeline script for the spacer collection is provided in the repository (pipeline_scripts/collect_spacers.sh).9.Extract direct repeat-containing reads. This initial filtering step significantly reduces the number of reads, effectively speeding up the following spacer extraction processes.$ bbduk.sh in=ecc.fastq.gz ref=merged_crispr_dr.repr.fasta outm=dr_containing_reads.fastq.gz interleaved=t k=21 hdist=1 rename=t10.Second filter to mask the direct repeats in the reads.$ bbduk.sh in=dr_containing_reads.fastq.gz ref=merged_crispr_dr.repr.fasta kmask=R k=19 hdist=1 out=dr_masked.fastq mink=1511.Extract spacers from the masked reads using our python script.$ {path to the virome_scripts directory}/pipeline_scripts/extract_spacers.py -s {sample name} dr_masked.fastq\u00a0>\u00a0spacers.fastaNote: The python script used above is available from our repository (pipeline_scripts/extract_spacers.py).Optional: The python program used above does not output short (<20\u00a0bp) and long (>50\u00a0bp) sequences by default. These parameters can be changed by options (-s and -l).Note: Spacer extraction is conducted for each preprocessed FASTQ file.12.Merge all discovered spacers, assign unique identifiers, and remove redundancy.$ cat {all spacer fasta files}\u00a0>\u00a0all_spacers.fasta$ awk '/\u02c6>/ {sub; n++; printf(\u201c>spacer_\"n\"\u2216t\"$0);} !/\u02c6>/ {print}' all_spacers.fasta\u00a0>\u00a0all_spacers.renamed.fasta #giving new unique identifiers$ cd-hit-est -T 8 -M 10000 -d 0 -sf 1 -s 0.9 -c 0.98 -i all_spacers.renamed.fasta -o all_spacers.repr.fastaOptional: It is highly advisable to make each spacer and direct repeat trackable to the original samples, contigs, and/or associated direct repeats. This can be achieved using FASTA descriptions in each record.Optional: We provided the spacer sequences extracted from the human gut metagenomes in the previous study. The sequences are stored in a FASTA formatted file located in the supplementary folder .Here, we return to the preprocessed FASTQ files. From them, we extract CRISPR spacers from reads containing CRISPR direct repeats.Timing: 1\u20132 h13.Search CRISPR direct repeats from the contigs.$ makeblastdb -dbtype nucl -in scaffolds.renamed.fasta$ blastn -evalue 1e-5 -task \u2018blastn-short\u2019 -outfmt 6 -num_threads 8 -query merged_crispr_dr.repr.fasta -db scaffolds.renamed.fasta\u00a0>\u00a0dr.blastn14.Mask sequences around direct repeat aligned regions.$ samtools faidx scaffolds.renamed.fasta$ awk \u2018BEGIN{FS=\"\u2216t\"; OFS=\"\u2216t\"} $10<$9{t=$9; $9=$10; $10=t} {print $2,$9\u20131,$10}' dr.blastn | sort -k1,1 -k2,2n | bedtools merge -i /dev/stdin -d 60 | bedtools slop -b 60 -i /dev/stdin -g scaffolds.renamed.fasta.fai | bedtools maskfasta -bed /dev/stdin -fi scaffolds.renamed.fasta -fo crispr_masked.fasta15.Align spacers to the masked contigs.$ makeblastdb -dbtype nucl -in crispr_masked.fasta$ blastn -evalue 1e-5 -perc_identity 93 -task \u2018blastn-short\u2019 -outfmt 6 -num_threads 8 -query all_spacers.repr.fasta -db crispr_masked.fasta\u00a0>\u00a0spacers.blastnNote: Protospacer search is conducted for each assembled FASTA file.16.Merge all discovered protospacers into a single BED-formatted file.$ cat {all spacer blastn output files} | awk \u2018BEGIN{FS=\"\u2216t\"; OFS=\"\u2216t\"} $10<$9{t=$9; $9=$10; $10=t} {print $2,$9\u20131,$10,$1}' | sort -k1,1 -k2,2n\u00a0>\u00a0all_protospacers.bedOptional: We provided the protospacers discovered from the assembled human gut metagenome contigs in the previous study. The protospacers are stored in a BED-formatted file located in the supplementary folder .Here, we discover protospacers by aligning the spacer sequences to CRISPR-masked contigs.Timing: 2\u20134 h17.Initial clustering is based on distance.$ bedtools cluster -d 50000 -i all_protospacers.bed | awk \u2018BEGIN{FS=\"\u2216t\"; OFS=\"\u2216t\"} {print $1\"_cluster_\"$5,$2,$3,$4}'\u00a0>\u00a0initial_cluster.bed18.Calculate a weighted graph from the spacer co-occurrence and generate an abc formatted file.$ {path to the virome_scripts directory}/graph_clustering/generate_abc_edgefile.py initial_cluster.bed\u00a0>\u00a0protospacers.abcNote: The python script used above is available in our repository (graph_clustering/generate_abc_edgefile.py).19.Convert the abc file into an mci format.$ mcxload -abc protospacers.abc --stream-mirror -write-tab data.tab -o protospacers.mci20.Run an mcl program to discover graph communities.$ mcl protospacers.mci -I 4 -pi 0.4 -te 821.Convert the mcl output to a tabular format.$ mcxdump -icl out.protospacers.mci.I40pi04 -tabr data.tab\u00a0>\u00a0protospacers_cluster.tabCRITICAL: Each tab-delimited line within this output file consists of a spacer cluster.22.Mark CRISPR-targeted regions using the clustering result and the merged protospacer bed file.$ {path to the virome_scripts directory}/graph_clustering/mark_crispr_targeted.py all_protospacers.bed protospacers_cluster.tab\u00a0>\u00a0crispr_targeted.bedNote: The python script used above is available in our repository (graph_clustering/mark_crispr_targeted.py).Note: The fourth column in the output file is formatted as {cluster id}:{protospacer count in the region}.23.Merge the regions to rescue the fragmented clusters.$ sort -k1,1 -k2,2n crispr_targeted.bed\u00a0>\u00a0crispr_targeted.sorted.bed$ bedtools merge -d 1000 -i crispr_targeted.sorted.bed -o collapse -c 4\u00a0>\u00a0crispr_targeted.merged.bed24.Extract CRISPR-targeted regions from the assembled contigs.$ samtools faidx scaffolds.renamed.fasta$ cut -f 1,2 scaffolds.renamed.fasta.fai | sort -k 1,1 | join -t $'\u2216t' - < | awk 'BEGIN{OFS=\"\u2216t\"; FS=\"\u2216t\"} $4>$2{$4=$2} {print $1,$3,$4,$5}' | bedtools getfasta -fi scaffolds.renamed.fasta -bed /dev/stdin -fo crispr_targeted.fastaNote: The extraction of CRISPR-targeted sequences is conducted for each assembled FASTA file.Optional: We provided the CRISPR-targeted terminally redundant sequences extracted from the assembled human gut metagenome contigs in the previous study. The sequences are stored in a FASTA formatted file located in the supplementary folder (supplementary_data/targeted_sequences/tr/tr_sequences.fasta).Here, we cluster spacers using a co-occurrence network calculated from the spacer alignment result. From this network, we discover the graph communities, representing subsets of spacers that corresponding protospacers co-occur together across the assembled contigs. We extract the protospacer-enriched regions using these graph communities, i.e., spacer clusters. This process effectively reduces the false-positive ratio by excluding lone or randomly scattered protospacers.The final output FASTA files contained CRISPR-targeted sequences. These sequences typically range in size between a few hundred and several hundred thousand bases. The output sequences likely contain multiple kinds of MGEs .Figure\u00a01\u2022Sequence clustering should be conducted to remove redundancy and fragmentations from the final output sequences.\u2022Genomic completeness could be investigated by checking terminal redundancy or inverted terminal repeats. In the repository, we provided python scripts that extract terminally redundant or inverted repeat sequences from a FASTA file (utils/circular_contigs.py and utils/inverted_repeat_contigs.py).\u2022CRISPR-Cas systems target various elements, including viruses, plasmids, transposons, and chromosomes. A protein homology search is a common method to classify the novel genomes. We suggest using a high-sensitivity homology search method based on hidden Markov models.It is possible to predict the CRISPR-targeting hosts by searching protospacer-associated direct repeats in the reference genome database. However, CRISPR undergoes intense horizontal gene transfer. Therefore, combining it with other methods, such as tRNA alignment, is highly advisable to predict the infecting host.Genome assembly takes time (step 4).in silico. Both approaches can reduce the file size and potentially the assembling time.The time metagenome assembly takes strongly varies between libraries. If an assembly takes longer than a day, and the library size is very large (>100 Gb), one can downsample the FASTQ file by randomly selecting pairs from the original files or normalizing the depth Why did you skip the error-correction step in the SPAdes program (step 4)?We found that the SPAdes program is sometimes trapped in the error-correction step and seems to endlessly run for more than four or five days. This phenomenon is sporadic, hard to replicate, and might depend on the system specifications. Such a problem was highly problematic when we were analyzing more than a thousand samples. To avoid this issue, we skipped the error-correction step in the SPAdes program and instead used the tadpole program for the preassemble error correction. We compared the statistics between the assembled contigs, using SPAdes or tadpole, and found no significant difference between them.Assembled contigs are short (step 4).The less populated organisms in the sample are likely to be shallowly sequenced, leading to highly fragmented and partial contigs. Therefore, they might require deeper sequencing to assemble the complete genome. Conversely, the metadata of SRA recorded libraries could be wrong sometimes. For example, we encountered several libraries likely produced from 16S amplicon sequencing but recorded as a whole genomic sequencing. It is advisable to avoid such libraries and/or refer to the experimental method described in the original article if necessary.The constructed spacer co-occurrence network is small and fragmented (step 21).In the original study, we used millions of spacers extracted from thousands of human gut metagenome datasets to construct a network. A significant number of spacers and spacer-aligned contigs are required to build a versatile network and predict graph communities. If you are attempting to analyze other than the human gut metagenome using a few materials, it might be advisable to skip the network building process entirely and manually check each protospacer containing contigs.Protein homology searches do not hit the database .Many MGE encoded protein sequences such as capsids and polymerases are extremely diversified, therefore it is often difficult to detect their homology to known sequences using the pairwise alignment-based method which is adopted by such as the BLAST program. In order to overcome this issue, hidden Markov models (HMMs) based programs such as HMMER  or HH-suitinoue@nig.ac.jp.Ituro Inoue: our repository.CRISPR spacers and direct repeats extracted in the previous study are available from"}
+{"text": "Lung adenocarcinoma (LUAD) is a major cause for global cancer-related deaths. Research reports demonstrate that lymph node metastasis (LNM) is pertinent to the survival rate of LUAD patients, and crux lies in the lack of biomarkers that could distinguish patients with LNM. We aimed to verify the LNM-related prognostic biomarkers in LUAD.  We firstly accessed the expression data of mRNA from The Cancer Genome Atlas (TCGA) database and then obtained samples with LNM (N+) and without LNM (N-). Differential expression analysis was conducted to acquire differentially expressed genes (DEGs). Univariate-LASSO-multivariate Cox regression analyses were performed on DEGs to build a risk model and obtain optimal genes. Afterwards, effectiveness and independence of risk model were assessed based on TCGA-LUAD and GSE31210 datasets. Moreover, a nomogram was established combining clinical factors and riskscores. Nomogram performance was measured by calibration curves. The infiltration abundance of immune cells was scored with CIBERSORT to explore the differences between high- and low-risk groups. Lastly, gene set enrichment analysis (GSEA) was used to investigate differences in immune features between the two risk groups.  Nine optimal feature genes closely related to LNM in LUAD were identified to construct a risk model. Prognostic ability of the risk model was verified in independent databases. Patients were classified into high- and low-risk groups in accordance with their median riskscores. CIBERSORT score displayed differences in immune cell infiltration like T cells CD4 memory resting between high/low-risk groups. LNM-related genes may also be closely relevant to immune features. Additionally, GSEA indicated that differential genes in the two risk groups were enriched in genes related to immune cells.  This research built a risk model including nine optimal feature genes, which may be potential biomarkers for LUAD. Lung cancer is the leading cause of cancer death globally, with adenocarcinoma as the most prevalent histologic type . Recent The establishment and improvement of many databases further promote our understanding of disease genomic alterations, including identifying biomarkers related to tumor diagnosis and prognosis. For instance, Fu et al.  analyzedHere, LUAD samples with or without LNM were obtained through TCGA database, which were subjected to differential expression analysis. Enrichment analysis was conducted on the acquired differential genes, and a risk model was built through regression analyses. Besides, immune infiltration abundance of LUAD samples was scored to unravel the correlation between immune infiltration and riskscores. The results may provide an effective prognostic tool for LUAD patients and assist doctors in identifying patients with high risk of mortality to increase their survival rate.https://portal.gdc.cancer.gov/) database along with clinical data  between LNM samples (N+) and non-LNM samples (N-). To that end, \u201cedgeR\u201d package  was applpadjust <0.05 andqvalue <0.05 were considered statistically significant [For better understanding of biological processes that DEGs may participate, \u201cclusterProfiler\u201d package  was emplnificant .p < 0.001) [Univariate Cox regression analysis was performed on DEGs with \u201csurvival\u201d package (< 0.001) . To avoi< 0.001) . Penalty< 0.001) . Multiva< 0.001)  to buildi is the cooperativity coefficient and xi is the relative gene expression standardized by Z-score.In this formula, Coefhttps://cran.r-project.org/web/packages/survminer/index.html). High- and low-risk groups were further divided by LNM occurrence to undertake survival analysis. Receiver operator characteristic (ROC) curve was drawn by using \u201ctimeROC\u201d package [We assessed the validity of the model in training set and validation set. Riskscores of samples were calculated according to the formula. Samples were divided into high- and low-risk groups by median score. Survival analysis was undertaken with survminer package  along with patient's clinical data  to predict the possibility of patient's 1-, 3-, and 5-year survival . Its perp value<0.05 (Supplementary Table The infiltration abundance of each immune cells in samples was scored by using CIBERSORT algorithm. A total of 184 low-risk samples and 170 high-risk samples were obtained after screening the samples with Lastly, to explore the differences in immune features between the groups, GSEA was performed in these two groups. As the results suggested, differential genes in the high- and low-risk groups were enriched in FETAL_VS_AUDULT_TREG_DN, NAIVE_TCELL_VS_MONOCYTE_UP, and CD16_POS_MONOCYTE_VS_DC_DN Figures . It was Accumulating evidence demonstrated that LUAD patients with LNM often have a poor response to standard treatment and shorter survival time . It is uIn the present study, DEGs were obtained via analyzing gene expression profiles of N+ and N- samples in TCGA-LUAD dataset. Enrichment analyses suggested that the expression of the DEGs was associated with tumor development. To further screen genes relevant to patient's prognosis, regression analyses were undertaken. Finally, 9 optimal feature genes were acquired, and a risk model was constructed. Riskscore = 0.066\u2217PITX3 + 0.087\u2217RHOV + 0.111\u2217MARCH4\u20130.033\u2217ZNF536\u20130.047\u2217SLC14A2\u20130.079\u2217CYP17A1 + 0.053\u2217IGFBP1 + 0.044\u2217KRT76\u20130.072\u2217GFI1B. Previous studies demonstrated that the above genes are relevant to patient's survival and prognosis. For instance, elevated RHOV expression level correlates with NSCLC patient's poor survival . High KRet al. [et al. [A clinical study illustrated that immune activation in tumor cells is closely relevant to LNM . To thiset al.  indicateet al. . In addiet al.  confirmeet al. . Haak et [et al.  found th [et al. . This exIn sum, according to TCGA-LUAD data, we constructed an effective 9-gene risk prognostic model that could predict patient's prognosis independent of other clinical factors. Our model was able to divide LUAD patients into two groups and effectively distinguish patients with poor prognosis. Moreover, the identified feature genes may play a predictive role to a certain extent in immune treatment. However, limitations still exist here. In the future, we plan to analyze the expression of optimal feature genes and immune checkpoints to further validate underlying value of the optimal feature genes in predicting the efficacy of treatment with immune checkpoint inhibitors."}
+{"text": "It has been extensively shown that circRNAs are involved in regulating CC development. Nevertheless, the function and mechanisms of hsa_circ_0004543 in regulating CC need to be clearly elucidated. Herein, hsa_circ_0004543 expressions were compared between 40 paired paracancerous and cancerous specimens from CC patients and between 6 CC cell lines and a normal human cervical epithelial cell line based on qRT-PCR. Potential complementary binding sites between hsa-miR-217 and hsa_circ_0004543 were predicted using the interactome, while binding sites for the hypoxia-inducible factor-1a (HIF-1a) were predicted by TargetScan. The function and mechanism of hsa_circ_0004543 in the development of CC were estimated by silencing hsa_circ_0004543 with/without hsa-miR-217 or HIF-1a overexpression. The association between gene expressions was evaluated with Pearson's correlation analysis. Molecular mechanisms were explored by ribonucleic acid (RNA) pulldown, dual-luciferase activity, and rescue experimental assays. Our results revealed that the hsa_circ_0004543 expression was considerably increased in CC tissues and cells. Its silencing repressed proliferation and metastasis, while it increased apoptosis of CC cells. The investigation of the mechanism showed that hsa-miR-217 silencing or HIF-1a overexpression rescued hsa_circ_0004543, and silencing inhibited malignant phenotypes of CC cells. hsa_circ_0004543 upregulated the HIF-1\u03b1 expression by sponging hsa-miR-217 in CC development. Therefore, the hsa_circ_0004543 functioned as a competing endogenous RNA (ceRNA) of hsa-miR-217 to increase CC oncogenesis and metastasis by the upregulation of the HIF-1\u03b1 expression. Consequently, targeting the hsa_circ_0004543/hsa-miR-217/HIF-1\u03b1 axis might be a potential treatment approach for CC.Cervical cancer (CC) is the 4 Cancer is now commonly acknowledged as a worldwide hazard to international development . The latth most commonly diagnosed malignancy, cervical cancer (CC) is the 4th principal source of female cancer deaths, with 604,000 new patients and 342,000 deaths in 2020 worldwide [As the 4CC is commonly asymptomatic and may be diagnosed during pelvic examination or routine screening in the early stages, with the symptoms of abnormal or postcoital vaginal bleeding . The preDespite the fact that diagnostic and therapeutic advances of surgical treatment with concurrent chemoradiotherapy have improved the overall five-year survival to about 70% in advanced CC patients, the metastasis and recurrence of CC still result in a quite poor prognosis with a 5-year overall survival <30% in the majority of regions and countries owing to restricted clinical strategies [As a newly identified noncoding RNA (ncRNA) class, circular RNAs (circRNAs) are preserved throughout species and are more stable than linear RNAs \u201310. Incrhsa_circ_0004543 was significantly increased in CC patient tissues based on circRNA microarray analysis . However\u03b1 (HIF-1\u03b1) is a key responser adapted to cancer hypoxia. HIF-1\u03b1 signaling activated in hypoxia conditions contributes to cell biology associated with oncogenesis, a key issue restraining the chemotherapy efficiency in various cancer treatment including CC [\u03b1, which may activate many metastatic sequences to promote local and distant site cancer recurrence [Hypoxia-inducible factor-1 uding CC , 17. As uding CC , 19, hypuding CC , 21. Inccurrence , 23. As currence .\u03b1/AXL signaling has been reported to be involved in lncRNA-HOTAIR-promoted renal cell carcinoma carcinogenesis, which provides a new target for the diagnosis and treatment of renal cell carcinoma [Moreover, miR-217/HIF-1arcinoma .\u03b1 signaling. Herein, we intend to explore the function and molecular mechanisms of hsa_circ_0004543 in CC oncogenesis and development, thus providing a potential biomarker for better management of CC. After analyzing the expressions of hsa_circ_0004543, hsa-miR-217, and HIF-1\u03b1 in 40 paired CC and paracancerous tissues with qRT-PCR, which revealed that hsa_circ_0004543 and HIF-1\u03b1 were increased, while hsa-miR-217 was decreased in tissues of CC patients; hsa_circ_0004543 was further found to increase HIF-1\u03b1 expression via sponging hsa-miR-217. Thus, this promoted CC oncogenesis and development. These findings may enable the progress of clinical management strategies against CC.Therefore, we speculated that hsa_circ_0004543 may stimulate CC development by hsa-miR-217/HIF-1TM Magnetic RNA-Protein Pull-Down kit, Lipofectamine 3000, M-MLV reverse transcriptase kit, miRNA reverse transcriptase kit, TRIzol reagent, and SuperSignal West Dura Extended Duration Substrate . Antibodies were purchased from Santa Cruz . Propidium iodide and APC-Annexin V were purchased form Sigma-Aldrich . The PsiCHECK\u2122-2 vector was purchased from Promega .The following reagents and instruments were used in this study: dual-luciferase reporter assay system , fetal bovine serum (FBS), Dulbecco's modified eagle medium (DMEM) cell culture medium , radio immunoprecipitation assay (RIPA) buffer , Matrigel , cell counting kit-8 (CCK8) assay kit , Gene Mutation Kit and SYBR Green Premix Ex Taq\u2122 II , PierceWe collected the cancerous and paracancerous specimens from 40 CC patients during surgical treatment and stored them at -80\u00b0C. All patients provided written informed consent. All experimental procedures were approved by the Ethics Committee of the Beijing Chao-Yang Hospital at Capital Medical University.The Committee on Type Culture Collection of the Chinese Academy of Sciences  provided all CC and normal human cervical epithelial (End1/E6E7) cells, which were routinely cultured in DMEM medium containing FBS (10%), penicillin (100\u2009IU/mL), and streptomycin (100\u2009mg/mL) .\u03bcL of CCK-8 reagent was applied to each well of cells cultured in a 96-well plate with an original 2000\u2009cells/well and incubated in the dark for 2\u2009h at 37\u00b0C. The optional density (OD) value was determined at a wavelength of 450\u2009nm with a microplate reader  [Cell viability was evaluated using the CCK-8 kit following the manufacturer's guidelines. In brief, 10\u2009ercules) .Cells were seeded in 6 well plates with 1000 cells/well and incubated for 10 days at 37\u00b0C to form colonies, followed by fixation in 4% paraformaldehyde for 10\u2009min and staining in 0.5% crystal violet for 5\u2009min. Colony numbers were determined using the software ImageJ  and images were acquired with a light microscope  .Cells washed with precold phosphate buffer solution were fixed and incubated for 15\u2009min with propidium iodide and APC-Annexin V, respectively, in dark conditions at room temperature. Apoptotic cells were then detected using a BD FACSCalibur flow cytometer. Apoptotic rates were determined using Cell Quest software  .\u03bcL of 10% FBS-supplied culture medium was loaded in the lower chamber, and 3\u2009\u00d7\u2009105 cells in 200\u2009\u03bcl of serum-free medium were loaded in the upper chamber. After being cultured for 24\u2009h (migration assay) or 48\u2009h (invasion assay), the commonly used time points for cell migration and invasion assays to avoid the influence of cell proliferation, the migrated or invaded cells were fixed and stained [A transwell  was applied to evaluate cell invasion (with Matrigel coating) and migration (without Matrigel coating) abilities. In brief, 600\u2009 stained .https://circinteractome.nia.nih.gov/) [http://www.targetscan.org/) [Potential hsa_circ_0004543 sponged miRNAs were predicted with the online tool Circular RNA Interactome (ih.gov/) . Potentian.org/) .Ct\u2212\u0394\u0394 method with GAPDH as an internal control for mRNAs and circRNAs, and U6 as an internal control for miRNAs [Sangon  synthesized the primers. Cells were lysed in Trizol to isolate the total RNA according to the protocols. RNAs were reversely transcribed using the M-MLV reverse transcriptase kit or miRNA reverse transcriptase kit following the accompanying instructions. SYBR Green Premix Ex Taq\u2122 II was mixed with cDNA and specific primers for qRT-PCR assay on a CFX96\u2009TM real-time PCR detection system . Relative gene expressions were calculated using the 2r miRNAs , 29.Cells cotransfected with hsa-miR-217 mimics or negative control (miR-NC), psiCHECK-2/hsa_circ_0004543 3\u2032-UTR (WT), or psiCHECK-2/hsa_circ_0004543 3\u2032-UTR mutated (MT) plasmid were used for hsa_circ_0004543 activity analysis, while cotransfected with psiCHECK-2/HIF-1\u0391 3\u2032-UTR (WT) or psiCHECK-2/HIF-1\u0391 3\u2032-UTR mutated (MT) plasmid were used for HIF-1\u0391 activity analysis using Lipofectamine 3000. The dual-luciferase reporter assay kit was then used following the manufacturer's procedures , 29.GenePharma  provided biotin-labeled hsa_circ_0004543 probes (hsa_circ_0004543) and negative controls (oligoes). PierceTM Magnetic RNA-Protein Pull-Down kit was used for RNA pull-down assay. Briefly, the miRNA binding to hsa_circ_0004543 was determined by qRT-PCR after it was enriched by incubating streptavidin agarose magnetic beads with biotin-labeled hsa_circ_0004543 probes or negative control first, and then with the cell lysates from SiHa or C-4I cells .\u03bcg) were used for target protein expression determination based on separation on an 8% SDS-PAGE gel, followed by the transfer on a polyvinylidene fluoride (PVDF) membrane, incubating at 4\u00b0C overnight in primary and secondary antibodies for 1\u2009h at RT, and developing with SuperSignal West Dura Extended Duration Substrate after it was washed three times with TBST [Total protein was extracted with RIPA lysis buffer. Proteins  was used. Tests were conducted with one-way analysis of variance followed by Tukey's post hoc test for multiple groups and Student's \u03b1 axis. Hsa_circ_0004543 levels in human CC tissues and cells were determined with qRT-PCR. Direct binding between hsa-miR-217 and hsa_circ_0004543 or HIF-1a was predicted by the interactome or TargetScan and was verified with a dual-luciferase reporter gene assay with or without RNA pull-down. Aggressive phenotypes of CC cells including cell viability, colony proliferation, apoptosis, migration, and invasion were detected with the CCK-8 assay, colony formation assay, flow cytometry assay, and transwell assay, respectively. The mechanism of hsa_circ_0004543 in CC development was further assessed by silencing hsa_circ_0004543 with/without hsa-miR-217 silencing or HIF-1a overexpression. Associations between gene expressions were evaluated with Pearson's correlation analysis.The purpose of the current work was to explore the role and the ceRNA mechanism of hsa_circ_0004543 in regulating CC oncogenesis and metastasis. Based on the bioinformatics analysis and literature review, we hypothesized that hsa_circ_0004543 expression was upregulated in CC cells and tissues, which contributed to increased CC cell viability, colony proliferation, migration, and invasion. It also inhibited cell apoptosis by regulating the hsa-miR-217/HIF-1To explore the function of hsa_circ_0004543 in CC oncogenesis and progress, we first collected paired CC and self-matched negative control (NC) paracancerous tissues from 40 CC patients, followed by the analysis of the hsa_circ_0004543 expression by qRT-PCR. The data proved that hsa_circ_0004543 expression was extremely higher in CC tissues versus NC tissues . We alsoTo explore the effects of hsa_circ_0004543 on CC malignant phenotypes, silencing RNA (siRNA) specifically targeting hsa_circ_0004543 (si-hsa_circ_0004543) was respectively transfected to two CC cells (SiHa and C-4I) and yielded the highest hsa_circ_0004543 expression for hsa_circ_0004543 knockdown. The results showed that transfecting si-hsa_circ_0004543 in SiHa and C-4I cells produced a significantly reduced hsa_circ_0004543 expression . Viabilihttps://circinteractome.nia.nih.gov/)  . This anih.gov/) . The intih.gov/) . Moreoveih.gov/)  and was ih.gov/) . Meanwhiih.gov/) . Associaih.gov/) .http://www.targetscan.org/)  , followean.org/) . Moreoveoth mRNA  and prototh mRNA  levels, oth mRNA . Furtheroth mRNA . Moreoveoth mRNA . These fAfter being cotransfected using si-NC, si-hsa_circ_0004543, si-hsa_circ_0004543 + inh-hsa-miR-217, or si-hsa_circ_0004543+HIF-1a, viabilities of SiHa and C-4I cells were first determined based on the use of the CCK-8 assay, and the data validated that hsa_circ_0004543 silencing time-dependently decreased OD value (0\u201372\u2009h) and was partly rescued by hsa-miR-217 silencing or HIF-1a overexpression . Colony circRNAs are noncoding RNAs that are highly stable in eukaryotic cells. Hsa_circ_0004543 has been newly identified as a significantly upregulated circRNA in CC tissues by circRNA microarray . NeverthAccumulating evidence has shown that transcriptional regulation between ncRNAs and mRNAs plays an essential function in CC cancer progression, including growth, migration, invasion, and multidrug resistance. As an important class of ncRNAs, miRNAs also play a critical role in regulating cell functions via degradation of target genes, therefore regulating cell proliferation, apoptosis, and metastasis. circRNA may be competitive endogenous RNA (ceRNA) for miRNAs to regulate expression of downstream targets of mRNAs, and the ceRNA networks are important mechanisms to elucidate the posttranscriptional regulation in CC , 31\u201335. We used the online tool Circular RNA Interactome which predicted miRNAs harboring complementary binding sequences with hsa_circ_0004543 and identified the potential candidate of hsa-miR-217. The direct binding between hsa_circ_0004543 and hsa-miR-217 was further confirmed with dual-luciferase reporter activity examination in SiHa and C-4I cells. Meanwhile, hsa-miR-217 was identified to be inhibited in both CC cell lines and tissues, indicating the oncosuppressor role of hsa-miR-217 in CC. Moreover, hsa_circ_0004543 silencing in SiHa and C-4I cells considerably upregulated hsa-miR-217 expression, and Pearson's correlation assay discovered a negative association between hsa-miR-217 with hsa_circ_0004543 expressions in 40 CC patient tissues. These findings suggested that hsa_circ_0004543 directly interacted with hsa-miR-217 to promote the aggressive phenotypes of CC cells.\u03b1, encoded by HIF1A, is a commonly expressed HIF\u03b1 isoform in various cells and the most essential regulator of oxygen homeostasis [\u03b1 changes are associated with the outcomes of patients with various cancers, suggesting its critical role in carcinogenesis [\u03b1 at both the mRNA and protein levels, and led to reduced viability and colony proliferation ability in CC cells. These effects were partly reversed by inhibiting hsa-miR-217 or overexpressing HIF-1a. Outcomes indicated that HIF-1a directly interacted with hsa-miR-217 via the sponging activity of hsa_circ_0004543 to stimulate CC progression. Hence, hsa_circ_0004543 may function as a ceRNA of hsa-miR-217 to inhibit HIF-1\u03b1 degradation and thus stimulate the growth and metastasis of CC cells.We used the online tool TargetScan to predict the mRNAs harboring complementary binding sequences with hsa-miR-217 and identified the potential candidate of HIF1A. The direct binding between HIF1A and hsa-miR-217 was further confirmed with a dual-luciferase reporter activity assay in SiHa and C-4I cells. Hypoxia is a typical representative of middle-late stage solid cancers and plays a key role in promoting malignancy cells to adapt to the hypoxia microenvironment in cancer by regulating the HIF transcriptional factors . HIF-1\u03b1,eostasis . HIF-1\u03b1 ogenesis . Our adv\u03b1 level in CC cells. These changes thus contributed to the CC cell's oncogenesis and progression. Our results emphasized the potential function of hsa_circ_0004543 as a newly identified oncogenic ncRNA and that targeting the hsa_circ_0004543/hsa-miR-217/HIF-1\u03b1 axis may provide a new therapeutic strategy to treat CC.In conclusion, our current work showed that the hsa_circ_0004543 level was significantly amplified in CC patients and cells that promoted viability, colony proliferation, migration and invasiveness, and repressed apoptosis by sponging hsa-miR-217 to upregulate the HIF-1"}
+{"text": "HemaSphere. 2021;5(5):e555), the authors inform the readership that Achille Iolascon, based at the Dept. of Molecular Medicine and Medical Biotechnology, University of Naples Federico II, and at CEINGE Biotecnologie Avanzate, Naples, Italy, should be acknowledged as an author. This has been updated.Since the publication of the article entitled \u201cSelecting \u03b2-thalassemia Patients for Gene Therapy: A Decision-making Algorithm\u201d (https://journals.lww.com/hemasphere/Fulltext/2021/05000/Selecting_thalassemia_Patients_for_Gene_Therapy_.12.aspxThe changes have been made online:"}
+{"text": "Pavo cristatus) are attractive both to the female of the species and to humans. However, little is known about the evolution of the phenotype and phylogeny of these birds at the whole-genome level. So far, there are no reports regarding the genetic mechanism of the formation of leucism plumage in this variant of Indian peafowl.The dazzling phenotypic characteristics of male Indian peafowl , and contig and scaffold N50 were up to 6.2 and 11.4 Mb, respectively. Compared with other birds, Indian peafowl showed changes in terms of metabolism, immunity, and skeletal and feather development, which provided a novel insight into the phenotypic evolution of peafowl, such as the large body size and feather morphologies. Moreover, we determined that the phylogeny of Indian peafowl was more closely linked to turkey than chicken. Specifically, we first identified that This study provides an Indian peafowl genome of high quality, as well as a novel understanding of phenotypic evolution and phylogeny of Indian peafowl. These results provide a valuable reference for the study of avian genome evolution. Furthermore, the discovery of the genetic mechanism for the development of leucism plumage is both a breakthrough in the exploration of peafowl plumage and also offers clues and directions for further investigations of the avian plumage coloration and artificial breeding in peafowl. Pavo cristatus (NCBI:txid9049), commonly called the Indian peafowl or blue peafowl, represents elegance, honour, beauty, luck, and romance in many Asian cultures  reference genome of the Indian peafowl was constructed using third-generation All procedures used for this study that involved animals fully complied with guidelines for the care and use of experimental animals established by the Ministry of Agriculture of China. The ethics committee of South China Agricultural University approved this study. A blood sample was collected from a female Indian peafowl for genome assembly and 51 blood samples from 35 blue feather peafowls and 16 leucistic plumage peafowls for pooled resequencing in Leping Sentai special breeding Co., Ltd, in Jiangxi Province, China, under the principles and standards of animal welfare ethics. Meanwhile, 2 liver and 2 muscle tissue samples were obtained from the female Indian peafowl to assist the process of genome assembly. Additionally, feather pulp samples from 4 blue and 4 leucistic peafowls were collected for RNA sequencing (RNA-seq).Genomic DNA was extracted from blood samples using a routine phenol-chloroform protocol. The concentration of the extracted DNA was evaluated using a Nanodrop 2000 spectrophotometer , and diluted to a final concentration of 100\u00a0ng/\u03bcL. The integrity of DNA was checked via electrophoresis on 0.8% agarose gel. Total RNA of feather pulp was extracted using TRIzol reagent . The purity and degradation of RNA was detected by Nanodrop 2000 spectrophotometer and agarose gel electrophoresis.RRID:SCR_016387). For Pacific Biosciences (PacBio) sequencing, genomic DNA was sheared by a g-TUBE device (Covaris) with 20\u00a0kb settings for further preparing a 20\u00a0kb Single-Molecule Real Time (SMRT) bell, and then the single-molecule sequencing was completed on a PacBio RS-II platform . For 10\u00d7 genomics sequencing, each GEM was amplified by PCR and added P7 sequencing adapters for Illumina sequencing.Genomic DNA was used to make 350-bp insert fragment libraries using the Illumina TruSeq Nano method, starting with 100\u00a0ng DNA. Mate pair libraries were made by Nextera Mate Pair Sample Preparation Kit (Illumina) with the gel plus option, and sequenced using Illumina NovaSeq 6000 platform  software RRID:SCR_004726), , KEGG (K_012773) , and Int_006695) . tRNAsca_011809)  from Rfa_007891)  to prediCoturnix japonica) [Gallus gallus) [Meleagris gallopavo) [Colinus virginianus) [Anas platyrhynchos) [Taeniopygia guttata) [Ficedula albicollis) [Geospiza fortis) [Pseudopodoces humilis) [Columba livia) [Falco peregrinus) [Falco cherrug) [Homo sapiens) [Mus musculus) [RRID:SCR_017118) v2.3.7 [RRID:SCR_011811) v7.450 software [RRID:SCR_017334) with default parameters [The amino\u00a0acid\u00a0sequences of the following were downloaded from NCBI database to identify the gene families and single-copy orthologous genes. They are: Japanese quail (aponica) , chicken gallus) , turkey llopavo) , northerinianus) , common hynchos) , zebra fguttata) , collareicollis) , medium  fortis) , Tibetanhumilis) , rock pia livia) , peregriegrinus) , saker fcherrug) , human (sapiens) , and mouusculus) . The lon) v2.3.7  was usedrameters . The finRRID:SCR_017254) v2.1.2 software was first used to find the best model for constructing phylogenetic tree with options \u201c-m MF\u201d and the species tree with bootstrap 1000 based on the concatenated alignment of single-copy orthologues sequences from 15 species [RRID:SCR_006086) software was used to construct phylogenetic tree with parameters \u201c-m PROTGAMMALGX -f a\u201d with bootstrap 1000. Divergence time of 15 species was estimated by using MCMCtree program implemented in PAML packages , [RRID:SCR_021162) database [RRID:SCR_019121) v1.7.1.To determine the phylogenetic relationship of 15 species, IQ-tree , . Five cadatabase  were useRRID:SCR_001010) . Gene pairs of synteny blocks within the genome were identified using MCScanX [To compare the genome synteny of peafowl with chicken and turkey, the homologue of the genome was identified using BLASTp  v4.2.1 with a random birth and death model and significance of P-values <\u00a00.05 [To identify the gene family expansion and contraction in peafowl, the gene families in 15 species and phylogenetic tree with divergent times were taken into account to estimate the significance of gene gain and loss in gene family using the CAFE  with the option \u201c-codon\u201d [dN/dS (\u03c9) values between foreground branch and background branch were estimated using likelihood ratio test values based on the \u03a72 test. When the \u03c9-value in the foreground branch was greater than in the background branch, it suggested that the genes of the foreground branch were under positive selection (P <\u00a00.05) and the positively selected sites were determined using the Bayesian empirical Bayes (BEB) method. All the positively selected genes underwent functional enrichment analysis using KOBAS  [To determine adaptive evolution under positive selection in peafowl, the single-copy orthologous protein sequences shared among the 11 species  were searched, filtered, and then converted to coding gene sequence (CDS) using the EMBOSS backtranseq program . The CDS\u201c-codon\u201d . The abo\u201c-codon\u201d . A branc\u201c-codon\u201d . The pea_006350) .RRID:SCR_019389) and were quantified using real-time PCR. These libraries constructed above were sequenced on an Illumina NovaSeq platform, and 150-bp\u00a0paired-end reads were generated with insert size \u223c350\u00a0bp. The raw data were filtered by removing reads with \u226510% unidentified nucleotides (N), reads with >50% bases having phred quality <5, and reads with >10\u00a0nt aligned to the adapter allowing \u226410% mismatches. The clean reads were mapped to the assembled reference genome using BWA with parameters \u201cmem -t 4 -k 32 \u2013M \u2013R.\u201d Alignment files were converted to BAM files using SAMtools  software (settings: \u2013bS \u2013t) [RRID:SCR_001876) v 4.0 pipeline [The genomic DNA from 35 blue feather peafowls and 16 leucistic plumage peafowls were pooled, respectively. Then 1.5\u00a0\u03bcg DNA per pool was used for constructing the sequencing libraries using Truseq Nano DNA HT Sample preparation Kit  following manufacturer's constructions. Each pooled DNA sample was fragmented through sonication to a size of 350bp\u00a0and end repaired, A-tailed, and ligated with the full-length adapter for Illumina sequencing with further PCR amplification. PCR-amplified sequencing libraries were purified (AMPure XP system) and analysed for size distribution on Agilent2100 Bioanalyzer  . In addipipeline .RRID:SCR_016323) v1.3.3 [RRID:SCR_012919)[RRID:SCR_009803) software [RRID:SCR_015687)[P <\u00a00.01. Subsequently, the functional enrichment analyses of DEGs were annotated through the GO (Gene Ontology) [The complementary DNA (cDNA) of feather was acquired through PrimeScript\u2122 RT reagent Kit with gDNA Eraser  according to the manufacturer's instructions. The cDNA underwent damage repair, end repair, SMRT dumbbell-shaped adapters, and ligation of the adapters to construct a mixed library. Primers and DNA polymerase were then combined to form a complete SMRT bell library. The qualified library was used for sequencing on a PacBio Sequel platform. The clean data were aligned to the reference genome of Indian peafowl by STAR v2.5.3a . The tra) v1.3.3  and featR_012919) in Subresoftware . DiffereR_015687) in condicDNA of feathers was reversely transcribed with PrimeScript\u2122 RT reagent Kit with gDNA Eraser (Takara). The reverse transcription quantitative PCR (RT-qPCR) was conducted in a total volume of 10\u00a0\u00b5L including 5\u00a0\u00b5L SYBR Taq II kit (Takara), 0.3\u00a0\u00b5L Rox Reference Dye (50\u00d7), 2.7\u00a0\u00b5L distilled water, 1\u00a0\u00b5L cDNA, and 1\u00a0\u00b5L primers, and performed on a 7900HT RT-qPCR system (ABI). \u03b2-actin was selected as the internal reference gene. All primer sequences are shown in Third-generation PacBio SMRT sequencing technology and second-generation Illumina sequencing technology were used and combined with 10X genomics to assemble the Indian peafowl genome. We obtained a sequencing volume of 164.03 Gb using an Illumina NovaSep 6000 platform, 112.57 Gb of sequencing data on a 10X Genomics sequencing platform, and 110.74 Gb of sequencing data using the PacBio sequencing platform . In totaWe assessed the completeness and base accuracy of our Indian peafowl genome assembly using CEGMA and BUSCO. Assembly of the draft genome presented a high mapping rate (98.05%) and coverage rate (99.87%) and low homozygous SNP rate (0.0002%) by mapping to the short reads, generally reflecting the high accuracy of genome assembly . The BUSab initioprediction, the Indian peafowl genome comprised 15.20% non-redundant repeat sequences, including 1.27% tandem repeats, 14.12% transposable elements, and 7.35% transposable element proteins  could be used to infer positive selection and contribute to understanding the evolutionary characteristics in species. In this study, pairwise synteny was compared between peafowl and chicken, and peafowl and turkey, and the ratio of dN/dS was calculated. Scaffold lengths greater than scaffold N70 (5 Mb) in the peafowl genome and other collinear scaffolds were marked as others were displayed  in peafowl compared to chicken were associated with biological processes and immune-related pathways  (P <\u00a00.05); e.g., T helper 1 (Th1) and Th2 cell differentiation, T-cell receptor signaling pathway, and intestinal immune network for IgA production. Furthermore, compared with turkey, 43 positively selected genes were notebly enriched in GO terms of organelle (GO:0043226), extracellular space (GO:0005615), and epithelium migration (GO:0090132), and the pathways of glutathione metabolism  and thyroid hormone synthesis  (P <\u00a00.05)  widely distributed in various tissues in chicken [Collinearity analysis can reflect the homology of different species and genetic relationships. Genes with a pairwise ratio of nonsynonymous to synonymous substitutions  , which a chicken . These eP <\u00a00.05) were detected in peafowl  and immune response (CD244) (P <\u00a00.05), such as the GO terms of natural killer cell activation involved in immune response (GO:0002323), MHC class I protein binding (GO:0042288), positive regulation of interleukin-8 production (GO:0032757), positive regulation of interferon-\u03b3 production (GO:0032729), and lipid droplet (GO:0005811) (ALDH3A2) (GO:0001561), myocardium development (GO:0048739), muscle contraction and cardiac disease , olfactory receptor activity  (GO:0004984), and the pathways of olfactory transduction, metabolism, and cardiac muscle contraction  . Converstraction . For exatabolism , 95. Thetabolism . During P <\u00a00.05). These genes were annotated and classified through the analysis of GO ontology and KEGG pathways to further explore the impact of adaptive evolution on peafowl. According to the results of functional enrichment analyses, we briefly summarized that these positively selective genes mainly participated in the process of lipid metabolism , limb and skeletal development , immune response , pigmentation (GO:0042470 and GO:0030318), sensory perception , and other GO terms , which demonstrated that the sites were under positive selection in branch-site model A (foreground). In the branch of peafowl (foreground), 3,417 genes were under significantly positive selection based on BEB values . Significant GO terms were involved in melanocyte differentiation, skeletal muscle and bone development, immunity, and response to stress  >30\u00a0were extracted as potential candidate regions. As a result, we found that EDNRB in scaffold 196 and PMEL in scaffold 144 were significantly related to plumage pigmentation  were associated with melanin deposition  application. The results indicated that there was no difference in the transcript of EDNRB in the 2 types of feather pulp  of PMEL was conducted in blue and leucistic peafowl Supplementary Table S13. Functional categories of positively selected genes (dN/dS > 1) between peafowl and chickenSupplementary Table S14. Functional categories of positively selected genes (dN/dS > 1) between peafowl and turkeySupplementary Table S15. Functional enrichment of significantly expansive genes in peafowlSupplementary Table S16. Functional enrichment of significantly contractive genes in peafowlSupplementary Table S17. GO term enrichment of positively selected genes in peafowl under branch-site modelSupplementary Table S18. KEGG pathways of positively selected genes in peafowl under branch-site modelSupplementary Table S19. Functional categories of positively selected genes in peafowl under branch modelSupplementary Table S20. Primer sequences of PMEL for RT-qPCRgiac018_GIGA-D-21-00190_Original_SubmissionClick here for additional data file.giac018_GIGA-D-21-00190_Revision_1Click here for additional data file.giac018_GIGA-D-21-00190_Revision_2Click here for additional data file.giac018_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giac018_Response_to_Reviewer_Comments_Revision_2Click here for additional data file.giac018_Reviewer_1_Report_Original_SubmissionDustin Rubenstein -- 8/17/2021 ReviewedClick here for additional data file.giac018_Reviewer_1_Report_Revision_1Dustin Rubenstein -- 11/17/2021 ReviewedClick here for additional data file.giac018_Reviewer_2_Report_Original_SubmissionYang Liu, Ph.D -- 9/20/2021 ReviewedClick here for additional data file.giac018_Reviewer_2_Report_Revision_1Yang Liu, Ph.D -- 11/28/2021 ReviewedClick here for additional data file.giac018_Reviewer_2_Report_Revision_2Yang Liu, Ph.D -- 12/25/2021 ReviewedClick here for additional data file.giac018_Supplemental_FilesClick here for additional data file.BEB: Bayesian empirical Bayes; BLAST: Basic Local Alignment Search Tool; bp: base pairs; BUSCO: Benchmarking Universal Single-Copy Orthologs; BWA: Burrows-Wheeler Aligner; cDNA: complementary DNA; CDS: coding gene sequence; CEGMA: Core Eukaryotic Genes Mapping Approach; DEG: differentially expressed genes; GATK: Genome Analysis Toolkit; Gb: gigabase pairs; GO: Gene Ontology; kb: kilobase pairs; KEGG: Kyoto Encyclopedia of Genes and Genomes; KOBAS: KEGG Orthology-Based Annotation System; MAFFT: multiple alignment fast Fourier transfom; Mb: megabase pairs; Mya: million years ago; NCBI: National Center for Biotechnology Information; PacBio: Pacific Biosciences; PASA: Program to Assemble Spliced Alignments; RAxML: Randomized Axelerated Maximum Likelihood; RNA-seq: RNA sequencing; SMRT: Single-Molecule Real Time; SNP: single-nucleotide polymorphism; SRA: Sequence Read Archive; TE: tandem repeat; tRNA: transfer RNA.This work was supported by Educational Commission of Jiangxi Province of China (GJJ190177) and by the Key Research and Development Program of Jiangxi Province of China (20171BBF60003).All procedures used for this study and involving animals fully complied with guidelines for the care and use of experimental animals established by the Ministry of Agriculture of China. The Animal Care and Use Committee of the South China Agricultural University approved this study.The authors declare that they have no competing interests.X.Y., H.M., and J.R. designed the study and wrote the manuscript. S.L. and H.C. analyzed the data and wrote the manuscript. S.L., H.C., H.M., and W.L. revised the manuscript. S.L., H.Z., and B.L. conducted the validation experiments. S.L., J.O., M.H., S.Z., S.X., H.T., Y.G., Y.X., D.C., K.C., H.M., and X.Y. collected samples and performed the sequencing and genotyping experiments. All authors contributed to and approved the final manuscript."}
+{"text": "We theoretically demonstrated a class of plasmonic coupled elliptical nanostructure for achieving a spaser or a nanolaser with high intensity. The plasmonic ellipse is made up of gold film substrate. The proposed structure is then trialed for various light polarizations, moreover, a simple elliptical nanostructure has been chosen primarily from which different cases have been formed by geometry alteration. The structure supports strong coupled resonance mode i.e. localized surface plasmon (LSP). The localized surface plasmon resonance (LSPR) of the investigated system is numerically examined using the finite-element method (FEM). The calculations showed that the LSPR peaks and the local field intensity or near field enhancement (NFE) of the active nanosystem can be amplified to higher values by introducing symmetry-breaking techniques in the proposed ellipse and its variants. The coupled nanostructure having different stages of wavelengths can be excited with different plasmonic resonance modes by the selection of suitable gain media. In addition, a small-sized nanolaser with high tunability range can be developed using this nanostructure. The spaser phenomena are achieved at several wavelengths by changing light polarization and structure alteration methods. Giant localized field enhancement and high LSPR values enable the proposed model to be highly appealing for sensing applications, surface-enhanced Raman spectroscopy, and much more. Lasers play an important role in physics and optics due to their coherent light-sourced nature. However, with increasing speed and size reduction in photonic devices, the role of traditional lasers is challenged due to the diffraction limit of light that usually prevents the miniaturization of such devices less than half of its wavelength , 2. The We investigated the coupling of plasmons in a simple elliptical-shaped nanostructure and its variants made up from silica and truncated gold ellipse, which can be fabricated using techniques suggested by , 23. Then\u2019 and \u2018bn\u2019 where \u2018n\u2019 is an integer  for various configurations. The NE is transformed to a phase known as the symmetry breaking phase and the term used for nano-ellipse is symmetry broken nano-ellipse SBNE. From this set of designs, we have modeled different patterns mentioned above.In this work, we have investigated several patterns of single nano-ellipse (NE) such as nano-elliptical dimer (NED), linear chain nano-elliptical trimer (LCNET), linear chain nano-elliptical quadramer (LCNEQ), symmetry broken nano-ellipse (SBNE), symmetry broken nano-elliptical dimer (SBNED), symmetry broken linear chain nano-elliptical trimer (SBLCNET) and symmetry broken linear chain nano-elliptical quadramer (SBLCNEQ). All these configurations have been formed from a single NE by adding an identical NE or by extracting a portion and then forming above mentioned configurations which are briefly discussed in later sections. The transformation from single NE to various sets is shown in In this section, we have briefly described all the cases for which we have calculated our results and performed the simulations. For a detailed understanding/analysis of the optical properties of a gold\u2013silica elliptical nanostructure, it is necessary to study a gold-silica nanostructure. The optical outcome of an ellipsoid is investigated by using the plasmon hybridization theory .a/b/t = 50/30/25 nm respectively. Where a is the outer semi-axis b represents the inner semi-axis while t gives the thickness of the NE. The structure is placed along the x-axis, its response is calculated for the x-polarization case and y-polarization case. That is, the light is the first incident from the x-axis and in the second case, the position of the nano-ellipse remained unchanged while the light was incident from the y-axis. We calculated the extinction spectra in 915 nm due to transverse dipolar mode and corroboration by the charge distribution, while for the y-polaroid case the spectra show a blue shift with a minor peak obtained at 606.2 nm since the transverse dipolar mode is weakened. The lowest energy mode corresponds to an anti-symmetric coupling between the plasmon resonance and the metallic nanostructure which in this case is shown by the y-polaroid case. While high peak arises from symmetric coupling between light and the NE. All of the systems studied herein were assumed to be in a vacuum. Similarly, amplification of both cases can be seen in 126 and for y-direction, light is poorly coupled resulting the formation of bonding modes, hence, the strong plasmonic effect does not occur and we obtained less amplification of 18. The local field enhancement (LFE) for the they-polarized case was 7 times less than that of the x-polarized case but can still be used for applications falling in this frequency range.We consider the truncated gold nano-ellipse (NE) surrounded by a gain media shown in a/b/t = 50/30/25 nm respectively and this case is termed as symmetry broken nano-ellipse (SBNE). The SBNE is surrounded by a gain media to compensate against ohmic losses as shown in 1142 nm and shows a redshift compared to the other two peaks that occurred at 836.4 nm and 659.9 nm. The peaks at the wavelengths of 836.4 nm and 659.9 nm shows low energy and are blue-shifted since the alignment of dipolar modes was antisymmetric. Similarly, if we look at the extinction spectra for y-polarization in the same plot we see that the strongest peak occurred at 1367 nm and shows red shifting compared to x-polarization as well as for the they-polarized case, but still, it can be noticed that its height is much low as compared to x-polaroid due to antisymmetric interaction of light with the SBNE. Similarly, the other two peaks were appeared at 902.6 nm and 694.3 nm and were blue-shifted concerning y-polaroid but show a slight red shifting concerning x-polaroid peaks. Also, it is noticeable that symmetry breaking led to multi-wavelength operation compared to. 200, and for y-polarization, the enhancement value was recorded to about 118. Furthermore, amplification obtained from the x-polarized case is about 1.5 times the y-polarized case as well as the full elliptical nano-structure. It can be seen that the y-polarized case for SBNE configuration produced far better results as compared to NE, whose value was 18 which is 6.5 times less than the current case. Hence, symmetry breaking produced huge near-field enhancement (NFE) values along with x-y polarization with multiple peaks.Metal-based nanoparticles support plasmon resonance(s) whose energies are strongly dependent on the geometry of the nanostructure. The resonance tunability feature has brought considerable experimental and theoretical research . An impo1115 nm which typically represents the strong coupling of the dimer with an incoming field. Another peak appears at the wavelength of 691.7 nm for the same polarization but its magnitude is too small. A blue-shifted peak shown in green color presents a y-polaroid case and this shifting along with the small height of the peak depicts subradiant quadrupolar nature resulting due to poor interaction of the electric field with the NED at 600 nm. 620 which shows a very high amplification factor for the arrangement. Similarly, 13 about which is about 48 times smaller than the x-polaroid case.Plasmonic nano-elliptical dimer (NED) with closed spaced resonant particles is an arrangement in which two identical ellipses are brought close to each other and the arrangement is termed as nano-elliptical dimer (NED) structure. The coupling between two NE structures with a small separation can induce a strong, enhanced, and deep subwavelength-confined optical near field inside the narrow gap by incident light. In 1494 nm (blue-line) shows red shifting and represents the negative parity dipoles.In this section, we have engineered a symmetry broken nano-elliptical dimer SBNED by setting all the structural parameters the same as that of section B and adding SBNE as depicted in 1199 nm and 865.4 nm represent strong mixing of positive parity bright modes with dark modes. The peak at the wavelength of 680.6 nm shows blue shifting with a strong magnitude compared to the 1001 nm peak. Similarly, y-polarization produced a peak at 1381 nm, showing a dipolar mixture of both NEs. Dark quadrupolar modes created low magnitude peaks at the wavelengths of 921.6 nm and 699.3 nm respectively. The tuning in these plots may be developed by alteration in the geometry or gap variation which is a highly suitable biosensor Reviewers' comments:Reviewer's Responses to Questions <font color=\"black\"> Comments to the Author1. Is the manuscript technically sound, and do the data support the conclusions? </font> The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1:\u00a0YesReviewer #2:\u00a0Yes********** <font color=\"black\"> 2. Has the statistical analysis been performed appropriately and rigorously?  </font> Reviewer #1:\u00a0YesReviewer #2:\u00a0Yes********** <font color=\"black\"> 3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data\u2014e.g. participant privacy or use of data from a third party\u2014those must be specified. </font> The Reviewer #1:\u00a0YesReviewer #2:\u00a0Yes********** <font color=\"black\"> 4. Is the manuscript presented in an intelligible fashion and written in standard English? </font> PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1:\u00a0YesReviewer #2:\u00a0Yes********** <font color=\"black\"> 5. Review Comments to the Author </font> Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1:\u00a0The authors theoretically demonstrated a class of plasmonic coupled elliptical nanostructure for achieving a spaser or a nanolaser with high intensity. Technically, the authors employ the numerical methods with apparent authority and the results seem valid. This work is interesting for the readership and deserve publication after major revision. This work will be more impactful if the authors address the following comments.1. Figs. 2-9 (c) and (d) are blurred. Please replace them as the clear ones.2. The references used in introduction section should be improved. It is written in abstract section that \u201cGiant localized field enhancement and high LSPR values enable the proposed model to be highly appealing for sensing applications, surface enhanced Raman spectroscopy\u201d. To be beneficial for the readers to enrich the mechanism and background of plasmonic sensor and surface enhanced Raman spectroscopy, the authors should mention the other approaches of plasmonic sensor, i.e., Nanomaterials, 10, 1399 (2021), J Phys D: Appl Phys, 54, 115301(2021)), Results in Physics, 15, 102567(2019) and Nanomaterials, 9,1691 (2019), and for surface enhanced Raman spectroscopy ).Reviewer #2:\u00a0The authors reported a class of plasmonic coupled elliptical nanostructure for achieving a spaser or a nanolaser with high intensity. The FEM calculations showed that the LSPR peaks and the local field intensity or near field enhancement (NFE) of the active nanosystem can be amplified to higher values by introducing symmetry-breaking techniques in the proposed ellipse and its variants. Giant localized field enhancement and high LSPR values enable the proposed model to be highly appealing for sensing applications, surface enhanced Raman spectroscopy, and much more. The research is significant, the amount of data is large, and the contrast for characteristic parameters of different-type nano-structures is clear. However, before possible publication, some of the listed points should be explained and revised for further improving the manuscript.1. Authors theoretically investigated a class of plasmonic coupled elliptical nanostructure for achieving spaser, but the shape of SBNE structures were unusual and whether they can be prepared for practical application?2. Why the structure type with maximum NFE for Elliptical nano-structures for the x polarization was SBLCNET, but the structure type with maximum NFE for Elliptical nano-structures for the y polarization was LCNEQ rather than SBLCNET?3. The calculated results should be supported by some experimental data through experimental measurement or experimental results from literatures.4. How does the performances (such as NFE) of the structures in this work when compared with that in literatures.5. There were too many abbreviations, so it is recommended to put them in a table.6. The table note for Table 3 should be Table 2.********** <font color=\"black\"> 6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.If you choose \u201cno\u201d, your identity will remain anonymous but your review may still be made public.Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. </font> Reviewer #1:\u00a0NoReviewer #2:\u00a0Nohttps://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at\u00a0figures@plos.org. Please note that Supporting Information files do not need this step.While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool,\u00a0 18 Jan 2022Response to Reviewer 1 commentsReviewer #1: The authors theoretically demonstrated a class of plasmonic coupled elliptical nanostructure for achieving a spaser or a nanolaser with high intensity. Technically, the authors employ the numerical methods with apparent authority and the results seem valid. This work is interesting for the readership and deserve publication after major revision. Author Reply: We thank the respected Reviewer for appreciating our work and accepting our efforts. ________________________________________Concern 1. Figs. 2-9 (c) and (d) are blurred. Please replace them as the clear ones.Author Reply: We thank the respected Reviewer for this comment and for highlighting the blurred images. The Figs.2-9 (c) and (d) have been modified with the high pixel format in the revised version of the manuscript. ________________________________________Concern 2. The references used in introduction section should be improved. It is written in abstract section that \u201cGiant localized field enhancement and high LSPR values enable the proposed model to be highly appealing for sensing applications, surface enhanced Raman spectroscopy\u201d. To be beneficial for the readers to enrich the mechanism and background of plasmonic sensor and surface enhanced Raman spectroscopy, the authors should mention the other approaches of plasmonic sensor, i.e., Nanomaterials, 10, 1399 (2021), J Phys D: Appl Phys, 54, 115301(2021)), Results in Physics, 15, 102567(2019) and Nanomaterials, 9,1691 (2019), and for surface enhanced Raman spectroscopy ).Author Reply: We are greatly thankful to the respected Reviewer for highlighting the weaker portion of the manuscript and for suggesting valuable related referenced papers. The suggested papers were worth reading and results in the enhancement of our knowledge. The suggested references were valid too and has been added in the revised version of the manuscript. ________________________________________Thank you for your attention, valuable suggestions, and patience, and if you have any questions, please don't hesitate to contact me. Yours sincerely,Corresponding authors*Response to Reviewer 2 commentsReviewer #2: The authors reported a class of plasmonic coupled elliptical nanostructure for achieving a spaser or a nanolaser with high intensity. The FEM calculations showed that the LSPR peaks and the local field intensity or near field enhancement (NFE) of the active nanosystem can be amplified to higher values by introducing symmetry-breaking techniques in the proposed ellipse and its variants. Giant localized field enhancement and high LSPR values enable the proposed model to be highly appealing for sensing applications, surface enhanced Raman spectroscopy, and much more. The research is significant, the amount of data is large, and the contrast for characteristic parameters of different-type nano-structures is clear. However, before possible publication, some of the listed points should be explained and revised for further improving the manuscript.Concern 1. Authors theoretically investigated a class of plasmonic coupled elliptical nanostructure for achieving spaser, but the shape of SBNE structures were unusual and whether they can be prepared for practical application?Author Reply: The nanoparticles can be fabricated with electron beam lithography. Scanning electron microscopy (SEM) can be used to verify the success of the lithography process and to identify the best fabrication parameters such as exposure dose and development time. Both the nanostructures for the SEM and those for the optical characterization can be fabricated on the same substrate by using exactly the same electron beam lithography procedure. As mentioned in the manuscript, we have performed simulations to investigate the optical properties of ellipse and associated variants by changes in geometry and incident light. Our work is theoretical and is based on Finite Element Method (FEM). The SBNE is taken from the main ellipse, this elliptical model provide points for confining SPs that leads for the formation of hotspots. On the same time this configuration has rounded corners due to which it can be fabricated using above mentioned methods and imprinting lithography, atomic force microscopy or the methods explained by .________________________________________Concern 2. Why the structure type with maximum NFE for Elliptical nano-structures for the xpolarization was SBLCNET, but the structure type with maximum NFE for Elliptical nano-structures for the y polarization was LCNEQ rather than SBLCNET?Author Reply: Respected Reviewer, the high NFE for x-polarization was achieved by SBLCNET, due to more exposed area of nanostructures to light and plasmon confinement points specially on the tips. While, for y-polarization this configuration showed less NFE and high value achieved for LCNEQ for y-polaroid case because here the light direction was changed and all of the four ellipses were totally receiving incident light efficiently that led to maximum plasmon confinement in the structure and produced a high NFE value for this configuration.________________________________________Concern 3. The calculated results should be supported by some experimental data through experimental measurement or experimental results from literatures.Author Reply: Respected Reviewer, the following table has been added in the revised version of the manuscript which shows a comparison of our work with others studies with both theoretical and experimental work.Table A : Comparison of current study with other worksRef. No Near Field Enhancement (NFE) No. of Peaks6 300 17 50 38 80 (abundance %) 49 1.5 ev 310 90 111 14.8 112 143 3Current study 1019 5________________________________________Concern 4. How does the performances (such as NFE) of the structures in this work when compared with that in literatures.Author Reply: Table A in the revised version of the manuscript summarizes the performance of current study with the others study in terms of NFE and LSPR/extinction/scattering peaks. ________________________________________Concern 5. There were too many abbreviations, so it is recommended to put them in a table.Author Reply: We thank the respected Reviewer for suggesting abbreviation table. The point is valid, and table has been added in the revised version of the manuscript. ________________________________________Concern 6. The table note for Table 3 should be Table 2.Author Reply: We thank the respected Reviewer for highlighting this error. The correct note for tables has been performed in the revised version of the manuscript. ________________________________________Thank you for your attention, valuable suggestions, and patience, and if you have any questions, please don't hesitate to contact me. Yours sincerely,Corresponding authors*________________________________________ References:[1]. Kazanskiy, N.L., Khonina, S.N., Butt, M.A., Ka\u017amierczak, A. and Piramidowicz, R., 2021. A numerical investigation of a plasmonic sensor based on a metal-insulator-metal waveguide for simultaneous detection of biological analytes and ambient temperature. Nanomaterials, 11(10), p.2551.[2]. Chao, C.T.C., Chau, Y.F.C. and Chiang, H.P., 2021. Highly sensitive metal-insulator-metal plasmonic refractive index sensor with a centrally coupled nanoring containing defects. Journal of Physics D: Applied Physics, 54(11), p.115301.[3]. Chau, Y.F.C., Chao, C.T.C., Huang, H.J., Anwar, U., Lim, C.M., Voo, N.Y., Mahadi, A.H., Kumara, N.T.R.N. and Chiang, H.P., 2019. Plasmonic perfect absorber based on metal nanorod arrays connected with veins. Results in Physics, 15, p.102567.[4]. Chou Chau, Y.F., Chen, K.H., Chiang, H.P., Lim, C.M., Huang, H.J., Lai, C.H. and Kumara, N.T.R.N., 2019. Fabrication and characterization of a metallic\u2013dielectric nanorod array by nanosphere lithography for plasmonic sensing application. Nanomaterials, 9(12), p.1691.[5]. Tseng, M.L., Chang, C.M., Cheng, B.H., Wu, P.C., Chung, K.S., Hsiao, M.K., Huang, H.W., Huang, D.W., Chiang, H.P., Leung, P.T. and Tsai, D.P., 2013. Multi-level surface enhanced Raman scattering using AgO x thin film. Optics express, 21(21), pp.24460-24467.[6]. Haynes, C.L., McFarland, A.D. and Van Duyne, R.P., 2005. Surface-enhanced Raman spectroscopy.[7]. Kelly, K.L., Coronado, E., Zhao, L.L. and Schatz, G.C., 2003. The optical properties of metal nanoparticles: the influence of size, shape, and dielectric environment. The Journal of Physical Chemistry B, 107(3), pp.668-677.[8]. Chen, S.; Carroll, D. Nano Lett. 2002, 2, 1003\u20131007[9]. T.W. Ebbesen, H.J. Lezec, H. Ghaemi, T. Thio, P.A. Wolff, Extraordinary optical transmission through sub-wavelength hole arrays. Nature 391, 667\u2013669 (1998).[10]. Huo, Y.Y., Jia, T.Q., Zhang, Y., Zhao, H., Zhang, S.A., Feng, D.H. and Sun, Z.R., 2014. Spaser based on Fano resonance in a rod and concentric square ring-disk nanostructure. Applied Physics Letters, 104(11), p.113104.[11]. Zhang, H., Zhou, J., Zou, W. and He, M., 2012. Surface plasmon amplification characteristics of an active three-layer nanoshell-based spaser. Journal of Applied Physics, 112(7), p.074309.[12]. Huo, Y., Jia, T., Zhang, Y., Zhao, H., Zhang, S., Feng, D. and Sun, Z., 2013. Narrow and deep Fano resonances in a rod and concentric square ring-disk nanostructures. Sensors, 13(9), pp.11350-11361.Attachment3. Response Letter.docxSubmitted filename: Click here for additional data file. 24 Jan 2022Large Electromagnetic Field Enhancement in Plasmonic Nanoellipse for Tunable Spaser Based ApplicationsPONE-D-21-35833R1Dear Dr. Farooq,We\u2019re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you\u2019ll receive an e-mail detailing the required amendments. When these have been addressed, you\u2019ll receive a formal acceptance letter and your manuscript will be scheduled for publication.http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at onepress@plos.org.If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they\u2019ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact Kind regards,Yuan-Fong Chou ChauAcademic EditorPLOS ONEAdditional Editor Comments :Reviewers' comments:Reviewer's Responses to Questions <font color=\"black\"> Comments to the Author </font> 1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the \u201cComments to the Author\u201d section, enter your conflict of interest statement in the \u201cConfidential to Editor\u201d section, and submit your \"Accept\" recommendation.Reviewer #1:\u00a0All comments have been addressed********** <font color=\"black\"> 2. Is the manuscript technically sound, and do the data support the conclusions? </font> The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1:\u00a0Yes********** <font color=\"black\"> 3. Has the statistical analysis been performed appropriately and rigorously?  </font> Reviewer #1:\u00a0Yes********** <font color=\"black\"> 4. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data\u2014e.g. participant privacy or use of data from a third party\u2014those must be specified. </font> The Reviewer #1:\u00a0Yes********** <font color=\"black\"> 5. Is the manuscript presented in an intelligible fashion and written in standard English? </font> PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1:\u00a0Yes********** <font color=\"black\"> 6. Review Comments to the Author </font> Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1:\u00a0The authors have revised their mauscript according to my comments. This manuscript can now be accepted for publication.********** <font color=\"black\"> 7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.If you choose \u201cno\u201d, your identity will remain anonymous but your review may still be made public.Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. </font> Reviewer #1:\u00a0No 8 Mar 2022PONE-D-21-35833R1 Large Electromagnetic Field Enhancement in Plasmonic Nanoellipse for Tunable Spaser Based Applications Dear Dr. Farooq:I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. onepress@plos.org.If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact plosone@plos.org. If we can help with anything else, please email us at Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staffon behalf ofDr. Yuan-Fong Chou Chau Academic EditorPLOS ONE"}
+{"text": "Background: Colorectal cancer (CRC) is the third most common cause of cancer deaths worldwide. Numerous studies have reported that circular RNAs (circRNAs) have important functions in CRC. It was first thought that circRNAs were non-coding RNA; however, more recently they were discovered to encode peptides and play a pivotal role in cancer development and progression. It was shown that most circRNAs possess coding potential; however, not all of them can truly encode peptides. Therefore, a practical strategy to scan for coding circRNAs is needed.Method: Sequence analyses included open reading frame (ORF) prediction, coding peptide prediction, and the identification of unique sequences. Then, experimental assays were used to verify the coded peptides, liquid chromatography-tandem mass spectrometry (LC-MS/MS) was introduced to detect sequences of circRNAs with coding potential, and Western blot was used to identify the encoded peptides. Finally, the functions of the circRNAs were primarily explored.Result: An efficient strategy for searching circRNAs with coding potential was created. We verified this schedule using public databases and LC-MS/MS, then two of these circRNAs were selected for further verification. We used commercial antibodies that can also identify the predicted peptides to test the coded peptides. The functions of the circRNAs were explored primarily, and the results showed that they were mainly involved in the promotion of proliferation and invasion ability.Discussion: We have constructed an efficient strategy of scanning circRNAs with coding potential. Our strategy helped to provide a more convenient pathway for identifying circRNA-derived peptides, which can be a potential therapeutic target or a diagnostic biomarker. PCNA and inhibit the proliferation of CRC cells by binding to eIF4A3  is now ranked third in terms of incidence and second in terms of mortality of all cancers worldwide . Even wio eIF4A3 . It was o eIF4A3 . Moreoveo eIF4A3 . Moreoveo eIF4A3 . Furthero eIF4A3 , gastrico eIF4A3 , and colo eIF4A3 . Therefohttp://www.ncbi.nlm.nih.gov/gds/). The following search words were used: (circular RNA or circRNA) and . The filters were as follows: 1) at least five pairs of normal and cancer tissues, 2) no metastasis, 3) human solid tissues, and 4) arrays should have differentially expressed circRNAs between normal and cancer tissue after differential analysis. Two datasets were finally included in this study: GSE126095 and GSE142837. The R software was used to calibrate, standardize, and log2 transform the downloaded files.All eligible microarray datasets available up to December 2020 were downloaded from GEO (http://www.circbase.org/). Then the open reading frames (ORFs) were predicted by using the getorf database (http://emboss.bioinformatics.nl/cgi-bin/emboss/getorf) with the sequence set as \u201ccircular.\u201d After infinite ORFs were excluded  predicted ORF regions spanned the junction sites of circRNAs, 2) the predicted coded peptides had more than 50 a.a., and 3)the bases overlay less than 50\u00a0bps, which could make the peptides easier to identify. The databases circRNADb and circbank were introduced to evaluate the overall coding potential of differentially expressed circRNAs. Finally, the circRNA-derived peptides should have a unique sequence with more than two a.a. The flowchart is shown in The batch effects between the datasets GSE126095 and GSE142837 were normalized by the sva package. Differential analysis was performed using the Limma package. The differentially expressed circRNAs met the standards: |logThe CRC cells HCT116, SW480, SW620, Lovo, HT29, DLD-1, and CACO2 and normal control cells NCM460 were all cultured in Dulbecco's modified essential medium (DMEM) with 10% fetal bovine serum (FBS). Cells were obtained from the Chinese Academy of Sciences  and Chuan Qiu Biotechnology .\u2212\u0394\u0394Ct method was used to process the data.The TRIzol Reagent  (1\u00a0ml per well) was used to lyse cells, then the RNAs were extracted using RNAsimple Kit . The concentration of RNAs was tested by DS-11 spectrophotometer . RNA samples (2\u00a0\u03bcg) were mixed with PrimeScript\u2122 RT reagent Kit with gDNA Eraser  and then applied to the ProFlex\u2122 PCR system  to obtain cDNAs. The cDNA samples were mixed with SYBR Premix Ex Taq II  and quantified by using ABI QuantStudio 5 ; the reaction was initiated at 95\u00b0C for 1\u00a0min, then at 95\u00b0C for 5\u00a0s, and 60\u00b0C 30\u00a0s for a total of 40 cycles. The primers are listed in A7595) and DHTKD1  at 4\u00b0C. After washing away the primary antibody with Tris-buffered saline Tween-20 (TBST), the secondary antibody  was incubated for 1\u00a0h at room temperature. The bands were detected in automatic chemiluminescence and fluorescence analysis system  by adding enhanced chemiluminescent reagent .Radioimmunoprecipitation assay (RIPA) lysis buffer  and protease and phosphatase inhibitor cocktail for general use  were used to obtain protein samples. Protein samples were denatured at 100\u00b0C for 10\u00a0min. Electrophoresis was started at 80\u00a0V to concentrate the protein and 120\u00a0V to separate the protein, then the transfer was conducted for 1\u00a0h under 300\u00a0mA. After blocking for 1\u00a0h with 5% non-fat milk , the membranes were incubated overnight with primary antibodies BANP  sequencing and data analysis by Oebiotech Co., Ltd. . In brief, enzymolysis was performed using 0.02\u00a0\u03bcg/\u03bcl trypsin, and then the peptides were desalinated. The sample was analyzed using a Nano-HPLC liquid phase system EASY-NLC1200 and a Q-Exactive mass spectrometer. The acquired data were analyzed using ProteomeDiscover  software.The cDNA from DLD-1 were amplified by PCR using a primer specifically targeting hsa_circ_0000725 and hsa_circ_0008826 and 2\u00d7Hieff \u00aePCR Master Mix (with dye) . To confirm the junction sites, Sanger sequencing was conducted by using the primers listed in To silence the circRNAs, siRNAs were synthesized by RIBOBIO . Lipofectamine\u2122 RNAiMAX Transfection Reagent  was used to transfect the siRNAs, and the efficiency was evaluated by qPCR.Cells  were seeded in 96-well plates. EdU  and CCK-8  reagents were used to estimate cell proliferative ability following the manufacture instructions. The images of EdU were analyzed by ImageJ, and the GraphPad Prism 7.0 software  was used to analyze CCK-8 results.Cells  were seeded in each well of a six-well plate. After 14\u00a0days of culture, the cells were fixed by 4% paraformaldehyde and stained by crystal violet. The images were analyzed by ImageJ.To evaluate cell invasion ability, each upper chamber  with a 1:7 concentration of Matrigel membrane  was seeded with 50,000 cells with the serum-free culture medium. The lower chambers were put into complete culture medium. After 72\u00a0h, the cells in the chambers were fixed by 4% paraformaldehyde and stained by crystal violet. Then, the upper chamber cells were cleared. The images were analyzed by ImageJ.In the cell cycle test, the cells were collected with cold phosphate-buffered saline (PBS) and fixed in 70% cold ethanol at \u221220\u00b0C overnight. After staining with propidine iodide  for 30\u00a0min, the cells were loaded into a flow cytometer , then 20,000\u201330,000 cells per tube were collected, and the results were analyzed by FlowJo . In the apoptosis test, the cells were suspended in Annexin-V binding buffer and then dyed using Annexin V combined fluorescein isothiocyanate  and propidine iodide  simultaneously in the dark for 15\u00a0min. The cells were loaded into a flow cytometer , then 20,000\u201330,000 cells per tube were collected, and the results were analyzed by FlowJo .t-test was used to evaluate the difference. A two-sided p-value <0.05 was considered statistically significant. Statistical analysis and graphs were constructed using GraphPad Prism 7.0 software .The Student's According to our strategy , we scanExperimentally, we tested the basic expression levels of the 57 circRNAs in a panel of seven CRC cell lines and one normal colorectal cell line NCM460 (data available under request). From those circRNAs, hsa_circ_0000725, hsa_circ_0007429, hsa_circ_0008501, and hsa_circ_0067080 were expressed significantly higher in most CRC cells, whereas hsa_circ_0005654 was expressed significantly lower in most CRC cells, which is the same as the differential analysis. However, the expressions of hsa_circ_0006088, hsa_circ_0007364, hsa_circ_0008199, and hsa_circ_0008826 were slightly varied in different CRC cells .Sequencing analysis found the unique a.a. sequence coded by the candidate circRNAs . We alsoThe function of circRNAs-derived coding peptides may significantly relate to their derived circRNA. Therefore, we preliminarily explored the function of hsa_circ_0000725 and hsa_circ_0008826; their siRNAs were constructed and transfected into DLD-1. The siRNAs could efficiently silence the hsa_circ_0000725 and hsa_circ_0008826 in DLD-1 . The CCKFlow cytometry assays found that the silencing of hsa_circ_0000725 could significantly lead to the accumulation in the G0/G1 stage of CRC cell, while the silencing of hsa_circ_0008826 showed a subtle change of cell cycle stages . MeanwhiAs CRC has a high incidence and high mortality, early diagnosis and intervention methods are essential in the successful management of the disease. Thus, seeking efficient biomarkers for diagnosis or specific targets for treatment is an urgent priority. Numerous studies have found novel biomarkers and potential therapeutic targets for CRC . HoweverBefore our study, emerging evidence had shown the importance of the coding function of circRNAs. In 2017, Legnini and his colleagues first identified that circ-ZNF609, which specifically controls myoblast proliferation, can be translated into a protein in myogenesis . AnotherIn this study, we constructed a strategy to efficiently search the coding potential circRNAs in CRC. In total, the candidate list lasts nine circRNAs, including hsa_circ_0000725, has_circ_0007429, hsa_circ_0008501, hsa_circ_0067080, hsa_circ_0005654, hsa_circ_0006088, hsa_circ_0007364, hsa_circ_0008199, and hsa_circ_0008826 . Owing tOur findings helped to construct a new schedule to scan potential coding circRNAs, which may be very practical in functional studies on circRNAs. However, our study still had limitations and shortcomings. The function of the circRNA itself could not be excluded, including the competing endogenous RNA (ceRNA) mechanism or RNA-binding protein mechanism. Whether the circRNA-derived peptides functioned in a pivotal role still needs further validation; specific antibodies are needed to identify these peptides, and overexpression vectors are needed to clarify the findings. Therefore, with the limitations considered, our preliminary exploration indicated that hsa_circ_0000725 and hsa_circ_0008826 possessed the ability of coding peptides and could promote the proliferation and invasion of CRC cells, but only hsa_circ_0000725 could influence the cell cycle and apoptosis rate of colorectal cells. Our strategy for scanning potential coding circRNAs was found to be effective. And these potential coding circRNAs may be good choices when seeking CRC therapeutic targets or diagnosis biomarkers."}
+{"text": "Sceloporus have a long history as important ecological, evolutionary, and physiological models, making them a valuable target for the development of genomic resources.High-quality genomic resources facilitate investigations into behavioral ecology, morphological and physiological adaptations, and the evolution of genomic architecture. Lizards in the genus Sceloporus undulatus. We performed synteny analysis with other snake and lizard assemblies to identify broad patterns of chromosome evolution including the fusion of micro- and macrochromosomes. We also used this new assembly to provide improved reference-based genome assemblies for 34 additional Sceloporus species. Finally, we used RNAseq and whole-genome resequencing data to compare 3 assemblies, each representing an increased level of cost and effort: Supernova Assembly with data from 10X Genomics Chromium, HiRise Assembly that added data from HiC, and PBJelly Assembly that added data from Pacific Biosciences sequencing. We found that the Supernova Assembly contained the full genome and was a suitable reference for RNAseq and single-nucleotide polymorphism calling, but the chromosome-level scaffolds provided by the addition of HiC data allowed synteny and whole-genome association mapping analyses. The subsequent addition of PacBio data doubled the contig N50 but provided negligible gains in scaffold length.We present a high-quality chromosome-level reference genome assembly, SceUnd1.0 , and tissue/developmental stage transcriptomes for the eastern fence lizard, These new genomic resources provide valuable tools for advanced molecular analysis of an organism that has become a model in physiology and evolutionary ecology. Genomic resources, including high-quality reference genomes and transcriptomes, facilitate comparisons across populations and species to address questions ranging from broad-scale chromosome evolution to the genetic basis of key adaptations. Squamate reptiles, the group encompassing lizards and snakes, have served as important models in ecological and evolutionary physiology owing to their extensive metabolic plasticity ; diverseAnolis carolinensis), whose genome is only 60% assembled into chromosomes and is lacking assembled microchromosomes [Despite having evolved greater phylogenetic diversity than mammals and birds, 2 major vertebrate groups with extensive genome sampling, genomic resources for squamates remain scarce and assemblies at the chromosome level are even more rare , 9\u201313. Womosomes , 16. Howomosomes , underscSceloporus) to further our ability to address fundamental ecological and evolutionary questions within this taxon, across reptiles, and across vertebrates. The genus Sceloporus includes \u223c100 species extending throughout Central America, Mexico, and the United States [Sceloporus for decades as a model system in the study of physiology [Sceloporus species, applicability across multiple fields of biology, and the extensive diversity of the genus make this an ideal group to target for genomic resource development.Our goal was to develop high-quality genomic and transcriptomic resources for the spiny lizards , which is distributed in forested habitats east of the Mississippi River [S. undulatus has been the focus of studies on the development of sexual size dimorphism [S. undulatus, particularly a high-quality genome assembly, will support its role as a model species for evolutionary and ecological physiology and will have immediate benefits for a broad range of comparative studies in physiology, ecology, and evolution.We focus on the eastern fence lizard, pi River . Recentlmorphism , 34, as morphism  and climmorphism , 38\u201340 oS. undulatus. We apply this genome reference to datasets on 3 scales: (i) to address how assembly quality influences mapping of RNA sequencing (RNAseq) and low-coverage whole-genome sequencing (WGS) data, (ii) to improve upon the genomic resources for the Sceloporus genus by creating reference-based assemblies of draft genomes for 34 other Sceloporus species, and (iii) to draw broad comparisons in chromosome structure and conservation with other recently published squamate chromosome-level genomes through large-scale synteny analysis.To this end, we developed a high-quality chromosome-level reference genome assembly and transcriptomes from multiple tissues for S. undulatus collected at Solon Dixon Forestry Education Center, in Andalusia, AL . The animals were euthanized and tissues were dissected, snap-frozen in liquid nitrogen, and stored at \u221280\u00b0C. Procedures were approved by the Pennsylvania State University Institutional Animal Care and Use Committee (Protocol No. 44595-1).Genome sequence data were generated from 2 male S. undulatus genome assemblies using increasingly more data with correspondingly greater cost: (i) a SuperNova assembly containing data from 10X Genomics Chromium; (ii) a HiRise assembly containing the 10X Genomics data with the addition of Hi-C data; and (iii) a PBJelly Assembly containing the 10X Genomics data and Hi-C data, with the addition of Pacific Biosciences (PacBio) data. These assemblies are provided as supplemental files and their summary statistics are provided in Table\u00a0We developed 3 S. undulatus using 10X Genomics Chromium Genome Solution Library Preparation with SuperNova Assembly [RRID:SCR_016385), resulting in 774 million 150-bp paired-end reads that were assembled using the SuperNova pipeline. We refer to this assembly with 46\u00d7 coverage as the SuperNova Assembly.In the fall of 2016, we sequenced DNA from snap-frozen brain tissue of a single juvenile male Assembly  through In the fall of 2017, we sequenced a second male  [Finally, also in fall of 2017, DNA extracted from the second adult male was used by Dovetail Genomics to generate 1,415,213 PacBio reads with a mean size of 12,418.8 bp . These PacBio data were used for gap filling to further improve the lengths of the scaffolds of the HiRise Assembly using the program PBJelly  , with thS. undulatus assemblies and other squamate genomes, we graphed genome contiguity for these 3 assemblies with other squamate reptile genomes, building on the graph by Roscito et al. [S. undulatus SuperNova Assembly (containing only the 10X Genomics data) is as contiguous as the bearded dragon genome assembly  [Sceloporus occidentalis is 2.36 Gb on the basis of Feulgen densiometry [S. undulatus is similar, the 1.9 Gb of sequence in our SceUnd1.0 assembly is likely either missing some data, or repeat regions have been condensed, representing redundancies. To assess the level of contamination in our SceUnd1.0 genome assembly, we used Blobtools v1  [The SceUnd1.0 assembly contains 45,024 scaffolds  containing 1.9 Gb of sequence, with an N50 of 275 Mb. Importantly, 92.6% (1.765 Gb) of the assembled sequence is contained within the first 11 scaffolds. Chromosomal studies have determined that the mosomes) , 46. Sorsiometry . Assumin_017618)  workflowRRID:SCR_015008) Tetrapoda dataset  [To assess the completeness of our 3 genome assemblies, we used the BUSCO  , 49. Forde novo transcriptome were obtained from 3 gravid female S. undulatus collected in Edgefield County, SC , and transported to Arizona State University. These animals were maintained under conditions described in previous publications [Samples used for the ications , 51, whiRRID:SCR_020132) to generate 100-bp paired-end reads. Publicly available raw Illumina RNAseq reads from S. undulatus liver  were also added to our dataset [Total RNA was isolated from the embryo and 3 tissue samples from each adult female  using the mirVana miRNA Isolation Kit (Ambion) total RNA protocol. Samples were checked for quality on a 2100 Bioanalyzer (Agilent). One sample from each tissue was selected for RNAseq based on the highest RNA Integrity Number (RIN), with a minimum cut-off of 8.0. For each selected sample, 3 \u03bcg of total RNA was sent to the University of Arizona Genetics Core  for library preparation with TruSeq v3 chemistry for a standard insert size. RNA samples were multiplexed and sequenced using an Illumina HiSeq 2000  [RRID:SCR_018930) [All trimmed reads were pooled and assembled ze of 25 . From th_017647)  with hom_017647)  and PFAM_017647) . The tra_018930) , which ide novo transcriptome data, trimmed reads from the 4 tissues used for RNA sequencing  were aligned back to the Trinity-assembled contigs using Bowtie2 v2.2.6  [de novo transcriptome assembly. To assess quality and completeness of the assemblies, we first compared the de novo assembled transcripts with the BUSCO Tetrapoda dataset, with BLAST+ v2.2.31 [RRID:SCR_005305) [de novo transcriptome assembly captured 97.1% of the expected orthologues (sum of completed and fragmented), a result comparable to the 97.8% obtained for the green anole transcriptome using 14 tissues [de novo assembled transcripts with the longest ORFs were compared to the protein set of A. carolinensis  using BLASTX  . This comparison showed that 11,223 transcripts of S. undulatus have nearly full-length (>80%) alignment coverage with A. carolinensis proteins . Table\u00a0de novo transcriptome annotation results.The most comprehensive transcriptome, obtained using reads from 4 tissues, consists of 547,370 contigs with a mean length of 781.5 nucleotides Table\u00a0\u2014shorter _016368) . From th v2.2.31  and HMME tissues  (Table\u00a03proteins . Predictab initio gene prediction programs Augustus [RRID:SCR_010835) [RRID:SCR_005829) [RRID:SCR_002456) [RRID:SCR_004726) [RRID:SCR_007777) [GCA_019175285.1. This Whole Genome Shotgun project has been deposited at DDBJ/ENA/GenBank under the accession JAGXEY000000000.Using the 24 largest scaffolds of the SceUnd1.0 assembly (we refer to this set as SceUnd1.0_top24), we used the Funannotate v1.5.0 pipeline  for geneAugustus  and GeneAugustus . Evidenc_010835)  was used_005829) , eggNOG _002456) , Pfam P_004726) , UniProt_007777) , CAZyme,6 , UniPrSceloporus species, and the genus seems to have evolved multiple variations of XY systems [S. undulatus, do not seem to have morphologically distinct sex chromosomes [undulatus species group [Sceloporus are among the large portion of iguanian lizards with conserved sex chromosomes, and another Sceloporus species within the same broad 2n = 22 radiation, Sceloporus malachiticus, has an X chromosome homologous to the green anole X but fused to several microchromosomes [S. undulatus. We blasted 16 X-linked genes from the green anole downloaded from Ensembl  [S. undulatus sex chromosomes; higher sequence homology may have caused the Y chromosome to assemble with the X chromosome [We used annotation and sequence homology to identify the X chromosome. Sex chromosomes are highly variable among  systems . Howeveromosomes . While tes group . These homosomes . Given t ZCCHC8) , 76 to Sromosome . This reS. undulatus genome, we modeled repeats de novo by running RepeatModeler v1.0.8  [RRID:SCR_012954) [de novo consensus repeat library. To estimate evolutionary divergence within repeat families in the S. undulatus genome, we generated repeat-family\u2013specific alignments and calculated the average Kimura-2-parameter divergence from consensus within each family, correcting for high mutation rates at CpG sites with the calcDivergenceFromAlign.pl RepeatMasker tool. We compared the divergence profiles of S. undulatus and A. carolinensis by completing parallel analyses. We annotated repeats in the A. carolinensis genome (AnoCar2.0) with RepeatMasker and the \u201canolis\u201d repeat library from RepBase release 20170127 [To estimate the repetitive landscape of the _015027)  on the S_012954)  with the20170127 .S. undulatus assembly contained a diverse repertoire of repeats including transposable elements, the most abundant of which are the long interspersed nuclear elements . The distribution of recent LINEs was significantly different between the 2 genomes (P = 2.824e\u221206), and Anolis contained more recent insertions from the L1 family (P = 0.0001571), as well as RTE-BovB (P = 0.001152) and R4 (P = 0.0001571). The Anolis genome also contained more recent LTR retrotransposons (P = 1.153\u2212e07), as well as Mariner (P = 0.0002122), Tigger (P = 0.01017), and Chapaev (P = 0.001152) DNA transposons.The s LINEs,  comprisis genome , as wells genome . HoweverS. undulatus individuals from the RNAseq Dataset 4 (RRID:SCR_011848) [S. occidentalis mtDNA genome [RRID:SCR_010910) [S. occidentalis mtDNA genome with an average read depth of 5,164.42 reads per site per individual. After sorting and indexing mapped reads with SAMTOOLS v1.6  [A. carolinensis mtGenome with MAFFT v1.3.7  [RRID:SCR_010519) [A. carolinensis mtGenome  transferred well to the newly assembled S. undulatus mtGenome , with 13 protein-coding genes, 22 tRNA regions, 2 ribosomal RNA regions, and a control region  genomes . We usedataset 4 , which a_011848)  to cleanA genome  using BW_010910) . Of the _002105) , we used_011811)  and tran_010519) . AnnotatS. undulatus, we produced 3 assemblies using increasing amounts of data, for correspondingly greater costs. To assess the utility of each of the assemblies for addressing ecological genomic questions, we use 2 datasets: RNAseq and whole-genome resequencing.In developing the high-quality reference genome for S. undulatus genome assemblies. The percentages of reads that mapped were calculated using SAMTOOLS v1.6 flagstat [First, we used RNAseq Dataset 4 Table\u00a0 from n =flagstat . We founS. undulatus individuals  from the same Alabama population as the individuals that were used to develop the reference assemblies. We also prepared libraries for n = 5 S. undulatus individuals  from Edgar Evins, TN, and for n = 5 individuals  from St. Francis, AR. This Arkansas population is at the borders of the S. undulatus and Sceloporus consobrinus geographic distributions, making its taxonomic status uncertain [S. undulatus assemblies with bwa_mem [S. undulatus individuals used to create these reference assemblies had a higher percentage of reads map to the assemblies than individuals from the Tennessee or Arkansas populations   identified, similar to our S. undulatus SuperNova Assembly  . For S. 7% Table\u00a0. Across ars Fig.\u00a0.S. undulatus scaffolds. However, Sceloporus is notable among squamates for remarkable chromosome rearrangements with karyotypes ranging from 2N = 22 to 2N = 46 [S. undulatus reference) or with large chromosomal inversions will not be reliable for addressing questions related to genomic architecture or structural variation [IGF1, we found that 16 of the 34 species had >75% coverage across the protein-coding region of this gene and 24 of them had >50% coverage (It is important to note that the reference-based assemblies produced for these 34 species will correspond 1:1 with the synteny of the  2N = 46 . Therefoariation . Howevercoverage . TherebyS. undulatus) SceUnd1.0 assembly with the green anole  and with recently published chromosome-level assemblies for the Burmese python (Python bivittatus) [Salvator merianae) [As another benchmark of genome completeness, and to generate an initial look at chromosome evolution among squamates, we performed synteny analysis of the eastern fence lizard (ittatus)  and the erianae)  (availaberianae) . Using tS. undulatus species group compared to other Sceloporus lineages and the Iguanian group has long driven a hypothesis that a high number of fusions occurred in chromosomes in this species group, which is evident in the marker-based synteny painting of the S. undulatus genome. While the incomplete nature of the green anole genome, especially the lack of microchromosomes, makes many Sceloporus lineage-specific fusions difficult to identify, the inclusion of the tegu and python genomes provides guidance. For example, tegu chromosomes 6, 7, 9, and 12 are all syntenic to fence lizard chromosome 6. However, the tegu chromosomes 6 and 7 occur in a single block as the python X chromosome, and we cannot discern whether this was a fusion in a lineage preceding Iguanians and snakes or a fission in the tegu. The tegu chromosomes 9 and 12 are syntenic to python chromosomes 8 and 12, which may have been fused in the fence lizard, considering the considerable size difference between fence lizard chromosome 6 and the syntenic green anole chromosome 6. Similarly, tegu chromosomes 8 and 16 are syntenic to python chromosomes 10 and 16, fusing to form the fence lizard chromosome 9 but almost completely absent from the green anole assembly. These synteny results further support that the tenth largest chromosome in the SceUnd1.0 assembly is syntenic to the anole X chromosome  of the genome sequence; these 11 scaffolds likely represent the 6 macro- and 5 microchromosomes of S. undulatus, based on karyotype, genome size, BUSCO analysis, and synteny with other squamate genomes. The remaining small scaffolds may contain some chromosome segments that could not be assembled, misassembled regions, or duplicated genes.For the advancement of reptilian genomic and transcriptomic resources, we provide a high-quality, chromosome-level genome assembly for the eastern fence lizard, In comparing the 3 levels of reference genome assemblies, we found that the first level using only the 10X Genomics and the SuperNova Assembly contained all, or very nearly all, of the protein-coding regions of the genome within its contigs (based on BUSCO and mapping of RNAseq and whole-genome resequencing data). By including the Hi-C data, the contiguity of the HiRise Assembly dramatically improved, joining contigs into chromosome-length scaffolds, but had minimal effect on mapping percentages for either RNAseq or WGS. The inclusion of the PacBio data in the final PBJelly Assembly to produce SceUnd1.0 closed some gaps but yielded a relatively small improvement after the already dramatic improvements from the Hi-C data.While it is now becoming possible to obtain a reference genome assembly for almost any organism, the quality and cost of reference genome assemblies vary considerably depending on the technologies used. This presents researchers with an important question: what levels of sequencing effort and assembly quality are required for a particular ecological genomics study? Important factors that must be considered include the sequencing depth, sequence contiguity, and thoroughness of annotation. Our study demonstrates that the SuperNova Assembly was sufficient for mapping RNAseq and whole-genome resequencing, while the more expensive data from HiC and PacBio were necessary to achieve high-level continuity and chromosome-level scaffolding in the HiRise and PBJ Assemblies.S. undulatus, a high-quality reference genome opens the door for these molecular techniques to be used in this ecological model organism. For example, with the recent demonstration of CRISPR-Cas9 gene modification in a lizard, the brown anole [Sceloporus lizards. This reference will provide a foundation for whole-genome studies to elucidate speciation and hybridization among closely related species utilizing low-coverage re-sequencing, or as a point of comparison with more distantly related species relative to the chromosomal inversions and large-scale genome architectural changes common in the clade. Sceloporus undulatus and other lizards in the genus Sceloporus exhibit evolutionary reversals in sexual size dimorphism and dichromatism and they have been used to demonstrate that androgens such as testosterone can inhibit growth in species (such as S. undulatus) in which females are the larger sex [in silico analyses to identify sex hormone response elements. In addition, this assembly will facilitate the identification of signatures of exposure to environmental stressors in both gene expression and epigenetic modification [Sceloporus genus.Genome assemblies of high quality and contiguity are critical for understanding organismal biology in a wide range of contexts that includes behavior, physiology, ecology, and evolution, on scales ranging from populations to higher-level clades. From RNAseq to ChIPseq (chromatin immunoprecipitation sequencing) and epigenetics, large-scale sequencing is rapidly becoming commonplace in ecological genomics to address fundamental questions of how organisms directly respond to their environment and how populations evolve in response to environmental variation. Many advanced molecular tools are typically reserved for traditional model organisms, but with the large foundation of ecological and physiological data available for wn anole , a genomrger sex , 104\u2013106fication  to evaluJAGXEY000000000. The assembly Sceloporus undulatus AU_SceUnd_v1.1 (a slightly updated version of SceUnd1.0 based on NCBI requirements) is version JAGXEY010000000, GenBank accession GCA_019175285.1. These NCBI BioProjects contain RNAseq data associated with this article: PRJNA371829, PRJNA437943, PRJNA629371, along with SRA SRR629640. The NCBI BioProject with the raw data for heterozygosity estimates associated with this article is PRJNA656311. All supporting data and materials are available in the GigaScience GigaDB database [All raw data are available on NCBI. The BioProject for the Genome Sequencing is PRJNA612440. The Whole Genome Shotgun project has been deposited at DDBJ/ENA/GenBank under the accession database  and the database , includiAll 3 genome assemblies and their BUSCO results.SuperNova assembly containing data from 10X Genomics Chromium: GenomeAssembly_SuperNova_Sceloporus_undulatus_pseudohap.fasta.gzHiRise assembly containing the 10X Genomics data with the addition of the Hi-C data: GenomeAssembly_HiRise_Sceloporus_undulatus.fasta.gzPBJelly Assembly (SceUnd1.0) containing the 10X Genomics data and the Hi-C data, with the addition of PacBio data: GenomeAssembly_SceUnd1.0_PBJELLY.fasta.gzTissue-Embryo Transcriptomes and annotation are provided as supplemental data.TranscriptomeAssemblyAnnotation.zip folder containingTranscriptome File: TranscriptomeAssembly_Tissues-Embryo_Trinity.fastaAnnotation File: TranscriptomeAssembly_Tissues-Embryo_Transdecoder.gff3Truncated assembly used for the Funannotate annotation pipeline (SceUnd1.0_top24), and the annotation results are supplied as supplemental data.SceUnd1.0_top24.fasta. This file contains only the longest 24 scaffolds and they have been renamed 1\u201324 from longest to shortest.SceUnd1.0_top24_Annotation_FunnanotateResults.zip folder containing the following files:SceUnd1.0_top24.gff3SceUnd1.0_top24.proteins.faSceUnd1.0_top24.transcripts.faSceUnd1.0_top24.annotations.txtSceUnd1.0_top24_CompiledAnnotation.csvSceUnd1.0_top24.proteins.fa.report_EnsembleCombined.top.txtThe mitochondrial genomes and the annotation are provided as supplemental data.MitoGenomeAssembly_Sceloporus_undulatus.fastaMitoGenomeAssembly_Sceloporus_undulatus_Annotation.gffThe reference-based assemblies for the 34 Sceloporus species are provided as supplemental data.GenomeAssemblies_34Sceloporus.tar.gzCode for generating consensus sequences for each species: mkgenome_AW-AC.shSupplemental Results.Sceloporus undulatus genome assembly including data from 10X Genomics Chromium library with Illumina sequencing, Hi-C library with Illumina sequencing, and PacBio sequencing assembled using the program PBJelly. Also referred to as the PBJelly assembly; SceUnd1.0_top24: Sceloporus undulatus genome assembly including only the longest 24 scaffolds from SceUnd1.0; SINE: short interspersed nuclear element; SNP: single-nucleotide polymorphism; SRA: Sequence Read Archive; tRNA: transfer RNA; WGS: whole-genome sequencing.BLAST: Basic Local Alignment Search Tool; bp: base pairs; BUSCO: Benchmarking Universal Single Copy Orthologues; ChIPseq: chromatin immunoprecipitation sequencing; E90N50: N50 of the most highly expressed transcripts that represent 90% of the total normalized expression data; GATK: Genome Analysis Toolkit; Gb: gigabase pairs; HET SNP: heterozygote single-nucleotide polymorphism; INDEL: insertion/deletion; kb: kilobase pairs; KEGG: Kyoto Encyclopedia of Genes and Genomes; L50 (L90): The smallest number of scaffolds that make up 50% (90%) of the total assembly length; LINE: long interspersed nuclear element; LTR: long terminal repeat; Mb: megabase pairs; mtDNA: mitochondrial DNA; N50 (N90): The contig or scaffold length such that the sum of the lengths of all scaffolds of this size or larger is equal to 50% (90%) of the total assembly length; NCBI: National Center for Biotechnology Information; ORF: open reading frame; PacBio: Pacific Biosciences; QC: quality control; RIN: RNA Integrity Number; RNAseq: RNA sequencing; SceUnd1.0: The authors declare that they have no conflict of interest.This work was supported by National Science Foundation Graduate Research Fellowship Program ; National Science Foundation BCS-1554834 to GHP; National Science Foundation IOS-PMB 1855845 to A.D.L.; National Science Foundation IOS-1456655 to T.L.; Clemson University lab funds to M.S.; Georgia Southern Startup Funds to C.L.C.; University of Virginia start-up funding to R.M.C.; Hatch Multistate W3045 project no. NJ17240 to H.P.J.A.; Grant for Postdoctoral Interdisciplinary Research in the Life Sciences from the School of Life Sciences at Arizona State University to M.T.; Auburn University Start-up Funds to T.S.S.A.K.W.: Data curation; Formal analysis; Investigation; Validation; Visualization; Writing\u2014original; Writing\u2014review & editingR.S.T.: Conceptualization; Data curation; Formal analysis; Investigation; Validation; Visualization; Writing\u2014review & editingM.B.G.: Data curation; Formal analysis; Investigation; Validation; Visualization; Writing\u2014original; Writing\u2014review & editingD.S.W.: Data curation; Formal analysis; Software; Validation; Visualization; Writing\u2014original; Writing\u2014review & editingA.D.C.: Data curation; Formal analysis; Methodology; Software, Validation, Visualization; Writing\u2014review & editingD.Y.S.: Formal analysis; Software; Writing\u2014original; Writing\u2014review & editingR.L.K.: Methodology; Formal analysis; Writing\u2014original draft; Writing\u2014review & editingA.P.S.: Formal analysis; Writing\u2014original draft; Writing\u2014review & editingC.L.C.: Conceptualization; Data Curation; Investigation; Funding Acquisition; Writing\u2014review & editingG.H.P.: Funding acquisition; Supervision, Writing\u2014review & editingM.T.: Data curation; Formal analysis; Methodology; Funding acquisition; Writing\u2014original draft; Writing\u2014review & editingT.L.: Conceptualization; Funding acquisition; Resources; Writing\u2014review & editingK.K.: Conceptualization; Funding acquisition; Resources; Writing\u2014review & editingM.W.S.: Resources; Funding Acquisition; Writing- review & editingA.D.L.: Conceptualization; Data curation; Funding acquisition; Methodology; Writing\u2014original; Writing\u2014review & editingM.J.A.: Conceptualization; Funding acquisition; Writing\u2014review & editingM.E.G.: Conceptualization; Writing\u2014review & editingH.P.J.A.: Investigation; Funding acquisition; Writing\u2014review & editingR.M.C.: Conceptualization; Funding acquisition; Investigation; Writing\u2014review & editingT.S.S.: Conceptualization; Data curation; Funding acquisition; Formal analysis; Investigation; Project Administration; Resources; Supervision; Writing\u2014original; Writing\u2014review & editing.All authors have read and approved the final version of the manuscript.giab066_GIGA-D-20-00171_Original_SubmissionClick here for additional data file.giab066_GIGA-D-20-00171_Revision_1Click here for additional data file.giab066_GIGA-D-20-00171_Revision_2Click here for additional data file.giab066_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giab066_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giab066_Reviewer_1_Report_Original_SubmissionJeramiah J Smith -- 7/31/2020 ReviewedClick here for additional data file.giab066_Reviewer_1_Report_Revision_1Jeramiah J Smith -- 5/25/2021 ReviewedClick here for additional data file.giab066_Reviewer_2_Report_Original_SubmissionShanlin Liu -- 8/4/2020 ReviewedClick here for additional data file.giab066_Reviewer_2_Report_Revision_1Shanlin Liu -- 5/5/2021 ReviewedClick here for additional data file.giab066_Supplemental_FileClick here for additional data file."}
+{"text": "P-values to be shared with the scientific community.A joint analysis of location and scale can be a powerful tool in genome-wide association studies to uncover previously overlooked markers that influence a quantitative trait through both mean and variance, as well as to prioritize candidates for gene\u2013environment interactions. This approach has recently been generalized to handle related samples, dosage data, and the analytically challenging X-chromosome. We disseminate the latest advances in methodology through a user-friendly R software package with added functionalities to support genome-wide analysis on individual-level or summary-level data. The implemented R package can be called from PLINK or directly in a scripting environment, to enable a streamlined genome-wide analysis for biobank-scale data. Application results on individual-level and summary-level data highlight the advantage of the joint test to discover more genome-wide signals as compared to a location or scale test alone. We hope the availability of gJLS2 software package will encourage more scale and/or joint analyses in large-scale datasets, and promote the standardized reporting of their  G) and a quantitative phenotype (denoted by Y), by testing the mean differences in Y according to the genotypes, otherwise known as a location test. More recently, several reports have investigated association with phenotypic variance of complex quantitative traits  or gene\u2013environment (G\u00d7E) interactions; both referred to as G\u00d7E hereinafter. Unlike a direct test of G\u00d7E interaction, a scale test can be used to indirectly infer G\u00d7E without knowledge of the interacting covariates, thus alleviates the multiple hypothesis burden of testing all possible pairwise interactions, and the assumption that all interacting environmental variables could be (accurately) measured.Genetic association studies examine the relationship between the genotypes of a single-nucleotide polymorphism . Since methods for genome-wide association studies (GWAS) of location are well-established, the main focus was on improving scale tests tailored for genetic data. https://github.com/dsoave/gJLS; accessed 2022 February 13). Following the X-inclusive trend to genome-wide analyses, robust and powerful location . We hope the availability of this unifying software package will encourage more X-inclusive, genome-wide, gJLS2 association analysis for complex continuous traits, particularly for those believed to be enriched for genetic interactions.In this study, we describe a generalized joint location and scale analysis tool (gJLS2) as an update to the JLS and gJLS methodology for autosomes, with added functionalities that (1) support X-chromosome mean and variance association analyses, (2) handle imputed data as genotypic probabilities or in dosage format, (3) allow the incorporation of summary statistics for location and/or scale tests, (4) implement a flexible framework that can accommodate additional covariates in both the location and scale association models, and (5) improve the computational time required for large-scale genetic data such as the UK biobank or github for the most recent version:#install.packages(\u201cdevtools\u201d)devtools::install_github(\u201cWeiAkaneDeng/gJLS2\u201d)https://weiakanedeng.github.io/gJLS2/. Any feedbacks/bugs can be reported under github\u2019s issues tab (https://github.com/WeiAkaneDeng/gJLS2/issues).via the \u201cdevtools\u201d package , rs986810 (C/T), rs180495 (G/A), rs5911042 (T/C), and rs4119090 (G/A) that are outside of the pseudo-autosomal region, to cover observed minor allele frequency  of 0.1, 0.2, 0.3, 0.4, and 0.45, respectively. See Supplementary Material Section 2 for more details on the simulated dataset. The summary statistics data comprised of association n\u2009>\u20095,000), it is recommended to run the location association analysis using the state-of-art linear mixed models, such as LMM-BOLT  is flexible to accommodate additional covariates and the default option. To account for related samples, the generalized least square (GLS) method is used by specifying a covariance structure for error terms in smaller samples. Users can either provide the covariance matrix or specify a structure for the covariance matrix according to predefined subgroups. However, for large population studies head(chrXdat) > locReg;CHR SNP gL1 X rs5983012_A 0.98776742 X rs4119090_G 0.92015693 X rs5911042_T 0.38980294 X rs986810_C 0.46191655 X rs180495_G 0.8767590Although the resulting 3-df test is robust to the choice of baseline allele and status of XCI, a recent report CHR SNP gS1 X rs5983012_A 0.13919092 X rs4119090_G 0.98284303 X rs5911042_T 0.14870174 X rs986810_C 0.95633905 X rs180495_G 0.3476929> scaleRegCHR SNP gS1 X rs5983012_A 0.17390622 X rs4119090_G 0.99999993 X rs5911042_T 0.11630234 X rs986810_C 0.95815895 X rs180495_G 0.3619056G additively (2 df) without the dominance term, or by replacing the genotype indicators for each observation, with the corresponding group probabilities (3 df). Similar to the location association, sample relatedness is dealt with using GLS for autosomal markers at the second stage of linear regression via the correlation matrix. Additional models, such as a sex-stratified variance test, can also be specified for X-chromosome. In this case, the scale test result is given by the Fisher\u2019s method that combines female and male-specific variance test results.The imputed data are analyzed either by computing the dosage value and used in place of > scaleRegCHR SNP gS LevFemale LevMale Fisher Flagged1 X rs5983012_A 0.1391909 0.5296098 0.08223648 0.1800391 12 X rs4119090_G 0.9828430 0.9143432 0.97557661 0.9939473 03 X rs5911042_T 0.1487017 0.1625983 0.40384172 0.2444805 04 X rs986810_C 0.9563390 0.5349616 0.78109151 0.7824831 15 X rs180495_G 0.3476929 0.9717918 0.14364539 0.4144558 1Note that a \u201cFlagged\u201d column is appended for these results, indicating the minimum genotype count in either females/males is less than 30  or not (indicated by 0). This is based on the quality control that SNPs with a minimum count below 30 should be removed to avoid inflated type I errors CHR SNP gL gS gJLS1 X rs5983012_A 0.9943538 0.1198837 0.37274722 X rs4119090_G 0.8881506 0.9794193 0.99114013 X rs5911042_T 0.3488576 0.1514217 0.20817014 X rs986810_C 0.4898597 0.9244773 0.81160645 X rs180495_G 0.8702304 0.3619588 0.67886814P-values from location and scale tests are allowed and can be combined via Fisher\u2019s method to give the corresponding test statistic for the joint analysis:P-value and P-value.Alternatively, summary statistics, i.e. P-values for X-chromosome markers.An important assumption underlying this simple method to combine evidence is the normality of the quantitative trait, which leads to ibutions , we stroThe main contribution of the software is the ability to handle X-chromosome  analysis and it is not our aim to compete with existing autosomal association tools that are geared toward whole-genome computations. As a result, for both location and scale association, data splitting remain our primary strategy to deal with biobank scale data. We also implemented the summary statistics option to encourage the inclusion of genome-wide results (autosome) computed using existing software as inputs for the location portion. For X-chromosome associations, we recommend using gJLS2 in non-GUI settings and provide 2 practical options for biobank-scale data.Rserve package to establish communication between R and PLINK 1.9. The following script demonstrates the joint analyses for X-chromosome SNPs that included additional covariates.R CMD Rserve --RS-port 8221 plink --bfile ./input/chrX_5_snp \\--R run_gJLS2PLINK_ Xchr.R \\--pheno ./input/Pheno.txt \\--pheno-name pheno1 \\--R-port 8221 \\--covar ./input/Pheno.txt \\--covar-name SEX, covar1, covar2, covar3 \\--out ./output/testRunFor larger datasets, it is more convenient to run the joint analyses using the PLINK R plug-in following the a typical GWAS pipeline. The R plug-in relies on the https://github.com/WeiAkaneDeng/gJLS2/tree/main/inst/extdata).Rscript run_gJLS2.R --bfile ./input/chrX_5_snp \\--pfile ./input/Pheno.txt \\--pheno pheno1 \\--Xchr TRUE \\--write 10 \\--nTasks 2 \\--covar SEX, covar1, covar2, covar3 \\--out ./output/testRun.results.txtAnother option is to use the Rscript provided that allows additional arguments to change how frequently the results are written to the output (\u2013write) and to increase the number of cores used (\u2013nTasks). A core is an independent processing unit on a central processing unit (CPU). Though a modern computer, usually containing 4\u20138 cores, is capable of handling parallel computing, we recommend the \u201csplit-apply-combine\u201d strategy and employing high-performance computing for large-scale analyses. Scripts for both options are available from github  to achieve sample sizes of: 1,000, 5,000, 10,000, 50,000, 100,000, 200,000, 300,000, 400,000, and repeated the joint-location-scale analysis using a single core with 10GB memory via (1) PLINK R plug-in and (2) Rscript. The reason for keeping the 100 SNPs to estimate the performance metric is because the analysis can be easily divided to chunks and combined after. n\u2009=\u2009488,377, m\u2009=\u200915,179), the gJLS analysis took \u223c16\u2009h for PLINK (using 3.5GB memory) and \u223c 21\u2009h (using 1.2GB) for Rscript. The memory efficiency of Rscript is expected as the \u201cBEDmatrix\u201d only maps the required portion of genotype files into memory. However, the Rscript can be parallelized, when using 4 cores with 20GB allocated memory, the wall clock run time was reduced to \u223c13\u2009h (using 14.0\u2009GB).For an X-chromosome wide analyses on UKB  an X-chromosome gJLS analysis on UK Biobank (UKB) data  on 4 complex traits previously studied in The gJLS2. We restricted analyses to white British samples , and for BMI, we further excluded those with diagnosed type 2 diabetes . We included only bi-allelic SNPs and filtered based on MAF\u2009<\u20090.01, HWE P-value <1E-5. A further check on sex-stratified MAFs  and Rscript (run_gJLS2.R) are provided in the inst/extdata folder of the R package along with the input files. It is worth noting the main advantages of the Rscript option beyond its flexibility: (1) the gJLS2 R package supports multi-core computing via the parallel base package and the argument \u201cnTasks\u201d can be used to specify the number of cores; (2) another useful feature is the \u201cwrite\u201d option that specifies the chunk size for the results to be written while the analysis is running and thus minimizes loss in case an interruption occurred.The base scripts for PLINK , to complete the analysis for 276,694 unrelated European samples and 15,179 X-chromosome variants per trait. The gL, gS, and gJLS2 istogram  for eachRscript run_gJLS2.R --sumfile ./input/ GIANT_BMI_chr16_gJLS_summary.txt \\--out ./output/GIANT_BMI_Sum.chr16_results.txtRscript run_gJLS2.R --sumfile ./input/ GIANT_Height_chr16_gJLS_summary.txt \\--out ./output/ GIANT_Height_Sum.chr16_results.txtThe Rscript is more flexible than the PLINK plug-in solution as it can also handle analysis of summary statistics. The input file should contain at least 3 columns with headers \u201cSNP,\u201d \u201cgL,\u201d and \u201cgS,\u201d while the output file has an additional column \u201cgJLS.\u201d We re-formatted the subset of chromosome 16 summary statistics of location and scale obtained from the GIANT consortium for BMI and height as inputs.P-values are presented using a Manhattan plot, quantile-quantile plot, and a histogram , which can be freely accessed from the online data portal. The genotype and phenotype data used to demonstrate the X-chromosome analysis are available from UK Biobank (https://www.ukbiobank.ac.uk/) release in March 2018 and under project identification number 64875. Data access can be requested with information provided here: http://www.ukbiobank.ac.uk/using-the-resource/. The genome-wide summary statistics are made available from the GIANT data portal .The gJLS2 package and the example datasets are available from G3 online.jkac049_Supplementary_DataClick here for additional data file."}
+{"text": "Circular RNAs (circRNAs) play key roles in many malignant tumors, including pancreatic cancer (PC); however, whether circular RNA hsa_circ_0006117, a newly identified circRNA, has a role in PC has not been investigated. Here, in order to elucidate the role and potential molecular mechanisms of circRNAs, we utilized bioinformatic tolls to screen the differentially expressed circRNAs in PC. Subsequently, circular RNA hsa_circ_0006117 was identified as being highly expressed in PC tissues in a screen of two GEO datasets, which was further verified in PC cell lines and tissues. Then, its molecular characteristics were investigated using methods such as Sanger sequencing and fluorescence in situ hybridization (FISH). Functional experiments subsequently indicated that circular RNA hsa_circ_0006117 facilitated the malignant behaviors of PC cells, prompting that it plays an oncogenic role in PC. Moreover, we found that circular RNA hsa_circ_0006117 exerts its PC-promoting effects via activating the KRAS/mitogen-activated protein kinase (MAPK) signaling pathway. Through bioinformatics exploration and dual-luciferase reporter assays, miR-96-5p was identified as a downstream target of circular RNA hsa_circ_0006117. A series of assays confirmed that circular RNA hsa_circ_0006117 acted as a miR-96-5p sponge, thereby promoting the malignant features of PC in a miR-96-5p/KRAS axis-dependent manner. Taken together, our study indicated, for the first time, that the specifically highly expressed circular RNA hsa_circ_0006117 facilitates PC progression via the modulation of the miR-96-5p/KRAS/MAPK signaling pathway and might be a hopeful therapeutic target for PC. Pancreatic cancer (PC) is a leading cause of cancer-related deaths worldwide among all cancers with the lowest five-year overall survival rate 10%) [0% [1]. AIncreasing evidence suggests that noncoding RNAs have widely participated in PC development \u20138. CircuKRAS), Harvey-RAS (HRAS), and neuroblastoma-RAS (NRAS), exhibit the highest mutation frequency in human cancers, with associated mutations being identified in approximately 30% of all cancers [KRAS is a driver gene of many diseases and one of the most common and frequently mutated genes in PC [KRAS oncogenic mutations lead to the continued activation of downstream molecules, and the KRAS/mitogen-activated protein kinase (MAPK) signaling pathway is strongly associated with the development of PC, both of which enhance the malignant potential of this cancer [KRAS is also involved in the regulation of noncoding RNAs in some cancers [KRAS in PC has not been explored, nor has the underlying regulatory relationship.Members of the rat sarcoma (RAS) oncogene family, including Kirsten-RAS  database. Subsequent functional experiments illustrated that circular RNA hsa_circ_0006117 promoted the rapid development of PC cells. Moreover, our results suggested that circular RNA hsa_circ_0006117 could activate the MAPK signaling pathway by relieving the miR-96-5p-mediated posttranscriptional suppression of https://www.ncbi.nlm.nih.gov/geo/) database. The microarray datasets GSE69362 and GSE79634 were included and downloaded for screening potential DECs. After identifying DECs using the R \u201climma\u201d package, Venn diagram analysis (http://bioinformatics.psb.ugent.be/webtools/Venn/) was performed to overlap and focus candidate DECs.Using the keywords \u201ccircular RNA,\u201d \u201cpancreatic cancer\u201d or \u201cpancreatic ductal adenocarcinoma,\u201d the expression profile of circular RNAs in PC were searched from the GEO . Normal human pancreatic ductal epithelial cell (HPDE) was purchased from Beijing North Carolina Chuanglian Biotechnology Research Institute . All PC cell lines were purchased from American Type Culture Collection , including PANC-1, MIA PaCa-2, AsPC-1, BxPC-3, and SW1990. The cells were cultured in Dulbecco's modified Eagle's medium (DMEM)  or Roswell Park Memorial Institute (RPMI) medium (Gibco) supplemented with 10% fetal bovine serum (Gibco).\u2212\u0394\u0394CT method and standardized to those of the appropriate internal references. All primers are displayed in We used an RNA-easy Isolation Reagent  to extract RNA based on its protocol. Real-time quantitative reverse transcription PCR (RT-qPCR) was performed using cDNA Synthesis Kit (Vazyme) and qPCR Probe Kit (Vazyme). Relative mRNA expression was analyzed with the 2\u03bcg) was incubated with or without 3\u2009U/\u03bcg RNase R  at 37\u00b0C for 30 minutes. Then, RT-qPCR was used for analyzing the abundance of circular RNA hsa_circ_0006117 and its parent gene protein tyrosine phosphatase receptor type A (PTPRA). Meanwhile, PC cells were treated with 2\u2009\u03bcg/mL actinomycin D  or dimethyl sulfoxide (DMSO) (Sigma-Aldrich) for 12 hours. After harvesting the cells, the stability of circular RNA hsa_circ_0006117 and linear RNA PTPRA was tested by RT-qPCR.Total RNA  based on the protocol. While the cytoplasmic/nuclear RNA ratio was assessed by RT-qPCR, U6 was used as a positive reference for nuclear RNA and GAPDH as a positive reference for cytoplasmic RNA.A FISH Kit  was applied to visualize the subcellular localization of circRNA. PC cells were sowed in 24-well plates and reproduced until 60%\u201370% confluence. After fixation in 4% paraformaldehyde and permeabilization with TritonX-100, the cells were hatched with a prehybridization buffer for 30\u2009min at 37\u00b0C and then with a Cy3-labeled circular RNA hsa_circ_0006117 probe (RiboBio) in hybridization buffer overnight at 37\u00b0C. Representative pictures were obtained using a fluorescence microscope  at \u00d7400 magnification.Dissolving PC cells with RIPA lysis buffer , 10% SDS-PAGE was used to segregate proteins and then transferred them onto PVDF membranes . After blocking with 5% milk  or 5% BSA (phosphorylated protein), the membranes were hatched with the corresponding primary antibodies and an HRP-conjugated secondary antibody. Immunoreactive protein bands were exposed with an ECL reagent (Boster) and quantified by Image Lab Software. The following commercially antibodies were used in this experiment: KRAS, GAPDH, phosphorylated-MEK1/2 (P-MEK1/2), MEK1/2, phosphorylated-ERK1/2 (P-ERK1/2), and ERK .All oligonucleotides  targeting circular RNA hsa_circ_0006117, and their controls) were obtained from RiboBio . The KRAS expression vector (pcDNA) was purchased from Vigenebio . Opti-MEM and Lipofectamine 3000  were performed to cotransfect constructs into PC cells. Then, short hairpin RNA (shRNA) targeting circular RNA hsa_circ_0006117 was generated by GeneChem .PC cells transfected with 2000 cells/well were treated with CCK-8 reagent . At a specific time , a microplate reader  was used to record the absorbance at 450\u2009nm. Transfected PC cells were incubated with 800 cells/plate for 12 days for colony formation assay. After imaging, the number of clones was calculated and statistically analyzed.\u03bcL pipette tip was applied to scratch the monolayer in transfected PC cells. Representative pictures were obtained with an inverted microscope (Olympus) at the appointed times (0 and 48\u2009h). The migration rate was normalized using the 0\u2009h scratch area. At the same time, transwell assays was used to estimate the migratory and invasive capabilities of PC cells. The upper chamber was pretreated or not with 60\u2009\u03bcL Matrigel  before transfection of 200\u2009\u03bcL serum-free PC cells (5\u2009\u00d7\u2009104 cells/well). And 600\u2009\u03bcL of 20% FBS medium was placed in the lower chamber. Migrating or invading cells were counted after 24\u201328\u2009h of incubation. Representative pictures were captured with an inverted microscope (Olympus).After the transfected PC cells were planted, a 200\u2009http://www.circbank.cn/searchCirc.html), TargetScan (http://www.targetscan.org/vert_72/), miRDB (http://mirdb.org/), and miRTarBase (http://miRTarBase.cuhk.edu.cn/) were performed to postulate circular RNA hsa_circ_0006117/miRNA binding and mRNA/miRNA binding, respectively. Meanwhile, the \u201cclusterProfiler\u201d package in R was used for Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis to ascertain the molecular regulatory network of circular RNA hsa_circ_0006117 using a threshold of P value <0.05.CircBANK (http://starbase.sysu.edu.cn/) and TargetScan. The circular RNA hsa_circ_0006117 sequence and that of the 3\u2032untranslated region (3\u2032UTR) of KRAS were amplified and separately inserted into the pmiR-RB-Report (Ribobio), termed as circRNA-WT or KRAS-WT. Meanwhile, the corresponding mutant sequences were also amplified and inserted into the same vector to generate circRNA-MUT and KRAS-MUT, respectively. After the cotransfection of these vectors (50\u2009ng) and miR-96-5p control or miR-96-5p mimics (50\u2009nm) into PC cells, a GLOMAX 96 spectrophotometer (Promega) and the Dual-Glo Luciferase Assay System  were applied to detect luciferase activity.The circRNA-miRNA and miRNA-mRNA binding sites were predicted by StarBase  was applied for statistical analysis. Independent sample Student's http://www.circbase.org/), we found that circular RNA hsa_circ_0006117 is derived from 5\u2032 of exon 8 to 3\u2032 of exon 9 of PTPRA (Chr20: 2944917\u20132945848) by back-splicing (PTPRA), respectively. Circular RNA hsa_circ_0006117 could only be amplified from cDNA obtained from MIA PaCa-2 cells using divergent primers  in two datasets were both elevated in PC tissues  and S1B. primers . Next, S primers . After tdegraded . A stabiPTPRA  database (http://gepia2.cancer-pku.cn/#index)  and phosphorylated extracellular signal-regulated kinases 1/2 (P-ERK1/2) were lower in circular RNA hsa_circ_0006117-silenced PC cells than in the negative controls, whereas the total MEK1/2 and ERK 1/2 protein level remained unchanged (Studies have suggested that exon-derived circRNAs mainly exert their functions in tumor development by sponging miRNAs . To expl_0006117 \u2013S3C. In /#index) . Therefo/#index)  and prot/#index)  expressianalysis . It is wnchanged .KRAS, we overexpressed KRAS in circular RNA hsa_circ_0006117-silenced PC cells to rescue the inhibitory effects of circRNA-sh#2. Proliferative capacity assays revealed that ectopically expressed KRAS could partially rescue the effects induced by circular RNA hsa_circ_0006117 silencing and facilitated the growth of PC cells (Figures KRAS overexpression partially rescued the circular RNA hsa_circ_0006117 knockdown-induced effects and reinforced the migration and invasion potential of PC cells. Furthermore, in PC cells where circular RNA hsa_circ_0006117 was knocked down, KRAS transfection partially restored the expression of P-MEK1/2 and P-ERK1/2 when the total MEK1/2 and ERK1/2 remained constant (To verify whether circular RNA hsa_circ_0006117 facilitates PC progression through regulating  Figures . Similar Figures  and tran Figures  assays dconstant .KRAS, and miR-96-5p was identified as a possible candidate (KRAS in PC cells transfected with miR-96-5p mimics reduced (Figures KRAS. These results suggested that circular RNA hsa_circ_0006117 and KRAS may share miR-96-5p binding sites. We subsequently generated pmiR-RB-Report\u2122 constructs containing either circular RNA hsa_circ_0006117 or KRAS 3\u2032UTR sequences and confirmed them by sequencing (Supplementary figures KRAS 3\u2032UTR (the predicted binding sites are displayed in KRAS by binding to sequences in its 3\u2032UTR (The above studies have shown that circular RNA hsa_circ_0006117 may play PC-promoting effects by sponging miRNAs. Hence, we used bioinformatic analysis to explore which miRNAs can bind both circular RNA hsa_circ_0006117 and andidate . RT-qPCRandidate . On the andidate , whereas Figures . Furtherts 3\u2032UTR .To investigate whether circular RNA hsa_circ_0006117 facilitates PC development by regulating miR-96-5p, in circular RNA hsa_circ_0006117-silenced PC cells, miR-96-5p-inhibitor was transfected or cotransfected for subsequent rescue experiments. Experiments reflecting proliferative capacity Figures  suggestePC displays highly invasive and metastatic characteristics , 24. AdvKRAS promotes PC development through the regulation of noncoding RNA or nucleotide synthesis, while KRAS is a well-known activator that mediates the phosphorylation/activation of the MAPK signaling pathway [KRAS and circRNAs in PC has not been investigated. In our research, we first demonstrated that circular RNA hsa_circ_0006117 could upregulate the expression of KRAS via the competitive absorption of miRNA. And we found the cancer-promoting role of miR-96-5p in PC, which is consistent with previous work by others [KRAS as a candidate for genetic therapy for PC treatment. Further, a recently published study suggested that an inhibitor targeting mutated KRAS represented an effective treatment for some types of tumors [via up-regulating the phosphorylation of MEK/ERK and accelerate PC progression in a KRAS-dependent manner. Combined, we believed that circular RNA hsa_circ_0006117 may be a promising pharmacological target, and exploring the functions of KRAS-associated circRNAs may be of value for clinical application.The KRAS/MAPK signaling pathway is strongly related to the growth and survival of cancer cells , 30. Thi pathway , 33. Hery others . Intriguy others  have idef tumors . Besidesf tumors . Our stuvia sponging miRNAs [KRAS and was identified as the downstream target of circular RNA hsa_circ_0006117 in PC. This provides a novel idea for the investigation of the ceRNA mechanism. Of course, it is undeniable that not all ceRNA mechanisms can be successfully verified by this approach. Combined, all of our work indicated that circular RNA hsa_circ_0006117 promotes PC progression in a manner that is dependent on the downregulation of miR-96-5p.Accumulating evidence has indicated that circRNAs can play both oncogenic and tumor-suppressor roles g miRNAs , 38. Forg miRNAs  uncovereg miRNAs  that hsaIn summary, we have identified circular RNA hsa_circ_0006117 as a specifically highly expressed circRNA in PC. We further found that circular RNA hsa_circ_0006117 facilitates the malignant behaviors of PC through regulating the miR-96-5p/KRAS/MAPK signaling pathway . These r"}
+{"text": "Standard transcriptomic analyses cannot fully capture the molecular mechanisms underlying disease pathophysiology and outcomes. We present a computational heterogeneous data integration and mining protocol that combines transcriptional signatures from multiple model systems, protein-protein interactions, single-cell RNA-seq markers, and phenotype-genotype associations to identify functional feature complexes. These feature modules represent a higher order multifeatured machines collectively working toward common pathophysiological goals. We apply this protocol for functional characterization of COVID-19, but it could be applied to many other diseases.For complete details on the use and execution of this protocol, please refer to   \u2022Steps for meta-analysis of multiple transcriptomic studies and protein interactions\u2022Network analysis-based workflow to identify gene and functional modules\u2022Data-driven higher-order functional features provide a basis for characterizing disease Standard transcriptomic analyses cannot fully capture the molecular mechanisms underlying disease pathophysiology and outcomes. We present a computational heterogeneous data integration and mining protocol that combines transcriptional signatures from multiple model systems, protein-protein interactions, single-cell RNA-seq markers, and phenotype-genotype associations to identify functional feature complexes. These feature modules represent a higher order multifeatured machines collectively working toward common pathophysiological goals. We apply this protocol for functional characterization of COVID-19, but it could be applied to many other diseases. At least two gene expression studies are required in our meta-analysis approach for identifying consensus DEGs.2.https://www.ncbi.nlm.nih.gov/geo/info/). Conversely, other public repositories such as the ArrayExpress (We used the NCBI Gene Expression Omnibus (GEO) repository   models and one in vivo (Ad5-hACE2-sensitized mice) model of SARS-CoV-2 infection. The Calu-3 model (GSE147507)  in Vero E6 cells )   is based153940)) . The thi153940))  that devOptional: An optional step is to use disease-specific protein-protein interaction (PPI) data. In our SARS-CoV-2 protocol, we used the SARS-CoV-2-human virus-host interactome data  markers from three human lung studies  and geno6.-5.In case of lung single cell markers, only significant gene markers  are used for enrichment analysis. On the other hand, GWA trait associations are limited to vulnerability loci with an association p-value \u2264107.After selecting the relevant gene expression data sets, raw data needs to be downloaded and processed to generate raw counts and normalized transcript per million (TPM) for all samples. Our SARS-CoV-2 protocol used the CSBB-v3.0 toolkit for both sample processing and DE analysis as shown below:perl CSBB-v3.0_MacOS.pl (or) CSBB-v3.0_Linux.pl ProcessPublicData\u00a0Path_to_SRA-DATA_Table\u00a0Path_to_Folder_to_write_results8.https://github.com/praneet1988/Computational-Suite-For-Bioinformaticians-and-Biologists. The .sra files associated with each individual sample are downloaded using the Prefetch utility of the NCBI SRA Toolkit (https://github.com/ncbi/sra-tools). To download all the samples simultaneously, an SRA data table is required and can be created based on the format shown here https://github.com/praneet1988/Computational-Suite-For-Bioinformaticians-and-Biologists/blob/master/Test_Files/SRA_DATA_TABLE.txt.Instructions for installing the toolkit are available on the project GitHub page at 9.Next, the .sra files are converted to FASTQ format using fastq-dump utility of the SRA toolkit. The final step involves mapping to a transcriptome assembly of interest using bowtie2  and quan10.https://github.com/SudhirGhandikota/COVID19_secondary_analysis/tree/main/input_data/Count%20Data contains the raw counts from all the three studies. These raw counts can then directly be used in the DE analysis step (explained below).In this protocol, we implemented the above sample processing steps to generate raw sample counts in human Calu-3 model (GSE147507). For the other two studies, we downloaded the raw counts provided by the authors in the respective GEO repositories. The following path in our protocol GitHub repository 11.perl CSBB-v3.0_MacOS.pl (or) CSBB-v3.0_Linux.pl\u00a0DifferentialExpression\u00a0Path_To_Counts_File No_of_Control_Samples\u00a0No_of_Treatment_Samples\u00a0Counts_Threshold_Filtering No_of_samples_to_filter_per_Gene\u00a0Normalization_TypeThe final pre-processing step involves performing differential expression (DE) analysis, individually in each of the transcriptomic datasets. The following script can used in this step:a.Path_To_Counts_File: Raw counts file from the previous step.b.No_of_Control_Samples and No_of_Treatment_Samples: Number of controls and treatment samples respectively.c.Counts_Threshold_Filtering: Minimum raw count threshold (for filtering samples).d.No_of_samples_to_filter_per_Gene: Minimum number of samples in which a gene must be expressed (for filtering genes). In our SARS-CoV-2-specific implementations, we have set Counts_Threshold\u00a0= 10 and No_of_samples\u00a0= 3.e.Normalization_Type: Type of normalization .f.https://github.com/praneet1988/Computational-Suite-For-Bioinformaticians-and-Biologists.Further descriptions about these parameters can be found at Command-line parameters accepted for the DE module are:12.http://bioconductor.org/packages/release/bioc/html/EDASeq.html) and any unwanted variation from the raw data is removed using RUVSeq (https://bioconductor.org/packages/release/bioc/html/RUVSeq.html ).Prior to DE analysis, upper-quartile normalization  is applig RUVSeq  package 13.Then, edgeR  package perl CSBB-v3.0_MacOS.pl DifferentialExpression\u00a0/Path_to_Local_Git/input_data/Count Data/GSE147507/Series7.txt\u00a03 3 10 3\u00a0UpperQuantile14.The results from DE analysis module are written to a \u201ctemporaryfile.txt\u201d file (tab-delimited) where the statistical p-values are adjusted for multiple-testing using the Benjamini-Hochberg procedure.Timing: \u223c5\u00a0min1.Rscript GetConsensus.R\u00a0-\u2013files \u201cDE_results1.txt,DE_results2.txt\u201d\u00a0--org_assemblies GRCh38.p13,GRCm39\u00a0--logFC 0.6 --pvalue 0.05 --k 2 --outpath 'outdir/\u2019Using the individual DEG sets see , the fola.--files: comma-separated list of result files from the differential expression analysis see , one forb.: comma-separated list of Ensembl assembly IDs, one for each study. Used to identify and map the human ortholog gene symbols for studies with non-human samples. Supported assembly IDs can be found at: https://uswest.ensembl.org/info/about/species.html. If not provided, all gene symbols are assumed to belong to the same organism.--org_assemblies c.2fc threshold value for filtering significant DEGs --logFC: logd.--pvalue: P-value (FDR corrected) threshold e.--k: DEGs in k or more studies to be part of the consensus signature f.--outpath: Path to the output directory where the consensus DEGs are written to.The available command-line options include:In this step, a consensus transcriptomic signature will be obtained by combining DEGs from each individual study. The final conserved signature is saved in two separate files (upregulated and downregulated).2.2FC \u2265 0.6 or log2FC \u2264 \u22120.6) and a p-value (FDR) threshold of <0.05 is applied. Users are free to choose a different threshold value of their choice.Firstly, filter the significant DEGs in each individual study by applying a fold change and a p-value threshold. By default, a 1.5-fold change threshold  database.For studies involving other organisms, the corresponding human orthologs of the filtered DEGs are obtained using the biomaRt (2.46.3) R package . Convers4.Next, identify consensus transcriptomic signature containing genes that are up- or down-regulated in two or more studies. Alternatively, there are several well-established meta-analysis techniques that can be used for combining data from independent studies. Fisher\u2019s method (combined probability test)  can be u5.COVID19_secondary_analysis/input_data/. From the three SARS-CoV-2 infection models (two in vitro and one in vivo), we observed a total of 1,467 genes (833 upregulated and 634 downregulated) that are differentially expressed in the same direction in two or more models.Input files required to identify and reproduce the consensus signature in SARS-CoV-2 can be found in the protocol GitHub repository at Timing: \u223c5\u201310\u00a0min6.The consensus transcriptomic signature from 7.a.https://string-db.org/ and exporting all the PPIs using the Exports option (TSV) from the result screen. The resultant output file contains all edges among the uploaded proteins.This can be achieved by manually uploading the list of proteins to b.However, the web-interface is not designed to handle more than 2000 proteins as input, in which case it is better to download the full PPI data and filter the interactions manually. We provide a script (described below) as part of this protocol which takes in the full set of PPIs and applies a filtering criterion of choice.c.The STRING-based human PPI network data is known to suffer from noise and incompleteness. To overcome these issues, only likely interactions (true positives) are used in this protocol by filtering the edges based on combined_score \u2265 0.9 (column 13) or experimental_score \u2265 0.7 (column 10). Users can select a different threshold to filter the final set of interactions.d.https://github.com/SudhirGhandikota/COVID19_secondary_analysis/tree/main/input_data/other%20data/filtered_PPI.txt) from STRING (v11) that we used in our SARS-CoV-2-specific analysis.Our GitHub repository includes a filtered set of PPIs  determines the tightness of the identified clusters. Increasing its value leads to higher granularity  and vice-versa. The default inflation value of 2.5 is used in this protocol. Results from clustering analysis, including the membership information, can be downloaded directly from the Cytoscape tool.The inflation factor parameter of the MCL algorithm .ii.https://string-db.org/cgi/download?species_text=Homo+sapiens. Conversely, interactions from other sources such as HuRI or BioGRID can also be provided (minus the --filter parameter). By default, the script uses the filtered STRING PPIs used in the protocol, which could be found at https://github.com/SudhirGhandikota/COVID19_secondary_analysis/tree/main/input_data/other%20data on the project GitHub page.--PPI_file:  A tab-delimited file containing the set of human PPIs. The latest version of STRING human PPIs can be downloaded from the following link iii.combined_score \u2265 0.9 or experimental_score \u2265 0.7.--filter: A condition to filter the PPI links (STRING only) prior to running the clustering algorithm. In our protocol, we only retained the edges with a iv.--inflation_value: Inflation parameter in MCL algorithm. It determines granularity of the identified clusters. High inflation values would result in increased granularity  and vice-versa .v.--max_iter: Maximum number of iterations for the MCL algorithm .vi.--outpath: Path to the output directory where the file containing the final MCL cluster memberships are written to.The command-line parameters to the script include:d.While there are several other clustering algorithms to choose from, MCL is ideal for identifying dense gene modules  with morEventually, network modules from this joint interactome are identified using the Markov clustering (MCL) algorithm .a.To do In this step a consensus interactome will be built and analyzed to identify candidate protein modules. Timing: 35\u201340\u00a0min9.https://toppcluster.cchmc.org) of the ToppGene suite  are used towards building the feature network. While there are several enrichment analyses tools available , ToppClu11.Marker_enrichments.R, present in the project GitHub page, can be used to perform the cell-type marker enrichments with the syntax shown below:Rscript Marker_enrichments.R\u00a0--marker_file ../input_data/Lung_Markers/lung_markers_test.txt\u00a0--p_value 0.05 --logFC 0.5\u00a0--cluster_file ../input_data/SARS-CoV-2-Cons_MCL_Clusters.txt\u00a0--outpath outdira.--marker_file: Text file containing the cell type marker genes. This file should contain 4 mandatory columns corresponding to the cell type (\u201ccell\u201d), gene marker (\u201cgene\u201d), fold change (\u201clogFC\u201d) and the adjusted p-value .b.--p_value and --logFC: The p-value (multiple-testing adjusted) and fold change thresholds to filter the marker genes.c.--cluster_file:A two column, tab-delimited file containing genes (column 1) and their corresponding MCL cluster memberships (column 2)d.--min_genes: Minimum number of genes needed to be in a candidate cluster .e.--outpath: Path to the output directory where the marker enrichment results need to be stored.Next, evaluate the modules for enrichments of single-cell marker gene lists. For filtered SARS-CoV-2 gene modules we computed enrichment analysis against three different lung scRNA-seq studies . Using m12.a.In case of GWAS Catalog associations, child terms for each trait are parsed from the experimental factor ontology (EFO) hierarchy  and usedb.GWAS_enrichments.py which first parses the EFO tree to obtain the child terms for each GWAS Catalog trait and then computes their enrichments among the SARS-CoV-2-specific candidate modules.python GWAS_enrichments.py--obo_file ../input_data/other\\ data/efo.obo.txt--cluster_file ../input_data/SARS-CoV-2-Cons_MCL_Clusters.txt--assoc_file ../input_data/other\\ data/gwas_catalog_v1.0.2-associations_e100_r2020-07-14.tsv --outpath outdirWe provide a python script https://www.ebi.ac.uk/efo/efo.obo) and a tab separated file containing the GWAS Catalog associations (https://www.ebi.ac.uk/gwas/docs/file-downloads) used in our work are also included in the GitHub repository.The EFO open biomedical ontology (OBO) file : Path to the EFO OBO file (.txt) which can be found at ii.https://www.ncbi.nlm.nih.gov/gap/phegeni) or GWAS Catalog associations (https://www.ebi.ac.uk/gwas/docs/file-downloads).--assoc_file: A Tab-delimited file containing phenotype-genotype associations from NCBI PheGenI  and their corresponding MCL cluster memberships (column 2).iv.--min_genes: Minimum number of genes needed to be in a candidate cluster .v.--p_value (for PheGenI associations only): To specify a p-value threshold for filtering associations .vi.--remove_intergenic: A Boolean flag to indicate the removal of intergenic associations. In our SARS-CoV-2-specific protocol, all intergenic associations from both PheGenI and GWAS Catalog were ignored.vii.--outpath: Path to the output directory where the enrichment results need to be stored.Both these scripts support the following set of command-line parameters:Thereafter, test the filtered gene modules for enrichments of phenotypic traits using compiled genotype-phenotype associations from the NCBI PheGenI  and the 13.Finally, construct a term-term network using the enriched features  from the candidate gene modules. Only a subset of enriched functional terms  are considered to reduce the complexity and manage the density of the final feature network. This filtering step however is optional, especially if the candidate cluster counts and/or the enriched terms are smaller in number. Two feature nodes are connected by an edge if they share one or more of the module candidates.14.https://gephi.org). The input to the Gephi tool (version 0.9.2) is a tab-delimited file containing the source and destination enriched terms. Using the Louvain clustering  <0.05) in 2 or more input studies.\u201cConsensus_up.txt\u201d and \u201cConsensus_down.txt\u201d \u2013 Text files containing the lists of genes (one gene per line) corresponding to upregulated and downregulated consensus signatures, respectively. By default, these files consist of genes differentially expressed , these files also include a column with comma-separated list of genes that are in common between the given module and a phenotypic trait. In case of the GWAS Catalog traits, gene associations of the corresponding child traits (from EFO hierarchy) are also used in the enrichment tests. The counts of mapped child traits are also specified in the output file.\u201cmodule_marker_enrichments.txt\u201d \u2013 A tab-delimited output file containing cell type marker genes enriched among the candidate modules. The overlapping marker genes are included in a separate column, as a comma-separated list.The outcomes  in our protocol are dependent on certain assumptions and choices made during the analysis steps. Firstly, the composition of the protein modules is dependent on the consensus transcriptomic signature and the PPIs identified among them. Any noise or heterogeneity in the model systems or the transcriptomic data can impact the transcriptomic concordance between the different model systems and the consensus signature  used to The final module composition (gene/protein assignment to the modules) also depends on the choice of clustering algorithms and the parameter values used to generate them. For instance, increasing the inflation factor parameter value of the MCL algorithm would lead to smaller-sized clusters or modules and vice-versa . AlthougFinally, the functional complexes are based on the set of functional terms or annotations used in the enrichment step, which in turn, is dependent on the disease or phenotype being studied. Any errors or redundancy within the annotation sources  can affect the composition of the functional complexes identified.Error in getopt : long flag XXXX is invalid\u201d while using any of the R Scripts.Error message \u201c--files) is used to run an R script. The specified option flag needs to be fixed before proceeding with the execution of the corresponding R Script.This error message commonly occurs when a wrong command-line parameter option flag  in protocol This error occurs when the number of Ensembl assembly IDs (comma-separated), provided explicitly using the Unable to recognize the provided Ensembl assembly\u201d while running the script \u201cGetConsensus.R\u201d.Warning message \u201chttps://uswest.ensembl.org/info/about/species.html.This message indicates that the provided assembly ID(s) in  is unrecError in mcl_results$Cluster : $ operator is invalid for atomic vectors\u201d while implementing the MCL step using the RScript \u201cMCL_Clustering.R\u201d.Error message \u201c\u201c--max_iter\" command-line parameter could help prevent this error message.This message potentially occurs if the number of iterations of the MCL algorithm  were notError: Small inflation coefficient prevents that an equilibrium state matrix is reached within XXX iterations\u201d while using the R Script \u201cMCL_Clustering.R\u201d.Error message \u201c--inflation_value command line option or by increasing the maximum of iterations using the --max_iter command-line parameter.This message indicates that the given combination of inflation coefficient parameter and the number of MCL iterations are not sufficient for the modularity objective to converge during anil.jegga@cchmc.org).Further information and requests should be directed to and will be fulfilled by the lead contact, Anil G. Jegga (This study did not generate any unique reagents."}
+{"text": "PRKCA) as the target gene of miR-709. Currently, the expression and function of rno_circRNA_0001004 in the rat pituitary gland is not clarified; (2) Methods: In this study, both bioinformatics analysis and dual-luciferase report assays showed a target relationship between rno_circRNA_0001004 and miR-709. Furthermore, the rno_circRNA_0001004 overexpression vector and si-circ_0001004 were constructed and transfected into GH3 cells; (3) Results: We found that rno_circRNA_0001004 expression was positively correlated with the PRKCA gene and GH expression levels, while it was negatively correlated with miR-709. In addition, overexpression of rno-circ_0001004 also promoted proliferation and relieved the inhibition of miR-709 in GH3 cells; (4) Conclusions: Our findings show that rno_circ_0001004 acts as a novel sponge for miR-709 to regulate GH synthesis and cell proliferation, and are the first case of discovery of the regulatory role of circRNA_0001004 in pituitary GH.(1) Background: As a novel type of non-coding RNA with a stable closed-loop structure, circular RNA (circRNA) can interact with microRNA (miRNA) and influence the expression of miRNA target genes. However, circRNA involved in pituitary growth hormone (GH) regulation is poorly understood. Our previous study revealed protein kinase C alpha ( Protein Kinase C (PKC) is a class of phospholipid-dependent kinases that participate in regulation of protein secretion including GH and luteinizing hormone (LH) [Growth hormone (GH) is a key hormone secreted from the anterior pituitary, and has received much attention as it regulates key physiological functions such as growth and development . Studies3 cells  by targeone (LH) ,14, as wone (LH) .Circular RNA (circRNA) is a special type of non-coding RNA (ncRNA) molecule that, unlike traditional linear RNA, forms covalently closed loop structures generated by pre-mRNA back splicing. CircRNAs are highly stable, abundant and conserved molecules with the characteristics of cell tissue specificity  and haveThe rno_circ_0001004 was firstly discovered in the rat anterior pituitary by using Illumina sequencing . It is g3 cells, we firstly detected the expression of rno_circ_0001004. Convergent and divergent primers were designed to amplify the linear or back-splicing products and total RNA from GH3 cells with or without RNase R treatment was subjected to RT-PCR. As expected, endogenous circ_0001004, but not pre-mRNA, was resistant to RNase R digestion  was cultured in F12  medium supplemented with 2.5% fetal bovine serum (FBS) , 15% horse serum  and 1% penicillin/streptomycin . Hela cells were cultured in PRMI 1640  culture medium with 10% FBS and 1% penicillin/streptomycin. GH3 cells were transfected with miR-709 mimic, rno_circ_0001004 or si-circ0001004 using Lipofectamine 2000 . The cells were incubated at 37 \u00b0C in a humidified atmosphere of 5% CO2.GH3 cells by Trizol reagent  according to the manufacturer\u2019s protocol. The cDNAs were obtained by Color Reverse Transcription Kit (with gDNA remover) . Genomic DNA (gDNA) was extracted using a Genomic DNA Isolation Kit . Quantification of mRNA, miRNA, circRNA and gDNA was performed by using a SBRY Green PCR Kit , primers and Real-Time PCR System  The circRNA and mRNA levels were normalized to those of \u03b2-actin, while the miR-709 levels were normalized to the U6 and determined by 2-DDCt method. The primer sequences for the amplification of specific primers are listed in Total RNA was isolated from GHThe sequence for exons 9\u201311 of Wnk2 was PCR amplified using primers F (5\u2032-GGGGTACCTGAAATATGCTATCTTACAGCCTGGCCTATCAGTGGGC-3\u2032) and R (5\u2032-CGGGATCCTCAAGAAAAAATATATTCACCTGGGTCCCTGAGGCAGC-3\u2032), then cloned into KpnI and BamHI restriction sites of a circular expression vector, the pcd2.1-ciR , by digestion to create rno_circ_0001004-overespressing vector.Hela cells were seeded in 96-well cell culture plates. When their confluence reached about 80%, the miR-709 mimic and rno_circ_0001004-Wt or rno_circ_0001004-Mut were co-transfected into cells using Lipofectamine 2000. After incubation for 48 h, the cells were washed with PBS and the luciferase activity was measured by the Dual-GLO luciferase reporter assay system  according to the manufacturer\u2019s instructions. 3 proliferation was assessed by the cell counting kit-8 (CCK-8) method, 5-ethynyl-2\u2032 deoxy uridine (EdU) incorporation assay and proliferating cell nuclear antigen (PCNA) expression. Firstly, the rate of GH3 proliferation was determined with the CCK-8 kit  according to the manufacturer\u2019s instructions. The number of viable cells was assessed by measuring the absorbance at 450 nm using a Synergy 2 Multi-Mode Reader . Secondly, DNA synthesis was examined with EdU incorporation assay  to evaluate GH3 proliferation. The EdU positive cells were counted and normalized by the total number of Hoechst 33,342 stained cells. Lastly, GH3 proliferation was evaluated by PCNA expression, which is the auxiliary component of DNA polymerase \u03b4 and constitutes a useful proliferation marker.GH3 cells were lysed in a RIPA lysis buffer  containing 1mM phenyl methane sulfonyl fluoride (PMSF). The concentration of protein was measured using the BCA Protein Assay Kit  according to the manufacturer\u2019s instructions. Equal amounts of total protein were separated by SDS-PAGE and transferred to a PVDF membrane in a tris-glycine methanol buffer. The primary antibodies used in this study were as follows: GH monoclonal antibody , PKC\u03b1 polyclonal antibody , ERK1/2 monoclonal antibody , Phospho-ERK1/2 monoclonal antibody (Tyr204) , PCNA monoclonal antibody  and Tubulin polyclonal antibody , HRP conjugated goat anti-rabbit IgG  and HRP conjugated goat anti-mouse IgG  were used as secondary antibodies. The membranes were incubated with ImmobilonTM Western Chemiluminescent HPR Substrate  and scanned with a FlourChem M Fluorescent Western Imaging System . The protein band density was determined by the software Image J and normalized with a corresponding Tubulin intensity.GH3 cells transfected with rno_circ_0001004 and si-circ_0001004 was determined using the reagents in the Rat Growth Hormone ELISA kit  according to the manufacturer\u2019s protocols. Color alterations in the wells were read using the 96-well microplate reader .The concentration of GH in a cell medium of GHp < 0.05 was considered as statistically significant. * p < 0.05; ** p < 0.01.All experimental results are presented as the mean \u00b1 S.E.M, with at least three independent replications. Statistical analysis was performed using SPSS 17.0 software. The statistically significant differences among groups were tested by one-way analysis of variance (ANOVA). In summary, our study reveals that rno_circ_0001004 competitively binds miR-709 to regulate the GH synthesis and cell proliferation in rat pituitary cells . To the"}
+{"text": "Differentially expressed circRNAs were analyzed in plasma samples by quantitative RT-PCR and correlated to relevant systolic and diastolic parameters. The pathophysiological implications were explored through bioinformatics tools. Four circRNAs were overexpressed compared to controls: hsa_circ_0003258, hsa_circ_0051238, and hsa_circ_0051239 in\u00a0LMNA-related DCM and hsa_circ_0089762 in the ischemic DCM cohort. The obtained areas under the curve confirm the discriminative capacity of circRNAs. The circRNAs correlated with some diastolic and systolic echocardiographic parameters with notable diagnostic potential in DCM. Circulating circRNAs may be helpful for the etiology-based diagnosis of DCM as a non-invasive biomarker.Dilated cardiomyopathy (DCM) is the third most common cause of heart failure. The multidisciplinary nature of testing \u2014 involving genetics, imaging, or cardiovascular techniques \u2014 makes its diagnosis challenging. Novel and reliable biomarkers are needed for early identification and tailored personalized management. Peripheral circular RNAs (circRNAs), a leading research topic, remain mostly unexplored in DCM. We aimed to assess whether peripheral circRNAs are expressed differentially among etiology-based DCM. The study was based on a case\u2013control multicentric study. We enrolled 130 subjects: healthy controls .The circular RNA (circRNA) expression pattern is paramount for categorizing the DCM etiologies.Our peripheral circRNAs fingerprint discriminates between various among etiology-based DCM and correlates with some echocardiographic parameters.We provide a potential non-invasive biomarker for the etiology-based diagnosis of LMNA-related DCM and ischemic DCM.The online version contains supplementary material available at 10.1007/s00109-021-02119-6. LMNA) explain 5\u201310% of familial DCM cases.Heart failure is a global pandemic affecting more than 25 million people worldwide, with a continuously increasing prevalence . One of LMNA-related DCM presents highly aggressive outcomes and lethal ventricular arrhythmias [DCM is a heterogeneous entity that has different outcomes and may require diverse therapies . Notablyhythmias . Male sehythmias . Thus, tNon-coding RNAs have pivotal roles in regulating the network that governs the physiology and pathology of cardiovascular diseases . To dateThe present study aimed to identify differentially expressed circRNAs in the plasma of patients with DCM of various etiologies such as familial, idiopathic, or ischemic.. Patient samples and the dataset were collected from several centers . We enrolled 130 subjects distributed in five study groups: healthy controls (n\u2009=\u200920), idiopathic DCM (n\u2009=\u200930), ischemic DCM (n\u2009=\u200920), and familial DCM patients. The carriers of rare pathogenic variants included were (i) LMNA gene (n\u2009=\u200930) and (ii) BCL2-associated athanogene 3 (BAG3) gene (n\u2009=\u200930)  and genetically and phenotypically positive (LMNAPh+) as previously described [DCM etiology was determined by three independent clinical cardiologists, who are experts in cardiomyopathies. DCM was defined as either LVEF levels below 50% and/or LV end-diastolic diameter larger than 56\u00a0mm . BAG3 anlial DCM . The LMNescribed . Geneticescribed . A transescribed , 12. TheThe study protocol was approved by the Andalusian Biomedical Research Ethics committee. The study was performed in full compliance with the Declaration of Helsinki. All participants provided written informed consent.Genetic analysis was performed as previously described . DNA iso\u00a0g, 15\u00a0min, 4\u00a0\u00b0C), and the plasma layer was aliquoted and stored at\u2009\u2212\u200980\u00baC until further analysis.Ten milliliters of peripheral blood was collected in K2-ethylenediaminetetraacetic acid tubes (BD) after 10\u00a0h overnight fasting. None of the patients was under heparin therapy. The blood was processed within 4\u00a0h after isolation, centrifuged . This platform analyzed 36 samples of idiopathic and non-idiopathic DCM subjects. Total RNAs from each sample were obtained using the Arraystar\u2019s standard protocols . The enriched circRNAs were amplified and transcribed into fluorescent cDNA using a random priming method . The labelled cDNAs were hybridized onto the Arraystar Human circRNA Array V2.0 . Once the slides had been washed, they were scanned by the Agilent Scanner G2505C.2O and stored at\u2009\u2212\u200980\u00a0\u00b0C. For the circRNA quantification, circulating RNA preparations were reverse transcribed with a first-strand cDNA synthesis kit  using a random primer approach and following the manufacturer\u2019s instructions. Previous to reverse transcription, samples were spiked with MS2 RNA , which was used as an internal normalizer. Quantification of selected circRNAs was performed by qRT-PCR using divergent DNA primers designed with the circInteractome algorithm [Total RNA was isolated from 200 \u00b5L of plasma using a miRNeasy Serum/Plasma Kit (Qiagen). RNA was eluted with 20 \u00b5L of RNase-free Hlgorithm  (see Suphttp://www.circbase.org/). The Circular RNA Interactome (https://circinteractome.nia.nih.gov/) was used to predict miRNAs and RBP-binding sites. The regulatory network was performed with Navigator software [https://string-db.org) [https://www.mirnet.ca/miRNet/home.xhtml).Information about circRNAs is available on the circBase website (software . The set-db.org) . The set2 circRNAs. In addition, the association between circRNAs and echocardiography parameters was assessed using logistic bivariate regression. Several models were constructed using the Wilcoxon test and iterating combinations between our circRNA candidates, as well as echocardiographic and clinical covariates. The changes in p-values of their variables were evaluated by the Wald test and a likelihood ratio. To characterize the diagnostic performance of the circRNAs candidate, ROC curves were applied together with a logistic regression model to determine the AUC and the specificity and sensitivity of the optimal cutoffs. ROC curves were generated by plotting sensitivity against 100-specificity. Data were presented as the AUC and 95% CI. The statistical software package R (www.r-project.org) was used for all analyses.Continuous variables are expressed as the mean\u2009\u00b1\u2009standard deviation. Categorical variables are expressed in frequency and percentage (%). Analysis of variance was applied to compare intergroup circRNAs levels. The Pearson correlation was used to test the link between echocardiographic and clinical variables vs. logp\u2009<\u20090.05).A total of 36 idiopathic and non-idiopathic DCM age-matched patients were assessed to test the differences in circRNA expression profiles  and phenotypically positive (LMNAPh+). Circulating hsa_circ_0003258 levels were differentially expressed in the LMNAPh\u2212    than in healthy subjects  than in healthy subjects  area under the curve (AUC) analysis was assessed to investigate the circulating circRNAs diagnostic value in discriminating LMNAPh\u2212 group showed a negative correlation between hsa_circ_0003258 and hsa_circ_0051239 with early diastolic mitral annular velocity (E\u2019s TDI). The LMNAPh+ cohort showed a positive correlation of hsa_circ_0051238 with tissue Doppler imaging (TDI) septal atrial systolic mitral annular velocity (A\u2019s TDI) and a negative correlation of hsa_circ_0051238 and hsa_circ_0051239 with LV outflow tract (LVOT) velocity.The association between circulating circRNAs and echocardiographic and clinical features of DCM patients was also analyzed. As indicated in Table An additional study was performed to assess correlations between the echocardiographic and clinical variables and hsa_circ_0089762 for the ischemic DCM population. Hsa_circ_0089762 expression was negatively associated with diastolic blood pressure and LVEF  tricuspid annular plane systolic excursion was only independently negatively associated with hsa_circ_0003258. Pulmonary hypertension (PHT) was independently positively related to hsa_circ_0003258 and hsa_circ_0051239.To further explore the expression of circRNA-DCM disease association, a logistic regression analysis was carried out in our DCM population Fig.\u00a0. All thrIn the case of hsa_circ_0089762, the logistic regression analysis showed that its circulating levels within A\u2019s TDI wave or the RV dimension were independent influencing factors for ischemic DCM.LMNA mutation influences the proper development of megakaryocytes resulting in altered platelet production/function [LMNA effect in the enrichment , regulation of megakaryocyte differentiation (FDR\u2009=\u20090.0013), and fibroblast growth  (FDR\u2009=\u20090.0267).An examination of biological processes related to RBPs, with binding sites for circRNA candidates, reveals a set of possible pathways in which circRNAs play a regulative role. function . We recoThe analysis of the intersection set of RBPs predicted to interact with the selected circRNAs Fig.\u00a0; Table 6p\u2009=\u20090.075), and hsa-miR-377 is related with ischemic cardiomyopathy (p\u2009=\u20090.221). Hsa_circ_0089762 has sponge activity with multiple, energetically favorable binding sites. Of note is hsa-miR-21, as well as hsa-miR-183, hsa-miR-361-3p, hsa-miR-384, hsa-miR-873, hsa-miR-938, hsa-miR-1249, and hsa-miR-1283. The miRNAs sponged by the circRNAs with a context score over 90% was used to capture the set of mRNAs regulated by these miRNAs. Functional enrichment, using a hypergeometric association algorithm, shows that 148 proteins of the network were related with focal adhesion (p\u2009=\u20092.68e\u22128), and 128 proteins were linked with regulation of the actin cytoskeleton (p\u2009=\u20090.00002). Gene ontology biological processes, using the same hypergeometric algorithm, show a significant correlation with endoplasmic reticulum-nuclei signaling pathways (p\u2009=\u20090.1e\u22126) and pre- and post-Golgi vesicle transportation .The analysis of miRNAs sponged by validated circRNAs offers various candidates for further research. Hsa_circ_0003258 has only one functional binding site to hsa-miR-653. As a counterpart, hsa_circ_0051238 and hsa_circ_0051239 present a clear sponge effect over hsa-miR-210, with five binding sites that have \u2206U below zero. Thereby, the overexpression of hsa_circ_0051238 and hsa_circ_0051239 will actively reduce the availability of hsa-miR-210. Hsa-miR-210 regulates expression of hepatocyte growth factor gene, whose overexpression is considered a treatment for DCM . AdditioOver the last decade, the diagnostic process of DCM etiologies has focused on searching for new biomarkers. An efficient biomarker for DCM should be robust, stable, non-invasive, sensitive, specific to this entity, predictive of a particular DCM etiology, and show a preclinical and clinical relevance to be validated in animal and/or human cell models . We propLMNA-related DCM patients. Notably, hsa_circ_0051238 plasmatic levels were significantly present in the LMNAPh\u2212 cohort. Hence, it may be a promising diagnostic biomarker for the early identification of patients in an initial stage of LMNA-related DCM. This will allow personalized therapeutic measures to be applied that help to improve the progression and outcome of LMNA-related DCM. Furthermore, plasmatic hsa_circ_0089762 may provide discriminative power for the ischemic DCM cohort with high-yield diagnostic accuracy and an AUC of 0.92. These circRNAs have been identified mostly in various types of oncologic processes [Unlike linear RNA, single circulating circRNAs or circRNAs combined with various other biomarkers are a promising tool for clinical diagnosis of heart diseases, which would improve outcome . Thus, crocesses \u201333. ThusLMNA and LVEF\u2009<\u200950% have been established as independent factors associated with a more aggressive outcome and even death during follow-up [LMNA-DCM etiology were related to male gender [LMNAPh\u2212 group, the E\u2019s TDI is negatively related to hsa_circ_0003258 and hsa_circ_0051239. This E\u2019s TDI impairment suggests an underlying early diastolic dysfunction [LMNAPh+ group showed a positive correlation, which indicates that the left atrium is a prominent factor to maintain the LV filling pressure when diastolic dysfunction advances. This sequential TDI septal impairment mirrors the transition from LMNAPh\u2212 to LMNAPh+ and may be related to the progressive fibrosis of the interventricular septum located in the basal portion, which is characteristic of the LMNA related-DCM that has been associated with ventricular arrhythmias and worse prognosis [LMNAPh+ cohort. Dysfunction of RV is a final common step in DCM and heart failure [In the current study, circRNA were related to clinical and echocardiographic variables. Male gender, rare non-missense variants in ollow-up . Herein,e gender . On the e gender . TDI reve gender , 17. In function . A\u2019s TDIrognosis . LVEF wa failure . RV pres failure .Otherwise, hsa_circ_0089762 correlated to diastolic blood pressure and LVEF in the ischemic group, which supports our results as a specific, highly sensitive biomarker with high-yield diagnostic accuracy. Moreover, hsa_circ_0089762 was related to A\u2019s TDI, which suggests more advanced progression of this entity. Its association with an increase in RV dimension could add information for tailored management in this group, since RV impairment is a worse outcome marker in the ischemic population . In addiZNF652 gene. ZNF652 interacts with CBFA2T3, which acts as a transcriptional repressor [ZNF652 is associated with systolic or diastolic blood pressure and hypertension. However, its role remains unclear [ATP5SL gene. ATP5SL is required for the assembly of mitochondrial NADH: ubiquinone oxidoreductase complex (complex I). Complex I is essential to provide the energy for cardiac function and is related to DCM progression [ATP5SL has been associated with a congenital ventricular septal defect by the overexpression of hsa_circ_0051239 [MT-CO2 gene. MT-CO2 is part of the electron transport chain of the mitochondria. Reduced activity of the electron transport chain subunits has been described independently of etiology in ischemic or idiopathic DCM patients [Regarding biological implications, circRNAs spring from introns or exons of their parental genes by back-spliced circularization . Hence, epressor . ZNF652  unclear . Hsa_cirgression . ATP5SL _0051239 . Finallypatients .The functional enrichment of the intersecting set of RBSs reveals the role of FMRP in regulation of the membrane potential. Bao et al. described FMRP isoform 1, in rats, as an essential protection factor and a novel potential biomarker in the cardiovascular system . The parOur current study has several limitations. Firstly, our sample was prospectively recruited from the outpatient clinic. The size of the study sample, comprised of strictly DCM patients, did not allow us to obtain a robust multivariate logistic regression model. Furthermore, a larger sample size is needed to validate these data by gender categorization since gender may play a role in the DCM prognosis , 54. In LMNA-related DCM, and hsa_circ_0089762 levels are specifically upregulated in the ischemic DCM cohort. These circulating circRNAs and certain echocardiographic variables might improve the etiology-based diagnostic, which allows early identification of asymptomatic cases and tailored treatment of the DCM population.Exploring new biomarkers through circular transcriptome expression patterns will identify new targets in DCM pathogenesis. We propose a circulating circRNAs fingerprint to discriminate between various DCM etiologies. Circulating hsa_circ_0003258, hsa_circ_0051238, and hsa_circ_0051239 expression levels are higher in Supplementary file1 (PDF 456 KB)Below is the link to the electronic supplementary material."}
+{"text": "The BioChemical Library (BCL) cheminformatics toolkit is an application-based academic open-source software package designed to integrate traditional small molecule cheminformatics tools with machine learning-based quantitative structure-activity/property relationship (QSAR/QSPR) modeling. In this pedagogical article we provide a detailed introduction to core BCL cheminformatics functionality, showing how traditional tasks  can be readily combined with machine learning. In addition, we have included multiple examples covering areas of advanced use, such as reaction-based library design. We anticipate that this manuscript will be a valuable resource for researchers in computer-aided drug discovery looking to integrate modular cheminformatics and machine learning tools into their pipelines. These tools frequently include capabilities for tasks such as high-volume molecule processing (Computer-aided drug discovery (CADD) methods are routinely employed to improve the efficiency of hit identification and lead optimization . The impocessing , ligand-ocessing , conformocessing , 2019, pocessing , structuocessing , and ligocessing .Frequently, building a drug discovery pipeline with all of these parts requires users to combine multiple different software packages into their workflow. This can be challenging because of different version requirements in package dependencies. Moreover, file- and data-type incompatibilities between packages can lead to errors and pipeline inefficiencies. Here, we describe the BioChemical Library (BCL) cheminformatics toolkit, a freely available academic open-source software package with tightly integrated machine learning-based QSAR/QSPR capabilities.Rosetta . In addition, BCL applications are modular and can be easily combined into complex protocols with simple Shell scripts. Output files from the BCL are primarily common file types that can also be read as input by other software packages. Its command-line usage will be familiar to users of the popular macromolecular modeling software Rosetta . The simThe first thing to complete after downloading and installing the BCL is to add the license file to the/path/to/bcl folder. We further recommend adding/<path>/<to>/bcl to the LD_LIBRARY_PATH and PATH environment variables in the. cshrc/.bashrc. This allows users to access the BCL from any terminal window simply by typing bcl. exe into the command-line. For detailed setup instructions, read the appropriate operating system (OS)-specific ReadMe file in bcl/installer/.bcl.exe helpThe BCL is organized into application groups each of which contains multiple applications. To view the application groups and associated applications, run the BCL help command:bcl.exe molecule:HelpThe BCL has application groups for cheminformatics, protein folding, machine learning, and other tasks . To isolbcl.exe application_group:ApplicationGenerally, the syntax to access a BCL application is as follows:bcl.exe application_group:Application --helpThe help menu for any application cans similarly be accessed asThese help options list the basic arguments and parameters available for each application. More detailed help options are also frequently available for individual application parameters. In this way, all of the documentation required to run the BCL can be readily accessed from the command line. The application groups composing the core of the BCL cheminformatics toolkit include the following: Molecule, Descriptor, and Model .Molecules are input to the BCL in the MDL structure-data format (SDF) file. Often, molecules that are downloaded or converted from one source to another contain errors . Dataset sanitization is a critical component of computational chemistry and informatics projects. The BCL molecule: Filter application is the first step in correcting these errors or identifying molecules that cannot be easily and automatically corrected.bcl.exe molecule:Filter--helpTo see all of the options available in molecule:Filter, run the following command:or view the supplementary material .bcl.exe molecule:Filter \\-input_filenames platinum_diverse_dataset_2017_01. sdf.gz \\-output_matched platinum_diverse_dataset_2017_01. matched.sdf.gz \\-output_unmatched platinum_diverse_dataset_2017_01. unmatched.sdf.gz \\-add_h -neutralize \\-defined_atom_types\u2013simple \\-logger File platinum_diverse_dataset_2017_01. Filter.logFor the following examples we will make use of a set of the Platinum Diverse Dataset, a subset of high-quality ligands in their protein-bound 3D conformations .bcl.exe This command reads in the SDF platinum_diverse_dataset_2017_01. sdf.gz, saturates all molecules with hydrogen atoms, neutralizes any formal charges, checks to see whether the molecules have valid atom types , and then checks to see whether the molecules have simple connectivity . The neutralization flag identifies atoms with formal charge and tries to remove the formal charge. The default behavior allows modification of the protonation state of the atom  and/or the bond order. Other options  are also available and can be seen in the help menu. Adding hydrogen atoms and neutralizing charges are not required operations but are shown above to demonstrate the functionality.All molecules that match the filter  are output into platinum_diverse_dataset_2017_01. matched.sdf, and molecules that fail to pass the filters are output into platinum_diverse_dataset_2017_01. unmatched.sdf. In this case, all molecules pass the filter. This allows the user to review the molecules that failed the filter and choose to either fix them or continue without them.2, the following command can be used:bcl.exe molecule:Filter \\-input_filenames platinum_diverse_dataset_2017_01. sdf.gz \\-output_matched platinum_diverse_dataset_2017_01. veber_pass.sdf.gz \\-output_unmatched platinum_diverse_dataset_2017_01. veber_fail.sdf.gz \\-compare_property_values TopologicalPolarSurfaceArea less 140 \\NRotBond less_equal 10 \\-logger File platinum_diverse_dataset_2017_01. veber.logThe molecule:Filter application can also be used to separate molecules by property and/or substructure using the compare_property_values flag. For example, to filter out molecules that contain 10 or more rotatable bonds and a topological polar surface area (TPSA) less than 140\u00a0\u00c52, and then an additional 84 molecules that had greater than 10 rotatable bonds were filtered out. Notice that the filters are applied sequentially, and molecules must pass both filters to be output to the matched file. Alternatively, the any flag can be specified such that if a molecule meets any one of the filter criteria, then it is output to the matched file:bcl.exe molecule:Filter \\-input_filenames platinum_diverse_dataset_2017_01. sdf.gz \\-output_matched platinum_diverse_dataset_2017_01. any_pass.sdf.gz \\-output_unmatched platinum_diverse_dataset_2017_01. any_fail.sdf.gz \\-compare_property_values TopologicalPolarSurfaceArea less 140 \\NRotBond less_equal 10 \\-any -logger File platinum_diverse_dataset_2017_01. any.logOf 2,859 molecules, 395 were first filtered out for have a TPSA \u2265140\u00a0\u00c5In this example, 2,801 molecules passed at least one of the filters and only 58 were filtered out.bcl.exe molecule:Filter \\-input_filenames drugbank_nonexperimental.simple.sdf.gz \\-output_matched drugbank_nonexperimental.simple.anilines.sdf.gz \\-output_unmatched drugbank_nonexperimental.simple.clean.sdf.gz \\-contains_fragments_from aniline. sdf.gz \\-logger File drugbank_nonexperimental.simple.toxicity_check.logOne may also filter based on substructure similarity. This is particularly useful if there are specific substructures that are desired or that need to be avoided. For example, aromatic amines are a well-known toxicophore and cannot be incorporated into potential druglike molecules; however, it is not uncommon to find these substructures in datasets. Here, we will filter a subset of DrugBank  moleculeIn practice, we usually explicitly filter certain toxicophore substructures via graph search with the MoleculeTotalToxicFragments descriptor in conjunction with compare_property_values flag; however, this example illustrates the flexibility to filter by substructure similarity with molecule:Filter. In addition to the standard use cases presented here, molecule:Filter can identify molecules with clashes in 3D space, conformers outside of some tolerance value from a reference conformer, exact substructure matches, specific chemical properties, and more. Some of these filters will be further explored in other subsections.Another critical aspect of dataset sanitization is removing redundancy. This is especially important when preparing datasets for QSAR model training and testing. If molecules appear more than once in a dataset, then it is possible that they could appear simultaneously in the training and test sets, leading to an artificial inflation in test set performance.1. Constitutions\u2013compares atom identities and connectivity disregarding stereochemistry;2. Configurations\u2013compares atom identities, connectivity, and stereochemistry;3. Conformations\u2013compares configurations as well as 3D conformations;4. Exact\u2013checks to see whether atom identities and order are equal with the same connectivities, bond orders, stereochemistry, and 3D coordinates.The BCL application molecule:Unique can help with this task. It has four levels at which it can compare and differentiate molecules:The first time the BCL encounters a molecule in an SDF it will store it in memory. Any additional encounters with the same molecule (at the chosen level described above) will be marked as duplicate encounters. The default behavior is to output only the first encounter of each molecule. There are cases in which a molecule appears multiple times but has different MDL properties and/or property values. It may not be desirable to lose the stored properties on duplicate compounds. In such cases, the user can choose to merge the properties or overwrite the duplicate descriptors instead.bcl.exe molecule:Unique \\-input_filenames 1798_actives.sdf.gz 1843_actives.sdf.gz \\2258_actives.sdf.gz 2689_actives.sdf.gz \\435008_actives.sdf.gz 435034_actives.sdf.gz \\463087_actives.sdf.gz 485290_actives.sdf.gz 488997_actives.sdf.gz \\-compare Constitutions \\\u2013output_dupes all_actives.dupes.sdf.gz \\\u2013logger File all_actives.unique.logFor example, one may want to see if any high-throughput screening (HTS) hits have activity on multiple targets. Previously, we published nine high-quality virtual HTS (vHTS) benchmark sets for QSAR modeling binary classification tasks . Here, wbcl.exe molecule:Unique \\-input_filenames 1798_actives.sdf.gz 1843_actives.sdf.gz \\2258_actives.sdf.gz 2689_actives.sdf.gz \\435008_actives.sdf.gz 435034_actives.sdf.gz \\463087_actives.sdf.gz 485290_actives.sdf.gz 488997_actives.sdf.gz \\-compare Constitutions\u2013merge_descriptors \\-output all_actives.unique_merged.sdf.gz \\\u2013logger File all_actives.unique_merged.logThe output file all_actives.dupes.sdf.gz contains 22 molecules that are active in at least two different datasets . If we want to merge the properties of these 22 compounds and isolate them from the rest of the actives, we can perform a second molecule: Unique with the merge_descriptors flag set, and then use molecule:Filter with the contains flag to isolate the duplicated compounds:bcl.exe molecule:Filter \\-input_filenames all_actives.unique_merged.sdf.gz \\-contains all_actives.dupes.sdf.gz \\-output_matched all_actives.dupes_merged.sdf.gz \\\u2013logger File all_actives.dupes_merged.logfollowed byWhen merge_descriptors is passed, all unique properties are included in the resultant output file. If the same property is present on duplicates, then the first observation of that property is stored on the output molecule. If overwrite_descriptors is passed instead of merge_descriptors, the last observation of a duplicate property is stored. By default, without either of these flags only the MDL properties on the first occurrence of a molecule are stored in the output.It may be that some of the compounds in the previous example that have activity on multiple targets are actually stereoisomers. Here, the molecules were compared based on atom identity and connectivity (Constitutions). Iterative runs of molecule:Unique coupled with molecule:Filter can be used to identify such cases.bcl.exe molecule:Reorder \\-input_filenames < screened_molecules.sdf> \\-output < screened_molecules.best.sdf > -output_max 100 \\-sort <QSAR_Score> -reverse \\\u2013logger File < screened_molecules.best.log>Sorting molecules is also useful during vHTS. After making predictions on a million compounds with a QSAR model, frequently users will want to identify some small top fraction of most probable hits for experimental testing. This can be readily achieved with molecule:Reorder (note\u2013this example utilizes pseudocode for filenames):In this example, the reverse flag indicates that the scores will be sorted from largest to smallest . Not more than 100 molecules will be output into the file screened_molecules.best.sdf.gz because of the output_max specification .In the previous section, we demonstrated that the BCL could identify duplicate compounds at multiple levels of discrimination. One important note is that redundant molecules are excluded  in the order in which they are observed in the original input. Often, the user may want to control this sequence by sorting the molecules according to some property. In these cases, molecule:Reorder can be used to do just that.Finally, a general note on SDF input and output. Aromaticity is automatically detected when reading input files; however, output structures are Kekulized  by default. To output an SDF that contains explicit aromatic bonds (achieved by labeling bond order as 4 in the MDL SDF), pass the explicit_aromaticity flag on the command line.de novo drug design. There are many different types of fragments molecule:Split is able generate from whole molecule(s)  .bcl.exe molecule:Split \\-input_filenames osimertinib. sdf.gz \\-output osi. murcko.sdf.gz \\-implementation ScaffoldsFor example, we can derive the Murcko scaffold from the FDA-approved 3rd generation tyrosine kinase inhibitor (TKI) osimertinib  as follobcl.exe molecule:Split \\-input_filenames osimertinib. sdf.gz \\-output osi. inverse_scaffold.sdf.gz \\-implementation InverseScaffoldAlternatively, we could remove the Murcko scaffold and return the other components:Substructure comparisons are described in more detail in The last application of interest for molecule processing is molecule: Coordinates molecule: Coordinates is a minor application that performs several convenience tasks. First, molecule: Coordinates can recenter all molecules in the input file(s) to the origin. Second, it can compute molecular centroids. Third, molecule: Coordinates can compute statistics on molecular conformers.For example, passing the statistics flag compute statistics on bond lengths, bond angles, and dihedral angles. Passing the dihedral_scores flag will compute a per-dihedral breakdown of the BCL 3D conformer score. The BCL 3D conformer score, or ConfScore, computes an amide non-planarity penalty in addition to a normalized dihedral score. Passing the amide_deviations and amide_penalties will output the amide deviations and penalties on a per-amide basis, respectively. This can be useful when comparing conformations obtained from conformation sampling algorithms, crystal structures, and/or molecular dynamics (MD) trajectory ensembles. See bcl.exe molecule:Properties\u2013helpComputing molecular descriptors/properties is a critical component of cheminformatics model building. We use the term \u201cproperties\u201d to refer to individual chemical features and \u201cdescriptors\u201d to refer to combinations of properties, often used to train QSAR/QSPR models; however, the terms are often used interchangeably in the BCL. In conjunction with substructure-based comparisons, generating molecular descriptors is arguably the foundation of LB CADD. The BCL was designed with a modular descriptor interface and extensible property definitions framework. This allows both developers and users alike to write new descriptors for specific applications as needed. To see a list of available predefined molecular properties, perform the following command:The property interface is organized into two general categories: 1) Descriptors of Molecules, and 2) Descriptors of Atoms. As you will see throughout this section and bcl.exe molecule:Properties \\-input_filenames osimertinib. sdf.gz \\-output osi. mol_properties.sdf.gz \\-add Weight NRotBond NRings TopologicalPolarSurfaceArea \\-tabulate Weight NRotBond NRings TopologicalPolarSurfaceArea \\-output_table osi. mol_properties.table.txtAs the names suggest, some descriptors are intrinsic to the whole molecule, while others are specific to atoms. For example, compute some whole molecule descriptors for the EGFR kinase inhibitor osimertinib:The flag add will add the specified properties to the SDF as MDL properties. The tabulate flag will output the properties for each molecule in row-column format in the file specified by output_table. There is also a statistics flag that will compute basic statistics for each of the specific descriptors across all the molecules in the input SDFs and output to output_histogram. The key observation regarding the output file is that the values for Weight, NRotBond, etc., are emergent properties of the whole molecule.bcl.exe molecule:Properties \\-add_h\u2013neutralize \\-input_filenames osimertinib. sdf.gz \\-output osi. atom_properties.sdf.gz \\-add Weight Atom_SigmaCharge Atom_TopologicalPolarSurfaceArea \\-tabulate Atom_SigmaCharge Atom_TopologicalPolarSurfaceArea \\-output_table osi. atom_properties.table.txt \\-statistics Atom_SigmaCharge Atom_TopologicalPolarSurfaceArea \\-output_histogram osi. atom_properties.hist.txtNext, compute some atomic descriptors for osimertinib:Notice here that the statistics flag outputs statistics across each atom property rather than across each molecule property. This is also the behavior when there are multiple input molecules. Importantly, here we see that the output is an array of values for each property. The indices of the array correspond to the atom indices of the molecule.bcl.exe molecule:Properties \\-add_h\u2013neutralize \\-input_filenames osimertinib. sdf.gz \\-output osi. mol_properties.sdf.gz \\-add TopologicalPolarSurfaceArea \\\u201cMoleculeSum \u201dEach category of descriptors can further be modified by molecule-specific or atom-specific operations. For example, some whole molecule properties can be obtained by performing simple operations on the per-atom properties. TopologicalPolarSurfaceArea (whole molecule property) is the sum of Atom_TopologicalPolarSurfaceArea (atomic property) across the whole molecule.Check to verify that TopologicalPolarSurfaceArea and MoleculeSum  yield the same value for osimertinib.Examples of additional operations include other basic statistics , property radial distribution function (RDF), Coulomb force, and shape moment. See the help menu for additional options and details.2). Several familiar druglikeness metrics come prepackaged in the BCL , as well as several others inspired by the literature and conventional medicinal chemistry practices. For each molecule in the Platinum Diverse dataset, count how many Lipinski and Veber violations there are. In addition, count as drug-like all molecules that have fewer than two Lipinski violations:bcl.exe molecule:Properties \\-input_filenames platinum_diverse_dataset_2017_01. sdf.gz \\-output_table platinum_diverse_dataset_2017_01. druglike.txt \\-tabulate LipinskiViolations LipinskiViolationsVeber LipinskiDruglikeIn 2 and/or number of rotatable bonds >10). The LipinskiDruglike property is a Boolean that returns 1 if fewer than two Lipinski violations occur; 0 otherwise. There is no equivalent Boolean operator for Veber druglikeness; however, it is simple to implement one using the aforementioned operators.bcl.exe molecule:Properties \\-input_filenames platinum_diverse_dataset_2017_01. sdf.gz \\-output_table platinum_diverse_dataset_2017_01. veber_druglike.txt \\-tabulate \u201cDefine [VeberDruglike = Less ]\u201d VeberDruglikeThe property LipinskiViolations counts how many times a molecule violates one of Lipinski\u2019s Rules , \u226410 hydrogen bond acceptors , molecular weight (MW) < 500 Daltons, and water-octanol partition coefficient (logP) < 5). The LipinskiViolationsVeber property computes the number of times a molecule violates Veber\u2019s Rule ]\u201d \\\u201cDefine [VeberAndLipinskiDruglike = Multiply ]\u201d \\VeberAndLipinskiDruglikeThis command makes use of the Define and Less operators to return 1 if there are no violations to Veber\u2019s Rule and 0 otherwise. New properties created with Define can also be passed to subsequent operators on the same line. For example, one could create a descriptor called VeberAndLipinskiDruglike by doing the following:This new descriptor returns 1 if a molecule passes both druglikeness filters, and 0 otherwise.Many metrics can be created using the BCL descriptor framework without modifying the source code. This can be useful to users who come across novel methods in the literature and wish to implement them in their own work. Take as an example a seminal work from Bickerton et al., which sought to quantify the chemical aesthetics of potential druglike compounds. Bickerton et al. asked 79 medicinal chemists at AstraZeneca to answer \u201cwould you undertake chemistry on this compound if it were a hit?\u201d for \u223c200 compounds each, to which chemists replied either \u201cyes\u201d or \u201cno\u201d . They geUsing the same dataset and descriptors as Bickerton et al.  curve . Bickert/path/to/bcl/scripts/machine_learning/analysis/SimplifyDecisionTree.py \\./models/DT/model000000. model > DT. logic_summary.txtNow that we have our DT, we can reduce it to a readable if-else style format that can be converted into a BCL descriptor. Run the script SimplifyDecisionTree.py, passing as an argument the DT model:bcl.exe molecule:Properties \\-add_h -neutralize \\-input_filenames platinum_diverse_dataset_2017_01. sdf.gz \\-output_table platinum_diverse_dataset_2017_01. dt_druglike.txt \\-tabulate \u201cDefine (Hitlike = @dt.obj)\u201d HitlikeWe can see in the contents of DT. logic_summary.txt that the first thing the DT checks is whether the small molecule has less than two aromatic rings. Molecules with no aromatic rings are excluded, and molecules with one aromatic ring are subject to different criteria than molecules with two or more. Subsequent criteria are then evaluated. We can rewrite the logic summary as a descriptor and save it in a file called \u201cdt.obj\u201d. Then, we pass that file to molecule:Properties as a descriptor definition and use it to classify molecules:The \u201cdt.obj\u201d code object file is a plain text file that can be opened with any text editor. The syntax mimics the BCL command-line syntax. Code object files are a convenient way to write a long, multi-line BCL command-line that makes it easier to build and reuse feature sets.On the topic of druglikeness, it is worth noting that additional advanced methods are also available to classify the chemical space of molecules in a dataset. In some cases, it is useful to identify potential drug-like compounds that not only fit the criteria discussed above but are also similar to some known class (es) of drugs. For example, when performing fragment-based combinatorial library design for kinase inhibitors, in addition to filtering out molecules that violate Veber\u2019s rules, it may also be desirable to filter molecules that are not sufficiently chemically similar to existing kinase inhibitors. This can be accomplished by building and scoring against an applicability domain (AD) model. For further details on creating and using AD models in the BCL, see We have described multiple uses of the molecule:Properties application, placing special emphasis on how it can be utilized to build different types of druglikeness metrics. As it is fundamentally a tool to obtain information from small molecule chemical structures, molecule:Properties can also be used to help generate statistical potentials, chemical filters, QSAR/QSPR models, and more. Some of these use-cases will be explored in later sections.a priori. In SB molecular docking, small molecule flexibility is often represented through the inclusion of multiple discrete pre-generated conformers  to combine rotamers consisting of one or more dihedral angles according to a statistically-derived energy . ClashesThe BCL small molecule conformation sampler is a leader among general purpose small molecule conformer generation algorithms . In thisbcl.exe molecule:ConformerGenerator \\-ensemble_filenames osimertinib. sdf.gz \\-conformers_single_file osimertinib. global_confs.sdf.gzStart by generating conformers of osimertinib with the default settings. Here, all that is needed is an input filename and an output filename:The ensemble_filenames argument is equivalent to the input_filenames argument used elsewhere . The conformers_single_file argument is one of two output options. The other option is conformers_separate_files. As implied by the name, in the former case all conformers are output to a single file. In the latter case, if multiple molecules are input to ensemble_filenames, then a unique SDF will be written for the conformational ensembles of each of the input molecules .By default, BCL:Conf will perform 8,000 conformer generation iterations, each of which rebuilds the molecule essentially from scratch . Without any other options, the top conformations will be clustered, yielding the 100 best-scoring representatives of each different cluster. An unbiased view of the conformational space around the ligand can be obtained by setting the skip_cluster flag. For this application, it is advisable to lower the number of iterations to roughly double the number of desired conformations; the conformers are rebuilt from scratch at every iteration, so there is little gain from doing more iterations than conformers desired. The returned conformers are sorted by score. Number of iterations and final conformers can be specified with the max_iterations and top_models flags, respectively.Conformations can be filtered to remove highly-similar conformations using the conformation_comparer flag  and the tolerance for what constitutes an \u201cidentical\u201d conformer increased from the default (0.0) to an arbitrarily large value  . For mosFor high-throughput applications, we recommend reducing iterations from 8,000 down to 800 or even 250. BCL:Conf\u2019s speed is nearly linear in number of iterations. Generally, more iterations yield better performance, at a trade-off of slightly-faster than linear increase in time per conformation when clustering is used .Alternatively, if conformation_comparer is set to \u201cRMSD 0.0\u201d, then no filtering or clustering is specified, and BCL:Conf will perform max_iterations conformer generation iterations, randomly select top_models conformers, sort them from best to worst by score, and return them. This option is the fastest, and the ensembles returned are arguably the most Boltzmann-like. For a recent comparison of each set of parameters to one another and other conformer generation algorithms, please see bcl.exe molecule:ConformerGenerator \\-add_h -ensemble_filenames osimertinib. sdf.gz \\-conformers_single_file osimertinib. symrmsd_cluster.confs.sdf.gz \\-max_iterations 8,000 \u2013top_models 25 \\-conformation_comparer SymmetryRMSD 0.25Generate conformers using two of the protocols described protocols. First, runbcl.exe molecule:ConformerGenerator \\-add_h -ensemble_filenames osimertinib. sdf.gz \\-conformers_single_file osimertinib. raw.confs.sdf.gz \\-max_iterations 8,000 \u2013top_models 250 \u2013skip_cluster-conformation_comparer RMSD 0.0Then,Notice that the ensemble generated with the SymmetryRMSD comparer and clustering enabled occupies the densest part of the broader conformational space sampled in the raw distribution.Local sampling was implemented in the recent algorithmic improvements to BCL:Conf . The ide1. -skip_rotamer_dihedral_sampling\u2013preserve input dihedrals to within 15-degrees of closest 30-degree bin (centered on 0\u00b0) in non-ring bonds.2. -skip_bond_angle_sampling\u2013preserve input conformer bond lengths and angles3. -skip_ring_sampling\u2013preserve input ring conformations4. \u2013change_chirality\u2013by default, input chirality and isometry are preserved. Use this flag to allow for generation of enantiomers and stereoisomers.Local sampling in the BCL is accomplished by restricting the rotamer search in one of four ways:bcl.exe molecule:ConformerGenerator \\-ensemble_filenames osimertinib. sdf.gz \\-conformers_single_file osimertinib. skip_all.local_confs.sdf.gz \\-skip_rotamer_dihedral_sampling -skip_bond_angle_sampling \\-skip_ring_sampling\u2013skip_clusterThese options are not mutually exclusive. Depending on how they are combined, different levels of sampling can be achieved. Moreover, they can be used in combination with any of the other options  described above. Generate local conformational ensembles of osimertinib by first placing all three restrictions:bcl.exe molecule:ConformerGenerator \\-ensemble_filenames osimertinib. sdf.gz \\-conformers_single_file osimertinib. skip_dihed_ring.local_confs.sdf.gz \\-skip_rotamer_dihedral_sampling -skip_ring_sampling\u2013skip_clusterNext, apply only the skip_rotamer_dihedral_sampling and skip_bond_angle_sampling restrictions to generate a local ensemble:Both of the ensembles show less conformational diversity than the global conformational ensemble created in the previous section. Notice the relative sampling differences between each of the local conformation sampling protocols described.Often times one may wish to only sample conformations of part of a molecule. For example, in docking congeneric ligand series, the core scaffold pose may be known with a high degree of confidence, and the goal is to optimize the pose of the rest of the molecule while keep the core scaffold fixed. Alternatively, crystal structures of protein-ligand complexes often have low or missing density for part of a bound ligand, and thus coordinate assignment may not accurate. Discretely sampling specific small molecule rotamers thus becomes a useful task to perform.In the BCL, this is accomplished by first assigning an MDL miscellaneous property named \u201cSampleByParts\u201d to the molecule(s) of interest. The value of the SampleByParts property corresponds to the 0-indexed atom indices of atoms in dihedrals that are allowed to be sampled by molecule:ConformerGenerator. By encoding this as a molecule-specific property, we avoid multiple command-line calls with different atom index specifications, allowing users to generate conformers more rapidly for multiple molecules and/or different independent rotamers within a molecule.bcl.exe molecule:Properties \\-add \u201cDefine [SampleByParts = Constant ]\u201d SampleByParts \\-input_filenames osimertinib. sdf.gz\u2013output \\osimertinib.sample_by_parts.sdfAs an example, consider a crystal structure of epidermal growth factor receptor (EGFR) kinase in complex with osimertinib (PDB ID 4ZAU) . This isbcl.exe molecule:ConformerGenerator \\-ensemble_filenames osimertinib. sample_by_parts.sdf.gz \\-conformers_single_file osimertinib. sample_by_parts.confs.sdf.gz \\-top_models 250 \u2013clusterAlso, note that if you have many molecules for which you want to assign SampleByParts atom indices and you do not want to have to manually identify the relevant indices, you can also use the molecule:SetSampleByPartsAtoms application. This application sets SampleByParts indices based on comparison to user-supplied substructures. With the SampleByParts property defined in the SDF, generate global conformers as previously described:Observe that sampling global conformers  with SampleByParts maintains the coordinates of all unspecified atoms. In this case, only dihedrals containing strictly the ethyldimethylamine atoms are sampled . Similarbcl.exe molecule:Compare < mandatory_parameter_one.sdf> \\<optional_parameter_two.sdf> \u2013output < mandatory_output.file> \\A critical component of LB CADD is molecular similarity analysis. Provided a set of molecules, we frequently want to know how similar each molecule is to a reference molecule(s). Fundamentally, this requires 1) defining what specifically will be compared between the molecules, and 2) defining the metric with which similarity will be measured. In the BCL, this is accomplished primarily through use of the molecule:Compare application. The command-line syntax of molecule:Compare differs from the syntax of other applications discussed so far. The SDF input files to molecule:Compare are passed as parameters instead of argument flags.1. If a single SDF is specified as a parameter, then all molecules in the file are compared with one another2. If two SDFs are specified, then the molecule(s) in the second file will be compared against the molecule(s) in the first file.This syntax strictly enforces two types of behavior:Finally, it is worth noting that molecule:Compare\u2019s performance scales approximately linearly with number of threads for costly comparisons. To enable threads, set -scheduler PThread <number_threads>. We suggest setting number_threads to number of physical cores on the device for maximum performance.bcl.exe molecule:Compare \\-method \u201cLargestCommonSubstructureTanimoto (help)\u201dThe BCL encodes molecules as graphs where the edges are bonds, and the atoms are nodes. For substructure-based comparisons, we can define equivalence between bonds and atoms using various comparisons dubbed comparison types. For any substructure-based comparison between two or more molecules, some combination of atom and bond comparison types is required, which defines the equivalence class for the atoms and bonds, respectively. The default combination differs between tasks. For a summary of available atom and bond type comparisons, examine the help menu options of any comparer that utilizes substructures. For example,will display the default atom and bond comparison types for this comparison method as well as list the available comparison types. For example, if atom type resolution occurs at AtomType, then an SP3 carbon would match another SP3 carbon but not an SP2. If the resolution is lowered to ElementType, then all carbon atoms can match one another independent of their orbital configuration. Similarly, bond type resolutions of BondOrder and BondOrderAmideWithIsometryOrAromaticWithRingness will yield different substructure matches.Not all similarity comparisons occur at the structural/substructural level. A number of comparison metrics in the BCL occur between properties computed at the whole molecular, substructural, or atomic level. Further, distance-based comparisons between molecules that are constitutionally identical can also be made.In cases where the similarity between unique molecules is desired there are broadly two approaches for measuring similarity: by substructure and by property. These are not mutually exclusive; depending on the desired resolution of the substructure comparisons, one can further measure property differences between substructures of different molecules.One common substructure similarity metric is the Tanimoto coefficient (TC), expressed between two molecules as the ratio of matched-to-unmatched atoms:atoms in  is the sbcl.exe molecule:Split \\-implementation \u201cLargestCommonSubstructure (file = afatinib.sdf)\" \\-input_filenames gefitinib. sdf.gz\u2013output mcs_gef_afa.sdf.gzThe first-generation EGFR tyrosine kinase inhibitor gefitinib and the second-generation inhibitor afatinib are structurally very similar. Afatinib is modified from the gefitinib scaffold and incorporates an acrylamide linker. Visualize the maximum common substructure (MCS) of afatinib and gefitinib using molecule:Split :bcl.exe bcl.exe molecule:Compare gefitinib. sdf.gz afatinib. sdf.gz \\-method LargestCommonSubstructureTanimoto\u2013output gef_afa_mcs_tani.txtNext, calculate the MCS TC of the gefitinib and afatinib:bcl.exe molecule:Compare gefitinib. sdf.gz afatinib. sdf.gz \\-method LargestCommonDisconnectedSubstructureTanimoto \\\u2013output gef_afa_mcds_tani.txtThis method searches for the single largest common connected substructure as the intersection of two molecules and computes the TC. In this case, the MCS TC is approximately 0.48. Sometimes searching for a single connected substructure can be disadvantageous. For example, if the primary differences between molecules results from core substitutions bridging two otherwise identical halves, then the single largest common substructure approach will fail to account for the complete degree of similarity. Alternatively, the user can calculate the maximum common disconnected substructure (MCDS) TC:As expected, the MCDS TC is greater than the MCS TC at approximately 0.86.bcl.exe molecule:ConformerGenerator \\-add_h -ensemble_filenames osimertinib. sdf.gz \\-conformers_single_file osimertinib. confs.sdf.gz \\-max_iterations 8,000 \u2013top_models 50 \u2013cluster \\-conformation_comparer SymmetryRMSD 0.25 \u2013generate_3DIn de novo ignoring all information from input coordinates by using generate_3D. Measure the heavy-atom symmetric RMSD to the native conformation:bcl.exe molecule:Compare osimertinib. sdf.gz osimertinib. confs.sdf.gz \\-method SymmetryRMSD -logger File osi. sym_rmsd_native.log \\-output osi. sym_rmsd_native.txt -remove_hNote that we are generating the molecule completely On examination of osi. sym_rmsd_native.txt, we see that see that of our 25 generated conformers, 3 are less than 2.0\u00a0\u00c5 from the native conformer, and the best is approximately 0.66\u00a0\u00c5 from native. If we repeat this process for two additional TKIs, the first-generation inhibitor erlotinib and the second-generation inhibitor afatinib, we also see that we are able to obtain multiple conformers less than 1.0\u00a0\u00c5 from native.In addition to RMSD-based metrics, molecule:Compare can also measure distance in the form of dihedral angle sums and dihedral distance bins. For additional information, examine the help menu options.bcl.exe molecule:AlignToScaffold <scaffold> <ensemble> <output>The BCL can be used to align small molecules according to their MCS. Unlike most of the examples in this section, this is accomplished through the molecule:AlignToScaffold application by passing three parameters:bcl.exe molecule:AlignToScaffold gefitinib. sdf.gz afatinib. sdf.gz \\afatinib.ats.sdf.gz \\For example, to align afatinib to gefitinib based on their MCS, use the following command:Instead of aligning by MCS, the user may also align the target ensemble to the largest rigid component of the scaffold structure by passing the align_rigid flag. Moreover, if the user wants to a define an alternative set of atoms to be aligned instead of the defaults, this can be accomplished by specifying those atoms for each the scaffold and target ensemble with align_scaffold_atoms and align_ensemble_atoms, respectively.In addition to substructure-based alignment, we can also perform property-based alignment. Property-based alignment algorithms typically maximize the overlap or minimize the distance between molecular and/or atomic properties . We haveBCL:MolAlign combines the conformational sampling ability of BCL:Conf with the property framework described in bcl.exe molecule:Compare mtx. perturbed.sdf.gz dhf. sdf.gz \\-add_h\u2013neutralize \\-output mtx_dhf_rigid_rmsdx.output \\-logger File rigid_alignment.log -random_seed \\-method \u201cPsiField \\\"To demonstrate how BCL:MolAlign can be used to perform each of these alignments, consider the classic problem of obtaining the crystallographic alignment of methotrexate (MTX) and dihydrofolic acid (DHF). This example is a good one because the intuitive heterocyclic overlap is not the correct one . Insteadbcl.exe molecule:Compare mtx. perturbed.sdf.gz dhf. sdf.gz \\-add_h\u2013neutralize \\-output mtx_dhf_rigid_rmsdx.output \\-logger File semi-flexible_alignment.log -random_seed \\-method \u201cPsiFlexField)\u201dThe rigid alignment ranks the correct alignment mode as the top scoring alignment . Rigid a.  the acidic groups . Note th. . For perbcl.exe molecule:Compare mtx. perturbed.sdf.gz dhf. perturbed.sdf.gz \\-add_h\u2013neutralize \\-output mtx_dhf_rigid_rmsdx.output \\-logger File fully-flexible_alignment.log \\-random_seed\u2013scheduler PThread 8 \\-method \u201cPsiFlexField \\ \\)\u201dFully-flexible alignment is useful when one is trying to recover pharmacophore features without knowing the binding pose of either molecule. Here, the goal is to align pharmacophore features of the molecules, not recover the native pose of the target molecule(s) by aligning to another molecule with a known binding mode. Perform a fully-flexible alignment of MTX and DHF.Fully-flexible alignment of MTX and DHF does not recover the most native-like conformations of MTX and DHF; however, it does recover correct alignments of the heterocycles, central aromatic rings, and acidic groups . Notice The descriptor application group is the workhorse for molecule featurization. Similar to the molecule:Properties application, the descriptor application group provides command-line access to the internal descriptor framework. Unlike molecule, descriptor is dataset centric; its primary purpose is to generate, manipulate, and analyze feature datasets for QSAR/QSPR. In this section, we will demonstrate core applications in descriptor and how they can be utilized in QSAR/QSPR modeling.1. The molecules for which to generate the features; these can be any valid SDF.2. The types of features to generate; these are properties such as those described in 3. The feature result label; this indicates the output(s) that models will train toward. This can be a constant value , a property , or another label .4. The output filename; three output types are available. The BCL has a partial binary format with the \u201c.bin\u201d suffix that is used for all model training. Feature datasets can also be output with the \u201c.csv\u201d suffix for a comma-separated values (CSV) file. Moreover, \u201c.csv\u201d files and \u201c.bin\u201d files can be interconverted. In this way, features generated with the BCL can be used by other software, and vice versa. For inter-operability with Weka software, \u201c.arff\u201d format is also supported, with a limitation of only working with continuous variables.Four specifications are required to generate feature datasets from small molecules:bcl.exe descriptor:GenerateDataset \\-source \u201cSdfFile (filename = 1798. combined.sdf)\u201d \u2013id_labels \u201cString (M1)\u201d \\-result_labels \u201cCombine (IsActive)\u201d \\-feature_labels \u201cCombine \u201d \\-output 1798. combined.scalars.binGenerate a simple feature dataset consisting of several scalar descriptors for a set of confirmed active M1 Muscarinic Receptor positive allosteric modulators (PAMs) and corresponding true negatives . The SDFbcl.exe descriptor:GenerateDataset\u2013compare 1798. combined.scalars.binBinary files were designed for rapid non-consecutive reading and writing, but the interested reader will find that the file format consists of a textual header specifying the properties and their sizes followed by a simple binary output of all features. Dataset information and statistics can be obtained by calling descriptor:GenerateDataset compare. For example:bcl.exe descriptor:GenerateDataset \\-source \u201cSubset \u201d \\-output 1798. combined.scalars.csvTo better understand the binary file encodings, convert 1798. combined.scalars.bin to a CSV file:The first column of every row contains the ID label \u201cM1\u201d as specified when the binary file was generated. The next four columns contain the descriptors specified above: Weight, LogP, HbondDonor, and HbondAcceptor. The very last column is the result value, which contains either 0 or 1 depending on the value in the SDF MDL property \u201cIsActive\u201d.bcl.exe descriptor:GenerateDataset \\-source \u201cCsv\u201d \\-output 1798. combined.scalars.binConvert CSV file back to a binary file:CSV files do not contain all of the supplementary information contained within the partial binary file format. Thus, certain information needs to be provided directly. For example, we need to specify the number of characters that are part of the row ID label, otherwise the BCL will try to convert the string  ID into feature values. ID labels therefore must be fixed-width. In addition, we need to tell the BCL how many of the columns are result values. By default, the BCL will assume that only the last column is the result label. By specifying number result cols = N, we tell the BCL to take the last N columns of the CSV as the result value(s).bcl.exe descriptor:GenerateDataset \\-source \u201cCsv\u201d \\\u2013id_labels \u201cString (M1)\u201d \\-result_labels \u201cCombine (IsActive)\u201d \\-feature_labels \u201cCombine \u201d \\-output 1798. combined.scalars.binAlso notice that the feature and result label information is not informative after converting from CSV to binary. The values are transferred to the new file format, but the BCL obviously cannot know where those values came from. These must be manually specified.In this case, the feature labels are internal parsable properties of the BCL; however, when relabeling feature labels upon converting from CSV to binary format, the user can specify any labels so long as the total number of labels is consistent with the number of feature columns.bcl.exe descriptor:GenerateDataset \\-source \u201cRandomize [Subset ]\u201d \\-output 1798. combined.scalars.rand.binAfter generating a dataset or importing a CSV file and converting it to binary format, feature datasets can be modified. The most frequent form of modification is randomization. Training a machine learning model, for example a neural network, often requires dataset randomization.Binary files are read by the \u201cSubset\u201d retriever. The Randomize operator is passed through the source flag and provided the dataset retriever option corresponding to the binary file.Additional dataset operators can be classified by how they modify the dataset. For example, the PCA  and EncodeByModel operators perform dimensionality reduction across feature (column) space, while the KMeans operator reduces dimensionality across molecule (row) space. Other operators are useful during model training and validation, such as Balanced, Chunks, and YScramble. Still others can be used to select particular ranges of rows from a dataset, such as Rows. Here, we will take a look at a few dataset operators. For full details on all available dataset operators, see the descriptor:GenerateDataset help menu.bcl.exe descriptor:GenerateDataset \\-source \u201cSdfFile (filename = 1843. combined.sdf.gz)\u201d \u2013scheduler PThread 8 \\-feature_labels MendenhallMeiler2015. Minimal.object \\-result_labels \u201cCombine (IsActive)\u201d \\\u2013output 1843. Minimal.bin\u2013logger File 1843. Minimal.logStart by generating a dataset for the Kir2.1 inward rectifying potassium channel using the dataset compiled in Butkiewicz et al.  and the bcl.exe descriptor:GenerateDataset \\-source \u201cRandomize (Subset (filename = 1843. combined.bin))\u201d \\-output 1843. combined.rand.bin\u2013logger File 1843. Minimal.rand.logRandomize the dataset:bcl.exe descriptor:GeneratePCAEigenVectors \\-training \u201cSubset \u201d \\-output_filename 1843. Minimal.PCs.dat\u2013opencl \\-logger File 1843. Minimal.PCs.logNote that we could have generated a randomized dataset with a single command by wrapping the SdfFile dataset retriever with Randomize; however, the Randomize dataset retriever is unable to support hyperthreading. Consequently, it is faster to generate larger datasets first using multiple threads and randomize them afterward. Next, perform PCA on the dataset using OpenCL to accelerate the calculation with a GPU. The flag opencl is optional and may not be supported on all platforms, but may provide a substantial speedup, depending on the GPU and dataset size:bcl.exe descriptor:GenerateDataset \\-source \u201cPCA, fraction = 0.95, filename = 1843. Minimal.PCs.dat)\u201d \\-output 1843. Minimal.rand.pca_095. bin\u2013opencl \\-logger File 1843. Minimal.rand.pca_095. logFinally, generate a new feature dataset accounting for 95% of the variance:bcl.exe descriptor:GenerateDataset/-source \u201cEncodeByModel \u201d \\-output < my_encoded_binary_file.bin>Performing PCA on the dataset has reduced the number of descriptors from 1,315 to 695. Alternatively, one could use EncodeByModel to reduce the number of feature columns using a pre-generated model. The following example utilizes pseudocode and a hypothetical pre-generated ANN with the MendenhallMeiler2015. Minimal.object features.The input file < my_binary_file.bin > would have 1,315 descriptors from MendenhallMeiler2015. Minimal.object, and the output file < my_encoded_binary_file.bin > would have a number of descriptors corresponding to the number of neurons in the final hidden layer preceding the output layer of our hypothetical pre-generated ANN.As a practical note, we have found that PCA-based dimensionality reduction useful for dataset visualization, but of limited value in improving model performance. Performance can often be recovered to that of the initial dataset when requiring at least 95% of the variance to be preserved, but performance improvement is rare from PCA, when using a regularized method such as dropout-ANNs.bcl.exe descriptor:GenerateDataset \\-source \u201cCombined \u201d \\-output < my_combined_binary_file.bin>Suppose you encoded the same original feature set using two different models and now want to combine the new encoded files for further training. This can readily be accomplished with the Combine operator.bcl.exe descriptor:GenerateDataset \\-source \u201cKMeans \u201d \\-output 1843. combined.rand.k300. bin \\-logger File 1843. combined.rand.k300. logNext, instead of performing dimensionality reduction along the column (features) axis, we will reduce the dimensionality along the row (molecule) axis. Perform K-means clustering of the feature dataset to reduce our row number from 301,493 to 300.This form of dimensionality reduction is unlikely to be as useful for training a deep neural network (DNN); however, it can be useful in similarity analysis in low dimensional feature space. Some of the datasets generated in this section will be referenced again in a and rb are the boundaries of the current distance interval, N is the total number of atoms in the molecule, r is the distance between the two atoms being considered, \u03b4 is the Kronecker delta, and P is the property computed for each atom. 2DAs are conformation-independent, while 3DAs are conformation-dependent \u201d \\-feature_labels \u201cCombine (3daSmoothSign (property = Atom_SigmaCharge))\u201d \\-result_labels \u201cCombine [Constant (999)]\u201d -output dasatinibs.3da.csv \\\u2013logger File dasatinibs.3da.logbcl.exe descriptor:GenerateDataset \\\u2013source \u201cSdfFile (filename = dasatinibs.sdf)\u201d \\-feature_labels \u201cCombine [2DASmoothSign (property = Atom_SigmaCharge)]\u201d \\-result_labels \u201cCombine [Constant (999)]\u201d -output dasatinibs.2da.csv \\\u2013logger File dasatinibs.2da.logThe \u201cdasatinibs.sdf\u201d file contains the coordinates and connectivity for two dasatinib molecules: one with 2D coordinates, the other with 3D coordinates. Compute the signed 2DA and 3DA for Atom_SigmaCharge on both dasatinib molecules.Upon examination of the tabulated 2DA and 3DA values for the two different dasatinib molecules, we observe that the 2DA contains the same values in both cases, while the 3DA contains unique values for the different conformers. To visualize the variance in each 3DA distance bin, we can tabulate the 3DAs for Atom_SigmaCharge on an ensemble of 3D conformations for several different molecules . DasatinWe can see that the variance in each descriptor column increases as a function of distance and number of rotatable bonds. In ethinyl estradiol there is little change in descriptor column variance as a function of distance. In contrast, molecules with increasing numbers of rotatable bonds display increasingly large variances at longer distance bins. This suggests that increasing conformational heterogeneity at longer distance bins leads to increased noise. Indeed, we have previously found that extending LB 3DAs beyond approximately 6.0\u00a0\u00c5 generally results in reduced performance on QSAR classification tasks , consistIt is also possible to use molecule:Properties to tabulate and compute statistics for molecules instead of plotting the CSV file data from descriptor:GenerateDataset. Here, we used descriptor:GenerateDataset to illustrate its usage. In practice, we do not just use a single 3DA or 2DA, but instead build sets of descriptors for feature and result labels and store them as separate code object files. As mentioned previously, the code object file format is the same format as allowed on the command line.bcl.exe model:HelpThe BCL supports multiple machine learning algorithms for QSAR/QSPR modeling. Among the methods available are ANNs  , supportbcl.exe model:Train --helpHere, we will first introduce the user to the overall workflow involved in training, analyzing, and subsequently testing BCL machine learning models. The basic workflow for model training is the same for each machine learning method and can be completed via the model:Train application. To see the available machine learning methods, access the help options within model:Train.As of this writing, the available model types can be found in bcl.exe model:Train \u201c<training algorithm>(help)\u201dTo expose all options for a particular machine learning method, pass the algorithm name as the first parameter to the application with the help menu request:bcl.exe model:Train < training algorithm> \\-max_minutes < maximum time of training in minutes> \\-max_iterations < maximum number of training iterations> \\-final_objective_function < performance metrics for model evaluation> \\-feature_labels < names of descriptors> \\-training < training set> \\-monitoring < monitoring set> \\-independent < independent set> \\-storage_model < location in which to store the model> \\-opencl < enables GPU acceleration> \\-logger File < log file>The following is a typical command-line format to train a model beginning with a pre-generated descriptor binary file:Model performance is evaluated with the user-specified objective function. The choice of objective function is typically related to the task being performed  .BCL model:Train is designed to readily enable cross-validation. The application is flexible with respect to serialization of model predictions for each of the monitoring, independent, and training partitions as well as writing of the model itself. For example, in five-fold cross-validation, the dataset is split into five chunks. For each round of cross-validation, the model is trained on four-fifths of the dataset, and the other fifth \u201cindependent\u201d set is left out for testing. One of the chunks can additionally be specified as the monitoring dataset. The monitoring dataset can be used for early termination of the model training session to prevent overtraining .-training \u201cSubset \u201d-monitoring \u201cSubset \u201d-independent \u201cSubset \u201dThe initial dataset set is split into monitoring, independent, and training partitions with model:Train by assigning chunks with the dataset retriever responsible for binary format files, Subset. In the following pseudocode example, we will set the options to divide the training set into the following five chunks (0-indexed): chunks one to four will be used as the training set, and chunk 0 will be used as both the monitoring set and the independent set (this is appropriate only if the monitoring dataset is not being used for early termination).bcl.exe model:PredictionMerge \\-input_model_storage \u2018File ' \\-output < output_pooled_predictions>Dataset partitioning is repeated for each round of cross-validation until each chunk takes a turn as the independent set. Then, the predictions of all the test sets are pooled together by the model:PredictionMerge application:bcl.exe model:ComputeStatistics \\-input < output_pooled_predictions> \\-obj_function < performance_metric> \\-filename_obj_function < output_performance_metric_file>This command line averages predictions made on the same independent set, though other pooling operations are available (see help). Prediction performance is evaluated with the specified objective function on the pooled predictions using the model:ComputeStatistics application:To simplify model training, we have written a Python script \u201claunch.py\u201d to perform training and cross-validation with one command./path/to/bcl/scripts/machine_learning/launch.py\u2013hTo see a list of model training operations :/path/to/bcl/scripts/machine_learning/launch.py\u2013t cross-validation\u2013hTo see the list of available flags for cross-validation, call/path/to/bcl/scripts/machine_learning/launch.py -t cross-validation \\--cross-validation 5 --local \\--learning-method LinearRegression ) \\--id linear_regression --final-objective-function RMSD \\--datasets < my_dataset.bin > --override-memory-multiplier: 1.25The following pseudocode example generates a simple linear regression model:bcl/trunk/scripts/machine_learning/launch.py\u2013t cross_validation \\--config-file config. example.iniMore complex commands can be easily prepared inside of a configuration file to be passed to the \u201claunch.py\u201d script. A sample configuration file is available in the The \u201claunch.py\u201d script will automatically generate three new directories titled \u201clog_files\u201d, \u201cresults\u201d, and \u201cmodels\u201d. Into each of those three directories a labeled directory (name specified with the id flag) is made. Model prediction output files and results of the final objective function are stored in the labeled directory within the \u201cresults\u201d folder. Log files, commands, and autogenerated scripts are stored in the labeled directory within the \u201clog_files\u201d folder. Finally, final model details are stored in the labeled directory within the \u201cmodels\u201d folder.In addition to running the training jobs locally, training can be run on a SLURM cluster using the slurm flag. In this way, large cross-validation jobs may leverage high-performance computing with minimal changes to the configuration. See additional configuration operations, such as slurm-host, using launch. py\u2013t cross-validation\u2013h.bcl.exe model:Test \\-retrieve_dataset \u201cSubset (filename=<vHTS.test.bin>)\u201d \\-storage_model \u201cFile \u201d \\-average output < vHTS.model_test.csv> \u2013logger File < vHTS.model_test.log>Note that in the above examples the training and test splits are derived from the same binary format file. This is not strictly necessary, and the user can supply alternatively derived validation splits prepared in separate files. Moreover, using a dataset split as the independent test set is generally only useful for model validation. To apply trained model predictions to new molecules in a vHTS, either model:Test or molecule:Properties can be used. For example, if a model is trained and validated using five-fold cross-validation, then the merged prediction on an external test set can be made as follows with model:Test:bcl.exe molecule:Properties\u2013input_filenames < vHTS.test.sdf> \\\u2013tabulate \\\u201cDefine {predicted_activity = PredictionMean [storage = File ])}\u201d predicted_activity \\\u201cDefine {local_ppv = PredictionInfo }\u201d local_ppv \\\u201cDefine {XActive = Multiply }\u201d XActive \\-output_table < vHTS.prop.test.csv > -logger File < vHTS.prop.test.log>Likewise, predictions can be made with molecule:Properties using the Prediction operators:Notice that scoring new compounds via molecule:Properties allows multiple outcome metrics to be reported and modified on-the-fly, while scoring with model:Test just outputs the raw prediction values . In this case, the output of model:Test is equivalent to \u201cpredicted_activity\u201d from molecule:Properties. The property \u201cXActive\u201d is the \u201cpredicted_activity\u201d score when the local PPV is greater than 0.5, and 0.0 otherwise. The localPPV metric calibrates model output values to local classification probability on the test sets. It is an estimate of the PPV at a singular model output value. This is in contrast to traditional PPV, which specifies the value of a prediction at, or above, a given output value (assuming positive parity). This metric assumes that the trained model prediction value varies monotonically with the actual prediction likelihood.bcl.exe model:Train \u201cNeuralNetwork (help)\u201dANNs are one of the most commonly employed classes of non-linear classifiers in QSAR modeling for LB-CADD due to their strong predictive power . To see l connected to the input vector l+1 by weights w and biases b, and f is the transfer function applied to each set of inputs into the l+1 layer. Correspondingly, the activation of a single neuron i in hidden layer l+1 can be represented asl+1. We have found that for classical QSAR tasks a simple mean-squared error (MSE) cost function is adequate.The BCL supports shallow and deep single- and multi-tasking neural networks. Transfer functions include linear, sigmoid, rectified linear, and leaky rectified linear. For a network with L hidden layers indexed Historically, overtraining in ANNs has been prevented by early termination of training when the monitoring dataset improvement rate or improvement scores fail to progress beyond a pre-determined extent. More recently, we have demonstrated that dropout is a better alternative to prevent model overtraining in QSAR tasks . The drop) or 1 (at fraction 1\u2014p) and multiplied elementwise by the values in p, then at test time the corresponding weights are scaled down by the factor 1\u2014p.Here, launch.py -t cross-validation --local \\--datasets 1843. combined.rand.bin --id 1843. ann.1x32_005_025 \\--config-file config. example.ann.ini \\Train a shallow (single hidden layer) neural network to classify molecules as either active or inactive PAMs of Kir2.1 beginning with the randomized dataset we generated in learning-method: \u2018NeuralNetwork , \\dropout , \\objective function = % (objective-function)s, \\scaling = AveStd, steps per update = 1, hidden architecture (32), \\balance = True, balance target ratio = 0.10, \\shuffle = True, input dropout type = Zero \\)\u2019The configuration file specifies the learning method as follows:\u201cAucRocCurve \u201dNote that we are asking for an ANN with one hidden layer composed of 32 neurons. The input and hidden layers will have 5 and 25% dropout, respectively. In addition, we have enabled class balancing. We have far fewer active (172) than inactive  compounds. Balancing oversamples the underrepresented (minor) class to achieve a ratio of (in this case) 0.10 with the most common class (major). The balance max repeats flag can also be set to specify the maximum number of times that a feature can be repeated. This does not lead to overtraining because of dropout. Batch size is controlled with the steps per update flag. The objective-function variable is defined in the configuration file asAdditional variables, such as the maximum number of training iterations (20), number of rounds of cross-validation (5), monitoring dataset (independent set), etc. are also set in the configuration file.launch.py -t cross-validation --local \\--datasets 1843. combined.rand.pca_095. bin \\--id 1843. pca_095. ann.1x32_005_025 \\--config-file config. example.ann.ini \\As a comparison, train an additional ANN with the same parameters using the feature set whose dimensions were reduced with PCA in The \u201claunch.py\u201d pipeline automatically generates a ROC curve for each model with and without a log scaled x-axis . The ovePredicting physicochemical properties such as solubility is a challenging but critical component of lead compound optimization. Many substitutions to a candidate molecule may increase the potency or selectivity, but at the cost of worsening solubility, metabolic stability, or other properties. Therefore, it is advantageous to prioritize synthesis and evaluation of derivatives that are simultaneously predicted to be active and have a promising chemical profile. To do this, we need a target-agnostic QSPR model.hydration). Note that not the descriptors, model architecture, nor hyper-parameters have been optimized for performance. This can be seen as an \u201cout of the box\u201d model a user might create.Dahl and colleagues demonstrated that multitask learning could improve the prediction of multiple outputs simultaneously if the training tasks are correlated . As an ebcl.exe descriptor:GenerateDataset \\-source \u201cSdfFile \u201d \\-feature_labels VuMendenhallMeiler2019. Scalar_Mol2D.object \\-result_labels \u201cCombine \u201d \\-output all_logp_logs_dgsolv.Scalar_Mol2D.bin \\-logger File all_logp_logs_dgsolv.Scalar_Mol2D.log \\-scheduler PThread 8 \u2013compareMolecules for training and validation are sourced from previously published databases  and combTo generate the Dense feature set, add the\u2013forbid_incomplete_records flag. The two binary format files should contain 35,874 and 448 rows, respectively, and the third dataset should contain the difference between them, 35,426. The distribution of result values overlaps reasonably well between the Full and Dense datasets, with the exception of the LogS distributions .learning-method: \u201cNeuralNetwork ( \\transfer function = Rectifier (0.05), \\weight update = Simple , \\dropout , \\objective function = % (objective-function)s, \\scaling = AveStd, steps per update = 10, hidden architecture , \\balance = False, shuffle = True, input dropout type = Zero \\)\u201dRandomize the datasets before training the model. The configuration file config. exmple.mdnn.ini sets up the neural network architecture:launch.py -t cross-validation --local \\--datasets all_logp_logs_dgsolv.Scalar_Mol2D.rand.bin \\--id all_logp_logs_dgsolv.Scalar_Mol2D.2x256-32_005_025_005 \\--config-file config. example.mdnn.ini --just-submitNote that our network contains 2 hidden layers with 128 and 32 neurons, respectively, with 5% dropout on the input layer, 25% dropout on the first hidden layer, and 5% dropout on the second hidden layer. Our objective function will be MAE_NMAD since this is a regression task. We will perform five-fold cross validation (specified in the configuration file). Train the network:The just-submit flag sends the process to the background. Train the dense network as well; it should take less time since there are relatively few examples in the training sets. Check the log_merge.txt file in the corresponding \u201clog_files\u201d subdirectory to view the final objective function for each of the three result labels .In cases where the training set has small deviation from the mean value, MAE will be lower, which can be misleading. To address this, we normalize MAE by MAD. Here, we see that the model trained on the Dense set of features learned LogP the best. However, this may be an artifact of the reduced training space. If we were to evaluate whether the Dense model was able to extrapolate beyond the very small training set, we would almost certainly see worse performance.To illustrate this, evaluate the predictive power of our Dense model on molecules in our Full\u2013Dense training set, and vice versa. This can be accomplished using either model:Test or molecule:Properties as described in Taken together, these data suggest that there is likely a significant fraction of molecules in the Full\u2013Dense set that occupy an area of feature space not represented in the 448 molecule Dense set. This is a good example that internal randomized cross-validation on a small training set is not an accurate predictor of external test set performance unless the external test set is within a similar domain of applicability . Applicabcl.exe model:Train \u201cDecisionTree (help)\u201dDT is a tree-based machine learning algorithm that partitions the dataset into smaller subsets as it develops. A DT starts from a root node, branches out to internal nodes, and ends at leaf nodes. To see the different options of a decision tree, calllearning-method: DecisionTree The default option of the decision method chooses the features for data splitting with the maximum information gain, and its prediction performance is scored by accuracy.There are two factors that determine the order of features and their corresponding splitting values in dataset partitioning in a decision tree: partitioners and node scores. Four types of partitioners are currently implemented in the BCL: InformationGain, Gini, ROC, and Sequence. The first three options rate the feature to split the dataset by information gain, Gini index, and area under the curve of the local ROC curves , respectWhile the partitioner determines how to calculate the split rating of different configurations of dataset partition, the node score type dictates how to rank different combinations of feature order and their corresponding splitting values. Four types of node scores are currently implemented in the BCL: split rating (SplitRating), number of correct predictions before splitting , split rating times initial number of correct predictions , and sum of number of incorrect predictions before and after data splitting . The users can also control the minimum number of incorrect classifications of a node by assigning a value to the min split flag.A DT was employed in bcl.exe model:Train \u201cKohonen (help)\u201dA self-organizing map (SOM), also commonly referred to as a Kohonen map, is an unsupervised learning method that is commonly used in clustering and dimensionality reduction. The SOM produces a low-dimensional , discretized representation of the input space of the training samples, called a map. This method applies competitive learning to reach a solution, as opposed to conventional feed-forward neural networks, which utilize error-correction learning. To see the options available to a Kohonen map model, calllearning-method: Kohonen , \\steps per update = 0,radius = 7.5, length = 140, Neighbor kernel = Bubble, \\Initializer = RandomlyChosenVectors, cutoff = 0.5, objective function = RMSD \\)Here is the typical configuration file setup to build a Kohonen map model:Before training a Kohonen map, users may shuffle the training set (shuffle = True). Similar to the ANNs, there are two options for scaling the input: AveStd and MinMax. The former works best when the input descriptors are continuous, and the latter is ideal for sparse and/or discretized input data. Regarding the configuration of the SOM, the map dimensions option dictates the number of nodes, or neurons, in each direction of the map. Setting the steps per update flag  to 0 indicates that all training rows will be used for each iteration.The initial radius of the neighborhood function, radius, is the maximum distance between the neighbor neuron and the best matching unit (BMU). Increasing the radius generally increases model quality at the expense of training time. In our experience, diminishing returns are met when the radius approaches 1/3 to 1/2 the total distance of the map. The number of iterations it takes for the radius to decrease to 0 in the given neighbor kernel function is given by length. The radius of the neighborhood is gradually reduced as the number of the iterations t increases, such that by Each iteration, the neurons compete by measuring their distances to the input dataset. The neuron j, with associated weight vector w, with the lowest distance d to the randomly selected input vector x is the winner.Iterations proceed for the entire batch size prior to updating neuron weights. The next step is updating the weights within the neighborhood of the winning node. There are two options for the neighbor kernel function: Bubble and Gaussian. The new weights are updated asFinally, users can select one of the objective functions mentioned above to evaluate the prediction performance of the model. At test time, the model will assign an AD score for each external compound. This AD score is the normalized distance of that compound to the closest node of the training set. For instance, a tested molecule with an AD score of 0.90 is further from the closest node than 90% of other molecules in the training set. In other words, that molecule\u2019s feature space was not so well-represented in the training dataset.learning-method: \u201cApplicabilityDomainKohonen s), steps per update = 0, \\length = 140, radius = 7.5, neighbor kernel = Bubble, \\initializer = RandomlyChosenVectors, scaling = AveStd, cutoff = 0.5, \\share distance metric = True)\u201dWe will use the BCL to build class-specific druglikeness applicability domain (AD) models from the structures of FDA approved drugs: 58 opioid receptor modulators and 82 kinase inhibitors . From ealaunch.py -t cross_validation --config-file AD. config \\--datasets kinase. train.Scalar_UMol2D.bin \\--id kinase. Scalar_UMol2D.AD --max-iterations 200 \\--local --no-cross-validation --cluster_num 5Note that the map dimensions are set by the cluster_num flag in the training command. Generate feature set for each molecule file using descriptor:GenerateDataset. Train the kinase set AD model:bcl.exe model:Test -retrieve_dataset \\\u201cSdfFile (filename = kinase.test.sdf.gz)\u201d \\-storage_model \\\u201cFile \u201d \\-output kinase_kinaseAD.test.outAfterward, train the opioid receptor set AD model. Next, we can evaluate the test sets with each AD model, beginning with the kinase inhibitor test set with the kinase inhibitor AD model:The AD scores are listed in the output data file. The first two lines are the format name, and the dimension of the data table. The AD scores of five test compounds are stored in the second columns of the last 5 lines. We can see that our test set compounds from the FDA approved kinase inhibitor list have a shorter AD distance than our molecules in the opioid receptor test set, and vice versa . These sUp to this point we have demonstrated vHTS predictions on pre-existing external datasets. Screening external datasets can be very valuable because of the ever-increasing number and availability of public, commercial, and institutional small molecule repositories. Nevertheless, it is also frequently the case that computation can be applied to assist specific medicinal chemistry projects. For example, in silico drug design can conceivably be utilized for library design, hit explosion, or scaffold hopping. Here, we will demonstrate how to perform multicomponent reaction (MCR)-based drug design with the BCL.Reaction-based drug design in the BCL proceeds according to user-defined MDL RXN (.rxn) files. There are a number of predefined reactions located in bcl/rotamer_library/functional_reactions. Reactions can be single-component intramolecular reactions, or multi-component intermolecular reactions of up to four unique reagents. Reactants must have their atoms mapped to corresponding atoms in the product(s). Atom mapping is required for substituents on the input reagents to be merged with the product(s).The reaction design framework functions in part by performing substructure comparisons of candidate reagents to reactant structures drawn in the RXN file. Substructure matching occurs at a resolution of ElementType for atoms and BondOrderOrAromatic for bonds. If there are candidate reagents that collectively can match all reactant positions in a reaction, then the reaction can proceed. Note that unlike input SDFs for molecule files, aromaticity must be shown explicitly in the RXN file to be interpreted. Also note that reactant matching will only match hydrogen atoms if they are drawn explicitly.bcl.exe molecule:React \\-starting_fragments piperazine. sdf -reagents reagents_le_20. sdf \\-reactions./rxns_dir/ -routine Random -repeats 9 -ligand_based \\-fix_geometry -fix_ring_geometry -extend_adjacent_atoms 2 \\-output_filename ugi_products.sdf -logger File ugi_reaction.logIn this example, we will generate products according to a 4-component split-Ugi reaction utilizing piperazine as the diamine scaffold in all designs .bcl.exe The individual molecule fragments passed via starting_fragments are treated as required reaction components. The reactions flag is given the path to a directory containing all RXN files the user wishes to include in the reaction. The reagents flag specifies candidate reactants with which the starting_fragments molecules are reacted. Thus, for every entry in the SDF passed via starting_fragments, the molecule: React application will check to see if it is a valid reactant for any of the reactions in the directory specified by reactions; for those reactions that the current starting_fragments molecule is a valid reactant, the remaining possible reactant positions are fit against the molecule fragments provided via reagents.bcl.exe molecule:React --helpThe routine flag specifies how to continue with reaction sampling. Currently, there are two options, though additional options are under development. The default is Random, which will perform one valid reaction (if any exist) for each molecule in starting_fragments using a randomly selected reaction and reagents from the user input. By default, the Random routine will run one time; however, by specifying repeats users can increase the number of cycles. If the starting_fragments SDF contains 100 entries and repeats is set to 4, then the molecule:React application will run 500 times\u2013one initial run for all entries followed by four repeats of all 100 entries. Alternatively, users may specify Exhaustive, which will enumerate all possible products from all given reactions and reagents for each starting_fragments molecule. Ongoing efforts to expand the reaction-based drug design framework include additional optimization routines, such as evolutionary fragment generation and simulated annealing, as well as mixed intra- and inter-reagent reactions. Other options are related to generation of 3D conformers for the product molecules and are explained in the help menu.For illustration purposes, we generated \u223c700,000 configurationally unique molecules with the split-Ugi reaction . As our Piperazine rings and related substructures are well-defined core components of dopamine receptor (DR) antagonists . UtiliziAs might be expected, there are a high density of molecules with a low (< 0.20) local PPV for activity at 10\u00a0nM; however, as the threshold for activity increases, the density of molecules that are identified as active increases . We also2. One could also filter out molecules from the library that have TPSA greater than 150\u00a0\u00c52 and/or greater than 10 rotatable bonds (Veber rules for druglikeness). We performed logP estimates with three unique methods: 1) the DNN we trained in We also estimated topological polar surface area (TPSA)  and wateFinally, we display predicted activity at 100\u00a0nM as a function of synthetic accessibility score (SAScore)  Figure . Encourade novo drug design, pharmacophore mapping, and more.The BCL is an academic research project made available for public use. As an academic research project, the BCL is under continuous development. Ongoing improvements are anticipated for many of the applications described here, including small molecule conformer sampling, small molecule flexible alignment, descriptor/feature generation, and additional machine learning architectures , strategies, and pre-generated models. In addition, several new tools are currently under active development for tasks such as library design, This manuscript has focused extensively on LB in silico drug discovery tools; however, we have also begun incorporating SB tools, such as deep learning-based protein-ligand interaction scoring . Two prihttp://www.meilerlab.org/bclcommons. Example files mentioned throughout the manuscript are freely available on the Meiler Lab GitHub page.Our hope is that this manuscript will serve as a resource for those interested in utilizing the BCL for cheminformatics research. Several high level BCL applications can also be accessed via webserver for non-expert users. The webserver is available through the BCL Commons website at http://www.meilerlab.org/bclcommons and requires a supporting license from http://meilerlab.org/servers/bcl-academic-license that is free for academic and non-profit users, with commercial licenses available for a fee.The BCL can be downloaded freely from"}
+{"text": "The potential physiological functions of hsa_circ_0002062, hsa-miR-942-5P, and CDK6 in hypoxic PASMCs were investigated through expression modulation. Our experiments demonstrated that hsa_circ_0002062 functions as a ceRNA, acts as a sponge for hsa-miR-942-5P, and consequently activates CDK6, which further promotes pulmonary vascular remodeling. Therefore, we speculate that hsa_circ_0002062 could serve as a candidate diagnostic biomarker and potential therapeutic target for HPH.Currently, new strategies for the diagnosis and treatment of hypoxia-induced pulmonary hypertension (HPH) are urgently required. The unique features of circRNAs have unveiled a novel perspective for understanding the biological mechanisms underlying HPH and the possibility for innovative strategies for treatment of HPH. CircRNAs function as competing endogenous RNAs (CeRNA) to sequester miRNAs and regulate the expression of target genes. This study aimed to explore the roles of hsa_circ_0002062 on the biological behaviors of pulmonary artery smooth muscle cells (PASMCs) in hypoxic conditions. A number of  Hypoxic pulmonary hypertension (HPH) presents as an elevated mean pulmonary artery pressure, results from hypoxic pulmonary vasoconstriction and abnormal vascular remodeling, and ultimately leads to right ventricular hypertrophy and heart failure . HPH is Circular RNAs (circRNAs) are a novel type of non-coding RNA with a covalently closed loop where the 3\u2032 and 5\u2032 ends are joined together . The funPrevious evidence has demonstrated that mmu_circ_0000790 binds to miR-374c and acts as an endogenous miR-374c sponge by activating the Notch pathway via FOXC1 in mice model with HPH . A recen2. PASMCs was randomly divided into a hypoxic group  and a normoxic group , which were maintained in hypobaric and normoxic incubators, respectively.Human pulmonary artery smooth muscle cells (hPASMCs) were purchased from Lonzapet . All of the cells were cultured in DMEM  supplemented with 10% fetal bovine serum , and 1% penicillin/streptomycin at 37\u00b0C in incubators containing 5% COTM First Strand cDNA Synthesis Kit . Amplification reactions were performed with an ABI PRISM 7300 Fluorescent Quantitative PCR System . The primers used in the study were synthesized by Beijing Genomics Institute (BGI) . All procedures were performed in triplicate, and the sequences of the forward and reverse primers are as follows: Hsa_circ_0002062 Primer F 5\u2032TTATTGACTGGGTCTTCC3\u2032, Primer R 5\u2032CATACATACATACATAGGGTG 3\u2032; CDK6 Primer F 5\u2032 TAACCTCAGTGGTCGTCAC 3\u2032, Primer R 5\u2032GTCTT TGCCTAGTTCATCG 3\u2032;According to the manufacturer\u2019s protocol, total RNA was extracted from hPASMCs using the Trizol reagent . RNA samples were digested with Ribonuclease R  to degrade linear RNA and improve the purity of circRNA. The expression of hsa-miR-942-5P was measured using TaqMan miRNA assays based on the provided instructions, U6 was used for normalization. For the analysis of the levels of hsa_circ_0002062 and CDK6, glyceraldehyde 3-phosphate dehydrogenase (GAPDH) was used as an internal control. Reverse transcription (RT) was conducted using the Revert Aid\u2013\u0394\u0394Ct).GAPDH Primer F 5 GGATTGTCTGGCAGTAGCC 3\u2032, Primer R 5\u2032ATTGTGAAAGGCAGGGAG3\u2032; hsa-miR-942-5p RT Primer, 5\u2032GTCGTATCCAGTGCAGGGTCCGAGGTATTCG CACTGGATACGACCACATG 3\u2032, PCR Primer, Primer F 5\u2032CG CGTCTTCTCTGTTTTGGC 3\u2032, Primer R 5\u2032AGTGCAGGGT CCGAGGTATT 3\u2032; U6 Primer F 5\u2032CTCGCTTCGGCA GCACA 3\u2032,Primer R 5\u2032AACGCTTCACGAATTTGCGT 3\u2032. The expression of target genes was measured using the relative quantitative method . Human CDK6 eukaryotic overexpression vector, mouse CDK6 shRNA Adeno-associated Viral virus (AAV), and mouse CDK6 overexpression AAV all were purchased from Gene Pharma . Overexpression hsa_circ_0002062 and shRNA hsa_circ_0002062 plasmids were purchased from Gisai Biotechnology Co., Ltd. . The FuGENE HD Transfection Reagent  was used in PASMCs with specific shRNAs and plasmid vectors based on the provided instructions. The sequences of the shRNA primers are as follows: Sh-CDK6: 5\u2032-GUUUGAACAUGUCGAUCAATT-3\u2032; sh-NC: 5\u2032-UUCUCCGAACGUGUCACGUTT-3\u2032;hsa-miR-942-5P inhibitor: 5\u2032-CACAUGGCCAAAACAGAG AAGA-3\u2032; hsa-miR-942-5p mimics: 5\u2032-UCUUCUCUGUUUU GGCCAUGUG-3\u2032; miR-N.C5\u2032-CAGUACUUUUGUGUAGUACAA-3\u2032.2. The cells were stained with the Click Additive Solution  and Hoechst 33342, and an anti-Edu working solution according to the manufacturer\u2019s instructions. After this, cells were treated with 4% paraformaldehyde and PBS containing 0.3% Triton X-100. The percentage of Edu-positive cells was finally measured using fluorescence microscopy analysis.Edu incorporation assays were used to determine cell proliferative abilities. Cells in logarithmic growth were taken and cultured in 6-well plates. Edu labeling solution  was added to the 6-well plates 48 h after transfection and then incubated for 2 h at 37\u00b0C and 5% CO7 cells/ml were fixed in 4% paraformaldehyde for 30 min and further treated with PBS containing 0.3% Triton X-100. The samples were first incubated in a TUNEL solution at 37\u00b0C for 1 h and stained according to the manufacturer\u2019s instructions . TUNEL-positive myocytes were determined using the IMS Image Analysis System , and the apoptotic rate was then calculated based on the number of the TUNEL-positive cells.Approximately 5 \u00d7 104 cells/well were seeded into the top chamber containing 0.2-ml serum-free DMEM. After incubation for 24 h, the cells migrated to the bottom chamber containing 0.7-ml medium supplemented with 10% FBS. The cells that did not migrate were carefully swabbed out of the chamber. The cells were fixed with 4% paraformaldehyde solution and stained with 0.5% crystal violet, imaged, and migration rates were calculated using an Olympus microscope .Cell migration was determined using a transwell chamber . Cells were first cultivated in a 24-well transwell plate in starvation medium for 24 h. For migration assays, 5 \u00d7 10Cells were homogenized in an ice-cold RIPA buffer . Protein concentrations were determined using a bicinchoninic acid (BCA) protein assay kit . Equal amounts of protein were separated by 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE)  and transferred onto polyvinylidene difluoride (PVDF) membranes . The membranes were blocked with 5% non-fat dry milk in Tris-buffered saline with 0.05% Tween 20 (TBST) at room temperature for 2 h, incubated overnight with the primary anti-CDK6 , anti-VEGF , antiAT1R , or anti-GAPDH antibodies . After washing with TBST three times for 10 min, the membranes were treated with HRP-labeled secondary antibodies  for 1 h and visualized using the chemiluminescence method . The relative protein expression was measured using GAPDH as an internal control.RIP assays were conducted using the EZ-Magna RIP kit . hPASMCs at 80\u201390% confluency were lysed in a full lysis buffer and then incubated with a RIP buffer containing magnetic beads conjugated with an anti-Ago2 antibody  or a negative control IgG antibody . The samples were incubated with 150-\u03bcl Proteinase K buffer at 55\u00b0C for 30 min to remove protein and purify RNA. Next, the immunoprecipitated RNA was reversed transcribed and quantified by qRT-PCR.Dual-luciferase reporter assays were performed to verify further whether hsa_circ_0002062 or CDK6 was the target of hsa-miR-942-5P.hsa_circ_0002062 wild type, hsa_circ_0002062 mutant, CDK6 wild type, or CDK6 mutant inserts were inserted into the pGL3-basic luciferase vector  in order to generate report plasmids. Afterward, cells transfected with indicated reporter plasmid were cultured in a 6-well plate and co-transfected with 5-\u03bcL miR-NC, or hsa-miR-942-5p mimic using Lipofectamine 2000 (Invitrogen). The Dual-Luciferase Reporter Gene Assay System  was used to detect luciferase activity according to the manufacturer\u2019s instruction, and the ratio of firefly to renilla luciferase activity was calculated.In order to construct CDK6 overexpression vector, the coding region of CDK6 (NM_009873) gene was inserted into pAAV-CMV-MCS vector carrying CMV promoter. To build CDK6 shRNA vector, the sequence of sh-CDK6 (5\u2032-GGATATGATGTTTCAGCTTCTCGAGAAGCTGAAACATCA TATCCTTTTT\u20143\u2032) was inserted into pAAV-U6-MCS carrying the U6 promoter. The CDK6 overexpression vector and CDK6 shRNA vector were co-transfected into AAV-293 cells with a packer plasmid (pAAV-RC) and a helper plasmid (phelper), respectively. Mouse CDK6 overexpression AAV and mouse CDK6 shRNA AAV were packaged and collected, while AAV without target gene was collected as controls.11 vg/ml),CDK6 overexpression AAV . and CDK6 shRNA AAV , respectively. Mice in the Control group were fed under normoxia. All animals in each group were repeatedly inoculated with AAVs through tail vein once a week during modeling. Four weeks later, the mice were sacrificed, and several indices were measured. At the end of the experiment, the lung tissues were fixed with 4% paraformaldehyde and embedded in paraffin. The vascular remodeling of mice in different groups was analyzed by HE and Masson staining. RNA and protein were extracted from the pulmonary arteries in other lung tissues. The expression levels of CDK6 mRNA were determined by qPCR, while the expression levels of CDK6, VEGF, and AT1R were analyzed by Western blot.All the animal experiments were performed with the approval of the Animal Experiment Ethics Committee of Renji Hospital affiliated with Shanghai Jiao Tong University. A total of 42 healthy male C57/BL6 mice 6\u20139 weeks old and weighing 22\u201326 g were purchased from SIPPR-BK Lab Animal Co., Ltd. , with six mice in the normoxic group and six mice in the hypoxic group. Mice in the Hypoxia + Vector group, the Hypoxia + CDK6 group, and the Hypoxia + sh-CDK6 group were inoculated with control AAV  were used to estimate significant differences between different groups. A p-value < 0.05 was considered to be statistically significant.Statistical analyses were conducted using SPSS 22.0  and GraphPad Prism 8 . All data are presented as the mean \u00b1 standard deviation (SD). Student\u2019s K6 mRNA were significantly upregulated in the hypoxic group compared with the control normoxic group  targeting hsa_circ_0002062 or overexpressing hsa_circ_0002062, and then stably transfected these into hPASMCs, which we confirmed by qRT-PCR (Previous evidence has indicated that hsa_circ_0002062 can serve as a sponge to regulate gene expression via sequestering miRNAs in CTEPH. The Encyclopedia of RNA Interactomes (ENCORI) was used to predicate the potential miRNAs that may bind to hsa_circ_0002062. Eight miRNAs were found that be possible targets of hsa_circ_0002062. The binding of circRNAs to miRNAs is mediated by Argonaute 2 (Ago2). We therefore used RNA immunoprecipitation (RIP) with an antibody against Ago2 in PASMCs, and determined whether the 8 miRNAs related to hsa_circ_0002062 found in previous studies were enriched by RIP using qPCR. The RIP results showed that hsa_circ_0002062, has-miR-942-5p, and CDK6 were enriched in Ago2-containing immunoprecipitates . Our fin6. As illustrated in Furthermore, a dual-luciferase reporter assay was performed to assess the effect of hsa_circ_0002062 on hsa-miR-942-5P activity and test whether CDK6 was the target of hsa-miR-942-5P. The luciferase assay results showed that the luciferase activity was reduced in hPASMCs co-transfected with miR-942-5P and WT-hsa_circ_0002062 or WT-CDK6 3\u2032 UTR but was not reduced in hPASMCs containing MUT-hsa_circ_0002062  or MUT-CP = 0.0232). Transfection of hPASMCs with hsa-miR-942-5p mimic significantly upregulated the expression level of hsa-miR-942-5p under hypoxia (P < 0.0001), which was confirmed by qRT-PCR , and silicosis . HoweverIn vitro experiments demonstrated that when hsa_circ_0002062 was upregulated, cell proliferation was induced, and cell apoptosis was inhibited. Meanwhile, when hsa_circ_0002062 was downregulated, the results were opposite. As hsa_circ_0002062 may play a stimulative role in HPH, these results supported the hypothesis that downregulating hsa_circ_0002062 inhibits HPH progression.In our study, hsa_circ_0002062 was found to be highly expressed in hypoxic PASMCs, and promoted pulmonary vascular remodeling, demonstrating that hsa_circ_0002062 might promote HPH development. CircRNAs, as a recent identified member of the non-coding RNA family, will be a promising diagnostic and therapeutic target in PH . A serieCTEPH within the miRNA-circRNA network. Nevertheless, previous studies are predominantly phenomenological, in that they are mostly profiling dysregulated circRNAs and lack in-depth mechanisms. Our result revealed that hsa_circ_0002062 acts as hsa-miR-942-5P sponge to upregulate CDK6 in HPH. Further experiments showed that knockdown of hsa_circ_0002062 attenuated pulmonary vascular remodeling, inhibited cell growth, and promoted cell apoptosis.Studies on circRNAs have opened a new chapter in respiratory diseases, as they act as endogenous miRNAs sponges to bind miRNAs through their binding sites to prevent their interactions with target genes . Liu et It has been revealed that miR-942-5p plays an important role in many cell types . miR-942CDK6 is a member of the cyclin-dependent kinase (CDK) family, which governs cell cycle transitions during quiescence, senescence, and differentiation . CDK6 caTaken together, we found that hsa_circ_0002062 was a significant regulator of HPH development through its regulation of the hsa-miR-942-5P/CDK6 axis. Our results suggested that hsa_circ_0002062 plays a stimulative role in pulmonary vascular remodeling. It can induce PASMC proliferation by positively regulating CDK6 expression. The hsa_circ_0002062/hsa-miR-942-5P/CDK6 axis thus becomes vital to our understanding of the molecular mechanisms involved in pulmonary vascular remodeling, and may serve as a novel therapeutic target for HPH. Due to its importance, in the future it will be necessary to excavate the downstream targets of hsa_circ_0002062 in detail and gain further insight into the hsa_circ_0002062 regulation mechanism of PH.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.All animal experiments were performed with the approval of the Animal Care and Use Committee of Renji Hospital Affiliated to Shanghai Jiao Tong University.YWa designed the research, wrote the manuscript, and submitted the article for publication. FH, LZ, and YL collected and analyzed the data. FH, XT, and YWu performed cell experiments. FH, YWa, and SC conducted animal experiments. All authors contributed to the manuscript and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest."}
+{"text": "The prediction of binding sites  is a common task in the data analysis of methods such as cross-linking immunoprecipitation in combination with high-throughput sequencing (CLIP-Seq). The predicted binding sites are often further analyzed to predict sequence motifs or structure patterns. When looking at a typical result of such high-throughput experiments, the obtained peak profiles differ largely on a genomic level. Thus, a tool is missing that evaluates and classifies the predicted peaks on the basis of their shapes. We hereby present StoatyDive, a tool that can be used to filter for specific peak profile shapes of sequencing data such as CLIP.With StoatyDive we are able to classify peak profile shapes from CLIP-seq data of the histone stem-loop-binding protein (SLBP). We compare the results to existing tools and show that StoatyDive finds more distinct peak shape clusters for CLIP data. Furthermore, we present StoatyDive\u2019s capabilities as a quality control tool and as a filter to pick different shapes based on biological or technical questions for other CLIP data from different RNA binding proteins with different biological functions and numbers of RNA recognition motifs. We finally show that proteins involved in splicing, such as RBM22 and U2AF1, have potentially sharper-shaped peaks than other RNA binding proteins.StoatyDive finally fills the demand for a peak shape clustering tool for CLIP-Seq data that fine-tunes downstream analysis steps such as structure or sequence motif predictions and that acts as a quality control. The biological function of a protein is determined by its interaction partners and the mode of interaction. Studying these interactions broadens our horizon about the cellular mechanisms such as alternative splicing and post-transcriptional regulation. Cross-linking immunoprecipitation in combination with high-throughput sequencing (CLIP-Seq) fathoms these interactions. CLIP-Seq investigates all interactions between an RNA binding protein (RBP) and its target RNAs . CLIP-SeMany RBPs have several binding domains with different binding affinities, and are often part of protein complexes, leading to an intricate binding pattern. As described in a review by Jankowsky and Harris , there aIn addition, technical biases might change the peak profile landscape. Binding artifacts might be introduced during read library preparation. Protocol biases, e.g., PAR-CLIP biases that are introduced by endonuclease and photoactivatable nucleosides , might aThis leads to many questions in the data analysis of binding sites that currently cannot be answered adequately. Examples are: Does my protein of interest bind generally specifically Fig.\u00a0 or unspeWe hereby present StoatyDive, a tool to evaluate and classify peak profiles to help answer the aforementioned questions. StoatyDive uses the whole peak profile, as well as predefined features, to do a peak shape clustering for sequencing data. In this article, we test StoatyDive on CLIP data of the eCLIP protocol from the histone stem-loop-binding protein (SLBP) from the study by Van Nostrand et\u00a0al. . SLBP haWe used eCLIP data of the protein SLBP . The daP-value = 0.03), both contained a lot of regions with a CV \u22480 , calculated for each peak, to get a broad overview of the peak profile landscape of their experiment (see Methods). Broader peaks tend to have a CV \u22480. Although the CV distributions of the input control and Replicate 1 of the SLBP data differed significantly . However, this does not mean a low quality of the data and just highlights that it is important to do replicates in order to quantify biological and technical variance as noted in a previous CLIP study  and the normalized score, in the range With the results of BEDTools, StoatyDive evaluates every peak k-means clustering [iX has x1, x2, jx..., nx nucleotides, we normalized the peaks by k-means clustering to the new data with 100 initializations, and maximal 10,000 iterations. The number of clusters k is found by convergence of the total within-cluster sum of squares and checked with the Akaike information criterion [StoatyDive classifies the peak profiles in an unsupervised manner using uMAP  and k-meustering . Yet befhed Fig.\u00a0 Step 4 writerion . We alsoP-value <0.05, see For the peak evaluation, StoatyDive generates a plot of the CV Equation\u00a0 and normThe normalized CV distribution helps to evaluate the peaks based on the individual experiments. An empirical threshold is set at a CV of 0.2 Equation\u00a0, below wk-means optimization and a plot of the dimensional reduction with uMAP, which can be used to readjust the number of k clusters if this is necessary. The user also receives a set of example peak profiles and smoothed peak profiles of each cluster, which can be used to investigate the identified shapes. For a general trend, StoatyDive delivers average profiles for each cluster.For the peak classification, StoatyDive generates a plot of the The final output of StoatyDive is a CV-sorted table of the whole peak set, from the highest to the lowest CV. Each peak is labeled with 0, for more specific binding sites, and 1, for more unspecific sites. The table also lists for each peak the cluster number (group number) of the peak profile shape.The peak correction Fig.\u00a0 Step 1 ck-means clustering. Yet, the final number of peak clusters will be optimized by StoatyDive. The parameter is an upper bound. However, the user has the option to force StoatyDive to use k specific clusters. The smoothing : Unixbiotools:StoatyDiveLicense: GPLv3RRID:SCR_018796GigaScience Database [StoatyDive provides a small dataset for a test run, which can be found in the github repository. The whole eCLIP dataset used in this article, such as SLBP or RBFOX2, is listed in the supplementary material of the study by Van\u00a0Nostrand\u00a0et\u00a0al.\u00a0 and in tDatabase .Supplementary Figure S1: CV distributions of all other proteins analyzed for Fig.\u00a0P-value, the number of uniquely mapped reads, and the mean CV for each replicate. The 2 replicates quite often have different CV distributions. Furthermore, we report a plot for the mean CV for the CLIP data in comparison to the size-matched input control of each protein. The control data tend to have a CV close to 0. The CV distributions between CLIP and control data always have a 2-sided Wilcoxon test P-value <0.05.Supplementary Figure S2: We applied StoatyDive to the size-matched input control of the SLBP data [LBP data . StoatyDSupplementary Figure S3: Peak lengths of the peak set (ENCFF127WAK) for the second replicate of SLBP and peak lengths of all other proteins of the eCLIP data from the study by Van\u00a0Nostrand\u00a0et\u00a0al.\u00a0[d\u00a0et\u00a0al.\u00a0.Supplementary Figure S4: All scatter plots from SIC-ChIP [SIC-ChIP  for the Supplementary Figure S5: We tested different dimensional reduction methods such as PCA, SOM, and t-SNE on the CLIP data of SLBP. The PCA has no clear clusters for Replicate 2, which is similar for t-SNE on Replicates 1 and 2. Using an optimized SOM delivers a feature layer with a very high activated hidden unit for Replicate 2. It is hard to see any distinct clusters from the counts (activation) of each hidden unit. uMAP can clearly separate the data into more defined clusters. Furthermore, it is much easier to interpret the results of uMAP, whereas an artificial neural network, such as a SOM, generates a feature layer (hidden layer) that is hard to explain.Supplementary Table S1. Mean CV, variance of the CV, mean log2 fold change (LFC) enrichment between the control and CLIP experiment, median P-value, true-positive rate (TPR), true-negative rate (TNR), accuracy (ACC), and Matthews correlation coefficient (MCC) for the analyzed peaks  of Replicate 2 (ENCFF127WAK) from the study by Van\u00a0Nostrand\u00a0et\u00a0al.\u00a0[d\u00a0et\u00a0al.\u00a0. Feature2 fold change; MCC: Matthews correlation coefficient; mRNA: messenger RNA; PCA: principal component analysis; RBP: RNA-binding protein; RRM: RNA recognition motif; SOM: self-organizing map; TPR: true-positive rate; TNR: true-negative rate; t-SNE: t-distributed stochastic neighbor embedding.ACC: accuracy; CLIP-Seq: cross-linking immunoprecipitation in combination with high-throughput sequencing; CV: coefficient of variation; IQR: interquartile range; LFC: logThe authors declare that they have no competing interests.This study was funded by the Deutsche Forschungsgemeinschaft  grant 322977937/GRK2344 2017 MeInBio \u2013 BioInMe Research Training Group, and Germany\u2019s Excellence Strategy (CIBSS - EXC-2189 - Project ID 390939984). The article processing charge was funded by the University of Freiburg in the funding programme Open Access Publishing.F.H. performed the computational analysis and tool developement. R.B. initialized the project and supervised the research. Both authors wrote the manuscript. Both authors read and approved the final manuscript.giab045_GIGA-D-20-00218_Original_SubmissionClick here for additional data file.giab045_GIGA-D-20-00218_Revision_1Click here for additional data file.giab045_GIGA-D-20-00218_Revision_2Click here for additional data file.giab045_GIGA-D-20-00218_Revision_3Click here for additional data file.giab045_GIGA-D-20-00218_Revision_4Click here for additional data file.giab045_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giab045_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giab045_Response_to_Reviewer_Comments_Revision_2Click here for additional data file.giab045_Response_to_Reviewer_Comments_Revision_3Click here for additional data file.giab045_Reviewer_1_Report_Original_SubmissionEric Van Nostrand -- 8/22/2020 ReviewedClick here for additional data file.giab045_Reviewer_1_Report_Revision_1Eric Van Nostrand -- 12/15/2020 ReviewedClick here for additional data file.giab045_Reviewer_1_Report_Revision_2Eric Van Nostrand -- 3/16/2021 ReviewedClick here for additional data file.giab045_Reviewer_2_Report_Original_SubmissionNejc Haberman -- 8/25/2020 ReviewedClick here for additional data file.giab045_Reviewer_2_Report_Revision_1Nejc Haberman -- 12/15/2020 ReviewedClick here for additional data file.giab045_Reviewer_3_Report_Original_SubmissionWilliam Lai -- 8/28/2020 ReviewedClick here for additional data file.giab045_Reviewer_3_Report_Revision_1William Lai -- 12/16/2020 ReviewedClick here for additional data file.giab045_Supplemental_FilesClick here for additional data file."}
+{"text": "Responses of the nitrification rate (NR), numbers of ammonia monooxygenase (amoA) gene copies, and community structures of ammonia-oxidizing bacteria (AOB) and archaea (AOA) to biochar application were investigated. The results indicated that, under N fertilization, the NR and numbers of amoA-AOB and amoA-AOA gene copies negatively responded to biochar addition. Biochar application increased the community diversity of AOB but decreased that of AOA. Biochar addition and N fertilization shifted the AOB community from Nitrosospira-dominated to Nitrosospira and Nitrosomonas-dominated, and altered the AOA community from Nitrososphaera-dominated to Nitrososphaera and Nitrosopumilus-dominated. The relative abundance of Nitrosospira, Nitrosomonas and Nitrosopumilus decreased, and that of Nitrosovibrio and Nitrososphaera increased with biochar application rate. Soil SOC, pH and NO3\u2212-N explained 87.1% of the variation in the AOB community, and 78.1% of the variation in the AOA community was explanatory by soil pH and SOC. The SOC and NO3\u2212-N influenced NR through Nitrosovibrio, Nitrosomonas, Norank_c_environmental_samples_p_Crenarchaeota and amoA-AOB and amoA-AOA gene abundance. Therefore, biochar addition inhibited nitrification in salt-affected irrigation-silting soil by shifting the community structures of AOB and AOA and reducing the relative abundance of dominant functional ammonia-oxidizers, such as Nitrosospira, Nitrosomonas and Nitrosopumilus.Biochar has been widely recognized as an effective and eco-friendly ameliorant for saline soils, but information about the mechanism of how biochar influences nitrification in salt-affected agroecosystem remains fragmented. An incubation experiment was performed on the salt-affected soil collected from a three-consecutive-year experiment at biochar application gradients of 7.5 t\u22c5ha Nitrogen is an indispensable nutrient element for sustaining ecosystem productivity and plays a pivotal role in closing crop yield gaps to meet ever-increasing food demands . In salt4+-N [Numerous measures have been developed to amend soil salinization by physically improving soil porosity, chemically accelerating ion exchange and leaching, and biologically promoting soil biochemical function ,6. Among4+-N ,13.amoA-AOB and amoA-AOA gene copies, but the effects of biochar on the abundance of AOB and AOA in saline soil are inconsistent. The abundance of AOB and AOA decreased with biochar addition in saline-alkali soil, and AOB were more susceptible than AOA to biochar addition [Nitrosospira, Nitrosomonas, Nitrosovibrio, Nitrososphaera and Nitrosopumilus are the most frequently reported taxa that were sensitive to biochar addition [amoA-AOB gene and shifted the AOB community structure from Nitrosospira-dominated toward Nitrosomonas-dominated [amoA-AOB and amoA-AOA genes, and the genera Nitrosospira (AOB) and Nitrososphaera (AOA) achieved absolute superiority [Ammonia oxidation, catalyzed by ammonia-oxidizing bacteria (AOB) and ammonia-oxidizing archaea (AOA), is the rate-limiting step of autotrophic nitrification . Previouaddition . It was addition . In addiaddition . It was ominated . It was eriority .amoA gene copies, diversity and community structures of AOB and AOA were measured using quantitative PCR amplification and Illumina MiSeq sequencing. The primary objectives were to: (i) investigate the responses of the nitrification rate and numbers of amoA-AOB and amoA-AOA gene copies to biochar at different application rates; (ii) clarify the effect of biochar addition on the diversity, community ordinations and structures of AOB and AOA; and (iii) identify the causal relationships among soil properties, the numbers of amoA-AOB and amoA-AOA gene copies, relative abundance of dominant genera, and nitrification rate.In summary, the influencing mechanism of biochar application on the potential nitrification rate (PNR), abundance and community structure of ammonia-oxidizing microorganisms has attracted increasing interest. However, most of the present conclusions concerning salt-affected soils are derived from experiments conducted under certain conditions and are fragmented. Little is known about the mechanism of the functional and structural responses of nitrification to biochar addition in salt-affected irrigation-silting soil. In the present study, an aerobic incubation experiment was conducted on soils sampled from a three-consecutive-year field plot trial at different biochar application rates under N fertilization. The nitrification rate and the \u22121\u22c5yr\u22121), NB1 , NB2  and NB3 . Using the conventional irrigation and agronomic management practices, the sunflower (Helianthus annuus L.) variety \u201cSH361\u201d was planted in the field plot trial throughout the experimental period, i.e., from May 2017 to September 2019.Soil samples used in the present experiment were collected from a three-consecutive-year field plot trial conducted in salt-affected farmland in Dengni Village , Hanggin Rear Banner of Inner Mongolia, China. The field trial is located in the northwestern of Hetao Irrigation District (HID), which is a typical irrigated area in the upper and middle reaches of the Yellow River basin in China . DetailsThe biochar used in the field plot trial was produced by pyrolyzing and charring wheat straw at 400~450 \u00b0C for 4 h under oxygen-restricted circumstances. The basic properties of the biochar were detailed described in . Biochar4)2SO4 solution at a rate of 200 mg N kg\u22121 dry weight soil and moistened to keep the soil moisture at 65% WFPS. Using the weighing method, deionized water was added to each microcosm to maintain constant moisture content during the incubation. The incubation lasted 35 days, and soil samples were collected from the microcosms on the 1st, 3rd, 7th, 10th, 15th, 25th and 35th days. Each treatment had 24 replicate microcosms and triplicate microcosms were used for each sampling, and 24 replicas were used up after eight soil samplings (including soil sampling after pre-incubation). The collected soil samples were sieved through a mesh size of 2 mm and subdivided into two subsamples: one was stored at 4 \u00b0C for soil chemical analysis, and the other was stored at \u221280 \u00b0C for soil microbiological analysis.Prior to the incubation experiment, soil microcosms were established for each treatment by adding 30 g of soil or a soil-biochar mixture (on an oven-dried basis) to a 250 mL Mason jar. All jars were moistened to 60% water-filled pore space (WFPS) using distilled water and placed in a thermostatic incubator for pre-incubation at 25 \u00b1 1 \u00b0C in the dark for 3 days to revive soil microbial activity. The top of each jar was wrapped using plastic films with small holes to ventilate and prevent moisture losses. After pre-incubation, soil samples were collected as the initial soil status. Meanwhile, the soil microcosms were fertilized with (NH1:5), pH, cation exchange capacity (CEC), soil organic carbon (SOC), total nitrogen (TN), NH4+-N and NO3\u2212-N contents, and available potassium (AP). The EC1:5 and pH were measured on 1:5 soil:water (w/v) suspensions. The CEC was measured using the ammonium acetate extraction method. The SOC and TN were analyzed by wet digestion with H2SO4-K2Cr2O7 and semimicro Kjeldahl digestion. Soil NH4+-N and NO3\u2212-N contents were determined on a 1:5 soil:KCl (2 M) extract using ultraviolet spectrophotometry. The AP (Olsen P) was analyzed by the sodium bicarbonate extraction and colorimetric analysis. Detailed analytical procedures for the above soil attributes referred to [For the soil samples used in microcosm construction and incubation, the analyzed physio-chemical properties consisted of soil salinity  gene copies and community structures of ammonia oxidizing bacteria (AOB) and ammonia-oxidizing archaea (AOA). This was done considering the dynamic characteristics of nitrification in salt-affected soils, and the differences in nitrification rate and amoA gene number were significant among different treatments on the 10th day.For the incubation soil samples, concentrations of NH\u00ae Soil DNA Kit  according to the procedure suggested by [\u00ae ND-2000c UV-Vis spectrophotometer , the content and purity of the extracted genomic DNA was quantified using agarose gel electrophoresis (AGE) at 1% content. Quantitative PCR (qPCR) was performed with duplicate sets of extracted DNA in an Mx3005P instrument  with a Brilliant II SYBR Green QPCR Master Mix . The amplification primers, sequences and reaction conditions of qPCR for amoA-AOB and amoA-AOA genes are shown in qPCR reaction, standard DNA preparation and count of gene copies for AOB and AOA were given in [qPCR amplification efficiencies were 98.32% and 97.55% for AOB and AOA, respectively.Soil total genomic DNA was extracted using the E.Z.N.A.ested by . Using agiven in ,26. The The above obtained PCR products were checked using 2% agarose gel electrophoresis and purified using an AxyPrep DNA gel extraction kit  to remove any unspecified products. Then, the PCR products were eluted with Tris-HCl buffer and checked using 2% agarose gel electrophoresis again. Purified amplicons were mixed in equimolar amounts and sequenced on an Illumina MiSeq Benchtop Sequencer . After sequencing, the sequences were checked and optimized using the Trimmomatic software. Operational taxonomic units (OTUs) were defined according to 97% similarity using USEARCH v7 . The repamoA-AOB and amoA-AOA gene copies, and community richness and diversity among different treatments. The relative abundance of dominant taxa at the order and genus levels was also compared using one-way ANOVA for the AOB and AOA, respectively. CANOCO 5.0  was used to perform principal coordinate analysis (PCoA) and redundancy analysis (RDA) to reveal the community ordinations and environmental relationships. Using AMOS 23.0 , structural equation modeling (SEM) was conducted to identify the causal effects of soil microhabitat traits, relative abundance of dominant genera of AOB and AOA, and the numbers of amoA-AOB and amoA-AOA gene copies on nitrification rates under biochar addition. The criteria for assessing the performance of model fitting referred to [The nitrification rate (NR) was calculated according to the formulae proposed by . The diverred to .amoA-AOB and amoA-AOA gene copies are given in \u22121\u22c5d\u22121 for the CK, N, NB1, NB2 and NB3 treatments, respectively. One-way ANOVA results showed that the NR value under the CK treatment was not different from that under the N treatment, and the NR values under the CK and N treatments were significantly higher than those under the NB2 and NB3 treatments. Overall, biochar addition resulted in the decrease of NR and the treatment with the highest amount of biochar (30 t\u22c5ha\u22121) had the lowest NR value. Actually, the NR varied temporally during the autotrophic nitrification process (The nitrification rate (NR) within the 7th and 10th day of incubation and the numbers of  process , and theamoA-AOB gene copies was 8.21 \u00b1 0.54, 4.61 \u00b1 0.64, 3.52 \u00b1 0.63, 2.97 \u00b1 0.81 and 2.50 \u00b1 0.43 \u00d7 107 g\u22121 soil under the CK, N, NB1, NB2, and NB3 treatments, respectively , Nitrosomonas (16.36 \u00b1 10.07%), Unclassified_o_Nitrosomonadales (9.73 \u00b1 6.01%) and Unclassified_k_norank_d_Bacteria (20.49 \u00b1 12.85%). Genera Norank_p_ammonia_oxidizing_bacteria_ensemble (4.60 \u00b1 4.38%) and Nitrosovibrio (1.09 \u00b1 1.16%) belonged to a second group with a lower but still important percentage (1% \u2264 average relative abundance < 5%). N fertilization significantly decreased the relative abundance of Nitrosospira but increased that of Nitrosomonas and Unclassified_k_norank_d_Bacteria. Furthermore, biochar addition increased the relative abundance of Nitrosovibrio, Unclassified_k_norank_d_Bacteria and Norank_p_ammonia_oxidizing_bacteria_ensemble, but decreased that of Nitrosospira under N fertilization. Genera Nitrosomonas and Unclassified_o_Nitrosomonadales showed no distinct response to biochar addition.Nitrososphaera (50.22 \u00b1 11.27%), Norank_c_environmental_samples_p_Crenarchaeota (16.77 \u00b1 21.49%), Nitrosopumilus (16.69 \u00b1 13.88%) and Unclassified_k_norank_d_Archaea (15.78 \u00b1 9.64%). The genus Nitrosopumilus was barely observed under the CK treatment. The N fertilization significantly enhanced the percentage of Nitrosopumilus and Unclassified_k_norank_d_Archaea, and decreased that of Norank_c_environmental_samples_p_Crenarchaeota. The further application of biochar, by contrast, decreased the relative abundance of Nitrosopumilus and Norank_c_environmental_samples_p_Crenarchaeota, whereas increased that of Nitrososphaera and Unclassified_k_norank_d_Archaea. The relative abundance of the AOA community at the genus level is shown in Principal coordinates analysis (PCoA) was used to evaluate the dissimilarity of the AOA and AOB communities. The obtained unconstrained ordination of the AOB and AOA communities and basic soil properties demonstrated that all treatments were clearly separated according to management practices . The fir3\u2212-N and pH were the significant environmental factors shaping the AOB community structure. The contribution of the environmental variable to the total explanatory variance is followed by the solely explanatory variance by this variable: SOC-61.8% (53.1%), pH-14.3% (12.3%), and NO3\u2212-N-11.0% (9.4%). The first axis explained 69.90% of the variation (p < 0.01) and was correlated with soil NO3\u2212-N and SOC, indicating that the first axis may characterize soil nutrient status. The second axis explained 4.52% of the variation (p < 0.05) and was correlated with soil pH, representing the status of the soil alkalinity. The differences in soil SOC, NO3\u2212-N and pH, as induced by N fertilization and biochar addition, contributed to the structural variation of the AOB community. For AOA, the community structure was mainly dominated by SOC and pH, and the explanatory contribution and variance related to the environmental variables were SOC-67.5% (54.3%) and pH-10.6% (8.5%). The first axis explained 54.86% of the variation (p < 0.01) and characterized the soil nutrient status. The second axis represented the soil alkalinity status and explained 7.94% of the variation (p < 0.01). The structural variation of the AOA community could be mainly ascribed to the difference in soil SOC and pH.The relationships between environmental variables and the community compositions of AOB and AOA were determined using redundancy analysis (RDA). amoA gene copies and basic soil properties, as expressed by Spearman\u2019s rank correlation coefficients, is given in Nitrosospira and Norank_c_environmental_samples_p_Crenarchaeota, but a negative correlation with that of genera Nitrosovibrio, Unclassified_k_norank_d_Bacteria, Norank_p_ammonia_oxidizing_bacteria_ensemble, and Unclassified_k_norank_d_Archaea. Likewise, the numbers of both amoA-AOB and amoA-AOA gene copies showed significantly positive responses to the relative abundance of genera Nitrosospira and Norank_c_environmental_samples_p_Crenarchaeota, but negative responses to that of genera Nitrosomonas, Unclassified_o_Nitrosomonadales, Nitrosovibrio, Unclassified_k_norank_d_Bacteria, Nitrososphaera, and Unclassified_k_norank_d_Archaea. Interestingly, the relative abundance of genera Nitrosospira and Norank_c_environmental_samples_p_Crenarchaeota showed negative correlation with most basic soil properties, but that of genera Unclassified_k_norank_d_Bacteria and Unclassified_k_norank_d_Archaea showed positive correlation with most basic soil properties.The dependence between the relative abundance of dominant genera of AOB and AOA and nitrification rates, amoA gene, and nitrification rates using structural equation models (SEMs). The causality among the above attributes was accurately captured by the SEM from the criteria of model performance, i.e., \u03c72/df of 0.972, p of 0.478, GFI of 0.908, and RMSEA close to 0. SOC showed positive direct influences on the relative abundance of the genera Nitrosovibrio (p < 0.05) and NO3\u2212-N (p < 0.001), but negative direct influences on the relative abundance of Norank_c_environmental_samples_p_Crenarchaeota (p < 0.01) and number of amoA-AOA gene copies (p < 0.01). Soil NO3\u2212-N content had a positive direct influence on the relative abundance of Nitrosomonas (p < 0.001), and a negative direct influence on that of Norank_c_environmental_samples_p_ Crenarchaeota (p < 0.001). Additionally, the numbers of amoA-AOB and amoA-AOA gene copies were positively altered by the relative abundance of Nitrosomonas (p < 0.05) and Norank_c_environmental_samples_p_ Crenarchaeota (p < 0.001), but negatively altered by the relative abundance of Nitrosovibrio (p < 0.001). In addition to the direct influence, SOC had an indirect influence on NR through the numbers of amoA-AOB and amoA-AOA gene copies, which were directly responsive to the relative abundance of Nitrosovibrio (p < 0.001). Soil NO3\u2212-N content also had an indirect positive influence on NR through the numbers of amoA-AOB and amoA-AOA gene copies, which were directly influenced by Nitrosomonas (p < 0.05). It was interesting to find that a common pathway through the relative abundance of Norank_c_environmental_samples_p_Crenarchaeota existed for SOC and NO3\u2212-N content. That was, both SOC and NO3\u2212-N content had indirect influence on NR through the relative abundance of Norank_c_environmental_samples_p_Crenarchaeota, and numbers of amoA-AOB and amoA-AOA gene copies. A total of 86.3% of the variation in NR was explained by the SEM model, and the proportions of the explainable variation in the relative abundance of Nitrosomonas, Nitrosovibrio, Nitrososphaera, and Norank_c_environmental_ samples_p_Crenarchaeota, and numbers of amoA-AOB and amoA-AOA gene copies were 61.1%, 27.5%, 41.6%, 84.6%, 96.2% and 96.1%, respectively.4+-N and AP  [Biochar has been extensively used as an effective amendment for soil salinization hazards because it enhances the nutrient supply capacity, modulates porosity and pore size, improves hydraulic parameters, and promotes soil aggregate structure . Usman eN and AP . Zhu et (<1 wt%) . This wa(<1 wt%) ,37, and amoA gene, and AOB-dominated nitrification and amoA-AOB gene abundance were predominant in alkaline soil, whereas AOA-dominated nitrification and amoA-AOA gene abundance were dominant in acidic soil [amoA gene abundance in saline soils are currently inconsistent for different soil textures, salinization types, and nutrient statuses, i.e., promotion [4+-N concentration, which acts as a substrate for nitrification [4+-N through physical adsorption and pore filling [Soil nitrification rate was closely associated with the abundance of dic soil . Data onromotion ,39, inhiromotion ,41, or iromotion . The posromotion ; (2) theromotion ; and (3)fication . In contfication ; (2) the filling ; and (3) filling . In factamoA-AOB gene contributed to soil potential nitrification rates (PNR), whereas the abundance of amoA-AOA gene was almost not responsive to biochar addition [amoA-AOB gene abundance, rather than amoA-AOA gene abundance, was observed under biochar application, although biochar addition significantly increased the diversity indices of AOB and AOA [amoA-AOB gene abundance, which was significantly more abundant than amoA-AOA gene abundance, but the amoA activity showed a significant negative correlation with soil salinity and water-soluble carbon [amoA-AOB and amoA-AOA gene abundance. The explanation was that biochar addition decreased the frequency of functional microorganisms by increasing the diversity of AOB and AOA, and increased soil pH  communities [amoA-AOA gene abundance on NR. In the present study, the potential mechanism of nitrification inhibition is that biochar addition improved the alpha diversity of AOB and AOA communities, shifted the community structure and decreased the relative abundance of dominant ammonia-oxidizers. This could be witnessed from the correlation among NR, amoA gene copies and Shannon index for AOB and AOA  showed that SOC had indirect influence on NR through Nitrosovibrio, Norank_c_environmental_samples_p_Crenarchaeota and amoA-AOB and amoA-AOA gene abundance, and NO3\u2212-N had indirect influence on NR through Nitrosomonas, Norank_c_environmental_samples_ p_Crenarchaeota and amoA-AOB and amoA-AOA gene abundance. Our conclusion is that biochar addition inhibits nitrification by improving the community diversity of AOB, shifting the community structures of AOB and AOA, and reducing the relative abundance of dominant functional ammonia-oxidizers. Soil microhabitat traits and unclassified ammonia-oxidizing microorganism also play an important role in nitrification inhibition, which still needs further efforts to validate using long-term observation experiments.Under N fertilization conditions, biochar addition inhibited the average nitrification rate and numbers of"}
+{"text": "Following the publication of the original article , the autThe original article  has beenAdditional file 1: Figure S1. Expected vs. Actual ERCC concentration. SPEAQeasy produces plots for each sample, for easy visual comparison of expected ERCC transcript abundance with the kallisto-measured concentration.Additional file 2: Figure S2. SPEAQeasy logs tracing computational steps by sample. To aid transparency and greatly simplify the source of execution errors, SPEAQeasy automatically generates logs with several pieces of information for every sample. In order of submission, the name of each Nextflow process is printed, along with (1) the working directory: where all relevant files are present, (2) the exit code: a standard indication of whether the process succeeded or how it failed, (3) a list of the specific commands run during the given process. Above is a snapshot of the top of an example logAdditional file 4: SNVs supplementary BED files. The common SNVs used for sample identification are stored in the BED files (A) common_missense_SNVs_hg19.bed and (B) common_missense_SNVs_hg38.bed.Additional file 4: SNVs supplementary BED files. The common SNVs used for sample identification are stored in the BED files (A) common_missense_SNVs_hg19.bed and (B) common_missense_SNVs_hg38.bed."}
+{"text": "Atherosclerosis (AS) is a typical vascular disease. Emerging evidence has shown that circRNAs play key roles in the progression of AS, but the potential function and underlying mechanism of hsa_circ_0001879 remains unknown. We detected the expression level of hsa_circ_0001879 was determined by qRT-PCR, and the proliferation rate and migration ability of HUVECs were measured by CCK-8 assay and Transwell assay, respectively. Proliferative markers and epithelium mesenchymal transition (EMT) markers were measured through immunoblotting. A dual luciferase activity assay was performed to detect the interaction between circRNAs, miRNAs, and mRNAs. Hsa_circ_0001879 was upregulated in AS patients. Hsa_circ_0001879 inhibited the proliferation and migration ability of Human umbilical vein endothelial cells (HUVECs). Hsa_circ_0001879 directly bound to miR-6873-5p and acted as a sponge. miR-6873-5p-induced HDAC9 mRNA degradation was inhibited by hsa_circ_0001879. Hsa_circ_0001879 decreased the proliferation and migration of HUVECs by inhibiting miR-6873-5p-induced HDAC9 degradation. Atherosclerosis (AS) is a major chronic disease characterized by the formation of atherosclerotic plaques, which pose a high risk to the cardiovascular system . During circRNAs are noncoding RNAs formed through the back-splicing of pre-mRNAs. circRNAs do not have a polyA tail or cap . With thIn this study, we hypothesized that hsa_circ_0001879 was engaged in the progression of AS and we tried to uncover the underlying mechanism, thus, we evaluated the expression of hsa_circ_0001879 in AS patients and found that hsa_circ_0001879 was upregulated. After establishing stable cell lines, the proliferation rate and migration ability of the cells were detected by CCK-8 assay and Transwell assay, respectively. Hsa_circ_0001879 inhibited the proliferation and migration of HUVECs. A dual luciferase activity assay showed that hsa_circ_0001879 inhibited miR-6873-5p-induced HDAC9 degradation by acting as a competing endogenous RNA.We hypothesized that hsa_circ_0001879 was engaged in the progression of AS and we tried to uncover the underlying mechanism, thus, we evaluated the expression of hsa_circ_0001879 in AS patients and found that hsa_circ_0001879 was upregulated. After establishing stable cell lines, the proliferation rate and migration ability of the cells were detected by CCK-8 assay and Transwell assay, respectively. Hsa_circ_0001879 inhibited the proliferation and migration of HUVECs. A dual luciferase activity assay showed that hsa_circ_0001879 inhibited miR-6873-5p-induced HDAC9 degradation by acting as a competing endogenous RNA.To detect the expression level of hsa_circ_0001879 in AS, we collected 20 samples from 20 AS patients and 20 normal tissues from volunteers. qRT-PCR was performed to measure the expression of hsa_circ_0001879. The results are shown in To determine the biology and underlying mechanism of hsa_circ_001879, we established stable overexpression and knockdown cell lines. The results are shown in After showing that hsa_circ_0001879 inhibited the proliferation and migration of HUVECs, we measured the expression of PCNA and EMT markers, which are key markers reflecting the proliferation status and migration ability of cells. The results showed that PCNA expression was decreased in ox-LDL-treated cells and was restored in sh-hsa_circ_0001879 cells and decreased in hsa_circ_0001879-overexpressing cells .Figure 3circRNAs exerts their functions by acting as competing endogenous RNAs (ceRNAs). We searched the circRNA database and identified miR-6873-5p as a potential target miRNA. We first measured the expression of miR-6873-5p in AS patient samples. The results showed that miR-6873-5p was downregulated in the samples obtained from AS patients . We nextBy searching TargetScan, we identified HDAC9 as a potential target gene of miR-6873-5p. We first measured HDAC9 mRNA levels in cell lines. HDAC9 was decreased in hsa_circ_0001879-knockdown cells at both the mRNA and protein levels . We nextPatient information and clinical sample collectionAS samples and normal tissues were randomly collected from patients in the department of Cardiology, Guangdong Provincial Hospital of Traditional Chinese Medicine; The Second Affiliated Hospital of Guangzhou University of Chinese Medicine. The including criteria was detailed as below: samples from patients with peripheral vascular AS were included and the samples were confirmed by pathologists. The normal tissues were normal peripheral vascular tissues from donors. This study was approved by the Ethics Committee of The Department of Cardiology, Guangdong Provincial Hospital of Traditional Chinese Medicine; The Second Affiliated Hospital of Guangzhou University of Chinese Medicine. Clinical samples were collected from patients after written and informed consent was obtained.The hsa_circ_0001879 plasmid was generated by chemical synthesis of the complete sequence of hsa_circ_0001879, and additional circularization promoter ALU sequences were added upstream and downstream, the sequence was detailed as below: 5\u02b9TCTGACAACTGAACTGCTCTCGCCTTGAACCTGTTTTGGCACTAA AATAAAATCTGTTCAATTAACGAATTCTGAAATATGCTATCTTACAG -GTGAATATATTTTTTCTTGAGGATCCACTAATTTGGGATGATAACGCCAAAACAGGTTCAAGGCGAGAGCAGTTCAGTTGTCAGAA3\u02b9, the sequence of hsa_circ_0001879 was cloned between AG and GT in the blank. The hsa_circ_0001879 shRNA-1 sequence was GGCTGTTCGGGAAAGTGTCAA, and the shRNA-2 sequence was CGGCTGTTCGGGAAAGTGTCA. The plasmids were transfected with Lipofectamine 3000  according to the manufacturer\u2019s instructions. After transfecting, cells were treated with puromycin for 3\u00a0days and stable cell lines were established.Tissues and cells were harvested and lysed with TRIzol  following the manufacturer\u2019s protocol. After purification and reverse transcription, cDNA was harvested. Specific primers were used for gene amplification. qPCR was performed using the SYBR Green detection RT\u2011PCR system  with RT mix  and SYBR Green . The thermocycling conditions were as follows: initial denaturation at 95\u00b0C for 5 s; 40 cycles at 95\u00b0C for 5 s of denaturation, 95\u00b0C for 35 s and 60\u00b0C for 30 s for annealing and elongation and 60\u00b0C for 30 s for final extension; and GAPDH was used for normalization. RNA was examined by detecting the absorption of OD260/OD230, RNA with ratio: > 2 was retained.The following key primers were used:hsa_circ_0001879 (F) CCCTCCAAGTCCAGTAAGAAGThsa_circ_0001879 (R) AGATCCTAAGAGGTGCGAGTTTAmiR-6873-5p (F): CTTCTCTGTAAGGCAAAGTmiR-6873-5p (R): CTCTACAGCTATTCCGAACHDAC9 (F): AGTAGAGAGGCATHDAC9 (R): GGAGTGTCCTTTCGGU6 (F): CTCGCTTCGCAGCACAU6 (R): AACGCTTCACGAATTTGCGGAPDH (F) GGAGCGAGATCCCTCCAAAATGAPDH (R) GGCTGTTGTCATACTTCTCATGGHuman umbilical vein endothelial cells (HUVECs) were acquired from the American Type Culture Collection. Cells were cultured with complete F12K (Sigma D0697) containing 10% fetal bovine serum (Invitrogen F8687). The cells were treated with different concentrations of ox-LDL for 48\u00a0h.The cells were harvested and washed three times with ice-cold PBS and lysed with RIPA buffer (Sigma 20\u2013188). The protein was quantified using a BCA kit (Beyotime Institute of Biotechnology) according to the manufacturer\u2019s protocol. After quantification, equal amounts of protein were separated by SDS-PAGE. Then, the proteins were transferred onto a polyvinylidene fluoride membrane and blocked with 5% milk for 1\u00a0h at room temperature. The membrane was incubated overnight with primary antibodies at 4\u00b0C, washed with TBST and then incubated with secondary antibodies  for 1\u00a0h at room temperature. Target proteins were detected using the ECL  method. The following antibodies were used:anti-PCNA , anti-\u03b2-actin , anti-N-cadherin , anti-E-cadherin , and anti-vimentin .The fragment of hsa_circ_0001879 or HDAC9 3\u2032UTR containing the wild-type or mutant allele was inserted into the pmirGLO vector  to create a WT-hsa_circ_0001879, MUT-hsa_circ_0001879, WT-HDAC9, or MUT-HDAC9 reporter. The plasmids were transfected into HUVECs, and the relative luciferase activities were measured with a Dual-Lucy assay kit (Solarbio).RIP analysis was performed using an EZ-Magna RIP kit  following the manufacturer\u2019s instructions. After lysing cells with RIP lysis buffer, cell lysates were incubated with magnetic beads coated with anti-Ago2 or anti-IgG (as the control). Additionally, qRT-PCR analysis was performed to measure the levels of hsa_circ_0001879 and miR-6873-5p.All data analyses were performed with SPSS 20.0 statistical software. The differences between two groups were compared by Student\u2019s t-test. For multigroup analysis, one\u2011way ANOVA followed by Tukey\u2019s post hoc test was performed when making comparisons within datasets. A p value <0.05 was considered to indicate a significant difference compared to the control.Evidence has demonstrated that endothelial cell dysfunction contributes to the progression of atherosclerosis ,17. The In our study, we identified a novel circRNA induced by ox-LDL and inhibited the proliferation and migration of HUEVCs through directly binds with miR-6873-5p and finally inhibited the degradation of HDAC9, EMT of endothelium cells and smooth muscle cells contributes critically to the progression of AS and understanding the underlying mechanism helped us better understand the disease .CircRNAs play key roles in the progression of AS, especially in the function of endothelium vein cells. Hsa_circ_0030042 regulates autophagy and protects atherosclerotic plaque stability by targeting eIF4A3 . The cirIn summary, circRNAs exert their functions in three main ways: as competing endogenous RNAs, RNA-binding protein partners and facilitators of peptide translation . In the The absence of HDAC9 attenuated AS progression by reducing inflammation and reversing cholesterol transport . HDAC9 hIn conclusion, hsa_circ_0001879 increased the expression of HDAC9 by sponging miR-6873-5p, thereby preventing the cell growth and migration of HUVECs. These findings suggest that hsa_circ_0001879 is a potential therapeutic target for AS.Our study indicated that hsa_circ_0001879 promotes the progression of AS through inhibit miR-6873-5p induced HDAC degradation by acting as a competing endogenous RNA.Click here for additional data file."}
+{"text": "The relationship between circular RNA (circRNA) and cancer stem cells (CSCs) is uncertain. We have investigated the combined influence of CSCs, circRNA (hsa_circ_0003222), and immune checkpoint inhibitors in NSCLC progression and therapy resistance. We constructed lung CSCs . The effects of hsa_circ_0003222 in vitro were determined by cell counting, colony and sphere formation, and Transwell assays. A tumor xenograft model of metastasis and orthotopic model were built for in vivo analysis. We found that hsa_circ_0003222 was highly expressed in NSCLC tissues and LCSCs. Higher levels of hsa_circ_0003222 were associated with the stage, metastasis, and survival rate of patients with NSCLC. Reduced levels of hsa_circ_0003222 decreased tumor cell proliferation, migration, invasion, stemness-like properties, and chemoresistance. The silencing of hsa_circ_0003222 was found to downregulate PHF21B expression and its downstream, \u03b2-catenin by relieving the sponging effect of miR-527. Moreover, silencing hsa_circ_0003222 alleviated NSCLC resistance to anti-programmed cell death-ligand 1 (PD-L1)-based therapy in vivo. Our data demonstrate the significant role of hsa_circ_0003222 in NSCLC cell stemness-like properties. The manipulation of circRNAs in combination with anti-PD-L1 therapy may alleviate NSCLC stemness and progression. Lung cancer encompasses 11.6% of all diagnosed cancer and contributes to 18.4% of deaths related to cancer worldwide . The majCSCs are increasingly implicated in drug resistance and metastasis in NSCLC and the prognosis in patient-derived samples enriched with CSCs is known to be poor , 17. TheLARP4 gene. The LARP4 gene is involved in mRNA and is known to regulate cell migration and invasion [MicroRNAs (miRNAs) are viewed as a potential therapy for NSCLC because of their role in regulating genes involved in tumorigenesis and are regularly exploited to investigate the mechanisms of cancer as miRNA mimics, anti-miRNA, and RNA sponges . In partinvasion , 27. We invasion . In addiinvasion . Therefoinvasion . TherefoTo confirm whether there is an association between hsa_circ_0003222 and NSCLC, we determined its relative expression using RT-qPCR in NSCLC tumor tissues and adjacent non-tumor tissues from 30 patients. The clinical characteristics of the patients can be found in Table P\u2009<\u20090.001, Fig. P\u2009<\u20090.001, Fig. The stem-like characteristics of LCSCs generated from PC9 and A549 cell lines were confirmed by assessing the expression of CD44, a cell surface marker for CSC populations. Flow cytometry indicated that the LCSCs generated from PC9 and A549 cell lines had a significantly higher expression of CD44 than the controls (P\u2009<\u20090.001) whereas migration and invasion were significantly reduced when hsa_circ_0003222 was inhibited. (P\u2009<\u20090.001). Likewise, the expression levels of stem cell-associated proteins, CD44, CD133, OCT4, SOX2, and PD-L1, in PC9 and A549 LCSCs appeared to be elevated when hsa_circ_0003222 was overexpressed but was lowered when hsa_circ_0003222 expression was inhibited. Moreover, cell viability in response to different concentrations of cisplatin was increased significantly (P\u2009<\u20090.001) when hsa_circ_0003222 is overexpressed and reduced significantly when it is inhibited (P\u2009<\u20090.001)  Fig. . Further01) Fig. . BesidesIn addition, we transfected SW900 LCSCs, a squamous carcinoma cell line with has_circ_0003222 expression and inhibition vectors and measured has_circ_0003222 levels with RT-qPCR to confirm over and under expression . We mutated the predicted binding sites of miR-527 in hsa_circ_0003222 . Similarly, the inhibition of miR-527 significantly increased colony and spheroid formation in both PC9 (P\u2009<\u20090.001) and A549 (P\u2009<\u20090.001) LCSCs and restored the colony and spheroid formation that were reduced by the inhibition of hsa_circ_0003222  whereas the inhibition of hsa_circ_0003222 resulted in a tumor volume minor than control (P\u2009<\u20090.01)  Fig. , B. Repr01) Fig. , immunoh01) Fig. , and a T01) Fig.  all indi01) Fig. . The lev01) Fig. , J. Orth01) Fig. , L. Live01) Fig. . Moreove01) Fig. , O. Our Despite current advances in the therapy of cancer with immune checkpoint inhibitors, NSCLC remains a leading cause of cancer fatality, partly owing to drug resistance and metastasis caused by stem cells , 32. To In our study, we searched for circRNA that may be potential regulators of LCSCs and subsequent metastasis and drug resistance in NSCLC. Using immunochemistry, we found that the level of hsa_circ_0003222 was upregulated in the tumor tissue of patients with NSCLC and that it could predict an unfavorable prognosis. Hsa_circ_0003222 is derived from LARP4, a gene associated with cell division and RNA stability , 27. MutSeveral circRNAs have been found to control cell cycle events and gene expression in NSCLC by sponging miRNA \u201338. In sIn this study, we found that both the expression of hsa_circ_0003222 and that of PD-L1 were upregulated in LCSCs. The upregulation of PD-L1 signifies a poor prognosis in NSCLC because it negatively regulates levels of CD4+ and CD8+ tumor-infiltrating T lymphocytes, which are associated with a better prognosis . TherefoWe then focused our attention on finding the potential targets of hsa_circ_0003222. Small sequencing and online tools predicted that miR-527 could be a potential candidate. It has been reported that miR-527 is associated with the inhibition of the TGF-\u03b2/SMAD signaling pathway through the regulation of SULF2 to suppress epithelial-mesenchymal transition in NSCLC . We founThe results we obtained in vitro were replicated convincingly in vivo in a murine xenograft model. Cell proliferation and tumor volume were increased when miR-527 is inhibited whereas apoptosis is increased when hsa_circ_0003222 is inhibited. An orthotopic model of tumors in nude mice indicated that hsa_circ_0003222 inhibition prevents metastasis and that treatment with a combination of anti-PD-L1 and hsa_circ_0003222 inhibition was found to significantly reduce tumor volume after 35 days. These results imply that the inhibition of PD-L1 and hsa_circ_0003222 could be used in combination to alleviate metastasis and drug resistance in NSCLC.To conclude, hsa_circ_0003222 accelerates stemness and the progression of NSCLC by sponging miR-527. MiR-527 expression levels were lower in tumor tissue and inhibit stemness and the progression of NSCLC. The inhibition of hsa_circ_0003222 may alleviate NSCLC resistance to anti-PD-L1. These findings illustrate the importance of circRNAs in the stemness and progression of NSCLC and in PD-L1 therapy.We collected 30 cases of fresh NSCLC tumor and paired paratumor from patients who agreed to the informed consent at the Shanghai Chest Hospital of Shanghai Jiao Tong University, China. None of these patients received chemotherapy or radiotherapy before collecting of tissue samples, which were then immediately snap-freezing and kept at \u221280\u2009\u00b0C. This study was permitted by the Ethics Committee of Shanghai Chest Hospital at Shanghai Jiao Tong University (ks(y)21167). All patients agreed that their lung tissues and information were used for research and signed written informed consent before the collection of lung tissues and information.We purchased Human lung cancer cells (PC9 and A549 cells) from the Cell Bank of the Chinese Academy of Sciences . Cells in 90% Dulbecco modified Eagle\u2019s medium  were in a 37\u2009\u00b0C humidified incubator with 5% carbon dioxide.We used repeated increase cisplatin therapy and sphere formation methods to construct the PC9 and A549 LCSC models, which were also described in our previous study . In shorCells were transfected with an appropriate amount of vector by using Lipofectamine 2000  and then cultured for 48\u2009h on the basis of the manufacturer\u2019s protocol.The full-length cDNA of hsa_circ_0003222 was synthesized by GeneChem  and then cloned into the circRNA vector . Two siRNAs against hsa_circ_0003222, shown in Supplementary Table 7\u2009ml and mixed 50\u2009\u03bcl cells suspension and 50\u2009ml Matrigel and subsequently injected into the left lung of the mice through the chest wall at depth of 3\u2009mm. Magnetic resonance imaging (MRI) examinations were used 1 week later to exam tumor formation. Each group contained six mice.The orthotopic assay was consistent with what was mentioned in our previous study . We prepWe fostered and handled all experimental animals approved by the Animal Care Committee of Shanghai Chest Hospital of Shanghai Jiao Tong University.T-test was performed between two independent groups; one-way ANOVA test was applied among various groups; Kaplan\u2013Meier curves and the log-rank test were used to analyze the survival rate of patients. p\u2009<\u20090.05 was considered a statistical significance.All data were statistically examined by GraphPad 7.0. More detailed materials and methods can be found in the Supplementary Methods.Supplemental Informationsupplement figure 1supplement figure 2"}
+{"text": "A hsa-miR-650 inhibitor reversed the promotion of rapamycin-induced autophagy and the inhibition of cell proliferation by the hsa_circRNA_103124 siRNA. However, hsa-miR-650 mimics reversed the inhibition of rapamycin-induced autophagy and the promotion of cell proliferation through hsa_circRNA_103124 overexpression. These results indicate that hsa_circRNA_103124 upregulation in patients with CD promotes cell proliferation and inhibits autophagy by regulating the hsa-miR-650/AKT2 signaling pathway.Circular RNAs (circRNAs) play important roles in the pathogenesis of Crohn\u2019s disease (CD). We discovered that hsa_circRNA_103124 was upregulated in CD patients in our previous study. Nonetheless, the function of hsa_circRNA_103124 is unclear. In this study, hsa_circRNA_103124 was predicted to interact with hsa-miR-650. Gene Ontology (GO) and pathway analyses identified AKT serine/threonine kinase 2 (AKT2) as the downstream target protein of hsa-miR-650. Activated AKT2 inhibits autophagy, but promotes cell proliferation. Recent studies suggest that the inhibition of autophagy is one of the mechanisms of CD pathogenesis. Therefore, we inferred that hsa_circRNA_103124 might regulate autophagy and proliferation by targeting AKT2 as a sponge for hsa-miR-650. Here, quantitative reverse transcription PCR (RT-QPCR) results revealed that upregulated hsa_circRNA_103124 expression in patients with CD was negatively correlated with hsa-miR-650 expression but positively correlated with the white blood cell count and calprotectin levels. TSC complex subunit 1 (TSC1), one of the proteins upstream of autophagy was downregulated in patients with CD. Consisting with the bioinformatics prediction, it was verified that hsa_circRNA_103124 targeted to hsa-miR650 by fluorescence  The pathogenesis of inflammatory bowel disease (IBD), which includes Crohn\u2019s disease (CD) and ulcerative colitis (UC), is not clearly understood to date. Researchers have universally acknowledged that the complex etiology of IBD is related to the host genetic background, microbial and environmental factors . The senSpecific circular RNAs (circRNAs) were discovered to be biomarkers of various diseases . The funIn this study, the correlation between hsa_circRNA_103124 and CALP levels or WBC counts was assessed in patients with CD. We also predicted by bioinformatics that one of the downstream targets of upregulated hsa_circRNA_103124 in patients with CD was hsa-miR-650. Hsa-miR-650 play different roles in various kinds of diseases. In some reports, hsa-miR-650 targets AKT serine/threonine kinase 2 (AKT2) and inhibits the proliferation, migration and invasion of synovial fibroblasts in individuals with rheumatoid arthritis . In addiATG16L1) and GTPase family M (IRGM) (Recently published studies have provided a better understanding of the mechanism of autophagy in IBD. Genome-wide association studies (GWAS) have revealed genes associated with autophagy, such as autophagy-related gene 16 like 1 (M (IRGM) . MoreoveM (IRGM) . TSC comM (IRGM) . TSC1 deM (IRGM) . TherefoM (IRGM) , may chaThus, according to our previous study, we inferred that upregulated hsa_circRNA_103124 in patients with CD may activate AKT2 by sponging hsa-miR-650. Hsa_circRNA_103124 overexpression may inhibit autophagy and promote cell proliferation. The specific mechanism of hsa_circRNA_103124 in CD pathogenesis requires intensive study.Patients with CD and healthy donors (HDs) were recruited from 2018 to 2019 at Suzhou Affiliated Hospital of Nanjing Medical University . Ethical approval was obtained from the Ethics Committee of Nanjing Medical University. Informed consent was obtained from all participants. Specimens were collected using the method described in our previous study . Informa\u03b2-actin as an internal reference, according to the methods described in our previous studies . Complementary DNA (cDNA) was synthesized with PrimeScript RT Master Mix . The relative expression of hsa_circRNA_103124 in PBMCs was detected using RT-QPCR, with  studies . The relPeripheral blood (1.5\u00a0ml) was collected from each patient with CD or healthy control, using a vacuum blood collection tube with EDTA-K2. The WBC count in peripheral blood was detected using an automated hematology analyzer . Fresh stool samples (500\u00a0mg) were collected from patients with CD. Fecal CALP levels were measured using enzyme-linked immuno sorbent assay (ELISA) . Please refer to the manual for specific operational procedures.The interaction between circRNA and microRNA was predicted with the Arraystar homemade miRNA target prediction software based on the TargetScan and miRanda databases. The 5 most likely miRNAs to which hsa_circRNA_103124 binds were predicted based on the arrangement of free energy and number of seed sequences. The intersection of the downstream genes of the target miRNAs in the Targetscan, miRDB and miRWalk databases was obtained using Venny 2.1. Gene Ontology (GO) and KEGG pathway analyses were performed to predict the biological function of hsa_circRNA_103124 in DAVID 6.7. Subsequent pairing of target miRNAs and genes was predicted using TargetScan.Caco2 cells were cultured in Dulbecco\u2019s modified Eagle\u2019s medium (DMEM), and human intestinal epithelial cells (HIECs) were cultured in Roswell Park Memorial Institute 1,640 medium (RPMI 1640 medium), at 37\u00b0C with 5% CO2. Over-expression of hsa_circRNA_103124 was induced by transiently transfecting overexpression plasmid pLC5-ciR-circRNA_103124  in Caco2 cells and HIECs. The downregulation of hsa_circRNA_103124 was induced by transfecting a siRNA . The oveThe cell cycle distribution was detected using flow cytometry  after propidium iodide  staining. Cell proliferation was examined using Cell Counting Kit-8 assay  and 5-ethynyl-2\u2032-deoxyuridine staining . The detailed methods are described in our previous study .in situ hybridization (FISH) was performed to detect the location and expression levels of hsa_circRNA_103124 and hsa-miR-650 using FAM- and CY3-labeled specific probes, respectively , in Caco2 cells or HIECs. Please refer to the manual for specific operational procedures. A confocal laser scanning microscope  was used to capture the cellular fluorescence images. The sequences of probes used in this experiment are listed in Fluorescence 4 cells/ml. Cells were incubated in a humidified atmosphere at 37\u00b0C with 5% CO2. Three hundred 1\u00a0bp of the hsa-miR-650 response element in hsa_circRNA_103124 and its adjacent sequences were recombined  with psiCHECK-2 . A control plasmid including mutated sequences of the hsa-miR-650 response element was inserted into psiCHECK-2. The plasmid and the hsa-miR-650 mimics were transfected into HEK293T cells using Lipofectamine 3,000 Reagent, according to the manual. The Dual-Luciferase\u00ae Reporter Assay System  was used to detect the activities of Renilla luciferase and firefly luciferase with Biotek Synergy H1, at 48\u00a0h following transfection.HEK293T cells were cultured in 24-well plates with DMEM, at a density of 2 \u00d7 104 cells/ml for 24\u00a0h. Rapamycin (20\u00a0ng/ml) was supplemented to induce autophagy for 24\u00a0h. The cells were stained with acridine orange  for 30\u00a0min. Fluorescent images of the AO positive cells (orange) were acquired using a confocal laser scanning microscope. The proportion of positively stained cells that underwent autophagy was calculated by normalization to the cells stained green.HIECs or Caco2 cells were grown on coverslips in 24-well plates with RPMI 1640 or DMEMmedium at a density of 2 \u00d7 10\u03b2-actin .The western-blotting methods were described in our previously published paper . Primaryp < 0.05 was considered as a standard to determine statistical significance. Differences between groups were determined using Student\u2019s t-test (between groups) or one-way analysis of variance (ANOVA) (among groups). Pearson\u2019s correlation coefficients were used to describe correlations between variables.GraphPad Prism 5  was used for data analysis in this study. r = 0.3052, p = 0.0330) or CALP . The WBC count and CALP level are relatively preferred noninvasive biomarkers for CD diagnosis and treatment as we mentioned in the introduction. Based on this result, hsa_circRNA_103124 potentially served as a diagnostic biomarker for CD.Hsa_circRNA_103124 was discovered as a potential diagnostic biomarker of CD in our previous study . The spe showed that hsa_circRNA_103124 and hsa-miR-650 localized in both the cytoplasm and nucleus. When we reduced the expression of hsa_circRNA_103124 transfecting a siRNA in cells, the localization of hsa-miR-650 in the cytoplasm was enhanced. Moreover, luciferase reporter assays (p = 0.043) compared with that in healthy controls. Moreover, hsa_circRNA_103124 expression negatively correlated with hsa-miR-650 expression  that most likely bind to hsa_circRNA_103124 are listed in  showed r assays  indicatepression . TherefoGO and KEGG pathway analyses indicated that hsa-miR-650 participates in pathways in cancer and TNF signaling . The VenOne of the most important biological functions of AKT2 is regulation of proliferation. Therefore, 5-Ethynyl-2\u2032-deoxyuridine (EdU) staining, Cell Counting Kit-8 (CCK-8) assay and the cell cycle distribution test were performed to study the status of cell proliferation following changes in hsa_circ_103124 expression. In EdU staining, thymine in the DNA of proliferating cells was replaced with EdU, which was stained by Apollo, as shown in red. The ratio of red to blue cells, represents the percentage of proliferating cells. In the CCK-8 assay, the OD450 absorbance value showed cell viability. The cell cycle distribution was analyzed using flow cytometry revealed the proportion of cells in G1, S or G2 phases.The expression of hsa_circRNA_103124 was downregulated in Caco2 cells or HIECs transfected with hsa_circRNA_103124 siRNAs . The resWe confirmed that hsa_circRNA_103124 overexpression in Caco2 cells and HIECs promoted proliferation by performing EdU staining , the CCKImpaired autophagy plays an important role in the pathogenesis of IBD . AKT2 isThe overexpression of hsa_circRNA_103124 in Caco2 cells and HIECs inhibited rapamycin-induced autophagy and reduced the number of AO-positive particles . The expCircRNAs are considered important regulatory molecules in large numbers of biological processes involved in various diseases . An incrIn the present study, the mechanism that hsa_circRNA_103124 may participate in the pathogenesis of CD was explored. The results in this study demonstrated positive correlations of hsa_circRNA_103124 with WBC count and CALP level in patients with CD. Fecal CALP and WBC are useful biomarkers in the diagnosis of patients with CD . TherefoATG16L1 T300A variant results in defective autophagy-induction, which disrupts the homeostasis of the intestinal epithelium, leading to disordered inflammatory immune responses (GO and KEGG pathway analyses showed that AKT2 is a downstream target of hsa-miR-650. Hsa-miR-650 was reported to suppress proliferation, migration and invasion in individuals with rheumatoid arthritis by targeting AKT2 . One of esponses . TherefoIn our study, overexpression of hsa_circRNA_103124 in Caco2 cells and HIECs promoted cell proliferation. The rapamycin induced autophagy was inhibited by overexpression of hsa_circRNA_103124, as well. The expression of AKT2 or CDK2 was increased in cells with hsa_circRNA_103124 overexpressed, while the expression of TSC1 or LC3B was downregulated. Hsa-miR-650 mimics reversed the effects of hsa_circRNA_103124 overexpression. The results achieved with the hsa_circRNA_103124 siRNA and hsa-miR-650 inhibitor were consistent with these findings. Therefore, we inferred that hsa_circRNA_103124 acted as a ceRNA by targeting hsa-miR-650 to promote cell proliferation and inhibit autophagy. Autophagy inhibition in CD could induce disordered inflammatory immune responses, which may be the probable mechanism that hsa_circRNA_103124 participates in CD progression.In vivo studies with animal models should be carried out to further prove the role of hsa_circRNA_103124 in the mechanism of CD pathogenesis. Second, this research included only samples from patients with CD. Further research is needed to determine whether hsa_circRNA_103124 participates in the progression of UC, which is another common type of IBD. Third, the mechanism by which hsa_circRNA_103124 participates in clinical CD via autophagy requires in-depth investigation. Fourth, the Arraystar chip was applied in this study to discover differentially expressed circRNAs in patients with CD. Nonetheless, many less abundant circRNAs or circRNAs expressed at low levels may be missed due to the limitation of this method. Deep RNA sequencing may be an ideal method to capture maximum circRNAs, which will be conducted in our future research.However, several limitations exist in this study. First, our research was conducted only on clinical samples and cells. In conclusion, we first studied the specific mechanism by which hsa_circRNA_103124 participates in CD. Based on the research results from this study, hsa_circRNA_103124 inhibited rapamycin induced autophagy by targeting the AKT2/TSC1/LC3B pathway as a sponge of hsa-miR-650. The proliferation of HIECs and Caco2 cells was promoted by hsa_circRNA_103124 in a hsa-miR-650/AKT2/CDK2 dependent manner ."}
+{"text": "Colorectal cancer (CRC) mortality is principally due to metastatic disease, with the most frequent organ of metastasis being the liver. Biochemical and mechanical factors residing in the tumor microenvironment are considered to play a pivotal role in metastatic growth and response to therapy. However, it is difficult to study the tumor microenvironment systematically owing to a lack of fully controlled model systems that can be investigated in rigorous detail.in vivo\u2013mimicking conditions. They consist of tumor cells grown in various biochemical and biomechanical microenvironmental contexts. These contexts include varying oxygen and drug concentrations, and growth on conventional stiff plastic, softer matrices, and bioengineered acellular liver extracellular matrix. Growth rate analyses under these conditions were performed via the cell phenotype digitizer (CellPD).We present a quantitative imaging dataset of CRC cell growth dynamics influenced by in situ relevant environmental perturbations provides insights into critical tumor microenvironment features contributing to metastatic seeding and tumor growth. Such insights are essential to dynamical modeling and understanding the multicellular tumor-stroma dynamics that contribute to metastatic colonization. It also establishes a benchmark dataset for training and testing data-driven dynamical models of cancer cell lines and therapeutic response in a variety of microenvironmental conditions.Our data indicate that the growth of highly aggressive HCT116 cells is affected by oxygen, substrate stiffness, and liver extracellular matrix. In addition, hypoxia has a protective effect against oxaliplatin-induced cytotoxicity on plastic and liver extracellular matrix. This expansive dataset of CRC cell growth measurements under  Colorectal cancer (CRC) is the third most deadly cancer in both men and women in the United States . CurrentLiver is the most common organ of distant metastasis. More than 50% of CRC patients with advanced disease develop liver metastases (LM) . Many suAltered extracellular matrix (ECM) has also been considered a key feature of the TME . ECM phyin vitro [The TME is a highly complex system with many factors working in concert. However, traditional biological assays often only examine a single environmental factor in a qualitative way, and thereby lack the ability to recapitulate essential features of metastatic growth. Multicellular computational modeling can provide novel insights to link cancer progression to heterogeneous TME conditions and the dynamical interactions between tumor cells and the resident cell ecosystem , 29. Howin vitro . This plin vitro , 33. HerP = 0.008), which is not present in the less aggressive cell lines, HT29 and Caco2  in HCT116 cells  increased the relative growth rate of low-dose oxaliplatin-treated HT29 cells under 1% oxygen concentration compared to primary tumor-mimicking stiffness (0.2\u00a0kPa). Increased matrix stiffness has been shown to increase stemness characteristics and result in oxaliplatin resistance through Akt/mTOR pathway . mTOR caOur CRC imaging dataset has the potential for extensive reuse in multicellular systems biology. Converting quantitative measurements into cell phenotype parameters with CellPD facilitates data sharing and implementation into dynamical computational models. Several computational models have been developed to investigate the dynamics of more invasive phenotypes driven by oxygen-limited environments, as well as the feedback between multicellular cancer systems and the chemical/biophysical microenvironment . The impRecent work, using generic tumor cell phenotypic parameters, showed that relatively simple hypotheses on tumor-stromal mechanobiologic feedbacks can lead to complex emergent behaviors in LM including tumor dormancy . The datHigh-throughput quantitative imaging datasets may bridge the gap between traditional biology and computational modeling to enable a systematic investigation of multiple linked microenvironmental factors contributing to CRC metastatic growth and potential therapeutic strategies. The cell phenotype parameters generated from our HCS platform will help build experimentally driven computational models of metastatic colon cancer cell growth as a function of microenvironment conditions in the liver parenchyma. We can then use these models of metastatic tumor growth to probe the relationships between growth dynamics and heterogeneous microenvironments to facilitate a deeper understanding of complex metastatic processes, and to develop new hypotheses and possible therapeutic interventions. We also envision that multifactorial datasets (including this one) will serve as gold standard data to help drive refinements in dynamic simulation model calibration and validation protocols.2).The human colorectal cell lines HCT116 and HT29 were acquired from ATCC and cultured in McCoy's 5A medium supplemented with 10% fetal bovine serum  and 1% penicillin/streptomycin . Caco2 cells were acquired from ATCC and maintained in Eagle's Minimum Essential Medium supplemented with 10% fetal bovine serum and 1% penicillin/streptomycin. For live cell imaging, HCT116-H2BGFP and HT29-H2BGFP were created by transducing HCT116 and HT29 with LentiBrite Histone H2B-GFP lentivirus . A positive green fluorescent protein (GFP) cell population was selected by a fluorescence-activated cell sorter. Cell lines were authenticated by a professional authentication service (University of Arizona Genetic Core) and routinely tested for mycoplasma contamination using MycoAlert . Hypoxia experiments were carried out in a hypoxia workstation  with separate chambers that allow for precise control over oxygen culture conditions . Thirty minutes prior to imaging, cells were stained with 5\u00a0\u03bcg/mL of Hoechst 33342  and 5\u00a0\u03bcg/mL of propidium iodine  to determine live or dead cells, respectively. For live cell experiments, cells were seeded on 0.2\u00a0or 2\u00a0kPa softwell (Matrigen) or CellCarrier plates in the presence or absence of liver ECM disc. Images were taken on the Operetta HCS in confocal mode using the z-stack function. For all experiments, image analysis was performed using the Harmony 3.5.2 software . Cells were identified and segmented at the nuclear level to determine live and dead cell counts over time as described previously [End point growth rate experiments of HCT116, HT29, and Caco2 were carried out in 96-well CellCarrier plates  at an initial cell seeding of 1,500, 4,000, and 2,000 cells per well, respectively. One day after seeding, cells were treated with the indicated dilutions of oxaliplatin . At the stated time points, images were acquired on an Operetta HCS System (PerkinElmer No. HH12000000) equipped with environmental controls  filter followed by a median filter to find the main structure of the disc and then applied a series of morphological operations to include the small details and trim the noisy non-disc regions close to the borders. We also imaged empty wells to make a light profile for the images and then compared this profile with the images of the wells to add some candidate on-disc pixels before applying morphological operations. We ran the disc segmentation by sweeping over the parameters that control the segmentation and manually chose the best segmentation. The 3 main parameters used were (i) the kernel size for the STD filtering, (ii) the threshold for marking a pixel as a candidate on-disc pixel, and (iii) the size of structural elements used for morphological operations.Cell segmentation and the cell's center coordinates were acquired from the Harmony 3.5.2 software. The center of the cell was overlayed with the segmented mask region. By iterating over all the cells, we separated the cells on the basis of location on or off the disc.2 levels was tested using a 2-sided sign test across all same-experiment increasing O2 levels. This nonparametric procedure is insensitive to cross-experiment measurement variation, and uses only the order of O2 levels and not their specific values. The tests were sufficiently powered to detect instances where all data trended in a single direction at a significance level of P = 0.05 . This criterion was met only for HCT116. Sign tests were performed in R using the SIGN.test function in the BSDA package (v1.2.0).Figure 50 differences between hypoxia (1% or 0.1% O2) and normoxia (21% O2) using posterior estimates from an empirical Bayesian model (brms package v2.13.5 under R v4.0.2). The model included effects for each hypoxia comparison to normoxia for each cell line. Weakly informative priors were used both for IC50 differences (Gaussian) and noise level (Cauchy), each scaled loosely to the data. Reported credible intervals are symmetric 95% intervals of the IC50 difference posterior distributions.Figure Figure P-values are computed from 2-sided Welch t-tests.Figure GigaScience GigaDB database [The datasets underlying this article are available in thedatabase .50: half maximal inhibitory concentration; LM: liver metastasis; MMR: mismatch repair; OCT: optimal cutting temperature; PBS: phosphate-buffered saline; STD: standard deviation; TME: tumor microenvironment.95% CI: 95% credible interval; CRC: colorectal cancer; ECM: extracellular matrix; GFP: green fluorescent protein; HCS: high-content screening; ICThis work was supported by a National Institutes of Health/National Cancer Institute R01 Provocative Questions (PQ) Grant, CA180149 . Additional support was provided by the University of Southern California Medical Faculty Women's Association (awarded to S.M.M.).The authors declare that they have no competing interests.C.T.C. and R.L. conducted experiments and analyzed data. A.G. and P.M. wrote MATLAB script to co-register cell locations with disc. E.F.J. and P.M. wrote CellPD to calculate the cell growth rate. D.R. performed the statistical analyses. D.V., M.B., S.S., and A.A. generated liver ECM discs. C.T.C., D.B.A., D.R., P.M., and S.M.M. wrote the manuscript and conceptualized the framework for this research. All authors helped edit the manuscript.giab026_GIGA-D-20-00103_Original_SubmissionClick here for additional data file.giab026_GIGA-D-20-00103_Revision_1Click here for additional data file.giab026_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giab026_Reviewer_1_Report_Original_SubmissionChris Armit -- 4/17/2020 ReviewedClick here for additional data file.giab026_Reviewer_2_Report_Original_SubmissionRamya Sivakumar -- 6/16/2020 ReviewedClick here for additional data file.giab026_Reviewer_3_Report_Original_SubmissionJessica Bauer -- 6/22/2020 ReviewedClick here for additional data file.giab026_Reviewer_3_Report_Revision_1Jessica Bauer -- 12/10/2020 ReviewedClick here for additional data file.giab026_Supplemental_FileClick here for additional data file."}
+{"text": "Eriocheir sinensis genome, and the genes were classified into four subfamilies according to phylogenetic analysis. Gene expansions occurred among E. sinensis genome, and synteny analysis revealed that some DDE_Tnp_4 family genes were caused by tandem duplication. In addition, the expression profiles of DDE_Tnp_4 family genes in E. sinensis indicated that subfamily I and II genes were up-regulated in response to acute high salinity and air exposure stress. E. sinensis is a kind of economical crustacean with strong tolerance to environmental stress. We confirmed the expansion of DDE_Tnp_4 family genes in E. sinensis and speculated that this expansion is associated with strong tolerance of E. sinensis. This study sheds light on characterizations and expression profiles of DDE_Tnp_4 family genes in E. sinensis and provides an integrated framework for further investigation on environmental adaptive functions of DDE_Tnp_4 family in crustaceans.DDE transposase 4 (DDE_Tnp_4) family is a large endonuclease family involved in a wide variety of biological processes. However, little information is available about this family in crustaceans. In this study, we used HMMER to identify 39 DDE_Tnp_4 family genes in  Eriocheir sinensis, is one of the most commercially important crustaceans [E. sinensis is more complicated than other economic crabs due to a special breeding migration. Adult E. sinensis mostly live in freshwater areas far from estuaries [E. sinensis migrates downstream from fresh to brackish water, where they suffer complicated stress [E. sinensis is a euryhaline species and a strong osmoregulator as they can function equally well in freshwater or brackish environments [E. sinensis is a species capable of aerial respiration and can survive for extended periods of time, even days, without water [Scylla paramamosain and Portunus trituberculatus, which are also commercially important crustaceans in China, E. sinensis exhibits enhanced tolerance to abiotic factors, including high salinity and air exposure [E. sinensis is linked to genes participating in innate immune system and many other physiological activities [+-K+-ATPase, heat shock proteins (like Hsp70 and Hsp90), and superoxide dismutase (SOD) [The Chinese mitten crab, staceans . The lifstuaries . During d stress . E. sineronments . In addiut water . Compareexposure ,5,6,7. Etivities ,9, such se (SOD) ,11,12.Chlamys farreri, the expansive Cu/Zn SOD family genes are considered to assist in protecting the body against paralytic shellfish toxins (PSTs) [Daphnia pulex are thought to be critical in pollutant efflux and cell defense activities [E. sinensis is linked to some expansive genes in E. sinensis remains unknown.Furthermore, gene expansion has been associated with environmental adaptation . In Chlas (PSTs) . The exptivities . HoweverDDE superfamily is a huge endonuclease family characterized by the presence of three conserved acidic residues (Asp-Asp-Glu) in their RNase H\u2013like domain (RNH) active sites that can bind to magnesium ion and catalyze phosphodiester bond hydrolysis ,17. DDE Latimeria chalumnae genome, which benefits vertebrate invasion of terrestrial environment [Acyrthosiphon pisum, implying that these genes might have a significant role in the diversification of morphology and adaptations [DDE_Tnp_4 family genes have been reported to expand during the evolution of some species, providing them with some evolutionary advantages. One member of DDE_Tnp_4 family is costly and exhibits evolutionary conservation patterns in coelacanth, ironment . Severalptations . E. sinensis DDE_Tnp_4 family has expanded and whether this family is linked to stress tolerance of E. sinensis. We first identified and compared DDE_Tnp_4 family genes among different genomes in crustaceans and then investigated the localization of DDE_Tnp_4 family genes on E. sinensis chromosomes. Transcriptome data were employed to analyze the expression changes of E. sinensis DDE_Tnp_4 family genes under high salinity and air exposure stress. This study provides the basic information and clues for further investigation of the environmental adaptive function of DDE_Tnp_4 family.This study was conducted to identify whether E. sinensis, S. paramamosain, P. trituberculatus, and D. pulex, respectively. Meanwhile, the number of DDE_Tnp_4 family genes accounted for 0.1391%, 0.0872%, 0.0072%, and 0.0131% of whole-genome protein-coding genes in E. sinensis, S. paramamosain, P. trituberculatus, and D. pulex, respectively (Amino acid sequences can be found in E. sinensis branch expanded significantly (p = 1.97756 \u00d7 10\u22126) compared with Brachyura ancestor species  software was used to identify 15 conserved motifs among these ubfamily . Motif 4E. sinensis DDE_Tnp_4 family genes was analyzed. Among 39 identified genes, 43.59% (17) of genes did not have introns, 33.33% (13) owned just one intron, 20.51% (8) possessed two introns, and only 2.56% (1) had three introns  of genes lacked having introns, while 33.33% (13) of genes owned just one intron. This result is similar to the situation found in Danio rerio, where DDE_Tnp_4 family genes found in D. rerio lacked or possessed only one intron [DDE_Tnp_4 is a large family containing numerous members which function in angiogenesis, cell cycle regulation, and innate immunity ,19,20. Hechanism . Howeverechanism , these trpuratus . Therefoe intron .E. sinensis have expanded, the genomic information of E. sinensis was compared with three other crustaceans. It was discovered that E. sinensis had a greater number and ratio of DDE_Tnp_4 family genes than S. paramamosain, P. trituberculatus, and D. pulex. Furthermore, expansion and contraction analyses confirmed that DDE_Tnp_4 family in E. sinensis genome expanded compared with other Brachyura species. The above findings are consistent with those reported for L. chalumnae; the expansion of DDE_Tnp_4 family genes is observed, and this phenomenon is speculated to be linked to adaptation of aquatic organisms to terrestrial environment [A. pisum, and these expansive genes might have a significant role in the diversification of morphology and adaptations [Transposons are a significant basic source of gene expansion. During species evolution, transposons replicate, move, amplify, and accumulate in invaded genomes . Followiironment . Expansiptations .C. farreri have expanded significantly, which protects the body against the damaging effects from PSTs [D. pulex, and these genes are involved in pollutant efflux and cell defense activities [E. sinensis has complicated life history and high tolerance to environmental stress [E. sinensis is linked to expansive DDE_Tnp_4 family genes, we analyzed expression profiles of E. sinensis DDE_Tnp_4 family genes under acute high salinity and air exposure stress conditions. Most DDE_Tnp_4 family genes were generally up-regulated under acute high salinity and air exposure stress conditions. The gene expression of subfamily I under high salt stress conditions was largely up-regulated compared to the control group. The aforementioned findings indicated that DDE_Tnp_4 family might be involved in high salinity and air exposure stress. These results are consistent with those obtained in Marsupenaeus japonicus, where the expression of DDE_Tnp_4 family genes was significantly up-regulated when challenged with white spot syndrome virus (WSSV) [During gene expansion, the positive selection pressure promotes species to form more novel functional genes, which is more conducive for species to adapt to various biological or abiotic stresses . For insrom PSTs . There ativities . E. sinel stress . To veris (WSSV) .E. sinensis. In addition, E. sinensis could adapt to abiotic stresses by activating innate immune system and many other physiological activities [E. sinensis [M. japonicus [E. sinensis adaptation to environmental stress by regulating the transcription of target genes in immune system and physiological activities.According to the above results, it is speculated that expensive DDE_Tnp_4 family genes may be linked to environmental tolerance of tivities ,9. High sinensis ,11,12. Asinensis . For insaponicus . As a reE. sinensis genome and annotation data were obtained from our previous study (accession number: LQIF00000000). The genome data of S. paramamosain, P. trituberculatus, and D. pulex were downloaded from the National Center for Biotechnology Information (NCBI) database . To identify DDE_Tnp_4 family genes, Hidden Markov Model (HMM) profile of DDE_Tnp_4 family genes (accession number: PF13359) was downloaded from Pfam database [E. sinensis was scanned using hmmsearch from HMMER v3.3 suite [\u22124 [http://smart.embl.de/, accessed on 8 October 2021). DDE_Tnp_4 family genes of S. paramamosain, P. trituberculatus, and D. pulex were also identified using the same methods.The database , and a s.3 suite  using HMuite [\u22124  .OrthoFinder  was usedDatabase ,34. The Database   was used to display the phylogenetic tree generated from MEGA-X program.Multiple sequence alignments were performed using MUSCLE tool . A MaximE. sinensis genome annotations. Gene Location Visualize (Advanced) [E. sinensis chromosomes. Tandem genes in E. sinensis were identified as previously reported standards [Location and synteny information of DDE_Tnp_4 family genes were obtained from dvanced)  was usedtandards . Multiplhttp://meme-suite.org/tools/meme, accessed on 28 September 2021) was used to scan conserved motifs. Parameters in MEME were as follows: the number of motifs, 15; minimum width, 6; maximum width, 200; other parameters were left at their default values. The global perspective of motifs in each DDE_Tnp_4 family genes was conducted by HeatMap [MEME 5.1.1  (http:// HeatMap . The Gen HeatMap  was emplUnder stress conditions, the profiles of DDE_Tnp_4 family genes were analyzed using transcriptome data obtained from previous studies  and the"}
+{"text": "More and more studies have shown that circular RNAs (circRNAs) play a critical regulatory role in many cancers. However, the potential molecular mechanism of circRNAs in prostate cancer (PCa) remains largely unknown.Differentially expressed circRNAs were identified by RNA sequencing. The expression of hsa_circ_0003258 was evaluated using quantitative real-time PCR and RNA in situ hybridization. The impacts of hsa_circ_0003258 on the metastasis of PCa cells were investigated by a series of in vitro and in vivo assays. Lastly, the underlying mechanism of hsa_circ_0003258 was revealed by Western blot, biotin-labeled RNA pulldown, RNA immunoprecipitation, luciferase assays and rescue experiments., while knockdown of hsa_circ_0003258 exerts the opposite effect. Mechanistically, hsa_circ_0003258 could elevate the expression of Rho GTPase activating protein 5 (ARHGAP5) via sponging miR-653-5p. In addition, hsa_circ_0003258 physically binds to insulin like growth factor 2 mRNA binding protein 3 (IGF2BP3) in the cytoplasm and enhanced HDAC4 mRNA stability, in which it activates ERK signalling pathway, then triggers EMT programming and finally accelerates the metastasis of PCa.Increased expression of hsa_circ_0003258 was found in PCa tissues and was associated with advanced TNM stage and ISUP grade. Overexpression of hsa_circ_0003258 promoted PCa cell migration by inducing epithelial mesenchymal transformation (EMT) in vitro as well as tumor metastasis in vivoUpregulation of hsa_circ_0003258 drives tumor progression through both hsa_circ_0003258/miR-653-5p/ARHGAP5 axis and hsa_circ_0003258/IGF2BP3 /HDAC4 axis. Hsa_circ_0003258 may act as a promising biomarker for metastasis of PCa and an attractive target for PCa intervention.The online version contains supplementary material available at 10.1186/s12943-021-01480-x. Prostate cancer (PCa) is a commonly diagnosed cancer in men and the leading cause of cancer-related death in western countries . LocalizCircular RNAs (circRNAs) are single-stranded, covalently closed RNA molecules, formed by back-splicing of pre-mRNAs, and originally considered to be a by-product of splicing errors . HoweverIn our study, we found a circRNA, which is derived from exons 4 and 5 of the ZNF652 (circBase ID: hsa_circ_0003258), as a novel oncogene in PCa. We found that hsa_circ_0003258 was significantly up-regulated in PCa tissues, and associated with the metastasis of PCa cells. Mechanistically, hsa_circ_0003258 directly interacted with the RNA-binding protein IGF2BP3, and enhanced the stability of histone deacetylase 4 (HDAC4) mRNA, consequently resulting in the aggressive nature of PCa. Moreover, hsa_circ_0003258 may upregulate the expression of Rho GTPase activating protein 5 (ARHGAP5) to promote tumor progression by sponging miR-653-5p. This work indicates the essential roles of hsa_circ_0003258 in PCa progression.Plasma and tissue samples were obtained from 18 PCa patients of the Nanfang Hospital . Inclusion criteria: 1. Patients who were diagnosed as PCa by pathological diagnosis before operation; 2. Patients signed an informed consent form. Exclusion criteria: 1. Patients with second primary tumors, HIV or syphilis virus, severe liver, kidney or other systemic diseases, other malignant diseases; 2. Patients who received preoperative chemotherapy or radiotherapy before surgery.Nine healthy individuals who were in good health determined by physical examinations in the Nanfang Hospital were included as controls. The research protocol and the use of human tissues and plasma samples were approved by the Ethics Committee of Southern Medical University and all participants signed informed consents. A PCa tissue microarray (TMA) was purchased from Shanghai Outdo Biotech Co. Ltd. and contained both normal prostate tissues and PCa tissues along with each patient\u2019s age, clinical stage, Gleason score, and metastasis status and tumor node metastasis (TNM) classification, that were recorded and archived in the National Engineering Center for Biochip. The detailed clinic features of enrolled patients were summarized in Supplementary Table Sp values <\u20090.05.The circRNAs from the plasm of PCa patients and control individuals were collected for microarray analysis. Methods of extracting plasma RNA was based on the previous protocols . Sample -\u0394\u0394Ct method to calculate. Genomic DNA (gDNA) is extracted from cells by the Easy Pure Genomic DNA kit . All primers were obtained from Sangon Biotech and listed in Supplementary Table STRIzol reagent (Invitrogen) was used to extract RNA from PCa cells and tissues. TB-Green PCR Master Mix Kit (Takara) and PrimeScript RT reagent Kit (TaKaRa) were used in qRT-PCR. Data were normalized to Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) and used the 2-\u0394\u0394Ct method to calculate.Nuclear and cytoplasm of cells were separated by Nuclear and Cytoplasmic Extraction Reagents  Following the manufacturer\u2019s instructions. Finally, Data were normalized to GAPDH (cytoplasmic control) and U6 (nuclear control) and used the 2Total RNA (2\u2009\u03bcg) was incubated with or without 3\u2009U/mg of RNase R  for 15\u2009min at 37\u2009\u00b0C. The expression of hsa_circ_0003258 and other RNA were detected by qRT-PCR. PCa cells were treated with Actinomycin D or DMSO  to evaluate the stability of hsa_circ_0003258 and its linear gene ZNF652. The stability of RNA was detected by qRT-PCR.The location of hsa_circ_0003258 was detected by FISH assay using a Cy3-labeled probes.FISH kit  was used to examine the signals following the manufacturer\u2019s instruction. The Nikon AISi Laser Scanning Confocal Microscope  was used to visualize the images.2. Use Lipofectamine 3000  to transfect cells with designated nucleotides or plasmids according to the manufacturer\u2019s instructions. Small interfering RNAs (siRNAs) targeting hsa_circ_0003258, IGF2BP3, HDAC4, ARHGAP5 and negative control (NC) siRNA were provided by RiboBio Co. . All siRNA sequences were listed in Supplementary Table SPCa cell lines  and human epidermal cell (RWPE-1) were purchased from the Stem Cell Bank, the Chinese Academy of Sciences. PCa cells lines were grown with RPMI-1640 medium  supplemented with 10% fetal bovine serum . RWPE-1 cells were cultured in Keratinocyte Serum Free Medium . Cells were incubated at 37\u2009\u00b0C in 5% CORNA-seq (RNA sequencing) between the control DU145-NC and sh-has_circ_0003258 cell was performed as previously described [Western blot analysis was performed as previously described . In brie4) were mixed with serum-free medium and added into the upper chamber of the insert. Then, 800\u2009\u03bcl complete medium was added to the bottom chamber. The cells in the chamber were mixed in 4% paraformaldehyde for 10\u2009min and stained with Giemsa  at different time points. After that, cells in the upper chamber were removed and the number of cells on the bottom surface were observed under a microscope and counted using Image J software.Transwell  with a multipolar (8.0\u2009\u03bcm) polycarbonate membrane was utilized to conducted Cell migration experiments. Cells , and mixed with 4% paraformaldehyde for 30\u2009min. Then the cells were permeabilized with 0.1% TritonX-100. Subsequently, the cells were incubated with Tris-buffered saline containing 5% bovine serum albumin (BSA) for 30\u2009min. Afterwards, samples were incubated with antibodies specific for IGF2BP3  at 4\u2009\u00b0C overnight. Finally, coverslips were treated with the fluorescent secondary antibody Alexa Fluor 488-conjugated goat anti-rabbit IgG  (1250 dilution) and DAPI (300\u2009nmol/L) staining. The images were photographed under a Nikon AISi Laser Scanning Confocal Microscope .Dual-Luciferase Reporter Assay kit  was used to verify the relationships between hsa_circ_0003258 and miR-653-5p. Wild-type (WT) or mutant (MUT) 3\u2032-UTR of hsa_circ_0003258 were cloned into the firefly-tagged pGL3 promoter luciferase vector . Using Lipofectamine 3000 (Invitrogen) to transfect HEK-293 cells with miR-653-5p mimic hsa_circ_0003258 or its mutant plasmid following the manufacturer\u2019s instructions. Luciferase activities were detected by a dual luciferase assay system (GeneCopoeia) after 48\u2009h. The experiment was performed with three replicates.Biotin-labeled hsa_circ_0003258 and oligonucleotide probes  were mixed with streptavidin magnetic beads  in RIP buffer for 4\u2009h. Subsequently, the DU145 cell lysate was incubated with the probes complex for 12\u2009h at 4\u2009\u00b0C. After purification, enriched hsa_circ_0003258 and miRNAs were quantified by qRT-PCR. Meanwhile, the bound proteins were identified by Western blotting.RIP assay was performed by using EZ-Magna RIP\u2122 Kit  with antibodies specific for IGF2BP3 following the manufacturer\u2019s instructions. The immunoprecipitated RNAs were detected by qRT-PCR to measure the level of hsa_circ_0003258 and HDAC4. Total RNAs (input) and isotype antibody (IgG) were applied as controls.7 cells per mouse) transfected with NC or sh-has_circ_0003258 were injected through lateral tail vein of BALB/c nude male mice (n\u2009=\u20095 for each group). Lung tissues were collected and examined for metastasis. After 40\u2009days, the tumors in vivo were evaluated by fluorescence imaging using the IVIS . The presence of cancer cells was confirmed by H&E (hematoxylin and eosin) staining. At the same time, immunohistochemistry (IHC) staining was conducted using antibodies against ARHGAP5  and HDAC4 .All experiments\u2019 animal procedures were approved by the Animal Care Committee of Southern Medical University. For tumor metastatic studies in vivo, DU145 cells .Statistical analyses were performed using the SPSS software version 20.0  or GraphPad Prism 7.0 . All values are expressed as mean\u2009\u00b1\u2009standard deviation (SD). Differences in mean values between groups were analyzed using ANOVA and Student\u2019s t tests. The correlation between hsa_circ_0003258 expression and clinicopathological properties was analyzed using a \u03c7In order to investigate the role of circRNA in the development of PCa, four pairs of plasma samples  were used for circRNA microarray analysis to examine the expression of circRNAs in these samples. The result showed that 7131 circRNAs were found in the PCa group and normal group , we found that the genomic length of the hsa_circ_0003258 is 731\u2009bp and the spliced length is 261\u2009bp. We identified that has_circ_0003258 is derived from ZNF652, which is located on chromosome 17q21 from CircBase (http://www.circbase.org/). Then, we examined the structure of hsa_circ_0003258. Hsa_circ_0003258 was formed by the back-splicing of exon 4 and 5 of linear gene ZNF652  acquired from the UCSC genome database  with a score\u2009\u2265\u200990 predicted by the CircInteractome database Fig.\u00a0A and S2CAccording to the results, we further investigated the target genes of miR-653-5p that play critical roles in the metastasis of PCa. Firstly, we predicted the target in miRDB, DIANA-microT, Targetscan and Starbase. Secondly, we analyzed the differential gene expression in DU145 cells (hsa_circ_0003258-sh vs. hsa_circ_0003258-nc) by RNA-seq. According to the prediction of target genes and the results of RNA-seq, we found 8 differentially expressed genes   and RBPmap (http://rbpmap.technion.ac.il/) , and found that nucleotides at 0\u201380 had a high potential to bind with proteins  Fig.\u00a0A. Furtheins Fig. B. The poins Fig. A up. Meains Fig. A down. Wins Fig. F and S4Bins Fig. G. Since ins Fig. H, confirhttp://starbase.sysu.edu.cn/starbase2/) and published RBP CLIP-SEQ data sets using IGF2BP3 Enhanced-CLIP SEQ data. We found 16 mRNAs bound to IGF2BP3 . We screened the IGF2BP3-binding 3\u2032UTRs from the Starbase  sponge, interacting with RBPs, and even translating proteins. One of the most extensively studies is circRNA terminating the regulation of miRNA to its target gene by binding to the miRNA as a competing endogenous RNA (ceRNA) through the base complementary pairing principle. For instance, CircHIPK3 promotes colorectal cancer via miR-7 , circEPSPrevious studies have shown conflicting results that miR-653-5p may be involved in tumor progression as an oncogene or tumor suppressor. MiR-653-5p has been identified as an oncogene in PCa  and gastThe tertiary structure of circRNAs leads to higher protein adsorbing capacity than linear RNA sequences. Thus, the circRNA-interacting RBPs act as an important molecular mode of action in the occurrence, translation, transcription regulation and extracellular transport of target genes . For exaThe Ras-Mitogen-Activated Protein Kinase (MAPK) pathway that comprise of the extracellular signal-related kinase1/2 (ERK1/2), JNK and p38 MAPK which play key roles in regulating multiple cellular processes like cell proliferation, apoptosis and migration . ERK actHowever, it is interesting that hsa_circ_0003258 was downregulated in the plasma level, but our results demonstrate that hsa_circ_0003258 was significant up-regulated in PCa cells and tissues. Besides, hsa_circ_0003258 acted as a promising biomarker for metastasis of PCa. These contradictory results between cell and plasma levels suggested that there could be more molecular mechanisms for hsa_circ_0003258 in regulating PCa progression. Based upon, we assumed that hsa_circ_0003258 may be affected by the tumor micro-environment during the process of being released into the plasma, and may be taken up by cells in the micro-environment. As far as we know, plasma is such a mixture of the secreta of the whole-body cells. In addition, some studies focusing on the tumor micro-environment also confirm our hypothesis. For example, Loss of miR-203 promotes tumor growth in some tumors  but someCollectively, our findings suggest that hsa_circ_0003258 functions as a novel positive regulator for PCa metastasis through both hsa_circ_0003258/miR-653-5p/ARHGAP5 axis and hsa_circ_0003258/IGF2BP3/HDAC4 axis Fig.\u00a0. These fAdditional file\u00a01: Supplementary Figure 1. The\u00a0expression and function of hsa_circ_0003258 in PCa.\u00a0(A). The heatmap showing some of the differentially expressed circRNAs between normal and prostate cancer patient plasma samples. (B). The representative images of FISH analysis of hsa_circ_0003258 in normal and PCa tissues. (C). The protein coding potential of hsa_circ_0003258. (D). The melting curve of hsa_circ_0003258 amplified product using the divergent primers. (E-H). Transwell assay detecting the migratory capacity of PCa cells after transfecting with siRNAs or vector plasmid. (I-J). Colony formation assays and CCK8 assays were utilized to evaluate the proliferation of PCa cells after knockdown of hsa_circ_0003258 in DU145 and C4\u20132 cells. ns: Not Significant. Supplementary Figure 2. Hsa_circ_0003258/ miR-653-5p/Arhgap5 pathway in C4-2.\u00a0(A). The heatmap of differentially expressed mRNAs in DU145/sh-nc and DU145/sh- hsa_circ_0003258 cells. Each sample was mixed in three replicates. (B). Transwell assay detecting the migratory capacity of PCa cells after SCH772984 treatment for 48\u2009h in DU145 Lv- hsa_circ_0003258 cells. (C). The potential binding miRNAs predicted by CircInteractome database. (D). Lysates from C4\u20132 cells was subjected to biotinylated-hsa_circ_0003258 pull-down assay and the expression levels of hsa_circ_0003258 were measured by qRT-PCR. (E). The expression levels of the top three candidate miRNAs predicted by Circinteractome database quantified by qRT-PCR after biotinylated-hsa_circ_00032583 pull-down assay in C4\u20132 cells. (F). The relative expression of hsa_circ_0003258 detected by qRT-PCR after the transfection of miR-653-5p mimic or inhibitor. (G-H). The qRT-PCR and Western blot showing the ARHGAP5 mRNA and protein level change of the predicated targets after silencing or overexpression of hsa_circ_0003258. (I-J). The qRT-PCR and Western blot showing the ARHGAP5 mRNA and protein level change of the predicated targets after silencing or overexpression of miR-653-5p. ns: Not Significant; * P\u00a0<\u20090.05; ** P\u00a0<\u20090.01; *** P\u00a0<\u20090.001. Supplementary Figure\u00a03. MiR-653-5p and ARHGAP5\u00a0promote\u00a0metastasis of\u00a0PCa.\u00a0(A). Cell migration observed in transwell assays after overexpression or silencing of miR-653-5p in C4\u20132 cells. (B). Transwell assay was performed after transfection with indicated vectors, lv- hsa_circ_0003258, mimic-nc or mimic-miR-653-5p in C4\u20132. (C). Transwell assay detected the migratory capacity of C4\u20132 cells after silencing ARHGAP5. ns: Not Significant; ** P\u00a0<\u20090.01; *** P\u00a0<\u20090.001; **** P\u00a0<\u20090.0001. Supplementary Figure\u00a04. The RNA-binding protein IGF2BP3 binds to hsa_circ_0003258 in C4-2.\u00a0(A). UP Western blot analysis of IGF2BP3 levels in pulldown assays using a biotinylated antisense oligomer targeting the junction of hsa_circ_0003258 in C4\u20132. Down RIP assay showing the association of IGF2BP3 with Hsa_circ_0003258in C4\u20132. Relative enrichment representing RNA levels associated with IGF2BP3 relative to an input control. IgG antibody served as a control. (B). IF and FISH assay showing hsa_circ_0003258 colocalized with IGF2BP3 protein in the cytoplasm in C4\u20132. (C). The m6A modification site of hsa_circ_0003258 predicted by SRAMP website tools. ** P\u00a0<\u20090.01. Supplementary Figure\u00a05. Hsa_circ_0003258\u00a0binds to IGF2BP3\u00a0and enhances its interaction with HDAC4. (A-B). Flow chart illustrates the criteria of identifying of HDAC4 as the target of hsa_circ_0003258. (C). The qRT-PCR showing the mRNA level change of the predicated targets after silencing of hsa_circ_0003258 in C4\u20132 cells. (D). RIP assay showing the association of IGF2BP3 with HDAC4 in C4\u20132 cells. Relative enrichment representing RNA levels associated with HDAC4 compared to an input control. IgG antibody served as a control. ns: Not Significant; * P\u00a0<\u20090.05; ** P\u00a0<\u20090.01; ***P\u00a0<\u20090.001. Supplementary Figure\u00a06. The\u00a0expression and function of HDAC4 in PCa cells and the\u00a0images\u00a0of lung metastasesin mice.\u00a0(A). HDAC4 protein and mRNA expression in the normal prostate epithelial cell line (RWPE-1) and five PCa cell lines detected by Western blot and qRT-PCR. (B). Transwell assay detected the migratory capacity of C4\u20132 cells after silencing HDAC4. (C). Transwell assay detected the migratory capacity after silencing the two signaling pathways. (D). Images of lungs derived from DU145 cells transfected with nc and sh- hsa_circ_0003258. (E). The luciferase images and the gross observation of lung metastases in mice injected with DU145/sh-nc and DU145/sh-hsa_circ_0003258 cells (n\u00a0=\u20095). * P\u00a0<\u20090.05; ** P\u00a0<\u20090.01; *** P\u00a0<\u20090.001.Additional file 2: Supplementary Table S1. Oligos used in the study.Additional file 3: Supplementary Table S2.\u00a0The detailed clinicopathological data of enrolled patient."}
+{"text": "Quality control checks are the first step in RNA-Sequencing analysis, which enable the identification of common issues that occur in the sequenced reads. Checks for sequence quality, contamination, and complexity are commonplace, and allow users to implement steps downstream which can account for these issues. Strand-specificity of reads is frequently overlooked and is often unavailable even in published data, yet when unknown or incorrectly specified can have detrimental effects on the reproducibility and accuracy of downstream analyses.To address these issues, we developed how_are_we_stranded_here, a Python library that helps to quickly infer strandedness of paired-end RNA-Sequencing data. Testing on both simulated and real RNA-Sequencing reads showed that it correctly measures strandedness, and measures outside the normal range may indicate sample contamination.https://github.com/betsig/how_are_we_stranded_here.how_are_we_stranded_here is fast and user friendly, making it easy to implement in quality control pipelines prior to analysing RNA-Sequencing data. how_are_we_stranded_here is freely available at The online version contains supplementary material available at 10.1186/s12859-022-04572-7. Common sequencing design of RNA-Seq libraries are either paired-end, where fragments are sequenced from both the 3RNA-Sequencing (RNA-Seq) is the mentclass2pt{minimFurther, library preparation protocols for RNA-Seq can be stranded or unstranded. In unstranded libraries, no information is preserved about the original transcript orientation. In contrast, stranded protocols retain strand information by attaching adapters, or through chemical modification of RNA or the paired cDNA during library preparation . StrandeDownstream RNA-Seq processing pipelines often incorporate information about library design in the workflow, e.g., via a strand-specificity (or strandedness) parameter in RNA assembly and read counting tools. Incorrect use of this parameter can significantly impact the output of RNA-Seq analyses. For example, defining a stranded library as unstranded can result in over 10% false positives and over 6% false negatives in downstream differential expression results .RNA-Seq sample strandedness and direction of strandedness is not available as metadata for RNA-sequencing samples in repositories such as The European Nucleotide Archive (ENA) or Sequence Read Archive (SRA), and in the cases where there is a corresponding paper, is often not reported in the methods. From a randomised investigation of 50 ENA \u201cPAIRED END\u201d studies with an associated publication, we found only 56% have strandedness either explicitly stated or mentioned in the methods section for library preparation  and IRFhow_are_we_stranded_here is written in Python3 and runs a series of commands to determine read orientation. First, a kallisto  index ofWe first tested how_are_we_stranded_here on simulated samples across three species\u2014human (Homo sapiens), yeast (Saccharomyces cerevisiae), and thale cress . Using lower numbers of reads resulted in greater variation in percent stranded reads for samples with non-strand-specific reads  or less than 0.6 (unstranded), when removing data points where less than 10% of sampled reads aligned to the transcriptome Fig.\u00a0. ReporteWhile the majority of samples matched their reported strand-specificity, there were seven which did not  should be detailed in the methods of published RNA-Seq data, as this is vital to being able to reproduce results, we found that over a third of publications do not provide this detail.how_are_we_stranded_here allows users to easily find the correct strandedness parameter for RNA-Seq datasets which is crucial for reproducibility of published results.All read runs matching the rules library_strategy=\u201cRNA-Seq\u201d, library_source=\u201cTRANSCRIPTOMIC\u201d, and library_layout=\u201cPAIRED\u201d were retrieved from ENA . The runAll read runs matching the following rules were retrieved from ENA for each human , yeast  and thale cress : instrument_platform = \u201cILLUMINA\u201d, library_strategy=\u201cRNA-Seq\u201d, library_layout=\u201cPAIRED\u201d, library_source=\u201cTRANSCRIPTOMIC\u201d, library_selection=\u201ccDNA\u201d. These runs were then filtered for those sequenced on an Illumina HiSeq 2000, 2500, 3000, or 4000, between 10 and 30 samples per study, and randomly reordered. Studies were searched for any publications that were associated with the data, and only retained if strandedness was stated or able to be inferred by the library preparation methods (see above). The first 20 studies matching these requirements were used for analyses for each species.Reads were simulated using polyester . KallistFor each study the first three samples were taken to profile for strandedness. A kallisto  index waAll steps were performed on samples of 200,000 reads from each fastq file, to match the number of reads used for how_are_we_stranded_here. FastQC 0.11.5 (Babraham Bioinformatics) was used to assess the quality of reads, and Trimgalore! 0.6.0 (Babraham Bioinformatics) in paired end mode to quality trim reads and remove adapter sequences. We used FastQ Screen 0.14.0 (Babraham Bioinformatics) to screen for contaminants in trimmed fastq files, with the default databases downloaded by \u201cfastq_screen\u2013get_genomes\u201d. Reads pairs which both mapped only to the correct genome were extracted for each sample using the\u2014tag flag in FastQ Screen, and a custom R script (see Code availability). We then ran how_are_we_stranded_here on the trimmed, and the exclusively correctly-mapping fastq files. For genome alignment with STAR 2.7.0e , the firAdditional file 1. Figure S1: Strandedness proportions in simulated data. Figure S2: Reads mapping to intergenic regions in s. cerevisiae samples. Figure S3: Strandedness proportions are not altered by trimming or filtering readsAdditional file 2. Table S1: Library preparation and strand specificity reporting in paired-end RNA-Seq studies. Table S2: Differences in variation of reported how_are_we_stranded_here strand-specificity values using increasing numbers of reads. Table S3: Run times for how_are_we_stranded_here using the default 200,000 reads on a 2020 M1 Macbook Pro. Table S4: how_are_we_stranded_here results on RNA-Seq data. Table S5: List of RNA-Seq studies with strand specificity searched in methods. Table S6: Proportion of reads aligning to different genomes with fastqscreen"}
+{"text": "HemaSphere. 2020;4(3):e401), the authors wish to adjust the bendamustine dosing unit of measure in the methods section so as to keep the units consistent throughout the manuscript. The original unit of measure used in the methods section was mg/kg; this is now updated to show as mg/m2.Since the publication of the article entitled \u201cA Study of Safety and Efficacy of Nivolumab and Bendamustine (NB) in Patients With Relapsed/Refractory Hodgkin Lymphoma After Nivolumab Monotherapy Failure\u201d (https://journals.lww.com/hemasphere/Fulltext/2020/06000/A_Study_of_Safety_and_Efficacy_of_Nivolumab_and.13.aspxThe changes have been made online:"}
+{"text": "With the development of HiC technology, more and more HiC sequencing data have been produced. Although there are dozens of packages that can turn sequencing data into contact maps, there is no appropriate tool to query contact maps in order to extract biological information from HiC datasets.We present HiCmapTools, a tool for biologists to efficiently calculate and analyze HiC maps. The complete program provides multi-query modes and analysis tools. We have validated its utility on two real biological questions: TAD loop and TAD intra-density.R and is freely available at https://github.com/changlabtw/hicmaptools and documented at https://hicmaptools.readthedocs.io/.HiCmapTools supports seven access options so that biologists can quantify contact frequency of the interest sites. The tool has been implemented in C++\u2009and The online version contains supplementary material available at 10.1186/s12859-022-04589-y. HiCmapTools, which helps biologists efficiently query HiC maps and perform permutation tests. It supports seven query modes and attempts to cover the most frequent needs of biologists who use HiC to study chromatin contacts and their putative function.With the invention of the microscope, researchers gained a preliminary understanding of the chromosome's tertiary structure. However, it was difficult to gain a more global picture, that is, until the development of chromosome conformation capture 3C)  and its C  and it.hic format generated by Juicer [hic) or depends on the input file (bin-contact pair files). A query is binned into a corresponding key based on its position to facilitate efficient extraction of contact frequency via STL\u00a0hash operation (O(1) for lookup). Also, we measure the significance of the extracted frequencies using permutation tests which rank the frequency among random samples. The usage of the query mode and random test are explained below.HiCmapTools is implemented in C+\u2009+\u2009, which facilitates using common programming data structures and functions from the Standard Template Library\u00a0(STL). Users input HiC maps in either\u00a0y Juicer  or bin-cy Juicer , 12. Thehttps://hicmaptools.readthedocs.io/en/latest/format.html#query-file.bait: calculate average contacts from downstream to upstream (controlled by -ner_bin) of a position of interest (white rectangle). For example, biologists can measure the average contact frequency around a PRE binding site.local: list all contacts inside an interval (white cross). All contacts inside a gene body can be extracted by querying specific gene loci.loop: contact frequency between two ends of a loop. As an example query, biologists can test whether gene looping exists\u00a0 by calcuX and Y, white crosses). For instance, contact frequencies between a gene promoter and an enhancer are extracted by querying their positions.pair: contacts between a pair of regions . As an example, given the list of chromatin insulator sites, HiCmapTools calculates all pairwise contacts among these sites, such that users can check whether any pair of binding sites interact with each other.Drosophila Antp-C and the BX-C).submap: sub contact map of regions of interest. The HiC map is stored efficiently by keeping only selected regions . Then, we perform the same query mode for the alternative queries and calculate their contact frequencies. Finally, the query's contact frequency is evaluated as its ranking among the sampled frequencies.Here, one is interested in whether there is local contact enrichment around specific loci, such as the Ubx gene in the BX-C locus. Then, we perform a -bait query where a 30k map is used, up/downstream is 150kb (=\u200930k x 5bins) with 100 permutation tests.hicmaptools -in_map fly_30k.n_contact -in_bin fly_30k.cbins -bait Ubx.bed -near_bin 5 -random 100 -output Ubxt-bait.tsvUbxt-bait.tsv and Ubxt-bait_random_1.txt. The former contains the contact frequency of the query (for example \u201c7237.85\u201d) and the average of the sampled frequencies (for example \u201c6363.51\u201d) with comparison provided as ratio (for example \u201c1.14\u201d) and rank (for example \u201ctop 19%\u201d). The latter provides each sampled frequency, including that of the query (the second line), where the suffix \u201c_1\u201d indicates one query entry in the input file. Output details are available online at https://hicmaptools.readthedocs.io/en/latest/format.html#output.\u00a0We provide tools/visualPermutationTest.R, a R\u00a0script to visualize the query's output against the distribution of the random samples  \u201316. In Dprofiles . Althougprofiles , 18, fewprofiles . These lDrosophila\u00a0HiC data .We present a C+\u2009+\u2009package that provides an efficient way to query HiC maps. HiCmapTools supports seven access options so that biologists can quantify the contact frequency of the interest sites. Furthermore, the frequency probability is estimated based on a null hypothesis that shuffles the query position. Finally, the frequency is visualized as an output plot: a vertical line in the density plot of the random samples. The authors will continue to develop new functions for comparative HiCs to pursue HiC quantitative analysis.Project name: HiCmapToolshttps://github.com/changlabtw/hicmaptoolsProject home page: https://hicmaptools.readthedocs.io/Project document page: Operating system(s): platform-independentProgramming language: C+\u2009+\u2009and ROther requirements: noneLicense: GNU GPLAny restrictions to use by non-academics: license neededAdditional file 1. The list of epiTADs in bed format where the fourth column notes the epi-class: 1- active-red, 2- null-gray, 3-PcG-blue and 4-HP1-green.Additional file 2.Bash and R scripts for the experiment of \u201c3.1 TAD loop\u201d.Additional file 3.Bash and R scripts for the experiment of \u201c3.2 TAD intra-density\u201d."}
+{"text": "Circular RNAs are novel regulators in endometrial carcinoma. Hsa_circ_0039569 was reportedly upregulated in endometrial carcinoma; however, the functional roles and mechanisms of hsa_circ_0039569 need further investigation. Therefore, we used quantitative real-time PCR (qRT\u2013PCR) to determine the mRNA levels of hsa_circ_0039569, miR-197 and high mobility group protein A1 (HMGA1). The protein level of HMGA1 was determined by Western blot. Cell Counting Kit-8 and colony formation assays were used to assess cell proliferation. Cell migration was measured via wound healing and Transwell assays. Transwell assay was also performed to determine cell invasion ability. Direct binding of the indicated molecules were verified by RNA binding protein immunoprecipitation (RIP) assay and dual luciferase reporter assay. The results revealed that hsa_circ_0039569 and HMGA1 were elevated, while miR-197 was downregulated in endometrial carcinoma. Moreover, hsa_circ_0039569 was positively correlated with the expression of HMGA1 and was negatively correlated with the level of miR-197. In addition, hsa_circ_0039569 facilitated the proliferation, migration and invasion of endometrial carcinoma cells. The underlying mechanism is that hsa_circ_0039569 serves as a sponge of miR-197 to repress the inhibitory effect of miR-197 on HMGA1. Furthermore, the miR-197/HMGA1 axis was implicated in endometrial carcinoma progression accelerated by hsa_circ_0039569. Collectively, hsa_circ_0039569 may promote the development of endometrial carcinoma by serving as an endogenous sponge of miR-197, increasing HMGA1 expression and identifying a novel target for endometrial carcinoma treatment. Endometrial carcinoma is a common gynecological malignancy in women worldwide . Risk faCircular RNA (circRNA) is a type of new noncoding RNA with a covalently closed structure . CircRNAMiR-197 is a microRNA (miRNA) that is dysregulated in several types of cancers. In prostate cancer, miR-197 was downregulated, and overexpression of miR-197 suppressed cell proliferation via voltage-dependent anion channel 1 (VDAC1)/AKT/\u03b2-catenin signaling . In ovarin vitro. We demonstrated a novel miR-197/HMGA1 axis modulated by hsa_circ_0039569. Collectively, our findings might provide new insight into endometrial carcinoma treatment.In the present study, we hypothesized that hsa_circ_0039569 might serve as a sponge of miR-197 to facilitate the progression of endometrial carcinoma. The aim and goal of this study was to illustrate the oncogenic role and mechanism of hsa_circ_0039569 in endometrial carcinoma. Our data revealed that hsa_circ_0039569 was upregulated in endometrial carcinoma and that inhibition of hsa_circ_0039569 impaired the progression of endometrial carcinoma Endometrial carcinoma tissues and matched adjacent normal tissues were collected from 36 patients with a hysterectomy at Hunan Cancer Hospital from March 2018 to May 2020 as previously described [2 at 37\u00b0C as previously described [Endometrial carcinoma cell lines, including HEC-1-B, AN3-CA, KLE, HEC1-A, Ishikawa, and hEEC , were purchased from ATCC or Shanghai Cell Bank of the Chinese Academy . Cells were cultured in RPMI-1640  with 10% FBS  in 5% COSmall interfering RNA (siRNA) targeting hsa_circ_0039569  and HMGA1 (si-HMGA1: ACTGGAGAAGGAGGAAGAG), miR-197 inhibitor (GCUGGGUGGAGAAGGUGGUGAA), and their negative controls were synthesized by Guangzhou RiboBio (China). The overexpression vectors pcDNA-hsa_circ_0039569 (ov-circ) and pcDNA-HMGA1 (ov-HMGA1) were constructed by Shanghai GenePharma. HEC-1-B or Ishikawa cells were transfected with the above plasmid via Lipofectamine 3000 (Invitrogen) as previously reported . Transfe3/well) were cultured in 96-well plates. After transfection, 10\u00a0\u03bcL CCK8 reagent  was added to fresh medium and incubated with treated cells for 2\u00a0h. Then, absorbance was measured at 450\u00a0nm. For apoptosis detection, cells were obtained and stained with 5\u00a0\u03bcL PI and FITC-Annexin V (Thermo Fisher Scientific) for 15\u00a0min, and FACS was used to assess the apoptosis rate.Colony formation assays and CCK-8 tests were performed to assess cell proliferation according to a previous report . In the \u2212\u0394\u0394Ct method normalized to U6 or GAPDH. A Cytoplasmic & Nuclear RNA Purification Kit  was used for nucleo-cytoplasmic separation according to the manufacturer\u2019s instructions. The primer sequences used in this study were as follows: hsa_circ_0039569 forward: 5\u2019-AAAATAGTGCCCCTACGGCG-3\u2019, hsa_circ_0039569 reverse: 5\u2019-GGCAGACGGTAACGGACGTA-3\u2019; miR-197 forward: 5\u2019-GCCTTCACCACCTTCTCCA-3\u2019, miR-197 reverse: 5\u2019-CGGCCCAGTGTTCAGACTAC-3\u2019; HMGA1 forward: 5\u2019-CCTCCAAGCAGGAAAAGGAC-3\u2019, HMGA1 reverse: 5\u2019-CTTCCTGGAGTTGTGGTGGT-3\u2019; U6 forward: 5\u2019-CTCGCTTCGGCAGCACA-3\u2019, U6 reverse: 5\u2019-AACGCTTCACGAATTTGCGT-3\u2019; and GAPDH forward: 5\u2019-AGGTCGGAGTCAACGGATTT-3\u2019, GAPDH reverse: 5\u2019-TGACGGTGCCATGGAATTTG-3\u2019.qRT\u2013PCR was performed as previously described . Total RWestern blotting was performed as previously described . Tissue 5 cells/mL. Then, 700 mL medium with 10% FBS was added to the lower chambers, and 500\u00a0\u03bcL cell suspension was seeded in the upper Transwell chambers . For the invasion assay, Matrigel (Corning) was used to coat the Transwell chambers for 3\u00a0h before cells were seeded. After 48\u00a0h, in the upper chamber, cells were swabbed, and cells adhered to the lower chamber were fixed with 4% formaldehyde and stained with crystal violet. Finally, chambers were visualized under a microscope for cell counting.Transwell assays were performed as previously described . Cells wA wound healing assay was performed as previously described . After HRNA immunoprecipitation (RIP) assayRIP was performed as previously described . Cell lyhttps://circinteractome.nia.nih.gov/) and starBase (http://starbase.sysu.edu.cn/) analyses were performed to predict the potential binding sites for miR-197 in hsa_circ_0039569 and HMGA1, respectively. The sequences were cloned into the pGL3 vector  to construct pGL3-WT and pGL3-MUT for HMGA1 or hsa_circ_0039569. After cotransfection of the reporter plasmid and NC/miR-197 inhibitor or miR-NC/miR-197 mimics, the luciferase activity was examined using a Luciferase Reporter Gene Assay kit (Yeasen).A dual-luciferase reporter assay was performed as previously described . Circulat tests and one-way ANOVA were applied for significant differences. The correlation in the expression levels of hsa_circ_0039569, miR-197 and HMGA1 was reflected via Pearson\u2019s correlation coefficient test. P <\u00a00.05 was accepted as statistically significant.Data for statistical analysis were obtained from three independent experiments analyzed using GraphPad Prism 8.0  and are presented as the mean \u00b1 SD. Student\u2019s The present study illustrates the oncogenic role and mechanism of hsa_circ_0039569 in endometrial carcinoma, and the flow chart diagram of the design is illustrated in We first investigated the expression of hsa_circ_0039569 in endometrial carcinoma. As shown in To investigate the functional roles of hsa_circ_0039569 in endometrial carcinoma cells, we overexpressed hsa_circ_0039569 in HEC-1-B cells and knocked down hsa_circ_0039569 in Ishikawa cells. The efficiency of transfection was validated by qRT\u2013PCR. Overexpression of hsa_circ_0030569 (ov-circ) increased its level, and si-circ2 (siRNA targeting hsa_circ_0039569) showed a better knockdown effect in Ishikawa cells and was applied to the following experiments . As showWe next investigated whether hsa_circ_0039569 mediated the migration and invasion of endometrial carcinoma cells. As indicated by wound healing assay and Transwell assay, overexpression of hsa_circ_0039569 increased the migrative ability of HEC-1-B cells, while hsa_circ_0039569 suppression showed the opposite effect in Ishikawa cells . AdditioSubsequently, we explored the potential mechanism of hsa_circ_0039569 in modulating endometrial carcinoma. MiR-197 is downregulated in endometrial carcinoma . ConsistHMGA1 was confirmed as a potential prognostic factor in endometrial carcinoma . In our Hsa_circ_0039569 regulated the proliferation of endometrial carcinoma cells via the miR-197/HMGA1 axisFirst, we overexpressed HMGA1 in HEC-1-B cells and knocked down HMGA1 in Ishikawa cells . As showHsa_circ_0039569 modulated the migration and invasion of endometrial carcinoma cells via the miR-197/HMGA1 axisFinally, we detected whether hsa_circ_0039569 regulated migration and invasion via the miR-197/HMGA1 axis. The results showed that overexpression of hsa_circ_0039569 promoted the migration of HEC-1-B cells, while simultaneous overexpression of miR-197 or HMGA1 knockdown partially reversed the effect of ov-circ. Accordingly, knockdown of hsa_circ_0039569 in Ishikawa cells weakened the migrative ability of cells, while simultaneous knockdown of miR-197 or HMGA1 overexpression restored the migrative ability . Similarin vitro. Moreover, hsa_circ_0039569 promoted endometrial carcinoma progression by sponging miR-197 and thus upregulating the expression of HMGA1.Endometrial carcinoma is a group of epithelial malignancies occurring in the endometrium, most commonly in perimenopausal and postmenopausal women . EndometRecently, circRNAs were revealed as biomarkers and regulators in endometrial carcinoma. CircTNFRSF21 , circ_00CircRNAs can serve as competitive endogenous RNAs (ceRNAs) to sponge miRNAs and impair the suppressive function of miRNAs on downstream mRNAs . In our A previous study suggested that lower miR-197 was found in endometrial carcinoma and that miR-197 mediated the functional role of hsa_circ_0002577 . In thisin vitro studies. No in vivo animal study was performed to support the conclusion. In our further work, we should use in vivo tumorigenesis and metastasis models to validate our in vitro findings.The major limitation of our present work is that the findings were based on Taken together, these results demonstrated that hsa_circ_0039569 may play an oncogenic role in endometrial carcinoma progression by acting as a sponge of miR-197 and upregulating HMGA1 expression, which might support hsa_circ_0039569 as a potential therapeutic target for endometrial carcinoma."}
+{"text": "Barcode-based multiplexing methods can be used to increase throughput and reduce batch effects in large single-cell genomics studies. Despite advantages in flexibility of sample collection and scale, there are additional complications in the data deconvolution steps required to assign each cell to their originating samples.To meet computational needs for efficient sample deconvolution, we developed the tools BarCounter and BarMixer that compute barcode counts and deconvolute mixed single-cell data into sample-specific files, respectively. Together, these tools are implemented as the BarWare pipeline to support demultiplexing from large sequencing projects with many wells of hashed 10x\u2009Genomics scRNA-seq data.https://github.com/AllenInstitute/BarWare-pipeline.BarWare is a modular set of tools linked by shell scripting: BarCounter, a computationally efficient barcode sequence quantification tool implemented in C; and BarMixer, an R package for identification of barcoded populations, merging barcoded data from multiple wells, and quality-control reporting related to scRNA-seq data. These tools and a self-contained implementation of the pipeline are freely available for non-commercial use at The online version contains supplementary material available at 10.1186/s12859-022-04620-2. The use of single-cell genomics has rapidly expanded due to high throughput, widely used commercial technologies. Microfluidic droplet based platforms , 21 are With the advantages of Cell Hashing come additional complications related to data processing: samples are no longer directly associated with a single set of well indices and must be demultiplexed at two levels, both by well and by sample, for downstream analysis Fig.\u00a0B. PrevioWe show that BarCounter outperforms other Hash Tag Oligo (HTO) counting tools and demonstrate the BarWare Cell Hashing pipeline using a large benchmark dataset generated by progressive overloading of the 10x\u2009Chromium v3 3\u2032 RNA-seq assay. These capabilities, combined with an emphasis on automated quality control reporting, make BarWare a scalable, user-friendly, and comprehensive toolkit for Cell Hashing that can be efficiently applied to large-scale sequencing projects with many wells of 10x\u20093\u2032 RNA-seq data.We identified HTO counting as a significant bottleneck in the processing of Cell Hashing data. In particular we found that a popular and widely used tool, CITE-seq Count , scaled The BarMixer package includes tools to convert raw HTO counts from BarCounter into assignments of each cell to their sample of origin. BarMixer assigns barcodes as \u201csinglet\u201d, \u201cdoublet\u201d, \u201cmultiplet\u201d, or \u201cno hash\u201d based on dynamically determined UMI cutoffs specific to each hash sequence in each well. For each hashtag, a distribution of HTO counts across all cell barcodes is generated, and a cutoff value delineating positive and negative barcodes is assigned Fig.\u00a0\u201d. BarcodSample-specific datasets are prepared via BarMixer by performing three key steps. For each well, BarMixer annotates Cell Ranger filtered HDF5 files with QC characteristics and cell metadata. Then, BarMixer uses the sample assignments for each cell to split data into separate HDF5 files by sample. Finally, BarMixer merges data across all processed wells based on the sample assignments. This yields a separate, merged HDF5 file for each sample, a merged HDF5 file for all multiplets, and metric reports in JSON and HTML format. Reports include relevant sequencing QC metrics, alignment distributions by barcode category, UMI and gene count distributions by hashtag, and median count data by both sample and well.We evaluated the BarWare pipeline and related tools by conducting a progressive cell overloading experiment Fig.\u00a0. We usedWe compared BarCounter to other popular software tools for HTO counting, including CITE-seq-Count , Cell Ranger count (10x\u2009Genomics), and kallisto indexing and tag extraction (KITE) in both single and multithreaded modes . Some ofTo evaluate the accuracy of BarCounter compared to a method including UMI correction, we ran BarMixer (described below) with HTO counts from either BarCounter or CITE-seq count with UMI correction and compared overlap in barcode classification and sample identification. For each of the six mixed wells, over 99.8% of barcodes identified as singlets were identical between the two methods , lowest CPU usage, and lowest user (CPU) time. BarCounter was fastest in real  time across all comparisons with the exception of the 64,000 and 80,000 cell wells, in which eight-threaded KITE processing was 7% and 15% faster, respectively Fig.\u00a0D\u2013F. Due Based on these performance metrics, we estimate that data from an eight well experiment loading 16,000 cells and sequencing to a depth of 40M reads (~\u20092500 reads per cell in this experiment) per well could be processed in parallel on a modest 8 CPU, 20\u00a0GB RAM computer in less than five minutes. These results demonstrate that BarCounter is ideally suited for the parallel processing of large Cell Hashing datasets, including when well number, cell recovery, and sequencing depth are high.We developed a second tool to apply Cell Hashing to samples distributed across multiple wells Fig.\u00a0B. BarMixTo evaluate the fidelity of BarWare\u2019s sample assignments, we utilized the Barware pipeline to process a progressive cell overloading dataset, then performed analysis using Seurat v4  to confiFollowing simple QC filtering, we performed dimensionality reduction, clustering, and visualized the results using uniform manifold approximation and projection (UMAP). We observed that cells from all eight wells were mixed evenly, there was complete overlap between technical replicates, and that each sorted FACS population showed a high degree of separation from the others Fig.\u00a0C, E. We We also visualized cell type-specific marker genes on our UMAP and found highly specific gene expression patterns that support the labelled cell type identities. Expression of CD3D was restricted to the na\u00efve and memory T cell populations, CD14 and MS4A1 (CD20) expression identified classical monocytes and B cells, respectively in the non T cell population, and GNLY was specific to labelled NK and CD8 T cells , were cryopreserved using Cryostor10 , and stored in liquid nitrogen until use. PBMCs were thawed at 37\u00a0\u00b0C using AIM V medium .PBMCs were fluorescence activated cell sorted (FACS) into na\u00efve T-cells (CD45+ CD3+ CD45RA+ CD27+), memory T-cells  and a non-T-cell bulk population (CD45+ CD3\u2212). Briefly, cells were incubated with TruStain FcX  for 10\u00a0min on ice, followed by staining with antibodies . Briefly, one million cells of each population were resuspended in 100\u00a0\u03bcl of staining buffer: DPBS without calcium and magnesium (Corning 21-031-CM) supplemented with 2% w/v BSA . 10\u00a0\u03bcl TruStain FcX  was added and cells were incubated on ice for 10\u00a0min, after which they were stained with 0.5\u00a0\u03bcg of a TotalSeq-A hashing antibody  following the 10x\u2009Genomics User Guide (CG000183 Rev A), with the only modification being cell overloading. All libraries were sequenced on an Illumina NovaSeq S4 flowcell. Target read counts were 30,000 reads per cell for RNA libraries and 2,000 reads per cell for HTO libraries.Raw sequencing data was converted from BCL to FASTQ format using bcl2fastq2 . Gene expression data was processed using Cell Ranger count(10x\u2009Genomics v4.0.0) and aligned to the GRCh38 (hg38) reference genome (refdata-cellranger-atacGRCh38-1.1.0) with the option \u2013expect-cells set to 40,000 for all wells. After running Cell Ranger count, the BarMixer Rmarkdown notebook add_tenx_rna_metadata.Rmd was used to prepare Cell Ranger outputs for downstream analysis.https://www.gnu.org/software/time/) on a Google Cloud Platform Compute Engine VM Instance with 12 vCPUs (Intel Skylake or later) and 78\u00a0GB of RAM. A list of filtered cell barcodes provided by Cell Ranger count as \u201cbarcodes.tsv\u201d files were used as the barcode whitelist input to HTO counting software tools where necessary. BarCounter was run with default parameters. KITE , as well as without UMI correction by including the additional parameter \u2013no_umi_correction.Hashtag counting was profiled using the Linux \u201ctime -v\u201d command  was runab/kite, , https:/Cells were assigned to individual HTO-defined samples, doublet, multiplet, or no hash categories using a multi-step process contained in the BarMixer package for R, all of which are performed in sequence for a given well using the hto_processing.Rmd script provided in BarMixer. The matrix of HTO counts per cell barcode is read from BarCounter outputs, and cutoffs for positive or negative cell barcodes are defined for each HTO separately. Cutoffs are determined by removing all counts below 10. Then, a test of unimodality is performed using the modetest function from the multimode package for R . For each well, this script generates a separate HDF5 file for each sample per well. After performing this split step for each well in the experiment, a third script from BarMixer, merge_h5_by_hash.Rmd, assembles the HDF5 files for each sample across all wells into a single HDF5 output, and uses the combined information from these files to generate a comprehensive QC report for data from all wells. All steps for category assignment, splitting, and merging can be performed using wrapper script provided in the BarWare-pipeline repository, 02_run_BarMixer.sh, available at Merged HDF5 files from the final step of the BarWare pipeline were used as input and analyzed using Seurat (v4.0.3 ). SingleVisualization of HTO profiling results and gene expression data was performed using R v.3.6.3 and greater  in the RProject name: BarWare pipelineProject home page:https://github.com/AllenInstitute/BarWare-pipelineOperating system(s): UNIX/Linux operating systems.Programming language: C, R, and bashOther requirements: R v3.6.3 or higherLicense: Allen Institute Software License (modified 2-clause BSD license)Any restrictions to use by non-academics: redistribution and use for commercial purposes restricted without further permission.Additional file 1: Table S1. HTO category agreement across wells. Fraction of agreement of cell barcode assignment to each HTO category for each pooled sample well based on BarCounter and CITE-seq-Count processing. Well: Pooled sample well. Frac_singlet: Fraction of singlet calls that agree using BarCounter and CITE-seq-Count. Frac_doublet: Fraction of doublet calls that agree using BarCounter and CITE-seq-Count. Frac_multiplet: Fraction of multiplet calls that agree using BarCounter and CITE-seq-Count. Frac_no-hash: Fraction of no hash detected calls that agree using BarCounter and CITE-seq-Count.Additional file 2: Table S2. Barcode category assignment discrepancies. Counts and count-derived metrics obtained for each of the top two hashes are shown for each cell barcode assigned to the singlet category BarCounter results. but considered a doublet based on CITE-Seq-Count results. Well: Pooled sample well. Barcode: Cell barcode. BarCounter_1st: Counts for the highest-scoring hash based on BarCounter. BarCoutner_2nd: Counts for the second highest-scoring hash based on BarCounter. CITE_1st: Counts for the highest-scoring hash based on CITE-seq-Count. CITE_2nd: Counts for the second highest-scoring hash based on CITE-seq-Count. Change_1st: Difference in counts for the highest-scoring hash (BarCounter_1st\u2014CITE_1st). Change_2nd: Difference in counts for the second highest-scoring hash (BarCounter_2nd\u2014CITE-seq-Count_2nd). Prop_Change_1st: Difference in counts for the highest-scoring hash as a proportion of BarCounter counts (BarCounter_1st\u2014CITE_1st) / BarCounter_1st. Prop_Change_2nd: Difference in counts for the second highest-scoring hash as a proportion of BarCounter counts (BarCounter_2nd\u2014CITE_2nd) / BarCounter_2nd. Ratio_1st:2nd: Ratio of the highest-scoring BarCounter counts to the second high-scoring BarCounter counts (BarCounter_1st / BarCounter_2nd).Additional file 3: Table S3. Sequenced read count statistics. Library ID: Pooled library ID. # Cells: Number of cells loaded. Reads: Number of sequenced read trios for each library . Reads per cell: Mean number of sequenced reads per cell barcode.Additional file 4: Table S4. Benchmarking statistics for HTO counting methods. Tool: Software tool used for benchmarking. Well: Pooled sample well. Elapsed Time (h:mm:ss): Elapsed (clock) time passed to analyze each well. User Time (s): User Time elapsed to analyze each well. % CPU: Maximum CPU load during well analysis. Max Resident Set Size (KB): Maximum resident memory set size during well analysis. Output Size (bytes): Output file size after analysis.Additional file 5: Table S5. BarWare HTO category assignment counts. Well: Pooled sample well. Total Barcodes: Number of cell barcodes identified for each well. Singlet: Number of cell barcodes assigned to the singlet category. Doublets: Number of cell barcodes assigned to the doublet category. Multiplets: Number of cell barcodes assigned to the multiplet category. No Hash: Number of cell barcodes assigned to the no hash detected category.Additional file 6: Table S6. Antibodies used for Cell Hashing and FACS. Manufacturer: Reagent manufacturer. Catalog No.: Manufacturer catalog number. Conjugate: Moiety (fluorophore or oligonucleotides) conjugated to each antibody. Target: Antibody binding target(s). Clone: Antibody clone. Vol per M cells (\u00b5L): Volume of antibody added per million cells for staining."}
+{"text": "HemaSphere. 2020;4:e356), the authors requested a change to their selected Creative Commons (CC) license. The license previously chosen was CC BY-NC-ND, but is now CC BY to comply with funding mandates. This adjustment has been made and does not affect the outcome of this publication.Since the publication of the article entitled \u201cRapid Emergence of Chronic Lymphocytic Leukemia During JAK2 Inhibitor Therapy in a Patient With Myelofibrosis\u201d (https://journals.lww.com/hemasphere/Fulltext/2020/06000/Rapid_Emergence_of_Chronic_Lymphocytic_Leukemia.9.aspxThe change has been made online:"}
+{"text": "Klebsiella pneumoniae, Pseudomonas aeruginosa, Staphylococcus aureus, and Moraxella catarrhalis. The analytical performance of mRT-PCR against target pathogens was evaluated by the limit of detection (LOD), specificity, and repeatability. Two hundred and ten clinical specimens from pneumonia patients were processed using an automatic nucleic acid extraction system for the \u201crespiratory bacteria four\u201d (RB4) mRT-PCR assay, and the results were directly compared to references from bacterial culture and/or Sanger sequencing. The RB4 mRT-PCR assay detected all target pathogens from sputum specimens with a coefficient of variation ranging from 0.29 to 1.71 and conservative LOD of DNA corresponding to 5 \u00d7 102 copies/reaction. The concordance of the assay with reference-positive specimens was 100%, and additional bacterial infections were detected from reference-negative specimens. Overall, the RB4 mRT-PCR assay showed a more rapid turnaround time and higher performance that those of reference assays. The RB4 mRT-PCR assay is a high-throughput and reliable tool that assists decision-making assessment and outperforms other standard methods. This tool supports patient management by considerably reducing the inappropriate use of antibiotics.Classification of clinical symptoms and diagnostic microbiology are essential to effectively employ antimicrobial therapy for lower respiratory tract infections (LRTIs) in a timely manner. Empirical antibiotic treatment without microbial identification hinders the selective use of narrow-spectrum antibiotics and effective patient treatment. Thus, the development of rapid and accurate diagnostic procedures that can be readily adopted by the clinic is necessary to minimize non-essential or excessive use of antibiotics and accelerate patient recovery from LRTI-induced damage. We developed and validated a multiplex real-time polymerase chain reaction (mRT-PCR) assay with good analytical performance and high specificity to simultaneously detect four bacterial pathogens causing pneumonia:  According to the World Health Organization (WHO), lower respiratory tract infections (LRTIs) were the main cause of morbidity and mortality leading to 3 million deaths worldwide in 2016 ..27]. TheThe RB4 mRT-PCR assay can potentially serve as an improved decision-making tool during LRTI treatment. Faster and more accurate diagnosis of pathogens would promote the use of narrow- over broad-spectrum antibiotics and substantially reduce the inappropriate use of antibiotics.S1 TableThe RB4 mRT-PCR assays detected an additional 19 bacterial infections from reference-positive assays.(PDF)Click here for additional data file. 6 May 2021PONE-D-21-10845Development of a multiplex real-time PCR assay for the simultaneous detection of four bacterial pathogens causing pneumoniaPLOS ONEDear Dr. Yang,Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE\u2019s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.The reviewers have no significant comments on this work. Authors should look at the reviewers' comments and respond to these comments.plosone@plos.org. 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Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1:\u00a0YesReviewer #2:\u00a0Yes**********5. Review Comments to the AuthorReviewer #1:\u00a0The manuscript was well designed and has technical rigor. The study question was answered. The proposed technique may help the diagnosis of pneumonia and will prevent the indiscriminate use of antibiotics.Reviewer #2:\u00a0The new mPCRs are promising tools to help clinicians to make clinical decisions in antibiotic treatments. The authors presented a promising array which could be useful in a clinical setting. It would be extremely useful information (and significantly strengthens the utility of the assay) to know if the assay is able to differentiate antibiotic resistant strain of the different types of bacteria.1) Any analysis for hairpins etc for the PCR primers?2) Have you investigated your assay against antibiotic resistant strains?3) Have you investigated the antibiotic susceptibility data against the results from the assay?**********what does this mean?). If published, this will include your full peer review and any attached files.6. PLOS authors have the option to publish the peer review history of their article . The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data\u2014e.g. participant privacy or use of data from a third party\u2014those must be specified.Reviewer #1: YesReviewer #2: Yes________________________________________4. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: Yes________________________________________5. Review Comments to the AuthorReviewer #1: The manuscript was well designed and has technical rigor. The study question was answered. The proposed technique may help the diagnosis of pneumonia and will prevent the indiscriminate use of antibiotics.Reviewer #2: The new mPCRs are promising tools to help clinicians to make clinical decisions in antibiotic treatments. The authors presented a promising array which could be useful in a clinical setting. It would be extremely useful information (and significantly strengthens the utility of the assay) to know if the assay is able to differentiate antibiotic resistant strain of the different types of bacteria.1) Any analysis for hairpins etc for the PCR primers?A: We checked the secondary structures of oligomers (primers and probes) using the GeneRunner software. We tested all 15 oligomers used in this study, and five showed no secondary structures. However, the predicted secondary structures did not significantly affect our PCR-based methods using dual-priming oligonucleotides (DPO) because the 30-mer oligo alone, unlike the 50-mer oligo , was not stable enough to form secondary structures. The GC content (<65 %) and the avoidance of repetitive sequences met the oligo design criteria (#3).- References \u20131. Chun JY, Kim KJ, Hwang IT, Kim YJ, Lee DH, Lee IK, et al. Dual priming oligonucleotide system for the multiplex detection of respiratory viruses and SNP genotyping of CYP2C19 gene. Nucleic Acids Res. 2007;35(6):e40. Epub 2007/02/09. doi: 10.1093/nar/gkm051. PubMed PMID: 17287288; PubMed Central PMCID: PMCPMC1874606.2. Fredman D, Jobs M, Str\u00f6mqvist L, Brookes AJ. DFold: PCR design that minimizes secondary structure and optimizes downstream genotyping applications. Hum Mutat. 2004;24(1):1-8. doi: 10.1002/humu.20066. PMID: 15221783.3. Riet J, Ramos LRV, Lewis RV, Marins LF. Improving the PCR protocol to amplify a repetitive DNA sequence. Genet Mol Res. 2017;16(3). doi:10.4238/gmr16039796.2) Have you investigated your assay against antibiotic resistant strains?A: Yes, RB4 mRT-PCR assays worked for antibiotic-resistant pathogens. Fifty-eight samples used in this study were tested for antibiotic resistance as a part of the minimal inhibitory concentration (MIC) test procedure. <Data table>Pathogen Total samples (N) MIC test (%) Antibiotic-resistant (N)K. pneumoniae 66 20 (30.3%) 20P. aeruginosa 70 29 (41.4%) 27S. aureus 55 10 (18.2%) 10M. catarrhalis 18 0 (0%) 0 Data are representative of the total samples, MIC test, and antibiotic resistance for target pathogens in 58 samples.3) Have you investigated the antibiotic susceptibility data against the results from the assay?A: Yes. K. pneumoniae, P. aeruginosa, and S. aureus were analyzed by MIC tests for resistance to 37 antibiotics and classified into three categories: antibiotic-resistant, antibiotic-intermediate, or antibiotic-susceptible. We analyzed the association of antibiotic resistance with extended-spectrum beta-lactamase (ESBL) gene types in K. pneumoniae strains. We screened 17, 17, and 16 antibiotics for K. pneumoniae, P. aeruginosa, and S. aureus, respectively.<Data table>Antibiotic K. pneumoniae P. aeruginosa S. aureusAmikacin S S N/AAmoxicillin/Clavulanic acid R N/A N/AAmpicillin R N/A N/AAmpicillin/Sulbactam N/A R N/AAztreonam R S N/ACefazolin R N/A N/ACefepime R S N/ACefotaxime R R N/ACefoxitin S N/A N/ACeftazidime R S N/ACiprofloxacin R S RClindamycin N/A N/A RColistin N/A S N/AErtapenem S N/A N/AErythromycin N/A N/A REsbl + N/A N/AFusidic acid N/A N/A SGentamicin S S SImipenem S S N/ALevofloxacin N/A N/A N/ALinezolid N/A N/A SMeropenem N/A S N/AMikacin N/A N/A N/AMinocycline N/A R N/AOxacillin N/A N/A RPenicillin G N/A N/A RPiperacillin N/A I N/APiperacillin/Tazobactam R I N/AQuinupristin/Dalfopristin N/A N/A SRFP (Rifampicin) N/A N/A STeicoplanin N/A N/A STelithromycin N/A N/A RTetracycline N/A N/A STicarcillin/Clavulanic acid N/A S N/ATigecycline S R STobramycin N/A N/A N/ATrimethoprim/Sulfamethoxazole S R SVancomycin N/A N/A SData are representative of antibiotics for target pathogens in 58 samples. Abbreviations: R; resistant; I, intermediate; S, susceptible; N/A; not available.We hope you are satisfied with our answer.Thank you very much. ________________________________________6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.If you choose \u201cno\u201d, your identity will remain anonymous but your review may still be made public.Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.Reviewer #1: NoReviewer #2: No ________________________________________In compliance with data protection regulations, you may request that we remove your personal registration details at any time. (Remove my information/details). Please contact the publication office if you have any questions.AttachmentResponse_to_Reviewers.docxSubmitted filename: Click here for additional data file. 4 Jun 2021Development of a multiplex real-time PCR assay for the simultaneous detection of four bacterial pathogens causing pneumoniaPONE-D-21-10845R1Dear Dr. Yang,We\u2019re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you\u2019ll receive an e-mail detailing the required amendments. When these have been addressed, you\u2019ll receive a formal acceptance letter and your manuscript will be scheduled for publication.http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. 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For more information, please contact Kind regards,Ruslan Kalendar, PhDAcademic EditorPLOS ONE 9 Jun 2021PONE-D-21-10845R1 Development of a multiplex real-time PCR assay for the simultaneous detection of four bacterial pathogens causing pneumonia Dear Dr. Yang:I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. onepress@plos.org.If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact plosone@plos.org. If we can help with anything else, please email us at Thank you for submitting your work to PLOS ONE and supporting open access. 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+{"text": "High hsa_circ_0005986 expression was associated with improved survival and was an independent prognostic factor for overall  and progression-free  survival. Moreover, the circRNA\u2013miRNA\u2013mRNA network was constructed using RNA-seq/miRNA-seq data and clinical information from TCGA-LIHC dataset. Our findings indicate a promising role for hsa_circ_0005986 as a prognostic biomarker in patients with HCC.Circular RNAs (circRNAs) represent potential biomarkers because of their highly stable structure and robust expression pattern in clinical samples. The aim of this study was to evaluate the expression of a recently identified circRNA, hsa_circ_0005986; determine its clinical significance; and evaluate its potential as a biomarker of hepatocellular carcinoma (HCC). We evaluated hsa_circ_0005986 expression in 123 HCC tissue samples, its clinical significance, and its association with patients\u2019 clinicopathological characteristics and survival. Hsa_circ_0005986 expression was downregulated in HCC tissues. Low hsa_circ_0005986 expression was more common in tumors larger than 5\u00a0cm [odds ratio (OR), 3.19; 95% confidence interval (CI), 1.51\u20136.76;  In 2012, there were an estimated 14 million and 8 million new cancer cases and cancer-related deaths, respectively. This increased to more than 18 million and 9 million, respectively, in 2018, which is evidence of the rapidly increasing rates of cancer incidence and mortality3. In particular, liver cancer is a commonly diagnosed cancer worldwide and is the fourth leading cause of cancer-related mortality. Hepatocellular carcinoma (HCC) accounts for approximately 80% of primary liver cancers2 and is associated with poor patient prognosis. HCC originates primarily in chronically damaged liver . Frequent recurrence of HCC limits treatment options because of the underlying liver disease and impaired liver function.Cancer remains a public health concern worldwide. It represents a major economic and social burden as well as a significant cause of mortality4. Noncoding RNAs such as long noncoding RNAs, microRNAs (miRNAs), and circular RNAs (circRNAs) are some potential biomarkers that may have relevance to HCC.Because of the poor prognosis associated with HCC, the identification of biomarkers is essential for predicting patient prognosis and survival and tumor recurrence as well as for determining suitable treatment options. The recent development of high-throughput sequencing techniques and advances in bioinformatics has resulted in an increase in the number of candidate biomarkers5. In contrast to linear RNAs, circRNAs are formed through a back-splicing event, which occurs via the linkage of downstream 3\u2032 and upstream 5\u2032 splice sites to form covalent and canonical bonds6. Exons, introns, or both may serve as substrates for circRNA back-splicing. This produces four types of circRNAs: exonic (EcircRNAs), circular intronic (ciRNAs), exon\u2013intron (EIciRNAs), and tRNA intronic (tricRNAs)9. Most circRNAs are EcircRNAs. ciRNAs are localized abundantly in the nucleus and show minute enrichment for target miRNA sites. Importantly, the fact that ciRNA knockdown can lead to the downregulation of the expression of its corresponding parental gene suggests that ciRNAs are involved in positively modulating transcription catalyzed by RNA polymerase II9. EIciRNAs are RNA molecules in which the exons are separated by retained introns. The nuclear abundance of both ciRNAs and EIciRNAs suggests that they are involved in transcriptional and post-transcriptional events11. Pre-tRNA splicing into two parts by specific enzymes gives rise to tRNA and tricRNA\u2014a unique class of ciRNA12.Circular RNAs (circRNAs) are a class of highly stable, single-stranded RNAs that form a loop through covalent binding. They are synthesized either from coding or noncoding genomic regions. Whereas circRNAs are formally known to be noncoding, recent evidence indicates the existence of protein-coding circRNAsCircRNAs are present predominantly in the cytoplasm. They contain miRNA response elements (MREs) and serve as sponges for miRNAs, thereby downregulating their expression. This results in decreased miRNA-mediated mRNA degradation or translational repression.13, including cell aging14, tissue development16, and neurological disorders such as Alzheimer\u2019s disease17. Furthermore, circRNAs are expressed in various cancers, including glioblastoma multiforme18, colorectal20, breast22, gastric23, and bladder24 cancers as well as HCC26. circRNAs may serve as miRNA sponges27 and may be involved in epithelial\u2013mesenchymal transition (EMT)28 and development29 of various cancers. Collectively, these findings indicate that circRNAs play important roles in various cellular processes and may serve as clinical biomarkers.Although the exact function of circRNAs remains unclear, many studies have revealed their involvement in both physiological and pathological processesThe aim of this study was to evaluate the expression of a recently identified circRNA, hsa_circ_0005986, determine its clinical significance, and evaluate its potential as a biomarker for HCC.n\u2009=\u200936) as non-curative treatment. This study was conducted according to local ethical guidelines, in accordance with the Declaration of Helsinki.This study included 162 patients with HCC  or gadoxetic acid disodium-enhanced liver magnetic resonance imaging (MRI). We defined overall survival as the time between the date of initial HCC diagnosis and either the date of death from any cause or the date of last contact with the patient during follow-up examination. Progression-free survival was defined as the time between the initial date of HCC diagnosis and either the first event of recurrence or progression or until death from any cause. The recurrence of HCC was recognized if a tumor exceeded 1\u00a0cm and showed characteristic CT or MRI contrast enhancement in the arterial phase and washout in the venous or delayed phase. Response Evaluation Criteria in Solid Tumors (version 1.1) was used to evaluate tumor response. HCC specimens and adjacent non-tumor tissue specimens were immediately stored at 4\u00a0\u00b0C for 24\u00a0h in RNAlater reagent  and then stored at\u2009\u2212\u200980\u00a0\u00b0C. We recorded the patients\u2019 age and sex, number and size of tumor, presence of macrovascular invasion, tumor node metastasis (TNM) stage, Barcelona Clinic Liver Cancer (BCLC) stage, Child\u2013Turcotte\u2013Pugh (CTP) category of liver function, alpha-fetoprotein (AFP) level, and other pertinent laboratory data. Cancer staging was performed according to the criteria of the American Joint Committee on Cancer (8th edition) and also following BCLC staging criteria. The ethical committee of our institution  approved the study (#KNUH-2014-04-056-001), and all patients provided written informed consent prior to sample collection.QIAzol Lysis Reagent  was used to extract total RNAs from the frozen specimens according to the manufacturer\u2019s instructions. We used a NanoDrop 2000 spectrophotometer  to determine RNA concentration as well as its purity. A High-Capacity cDNA Reverse Transcription kit  was used to reverse transcribe cDNA according to the manufacturer\u2019s instructions.\u2212\u0394\u0394Ct method. All primers were synthesized by Bionics . In order to amplify only hsa_circ_0005986, but not linear form of RNA, the primers were designed by considering the backsplice junctions of circRNA  to perform qRT-PCR. The expression of hsa_circ_0005986 was normalized to that of glyceraldehyde 3-phosphate dehydrogenase (GAPDH) and then quantified using the 230 of hsa_circ_0005986 were expressed as specificity, sensitivity, and area under the receiver operating curve (AUC). To determine the predictors of survivals, univariate and multivariate analyses based on a Cox proportional hazards model were performed. p-values of\u2009<\u20090.05 were considered statistically significant. Values that were statistically significant in univariate analyses were included in multivariate analyses, with a p-value of\u2009<\u20090.1. We conducted all the analyses using SAS version 9.4 software , and GraphPad Prism 6 program for Windows  was used to generate figures.For descriptive statistics, categorical data were expressed as number (%) and numerical data as the mean and standard deviation for normally distributed data and as the median with interquartile range for non-normally distributed data. A paired t-test was used to analyse differences in hsa_circ_0005986 expression between HCC and adjacent non-tumor tissue. We used the chi-square or Fisher\u2019s exact probability test to compare clinicopathological characteristics between two groups with different hsa_circ_0005986 expressions. The Kaplan\u2013Meier method was used to generate survival curves, and the log-rank test was conducted to compare survival curves between groups. The prognostic performanceshttps://github.com/cpreid2/gdc-rnaseq-tool). Differentially expressed genes and miRNA  were analyzed using the DEseq233 R package (version 1.30.1) for further analysis. A co-expression network was constructed using a WGCNA package34 (version 1.70-3) in the R software (version 4.0.3). Potential target miRNAs of hsa_circ_0005986 were predicted via circBank35. The overlapping part of the target miRNAs from co-expressed miRNAs from WGCNA were selected. The potential target genes of the selected miRNA were predicted using mirDB36. The overlapping part of the target genes from co-expressed genes from WGCNA were selected. The circRNA\u2013miRNA\u2013mRNA network was visualized using Cytoscape (version 3.8.2 for Mac).Publicly available sequencing data  related to HCC were obtained from The Cancer Genome Atlas (TCGA) using gdc-rnaseq-tool  approved the study (#KNUH-2014-04-056-001), and all patients provided written informed consent prior to sample collection.Informed consent for publication was obtained from all participants.Table We found that the expression of hsa_circ_0005986 was significantly downregulated in HCC tissues compared with that in adjacent non-tumor tissues Fig.\u00a0. In addip\u2009=\u20090.002), advanced TNM stage , and higher BCLC stage .Table The overall survival of patients was significantly different according to hsa_circ_0005986 expression Fig.\u00a0A. The cup\u2009=\u20090.009) with a sensitivity of 54.7% and specificity of 71.2%. The AUC of hsa_circ_0005986 for predicting progression was 0.673  with a sensitivity of 53.7% and specificity of 80.5%.The AUC of hsa_circ_0005986 for predicting survival was 0.633 , large tumors , vessel invasion , AFP level\u2009>\u2009400\u00a0ng/mL , poor CTP class , and curative treatment .The univariate analysis of prognostic factors for overall survival in patients with HCC Table  demonstrp\u2009=\u20090.037), vessel invasion , AFP level\u2009>\u2009400\u00a0ng/mL , and curative treatment .Multivariate analysis identified the following independent prognostic factors for overall survival: high hsa_circ_0005986 expression , multiple tumors , tumor size\u2009<\u20095\u00a0cm , vessel invasion , AFP level\u2009>\u2009400\u00a0ng/mL , poor CTP class , and curative treatment .Univariate analysis of the factors associated with progression-free survival Table  revealedp\u2009=\u20090.017), vessel invasion , and AFP level\u2009>\u2009400\u00a0ng/mL .Multivariate analysis revealed the following independent prognostic factors for progression-free survival: high hsa_circ_0005986 expression , negative regulation of cell proliferation (p\u2009=\u20090.0044), skeletal system development (p\u2009=\u20090.0062), regulation of inflammatory response (p\u2009=\u20090.013), somatic stem cell maintenance (p\u2009=\u20090.014), and positive regulation of inflammatory response (p\u2009=\u20090.017), all of which were statistically significant.We obtained RNA-seq/miRNA-seq data and clinical information from the TCGA-LIHC dataset, including 425 RNA-seq and 425 miRNA-seq samples , and differentially expressed mRNAs and miRNAs were identified (9004 for mRNA and 310 for miRNA). To further investigate the target of hsa_circ_005986, circBank and WGCNA were performed. Two miRNAs were predicted as target of hsa_circ_005986. The target genes of hsa-mir-3677 and hsa-mir-188 were predicted using mirDB and WGCNA. Finally, we used 2 miRNAs and 52 genes to construct a circRNA\u2013miRNA\u2013mRNA network  as HCC diagnostic biomarkers has been suggested49. For example, circ-CDYL was upregulated in the early stages of HCC but showed a low AUC value, i.e., 0.6450, which was lower than those reported for circ_005075 and circ_0016788 49. Similarly, the development of prognostic biomarkers for HCC has progressed, and some candidates have been identified. Upregulated circ_001569, circ_0008450, and circ_0000267 expressions were associated with poor HCC prognosis suggesting that these circRNAs are independent prognostic markers for HCC53.CircRNAs have recently been shown to be potential biomarkers for many cancers, including HCC57. The majority of these processes are regulated by circRNAs via miRNA sponging. For instance, to facilitate tumorigenesis, hsa_circ_101280 serves as a sponge for miR-375 and upregulates JAK2 expression, thereby promoting the proliferation of HCC cells as well as suppressing tumor cell apoptosis54. In addition, the oncogenic role of circSLC3A2 was shown to be dependent on the regulation of PPM1F expression by sponging miR-490-3p56. Another study involving HCC cells revealed higher circASAP1 expression in cells with higher metastatic potential. In addition, circASAP1 was shown to regulate the miR-326/miR-532-5p-MAPK1 pathway, thereby promoting the proliferation of tumor cells in HCC as well as metastasis43. Conversely, circMTO1 and hsa_circ_0001445 were found to promote the expression of the tumor suppressor genes p2125 and TIMP357, respectively. These actions are based on the sponging of miR-925, miR-17-3p, and miR-181b-5p57.By regulating cell proliferation, migration, invasion, apoptosis, metastasis, and EMT, circRNAs play either an oncogenic or a suppressive role in the progression of HCC58. The authors also showed an association between hsa_circ_0005986 expression and patient data, which is consistent with our results, except for the correlation of family history with chronic hepatitis B. We did not find an association between hsa_circ_0005986 expression and chronic hepatitis B infection. Moreover, the previous study did not include survival or progression data from 81 patients with HCC. The study by Fu et al. focused on the mechanistic aspect of hsa_circ_0005986 to be considered as a biomarker. On the contrary, our study focused more on the validation of hsa_circ_0005986 as a prognostic biomarker, along with other clinical predictors, based on survival and progression data from a larger number of patients with HCC. Therefore, the difference between this study and the previous study is the validation of a potential prognostic biomarker, hsa_circ_0005986, in a larger HCC cohort, which might be the originality of our study.Our study revealed a clear relationship between the patients\u2019 characteristics and hsa_circ_0005986 expression. We showed that hsa_circ_0005986 exhibited reduced expression in HCC and demonstrated that it was associated with clinical and pathological characteristics of patients with HCC. To our knowledge, this is the first study to validate hsa_circ_0005986 as a prognostic biomarker in a HCC cohort by performing survival and regression analyses. In 2017, Fu et al. showed that hsa_circ_0005986 sponged miR-129-5p to regulate NOTCH1 expression in HCC. Downregulated hsa_circ_0005986 expression led to the liberation of miR-129-5p, leading to lower NOTCH1 expression. This was coupled with an accelerated G0/G1 to S phase transition to promote cell proliferation59. On the other hand, there might be a regulation network among immunodeficiency, a microbiome, and circRNA. Immunodeficiency can promote adaptive alterations of the host\u2013gut microbiome and affect cancer development and progression60.We analyzed the whole genome mRNA\u2013miRNA\u2013hsa_circ_0005986 and its interaction network for predicting the role of hsa_circ_0005986 in HCC. On gene ontology analysis, cell adhesion, inhibition of cell proliferation, and skeletal system development were all possibly related to the migration, proliferation, and invasion of HCC. In our study, hsa_circ_0005986 is downregulated in HCC compared with the background liver and also in the higher stages of HCC. These findings were compatible with the potential function of hsa_circ_0005986 as an inhibitor of proliferation. Regulation of the inflammatory response, promotion of the inflammatory response, and somatic stem cell population maintenance might be related to HCC development. Moreover, for predicting the potential mechanism of hsa_circ_0005986, RNA modification or a microbiome should be discussed. Aside from circRNAs relevant to HCC progression and metastasis, RNA modification has also become a popular topic in cancer. Specifically, N4-acetylcytidine modification in other highly stable RNAs, such as circRNA, can be a possible mechanism of aberrant RNA modifications in HCC, which has rarely been studied in the current literatureThis study has some limitations. First, we did not examine hsa_circ_0005986 expression at a mechanistic level but rather evaluated the association between its expression and clinical endpoints. Further investigation of the mechanism of action of hsa_circ_0005986 is essential. Second, considering the retrospective nature of this study, there may have been some selection bias, considering that patients with missing medical records were not included. We excluded 39 patients who were either lost to follow-up or were previously treated for HCC, which reduced the total number of patients available for analysis. A larger number of patients are needed to validate the associations with hsa_circ_0005986 expression. It is necessary to improve the performance of hsa_circ_0005986 predicting prognoses, specifically for survival and progression. Third, percutaneous needle biopsy performed to obtain the specimens may not adequately reflect tumor heterogeneity. Some pathological features that affect the survival of patients with HCC cannot be assessed using needle biopsies . This also reinforces the need to identify noninvasive biomarkers. Noninvasive diagnostic approaches such as serum or exosome collection are needed to validate whether hsa_circ_0005986 can be used as a prognostic biomarker in patients with HCC.63. In addition, it is important to develop effective individualized therapeutic strategies to help improve the outcomes of patients with HCC.In conclusion, our results showed the association between hsa_circ_0005986 expression and HCC proliferation and progression. Considering that hsa_circ_0005986 was shown to be a predictor of HCC progression and survival of patients with HCC, we believe that it has potential to become both a prognostic biomarker and a therapeutic target. However, additional studies are needed to clarify the mechanisms underlying the causal role of hsa_circ_0005986 in HCC progression under the Mendelian Randomization framework through integrating multi-omics datasetsSupplementary Information."}
+{"text": "CircRNAs are functional in cancer-related processes and are promising candidates for cancer prognostic biomarkers. The study aimed to evaluate the functional and clinical significance of has_circ_0001944 in colorectal cancer (CRC), including predictive value for overall survival (OS) and recurrence-free survival (RFS), and its effect on cell growth and metastasis.This retrospective study included 133 patients with CRC. The expression of has_circ_0001944 in tissues and cells was quantified by real-time quantitative reverse transcription PCR. Receiver operating characteristics and Kaplan\u2013Meier survival analysis were used to assess the significance of has_circ_0001944 as a prognostic marker, and its reliability was validated using multivariate regression analysis. Subsequently, XTT, transwell migration, and modified-transwell invasion assays were used to determine the behavior of the CRC cells in response to has_circ_0001944 inhibition.Results of the qRT-PCR showed upregulation of has_circ_0001944 in the CRC samples compared to the normal samples. High has_circ_0001944 expression indicated shorter OS and RFS, comes down to poor prognosis. Multivariate regression analysis showed that elevated has_circ_0001944 increased the risk of death or recurrence and is a valuable prognostic factor. Following the has_circ_0001944 inhibition, the proliferation, migration and invasion of the CRC cells were reduced. miR-548b-3p was target miRNA of has_circ_0001944.Up-regulation of has_circ_0001944 is associated with a poor prognosis of CRC. has_circ_0001944 downregulation can slow the progression of CRC partly by targeting miR-548b-3p.The online version contains supplementary material available at 10.1007/s12672-022-00485-2. Colorectal cancer (CRC), originating from the non-cancerous proliferation of mucosal epithelial cells, is one of the common malignant tumors in the digestive system . AccordiCircRNAs are closed circular non-coding RNAs that are ubiquitously present in eukaryotic cells . ConsideThe present study collected CRC tissues and cell lines to determine the expression level of hsa_circ_0001944. Then used a cutoff value from receiver operating characteristic (ROC), stratification between the high-expression group and the low-expression group was archived and Kaplan\u2013Meier curves were established to evaluate the association of hsa_circ_0001944 with overall survival (OS) and recurrence-free survival (RFS) in CRC patients. The cell experiments were used to determine the behavior of the CRC cells in response to has_circ_0001944 inhibition.This retrospective study recruited medical information and tissue samples (both cancerous and corresponding non-neoplastic tissues) from 133 confirmed histological-CRC patients who underwent enterotomy at The Fourth Affiliated Hospital of China Medical University between January 2012 and January 2015. The ethics committee of The Fourth Affiliated Hospital of China Medical University has approved this study, and all patients have signed the written formed consent about samples for scientific research. None of the patients had previously received any treatment related to cancer. Other exclusion criteria were rare and complex types of tumors including hereditary nonpolyposis colon cancer and familial adenomas. The pivotal clinicopathological data were retrieved and extracted from institutional medical records, electronic medical records systems or doctors\u2019 notes. The Chinese Protocol of Diagnosis and Treatment of Colorectal Cancer from Chinese Society of Clinical Oncology, which is based on the AJCC/UICC, was used for the CRC clinical tumor-node-metastasis (cTNM) staging classification and pathological evaluation  . All CRC2 for HCT 116; Ham's F-12K Medium \u2009+\u200910% FBS and 5% CO2 for LoVo; Leibovitz's L-15 Medium \u2009+\u200910% FBS  and 100% air for SW1116, and SW620; RPMI-1640 \u2009+\u200910% FBS  for FHC. The culture temperature was 37\u00a0\u2103.The human CRC cell lines HCT 116, LoVo, SW1116, and SW620 were acquired from Shanghai Cell bank of Chinese Academy of Sciences , apart from the human colon epithelial cell line FHC which were from Kunming Cell bank of Chinese Academy of Sciences . The cells were cultured in the respective recommended media and culture conditions: McCOY's 5A \u2009+\u200910% fetal bovine serum  and 5% COFor a transient transfection with the aim to reduce the hsa_circ_0001944 or miR-548b-3p expression, LoVo and SW620 cells were transfected with the siRNA specifically targeting hsa_circ_0001944 (si-circ_0001944), miR-548b-3p inhibitor (anti-miR-miR-548b-3p) or their negative control (si-ctr and anti-ctr), from wcgene biotech  using the Lipofectamine 2000 . The transfected cells were incubated under their respective growth conditions and the measurement of RNA expression after hsa_circ_0001944 inhibition was conducted by real-time quantitative reverse transcription PCR (qRT-PCR) after 48\u00a0h.\u2212\u0394\u0394Ct method.The total RNA from cells and the homogenate of tissue samples was isolated using Invitrogen TRIzol (USA). The circRNA was enrichened using RNase R . hsa_circ_0001944 and miR-548b-3p expression levels were evaluated using up to 1\u00a0mg of total RNA, first subjected to reverse transcription using the PrimeScript RT Master Mix  and then quantification using Bulge-Loop miRNA qRT-PCR Starter Kit  and the responding primers specific for hsa_circ_0001944 and miR-548b-3p and their respective housekeeping gene GAPDH and U6 on a LightCycler LC480 II . The fold changes of hsa_circ_0001944 and miR-548b-3p expression were calculated using 23 cells/well in 100\u00a0\u00b5L culture medium into 96-well microplate . The cell cultures were incubated for indicated periods  at 37\u00a0\u00b0C. At each time point, 50\u00a0\u00b5L XTT was added per well and the microplate was incubated for another 4\u00a0h at 37\u00a0\u00b0C. The spectrophotometrical absorbance was measured using a Spectra Max M2e  at 492 and 690\u00a0nm. The cell proliferation was reflected by (A492nm\u2013A690nm).The cell proliferation was quantified using the Cell Proliferation Kit II (XTT) . Briefly, transfected LoVo and SW620 cells were seeded at a concentration of 5\u2009\u00d7\u2009105) were collected and washed. 100 \u03bcL of 1\u2009\u00d7\u2009Binding Buffer was added into the cells, and gently made to be a single cell suspension. 5 \u03bcL Annexin V-FITC and 5 \u03bcL PI Staining Solution were added into the cell suspension and mixed gently. The incubation was conducted in the dark at room temperaturefor 10\u00a0min; After the addition of 400 \u03bcL 1\u2009\u00d7\u2009Binding Buffer, the samples were detected by flow cytometry within 1\u00a0h.The cell apoptosis was detected by Annexin V-FITC/PI Apoptosis Detection Kit . Briefly, transfected LoVo or SW620 cells . At the bottom chamber, a medium with 10% FBS was added. Migrated or invaded cells in the bottom chamber were stained by crystal violet after 24\u00a0h of culture. The number of migrated cells was counted using using a Leica DMI6000B epifluorescence system  at five random microscopic fields.Transwell assay and modified transwell assay with Matrigel-coated upper inserts were used to evaluate the cell migration and invasion. A total of 4\u2009\u00d7\u200910https://circinteractome.nia.nih.gov/). miR-548b-3p, a downregulated and proliferation-suppressive miRNA, was one of hsa_circ_0001944 targets  and an independent prognostic marker for RFS in CRC . Taken together these results suggest that hsa_circ_0001944 level is a predictive factor for OS and DFS in CRC.To uncover the potential associations between altered hsa_circ_0001944 expression and clinical prognosis in CRC, the expression data were subjected to the establishment of ROC curve   and derived from a long non-coding RNA region within the FIRRE locus. It has been verified to exist in a circular form and was mainly located in the cytoplasm . Our stuHsa_circ_0001944 plays a role in the regulation of multiple cellular processes. In bladder cancer, hsa_circ_0001944 promotes cell proliferation and invasion , 21. HsaWNT2 [WNT2. This finding expands the understanding of the circRNA/miRNA regulatory network in CRC and suggests the therapeutic potential of silencing has_circ_0001944 in CRC.CircRNAs have been found to have many functions, ranging from acting as miRNA sponges, interacting with proteins to regulate transcription to allowing translation . CircRNAWNT2 . AltogetIn conclusion, the present retrospective study shows that the upregulation of has_circ_0001944 in CRC was associated with unfavorable prognosis, OS and RFS, for patients. This suggests that has_circ_0001944 may serve as promising prognosis-predictive biomarkers. Has_circ_0001944 may promote CRC by sponging miR-548b-5p. This suggests the therapeutic potential of silencing has_circ_0001944 in CRC.Additional file 1: Figure S1. si-circ_0001944-2 decreased cell proliferation and, reduced the migrated and invaded cells, but induced the cell apoptosis. (A) Verification of the transfection. (B) (C) Cell proliferation was evaluated in LoVo and SW620 cells transfected with si-circ_0001944-2, using the negative siRNA as reference. (D) Numbers of migrating cells were determined using Transwell assay. (E) Numbers of invading cells were determined using Matrigel-modified Transwell assay. *P\u2009<\u20090.05, **P\u2009<\u20090.01."}
+{"text": "Acinetobacter baumannii OCU_Ac16a, a clinical isolate co-harbouring three acquired carbapenemase genes, blaNDM-1, blaTMB-1, and blaOXA-58, and assess the clinical significance of so-called multiple-carbapenemase producers.To characterize blaNDM-1, were isolated from sputum cultures of a patient at Osaka City University Hospital. We subjected these strains to whole-genome analysis, particularly focusing on the genetic context of each carbapenemase gene. The transmissibility and functionality of each carbapenemase gene were analysed by conjugation and transformation experiments and antimicrobial susceptibility tests.OCU_Ac16a and its close relative, OCU_Ac16b, which lacks the blaTMB-1 was located in a class 1 integron on the chromosome, whereas blaNDM-1 and blaOXA-58 were found on plasmids named pOCU_Ac16a_2 and pOCU_Ac16a_3, respectively. pOCU_Ac16a_2 (which exhibited highly efficient self-transmissibility) and pOCU_Ac16a_3 (which did not show transmissibility but could be introduced into another A. baumannii strain via electroporation) could both confer carbapenem resistance  on the recipient strain. The functionality of blaTMB-1 was evident from the high resistance of OCU_Ac16b to ceftazidime and cefepime , and the high resistance of OCU_Ac16a to cefiderocol (MIC 32 mg/L) could be explained by the additive effect of blaNDM-1 and blaTMB-1.Our data revealed the genomic organization of OCU_Ac16a and demonstrated that all the carbapenemase genes are functional, each contributing to the extremely high broad-spectrum resistance of OCU_Ac16a to \u03b2-lactams. As multiple-carbapenemase producers can be serious health threats as drug-resistant pathogens and disseminators of carbapenemase genes, close attention should be paid to their emergence. Acinetobacter baumannii, which has acquired clinically relevant AMR genes, such as carbapenemase genes, owing to the horizontal gene transfer of mobile genetic elements, such as plasmids.,Acinetobacter spp., the most common group of carbapenemases is Ambler\u2019s class D, which consists of enzymes referred to as oxacillinases (OXAs). In addition, Ambler\u2019s class B, consisting of metallo-\u03b2-lactamases [e.g. New Delhi metallo-\u03b2-lactamase (NDM) and Tripoli metallo-\u03b2-lactamase (TMB)],,The increase in antimicrobial-resistant (AMR) bacteria is posing a serious threat to human health worldwide. One such AMR bacterial species is ,Notably, over the past decade, a significant number of studies have reported the emergence of bacterial strains that simultaneously possess two different carbapenemase genes. While the emergence of triple-carbapenemase producers has also been reported,A. baumannii strain, OCU_Ac16a that was isolated from the intratracheal aspirate of a patient with oesophageal cancer at Osaka City University Hospital in Japan in 2015.blaNDM-1, blaTMB-1, and blaOXA-58, in addition to the intrinsic blaOXA-51-like and blaADC-25-like \u03b2-lactamase genes. It should be noted that two of these genes encode metallo-\u03b2-lactamases (NDM-1 and TMB-1). Post isolation of OCU_Ac16a, a possible variant of OCU_Ac16a (named OCU_Ac16b), co-harbouring blaTMB-1 and blaOXA-58, but not blaNDM-1, was isolated from the same patient. In this study, we aimed to elucidate their genomic organization and also assess the impact of each carbapenemase gene on carbapenem resistance in order to evaluate the clinical significance of the emerging strains called multiple-carbapenemase producers.We recently identified a carbapenem-resistant The study conformed to the principles of the Declaration of Helsinki and was approved by the Institutional Ethics Review Board . Informed consent was waived according to the ethical guidelines for human research in Japan.A. baumannii strains OCU_Ac16a and OCU_Ac16b were isolated from a patient with type 3 oesophageal cancer in the middle thoracic oesophagus. The patient underwent transthoracic oesophagectomy followed by gastric tube reconstruction. OCU_Ac16a was isolated from suctioned sputum culture on postoperative day (POD) 32, whereas OCU_Ac16b was isolated from sputum obtained by bronchoscopy on POD 35 (for more detail on the methods see JAC Online).The Antimicrobial susceptibility tests were conducted in accordance with the criteria specified by the Clinical and Laboratory Standards InstituteAP023077\u2013AP023080. Whole-genome shotgun assembly of the OCU_Ac16b genome has been deposited under accession numbers BLWH01000001\u2013BLWH01000743.Whole-genome sequencing of OCU_Ac16a and OCU_Ac16b were performed using the MiSeq system  as previously described.http://pubmlst.org/abaumannii/). Antimicrobial resistance genes were detected using ResFinder v3.2 (http://cge.cbs.dtu.dk/services/ResFinder/). Genetic elements related to plasmid mobility were detected using the web-based tool, oriTfinder.et al.rep gene sequence.MLST was performed using the Institut Pasteur MLST scheme  of rep-like gene. blaNDM-1 and blaOXA-58 were found to be located on pOCU_Ac16a_2 and pOCU_Ac16a_3, respectively, whereas blaTMB-1 was found to be located on the chromosome (aph(3\u2032)-VIa, which was located on pOCU_Ac16a_2. The OCU_Ac16b draft genome consisted of 743 contigs, and MLST analysis revealed that this isolate belonged to the same sequence type (ST412) as OCU_Ac16a, indicating their clonality. Additionally, a comparative analysis involving the genome sequences showed that OCU_Ac16b was nearly identical to OCU_Ac16a, except that it lacked the whole pOCU_Ac16a_2 plasmid.The complete genome of OCU_Ac16a consisted of a chromosome and three plasmids, named pOCU_Ac16a_1, pOCU_Ac16a_2, and pOCU_Ac16a_3, with characteristics as summarized in romosome . All theOCU_Ac16a was highly resistant to all the \u03b2-lactam antimicrobials tested, including cefiderocol, a novel siderophore cephalosporin . However, it was susceptible to gentamicin, amikacin, levofloxacin, colistin, minocycline, and tigecycline  . The antblaTMB-1 was found to be located in a class 1 integron and was similar to its counterpart in A. baumannii strain A1, which was the first Acinetobacter strain clinically isolated in Japan in 2009 reported to possess blaTMB-1.blaNDM-1 was found to be located in a cluster with high overall identity with species previously reported to carry this gene  (pOCU_Ac16a_1 and pOCU_Ac16a_2 were possibly self-transmissible given that they contained a set of mobilization elements. Our conjugation experiments further demonstrated the transmissibility of pOCU_Ac16a_2 . pOCU_Acmikacin) . NotablyblaOXA-58 in our conjugation experiments, we successfully transformed the plasmid into A. baumannii ATCC 19606T RFP50R via electroporation. The transformant showed significant resistance to piperacillin, imipenem, and meropenem   . These dA. baumannii to cefiderocol. This is a good example that explains how challenging it is to combat multiple-carbapenemase producers. Although several promising \u03b2-lactamase inhibitors with efficacy against carbapenemases have been or are being developed, including avibactam and vaborbactam, they are less promising as a weapon against bacteria such as OCU_Ac16a because there are only a few candidate compounds that can inhibit metallo-\u03b2-lactamases and no one compound can universally inhibit multiple classes of \u03b2-lactamases.We found it particularly interesting that OCU_Ac16a was resistant to cefiderocol despite the fact that oxacillinases and metallo-\u03b2-lactamases do not generally confer resistance to this drug.dlab191_Supplementary_DataClick here for additional data file."}
+{"text": "Cryptosporidium parvum is an apicomplexan parasite commonly found across many host species with a global infection prevalence in human populations of 7.6%. Understanding its diversity and genomic makeup can help in fighting established infections and prohibiting further transmission. The basis of every genomic study is a high-quality reference genome that has continuity and completeness, thus enabling comprehensive comparative studies.Cryptosporidium parvum. The assembly is based on Oxford Nanopore reads and was improved using Illumina reads for error correction. We also outline how to evaluate and choose from different assembly methods based on 2 main approaches that can be applied to other Cryptosporidium species. The assembly encompasses 8 chromosomes and includes 13 telomeres that were resolved. Overall, the assembly shows a high completion rate with 98.4% single-copy BUSCO genes.Here, we provide a highly accurate and complete reference genome of C. parvum subtype isolate provides the basis for subsequent comparative genomic studies across the Cryptosporidium clade. This will enable improved understanding of diversity, functional, and association studies.This high-quality reference genome of a zoonotic IIaA17G2R1  Cryptosporidium is an apicomplexan parasite of public health and veterinary significance with a recent analysis reporting a global infection prevalence of 7.6% ) with low error rates and is frequently used to improve reference genome assembly , thus en_015008)  comparis_015880] , Flye [F_017016] , Shasta _017016] , Falcon _017016] ) makes iC. parvum by using long-read sequencing on the ONT PromethION  supplemented with short-read data generated on NovaSeq 6000  for error correction  and obtained a total of \u223c480 Mb of sequence  [Using these short reads we ran a genome estimation using GenomeScope   to obtaiThe initial assembly was carried out with only the ONT reads using Canu  see Met. The larC. parvum genome using genome alignments and remapping of short reads.We also generated an assembly with Flye assembler  see Met. DespiteC. parvum genome reference [To validate our findings, we first aligned the Canu and Flye assemblies to the previously published eference  using nueference  v3.23).eference , which w.23.efereRRID:SCR_018171) alignment analysis indicates that the GCA_015245375.1 [C. parvum. Upon closer inspection small segments that aligned to other chromosomes were shown to be telomeric sequences. Thus, these segments did not indicate inaccurate alignments per se but highlighted their repetitive nature (see below for details on telomere reconstruction). However, when assessing the dot plot generated for the Flye-assembled genome , which was too small (\u223c62 kb)\u00a0to represent a chromosome. Thus, the missing two chromosomes were merged with other chromosomes within two contigs from Flye. When comparing both of our assemblies (Canu and Flye) to the previously established GCA_000165345.1, we saw large structural disagreements on both assembly comparisons  and the long-read assemblies. We mapped the reads and found structural variants (SVs) based on discordant paired-end reads (see Methods) . We idenC. parvum reference GCA_015245375.1 [The quality of the Canu-generated draft assembly was further improved by 2 rounds of assembly polishing using the short reads (see Methods). After the first round of polishing, the number of corrections were reduced to \u223c20 along the entire genome. The 8 largest contigs available in the final polished assemblies are aligned (see Methods) to the previously published 245375.1 . The aliTo further assess the completeness of our assembly, we used BUSCO  with theA further comparison with the previous reference genome (GCA_015245375.1)  revealedLast we used the Illumina data set to identify single-nucleotide variants (SNVs) with respect to the new assembly (GCA_019844115.1). C. parvum genome. The final assembled genome has been deposited at GenBank (accession GCA_019844115.1).Telomeric ends present on either end of each chromosome were identified in the Canu genome assembly (see Methods). To search for telomeres, we identified matching sequences of \u201cTTTAGG\u201d repeats  in our aCryptosporidium spp. are usually typed and characterized widely by using a small set of genetic markers including gp60, COWP, HSP70, and 18S [ and 18S . Most ofgp60 sequence from the present assembly was aligned with reference sequences retrieved from GenBank. Reference sequences selected for alignment consisted of multiple IIa (C. parvum) subtypes, including a IIaA17G2R1 reference (MK165989) corresponding to the sequenced C. parvum isolate in our study. ClustalW alignment was carried out using BioEdit V7.2.5  with no gaps or large mismatches. The assembled genome has 100% identity with the reference genome IIaA17G2R1, and the genetic markers were observed (see The rved see .Cryptosporidium spp. (IIaA17G2R1 and IIaA15G2R1), which might affect further comparison or association studies. In contrast, our study was able to boost the fidelity and robustness of the assembly by focusing on one subtype only, IIaA17G2R1, resulting in a better telomere-to-telomere assembly representation (GCA_019844115.1). Studies of Cryptosporidium spp.are based on genetic markers previously identified for some regions of chromosome 6 and are not able to provide a better understanding of the genetic variation and recombination occurring within the species. Thus, establishing stronger marker genes and perhaps enabling improved recovery of Cryptosporidium-specific sequencing reads by mapping to a high-resolution reference genome will enable better understanding of Cryptosporidium transmission.The present work highlights how next-generation sequencing, including third-generation long-read sequencing, can be used to generate a high-quality genome assembly complete with centromeric regions and numerous telomeres. The genome assembly generated provides a gapless reference compared to the previously published GCA_000165345.1  and exteC. parvum subtyping is based on tandem repeat analysis of gp60, a highly polymorphic gene that encodes for an immunodominant glycoprotein (15/40\u00a0kDa) located on the surface of sporozoites and merozoites of many Cryptosporidium species [C. parvum IOWA II belonging to subtype IIaA15G2R1 based on gp60 sequencing. This strain has now been replaced with a closely related local isolate belonging to the IIaA17G2R1 subtype. In our work, this isolate is referred to as C. parvum (GCA_019844115.1). It is unclear whether the IIaA17G2R1 evolved from IOWA II, possibly from recombination with another local isolate, or whether it represents a distinct isolate on its own. To our knowledge the assembly done here represents the first IIaA17G2R1 subtype isolate for which long-read sequencing has been performed. C. parvum isolates belonging to the IIaA17G2R1 subtype have been identified in farms in various regions of the world [A commonly used approach for  species . The prehe world , were thhe world , and arehe world ,39.Cryptosporidium spp., which are responsible for surmounting challenges from the host and are subject to spontaneous mutation rates [Cryptosporidium spp. will lead to a better understanding of the organism's adaptation to a variety of environmental and host settings.Published studies have shown the presence of contingency genes in on rates . The majon rates ,43. In tde novo assembly approaches here to obtain a better representation for Cryptosporidium spp. and demonstrated two methods for validating these two assemblies. First, we compared the assemblies from Flye and Canu to pre-existing assemblies from Cryptosporidium spp. from different subtypes and were able to identify certain structural differences. Furthermore, the detection of SVs proved helpful in deciding which assembly best represents the species at hand [Cryptosporidium spp. this was not necessary because the genome is of relatively small size (\u223c9 Mb) and encompasses eight chromosomes. The analysis of BUSCO is also an important indication of quality  but did not indicate incorrect rearrangements identified with the Flye assembly. These types of misassemblies can be readily identified only by comparing closely related reference genomes and/or orthologous data sets .We utilized two  at hand . This wa at hand . For CryCryptosporidium spp. assembly will be a helpful resource to advance the study of this important pathogen, further investigate its complexity during growth and development in vitro, and serve as a reference for the study of genetic diversity among different isolates. Furthermore, we hope that it also facilitates translational research that focuses on characterizing virulence, pathogenicity, and host specificity. In this way, new targets may be found leading to vaccines or effective antiparasitic agents to treat this important pathogen.The final Cryptosporidium parvum oocysts were obtained from Bunchgrass Farm in Deary, ID , and are propagated from IOWA-1 subtype IIaA15G2R1, which was recently replaced by a local isolate subtype IIaA17G2R1 [8) were washed in PBS and treated with diluted bleach for 10 minutes on ice to allow for sporozoite excystation. Parasites were pelleted, washed in PBS, and DNA was extracted using Ultrapure\u2122 phenol:chloroform:isoamyl alcohol  followed by ethanol precipitation. Glycoblue\u2122 co-precipitant  was used to facilitate visualization of DNA during extraction and purification steps.aA17G2R1 . PurifieNEBNext FFPE DNA Repair Mix was used to repair 620 ng\u00a0of genomic DNA, which was then followed by end-repair and dA-tailing with NEBNext Ultra II reagents. The dA-tailed insert molecules were further ligated with an ONT adaptor via ligation kit SQK-LSK110 . Purification of the library was carried out with AMPure XP beads , the final library of 281 ng\u00a0was loaded to 1 PromethION 24 flow cell , and the sequencing data were collected for 24 hours.DNA (100\u00a0ng) was sheared into fragments of \u223c300\u2013400\u00a0bp in a Covaris E210 system  followed by purification of the fragmented DNA using AMPure XP beads . DNA end repair, 3\u2032-adenylation, ligation to Illumina multiplexing dual-index adaptors, and ligation-mediated PCR (LM-PCR) were all completed using automated processes. The KAPA HiFi polymerase  was used for PCR amplification (10 cycles), which is known to amplify high-GC and low-AT rich regions at greater efficiency. A fragment analyzer  electrophoresis system was used for library quantification and size estimation. The libraries were 630\u00a0bp (including adaptor and barcode), on average. The library was pooled with other internal samples, with adjustment carried out to yield 3 Gb of data on a NovaSeq 6000 S4 flow cell.RRID:SCR_005491) (version 2.3.0) to generate a k-mer\u2013based histogram of our raw reads to estimate the genome size based on our short-read data. To obtain this we ran Jellyfish [k-mer size of 21) and haploid genome. GenomeScope provided the overall statistics across the short reads.We used Jellyfish\u00a0 -maxmatch -l 100 -c 500 [We aligned the assembly of Canu (version 2.0)  and Flye0 -c 500 . Next, t0 -c 500  (version0 -c 500  (0.7.17-0 -c 500  (v1.6.0)RRID:SCR_002105) [RRID:SCR_014731) [We used Canu  (v2.0) f_002105)  (v1.9) w_014731)  (v 1.24)We ran BUSCO  (v5.2.2)RRID:SCR_016368) [We used the sequence \u201cTTTAGGTTTAGGTTTAGG\u201d to identify telomeric sequences at the start and end of every contig from our assembly. To do so we used Bowtie    [gp60 gene sequence for tandem repeats to determine subtype designation was done following the methods of Alves et al. [Genetic marker gp60 was used to subtype the assembled genome against available GenBank reference genomes for _017277)  . All supporting data and materials are available in the\u00a0database .Supplemental Figure S1. Genomescope estimation of genome size.Supplemental Figure S2. ClustalW alignment of the gp60 coding sequence with the assembly.giac010_GIGA-D-21-00321_Original_SubmissionClick here for additional data file.giac010_GIGA-D-21-00321_Revision_1Click here for additional data file.giac010_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giac010_Reviewer_2_Report_Original_SubmissionMatthew Knox -- 11/2/2021 ReviewedClick here for additional data file.giac010_Reviewer_2_Report_Revision_1Matthew Knox -- 12/8/2021 ReviewedClick here for additional data file.giac010_Reviewer_3_Report_Original_SubmissionJuan Alzate -- 11/2/2021 ReviewedClick here for additional data file.giac010_Reviewer_3_Report_Revision_1Juan Alzate -- 12/10/2021 ReviewedClick here for additional data file.giac010_Supplemental_FileClick here for additional data file.bp: base pairs; BUSCO: Benchmarking Universal Single-Copy Orthologs; Gb: gigabase pairs; Mb: megabase pairs; NCBI: National Center for Biotechnology Information; ONT: Oxford Nanopore Technologies; PBS: phosphate-buffered saline; SNV: single-nucleotide variant; SV: structural variant.F.J.S. has presented at both ONT- and Pacific Biosciences\u2013sponsored conferences. The authors declare that they have no other competing interests.This work was supported by the National Institute of Allergy and Infectious Diseases (Grant No. 1U19AI144297).F.J.S. and V.K.M.: Conceptualization, Analysis, and Writing\u2014Original Draft PreparationC.L.C. and G.A.M.: Conceptualization and Writing\u2014Review & EditingP.C.O.: Conceptualization, Resources, and Writing\u2014Review & EditingH.D., Q.M., and D.M.M.: Conceptualization and Writing\u2014Review & EditingS.S., S.B., K.K., G.W., H.S., V.V., and Y.H.: Methodology and InvestigationM.C.R., K.L.H., and S.J.C.: ConceptualizationM.M. and M.M.: AnalysisR.A.G. and J.F.P.: Conceptualization, Funding Acquisition"}
+{"text": "Cupressaceae is the second largest family of coniferous trees (Coniferopsida) with important economic and ecological values. However, like other conifers, the members of Cupressaceae have extremely large genome (>\u20098 gigabytes), which limited the researches of these taxa. A high-quality transcriptome is an important resource for gene discovery and annotation for non-model organisms.Juniperus squamata, a tetraploid species which is widely distributed in Asian mountains, represents the largest genus, Juniperus, in Cupressaceae. Single-molecule real-time sequencing was used to obtain full-length transcriptome of Juniperus squamata. The full-length transcriptome was corrected with Illumina RNA-seq data from the same individual. A total of 47,860 non-redundant full-length transcripts, N50 of which was 2839, were obtained. A total of 57,393 simple sequence repeats were identified and 268,854 open reading frames were predicted for Juniperus squamata. A BLAST alignment against non-redundant protein database was conducted and 10,818 sequences were annotated in Gene Ontology database. InterPro analysis shows that 30,403 sequences have been functionally characterized against its member database. This data presents the first comprehensive transcriptome characterization of Juniperus species, and provides an important reference for researches on the genomics and evolutionary history of Cupressaceae plants and conifers in the future. Sequoiadendron gigantea, Pinus taeda L. and Picea abies [Compared with other plant groups, the genome analysis of coniferous species lags behind because of their larger genome , 2. At pea abies \u20135. WholeJuniperus squamata is an evergreen shrub of the family Cupressaceae reaching 1\u20133\u2009m tall, with brownish-gray bark [Juniperus squamata. Considering the importance of simple sequence repeats (SSRs) to plant population genetic analysis, we also developed SSRs for this species [Juniperus squamata can provide an important reference for its downstream analysis, such as genomic basis of environmental adaptation and genome evolution of Cupressaceae and even conifers.ray bark . It is fray bark . This te species , 9. To f species . To func species . The fulJuniperus squamata individual were collected from Kangding, Sichuan Province, China. For each tissue, the short paired reads were sequenced by Illumina platform. We also mixed the samples of each tissue and generated the long reads by the PacBio Sequel platform. Total RNA of the samples was isolated using the Plant RNA kit  and then treated with RNase-free DNase I (NEB) to remove DNA. RNA degradation and contamination were monitored on 1% agarose gels and RNA purity was checked using the NanoPhotometer\u00ae spectrophotometer . RNA concentration was measured using Qubit\u00ae RNA Assay Kit in Qubit\u00ae 2.0 Fluorometer . RNA integrity was assessed using the Bioanalyzer 2100 system . The Single-molecule real-time (SMRT) bell library was constructed with the Pacific Biosciences DNA Template Prep Kit 2.0 and SMRT sequencing was then performed on the Pacific Bioscience Sequel System. The sample used for Illumina sequencing was harvested using the same methods. The library was constructed using Illumina HiSeq X Ten. Adapter clipping and quality filtering of the Illumina raw reads was done using Trimmomatic version 0.36 [Fresh leaves, stems, and strobiles of one ion 0.36 . Based ohttps://www.pacb.com/support/softwaredownloads). Subread BAM files were generate from raw reads, parameters: -minLength 200, \u2212minReadScore 0.75. Circular consensus sequence (CCS) was generated from subread BAM files, parameters: -min_length 50, \u2212max_drop_fraction 0.8, \u2212no_polish TRUE, \u2212min_zscore \u2212\u20099999.0, \u2212min_passes 2, \u2212min_predicted_accuracy 0.8, \u2212max_length 15,000. CCS BAM files were output, which were then classified into Full-Length non-chimeric (FLNC) and non-full length (NFL) fasta files by examining the 5\u2032 and 3\u2032 adapters and the poly(A) tail. Iterative Clustering and Error Correction (ICE) algorithm was utilized to cluster FLNC fasta files to obtain cluster consensus. Quiver from SMRT link  were then utilized to polish cluster consensus sequence with NFL fasta files to obtain polished consensus sequence.The raw full-length transcriptome sequencing data of samples were processed using the SMRT link version 4.0 software  was employed to identify ORF within the transcripts of Juniperus squamata. The results of ORF prediction are shown in Data file\u00a03.MISA version 1.0 was employed to identify SSRs from final unique transcript isoforms of squamata (paramete\u2212\u20095 [https://www.python.org/) script was used to carry out GO annotation (available at https://github.com/shanzha09/GO-annotation.git). InterProScan version 5.52\u201386.0 was used to search the final isoforms against interPro database [DIAMOND version 2.0.9.147 was used to align the final unique transcript isoforms against non-redundant protein database with a significance threshold of E\u2009\u2264\u200910\u2212\u20095 . A custodatabase . The resThere is a shortcoming that we only collected one sample for single-molecule real-time sequencing of transcriptome."}
+{"text": "Coral reefs are the world\u2019s most diverse marine ecosystems that provide resources and services that benefit millions of people globally. Yet, coral reefs have recently experienced an increase in the frequency and intensity of thermal-stress events that are causing coral bleaching. Coral bleaching is a result of the breakdown of the symbiosis between corals and their symbiotic microalgae, causing the loss of pigments and symbionts, giving corals a pale, bleached appearance. Bleaching can be temporary or fatal for corals, depending on the species, the geographic location, historical conditions, and on local and regional influences. Indeed, marine heat waves are the greatest threat to corals worldwide. Here we compile a Global Coral-Bleaching Database (GCBD) that encompasses 34,846 coral bleaching records from 14,405 sites in 93 countries, from 1980\u20132020. The GCBD provides vital information on the presence or absence of coral bleaching along with site exposure, distance to land, mean turbidity, cyclone frequency, and a suite of sea-surface temperature metrics at the times of survey. Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.16958353 The symbionts photosynthesize and translocate photosynthates to the coral animals, and in return corals produce organic wastes upon which the symbionts thrive2. This mutually beneficial relationship between corals and their symbionts has allowed corals to thrive in shallow, tropical and subtropical localities and build coral reefs for millennia. Recently, however, this relationship has become dysfunctional during marine heat waves, when seawater temperatures are anomalously high4. This dysfunctionality leads to the paling of corals through loss of pigmentation or loss of symbionts \u2014 more commonly referred to as coral bleaching , which was terminated around 2010, and the second by Donner et al.10 who collated 7429 data records on coral bleaching. Here we follow the previous database conventions to present a Global Coral-Bleaching Database (GCBD), obtained from seven data sources that encompasses 34,846 coral bleaching records from 14,405 sites in 93 countries, over 40 years, from 1980\u20132020   is available as a Microsoft Access database file and as a SQLite database file, the latter of which is directly accessible through R12. Veron\u2019s ecoregions shapefiles were used to determine the ecoregion of each site13. The Coral Reef Temperature Anomaly Database (CoRTAD version 6), which is a collection of sea surface temperature variables, were extracted for each sampling event14. CoRTAD values were only extracted for a sampling event if the coral bleaching data had a clearly defined month and year \u2014 where sampling events were missing a date, the 15thday of the month was used. Cyclone frequency and turbidity data were added for each site15. For turbidity, we used a 4-km resolution data from NASA\u2019s  Earth Observing System Data and Information System (EOSDIS) Modis-Aqua satellite database. We acquired these data from mid-2002 through to December 2017 (https://oceandata.sci.gsfc.nasa.gov/MODIS-Aqua/Mapped/Monthly/4km/Kd_490/). Cyclone data were collected from International Best Track Archive for Climate Stewardship  as spatial points and imported into R11. These data were subset into storm categories based on wind speed, according to the Saffir\u2013Simpson scale15. A raster file for the spatial frequency of cyclones was made in Quantum Geographical Information Systems (QGIS) using the \u2018heatmap\u2019 function, with a radius matching the radius of damaging winds (>26\u2009ms\u22121) for each cyclone category. These radii followed Moyer et al.16 and considered 50\u2009yr of consistent sampling effort, between 1964 and 2014. Individual yearly raster files were summed to determine the number of cyclones per 9.2\u2009km cell for the 50-year period. A raster file for the frequency of cyclones was created by interpolating wind speeds across all storm tracks using the inverse distance weighted interpolation in QGIS15. The Atlantic and Gulf Rapid Reef Assessment (AGRRA)17 and the Florida Reef Resilience Program (FRRP)18 had bleaching codes that were presented by transect instead of by site; these data were averaged and presented here at the site level. We did not include coral cover estimates for AGGRA and FRRP because both sampling strategies were designed to estimate coral populations at regional scales and not specifically to examine coral cover on reefs. Average depths (m) were used for the Donner et al.10 data that had ranges in depth.If the site coordinates were not already in decimal degrees, they were converted to decimal degrees. The coordinates were entered into Google Earth and the location names, distance to land in meters, and exposure were determined for each site. Exposure was defined based on a site\u2019s potential exposure to predominate winds, swell, and fetch . Sampling points that fell on land or were >1\u2009km from any coral reef were removed. The Marine Ecoregions of the World (MEOW) shapefiles were used to determine the marine realm of each sitehttp://data.reefcheck.us/)19, (2) Donner et al.10, (3) McClanahan et al.20, (4) AGRRA (https://www.agrra.org)17, (5) FRRP: http://frrp.org/data/18, (6)\u00a0Safaieet\u00a0al.21, and (7) Kumagai et al.22  Reef Check Site Information et al.13.Ecoregion_Name: identification of the Ecoregions (150) as defined by Veron Country_Name: the country where sampling took place.State_Island_Province_Name: the state, territory  or island group  where sampling took place.City_Town_Name: the region, city, or nearest town, where sampling took place.Site_Name: the accepted name of the site or the name given by the team that sampled the reef.Distance_to_Shore: the distance (m) of the sampling site from the nearest land.Exposure: a site was considered exposed if it had >20\u2009km of fetch, if there were strong seasonal winds, or if the site faced the prevailing winds. Otherwise, the site was considered sheltered or \u2018sometimes\u2019. \u2018Sometimes\u2019 refers to a few sites with a >20\u2009km fetch through a narrow geographic window, and therefore we considered that the site was potentially exposed during cyclone seasons. We left the category \u2018sometimes\u2019 in the database because those sites were not clearly exposed sites, nor were they clearly sheltered sites, and future researchers may be interested in temporary exposure.Turbidity: kd490 with a 100-km buffer.Cyclone_Frequency: number of cyclone events from 1964 to 2014.Comments: comments of any issues with the site or additional information.Sample_Event_tbl)Sample Event Information (Site_ID: site ID field from Site_Info_tbl.Reef_ID: name of reef site that was adopted by sampling group (from ReefCheck).et al.)20.Quadrat_No: quadrat number (from McClanahan Date_Day: the date of the sampling event.Date_Month: the month of sampling event.Date_Year: the year of sampling event.Depth: depth (m) of sampling site. Comments: comments of any issue or additional information of sampling event.R_Scripts_tbl)R Code (Relevant_Papers_ID: relevant papers ID field from Relevant_Papers_tbl.Project name: name of project associated with R code.Paper_Title: title of paper where R code was published.Code_Name: name of R code file.Description: description of the R code.Data_Source: data source ID field from Data_Source_LUT.R_Code: attachment of R code file.URL: hyperlink to R code or link to github.Cover_tbl)Coral Cover Information Bleaching Information Environmental Parameter Information ] based on weekly SSTs for the study time frame, created using a harmonics approach.Temperature_ Kelvin: CoRTAD. SST in Kelvin.Temperature_Mean: CoRTAD. Mean SST in degrees Celsius.Temperature_Minimum: CoRTAD. Minimum SST in degrees Celsius.Temperature_Maximum: CoRTAD. Maximum SST in degrees Celsius.Temperature_Kelvin_Standard_Deviation: CoRTAD.\u00a0Standard deviation of SST in Kelvin.Windspeed: CoRTAD. meters per hour.SSTA: CoRTAD.  weekly SST minus weekly climatological SST.SSTA_Standard_Deviation: CoRTAD. The Standard Deviation of weekly SSTA in degrees Celsius over the entire period.SSTA_Mean: CoRTAD. The mean SSTA in degrees Celsius over the entire period.SSTA_Minimum: CoRTAD. The minimum SSTA in degrees Celsius over the entire period.SSTA_Maximum: CoRTAD. The maximum SSTA in degrees Celsius over the entire period.SSTA_Frequency: CoRTAD.  number of times over the previous 52 weeks that SSTA\u2009>\u2009\u2009=\u20091 degree Celsius.SSTA_Frequency_Standard_Deviation: CoRTAD. The standard deviation of SSTA Frequency in degrees Celsius over the entire time period of 40 years.SSTA_FrequencyMax: CoRTAD. The maximum SSTA Frequency in degrees Celsius over the entire time period.SSTA_FrequencyMean: CoRTAD. The mean SSTA Frequency in degrees Celsius over the entire time period of 40 years.SSTA_DHW: CoRTAD. (Sea Surface Temperature Degree Heating Weeks) sum of previous 12 weeks when SSTA\u2009>\u2009\u2009=\u20091 degree Celsius.SSTA_DHW_Standard_Deviation: CoRTAD. The standard deviation SSTA DHW in degrees Celsius over the entire period.SSTA_DHWMax: CoRTAD. The maximum SSTA DHW in degrees Celsius over the entire time period of 40 years.SSTA_DHWMean: CoRTAD. The mean SSTA DHW in degrees Celsius over the entire time period of 40\u00a0years.TSA: CoRTAD.  weekly SSTs minus the maximum of weekly climatological SSTs in degrees Celsius.TSA_Standard_Deviation: CoRTAD. The standard deviation of TSA in degrees Celsius over the entire time period of 40 years.TSA_Minimum: CoRTAD. The minimum TSA in degrees Celsius over the entire time period of 40 years.TSA_Maximum: CoRTAD. The maximum TSA in degrees Celsius over the entire time period of 40 years.TSA_Mean: CoRTAD. The mean TSA in degrees Celsius over the entire time period of 40 years.TSA_Frequency: CoRTAD.\u00a0The number of times over previous 52 weeks that TSA\u2009>\u2009\u2009=\u20091 degree Celsius.TSA_Frequency_Standard_Deviation: CoRTAD. The standard deviation of frequency of TSA in degrees Celsius over the entire time period of 40 years.TSA_FrequencyMax: CoRTAD. The maximum TSA frequency in degrees Celsius over the entire time period of 40 years.TSA_FrequencyMean: CoRTAD. The mean TSA frequency in degrees Celsius over the entire time period of 40 years.TSA_DHW: CoRTAD.  sum of previous 12 weeks when TSA\u2009>\u2009\u2009=\u20091 degree Celsius.TSA_DHW_Standard_Deviation: CoRTAD. The standard deviation of TSA DHW in degrees Celsius over the entire time period of 40 years.TSA_DHWMax: CoRTAD. The maximum TSA DHW in degrees Celsius over the entire time period of 40\u00a0years.TSA_DHWMean: CoRTAD. The mean TSA DHW in degrees Celsius over the entire time period of 40 years.Authors_LUT)Author Names Bleaching Level Information City, Town Names Country names (Country_Name: name of the country where sampling took place.Data_Source_LUT)Data Source Information Ecoregion Names Exposure Type (Exposure_Type: site exposure to fetch.Ocean_Name_LUT)Ocean Name Information Name of Realm State_Island_Province_Name_LUT)State, Island, Province Name  or island group (e.g. Hawaiian Islands) where sampling took place.Substrate_Type_LUT)Substrate Type (Substrate_Type: type of substrate from Reef Check data.Relevant_Papers_tbl)Relevant Publications Severity Index Code Bleaching Prevalence Code  they were erroneous , (ii) they occurred on land, or (iii) they were >1\u2009km from a coral reef.Environmental and site data were added to each site, which included reef site exposure, distance to land, mean turbidity, cyclone frequency, and a suite of sea-surface temperature metrics at the times of survey.The GCBD was curated by a Database Administrator (CK). No outside contributions are expected at this time. When coral bleaching datasets were added, there was a procedure to validate and standardize the site localities, including the following:"}
+{"text": "Circular RNAs (circRNAs) have been shown to play vital biological functions in various tumors, including prostate cancer (PCa). However, the roles of circRNAs in the metastasis of PCa remain unclear. In the present study, differentially expressed circRNAs associated with PCa metastasis were screened using high-throughput RNA sequencing, from which hsa_circ_0004296 was identified.Quantitative real-time PCR (qRT-PCR) was used to detect the expression of circ_0004296 in PCa tissues and adjacent normal tissues as well as in blood and urine. Gain and loss of function experiments were performed to investigate the function of circ_0004296 in PCa. Bioinformatics analyses, RNA pull-down assay, and mass spectrometry were conducted to identify RNA-binding proteins. RNA immunoprecipitation and RNA and protein nuclear-cytoplasmic fractionation were performed to investigate the underlying mechanism. A xenograft mouse model was used to analyze the effect of circ_0004296 on PCa growth and metastasis in vivo.The expression of circ_0004296 was decreased in PCa tissues, blood, and urine, which was negatively associated with metastasis. Furthermore, gain and loss of function experiments in vitro and in vivo showed that circ_0004296 inhibited the proliferation, migration, invasion, and epithelial-mesenchymal transition of PCa cells. Mechanistically, circ_0004296 regulated host gene ETS1 expression at the post-transcriptional level. EIF4A3 was identified and confirmed as the downstream binding protein of circ_0004296. EIF4A3 expression was significantly upregulated in PCa tissues and associated with PCa metastasis. Silencing EIF4A3 suppressed PCa cell proliferation, migration, invasion, and EMT.Circ_0004296 overexpression efficiently inhibited ETS1 mRNA nuclear export by promoting EIF4A3 retention in the nucleus, leading to the downregulation of ETS1 expression and suppression of PCa metastasis; thus, circ_0004296 might be a potential biomarker and therapeutic target for patients with PCa.The online version contains supplementary material available at 10.1186/s13046-021-02138-8. Prostate cancer (PCa) is the second most common malignancy in men and causes around 300,000 deaths worldwide annually . MetastaCircular RNAs (circRNAs) are a class of covalently closed circular noncoding RNAs without 5\u2032 and 3\u2032 ends . CircRNAThe EMT process has been found to play a crucial role in PCa metastasis, wherein epithelial cells acquire mesenchymal phenotype along with loss of intercellular adhesion and conversion to migratory and invasive cells . It is wIn the present study, we identified circ_0004296 by RNA sequencing and qPCR and evaluated its role in PCa. The expression of circ_0004296 was decreased in PCa tissues, blood, and urine, and it was negatively associated with metastasis. Furthermore, gain and loss of function experiments in vitro and in vivo showed that circ_0004296 inhibited the proliferation, migration, invasion, and EMT of PCa cells. Mechanistically, circ_0004296 was localized in the nucleus, interacted with the RNA-binding protein (RBP) EIF4A3 to prevent nuclear export of host gene ETS1 mRNA, and subsequently inhibited its expression. Our study implied that circ_0004296 may serve as a specific biomarker and therapeutic target for patients with metastatic PCa (mPCa).All patients provided written informed consent. The present study was approved by the Ethics Committee of Shanghai Tenth People\u2019s Hospital . Clinical pathological data were also collected through the hospital medical record system. Five pairs of PCa tissues and matched local metastatic lymph node tissues were obtained from radical resection of PCa and dissection of enlarged lymph nodes. Forty pairs of PCa tissues and matched adjacent normal tissues were obtained from radical resection of PCa. Forty-six urine specimens and 39 blood samples were obtained from patients with benign prostatic hyperplasia, localized PCa, and metastatic PCa. Tissue microarrays included 359 specimens from patients with PCa. These cases had corresponding follow-up information for an average of 32\u2009months. All cases were confirmed by clinical and pathological diagnosis.Five pairs of PCa tissues and matched local metastatic lymph node tissues were used for RNA-seq. Total RNA was isolated using the QubitRNA Assay Kit  following the manufacturer\u2019s protocol. RNA-seq and data analysis were performed by Oebiotech .PCa cell lines  and the normal human prostate epithelial cell line RWPE-1 were purchased from the cell library of Shanghai Chinese Academy of Sciences . PCa cell lines were cultured in RPMI-1640 medium, supplemented with 10% fetal bovine serum (FBS), penicillin (100\u2009U/ml), and streptomycin (100\u2009\u03bcg/ml). RWPE-1 cells were cultured in Defined Keratinocyte SFM (1X) medium .Total RNA for PCa tissues and cells were isolated using TRIzol as described previously . Total RPC3 and DU145 cells were transfected using Lipofectamine\u00ae 3000  following the manufacturer\u2019s instructions. Plasmid and lentivirus expression vector constructs for overexpressing circ_0004296 were designed and synthesized by Zuorun  (Supplementary Table Cell proliferation was assessed by the Cell Counting Kit-8 (CCK8) assay and expressed as colony formation as described previously . Three iCell migration was measured by the wound healing assay as described previously . Three iCell migration and invasion were assessed by the Transwell migration and invasion assay as described previously . Three iTotal protein was lysed from cells using radioimmunoprecipitation assay buffer . Protein quantification and western blotting assays were performed as described previously . The folProtein expression was evaluated by immunofluorescence staining (IF) of PCa tissues and cells. Briefly, 5-\u03bcm paraffin-embedded cross-sections of tissues or cells uniformly grown on slides were fixed with 4% paraformaldehyde, permeabilized with 0.1% Triton-100, and blocked with 5% bovine serum albumin (BSA). Subsequently, tissues or cells were incubated with anti-E-cadherin , anti-EIF4A3 , and anti-Vimentin  overnight at 4\u2009\u00b0C. Then, the cells were treated with the corresponding secondary antibody after washing for three times. DAPI (Beyotime) staining was used for nuclear localization. Images were captured with a confocal microscope .6 DU145-vector or DU145-circ_0004296 cells. Tumor size was measured using a vernier caliper twice a week. Tumor volume was calculated as follows: 0.5\u2009\u00d7\u2009length \u00d7 width2. Five weeks later, mice were sacrificed, and all dissected tumor xenografts were weighed and subjected to immunohistochemistry (IHC). IHC was performed as described previously [5 DU145-vector or DU145-circ_0004296 cells in 10\u2009\u03bcL of PBS, with six mice in each group. To develop a pulmonary metastatic model, 1\u2009\u00d7\u2009106 DU145-vector or DU145-circ_0004296 cells were injected into the tail vein of 8-week-old male nude mice, with six mice in each group. The intraprostatic and metastatic tumors were observed weekly using the in vivo imaging system (IVIS). Mice were sacrificed when the weight loss was \u226510%, and the lung metastatic foci were examined microscopically by H&E staining.All animal experiments were approved by the Animal Care and Use Committee of Shanghai Tenth People\u2019s Hospital of Tongji University . BABL/c male nude mice were housed under specific pathogen-free conditions. To develop a subcutaneous xenograft mouse model, twelve 4-week-old male nude mice were randomly divided into two groups, which were subcutaneously injected with 5\u2009\u00d7\u200910eviously . To deveNuclear and cytoplasmic RNA were extracted using the PARIS\u2122 kit  following the manufacturer\u2019s protocol. The qRT-PCR was used to detect RNA abundance in different cell fractions. The efficiency of nuclear and cytoplasmic RNA isolation was controlled by qRT-PCR using U6 and actin, respectively.Nuclear and cytoplasmic protein fractionation assays were performed with nuclear and cytoplasmic protein extraction kit (Beyotime) following the manufacturer\u2019s protocol. The purity of the subcellular fractions were detected using anti-GAPDH  and anti-Histone 3  antibodies by western blotting assay.RNA pull-down assay for circ_0004296 and circ_0004296-binding proteins was performed using RNA pull-down kit . The biotinylated circ_0004296 probe used to pull down for circ_0004296 and RBPs was synthesized by GenePharma . DU145 cells were harvested and lysed with lysis buffer. The probes were incubated with streptavidin-coated magnetic beads to generate probe-coupled magnetic beads. The cell extract (800\u2009\u03bcL) was incubated with magnetic beads coupled with the circ_0004296 probe or a negative control (NC) probe. Proteins pulled down by the probe were collected using a protein elution buffer. Mass spectrometry was used to identify differentially interacted proteins. Mass spectrometry and data analysis were performed by Wayenbio . The differential abundance of circ_0004296-binding proteins was confirmed by western blotting assay.RNA-FISH experiments were completed using a Ribo\u2122 FISH kit  following the manufacturer\u2019s instructions. The FISH probe of circ_0004296 was synthesized by GenePharma . DU145 cells were harvested and lysed with lysis buffer. The cell extracts were incubated with magnetic beads coated with anti-EIF4A3 antibodies  or control . Subsequently, total RNA was extracted from the magnetic beads. qRT-PCR was used to determine the presence of ETS mRNA and circ_0004296.The circRNA-binding proteins were predicted using the online program CircInteractome , Starbast-test was used to evaluate the difference between two groups, while ANOVA test was used for more than two groups. All experiments were conducted in triplicate. Linear correlations were analyzed by Pearson\u2019s correlation coefficient. The disease-free survival and biochemical recurrence-free survival was analyzed by Kaplan-Meier method and the log-rank test. P\u2009<\u20090.05 was considered to be statistically significant.Data were analyzed and plotted using SPSS 25.0  and GraphPad Prism 7 , which were shown as mean\u2009\u00b1\u2009standard deviation (SD). Student\u2019s P\u2009<\u20090.05) , circ_0004296 was reduced in PCa tissues  Fig.\u00a0A. Next, 05) Fig.\u00a0. We chos05) Fig.\u00a0A; compar05) Fig.\u00a0B-D; its ues Fig. C. Circ_0ues Fig. D and bloues Fig. E from paues Fig. D and E. ues Fig. F. Sangerues Fig. G. To furues Fig. H and I. To investigate the function of circ_0004296 in PCa cells, the recombinant pLenO-GTP-circ_0004296-overexpression plasmid was constructed. The integrity of the linear circ_0004296 RNA sequence carried on the plasmid was confirmed by northern blotting and Sanger sequencing Fig.\u00a0. OverexpIt is known that EMT plays a critical role in mediating the metastasis of human tumors, including PCa . Next, IWe also designed three siRNAs targeting the back-spliced junction (si-circ_0004296). The qRT-PCR results showed that siRNA#1 had the highest knockdown efficiency for circ_0004296 and had no effect on the linear mRNA level Figure A and B. p\u2009=\u20090.017, Fig. p\u2009=\u20090.011, Fig. p\u2009<\u20090.001, Figure First, the intracellular localization of circ_0004296 was detected in PCa cells by the RNA-FISH assay and the nuclear and cytoplasmic fractionation assay. The results showed the predominant nuclear distribution of circ_0004296 Fig.\u00a0A and B. Taken together, these findings demonstrated that circ_0004296 inhibited PCa cell proliferation, migration, invasion, and EMT through the EIF4A3/ETS1 axis.Furthermore, GSEA supported that the mRNA splicing and mRNA export functional signatures were significantly enriched for the high versus low EIF4A3 expression in TCGA databases Fig.\u00a0A; this fTo further examine the effect of circ_0004296 in vivo, we constructed luciferase-labeled stably DU145-overexpressing circ_0004296 and luciferase-labeled DU145-vector cells Figure I and J. The present study first demonstrated that circ_0004296 was downregulated in PCa tissues and cell lines. We also confirmed that circ_0004296 was stably downregulated in the blood plasma and urine of patients with metastatic PCa, which predicted distant metastasis. Further in vitro and in vivo experiments confirmed that circ_0004296 inhibited the proliferation, migration, invasion, and EMT of PCa cells. Mechanistically, we showed that circ_0004296 was mainly located in the nucleus, and it interacted with RBP EIF4A3 and regulated the host gene ETS1 expression to inhibit the function of PCa cells. Furthermore, circ_0004296 efficiently inhibited nuclear export of ETS1 mRNA by promoting EIF4A3 retention in the nucleus Fig.\u00a0. Taken thttp://www.exoRBase.org), is a repository of circular RNA (circRNA), long non-coding RNA (lncRNA) and messenger RNA (mRNA) derived from RNA-seq data analyses of human blood exosomes, indicating that circulating circRNAs may be diagnostic and prognostic biomarkers [Increasing evidence suggests that circRNAs are aberrantly expressed in multiple human tumors, and they play vital roles in tumor growth, tumor progression, metastasis, and drug resistance . Previouomarkers . CircSHKomarkers . Circ-CComarkers . In thisIt is known that EMT is important for the progression and metastasis of cancer cells. The primary PCa epithelial cells gain migratory and invasive properties by undergoing EMT . ImportaAlthough the role of circRNAs in cancers has been widely reported, the downstream regulatory mechanisms remain largely obscure. To date, most circRNAs have been reported to play an important role by acting as ceRNAs to inhibit miRNAs , 9. HoweThe upstream regulatory mechanisms of circRNA downregulation should thus be addressed in future research. The specific motif sites of EIF4A3 binding to circ_0004296 and ETS1 mRNA have not been elucidated and require further studies.Overall, the present study demonstrated that circ_0004296 was downregulated in PCa and inhibited cancer metastasis by suppressing EMT. We also confirmed circ_0004296 was stably downregulated in the blood plasma and urine of patients with PCa, thus indicating its potential for precision targeted treatment. Mechanistically, EIF4A3 protein mediated circ_0004296-induced inhibition of host gene ETS1 expression at the post-transcriptional level. Furthermore, our research revealed a novel role of the circRNA circ_0004296, wherein it directly interacted with EIF4A3, suppressed nuclear export of ETS1 mRNA, and subsequently suppressed EMT in PCa. Thus, the present study provides novel insights into the potential roles of the circ_0004296/EIF4A3/ETS1 axis in the therapeutic management of PCa.Additional file 1: File S1. The Sanger sequencing of the linear circ_0004296 RNA sequence carried on the plasmid.Additional file 2: Table S1. Primers, siRNAs, probes, and plasmid used in the present study.Additional file 3: Table S2. The specific RBPs interacted with circ_0004296.Additional file 4: Figure S1. A. The expression abundance of six screened circRNA candidates. B. qRT-PCR showed the expression of circ_0001708 (circRNA_19382) in PCa cell lines. C. qRT-PCR showed the expression of circ_0002842 (circRNA_22335) in PCa cell lines. D. qRT-PCR showed the expression of circ_0004390 (circRNA_00938) in PCa cell lines. E. Northern blotting assay confirmed the integrity of the linear circ_0004296 RNA sequence carried on the plasmid.Additional file 5: Figure S2. The circ_0004296 knockdown promoted the proliferation, migration, invasion, and EMT induction of PCa cells. A. Schematic illustration of designed siRNAs  targeting the back-splice junction of circ_0004296. B. The expression of circ_0004296 and ETS1 was detected by qRT-PCR in PC3 and DU145 cells transfected with siRNA-circ_0004296 or si-NC. C, D. CCK8 and colony formation assays were performed to measure the viability of PCa cells after circ_0004296 knockdown. E-H. The migration and invasion abilities of PCa cells were analyzed by wound healing and Transwell assays for migration and invasion after circ_0004296 knockdown. Scale bars =100um. I. Western blotting assay was performed to detected the expression of EMT-related proteins, namely E-cadherin, N-cadherin, Vimentin, and\u00a0Snail, after circ_0004296 knockdown. J. Protein profiling was used to screen specific proteins bound by circ_0004296 in PCa cells. *P\u2009<\u20090.05, **P\u2009<\u20090.01, ***P\u2009<\u20090.001.Additional file 6: Figure S3. A. Knockdown of EIF4A3 had no effect on circ_0004296 expression. B. The efficiency of ETS1 knockdown at protein level. C. The efficiency of ETS1 overexpression at protein level. D. Colony formation assays were used to measure viability of PCa cells after ETS1 knockdown. E. The migration abilities of PCa cells were analyzed by Transwell assays for migration after ETS1 knockdown. Scale bars =100um. F. Colony formation assays were used to measure viability of PCa cells after ETS1 overexpression. G. The migration abilities of PCa cells were analyzed by Transwell assays for migration after ETS1 overexpression. Scale bars =100um. H. Kaplan-Meier analysis for different ETS1 protein expression in PCa patients. I, J. Luciferase-labeled stably DU145-overexpressing circ_0004296 and luciferase-labeled DU145-vector cells were constructed. Scale bars =200um. *P\u2009<\u20090.05, **P\u2009<\u20090.01, ***P\u2009<\u20090.001.Additional file 7."}
+{"text": "Surface ozone (O Despite the fact that anthropogenic emissions of key air pollutants have decreased significantly as a result of stringent emission control measures implemented over the last two decades, air quality in many parts of Europe remains poor1. Particularly, secondary air pollutants  formed by complex atmospheric photo-chemical reactions did not show the same trend of decreasing as primary air pollutants, which are emitted directly from primary sources2. Ozone  are widely used to study the ozone variability19. However, CTMs have a large bias in resolving complex topography and chemistry mechanisms due to coarser resolution21, for example, urban areas are typically in a NO22. CTM, on the other hand, necessitate massive computational resource.In today\u2019s world, air quality is a major environmental threat to human health; additionally, some key air pollutants, either directly or indirectly, contribute to climate change  is gaining traction as an alternative modeling tool to complement CTM in Earth system science fields46. Because HCHO is an intermediate gas-product of VOC oxidation, it can be used as a proxy for VOC emissions. As CTMs resolve the physical-chemical processes, whereas ML algorithms do not, a hybrid modelling approach that incorporates the CTM prediction as a predictor variable into the ML model may improve the performance47. To this end, the objectives of this study are formulated as follows: 1) investigate the importance of limited available (in-situ and satellite) ozone precursor information and coarse CTM ozone simulations in modeling urban surface ozone variability using ML algorithm; and 2) investigate the potential of ML model\u2019s transfer-ability; how well the ML algorithm trained for one location explains ozone variability in other locations. The ultimate goal of these two objectives is to provide us confidence in modeling the surface ozone variability of locations with sparse or no ozone measurements and filling the data gap.In-situ VOC and OThis study focuses on Munich, a southern German metropolitan area where air pollutants are currently measured at five different locations. Given the long-term availability of all pollutants data, we chose an urban measurement station (Lothstrasse) to train and test the ML model, which continuously measured O48. Surface ozone simulations of CAMS (Copernicus Atmosphere Monitoring Service) global reanalysis dataset (EAC4) are also obtained from CAMS data store, which has a spatial resolution of 0.75\u00b0 and a temporal resolution of 3 h.Meteorological variables  are obtained from the ERA 5 reanalysis dataset, with spatial and temporal resolutions of 0.25\u00b0 and 1 h, respectively49. OMI data has a spatial resolution of 13 * 24 km and a daily temporal resolution. The OMI local overpass occurs between 1 p.m. and 2 p.m. OMI data are available beginning in October of 2004. We filtered the OMI data before using it to include only data with no processing errors, less than 10% snow or ice cover, a solar zenith angle of less than 80\u00b0 for NOThe tropospheric column NO50, is used in this study to model surface ozone concentrations. Since our objective is to investigate the importance of precursor information in surface ozone modeling using ML, the ML algorithm we choose should be more interpretable. A tree-based ML algorithm, such as XGBoost, is more interpretable than neural networks, which are typically black box systems, and also achieves higher interpretability than simple linear regression algorithms 51. We train the XGBoost ML algorithm with different predictor categories or combinations of predictor categories (Table https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html) and, we find that XGBoost algorithm is not sensitive to hyper-parameters in this study. Therefore, the hyper-parameters were set to their default values (https://xgboost.readthedocs.io/en/latest/parameter.html). We also discuss the predictor variable (feature) importance in the ML model using the results derived from sklearn python library\u2019s \u201cfeature_importance\u201d function, which calculates feature importance by taking the average gain across all splits (https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_regression.html). For this study, we focus on the afternoon (1 p.m. to 2 p.m.) when ozone levels are at their highest , matching with the OMI satellite overpass time. We also performed a similar analysis with the Random Forest (RF) ML algorithm.The Extreme Gradient Boosting (XGBoost) algorithm, a supervised learning-gradient boosting tree-based ML algorithm52. The first nine parts are used to train the ML algorithm, and the final one is used to test the ML model; this process is repeated ten times for the remaining combinations. The mean of R13. The ML algorithm trained solely with CAMS (\u201cML_cams\u201d) or in-situ precursors (\u201cML_insitu\u201d) show poor performance in all terms when compared to ML algorithm trained with the meteorology category alone (\u201cML_met_ds\u201d)  for training, and remaining 1575 days (30%) for testing the ML predictions. The k-fold cross validation (CV) is used to evaluate the performance of the ML model for different dataset combinations for training and testing. Here we choose k as 10, i.e., 5375 days of data split into 10 parts. To avoid spurious correlation between training and test datasets, we adopted a block sampling approachre) Fig. . In addihttps://christophm.github.io/interpretable-ml-book/feature-importance.html) and SHAP values (https://christophm.github.io/interpretable-ml-book/shap.html) agree with the feature importance calculated using each feature\u2019s gain. For example, Fig. The ML algorithm trained with meteorology and in-situ precursors category (\u201cML_met_ds_insitu\u201d) performs better than \u201cML_met_ds\u201d, with Rmentclass2pt{minimmentclass2pt{minimure Fig. . The feaFor 689 days between 2001 and 2017, all in-situ and satellite ozone precursors information, meteorological variables and CAMS data are available. Similarly, we use the first 70% of data (480 days) for training and remaining 30% (209 days) for testing the model. Also, we performed the k(10)-fold CV for 689 days of dataset. The performance of the ML algorithm trained with meteorology and satellite precursors (\u201cML_met_ds_satellite\u201d) is, however, equal to the performance of the ML algorithm trained with meteorology alone  to predict the ozone concentrations of other three stations in Munich, two  of which are sub-urban and remaining one (Stachus) is urban station. When compared to ground-truth, the performance of \u201cML_met_ds\u201d for two sub-urban station is better (R-3) Fig. . The pre75 days tnce Fig. .Figure 3Similarly, we use the \u201cML_met_ds\u201d, \u201cML_s_rh_t_wd_blh_no\u201d and \u201cML_s_rh_t_wd_blh_no_cams\u201d trained for \u201cLothstrasse\u201d station to predict the ozone concentration of two major German cities (3 stations for each city) Figs. , S7. Herg Tables \u2013S5. In aIn this study, the potential of a machine learning algorithm in simulating urban surface ozone has been demonstrated. As ozone is primarily produced by complex photo-chemical reactions in the atmosphere, the performance of the ML algorithm with meteorology information alone is promising; however, including the precursor emission (NOSupplementary Information."}
+{"text": "Owing to massive genome sequencing investment and taxonomic curation, this is an excellent group to explore genome quality.The availability of thousands of genomes has enabled new advancements in biology. However, many genomes have not been investigated for their quality. Here we examine quality trends in a taxonomically diverse and well-known group, butterflies (Papilionoidea), and provide draft, de novo assemblies for all 822 available butterfly genomes and interpret their quality in terms of completeness and continuity. We identify the 50 highest quality genomes across butterflies and conclude that the ringlet, Aphantopus hyperantus, has the highest quality genome. Our post-processing of draft genome assemblies identified 118 butterfly genomes that should not be reused owing to contamination or extremely low quality. However, many draft genomes are of high utility, especially because permissibility of low-quality genomes is dependent on the objective of the study. Our assemblies will serve as a key resource for papilionid genomics, especially for researchers without computational resources.We provide de novo genomes. We recommend that studies presenting genome sequences provide the assembly and some metrics of quality because quality will significantly affect downstream results. Transparency in quality metrics is needed to improve the field of genome science and encourage data reuse.Quality metrics and assemblies are typically presented with annotated genome accessions but rarely with  The explosion of available genomes across the Tree of Life has created entirely new fields of science and is changing how we investigate long-standing questions in biology. Studies of gene family evolution and gene mutation have expanded from single genes to mapping the architecture of entire genomes. Macroevolutionary studies using genomic data are now regularly being generated at impressive scales, e.g., complete class , continede novo genome assemblies and quality metrics for butterflies that will be useful for studying Lepidoptera evolution, gene discovery, and genomics. To understand how genome quality varies across taxa, we examine genome assembly quality in this exemplar group of organisms that has >935 published genomes. Additionally, we explore potential uses of these data, bearing in mind their draft nature, and discuss the state of butterfly genomics in light of genome quality.Here, we provide draft De novo genome assemblies allow for the discovery of novel genes with important ecological implications. For example, genes and gene duplications associated with plant detoxification can be identified [Gene family evolution and mutation holds immense potential in uncovering the mechanisms behind rapid functional adaptation and potential subsequent speciation , 6, and entified . Additioentified , 9). Howentified  documententified . These tentified , 6, and entified . Includientified  may mitiPhylogenetic studies stand to gain enormous taxonomic ground into the 2020s,\u00a0primarily\u00a0owing to the explosion of low-coverage genomes that are particularly well suited for phylogenetic studies. Taxonomic coverage in phylogenetic studies is increasing exponentially with the ability to sequence genomes from historical or museum specimens. Advances in both cost and quality of sequencing, as well as the ability to sequence DNA from degraded museum samples , allow rHere, we provide 822 draft de novo genome assemblies and quality metrics for a taxonomically diverse, well known group, butterflies, that will be useful for studies on their evolution, gene discovery, and genomics. We explore potential uses of these data, bearing in mind their draft nature, and discuss the state of butterfly genomics in light of genome quality.We obtained all published genome assemblies and genomic reads of butterflies (Lepidoptera: Papilionoidea) from NCBI  and LepBRRID:SCR_000131) v3.13 [de novo genomes, 32 CPUs and 128 GB of memory were sufficient. Forty genomes required additional memory; we ran these genomes with 24 threads with 720 GB of memory, potentially due to deeper sequencing or greater genomic complexity.We trimmed reads using TrimGalore  requirin1) v3.13  using paRRID:SCR_018927) [RRID:SCR_016577) [Following assembly, we performed several post-processing steps to ensure sequence integrity. First, we identified and removed contigs composed of <200 bp using SeqTK  . We scan_016577)  and remoRRID:SCR_015008) v3.02 [To assess assembly quality, we first used assembly-stats  to quant8) v3.02  to deterWe assembled 873 papilionoid genomes using raw reads from the NCBI SRA database and downloaded 62 pre-assembled genomes from the NCBI Assembly database . These 9de novo assembled genomes represent 6 families and 24 subfamilies  have notably lower mean quality scores . The H. hierax genome (GCA_900068475.1) had the lowest quality measures of the pre-assembled genomes that we investigated . The satyrine Aphantopus hyperantus (GCA_902806685.1) had the highest quality scores of all genomes investigated .The metrics that we used revealed large variance in genome assembly quality. N50 and BUSCO scores were often similar, such that the highest quality genomes typically had both high N50 and high BUSCO scores, although this was not always the case Fig.\u00a0. These qde novo genome assemblies  to 43,550 bp in Sertania guttata guttata . Proboscis propylea  or phylogenetic systematics (prioritize BUSCO).J. evarete nigrosuffusa , as \u201cgenomic data,\u201d as opposed to the potentially misleading term, \u201cgenome.\u201d Next, accessioning all assemblies would save countless hours of computation time and allow for the validation of results. In addition, assemblies would also allow results  from previous studies to be validated. Accessioning should include low-coverage draft genome assemblies, which can also be deposited in the NCBI's Assembly database. These assemblies have notably lower N50 and BUSCO scores when compared to the average assembly from NCBI and LepBase [de novo assembled genomes were, in many cases, comparable to the 5 Heliconius genomes that we investigated, suggesting that even low-quality genome assemblies can and should be accessioned. Including quality scores  for each draft assembly via the NCBI Assembly database . If resContamination has been shown to be a pervasive pattern in genome and transcriptome sequencing projects, especially those that use multiplexed sequencing approaches . In a reOur study reveals a significant lack of standardization and reporting across genomic studies because many do not provide genome assemblies and necessary quality metrics. Our main conclusions are that:We provide draft assemblies and quality metrics for all butterfly genomes available at the time of this study (available through NCBI TPA database) . We syntAphantopus hyperantus, has the highest quality papilionoid genome, and that \u226551 of 873 genomes that we assembled are ultimately unusable, and another 67 should be reused only with caution. Long and contiguous reads, indicated by high N50 values, are 1 quality metric that should be reported in all studies, especially those of gene mutation, duplication, or genomic architecture.We found that the ringlet, Quality metrics, such as sequence length, whether sequences are contiguous, and N50 and BUSCO scores, should be reported in all studies. Phylogenetic studies are strengthened when genomes with a high completeness score, such as BUSCO, are used.Researchers should provide draft assemblies in all genome publications and databases. Accessioning quality scores will enhance transparency and avoid unnecessary use of computational resources. Accessioning assemblies further promotes the FAIR principles of interoperability and reuse by limiting contaminant sequences and allowing confirmation of results.GigaScience database, GigaDB [See , GigaDB .Supplementary Table S1. Sample ID, N50, BUSCO, and sequencing metadata for de novo assembled genomes.Supplementary Table S2. Sample ID, N50, BUSCO, and sequencing metadata for pre-assembled genomes.Supplementary Table S3. Sample ID, N50, BUSCO, and sequencing metadata for de novo genomes resulting in extremely poor quality assemblies.Supplementary File S1. Filter_seqs_by_NCBI.py script used to automatically update assemblies with the feedback file from NCBI during the NCBI Accession process.bp: base pair; BUSCO: Benchmarking Universal Single-Copy Orthologs; CPU: central processing unit; FAIR: Findability, Accessibility, Interoperability, and Reuse; NCBI: National Center for Biotechnology Information; SPAdes: St. Petersburg genome Assembler; SRA: Sequence Read Archive; TPA: third party database.The authors declare that they have no competing interests.This work was funded by the National Science Foundation Grants DEB No. 1,541,500 and No. 1,557,007 to A.Y.K.A.Y.K. conceived of the study. E.A.E. performed data collection, data analysis, and produced the figures and scripts, with overall guidance from A.Y.K. All authors wrote the manuscript. C.G.S. and E.A.E. deposited the data.giab041_GIGA-D-20-00047_Original_SubmissionClick here for additional data file.giab041_GIGA-D-20-00047_Revision_1Click here for additional data file.giab041_GIGA-D-20-00047_Revision_2Click here for additional data file.giab041_GIGA-D-20-00047_Revision_3Click here for additional data file.giab041_GIGA-D-20-00047_Revision_4Click here for additional data file.giab041_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giab041_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giab041_Response_to_Reviewer_Comments_Revision_2Click here for additional data file.giab041_Response_to_Reviewer_Comments_Revision_3Click here for additional data file.giab041_Reviewer_1_Report_Original_SubmissionChris Wheat, PhD -- 3/31/2020 ReviewedClick here for additional data file.giab041_Reviewer_1_Report_Revision_1Chris Wheat, PhD -- 8/5/2020 ReviewedClick here for additional data file.giab041_Reviewer_2_Report_Original_SubmissionSujai Kumar -- 4/20/2020 ReviewedClick here for additional data file.giab041_Supplemental_FilesClick here for additional data file."}
+{"text": "Early diagnosis is the key to improving the prognosis of breast cancer (BC) patients; however, there are currently no circulating biomarkers that demonstrate good sensitivity and specificity. This study applied circular RNA (circRNA) microarray analysis, screening, and verification in BC plasma samples to identify three tumor-derived differentially expressed circRNAs: hsa_circ_0000091, hsa_circ_0067772, and hsa_circ_0000512. We constructed a diagnostic model using logistic regression analysis in the training set and established an optimal diagnostic model based on the three circRNAs, which showed sensitivity, specificity, and area under the curve (AUC) values of .971, .902, and .974, respectively. We then verified the diagnostic model in the test set which showed satisfactory stability for BC diagnosis. Additionally, the expression of hsa_circ_0000091 in plasma correlated with axillary lymph node (ALN) metastasis, TNM stage, and prognosis of BC patients. Furthermore, hsa_circ_0000091 combined with ultrasound showed predictive ability for ALN metastasis, with an AUC of .808. These findings suggested that the three identified circRNAs can be used as circulating biomarkers for BC diagnosis, with hsa_circ_0000091 potentially representing a prognostic biomarker for BC and novel approach for predicting ALN metastasis. Breast cancer (BC) is a complex malignant tumor that shows the highest morbidity among women worldwide , with diCircular RNA (circRNA) is a type of noncoding RNA produced by back-splicing. Unlike linear RNAs, circRNAs are circular in structure and do not possess 5\u2032 caps or 3\u2032 tails, making them highly stable. Additionally, circRNAs play critical roles in various cancers  and are Tumor cells can secrete RNAs, including circRNAs, into the circulatory system, and these secreted RNAs are closely related to tumor proliferation and metastasis . Thus, wThe inclusion criteria for the BC group were as follows: 1) BC confirmed by pathological report, 2) patients having not undergone preoperative radiotherapy and/or chemotherapy, and 3) absence of other malignant tumors or serious chronic diseases. BC patients who underwent surgery at the Shanghai Tenth People\u2019s Hospital and met the aforementioned criteria between 2017 and 2020 were enrolled in this study. The inclusion criterion for the normal group was healthy individuals without any benign tumors, malignant tumors, or serious chronic diseases.g for 10\u00a0min, the plasma was separated and stored at \u221280\u00b0C. A total of 523 blood samples  were included in this study. BC preoperative and normal plasma samples were randomized into training and test sets. This study was approved by the Institutional Ethics Committee of Shanghai Tenth People\u2019s Hospital, and the study methodology met the criteria outlined in the Declaration of Helsinki.Venous blood samples were collected from all participants, with preoperative blood samples collected before surgery for BC patients (BC group) and normal plasma samples collected from healthy individuals . Postoperative blood samples were collected 3 days after BC surgery, and metastatic blood samples were collected after confirmation metastasis. All blood samples were centrifuged at 3,000\u00a0rpm for 10\u00a0min at 4\u00b0C. Following high-speed centrifugation at 12,000 \u00d7We used the human circRNA array  for analyses of five BC tissues and matched adjacent normal tissues. Quantification of total RNA extracted from each tissue sample was performed using a NanoDrop ND-1000 , with sample preparation and microarray hybridization performed according to the manufacturer\u2019s instructions.\u2212\u0394\u0394CT method.Total RNA in plasma was extracted using an EZ-press Serum/Plasma RNA purification kit  according to the manufacturer\u2019s instructions. cDNA was synthesized using the HiScript III RT SuperMix kit , and qRT-PCR was conducted using SYBR Green Master Mix . Primer sequences were designed and synthesized by RiboBio , with the 18S gene sequence used as an internal reference for circRNAs. The primer sequences were as follows: hsa_circ_0000091 forward, 5\u2032-CAG\u200bCTG\u200bTTT\u200bACC\u200bAGA\u200bGTG\u200bCAT\u200bGA-3\u2032 and reverse, 5\u2032-CGA\u200bTGC\u200bGTT\u200bTTC\u200bTAA\u200bTCT\u200bGGT\u200bTC-3\u2032; hsa_circ_0067772 forward, 5\u2032-TGC\u200bCAG\u200bCAG\u200bTTC\u200bTGA\u200bCAT\u200bT-3\u2032 and reverse, 5\u2032-TCT\u200bTTG\u200bGGT\u200bACT\u200bCCC\u200bTCT\u200bT-3\u2032; hsa_circ_0000512 forward, 5\u2032-TTT\u200bGCC\u200bGGA\u200bGCT\u200bTGG\u200bAAC-3\u2032 and reverse, 5\u2032-ATC\u200bTCC\u200bTGC\u200bCCA\u200bGTC\u200bTGA\u200bCC-3\u2032; and 18S forward, 5\u2032-TAG\u200bAGG\u200bGAC\u200bAAG\u200bTGG\u200bCGT\u200bTC-3\u2032 and reverse, 5\u2032-CGC\u200bTGA\u200bGCC\u200bAGT\u200bCAG\u200bTGT-3\u2032. The relative expression of circRNAs was assessed using the 2U test was used for unpaired samples. Comparisons between circRNA expression and patient clinical features were conducted using the chi-squared test. The receiver operating characteristic (ROC) curve and corresponding area under the curve (AUC) were determined for individual and combined circRNAs. Disease-free survival (DFS) was defined as the time interval from the date of surgery to the date of recurrence or final contact. The Kaplan\u2013Meier method was applied for survival analysis. Log-rank tests were used to determine statistical significance. Statistical analysis was performed using SPSS  and GraphPad Prism . Data are presented as the mean \u00b1 standard deviation (SD) and were considered statistically significant at p < .05.Comparisons between paired specimens were analyzed using the Wilcoxon matched-pairs signed rank test, whereas the Mann\u2013Whitney p < .01, and the data were used to construct heat maps and volcano plots to visualize the results. We identified 82 differentially expressed circRNAs, including 19 upregulated and 63 downregulated circRNAs, between BC tissues and normal adjacent tissues  \u22652 and  tissues .To screen candidate tumor-derived circRNAs in plasma, we analyzed the expression of the top 30 differentially expressed circRNAs (ranked by |FC|) in 10 BC plasma samples versus 10 normal plasma samples using qRT-PCR. We identified five circRNAs  as differentially expressed in BC plasma samples relative to normal plasma samples , resultiWe expanded the number of BC and normal plasma samples in each group to 102 and considered this the training set. QRT-PCR results revealed that hsa_circ_0000091 expression decreased, whereas expression of hsa_circ_0067772 and hsa_circ_0000512 increased in BC plasma samples relative to that in normal plasma samples . HoweverWe applied the ROC curve to the training set to evaluate the sensitivity and specificity of the circRNAs for BC diagnosis. Separate analysis of individual circRNAs and the three circRNAs combined revealed AUCs for hsa_circ_0000091, hsa_circ_0067772, and hsa_circ_0000512 of .825 [95% confidence interval (CI): .769\u2013.880], .730 (95% CI: .659\u2013.801), and .704 (95% CI: .632\u2013.776), respectively. To establish the diagnostic model, we performed logistic regression of the three circRNAs using the following equation:As expected, the AUC of the combination of the three circRNAs was as high as .974 (95% CI: .952\u2013.996) along with significantly improved sensitivity and specificity . TherefoTo confirm that these three circRNAs were breast tumor\u2013derived circRNAs, we detected their expression in postoperative plasma samples from the 102 BC patients enrolled in the training set using qRT-PCR. After resection of the breast tumors, we found that hsa_circ_0000091 expression was significantly higher and hsa_circ_0067772 and hsa_circ_0000512 expression was significantly lower in postoperative plasma samples than in preoperative plasma samples , suggestWe used a total of 100 BC plasma samples and 100 normal plasma samples as the test set. Analysis using the chi-squared test revealed no difference in the features of patients enrolled between the training and test sets . MoreoveLevels of traditional biomarkers, including CEA, CA125, CA15-3, and CA19-9, above the reference range are considered to indicate positive diagnosis of BC. Here, we identified a demarcation point for distinguishing high or low expression of circRNAs according to the ROC curve generated from the training set. Following calculation of the Youden index, we determined the cutoff value for circRNA expression as the maximum of the Youden index , with thWe collected 17 plasma samples from patients with metastatic tumors during the course of postoperative follow-up and examined the expression of hsa_circ_0000091, hsa_circ_0067772, and hsa_circ_0000512. We found that the relative expression of hsa_circ_0000091 in metastatic plasma samples decreased compared with that observed in postoperative plasma samples, whereas no significant difference was observed in hsa_circ_0067772 and hsa_circ_0000512 expression between postoperative and metastatic plasma samples . MoreoveUsing the identified cutoff values as demarcation points, we found that hsa_circ_0000091 expression positively correlated with axillary lymph node (ALN) metastasis and TNM stage in 202 samples from BC patients  , with anhttp://bibiserv.techfak.uni-bielefeld.de/rnahybrid/) to predict the potential miRNA targets of the circRNAs. Integration of the data into a Venn diagram and selection of miRNAs from the intersections of the two databases identified 17, 34, and 32 miRNAs possibly targeted by hsa_circ_0000091, hsa_circ_0067772, and hsa_circ_0000512 for miRNA sponging, respectively (http://www.funrich.org/) to perform biological pathway enrichment analysis for these 82 miRNAs. The results showed that hsa_circ_0000091, hsa_circ_0067772, and hsa_circ_0000512 are involved in 40 biological signaling pathways  and ALN dissection (ALND) are the main methods used to evaluate ALN status for BC patients during surgery. SLNB is less invasive than ALND and has become the gold standard for evaluating ALN status . Moreovein vitro and in vivo to obtain more definitive evidence.The most common function of circRNAs is as a miRNA sponge . In the The present study established a BC diagnostic model using a combination of three tumor-derived plasma circRNAs , which potentially offers a valuable liquid biopsy method for diagnosing BC. Furthermore, the results indicated that the plasma hsa_circ_0000091 level might represent a prognostic biomarker for BC and that its combination with ultrasound can potentially serve as a new approach to predicting ALN metastasis."}
+{"text": "Circular RNA (circRNA) hsa_circ_0077837 inhibits colorectal cancer. Our research studied the participation of hsa_circ_0077837 in non-small cell lung cancer (NSCLC). Hsa_circ_0077837 and phosphatase and tensin homolog (PTEN) expression in cancer and paired non-cancer tissues from a total of 64 NSCLC patients were studied with RT-qPCR. To evaluate the prognostic value of hsa_circ_0077837 for NSCLC, these 64 patients were monitored for 5\u00a0years. Expression of PTEN in NSCLC cells with hsa_circ_0077837 overexpression was determined by RT-qPCR and Western blot. The methylation of PTEN gene in cells transfected with hsa_circ_0077837 expression vector was analyzed by methylation specific PCR (MSP). The roles of hsa_circ_0077837 and PTEN in NSCLC cell proliferation were evaluated using cell apoptosis assay. Our data showed that hsa_circ_0077837 was upregulated in NSCLC and predicted poor survival. Besides, hsa_circ_0077837 expression level was higher in 36 advanced cases (stage III and IV) than in 28 early-stage cases (stage I and II). Hsa_circ_0077837 was inversely correlated with PTEN across cancer tissues. In NSCLC cells, hsa_circ_0077837 overexpression decreased PTEN expression, increased PTEN gene methylation, and reduced HCC827 cell apoptosis via PTEN. Overall, hsa_circ_0077837 is upregulated in NSCLC and downregulates PTEN by increasing its gene methylation to suppress cell apoptosis.List of abbreviations:Non-small cell lung cancer (NSCLC); circRNAs (circular RNAs); methylation-specific PCR (MSP) Lung cancer is the most common cancer in terms of incidence and mortality among males and females in most countries . CompareAlthough efforts have been made to prevent and treat NSCLC, patients\u2019 survival has been significantly improved in recent decades . TherefoFrom July 2013 to July 2015, 64 NSCLC patients  were enrolled in Qilu Hospital of Shandong University after Ethics approval was obtained from the Ethics Committee. NSCLC was diagnosed by computed tomography (CT) scan and confirmed by histopathological biopsy. Patients were included if they were newly diagnosed and had not been treated previously. Patients were excluded if they were recurrent cases and complicated with other diseases. Primary tumors were resected from patients and dissected to isolate paired NSCLC and non-tumor tissues. All participants signed informed consent. The clinical characteristics of NSCLC patients are summarized in The 64 NSCLC patients were grouped into the stage I or II (n\u00a0=\u00a028) and stage III or IV (n\u00a0=\u00a036) according to the 7th edition of the American Joint Committee on Cancer (AJCC) staging systems. All patients did not undergo treatments prior to the surgery. From the day of admission, patients were visited every month for 5\u00a0years to monitor their survival. The 64 patients either died of NSCLC during the follow-up or completed the follow-up.NSCLC cell line HCC827 was obtained from ATCC and cultured in RPMI-1640 medium with 10% fetal bovine serum (FBS) to about 80% confluence for subsequent assays.8 HCC827 cells.To overexpress hsa_circ_0077837 or PTEN, cells were transfected with hsa_circ_0077837 or PTEN pcDNA3.1 vector using Lipofectamine 2000 (Invitrogen). In each transfection, 1\u00a0\u00b5g hsa_circ_0077837 or PTEN expression vector was transfected into 10Total RNAs were isolated from paired tissue samples and HCC827 cells using EZ RNA Miniprep Kit (EZ BioResearch) and treated with DNase I (Invitrogen) to remove genomic DNAs. RNA purity was checked by measuring OD 260/280 ratios.\u2212\u0394\u0394CT method.RNA samples were subject to reverse transcriptions to synthesize cDNA. hsa_circ_0077837 and PTEN mRNA levels were determined using RT-qPCRs with GAPDH as the internal control. The primer sets used for RT-qPCR were 5\u2019-CCTGGAGAAACATGCCAAGGG-3\u2019 and 5\u2019-TCACTTCAGACACAGAGCCTACT-3\u2019 for hsa_circ_0077837, 5\u2019-GTTTACCGGCAGCATCAAA-3\u2019 and 5\u2019-CCCCCACTTTAGTGCACAG-3\u2019 for PTEN, and 5\u2019-AGCCTCCCGCTTCGCTCTC-3\u2019 and 5\u2019-GCGCCCAATACGACCAAATCCG-3\u2019 for GAPDH. PCR data were processed using the 2Following formalin fixation and dehydration in ethanol, tissues were embedded in paraffin and prepared as 5\u2009\u00b5m thick sections. The sections were blocked in 5% normal goat serum and were incubated in turn with rabbit anti-PTEN polyclonal antibody  and goat anti-rabbit IgG . The sections were observed under a phase-contrast light microscope (Olympus) and photographed. Integrated optical density (IOD) was calculated using Image-Pro Plus software.TM kit (ZYMO RESEARCH). After that, genomic DNA samples were used as templates to perform both routine PCRs and MSP using 5\u2019-TAGATAGGTGCCCTTTGGGCCCTTG-3\u2019 and 5\u2019-CCCCCAAATCTGTGTCCTCATGGTGT-3\u2019 for routine PCR and 5\u2019-TAGATAGGTGTTTTTTGGGTTTTTG-3\u2019 and 5\u2019-CCCCCAAATCTATATCCTCATAATAT-3\u2019 for MSP.A total of 5\u00a0\u03bcg isolated genomic DNAs were used for bisulfite modification using DNA Methylation-GoldTotal proteins were isolated using RIPA solution (Invitrogen) and quantified using BCA assay. After denaturation, proteins were separated by 10% SDS-PAGE gel electrophoresis and transferred onto PVDF membranes. After blocking, the membranes were incubated in turn with GAPDH  or PTEN  primary antibodies and HRP IgG secondary antibody . Signals were then developed using ECL Western blotting Substrate Kit . Data were normalized using QuantityOne software.HCC827 cells were cultured in a 96-well plate with 3,000 cells per well. Three wells were set for each experiment. After culturing for 48\u00a0h, cells were harvested, washed, and stained with Annexin-V FITC and PI. Finally, apoptotic cells were analyzed using FACSCalibur instrument.7cells were incubated with cell lysis buffer on ice for at least 20\u00a0min. The cell lysates were centrifuged for 10\u00a0min at 1200\u00a0g. The supernatants, which were the cytoplasm fractions, were collected, transferred to a new tube, and subjected to RNA isolation. Cell pellets, which were the nucleus fractions, were further incubated with nucleus lysis buffer for 10\u00a0min on ice and subjected to RNA isolation. Both RNA samples were prepared as cDNA samples and used to determine the expression of hsa_circ_0077837 using PCRs. GAPDH was included in this assay as a cytoplasm marker.Both nucleus and cytoplasm fractions of HCC827 cells were prepared using a Cell Fractionation Kit from Abcam (ab109719). In brief, cells were washed using ice-cold PBS and counted. A total of 10P <\u00a00.05 was statistically significant.Data were expressed as mean \u00b1 standard deviation (SD). Differences among multiple groups were analyzed using ANOVA Tukey\u2019s test. The 64 patients were divided into high and low hsa_circ_0077837 level groups . Survival curves were plotted based on follow-up analysis and compared using log-rank test. The associations between patients\u2019 clinical characteristics and hsa_circ_0077837 expression were analyzed using Chi-squared test. Correlations were analyzed by Pearson\u2019s correlation coefficient. The expression levels of genes may indicate their functions. To this end, Hsa_circ_0077837 and PTEN expression levels in cancer and paired non-cancer tissue samples from 64 NSCLC patients were detected by RT-qPCR. The expression data of hsa_circ_0077837 and PTEN in paired tissues were used to plot heatmaps using Heml 1.0 software. The results showed that hsa_circ_0077837 was upregulated , and PTECorrelations suggest interactions. Therefore, correlations between hsa_circ_0077837 and PTEN were explored with Pearson\u2019s correlation coefficient. Hsa_circ_0077837 and PTEN were inversely correlated across cancer tissues  but not The prognostic value of hsa_circ_0077837 for NSCLC was explored by plotting survival curves. Compared to patients with low hsa_circ_0077837 levels, patients with high hsa_circ_0077837 levels experienced worse survival . Therefop <\u00a00.05). Hsa_circ_0077837 overexpression decreased PTEN mRNA (p <\u00a00.05). The effect of hsa_circ_0077837 overexpression on PTEN gene methylation was evaluated by MSP. Compared to the empty vector group, cells transfected with hsa_circ_0077837 showed increased PTEN gene methylation . Therefore, hsa_circ_0077837 may suppress NSCLC cell apoptosis via PTEN.The role of hsa_circ_0077837 and PTEN in HCC827 cell apoptosis was analyzed. It was observed that hsa_circ_0077837 overexpression decreased HCC827 cell apoptosis, while PTEN overexpression increased HCC827 cell apoptosis. Moreover, PTEN overexpression reduced the inhibitory effect of hsa_circ_0077837 overexpression on cell apoptosis , and hsa_circ_0077837 overexpression suppressed CRC cell proliferation, suggesting the role of hsa_circ_0077837 as a tumor suppressor in CRC . Our resIt has been well established that treatment strategies are closely related to the survival of patients . Hsa_cirPTEN is a tumor suppressor that participates in cancer biology by inducing cancer cell apoptosis via suppressing the PI3K-Akt pathway, a main cell survival pathway in cancers . ConsistThe roles of circRNAs in NSCLC have been extensively explored in recent years . HoweverHsa_circ_0077837 is upregulated in NSCLC and predicts poor survival of NSCLC patients. Moreover, hsa_circ_0077837 may downregulate PTEN through methylation to suppress cancer cell apoptosis.Click here for additional data file."}
+{"text": "Gull Point State Park is located on a peninsula on the west shore of West Okoboji Lake . It is the primary state park in the Iowa Great Lakes region. Sediment and water samples from three locations at the Gull Point pond were analyzed for their microbial composition. A260/280 ratios between 1.60 (Gull_B) and 1.90 (Gull_A_sed). A 16S rRNA amplicon sequencing library was prepared for each sample, following the 16S metagenomic sequencing library preparation protocol , with smaller amounts of Bacteroidetes (3 to 15%), Actinobacteria (5 to 19%), and Firmicutes (6 to 11%). Over 72% of the reads were classified to the genus level. A principal coordinates analysis (PCoA) chart was generated within the 16S Metagenomics app, using Classical MDS on a Pearson covariance distance matrix generated from per-sample normalized classification abundance vectors ; Mycobacterium and Hydrogenophaga (Gull_B); and Malikia, Vogesella, and Hydrogenophaga (Gull_C). These genera have been found in various environmental samples , SRR15141921 (Gull_A_sed), SRR15141697 (Gull_B), SRR15142125 (Gull_B_sed), SRR15142122 (Gull_C), and SRR15142121 (Gull_C_sed).The 16S rRNA gene amplicon data sets have been deposited at DDBJ/ENA/GenBank under BioProject accession number"}
+{"text": "Glioma is a primary intracranial tumor with high morbidity and mortality. We acquired miR-338-5p, which suppresses the development of glioma, from the GEO and CGGA databases. In addition, we predicted that hsa_circ_0072389, hsa_circ_0072386, hsa_circ_0008621, hsa_circ_0072387, and hsa_circ_0072391 could relieve the silencing of IKBIP by miR-338-5p. By analyzing genes related to IKBIP expression, possible pathways affecting glioma were identified. This study provides new ideas for investigating multiple circRNAs in ceRNAs. Glioma is the most common primary malignant brain tumor and accounts for approximately half of all intracranial primary tumors . The curCircular RNA is a type of noncoding RNA lacking both a 5\u2032 end cap and a 3\u2032 end polytail that induces a circular RNA structure via covalent bonds . An incrIn this study, we found that miR-338-5p is differentially expressed between glioma tumor and normal tissues in GSE datasets , indicating that miR-338-5p may have a stable effect on the occurrence or development of glioma. By analyzing the data in several databases, including circBANK, GEO, and circinteractome, we deduced that hsa_circ_0072389, hsa_circ_0072386, hsa_circ_0008621, hsa_circ_0072387, and hsa_circ_0072391 may aggravate glioma by combining with miR-338-5p. In addition, with the circPrimer and circBase databases, we found that these 5 circRNAs all originated from HMGCS1. Moreover, we found that IKBIP may be the target gene of miR-338-5p by analyzing multiple databases, which involved miRwalk, miRDB, TargetScan, GEO, and GEPIA. Therefore, we believe that hsa_circ_0072389, hsa_circ_0072386, hsa_circ_0008621, hsa_circ_0072387, and hsa_circ_0072391 promote the expression of IKBIP by binding with miR-338-5p. Then, we verified the relationship among circRNAs , miR-338-5p and IKBIP by Western blot. With the Pathcards and GEPIA databases, we inferred that IKBIP may promote the development of glioma NF-\u03baB, JAK/STAT and TGF\u03b2/SMAD signaling pathways. In summary, we believe that hsa_circ_0072389, hsa_circ_0072386, hsa_circ_0008621, hsa_circ_0072387, and hsa_circ_0072391 induce the NF-\u03baB and JAK/STAT pathways to aggravate glioma via miR-338-5p/IKBIP.p <0.05 and |logFC| \u2265 2 p < 0.05 for miRNA and mRNA. The whole screening process was implemented with the limma R package [High-throughput data on circRNAs, miRNAs and mRNAs in glioma were obtained from the GEO database. The screening standard for circRNA was |logFC| \u2265 1.5  package .http://www.circbank.cn/), and mRNAs that targeted miR-338-5p were discovered according to the results from the miRwalk , miRDB (http://mirdb.org/), and targetscan databases (http://www.targetscan.org/vert_72/). All these results indicated the presence of conserved 8-mer and 7-mer sites that match the seed region of miR-338-5p [The prediction of circRNAs binding with miR-338-5p was based on the circBANK database  was employed to reveal proteins that interact with circRNA. In addition, the TargetScan prediction tool enables the prediction and binding sites for RBPs on reported circRNAs that have yet to be mapped [The circinteractome database , biological process (BP), and cellular component. All operations were implemented by using the Clusterprofiler R package .https://string-db.org/) [The protein\u2013protein interaction (PPI) network was constructed by the STRING v11 database (db.org/) .http://circrna.org/) and further entered into the RNAfold database (http://rna.tbi.univie.ac.at/) to acquire the secondary structures and MFE structures of circRNAs.The sequences of circRNAs were obtained from circbase (http://gepia.cancer-pku.cn/). Kaplan\u2013Meier plots were used to analyze the relationship between the survival time and gene expression of glioma patients. The hazard ratio [The overall survival time and gene expression of glioma patients were obtained from the GEPIA database (rd ratio  and 95% https://pathcards.genecards.org/), and the correlation between IKBIP and gene expression in glioma was obtained through the GEPIA database.Pathway-related genes were obtained from the Pathcards database . The miR-338-5p inhibitor was obtained from Boshang Biotechnology (Shanghai). Taking GAPDH as a reference, the primer sequences were as follows:A BCA Protein Detection Kit  was used to detect the protein concentration. SDS (5\u00d7) was added to the total protein, and the mixture was further heated at 100\u00b0C for 10 minutes. The protein was isolated by SDS\u2013PAGE and transferred to polyvinylidene fluoride (PVDF) film. Five percent skim milk was sealed at room temperature for 2 hours and further incubated with primary antibody in a shaking bottle at 4\u00b0C for 8\u201312 hours. Then, we washed the film with Tris-buffered saline and Tween 20 (TBST) 3 times, and each wash lasted 10 minutes. After that, the secondary antibody was incubated with film at room temperature for 1 hour and washed with TBST once more 3 times (10 min each time). Proteins were observed by enhanced chemiluminescence.t-test was used to determine statistically significant differences. When P < 0.05, the difference was statistically significant. The statistical analysis software used in this study was SPSS 19.0.Student\u2019s https://www.ncbi.nlm.nih.gov/geo/.The datasets used in the project are available from the corresponding author. The data that support the findings of this study are openly available in GEO at p < 0.05), we found 1426 differential genes in GSE139031 (1312 upregulated genes and 114 downregulated genes), 32 differential genes in GSE25632 (21 upregulated genes and 11 downregulated genes), 26 differential genes in GSE103228 (4 upregulated genes and 22 downregulated genes), and 30 differential genes in GSE65626 (20 upregulated genes and 10 downregulated genes)  with large sample sizes for analysis. When comparing gene expression in tumor tissues with gene expression in normal tissues, there were 3556 differential genes in GSE139031, 1145 differential genes in GSE25632, 6658 differential genes in GSE103228, and 3556 differential genes in GSE65626 \u20131D. Afted genes) \u20131H. To id genes) . By anald genes) \u20131K; therp < 0.05) were differentially expressed between glioma tissues and normal tissues, which included 351 upregulated circRNAs and 121 downregulated circRNAs , which involved 451 upregulated genes and 868 downregulated genes , MALT1 , LY96 , NFKB2 , CD14  in the NF-\u03baB signaling pathway, SOCS3 , MAP2K3 , JAK3 , IL27RA , IL13RA1  in the JAK/STAT signaling pathway, IL27RA , and IL13RA1 (p = 3.5). These were not only correlated with IKBIP expression  [Because genes play different roles in different cancers , we explhsa_circ_0072386, hsa_circ_0008621, hsa_circ_0072387, and hsa_circ_0072391 can bind to miR-338-5p, and miR-338-5p can target IKBIP, which is coexpressed with genes in the NF-\u03baB signaling pathway and JAK/STAT signaling pathway. Therefore, we speculate that after being transcribed from the host gene HMGCS1, pre-RNAs are cleaved into mRNAs and circRNAs through splicing and modification. In these circRNAs, hsa_circ_0072389, hsa_circ_0072386, hsa_circ_0008621, hsa_circ_0072387, and hsa_circ_0072391 are transported between or within cells, which bind to miR-338-5p and hinder the silencing of IKBIP mRNA. Then, the upregulation of IKBIP results in the activation of the NF-\u03baB signaling pathway, JAK/STAT signaling pathway and TGF\u03b2/SMAD signaling pathway, which worsen the deterioration of glioma .p < 0.001, |R| > 0.3) [p = 5.9 e-9, R = 0.44), MALT1 , LY96 , NFKB2 , CD14  in the NF-\u03baB signaling pathway, SOCS3 , MAP2K3 , JAK3 , IL27RA , and IL13RA1  in the JAK/STAT signaling pathway, and USP15  in the TGF\u03b2/SMAD signaling pathway were satisfactory. Therefore, we speculated that these 3 signaling pathways may interact with IKBIP to affect the progression of glioma directly, supporting the view that hsa_circ_0072389, hsa_circ_0072386, hsa_circ_0008621, hsa_circ_0072387, and hsa_circ_0072391 upregulated IKBIP by sponging the adsorption of miR-338-5p, which further promoted glioma through the NF-\u03baB signaling pathway, JAK/STAT signaling pathway, and TGF\u03b2/SMAD signaling pathway.In GSE139031, GSE25632, GSE103228, and GSE65626, we found that miR-338-5p was expressed at low levels in glioma. In the CGGA database, we further verified that glioma patients with high miR-338-5p expression had a better prognosis. Meanwhile, a great number of studies have also shown that miR-338-5p is able to inhibit the proliferation and invasion of glioma cells . Therefo| > 0.3) . Then, wIn most studies of ceRNAs in tumors, a single circRNA or mRNA could be screened from circRNA chips or mRNA chips. Then, the targeted miRNA could be predicted easily . HoweverSupplementary Figures"}
+{"text": "Detecting copy number variations (CNVs) and copy number alterations (CNAs) based on whole-genome sequencing data is important for personalized genomics and treatment. CNVnator is one of the most popular tools for CNV/CNA discovery and analysis based on read depth.Herein, we present an extension of CNVnator developed in Python\u2014CNVpytor. CNVpytor inherits the reimplemented core engine of its predecessor and extends visualization, modularization, performance, and functionality. Additionally, CNVpytor uses B-allele frequency likelihood information from single-nucleotide polymorphisms and small indels data as additional evidence for CNVs/CNAs and as primary information for copy number\u2013neutral losses of heterozygosity.https://github.com/abyzovlab/CNVpytor under the MIT license.CNVpytor is significantly faster than CNVnator\u2014particularly for parsing alignment files (2\u201320 times faster)\u2014and has (20\u201350 times) smaller intermediate files. CNV calls can be filtered using several criteria, annotated, and merged over multiple samples. Modular architecture allows it to be used in shared and cloud environments such as Google Colab and Jupyter notebook. Data can be exported into JBrowse, while a lightweight plugin version of CNVpytor for JBrowse enables nearly instant and GUI-assisted analysis of CNVs by any user. CNVpytor release and the source code are available on GitHub at  The continuous reduction of cost has enabled whole-genome sequencing (WGS) to be widely used in different research projects and clinical applications. Consequently, many approaches for processing, analyzing, and visualizing WGS data have been developed and are being improved. Detection and analysis of copy number variations (CNVs) based on WGS data is one of them. Research directions related to cancer genomics, single-cell sequencing, and somatic mosaicism create huge amounts of data and demands for processing on the cloud that require further improvements in CNV callers, moving to parallel processing, better compression, modular architecture, and new statistical methods.CNVnator is a method for CNV analysis based on read depth (RD) of aligned reads. It has been determined to have high sensitivity 86\u201396%), low false-discovery rate 3\u201320%), and high genotyping accuracy (93\u201395%) for germline CNVs in a wide range of sizes from a few hundred base pairs to chromosome size events . Since i\u201396%, low0%, and hHere we describe CNVpytor, a Python extension of CNVnator. CNVpytor inherits the reimplemented core engine of CNVnator and extends visualization, modularization, performance, and functionality. Along with RD data, it enables consideration of allele frequency of single-nucleotide polymorphisms (SNP) and small indels as an additional source of information for the analysis of CNV/CNA and copy number\u2013neutral variations. Along with RD data, this information can be used for genotyping genomic regions and visualization.P-values, are all stored in the same .pytor file and can be extracted into Excel (TSV file) or a VCF file.CNVpytor inherits the RD analysis approach developed in CNVnator . BrieflyP-values calculated from RD and BAF signal. Variant data can also be plotted in parallel with RD signal  . The mainal Fig.\u00a0. Same asCNVpytor is to be run in a series of steps Fig.\u00a0. For enhRoutine processing steps can be followed by CNV visualization and analysis in the Viewer session, which can be interactive or hands-off. Implementation of interactive mode is inspired by a Linux shell with tab completion and with a help page similar to the man pages. In this mode, a user can instantly make various visualizations, preview and filter CNV calls, annotate calls, create joint calls across multiple samples, and genotype specified regions. The viewer does not save results into the .pytor file, and outputs are printed and plotted on the screen or exported to an output file(s). Hands-off mode executes user-written script(s) with CNVpytor commands. Such scripts can be used as part of the processing pipeline where, e.g., images of signals around called CNVs are generated and stored for possible future inspection. Through the viewer interface, it is possible to directly access Python and run code. This allows user to access some standard features of underlying libraries, e.g., matplotlib library can be used to customize plots.CNVpytor can be used as the Python module. All functionalities, like reading and editing CNVpytor data files, and all calculation steps and visualizations can be performed by calling functions or classes. This way CNVpytor can be easily integrated in different platforms and computing environments; e.g., CNVpytor can be run from Jupyter Notebook on a local machine or in cloud services, e.g., Google Colab. CNVpytor is also integrated into OmniTier's \u201cCompstor Novos\u201d variant calling workflow .Visualization of multiple tracks/signals can be done interactively by mouse and by typing relevant commands, as well as by running scripts with CNVpytor commands provided as inputs to CNVpytor. CNVpytor has extended visualization capabilities with multiple novel features as compared to CNVnator. For each sample  multiple data tracks such as RD signal, BAF of SNPs, and binned BAF likelihood can be displayed in an adjustable grid layout as specified by a user. Specifically, multiple regions across multiple samples can be plotted in parallel, facilitating comparison across samples and different genomic loci Fig.\u00a0. To get Some additional features include GC-bias curve plot and 2D histogram Fig.\u00a0, allele CNVpytor also has implemented functionality to export data into formats that can be embedded into JBrowse, a web-based genome browser used to visualize multiple related data tracks . The export enables users to utilize JBrowse capabilities to visualize, compare, and cross-reference CNV calls with other data types  and annotations across genome. Exported data provide 3 resolutions of RD and BAF tracks  while the appropriate resolution is chosen automatically by JBrowse depending on the size of the visualized genomic region. Multiple .pytor files can be exported at once.Alternatively, a user can utilize a lightweight CNVpytor plugin for JBrowse. The plugin takes information about coverage from a relatively small (as compared to BAM) VCF file and on the fly performs the read depth and BAF estimation, segmentation, and calling. For read depth analysis, the plugin fetches the information from the DP field in the VCF file and uses it as a proxy for actual coverage. Since for large bin sizes such an estimate corresponds well to the actual value , the plugin enables quick and easy review of large copy number changes in a genome. For BAF analysis, the plugin conducts analyses the same way as a stand-alone application. All temporary values are stored in the browser cache for fast and interactive visualization of a genomic segment. As well as improving responsiveness by eliminating the network lag of a client-server application, this ensures that no information about a personal genome is transferred to external servers. Once the analyses are complete, the results are instantly visualized using JBrowse's native capabilities. Usage cases of the plugin are: 1) quick and visual cross-referencing of copy number profiles between multiple samples and in relation to other data types, and 2) a review of a personal genome(s) for large CNVs in a simple user-friendly environment.Development of new, maintenance, and improvement of existing bioinformatics tools are driven by changing data types, demands for newer and user-verifiable analyses, necessity for processing larger datasets, and the evolving nature of computational infrastructures and platforms. CNVpytor brings the functionality of its predecessor CNVnator to a new level and significantly expands it. CNVpytor is faster and virtually effortless to install, requires minimum space for storage, enables analysis of BAF for call confirmation and genotyping, provides users with instant and extended visualization and convenient functionality for result curation (including merging over multiple samples), and is equipped for integration with other tools. The utilized method is suitable to segment RD signal in the case of mosaic or somatic cancer sample CNAs, and the alteration will be called if its cell frequency is >50% . A more Calculations for RD binning, mean-shift algorithm, partitioning, and calling CNVs are explained in detail in the CNVnator article . The onli), alternative count , quality, and genotype (0/1 or 1/1).CNVpytor imports information about SNPs and single-letter indels from the variant (VCF) file. All other variants are ignored. For each variant the following data are stored in the CNVpytor file: chromosome, position, reference base, alternative base, reference count : Minor allele frequency (MAF): One of the characteristics of next-generation sequencing is that some bases are not accessible for variant discovery using short reads, owing to the repetitive nature of the human genome. In the 1000 Genomes Project, a genome mask is created to tabulate bases for variant discovery. There are \u223c74% of bases marked passed (P), which corresponds to \u223c77% of non-N bases  and for each the point function is calculated by multiplying values of symmetrized beta distribution for each variant. The position of maximum likelihood represents the most probable BAF value in a particular bin. Along with the likelihood function average values of variant BAF and MAF are calculated per bin and stored in thet CNVpytor file, together with counts of homozygous and heterozygous variants.The likelihood function for each bin is calculated as a product of individual likelihood functions of variants within that bin:P-value calculated using i-test statistics between RD difference in the region and global  mean; (vi) e-val2: P-value from the probability of RD values within the region to be in the tails of a Gaussian distribution of binned RD; (vii) e-val3: same as e-val1 but without first and last bin; (viii) e-val4: same as e-val2 but without first and last bin; (ix) q0: fraction of reads mapped with zero quality within call region; (x) pN: fraction of N bases  within call region; (xi) dG: distance to nearest gap in reference genome.For each CNV call the following values are calculated: (i) event type: \u201cdeletion\u201d or \u201cduplication\u201d; (ii) coordinates in the reference genome; (iii) CNV size; (iv) RD normalized to 1; (v) e-val1: There are 5 parameters in viewer mode used for filtering calls: CNV size, e-val1, q0, pN, and dG. Those parameters will define which calls CNVpytor will plot or print out. When calls are printed or exported, CNVpytor optionally can generate graphical file(s) with a plot of the CNV call region containing user-specified tracks.To annotate called regions, we use Ensembl REST API (overlap/region resource). It is an optional step that requires web connection and is executed when calls are previewed by the user or exported to an output file. The annotation is added in an additional column in the output and contains a string with gene names, Ensembl gene IDs, and information about the position of genes relative to CNV .P-value based on BAF signal.The copy number of a provided genomic region is calculated as a mean RD within the region divided by mean autosomal RD scaled by 2. To achieve better precision, first and last bin content are weighted by the fraction of overlap with the provided region. Optionally CNVpytor can provide additional values: (i) e-value from the probability of RD values within the region to be in the tails of a Gaussian distribution of binned RD ; (ii) q0: fraction of reads mapped with q0 quality within call region; (iii) pN: fraction of reference genome gaps (Ns) within call region; (iv) BAF level estimated using maximum likelihood method; (v) number of homozygous variants within the region; (vi) number of heterozygous variants within the region; (vii) To make a joint call set for multiple samples, CNVpytor proceeds in the following way:Filter calls using user-defined ranges for size, p-val, q0, pN, and dG;Sort all calls for all samples by start coordinate;Select first call in that list that is not already processed and select calls from other samples with reciprocal overlap >50%;For selected calls, calculate genotypes within the region of intersection and, optionally, annotate with overlapping genes.If specified, for each joint CNV call CNVpytor will create a graphical file with a plot of the call region containing user-specified tracks.For data storage and compression, we used HDF5 file format and h5py Python library. Additional compression is obtained by storing RD signal using 100-bp bins. The same bin size is used for storing reference genome AT, GC, and N content. Data organization within the .pytor file is implemented in an IO module, which can be used to open and read different datasets from an external application. The Python library xlwt is used to generate spreadsheet files compatible with Microsoft Excel.The Matplotlib  Python lCNVpytor depends on several widely used Python packages: requests 2.0 or higher, gnureadline, pathlib 1.0 or higher, pysam 0.15 or higher, numpy 1.16 or higher, scipy 1.1 or higher, matplotlib 2.2 or higher, h5py 2.9 or higher, xlwt 1.3 or higher. All dependences are available through pip installer, which makes installation of CNVpytor straightforward.GigaScience database, GigaDB [An archival copy of the code and links to data used to create figures are available via the , GigaDB .Project name: CNVpytorhttps://github.com/abyzovlab/CNVpytorProject home page: Operating systems: Platform independentProgramming language: PythonOther requirements: requests \u22652.0, gnureadline, pathlib \u22651.0, pysam \u22650.15, numpy \u22651.16, scipy \u22651.1, matplotlib \u22652.2, h5py \u22652.9, xlsxwriter \u22651.3, pathlib \u22651.0License: MIT LicenseRRID:SCR_021627bio.tools ID: cnvpytorSupplementary Figure 1: Genome-wide plot for K562 cell line: normalized read depth (top), B-allele frequency of individual SNPs (middle), and BAF likelihood function (bottom). Bin size is 100\u00a0kb.Supplementary Figure 2: Circular plot for K562 cell line. Inner circle corresponds to read depth; outer, to binned MAF. Bin size is 100\u00a0kb.Supplementary Figure 3: Comparison of read depth statistics between 2 regions.Supplementary Figure 4: JBrowse export example.  CNVpytor-produced read depth and binned BAF data for a glioblastoma cancer sample for chromosome 1 deletion are visible. (c) JBrowse view of the same data. Same color-coding schema is followed here.Supplementary Figure 5: Comparison between read depth signal parsed from alignment file and variant file for 3 samples: RD Manhattan plot comparison for NA12878 sample (a), K562 sample (b), and HepG2 (c); distribution of differences in copy number within bins for same samples (d\u2013f). Bin size is 10\u00a0kb.Supplementary Figure 6: CNVpytor application on polyp subclonal CNA [onal CNA . Raw RD Supplementary Table 1: Recommended minimal bin size for given coverage to ensure relative deviation of RD signal <10% for 150-bp reads. For 100-bp reads one can use 33% smaller bin size.Supplementary Table 2: Comparison between CNVpytor features with other similar tools.API: application programing interface; BAF: B-allele frequency; bp: base pairs; CNA: copy number alteration; CNV: copy number variation; CPU: central processing unit; Gb: gigabase pairs; GUI: graphical user interface; HETs: heterozygous variants; kb: kilobase pairs; Mb: megabase pairs; RD: read depth; REST: representational state transfer; SNP: single-nucleotide polymorphism; WGS: whole-genome sequencing.The authors declare that they have no competing interests.This study is supported by National Cancer Institute grant U24CA220242 and funds from the Center for Individualized Medicine at Mayo Clinic.A.A. conceived and supervised this study. M.S. designed and developed CNVpytor software. A.P. and M.S. tested the software. A.P. and C.D. developed the plugin for JBrowse. I.H. co-supervised the development of the plugin for JBrowse. M.S., A.P., and A.A. wrote the manuscript. All authors read and approved the final manuscript.giab074_GIGA-D-21-00151_Original_SubmissionClick here for additional data file.giab074_GIGA-D-21-00151_Revision_1Click here for additional data file.giab074_GIGA-D-21-00151_Revision_2Click here for additional data file.giab074_GIGA-D-21-00151_Revision_3Click here for additional data file.giab074_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giab074_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giab074_Response_to_Reviewer_Comments_Revision_2Click here for additional data file.giab074_Reviewer_1_Report_Original_SubmissionWhitney Whitford, PhD -- 6/13/2021 ReviewedClick here for additional data file.giab074_Reviewer_1_Report_Revision_1Whitney Whitford, PhD -- 8/29/2021 ReviewedClick here for additional data file.giab074_Reviewer_2_Report_Revision_1Sheida Nabavi -- 9/11/2021 ReviewedClick here for additional data file.giab074_Reviewer_3_Report_Original_SubmissionXiao Dong -- 7/5/2021 ReviewedClick here for additional data file.giab074_Reviewer_3_Report_Revision_1Xiao Dong -- 9/1/2021 ReviewedClick here for additional data file.giab074_Supplemental_FilesClick here for additional data file."}
+{"text": "Objective: Diabetic kidney disease (DKD) has become the major cause of end-stage renal disease (ESRD) associated with the progression of renal fibrosis. As gut microbiota dysbiosis is closely related to renal damage and fibrosis, we investigated the role of gut microbiota and microbiota-related serum metabolites in DKD progression in this study.Methods: Fecal and serum samples obtained from predialysis DKD patients from January 2017 to December 2019 were detected using 16S rRNA gene sequencing and liquid chromatography-mass spectrometry, respectively. Forty-one predialysis patients were divided into two groups according to their estimated glomerular filtration rate (eGFR): the DKD non-ESRD group (eGFR \u2265 15\u00a0ml/min/1.73\u00a0m2) (n = 22), and the DKD ESRD group (eGFR < 15\u00a0ml/min/1.73\u00a0m2) (n = 19). The metabolic pathways related to differential serum metabolites were obtained by the KEGG pathway analysis. Differences between the two groups relative to gut microbiota profiles and serum metabolites were investigated, and associations between gut microbiota and metabolite concentrations were assessed. Correlations between clinical indicators and both microbiota-related metabolites and gut microbiota were calculated by Spearman rank correlation coefficient and visualized by heatmap.Results: Eleven different intestinal floras and 239 different serum metabolites were identified between the two groups. Of 239 serum metabolites, 192 related to the 11 different intestinal flora were mainly enriched in six metabolic pathways, among which, phenylalanine and tryptophan metabolic pathways were most associated with DKD progression. Four microbiota-related metabolites in the phenylalanine metabolic pathway  and indole-3 acetic acid (IAA) in the tryptophan metabolic pathway positively correlated with DKD progression, whereas L-tryptophan in the tryptophan metabolic pathway had a negative correlation. Intestinal flora g_Abiotrophia and g_norank_f_Peptococcaceae were positively correlated with the increase in renal function indicators and serum metabolite HA. G_Lachnospiraceae_NC2004_Group was negatively correlated with the increase in renal function indicators and serum metabolites [L-(\u2212)-3-phenyllactic acid and IAA].Conclusions: This study highlights the interaction among gut microbiota, serum metabolites, and clinical indicators in predialysis DKD patients, and provides new insights into the role of gut microbiota and microbiota-related serum metabolites that were enriched in the phenylalanine and tryptophan metabolic pathways, which correlated with the progression of DKD. Diabetic kidney disease (DKD) reflects one of the most common microvascular complications of diabetes, typically characterized by albuminuria or reduced estimated glomerular filtration rate (eGFR) . AlthougAccording to the \u201cgut\u2013kidney axis\u201d hypothesis, dysregulation of intestinal microbiota irritates renal tissue through uremic toxins, causing systemic micro-inflammation, renal injury, and fibrosis . Recent Metabolomics is a powerful tool to screen for changes in metabolic profiles and to characterize mechanisms of pathological changes . It can The large and complex microbial community in the human intestinal tract has a profound impact on human metabolic phenotype. As the mediator of the interaction between intestinal flora and diseases, the metabolites can more directly show the relationship between intestinal flora and diseases. Ma et al. combined 16S rRNA and metabolomics technology and determined that flora-metabolites combined with the flora-bacteria might represent a new detection method for breast cancer . EvidencIn the present study, we aimed to investigate gut microbiota profiles and serum metabolic characteristics in predialysis DKD patients that were associated with DKD progression and to explore the correlation between intestinal flora and metabolic disorders using multiomics technology of 16S rRNA gene sequencing and metabolomics.2), and the DKD ESRD group (eGFR < 15\u00a0ml/min/1.73\u00a0m2). The study protocol was approved by the Institutional Ethics Committee of Guangdong Provincial Hospital of Chinese Medicine (No. ZE2020-193-01), and informed consent was obtained before sample collection.This study detected fecal and serum samples of 41 predialysis DKD patients from January 2017 to December 2019 in Guangdong Provincial Hospital of Chinese Medicine. The patients were divided into two groups according to their renal function (eGFR): the DKD non-ESRD group  was calculated using the chronic kidney disease epidemiology collaboration (CKD-EPI) equation .The inclusion criteria were age from 18 to 85 years, diagnosis of DKD, and nonrenal replacement therapy. Note: renal replacement therapy refers to hemodialysis, peritoneal dialysis, and renal transplantation.The exclusion criteria were incomplete clinical data, concomitant active malignant tumor, pulmonary infection, acute coronary heart disease, and other acute complications, antibiotics or probiotics having been taken 3\u00a0months prior to sample collection, and corticosteroid or immunosuppressive therapy prior to sample collection.At least 1\u00a0g of fresh feces was collected by sterilized cotton swabs in a special fecal collection tube. Blood samples were collected by venipuncture in EDTA tubes; serum was separated by centrifugation. Feces and serum were stored immediately at \u221280 \u00b0C until further processing.\u00ae soil DNA Kit . The DNA extract was checked on 1% agarose gel, and DNA concentration and purity were determined with NanoDrop 2000 UV-vis spectrophotometer . The hypervariable region V3\u2013V4 of the bacterial 16S rRNA gene were amplified with primer pairs 338F (5\u2032-ACT\u200bCCT\u200bACG\u200bGGA\u200bGGC\u200bAGC\u200bAG-3\u2032) and 806R (5\u2032-GGACTACHVGGGTWTCTAAT-3\u2032) by an ABI GeneAmp\u00ae 9700 PCR thermocycler . The PCR product was extracted from 2% agarose gel and purified using the AxyPrep DNA Gel Extraction Kit  and quantified using Quantus\u2122 Fluorometer .Microbial community genomic DNA was extracted from feces samples using the E. Z.N.A.Purified amplicons were pooled in equimolar and paired-end sequenced on an Illumina MiSeq PE300 platform/NovaSeq PE250 platform  according to the standard protocols by Majorbio Bio-Pharm Technology Co. Ltd. .The raw 16S rRNA gene sequencing reads were demultiplexed, quality-filtered by fast version 0.20.0  and mergThe metabolites were extracted from 100\u00a0\u00b5l of liquid sample and treated by high-throughput tissue crusher Wonbio-96c , then followed by ultrasound for 30\u00a0min. After centrifugation, the supernatant was carefully transferred to sample vials for LC-MS/MS analysis.Chromatographic separation of the metabolites was performed on an ExionLC\u2122 AD system  equipped with an ACQUITY UPLC HSS T3 column . The UPLC system was coupled to a quadrupole-time-of-flight mass spectrometer  equipped with an electrospray ionization (ESI) source operating in positive mode and negative mode. Data acquisition was performed with the data-dependent acquisition (DDA) mode.http://www.hmdb.ca/) and Metlin database (https://metlin.scripps.edu/).The raw data were imported into the Progenesis QI 2.3  for peak detection and alignment. Mass spectra of these metabolic features were identified by using the accurate mass, MS/MS fragments spectra, and isotope ratio difference with search in reliable biochemical databases, such as the Human Metabolome Database  and \u201cropls\u201d  R package from Bioconductor on the Majorbio Cloud Platform (www.majorbio.com) with a two-sided p-value less than 0.05 considered significant.Results were expressed as frequencies and percentages for categorical variables, mean \u00b1 SD for continuous normally distributed variables, and median  for continuous variables that were not normally distributed. Categorical variables for the patient characteristics were compared using the chi-square test or Fisher\u2019s exact test, and the continuous variables were tested with http://www.mothur.org/), tested by nonparametric Wilcoxon rank sum test, and p < 0.05 was considered statistically significant. Beta diversity measured the difference in OTU composition between different samples and was assessed using partial least squares discriminant analysis (PLS-DA), which is a supervised analysis suitable for high-dimensional data. The corresponding statistical significance of the beta diversity was measured separately by ANOSIM.We used rarefaction curves and species accumulation curves to ensure that the sample size or sequencing depth reached saturation in our study. Gut microbiota alpha diversity index  was analyzed on mothur software  calculated by the LEfSe software (http://huttenhower.sph.harvard.edu/). The correlation between biochemical indicators and various microbes was calculated by Spearman rank correlation coefficient and visualized by heatmap in R using the \u201cheatmap\u201d package.Compositional differences between the two groups from the phylum to genus level were tested with nonparametric Wilcoxon rank-sum test. Variation at the taxonomic level was determined by linear discriminant analysis (LDA) effect size  was calculated in the OPLS-DA model. Values of p were estimated with paired Student\u2019s t-test on single-dimensional statistical analysis. Metabolites with VIP >1 and p < 0.05 were considered statistically significant. We used the area under the receiver operating characteristic (ROC) curve to assess the accuracy of the metabolites in predicting DKD progression.Orthogonal partial least squares discriminate analysis (OPLS-DA) was used for statistical analysis to determine global metabolic changes between comparable groups. All metabolite variables were scaled to Pareto scaling prior to conducting the OPLS-DA. The model validity was evaluated from model parameters http://www.genome.jp/kegg/). These metabolites could be classified according to the pathways they involved or the functions they performed. Enrichment analysis was used to analyze a group of metabolites in a function node whether it appears or not. Scipy. stats (Python packages) (https://docs.scipy.org/doc/scipy/) was exploited to identify statistically significantly enriched pathways using Fisher\u2019s exact test.Differential metabolites between the two groups were summarized and mapped into their biochemical pathways through metabolic enrichment and pathway analysis based on database search  (n = 22) or the DKD ESRD group (eGFR < 15\u00a0ml/min/1.73\u00a0m2 group) (n = 19), with mean ages of 69.63 \u00b1 13.01 and 61.89 \u00b1 9.85 in the two groups, respectively. Compared with the DKD non-ESRD group, the levels of serum creatinine and blood urea nitrogen were higher in the DKD ESRD group (p < 0.001). There were no significant differences in other baseline indicators between the two groups  (The rarefaction curve indicated that the sequencing depth of each sample approached the expected level . Alpha d > 0.05) . The res > 0.05) .Bacteroides represented the highest abundance of OTU in the two groups. The mean relative abundance for Bacteroides was similar in the two groups, accounting for 28.74 \u00b1 17.60% in the DKD ESRD group and 30.33 \u00b1 22.34% in the DKD non-ESRD group. The mean relative abundance for Faecalibacterium was also similar in the two groups, accounting for 3.99 \u00b1 2.91% in the DKD ESRD group and 5.84 \u00b1 7.04% in the DKD non-ESRD group. Likewise, other gut microbiota, such as Blautia, Escherichia\u2013Shigella, Fusobacterium, etc., did not demonstrate a significant difference in their relative abundance in either DKD ESRD or DKD non-ESRD group . Compared with the DKD non-ESRD group, the levels of g_Tyzzerella, g_Ruminococcaceae, g_Catenibacillus, g_Abiotrophia, g_norank_f_Peptococcaceae, g_norank_f_norank_o_Oscillospirales, and f_Aerococcaceae were significantly higher, and the levels of g_Olsenella, g_Faecalicoccus, g_Lachnospiraceae_NC2004_group, and g_Staphylococcus were significantly lower in the DKD ESRD group . G_norank_f_Peptococcaceae had a positive correlation with serum creatinine and a negative correlation with eGFR (p < 0.05). In contrast, g_Lachnospiraceae_NC2004_group had a strong negative correlation with serum creatinine and a positive correlation with eGFR (p < 0.05). G_norank_f_norank_o_Oscillospirales and g_unclassified_f_Ruminococcaceae had a strong negative correlation with glycosylated hemoglobin (HbA1c) (p < 0.05)  .p < 0.05) based on the OPLS-DA model , L-(\u2212)-3-phenyllactic acid, dihydro-3-coumaric acid, and p < 0.05 according to previous studies -3-phenylactic acid. F_Aerococcaceae and g_ Abiotrophia were positively correlated with trans-3-hydroxy-cinnamate. G_ Tyzzerella was positively correlated with dihydro-3-coumaric acid -3-phenylactic acid, ric acid .g_Olsenella, g_Faecalicoccus, and g_Lachnospiraceae_NC2004_Group were negatively correlated with indole-3 acetic acid. G_Tyzzerella was negatively correlated with L-tryptophan  was highly expressed, and L-tryptophan had low expression in the DKD ESRD group compared with the DKD non-ESRD group. Among 11 differential intestinal floras, yptophan .To further verify the role of microbiota-related metabolites enriched on the phenylalanine and tryptophan metabolic pathways in DKD progression, a correlation analysis between the above six microbiota-related metabolites and clinical indicators was undertaken. Consistent with the results of comparison between groups, HA, L-(\u2212)-3-phenyllactic acid, and dihydro-3-coumaric acid in the phenylalanine metabolic pathway and IAA in the tryptophan metabolic pathway were positively correlated with serum creatinine and negatively correlated with eGFR, whereas L-tryptophan in the tryptophan metabolic pathway was opposite .trans-3-hydroxy-cinnamate, dihydro-3-coumaric acid], and IAA in the tryptophan metabolic pathway positively correlated with DKD progression, whereas L-tryptophan in the tryptophan metabolic pathway had a negative correlation. Intestinal flora g_Abiotrophia and g_norank_f_Peptococcaceae, both of which positively correlated with DKD progression, had a positive correlation with a high level of HA. G_Lachnospiraceae_NC2004_Group, which negatively correlated with DKD progression, also had a negative correlation with a high level of IAA and L-(\u2212)-3-phenyllactic acid, simultaneously. In addition, g_Tyzzerella was positively correlated with dihydro-3-coumaric acid and negatively correlated with L-tryptophan. G_unclassified_f_Ruminococcaceae was positively correlated with HA, but negatively with HbA1c. These results indicated the potential role of specific gut microbiota in the DKD progression associated with the phenylalanine and tryptophan metabolism.In this study, 11 significantly different intestinal flora and 239 significantly different metabolites were identified between the DKD non-ESRD group and the DKD ESRD group. The phenylalanine and tryptophan metabolic pathways were most associated with DKD progression. Four microbiota-related metabolites in the phenylalanine metabolic pathway  in the phenylalanine metabolic pathway were positively correlated with deterioration of renal function in DKD patients. Abnormal phenylalanine metabolism has previously been demonstrated in patients with diabetes  and typeAs intermediates of phenylalanine metabolism, HA, which is a common protein-bound uremic toxin (PBUT) in patients with ESRD, is related to the progress of renal fibrosis due to its oxidative stress-associated toxicity . It is gThe gut microbiota makes up the largest microecosystem in the human body and is closely related to metabolic disorders in kidney disease. Several studies have reported the relationship between gut microbiota and phenylalanine metabolism in CKD patients , but theStudies have demonstrated the significance of the gut microbiota in contributing to the synthesis of HA in phenylalanine metabolism . For exag_Abiotrophia and g_norank_f_Peptococcaceae in DKD progression, and their positive correlation with serum HA concentration in DKD, which has not been previously reported. However, an increasing amount of evidence has suggested their involvement in abnormal glucose and lipid metabolism and insulin resistance  to indole  and serum IAA, among which, g_Lachnospiraceae_NC2004_Group was negatively correlated with L-(\u2212)-3-phenyllactic acid and serum creatinine level, indicating its potential role in the DKD progression via both the phenylalanine and tryptophan metabolic pathways. G_Lachnospiraceae_NC2004_Group is a Firmicutes member belonging to f_Lachnospiraceae, which was mainly involved in the generation of IAA (g_Olsenella, g_Faecalicoccus, and IAA synthesis, and their role in DKD progression.In concert with previous studies , our stuo indole , which co indole . Therefon of IAA . It is an of IAA , convertn of IAA . There hG_Tyzzerella was negatively correlated with L-tryptophan and positively correlated with dihydro-3-coumaric acid, indicating its association with the phenylalanine and tryptophan metabolic disorders. As previously reported, g_Tyzzerella expression was increased in people at high cardiovascular risk (_Tyzzerella and DKD renal function indicators in this study. The role of g_Tyzzerella in DKD progression needs further investigation.lar risk , and corlar risk , which mg_Abiotrophia, g_norank_f_Peptococcaceae, and g_Lachnospiraceae_NC2004_Group in DKD progression, and their involvement in phenylalanine and tryptophan metabolism. These findings offer real promise in finding a new therapeutic strategy that targets protein-bound uremic toxin HA and IAA in DKD. However, our study has some limitations. First, because this was a retrospective study, we lack records of patient drug and dietary intake, so it was not possible to account for the influence that drugs and dietary habits might have had on intestinal flora and the metabolic profile. Second, the sample size was small and would need to be expanded in future studies. Nevertheless, all participants were residents of Guangdong Province, with characteristics and living habits that were relatively concentrated and consistent. Third, the result of gut microbiota is based on 16S rRNA gene sequencing. Further analysis based on gut metagenome, which could provide more bacterial information, is needed.This study reported the relationship between intestinal microecology and DKD progression by associating intestinal microflora with metabolites via multiomics-integrated methods. The results identified the potential role of In conclusion, this study highlights the complex, interactive network of gut microbiota, serum metabolites, and clinical indicators of predialysis DKD patients and provides new insights into the role of gut microbiota and microbiota-related serum metabolites enriched in phenylalanine and tryptophan metabolic pathways in the progression of DKD."}
+{"text": "Here, we report the nearly complete genome sequences of nine severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants with the D614G mutation. These viruses were detected from various infected individuals with different levels of severity from Pahang, Malaysia. In addition, this study described the presence of lineage B.1.351 as a type of variant of concern (VOC) and lineages B.1.466.2 and B.1.524 as local variants. Coronaviridae and genus Betacoronavirus  and traced from active contact tracing during severe acute respiratory infection (SARI) surveillance. The study was approved by the International Islamic University Malaysia Research Ethics Committee (IREC 2021-080).The current pandemic of coronavirus disease 19 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which belongs to the viral family onavirus . The clihttps://github.com/CDCgov/SARS-CoV-2_Sequencing/tree/master/protocols/BFX-UT_ARTIC_Illumina. Briefly, the raw reads were aligned to the reference strain WuHan-Hu-1 genome (GenBank accession number MN908947) using the Burrows-Wheeler Aligner MEM algorithm (BWA-MEM) v0.7.17-r1188  and converted into cDNA using SuperScript IV reverse transcriptase (Invitrogen) with some modifications; a hexamer annealing and extension step of 25\u00b0C for 2 min was performed, followed by cDNA synthesis at 42\u00b0C for 50 min. A portion (1:10 volume) of the cDNA from sample IIUM91 was used as the template for multiplex PCR using Q5 high-fidelity DNA polymerase  and the Artic v3 primer pools. The amplicons for more recent samples were generated using the commercially available NEBNext ARTIC SARS-CoV-2 companion kit (NEB). Equal volumes of PCR products obtained from the two primer pools were mixed; pool 1 and pool 2 were mixed according to the designated protocols  are SRP286590 and SRP324679. The sequences in the GISAID database are as follows: EPI_ISL_455313, EPI_ISL_2622006, EPI_ISL_2622007, EPI_ISL_2622045, EPI_ISL_2622046, EPI_ISL_2622047, EPI_ISL_2622079, EPI_ISL_2622088, and EPI_ISL_2622089.These sequences were deposited in GenBank under the accession numbers"}
+{"text": "Candida nivariensis. We sequenced both the DNA and RNA of this species using both the Oxford Nanopore Technologies and Illumina platforms. We assembled the genome into an 11.8\u2009Mb draft composed of 16 contigs with an N50 of 886 Kb, including a circular mitochondrial sequence of 28 Kb. Using direct RNA nanopore sequencing and Illumina cDNA sequencing, we constructed an annotation of our new assembly, supplemented by lifting over genes from Saccharomyces cerevisiae and Candida glabrata.We present a highly contiguous genome and transcriptome of the pathogenic yeast,  Candida genus are a major source of morbidity and mortality . This protein family includes many genes that encode for adhesion proteins that are found in various members of the Candida genus, and play a key role in pathogenicity, being involved in regulation of biofilm formation, cell-to-cell contact, and host\u2013pathogen interactions (C. nivariensis (GenBank: GCA_001046915.1) is highly fragmented. Constructed from sequencing of strain CBS9983, the reference genome consists of 123 contigs with an N50 of 248 Kb , and each was sequenced on a separate MinION flowcell (R9.4). Two Illumina libraries were prepared with the Nextera Flex Library Prep Kit, each using 400\u2009ng of extracted DNA. Both Illumina libraries were then sequenced on a single iSeq 100 run.RNA was extracted from liquid culture using the Zymo Fungal/Bacterial RNA MiniPrep Kit. Using the NEBNext Poly(A) mRNA Magnetic Isolation Module, polyA tailed mRNA was isolated from the total RNA. Two ONT direct RNA sequencing libraries were prepared and sequenced on separate MinION flowcells, each using \u223c200\u2009ng of polyA selected RNA and the SQK-RNA002 sequencing kit. With the NEBNext Ultra II RNA First-Strand Synthesis Module and the NEBNext Ultra II Non-Directional RNA Second Strand Synthesis Module, cDNA was prepared from the isolated mRNA. Two individual Illumina libraries were then prepared with the Nextera Flex Library Prep Kit, each using 400\u2009ng of cDNA. Both library replicates were then sequenced on a single iSeq 100 run, generating 2 \u00d7 150 paired-end reads.Nanopore data were basecalled using Guppy v3.2.4 on default settings. Reads greater than 3\u2009kb long with an average basecalling quality score greater than 7 were assembled into 21 contigs using Canu v2.1  with aliC. nivariensis (NCBI: NC_036379.1) using Mummer, and observed a 3662-bp sequence in the reference mitochondrial genome which appears at both ends of our 32-kb circular contig. Using the Mummer alignments , we removed the extraneous 3662\u2009bp from the end of our contig, resulting in a 28-kb mitochondrial genome, which we named \u201cJHU_Cniv_v1_mito.\u201d Lastly, we remapped the ONT and Illumina reads back to the assembly, and found no bases with zero coverage, indicating that none of our contigs need to be further broken . Henceforth, we refer to this assembly as \u201cJHU_Cniv_v1.\u201dOf our 21 corrected contigs, 5 were flagged as repeats by Canu and originally constructed from fewer than 180 nanopore reads. The remaining 16 contigs were constructed from over 1800 nanopore reads each. Because the five repetitive contigs were constructed from so few reads and were found to occur elsewhere in the assembly through Mummer v4.0.0rc1 , we exclRepeat regions were identified by Tandem Repeats Finder v4.09  with setet al. 2014) in order to check for any remaining adapter sequences and to filter out reads with low base quality. HISAT2 v2.1.0 was used on default settings to align the trimmed cDNA reads to the assembly. The BRAKER v2.1.5 (C. glabrata (NCBI: GCF_000002545.3), Saccharomyces cerevisiae (NCBI: GCF_000146045.2), Candida albicans (NCBI: GCF_000182965.3).Illumina RNA-seq reads were trimmed using Trimmomatic v0.39 . Code used for analysis is available at https://github.com/timplab/nivar. Supplementary materials and data files are available on figshare: https://doi.org/10.25387/g3.14381858.All sequence data are available in the Sequence Read Archive, under BioProject PRJNA686979. This Whole Genome Shotgun project has been deposited at DDBJ/ENA/GenBank under the accession C. nivariensis, JHU_Cniv_v1 (Methods). Our assembly consists of 11.8\u2009Mb of sequence in 16 contigs with an N50 of 886 Kb , as opposed to a nearly perfect 1:1 alignment between JHU_Cniv_v1 and the current C. nivariensis reference genome . This indicated that the C. glabrata genome is not sufficiently similar to C. nivariensis to use as a reference for contig scaffolding. Using the C. nivariensis reference genome for scaffolding similarly results in erroneous placement of telomere repeats in the middle of scaffolds, or no change to our assembly. This is unsurprising, as the C. nivariensis reference genome is so highly fragmented.We tried to further scaffold our assembly using the more contiguous and highly related To assess assembly completeness, fungal single-copy orthologs were checked using BUSCO v5.0.0 . This miMethods). Our final annotation of JHU_Cniv_v1 comprises 25,979 features, 5859 of which are genes (Supplementary Table S2), the rest of which are more detailed features including individual exons, coding sequence, and start/stop codons. Current annotations of closely related yeasts report similar gene counts (Supplementary Table S3). In order to assess transcriptome completeness, BUSCO was used in transcriptome mode, again with its saccharomycetes_odb10 database. Because no annotation of the C. nivariensis reference genome currently exists, we compared our transcriptome to those of C. glabrata, S. cerevisiae, and C. albicans. Compared to these highly characterized yeast transcriptomes, ours contains slightly fewer complete and single-copy BUSCOs (1876 of 2137 searched) and roughly double the number of complete and duplicated BUSCOs (232 of 2137 searched). The numbers of missing and fragmented BUSCOs between the three are comparable (We annotated our new assembly by lifting over genes from related yeasts and adding gene predictions based on long- and short-read RNA sequencing from the same strain (mparable .C. glabrata subtelomeric regions have been proven to be difficult to correctly assemble using short-read data (C. glabrata subtelomere gene homologs between the C. nivariensis reference genome and JHU_Cniv_v1. Using the assembly and re-annotation of C. glabrata from C. glabrata subtelomere genes and used BLAST (v2.6.0+) to find any matches in the C. nivariensis reference and JHU_Cniv_v1. We observed an identical set of 48 C. glabrata subtelomere genes in both C. nivariensis genomes but found that the copy number for several genes was greater in JHU_Cniv_v1 (C. glabrata genes with homology in C. nivariensis, 35 are ribosomal. With the exception of just three ribosomal genes, which occur a similar number of times in both C. nivariensis genomes, all homologous ribosomal genes appear once in the reference, and either four or six times in JHU_Cniv_v1 (As _Cniv_v1 . To accoC. glabrata, the putative adhesins typically spanned multiple kilobases (glabrata GPI-CWPs. To find the corresponding adhesin genes in the C. nivariensis reference genome, we again used BLAST, and compared the longest hit of each adhesin gene to the true length of the gene as predicted in JHU_Cniv_v1 (Using JHU_Cniv_v1, we identified GPI-anchored membrane proteins among annotated genes >1000-nt long. Using GffRead , we consilobases , though _Cniv_v1 . NotablyCandida nivariensis constructed by long reads and polished by short reads. It spans large, repetitive gaps in the nivariensis genome that have fragmented short-read assemblies thus far, and includes a full mitochondrial chromosome, as well as telomere repeats. These telomere repeats are identical to those in C. glabrata and have been found to be shared within the entire \u201cglabrata group\u201d (C. nivariensis has 13 chromosomes, which is in agreement with previous PFGE data (JHU_Cniv_v1 is a high quality, extremely contiguous assembly of C. nivariensis reference and JHU_Cniv_v1 are comparable to other related yeasts, with our genome slightly improved over the previous reference. However, while JHU_Cniv_v1 is a much more contiguous assembly than any C. nivariensis genome preceding it, the few remaining sequence errors still can pose a problem to downstream analyses, as evidenced by the seemingly absent BUSCO we manually identified.As assessed by BUSCO, genome completeness of the current Our accompanying RNA-seq data enabled us to annotate this genome, achieving a similar level of BUSCO completeness to some of the most highly studied model organisms. Our annotation has comparable or lower levels of missing and fragmented BUSCOs compared to the reference annotations, though more duplicated ones. While our annotation is largely comparable to those of similar yeasts, it has not been manually curated, and should thus be treated as preliminary. Of course, as these organisms were grown under only one condition before RNA extraction, it remains unlikely that this annotation is fully complete.C. glabrata. For each subtelomeric C. glabrata gene with homology in C. nivariensis, more copies were found in JHU_Cniv_v1, as its contiguity allows it to more easily capture repeated genome elements. We note that of subtelomeric glabrata genes found, the majority are ribosomal, and of these, only three do not show a four or six times increased copy number in JHU_Cniv_v1. Due to the repetitive nature of rDNA arrays, it can be difficult for short-read genome assemblies to capture them in their full complexity. Conversely, our long-read assembly more easily spans these regions, potentially providing a clearer look at the biology in which they are involved.To demonstrate the utility of genome and annotation contiguity, we examine genes from a difficult to assemble region in In addition to genes arranged in complex and repetitive patterns, our more contiguous assembly enables analysis of large genes with internal repeats, such as GPI adhesins. Since these genes are so large, it can be difficult or impossible to predict them from fragmented assemblies which are unable to capture them in their full length. As adhesins are critical to understanding elements of pathogenicity in these yeasts, fragmented genome assemblies and missing gene annotations can be crippling to this dimension of research in these organisms."}
+{"text": "Paphiopedilum hirsutissimum is a member of Orchidaceae family that is famous for its ornamental value around the globe, it is vulnerable due to over-exploitation and was listed in Appendix I of the Convention on International Trade in Endangered Species of Wild Fauna and Flora, which prevents its trade across borders. Variation in flower color that gives rise to different flower patterns is a major trait contributing to its high ornamental value. However, the molecular mechanism underlying color formation in P. hirsutissimum still remains unexplored. In the present study, we exploited natural variation in petal and labellum color of Paphiopedilum plants and used comparative transcriptome analysis as well as pigment measurements to explore the important genes, metabolites and regulatory pathways linked to flower color variation in P. hirsutissimum.P. hirsutissimum. Importantly, over-expression of some of these candidate TFs increased anthocyanin accumulation in tobacco leaves which provided important evidence for the role of these TFs in flower color formation probably via regulating key structural genes of the anthocyanin pathway.We observed that reduced anthocyanin and flavonoid contents along with slightly higher carotenoids are responsible for albino flower phenotype. Comparative transcriptome analysis identified 3287 differentially expressed genes (DEGs) among normal and albino labellum, and 3634 DEGs between normal and albino petals. Two genes encoding for flavanone 3-hydroxylase (F3H) and one gene encoding for chalcone synthase (CHS) were strongly downregulated in albino labellum and petals compared to normal flowers. As both F3H and CHS catalyze essentially important steps in anthocyanin biosynthesis pathway, downregulation of these genes is probably leading to albino flower phenotype via down-accumulation of anthocyanins. However, we observed the downregulation of major carotenoid biosynthesis genes including VDE, NCED and ABA2 which was inconsistent with the increased carotenoid accumulation in albino flowers, suggesting that carotenoid accumulation was probably controlled at post-transcriptional or translational level. In addition, we identified several key transcription factors  that may regulate structural genes involved in flower color formation in P. hirsutissimum with different flower color patterns by manipulating the anthocyanin and carotenoid biosynthesis pathways.The genes identified here could be potential targets for breeding The online version contains supplementary material available at 10.1186/s12870-021-03256-3. Paphiopedilum hirsutissimum is one of the important members of Orchidaceae family having 736 genera. It is the second largest family of flowering plants mainly famous for its aesthetic and ornamental values  that catalyzes the formation of leucoanthocyanidins from dihydroflavonols in anthocyanin biosynthesis [TRINITY_DN51455_c0_g1_i1|m.23490) was observed in both labellum and petal tissues of albino flower  and CCD8 (TRINITY_DN53000_c0_g1_i1|m.68975) were upregulated while CCD7 (TRINITY_DN64103_c1_g1_i3|m.74100) was downregulated in the albino flower tissues compared to the normal tissues  for lutein biosynthesis from \u03b1-carotene was upregulated in albino petals compared to normal petals, however, no change was observed in labellum tissues. Violaxanthin de-epoxidase (VDE) is another important enzyme that catalyzes the formation of violaxanthin from zeaxanthin [TRINITY_DN58026_c0_g1_i1|m.78883) was significantly downregulated in the albino labellum compared to the normal sample  was significantly downregulated in albino petals (AP) compared to the normal sample which suggests the down-accumulation of xanthoxin  (TRINITY_DN54486_c0_g1_i1|m.33185) was upregulated in AL tissues that seems to enhance the formation of dihydroxy-phaseic acid, a by-product of carotenoid biosynthesis pathway. Xanthoxin dehydrogenase (ABA2) is another key enzyme that catalyzes the last step of ABA biosynthesis. Gene encoding ABA2 (TRINITY_DN52666_c0_g2_i1|m.83328) was also downregulated in AL tissues. Notably, we observed downregulation of major carotenoid pathway genes  which was inconsistent with increased carotenoid accumulation.Carotenoids are important pigments in photosynthetic and non-photosynthetic organs of plants . To idenway Fig.\u00a0. \u03b2-carotaxanthin . A VDE eP. hirsutissimum.Some transcription factor (TF) families including MYB, bHLH, MADS-Box and ERF play important roles in color formation via anthocyanin biosynthesis by regulating the expression of key structural genes. We therefore analyzed the expression pattern of these TF families in AL vs NL and AP vs NP tissues Fig.\u00a0. Twenty-Nicotiana benthamiana) leaves and measured the anthocyanin accumulation, the major regulator of flower color. 4 out of 5 TFs belonging to each of the four tested TFs families significantly increased the anthocyanin accumulation in tobacco leaf compared to the control leaves  with contrasting expression patterns in our RNA-seq data using qRT-PCR analysis. All the TFs encoding genes showed more or less similar expression trend in our qRT-PCR data as observed in RNA-seq data Fig.\u00a0A. This gves Fig. B. This shttps://www.petalrepublic.com/floristry-and-floriculture-statistics/) and Kenya (http://www.kenyarep-jp.com/business/flower_e.html). P. hirsutissimum belongs to the family of orchids and have ornamental plants with unique flower patterns [P. hirsutissimum plants by using natural variation in flower color. The wild-type flowers of P. hirsutissimum have pink-rose petals and yellow labellum with purple spots which are very attractive to people and give a pleasant feeling. Its natural albino variant shows off-white petals with yellowish-green labellum that provides a good source of study the mechanism of flower color variation. In accordance with the albino phenotype, we observed reduced anthocyanins and increased carotenoid contents in mutant  and ABA2 (TRINITY_DN52666_c0_g2_i1|m.83328) were downregulated in albino petals and labellum, respectively  positively regulate the expression of carotenoid biosynthesis genes and affect carotenoid accumulation and flower color in Mimulus lewisii [Medicago truncatula [P. hirsutissimum. Previously, the function of MYB genes was tested using VIGS approach by silencing some MYB genes [MdERF38 positively regulates anthocyanin accumulation by interacting with MdMYB1 [VmTDR4 was shown to regulate anthocyanin accumulation and fruit color by interacting with MYB TFs [P. hirsutissimum flowers  that are probably regulating the expression of important structural genes. Overexpression of 4 out of 5 TFs significantly increased the anthocyanin accumulation in tobacco which provided important evidence of the role of these TFs in flower color formation. Further functional characterization of these TFs via overexpression, knock-out and protein-DNA interaction approaches could further improve our understanding of the mechanism of their action and flower color formation.In this study, we used comparative transcriptome and biochemical analysis to explore the important genes and regulatory pathways linked to flower color variation in Additional file 1: Table S1: Primer sequences of genes used for qRT-PCR and vector construction.Additional file 2: Table S2: Differentially expressed genes (DEGs) among normal and albino labellum.Additional file 3: Table S3: DEGs between normal and albino petals.Additional file 4: Figure S1a: DEGs expressed only in normal flower tissues.Additional file 5: Figure S1b: DEGs expressed only in normal labellum.Additional file 6: Figure S2: Gene ontology enrichment analysis among normal and albino tissues.Additional file 7: Figure S3A: KEGG enrichment analysis among normal and albino labellum.Additional file 8: Figure S3B: KEGG enrichment analysis among normal and albino petals."}
+{"text": "Circular RNAs (circRNAs) are involved in the pathogenesis of certain renal diseases, however, the function and mechanism of them in renal fibrosis remains largely unknown. In the present study, RNA expression data in unilateral ureteral obstruction (UUO) kidneys was obtained from our previous circRNA Microarray and public Gene Expression Omnibus datasets to construct a ceRNA network. The effects of target circRNA as long as the homologous human circRNA on renal fibrosis was examined in vitro and in vivo. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was further performed among genes regulated by the human circRNA. We found that circRNA_37492, showing well connection degree in the ceRNA network, was abundant expression and high sequence conservation. We observed that the expression of circRNA_37492 was induced by the TGF-\u03b21 or UUO in BUMPT cells and C57BL/6 mice, respectively. In vitro, cytoplasmic circRNA_37492 inhibited type I, III collagen and fibronectin deposition by sponging miR-7682-3p and then upregulated its downstream target Fgb. In vivo, overexpression of circRNA_37492 attenuated fibrotic lesions in the kidneys of UUO mice via targeting miR-7682-3p/Fgb axis. Furthermore, hsa_circ_0012138, homologous with circRNA_37492, may potentially target miR-651-5p/FGB axis in human renal fibrosis. Not only that, GO and KEGG enrichment revealed that hsa_circ_0012138-regulated genes were previously demonstrated to related to the fibrosis. In conclusion, we for the first time demonstrated that circRNA_37492 attenuated renal fibrosis via targeting miR-7682-3p/Fgb axis, and the homologous hsa_circRNA_0012138 was speculated as a possible ceRNA to regulate multiple gene expressions and involve in human renal fibrosis, suggesting that circRNA_37492/hsa_circ_0012138 may serve as potent therapy target for obstructive renal fibrosis disease. Chronic kidney disease (CKD) is a major public health problem with significant morbidity and mortality all over the world, obstructive nephropathy (ON) is the main cause of CKD . Renal fCircular RNA (circRNA), lacking 5\u2032cap and a 3\u2032poly (A) tail, is a stable ring structure with highly conservative sequences across species , 7, and In this study, non-coding RNA expression profiles from public Gene Expression Omnibus datasets and our previous circRNA array data was used to comprehensively evaluate the landscape of competing endogenous RNAs (ceRNAs) network and to determine the key ceRNA pathway with targeted circRNA. After selection, the key circRNA, miRNA and mRNA still go through further experimental validation in vitro and vivo. In addition, to push these results towards clinical use, the homologous circRNA in human was searched out and verified by experiments and bioinformatics. Identification of these circRNAs and their role, may reveal novel therapeutic targets for renal fibrosis caused by ON.As the flow diagram Fig.  shows, RThese circular RNAs  from sankey diagram were selected and the top 7 circular RNAs ranked by sequence conservation score were shown in Fig. The fluorescent in situ hybridization (FISH) and confocal microscopy showed that circRNA_37492 was mainly enriched in the cytoplasm of Boston University mouse proximal tubular (BUMPT) cells Fig. S. In addiTo further clarify the role of circRNA_37492 in renal fibrosis, siRNA circRNA_30032 was transfected into BUMPT cells, and then treated with or without TGF-\u03b21 for 24\u2009h. The RT-qPCR analysis showed that siRNA circRNA_30032 silenced the expression of circRNA_30032 under basic and TGF-\u03b21 treatment condition Fig. S. FurtherThe above data verified that circRNA_37492 has an anti-fibrosis role, hence, we proposed that overexpression of it might ameliorate renal fibrosis. Here, The RT-qPCR analysis showed that overexpression of circRNA_30032 enhanced the expression of circRNA_30032 under basic and TGF-\u03b21 treatment condition Fig. S. WesternThe prediction from Arraystar\u2019s home-made miRNA target prediction software showed that circRNA_37492 contained the binding sites of five miRNAs, among them, miR-7682-3p, as a component of the ceRNA network, was a potential downstream target of circRNA_37492 Fig. . The lucMiR-7682-3p was predicted to target 5913 genes with 8734 sites in the 3\u2019UTR, however, its role in renal fibrosis remains unclear. The RT-qPCR analysis indicated that the expression of miR-7682-3p was notably enhanced under basic and TGF-\u03b21 treatment Fig. . In addiFgb (fibrinogen beta chain gene) can facilitate early wound healing by polymerizing into insoluble fibrin matrix to stabilize the lesion, and promote cell migration and proliferation during re-epithelialization. It was also was involved in anti-liver fibrosis and functioned in protecting against kidney ischemia/reperfusion injury \u201318. In tTo further confirm whether antifibrotic role of circRNA_3749 was mediated by the miR-7682-3p, we performed a recovery assay with siRNA circRNA_37492 and miR-7492-3p inhibitor. RT-qPCR showed that they were successfully transfected into BUMPT cells and worked well Fig. . WesternTo further explore the antifibrotic role of circRNA_37492 in vivo, circRNA_37492 plasmids were injected via tail vein, and then subjected to UUO as described above. In corresponding with the experiment in vitro, overexpression of circRNA_37492 attenuated UUO-induced tubular dilation, renal cortical atrophy, and ECM accumulation on HE and Masson\u2019s trichrome staining Fig. , which whsa_circ_0012138 originates from the best transcript NM_024066) of ERI3 (gene symbol) and was matched with circRNA_37492  to be the homologous circRNA Table . Eri3 ge066 of ERAs a result of the special loop structure, circRNAs are more stable than their linear host genes, so multiple sequence-conserved mouse circRNAs may exist in human. The role and regulatory mechanism of circRNAs in renal fibrosis need urgently to be uncovered. In this study, by construction of the ceRNA network, we for the first time identified a mouse circRNA with its homologous hsa_circ_0012138 associated with the pathophysiology of renal fibrosis. Mechanistically, circRNA_37492 bound to miR-7682-3p as its ceRNA to induce the expression of Fgb. Interestingly, overexpression of circRNA_37492 attenuated the TGF-\u03b21/UUO-induced renal fibrosis via targeting the miR-7682-3p/Fgb axis.Several studies reported that circRNAs mediated the progression of diabetic nephropathy, lupus nephritis, and focal segmental glomerulosclerosis \u201315, and As we know, circRNAs usually sponged miRNA to regulate mRNA \u201331. HereInterestingly, hsa_circ_0012138/miR-651-5p/FGB may constitute potential ceRNA axis in human renal fibrosis, according to further study  & miRanda (http://www.miranda.org/) [P value\u2009<\u20090.05, |log2FC|>\u2009=\u20091). The predicted microRNAs were intersected with downregulated microRNAs, and then intersection was taken as candidate circRNAs-miRNAs. Next, the targeted genes of candidate miRNAs were predicted using TargetScan database and were intersected with upregulated genes in GSE145053  as candidate miRNAs-mRNAs. Finally, the candidate circRNAs-miRNAs were intersected with candidate miRNAs-mRNAs to establish the circRNA-miRNA-mRNA ceRNA network, which was visualized by \u201cggalluvial\u201d R package. In addition, the NetworkAnalyzer plug-in in Cytoscape software (http://www.cytoscape.org) was used to calculate the topological parameters of the network [The circRNA chip assay was used to detect expression of circRNA in kidney of UUO model. Differential expression analysis was performed for the identification of upregulated circRNAs (fold change >2). Predicted miRNAs were determined using Arraystar\u2019s home-made miRNA target prediction software based on TargetScan (da.org/) . Express network , the cir network .2/95% air, and subsequently transfected with miR-7682-3p inhibitor (100\u2009nM), miR-7682-3p mimics (100\u2009nM), circRNA_37492 siRNA (50\u2009nM), circRNA_37492 plasmids (50\u2009nM), siRNA Fgb (100\u2009nM), or negative-control plasmid  using Lipofectamine 2000 Transfection Reagent . Twenty-four hours after transfection, the cells were starved and treated with or without 5\u2009ng/mL of TGF-\u03b21  for different times.The BUMPT cells were initially obtained from Drs. John Shwartz & William Lieberthal at Boston University , and incThe dual luciferase assay kit (cat. no. KGAF040) was purchased from KeyGEN BioTECH , and all the plasmids were constructed by Sangon Biotech Company . Reporter assays were performed using the dual luciferase assay system as described previously , 37. Then\u2009=\u20093 per group). The mice were randomly allocated to experimental groups by Random function in Excel software  and no blinding was needed. The mice were injected with saline or 25\u2009\u00b5g circ_37492 plasmid  by tail vein (once a day for consecutive 3 days), and then the left ureter was ligated to construct the UUO model according to the previous studies [Male C57BL/6 mice (8\u201310 weeks of age) were purchased from Sippr-BK Laboratory Animal Corporation . Animal experiments were reviewed and approved by the Animal Ethical and Welfare Committee of The Second Xiangya Hospital (China). Mice were bred with free water and food in a specific pathogen-free conditions under a 12-h light/12-h dark cycle. The sample size was estimated to 12  [Antibodies for Collagen I (cat. no. ab34710), III (cat. no. ab7778), FN (cat. no. ab2413), and Fgb (cat. no. ab189490) were purchased from Abcam , whereas anti-GAPDH (cat. no. T0004) were obtained from Affinity Biosciences . Mice kidney tissues were fixed and cut into slices, then stained with hematoxylin and eosin (H&E) and Masson\u2019s trichrome as we previously described . We usedgov/ij/) .All PCR primers were synthesized by Sangon Biotech Company  and listed in Table FISH Kit (cat. no. C10910) was purchased from RiboBio, RNA FISH probes were also synthesized by RiboBio . The experiment was performed based on the instructions of the FISH Kit. For FISH analysis, BUMPT cells and mice kidney were fixed with 4% paraformaldehyde (Sigma) and hybridized with the fluorescence probes of miR-7682-3p and circRNA_37492. U6 and 18S rRNA served as the nuclear and cytoplasmic controls, respectively. DAPI was used to stain nuclei, U6, 18S rRNA, and circRNA37492 were labeled by CY3, miR-7682-3p was labeled by FAM. The slides were hybridized at 37\u2009\u00b0C overnight with the probes. The confocal Laser Scanning Microscope  was used to take fluorescence images.n\u2009=\u20098) and radical nephrectomy (n\u2009=\u20094) at the clinic , as a result, hsa_circ_0012138 was matched with it to be the homologous circRNA, and was further tested expression levels in human obstructive hydronephrosis kidney samples by RT-qPCR. miRNAs interacting with hsa_circ_0012138 were predicted based on the starBsae (http://starbase.sysu.edu.cn/) [http://www.targetscan.org/), on the other hand, differential expression analysis were performed to identify upregulated genes based on gene expression profiles in GSE66494 dataset  using the R package \u2018limma\u2019 . Only those predicted genes truly upregulated in GSE66494 can be recognized as targeted genes. Accordingly, the expression of homologous gene and its predicted binding miRNA were assayed by western blot and RT-qPCR. Moreover, to further clarify the potential biological process and understand the potential pathways of differential expression mRNAs regulated by hsa_circ_0012138, we performed Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis using the R package \u2018clusterProfiler\u2019, and then visualized the top 10 terms [Sequence conservation for circRNA_37492 was tested by blast function in circBase website (edu.cn/) , 48. On 10 terms .t-tests, for multiple group comparison, we used one-way ANOVA test. Quantitative data are presented as the mean and SD (mean\u2009\u00b1\u2009SD). The difference was considered statistically significant when P\u2009<\u20090.05.Statistical analyses were performed using GraphPad Prism . For comparisons between two groups, we used two-tailed Student Supplementary InformationOriginal Dataaj-checklist"}
+{"text": "Purpose. We investigated the disparate influence of lesion location on functional damage and reorganization of the sensorimotor brain network in patients with thalamic infarction and pontine infarction. Methods. Fourteen patients with unilateral infarction of the thalamus and 14 patients with unilateral infarction of the pons underwent longitudinal fMRI measurements and motor functional assessment five times during a 6-month period . Twenty-five age- and sex-matched controls underwent MRI examination across five consecutive time points in 6 months. Functional images from patients with left hemisphere lesions were first flipped from the left to the right side. The voxel-wise connectivity analyses between the reference time course of each ROI , pons, ventral anterior (VA), and ventral lateral (VL) nuclei of the thalamus) and the time course of each voxel in the sensorimotor area were performed for all five measurements. One-way ANOVA was used to identify between-group differences in functional connectivity (FC) at baseline stage (<7 days after stroke onset), with infarction volume included as a nuisance variable. The family-wise error (FWE) method was used to account for multiple comparison issues using SPM software. Post hoc repeated-measure ANOVA was applied to examine longitudinal FC reorganization. Results. At baseline stage, significant differences were detected between the contralateral VA and ipsilateral postcentral gyrus , contralateral VL and ipsilateral precentral gyrus . Repeated measures ANOVA revealed that the FC change of cl_VA-ip_postcentral differ significantly among the three groups over time. The significant changes of FC between cl_VA and ip_postcentral at different time points in the thalamic infarction group showed that compared with 7 days after stroke onset, there was significantly increased FC of cl_VA-ip_postcentral at 1 month, 3 months, and 6 months after stroke onset. Conclusions. The different patterns of sensorimotor functional damage and reorganization in patients with pontine infarction and thalamic infarction may provide insights into the neural mechanisms underlying functional recovery after stroke. Motor function impairment, as well as rehabilitation, depends highly on infarction locations in patients with stroke , 2. A prBased on the anatomical site of cerebral infarction, patients can be subdivided into supratentorial and infratentorial cerebral infarctions. Most supratentorial infarction-associated functional magnetic resonance imaging (fMRI) studies have focused on stroke patients with lesions in the basal ganglia or corona radiata , 6, yet Previous evidence suggests that thalamic and pontine infarction could cause impairment of functional connections in brain regions outside of the lesion , 11. HowTwenty-eight right-handed stroke patients with different degrees of neurological dysfunction were recruited from inpatient services at Xuanwu Hospital of Capital Medical University . All participants provided written informed consent prior to assessment. The inclusion criteria were as follows: (1) first-ever ischemic stroke (within 7 days of symptom onset), (2) unilateral lesions involving pons or thalamus were confirmed by diffusion-weighted imaging (DWI), and (3) age 18 to 75 years old. Exclusion criteria were as follows: (1) unclear onset time, (2) lesions outside the pons or thalamus, (3) recurrence of infarction or secondary hemorrhage during follow-up, and (4) deafness and/or blindness, or aphasia that might prevent completion of the study. Fourteen patients with unilateral infarction of the thalamus (TI group) and 14 patients with unilateral infarction of the pons (PI group) were enrolled in the current study. Twenty-five age-and sex-matched healthy control participants were included as the normal control group (NC group).The current study protocol was planned as a 6-month longitudinal design, during which patients with stroke underwent assessment using the Fugl-Meyer (FM) scale and magnetic resonance imaging (MRI). Those data were collected five times after infarction occurred, during the first week after symptom onset (<7 days), 2 weeks, 1 month, 3 months, and 6 months after stroke onset. We then acquired MRI data on day 0 (baseline), 2 weeks, 1 month, 3 months, and 6 months for the normal controls .3. Resting-state functional MRI (fMRI) data were obtained using a gradient-echo echo-planar imaging sequence with the following parameters: TR/TE = 3000/30\u2009msec, flip\u2009angle = 90\u00b0, voxel\u2009size = 3 \u00d7 3 \u00d7 3\u2009mm3, matrix\u2009size = 64 \u00d7 64, gap = 0\u2009mm, number\u2009of\u2009slices = 43, and 124 time points. During fMRI scanning, all participants were instructed to remain motionless, stay awake, and keep their eyes open. Axial fast spin-echo T2-weighted, fluid attenuation inversion-recovery, and DWI examinations were also performed.All participants  were invited to participate in 5 imaging sessions. MRI data were acquired using a 3T MR scanner equipped with a 12-channel coil . Full brain structural images were collected using a sagittal 3D-magnetization-prepared rapid acquisition gradient echo (3D-MPRAGE) T1-weighted sequence with the following parameters: TR/TE = 1600/2.15\u2009msec, flip\u2009angle = 9\u00b0, FOV = 256\u2009mm \u00d7 256\u2009mm, matrix\u2009size = 256 \u00d7 256, and voxel\u2009size = 1 \u00d7 1 \u00d7 1\u2009mmThe degree of motor deficit was assessed independently by two neurologists on the same day as the MRI data acquisition. The two scores were averaged to provide an estimate. Thirty-three tasks in the FM scale were used to evaluate patients' motor function, limb coordination, and active joint function of the upper limbs , 13. EacMeasurement of infarction lesion volumes of PI and TI was performed manually using MRIcron software (version 1.40), including the following steps: (1) infarction volume measurement: two experienced physicians individually measured the infarction size using DWI images for patients during the subacute stage and fluid-attenuated inversion recovery (FLAIR) images during the chronic stage. The scores measured by the two physicians were then averaged. (2) Normalized infarction volume measurement: the infarction volume of patients was normalized to reduce the individual differences in brain volume. The median sagittal plane area was measured in the 3D-MPRAGE image , which ihttp://www.fil.ion.ucl.ac.uk/spm/software/spm12). The following steps were performed: (1) discard the first 10 EPI volumes, (2) slice timing correction, (3) head motion correction, (4) spatial normalization to MNI space using an EPI template with a voxel size of 3\u2009mm \u00d7 3\u2009mm \u00d7 3\u2009mm by DARTEL, (5) data were smoothed using a Gaussian kernel of 6\u2009mm full width at half maximum (FWHM), (6) linear regression was performed to remove the effects of the white matter and cerebrospinal fluid by 99% mask, (7) band-pass filtering between 0.01-0.1\u2009Hz, and (8) preventing focal infarct tissue from affecting the algorithm, the imaging data from the stroke patients with lesions in the left hemisphere were flipped from left to right along the median sagittal line. The right hemisphere was defined the ipsilesional side, and the left hemisphere was defined as the contralesional side in all patients with stroke.Resting-state fMRI data were preprocessed using data processing and analysis of brain imaging (DPABI) software  and SPM1Subcortical areas, such as the basal ganglia (putamen), thalamus, cerebellum, and brainstem nuclei, are important components of the motor network . They haz values using Fisher's z-transformation to improve the normality of the correlation coefficient.The voxel-wise functional connectivity analyses between the reference time course of each ROI  and the time course of each voxel in the brain areas were performed to generate seed-based FC maps at baseline stage, 2weeks, 1 month, 3 months, and 6 months after stroke. Pearson's correlation coefficients between the average time series of the ROIs and sensorimotor brain areas were computed to obtain seed-based FC maps. For group analyses, the correlation coefficients were transformed to p < 0.05. A Chi-square test was used to identify differences in sex and handedness among the PI, TI, and NC groups. For clinical variables, the differences in age, normalized infarction volume, and FM scores were analyzed by one-way ANOVA in the PI and TI groups.Statistical analyses of demographic and clinical data were conducted using SPSS 17.0. The statistical significance threshold was set at The infarction volume was the largest within 7 days of the onset of ischemic stroke compared to the follow-up time points. Therefore, we considered that larger infarct volumes would indicate more significant effects of infarct volume on FC in patients with stroke. The FC in patients with stroke (PI and TI groups) differed the most from normal controls at baseline stage. One-way ANOVA controlling for infarction volume of patients with stroke  was used to identify group differences of FC among PI, TI, and NC groups at baseline stage. The peak voxel of the corresponding sensorimotor area  of automated the anatomical labeling (AAL)  mask wasp < 0.05.Next, a post hoc \u201c5 (time) \u00d73 (group)\u201d repeated-measure ANOVA model was established to explore the differences in longitudinal FC changes among the three groups. The changes in FC in different brain regions among the three groups over a long-term follow-up of 6 months were examined for interaction effects of \u201ctime\u201d by \u201cgroup.\u201d The significance threshold was set at p = 0.103, one-way ANOVA), sex , or handedness . The normalized infarction volume decreased significantly during the observation period in the PI  = 4.77, p = 0.002) and TI groups  = 7.16; p \u2264 0.001). Longitudinal FM examination revealed significant improvement over time in the PI  = 8.92, p \u2264 0.001) and TI groups  = 4.94, p = 0.002).Detailed demographic and clinical findings for the PI, TI, and NC groups are provided in F = 14.49, pFWE = 0.043, MNI: 27, -30, 54) among the three groups  = 12.49, pFWE = 0.037, MNI: 15, -27, 69) among the three groups  and PI groups . However, there was no significant difference in FC of cl_VA-ip_postcentral between normal control and PI groups . Meanwhile, the post hoc comparisons of the baseline stage among the three groups showed significant difference of cl_VL-ip_precentral FC between PI and both normal control  and TI groups . However, there was no significant difference in FC of cl_VL-ip_precentral between normal control and TI groups .At baseline stage (within 7 days after stroke), one-way ANOVA analysis of FC between seed-based  and whole-brain regions indicated a significant difference in FC between the contralateral VA and ipsilateral postcentral gyrus   = 0.220, p = 0.927). The \u201cgroup\u201d main effect of cl_VA-ip_postcentral differed significantly among the three groups  = 7.193, p = 0.002). There was a significant \u201cgroup\u00d7time\u201d interaction effect of cl_VA-ip_postcentral  = 2.702, p = 0.008, p = 0.282) or \u201cgroup\u201d (repeated-measure ANOVA: p = 0.314) main effect of cl_VL-ip_precentral, cl_pon-ip_postcentral, cl_pon-ip_precentral, cl_putamen-ip_postcentral, and cl_putamen-ip_precentral. The interaction effect of FC changes of cl_VL-ip_precentral, cl_pon-ip_postcentral, cl_pon-ip_precentral, cl_putamen-ip_postcentral, and cl_putamen-ip_precentral did not differ significantly among the three groups over time (p > 0.504).We further explored longitudinal FC changes during the follow-up period in the TI, PI, and NC groups by extracting FC values in cl_VA-ip_postcentral, cl_VL-ip_precentral, cl_pon-ip_postcentral, cl_pon-ip_precentral, cl_putamen-ip_postcentral, and cl_putamen-ip_precentral. No significant differences in \u201ctime\u201d main effect were detected for cl_VA-ip_postcentral  = 2.980, p = 0.023. However, the \u201ctime\u00d7group\u201d repeated-measure ANOVA failed to detect a significant interaction effect among the three groups from 1 month to 6 months after stroke, F = 1.523, p = 0.201.Results of the plot of the study indicated that the interaction effect results were mainly detected one month ago; hence, we considered one month as the cut-off point and divided the follow-up time of the study into two stages. A \u201ctime\u00d7group\u201d repeated-measure ANOVA indicated a significant interaction effect of cl_VA-ip_postcentral that varied significantly among three groups at one month after stroke, T(13) = 2.550, p = 0.024), 3 months (T(13) = 2.859, p = 0.013), and 6 months (T(13) = 3.178, p = 0.007) after stroke onset in the TI group, and the difference in FC in cl_VA-ip_postcentral between two time points in the TI group became increasingly greater with the prolongation of time. Compared with 7 days after stroke onset, there was no significant increase in FC of cl_VA-ip_postcentral in the TI group at 2 weeks after stroke onset (p = 0.115).Repeated measurement analysis was used to detect changes in FC within each group, and the results showed the FC value of cl_VA-ip_postcentral with no significant difference between the PI and NC groups. However, the FC value of cl_VA-ip_postcentral was significantly different in the TI group. Compared with 7 days after stroke onset, there was a significant increase in FC of cl_VA-ip_postcentral at 1 month  and whole-brain regions between the PI1 and PI2 groups indicated significant differences in FC of cl_VA-cl_postcentral ; therefore, we suspected that heterogeneity of infarction lesion volume may have affected FC changes in the PI group. To confirm our hypothesis, the pontine infarction group was further divided into two subgroups according to the infarction volume at baseline. Patients with infarction\u2009volume < 10\u2009ml were assigned to the PI1 group (7 patients) and the rest (>10\u2009ml) were assigned to the PI2 group (7 patients). A r = 0.104, p = 0.724). Pearson correlation analyses indicated that the FC changes (FC\u2009changes = FCTime5 \u2212 FCTime1) of cl_VA-ip_postcentral_postcentral were not correlated with the changes in the FM scale (FM\u2009changes = FMTime5 \u2212 FMTime1) in the TI group . The correlation between infarct volume and FM was also analyzed in the TI group. No significant correlation was observed between infarction volume at baseline stage and FM at 6 months after stroke onset . Infarction volume changes (infarction\u2009volume\u2009changes = infarction\u2009volumeTime5\u2013infarction\u2009volumeTime1) did not correlate with the changes in the FM scale (FM\u2009changes = FMTime5 \u2212 FMTime1) in the TI group .The brain region of the cl_VA-ip_postcentral functional connectivity at baseline was not significantly correlated with motor improvement, as measured by the FM scale at 6 months after onset . Nevertheless, the postcentral gyrus is also involved in sensory processing, and the decreased activation in the postcentral cortex is indicative of attenuated sensory processing , 36. AltIn our study, there were no significant differences in FC changes between ROIs and the sensorimotor cortex in patients with PI. Wei et al.  exploredIn this study, we analyzed the FC changes in sensorimotor brain areas in patients with PI versus those with TI during follow-up of 6 months. The main findings were that FC significantly decreased between cl_VA and ip_postcentral in patients with TI at baseline as compared with the PI and NC groups, and the TI group exhibited gradual increases in FC between cl_VA and ip_postcentral thereafter. Therefore, FC increase between cl_VA and ip_postcentral suggests that the sensorimotor brain area may be responsible for the recovery of motor function in patients with thalamic stroke. Additionally, we did not detect any significant differences in FC changes between ROIs and the sensorimotor cortex in patients with PI. Heterogeneity within the pontine group may be associated with nonsignificant results during the intergroup comparisons.There are a few limitations to consider. One of the weaknesses of our pilot study was the lack of data regarding assessment of other functions, for example, sensory and cognitive function. Future studies should address the sensory and cognitive function changes in patients with thalamic infarction and pontine infarction. Second, a limited number of cases due to the rarity of isolated unilateral thalamic strokes and isolated unilateral pontine strokes may have prevented us from providing conclusive evidence for FC changes in patients with thalamic infarction and pontine infarction. We expect to expand the sample size in our future work to further elucidate our findings."}
+{"text": "Biological aging is revealed by physical measures, e.g., DNA probes or brain scans. Incontrast, individual differences in mental function are explained by psychologicalconstructs, e.g., intelligence or neuroticism. These constructs are typically assessedby tailored neuropsychological tests that build on expert judgement and require carefulinterpretation. Could machine learning on large samples from the general population beused to build proxy measures of these constructs that do not require humanintervention?Here, we built proxy measures by applying machine learning on multimodal MR images andrich sociodemographic information from the largest biomedical cohort to date: the UKBiobank. Objective model comparisons revealed that all proxies captured the targetconstructs and were as useful, and sometimes more useful, than the original measures forcharacterizing real-world health behavior . We observed this complementarity of proxy measures and original measuresat capturing multiple health-related constructs when modeling from, both, brain signalsand sociodemographic data.Population modeling with machine learning can derive measures of mental health fromheterogeneous inputs including brain signals and questionnaire data. This may complementor even substitute for psychometric assessments in clinical populations. Quantitative measures of mental health remain challenging despite substantial efforts. The fieBy comparison, it is easier to collect data on the general population without informationon clinical conditions. For brain health, such data have led to the development of proxymeasures that quantify biological aging , 16\u201322. One high-stake target is intelligence, which is measured through socially administeredtests and is one of the most extensively studied constructs in psychology. Fluidintelligence refers to the putatively culture-free, heritable, and physiological componentof intelligence , 27 and Neuroticism is a second promising target. As a key representative of the extensivelystudied Big Five personality inventory, neuroticism has a long-standing tradition in thepsychology of individual differences\u00a0, 32. NeuDespite strong population-level heritability ,41, theAnother challenge is that psychological traits are often measured using arbitrarynon-physical units, e.g., education degree or monthly income. In fact, society treatsindividual differences as categorical or continuous, depending on the practical context.While personality has been proposed to span a continuum\u00a0, psychiaConfronting the promises of population phenotyping with the challenges of measuringpsychological traits raises the following questions: (i) Can the success of brain age atcharacterizing health be extended to other proxy measures directly targeting mentalconstructs? (ii) How well can various constructs related to mental health be approximatedfrom general-purpose inputs not designed to measure specific latent constructs? (iii) Whatis the relative merit of brain imaging and sociodemographic characteristics? We tackledthese questions by using machine learning to craft proxy measures in order to approximatewell-characterized target measures from brain-imaging and sociodemographic data. We studiedage, fluid intelligence, and neuroticism. These targets have been, traditionally, consideredas proxies for mental health and are fundamentally different in terms of scope and nature.Our results suggest that, the same way brain age can enrich age as a predictor ofneurological complications, the additional proxy measures proposed in this work can bringvalue for the study of mental health by enriching the mental asessments they wereconstructed from.The article is organized as follows: We first present a summary of the methodology and theworkflow of building distinct proxy measures for age, fluid intelligence, and neuroticismusing machine learning\u00a0Fig.\u00a0. We thenTo approximate age, fluid intelligence, and neuroticism, we applied random forestregression on sociodemographic data and brain images. The data were split into validationdata for model construction\u00a0and generalization data for statistical inference on out-of-sample predictions withindependent data\u00a0. Our findings suggested that someinformation on psychological constructs can be assembled from general inputs notspecifically tailored to measure these constructs, such as brain images andsociodemographic variables. The resulting proxy measures can be regarded as crudeapproximations of the psychological measures, but they can nonetheless capture essentialaspects of the target constructs. To probe the external validity of the proxy measures, weused left-out data to investigate their link with real-world behavior, e.g., sleep,physical exercise, and alcohol and tobacco consumption. To relate such health behaviors toour proxy measures, we modeled them separately as weighted sums of predicted brain age \u0394,fluid intelligence, and neuroticism using multiple linear regression . To avoid circularity, we used the out-of-sample predictions for all proxymeasures .The estimated regression coefficients  revealed complementaryassociations between the proxy measures and health-related behavior\u00a0Fig.\u00a0. SimilarThe 3 proxy measures are difficult to compare on an equal footing because a \u0394 wasconsidered for brain age only  andaging-specific deconfounding was applied. The brain age \u0394 is indeed the standard practice,theoretically justified because age is on a metric scale\u00a0 for whicA question that remains is whether the proxy measures bring additional value compared tothe original target measures from which they were derived. These original target measuresshowed similar associations with health behavior, with the same signs in mostcases\u00a0Fig.\u00a0. At the In a second step, we investigated the relative performance of proxy measures built frombrain signals and distinct sociodemographic factors for the 3 targets: age, fluidintelligence, and neuroticism. Among the sociodemographic variables there was 1 block foreach target explaining most of the prediction performance Fig.\u00a0. For ageCombining MRI and sociodemographic characteristics enhanced age prediction systematicallyacross all 4 blocks of variables \u00a0Fig.\u00a0. The benPsychological measures often come without physical scales and units\u00a0. In pracGuided by machine learning, we empirically derived proxy measures that combine multiplesources of information to capture extensively validated target measures from psychology.These proxy measures all showed complementary associations with real-world health indicatorsbeyond the original targets. The combination of brain imaging and target-specificsociodemographic inputs often improved approximation performance.In our study, construct validity\u00a0, 7, 55 oCan our empirically derived proxy measures thus substitute for specific psychometricinstruments? A mental health professional may still prefer an established routine forclinical assessment, relying on interviews and personality questionnaires with implicitexperience-based thresholds. Inclusion of brain imaging may even seem to yield diminishingreturns when approximating high-level psychological traits. Yet, it could simply be amatter of time until more effective acquisition techniques will be discovered alongsidemore powerful signal representations. Including brain imaging rather seems a \u201csafe bet\u201dbecause machine learning is often capable of selecting relevant inputs\u00a0, 59 andBrain age has served as landmark in this study. It has been arguably the most discussedcandidate for a surrogate biomarker in the brain-imaging literature\u00a0, 17, 24.It is important to recapitulate that approximation quality on these differently measuredtargets has a different meaning. Age is measured with meaningful physical units (years) ona ratio scale\u00a0  with brain-imaging data available. Asa result, we could not directly assess the performance of proxy measures in clinicalpopulations. The low number of diagnosed mental disorders in the UKBB highlights thepractical importance of studying mental health as a continuous variable, in addition todiagnosed conditions. Indeed, a public health perspective calls for targeting individualdifferences in health, not only pathology. Psychological constructs such as IQ andneuroticism are important factors of the epidemiology of psychiatric disorders ,38, 68,In population studies of mental health, individual traits are captured via lengthyassessments, tailored to specific brain and psychological constructs. We have shown thatproxy measures built empirically from general-purpose data can capture these constructs andcan improve upon traditional measures when studying real-world health patterns. Proxymeasures can make psychological constructs available to broader, more ecological studiesbuilding on large epidemiological cohorts or real-world evidence. This can make thedifference where psychological constructs are central to developing treatment and preventionstrategies but direct measures have not been collected.To facilitate reproduction, understanding, and reuse, we have made all data analysis andvisualization source code available on GitHub\u00a0.The UKBB database is to date the most extensive large-scale cohort aimed at studying thedeterminants of the health outcomes in the general adult population. The UKBB is openlyaccessible and has extensive data acquired on 500,000 individuals aged 40\u201370\u00a0yearscovering rich phenotypes, health-related information, brain-imaging, and genetic data. ParticiAll participants gave informed consent. The UKBB study was examined and approved by theNorth West Multi-centre Research Ethics Committee. We considered participants who haveresponded to cognitive tests and questionnaires and provide access to their primarydemographic characteristics and brain images . Out of Learning curves documented that the training split was sufficiently large forconstructing stable prediction models\u00a0 with proTo establish specific comparisons between models based on sociodemographiccharacteristics, brain data, or their combinations, we exclusively considered the casesfor which MRI scans were available. The final sample sizes used for model construction andgeneralization testing then depended on the availability of MRI: For age and fluidintelligence, our randomized split-half procedure  yielded 4,203 cases for model building and 4,157 forgeneralization. For cases with valid neuroticism assessment, fewer brain images wereavailable, which yielded 3,550 cases for model building and 3,509 for generalization.Sociodemographic data (non-imaging) were collected with self-report measures administeredthrough touchscreen questionnaires, complemented by verbal interviews, physical measures,biological sampling, and imaging data. MRI data were acquired with the Siemens Skyra 3Tusing a standard Siemens 32-channel RF receiver head coil . We consAs our target measures for brain age modeling, we use an individual\u2019s age at baselinerecruitment (UKBB code \u201c21022-0.0\u201d). Fluid intelligence was assessed using a cognitivebattery designed to measure an individual\u2019s capacity to solve novel problems thatrequire logic and abstract reasoning. In the UKBB, the fluid intelligence test (UKBBcode \u201c20016-2.0\u201d) comprises 13 logic and reasoning questions that were administered viathe touchscreen to record a response within 2 minutes for each question. Therefore, eachcorrect answer is scored as 1 point, with 13 points in total  mood and sentiment,(2) primary demographic characteristics such as age and sex,(3) lifestyle, (4) education, and (5) early life.We then investigated the intercorrelation between all 86 variables to ensure that theproposed grouping is compatible with their empirical correlation structure\u00a0with 10% of the data used for testing. To compare model performance based on paired tests,we used the same splits across all models. Split-wise testing performance was summarizedfor informal inference using violin plots\u00a0Figs\u00a0and\u00a04. FOn the held-out set, unique subject-wise predictions were obtained by averaging acrossfolds and occasional duplicate predictions due to Monte Carlo sampling, which couldproduce multiple predictions per participant . Such a strategy is knownas CV-bagging , 106 andR2 score for regression and AUC score forclassification.To assess the statistical significance of the observed model performance and thedifferences in performance between the models, we computed resampling statistics of theperformance metrics on the held-out generalization data not used for modelconstruction\u00a0. Once unP-value for baseline comparisons (\u201ccould the predictionperformance of a given model be explained by chance?\") on the held-out data, wepermuted targets 10,000\u00a0times and then recomputed the test statistic in eachiteration. P-values were then defined as the probability of the teststatistic under null distribution being larger than the observed test statistic. Tocompute uncertainty intervals, we used the non-parametric bootstrap method,recomputing the test statistic after resampling 10,000\u00a0times with replacement andreporting the 2.5 and 97.5 percentiles of the resulting distribution. Note that thisprocedure is unrelated to the parametric bootstrap used for the analyses presented inFig. To obtain a R2 or AUC between any 2 models. To obtain aP-value for model comparisons (\u201ccould the difference in predictionperformance between 2 given models be explained chance?\") on the held-out data, forevery testing-data point, we randomly swapped the predictions from Model A and Model B10,000\u00a0times and then recomputed the test statistic in each iteration. We omitted allcases for which only predictions from 1 of the models under comparison was present.P-values were then defined as the probability of the absolute valueof the test statistic under null distribution being larger than the absolute value ofthe observed test statistic. The absolute value was considered to account fordifferences in both directions. Uncertainty intervals were obtained from computing the2.5 and 97.5 percentiles of the non-parametric bootstrap distribution based on 10,000iterations. Here, predictions from Model A and Model B were resampled using identicalresampling indices to ensure a meaningful paired difference. Again, note that thisprocedure is unrelated to the parametric bootstrap used for the analyses presented inFig. For model comparisons, we considered the out-of-sample difference inresid from the measure of interest  byapplying the following quadratic fit on the validation data:val1 and \u03b2val2 obtainedfrom\u00a0Equation\u00a0For association with health-contributing habits\u00a0Table\u00a0, we compduration\u00a0. To mitiduration\u00a0 and [eqsentclass1pt{minimaWe then investigated the joint association between proxy measures of interest andhealth-related habits\u00a0Table\u00a0 usingmuentclass1pt{minimaThe parametric bootstrap was a natural choice for uncertainty estimation because weused standard multiple linear regression, which provides a well-defined procedure formathematically quantifying its implied probabilistic model. Computation was carriedout using the \u201csim\" function from the arm package as described in\u00a0, 54: Platform independentProgramming language: Python and ROther requirements: Python 3.6.8 or higher, R 3.4.3 or higherLicense:\u00a0BSD-3Aggregated data supporting the results and figures of this article are available throughthe GigaScience Database\u00a0 and theA summary of the planned researchThe UK Biobank data fields required for the projectA description of derivatives  generated by the projectFigure 1 \u2013 Figure supplement 1: Learning curves on the random split-halfvalidation used for model building. To facilitate comparisons, we evaluated predictions ofage, fluid intelligence and neuroticism from a complete set of socio-demographic variableswithout brain imaging using the coefficient of determination 2Rmetric (y-axis) to compare results obtained from 100 to 3000 training samples (x-axis). Thecross-validation (CV) distribution was obtained from 100 Monte Carlo splits. Across targets,performance started to plateau after around 1000 training samples with scores virtuallyidentical to the final model used in subsequent analyses. These benchmarks suggest thatinclusion of additional training samples would not have led to substantial improvements inperformance.Figure 2 \u2013 Figure supplement 1: Marginal associations between proxy measuresand health-related habits. Marginal  estimates using univariateregression. Same visual conventions as in Fig. Figure 2 \u2013 Figure supplement 2: Conditional associations between proxy measuresand health-related habits without explicit brain age delta. Conditional estimates usingmultivariate regression. Instead of the brain age delta, the brain-predicted age is includedalongside an age-deconfounder as used in the main analysis. Same visual conventions as inFig. Figure 2 \u2013 Figure supplement 3: Conditional associations between proxy measuresand health-related habits with-proxy-specific deconfounding. Conditional estimates usingmultivariate regression. Instead of the brain age delta, the brain-predicted age is includedalongside an age-deconfounder as used in the main analysis. Moreover, predicted fluidintelligence and neuroticism are deconfounded for the target values at training time,analogous to the brain age predictions. Same visual conventions as in Fig. Figure 2 \u2013 Figure supplement 4: Joint modeling of health-related habits fromproxy and target measures. Conditional estimates using multivariate regression. Everyhealth-related habit (double rows) is modeled simultaneously from multiple proxies andtargets. Same visual conventions as in Fig. Figure 3 \u2013 Figure supplement 1: Prediction of individual differences in proxymeasures from MRI. Approximation performance using multiple MR modalities on the validationdataset: sMRI, dMRI, rfMRI and their combinations . We firstapplied Yeo-Johnson power transform to the variables, yielding approximately symmetricaldistributions. Then we computed Pearson correlations. One can see that most variables showlow if any intercorrelations. Strongly intercorrelated blocks emerged, in particular forMood and Sentiment and Lifestyle. Note that within the Lifestyle category many smallerblocks with strong intercorrelation occurred, some of which were obviously related to thecircumstances of living, such as household or employment status.Supplementary Figure S2: Investigating the age gap between the first visit andthe MRI visit time point. (A) Individual gap between age at first visit and MRIscan time. MRI scans never happened at the first visit, leading to a strictly positive gapof >5 years for most participants. Pearson correlation coefficient indicates high rankstability, suggesting that, from a statistical perspective, age at first visit and age atscan time are, essentially, interchangeable. (B) Direct comparison ofindividual-specific age predictions from brain images and sociodemographic data. Same modelas in the main analysis  for joint proxy-targetmodels of health-related habits.Supplementary Table S5: Regression statistics on the held-out set for purelyMRI-based approximation.Supplementary Table S6: Classification difference statistics on the held-outset for MRI-based approximation.Supplementary Table S7: List of variables contained in each block ofsociodemographic models: Mood and Sentiment (MS), Age, Sex (AS), Education (EDU), Early Life(EL).AUC: area under the classification accuracy curve; ICA: independent component analysis;ICD-10: International Statistical Classification of Diseases and Related Health Problems,10th Revision; MRI: magnetic resonance imaging; UKBB: UK Biobank; VIF: variance inflationfactors.The authors declare that they have no competing interests.D. B. acknowledges funding by the Canadian Institutes of Health Research (438531).G. V. acknowledges funding by the Canada First Research Excellence Fund.Conceptualization: B.T., D.B., D.E., G.V., J.H.Data curation: D.B., K.D.Software: B.T., D.E., G.V., K.D.Formal analysis: D.E., G.V., K.D.Supervision: B.T., D.E., G.V.Funding acquisition: G.V., J.H.Validation: D.E., K.D.Investigation: D.E., K.D.Visualization: D.E., G.V., K.D.Methodology: B.T., D.E., G.V.Project administration: D.E., G.V.Writing\u2014original draft: D.E., K.D.Writing\u2014review and editing: D.B., B.T., D.E., G.V., J.H., K.D.giab071_GIGA-D-21-00080_Original_SubmissionClick here for additional data file.giab071_GIGA-D-21-00080_Revision_1Click here for additional data file.giab071_GIGA-D-21-00080_Revision_2Click here for additional data file.giab071_GIGA-D-21-00080_Revision_3Click here for additional data file.giab071_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giab071_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giab071_Response_to_Reviewer_Comments_Revision_2Click here for additional data file.giab071_Reviewer_1_Report_Original_SubmissionBo Cao -- 4/24/2021 ReviewedClick here for additional data file.giab071_Reviewer_1_Report_Revision_1Bo Cao -- 7/28/2021 ReviewedClick here for additional data file.giab071_Reviewer_1_Report_Revision_2Bo Cao -- 8/12/2021 ReviewedClick here for additional data file.giab071_Reviewer_2_Report_Original_SubmissionHugo Schnack -- 4/27/2021 ReviewedClick here for additional data file.giab071_Supplemental_FileClick here for additional data file."}
+{"text": "Cerebral palsy (CP) is a spectrum of non-progressive motor disorders caused by brain injury during fetal or postnatal periods. Current diagnosis of CP mainly relies on neuroimaging and motor assessment. Here, we aimed to explore novel biomarkers for early diagnosis of CP.Blood plasma from five children with CP and their healthy twin brothers/sisters was analyzed by gene microarray to screen out differentially expressed RNAs. Selected differentially expressed circular RNAs (circRNAs) were further validated using quantitative real-time PCR. Receiver operating characteristic (ROC) curve analysis was used to assess the specificity and sensitivity of hsa_circ_0086354 in discriminating children with CP and healthy controls.p\u2009<\u20090.05), among which five circRNAs related to neuron differentiation and neurogenesis were chosen for further validation. Additional 30 pairs of children with CP and healthy controls were recruited and five selected circRNAs were further detected, showing that hsa_circ_0086354 was significantly down-regulated in CP plasma compared with control, which was highly in accord with microarray analysis. ROC curve analysis showed that the area under curve (AUC) to discriminate children with CP and healthy controls using hsa_circ_0086354 was 0.967, the sensitivity was 0.833 and the specificity was 0.966. Moreover, hsa_circ_0086354 was predicted as a competitive endogenous RNA for miR-181a, and hsa_circ_0086354 expression was negatively correlated to miR-181a expression in children with CP.43 up-regulated circRNAs and 2 down-regulated circRNAs were obtained by difference analysis (fold change\u2009>\u20092, Hsa_circ_0086354 was significantly down-regulated in blood plasma of children with CP, which may be a novel competent biomarker for early diagnosis of CP.The online version contains supplementary material available at 10.1186/s12920-022-01163-6. Since W.J. Little first described in the 1840s, the concept of cerebral palsy (CP) has been revised for several times and is now defined as a non-progressive motor disorder induced by brain injury during prenatal (80%), perinatal 10%) or postnatal (10%) . The inc% or postNoncoding RNAs represents more than 98% of all human transcripts, among which circular RNAs (circRNAs) are a special subtype without 5\u2032 cap or 3\u2032 poly-A tail , 15. cirWith the rapid development of next-generation sequencing, over 1000 circRNAs in human serum exosomes were identified , 24. In 2EDTA tubes. Plasma was isolated by centrifugation, followed by total RNA extraction using TRIzol reagent . All blood samples were collected with the consent of parents of children with CP. And all experiments performed in this study were in accord with the ethical guidelines of the Declaration of Helsinki and approved by the Ethics Committee of Xi\u2019an International Medical Center Hospital.Five children with CP and their healthy twins were selected in our study to minimize individual differences . Detailed clinical information of participants was provided in Additional file p value\u2009<\u20090.05 were presented as heatmap plots using R package \u201cpheatmap\u201d. Then differential expressed circRNAs with flag-signal of \u201cAbsent\u201d in CP group or healthy controls were removed. Gene Ontology (GO) enrichment analysis were performed use Fisher's exact test by a R package \u201cclusterProfiler\u201d of the target genes. For CP etiology and biomarker investigations, five circRNAs regarding neuron differentiation and neurogenesis were chosen for further quantitative real-time PCR verification. The TargetScan prediction tool was used to identify interactions between hsa_circ_0086354 and target miRNAs. miRNAs that had perfect nucleotide pairing with hsa_circ_0086354 were selected. Further Pearson correlation was carried out to analyze the correlation between hsa_circ_0086354 and miRNAs, only interactions with significant negative correlation was retained. The circRNAs-miRNAs network was visualized by Cytoscape software  [After RNA integrity assessment using Agilent Bioanalyzer 2100 , total RNAs were reversely transcribed into cDNA, which was further used to generate biotinylated cRNAs. Then cRNAs were hybridized with Hybridization Slides  in a Hybridization Oven at 65\u00a0\u00b0C for 17\u00a0h. Sides were scanned under a Microarray Scanner  and raw data were obtained by the Feature Extraction software 10.7 , followed by raw data normalization using Quantile algorithm. cirRNAs with a fold change\u2009>\u20092, ape.org) .\u2212\u25b3\u25b3 method. Specific primers used for circRNAs detection were listed in Table Additional thirty pairs of children with CP and their healthy controls were recruited to verify the differential expressed circRNAs screened by the microarray. In brief, total RNAs of plasma were extracted using UNIQ-10 RNA extraction kit  and reversely transcribed into cDNA using Maxima Reverse Transcriptase . Then cDNAs were quantified using Fast qPCR Master Mix (High Rox)  in an ABI Stepone plus PCR instrument. Similar methods were used to detect miR-181a level. 18S ribosomal RNA was used as internal control for hsa-circRNAs and RNU6B was used as an internal control for miR-181a. All data were analyzed using the 2\u2212\u25b3\u25b3 method and mean values were compared using unpaired t-test . All experiments were repeated for at least three times and p values less than 0.05 were regarded as statistically significant.Data from quantitative real-time PCR was analyzed using the 2p\u2009<\u20090.05) . As listed in Table In order to minimize individual differences, the blood samples from five pairs of twin children  were collected in our study. Sino human ceRNA array V3.0 which includes 53,625 human circRNAs was used to screen out differentially expressed circRNAs between the twins. Volcano plot showed that 134 circRNAs were differentially expressed in children with CP compared to their healthy controls, among which 77 circRNAs were up-regulated and 57 were down-regulated , hsa_circ_0083264 was \u2212\u00a01.031 (microarray: 2.039), hsa_circ_0035127 was \u2212\u00a01.408 (microarray: 2.144), hsa_circ_0015069 was 1.76 (microarray: 2.113) and hsa_circ_0086354 was \u2212\u00a06.15 (microarray: \u2212\u00a03.676) (Fig.\u00a0Hsa_circ_0086354 associated ceRNA network was obtained using Cytoscape analysis. miR-181a, miR-4741 and miR-4656 were down-stream target microRNAs of hsa_circ_0086354 (Fig.\u00a0Owing to its enigmatic etiology, the diagnosis of CP can barely rely on neuroimaging and assessment of motor dysfunction . CirRNAsIn the present study, blood samples from five children with CP and their twin brothers/sisters were collected to screen out differentially expressed circRNAs using microarray. Twin participants at identical preterm conditions can exclude additional risk factors of CP, which makes our results more reliable. Five circRNAs enriched in neuron differentiation and neurogenesis were selected from 45 differentially expressed circRNAs for further validation. Another 30 pairs of plasma samples from children with CP and healthy controls were collected, and the expression levels of five selected circRNAs were quantified. It was remarkable that the expression pattern of hsa_circ_0086354 measured by quantitative real-time PCR was highly in consistent with that detected by microarray. Yet the expression differences between children with CP and healthy controls of hsa_circ_0042123, hsa_circ_0083264, hsa_circ_0035127 and hsa_circ_0015069 were either not significant or contradictory with microarray analysis. Therefore, our findings suggest that hsa_circ_0086354 might serve as a promising biomarker for CP diagnosis.circRNAs have been reported to serve as competent biomarkers for diagnosis of various diseases. For instance, plasma hsa_circRNA_002453 was a potential biomarker for severity of renal involvement and diagnosis of lupus nephritis with an AUC of 0.906 . Hsa_cirWe further discovered that hsa_circ_0086354 acts as a ceRNA of miR-181a. miR-181a is up-regulated in patients with mild cognitive impairment which later progressed to Alzheimer\u2019s disease . miR-181Hsa_circ_0086354 is significantly down-regulated in children with CP in contrast with their healthy control with an AUC of 0.967, making it as a promising biomarker for the early diagnosis of CP. Hsa_circ_0086354 may also be involved in the etiology of CP through targeting miR-181a.Additional file 1: Table S1. Relative clinical information of children with cerebral palsy and their healthy controls.Additional file 2: Fig. S1. Top 30 of biological_process, cellular_component and molecular_function obtained using Gene Ontology enrichment. Plot size refers to gene number."}
+{"text": "Saccharopolyspora erythraea have broad-spectrum antibacterial activities. Recently, several TetR-family transcriptional regulators (TFRs) were identified to control erythromycin production by multiplex control modes; however, their regulatory network remains poorly understood. In this study, we report a novel TFR, SACE_0303, positively correlated with erythromycin production in Sac. erythraea. It directly represses its adjacent gene SACE_0304 encoding a MarR-family regulator and indirectly stimulates the erythromycin biosynthetic gene eryAI and resistance gene ermE. SACE_0304 negatively regulates erythromycin biosynthesis by directly inhibiting SACE_0303 as well as eryAI and indirectly repressing ermE. Then, the SACE_0303 binding site within the SACE_0303-SACE_0304 intergenic region was defined. Through genome scanning combined with in vivo and in vitro experiments, three additional SACE_0303 target genes  were identified and proved to negatively affect erythromycin production. Finally, by coupling CRISPRi-based repression of those three targets with SACE_0304 deletion and SACE_0303 overexpression, we performed stepwise engineering of the SACE_0303-mediated mini-regulatory network in a high-yield strain, resulting in enhanced erythromycin production by 67%. In conclusion, the present study uncovered the regulatory network of a novel TFR for control of erythromycin production and provides a multiplex tactic to facilitate the engineering of industrial actinomycetes for yield improvement of antibiotics.Erythromycins produced by  The high G+C Gram-positive bacterial actinomycetes are well known as one of the most abundant sources of bioactive secondary metabolites . The bioSaccharopolyspora erythraea, an important industrial actinomycete, is commonly used for the large-scale fermentation manufacturing of the valuable polyketide antibiotic erythromycin A (Er-A). Er-A and its derived macrolide drugs exhibit nice activities of many Gram-positive and some Gram-negative bacteria and have high annual sales in the billions of dollars (ery) cluster consists of 20 genes arranged in four main polycistronic units in Sac. erythraea . In particular, little is known about the TFR-mediated regulatory network concerning erythromycin biosynthesis.As a typical representative of TFs, TetR family transcriptional regulators (TFRs), consisting of an N-terminal DNA-binding domain and a C-terminal ligand-responsive domain, usually participate in the control of antibiotic production in actinomycetes . A totala genome , and onla genome . These iSac. erythraea remained limited owing to much time and effort regarding multigene engineering. In particular, the low efficiency of the homologous recombination-based gene knockout in Sac. erythraea has always restricted genetic engineering of the industrial actinomycetes. In the past 3 years, clustered regularly interspaced short palindromic repeats interference (CRISPRi) mediated multiplex gene repression has been developed in the model actinomycetes Streptomyces coelicolor  their targets resulted in yield improvement of erythromycin, traditional genetic engineering in elicolor  and was ectively .eryAI and resistance gene ermE, but directly suppressed its adjacent gene SACE_0304, encoding a MarR-family regulator (MFR). SACE_0304 was shown to directly repress SACE_0303 and eryAI but indirectly inhibit ermE. Three new SACE_0303\u2019 target genes, SACE_2467, SACE_5222, and SACE_3156, were discovered and validated to negatively affect erythromycin production. Further, we performed stepwise engineering of the SACE_0303-mediated mini-regulatory network in a high-yield strain by coupling CRISPRi-based repression of those three targets with SACE_0304 deletion and SACE_0303 overexpression, resulting in obvious titer improvement of erythromycin.In this study, we report a novel TFR, SACE_0303, which indirectly triggered the erythromycin structure gene Escherichia coli were cultured in Luria-Bertani (LB) broth medium or on LB agar plate at 37\u00b0C. E. coli DH5\u03b1 were used to construct plasmid. E. coli BL21 (DE3) was used for protein expression. Sac. erythraea A226, WB, and their derivative mutants were grown on the R3M agar plate medium for sporulation, protoplast regeneration, and phenotypic observation and in tryptone soya broth (TSB) medium for seed stock culture, genomic DNA extraction, and protoplast preparation at 30\u00b0C.All strains, plasmids, and primers used in this study are listed in SACE_0303 gene were amplified via PCR with the primer pairs SACE_0303-up-F/R and SACE_0303-down-F/R, respectively (0303 (0303 was introduced into Sac. erythraea A226 by PEG-mediated protoplast transformation. A 424-bp DNA fragment of SACE_0303 was replaced by the thiostrepton resistance gene (tsr) by the method of chromosomic homologous recombination. The \u0394SACE_0303 mutant with thiostrepton resistance was confirmed by PCR with the primers SACE_0303-C1/C2   , which i9 (v3.0)  was dige156-5222 . Finallyectively , and pSE156-5222 .Flask fermentation of A226, WB, and their derived mutants were performed as previously described. Spores of A226 and its derivative strains were inoculated into TSB seed medium and grown for 2 days. Then, 5 mL seed cultures were inoculated into the R5 liquid medium to grow at 220 rpm, 30\u00b0C for 6 days. For WB and its derivatives, strains were cultivated in the industrial seed and fermentation media with the same culture conditions as A226 . Er-A exSACE_0303 gene was amplified using the primers SACE_0303-C5/C6 (E. coli BL21 (DE3), and SACE_0303 expression was induced by 0.5 mM IPTG at 30\u00b0C for 8\u201310 h. Purification of His6-tagged SACE_0303 protein was performed on a Ni2+-NTA spin column (BIO-RAD). BCA protein assay kit (Thermo Fisher Scientific) was used to analyze the concentration of purified protein, and its quality was estimated by SDS-PAGE.The 03-C5/C6  and was 6-tagged SACE_0303. The binding reaction system contained 60 mM KCl, 50 mM EDTA, 10 mM Tris-HCl (pH 7.5), 10 mM DTT, 5 mM MgCl2, 10% glycerol, 150 ng DNA probe labeled by 5\u2032-FAM/3\u2032-HEX and purified His6-SACE_0303 protein. Unlabeled DNA fragments or poly-dIdC were used for competitive assays. After incubation at 30\u00b0C for 20 min in 20 \u03bcL reaction mixtures, the reactants were fractionated on 6% native PAGE gels in 1 \u00d7 TAE buffer at 40 mA for 35\u201345 min.EMSAs were performed as previously published report . DNA proTM 6 Flex (Thermo Fisher Scientific) using the primers listed in hrdB (SACE_1801) gene in Sac. erythraea was served as an internal control to normalize samples.Using the TransZol up plus RNA kit (Transgen), total RNA was isolated from A226 and its derivatives after 24 h fermentation in R5 liquid medium or WB derivatives after 12 h culture in industrial fermentation medium. The RNA concentration was measured with the microplate reader (BioTek). RNA was treated with DNase I (MBI Fermentas), and reverse transcription was achieved using a cDNA synthesis kit (MBI Fermentas). The relative transcriptional levels of genes were examined with QuantStudio0303, P0304, PeryAI, and PermE regions were successively amplified using the primer pairs in egfp) fragment obtained by XbaI/BamHI digestion of pKC-DE . All fluorescence values were normalized to growth rates (OD600).0303\u20130304 was successively incubated with 0, 70, and 490 nM His6-SACE_0303 in a total 50 \u03bcL of binding buffer at 20\u00b0C for 20 min, and then 2 \u03bcL DNase I  was performed at 20\u00b0C for 30 s, 10 \u03bcL DNase I stop solution was added to the mixture and reacted at 65\u00b0C for 10 min. The ethanol precipitation method was used to purify and recover DNA samples. Purified DNA was sequenced with a 3730XL DNA genetic analyzer (Applied Biosystems), and GeneMarker software program v2.2 for data analysis.The DNase I footprinting assay was performed as previously described . To prect-test, with \u2217p <0.05, \u2217\u2217p < 0.01, and \u2217\u2217\u2217p < 0.001, ns, not significant.All data in this study were stated as means \u00b1 standard error of the mean (SD), and analyzed by Student\u2019s SACE_0303 and its adjacent genes of the Sac. erythraea chromosome is shown in SACE_0304, the neighboring gene of SACE_0303, encodes an MFR. To clarify the function of SACE_0303, the fragment homologous recombination method was performed in Sac. erythraea A226 to obtain the SACE_0303-deleted mutant strain \u0394SACE_0303  and the resistance gene ermE encoding rRNA methyltransferase (PermE) for binding to His6-SACE_0303 to uncover its potential action mode in the ery cluster. Results found from the gel-shift assays that SACE_0303 could not bind to PeryAI and PermE  and SACE_0304 (P0304)  was individually ligated into pKC-TE and pKC-ME and transformed into DH5\u03b1, the green fluorescence of pKC-MR-TE and pKC-MR-ME was, respectively, decreased by 81% and increased twofold compared with the absence of SACE_0304  . PlasmidACE_0304 , indicat4 by 25% . Therefoery cluster, we likewise constructed eGFP reporter plasmids  was protected by SACE_0303, in which an 18 bp palindrome sequence  was obviously detected  (data not shown), during which 10 predicted high-score sites flanked by well-annotated genes were chosen for EMSAs (SACE_2467 encoding cation-transporting ATPase (P2467), SACE_3156 encoding a large transcriptional regulator (P3156), and SACE_5222 encoding alpha-ketoglutarate permease (P5222)  . Further in A226 . These rSACE_2467, SACE_3156, and SACE_5222 were individually overexpressed in A226. By fermentation and HPLC analyses, Er-A yields in A226/pIB-2467, A226/pIB-3156, and A226/pIB-5222 were, respectively, reduced by 26.6, 37.6, and 26.1% relative to those in A226  and WB\u03940304/pSETdCas9-0303 (928 mg/L) exhibited a stepwise increase in Er-A yield over WB (685 mg/L) , respectively, exhibited \u223c23% and \u223c67% increase in Er-A production compared with WB\u03940304/pSETdCas9-0303 and WB  . Furtheras9-0303 , and theanalyses . Corresp3 and WB .Sac. erythraea were successively shown to be involved in the repression or activation of erythromycin biosynthesis, in which SACE_7301 and SACE_3446 exerted a direct interaction to the promoters of the ery cluster  to directly inhibit the genes for erythromycin biosynthesis, export, and resistance  family of TFs, widely distributing among prokaryotes, could modulate diverse physiological processes, including stress response, antibiotic resistance, and export, etc. . In spitptomyces , and thesistance . This stTFRs and MFRs, both serving as one-component regulators, could control the expression of upstream targets by responding to specific ligands . Typical0303\u20130304 , an intermediate of the tricarboxylic acid (TCA) cycle, intersects between carbon and nitrogen metabolic pathways (S. coelicolor, increased \u03b1-KG could promote the TCA cycle to form more NADH for maintaining intracellular redox homeostasis (SACE_5222 encoding \u03b1-ketoglutarate permease might unbalance intracellular redox status, exhibiting an adverse effect on erythromycin biosynthesis. SACE_3156 encodes a large transcriptional regulator belonging to a LuxR family, and its overexpression likewise decreased the erythromycin yield (Based on defined DNA binding site of SACE_0303 within P303\u20130304 , we utiloduction . SACE_24oduction . A previeostasis . As the rythraea , we infepathways . In S. ceostasis . We specin yield . Howeverin yield , which nSac. erythraea WB for enhanced erythromycin production (SACE_0303 under PermE\u2217 was ligated into the CRISPRi system and the obtained plasmid was introduced into an existing WB mutant with SACE_0304 deletion for concurrent transcriptional downregulation of three new SACE_0303 targets as well as SACE_0303 overexpression (Rewiring the regulatory network with engineering of TFs and their targets is an effective approach to boost the productivity of antibiotics in actinomycetes . For exaoduction , 2019. Noduction . Herein,pression . Expectepression . The preThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.HW and BZ conceived and designed the study. YL, SK, PW, BL, LL, JN, HZ, and KC performed the experiments. YL and HW analyzed the data. HW wrote the manuscript. BZ modified the manuscript. All authors have read and approved the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher."}
+{"text": "Retinoblastoma is the commonest eye cancer occurring in the pediatric population. Circular RNAs (circRNAs) are essential regulators of tumorigenesis and development. The current experiment delves into the function and molecular basis of hsa_circ_0000034 in retinoblastoma progression. In the study, these series of experiments noted an upregulation of hsa_circ_0000034 in retinoblastoma cell lines and tissues. Retinoblastoma patients with raised hsa_circ_0000034 expressions were more likely to possess a more progressive International Integrated Reporting Council (IIRC) stage and optic nerve invasion. hsa_circ_0000034 knockdown caused a marked suppression in the proliferation and invasion of retinoblastoma cells in vitro. Mechanistically, hsa_circ_0000034 appeared to serve as a competitive endogenous RNA (ceRNA) in retinoblastoma through miR-361-3p sponging. In conclusion, our data proved that hsa_circ_0000034 promoted the oncogenicity of retinoblastoma via regulation of miR-361-3p expression, a finding that may contribute toward retinoblastoma therapeutics.  Retinoblastoma is a common malignant eye cancer with the highest incidence in infants and children . With thCircular RNAs (circRNAs) represent endogenous non-coding RNA possessing a covalent closed-loop structure ,6. IncreRecently, a large number of miRNA molecules play critical roles in the occurrence and progression of retinoblastoma . Li et aThe aim of the current study is to explore the roles of hsa_circ_0000034 on the progression of retinoblastoma and reveal the possible mechanisms. Our results demonstrate that upregulated hsa_circ_0000034 was associated with advanced clinical features in retinoblastoma patients and promoted the proliferation and invasion abilities. Furthermore, hsa_circ_0000034 directly sponged miR-361-3p and consequently promoted the progression of retinoblastoma cells in vitro. Thus, hsa_circ_0000034 may act as a potential candidate target in retinoblastoma therapy.Thirty-eight retinoblastoma tissue specimens, as well as 12 normal retina tissue specimens, were obtained from the Affiliated Hospital of Qingdao University. All retinoblastoma patients did not receive any therapy before surgery. All experiments were approved by the ethics committee of the Affiliated Hospital of Qingdao University and the signed informed consent had been obtained from all the patients. The clinical features of retinoblastoma patients are shown in 2 at 37\u00b0C and supplemented with 10% fetal bovine serum  in RPMI 1640 medium .The American Type Culture Collection  provided the human normal human retinal pigment epithelial cell line (ARPE\u201019) and retinoblastoma cell lines . All specimens were maintained under 5% COSmall interfering RNA specific to hsa_circ_0000034 (si-circRNA), miR-361-3p mimics, miR-361-3p inhibitors and negative controls were synthesized by GenePharma . Transfection was carried out via the lipofectamine 2000 in accordance with the manufacturer\u2019s instruction.RT Master Mix . An ABI 7500 Fast Real-Time PCR system (Applied Biosystems) using an SYBR Green PCR Kit (Takara) was used to carry out qRT-PCR. GAPDH, as well as U6, were regarded as controls. The 2\u2013\u0394\u0394CT method allowed for the calculation of the relative gene expression. The primers are as follows: hsa_circ_0000034, 5\u02b9-TCCCGTCATGAGATCAGCAAT-3\u02b9 (forward) and 5\u02b9-GCCTGTACAGCTTGTGCAAT-3\u02b9 (reverse); miR-361-3p, 5\u02b9-UCCCCCAGGUGUGAUUCUGAUUU-3\u02b9 (forward) and 5\u02b9- GCAAATCAGAATCACACCTG-3\u02b9 (reverse) [Trizol reagent allowed for extract tissue and cell total RNA which was then used to produce cDNA through reverse transcription with the PrimeScript reverse) .Isolation of RNA from retinoblastoma cells cytoplasmic and nuclear fractions was done with the Cytoplasmic & Nuclear RNA Purification Kit . qRT\u2013PCR allowed for quantification of hsa_circ_0000034, GAPDH and U6 in the RNA samples. Internal cytoplasmic reference was GAPDH while U6 represented the nuclear RNA control.Retinoblastoma cell proliferation was analyzed using 5-ethynyl-2\u02b9-deoxyuridine (EdU) assay Kit (RiboBio) and the previous study .5 cells/well. Cells were allowed to achieve 85% confluence before being scratched by a sterilized pipette tip. Images were captured by an inverted microscope at each indicated time (0\u00a0h and 24\u00a0h).To study retinoblastoma cell migration, wound healing assays were used. Six-well plates were used to house retinoblastoma cells that were plated at 1\u00a0\u00d7\u00a0105 retinoblastoma cells were seeded in the top chamber, with the bottom chamber flushed with cell media. Cells were left for 48\u00a0h to incubate before the clearance of the cells on the top layer. Cells present in the lower chamber were methanol-fixed before undergoing 0.1% crystal violet staining. Cell numbers were counted using a microscope .Invasion assay was performed via Matrigel-coated membranes. In brief, 1\u00a0\u00d7\u00a010The Magna RIP Kit  was used to perform the RIP assay based on protocols set by the manufacturer. Antibodies against IgG and argonaute 2 (anti-AGO2) were utilized for the RIP assays. Purified RNAs were extracted and the enrichment of hsa_circ_0000034 and miR-361-3p was processed by RT-qPCR.hsa_circ_0000034-WT (wild-type) or hsa_circ_0000034-MUT (mutant) were inserted into pmirGLO (Promega) to construct 2 reporter plasmids. When retinoblastoma cells reached approximately 70% confluence, Lipofectamine\u00ae 2000 was used to transfect the reporter plasmids with either miR-361-3p mimic or inhibitor. The activity of luciferase was determined after 48\u00a0h of transfection in compliance with instructions set by the manufacturer.The SPSS 21.0 (IBM) statistical software was used for this experiment. All data were depicted in terms of mean\u202f\u00b1 standard deviation (SD) of three separate experiments. Either the Student\u2019s t-test (for two groups) or one-way analysis of variance  was used to determine intergroup differences. Statistical significance was determined with a p value of <0.05 indicated that the difference was statistically significant.In a previous study, Lyu et al.  demonstrhsa_circ_0000034 was found to be higher in retinoblastoma cell lines in contrast to human normal human retinal pigment epithelial cell line (ARPE\u201019) ). Next, In order to delineate how hsa_circ_0000034 contributes to retinoblastoma progression, the molecule was first localized in retinoblastoma cells. Subcellular fractionation assay showed that the molecule was found in highest concentrations in WERI-Rb1 cell cytoplasm ). Next, Next, miR-361-3p levels were determined in retinoblastoma. qRT-PCR found that miR-361-3p expression was markedly reduced in retinoblastoma tissues and cell lines ,b). MiR-To further confirm that hsa_circ_0000034 exhibited oncogenic effects on retinoblastoma progression through sponging miR-361-3p, miR-361-3p inhibitors were co-transfected with si-circRNA into retinoblastoma cells ). ColonyIncreasing evidence has indicated that dysregulated circRNAs contribute to carcinogenesis and tumor progression . RecentlRecently, numerous studies have revealed that circRNAs indirectly regulate gene expression by serving as competing endogenous RNAs (ceRNAs) ,21. Li eTo verify our hypothesis, bioinformatics analyses were conducted to search for miRNAs that may bind to hsa_circ_0000034, and miR-361-3p was found to be a potential candidate. qRT\u2013PCR analysis uncovered that hsa_circ_0000034 knockdown increased miR-361-3p expressions in retinoblastoma cells. RIP and luciferase reporter assay further confirmed the relationship between miR-361-3p and hsa_circ_0000034. In addition, rescue assays demonstrated that suppressing miR-361-3p reversed the effects of hsa_circ_0000034 knockdown on retinoblastoma cell progression. Collectively, these results suggested that hsa_circ_0000034 might promote the malignancy of retinoblastoma cells by sponging miR-361-3p.In summary, our findings demonstrated that hsa_circ_0000034 promoted retinoblastoma cell metastasis and growth in vitro by sponging miR-361-3p, which might provide a valuable insight into this molecule as a therapeutic target for retinoblastoma treatment. However, our research has the following limitations, first, the number of patients is not big enough, and we will increase the number of patients in further study. Second, the effects and underlying mechanisms of hsa_circ_0000034 in vivo are still needed to be explored."}
+{"text": "This study aimed to investigate the circular RNAs (circRNAs) involved in the development of HCC and elucidate the mechanism. RNA sequencing found 72 downregulated circRNAs and 88 upregulated circRNAs in human HCC tissues, including hsa_circ_0098181, hsa_circ_0072309, hsa_circ_0000831, and hsa_circ_0000231. The reduction of hsa_circ_0098181 was confirmed in eight paired human HCC tissues, hepatoma cell lines, and CCL4/DEN-induced mouse HCC models by RT-qPCR. The FISH assay revealed that hsa_circ_0098181 is mainly located in the cytoplasm of hepatocytes in the paratumor tissues. Further log-rank analysis performed in 91 HCC patients demonstrated that low expression of hsa_circ_0098181 was related to poor prognosis. The plasmid and lentivirus overexpressing hsa_circ_0098181 were delivered into HCC cell lines. After hsa_circ_0098181 was upregulated, the proliferation, invasion, migration, and colony formation of HCC cell lines were inhibited, and the apoptosis was promoted. Moreover, exogenous hsa_circ_0098181 delivery mitigated the tumor formation ability of Huh7 in Balb/C nude mice. The dual-luciferase reporter assay and the RIP assay verified that hsa_circ_0098181 sponged miR-18a-3p to regulate PPARA. In addition, a rescue experiment found miR-18a-3p mimic partly reversed the suppression of hsa_circ_0098181 on proliferation, invasion, and migration of HCC cell lines. In conclusion, hsa_circ_0098181 can repress the development of HCC through sponging miR-18a-3p and promoting the expression of PPARA  Primary liver cancer is one of the leading causes of death from cancer ., 2017. Circular RNA (circRNA) is a kind of non-coding RNA (ncRNA) which does not contain 5\u2032 polarity and a 3\u2032 polyadenylated tail, and has closed ring structures to ensure their stability . CircRNARecently, accumulating evidence indicates that circRNAs can contribute to tumorigenesis, liver fibrosis, nonalcoholic fatty liver (NASH), liver regeneration, and other liver diseases . Some reIt is well known that the adaptation of tumors to the local microenvironment is closely related to metabolic changes . As an iin vitro and in vivo, and further revealed the downstream miRNA and its targets.In the current study, we identified the differentially expressed circRNAs in human HCC tissues by RNA sequencing. The expression of hsa_circ_0098181 was further detected in human HCC tissues, hepatoma cell lines, and mouse HCC models by RT-qPCR, and the impact of hsa_circ_0098181 on survival was analyzed in HCC patients. Moreover, we focused on investigating the effect of hsa_circ_0098181 on the development of HCC and illustrating the internal mechanism. Our results confirmed that increasing hsa_circ_0098181 mitigated the malignant phenotype of HCC The tumor and paratumor tissues were obtained from patients who underwent hepatectomy in Oriental Hepatobiliary Hospital and were pathologically diagnosed with HCC . RNA sequencing was performed using five paired human HCC tissues and their paratumor tissues to explore the circRNA signatures for HCC . The study was approved by the Ethics Committee . Written informed consent was obtained from each patient.2 humidified atmosphere at 37\u00b0C.Human hepatoma cell lines (Huh7 and Hep3B) were provided by the Type Culture Collection of the Chinese Academy of Sciences . Huh7 cells were cultured in Dulbecco\u2019s modified Eagle\u2019s medium (DMEM) containing 10% fetal bovine serum (FBS) and 1% penicillin\u2013streptomycin. Hep3B cells were cultured in MEM with 10% FBS and 1% penicillin\u2013streptomycin. All cells were cultured in a 5% COPrimary HCC C57BL/6 mice  models were induced by N-nitrosodiethylamine (DEN) and carbon tetrachloride (CCL4). One dose of DEN (25\u00a0mg/kg) was injected intraperitoneally on the 14th day after birth. Carbon tetrachloride (CCL4) was administered intraperitoneally  since the mice reached 10\u00a0weeks of age. Then the mice were sacrificed after 16\u00a0weeks. The liver of the mice was removed and collected for RNA extraction to further detect the level of hsa_circ_0098181.Tissue and cell RNAs were isolated by TRIzol . For RNase R treatment, 2\u00a0\u03bcg total RNA was incubated for 15\u00a0min at 37\u00b0 with 6\u00a0U/\u03bcg RNase R  and mixed with PrimeScript RT Master Mix  to synthesize cDNA. TB Green Premix EX Taq  was applied for RT-qPCR. In addition, miRNAs\u2019 reverse transcription and RT-qPCR were performed with the miDETECT A Track miRNA qRT-PCR Starter Kit . The primers are listed in in situ hybridization (FISH) was used to explore the expression of hsa_circ_0098181 in human HCC tissues and determine its subcellular localization. The probe was provided by the RiboBio Company . Each section was added with 20\u00a0\u03bcl pre-hybridizing solution in a wet box for 2\u20134\u00a0h and subsequently incubated with a 20-\u03bcM probe at 38\u201342\u00b0C overnight. Finally, the nuclei were stained with DAPI, and the sections were observed under a fluorescence microscope .Fluorescence The plasmid (PLC5-CIR carriers) carrying hsa_circ_0,098,181 or its control  was transfected into hepatoma cell lines by Lipofectamine 2000 (Lipo 2000) and treated for 48\u00a0h. The lentivirus overexpressing hsa_circ_0098181 was constructed by Geneseed Biotech . After virus infection, Huh7 cells were screened with Puro at a concentration of 2\u00a0mg/L, and a cell line stably expressing hsa_circ_0098181 was established in about 2\u20133\u00a0weeks. The cells were used to perform subcutaneous tumor formation and the rescue experiment.Cell proliferation was detected using CCK-8 . About 3,000 cells (per plate) were incubated in 96-well plates. CCK-8 solution reagent (10\u00a0\u03bcl) was added to each well, and the cells were incubated at 37\u00b0 for 1\u00a0h for five successive days. The absorbance at 450\u00a0nm was measured by a microplate daily .4 cells were added into the upper chambers and incubated for 72\u00a0h. After dyeing with 0.1% crystal violet for 20 min, the invasion of cells was observed under a microscope. The migration of HCC cell lines was determined with a similar method, while the upper chambers did not have to be coated with Mitrogel. ImageJ software was used to quantify the degrees of invasion and migration of Huh7 and Hep3B cells.For the invasion assay, the upper chambers were coated with Mitrogel  at 37\u00b0C for 30\u00a0min. After adding trypsin into the plates, 10% of the FBS culture medium was used to stop digestion, and then the cells were washed with PBS twice. 500\u00a0\u03bcL of the culture medium with 10% FBS was put into the lower chamber of 24-well plates. Then 3 \u00d7 10Apoptosis of cells was performed with AnnexinV-APC/7-AAD staining using the Apoptosis Detection Kit with 7-AAD . The cell cycle was determined by the Cell Cycle Staining Kit  according to the instructions. After staining, the apoptosis and cell cycle were detected by flow cytometry .6 cells per mouse). When the long diameter of the tumors reached about 2\u20133\u00a0mm, then the tumor volume was detected every 2\u00a0days. After 10\u00a0days, the mice were sacrificed. The stripped tumor tissues were used for subsequent RT-qPCR, Western blot assay, and immunohistochemistry staining.Balb/C nude mice  were used to construct the xenograft model. Huh7 cells stably carrying hsa_circ_0098181 were subcutaneously injected into the backs of the nude mice (1 \u00d7 10www.circbank.cn) and Circinteratome (https://circinteractome.nia.nih.gov) websites. Similarly, the miRDB (www.mirdb.org) and miRmap (https://mirmap.ezlab.org) websites were used to determine the targets of miRNAs. Combined with the result of RNA sequencing, the target miRNA and mRNA were screened, and the binding sites were further predicted by Miranda v3.3a software.The potential downstream miRNAs of hsa_circ_0,098,181 were predicted using Circbank (A Western blot assay was carried out to detect the protein expression. The protein samples extracted with lysis buffer (with protease inhibitor) were loaded into the prepared SDS-PAGE gel and separated using gel electrophoresis. After the separated protein was transferred onto a PVDF membrane, the membrane was blocked with 5% milk for 1\u00a0h and incubated with the primary antibody  overnight. Consequently, the secondary antibody was applied. One hour later, the membrane was scanned with the Odyssey infrared imaging scanner (LI-COR Odyssey system).After dewaxing and hydration, the sections of mouse subcutaneous tumor tissues were treated with 3% hydrogen peroxide for 20 min and incubated with primary antibody  overnight and then with secondary antibody for 60\u00a0min.To confirm the binding between circRNA and miRNA, the HEK-293\u00a0T cells were co-transfected with psiCHECK2-hsa_circ_0098181-WT/-MUT  and miR-18a-3p NC/mimic . Similarly, psiCHECK2 PPARA-3\u2032UTR-576bp-WT/MUT1/MUT2/MUT1+MUT2 (MUT1 and MUT2 were single mutations) and miR-18a-3p NC/mimic were co-transfected in HEK-293\u00a0T cells to verify the combination between miR-18a-3p and PPARA. The cells were quantified using a dual-luciferase reporter analysis kit .7 Huh-7 cells were washed with PBS, and lysis buffer (containing protease inhibitor and RNase inhibitor) was added into the cells for 10 min. After centrifugation at 14000\u00a0g for 10\u00a0min, the supernatant was divided into three groups: input, Ago2, and IgG groups. 5\u00a0\u03bcg Ago2  and IgG  antibodies were added separately into the Ago2 and IgG groups. After the binding of the antibody and the beads, the antigen was added. Finally, RNA was collected and reversed into cDNA for further RT-qPCR.The radioimmunoprecipitation (RIP) assay was used to verify the direct combination of hsa_circ_0098181 and Ago2, miR-18a-3p and Ago2 by the RNA Immunoprecipitation Kit . First, 1 \u00d7 10t-test and presented as the mean \u00b1 standard deviation (SD). The Kaplan\u2013Meier method was used to analyze the survival in patients with different expression levels of hsa_circ_0098181. p-value < 0.05 was considered statistically significant .All data were analyzed by SPSS V.23.0 or GraphPad Prism 8 software. The results were analyzed by Student\u2019s n = 8), the levels of hsa_circ_0098,181 and hsa_circ_0072309 decreased obviously  and low expression of hsa_circ_0098181 was related to the poor prognosis (p = 0.037). These results indicated the potential of hsa_circ_0098181 as a prognosis biomarker and therapeutic target for HCC.RNA sequencing was performed to identify the differential circRNA profiles between human HCC tissues and their paratumor sections, and determine the circRNA signatures in HCC. It was shown that 72 circRNAs were less expressed and 88 circRNAs were up-regulated in human HCC tissues compared with their paratumor tissues . Considebviously . Moreovebviously . Accordibviously . After dbviously . These rp < 0.001, p < 0.001) and Hep3B cells  and promoted the transcription of PPARA in xenograft tissues  is a leading cause of cancer-related death in humans with a poor prognosis, and the median survival time is about 21 months ., 2017. Previous research works have screened out several circRNAs whose levels varied in HCC tissues, including ciRS-7, hsa_circ_0005075, hsa_circ_0005986, and hsa_circ_0004018 . Among tIt is generally believed that the proliferation, invasion, migration, and colony formation abilities of tumor cells were related to the malignant phenotype of HCC. In this study, overexpression of hsa_circ_0098181 obviously inhibited the proliferation, invasion, migration, and colony formation of Huh7 and Hep3B cells, and the tumorigenicity of HCC cell lines was remarkedly repressed after hsa_circ_0098181 delivery. This observation suggested the prominent antitumor effect of hsa_circ_0098181 on HCC.H. pylori-associated gastric cancer, miR-18a-3p was increased and upregulating miR-18a-3p stimulated the growth and motility of gastric cancer cell lines in vitro  and reducing lipid deposition. As the core regulator of the lipid metabolism, PPARA can be activated by fatty acids and has the ability to suppress inflammation . Nowadayvia inhibiting miR-18a-3p and promoting PPARA expression. The rescue experiments confirmed that miR-18a-3p mimic partially reversed the antitumor effect of hsa_circ_0098181 on HCC and its impact on PPARA expression. This finding further clarified the regulation of hsa_circ_0,098,181 on miR-18a-3p and PPARA.Considering the essential role of inflammation and lipid metabolism in HCC, PPARA is believed to be a potential anti-HCC target . Recentlin vitro and in vivo. Moreover, the further study found that hsa_circ_0098181 played anti-HCC effect via sponging miR-18a-3p and targeting PPARA. Thus, hsa_circ_0098181 might be a promising biomarker and therapeutic target for liver cancer.Taken together, our current study demonstrated the reduction of the hsa_circ_0098181 level in human HCC and revealed the repression of hsa_circ_0098181 on the malignant phenotype of HCC"}
+{"text": "Objective: Circular RNAs (circRNAs) have been demonstrated in playing an important role in the physiological and pathological processes (such as cancer). This paper aims to clarify the role of Circ_0006677 in non\u2013small-cell lung cancer (NSCLC) progression.Methods: Using clinical data and in vitro cell line models, we revealed the tumor-suppressive role of circ_0006677 in lung cancer. Using the online bioinformatics tool, we predicted the target of circ_0006677 and further validated its regulatory mechanisms responsible for its tumor suppressor function in NSCLC.Results: Circ_0006677 expression was reduced in NSCLC tissues of patients and lung cancer cells in comparison to adjacent normal tissues. Lower expression of circ_0006677 was significantly associated with poorer patient survival. Overexpression of circ_0006677 significantly inhibited the ability of NSCLC cell proliferation, migration, invasion, and glycolysis. Mechanically, circ_0006677 could inhibit NSCLC progression and glycolysis by regulating the expression of the signal transducer inhibitor SOSC2 through sponging microRNA-578 (miR-578).Conclusion: Circ_0006677 prevents the progression of NSCLC via modulating the miR-578/SOSC2 axis. Circ_0006677 acts as a tumor suppressor in NSCLC progression2. Circ_0006677 works as a sponge for miR-578 in NSCLC cellsvia the miR-578/SOSC2 axis3. Circ_0006677 inhibits NSCLS Lung cancer is the leading cause of cancer-related deaths worldwide . Non-smavia the Wnt/\u03b2-catenin signaling pathway by acting as a sponge for miR-7 and miR-214 (Circular RNAs (circRNAs) represent a large class of endogenous RNAs with covalently closed continuous loop . With th miR-214 . However miR-214 . In many miR-214 . SOCS2 i miR-214 .in vitro and represses tumor growth in the xenograft mouse models. Mechanically, circ_0006677 functions as a sponge of miR-578 to induce the expression of SOCS2, thereby suppressing NSCLC progression and glycolysis. Therefore, circ_0006677 may be a promising biomarker for NSCLC diagnosis and a potential therapeutic candidate for NSCLC treatment.Here, we found that circ_0006677 is significantly downregulated in NSCLC tissues. We further showed that circ_0006677 inhibits NSCLC cell proliferation, migration, invasion, and glycolysis via biopsy were selected for this study. These patients received no prior radiotherapy or chemotherapy before surgery. This study was approved by the Research Ethics Committee at Cangzhou Central Hospital. All patients signed the informed consent for the use of their patient information and tissues. Tumor tissues and adjacent noncancerous tissues were collected and stored at 80\u00b0C until use.A total of 88 NSCLC patients whose diagnoses were confirmed We compared the levels of circ_0006677 in NSCLC tissues and adjacent normal tissues using the GEO dataset (GSE112214).The potential miRNAs that might interact with circ_0006677 was predicted using two online tools (circBank and CircInteractome). The interaction between miRNA and its target genes was analyzed using the TargetScan database.NSCLC cell lines  and human bronchial epithelial cells (HBE) were purchased from the Cell Bank of Type Culture Collection of Chinese Academy of Sciences, Shanghai, China). All these cell lines were cultured in RPMI-1640 medium  supplemented with 10% fetal bovine serum, streptomycin, and penicillin at 37\u00b0C in 5% CO2. HEK293T cells were cultured in DMEM supplemented with 10% fetal bovine serum, 100\u00a0U/ml penicillin , and 100\u00a0mg/ml streptomycin.To generate circ_0006677 overexpression lentiviral plasmid, circ_0006677 was constructed into a pLV plasmid. For virus packaging, pLV-circ_0006677, PsPAX2, and pMD2G plasmids were co-transfected into HEK293T cells. The viral supernatant was collected to construct stable circ_0006677\u2013overexpressing cell lines.RNase R  was used to eliminate the linear RNAs. The expression of GAPDH and circ_0006677 before and after RNase R treatment was assessed.Cells growth was monitored with CCK-8 reagent every day following the manufacturer\u2019s instruction . In brief, 1, 000 cells were seeded in 96-well plates and cultured overnight. CCK-8 reagent was added at the time point, and the absorbance at 450\u00a0nm was measured using a DNM-9602 Microplate Reader .One thousand cells were seeded in six-well plates and cultured for 14\u00a0days. Cell colonies were fixed and stained with 0.5% crystal violet in methyl alcohol. Images of cell clones were taken, and colonies were counted with ImageJ.For migration assay, cells in the logarithmic growth stage were starved for 24\u00a0h, and the cells were digested the next day, centrifuged, and resuspended for a final concentration of 1 \u00d7 10 /ml. Celld-glucose (2-DG) for 20\u00a0min at 37\u00b0C. Cells were then washed with PBS to remove exogenous 2-DG. Cells were lysed with extraction buffer, heated at 85\u00b0C for 40\u00a0min, cooled on ice for 5\u00a0min, and centrifuged at 13,000g. The supernatant was transferred to new tubes and incubated with mix A for 1\u00a0h at 37\u00b0C. After mix B was added to the reaction mixture, the absorbance of each well was analyzed under the wavelength of 450\u00a0nm every 2\u20133\u00a0min on a microplate reader, set in the kinetic mode.Glucose consumption and lactic acid production of the cells were measured by glucose assay and lactate assay following the manufacturers\u2019 instruction . In brief, cells were incubated with 2-deoxy-Samples of patient tissue and NSCLC cell lines were lysed with TRIzol reagent  and total RNAs were isolated following the manufacturer\u2019s instructions. The RNA sample was reverse transcribed by the Reverse Transcriptase Kit . Real-time PCR was performed with the CFX Connect Real-time PCR Machine (Bio-Rad) and SYBR green reagent, as described previously . GAPDH wSOCS2 mRNA were synthesized and cloned into the pMIR-Reporter Vector . Using Lipofectamine 2000 reagent, A549 and H1299 cells were co-transfected with SOCS2 wild-type 3\u2032UTR or mutant-type SOCS2 3\u2032-UTR with miR-578 mimic or control mimic (miR-NC). 48\u00a0h after transfection, cells were harvested, and the luciferase activity was measured using a dual luciferase assay . Each experiment was repeated three times.The wild-type (WT) or mutated (MUT) sequences at 3\u2032-UTR of Biotin-labeled RNAs were transcribed using the Biotin RNA Labeling Mix  and T7 RNA polymerase , treated with RNase-free DNase I , and then purified with the RNeasy Mini Kit . Transcriptional production of a biotinylated circ_0006677 probe or control probe (NC-probe) was heated at 95\u00b0C for 5\u00a0min, placed on ice for 5\u00a0min, and placed at room temperature for 20\u00a0min to form the secondary structure. Folded RNA was mixed with cell extracts for 2\u00a0h. Then, 50\u00a0\u00b5l of streptavidin agarose beads  were added to each binding reaction and incubated for 1.5\u00a0h. After washing the beads, the samples and inputs were digested by Proteinase K, and RNA was isolated and quantified by qRT-PCR assay.Cells were lysed by RIPA lysis buffer . Protein concentration was determined using BCA assay (Beyotime). Proteins were subjected to 10% SDS\u2013polyacrylamide gel electrophoresis. Separated proteins were transferred onto polyvinylidene difluoride (PVDF) membranes  and immunoblotted with primary antibodies: anti-SOCS2 , anti-GAPDH , anti-MMP2 , and anti-MMP9 , and secondary antibody: goat anti-rabbit . Bands were visualized using the EasyBlot ECL Kit . The intensity of the blots was quantified by ImageJ .nu/nu nude mice (20\u00a0g) were purchased from Beijing Vital River Laboratory Animal Technology  and employed to establish the mouse models, as reported previously . All experiments were repeated three times. All the data were presented as mean \u00b1 standard deviation (SD). Differences between two groups compared with unpaired two-tailed n = 88) than the adjacent normal tissues (n = 88) . We found that circ_0006677 was downregulated in NSCLC tissues compared with adjacent normal tissues . By perf(n = 88) . Moreove(n = 88) . Moreove(n = 88) . The qRT(n = 88) . Further(n = 88) . Nuclear(n = 88) . These rTo validate the function of circ_0006677 in NSCLC, we sought to ectopically express circ_0006677 in A549 and H1299 cells, which have the lowest levels of circ_0006677. To dos so, we infected A549 and H1299 cells with lentivirus to construct stable cell lines overexpressing circ_0006677 (designated A549-circ_000667 and H1299-circ_000667). Compared with control cells, the circ_0006677 expression level was significantly increased in A549-circ_000667 and H1299-circ_000667 cells .Overexpression of circ_0006677 significantly inhibited the proliferation  and coloTo investigate the downstream regulatory mechanism of circ_0006677 in NSCLC cells, we predicted the potential miRNAs using online prediction tools. As a result, we identified 5 miRNAs  that might bind to circ_0006677 . SubsequTo explore whether miR-578 exhibits a tumor-promoting role in NSCLC cells, we analyzed the expression of miR-578 in NSCLC cell lines and HBE. We validated an increased expression of miR-578 in NSCLC cells compared to HBE . RelativOur qRT-PCR experiments showed that the transfection with miR-578 inhibitor significantly decreased the level of miR-578 in A549 and H1299 cells . CellulaSOCS2 mRNA. Thus, we explored whether the tumor-promoting role of miR-578 was mediated by the suppression of SOSC2. Overexpression of miR-578 significantly decreased the luciferase reporter activity of wild-type SOCS2 3\u2032-UTR in A549 and H1299 cells . As expected, overexpression of circ_0006677 significantly inhibited the proliferation, colony formation, migration, and invasion of A549 and H1299 cells; however, either miR-578 overexpression or knockdown of SOCS2, could partly restore these phenotypes of A549 and H1299 cells (To further explore whether circ_0006677 regulates NSCLC progression 99 cells . Similar99 cells . Collectin vivo, we established the xenograft mouse models by subcutaneously inoculating the stable A549 cells overexpressing circ_0006677 or control cells into nude mice. The volume and weight of tumors in mice bearing A549 cells overexpressing circ_0006677 were markedly lower than those in mice bearing control A549 cells (in vivo.To investigate the role of circ_0006677 49 cells . The qRT49 cells . Immunoh49 cells , confirmvia mediating the miR-578/SOCS2 pathway. Thus, circ_0006677/miR-578/SOCS2 signaling could be a potential target for NSCLC diagnosis and treatment.CircRNAs have been considered as important regulators in cancer progression . ElucidaNSCLC is the most common type of lung cancer, accounting for 84% of all lung cancer cases . Comparein vitro and in vivo experiments clearly showed that circ_0006677 plays a critical tumor-suppressive role in NSCLC progression by inhibiting the proliferation, migration, invasion, and glucose metabolism of NSCLC cells, as well as the growth properties of NSCLC cells in a subcutaneous tumor xenograft models, indicating that circ_0006677 might be a new therapeutic target for NSCLC treatment.To date, little is known about the cellular function of circ_000667 in NSCLC cells. Here, our Recent studies have suggested that circRNAs may function as miRNA sponges to modulate cancer development . In our Abnormal expression of miR-578 has been linked to cancer progression . HoweverSOCS2 suppresses the cytokine-induced signaling transduction and its downregulation is observed in many cancers . Here, wvia increasing SOCS2 expression through sponging miR-578. Our findings uncover a mechanistic role for the circ_0006677/miR-578/SOCS2 signaling pathway in NSCLC progression and metabolic reprogramming, providing a potential therapeutic strategy for patients with NSCLC.In conclusion, circ_0006677 is significantly downregulated and negatively associated with poor outcomes in NSCLC patients. Furthermore, circ_0006677 regulates the Warburg effect and NSCLC progression"}
+{"text": "Rhodothermus marinus is a halophilic extreme thermophile, with potential as a model organism for studies of the structural basis of antibiotic resistance. In order to facilitate genetic studies of this organism, we have surveyed the antibiotic sensitivity spectrum of R. marinus and identified spontaneous antibiotic-resistant mutants. R. marinus is naturally insensitive to aminoglycosides, aminocylitols and tuberactinomycins that target the 30S ribosomal subunit, but is sensitive to all 50S ribosomal subunit-targeting antibiotics examined, including macrolides, lincosamides, streptogramin B, chloramphenicol, and thiostrepton. It is also sensitive to kirromycin and fusidic acid, which target protein synthesis factors. It is sensitive to rifampicin (RNA polymerase inhibitor) and to the fluoroquinolones ofloxacin and ciprofloxacin (DNA gyrase inhibitors), but insensitive to nalidixic acid. Drug-resistant mutants were identified using rifampicin, thiostrepton, erythromycin, spiramycin, tylosin, lincomycin, and chloramphenicol. The majority of these were found to have mutations that are similar or identical to those previously found in other species, while several novel mutations were identified. This study provides potential selectable markers for genetic manipulations and demonstrates the feasibility of using R. marinus as a model system for studies of ribosome and RNA polymerase structure, function, and evolution. Thermus thermophilus, studies of other, phylogenetically distant thermophiles could potentially facilitate a comparative approach. This is especially relevant to the extent that species-specific idiosyncrasies, such as differences in DNA repair patterns or codon usage bias, can influence the spectrum of mutants arising. Such idiosyncrasies provide a compelling motivation to explore novel model systems.Extremophilic organisms are important model systems for investigating macromolecular structure, function and evolution. Macromolecular complexes such as the ribosome are important antibiotic targets and their structural studies have significantly advanced our understanding of antibiotic modes of action and mechanisms of antibiotic resistance ,2,3. RibRhodothermus marinus R-10T is a Gram-negative, non-motile, non-spore-forming, thermophilic and halophilic bacterium isolated from a submarine hot spring off the coast of Iceland [Rhodothermus has been classified as a member of the Rhodothermaceae, branching deeply within the phylum Bacteroidetes, with its closest relative being the mesophilic, extremely halophilic genus Salinibacter [Rhodothermaceae include Salisaeta [Rubricoccus [Rubrivirga [Longimonas [Longibacter [Rhodothermus are mesophilic, suggesting that adaptation of Rhodothermus to growth at high temperature is a derived rather than a primitive character. Its affinity with Salinibacter suggests that adaptation to hypersaline environments predates development of thermostability. This stands in contrast to members of the genus Thermus, which form part of a phylum that branches deeply in the universal phylogenetic tree, and for whom thermal adaptation is likely a primitive character. Interestingly, R. marinus was isolated from the same environmental sample as the halotolerant IB-21 strain of Thermus thermophilus [ Iceland . It grow Iceland . Based onibacter ,7. Otheralisaeta , Rubricoricoccus , Rubrivingimonas , and Longibacter . All memmophilus , providiR. marinus as a model system for genetic and structural studies of the ribosome and potentially other macromolecular complexes. Like other extremophiles, R. marinus has become an important subject of protein structural studies. Notable examples include a novel respiratory complex III [R. marinus have yet to be solved, these would seem promising subjects for structural studies given their important roles as targets for major antibiotic classes. Here, we describe antibiotic-resistant mutants of R. marinus with alterations in cellular components responsible for gene expression.We have begun to develop plex III  and the plex III . AlthougR. marinus has a number of advantages as a potential model organism for the study of the protein synthesis. In contrast to most other bacteria, the R. marinus genome has a single rrn operon [E. coli rRNA mutants were hampered by the presence of seven rrn operons such that even dominant mutations arising in a single operon fail to express a selectable phenotype; isolation of such mutants required either expression of rRNA from multi-copy plasmids [rrn operons [rrn operons. More recently, isolation of pure rRNA mutants of Mycobacterium spp. [T. thermophilus [R. marinus is not naturally competent for transformation, a method of DNA transfer by electroporation has been described [n operon ,17, faciplasmids  or delet operons . In geneium spp.  or T. thmophilus  has beenmophilus . While Rescribed  and targescribed . There iescribed .R. marinus mutants having base substitutions in rRNA, or amino acid substitutions or deletions in ribosomal proteins or RNA polymerase. In most instances, these mutations are similar or identical to those found in T. thermophilus or mesophilic bacteria. Some mutations, specifically those affecting ribosomal protein uL4, have not been previously observed.Here, we describe a collection of R. marinus. Although the sensitivity of R. marinus to several antibiotic classes was reported in the initial description of the genus [Before selecting resistant mutants, we established the range of antibiotics inhibitory to he genus , we undeR. marinus to be insensitive to all 30S ribosomal subunit antibiotics we tested. We confirmed the previous report of intrinsic resistance of R. marinus to the aminoglycosides streptomycin, kanamycin and gentamicin [R. marinus is also insensitive to the aminocylitols spectinomycin and kasugamycin. We observed resistance to capreomycin, a member of the tuberactinomycins, which binds at the 30S\u201350S subunit interface, consistent with the previously observed cross-resistance of aminoglycoside-resistant mutants to capreomycin [R. marinus. In summary, R. marinus is resistant to all 30S inhibitors tested and sensitive to all 50S subunit inhibitors tested. Sensitivity was also found to kirromycin and fusidic acid, which target protein synthesis factors EF-Tu and EF-G, respectively. Among non-ribosomal drugs, we found R. marinus to be sensitive to the RNA polymerase inhibitor rifampicin and to the DNA gyrase inhibitors ofloxacin and ciprofloxacin, but, as previously reported [We found ntamicin  and alsoreomycin ,26, suggreported , insensiT. thermophilus [Selection of resistant mutants was attempted with a number of drugs, including the RNA polymerase inhibitor rifampicin, the protein synthesis inhibitors chloramphenicol, lincomycin, erythromycin, spiramycin, tylosin, oleandomycin, thiostrepton, and fusidic acid, and the gyrase inhibitors ofloxacin and ciprofloxacin. Resistant mutants arose on rifampicin, chloramphenicol, lincomyin, erythromycin, spiramycin, tylosin and thiostrepton. No mutants appeared on oleandomycin, fusidic acid, ofloxacin or ciprofloxacin, although a more exhaustive search on a wider range of drug concentrations could potentially reveal mutants resistant to these drugs. Individual isolates were purified and analyzed by sequencing the genes known from previous studies to be the likely sites of mutations. Based on our own studies with mophilus , we had rpoB, the gene encoding the \u03b2 subunit. We identified three independent R. marinus RifR alleles of rpoB (locus tag RMAR_RS05525). These included rpoB1 (GTC to TTC) producing the amino acid substitution V146F; rpoB2 (GCC to GTC) producing A522V; and rpoB3 (GCA to TCA) producing H526Y (E. coli amino acid residue numbering). Mutations at these positions have been found in other species  and areiewed by ).O-methylation of A1067, or by amino acid substitutions, deletions or insertions in uL11 (reviewed by [rrlA encoding 23S rRNA (locus tag RMAR_RS000900) and rplK encoding uL11 (locus tag RMAR05505) of the ThiR mutants. While we did not identify any changes in uL11, all mutants were found to have alterations at or near A1067 of 23S rRNA  brought together by a series of tertiary interactions A. Thiostiewed by ). We seq23S rRNA . These i complex , A1067 iThe peptidyltransferase active site is situated deep within the 50S ribosomal subunit, and X-ray crystal structures of the bacterial ribosome in complex with transition state analogs indicate that all direct contacts with substrates in the active site involve residues of 23S rRNA . ChloramR) mutants of R. marinus were readily identified , spiramycin- (SpiR), tylosin- (TylR), and lincomycin-resistant (LinR) mutants.Chloramphenicol-resistant . A largsistance .R. marinus mutants selected for resistance to several macrolides, including erythromycin, spiramycin, and tylosin, and sequenced both rplD and rplV of each of these. No mutations in rplV were found in any mutant. Several EryR mutants were found to have deletions within rplD (locus tag RMAR_RS04205), initially noted by the diminished size of PCR products as viewed by agarose gel electrophoresis. Sequencing confirmed that the rplD genes of these mutants had deletions in the region corresponding to the loop of uL4 that extends toward the peptidyltransferase center and polypeptide exit channel where erythromycin binds.We identified a number of rplD . The saT. thermophilus RNA polymerase have been determined in various complexes [M. smegmatis RNA polymerase-rifampicin complex has been solved [R. marinus RNA polymerase residues mutated in RifR mutants, V146 and H526 are both quite conserved, whereas A522 is less so. Substitutions at H526 are among the more frequently observed RifR mutations in Mycobacterium tuberculosis [M. smegmatis RNA polymerase-rifampicin complex [E. coli, M. tuberculosis, and T. aquaticus. While none of these residues make direct contact with the drug, their exchange with bulkier residues is likely to have a strong steric effect on drug binding.A number of high-resolution structures of the omplexes ,42,43, iomplexes . More ren solved . Of the rculosis . As seen complex , all thrrplD observed in R. marinus are the result of recombination between short, fortuitously repeated DNA sequences. Synonymous codons at either of these repeats would presumably prevent these particular deletions from arising.Crystal structures of antibiotics bound to the peptidyltransferase center help to explain their mechanism of action . As illuE. coli eryA allele of rplD was found to result in a single amino acid substitution, K63E [R. marinus, it should be possible to assess their effects by reconstructing the analogous deletions in E. coli rplD. While there is as yet no high-resolution structure of the R. marinus ribosome, structures of ribosomes from a variety of organisms indicate that the loop subjected to these deletion mutations is located in close proximity to 23S rRNA residues involved in erythromycin binding. A wide variety of amino acid substitutions as well as small deletions  in this loop have been found in multiple species.The finding of deletion mutations in uL4 is consistent with previously identified mutations in this protein. The original on, K63E   [R. marinus, a sequence found in only 0.1% of bacterial 16S rRNA sequences. The mesophilic extreme halophile Salinibacter ruber, whose closest relative is R. marinus, is also resistant to kanamycin and its 16S rRNA also has the A1409-U1491 base pair [http://rnacentral.org/; accessed on 9 August 2020) [Rhodothermaceae. Whether or how either of these base pair identities might influence the aminoglycoside binding site is not obvious. Previous studies have found the aminoglycoside-resistance mutations C1409G of yeast mitochondrial 17S rRNA [Tetrahymena thermophila 18S rRNA [R. marinus, it is not yet possible to distinguish between these two possible explanations for resistance to aminoglycosides and capreomycin.The finding that t system . This exst 2020)  indicatease pair . Based ost 2020) , this sa17S rRNA , or a G118S rRNA . This woR. marinus R-10T ATCC 43812/DSM 4252 [R. marinus was cultivated in liquid TEM medium (ATCC Medium 1598) containing 2% NaCl (referred to hereafter as TEMS medium) or on TMG medium containing 2% NaCl (referred to hereafter as TMGS medium). TMG medium consists of TEM lacking phosphate buffer and solidified with gelrite at a concentration of 1.1%. All cultures were grown at 65 \u00b0C under aerobic conditions with vigorous aeration at 200 rpm in a New Brunswick Innova 42 Shaker Incubator. Overnight cultures were typically cultivated in 20 mL of medium in 125 mL baffled culture flasks (Corning).All mutants were derived from 9 cells from a saturated overnight culture onto TMGS plates containing various antibiotic concentrations; chloramphenicol, 25, 50, or 100 \u03bcg/mL; erythromycin, 50, 100, or 200 \u03bcg/mL; tylosin, 100 \u03bcg/mL; spiramycin, 100 \u03bcg/mL; lincomycin, 100 \u03bcg/mL; thiostrepton, 25, 50, 100, or 200 \u03bcg/mL; rifampicin, 50, 100, or 200 \u03bcg/mL. Mutants were purified by restreaking onto TMGS medium containing antibiotic at the same concentration used in selection, then a second time on antibiotic-free TMGS. Mutants were never exposed to antibiotic after the initial single colony isolation. Single colonies were used to inoculate TEMS medium and shaken at 65 \u00b0C to saturation. Mutants were archived as 25% glycerol stocks at \u221280 \u00b0C.To assay antibiotic sensitivity, 100 \u03bcL of a saturated overnight culture grown in TMGS broth was spread-plated onto TMGS plates. A disc infused with 100 \u03bcg of antibiotic was placed onto the surface of the plate, which was then incubated at 65 \u00b0C overnight; zones of inhibition were subsequently measured. Spontaneous mutants were selected by spreading approximately 10rrnA operon encoding 16S rRNA (locus tag RMAR_RS00885), tRNAIle (locus tag RMAR_RS00890), tRNAAla (locus tag RMAR_RS00895), 23S rRNA (locus tag RMAR_RS00900), and 5S rRNA (locus tag RMAR_ RS00905), was amplified using primers Rma_rrnA_f3 and Rma_rrnA_r3, with a 58 \u00b0C annealing temperature and a 6 min extension time. The rplD gene encoding ribosomal protein uL4 (locus tag RMAR_RS04205) was amplified using primers Rma_rplD_f1 and Rma_rplD_r1, with a 52.5 \u00b0C annealing temperature and a 1 min extension time. The rplV gene encoding ribosomal protein uL22 (locus tag RMAR_RS04225) was amplified using primers Rma_rplV_f1 and Rma_rplV_r1, with a 49 \u00b0C annealing temperature and a 1 min extension time. The rplK gene encoding ribosomal protein uL11 (locus tag RMAR_RS05505) was amplified using primers Rma_rplK_f1 and Rma_rplK_r1, with a 60 \u00b0C annealing temperature and a 1 min extension time. The rpoB gene encoding the \u03b2-subunit of RNA polymerase (locus tag RMAR_RS05525) was amplified using primers Rma_rpoB_f1 and Rma_rpoB_r1, with a 49 \u00b0C annealing temperature and a 4 min extension time. Sequencing of the rrlA gene encoding 23S rRNA was performed using primers Rma_rrnA_f4, Rma_rrnA_f7, Rma_rrnA_f8, Rma_rrnA_r5, Rma_rrnA_r6, Rma_rrnA_r7.Chromosomal DNA (gDNA) was prepared using Wizard Genomic DNA Kit . Oligonucleotide primers were synthesized by IDT and are described in Antibiotic sensitivity spectra and patterns of cross resistance can potentially be informative from both phylogenetic and ribosome structure-function perspectives. Extensive surveys of antibiotic-resistance mutations have been conducted for only a handful of species, making broad generalizations difficult. In this study, we have isolated and characterized a number of antibiotic-resistant mutants of a single species, potentially allowing direct comparisons of mutant phenotypes. Importantly, we find that mutations arising in a thermophilic-halophilic species closely resemble those found in mesophilic species, consistent with the extreme sequence conservation  of antibiotic-binding sites in RNA polymerase and the ribosome. Surprising was the inherent resistance of this species to a range of structurally-unrelated 30S subunit inhibitors. The basis for this resistance remains to be determined. The ability to readily isolate rRNA mutations in this species makes it a candidate for future structural studies to address this question."}
+{"text": "Transcranial alternating current stimulation (tACS) can affect perception, learning and cognition, but the underlying mechanisms are not well understood. A promising strategy to elucidate these mechanisms aims at applying tACS while electric or magnetic brain oscillations targeted by stimulation are recorded. However, reconstructing brain oscillations targeted by tACS remains a challenging problem due to stimulation artifacts. Besides lack of an established strategy to effectively supress such stimulation artifacts, there are also no resources available that allow for the development and testing of new and effective tACS artefact suppression algorithms, such as adaptive spatial filtering using beamforming or signal-space projection. Here, we provide a full dataset comprising encephalographic (EEG) recordings across six healthy human volunteers who underwent 10-Hz amplitude-modulated tACS (AM-tACS) during a 10-Hz steady-state visually evoked potential (SSVEP) paradigm. Moreover, data and scripts are provided related to the validation of a novel stimulation artefact suppression strategy, Stimulation Artifact Source Separation (SASS), removing EEG signal components that are maximally different in the presence versus absence of stimulation. Besides including EEG single-trial data and comparisons of 10-Hz brain oscillatory phase and amplitude recorded across three conditions , also power spectra and topographies of SSVEP amplitudes across all three conditions are presented. Moreover, data is provided for assessing nonlinear modulations of the stimulation artifact in both time and frequency domains due to heartbeats. Finally, the dataset includes eigenvalue spectra and spatial patterns of signal components that were identified and removed by SASS for stimulation artefact suppression at the target frequency. Besides providing a valuable resource to assess properties of AM-tACS artifacts in the EEG, this dataset allows for testing different artifact rejection methods and offers in-depth insights into the workings of SASS. This paradigm was used to validate SASS, a novel spatial filtering algorithm for rejection of AM-tACS artifacts in the EEG signal p1/no_stim.eeg  \u2013 p6/no_stim.eeg These files contain 64-channel EEG data featuring steady-state visually evoked potentials (SSVEPs) in absence of AM-tACS in the Brainvision format. Apart from channels in the standardized 10\u201320 system nomenclature, this dataset contains an \u2018audio\u2019 channel containing analogue pulses synchronized with the visual flicker.p1/open.eeg  \u2013 p6/open.eeg These files contain 64-channel EEG data featuring steady-state visually evoked potentials (SSVEPs) during AM-tACS in the Brainvision format. Apart from channels in the standardized 10\u201320 system nomenclature, this dataset contains an \u2018audio\u2019 channel containing analogue pulses synchronized with the visual flicker.amplitudes_phases.pyThis script computes single-trial amplitudes (S3_1.pdf \u2013 S3_6.pdf) and phases (S4_1.pdf \u2013 S4_6.pdf) for each participant.filter_characteristics.pyThis script computes eigenvalue spectra (S6_1a.pdf \u2013 S6_6a.pdf) and topographies (S6_1b.pdf \u2013 S6_6b.pdf) of components found by Stimulation Artifact Source Separation (SASS) for each participant.group_amplitudes_phases.pyThis script computes the mean amplitude (S8a.pdf) and inter-trial phase-locking value (S8b.pdf) of single-trial steady-state visually evoked potentials (SSVEPs) for each participant when Stimulation Artifact Source Separation (SASS) is computed as usual on the full-length datasets.group_amplitudes_phases_validation.pyThis script computes the mean amplitude (S9a.pdf) and inter-trial phase-locking value (S9b.pdf) of single-trial steady-state visually evoked potentials (SSVEPs) for each participant when Stimulation Artifact Source Separation (SASS) is computed on the first half of data with AM-tACS and applied to the second half.heartbeat_modulation.pyThis script computes the high-resolution multitaper power spectral density (S5_1.pdf \u2013 S5_6.pdf) necessary to detect possible modulations of the stimulation artifact by heartbeats in the frequency domain.topoplot_amplitude.pyThis script computes the topographic plots of mean-single trial steady-state visually evoked potential (SSVEP) amplitude (S2_1.pdf \u2013 S2_6.pdf).22.1EEG  was recorded from seven participants (22\u201328 years old) while they viewed white flickering gratings on a black background presented through a head-mounted display . EEG was recorded in DC mode with a dynamic range of +/\u2212430\u00a0V, a resolution of 51 nV/bit, and a range of 24 bit. It was ensured that electrode impedances stayed below 10 kOhm. Visual stimuli flickered at 10\u00a0Hz, and were presented for 2\u00a0s in each trial, with a random inter-trial interval of between 0.5 and 1\u00a0s. A trigger signal marking the flicker onset was fed into the EEG system to record stimulus timing. Two 10\u00a0min recording sessions were performed per participant, with a break of 5\u00a0min in between. In the first session, visual flickers were presented in absence of AM-tACS. In the second session, visual flickers were presented while AM-tACS targeting 10-Hz oscillations was applied. AM-tACS with a carrier frequency of 220\u00a0Hz, an envelope frequency of 10\u00a0Hz, and a peak-to-peak amplitude of 2\u00a0mA was applied through 4\u00a0\u00d7\u00a05\u00a0cm rubber electrodes positioned over positions CPz and on the inion using a commercially available stimulator .2.2The following describes the processing steps featured in the analysis scripts provided in the linked GitHub repository (see Specifications Table). The scripts, along with their output (i.e. the figures described in the Data Description section) are available along with the raw data on Mendeley Data (see Specifications Table). All analyses were implemented in MNE-Python All EEG data was bandpass-filtered around 10\u00a0Hz using finite impulse response filters with a length of 1.65\u00a0s, which was used to compute SASS (see next section). The Hilbert transform was then applied to obtain sample-wise phase and amplitude of EEG signals at each electrode, which was then subsequently averaged within each trial to obtain single-trial phase and amplitude (amplitudes_phases.py). Phases obtained via the Hilbert transform were always transformed into the phase difference relative to the visual flicker before further analysis. Unless topographically plotted, these outcome measures were computed on a representative EEG channel computed as the average of all occipital sensors. For topographic representation of SSVEP amplitude (topoplot_amplitude.py), an average was taken across single trials for each participant and a log scale applied to allow for a visualization of artifact-cleaned and artifact-contaminated data on the same scale.To obtain group-level measures of phase locking and amplitude of SSVEPs recovered by SASS (group_amplitudes_phases.py), we computed the phase-locking value R-peak were demeaned and averaged. The significance of this average was tested at each timepoint by randomly placing the window centers 1000 times and computing the resulting permutation p-value, corrected for multiple comparisons. This procedure was performed independently for each channel.To assess modulation of the stimulation artifact by the heartbeat To investigate the properties of the linear data decomposition described in the next section  forming the basis of SASS (filter_characteristics.py), we computed the eigenvalue spectra for each participant. The eigenvalue spectrum represented the ratio of power in the respective component in the condition in the presence versus absence of AM-tACS. We also plot the spatial patterns (rows of the matrix projecting from hidden space to data space) topographically.2.3Covariance matrices B and A were computed without regularization separately from data in absence of and during AM-tACS, respectively. The projection matrix implementing stimulation artifact source separation(SASS) was computed fromthese two covariance matrices The number of rejected components (number of nulls) in the matrix S was chosen such that the mean squared difference of power across all sensors between cleaned data in the presence of AM-tACS and data in absence of AM-tACS was minimized.This projection matrix P was then applied to broadband EEG data to visualize power spectra, and to narrowband EEG data to compute single-trial phase and amplitude of 10\u00a0Hz SSVEPs.The authors declare that they have no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article."}
+{"text": "Hsa_circ_0006401 promoted CRC proliferation and migration by encoding the hsa_circ_0006401 peptide. Hsa_circ_0006401 peptides decreased the mRNA and protein level of the host gene col6a3 by promoting col6a3 mRNA stabilation. In conclusion, our study revealed that circRNAs generated from col6a3 that contain an open-reading frame (ORF) encode a novel 198-aa functional peptide and hsa_circ_0006401 peptides promote stability of the host gene col6a3 mRNA to promote CRC proliferation and metastasis.Dysregulation of circular RNA (circRNA) expression is involved in the progression of cancer. Here, we aimed to study the potential function of hsa_circ_0006401 in colorectal cancer (CRC). CircRNA hsa_circ_0006401 expression levels in CRC and adjacent nontumor tissues were analyzed by real-time quantitative PCR (qRT-PCR) and circRNA in situ hybridization (RNA-ISH). Then, CRC cell proliferation was assessed by cell counting. Wound-healing and transwell assays were utilized to detect the effect of hsa_circ_0006401 on CRC migration. A circRNA-ORF construct was created, and a specific antibody against the splice junction of hsa_circ_0006401 was prepared. Finally, the proteins directly binding to hsa_circ_0006401 peptides were identified by immunoprecipitation combined with mass spectrometry. In our study, we found hsa_circ_0006401 was closely related to CRC metastasis and exhibited upregulated expression in metastatic CRC tissue samples. Proliferation and migration were inhibited in vitro when hsa_circ_0006401 expression was silenced. Downregulation of hsa_circ_0006401 expression decreased CRC proliferation and liver metastasis in vivo. A 198-aa peptide was encoded by sequences of the splice junction absent from  In recent years, the incidence of CRC has gradually increased, and the age of onset has become younger. Although great improvements in the diagnosis and treatment of CRC have been made, the prognosis is still not promising. Therefore, the development of new therapeutic strategies is urgently needed.Globally, the morbidity and mortality of colorectal cancer (CRC) rank third and fourth among those of malignant tumors, respectively2. Compared with linear noncoding RNAs, circRNA molecules have a closed circular structure that is not affected by RNA exoenzymes and is more stable3. Although circRNAs are usually expressed at low levels, they play important roles in regulating various physiological and pathological processes in the human body. Recent studies have found that aberrant circRNA expression is involved in tumorigenesis and progression6. For example, circRNA_102171 promotes the progression of thyroid cancer by interacting with CTNNBIP1 to activate the Wnt/\u03b2-catenin pathway7, high expression of circ-Foxo3 blocks the progression of the cell cycle by interacting with CDK2 through the action of promoting cell division8, and the circRNA cSMARCA5 promotes the expression of the tumor suppressor TIMP3 by acting as a \u201csponge\u201d for miR-17-3p and miR-181b-5p, which inhibits the proliferation and migration of liver cancer cells9. Although an increasing number of studies suggest that circRNAs are involved in the development of tumors, circRNA research in CRC is still in its infancy. The identification of tumor-related circRNAs and the study of functional mechanisms are of great significance for the development of new diagnostic methods.Circular RNA (circRNA) is one of the largest classes of noncoding RNA. CircRNAs are formed by a back-splicing mechanism and are abundantly present in eukaryotic transcriptomes10. Emerging evidence has also shown that circRNA-derived proteins play important biological roles in the cell stress response, myogenesis control, and tumor progression11.Because of the lack of the 5\u2032 cap structure, which is considered necessary for RNA translation, circRNAs have long been considered noncoding RNAs. In recent years, studies have found that circRNAs can be used as a protein synthesis template to encode and translate proteins under certain conditionsHere, we revealed that the expression of hsa_circ_0006401 in metastatic CRC was significantly increased compared with that in nonmetastatic CRC. Further studies revealed that silencing hsa_circ_0006401 expression with siRNA could significantly inhibit the proliferation and migration of CRC cells and promote their apoptosis and that inoculation of hsa_circ_0006401-silenced CRC cells into nude mice significantly reduced the size of subcutaneous tumors and the number of liver metastases. These results strongly suggest that hsa_circ_0006401 may play an important regulatory role in the development of CRC. In this project, we also found a novel 198-aa peptide produced from hsa_circ_0006401 that regulates the aggressive phenotype of CRC cells.2. Lipofectamine\u00ae 3000 reagent  was used for cell transfection with small interfering RNAs (siRNAs)  or constructed plasmids  according to the manufacturer\u2019s instructions. The siRNA sequences were as follows:The CRC cell lines SW480 and SW620 were purchased from the Shanghai Institute of Biochemistry and Cell Biology and cultured in DMEM  supplemented with 10% fetal bovine serum (FBS)  and 0.5% penicillin/streptomycin . Cells were maintained at 37\u2009\u00b0C in a humidified atmosphere containing 5% COsiRNA#1 5\u2032 ACAGAAAUGUUCCGAAUAA dTdT 3\u2032,siRNA#2 5\u2032 CUCUCACUGAAACAGAAAU dTdT 3\u2032.This work was approved by the Research Ethics Committee of Zhejiang Provincial People\u2019s Hospital, Hangzhou Medical College (code: 2020QT084). Twelve samples from CRC patients were collected from Zhejiang Provincial People\u2019s Hospital between May 2017 and July 2020. All the enrolled patients in this study had never received preoperative therapy. All tissues were histologically diagnosed as CRC. Patient information is shown in Table TM IV First-Strand Synthesis System  according to the instructions provided by the manufacturer.Total RNA was extracted from tissue and cell samples with TRIzol , and the RNA concentration and purity were checked by the Agilent 2100 Bioanalyzer according to the manufacturer\u2019s protocol . RNA was then reverse transcribed into cDNA using the SuperScriptTM Premix DimerEraserTM  on the ABI 7900 Real-Time PCR System . The reaction conditions were set according to the manufacturer\u2019s protocol . The human GAPDH reference gene was used as an internal control. For mRNA decay assay, 28S RNA was used as an internal control. All assays were conducted in triplicate. The PCR products were sent to GENESEED  for sequencing to ensure the accuracy of circRNA detection. Primer information is as follows:Cells transfected with indicated siRNAs were directly harvested or treated with 1\u2009\u03bcg/ml Actinomycin D  and harvested at indicated time points. Hsa_circRNA_0006401 and mRNA level was detected by qRT-PCR utilizing TB Greenhsa_circ_0006401 Forward, 5\u02b9-TGGCTCTCACTGAAACAGAAATG-3\u02b9;Reverse, 5\u02b9-GTCGTCAC TGGGTTGGATGTAG-3\u02b9TGF\u03b21 Forward, 5\u02b9-CGGCTGC TGCTGAAAGCCGACCA-3\u02b9;Reverse, 5\u02b9-GGTCGGGGCCAAAAGCGTGT-3\u02b9COL6A3 Forward, 5\u02b9-ATGAGGAAACATCGGCACTTG-3\u02b9;Reverse, 5\u02b9-GGGCATGAGTTGTAGGAAAGC-3\u02b9GAPDH Forward, 5\u02b9-AGTCAGCATT TCACAAGACCTC-3\u02b9;Reverse, 5\u02b9-CAGGCGAAGATGTTCTGGC-3\u02b9;28S RNA Forward, 5\u02b9-CCCAGTGCTCTGAATGTCAA-3\u02b9;Reverse, 5\u02b9-AGTGGGAATCTCGTTCATCC-3\u02b9.TM Fluorescent in Situ Hybridization Kit and RiboTM hsa_circ_0006401 FISH Probe Mix were purchased from Ribobio company. CRC tumor and para tumor tissue slides were deparaffinized, rehydrated, and then disposed of according to the instructions provided by the manufacturer . Finally, the slides were evaluated with a fluorescence microscope.RiboAdherent CRC sw480 cells were harvested and washed with PBS supplemented with 0.5% bovine serum albumin (BSA). After fixation with 70% ethanol at \u221220\u2009\u00b0C overnight, the sw480 cells were resuspended in PBS supplemented with 40\u2009\u03bcg/ml PI at 37\u2009\u00b0C for 30\u2009min, and 100\u2009\u03bcg/ml RNase A was subsequently added to the cells and incubated in a 4\u2009\u00b0C dark room for 30\u2009min. Cell apoptosis was determined with a flow cytometer (BD Biosciences). All assays were conducted in triplicate.6) in 150\u2009\u03bcl of PBS were randomly subcutaneously injected into 4-week-old male BALB/c nude mice. Two weeks after cancer cell inoculation, the tumors were isolated, and tumor volume was determined using the following formula: 0.5236\u2009\u00d7\u2009L1\u2009\u00d7\u2009(L2)2, L1 is the long axis and L2 is the short axis of the tumor. The tumor tissue and liver of nude mice were collected and fixed in a 10% buffered formaldehyde solution, and hematoxylin and eosin (H&E) staining was applied to assess tumor invasion and metastasis independently by two physicians. The physicians were blind to these slides. The sample size was estimated according to published articles.The studies received approval from the medical ethical committee of The Zhejiang Provincial People\u2019s Hospital. SW620 cells . Images of tumors were acquired with a light microscope. Ten sections were randomly chosen to analyze local invasion and liver metastasis. The liver lesion number was quantified by ImageJ software (version 2.1.4).Fresh CRC and paratumor tissues were washed with PBS and processed into tissue blocks. Then, the tissue blocks were fixed, embedded, sectioned at a thickness of 5\u2009\u00b5m, attached to a polylysine slide overnight at 60\u2009\u00b0C and dewaxed. An antigen retrieval solution and a blocking solution were added to the sections. Then, primary antibodies  were applied to the sections and incubated at 4\u2009\u00b0C overnight. Next, the sections were incubated with a biotinylated secondary antibody for 20\u2009min at room temperature. The staining intensity of the hsa_circ_0006401 peptide was scored independently by two physicians.Proteins were isolated from CRC cells and incubated with primary antibody detecting hsa_circ_0006401 peptide , Col6a3 , HA-probe , and GAPDH  was used as a control. Amino acid sequence for hsa_circ_0006401 peptide antibodies were as follows: 1. HAPL0559 147-161aa CSFSTKKSQPPPPQPA; 2. HAPM0617 splice junction TEMFRITLLQVLHPTQC. The anti-rabbit secondary antibody was then applied . Finally, enhanced chemiluminescence was utilized to observe immunoreactive proteins.SW480 cells were washed with PBS and lysed by cold lysis buffer . The supernatant was collected and incubated with antibody HAPM0617. Then, the protein G beads were incubated with the lysates. The beads were washed by cold lysis buffer, and the protein loading buffer was added to the beads, then were forwarded to Hangzhou Molecular Diagnostic Bio-tech Company. Ltd for Mass spectrometry analysis.t-test. All data are presented as the means\u2009\u00b1\u2009standard deviation (SD). A P-value\u2009<\u20090.05 was considered significant. Data from the experiments are expressed as the means\u2009\u00b1\u2009SD from at least three independent experiments.Statistical analysis was carried out by using SPSS 21.0 software . Differences between individual groups were compared by Student\u2019s col6a3 on Chr2(q37.3)   of .3) Fig. . To inve41) Fig. . An RNA-41) Fig. . Moreove41) Fig. .Fig. 1AnTo further confirm the function of hsa_circRNA_0006401, the location and expression of hsa_circRNA_0006401 were explored by circRNA in situ hybridization. As shown in Fig. To evaluate the function of hsa_circRNA_0006401 in vivo, control SW620 CRC cells and hsa_circRNA_0006401-silenced SW620 CRC cells were subcutaneously injected into the right and left subaxillary regions, respectively, of nude mice. Two weeks after cancer cell inoculation, the mice were sacrificed, and the tumors were isolated. As shown in Fig. col6a3. A 594-nt open reading frame (ORF) is present in hsa_circ_0006401, spanning across the splice junction, which has the potential to encode a 198-aa peptide . A construct was derived from p-Circ-GFP that contained a GFP sequence without an AUG initiation codon immediately upstream of the STOP codon, such that a GFP fusion protein could be produced when a circular template formed. We observed GFP expression in P-Circ-GFP-transfected 293T cells. However, a construct with a mutation in the ORF start codon (the start codon ATG was mutated to TTG) did not exhibit GFP fusion protein expression after transfection  against the hsa_circ_0006401 peptides, one of which was against unique reads of the circular junction (HAPM0617). As shown in Fig. To investigate whether hsa_circ_0006401 regulates CRC growth, migration and metastasis by encoding the hsa_circ_0006401 peptides, the p-Circ and ORFmut constructs were transfected into CRC cells. Then, clone formation assay, transwell assay and wound-healing assay were applied to assess the function of hsa_circ_0006401 peptides on ability of proliferation and migration of CRC cells. As shown in Fig. 12. Endotrophin, the cleaved C5 domain fragment of COL6A3, can directly regulate the malignancy of cancer cells via TGF\u03b2-dependent mechanisms12. Data from TCGA showed that compared with normal tissues, COL6A3 expression level is higher in colon cancer tissues  chain COL6A3) is a key component of the extracellular matrix and highly expressed in multiple malignants3 is a keues Fig. . To furtues Fig. . Silenci6a3 Fig. . Moreove6a3 Fig. . Down-re6a3 Fig. .Fig. 6HsTaken together, the results indicated linear RNA COL6A3 expression level was regulated by hsa_circ_0006401 peptides expression.col6a3 mRNA, but did not affect the decay of GAPDH mRNA  into a plasmid with HA tag Fig. . The immRNA Fig. , suggestcircRNA is a novel type of endogenous RNA that is widely and stably present in eukaryotic cells. Due to the lack of a cap structure and polyA tail, circRNAs form a covalently closed loop, are highly resistant to RNase activity and are conserved across species. With the rapid development of RNA deep-sequencing technology, a few cancer-related circRNAs have been identified. However, the biological functions and mechanisms of most circRNAs are largely unexplored. Accumulating studies also suggest that circRNAs participate in the tumorigenesis of human cancer and hold promise to become new diagnostic and prognostic biomarkers for various cancers.col6a3 and has unknown molecular structures in CRC. In this study, we found that the expression of hsa_circ_0006401 was closely related to lymph node metastasis. Further investigation suggested that compared to CRC tissue samples from nonmetastatic patients, cancer tissue samples from metastatic patients showed hsa_circ_0006401 upregulation. In vitro and in vivo studies showed that hsa_circ_0006401 promoted CRC growth, migration, and metastasis and inhibited CRC apoptosis. Together, these results indicate that hsa_circ_0006401 may have a potential function in the tumorigenesis of CRC. The potential diagnostic capacity of hsa_circ_0006401 was also evaluated in our study, and we revealed that hsa_circ_0006401 might serve as a novel biomarker for metastatic CRC patients. To the best of our knowledge, this is the first report to reveal the functional and diagnostic value of hsa_circ_0006401 in CRC. Our results indicate that hsa_circ_0006401 may serve as a potential biomarker of CRC and is involved in the regulation of CRC tumorigenesis, which provide the new insight that the circularization of the three exons spliced from the pre-mRNA col6a3 may maintain functions consistent with those of the host gene.Hsa_circ_0006401 is derived from its host gene 13 or interact with RNA-binding proteins (RBPs)14 to moderate gene expression. Recently, several studies have suggested that circRNAs can be translated and encode functional proteins in a cap-independent manner through the internal ribosomal entry site (IRES)16. Nevertheless, genome-wide studies have demonstrated that translation of circRNAs, which is driven by short sequences containing N6-methyladenosine (m6A) as the IRES, is widespread in human cells17. Emerging evidence also suggests that circRNA-derived proteins play important biological roles in cellular responses to environmental stress, myoblast proliferation and tumorigenesis.The mechanisms involved in the regulatory function of circRNAs are more complex. Some circRNAs may be sponges for microRNAs (miRNAs)col6a3 and is localized in the cytoplasm. In our study, we found ORFs across the back-spliced junction of hsa_circ_0006401. To study whether hsa_circ_0006401 has protein-coding potential, a construct producing a high hsa_circ_0006401 circRNA expression level was generated, and a GFP sequence without an AUG initiation codon was inserted immediately upstream of the ORF termination codon. The GFP-fusion protein was detected by fluorescence microscopy in 293T cells. However, most GFP expression was blocked when the AUG codon of the hsa_circ_0006401 ORF was mutated. Western blotting results were consistent with the immunostaining results. With comparison to the host gene, we found that a novel 198-aa peptide with an additional amino acid might be produced from this ORF, which was absent in the host gene col6a3 mRNA transcript.Hsa_circ_0006401 is produced by 2\u20134 exons of its host gene To further confirm peptide expression, the peptide sequence spanning the back\u2013splice junction, unambiguously identified as hsa_circ_0006401-encoded products, was detected by IHC. The peptide was found to be expressed in both the nucleus and cytoplasm in human colon cancer and paratumor tissue specimens. Therefore, the hsa_circ_0006401 peptide was considered to be naturally endogenously produced in human colon cancer tissues. Hsa_circ_0006401 peptide expression was also confirmed in SW480 colon cancer cells. Moreover, mutation of the hsa_circ_0006401 ORF prevented the increased proliferation and migration of CRC cells induced by overexpression of hsa_circ_0006401, suggesting that hsa_circ_0006401 may promote an aggressive phenotype in CRC cells by encoding the hsa_circ_0006401 peptide. Recently, a few studies also reported that circRNAs might encode functional peptides or proteins. For example, the circRNA Circ-ZNF609 is translated into a protein and regulates myogenesis. Furthermore, the circRNA-derived protein SHPRH-146aa suppresses glioma tumorigenesis by protecting full-length SHPRH from degradation by the ubiquitin\u2013proteasome. Taken together, these results emphasize the potentially important roles of peptides encoded by circRNAs.col6a3, is an essential component of the extracellular matrix and structurally has a short triple-helical domain and two large globule-like N-terminal and C-terminal non-collagenous domains18. The cleaved C5 domain fragment, called endotrophin, can directly regulate the cancer phenotype by activating the TGF\u03b2-dependent pathway12. Previous studies showed COL6A3 is a potential prognosis marker of colorectal carcinoma19 and alternatively spliced COL6A3 transcripts are associated with the progression of colon cancer20. In our study, we found COL6A3 is highly expressed in colon cancer, and its expression correlate with poor survival outcomes, which further emphasized the crucial role of COL6A3 as a tumor promoter in colorectal carcinoma. Moreover, hsa_circ_0006401 peptides decreased the mRNA and protein level of the host gene col6a3 and TGF\u03b21 expression, the knockdown of hsa_circ_0006401 peptide also led to accelerated decay of the col6a3 mRNA, suggesting hsa_circ_0006401 peptides may promote proliferation and metastasis of CRC by protecting col6a3 mRNA from degradation. Notably, hsa_circ_0006401 peptides were found to be closely related to poly(A) RNA binding and mRNA processing. The most important function of the poly A tail is to modulate the stability of mRNA as a complex21. Therefore, we speculated that hsa_circ_0006401 peptide may involve in the poly(A) mRNA decay process as RNA binding protein.To date, there are no reports related to the molecular activity of the peptide derived from hsa_circ_0006401. We found that hsa_circ_0006401 could promote an aggressive CRC phenotype by encoding hsa_circ_0006401 peptides. Many circRNA-derived proteins have sequence overlap with proteins conventionally generated from linear mRNA. Therefore, it is possible that hsa_circ_0006401-encoded proteins could interfere with the function of counterparts derived from linear mRNA COL6A3. COL6A3, encoded by the host gene col6a3 mRNA.In conclusion, we revealed that hsa_circ_0006401 promotes proliferation and metastasis in vivo and in vitro by encoding a novel peptide, the peptide is essential and promotes stabilization of"}
+{"text": "Scientific Reports 10.1038/s41598-020-78703-6, published online 10 December 2020Correction to: The original version of this Article contained errors.During revision of some of the nomenclature in the manuscript the Authors introduced errors in the description of recombination events, suggesting that SARS-CoV-2 is a DNA virus. SARS-CoV-2 is an RNA virus; the Article has been corrected as follows.In the Abstract,\"Population genetic analyses provide estimates suggesting that the putative introduced DNA within the RBD is undergoing directional evolution.\"now reads:\"Population genetic analyses provide estimates suggesting that the putative introduced genetic sequence within the RBD is undergoing directional evolution.\"In the Results, subheading 'Recombination between bat and pangolin coronaviruses may represent to the origin of SARS-CoV-2',\"One of these two recombinationally intergrated DNA fragments is located inside polyprotein 1ab ), referred to as RI_DNA_ORF1 in this manuscript, and the other fragment spans the 3\u2032 end of ORF1 and the 5\u2032 beginning of the S protein, referred to as RI_DNA_Boundary in this manuscript .\"now reads:\"One of these two recombinationally intergrated RNA fragments is located inside polyprotein 1ab ), referred to as RI_RNA_ORF1 in this manuscript, and the other fragment spans the 3\u2019 end of ORF1 and the 5\u2019 beginning of the S protein, referred to as RI_RNA_Boundary in this manuscript .\"and\"Our results suggested with high probability that SARS-CoV-2 originated from a bat coronavirus after recombinational integration of a DNA fragment from a pangolin coronavirus into the S protein gene . This putative integrated DNA fragment, referred to as RI_DNA_S in this manuscript, encodes a 76 AA long peptide and is located in the RBD , which may influence the host preference of the virus.\"now reads:\"Our results suggested with high probability that SARS-CoV-2 originated from a bat coronavirus after recombinational integration of a RNA fragment from a pangolin coronavirus into the S protein gene . This putative integrated RNA fragment, referred to as RI_RNA_S in this manuscript, encodes a 76 AA long peptide and is located in the RBD , which may influence the host preference of the virus.\"In Figure\u00a02 legend,\"Coordinate positions or positions of three recombinationally integrated DNA regions (indicated out by orange dotted lines) in the genome of SARS-CoV-2 (MN908947), with major proteins marked. \u2018a\u2019, \u2019b\u2019 and \u2018c\u2019 refer to RI_DNA_ORF1, RI_DNA_Boundary and RI_DNA_S, respectively.\"now reads:\"Coordinate positions or positions of three recombinationally integrated RNA regions (indicated out by orange dotted lines) in the genome of SARS-CoV-2 (MN908947), with major proteins marked. \u2018a\u2019, \u2019b\u2019 and \u2018c\u2019 refer to RI_RNA_ORF1, RI_RNA_Boundary and RI_RNA_S, respectively.\"Additionally, throughout the Results, the Discussion, the Methods sections, in Figures\u00a01 and 2, all figure legends, in Table 2 and the table legend, and in the Supplementary Files all instances of RI_DNA_ORF1, RI_DNA_Boundary, and RI_DNA_S have been replaced with RI_RNA_ORF1, RI_RNA_Boundary, and RI_RNA_S, respectively.Finally, since RDP v4 was used in this study, Reference 32, which was:Martin, D. P. et al. RDP3: a flexible and fast computer program for analyzing recombination. Bioinformatics 26, 2462\u20132463 (2010).now reads:Martin, D. P. et al. RDP4: Detection and analysis of recombination patterns in virus genomes. Virus Evol 1, vev003 (2015).The errors have been corrected in the original Article and in the Supplementary Information file that accompanies the original Article."}
+{"text": "Recently, circular RNAs (circRNAs) have become an intense focus of research and large numbers of circRNAs have been identified, awaiting functional elucidation. Thus, the present study aims to examine the regulation of circRNAs and its molecular mechanism in lung cancer growth. Here, we show that circular RNA circ_0000677 was overexpressed and correlated with poor prognosis in non\u2010small cell lung cancer (NSCLC) patients. Functionally, circ_0000677 knockdown markedly inhibited proliferation of NSCLC cells by observing of immunofluorescence staining of Ki67, clone formation assay, and xenograft experiments. In mechanism, circ_0000677 acted as a sponge of microRNA-106b and further regulated CCDND1 gene expression in NSCLC cells by dual luciferase activity assay and their expression examination. Taken together, these findings suggest a role for circ_0000677/miR-106b/CCND1 regulation axis in promoting NSCLC growth and progression. Lung cancer is the most common cancer, and the leading cause of death around the world . The morCircular RNAs (circRNAs), a type of recently discovered noncoding RNA, are widely found in biological systems . CircRNACircular RNA circ_0000677 (Alias: circ_001569) is a newly discovered circRNA of 1776 bp and located on chromosome 16q13.1. Circ_0000677 has been found to be overexpressed in multiple types of tumors . It has The human NSCLC specimens and paired normal adjacent tissues were obtained from 35 patients underwent a surgical procedure at the Nantong Maternity and Child Health Hospital andThe Second Affiliated Hospital of Nanjing Medical Universityfrom August 2019 to March 2021. After surgery, all specimens were immediately frozen in liquid nitrogen and stored at \u221280\u00b0C. All the patients provided written consent, and the Ethics Committee from Nantong Maternity and Child Health Hospital (No. Y2017096) approved all aspects of this study.2 incubator. Small interfering RNAs (siRNAs) transfection and plasmid were conducted as described [The human NSCLC lines  were purchased from Shanghai Institute of Cell Biology, Chinese Academy of Sciences . HEK-293\u00a0T cell was maintained in DMEM medium  and other cells were cultured in RPMI-1640 , supplemented with 10% fetal bovine serum , and 1% penicillin and streptomycin . The cultured cells were maintained at 37\u00b0C in a humidified 5% COescribed . When ce\u0394\u0394CT.Total RNA was extracted from cultured cells and tissues with TRIzol reagent , according to the manufacturer\u2019s protocol. Reverse transcription of miRNA was then conducted using PrimeScript RT Reagent Kit  with stem-loop primers. For reverse transcription of mRNA and circRNA, PrimeScript RT Master Mix  with random primers was used. The real-time PCR was performed on an Applied Biosystems 7500 Sequence Detection System  with TB Green Premix Ex Taq II  as described . GAPDH aWestern blot analysis was performed as described . Total pFor the luciferase reporter assays, the wide type (WT) sequences of circ_000067 and CCND1, and their corresponding mutation (MT) were synthesized and inserted into luciferase reporter vector GP-miRGLO , as detailed in the \u2018Results\u2019 section. All these WT- or MT-plasmids were co-transfected with equal amounts of mim-miR-106b-5p, or mim-miR-control to HEK293T cells using Lipofectamine 3000 (Invitrogen), respectively. Twenty-four hours after transfection, the cells were assayed using a Dual Luciferase Assay kit  as described [6 Fluc-labeled NCI-H1299 cells transfected with si-circ_0000677 or control vector were subcutaneously injected into the left hinder leg, respectively. Luciferase activity, i.e. the growth of tumors in vivo, was measured by bioluminescence imaging after mice received D-luciferin  in PBS at a final concentration of 0.15 mg/ml. Machines for bioluminescence imaging used in this study was IVIS Lumina Series III . The study has obtained permission from the Ethics Committee from Yancheng Third People\u2019s Hospital, met the standards set out in the NC3Rs primate\u2019s guidelines and followed best practice procedures.For the tumorigenicity assay, 5-week-old male BALB/c nude mice were purchased from the Model Animal Research Center at hospital. Xenograft experiment was performed as described . Total oIF analysis was performed as described . Cells gColony formation assay was performed as described ,33. Cellt test and one-way ANOVA were used for statistical analysis. Survival was analyzed by Kaplan-Meier survival curve and correlation was analyzed using Pearson correlation test. P <\u00a00.001 was considered statistically significant.All statistical analyses were performed by GraphPad Prism 8.0. Data are presented as mean\u00b1s.e.m. Student\u2019s Circ_0000677 has been considered to be associated with tumorigenesis and prognosis in multiple tumor . We hypoIn order to reveal the expression profiles of circ_0000677 in non-small cell lung cancer, 30 paired NSCLC tumor tissues and their corresponding adjacent non-cancerous tissues were detected. PCR analysis demonstrated that circ_0000677 was significantly higher expressed in NSCLC tissues as compared to normal lung tissue . Considein vivo, NCI-H1299 cells were subcutaneously inoculated in BALB/c nude mice. After 4\u00a0weeks, the fluorescence image confirmed that knockdown of circ_0000677 attenuated the in vivo tumorigenic capacity of NCI-H1299 cells  . Thus, N99 cells . These rTo understand the molecular mechanism by which circ_0000677 contributed to NSCLC cell proliferation, potential relationships between circ_0000677 and its target microRNA (miRNA) were predicted using TargetScan and miRanda database. The results showed that miR-106b might be targeted by circ_0000677, whose 3\u2032-UTR region possesses a putative binding site for miR-106b . Base onIn order to verify detailed molecular regulation of circ_0000677/miR-106b axis, target genes of miR-106b were searched for. We identified that CCND1 (cyclin D1) might be targeted by miR-106b, whose 3\u2032-UTR region possesses a potential binding site for miR-106b . Base onTo further confirm CCND1 plays a role in CCND1/ miR-106b regulation, circ_0000677 knock down was conducted in NSCLC cell line NCI-H1299, and qRT-PCR analysis revealed an unambiguously reduced mRNA expression level of CCND1 . LikewisRecent years, circRNAs, as a newly identified noncoding RNA, have become an intense focus of research. In the present study, we reported overexpressed circ_0000677 in NSCLC patients and examined the relationship between relative circ_0000677 expression and clinical prognosis of NSCLC patients. Functionally, we found circ_0000677 could drive NSCLC cell proliferation and knockdown of circ_0000677 led to an attenuated tumorigenic capacity in BALB/c nude mice. In mechanism, circ_0000677 acted as a sponge of miR-106b and further drove miR-106b/CCND1 signaling. Taken together, these findings suggest a role for circ_0000677/miR-106b/CCND1 regulation axis in promoting NSCLC growth and progression.CircRNAs have long been traditionally considered as abnormal products of RNA splicing. Recently, there is a growing consensus that circRNAs, mostly derived from the exon of human genes, actually functioned in multiple biological processes . In the Existing researches established that circRNAs exert a variety of modes of function, of which acting as \u2018miRNA sponge\u2019 accounts for a significant part . In the MicroRNAs, another novel identified small non-coding RNAs, have been considered to play crucial roles in the regulation of cellular processes and maintaining tumor cell properties . MiR-106As compared with previous NSCLC studies, we confirmed our hypotheses by multiple means, including experiments with nude mice and robust clinical data. In addition, clinical observation from NSCLC patients indicated that higher expression of circ_0000677 predicts an unfavorable prognosis, which imply that circ_0000677 could be a reasonable target for both therapeutic and diagnostic applications. However, we are aware of the limitation that additional studies in other ethnic groups with larger number of NSCLC patients are needed to corroborate the clinical relevance of these findings. Another limitation of the present study is its lack of simultaneously comparison of the diagnostic performance of circ_0000677 with other reported biomarkers in a clinical setting. We consider this as a future direction.Taken together, the present study revealed that circ_0000677 was overexpressed in NSCLC tissues and associated with poor prognosis. Circ_0000677 functioned as a sponge of miR-106b and further regulated CCND1 in NSCLC cells, which promoted proliferation of tumor cells. These results suggested circ_0000677/miR-106b/CCND1 axis might be a promising therapeutic target in NSCLC patients."}
+{"text": "Multi-region sequencing (MRS) has been widely used to analyze intra-tumor heterogeneity (ITH) and cancer evolution. However, comprehensive analysis of mutational data from MRS is still challenging, necessitating complicated integration of a plethora of computational and statistical approaches.Here, we present MesKit, an R/Bioconductor package that can assist in characterizing genetic ITH and tracing the evolutionary history of tumors based on somatic alterations detected by MRS. MesKit provides a wide range of analysis and visualization modules, including ITH evaluation, metastatic route inference, and mutational signature identification. In addition, MesKit implements an auto-layout algorithm to generate phylogenetic trees based on somatic mutations. The application of MesKit for 2 reported MRS datasets of hepatocellular carcinoma and colorectal cancer identified known heterogeneous features and evolutionary patterns, together with potential driver events during cancer evolution.https://bioconductor.org/packages/MesKit under the GPL v3 license.In summary, MesKit is useful for interpreting ITH and tracing evolutionary trajectory based on MRS data. MesKit is implemented in R and available at  Cancer evolves through a process of somatic alterations , of whicRecently, plenty of MRS studies have used phylogenetic trees to show the temporal sequence and heterogeneous divergence between samples , 11, 12.EGFR and FGFR1. They also showed that APOBEC mutations and aging predominated in the early stage of tumorigenesis of esophageal squamous cell carcinoma. These findings suggest that the MRS strategy has the potential to reveal mutational mechanisms and thereby could improve both diagnosis and treatment.Moreover, MRS provides insights into the dynamics of mutational processes during tumor progression. A previous study indicated that DNA damage repair dysfunction might be crucial for mutation accumulation during osteosarcoma evolution . Recentlde novo signature and enrichment analysis. MutationalPatterns [The downstream analysis of MRS data focuses on somatic alterations, including somatic single-nucleotide variants (sSNVs), small insertions and deletions (INDELs), and copy number alterations (CNAs). At present, many tools are available to analyze somatic alterations, which has greatly promoted the development of cancer genomics. For example, Maftools  providesPatterns  and decoPatterns  are powePatterns  enables Patterns , PhyloSuPatterns , and PyCPatterns  are basePatterns , DPClustPatterns , and PhyPatterns  adjust fPatterns . HoweverTo address these concerns, we present MesKit, an R/Bioconductor package that provides commonly used analysis and visualization modules for MRS studies. MesKit was designed as an easy-to-use R package that only requires a MAF file and a clinical file as inputs, enabling researchers to evaluate the contribution of point mutations to heterogeneity within/between tumors from the same patient. MesKit can also be used to depict mutational profiles, track evolutionary dynamics, and characterize mutational patterns at different levels. Notably, we implemented an auto-layout algorithm to visualize rooted phylogenetic trees with annotations. In addition, MesKit enables easy integration and analysis of segmentation data and CCF data and a Shiny application is provided to facilitate interactive analysis. Finally, we applied MesKit on 2 high-quality MRS datasets of hepatocellular carcinoma (HCC)  Supplem. We reprhttp://gdac.broadinstitute.org) repository .We used 2 cohorts in our analysis. The HCC cohort included tumor tissue (n = 52) and matched blood  samples from 11 patients, which were collected before treatment , while Imerged) of each mutation is computed by integrating multiple regions as previously described [i, respectively. The clonal status of sSNVs/INDELs are determined based on CCFs. A CCF value of 1 indicates that the mutation is present in 100% of the cancer cells in a sample, while a CCF value <1 indicates that the mutation is present in a subset of the cancer cells in a sample and thus is subclonal. In each sample, a mutation is classified as clonal when the upper bound of the 95% confidence interval (CI) of the CCF is \u22651 and subclonal otherwise [merged of mutation m <\u00a00.5 .MesKit includes several measures of ITH defined by recent genomic studies. For a single region/tumor, it is common to infer subpopulations of tumor cells by clustering VAFs or CCFs , 30. To FST) [FST index estimating between-region ITH for k regions was computed as described previously [a and b, m, and m in region a.Another approach to estimate ITH is calculating the area under the curve (AUC) of the cumulative density function based on the CCFs per tumor, and tumors with higher AUC values are considered to be more heterogeneous . MoreoveFST)  and Nei FST) . Calculaeviously :(3)\\dock regions within the same tumor was defined as follows [a and b. a and region b for mutation m, respectively.The Nei genetic distance for  follows :(5)\\docab and PCa/PCb represent shared subclonal sSNVs of lesion pair ab, and PCa/PCb of all sample pairs from lesion a and lesion b are used to compute the JSI for lesions with MRS data.For spatially separated lesions from the same patient, the potential metastatic route can be determined by comparing subclonal architecture between paired lesions. Here, MesKit integrated a Jaccard similarity index (JSI)-based method to identify seeding patterns based on the CCFs of sSNVs for paired lesions . The JacMesKit reconstructs the phylogeny of multiple specimens from individual patients on the basis of the presence or absence of somatic mutations. This process is implemented in getPhyloTree function via utilization R implementations of several standard phylogenetic approaches from the APE  and PHANA and B are non-zero vectors with n mutational types. Cosine similarity value can be used to test how well each mutational profile can be explained by the provided mutational signatures. Two mutational profiles are identical when the cosine similarity is 1 and are independent when the cosine similarity is 0.To illustrate the dynamic mutational spectrum during tumor progression, we implemented mutational signature analysis based on phylogenetic trees. The process starts with the construction of a mutation matrix accounting for 96 trinucleotide changes, where the sequence context of the base substitutions can be retrieved from the corresponding reference genome using the BSgenome R package. Six types of base substitution types are distinguished by convention: C>A, C>G, C>T, T>A, T>C, and T>G. As methylated cytosine at CpG sites\u00a0with the attendant risk of spontaneous deamination are mutagenic hot spots in the human genome , C>T mutMesKit was implemented as an open source R/Bioconductor package. With a MAF file and a clinical data file as standard inputs, MesKit provides a series of analysis and visualization functions to interpret mutational data from MRS experiments Fig.\u00a0. In addiKRAS and APC, were clonal and shared between paired primary tumors and metastases, indicating their early occurrence in colorectal carcinogenesis  and brain metastases (BM) of 2 patients (V824 and V930), while there is currently no strong evidence that shows that BRCA2 mutations are associated with CRC metastasis. In addition, the plotCNA function of MesKit can be used to characterize the CNA landscape across samples on the basis of copy number data. Consistent with TCGA projects and other previous studies of HCC [Generally, somatic mutations identified from MRS in a single tumor are classified as \u201cpublic mutations\u201d , \u201cshared mutations\u201d , or \u201cprivate mutations\u201d (existing in a single region) , 44, 58.sis Fig.\u00a0. Interess of HCC , 63, a nFST and Nei genetic distance, to enable pairwise comparisons between regions/lesions. Comparison of ITH between primary tumors and paired metastases in the CRC cohort showed no significant difference using these 2 indices  in truncal mutations than branch mutations  : Platform independentProgramming language: ROther requirements: R \u22654.0License: GPL-3SCR_020959RRID: biotools: meskitGigaScience database, GigaDB [The code for creating the figures in this article can be found and re-executed in a Code Ocean capsule . Support, GigaDB .Figure S1: Mutational landscape of HCC and CRC cohorts A. Mutational profile of HCC cohort. Oncoprint of top 15 most frequently mutated driver genes of HCC were grouped by public, shared, or private mutations including both clonal and subclonal drivers. Stacked bar charts on the top and right show the number of mutations for different types per sample and per driver gene, respectively. Genes were sorted by mutational frequency and samples were split by patients as indicated by the annotation bar (bottom). B. The consistent CNAs of CRC cohort with significant recurring CNAs identified from TCGA Colorectal Adenocarcinoma project by GISTIC2.0 (obtained from Broad GDAC website). Each track represents 1 tumor sample. BM: brain metastasis; LN: lymph node metastasis; LU: lung metastasis; P: primary tumor. Dark red indicates amplifications (CN \u2265 \u20094); light red, gains (2\u2009 < \u2009CN\u2009 < \u20094); dark blue, deletions (CN\u2009 = \u20090); and light blue, losses (0 \u2009<\u2009 CN\u2009 < \u20092).Figure S2: CCF heat maps of CRC cohort The heat maps of CCF values of tumor samples from the same patient. The color bar next to the heat map indicates the classification of mutations shared amongst different samples. The proportion of each classification is indicated in the legend. Putative CRC driver genes are labeled on the right.Figure S3: Comparison of phylogenetic trees constructed by different methods of the CRC cohort. Comparison of the MP-based phylogenetic trees against those constructed by NJ method and ML method for each patient with CRC. For each pair, the different clades between 2 phylogenetic trees are highlighted in red (the first tree) or blue (the second tree).Figure S4: Comparison of signature contributions measured by MesKit, MutationalPatterns, SignatureEstimation, and deconstructSigs. A. Relative contributions of all 30 COSMIC signatures for each patient in the HCC and CRC cohorts. B. Cosine similarity and RSS between the original and the reconstructed mutational profiles.Figure S5: Mutation spectra of truncal and branch mutations of HCC5647, HCC7608, and HCC8716. Stacked bar plots show the proportions of truncal and branch mutations accounted for by each of the 6 mutation types in HCC5647, HCC7608, and HCC8716. The number of analyzed mutations is displayed on top of each bar. A Fisher exact test was used to compare truncal and branch mutations for each mutation type (2-sided test: *P <\u00a00.01).Figure S6: Schematic diagram of visualizing phylogenetic trees. Node N refers to a non-mutated normal sample: node 0 represents the starting node. In tree T0: K = {node 0, node 2, node 4, node 5, node 8}, B = {node 1, node 3, node 6, node 7}, R = {node 1, node 7}, Table S1: Clinical features of the HCC and CRC cohortsTable S2: Distance between the MP-based phylogenetic tree and the NJ-/ML-based phylogenetic tree for each patient in CRC cohortTable S3: Relative contributions of all 30 COSMIC signatures for each patient in HCC and CRC cohorts, as measured by MesKit, MutationalPatterns, SignatureEstimation, and deconstructSigsTable S4: Signature contributions of truncal and branch mutations of HCC5647, HCC7608, and HCC8716Supplementary File S1: Phylogenetic visualization auto-layout algorithmFST: fixation index; GDAC: Genome Data Analysis Center; GUI: graphical user interface; HCC: hepatocellular carcinoma; INDELs: small insertions and deletions; ITH: intra-tumor heterogeneity; JSI: Jaccard similarity index; LN: lymph node; LU: lung metastasis; MAD: median absolution deviation; MAF: mutation annotation format; MATH: mutant-allele tumor heterogeneity; ML: maximum likelihood; MP: maximum parsimony; MRS: multi-region sequencing; NJ: neighbor-joining; RSS: residual sum of squares; sSNV: somatic single-nucleotide variant; TCGA: The Cancer Genome Atlas; VAF: variant allele frequency; WES: whole-exome sequencing.AUC: area under the curve; BM: brain metastasis; CCF: cancer cell fraction; CI: confidence interval; CNAs: copy number alterations; COSMIC: Catalogue of Somatic Mutations in Cancer; CRC: colorectal cancer; The authors declare that they have no competing interests.Q.Z. and J.R. conceived the project. M.L., J.C., X.W. and C.W. developed the methodology and implemented the method. X.Z., Z.Z. and Y.X. helped test the software. M.L., Q.Z., and J.R. wrote the manuscript. All authors read and approved the final manuscript.giab036_GIGA-D-21-00007_Original_SubmissionClick here for additional data file.giab036_GIGA-D-21-00007_Revision_1Click here for additional data file.giab036_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giab036_Reviewer_1_Report_Original_SubmissionRoland Schwarz -- 1/31/2021 ReviewedClick here for additional data file.giab036_Reviewer_1_Report_Revision_1Roland Schwarz -- 4/14/2021 ReviewedClick here for additional data file.giab036_Reviewer_2_Report_Original_SubmissionTheo Z Hirsch, Ph.D -- 2/2/2021 ReviewedClick here for additional data file.giab036_Reviewer_2_Report_Revision_1Theo Z Hirsch, Ph.D -- 4/8/2021 ReviewedClick here for additional data file.giab036_Reviewer_3_Report_Original_SubmissionMarc Williams -- 2/4/2021 ReviewedClick here for additional data file.giab036_Reviewer_3_Report_Revision_1Marc Williams -- 4/8/2021 ReviewedClick here for additional data file.giab036_Supplemental_FilesClick here for additional data file."}
+{"text": "This article describes a dataset that was generated as part of the article: Personalized prediction of transcranial magnetic stimulation clinical response in patients with treatment-refractory depression using neuroimaging biomarkers and machine learning (DOI: 10.1016/j.jad.2021.04.081). We collected resting-state functional Magnetic Resonance Imaging data from 70 medication-refractory depressed subjects before undergoing four weeks of repetitive transcranial magnetic stimulation targeting the left dorsolateral prefrontal cortex. The data presented here include information about the seed-based analyses such as regions of interest, individual/group functional connectivity maps and contrast maps. The contrast maps are controlled for age, gender, duration of the current depressive episode, duration since the first depressive episode, and symptom scores. Demographics, clinical characteristics, and categorical treatment response variables are reported as well. Further, the individual connectivity values of the identified neuroimaging biomarkers of long-term clinical response were used as features in the support vector machine models are presented in combination with the trained classifiers of the support vector machine models. Post hoc analyses that were not published in the original analyses are presented as well. Finally, the R or MATLAB code scripts for all figures published in the co-submitted paper are included. A 5mm sphere was created around these coordinates. ROI files are saved in anticorrelated_ROI.Contrasts_CC: All contrast files after pre-processing with component based noise correction (5 NIfTI and 5 text files) of the seed-based analyses. The basename of each file consists of 3 unique parts SBA_\u2019A\u2019_\u2019B\u2019_\u2019C\u2019.nii. A indicates whether the full sample or training sample was used (full or train). B corresponds to the seed region (DLPFC or sgACC). C shows what contrast the file contains . Each of these files consist of 2 volumes (dim 74\u00a0\u00d7\u00a092\u00a0\u00d7\u00a078). Volume 1 shows the statistical T values of the significant clusters and Volume 2 shows the same clusters with discrete values. The row numbers of the MNI coordinates in the text file with the same basename correspond to the discrete values in volume 2.Contrasts: contrast files after pre-processing with global signal regression (2 NIfTI and 2 text files) comparing long-term responders and nonresponders in the full sample using DLPFC and sgACC as seeds. The naming is similar as in the contrast_CC folder.Figures: This folder contains all 5 figures in .jpg format.Hopman_Fig. 1.jpg: This figure consists of seven (A-G) panels. Panel A and B show volume 1 and 2 of the FCmaps_average_CC.nii file, respectively. Panel C, D and F show the contrast files contrasts_CC/SBA_full_DLPFC_RvsN.nii, contrasts_CC/SBA_full_sgACC_RvsN.nii and contrasts_CC/SBA_full_sgACC_SvsR.nii respectively. Brain images were visualized with the BrainNet Viewer . Individual connectivity values were extracted and used to create the graphs in panel E and G. Data and code can be found in Plots_code_data.xlxs and Hopman_Rcode.RHopman_Fig. 2.jpg: This figure consists of 4 panels. Panel A was created with MATLABR2019b using the files in the /matlab folder. To re-create it in MATLAB, change directory to the matlab folder open and run the ROC_classification_metrics.m script. Panel C and D were based on the output data of the ROC_classification_metrics.m script and variables of interests were manually saved in Hopman_data.xlxs (sheet: SVM) The code for the plots can be found in Hopman_Rcode.R.Hopman_Fig. 3.jpg: This figure consists of 6 panels. Brain images (Panel A-D) were visualized with the BrainNet Viewer . Panel A and C show the average functional connectivity map (seed: sgACC) and most anticorrelated spots. The most anticorrelated spots were calculated with the matlab script: coordinates_anticorrelation.m. Then, correlation values were extracted, which can be found in Hopman_data.xlsx (sheet: anticorrelations). This file was also used to create the scatterplots in R, code can be found in Hopman_Rcode.R.Hopman_Fig. 4.jpg: This figure consists of 6 Panels. Brain images (Panel A/B) were visualized with the BrainNet Viewer . Panel A and B show the images /contrast_CC/SBA_train_sgACC_RvsN.nii and /contrast_CC/SBA_train_DLPFC_RvsN.nii, respectively. The connectivity values illustrated in Panel C are saved in Hopman_data.xlsx (sheet: SBA_train_RvsN) and the Rcode can be found in Rcode/Hopman_Rcode.R.Hopman_Fig. 5.jpg: This figure shows the seed-based analyses after global signal regression. Brain images (Panel A/B) were visualized with BrainNet Viewer  using the files in the contrast_GSR folder. The connectivity values illustrated in Panel C are saved in Hopman_data.xlsx (sheet:SBA_RvsN_GSR) and the Rcode can be found in Rcode/Hopman_Rcode.RFunctional_connectivity_maps\u00a0+\u00a0ROIs: The content of each file is described below. All XX in the filenames is replaced by either CC or GSR, indicating the applied denoising pre-processing strategy.FCmaps_average_XX.nii: average standardized MNI space group level functional connectivity maps of the seed-based analyses . The connectivity maps of subjects with excessive head movement were excluded .FCmap_DLPFC_individual_XX.nii: standardized MNI space subject-level functional connectivity maps of the left DLPFC seed-based analysis . The volume number is similar to the subject ID (SID). The values represent the Fisher transformed Z scores between the mean time series within the left DLPFC seed and that particular voxel.FCmap_sgACC_individual_XX.nii: standardized MNI space subject-level functional connectivity maps of the sgACC seed-to-voxel analysis . The volume number is similar to the subject ID (SID). The values represent the Fisher transformed Z scores between the mean time series within the sgACC seed and that particular voxel.Hopman_SBA_SEED.nii: MNI space regions of interest used for the seed-based analyses. One volume (dim 91\u00a0\u00d7\u00a0109\u00a0\u00d7\u00a091) coded with 1 for left DLPFC, and 2 for sgACC.Matlab: ROC_classification_metrics.m is the script used to perform the ROC analyses and extract classification metrics. Further, this folder contains the trained classifiers of all models (A\u2013O).Rcode: This folder contains two R scripts. Hopman_Rcode.R was used to create Tables: This folder contains 4 .docx file showing the tables.Hopman_Table 1.docx: This table shows the demographics and clinical characteristics for all subjects and by short-term and long-term response. Further, analyses of variance and binomial regression were performed to examine any differences across time points and responder group. All variables are saved in Hopman_data.xlsx  and Rcode for these analyses can be found in Rcode/ANOVA_binomial_logistic_clinical characteristics.R.Hopman_Table 2.docx: This table shows all significant clusters of the seed-based analyses after component based noise correction. The contrasts were long-term responders versus nonresponders and sustained response versus relapse.Hopman_Table 3.docx: Replication of the Seed-Based Analyses using the Training Sample, contrast: long-term responders versus nonresponders.Hopman_Table 4.docx: This table shows all significant clusters of the seed-based analyses after global signal regression. The contrasts were long-term responders versus nonresponders and sustained response versus relapse.Hopman_data.xlsx: file with all variables used for the analyses. The first sheet (Overview) gives the definition of each variable per sheet. The scripts in the Rcode folder need this file to run.22.1The derived data described in this article are shared at Mendeley Data 2.2n\u00a0=\u00a070) were referred by psychiatrists from the specialist outpatient clinics in the public sector funded by the local government of Hong Kong Special Administrative Region. Participants were right-handed, aged 18\u201357 years, met the criteria for major depressive disorder (MDD) based on the Diagnostic and Statistical Manual of Mental Disorders , moderate or severe episode defined by a scored of \u2265\u00a020 on the Montgomery-\u00c5sberg Depression Rating Scale  or week 8 measurement (n\u00a0=\u00a01) instead. The percentage change in MADRS symptom scores at week 4 (MADRSbaseline - MADRSwk4)/MADRSbaseline * 100%) and week 12 (MADRSbaseline - MADRSwk12)/MADRSbaseline * 100%) were calculated. Response was defined as a minimum reduction of 50% in symptom score measured with the MADRS Several demographic and clinical variables were collected during the pre-treatment measurement, including age, gender, handedness, years of education, duration of the current depressive episode in weeks, total duration since the first depressive episode, the number of depressive episodes, medication, and the level of treatment refractoriness 2.42.4.13, and matrix size\u00a0=\u00a0240\u00a0\u00d7\u00a0240. This scan was used to register with the resting-state fMRI data, and for segmentation into grey matter, white matter, and cerebrospinal fluid, and normalization to template space. The T1-structural scan was followed by a six-minute resting-state fMRI scan consisting of 170 volumes with the following parameters: repetition time\u00a0=\u00a02050ms, echo time\u00a0=\u00a025ms, flip angle\u00a0=\u00a090\u2070, 3.2 mm3 voxels, slice thickness\u00a0=\u00a03.2 mm, Field of View\u00a0=\u00a0205 mm\u00b2, and matrix size\u00a0=\u00a064\u00a0\u00d7\u00a064. Research has shown that six minutes of resting-state fMRI results in moderate to strong reliability for functional connectivity measures MRI scans were acquired up to two weeks before the start of the rTMS treatment on a 3.0T Philips Achieva Medical Scanner with an eight-channel SENSE head coil  at the Prince of Wales Hospital, Hong Kong Special Administrative Region in China. The first scan was a high resolution T1-weighted structural scan covering the whole brain acquired with the following parameters: repetition time\u00a0=\u00a07.54 ms, echo time\u00a0=\u00a03.53 ms, flip angle\u00a0=\u00a08\u2070, 1.1\u00a0\u00d7\u00a01.1\u00a0\u00d7\u00a00.6 mm voxels, number of slices\u00a0=\u00a0285, slice orientation\u00a0=\u00a0sagittal, slice thickness\u00a0=\u00a01.2 mm, Field of View\u00a0=\u00a0250 mm2.4.2Resting-state fMRI data were pre-processed using the default pipeline of the CONN toolbox v18.b 2 and to a depth of approximately 2 cm The region of interests for the seed-based analyses were defined in MNI standardized space. For the left DLPFC, a 20-mm sphere was drawn around previously determined optimum stimulation Montreal Neurological Institute . A Magstim Super-Rapid device was used with a 70-mm figure-of-eight double air film coil  and manually centred at MNI coordinates X\u00a0=\u00a0-46, Y\u00a0=\u00a045, Z\u00a0=\u00a038  and binomial logistic regression were performed to examine differences in demographics and clinical characteristics. For the continuous variables, repeated measures ANOVA were performed with two main terms including Time  and Response  and one interaction term (Time x Response). For the categorical variables, binomial logistic regression analyses were performed with Response (0 or 1) as outcome variable. Time and each categorical variable were added as predictors and dummy coded. For time , short-term was the reference group. For gender , the male group was the reference group. For medication , the none group was the reference group, antidepressants was dummy 1 and antidepressant\u00a0+\u00a0psychotropic was dummy 2. For treatment refractoriness , level 1 was the reference group, level 2 was dummy 1 and level 3+4 was dummy 2. Model comparison was performed using Akaike Information Criteria  and Chi-square test .2.6.2Subject-level bivariate Pearson's correlations between the mean time series within each seed (DLPFC/sgACC) and the blood-oxygen-level-dependent time series of each voxel in the brain were extracted and converted to normally distributed Fisher transformed z-scores to conform to the assumptions of generalized linear models using the CONN toolbox 2.6.3Machine learning was applied to examine whether combining the identified biomarkers could increase the accuracy of categorical rTMS treatment response prediction. Our sample was split into a training/validation dataset (70%) and a test dataset (30%). We used MATLAB's Machine Learning toolbox  to search for the best classification model type, including decision trees, discriminant analysis, support vector machines, logistic regression, nearest neighbors, naive Bayes, and ensemble classification. Hyperparameters optimization was automated by the toolbox. Subject-level Fisher transformed z-scores identified by the seed-based analyses above were entered as features, and long-term categorical treatment response was entered as a binary outcome (responders/nonresponders). The average accuracy scores and prediction speed from the 5-fold cross-validation procedures were used to determine the best classification model type. This classification model type was used to train classifiers for all feature combinations in the training/CV dataset. The trained classifiers subsequently were used to examine performance in the independent test dataset. A large decrease in performance in the test dataset compared to the training/CV dataset suggests overfitting 2.7Based on the reviewers\u2019 comments, two post hoc analyses were performed which were not included in the original article.2.7.1Correlational analyses were performed to directly examine the predictive value of the most anticorrelated areas. Functional connectivity maps were masked with the automated anatomical labeling atlas (AAL) regions of interest, including left middle frontal gyrus and subcallosal cortex A\u2013D. Then2.7.2In the original article This study was approved by the Joint Chinese University of Hong Kong\u2013New Territories East Cluster Clinical Research Ethics Committee [Ref No.: CRE-2014.041] and in line with the Helsinki Declaration Helene Hopman: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Visualization, Writing - original draft, Writing - review & editing; Sandra Chan: Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Writing - review & editing; Winnie Chu: Conceptualization, Methodology; Hanna Lu: Writing - review & editing; Chun-Yu Tse: Writing - review & editing; Steven Chau: Writing - review & editing; Linda Lam: Conceptualization, Methodology; Arthur Mak: Conceptualization, Methodology; Sebastiaan Neggers: Conceptualization, Methodology, Supervision, Writing - review & editing.S. Neggers holds a minority share in Brain Science Tools BV, a company manufacturing stereotactic navigation technology for TMS. This did not influence the design, analysis, or reporting of the current manuscript in any way. No potential conflict of interest was reported by the other authors."}
+{"text": "Intervertebral disc degeneration (IDD) is a chronic degenerative and age-dependent process characterized by aberrant apoptosis, proliferation, synthesis, and catabolism of the extracellular matrix of the nucleus pulposus (NP) cells. Recently, studies showed that circular RNAs play important roles in the development of many diseases. However, the role of circRNAs in IDD development remains unknown. We showed that circ_0134111 level was overexpressed in IDD tissue samples as compar-ed to control tissues. The upregulation of circ_0134111 was more drastic in the moderate and severe IDD cases than in those with mild IDD. In addition, we showed that interleukin-1\u03b2 and tumor necrosis factor-\u03b1 exposure significantly enhanced circ_0134111 expression in NP cells. Furthermore, ectopic expression of circ_0134111 induced proliferation, pro-inflammatory cytokine secretion, and ECM degradation in the NP cells. We also showed that circ_0134111 directly interacted with microRNA (miR)-578 in NP cells where elevated expression of circ_0134111 enhanced the ADAMTS-5 and MMP-9 expression. Moreover, miR-578 expression was significantly decreased in IDD patients and the miR-578 expression was negatively correlated with circ_0134111 expression in the IDD samples. Interleukin-1\u03b2 and tumor necrosis factor-\u03b1 exposure significantly decreased miR-578 levels in NP cells, in which ectopic miR-578 expression inhibited cell growth, pro-inflammatory cytokine expression, and ECM degradation. Finally, we showed that circ_0134111 overexpression induced the IDD-related phenotypic changes through inhibiting miR-578. These data suggested that circ_0134111 could promote the progression of IDD through enhancing aberrant NP cell growth, inflammation, and ECM degradation partly via regulating miR-578. Low back pain (LBP) is a leading cause of physical disability and is one of the most frequently encountered health problems in clinics, causing substantial global public health and economic burden \u20134. IDD iRecently, noncoding RNAs, including microRNAs, long noncoding RNAs, and circRNAs (circular RNAs) have been shown to act important roles in the development of many diseases \u201320. CircOur study identified circ_0134111 as one of the most highly upregulated circRNAs in the IDD tissue samples as compared to control specimens. Upstream, pro-inflammatory cytokines TNF-\u03b1 and IL-1\u03b2 were found to significantly increase circ_0134111 expression. Furthermore, enforced expression of circ_0134111 induced aberrant ECM degradation, proliferation, and inflammatory cytokine secretion in NP cells.To explore whether circ_0134111 is deregulated in IDD, its expression level was measured by qRT-PCR in 30 IDD tissues and 10 control disc samples. The expression of circ_0134111 was higher in the IDD samples than in the control tissues Fig. . Furthercirc_0134111 expression level in NP cells after exposure to two important pro-inflammatory cytokines, namely IL-1\u03b2 and TNF-\u03b1, was measured by qRT-PCR. As shown in Fig. To investigate the downstream signaling of circ_0134111, bioinformatics tools were used to predict the downstream targets. As displayed in Fig. To study whether miR-578 expression was changed in IDD, miR-578 expression was determined with qRT-PCR in disc tissues collected from the same cohort of IDD patients and 1control subjects. As shown in Fig. circ_0134111 overexpression promoted NP cell proliferation as shown by the CCK-8 assay Fig. . In lineOpposite to the actions of circ_0134111, miR-578 overexpression inhibited NP cell proliferation Fig. , decreasWe further studied whether circ_0134111 regulated proliferation, cytokine secretion, and ECM degradation through regulating miR-578 expression in NP cells. We found that overexpression of circ_0134111 promoted cell proliferation in NP cells, where miR-578 reversed this effect Fig. . OverexpWe found that the circ_0134111 level was upregulated in IDD tissues and the upregulation of circ_0134111 correlated with the clinical severity. Upstream, we showed that two pro-inflammatory cytokines\u2014IL-1\u03b2 and TNF-\u03b1 could induce circ_0134111 expression in NP cells. Functionally, circ_0134111 induced NP cell proliferation, pro-inflammatory cytokine secretion, and ECM degradation whereas miR-578 produced the opposite effects. Mechanistically, circ_0134111 directly interacted with miR-578 to mediate the phenotypic changes. These data suggested that circ_0134111 could promote IDD progression, at least in part, through regulating miR-578 expression to alter NP cell functions.Recently, several circRNAs play crucial roles in IDD development. For example, Hu et al.  showed tNumerous studies have suggested that circRNAs mediate their biological functions in IDD via sponging miRNAs. For example, exosome-transported circ_0000253 promoted IDD development via regulating miR-141-5p . Guo et In summary, our results demonstrated that circ_0134111 is aberrantly upregulated in the IDD tissues. The expression of this circRNA could also be induced by IL-1\u03b2 and TNF-\u03b1 in NP cells. circ_0134111 also alters NP cell phenotypes that are known to contribute to IDD progression. Mechanistically, miR-578 is the downstream target of circ_0134111. Our data suggested that circ_0134111 may be a novel therapeutic target in IDD.The intervertebral disc samples from the IDD patients and normal intervertebral disc samples from those with spondylolysis were collected from our hospital. These specimens were snap-frozen and stored in the liquid nitrogen until protein or RNA extraction.Total cellular and tissue RNA was extracted using Trizol . Expression of lncRNA and mRNA was detected by qRT-PCR using the SYBR Green PCR mix on the BioRad IQ5 PCR system. GAPDH and U6 nuclear RNA were utilized as controls for mRNA/lncRNA and miRNA, respectively. These primer sequences are as follows: circ_0134111, forward 5\u2019- GAAAACAGATGAGGAGAAGGCC-3\u2019 and reverse 5\u2019- CGTCTTTTTCTCAGCTTTGCC-3\u2019; IL-6, forward 5\u2019-GACTGATGTTGCTGACAGCCACTGC-3\u2019 and reverse 5\u2019-TAGCCACTGCTTCTGTGACTCTAACT-3\u2019; IL-8, forward 5\u2019-AAACCACCGGAAGGAACCAT-3\u2019 and reverse 5\u2019-GCCAGCTTGGAAGTCATGT-3\u2019. VEGF, forward: 5\u2032-GGACCCGAT GCGGTTAGAG-3\u2032and reverse 5\u2032-ATCAAGTGGATGCCCCACAG-3\u2032;NP cells were separated and cultured according to previous studies. In brief, NP tissues were dissected for digestion with collagenase II in Dulbecco\u2019s modified Eagle\u2019s Medium (Life Technologies). NP cells were cultured in DMEM supplement with fetal bovine serum (FBS), streptomycin, and penicillin. circ_0134111 and control plasmid, miR-578 mimic and miR-NC were purchased from Genechem  and were transfected into cells using Lipofectamine2000  according to the instructions of the manufacturer.3 cells/well and cultured for different times . Ten microliters CCK-8  solution was added in each well and continued to incubate for 2\u2009h at 37\u2009\u00b0C. The absorbance at 450\u2009nM was read on the microtiter reader.After transfecting, cells were seeded in 96-well plates with the density at 5\u2009\u00d7\u200910Isolation of total protein from IDD tissues or NP cells was performed with RIPA buffer. The concentration of protein was determined with the bicinchoninic acid (BCA) protocol. An equal amount of protein was resolved by SDS-polyacrylamide gel electrophoresis  and transferred to the PVDF membrane (Millipore). After blocking with 5% milk, the membrane was incubated with primary antibody . After washing three times with TBST, the membrane was incubated with an HRP-conjugated secondary antibody. The signals were generated with the chemiluminescent reagents. The primary antibodies used in this study are as follows: VEGF, MMP-9, ADAMTS-5, and GAPDH (Santa Cruz Biotechnology).t-test or one-way ANOVA where appropriate. P\u2009<\u20090.05 was considered as significant.Results were shown as the means\u2009\u00b1\u2009standard deviation (SD). All statistical tests were conducted using the SPSS 18.0 software . The significance of the difference between groups was determined using Student\u2019s"}
+{"text": "To investigate the clinical significance of differentially expressed circRNAs and candidate circRNAs in the transformation of oral leukoplakia (OLK) to oral squamous cell carcinoma (OSCC).We performed high-throughput circRNA sequencing in six cases of normal oral mucosal (NOM) tissues, six cases of OLK tissues, and six cases of OSCC tissues. Ten circRNAs with significant differential expression were verified by qRT-PCR. Enzyme tolerance assay and Sanger sequencing were performed on the screened target circRNA hsa_circ_0060927, and a qRT-PCR assay of hsa_circ_0060927 was performed in three tissues (24 cases in each group); this was followed by an ROC analysis. The ceRNA network was predicted using TargetScan and miRanda. MiR-195-5p and TRIM14 were selected as the downstream research objects of hsa_circ_0060927. The sponge mechanism of hsa_circ_0060927 was detected by AGO2 RIP. The interaction between hsa_circ_0060927 and miR-195-5p was verified by RNA pull-down assay and dual luciferase reporter gene assay. The expressions of hsa_circ_0060927, miR-195-5p, and TRIM14 were verified by normal oral epithelial primary cells and cell lines of LEUK1, SCC9, and SCC25. The hsa_circ_0060927 overexpressed plasmid and miR-195-5p mimics were constructed to transfection LEUK1 to detect the changes in cell proliferation, apoptosis, and migration.The results of qRT-PCR validation were consistent with the sequencing results. Hsa_circ_0060927 is a true circRNA with trans-splicing sites. The expression of hsa_circ_0060927 increased in NOM, OLK, and OSCC. Overexpression of\u00a0hsa_circ_0060927 enhanced the ability of cell proliferation and migration, and decreased cell apoptosis capacity. The prediction of ceRNA network suggested that hsa_circ_0060927 could regulate the target gene TRIM14 through sponging miR-195-5p. AGO2 RIP indicated that hsa_circ_0060927 had a sponge mechanism. RNA pull-down and dual luciferase reporter gene assay suggested that hsa_circ_0060927 interacted with miR-195-5p. Hsa_circ_0060927 was positively correlated with the expression of TRIM14, and could relieve the inhibition of miR-195-5p on TRIM14 to regulate cell proliferation, apoptosis, and migration of LEUK1 cells.Hsa_circ_0060927 acted as a potential key ceRNA to sponge downstream miR-195-5p and promote OLK carcinogenesis by upregulating TRIM14. Hsa_circ_0060927 was expected to be a molecular marker for the prevention and treatment of OLK carcinogenesis through the hsa_circ_0060927/miR-195-5p/TRIM14 axis. Oral squamous cell carcinoma (OSCC) is the most common cancer in the oral maxillofacial region, and the incidence rate of OSCC has remained on an upward trend . Due to Oral leukoplakia (OLK) cannot be pathologically or clinically defined as any other diseases with white plaques or patches that cannot be rubbed off . It is wCircular RNAs (circRNAs) are a large portion of endogenous non-coding RNAs with a covalently closed loop structure with no 5\u2032 cap or 3\u2032 polyadenylation tails , 10. Comin vitro and in vivo  tissue specimens were used to perform high-throughput sequencing in a total of eighteen cases, with six samples in each group; all of those samples were selected from patients who underwent surgery and treatment in Shanghai Ninth People\u2019s Hospital. The clinical manifestation of OLK and OLK canceration is shown in in situ as clusters and finally sequenced for 150 cycles on an Illumina HiSeq 4000 Sequencer .Total RNA was extracted from the experimental tissue samples using TRIzol . RNA concentrations and purity were tested by NanoDrop ND-1000 . Spectrophotometer OD260/OD280 values were used for RNA purity indexes. Quality control results indicated a range of OD260/OD280 between 1.8 and 2.1. Under the guidance of the manufacturer\u2019s instructions, the rRNAs were eliminated by Ribo-Zero rRNA Removal Kits . RNA libraries were constructed by TruSeq Stranded Total RNA Library Prep Kit . The BioAnalyzer 2100 system  was applied for controlling the quality and quantified of libraries. CloudSeq Biotech  provided the high-throughput sequencing service. Libraries (10 pM) were denatured as single-stranded DNA molecules, captured on Illumina flow cells, amplified via cutadapt software (v1.9.3). The eligible trimmed reads were aligned to a reference genome/transcriptome with STAR software (v2.5.1b). The high-quality circRNAs were detected and identified by DCC software (v0.4.4). Identified circRNAs were annotated using circBase and circ2Traits databases. According to sequencing depth and degree of variation, we normalized the data and screened for altered circRNAs between OSCC and OLK using edgeR software (v3.16.5). The profile of differentially expressed circRNAs between OLK and OSCC was generated by Cluster and TreeView software. Based on the expression levels of all identified circRNAs in OLK and OSCC, the hierarchical clustering analysis proceeded and the significant differential circRNAs were selected. Unprocessed and analyzed sequencing data, after standardization, were uploaded to the National Center for Biotechnology Information Gene Expression Omnibus (GEO). The number of the dataset that was successfully uploaded to GEO is GSE131182 and GSE131568.To filter high-quality trimmed reads for analyzing circRNAs, Q30 was applied for quality controlling, and trim 3\u2019 adaptors and low-quality reads were removed Reverse transcription was applied for total RNA by SuperScript III Reverse Transcriptase . According to the manufacturer\u2019s instructions, qRT-PCR reactions involved using qPCR SYBR Green master mix  with QuantStudio 5 Real-Time PCR System (Thermo Fisher). Divergent primer pairs designed for target 10 circRNAs were selected and are summarized in \u2212\u2206\u2206Ct measurement.RNeasy MinElute Cleaning Kit (Qiagen) was used for RNase R assay, and total RNA was extracted from experimental tissues and then incubated with RNase R . Next, the back-spliced junctions of hsa_circ_0060927 were verified by Sanger sequencing. Data analysis for relative expression of circRNAs was performed by 2p-value < 0.05 and FC \u2265 2.0 were selected from the result of sequencing data. Step 2: According to the degree of the differentially expressed circRNAs, the characteristic of circRNAs and the correlation of disease annotation, ten circRNAs were elected from the 389 circRNAs selected in step 1. The ten circRNAs were validated by qRT-PCR. Step 3: Given that the aim was to detect whether there were molecules involved in the progress of OLK carcinogenesis, the comparison of differentially expressed circRNAs in normal versus OLK (N-K) and OLK versus OSCC (K-S) groups was intersected. Hsa_circ_0060927 was the intersection between the two groups and was also the most significant upregulated circRNA expression in K-S. Step 4: RNAase R and Sanger sequencing assay were used to validate the structure of hsa_circ_0060927. The candidate circRNA, hsa_circ_0060927, was further validated in an independent cohort that contained 24 OSCC, 24 OLK, and 24 healthy oral mucosa tissue samples, and ROC curve analysis was depicted after expanding the sample size for verification of hsa_circ_0060927. Cell localization and cell experiment were performed further to explore the expression and function of hsa_circ_0060927. Step 5: Mechanism analyses were performed to determine the importance of the hsa_circ_0060927/miR-195-5p/TRIM14 axis.Step 1: A total of 389 circRNAs with a criterion of in situ hybridization (FISH) to locate hsa_circ_0060927 and miR-195-5p in OLK cells. In brief, FAM-labeled probes were specific to hsa_circ_0060927 and cy3-labeled probes were specific to miR-195-5p. Nuclei were stained by DAPI. All the procedures were conducted according to the manufacturer\u2019s instructions . All images were acquired on an upright fluorescence microscope system .The OLK tissue slices were used to perform fluorescence 2.The Leuk1 cell line was derived from the University of Texas MD Anderson Cancer Center, USA \u201317, provWe constructed an hsa_circ_0060927-overexpressed vector. Briefly, the sequence with a full length of 1,106 bp was subcloned into a pCDH-CMV-MCS-EF1-copGFP-T2A-Puro vector  to generate pLCDH-circ_0060927 constructs. The subcloned sequence containing a front circular frame (SA), a back circular frame (SD) of circRNA biogenesis, and a full length of hsa_circ_0060927 and 5\u2019-ATTTAAATCGGATCCGGCCACACCCTCCCATCAAA-hsa_circ_0060927-ATCCTTCGCGGCCGCTCAGAACACAGCCTTTGTAGG -3\u2019 was directly synthesized. The pLCDH-ciR empty vector was used as the control group. The procedure of transfection was conducted using Lipofectamine 2000 (Invitrogen) kits.Cell proliferation was tested using Cell Counting Kit-8 (CCK-8) assay according to the manufacturer\u2019s instruction. After 48\u00a0h of cell transfection, the cells were seeded in 96-well plates after cell counting, and the plates were cultured in the incubator. After cell attachment, 10 \u03bcl of CCK-8 solution  was added to each well on days 1\u20134. After incubation for 1\u00a0h, the absorbance was measured at 450 nm using a microplate reader . All results were expressed as the mean \u00b1 SD. Each experiment was proceeded at least three times independently.6 cells/ml, and cell apoptosis assay was detected by flow cytometry  after cell staining.Cell apoptosis assay was performed using an Annexin V\u2013FITC/PI kit . After 48\u00a0h of cell transfection, cells were collected after trypsin digestion. Mixing resuspended cells using the Annexin V\u2013FITC/PI kit was according to the manufacturer\u2019s guidance. The cell concentration was 1 \u00d7 10After transfection for 48\u00a0h, LEUK1 cells were suspended in growth factor-free Defined keratinocyte-SFM. One hundred microliters of suspension cells was added to the Transwell chamber  after cell counting. Defined keratinocyte-SFM containing 0.5% growth factor was added to the lower chamber. After 24 s, nonmigratory cells were removed. The cells migrating through the membrane were counted under a microscope  after fixing by formaldehyde and staining by 0.1% crystal violet.CircRNA\u2013miRNA-coding gene interactions were predicted by target prediction software, Target scan and miRanda, and the construction of the ceRNA network was used by the bioinformatics software Cytoscape (v2.8.0). In order to select the downstream target molecules in which hsa_circ_0060927 may regulate the process of OLK carcinogenesis, the miRNAs related to oral diseases that have been reported corresponded to the above predicted miRNAs. The combining capacity between circRNA and miRNA was predicted by bioinformation software.p-value indicates the statistical significance of GO terms enrichment in the host genes using Fisher\u2019s exact test . KEGG analysis was used to annotate host genes of differentially expressed circRNAs using the KEGG database. The Fisher p-value denotes the significance of each pathway involved, and p < 0.05 was deemed to indicate statistically significant differences.The functional mechanism analysis of the differentially expressed circRNAs between OLK and OSCC was predicted by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. GO terms were screened according to the source gene with significantly different circRNAs and GO annotation list. The RIP assays were performed in OLK tissues. Resuspended cells were collected from tissue homogenate. RNA lysis buffer was used to lyse\u00a0the cells. Then, the cells were incubated in RIP immunoprecipitation buffer containing magnetic beads conjugated with human anti-Argonaute2 (AGO2) antibody  or negative control Rabbit IgG . Proteinase K was added to the RIP sample and incubated at 55\u00b0C for 30\u00a0min. Then, immunoprecipitated RNA was isolated and analyzed by qRT-PCR to quantify the enrichment of hsa_circ_0060927.Biotin-labeled hsa_circ_0060927 probe (positive probe) and oligo probe  (negative probe) were synthesized. OLK tissues were lysed with lysis buffer and incubated with specific hsa_circ_0060927 probes. Then, OLK tissues were lysed with lysis buffer and incubated with probe-coated beads at 4\u00b0C overnight. The beads were washed, the RNA complexes were extracted with TRIzol , and qRT-PCR was performed to detect miR-195-5p.The wild-type hsa_circ_0060927 (hsa_circ_0060927-WT) vector, which contained miR-195-5p-specific binding sites, and the mutant fragment (hsa_circ_0060927-Mut), which contained the mutant miR-195-5p binding sites, were constructed by annealing double-stranded DNA and inserting them into the pSI-Check2 vector  . Lipofect-test was performed according to actual conditions. Statistical significance of difference between groups was determined by unpaired t-test and one-way ANOVA . Correlations were analyzed using Pearson\u2019s linear correlation analysis. p < 0.05 was considered statistically significant. Student\u2019s receiver operating characteristic (ROC) was performed to assess diagnostic and prognostic values of hsa_circ_0060927. Quantitative data were shown as mean \u00b1 standard error of the mean (SEM), and all the data were obtained through three times repetition at least.Statistical analysis proceeded using SPSS 19.0  and GraphPad Prism version 8.0 software. p-value  new circRNAs were identified with the rest found (95.9%) in the circRNA database . Most weTop eight upregulated and two downregulated circRNAs were selected for verification by qRT-PCR. Validation was first performed on the six pairs of OSCC and OLK tissue Figure\u00a02In order to better compare the differential circRNAs in OSCC, OLK,and NOM groups, we performed an intersection analysis on the upregulated, downregulated, and total differentially expressed gene of the three groups respectively; Venn diagrams were drawn (in situ hybridization (FISH) revealed that hsa_circ_0060927 localized in cytoplasm of OLK  assay was then performed in OLK tissues to determine the association between hsa_circ_0060927 and AGO2. AGO2 protein was a key molecule for circRNA to sponge miRNA.The quantitative real-time PCR results showed that the expression of hsa_circ_0060927 pulled down with anti-AGO2 antibodies was significantly higher compared to the anti-IgG group  can be found at: NCBI, GSE131182, and GSE131568.The studies involving human participants were reviewed and approved by the Ethics Committee of Shanghai Ninth People\u2019s Hospital. The patients/participants provided their written informed consent to participate in this study.HZ conceptualized the study. SX, YHS, and YXS developed the methodology. SX was in charge of the software. SX, YHS, and YXS validated the study. SX, YHS, and YXS conducted the formal analysis and the investigation. HZ provided the resources. SX, YHS, and YXS conducted the data curation. SX wrote and prepared the original draft. SX, YHS, YXS, and HZ wrote, reviewed, and edited the manuscript. SX and HZ conducted the visualization. HZ supervised the study. All authors contributed to the article and approved the submitted version.This work was supported by the National Key R&D Program of China (2017YFC0840100 and 2017YFC0840110) and Biobank Program of Shanghai Ninth People\u2019s Hospital (YBKA201912).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher."}
+{"text": "The accessions were distributed into three groups, following their carotenoid profiles, with accession C044 having the highest sprout carotenoid content in a single cluster. A total of 3120 genome-wide SNPs were tested for association analysis, which revealed that carotenoid biosynthesis in cowpea sprouts is a polygenic trait controlled by genes with additive and dominance effects. Seven loci were significantly associated with the variation in carotenoid content. The evidence of variation in carotenoid content and genomic regions controlling the trait creates an avenue for breeding cowpea varieties with enhanced sprouts carotenoid content.The development and promotion of biofortified foods plants are a sustainable strategy for supplying essential micronutrients for human health and nutrition. We set out to identify quantitative trait loci (QTL) associated with carotenoid content in cowpea sprouts. The contents of carotenoids, including lutein, zeaxanthin, and \u03b2-carotene in sprouts of 125 accessions were quantified via high-performance liquid chromatography. Significant variation existed in the profiles of the different carotenoids. Lutein was the most abundant (58 \u00b1 12.8 mg/100 g), followed by zeaxanthin (14.7 \u00b1 3.1 mg/100 g) and \u03b2-carotene (13.2 \u00b1 2.9 mg/100 g). A strong positive correlation was observed among the carotenoid compounds ( A balanced and healthy diet is a global priority, especially in the low-income and developing countries where hunger and malnutrition are more widespread ,2,3. HidVigna unguiculata L. Walp), are important sources of micronutrients and amino acids, exceeding or complementing the profiles of cereals, making them perfect target crops for addressing the global micronutrients deficiency  with the variation in carotenoid content among the cowpea sprouts. These loci were distributed on chromosome 6 , chromosome 7 (S_Vung_CA1838 and S_Vung_CA1840), chromosome 8 (S_Vung_CA2146), and chromosome 11 (S_Vung_CA3031). The loci explained 10.10 to 13.51% of the variation of carotenoid content, with locus S_Vung_CA1840 showing the largest effect (13.51%) for the variation of \u03b2-carotene contents among cowpea sprouts , although positive, was the lowest among the three pairs of comparisons among compounds , confirmCarotenoid content in plants is an important trait for human health and nutrition ,55. The r2 = 0.47) pairs of markers across the genome. Notably, high LD decay distance (1.4 Mbp) was obtained in the germplasm, which is within the range of ~500 kb to 1.88 Mb, previously reported in V. unguiculata subspecies  were defined as genomic regions or loci associated with carotenoid biosynthesis in cowpea sprouts [GWAS was performed using Tassel v5.2.60  and rMPV sprouts ,88. S_Vung_CA1511, S_Vung_CA1513, S_Vung_CA1519, S_Vung_CA1838, S_Vung_CA1840, S_Vung_CA2146, and S_Vung_CA3031, were identified to support molecular breeding for sprouting cowpea varieties with enhanced carotenoids contents.This study revealed that the level of carotenoid content varied among sprouts of cowpea accessions. The accessions were grouped into three clusters based on their carotenoid contents, with some of them exhibiting high profiles of carotenoids, and they can be recommended for production and promotion of high integration of cowpea sprouts in the daily diet consumption in food-insecure regions. The presence of subgroups in the population was also confirmed by analysis of the genetic structure. However, the germplasm had low genetic diversity, which calls for more research efforts to broaden the genetic basis of cowpea for high carotenoids content, as well as other important characteristics of cowpea sprouting varieties. Seven candidate loci,"}
+{"text": "Deriving the atomic form factors from Hirshfeld-partitioned periodic projector augmented wave calculations shows a great benefit for H-atom bond lengths and a smaller benefit for H-atom atomic displacement parameters. Hirshfeld atom refinement (HAR) is an X-ray diffraction refinement method that, in numerous publications, has been shown to give H-atom bond lengths in close agreement with neutron diffraction derived values. Presented here is a first evaluation of an approach using densities derived from projector augmented wave (PAW) densities with three-dimensional periodic boundary conditions for HAR. The results show an improvement over refinements that neglect the crystal environment or treat it classically, while being on a par with non-periodic approximations for treating the solid-state environment quantum mechanically. A suite of functionals were evaluated for this purpose, showing that the SCAN and revSCAN functionals are most suited to these types of calculation. Calculation of the phase was done in the established way . The agreement with neutron diffraction results was evaluated for bond lengths to H atoms and for the anisotropic displacement parameters of H atoms. Distances were compared directly. However, as differences in absorption or extinction, and small deviations in temperature, between neutron and X-ray diffraction experiments can influence anisotropic displacement parameters, the neutron displacement parameters were scaled using equation (1)Python script:q and \u0394ijU were determined by a least-squares fit to the non-H atoms and were determined for each HAR refinement individually. The factor q represents scaling due to differences in measurement temperature between the neutron and the X-ray experiments. To compare the performance of different HAR approaches, we benchmarked to both neutron data and The following quality indicators were used to estimate the relative performance of different functionals within the PAW-HAR method, as well as the performance compared with comparison and reference calculations:wR2(F2) is a scaled least-squares agreement factor. As such, a lower wR2(F2) should indicate a higher precision in determined values. However, due to possible systematic deviations, the wR2(F2) does not determine accuracy alone. This means that, in method development, a comparison of H-atom bond lengths and displacement parameters with values derived from other sources is inevitable.(i) r and |\u0394r|. Currently, the main application of HAR is the determination of H-atom positions: Therefore, the difference in X\u2014H bond lengths from reference neutron values is the central criterion to evaluate the quality of any endeavour. The difference is simply calculated as(ii) \u0394r| for most of our figures. This means that a lower value always indicates an improved agreement.To see immediately the performance in our figures, we used the absolute value of this difference |\u0394ijU and |\u0394ijU|. Additionally, a high agreement in the calculated displacement parameters is also desirable. As such, the difference from the scaled reference neutron values is calculated. Again, it is simply calculated as(iii) \u0394ijU| for most of our figures, so that a lower value always indicates an improved agreement.Consistent with the distances, we used the absolute value of this difference |\u0394S12. To compare the deviation between the probability distributions described by the H-atom anisotropic displacement parameters from different sources (in our case refinements from X-ray and neutron data), the S12 value has been proposed  2.4.We chose a variety of different datasets for use in the development of our method. We cannot rely on the least-squares agreement alone, as error compensation might lead to erroneous conclusions. The ultimate goal is to derive positions that are more accurate. To evaluate this target, both a neutron and an X-ray dataset need to be available. Additionally, the X-ray dataset should have a measured resolution of at least 0.6\u2005\u00c5. The scaling of the independent atom refinement should be independent of the resolution and no outliers should be visible.NoSpherA2 results, but also with state-of-the-art results from different groups. Finally, we tried to include structures that contain H atoms engaged in hydrogen bonds and C\u2014H bonds. This permits us to aggregate the H-atom quality descriptors in order to investigate the different performance of functionals for the two binding motifs.Additionally, we tried to include datasets which were already subjected to a benchmark HAR. This enables us not only to compare with The following datasets were included in our investigation Fig. 1.l-Alanine, referred to herein as A23K. The initial high-resolution X-ray data at 23\u2005K were originally collected in 1988 on a et al., 1988et al., 2019l-Alanine has comparatively few atoms and a small unit cell, while not having atoms on special positions. It features H atoms that are involved in classical hydrogen bonds (H1\u2013H3) as well as H atoms that are located in C\u2014H bonds (H4\u2013H7). This makes it ideally suited to exploring different theoretical approaches in a reasonable amount of time. We can compare with a recent refinement that uses densities calculated by the CCSD method  HMa-8HQ) and (iii) hexaaquamagnesium hydrogen maleate (HMa-Mg). Recently, high-resolution X-ray diffraction data for a group of different hydrogen maleate salts have been published , but contains additional C-bound H atoms (atoms H6\u2013H11). HMa-Mg comprises a hexaaquamagnesium dication counter-ion, exhibiting additional hydrogen bonds (atoms H4\u2013H9). We can compare the re-refinement of the two structures from 2021. From the available refinements we chose the calculations done with the B3PW91/def2-TZVP functional.(ii) 8-Hydroxyquinone hydrogen maleate (denoted Xy). The high-resolution X-ray dataset  Xylitol  2.5.In order to visualize the distribution of the given quality indicators for the investigated H atoms, this work relies heavily on box-whisker plots. In a box-whisker plot the edges of the box represent the 25th and 75th percentiles. The central line within the box marks the median value. The whisker on the left-hand side extends to the smallest point within the 25th percentile minus 1.5 times the interquartile range. The right-hand-side whisker is defined as the largest point with the 75th percentile plus 1.5 times the interquartile range. Values outside this range are potential outliers and are indicated separately by a glyph . For a more in-depth description see the work of Krzywinski & Altman 2014.Improvement can be seen in two ways, by a smaller median disagreement  or by a narrower distribution of the values, as indicated by the width of the boxes within the plots and, to some degree, the width of the whiskers, provided that the number of outliers does not increase at the same time.x axis on top for |\u0394r| and |\u0394ijU|. This axis is divided by the mean estimated standard deviation of the neutron distances and atomic displacement parameters for the relevant structure. This is not as accurate as dividing all deviations individually  calculations, a finer grid spacing should improve the quality of the calculation at the cost of computational resources. As the grid always spans the complete unit cell, the cost for a given spacing is also highly dependent on the unit-cell size, which means that for periodic PAW calculations structures with centrings are sometimes limited in how fine a grid spacing can be calculated for a given amount of system memory.k-points. A generally accepted method for calculating suitable meshes is the concept of Monkhorst\u2013Pack grids  improves from 3.24% at a grid spacing of 0.275\u2005\u00c5 to 3.09% at grid spacings of both 0.125 and 0.150\u2005\u00c5. Increasing the k-points from \u0393-point sampling to  improved the wR2(F2) slightly to 3.07%, with no further improvement with larger sampling.As a result, all quality indicators show converging improvement with finer grid spacing in real space. The r values also showed a converging improvement with finer real-space grid spacing, with convergence occurring below 0.2\u2005\u00c5. The introduction of additional k-points into the calculations actually led to a slight decrease in the agreement between calculated and neutron-derived X\u2014H distances, with the mean absolute difference at a real-space grid spacing of 0.125\u2005\u00c5 increasing from 0.007 to 0.009\u2005\u00c5 from a \u0393-point sampling to a sampling of . Again, a further increase in k-points did not yield a difference in results.The \u0394k-point grid spacing. The S12 at the \u0393 point converged at a real-space grid spacing of 0.175\u2005\u00c5. The introduction of a  k-point grid improved the agreement in displacement parameters, with the mean S12 falling for the H atoms.The anisotropic displacement parameters improved with both a finer real-space and l-alanine when the number of k-points is increased, while the agreement in displacement parameters increases. For this molecular compound with hydrogen bonds, a finer k-point spacing than  is not necessary, because of the relatively flat band dispersion in molecular crystals.To summarize, an increase in real-space grid points does always benefit the desired quantities, even though there are diminishing benefits. The agreement of distances with the neutron values actually decreases slightly for 3.2.HMa-Mg). The interested reader will find a summary of all other evaluations in the supporting information in Section S5. Details of the parameters for the theoretical calculations can be found in Section S3.We have tested the performance of PAW-HAR with a large number of functionals for all the structures evaluated in this work. The overall trends are similar. Therefore, for the sake of clarity, we limit the evaluation of functionals here to one structure, namely hexaaquamagnesium hydrogen maleate (wR2(F2) depicted in Fig. 3The hierarchy of functionals can clearly be seen in the X\u2014H distances. This clear an effect is unique to this dataset. However, PW usually shows an advantage over BLYP and a distance performance similar to GGA functionals. SCAN and revSCAN show the best overall performance. In this dataset revSCAN has only a small lead. The determined \u0394ijU values show one outlier for all functionals. This corresponds to the U11 value of the H atom located within the hydrogen maleate molecule, where the direction of the disagreement is approximately located along the intra\u00admolecular O\u2014H\u22efO hydrogen-bond interaction.Surprisingly, the PW functional performs well for the evaluation of In consequence, we decided to use the SCAN functional as reference for further comparisons in the following evaluations.3.3.et al., 2021l-alanine structure described with CCSD represents the limit of what can be reached on the first rung, which stands for no crystal field description. The reference for the two hydrogen maleate structures employed a cluster of classical charges and is therefore located on the second rung. Finally, the xylitol and urea references both used embedding in a quantum mechanically treated cluster for their HAR and are therefore located on the third rung, the quantum-mechanical description of the surroundings. We want to demonstrate that PAW-HAR also belongs to that level. A depiction of the described Jacob\u2019s Ladder can be found in the supporting information, Fig. S3.In this section, we compare our results with different reference calculations in order to investigate the performance in comparison with different density descriptions. We ordered the structures according to the positions of the reference structures on the Jacob\u2019s Ladder proposed for HAR  cut off of 3 was set in the refinement, we decided to do two PAW refinements for the dataset, one that enables the best comparison by using the same data and one where we used the full data. A summary of the performance of the different methods can be found in Fig.\u00a04wR2(F2) comparison, however, confirms that the crystal environment is needed for the appropriate density description. Both the TONTO refinement with cluster charges and the periodic calculation show an almost identical wR2(F2) value. Interestingly, a larger cluster radius in the cluster charge calculation led to a worse performance, while with both radii the calculation did not fully converge in 20 cycles. Meanwhile, both the comparison calculation based on a B3LYP calculation in ORCA and the calculation using atomic form factors from a CCSD calculation show a higher wR2(F2) value compared with our corresponding PAW-HAR refinements.The refinement against the PAW-derived atomic form factors and the comparison refinements in et al., 2020X\u2014H distances and H-atom atomic displacement parameters.Compared with the cluster charge calculation, the PAW-derived values show a better agreement for the distances and a slightly better agreement for the displacement parameters. Both the reference CCSD calculation  cut off of 4. Again, we did two separate refinements against the cut data and against the full data, which had been published before  and minimally improved agreement in the atomic displacement parameters for the periodic calculation, while there is a significant improvement in the X\u2014H distance agreement for the reference calculation. Disagreement in distances increases with refinement of extinction while the agreement in displacements improves slightly. The crystallographic agreement factor profits greatly from extinction refinement. In contrast with other datasets, the cluster charge calculation shows an improved agreement factor compared with the periodic calculation. However, both the distance agreement and the agreement in atomic displacement parameters are lower. Unsurprisingly, the single-molecule calculation shows a higher wR2(F2) and lower agreements in the H-atom distance and vibrational parameter.In a direct comparison of the reference calculation and the PAW-HAR without extinction we can see a slightly lower Comparison without the consideration of extinction gives good reason to believe that the method reported in the reference would compare favourably with PAW-HAR for this dataset. However, we believe the ultimate answer can only be given if a refinement including extinction were published.3.3.4.wR2(F2) compared with the reference B3LYP .The final difference electron density is on a low level but not completely featureless for all refinements done in this paper  The majority of H atoms are engaged in C\u2014H bonds (21 atoms/bonds). Within the hydrogen bonds we distinguish (ii) those where the atoms do not share the same calculated fragment  and (iii) those where X\u2014H donor and Y acceptor are within the same calculated fragment in the NoSpherA2-based calculations .We can now aggregate the agreement in n) values for both the distances and the atomic displacement parameters, in order to determine whether the differences are actually significant. Criteria aggregated in this way are depicted in Fig. 10Additionally, we want to scale the deviations by the estimated standard deviations from the neutron refinement  value is higher. Due to the different treatment of extinction, a final verdict on the second dataset is not possible at the moment, independent of the density description. In summary, we have established that PAW-HAR is a possible quantum-mechanical treatment of the crystal surroundings, at least on a par with other state-of-the-art approaches.In the investigated structures we have shown a significant improvement over published calculations that neglected the crystal environment or treated it classically. A comparison against calculations where the crystal environment was emulated by cluster embedding showed neither a clear advantage nor disadvantage. Distances show a very high agreement, while the NoSpherA2/TONTO. As expected, the overall relative speed is highly dependent on the respective settings. On average, however, our method seems to be faster with larger structures. The inclusion of more k-points can lead to a longer duration of the Hirshfeld atom refinement, especially as the smaller molecules were also calculated at very fine real-space grids in PAW-HAR.For structures where the periodic density functional theory calculation was limited to the \u0393 point, we could see significant speed-ups in comparison with the atomic form factor calculation in QUANTUM ESPRESSO is already available in the XHARPy library.The overall success of our method demonstrates that the density calculation and partitioning on a rectangular grid instead of an atom-centred one can only have a small influence. The combination of expansion on the grid and fast Fourier transform is fast and reliable when the spherical frozen-core density is calculated separately. As a number of quantum chemistry programs for the solid state rely on rectangular grids, this opens up new sources for the density. In addition to the GPAW interface employed for this work, an experimental implementation for Additionally, periodic calculations with the projector augmented wave scheme are a viable tool for obtaining atomic form factors and deriving very accurate H-atom positions and accurate H-atom displacement parameters. From a practical point of view, the central benefit is the absence of a fragment dependency. There is no potential bias from fortunate or unfortunate selections of the calculated fragment and/or cluster radii. The calculated fragment is the complete unit cell. We have demonstrated that using only cluster charges leads to a worse performance compared with the PAW-HAR for H atoms located at the border of the calculated fragment.XHARPy library itself is flexible enough to accommodate such investigations.Overall, we would state that the present approach offers great potential. This is the case both from a conceptual standpoint that a periodic system is calculated as such, and from the presented results. Now that the viability of the approach with the presented refinement library is established, the application to inherently periodic structures and highly charged species, especially when combined with other density partitioning methods, would be the logical next steps. The The library can be downloaded from the repository at https://github.com/Niolon/XHARPy under the GPL-3.0 license.10.1107/S2052252522001385/fc5060sup1.cifCrystal structure: contains datablock(s) A23_SHELXL_iam, A23_xHARPy_iam, A23_NoSpherA2_B3LYP_ORCA, A23_NoSpherA2_B3LYP_tonto, A23_NoSpherA2_PBE_ORCA, A23_NoSpherA2_SCAN_ORCA, A23_xharpy_SCAN, A23_xharpy_SCAN_iovs_cut, A23_xHARPy_BEEF-vdW, A23_xHARPy_BLYP, A23_xHARPy_PBE, A23_xHARPy_PW, A23_xHARPy_PW91, A23_xHARPy_RPBE, A23_xHARPy_TPSS, A23_xHARPy_revPBE, A23_xHARPy_revSCAN, A23_xHARPy_vdW-DF, A23_xHARPy_vdW-DF2, A23_xHARPy_RPBE_fd_rectangular, A23_xHARPy_RPBE_lcao_rectangular, A23_xHARPy_RPBE_lcao_spherical, HMa-8HQ_NoSpherA2_B3LYP_ORCA, HMa-8HQ_NoSpherA2_B3LYP_tonto, HMa-8HQ_NoSpherA2_PBE_ORCA, HMa-8HQ_NoSpherA2_SCAN_ORCA, HMa-8HQ_xharpy_SCAN, HMa-8HQ_xharpy_SCAN_iovscut, HMa-8HQ_xHARPy_BEEF-vdW, HMa-8HQ_xHARPy_BLYP, HMa-8HQ_xHARPy_PBE, HMa-8HQ_xHARPy_PW, HMa-8HQ_xHARPy_PW91, HMa-8HQ_xHARPy_RPBE, HMa-8HQ_xHARPy_TPSS, HMa-8HQ_xHARPy_revPBE, HMa-8HQ_xHARPy_revSCAN, HMa-8HQ_xHARPy_vdW-DF, HMa-8HQ_xHARPy_vdW-DF2, HMa-Mg_NoSpherA2_B3LYP_ORCA, HMa-Mg_NoSpherA2_B3LYP_tonto, HMa-Mg_NoSpherA2_PBE_ORCA, HMa-Mg_NoSpherA2_SCAN_ORCA, HMa-Mg_xharpy_SCAN, HMa-Mg_xharpy_SCAN_Iovs_cut, HMa-Mg_xHARPy_BEEF-vdW, HMa-Mg_xHARPy_BLYP, HMa-Mg_xHARPy_PBE, HMa-Mg_xHARPy_PW, HMa-Mg_xHARPy_PW91, HMa-Mg_xHARPy_RPBE, HMa-Mg_xHARPy_TPSS, HMa-Mg_xHARPy_revPBE, HMa-Mg_xHARPy_revSCAN, HMa-Mg_xHARPy_vdW-DF, HMa-Mg_xHARPy_vdW-DF2, Xy_NoSpherA2_B3LYP_ORCA, Xy_NoSpherA2_B3LYP_tonto, Xy_NoSpherA2_PBE_ORCA, Xy_NoSpherA2_SCAN_ORCA, Xy_xharpy_SCAN, Xy_xharpy_SCAN_noEXTI, Xy_xHARPy_BEEF-vdW, Xy_xHARPy_BLYP, Xy_xHARPy_PBE, Xy_xHARPy_PW, Xy_xHARPy_PW91, Xy_xHARPy_RPBE, Xy_xHARPy_TPSS, Xy_xHARPy_revPBE, Xy_xHARPy_revSCAN, Xy_xHARPy_vdW-DF, Xy_xHARPy_vdW-DF2, urea_NoSpherA2_B3LYP_ORCA, urea_NoSpherA2_B3LYP_tonto, urea_NoSpherA2_PBE_ORCA, urea_NoSpherA2_SCAN_ORCA, urea_NoSpherA2_SCAN_dijkl, urea_xharpy_SCAN, urea_xHARPy_SCAN_cijk, urea_xHARPy_SCAN_dijkl, urea_NoSpherA2_B3LYP_tonto_dijkl, urea_xHARPy_BEEF-vdW, urea_xHARPy_BLYP, urea_xHARPy_PBE, urea_xHARPy_PW, urea_xHARPy_PW91, urea_xHARPy_RPBE, urea_xHARPy_TPSS, urea_xHARPy_revPBE, urea_xHARPy_revSCAN, urea_xHARPy_vdW-DF, urea_xHARPy_vdW-DF2. DOI: 10.1107/S2052252522001385/fc5060urea_NoSpherA2_SCAN_dijklsup2.hklStructure factors: contains datablock(s) urea_NoSpherA2_SCAN_dijkl. DOI: 10.1107/S2052252522001385/fc5060A23_NoSpherA2_SCAN_ORCAsup3.hklStructure factors: contains datablock(s) A23_NoSpherA2_SCAN_ORCA. DOI: 10.1107/S2052252522001385/fc5060sup4.pdfSupporting Information on refinement quality. DOI: 2150631, 2150632, 2150633, 2150634, 2150635, 2150636, 2150637, 2150638, 2150639, 2150640, 2150641, 2150642, 2150643, 2150644, 2150645, 2150646, 2150647CCDC references:"}
+{"text": "In vitro precipitation of circRNAs, luciferase reporter assays, and biotin-coupled microRNA capture assays were carried out to investigate the mechanisms by which hsa_circ_0072309 regulates NSCLC. Through the above work, we found that hsa_circ_0072309 interacted with miR-607 via its miRNA response element to upregulate the expression of FTO, an m6A demethylase and downstream target of miR-607, thus promoting tumorigenesis of NSCLC. In total, our findings indicated the oncogenic role of hsa_circ_0072309 in NSCLC and provide a potential target for treatment.Emerging evidence has demonstrated that circular RNAs (circRNAs) are abnormally expressed in non-small cell lung carcinoma (NSCLC). However, the contributions of circRNAs to the tumorigenesis of lung adenocarcinoma (LUAD), one of the subtypes of NSCLC, remain unclear. Based on a microarray assay, we found that hsa_circ_0072309 was significantly upregulated in NSCLC compared with matched normal samples. Moreover, functional experiments demonstrated that hsa_circ_0072309 promotes the proliferation, migration, and invasion of NSCLC cells.  Cancer is a major public health issue worldwide, and lung cancer is one of the most common types. Studies have shown that the global incidence and mortality of lung cancer have reached 11.6% and 18.4%, respectively. More than 80% of all lung cancer cases are non-small cell lung cancer, which is the main cause of lung cancer-specific mortality . HoweverCircular RNAs are a novel category of endogenous noncoding RNAs formed by noncanonical splicing of exonic and intronic sequences , 6. Distin vivo and in vitro. Mechanistically, based on bioinformatic analysis, RIP assays and rescue experiments, hsa_circ_0072309 was proven to sponge miR-607 and thereby upregulate its target gene fat mass and obesity-associated protein (FTO). Collectively, our study identified a new potential biomarker, hsa_circ_0072309, for NSCLC and established the hsa_circ_0072309/miR-607/FTO axis in tumorigenesis, which shed light on its application in clinical treatment.In this study, we found that hsa_circ_0072309 was upregulated in NSCLC tissues and cells. Functionally, we found that si-hsa_circ_0072309 suppressed the proliferative, migrative and invasive capacity of NSCLC cells To investigate the roles of circRNAs in NSCLC, we determined the circRNA expression patterns in five pairs of lung adenocarcinoma tissues and corresponding normal tissues after depleting ribosomal RNA and linear RNA molecules to enrich the circRNAs . As indiUnfamiliar with hsa_circ_0072309, we explored the structure and localization of hsa_circ_0072309 before investigating its biological functions. First, we localized hsa_circ_0072309 on the LIFR gene on chromosome 5 (q13.1), and here, we also named hsa_circ_0072309 as circLIFR. Analyzing the sequence of hsa_circ_0072309, we found that hsa_circ_0072309 was formed by exon back-splicing of exon 8 to exon 11 of the LIFR gene and was 580 nucleotides long . To idenTo investigate the biological roles of hsa_circ_0072309 in NSCLC, we first reduced the expression level of hsa_circ_0072309 in the NSCLC cell lines H1975 and H1650 using siRNAs, which displayed similar knockdown efficiency , 3E. We The above results prompted us to explore the mechanisms underlying the oncogenic functions of hsa_circ_0072309 in NSCLC. Considering that circRNAs competitively sponge miRNAs to regulate biological processes, we established the regulatory relationship of hsa_circ_0072309 and miRNAs. We selected some potential target miRNAs from the circular RNA interactome database to conduct RNA pulldown experiments, and screened out five miRNAs, including hsa-miR-207, hsa-miR-336-5p, hsa-miR-781, hsa-miR-607 and hsa-miR-214-3p . The exphttps://circinteractome.nia.nih.gov/ and established miR-607-WT and miR-607-Mut constructs with a mutation in the predicted binding site. RNA pulldown assays demonstrated that hsa_circ_0072309 interacted with miR-607-WT instead of miR-607-Mut , we found several genes containing miR-607 binding sites in the 3'-UTR. To screen out the direct downstream target gene of miR-607, we transfected miR-NC and miR-607 mimic into HEK293 cells separately, and then RT-qPCR analyses were used to detect the expression levels of these candidate target genes. Successfully, we screened FTO as the downstream target gene of miR-607, whose expression level was significantly decreased upon miR-607 mimic transfection . We ectopically expressed FTO in the hsa_circ_0072309-WT and hsa_circ_0072309-KD NSCLC cells. Cell proliferation assays showed that FTO overexpression significantly promoted cell viability in the hsa_circ_0072309-WT cells and reversed the decreased cell viability in the hsa_circ_0072309-KD cells to the normal level , 7D. LikCircRNAs are commonly generated from the exons of protein-coding gene transcripts by RNA splicing and are more abundant than their corresponding linear RNAs , 23. MorConsidering that hsa_circ_0072309 was significantly upregulated in NSCLC, we explored its biological functions in NSCLC. Our results demonstrated that hsa_circ_0072309 promoted tumorigenesis and metastasis in NSCLC cell lines. To investigate the molecular mechanisms of hsa_circ_0072309 in tumorigenesis, we performed RNA pulldown assays and luciferase activity assays, and the results demonstrated that hsa_circ_0072309 sponged miR-607 to regulate FTO expression.FTO is one of the only two identified m6A demethylases . As an iMoreover, although the research showed the tumor-driving effects of hsa_circ_0072309 in the process of leading lung cancer, we also believe that there may be other important deregulated circRNAs that participate in the progression of lung cancer, because of the limited tissue samples used for screening. Hence, the deregulated circRNAs in NSCLC pathology still need further investigation.The 30 pairs of lung adenocarcinoma tissues and paired adjacent normal tissues were acquired from Changzhou Seventh People's Hospital from January 2019 to Mary 2019. NSCLC tissue specimens and corresponding normal tissues were obtained though puncture and packed in liquid nitrogen at -196\u00b0C. All experiments in our study were approved by the Ethics Review Committee of Changzhou Seventh People's Hospital. All patients gave informed consent.P-value of < 0.05 were considered differentially expressed.TRIzol reagent  was used to extract total RNA from lung adenocarcinoma and adjacent normal tissues according to the manufacturer\u2019s specifications. CircRNAs were enriched by removing linear RNAs with RNase R and then amplified and labeled using an Arraystar Super RNA Labeling Kit . Subsequently, an Arraystar Human circRNA Array applied to hybridization was scanned by an Agilent Scanner G2505C . CircRNAs with a fold change of \u2265 2 and a Full-length human FTO  cDNA was duplicated into the pLVX-IRES-Puro vector  to generate FTO expression plasmids. The siRNA targeting hsa_circ_0072309 (siRNA sequence: GCAGTCAGTCTAATTTTACG) was obtained from GenePharma .All cells in this study were obtained from ATCC. Human bronchial epithelial HBE cells and human NSCLC H1975 and H1650 cells were cultured in RPMI 1640 medium with 10% fetal bovine serum  and 1% penicillin/streptomycin solution . Lentivirus was used to establish individual stable cell lines. SiRNA duplexes specific to hsa_circ_0072309 (100 nmol/L), miR-607 mimic and their negative control (NC) oligonucleotides were transfected into cells with Lipofectamine 3000  under the manufacturer\u2019s protocol.\u00ae Non-Radioactive Cell Proliferation Assay (MTT) Kit  was used to perform the cell proliferation assays. Cells (1 \u00d7 104 cells/ml) were seeded into 96-well plates (100 ml/well) and incubated at 37\u00b0C in a humidified atmosphere containing 5% CO2. Ten milliliters of MTS solution was added to each well and then incubated at 37\u00b0C for 2 h. The absorbance at 590 nm for each sample was measured by using a spectrophotometer. All experiments were repeated three times and were performed in triplicate.Under the manufacturer\u2019s protocol, the CellTiter 96Transwell chambers  were used to perform the Transwell assays for the determination of cell invasion. H1975 and H1650 cells (1 \u00d7 105) were resuspended in RPMI 1640 medium without serum  and seeded into the upper chamber. RPMI 1640 medium containing 20% FBS was added to the bottom chamber. After 48 h of incubation, cells on the upper side of the chamber were scraped off with cotton swabs. Then, the filters were fixed in 4% paraformaldehyde  and stained with 0.1% crystal violet  for 15 min and 10 min, respectively. After three washes with phosphate buffered saline , cells invading through the Matrigel were imaged, and the number of cells was counted in five random views using a microscope . Each assay was performed in triplicate.A wound-healing assay was used to detect cell migration. First, cells were added to 6-well plates. When the confluence of cells reached approximately 90%, a 200 ml pipette tip was used to make the artificial wounds. After 24 h, the wound closure distance was measured by using a microscope. Each assay was performed in triplicate.Total RNA was isolated using TRIzol under the instructions of manufacturer. CircRNAs were then enriched by removing linear RNAs with RNase R. First strand cDNA was generated by using Superscript II , and 1 \u03bcg of total RNA was used in reverse transcription. SYBR Green Universal Master Mix reagent  and primer mixtures were used to conduct real-time qPCR assays. GAPDH was used as a control for circRNAs. The outward-facing primers used for RT-qPCR analysis were purchased from Geneseed  and the primer sequences are shown in Convergent Primer:F:5'-ATTGCACAGATGATGGATATTT-3';R: 5'-CAATGCAAACTTCATAATCAGTACC-3'.Divergent Primer:F:5'-CACTAAATGAACAAAACGTTTCC-3';R: 5'-TATAGAAGAAGAAATGTTGATA-3'.Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) gels were used to extract and separate the total protein of each group. FTO antibody  and AGO2 antibody  were used at a 1:2000 dilution. GAPDH antibody  was used as a loading control.Under the manufacturer\u2019s specifications, the SurePrep\u2122 Nuclear or Cytoplasmic RNA Purification Kit  was used to separate nuclear and cytoplasmic fractions. Real-time qPCR assays were used to detect the RNA expression levels of hsa_circ_0072309, RNU6-1 and GAPDH. RNU6-1 and GAPDH were used as internal controls.Cells were fixed in 4% PFA and then permeabilized with 0.5% Triton X-100 at 4\u00b0C for 15 min. Hsa_circ_0072309 probe labeled with digoxigenin (DIG) or the control probe mix was incubated with the cells for 4 h at 55\u00b0C. After three brief washes with 2 \u00d7 saline-sodium citrate for 5 min each time, signals were detected using horseradish peroxidase (HRP)-conjugated anti-DIG secondary antibodies . The nucleus was counterstained with 4\u2032,6-diamidino-2-phenylindole . A confocal microscope was used to acquire the images. ISH was performed as described elsewhere . The expFor construction of the hsa_circ_0072309-WT luciferase reporter vector, hsa_circ_0072309 cDNA, which had the predicted miR-607 binding site, was cloned into the pmirGLO vector . The hsa_circ_0072309-Mut vector was generated by inserting mutant hsa_circ_0072309 with point mutations in the miR-607 binding site. Likewise, the FTO 3'UTR-WT and FTO 3'UTR-Mut luciferase reporter vectors were constructed by cloning wild-type and mutant FTO 3'-UTR fragments into the pmirGLO vector.Lipofectamine 3000  was used to cotransfect miR-607 or miR-NC with the reporter vector into HEK293 cells. The luciferase activity was detected with a Dual Luciferase Reporter Assay System  according to the manufacturer\u2019s protocol after transfection for 48 h. Each assay was performed in triplicate.First, 3'-end biotinylated miR-607 and miR-607-Mut were transfected into cells when the final concentration reached 20 nmol/L. After 24 h, the cells were obtained, and streptavidin magnetic beads  were incubated in the cell lysate for RNA pulldown assays. Real-time qPCR assays were used to analyze the abundance of hsa_circ_0072309 or FTO.Five-week-old BALB/c nude mice obtained from Shanghai SLAC Laboratory Animal Center were used for the vivo xenotransplantation assays. The animal experiments were conducted with the permission of the Institutional Animal Care and Use Committee of Changzhou Seventh People's Hospital. H1975 cells were subcutaneously injected into nude mice. There were five mice per group. Tumor volumes were measured every five days. Tumor volumes were estimated by measuring their length and width and calculated using the following equation: V = 0.5 * length * width^2. Approximately one month later, all mice were euthanized, and then, the tumors were resected for weighing and imaging.Tumors separated from the nude mice were embedded in paraffin after fixation with 4% PFA. IHC assays were conducted using specific anti-FTO .t-test, one-way ANOVA and two-way ANOVA were applied to determine the statistical significance. For all statistical tests, a P value < 0.05 was defined as statistically significant.All of the above experiments were carried out using three independent repeated experiments with cells. GraphPad Prism 8.0  was used for statistical analyses. The results are described as the mean \u00b1 SEM. Pearson correlation coefficients were used to determine the correlation between the expression of hsa_circ_0072309 and miR-607. Student\u2019s Supplementary Figures"}
+{"text": "We report the genome sequences and the identification of genetic variations in eight clinical samples of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Samples were collected from nasopharyngeal swabs of symptomatic and asymptomatic individuals from five care homes for elderly and infirm persons in Israel. The sequences obtained are valuable, as they carry a newly reported nonsynonymous substitution located within the nucleoprotein open reading frame. We report the genome sequences and the identification of genetic variations in eight clinical samples of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Samples were collected from nasopharyngeal swabs of symptomatic and asymptomatic individuals from five care homes for elderly and infirm persons in Israel. The sequences obtained are valuable, as they carry a newly reported nonsynonymous substitution located within the nucleoprotein open reading frame. Betacoronavirus strain of the Coronaviridae family named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was identified as the etiological agent of a disease that was later termed coronavirus disease 19 (COVID-19) (TC) values ranging from 12.8 to 16.8, implying a high viral load. Partial clinical information indicated that at least 2 of the 8 samples  originated from asymptomatic individuals.Shortly after a severe acute respiratory syndrome emerged in Wuhan, China, in December 2019 (OVID-19) . In thisSamples were collected directly from swabs, and RNA was extracted with a QIAamp viral RNA minikit (Qiagen) according to the manufacturer\u2019s protocol, using 60\u2009\u00b5l of AVE buffer for elution. A SMARTer stranded total RNA-Seq pico input mammalian v2 kit (TaKaRa) was used for library construction prior to sequencing on a MiSeq instrument (Illumina). Whole-genome, paired-end sequencing was conducted in a duplex or triplex format with a read length of 150 nucleotides.https://www.bioinformatics.babraham.ac.uk/projects/fastqc) with default settings was used for quality control of the data. Trimming and removal of low-quality reads were performed using Trim Galore! v0.6.3  with default settings. Bowtie 2 (NC_045512). Reads mapped to SARS-CoV-2 were used as input data for the SPAdes assembler v3.13.0  replacements. A total of 31 substitutions were nonsynonymous, 4 of which mapped to the Spike coding region; 18 substitutions were of the synonymous type, and the remaining 3 substitutions occurred in noncoding regions . The eig8\u2013While most of the nonsynonymous replacements were previously reported , the A50Although several papers documented a list of viral factors that are correlated with COVID-19 severity \u201316, ther\u2013MW228070, MW194121, MW201576, MW227568, MW237708, MW201577, MW193889, and MW201578. The raw reads have been submitted to the NCBI Sequence Read Archive under the study reference number PRJNA672811.The genome sequences have been deposited at the GISAID EpiCoV coronavirus SARS-CoV-2 platform database under the identifiers EPI_ISL_594155, EPI_ISL_594156, EPI_ISL_594157, EPI_ISL_594158, EPI_ISL_594159, EPI_ISL_594160, EPI_ISL_594161, and EPI_ISL_594162 and in the NCBI GenBank database under the accession numbers"}
+{"text": "Extrachromosomal DNA (ecDNA) is a type of circular and tumor specific genetic element. EcDNA has been reported to display open chromatin structure, facilitate oncogene amplification and genetic material unequal segregation, and is associated with poor cancer patients\u2019 prognosis. The ability of immune evasion is a typical feature for cancer progression, however the tumor intrinsic factors that determine immune evasion remain poorly understood. Here we show that the presence of ecDNA is associated with markers of tumor immune evasion, and obtaining ecDNA could be one of the mechanisms employed by tumor cells to escape immune surveillance. Tumors with ecDNA usually have comparable TMB and neoantigen load, however they have lower immune cell infiltration and lower cytotoxic T cell activity. The microenvironment of tumors with ecDNA shows increased immune-depleted, decreased immune-enriched fibrotic types. Both MHC class I and class II antigen presentation genes\u2019 expression are decreased in tumors with ecDNA, and this could be the underlying mechanism for ecDNA associated immune evasion. This study provides evidence that ecDNA formation is an immune escape mechanism for cancer cells. Immunoediting, which includes three temporally distinct stages, termed elimination, equilibrium, and escape, has been proposed to explain the interactions between cancer cells and the immune system during the evolution of cancer5. The mechanisms responsible for the escape of tumor cells from immunosurveillance are not fully understood. Potential tumor intrinsic immune evasion mechanisms include: impaired antigen presentation machinery , overexpressed immune checkpoints or their ligands such as programmed death-ligand 1 (PD-L1) on cancer cells9. In addition, secreting of immune inhibitory cytokines, such as TGF-\u03b2, remarkably reshape the tumor immune microenvironment11.The immune system plays a crucial role in the protection and fight against cancer cells12. Later these DNA elements without centrioles and telomeres are found to be circular, a few Mb in size, and their size but not their number is stable during the proliferation of cancer cells13. With the recent advance of sequencing and bioinformatics techniques, ecDNA has been found to be prevalent in various types of cancers, however ecDNA is rarely detected in normal tissues, suggesting the presence of ecDNA is a specific feature for some cancer cells14. EcDNA promotes accessible chromatin (open chromatin) formation, facilitates oncogene amplification, drives genetic heterogeneity, and is associated with poor prognosis in multiple types of cancer17.Extrachromosomal DNA (ecDNA) is a type of tumor specific DNA element that is circular and about 1\u20133\u00a0Mb in size. Since the 1960s, double minute chromosomes have been observed in the metaphase spreads of human cancer cellsSomatic DNA alterations are major determinants of cancer phenotypes, including immune phenotypes. EcDNA formation is a type of somatic DNA alteration. We hypothesize that ecDNA formation could be one mechanism for cancer cells to evade immune surveillance.17. In total, 1684 samples with ecDNA status and gene expression information are available for analysis  levels and cytotoxic scores are significantly decreased in tumors with ecDNA  is dramatically decreased, while immune desert type TME (D) is significantly up-regulated  but not pan-cancer analysis of whole genomes (PCAWG) datasets. For downstream immune infiltration and gene expression analysis, we only keep TCGA samples. Tumor immune cell infiltration information for TCGA samples was downloaded from the TIMER webserver (http://timer.comp-genomics.org/), including the results calculated by TIMER, CIBERSORT, quanTIseq, xCell, and MCP-counter algorithms. Somatic mutation data detected by Mutect2 was download from UCSC xena (GDC-PANCAN.mutect2_snv.tsv). The pan-cancer gene-level RNA-Seq data of TCGA samples was downloaded from UCSC xena, including counts and normalized transcripts per million (TPM) data. Immune subtyping and tumor microenvironment (TME) information of TCGA samples are based on reports of Thorsson et al. and Bagaev et al. study respectively25. The leukocyte fraction data of TCGA samples are based on the results of Thorsson et al. study25. In the downstream analysis, we only keep cancer types where the count of ecDNA samples was more than 20. All methods were performed in accordance with the relevant guidelines and regulations.EcDNA status information was determined using AmpliconArchitect from whole genome sequencing (WGS) data as described previously34. ESTIMATE  is a tool using gene signatures to generate three scores: stromal score, immune score and estimate score, we used R package Estimate to calculate the immune score23. The cytolytic activity (CYT) score was a quantitative means of assessing cytotoxic T cell infiltration and activity and was calculated as the geometric mean of expression of GZMA and PRF1 genes35. The tumor inflammation signature (TIS) uses 18-gene signature to measure a pre-existing but suppressed adaptive immune response within tumors. The TIS has been shown to enrich for patients who respond to the anti-PD1 agent pembrolizumab. TIS was calculated by gene set variation analysis (GSVA) using the 18-gene signature mentioned by Danaher et al.36.In addition to immune cell infiltration quantification using gene expression data, we calculated a variety of additional immune microenvironment quantitative scores. The immunophenoscore (IPS) was used to measure the immune state of the samples. IPS was based on the expression of major determinants, identified by a random forest approach, and these factors were classified into four categories: major histocompatibility complex (MHC) molecules, effector cells, suppressor cells and checkpoint markers. We used R scripts and IPS genes provided by the origin paper to calculate IPS scores25. Mutect2 mutation files were first transformed into VCF format by maf2vcf tools, and we used NeoPredPipe to predict neoantigen37. We only evaluated single-nucleotide variants leading to a single amino acid change, and novel peptides of nine amino acids were considered. From the output results, if the IC50 of a novel peptide is less than 50\u00a0nM, and the TPM expression level is greater than 1, then this peptide is labeled as neoantigen. A mutation was considered neoantigenic if there was at least a single peptide produced from the mutated base that produce a neoantigen. Neoantigen burden was calculated similarly as TMB: /38.TMB was defined as the number of non-synonymous alterations per megabase (Mb) of genome examined. We used 38\u00a0Mb as the estimate of the exome size: TMB\u2009=\u2009(whole exome missense mutations)/38. Tumor neoantigen are generated by somatic mutations, and can be recognized as foreign by immune cells, conferring immunogenicity to cancer cells. Neoantigen was predicted based on somatic mutation and human leukocyte antigen (HLA) typing data. HLA typing data for TCGA cancer was obtained from Thorsson et al. study38. Then gene set enrichment analysis was performed by using R package \u201cfgsea\u201d. We downloaded gene list gmt file for the following pathways from MSigDB database, including \"REACTOME_MHC_CLASS_II_ANTIGEN_PRESENTATION\", \"REACTOME_CLASS_I_MHC_MEDIATED_ANTIGEN_PROCESSING_PRESENTATION\", \"GOBP_ANTIGEN_PROCESSING_AND_PRESENTATION_OF_PEPTIDE_ANTIGEN_VIA_MHC_CLASS_I\", and \"GOBP_ANTIGEN_PROCESSING_AND_PRESENTATION_OF_PEPTIDE_OR_POLYSACCHARIDE_ANTIGEN_VIA_MHC_CLASS_II\". The GSEA p values were corrected by FDR method, and was considered significant if less than 0.05. For each cancer sample, we also calculated corresponding pathway GSVA scores using R package \u201cGSVA\u201d39.For each cancer type, we used Deseq2 to identify differentially expressed genes between ecDNA and non-ecDNA samplesP values showed in boxplot were calculated by Wilcoxon tests using R. We used the following convention for symbols indicating statistical significance: ns: P\u2009>\u20090.05, *: P\u2009\u2264\u20090.05, **: P\u2009\u2264\u20090.01, ***: P\u2009\u2264\u20090.001, ****: P\u2009\u2264\u20090.0001. Immune subtype enrichment analysis was conducted by chi-squared test. All statistical tests and visualization analyses were performed with R.All Supplementary Information."}
+{"text": "Resource web link:https://www.shionogi.com/wp-content/uploads/Mechanisms_of_Carbapenem_Resistance_Exploring_the_Complexities_1.mp4?_=2 WHO region and country (World Bank): Western Pacific, Japan (HIC)Pseudomonas and Acinetobacter).This resource is an industry-prepared (Shionogi) animation that aims to explain the mechanisms of resistance to \u03b2-lactam antibiotics in Gram-negative bacteria, naming three priority groups  but the resource may be useful to students, laboratory scientists etc.This resource would be a useful teaching aid that could be incorporated into other educational resources."}
+{"text": "The interaction between miR-326 and circ_0008043 or RAB21 was assessed using dual-luciferase reporter analysis and RNA pull-down analysis. The data illustrated that circ_0008043 and RAB21 were highly expressed, while miR-326 was expressed at less levels in HCC tissues and cells. Interfering with circ_0008043 suppressed cellular proliferation, migration, invasion, and cell growth. Circ_0008043 was confirmed to be an miR-326 sponge that targets RAB21. Rescue experiments showed that inhibiting miR-326 abrogated the effect induced by knockdown of circ_0008043, and overexpressed RAB21 abolished the effect induced by miR-326 overexpression. In summary, silencing of circ_0008043 impeded HCC progression by regulating the miR-326/RAB21 axis. These data suggest that circ_0008043 may have clinical value in the treatment of HCC.Circular RNAs (circRNAs) are non-coding RNAs with covalently closed structures that modulate the progression of hepatocellular carcinoma (HCC). Here, we explored whether circ_0008043 regulated the biological function of HCC cells. Quantitative real-time polymerase chain reaction (qPCR) was used to detect circ_0008043, microRNA (miR)-326, and RAB21 levels. Expression of E-cadherin, N-cadherin, and vimentin was assessed using qPCR. Cell proliferation, migration, and invasion were evaluated using 3--2,5-diphenyltetrazolium bromide, colony formation, and transwell assays. Xenograft tumors were used to evaluate cell growth   Primary liver cancer is a malignant tumor that commonly originates from hepatocytes and intrahepatic bile duct cells and is the No. 2 leading cause of cancer related death . HepatocCircular RNAs (circRNAs) are newly discovered noncoding RNAs that are widely found in almost all species. They have covalently closed structures formed using a reverse splicing method . CircRNA15\u201317]. MiR-326 is usually downregulated in cancers, such as breast cancer, lung cancer, gastric cancer, as well as HCC [18\u201321]. However, the relationship between circ_0008043 and miR-326 in HCC remains unclear.CircRNAs are commonly act as competing endogenous RNAs (ceRNAs) to bind miRNAs, and further regulate gene expression . MiR-32624\u201326]. However, the expression of RAB21 and the function in HCC need explored.RABs are critical regulators of cell growth, cytoskeleton assembly, and membrane transport. Rab21 maintains its structure and function in the Golgi apparatus . InteresIn the present study, differentially expressed circRNAs were identified in HCC cells. Circ_0008043 is one of the differentially expressed circRNAs. We aimed to investigated the biological functions and molecular mechanisms of circ_0008043 were further studied. We hypothesized that silencing circ_0008043 inhibited HCC cell proliferation, migration, invasion, and metastasis. Mechanistically, circ_0008043 regulated cellular processes via the miR-326/RAB21 axis. The goal of this study is to provide theoretical basis for the potential of circ_0008043 in clinical therapy of HCC.Gene microarray GSE155949 for circRNA expression profile was downloaded from the GEO database. The R language package was used to screen differentially expressed circRNAs. The criteria for differentially expressed circRNAs were |log2(FC)| > 2 and P <\u00a00.05.Fresh HCC tissues and paracancerous non-tumor tissues were obtained from patients undergoing HCC surgery. All the tissues were frozen in liquid nitrogen, and then stored at \u221280\u00b0C until use. This study was approved by the Ethics Committee of the Third People\u2019s Hospital of Shenzhen. Written informed consent was obtained from all patients. Clinical information of all patients was listed in 2.HCC cell lines  and normal THLE3 cells were purchased from the Chinese Scientific Academy . The cells were maintained in DMEM  supplemented with 10% fetal bovine serum (FBS) (Gibco) at 37\u00b0C and 5% COst Strand cDNA Synthesis SuperMix for qPCR . Subsequently, qPCR was conducted using Hieff\u00ae qPCR SYBR Green Master Mix (Yeasen) on a CFX96 qPCR system  under the following conditions: 95\u00b0C for 5\u00a0min, 40 cycles of 95\u00b0C for 10s, and 60\u00b0C for 30s. The 2\u2212\u0394\u0394Ct method was used to detect relative expression (fold changes). GAPDH and U6 served as the internal controls.The MiRNeasy Mini kit  was used to isolate total miRNA, and an RNeasy mini kit (Qiagen) was used to isolate total RNA. Reverse transcription was performed using the 1After total RNA isolation, 10\u00a0U Rnase R  was incubated with 2.5\u00a0\u03bcg RNA for 20\u00a0min. In addition, Focus and HA22T cells were incubated with actinomycin D (2\u00a0\u03bcg/mL) for 0, 4, 8, 12, and 24\u00a0h. Circular and linear RNA levels were examined using qPCR.Short hairpin RNA (shRNA)-circ_0008043, sh-negative control (nc), mimic and inhibitor of miR-326, nc mimic and inhibitor, RAB21 overexpressing vector, and empty vector  were transfected into Focus and HA22T cells using Lipofectamine 3000 . The cells were cultured at 37\u00b0C for 48\u00a0h and qPCR was conducted to test the transfection efficiency.Cell viability was performed as previously described . Cells cColony formation analysis was performed as previously described . Cells dCell migration and invasion were assessed by transwell assay . Transwe6 cells) were injected into the backs of the mice. Tumor size was detected every 1\u00a0week for 4\u00a0weeks. The volume was calculated using the following formula: 0.5 \u00d7\u00a0length \u00d7 width2. Mice were sacrificed, and tumors were excised. Tumor weight was measured. The animal study was approved by the Ethics Committee of the Third People\u2019s Hospital of Shenzhen. The criterion of the animal study was in accordance with the Guide for the Care and Use of Laboratory Animals.BALB/c nude mice , obtained from Shanghai SLAC Laboratory Animal Co., Ltd. , were divided into adenovirus (Ad)-sh-nc and Ad-sh-circ_0008043. All mice were kept under standard conditions with free food and water. Four mice were included in each group. Sh-nc and sh-circ_0008043 fragments were inserted into the adenovirus and transfected into HA22T cells. The transfected cells (1\u00a0\u00d7\u00a010We cloned the possible binding sites of circ_0008043 and RAB21 to miR-326 into the pmir-GLO plasmid (Promega) to construct wild-type (wt)-circ_0008043 and wt-RAB21. Mutant (mut) sequences of circ_0008043 and RAB21 were designed and cloned into the pmir-GLO plasmid to construct mut-circ_0008043 and mut-RAB21. Focus and HA22T cells were co-transfected with wt-circ_0008043/wt-RAB21 or mut-circ_0008043/mut-RAB21 and mimic or NC mimic using Lipofectamine 2000. Luciferase activity was detected using the dual Glo Luciferase Assay System (Promega) after 48\u00a0h.The cells were transiently transfected with biotin-labeled miR-326 and nc. The cells were harvested after 48\u00a0h. After lysis, the cell lysates were incubated with Dynabeads M-280 Streptavidin (Invitrogen) at 4\u00b0C for 3\u00a0h. Subsequently, the beads were washed with lysis washing buffer. qPCR was conducted to examine the enrichment of circ_0008043 and RAB21.t-test was used to compare the two groups, and one-way analysis of variance was used to compare multiple groups. Statistical significance was set at P <\u00a00.05.The experimental data were acquired from three independent replicates. Data analysis was performed using GraphPad Prism 6 software, and the results are shown as mean \u00b1 standard deviation (SD). Student\u2019s in vivo. Knockdown of circ_0008043 inhibited cell biological behaviors by regulating the miR-326/RAB21 axis. The data may provide a new insight for circ_0008043/miR-326/RAB21 axis in HCC treatment.In this study, we explored the role of circ_0008043 and potential molecular mechanism. We evaluated cell proliferation, migration, invasion, and epithelial-mesenchymal transition (EMT) of HCC cells, and tumor growth First, we screened differentially expressed circRNAs in the tissues of HCC patients. Volcano plots showed numerous upregulated and downregulated circRNAs . Then, cAs illustrated in To explore the function of circ_0008043, Focus and HA22T cells were transfected with sh-circ_0008043 #1 and sh-circ_0008043 #2, and the levels of circ_0008043 were significantly reduced after transfection . Knockdoin vivo. Knockdown of circ_0008043 significantly reduced the size and weight of xenograft tumors .FigureTo understand the molecular mechanism, we predicted that miR-326 was a potential target of circ_0008043 ). The daCirc_0008043 was pulled down by biotin-labeled miR-326 ). KnockdAfter transfection, the inhibitor markedly downregulated miR-326 expression, whereas the mimic markedly induced miR-326 upregulation . miR-326RAB21 was predicted to be an miR-326 target, according to the results of TargetScan database . MiR-326After transfection with RAB21-overexpressing vector, RAB21 levels were significantly elevated . RAB21 oIn the present study, we found how circ_0008043 affected the progression of HCC. Moreover, circ_0008043 was confirmed to be an miR-326 sponge, which targeted RAB21. Circ_0008043 plays a role in HCC by regulating the miR-326/RAB21 axis.An increasing number of circRNAs have been discovered in recent years, and several studies showed that they play a crucial role in the development of malignancy, including HCC. For example, circ_0001955 facilitates tumorigenesis, cell growth, and metastasis in HCC ,30. Hsa_To clarify the molecular mechanism, we found that circ_0008043 functions as a ceRNA to sponge miR-326. MiR-326 is a promising biomarker in tumor diagnosis, prognosis, and therapy. It is commonly downregulated in cancers and is associated with poor prognosis, rapid cell growth, promotion of metastasis, and unfavorable drug resistance . MiR-326Furthermore, we found that RAB21 was a downstream candidate of miR-326. Interestingly, the role of RAB21 is contradictory in cancer. For example, RAB21 is enhanced in glioma tissues and cells, which knockdown inhibits the proliferation and induces apoptosis of glioma cells ,37. Invein vivo.However, there are still limitations of our study. Circ_0008043 could be diagnosis and therapy target. But it is a basic study, and a lot more research is needed before it can be used in the clinic. Our future work may focus the role of circ_0008043 and the circ_0008043/miR-326/RAB21 axis Circ_0008043 acts as an oncogene in HCC. Negative regulation of HCC progression was affected by silencing of circ_0008043 by regulating the miR-326/RAB21 axis. These findings suggest that the circ_0008043/miR-326/RAB21 axis may have important clinical value in the treatment of HCC."}
+{"text": "Escherichia coli. In this study, we used the adaptive laboratory evolution (ALE) strategy to improve violacein production using galactose as a carbon source. During the ALE, a tryptophan-responsive biosensor was employed to provide selection pressure to enrich tryptophan-producing cells. From the biosensor-assisted ALE, we obtained an evolved population of cells capable of effectively catabolizing galactose to tryptophan and subsequently used the population to obtain the best violacein producer. In addition, whole-genome sequencing of the evolved strain identified point mutations beneficial to the overproduction. Overall, we demonstrated that the biosensor-assisted ALE strategy could be used to rapidly and selectively evolve the producers to yield high violacein production.Violacein is a naturally occurring purple pigment, widely used in cosmetics and has potent antibacterial and antiviral properties. Violacein can be produced from tryptophan, consequently sufficient tryptophan biosynthesis is the key to violacein production. However, the complicated biosynthetic pathways and regulatory mechanisms often make the tryptophan overproduction challenging in  Chromobacterium violaceum and Janthinobacterium lividum have been used. However, these natural producers are pathogenic, and genetic engineering tools are insufficient, so development of such producers is limited [Escherichia coli have been used to construct heterologous violacein biosynthetic pathways [Violacein is one of the tryptophan derivatives and has attracted attention in the pharmaceutical industry due to its antibacterial, antiviral, antifungal, and antitumor activity ,2. In ad limited ,5,6. Altpathways ,8. In papathways ,10,11 anThe violacein production pathway from galactose can be divided into a tryptophan production pathway from galactose and a violacein production pathway from tryptophan a. For efE. coli that can effectively produce violacein by metabolizing galactose. Synthetic expression cassettes were introduced to maximize the synthetic violacein pathway, and culture conditions were evaluated. Subsequently, strain improvement of an evolutionary approach was performed to acquire a sufficient precursor pool, and a sensor consisting of tnaC recognizing tryptophan and tetracycline resistance marker was developed. Connecting this sensor to the adaptive laboratory evolution strategy allowed selection of strains that produced tryptophan well. Consequently, we obtained a strain that could effectively metabolize galactose and produce tryptophan, resulting in increased violacein production. Whole-genome sequencing of the evolved strain identified beneficial point mutations for the overproduction.In this study, we developed tnaA) and feedback inhibition (trpR). This strain produced 3.1 g L\u22121 tryptophan for 48 h [kanR and named the strain EPW.Since violacein can be synthesized from tryptophan through five enzymatic reactions catalyzed by VioA, VioB, VioC, VioD, and VioE , sufficifor 48 h . We replC. violaceum   c. Among lsewhere . We thenas added d. The amTween 80 e, indicaE. coli tnaC gene, encoding the TnaC leader peptide, which attenuates the tnaAB expression in the absence of tryptophan [tnaC gene with tetA-sgfp gene using primers listed in tetA-sgfp [tetA encoding a tetracycline/H+ antiporter was induced by tryptophan, allowing only tryptophan-producing cells to survive in the tetracycline-containing culture medium.As shown in yptophan . Thus, wetA-sgfp . In the \u22121 of tryptophan . Most of them were revealed as upstream and downstream gene variants, and only five of them were missense variants, which resulted in an amino acid substitution at that position  of tnaC is changed to T (Thr) [tnaC sensor to lower its affinity with tryptophan, and this modification provided a wider operational range of the sensor. Using the optimized tryptophan sensor, we adaptively evolved the parental strain, EPWS, and we then introduced pVio plasmid containing the heterologous violacein pathway to the evolved population to obtain a strain capable of effectively metabolizing galactose and consequently overproducing violacein. Notably, we could instantly screen violacein-overproducing strains on a solid medium with the naked eye due to the distinct color violacein.Since the tryptophan biosynthesis regulatory network is complicated, we took the ALE approach to enhance the tryptophan production. To assist the ALE, we developed a tryptophan-responsive genetic device using  T (Thr) . Based ogalR, which encodes a DNA-binding transcription factor that inhibits the transcription of operons involved in D-galactose transport and catabolism. In particular, the amino acid change occurred within the DNA binding domain of GalR, suggesting that the regulation on GalR-regulated genes might be alleviated due to the mutation. Therefore, the missense mutation of GalR may affect galactose catabolism and increase galactose utilization, leading to the increased production of violacein  and ExgeneTM Cell SV kit (GeneAll Biotechnology), respectively. DNA fragments were purified using the Expin\u2122 Gel SV kit (GeneAll Biotechnology). Q5 polymerase and NEBuilder\u00ae HiFi DNA Assembly Master Mix were purchased from New England Biolabs . Luria-Bertani (LB) broth and agar used for the cloning process of plasmids, and yeast extract, were obtained from BD Biosciences . Other chemicals were attained from Sigma\u2013Aldrich . The oligonucleotides were synthesized by Cosmogenetech   .VioA and vioB genes were amplified from pET15b-vioA and pET15b-vioB (GenBank: KX461959 and KX461960) with UTR_vioA_F/term_vioA_R and UTR_vioB_F/term_vioB_R, respectively. To insert a homology sequence at 5\u2032-end, each PCR product was amplified again with homology_vioA_F2/term_vioA_R and homology_vioB_F2/term_vioB_R2, respectively. The fragments of vector, vioA, and vioB were assembled using the assembly method, and the resultant was named pCPA. To clone the vioCDE genes, the vector was first amplified from pCPA with homology_vec_F/R. VioC, vioD and vioE genes were amplified from pET15b-vioC, -vioD, and pET21-vioE  with UTR_vioC_F/term_vioC_R, UTR_vioD_F/term_vioD_R and UTR_vioE_F/term_vioE_R, respectively. The PCR products were also amplified again with homology_vioC_F2/term_vioC_R2, homology_vioD_F2/term_vioD_R, and homology_vioE_F2/term_vioE_R, respectively. The vector containing pCPA and the PCR products of vioC, vioD, and vioE were assembled using the Gibson assembly. All sequences of 5\u2032-UTRs were designed and predicted using the UTR Designer  to enhance translation efficiency [The violacein production plasmid was constructed using the Gibson assembly method . First, ficiency .tnaC encoding a tryptophan-responsive leader peptide was amplified using a chromosome of E. coli MG1655 as a template with a set of primers, tnaC_homo_F/tnaA_homo_w_tetA. The tetA-sgfp encoding fusion protein was amplified with tetA_F/sfgfp_homo_R. The three PCR amplicons were assembled. To improve sensor performance, a single amino acid mutation was introduced using blunt-end ligation with a primer set of D21T_blunt_F/R.The tryptophan-responsive sensor plasmid was also constructed using the Gibson assembly. The vector was amplified from the pET23b vector with a set of primers named T7_term_F/pET23b_vec_R. A E. coli W3110, and the fluorescence intensity was monitored with different tryptophan concentrations. Cells were inoculated into the M9 medium , supplemented with 50 \u00b5g mL\u22121 kanamycin and 100 \u00b5g mL\u22121 of carbenicillin for plasmid maintenance. Overnight seeds were inoculated into the fresh medium to make an initial OD600 of 0.05. Tryptophan was added to the medium to achieve final concentrations of 0, 0.025, 0.05, 0.1, 0.2, 0.5, and 1 g L\u22121. Fluorescence was analyzed using flow cytometry  after 6 h of inoculation. Fluorescence of sGFP was measured on a FITC channel, excited with a 488-nm, and detected with a 525/40-nm bandpass filter. At least 20,000 events were recorded per sample.The sensor plasmids for tryptophan detection were transformed into mut was chosen as a tryptophan sensor, and tetA-sgfp fusion gene was used as a selection marker. This selection cassette expresses tetracycline resistance gene tetA, only in the presence of tryptophan. Using cells at the exponential growth phase (OD600 around 0.8), we first tested different tetracycline concentrations from 0 to 200 \u00b5g mL\u22121 and found a selection condition under which the cell growth was severely but not completely inhibited. The initial selection condition was 50 \u00b5g mL\u22121 of tetracycline. We then raised the tetracycline concentration up to 100 \u00b5g mL\u22121 to enrich tryptophan-producing cells but to eliminate other cells. To do so, cells were cultured in 2 mL of M9-YE (M9 medium containing 1g L\u22121 yeast extract) with an initial OD600 of 0.05. When OD600 reached 0.8, 50 \u00b5g mL\u22121 of tetracycline was added to the culture, and cells were cultured until it reached OD600 of 2.0. The cells were diluted into a fresh medium, and the enrichment procedure was repeated for six rounds. During the enrichment procedure, tetracycline was added at a concentration of 50, 50, 75, 75, 100, and 100 \u00b5g mL\u22121.To evolve the EPW strain, TrpSEN\u22121 galactose with appropriate antibiotics. To produce violacein, a single colony was obtained from a streak plate. The single colony was inoculated into the M9-YE medium. The overnight culture was inoculated into the fresh M9-YE medium containing 0.025 mM of IPTG and with an initial OD600 of 0.05. The cells were incubated at 37 \u00b0C for 4 h, and the temperature was lowered to 30 \u00b0C. When tested in small-scale liquid culture, 2 mL of M9-YE medium without surfactant was used. A 250 mL baffled flask containing 20 mL of M9-YE medium 3 g L\u22121 Tween 80 was used, and the agitation speed for fermentation was 200 rpm.All cultivation experiments for violacein production were performed using the modified M9-YE medium containing 10 g LTM 3000 analytical HPLC system, Dionex, Sunnyvale, CA, USA) equipped with Acclaim 120 C18 reverse-phase column (Dionex). The mobile phases were acetonitrile with 0.1% formic acid (A) and water containing 0.1% formic acid (B). The following gradient was carried out at a flow rate of 1 mL min\u22121: 0 min, 5% A; 1 min, 5% A; 5 min, 45% A; 7 min, 55% A; 9 min, 95% A; 10 min, 5% A; 12 min, 5% A. The signal was detected using an ultraviolet-visible (UV-Vis) diode array detector. Ultraviolet-visible (UV-Vis) diode array detector was used to detect signals. Galactose was analyzed with an Aminex HPX-87H column  maintained at 65 \u00b0C. The mobile phase was 5 mM H2SO4 at a flow rate of 0.6 mL min\u22121. The signals were monitored using a Shodex RI-101 refractive index detector .For the measurement of crude violacein, cells were first collected by centrifugation. After removing the supernatant, violacein was extracted by mixing the collected cells with ethanol in an ultrasonic water bath  at 60 \u00b0C until the cells were fully bleached. All ethanol extracts were collected, and the absorbance was measured at 570 nm to quantify the violacein using a Hidex Sense 425-301 microplate reader . The concentration of violacein was calculated based on a standard curve prepared using the purchased violacein (Sigma-Aldrich). In order to accurately quantify violacein in the flask fermentation, we used analytical high-performance liquid chromatography (HPLC) system (UltiMateTM Cell SV kit (GeneAll Biotechnology) according to the manufacturer\u2019s protocol. Sequencing libraries were constructed using the TruSeq DNA Nano DNA High Throughput Library Prep Kit  according to the manufacturer\u2019s instructions. Samples were sequenced using the Illumina NovaSeq 6000 system . Sequencing reads were then mapped onto the reference genome (GCF_000010245.2) using Burrows-Wheeler Aligner (version 0.7.12.) [The bacterial chromosomal DNAs from EPWSV and EPWSV2 were extracted from an overnight cultured sample using GeneAll Exgene0.7.12.) ."}
+{"text": "The expression of hsa_circ_0001306 was closely related to tumor size. Knockdown of hsa_circ_0001306 could downregulate F-box and WD repeat domain containing 7(FBXW7), a target of miR-527, thereby promoting HCC cell proliferation and invasion. Furthermore, hsa_circ_0001306 siRNA increased the multiplication rate of HCC tumors. Mechanistic studies indicated that hsa_circ_0001306 acts as a ceRNA for miR-527, which resulted in the reduction of its endogenous target, FBXW7. Hsa_circ_001306 is significantly downregulated in HCC, and the hsa_circ_0001306/miR-527/FBXW7 axis plays an important role in HCC progression.Hepatocellular carcinoma (HCC) is one of the most common types of cancer worldwide. Circular RNAs (circRNAs) have been reported to regulate many types of cancers, including HCC. The purpose of this study was to investigate the potential roles of hsa_circ_0001306 in HCC. Firstly, the downregulation of hsa_circ_0001306 was identified by high\u2011throughput RNA sequencing and further verified by qRT-PCR. Secondly, we evaluated the effects of hsa_circ_0001306 on HCC cell proliferation, invasion, cell cycle. Finally, we used an animal model to validate the  Hepatocellular carcinoma (HCC) is one of the most common types of cancer worldwide. More than 750,000 individuals are diagnosed with this disease annually circRNA is a type of non-coding RNA, whose study has recently gained momentum MicroRNAs (miRNAs) are highly conserved endogenous RNAs 20-24 nucleotides in length, which play a significant role in regulating the target mRNAs. The deregulation of miRNAs is usually related to the tumor development in multiple human cancers including HCC. MiR-527 was involved in several cancers FBXW7 (F-box and WD repeat domain containing 7) is a member of the F-box protein family, which is an essential tumor suppressor and is frequently inactivated in human cancer cells including HCC In the present study, we identified a novel HCC-related circRNA, hsa_circ_0001306, which was significantly downregulated in HCC specimens. The expression of hsa_circ_0001306 was closely related to tumor size. Moreover, we found that hsa_circ_0001306 may function as a ceRNA for miR-527, thereby reducing the level of its endogenous target, F-box and WD repeat domain containing 7 (FBXW7). Therefore, hsa_circ_0001306 may serve as a new target for HCC.Fifty HCC specimens and paired adjacent tissues were collected from patients who underwent hepatectomy at the First Affiliated Hospital of Soochow University from January 2014 to December 2016. All patients did not receive chemotherapy or radiotherapy before surgery. Based on the evaluations of experienced pathologists, the paired adjacent non-tumor tissues were harvested from the tumor edge at 5 cm, and no visible tumor cells were found. All tissue specimens were stored in liquid nitrogen. Clinical information was obtained under the study protocol approved by the Research Ethics Committee of the First Affiliated Hospital of Soochow University and written informed consent was obtained from each subject.The mirVana miRNA Isolation Kit  was used to extract the total RNA. The Agilent 2100 Bioanalyzer  was used to assess RNA integrity. The libraries sequenced on the Illumina sequencing platform (HiSeqTM 2500 or another platform) were instituted by TruSeq Stranded Total RNA with Ribo-Zero Gold.2 at 37 \u00b0C Four hepatoma cell lines , the normal hepatic cell line THLE-2, and the 293T cell line were purchased (6-12 months prior to experiments) from Type Culture Collection of the Chinese Academy of Science . All cell lines were analyzed by the short tandem repeat STR method recommended by American Type Culture Collection (ATCC) when we purchased. All six cell lines were cultured in Dulbecco's Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum (FBS) in humidified air containing 5% COWe extracted total RNA from each HCC specimen and paired paracancerous liver tissue using Ezol Reagent  according to the manufacturer's instructions. The purity and quality of total RNA was assessed using the NanoDrop 2000 Spectrophotometer . We synthesized the target cDNA by reverse transcription (RT) using random primers and the GoScript RT System . A non-template reaction was served as the control. The GoTaq qPCR Master Mix (Promega) and the qRT-PCR Plus System  were used for the real-time quantitative reverse transcription-polymerase chain reaction (qRT-PCR). Primers for hsa_circ_0001306 and glyceraldehyde 3-phosphate dehydrogenase (G3PDH) were synthesized by GenePharma . To validate the hsa_circ_0001306 was circular, the extracted total RNA was incubated at 37\u2103 with or without RnaseR (3U/ug) for 30 minutes to digest the linear RNA. The hsa_circ_0001306 expression level was then detected with its specific primers. The sequences of G3PDH, hsa_circ_0001306, miR-527, U6, and FBXW7 primers were in Supplementary File 3.hsa_circ_0001306 siRNA-1: sense 5'-GAUACAUUUCUAUUCCCCATT-3'; antisense 5'-UGGGGAAUAGAAAUGUAUCTT-3';hsa_circ_0001306 siRNA-2: sense 5'-CAUUUCUAUUCCCCAGGAATT-3'; antisense 5'-UUCCUGGGGAAUAGAAAUGTT-3';miR-527 mimic: 5'-CUGCAAAGGGAAGCCCUUUC-3'; and NC: 5'-AGUCGUCUAUAGAAGUUCGAGC -3';miR-527 inhibitor: 5'-GAAAGGGCUUCCCUUUGCAG-3'; and, NC: 5'-UCUGCACUAUUCAAGUGUGACC -3'.To alter the expression of the target gene, cells were transfected with siRNAs and the negative control (NC) using Lipofectamine 2000  according to the manufacturer's instructions. The hsa_circ_0001306 siRNA, which targets the junction region of the hsa_circ_0001306 sequence, was designed and synthesized by GenePharma. The miR-527 mimic, miR-527 inhibitor, and scrambled negative control siRNA (NC) were designed and synthesized by GenePharma. The sequences were as follows:3 cells/well) were seeded in a 96-well plate and cultured for 0, 24, 48, and 72 h. Thereafter, Cell Counting Kit-8 Reagent  was added to each well, and the plate was incubated for approximately 2 h. The absorbance was measured in a microplate reader at 450 nm.Transfected cells  for 30 min. This experiment was performed three independent times to reduce variations.Transfected cells were seeded in a 6-well plate and cultured in DMEM supplemented with 10% FBS in humidified air containing 5% CO5 cells) were added into the upper chamber with a Matrigel-coated membrane . DMEM supplemented with 10% FBS was added into the lower chamber. After incubation for 36 h, cells that invaded the Matrigel and migrated to the underside of the Transwell chamber membrane were fixed and stained with crystal violet for 30 min. The number of stained cells was counted in five randomly selected visual fields under a Leica DM3000 microscope.To evaluate the invasion capacity of the transfected cells, Transwell chambers  were used. Transfected cells . Transfected cells were incubated with 5 \u03bcM EdU according to the manufacturer's instructions. Thereafter, the cells were fixed in 4% formaldehyde for 30 min and permeabilized with 0.5% Triton-X100 for 10 min. EdU-positive cells were examined under a fluorescence microscope in dark conditions.We analyzed the proliferative capacity of HCC cells using the Cell-LightTransfected cells were stained with fluorescein isothiocyanate-Annexin V and propidium iodide and analyzed by flow cytometry (BD Biosciences). Transfected cells were stained with the CycleTEST\u2122 PLUS DNA Reagent Kit (BD Biosciences) according to the manufacturer's instructions. To examine the cell cycle, cells were analyzed by flow cytometry. The relative number of cells in G0/G1, S, and G2/M phases was determined and compared between the groups.For this experiment, ten male BALB/c-A nude mice purchased from the Shanghai Laboratory Animal Experimental Animal Center of the Chinese Academy of Sciences were used. After subcutaneous incubation of HCC tumors for seven days, we injected 10 OD cholesterol-modified hsa_circ_0001306 siRNA-1 or control siRNA every three days for five weeks. The tumor size was measured  twice a week. Finally, we removed and weighed the subcutaneous tumors. We performed all animal experiments according to the Animal Management Rules of the Chinese Ministry of Health .We extracted total proteins from HCC cells using RIPA extraction reagent  supplemented with a protease inhibitor cocktail (Roche). We collected the supernatants after centrifugation at 16000 g and 4 \u00b0C for 15 min. The total proteins were separated by 12% SDS and transferred onto polyvinylidene fluoride membranes . The membranes were blocked with 5% low fat powdered milk for 1 h and incubated with primary antibodies  overnight at 4 \u00b0C. The immunoreactive proteins were detected by the electrochemiluminescence detection system (Thermo Fisher Scientific).hsa_circ_0001306: GTGATGGC+TTCCTGGGGA+ATAGAAATGTATCC+T+AGGCT and,hsa-miR-527: GAAAG+GGCTTCCC+TTTGCAG.The expression levels and locations of hsa_circ_0001306 and hsa-miR-527 were detected by FISH in HCC cell lines and tumor tissues obtained from nude mice. The sequences of the probes were as follows:The hsa_circ_0001306 fragments containing the putative binding sites of miR-527 and its mutant sequence were synthesized and cloned into the luciferase reporter gene psiCHECK2 (Promega) and designated hsa_circ_0001306-WT and hsa_circ_0001306-Mut, respectively. The vectors were sequenced and respectively co-transfected with miR-527 or miR-NC into 293T cells. After co-transfection for approximately 48 h, luciferase activity was measured using the Dual Luciferase Reporter Assay Kit .7 miR-527-overexpressing Hep1 cells were washed with ice-cold phosphate-buffered saline (PBS), lysed in RNA lysis buffer and centrifuged then. The supernatant was incubated with streptavidin magnetic Dynabeads  to enrich the miR-527 probe overnight at 30 \u00b0C. Later the probes-dynabeads-miRNAs mixture was washed and the RNA was extracted using TRIzol Reagent and the content of hsa_circ_0001306 was analyzed by qRT-PCR.Firstly a biotinlabeled miR-527 probe was synthesized by GenePharma Co.,Ltd  to bind to the possible binding sites of hsa_circ_0001306. The probe sequence was Bio-5'-CTGCAAAGGGAAGCCCTTTC-3'. The antisense of miR-527 was used as negative control (NC). The NC sequence was Bio-5'-GAAAGGGCTTCCCTTTGCAG-3'.Approximately 1\u00d710t-test was used to analyze differences between the two groups. The Pearson correlation coefficient was used to indicate the relationship between hsa_circ_0001306 and miR-527. GraphPad Prism 6.0 Software  was used to analyze and present the data. All data are expressed as mean \u00b1 standard deviation (SD). P-values < 0.05 were considered statistically significant.Student's To identify differentially expressed circRNAs between HCC specimens and paired adjacent normal tissues, eight pairs of specimens (eight HCC specimens and eight matched non-tumor liver tissues) were analyzed by high throughput RNA sequencing. As shown in the heatmap Figure A, 40 uprin vivo, we inoculated Hep-G2 cells subcutaneously into male nude mice. All mice developed tumors. After continuous intra-tumoral injection of cholesterol-conjugated hsa_circ_0001306 siRNA-1 for six weeks, we found that tumor growth was significantly enhanced in the experimental group. The average size and weight of tumors in the hsa_circ_0001306 siRNA-1 group were significantly larger than those in the control group . The expression of hsa_circ_0001306 was significantly decreased in both hsa_circ_0001306 siRNA-1 and hsa_circ_0001306 siRNA-2 groups (Supplementary File 2A-B). Several experiments were then carried out to validate the roles of hsa_circ_0001306 in cell proliferation and invasion. Short-term cell proliferative capability was determined with the CCK-8 assay. As shown in Figure The invasion ability and cell cycle are key indicators of tumor cell proliferation. Therefore, to investigate whether hsa_circ_0001306 could affect HCC cell invasion, we performed an invasion assay with transfected HCC cells. The results showed that Hep-G2 and SK-HEP-1 cell invasion was enhanced in hsa_circ_0001306 siRNA-1 and hsa_circ_0001306 siRNA-2 groups compared with the NC group . As shown in Figure To investigate the detailed mechanism of miR-527 in HCC tumorigenesis, we first measured the expression of FBXW7, a direct target of miR-527 predicted by the miRBD Although great strides have been made on the effective and accurate diagnosis and treatment of HCC, the high malignant level of HCC is a huge challenge in clinical practice. Therefore, novel biomarkers and therapeutic targets are urgently required to improve the outcome of HCC patients.in vitro and in vivo experiments have confirmed that knockdown of hsa_circ_0001306 promoted cell proliferation and invasion. Consistent with previous studies, our findings indicated that hsa_circ_0001306 served as a ceRNA to exert its biological function in the development of HCC through a circRNA/miRNA/mRNA axis Increasing evidences have recently indicated that circRNAs are critically important in cell activities and cancer development. Progressive and uncontrolled tumor growth can be attributed to the dysregulation of circRNAs We further proved that miR-527 was the miRNA target of hsa_circ_0001306 by bioinformatic prediction and dual-luciferase reporter assay, which was mainly distributed in the cytoplasm. miR-527 was significantly upregulated in HCC specimens, and it promoted cell proliferation by downregulating FBXW7. Similarly, miR-527 was also upregulated in HCC samples obtained from TCGA compared with that of normal tissues , and expression levels of the top 10 circRNAs were examined in 50 pairs of HCC and normal tissues collected in our center by qRT-PCR. We finally selected hsa_circ_0001306 that possessed the most significant P value for the following experiments. The potential functions of other significantly differentially expressed circRNAs in HCC need further research.There are several limitations in our research. Firstly, although hsa_circ_0001306 was downregulated in HCC cells, its full-length was too long and it was difficult being artificially overexpressed. Therefore, we selected HCC cell lines expressing a relatively high level of hsa_circ_0001306 to carry out the knockdown experiments. hsa_circ_0001306 overexpression models established by novel technologies need to be further investigated. Secondly, we confirmed that miR-527 was the downstream target of hsa_circ_0001306 involved in the regulation of HCC development through mediating proliferation and invasion. Other existing miRNAs involved in should also be explored in the future. The development of HCC is mediated through complicated genetic regulatory systems involving interactions between DNAs, RNAs, proteins, and small molecules. Although our findings revealed the critical function of hsa_circ_0001306 in regulating proliferation and invasion of HCC, Kaplan-Meier survival curves did not identify the prognostic potential of it in HCC patients. A further larger study among HCC patients will be needed in future research. Thirdly, a total of 96 differentially expressed circRNAs between HCC and adjacent normal specimens from TCGA were identified by high throughput RNA sequencing (fold-change >2.0, In summary, we are the first to identify a novel HCC-related circRNA, hsa_circ_0001306, and to reveal that hsa_circ_0001306 is a tumor suppressor gene that can inhibit cell proliferation and invasion by the miR-527-FBXW7 axis in HCC. A schematic representation of the interplay among RNAs within the hsa_circ_0001306/miR-527/FBXW7 pathway is shown in Figure Supplementary figures and table.Click here for additional data file."}
+{"text": "Osteosarcoma (OS) is one of the most common malignant bone tumors in children and adolescents. Circular RNAs (circRNAs) are critical regulators involved in multiple physiological and pathological processes. However, the underlying regulatory mechanisms of circRNA in OS are still not fully understood.The circRNA expression profiles were downloaded from the Gene Expression Omnibus (GEO) database and analyzed by GEO2R. Bioinformatics analysis was performed to predict the potential target miRNAs of hsa_circ_0069117 and its downstream mRNAs. The co-expression of hsa_circ_0069117/miR-875-3p/PF4V1 axis was further validated in OS tissue samples via quantitative real-time PCR (qRT-PCR). Luciferase reporter gene plasmids containing the sequence of PF4V1 and hsa_circ_0069117 were constructed to verify the putative sites of miR-875-3p. Gain/loss-of-function assays were performed to verify the effect of hsa_circ_0069117 on miR-875-3p/PF4V1 expression and related pathways via qRT-PCR and Western blot. Cell counting kit-8 (CCK-8) and wound-healing assays were performed to evaluate the effect of hsa_circ_0069117 on cell proliferation and migration of MG63 and U2OS, respectively.We identified hsa_circ_0069117 as the most markedly dysregulated circRNA in OS cell lines. Bioinformatics analysis indicated that hsa_circ_0069117 might inhibit the expression of miR-875-3p, thereby promoting the expression of platelet factor 4 variant 1 (PF4V1). The expression of miR-875-3p was negatively correlated to hsa_circ_0069117 and PF4V1 in clinical samples. Luciferase reporter gene assays confirmed the binding sites of miR-875-3p on hsa_circ_0069117 and PF4V1. Gain/loss-of-function and rescue assays further indicated that hsa_circ_0069117 could significantly promote the expression of PF4V1 by sponging miR-875-3p, thereby inhibiting the proliferation and migration of OS cells by suppressing ERK1 and AKT.Our study revealed that hsa_circ_0069117 is an anti-OS molecule that could substantially attenuate cell proliferation and migration of OS, which may provide a novel and reliable molecular target for the treatment of OS patients. Osteosarcoma (OS) is the most common primary malignant bone tumor accounting for 10% of solid tumors in children and adolescents . It has Circular RNAs (circRNAs) were discovered decades ago , 9. It iIn the present study, we analyzed the GEO dataset GSE96964 and found that circRNA hsa_circ_0069117, a circular transcription of TBC1 domain family member 14 (TBC1D14), was most markedly dysregulated in OS cells . Howeverhttp://www.ncbi.nlm.nih.gov/geo/). The raw data of circRNA and miRNA expression profiles were then analyzed via GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/), an interactive web tool that allows users to identify genes or ncRNAs expressed differentially across experimental conditions [ p\u2009<\u20090.05.The circRNA and miRNA expression profiles of OS were downloaded from the Gene Expression Omnibus (GEO) database of the National Center of Biotechnology Information . The Functional Enrichment analysis tool (FUNRICH) was used to screen out the potential target mRNAs of miR-875-3p in miRDB, TargetScan, miRWalk, TargetMiner, and microT-CDS databases [The target miRNAs of hsa_circ_0069117 were predicted using circBank (atabases .Human osteosarcoma cell lines (OSCL) U2OS, 143B, MG63, HOS, and human mesenchymal stem cell line hMSCs were obtained from the American Type Culture Collection (ATCC). These cell lines were cultured in Dulbecco\u2019s minimum essential medium  mixed with 10% fetal bovine serum  and 1% antibiotics . The medium was replaced every 2\u20133\u00a0days.A total of 6 patients who were diagnosed with conventional osteosarcoma and underwent surgical resection at the Second Hospital of Jilin University were involved in our study. The diagnosis of OS was confirmed by pathological analysis. None of the patients received radiotherapy or chemotherapy before surgery. Osteosarcoma tissues (OST) and paired adjacent normal tissues were obtained after surgery and stored in liquid nitrogen. All the patients involved in this study provided written informed consents. The ethics committee of the Second Hospital of Jilin University approved this study (NO. 2016.169).\u2212\u25b3\u25b3Ct method. The primers used in this study are listed in Table Total RNA was extracted using TRIzol (Invitrogen) following the manufacturer\u2019s instructions. The cDNAs were synthesized using First-Strand Synthesis Kit . The primers of circRNA, miRNA and mRNA were synthesized by GenePharma . Quantitative real-time PCR (qRT-PCR) was performed using the TB Green\u2122 Kit . The expression of circRNA and mRNA were normalized relative to GAPDH, and miRNAs was normalized relative to U6, which was calculated using 2The 3\u2032-UTR of PF4V1 and hsa_circ_0069117 containing the putative binding site (wide and mutated type) of miR-875-3p were inserted into pGL6-miR vectors which were then validated by sequencing . The 293\u00a0T cells were co-transfected by reporter vectors and miR-875-3p mimics . Firefly luciferase activity was measured at 48\u00a0h after transfection.The hsa_circ_0069117 overexpression vector (ov-hsa_circ_0069117) and siRNA (si-hsa_circ_0069117) were designed and purchased from Hanbio . The miR-875-3p inhibitor and mimics were purchased from GenePharma Gene . Lipofectamine\u2122 3000 Transfection Reagent  was used as the transfection vehicle. The expression of hsa_circ_0069117, miR-875-3p and mRNAs was detected at 48\u00a0h post-transfection using quantitative real-time PCR.Proteins were separated by gel electrophoresis and transferred to a PVDF membrane. The PVDF membrane was incubated with primary antibodies against PF4V1, ERK1, p-ERK1, AKT, p-AKT, and GAPDH  overnight at 4\u00a0\u00b0C. Secondary antibodies  were used to incubate the blots. The ECL luminescence reagent  was then used to visualize the bands. The quantification analysis of protein was performed for PF4V1, ERK1, p-ERK1, AKT by normalizing to GAPDH.The effect of hsa_circ_0069117 on MG63 and U2OS proliferation was evaluated using the cell counting kit-8 (CCK-8) assay. OS cells were seeded in 96-well plates and cultured to 80\u201390% density. CCK-8 solution was added to the cell culture plates at 24\u00a0h and 48\u00a0h (for 2\u00a0h) after hsa_circ_0069117 vector transfection. The optical density was measured at a wavelength of 450\u00a0nm to reflect the effect of hsa_circ_0069117 on MG63 and U2OS proliferation.The effect of hsa_circ_0069117 on MG63 and U2OS cell migration was evaluated using a wound-healing assay. OS cells were seeded in 96-well plates and cultured to 80\u201390% density. A straight wound was created manually with a clean 100\u00a0\u03bcL plastic pipette tip post hsa_circ_0069117 vector transfection. The cell migration areas were recorded by microscopy at 24\u00a0h and 48\u00a0h post hsa_circ_0069117 vector transfection.p\u2009<\u20090.05 indicated that the difference was statistically significant. |FC|\u2265\u20092 and adjusted p\u2009<\u20090.05 were set as the threshold for detecting dysregulated circRNAs and miRNAs. Bar graphs were constructed using GraphPad Prism 7.0. The ceRNA network was generated using Cytoscape software.The expression data were using the t-test to compare differences between two groups. One-way ANOVA was used to compare the differences among multiple groups and p value: 0.00000881). Next, we predicted 60 potential target miRNAs of hsa_circ_0069117  and miRNA expression profile (GSE70367) were analyzed using GEO2R to detect the dysregulated circRNAs and miRNAs in OS. According to the criteria, eight upregulated and 102 downregulated circRNAs were screened out (Fig.\u00a0Next, we detected the expression of hsa_circ_0069117, miR-875-3p, and PF4V1 in four OSCLs. The results indicated that the expression of hsa_circ_0069117 was significantly decreased in OSCLs Fig.\u00a0A while tWe further validated the expression of hsa_circ_0069117/miR-875-3p/PF4V1 axis in OSTs, which was consistent with that in OSCLs Fig.\u00a0D\u2013F. InteThereafter, we explored the binding sites of miR-875-3p on hsa_circ_0069117 Fig.\u00a0, while tGain/loss-of-function assays were performed to verify the effect of hsa_circ_0069117 on miR-875-3p/PF4V1 expression and related pathways. The results showed that overexpressing hsa_circ_0069117 could significantly suppressed the expression of miR-875-3p (Fig.\u00a0ERK1 and AKT have been reported as downstream targets of PF4V1 . TherefoSubsequently, we validated the effect of hsa_circ_0069117 on the proliferation and migration of OS cells. As hsa_circ_0069117 was most remarkably decreased in MG63 and U2OS cell lines, we performed CCK-8 and wound healing assays on MG63 and U2OS cell lines after hsa_circ_0069117 overexpression. The results indicated that the proliferation Fig.\u00a0A and migRecent years have witnessed the great progression of circRNA research in functioning as a gene expression regulator in various diseases \u201322. SeveIn present study, hsa_circ_0069117 was found to be the most markedly dysregulated in OS cells. However, the regulatory effect of hsa_circ_0069117 in malignant diseases, including OS, remains unclear. To further investigate the potential regulatory roles of hsa_circ_0069117 in OS, we predicted its potential target miRNAs. Sixty miRNAs were predicted to be targets of hsa_circ_0069117. By co-analyzing with the miRNA expression profile in OS, we found that only miR-875-3p was differentially expressed. MiRNA-875-3p has already been reported as an oncogene regulator by targeting PF4V1 . PF4V1, We constructed several lines to confirm our hypotheses . First, In summary, we conducted this study to elucidate the regulatory effect of hsa_circ_0069117 on OS progression. We identified and further confirmed that miR-875-3p/PF4V1 axis was the target of hsa_circ_0069117. Over-expression of hsa_circ_0069117 substantially attenuated the proliferation and migration of OS cells. Our study provides novel and reliable molecular target for the diagnosis and therapy of patients with OS."}
+{"text": "Viral infections are prevalent in human cancers and they have great diagnostic and theranostic values in clinical practice. Recently, their potential of shaping the tumor immune microenvironment (TIME) has been related to the immunotherapy of human cancers. However, the landscape of viral expressions and immune status in human cancers remains incompletely understood.We developed a next-generation sequencing (NGS)-based pipeline to detect viral sequences from the whole transcriptome and used machine learning algorithms to classify different TIME subtypes.We revealed a pan-cancer landscape of viral expressions in human cancers where 9 types of viruses were detected in 744 tumors of 25 cancer types. Viral infections showed different tissue tendencies and expression levels. Multi-omics analyses further revealed their distinct impacts on genomic, transcriptomic and immune responses. Epstein-Barr virus (EBV)-infected stomach adenocarcinoma (STAD) and Human Papillomavirus (HPV)-infected head and neck squamous cell carcinoma (HNSC) showed decreased genomic variations, significantly altered gene expressions, and effectively triggered anti-viral immune responses. We identified three TIME subtypes, in which the \u201cImmune-Stimulation\u201d subtype might be the promising candidate for immunotherapy. EBV-infected STAD and HPV-infected HNSC showed a higher frequency of the \u201cImmune-Stimulation\u201d subtype. Finally, we constructed the eVIIS pipeline to simultaneously evaluate viral infection and immune status in external datasets.Viral infections are prevalent in human cancers and have distinct influences on hosts. EBV and HPV infections combined with the TIME subtype could be promising biomarkers of immunotherapy in STAD and HNSC, respectively. The eVIIS pipeline could be a practical tool to facilitate clinical practice and relevant studies.The online version contains supplementary material available at 10.1186/s12885-021-08871-9. Human oncogenic viruses have been implicated in causing 10\u201315% of human cancers worldwide . The expImmunotherapy has revolutionized the therapeutic strategies of human cancers. The presence of programmed cell death 1 ligand 1 (PD-L1), microsatellite instability-high (MSI-high) or DNA mismatch-repair deficiency (dMMR) and tumor mutation burden (TMB) are the most promising biomarkers for immunotherapy. However, these biomarkers have limited ability in selecting responders , 13. BesIn this study, we aim to investigate viral sequences across human cancers and find their influences on the genome, transcriptome and TIME of their hosts. Also, we aim to generate an integrated pipeline to detect viral infections and identify TIME subtypes. Hopefully, the revealed virus-cancer associations and the developed tools may provide insights into immunotherapy in human cancers.We downloaded 11,206 TCGA BAM format files of 33 cancer types from The Genomic Data Commons (GDC) data portal with official authorization. We aligned raw RNA-seq data in BAM files which came from STAR with reference sequences of human and viral genomes to detect viral expression. Next, we employed StringTie (version 1.3.3) to assemble transcripts for each BAM file, with GENCODE v22 as the reference annotation. Finally, the expression level of each transcript was normalized into TPM (transcripts per kilobase million). For transcripts of the same viral infection type, we selected the maximum TPM value as the final viral mRNA expression. Of note, we refer to an infected case as a tumor infected by a specific virus type, for example, an HPV-infected tumor of HNSC is an HPV infection case in HNSC. By comparison, an infected sample is a sample harboring viral sequences, no matter how many types of viral sequences were detected. Therefore, in samples co-infected by different viruses, the number of infected cases is not equal to that of infected samples or tumor samples.MAF format files, gene expression profiles  and corresponding clinical information of 11,206 tumor samples were also downloaded from the TCGA database. Information of total leukocyte fraction (LF), 22 types of lymphocyte infiltration, genomic features , aneuploidy score, homologous recombination defects (HRD), intratumor heterogeneity (ITH)) of each tumor sample was obtained from a previous study . All datTumor mutation burden (TMB) is defined as the number of somatic mutations (excluding germline mutations) within the whole genome. In this study, the TMB of each tumor sample was calculated by measuring mutations per megabase (Mb) based on MAF format files from TCGA. For each sample, we merged all somatic mutations calculated by four different techniques, including MuTect , MuSE 2, VarScanWe segregated 22 leukocyte subtypes into 9 subsets according to the criteria from a previous study :T.cells.CD8\u2009=\u2009T.cells.CD8,T.cells.CD4\u2009=\u2009T.cells.CD4.naive+T.cells.CD4.memory.resting+T.cells.CD4.memory.activated+T.Cells.Follicular.Helper+T.Cells.gamma.delta+T.Cells.Regulatory.Tregs,B.cells\u2009=\u2009B.cells.naive + B.cells.memory + Plasma.Cells,NK.cells\u2009=\u2009NK.cells.resting + NK.cells.activated,Macrophage\u2009=\u2009Macrophages.M0\u2009+\u2009Macrophages.M1\u2009+\u2009Macrophages.M2,Dendritic.cells\u2009=\u2009Dendritic.cells.resting + Dendritic.cells.activated,Mast.cells\u2009=\u2009Mast.cells.resting + Mast.cells.activated,Neutrophils\u2009=\u2009Neutrophils + Monocytes,Eosinophils\u2009=\u2009Eosinophils.The original data of 22 leukocyte subtypes was obtained from Ref . The oriKEGG_B_CELL_RECEPTOR_SIGNALING_ PATHWAYKEGG_CELL_ADHESION_MOLECULES_ CAMSKEGG_CHEMOKINE_SIGNALING_PATHWAYKEGG_COMPLEMENT_AND_COAGULATION_CASCADESKEGG_CYTOKINE_CYTOKINE_RECEPTOR_INTERACTIONKEGG_FC_EPSILON_ RI_SIGNALING_ PATHWAYKEGG_FC_GAMMA_R_ MEDIATED_ PHAGOCYTOSISKEGG_LEUKOCYTE_TRANSENDOTHELIAL_MIGRATIONKEGG_NATURAL_KILLER_CELL_MEDIATED_CYTOTOXICITYKEGG_NOD_LIKE_RECEPTOR_SIGNALING_PATHWAYKEGG_RIG_I_LIKE_RECEPTOR_SIGNALING_PATHWAYKEGG_T_CELL_RECEPTOR_SIGNALING_PATHWAYKEGG_TGF_BETA_SIGNALING_ PATHWAYKEGG_TOLL_LIKE_RECEPTOR_SIGNALING_PATHWAYREACTOME_PD1_SIGNALINGBIOCARTA_CTLA4_PATHWAYGene set variation analysis (GSVA) was implemented using the \u201cGSVA\u201d R package (3.8). Sixteen immune-related pathways were obtained from the Molecular Signatures Database (MSigDB) , 28, incDetailed information about GSVA is available in a previous study .P\u2009<\u20090.05. Enrichment analyses were performed using the \u201cclusterProfiler\u201d R package [Gene expressions (FPKM) were log2-transformed after adding one as the pseudo count and then processed by the \u201climma\u201d R package (3.36.5). Differentially expressed genes (DEGs) were defined as genes with  package .Using whole DEGs for enrichment analysis can cause redundancies which is much less biologically interpretable. In this case, subtle changes would be masked by dominant alterations. Therefore, we chose fuzzy c-means (FCM) to divide whole DEGs into different functional modules. FCM is a soft clustering approach and we implemented the method using the \u201cMfuzz\u201d R package (3.8) , 32. AftWe performed clustering analysis on the \u201cLF-high\u201d samples of six cancers including LGG, COAD, CESC, KIRC, UVM and SKCM . These cThe Kaplan-Meier analysis was used to estimate the empirical survival probabilities. Differences between survival curves were tested by the log-rank test using the \u201csurvival\u201d R package (version 3.2\u20137).http://tide.dfci.harvard.edu/). First, raw FPKM values were log2 transformed after adding a pseudo value of one (log2(FPKM+\u20091)). For each gene, we then subtracted the mean value calculated by averaging normalized FPKM values across all patients of the same cancer type. Finally, we uploaded the normalized tab files of the expression matrix to the website to get TIDE predictions. The results include \u201cResponder\u201d, \u201cTIDE score\u201d, \u201cIFNG score\u201d, \u201cDysfunction score\u201d, and \u201cExclusion score\u201d, etc.  distributions were first explored in 9692 tumors of 30 cancer types with available LF information . We found LFs of cancers lacking leucocyte infiltrations  were mosTo model a LF.Score that classifies tumors into the \u201cLF-high\u201d or the \u201cLF-low\u201d group based on gene expression profiles, we used TCGA datasets for both training and internal validations. Since TCGA datasets are comprised of different tumor types with unbalanced sample sizes, we adopted stratified sampling and randomly assigned samples of each cancer type into a training set and two validation sets at the same ratio of 4:3:3 . For feature selection, we first performed correlation analyses in the training cohort. We found 167 protein-coding genes were relevant to LF levels (Pearson\u2019s r\u2009>\u20090.5). Then we performed LASSO regression, which is an L1 regularization technique, to further shrink the size of the gene signature. 10-fold cross-validation LASSO regression was performed using the \u201ccv.glmnet\u201d function in the \u201cglmnet\u201d R package to define the optimal lambda (\u03bb\u2009=\u20090.00673)  of 0.855 in the training cohort (95% CI: 0.841\u20130.868) . An SVM model was then trained using the 10-fold cross-validation strategy by the \u201ce1071\u201d R package. The efficiency of the classifier was evaluated by the receiver operating characteristic curve (ROC) calculated by the\u201cpROC\u201d R package. The classifier could distinguish the \u201cImmune-Stimulation\u201d subtype from the \u201cImmune-Anergy\u201d subtype in both training cohort   and a validation cohort (513 samples). For feature selection, DEG analysis was performed in the training cohort where thirty-seven genes were found differentially expressed between two subtypes . TIME prediction was implemented based on the two models (LASSO regression and SVM) derived from the TCGA training cohort. For a given sample, eVIIS first calculates its LF.Score using the LASSO regression model based on FPKM values of 30 LF-relevant genes. The sample will be classified as the \u201cImmune-Exclusion\u201d subtype if LF.Score\u2009<\u200981.03. Samples with LF.Score\u2009>\u2009= 81.03 will be further processed by the SVM model. The SVM model evaluates each sample by the 37 DEGs (FPKM) and predict the sample as the \u201cImmune-Stimulation\u201d or the \u201cImmune-Anergy\u201d subtype. The eVIIS pipeline is available at https://github.com/HuangLab-Fudan/eVIIS.Based on the LF.Score classifier and the SVM classifier, we developed the eVIIS pipeline. The pipeline was built using Python (version 3.6.0) and shell language. It takes several steps to predict viral infections and the TIME subtype: (1) align RNA-seq data to human and viral reference genome sequences using STAR (version <= 2.5) ; (2) assp\u2009<\u20090.05 as a significance cutoff. All statistical analyses were performed using R (version 4.0.1).Wilcoxon rank-sum test was used to compare continuous variables. Pearson\u2019s Chi-squared test was used to compare unordered categorical variables. Correlation analysis was performed by Spearman\u2019s rank correlation. All tests were two-tailed with We designed a pipeline to detect 212 types of viral sequences by interrogating RNA-Seq data of 11,206 tumor samples of 33 cancer types from TCGA , in which the high-risk HPV16 and HPV18 dominantly accounted for 60.90 and 14.53%, respectively. Besides, we detected 2 cases of HBV infection in CESC. For LIHC, 93.6% tumors (117 out of 125) were infected by Hepatitis B virus (HBV). Additionally, four tumors were infected by HPV (HPV16 and HPV35), four tumors were infected by Hepatitis C virus (HCV), and one tumor was infected by Epstein-Barr virus (EBV). For STAD, 55.1% of tumors (43 out of 78) were infected by EBV and 51.3% of tumors (40 out of 78) were infected by Cytomegalovirus (CMV). Besides, one tumor was infected by HPV18, one tumor was infected by Kaposi\u2019s sarcoma-associated herpesvirus (KSHV), and one tumor was infected by Human Immunodeficiency Virus (HIV). Of note, we found co-infection of EBV and CMV occurred frequently in gastrointestinal tumors. Apart from STAD, ESCA showed 3.2% of tumors (6 out of 185) with EBV infection and 5.9% of tumors (11 out of 185) with CMV infection. For HNSC, 89.61% of tumors (69 out of 77) were infected by HPV and HPV16 was the dominant subtype . Also, we found one tumor infected by EBV, 3 tumors infected by CMV, 2 tumors infected by HBV. For LAML, we detected CMV infection in all tumors and SV40 infection in 83.6% of tumors (46 out of 55). We compared the above results with the clinical information provided by TCGA. For HPV infection, 22 out of 23 records of CESC and 91 out of 96 records of HNSC agreed with our detection. Additionally, we detected other subtypes of HPV, including HPV45, HPV52, HPV58 and HPV70. As for EBV infection, EBV infections were detected in 27 out of 30 tumors that were accordingly defined as the GI.EBV subtype by TCGA   , HPV-infected HNSC (P\u2009=\u20090.0034), and HBV-infected LIHC (P\u2009<\u20090.0001) showed an earlier age of diagnosis (Pearson\u2019s Chi-squared test)  Fig.\u00a0. Ancestrst) Fig. . HPV-infp\u2009<\u20090.05; two-tailed Mann-Whitney U test). By comparison, five features  were increased in HBV-infected LIHC   , mutation rate (silent and non-silent), copy number variation , aneuploidy, homologous recombination defects (HRD) and intratumor heterogeneity (ITH) for the most prevalent virus-cancer associations . We found all eight features decreased consistently in HPV-infected HNSC and features including CNV, HRD and ITH were decreased in EBV-infected STAD (st) Fig.\u00a0. Using Tst) Fig.\u00a0C. Besideon) Fig. D.Fig. 3To explore the impact of viral infections upon the transcriptome, we performed differential expression analysis. For HPV-infected HNSC, we identified 3367 differentially expressed genes (DEGs), of which 2446 DEGs were upregulated and 921 DEGs were downregulated. For EBV-infected STAD, we identified 986 DEGs, of which 511 DEGs were upregulated and 475 DEGs were downregulated. In contrast, only 163 DEGs were identified in HBV-infected LIHC, of which 93 DEGs were upregulated and 70 DEGs were downregulated. Compared to HPV-related DEGs and EBV-related DEGs, changes of HBV-related were much smaller. For all virus-related DEGs, 9 genes were overlapped, including CDT1, CENPM, HLA-DPA1, LMNB1, MCM2, MCM5, PAFAH1B3, RRM2 and TK1 . For LIHC, LF, CD4+ T cells and mast cells were decreased in HBV-infected tumors . For HNSC, infiltrations of CD8+ T cells and B cells were increased, while LF, macrophages and mast cells were decreased in HPV-infected tumors    Fig. . For allWe further examined how immune-related functions were regulated in these virus-infected tumors , 28. In Tumor immune microenvironment (TIME) is a prerequisite of applying immunotherapy in the clinic . In thisp-value <\u20092.2e-16, Pearson\u2019s correlation)  and the \u201cImmune-Stimulation\u201d subtype ; and the \u201cImmune-Anergy\u201d subtype is higher than the \u201cImmune-Stimulation\u201d subtype   and the \u201cImmune-Exclusion\u201d subtype ; and the \u201cImmune-Anergy\u201d subtype is higher than the \u201cImmune-Exclusion\u201d subtype   and the \u201cImmune-Exclusion\u201d subtype . and \u201cIFNG scores\u201d are higher in the \u201cImmune-Anergy\u201d subtype also shows a higher level of \u201cIFNG scores\u201d than the \u201cImmune-Exclusion\u201d subtype   and validated in the two validation datasets (2931 and 2911 samples) from the whole TCGA datasets. And the SVM model was trained (1200 samples) and validated (513 samples) on samples of the \u201cImmune-Anergy\u201d and the \u201cImmune-Stimulation\u201d TIME subtypes of LGG, COAD, CESC, KIRC, UVM and SKCM from TCGA datasets  and HNSC , respectively. However, HBV infection showed no significant impact on the TIME subtypes of LIHC  and progression-free interval (PFI)  in STAD, and the \u201cImmune-Stimulation\u201d subtype showed the best OS  and PFI  in HNSC .We combined the viral sequence detection pipeline and the TIME subtyping workflow to develop an integrated eVIIS pipeline. Given an RNA-seq dataset, eVIIS simultaneously evaluates viral infection and immune status Fig.\u00a0. eVIIS pFor independent validation, we used an extra dataset including 83 human primary gastric tumor tissue samples from the surgical specimen archives from Fudan University Shanghai Cancer Center (FUSCC). The results showed that 4 EBV-infected samples and 12 CMV-infected samples, in which 6 samples (7.23%) were predicted as the \u201cImmune-Stimulation\u201d subtype. The results were consistent with the prevalence of EBV and CMV infections in gastrointestinal tumors observed in the TCGA cohort.In this study, we designed an NGS-based pipeline to detect 212 types of viral sequences. We obtained a comprehensive landscape of viral expressions of 11,206 tumors of 33 cancer types from TCGA. Of all the infected cancers, stronger virus-cancer associations were observed in CESC, LIHC, LAML, STAD, HNSC and ESCA. And HPV, HBV, EBV and CMV were the most prevalent infection types. Our results are consistent with a similar study that reported HPV infection in 96.55% of tumors (84 out of 87) in CESC, HBV infection in 32.35% of tumors (11 out of 34) in LIHC, and HPV infection in 14.14% of tumors (43 out of 304) in HNSC . Furthern\u2009=\u20093). Besides, the study reported HPV6 and HPV45 infections in BLCA. These have also been detected in our results and we additionally detected HPV52 and HPV56. It\u2019s technically hard to discard all the false-positive results of viral infections, but the varied tissue tendencies and expression levels could be used as a reference. For example, HBV-infected LIHC tumors usually harbor high viral expressions. Therefore, in tumors with low HBV viral expressions, they would likely be considered as contaminations or from infected lymphocytes [Viral infections showed different tissue tendencies and their main hosts were highly selective. This could be explained by their different viral receptors that are required during infections. However, viral infections were also detected sporadically in some uncommon hosts. HPV16 was detected in a broad spectrum of cancers including uterus, lung, bladder carcinomas and low-grade gliomas tumors. These have also been reported in previous findings , 11, 53.phocytes , and thiphocytes . CompareCommonly, viral infections have the potential to cause perturbations in the host genome. In our study, EBV-infected STAD showed decreased CNV and HRD and HPV-infected HNSC exhibited consistently decreased genomic variations. This could partially be explained by the following transcriptional analyses that pathways involving DNA replication, mismatch repair, base excision repair, and nucleotide excision repair in S1 and H3 were upregulated, leading to decreased genomic instability in EBV-infected STAD and HPV-infected HNSC. A recent study reported that HPV-positive HNSC exhibited an almost complete mutual exclusivity with mutations in known drivers such as TP53, CDKN2A and TERT. Such decreased mutation burden and the independence from carcinogenic drivers confirmed the mutation-independent oncogenic and tumorigenic potential of HPV .The impacts of viral infections were different on the host transcriptomes. While the small number and small changes of expression levels of DEGs were seen in HBV-infected LIHC, much greater changes were observed in EBV-infected STAD and HPV-infected HNSC. The common genes that were changed in all types of infections were primarily concerning cell proliferation. This reflects the ability of viral infections to stimulate cell proliferation that leads to tumor development . The cluAnother group of genes that changed commonly were immune-related. These immune-related genes were consistently upregulated in EBV-infected STAD and HPV-infected HNSC. Accordingly, multiple immune cells were increased in the HPV-infected HNSC. This could be supported by a recent study that reported a significant increase in M1 macrophages and T-cells  in HPV-positive HNSC . Many imFor anti-PD immunotherapy, TIME is considered as a prerequisite to select appropriate patients . A largeWe provided a comprehensive virus-cancer association landscape and revealed different properties of viral infections. EBV-infection and HPV-infection led to decreased genomic variations, significantly altered gene expressions, and effectively triggered anti-viral immune responses in STAD and HNSC. EBV-infection and HPV-infection combined with the TIME subtype could be candidate biomarkers of the immunotherapy in STAD and HNSC, respectively. Finally, the eVIIS pipeline could be a practical tool to facilitate clinical practice and relevant studies.Additional file 1: SupplementaryFig. S1. Results of the fuzzy c-means (FCM) clustering. EBV-infected STAD showed 3 gene clusters , HBV-infected LIHC showed 4 gene clusters  and HPV-infected HNSC showed 4 gene clusters . Supplementary Fig. S2. Comparisons of GO enrichment analysis of DEGs in each cluster, in which left side denotes down-regulated DEGs and right side for up-regulated DEGs. The bubble size represents enriched genes in each GO term and color indicates adjust.P value. Supplementary Fig. S3. CD8 T cells infiltration in all EBV- and HPV-associated cancers. The red triangles and grey rounds represent virus-positive and virus-negative cases respectively. Supplementary Fig. S4. Correlation between CD8 T cells infiltration and EBV expression in all EBV-associated cancers. The different colored points stand for the corresponding cancer types. Supplementary Fig. S5. Comparisons of Neoantigen, TCR and BCR between infected cases and non-infected cases in STAD, LIHC and HNSC. Supplementary Fig. S6. Survival curves of OS and PFI outcomes of TIME subtypes in LIHC. Supplementary Fig. S7. ROC-curve of LF.Score classifier in training (3865 samples),validation1 (2931 samples) and validation2 (2911 samples) cohorts; the accuracy of the SVM diagnosis in two random subsampling cohorts were labeled. Supplementary Fig. S8. 10 cross-validation curve (red dotted line), and upper and lower standard deviation curves along the \u03bb sequence (error bars). We determined lambda.1se (0.00673) as the optimal \u03bb, which gives the most regularized model such that error is within one standard error of the minimum. Supplementary Fig. S9. ROC-curve and confusion table of SVM classifier in training (n\u2009=\u20091200) and validation (n\u2009=\u2009513) cohorts. Supplementary Fig. S10. Correlation analysis between TIME subtypes and TIDE. Supplementary Fig. S11. Correlation analysis between TIME subtypes and IFNG score.Additional file 2: Supplementary Table\u00a01. TCGA Dataset.Additional file 3: Supplementary Table\u00a02. Comparisons of genomic features between infected and non-infected tumors.Additional file 4: Supplementary Table\u00a03. Information of 76 immune-related genes.Additional file 5: Supplementary Table\u00a04. Gender, Race and Age analyses.Additional file 6: Supplementary Table\u00a05. TIDE and TIME information for TCGA SKCM dataset."}
+{"text": "Machine learning brings the hope of finding new biomarkers extracted from cohorts with rich biomedical measurements. A good biomarker is one that gives reliable detection of the corresponding condition. However, biomarkers are often extracted from a cohort that differs from the target population. Such a mismatch, known as a dataset shift, can undermine the application of the biomarker to new individuals. Dataset shifts are frequent in biomedical research, e.g., \u2009because of recruitment biases. When a dataset shift occurs, standard machine-learning techniques do not suffice to extract and validate biomarkers. This article provides an overview of when and how dataset shifts break machine-learning\u2013extracted biomarkers, as well as detection and correction strategies. Biomarkers are measurements that provide information about a medical condition or physiological state . For exaComplex biomedical measures may carry precious medical information, as with histopathological images or genome sequencing of biopsy samples in oncology. Identifying quantitative biomarkers from these requires sophisticated statistical analysis. With large datasets becoming accessible, supervised machine learning provides new promise by optimizing the information extracted to relate to a specific output variable of interest, such as a cancer diagnosis . These mCan such predictive biomarkers, extracted through complex data processing, be safely used in clinical practice, beyond the initial research settings? One risk is the potential mismatch, or \u201cdataset shift,\" between the distribution of the individuals used to estimate this statistical link and that of the target population that should benefit from the biomarker. In this case, the extracted associations may not apply to the target population . ComputeIn this article, we consider predictive biomarkers identified with supervised machine learning. We characterize the problem of dataset shift, show how it can hinder the use of machine learning for health applications , 13, andX and an output  Y: e.g., the relation between the absorption spectrum of oral mucosa and blood glucose concentration ).We now discuss some misconceptions and confusions with problems not directly related to dataset shift.Dataset shift is sometimes confused with the notion of confounding because both settings arise from an undesired effect in the data. Confounding comes from causal analysis, estimating the effect of a treatment\u2014an intervention, sometimes fictional\u2014on an outcome. A confounder is a third variable\u2014e.g., age or a comorbidity\u2014that influences both the treatment and the outcome. It can produce a non-causal association between the two . Preferential sample selection is ubiquitous and cannot always be prevented by careful study design  but X (but not Y) changes the distribution of the inputs. If the model is correctly specified, an estimator trained with uniform weights will lead to optimal predictions given sufficient training data , the distribution of  problem . Prior p problem , 38). WhIdeally, machine-learning biomarkers would be designed and trained using datasets carefully collected to be representative of the targeted population\u2014as in Liu et\u00a0al. . To be tWe gave an overview of importance weighting, a simple tool against dataset shift. Importance weighting needs a clear definition of the targeted population and access to a diverse training dataset. When this is not possible, distributionally robust optimization may be a promising alternative, although it is a more recent approach and more difficult to implement. Despite much work and progress, dataset shift remains a difficult problem. Characterizing its impact and the effectiveness of existing solutions for biomarker discovery will be important for machine-learning models to become more reliable in healthcare applications.We conclude with the following recommendations:Be aware of the dataset shift problem and the difficulty of out-of-dataset generalization. Do not treat cross-validation scores on 1 dataset as a guarantee that a model will perform well on clinical data.Collect diverse, representative data.Use powerful machine-learning models and large datasets.Consider using importance weighting to correct biases in the data collection, especially if the learning model may be over-constrained .Look for associations between prediction performance and demographic variables in the validation set to detect potential generalization or fairness issues.Do not remove \u201cconfounding signal\" in a predictive setting.These recommendations should help in designing fair biomarkers and their efficient application on new cohorts.The source files used to create this publication can be found in the suppording data in the GigaScience Database . They arAUC: area under the curve; CT: computed tomographic; FEV1: Forced expiratory volume in 1 second; RBF: Radial Basis Function; SVM: Support Vector Machines.The authors declare that they have no competing interests.All authors participated in conception, literature search, data interpretation, and editing the manuscript. J.D. wrote the software and drafted the manuscript.giab055_GIGA-D-21-00081_Original_SubmissionClick here for additional data file.giab055_GIGA-D-21-00081_Revision_1Click here for additional data file.giab055_GIGA-D-21-00081_Revision_2Click here for additional data file.giab055_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giab055_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giab055_Reviewer_1_Report_Original_SubmissionGuray Erus -- 5/10/2021 ReviewedClick here for additional data file.giab055_Reviewer_1_Report_Revision_1Guray Erus -- 7/13/2021 ReviewedClick here for additional data file.giab055_Reviewer_2_Report_Original_SubmissionSpencer Thomas -- 5/13/2021 ReviewedClick here for additional data file.giab055_Reviewer_3_Report_Original_SubmissionEnzo Ferrante -- 5/15/2021 ReviewedClick here for additional data file.giab055_Reviewer_3_Report_Revision_1Enzo Ferrante -- 7/18/2021 ReviewedClick here for additional data file."}
+{"text": "KOREF is the Korean reference genome, which was constructed with various sequencingtechnologies including long reads, short reads, and optical mapping methods. It is alsothe first East Asian multiomic reference genome accompanied by extensive clinicalinformation, time-series and multiomic data, and parental sequencing data. However, itwas still not a chromosome-scale reference. Here, we updated the previous KOREF assemblyto a new chromosome-level haploid assembly of KOREF, KOREF_S1v2.1. Oxford NanoporeTechnologies (ONT) PromethION, Pacific Biosciences HiFi-CCS, and Hi-C technology wereused to build the most accurate East Asian reference assembled so far.We produced 705 Gb ONT reads and 114 Gb Pacific Biosciences HiFi reads, and correctedONT reads by Pacific Biosciences reads. The corrected ultra-long reads reached higheraccuracy of 1.4% base errors than the previous KOREF_S1v1.0, which was mainly built withshort reads. KOREF has parental genome information, and we successfully phased it usinga trio-binning method, acquiring a near-complete haploid-assembly. The final assemblyresulted in total length of 2.9 Gb with an N50 of 150 Mb, and the longest scaffoldcovered 97.3% of GRCh38\u2019s chromosome 2. In addition, the final assembly showed high baseaccuracy, with <0.01% base errors.KOREF_S1v2.1 is the first chromosome-scale haploid assembly of the Korean referencegenome with high contiguity and accuracy. Our study provides useful resources of theKorean reference genome and demonstrates a new strategy of hybrid assembly that combinesONT's PromethION and PacBio's HiFi-CCS. The second one is KOREF_C, which is a consensuspopulation reference that includes variome information of Koreans. KOREF was initiated bythe Korean Ministry of Science and Technology in 2006 to generate a national genome andvariome references, and currently it is jointly developed by the Genome Research Foundation,National Standard Reference Research Center, and the Korean Genomics Center at UNIST . The first version of KOREF_S1, KOREF_S1v1.0,had a clear limitation of short reads and long-distance mapping-based approaches thatresulted in a relatively low-quality assembly compared to the current GRCh38. We used OxfordNanopore Technologies (ONT) PromethION and Pacific Biosciences (PacBio) HiFi sequencers toupgrade KOREF_S1 by using a publicly available KOREF cell line.Since the human genome reference was released in 2003, it has been updated and recently waspatched in 2019 (GRCh38.p13) by the Genome Reference Consortium (GRC) . The firRRID:SCR_017987).Base-calling the raw signals was performed using Guppy v4.0.11 with the Flip-flop hacmodel.Sample preparation steps were followed as in the previous study , 5. HumaRRID:SCR_017990)(PacBio) sequencing platform.Genomic DNA from KOREF blood samples was extracted using QIAGEN Blood & Cell CultureDNA Kit . A total of 5\u00a0\u03bcg of each sample was used as input for librarypreparation. The SMRTbell library was constructed using the SMRTbell\u00ae Express TemplatePreparation Kit (101\u2013357-000). Using the BluePippin Size selection system we removed thesmall fragments for a large-insert library. After sequencing primer v4 was annealed to theSMRTbell template, DNA polymerase was bound to the complex (Sequel Binding kit 2.0). Wepurified the complex using AMPure Purification to remove excess primer and polymeraseprior to sequencing. The SMRTbell library was sequenced using single-molecule real-time(SMRT) cells (PacBio) using Sequel Sequencing Kit v2.1 and 10 hr movies were captured foreach SMRT Cell 1M\u00a0v2 using the Sequel II .KOREF cell lines and blood samples were prepared for the construction of Hi-C libraries.Briefly, chromatin from cross-linked cells was solubilized and then digested usingrestriction enzymes MboI or Arima's multiple enzymes (GATC and GANTC). The digested endswere labelled using a biotinylated nucleotide, and ends were ligated to create ligationproducts. Ligation products were purified, fragmented, and selected by size using AMPureXP Beads. Illumina-compatible sequencing libraries were constructed on end repair,dA-tailing, and adaptor ligation using a modified workflow of the Hyper Prep kit . The bead-bound libraries were amplified and purified using AMPure XPbeads and sequenced using Illumina NovaSeq platform with a read length of 150\u00a0bp byNovogene .\u00a0Short paired-end raw reads using Illumina HiSeq 2000 platformwere acquired from a previous study, accession No. SRR2204706.For generating parental sequencing reads, we prepared samples from both of KOREF_S1\u2019sparents. DNA was extracted from a sample of the donor's blood using DNAeasy Blood &Tissue Kit from QIAGEN according to the manufacturer's instructions. The quality andconcentration of the extracted DNA were evaluated using NanoDrop\u2122 One/OneC UV-VisSpectrophotometer (Thermo Scientific). Library construction and whole-genome sequencingwere performed by Illumina HiSeq platform  with 100-bp paired-endsequencing.RRID:SCR_016967)[RRID:SCR_011848)[RRID:SCR_016968)[The sequenced long- and short-read data underwent preprocessing steps such as adaptertrimming, quality trimming, and error correction. For the long reads, adapter trimming wasperformed using Porechop v0.2.4 [RRID:SCR_017642)[To obtain more accurate and longer haplotype-resolved reads from ONT PromethIONsequencing, we applied a trio-binning with KOREF's parental sequencing data and errorcorrection with PacBio HiFi sequencing data. The whole procedure is described inFig.\u00a0_015880) with the_017642). We acquRRID:SCR_017225)and Flye assembler v2.8.1 [Contig assembly was processed using wtdbg2 v2.5  . For theRRID:SCR_017226)[RRID:SCR_021172)[RRID:SCR_014731)[To construct scaffolds with a chromosome scale, we conducted scaffolding using PromethIONreads and Hi-C data. To scaffold contigs using PromethION reads, LINKS v1.8.7  was used_017226) was appl_014731) with KORRRID:SCR_018550)[To assess base errors, we constructed high-confidence regions of KOREF_S1 v1 againstchromosome sequences of GRCh38.p13. The procedure was as in the study by Li et al. . We alig_018550). Alignmek-mers.To assess base errors of long reads and genome assemblies, we compared them to theKOREF_S1v1.0 assembly using the assembly_assess program from Pomoxis v0.3.4 . In addiRRID:SCR_001004)[RRID:SCR_015008)[To identify protein-coding genes on the KOREF_S1v2.1 genome, we performed a liftover witha gene annotation from GENCODE 38. The liftover was processed using Liftoff v1.6.1 . The res_001004). To asse_015008) was perfWe obtained 235\u00d7 coverage (705 Gb) of long reads from 12 ONT PromethION flow-cells and38\u00d7 coverage (114 Gb) of long reads from 6 PacBio HiFi cells . We alsoWe extended the contigs to chromosome-scale scaffolds using 76.5 Gb of PromethION reads(Flow-cell No. 2) and 884 Gb of Hi-C data (294\u00d7 sequencing depth). Scaffolds from themitochondrial genome were excluded by using KOREF's mitochondrial DNA sequence from theprevious study [We annotated genes in KOREF_S1v2.1 by integrating a liftover of gene annotations fromGENCODE release 38  andhomoUsing the Merqury program for quality assessment, we estimated quality value (QV) scoresof Q43.88 for the paternal assembly and Q44.49 for the maternal assembly. The finalassembly showed QV score of Q43.88, indicating >99.99%\u00a0accuracy Table , and it RRID:SCR_018171)[To identify missing regions on KOREF_S1v2.1, we made an alignment plot of KOREF againstCHM13 v1.1 using Mummer v4.0.0beta2  and Dot _018171). We founRRID:SCR_021069)[From a pilot study of KOREF_S1\u2019s PacBio HiFi sequencing by Hifiasm v0.15.5-r352  and contiguity from multiple sequencingtechnologies including ONT, PacBio, Illumina, and Hi-C. Furthermore, the new KOREF assemblywas phased with parental sequencing data. To generate ultra-long and highly accurate reads,we corrected the ONT reads using PacBio HiFi reads. Most genomic regions were covered by thecorrected reads, but some highly competitive regions including the telomere and centromerewere not covered. They had remaining gaps with unknown length. Especially on the Ychromosome, we found more gaps and less contiguity than other chromosomes. The genomicsequences of the X and Y chromosomes have highly similar regions and they probably make itdifficult to phase genomic sequences on sex chromosomes.de novo assembly pipelines, such as Hifiasm [Recently, new  Hifiasm  and HiCa Hifiasm , have be Hifiasm , and it In conclusion, we upgraded a high-quality Korean reference genome, KOREF. Our studyprovides useful resources of the Korean reference genome and demonstrates a new strategy ofhybrid assembly that combines use of ONT's PromethION and PacBio's HiFi-CCS.PRJNA735947. The version described in this article isversion JAHRJT000000000. Raw DNA and RNA sequence reads for KOREF and KPGP have beensubmitted to the NCBI SRA database  (1425157253)(2.220037.01). This work was also supported by the Establishment of DemonstrationInfrastructure for Regulation-Free Special Zones funded by the Ministry of SMEs and Startups (1425157301) (2.220036.01). This work was also supported by the Ministry ofTrade, Industry & Energy  under Industrial Technology Innovation Programs and Industrial Strategic Technology Development Program.J.B. supervised and coordinated the national Korean reference genome project and PersonalGenome Project Korea. J.B. conceived and designed the reference genome project. H.K.performed the analyses and assembly. SJ contributed to the analysis and editing themanuscript. YK and CK performed experiments. Jihun B. contributed to bioinformatic analyses.H.K. and J.B. wrote the manuscript."}
+{"text": "Pachyrhynchus sulphureomaculatus genome is the first chromosome scale genome for the hyperdiverse Phytophaga lineage and currently the largest insect genome assembled to this scale. The genome is significantly larger than those of other weevils, and this increase in size is caused by repetitive elements. Our results also indicate that, among beetles, there are instances of long-lasting (>200 Ma) localization of genes to a particular chromosome with few translocation events. While some chromosomes have a paucity of translocations, intra-chromosomal synteny was almost absent, with gene order thoroughly shuffled along a chromosome. This large amount of reshuffling within chromosomes with few inter-chromosomal events contrasts with patterns seen in mammals in which the chromosomes tend to exchange larger blocks of material more readily. To place our findings in an evolutionary context, we compared syntenic patterns across Insecta in a phylogenetic framework. For the first time, we find that synteny decays at an exponential rate relative to phylogenetic distance. Additionally, there are significant differences in decay rates between insect orders, this pattern was not driven by Lepidoptera alone which has a substantially different rate.Patterns of genomic architecture across insects remain largely undocumented or decoupled from a broader phylogenetic context. For instance, it is unknown whether translocation rates differ between insect orders. We address broad scale patterns of genome architecture across Insecta by examining synteny in a phylogenetic framework from open-source insect genomes. To accomplish this, we add a chromosome level genome to a crucial lineage, Coleoptera. Our assembly of the  Pachyrhynchus sulphureomaculatus genome is the first chromosome scale genome for the hyperdiverse Phytophaga lineage and currently the largest insect genome assembled to this scale. We are the first to identify in beetles that genes stay localized on chromosomes for hundreds of millions of years, while their order along chromosomes gets completely shuffled over time. We are also the first to empirically demonstrate that synteny decay rates different significantly between insect orders and that this pattern in not driven solely by Lepidoptera (moths and butterflies), which has a substantially different rate.Patterns of genomic architecture across insects remain largely undocumented or decoupled from a broader evolutionary context. For instance, it is unknown whether rates of gene order decay differ between insect orders. We address broad scale patterns of genome architecture across Insecta by examining synteny (shared gene order) in a phylogenetic framework from open-source insect genomes . To accomplish this, we add a chromosome level genome to a crucial lineage, Coleoptera (beetles). Our assembly of the Easter Egg Weevil  Pachyrhynchus sulphureomaculatus Schultze, 1922 , Bombyx mori [GCA_000151625.1], Clogmia albipunctata [clogmia.6], Culex quinquefasciatus [CpipJ3], and Rhodnius prolixus [Rhodnius_prolixus-3.0.3] as well as several others see Table A in dnazoo.org). The assemblies were based on the whole genome sequencing data from [Pachyrhynchus sulphureomaculatus to the other Coleoptera genomes. Following, we calculated the number of loci found in P. sulphureomaculatus chromosomes and those in the other Coleoptera and calculated the percent conserved within a chromosome. To visualize the shared synteny, we plotted the different pairs using the R package RIdeogram [To examine the gene synteny between other Coleoptera genomes, we downloaded chromosome-level genomes from NCBI or supplied form the journal or authors website . Instead, we sampled individual species across the phylogenetic breadth of the genus. In addition, we also gathered genomes from the literature. (See Table A in https://m.ensembl.org/) using a custom script . We computed the total GOC scores for all pairwise comparisons among the 143 taxa. Next, to consider the effect of the phylogenetic relationships, we reconstructed the relationship among our taxa using the BUSCO gene sets\u2019 amino acids. We used custom scripts to identify a 50% complete matrix and used mafft with 1000 iterations and the \u201clocalpair\u201d settings to align the sequences. Next, we used trimAI [ape v.5.4 \u2018makeChronosCalib\u2019 function [Next, we investigated whether the observed synteny was distinctive within Coleoptera relative to other orders of insects, such as Lepidoptera, in which high levels of synteny between taxa have been recorded ,28. We ud trimAI  with \u201caud trimAI  with thed trimAI ,104 usinfunction   violate the independence assumptions of ordinary least squares regression models, we will use a permutational approach to evaluate the significance of the regression models we fit. This approach is consistent with widespread methods in ecology and evolutionary biology that perform regression analyses with distance matrices ,47.We implement this permutational approach using a custom algorithm in the R programming language . We use We are forced to take a permutational approach because synteny can only be quantified in a pairwise fashion, obviating other methods such as independent contrasts (Harmon & Glor 2010). We use a simple permutation algorithm that does not take into account phylogenetic branch lengths   best fits the data using a permutational estimate of the F statistic  and its deviation from the null. We use the F statistic instead of AIC or BIC because these information theoretic and Bayesian model comparison criteria have been shown to perform poorly in distance matrix regression settings . SimilarS1 Anno ResultsP. sulphureomaculatus assembly. A: Table_A.gff: the gff file. B: Table_B.faa: the faa file. C: Table_C.tsv: the gene model scores file. D: Table_D.trna: the trna seqs.The faa, gff, and model scores results files as well as trna sequences of (ZIP)Click here for additional data file.S1 BUSCO Analyses ResultsContains: Table_A.xlsx, Table_B.csv. A: Table_A.xlsx: lists the BUSCO results from the different transcriptome assemblies by method used. B: Table_B.csv: lists the BUSCO results for the different versions of BUSCO insect e.g. 2, V4 and the associate percentages for single copy complete, complete and duplicated, fragmented and missing genes.(ZIP)Click here for additional data file.S1 FigScaffolds included are from the unfiltered assembly. Taxonomic annotation provided via blastn alignment to the NCBI nt database.(PDF)Click here for additional data file.S2 FigY-axis is the percent of BUSCO genes, X-axis labels are the genus names. The abbreviations in the legend are: D = duplicated, F = fragmented, M = missing and S = single.(PDF)Click here for additional data file.S1 Insecta Trees and CalibrationsContains. A: Tree_A.newick: chronogram used in (ZIP)Click here for additional data file.S1 P sulph HiC heatmap all chroms & scaffolds(PDF)Click here for additional data file.S1 P79 coI.fasta(FASTA)Click here for additional data file.S1 Raw Data ReportsContains: Table_A.xlsx, Table_B.docx. A: Table_A.xlsx: Raw data report for PacBio sequences. B: Table_B.docx: Summary of Hi-C reads mapped.(ZIP)Click here for additional data file.S1 RepeatMasker ResultsP. sulphureomaculatus.Contains the RepeatMasker result tables: Table_A.xlsx, Table_B.docx. A: Table_A.xlsx: The NCBI accession numbers used in repeatmasker analyses. B: Table_B.docx: Table of results from RepeatMasker for (ZIP)Click here for additional data file.S1 ScriptsContains: Script_A.sh, Script_B.sh. A: Script_A.sh: script to create scaffold ordered BUSCOs. B: Script_B.sh: uses results from Script_B.sh to compute synteny scores.(ZIP)Click here for additional data file.S1 Synteny AnalysesContains: A: Table_A.txt: the GOC pairwise distances matrix. B: Doc_A.pdf: instruction on how to preform synteny analyses. C: \u201csynteny analyses/synteny/data/Insecta_matrix_matched_to_phylo_mod3.txt\u201d: GOC pairwise distances matrix. D: \u201csynteny analyses/synteny/data/rescaled_tree_insecta6.csv\u201d: pairwise phylogenetic distance matrix. E: \u201csynteny analyses/synteny/R/syntPermAOV\u201d: R function to perform correlation of GOC distance and phylogenetic distance by insect order. F: \u201cRead_me_Example_by A. Rominger synteny_perm.pdf\u201d step by step instruction on how synteny correlations were performed.(ZIP)Click here for additional data file."}
+{"text": "Objective: Recently, abundant number of studies have revealed many functions of circular RNAs in multiple diseases, however, the role of circular RNA in the rupture of human intracranial aneurysm is still unknown. This study aims to explore the potential functions of circular RNA in the rupture of human intracranial aneurysms.Methods: The differentially expressed circular RNAs between un-ruptured intracranial aneurysms (n = 5) and ruptured intracranial aneurysms (n = 5) were analyzed with the Arraystar human circRNAs microarray. Quantitative real-time PCR (qPCR) was used to verify the results of the circRNA microarray. The role of circular RNA in intracranial aneurysm rupture was assessed in vitro. MTT assay, CCK-8 assay, Caspase3/7 assay, assay of cell apoptosis and Celigo wound healing was conducted to evaluate the relationship between circular RNA and the rupture of human intracranial aneurysms.Results: A total of 13,175 circRNA genes were detected. Among them 63 circRNAs upregulated and 54 circRNAs downregulated significantly in ruptured intracranial aneurysms compared with un-ruptured intracranial aneurysms (p < 0.05 Fold Change > 1.5). Five upregulated circRNAs were selected for further study . The results of qPCR showed only hsa_circ_0005505 significantly upregulated (p < 0.05). The expression of hsa_circ_0005505 was higher in ruptured intracranial aneurysm tissues. And our in vitro data showed that hsa_circRNA_005505 promotes the proliferation, migration and suppresses the apoptosis of vascular smooth muscle cell.Conclusion: This study revealed an important role of hsa_circ_0005505 in the proliferation, migration and apoptosis of vascular smooth muscle cell, and indicated that hsa_circ_0005505 may associate with the pathological process of intracranial aneurysms. Intracranial aneurysm (IA) is a cerebrovascular disorder characterized by a regional ballooning of intracranial arteries. The incidence of intracranial aneurysm in general population is 1.8\u20138.4% . RuptureCircle RNAs (termed circRNAs) are one type of non-coding RNAs (ncRNAs) that are formed covalently closed loop structures and widely expressed in human cells . In recein vitro.In this study we acquired differentially expressed circRNAs between ruptured intracranial aneurysm (RIA) tissues and un-ruptured intracranial aneurysm (UIA) tissues through circRNA microarray. We further detected hsa_circ_0005505 and assessed the role of hsa_circ_0005505 in intracranial aneurysm rupture A total of 10 pairs of ruptured and un-ruptured intracranial aneurysm tissues were obtained from surgical resection during the aneurysm clipping surgery in Beijing Tiantan Hospital. The collection of human specimens was approved by the Medical Ethics Committee of Beijing Tiantan Hospital, Capital Medical University. Written informed consent was obtained from each patient according to the policies of the committee. All specimens were stored in liquid nitrogen, and five pairs of samples were used to conduct circRNA microarray analysis, other samples were used to perform qPCR.We extracted RNA with the use of Trizol Reagent  from five paired ruptured and un-ruptured intracranial aneurysms according to the manufacturer\u2019s instructions. The quality and concentration of RNA was tested by the NanoDrop ND- 1000  .Sample labeling and array hybridization were performed according to the manufacturer\u2019s protocol . Briefly, total RNAs were digested with Rnase R  to remove linear RNAs and rich circular RNAs. Then, the enriched circular RNAs were amplified and transcribed into fluorescent cRNA utilizing a random priming method . The labeled cRNAs  were purAgilent Feature Extraction software (version 11.0.1.1) was used to analyze acquired array images. Quantile normalization and subsequent data processing were performed using the R software limma package. Differentially expressed circRNAs with statistical significance between two groups were identified through Volcano Plot filtering. Differentially expressed circRNAs between two samples were identified through Fold Change filtering. Hierarchical Clustering was performed to show the distinguishable circRNAs expression pattern among samples.http://circrna.org). Primers were produced by RiboBio   was used to purify the circRNAs again. The relevant cDNAs were composed  and stored in \u221220\u00b0C. QuantStudio5 Real-time PCR System  was used to perform qPCR. The sequence of circRNA results was acquired from the database \u201ccircBase\u201d  . Becausehttp://gb.whu.edu.cn/CSCD/) was used to recognize miRNAs binding on our target circRNA. Three algorithms including Targetscan . The p value after adjustment represents the significance of GO terms. We also perform KEGG pathway analysis of parental genes of circRNA-binding miRNAs, in order to reveal the biological or pathological processes which circRNAs participate in. The p value after adjustment represents the significance of pathway correlations as well.We assumed that our target circRNA may have molecular interactions with these genes, or our target circRNA may regulate biological functions through these genes. GO analysis on genes correlated with these miRNAs was performed by DAVID (Human brain vascular smooth muscle cells (HBVSMCs) were obtained from Bnbio . The cells were cultured in Smooth Muscle Cell Medium  containing 2% fetal bovine serum , 5\u00a0ml of smooth muscle cell growth supplement , and 5\u00a0ml of penicillin/streptomycin solution  at 37\u00b0C in an incubator of 5% CO2.The hsa_circ_0005505 specific shRNA, their relevant lentiviruses  and negative control lentivirus were obtained from Shanghai Genechem Co., LTD. . HBVSMCs were transduced with individual types of lentivirus at a multiplicity of infection (MOI) of 50 and the ideal value of infection efficiency was 80%.3 per well) were plate in 96-well plates and treated with 20\u00a0\u03bcl of 5\u00a0mg/ml MTT solution, then the spectrophotometrically at 490\u00a0nm was analyzed by automatic microplate reader . MTT assay was performed triplicate in our research.MTT assay was used to measure the viability of HBVSMCs according to the manufacturer\u2019s instructions . HBVSMCs (2 \u00d7 103 per well) were plate in 96-well plates and treated with 10\u00a0\u03bcl of CCK-8 solution, then the spectrophotometrically at 450\u00a0nm was analyzed by automatic microplate reader . CCK-8 assay was performed triplicate.CCK-8 assay was utilized to test the cell viability in order to verify the effect of the target gene on cell proliferation. The assay was performed according to manufacturer\u2019s instructions . HBVSMCs . The apoptosis of cells was analyzed after 3\u00a0days since infection. We also analyzed cell apoptosis by using the Annexin V-APC Apoptosis Detection Kit  according to the manufacturer\u2019s instruction. HBVSMCs were stained with APC and then analyzed by fluorescence-activated cell sorting using FACScan  after 5\u00a0days since infection. Both apoptosis analyses were performed three times.4 per well). After 24\u00a0h incubation, parallel wounds with similar width were made in each well by 96 Wounding Replicator . Wound closure level was monitored by Celigo  in 0, 8, and 24\u00a0h after wounded and lastly analyze the migration rate. Wound-healing assay was performed triplicate.The wound -healing assay was used to evaluate the migration rate of cells. The transfected HBVSMCs were seeded into 96 well plates . The protein content was assessed by using a BCA protein assay kit . Protein lysates (40\u00a0\u03bcg/sample) were separated on 10% SDS-PAGE and transferred to polyvinylidene difluoride membranes . Then membranes were probed with following primary antibodies: anti-YAP1 , anti-MMP2 , anti-MMP9 , anti-OPN  and anti-GAPDH , then incubated overnight at 4\u00b0C. After that, horseradish peroxidase-conjugated goat anti-mouse immunoglobulin G  and horseradish peroxidase-conjugated goat anti-rabbit immunoglobulin G  were used to detect protein band at room temperature for 30\u00a0min. Signals were detected using an enhanced chemiluminescence kit  according to manufacturers\u2019 instruction. The band density was quantified with the AlphaEaseFC software. GAPDH served as the loading control. Each experiment was performed at least three times.t-test and used to identify the differentially expressed circRNAs in the sample of intracranial aneurysms. CircRNAs were selected as differentially expressed with a p < 0.05 and a fold-change > 1.5, which means they were statistically significant. The significance of qRT-PCR was assessed by Student\u2019s t-test and p < 0.05 was considered statistically significant, it was analyzed by GraphPad Prism 8.4.0 . Other statistical methods such as chi-squared test, Wilcoxon signed-rank test and Mann Whitney U test were also performed. All statistical analyses were performed by SPSS 19.0 .The fold-changes were estimated by unpaired Student\u2019s p < 0.05)   and 93 o < 0.05) . However < 0.05) . The var < 0.05) . Five upp < 0.05) (The circRNA microarray profiling expression results were verified through quantitative reverse transcription PCR (qPCR) in five paired UIA and RIA samples. All these five selected circRNAs were upregulated which in agreement with the microarray results, but only hsa_circ_0005505 and hsa_circ_0043001 upregulated significantly ( < 0.05) . The par < 0.05) , which phttps://www.ncbi.nlm.nih.gov/orffinde) (https://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi) showed that, the ORF of hsa_circ_0005505 may encode PKC  _like super family . Besides encoding proteins, the other potential function of circRNA is microRNA (miRNA) sponges. Through CSCD, miRNAs binding on hsa_circ_0005505 have been found (in situ hybridization (FISH) against hsa_circ_0005505 showed the predominant cytoplasmic distribution of hsa_circ_0005505 (The hsa_circ_0005505 (chr12:66597490-66622150) is partly derived from exon 5\u201311 in human endogenous IRAK3. The genomic sequence of hsa_circ_0005505 is 24660nt and the spliced length is 754nt. To explore the potential functions of hsa_circ_0005505, we found hsa_circ_0005505 contain 14 open reading frames (ORFs) through ORFfinder (rffinde) , but onlen found . Also, w_0005505 . We also_0005505 .Based on the hsa_circ_0005505 predicted targeting miRNAs, the circRNA-miRNA-mRNA network has been built . Then GOThe pictures of infected HBVSMCs were shown in The development of high-throughput sequencing, gene chip technology and bioinformatics over the last decade have largely improved our knowledge on circRNA. It is becoming increasingly clear that circRNAs play a crucial role in the pathological process of many kinds of cancers, such as colorectal cancer , breast Research about the role of circRNAs in the formation and rupture of intracranial aneurysm is still scarce. However, several circRNAs have been found to be related with VSMCs. Besides function as miRNA sponges to control gene transcription, circRNAs\u2019 ability of encoding peptides or proteins also have been studied . We haveOur study also has some limitations. Firstly, the amount of our tissue sample is small. Our results need more intracranial aneurysm samples to testify. Second, due to the rare of aneurysm samples, some of our samples were stored in liquid nitrogen for several weeks, perhaps this would affect the amount of circRNAs. Lastly, the lack of studies about circRNAs in aneurysm make us can\u2019t compare our results with others in order to improve our methods.The above findings revealed an important role of hsa_circ_0005505 in the proliferation, migration and apoptosis of VSMCs and may take part in the phenotype modulation of VSMCs, indicating that hsa_circ_0005505 may associated with the pathological process of intracranial aneurysms."}
+{"text": "Circular RNAs (circRNAs) are powerful factors in regulating various cancer behaviors. It has been manifested in previous researches that circular RNA hsa_circ_0005909 (circ_0005909) exhibits a regulatory function in osteosarcoma. However, there are no other studies on whether circ_0005909 displays potential functions on the progression of non-small-cell lung cancer (NSCLC).  RT-PCR was applied to examine the expression of circ_0005909 in NSCLC. To study the specific behaviors of NSCLC cells after circ_0005909 knockdown, cell counting kit-8 (CCK-8) assays, colony formation assays, Transwell assays, and xenograft tumor model assays were conducted. Bioinformatics and luciferase reporter assays were employed to study the association among circ_0005909, miRNA-338-3p, and SOX4.  In this research, our group firstly showed that circ_0005909 expressions were distinctly increased in NSCLC specimens and cell lines. Clinical studies revealed that high circ_0005909 expressions were associated with poor prognosis of NSCLC patients. Functionally, knockdown of circ_0005909 was observed to suppress the proliferation, metastasis, and drug resistance of NSCLC cells. In the terms of mechanism, circ_0005909 could act as a sponge of miRNA-338-3p, and miRNA-338-3p could target SOX4. In addition, miRNA-338-3p inhibitors reversed the suppressor ability of circ_0005909 silence on NSCLC behaviors.  circ_0005909 promoted the progression of NSCLC via the modulation of the miRNA-338-3p/SOX4 axis, which may be a therapeutic target for NSCLC. As one of the most frequent malignancies in the world, lung cancer is rapidly becoming the main cause of tumor-related death nowadays , 2. Non-Circular RNAs (circRNAs), characterized by closed loop structures without 5\u2032 caps and 3\u2032 poly(A) tails, are a class of noncoding RNAs . circRNACircular RNA hsa_circ_0005909 (circ_0005909), a newly identified circRNA with a length of 371 nucleotides, originated from the reverse splicing of XPR1 mRNAs. Recently, the distinct overexpression of circ_0005909 was reported in osteosarcoma, and its oncogenic roles were also demonstrated in cellular experiments , 14. HowA total of 102 paired NSCLC specimens and the matched noncancer specimens were obtained from NSCLC patients undergoing surgery at the Weifang People's Hospital between July 2013 and March 2016. Based on histopathological evaluation, patients with NSCLC were diagnosed. The protocols used in this study were approved by the Ethical Review Committees of Weifang People's Hospital, and written informed consent was provided from all patients.2 in a humidified incubator.Human NSCLC cell lines  and normal human bronchial epithelial cells (NHBE) were provided by Jihe Technology . These cells were cultured in DMEM or RPMI 1640 medium , containing 10% fetal bovine serum  at 37\u00b0C with 5% COFor the dysregulation of various factors, small hairpin RNA of circ_0005909 (sh-circ_0005909), miRNA-338-3p inhibitors (5\u2032-GCAAAAAUUAGUGUGCGCCACC-3\u2032), miRNA-338-3p mimics (5\u2032-UUUGAGCAGCACUCAUUUUUGC-3\u2032), and their negative controls (5\u2032-CAGUACUUUCAGUGCCAUCACAC-3\u2032) were provided by Hewu Company . To increase circ_0005909 levels, full length circ_0005909 was cloned into a modified LV003 lentiviral vector . An empty vector served as a control. Based on the product guide, Lipofectamine 2000  was applied for the transient transfection into A549 and H460 cells. Cells were harvested at 48\u2009h posttransfection.To explore the potential networks among circRNAs, miRNAs, and target mRNAs, CircInteractome was applied to predict miRNA-338-3p binding sites to the circ_0005909 and TargetScan was applied for the prediction of the potential miRNA-338-3p binding sites to 3\u2032-UTR of SOX4.\u03bcl. The relative levels of lncRNAs and mRNAs were determined by RT-PCR, which was carried out by the use of the ABI Power SYBR Green PCR Master Mix . U6 and GAPDH were applied as internal controls. The relative expressions of the target genes were assessed via 2\u2212\u0394\u0394Ct methods. All primer sequences are listed in For the collection of total RNAs, the TRIzol Reagent  was used based on product guides. PrimeScript RT Reagent Kit  was applied to synthesize complementary DNA. By the use of a PrimeScript\u2122 RT Reagent Kit , one microgram of total RNA was reverse transcribed in a final volume of 25\u2009\u03bcg) were incubated with or without 4\u2009U\u00b7\u03bcg\u22121 of RNase R . After incubation at 37\u00b0C for 20\u2009min, the RNeasy MinElute Cleaning Kit  was used to purify the collected RNAs. Finally, RT-PCR assessed the expressions of RNAs.Total RNAs  and stained with 0.1% crystal violet . Finally, the collected cells were counted manually.Cellular proliferation was also determined using EdU assays. A549 and H460 were seeded in 48-well plates, followed by EdU medium  which was used to incubate the above cells. The EdU staining reagents were applied to color cells. After washing in PBS three times, A549 and H460 cells were cultured in DAPI solution. The images were obtained by inverted fluorescence microscopy.4transfected cells were seeded. To the lower chamber, our group added medium with 20% FBS. Matrigel  was used to precoat the membrane. On the top chamber, 1.0 \u00d7 105 cells were added. After one day, the chamber was fixed with 4% paraformaldehyde and stained using 0.05% crystal violet . The invasive cells were imaged and counted.On the upper Transwell chambers,5 \u00d7 10The 3\u2032-UTR of SOX4 was amplified and cloned downstream of the pGL3/Luciferase vector (SOX-Wt). Then, the mutant 3\u2032-UTR of SOX4 was amplified and cloned downstream of the pGL3/Luciferase vector (SOX-Mut). circ_0005909 wild-type (circ_0005909_Wt) and mutant (circ_0005909_Mut) reporter vectors were constructed and inserted into pGL3. A549 and H460 cells were cotransfected into the luciferase reporter vectors and miR-NC or miR-338-3p. After one day, cells were lysed. Dual-Luciferase Reporter System  was applied to examine relative luciferase activities.Based on the manufacturer's directions, the PARIS kit  was applied to perform nuclear and cytosolic fraction separation.The commercially synthesized biotin-labeled miRNA-338-3p was purchased from Chenhua Biology  and transfected into A549 and H460 cells for 48\u2009h. Then, the Dynabeads M-290 Streptavidin  was applied to incubate the cell lysate based on the product guide. For the purification of the interacted RNA complex, TRIzol was used. The levels of circ_0005909 were determined using RT-PCR. Three independent experiments were performed.To extract total protein from the NSCLC tissues and cell lines, RIPA buffer  was used. A BCA Protein Assay Kit (Beyotime) was used for the quantification of protein concentrations. The specific procedures of the western blot were described in the previous study. The primary antibodies against SOX4  was used in this experiment. GAPDH was used as a control.n = 6/group, females, 5-6 weeks old; Saiye Biology, Suzhou, Jiangsu, China). The Animal Care and Use Committee of Weifang People's Hospital has approved the animal experiments. At the indicated times, tumor volumes were examined, and the following equation was used for the calculation: volume = (length \u00d7 width2)/2. After seven weeks, Mice were killed, and the collected tumors were weighed and harvested for subsequent study.A549 cells were transfected with sh-circ_0005909 or sh-NC. Cells were injected subcutaneously into BALB/c nude mice  followed by the LSD post hoc test was conducted. The differences of categorical factors were determined using the Chi-squared test. The Kaplan-Meier method and the log-rank test were applied for the survival assays. The Cox regression model was conducted for univariate and multivariate assays. Values of p < 0.05 were considered significant.All data were analyzed using the SPSS 15.0 software. To explore the differences between two groups (or >two groups), Student's Firstly, we performed RT-PCR to examine whether circ_0005909 was abnormally expressed in NSCLC. As presented in p = 0.023) and distant metastasis (p = 0.028)  stage (= 0.028) . For furTo study the potential functions of circ_0005909 in NSCLC behaviors, we downregulated the expressions of circ_0005909. RT-PCR assays demonstrated the transfection efficiency of sh-circ_0005909 in both A549 and H460 cells. However, the expression of XPR1 mRNA remained unchanged after the transfection of sh-circ_0005909 . The groMany researches have suggested that cytoplasmic circRNAs might regulate the expressions of various genes via functioning as miRNA sponges . We haveUsing an online tool (TargetScan), we observed that SOX4 may be a potential target of miRNA-338-3p . Based oThe identification of novel modulators in NSCLC progression was very important for the improvements of targeted therapies . Many stFor the exploration of mechanism assays, a large number of researches have demonstrated that circRNAs modulate tumor progression via serving as miRNA sponges , 24. In SOX4 is a member of the SOX (SRY-related HMG box) gene family consisting of transcription factors involved in differentiations of cells and organs, and the progression of various types of tumors , 29. ManIn sum, the current data depicted a circ_0005909/miRNA-338-3p/SOX4 axis in NSCLC and implied that circ_0005909 promoted NSCLC progression. Therefore, circ_0005909 was a candidate biomarker and therapeutic target for NSCLC patients."}
+{"text": "Circular RNAs (circRNAs) are emerging as important regulators in bone metabolism, which is mediated by microRNA (miRNA) sponges. However, it is not clear how circRNA regulates osteogenic differentiation of human bone marrow mesenchymal stem cells (hBM-MSCs).Therefore, based on the previous circRNA chip results, hsa_circ_0006766, which is differentially expressed in the osteogenic differentiation of hBM-MSCs, was screened out, and bioinformatics analysis was performed to predict potential target miRNAs. During osteogenic differentiation of hBM-MSCs, hsa_circ_0006766 and its target miRNAs  were detected by quantitative Real Time-PCR (qRT-PCR). Target gene prediction for the differentially expressed target miRNAs was performed, and target genes were validated by dual-luciferase reporter gene assay and qRT-PCR. It is shown that hsa_circ_0006766 was up-regulated and miR-4739 was down-regulated during osteogenic differentiation of hBM-MSCs.Moreover, the target gene Notch2 was predicted to be highly expressed during osteogenic differentiation. And dual-luciferase assay proved that Notch2 was the gene targeting to miR-4739. Taken together, our finding confirmed that hsa_circ_0006766 may act as a major regulatory part in osteogenic differentiation of hBM-MSCs via an hsa_circ_0006766\u2013miR-4739\u2013Notch2 regulatory axis. Accordingly, hsa_circ_0006766 may affect the development of osteoporosis and may thus become a therapeutic target. Osteoporosis (OP) is the most common metabolic bone-related disease in which bone density and bone quality are decreased, bone microstructure is destroyed, and bone fragility is increased, thereby prone to fracture. One of circRNA exhibits a circular structure, which cannot be degraded by RNA exonucleases, and has strong stability , can serWe aimed to confirm the role of hsa_circ_0006766 in the process of osteogenic differentiation of hBM-MSCs in this study. We assume that hsa_circ_0006766 may promote the osteogenic differentiation of hBM-MSCs through the hsa_circ_0006766\u2013miR-4739\u2013Notch2 axis, and therefore may become an effective biomarker for the prognosis and diagnosis of osteoporosis.hBM-MSCs  were cultured in human bone marrow mesenchymal stem cell-specific medium , and HEK-293\u00a0T cells  were cultured in Dulbecco\u2019s Modified Eagle\u2019s Medium-high glucose , which contains 10% FBS,1% 100\u00d7\u00a0Penicillin-Streptomycin Solution (Gibco) in an incubator.5 per well and further cultured in a 37\u00b0C constant temperature. When grown to almost 80%, the medium was changed to mesenchymal stem cell osteogenic differentiation solution (Caygen), and then changed once every 2\u20133\u00a0days.hBM-MSCs cultured to passage 5 were inoculated into 6-well plate with 2\u00a0\u00d7\u00a010Cells that underwent osteogenic differentiation for 14\u00a0days were stained with Alizarin Red. After the cells were fixed with 4% paraformaldehyde for 30\u00a0min, washed with PBS. Added with 1 ml of Alizarin Red S dye solution, stood at room temperature for 3\u20135\u00a0min. Using PBS to wash cells one more time, and mineralization was subsequently observed and imaged using an inverted microscope.TargetScan 7.2 and miRanda miRNA target gene prediction software were used to predict miRNA response elements (MREs) within hsa_circ_0006766. After target gene prediction, the miRNAs were classified by functional enrichment analysis based on the Gene Ontology (GO) database. The accession numbers of the target genes were used as clues for tracking. GO annotations were obtained from the UCSC Genome Browser. Next, miRNAs with significant differences in expression levels were categorized and classified. Kyoto Encyclopedia of Genes and Genomes (KEGG) mapping was performed basing on GO annotations, and statistical methods such as hypergeometric distribution tests were used to determine significantly enriched biological signaling or metabolic pathways. Through pathway analysis of differentially expressed genes, we identified enriched pathways.U6 and Human \u03b2-actin were selected as house-keeping genes for miRNAs and circRNAs or mRNAs, respectively. Each sample was run in triplicate. Relative quantitation was performed by comparing threshold cycle (CT) values. Finally, differences in target gene expression were analyzed using the 2\u2212\u0394\u0394CT relative quantification.The gene sequences were downloaded from the NCBI or miRbase databases; the associated primer sequences are shown in HEK293T cells were co-transfected with miR-4739 mimic/negative control and Notch2, Wnt1, or ZFP36L1 wild-type or mutant 3\u02b9-untranslated region (UTR) reporter plasmid. Using Dual Luciferase Reporter Gene Assay Kit  to measure the firefly and Renilla luciferase values based on the provided manual after 48\u00a0h of transfection. All assays were repeated no less than thrice.P value of less than 0.05 was regarded as significant.With the use of GraphPad Prism v7.0 statistical software, one-way analysis of variance was employed for multiple group testing, and the Mann\u2013Whitney test for non-parametric was used to analyze the differences between groups. In this study, we inspected the changes of hsa_circ_0006766 during the osteogenic differentiation of hBM-MSCs by inducing the osteogenic differentiation of it. Through bioinformatics, qRT-PCR and dual luciferase, we proved that hsa_circ_0006766 may promote the osteogenic differentiation of hBM-MSCs via hsa_circ_0006766\u2013miR-4739 -Notch2 axis .Firstly, in order to confirm the successful induction of hBM-MSCs, Alizarin Red staining was performed. Cell aggregates were observed on day 7 of osteogenic differentiation of hBM-MSCs. Brown calcified nodules were observed on day 14, and Alizarin Red staining revealed red calcified nodules, indicating a positive staining result . CompareP <\u00a00.01, In order to confirm that hsa_circ_0006766 is up-regulated during the osteogenic differentiation of hBM-MSCs, we used qRT-PCR to verify and constructed a circRNA-miRNA-mRNA network to find the downstream target genes of hsa_circ_0006766. qRT-PCR results showed that during 7\u00a0days of induction of osteogenic differentiation, hsa_circ_0006766 expression in hBM-MSCs significantly increased with time compared with that on day 0  , particuP <\u00a00.05, Notch2, ZFP36L1, and Wnt1, which are associated with bone metabolism and were predicted to be target genes of miR-4739  which was discovered to be significantly downregulated in osteogenic differentiation of hBM-MSCs. MiR-4739 was reported to be closely associated with bone metabolism and is involved in the process of regulating the osteogenic differentiation of hBM-MSCs. These rStudies have indicated that circRNA can affect the development of diseases by regulating signal transduction pathways ,26. ThroThe study had some shortcomings: the functions and mechanisms of hsa_circ_0006766 and the osteogenic differentiation of miR-4739 in hBM-MSCs were not determined by their overexpression or silencing. In addition, the expression of MAPK signaling pathway-related genes was not confirmed by qRT-PCR or western blot. Therefore, the specific mechanism of hsa_circ_0006766 in osteogenic differentiation of hBM-MSCs requires further investigation.In summary, we hypothesize that hsa_circ_0006766 regulates the MAPK signaling pathway through a hsa_circ_0006766\u2013miR-4739\u2013Notch2 axis to promote osteogenic differentiation of hBM-MSCs and osteogenesis, which may prevent OP. Overexpression of hsa_circ_0006766 or inhibition of miR-4739 can promote osteogenesis, suggesting new targets for the diagnosis, treatment, and prevention of OP."}
+{"text": "Tuber shape and specific gravity (dry matter) are important agronomic traits in potato processing and impact production costs, quality, and consistency of the final processed food products such as French fries and potato chips. In this study, linkage and QTL mapping were performed for these two traits to allow for the implementation of marker-assisted selection to facilitate breeding efforts in the russet market class. Two parents, Rio Grande Russet  and Premier Russet  and their 205 F1 progenies were initially phenotyped for tuber shape and specific gravity in field trials conducted in Idaho and North Carolina in 2010 and 2011, with specific gravity also being measured in Minnesota in 2011. Progenies and parents were previously genotyped using the Illumina SolCAP Infinium 8303 Potato SNP array, with ClusterCall and MAPpoly (R-packages) subsequently used for autotetraploid SNP calling and linkage mapping in this study. The 12 complete linkage groups and phenotypic data were then imported into QTLpoly, an R-package designed for polyploid QTL analyses.Significant QTL for tuber shape were detected on chromosomes 4, 7, and 10, with heritability estimates ranging from 0.09 to 0.36. Significant tuber shape QTL on chromosomes 4 and 7 were specific to Idaho and North Carolina environments, respectively, whereas the QTL on chromosome 10 was significant regardless of growing environment. Single marker analyses identified alleles in the parents associated with QTL on chromosomes 4, 7, and 10 that contributed to significant differences in tuber shape among progenies. Significant QTL were also identified for specific gravity on chromosomes 1 and 5 with heritability ranging from 0.12 to 0.21 and were reflected across environments.Fully automated linkage mapping and QTL analysis were conducted to identify significant QTL for tuber shape and dry matter in a tetraploid mapping population representing the russet market class. The findings are important for the development of molecular markers useful to potato breeders for marker-assisted selection for the long tuber shape and acceptable dry matter required by the potato industry within this important market class.The online version contains supplementary material available at 10.1186/s12870-021-03265-2. Solanum tuberosum) is known as one of the four primary food sources worldwide . Subsequently additional validation methods were used to protect from false-positive QTL with the use of the three or high LOP which included checking the consistency of a QTL across locations and years. Furthermore, allele effect and single-marker analyses were conducted to check how the presence or absence of a target allele affected the phenotype data of the two traits. Detailed information on those validation processes will be explained in the following paragraphs. The QTLpoly (R-package) also provides information on support intervals defined as the QTL peak adjacent to zone with LOP higher than or equal to LOP \u2013 d, where d is a constant, which subtracts the highest LOP in that region [LOP \u2013 1.5. The fit_model function in QTLpoly calculated the heritability of the significant QTL. They were labeled as \u201ch2QTL\u201d in the current study. If a h2QTL of a QTL peak is higher than 10%, the QTL will be considered as a major QTL, while a h2QTL\u00a0\u2264\u200910% was considered a minor QTL [QTLpoly, an R-package designed for QTL analysis of polyploid organisms, was used to combine the 12 linkage groups with the BLUP datasets estimated from the phenotype data, and then to draw 12 QTL maps as described in da Silva Pereira et al. . In briecs tests  at every is used , 46. The the QTL . The QTLt region , 47. We inor QTL . After sThe QTLpoly software provided allele effects at each SNP position, which are indicated by bar graphs .After checking the allele effect of the QTL, single-marker analysis was pursued to check whether the allele effects were actually reflected in the original phenotype data or not, as well as to find the most fitted genetic models such as additive, simplex-dominant, etc. When a target QTL and the linked SNP marker were selected, we separated BLUP data by genotype, giving us two to five different groups. We then compared averages of the BLUPs of each genotype group to check whether a significant mean difference existed between two genotype groups or not. The presence of the significant mean difference can indirectly reveal allele effects on phenotype. For instance, if the \u201cB\u201d allele of an SNP marker is associated with an increase in specific gravity and has an additive impact, the greater number of B alleles in a genotype would be expected to confer a higher specific gravity. Tukey-Kramer mean comparison test  was used for the single-marker analysis  test, which matches expected genotype frequencies against observed frequencies and calculates the associated p-value. Bonferroni correction was used to distinguish informative markers . The make_seq_mappoly argument omitted additional 171 markers, which significantly did not meet the expected segregation ratios based on Mendelian inheritance. The \u201celim.redundant\u201d argument automatically identified and removed 215 redundant markers. During the two-point and MDS processes, 1020 markers were additionally omitted, which were uninformative, co-segregating, or not belonging to one of the 12 linkage groups.Two hundred five individuals of the A05141 mapping population and their parents, Rio Grande Russet and Premier Russet, were genotyped with the SolCAP Infinium 8303 potato SNP array. Illumina GenomeStudio software was used to analyze the array data and calculate theta value scores of each individual for 8303 SNP loci. ClusterCall uses the theta values to determine 5630 polyploid marker genotypes. Since MAPpoly cannot analyze the SNPs having no-call in either the two parents, 141 SNP markers were removed   at 47.66\u2009cM  and TS_QTL_ch07_a (51%). The influence of Premier Russet was greater at TS_QTL_ch04_b (53%), TS_QTL_ch04_c (53%), TS_QTL_ch04_a (54%), TS_QTL_ch10_a (61%), TS_QTL_ch10_b (55%), TS_QTL_ch10_c (70%), TS_QTL_ch10_d (61%), TS_QTL_ch10_e (63%), TS_QTL_ch10_f (64%), TS_QTL_ch10_g (55%), TS_QTL_ch10_h (62%), and TS_QTL_ch10_i (64%) (Supplementary Table\u00a0TS_QTL_ch10_a to_i) were 0.33, 0.39, 0.51, 0.36, 0.38, 0.28, 0.44, 0.39, and 0.42, respectively (Supplementary Table\u00a0TS_QTL_ch04_a to _c) were 0.16, 0.29, and 0.21, respectively. Finally, the TS_QTL_ch07_a showed 0.22 , 51 positive and 53 negative allele effects were observed  were 0.0019, 0.0043, 0.0019, 0.0026, and 0.0022, respectively (Supplementary Table\u00a0SG_QTL_ch05_a to _f) were 0.0023, 0.0026, 0.0021, 0.0021, 0.0022, and 0.0025, respectively , 22 positive and 22 negative effects were observed in Rio Grande Russet as well as 29 positive, and 15 negative effects were detected in Premier Russet. Rio Grande Russet and Premier Russet\u2019s contribution for specific gravity were 0.0155 (58%) and 0.0113 42%), respectively  of an allele of the SNPs linked to a QTL. Significant mean differences between genotype groups were detected for SNP markers, solcap_snp_c2_54790, solcap_snp_c2_26012, and solcap_snp_c1_11535 for tuber shape, and solcap_snp_c2_49905, solcap_snp_c2_3452, and solcap_snp_c2_42406 for specific gravity , Ro, was first postulated by Masson [Ro locus between TAc13b and Tac20 RFLP markers after analyzing a mapping population obtained from the cross between a female parent having S. phureja and Chippewa genetic background and a male parent carrying S. vernei and S. tuberosum. Chen et al. [Ro locus on chromosome 10 between two BACs, PA28 and PA13_16, based on a full-sib diploid population. Li et al. [Ro locus and another locus associated with eye depth on the same chromosome between STM0051 (SSR) and CT240 (RFLP) markers while studying a diploid family. Since physical map locations of STM0051, solcap_snp_c1_15594, and solcap_snp_c1_11535 were available in Spud DB [Ro locus. On the other hand, evidence showing the proximity of solcap_snp_c1_11535 (or solcap_snp_c1_15594) to Ro locus was observed in Endelman and Jansky [Ro locus . Sharma et al. [Ro, being associated with tuber appearance  on chromosome 10, [A candidate gene for the major QTL found on chromosome 10 in this study is the y Masson , and it y Masson  mapped tn et al.  localizei et al.  also map Spud DB , 55, we a et al.  reportedsome 10, . TherefoTS_clo_ID and TS_clo_ID_2010). Only one TS_clo_ID among the nine BLUP datasets showed skewness toward long tuber shape  was reported in Table Through the visualized allele effects  at 6.78\u2009cM on chromosome 4, positive allele effects were continuously detected on homolog b, d, and h where the allele A is located  produced QTL peak on 141.26\u2009cM, but the other three BLUP datasets  resulted in their QTL perks near 122\u2009cM. Furthermore, no QTL was detected from any BLUP data related to the 2010-year effect. Based on those results, there would be two (or more) QTL on chromosome 1, and the QTL would be significantly affected by year and location effects. Table On chromosome 1, three different positions  harbored significant QTL depending on BLUP datasets. Interestingly, two BLUP datasets associated with North Carolina .Long or oblong russet potato varieties with an appropriate specific gravity are required by the potato industry for the production of French fries. This study provided important genetic information associated with longer tuber shape across growing environments, which significantly affects the russet-skinned market class. Similar to previously published findings, a major QTL on chromosome 10 was identified in this russet mapping population associated with tuber shape across growing environments with environment-specific QTL also being identified on chromosomes 4 and 7 that were of consequence in Idaho and North Carolina environments, respectively. Significant QTL for specific gravity were oftentimes specific to certain growing environments. For example, a QTL for chromosome 5 was identified with significance in Idaho and Minnesota, but not in North Carolina. Two additional significant QTL were discovered in close proximity on chromosome 1, but with one being significant in North Carolina whereas the other was of more significance in Idaho and Minnesota. The results of this study have identified QTL that can be further explored for the development of markers useful for marker-assisted selection in the russet market.Additional file 1: Supplementary Figure S1. Scale for tuber shape measurement (SolCAP 2009)Additional file 2: Supplementary Figure S2. Allele effects of the SNPs at the mapped QTL. a. Tuber Shape. b. Specific Gravity. The \"a\" to \"d\" at the X-axis represents four phased homologs of Rio Grande Russet, and the \"e\" to \"h\" represent another four homologs of Premier Russet. Y-axis displayed the quantity of an allele effect on each homolog (Unit does not exist). \u201csolcap_snp_\u201d was omitted at the beginning of all the SNP marker names.Additional file 3: Supplementary Figure S3. Twelve tetraploid linkage groups of two parents. ** Supplementary Fig. 3 is uploaded as a separate PDF file. (PDF 18802 kb)Additional file 4: Supplementary Figure S4 Tuber shape segregation pattern with TS scoresAdditional file 5: Supplementary Figure S5 Distribution of the BLUP datasets of tuber shape and specific gravity. BLUP data abbreviations: tuber shape (TS), specific gravity (SG), a genetic effect of clones (clo), Idaho (ID), North Carolina (NC), Minnesota (MN) location effects, 2010 (2010), and 2011 (2011) year effects.Additional file 6: Supplementary Figure S6 Single-Marker analyses. a. Tuber Shape. b. Specific Gravity. Tukey-Kramer mean comparison test was used for the single-marker analysis . If the p-value is below 0.05, the two groups were significantly different from each other and are indicated as so under the Group heading by assignment of letters \u201cA\u201d and \u201cB\u201d; e.g., if the two genotypic groups are significantly different, they will be designated A and B, respectively. If a genotype group is not significantly different from another group, it will receive an \u201cAB\u201d designation. The differences are also visualized through circles. For instance, if two circles above do not overlap, the two means are considered significantly different from each other. \u201csolcap_snp_\u201d was omitted at the beginning of all the SNP marker names.Additional file 7: Supplementary Table S1 Genotype data of the A05141 mapping population. (CSV 2393 KB). ** Supplementary Table 1 is uploaded as a separate CSV file. (CSV 2337 kb)Additional file 8: Supplementary Table S2 Tuber shape and specific gravity phenotype data of the A05141 mapping population (XLSX 101 KB). ** Supplementary Table 2 is uploaded as a separate Excel file.Additional file 9: Supplementary Table S3 Summary of the BLUP datasets (DOCX 16.1 KB). ** Supplementary Table 3 is uploaded as a separate Microsoft Word file.Additional file 10: Supplementary Table S4 Detailed information on the allele effects of tuber shape and specific gravity (XLSX 14.7\u2009KB). ** Supplementary Table\u00a04 is uploaded as a separate Excel file."}
+{"text": "The tufted duck is a non-model organism that experiences high mortality in highly pathogenic avian influenza outbreaks. It belongs to the same bird family (Anatidae) as the mallard, one of the best-studied natural hosts of low-pathogenic avian influenza viruses. Studies in non-model bird species are crucial to disentangle the role of the host response in avian influenza virus infection in the natural reservoir. Such endeavour requires a high-quality genome assembly and transcriptome.This study presents the first high-quality, chromosome-level reference genome assembly of the tufted duck using the Vertebrate Genomes Project pipeline. We sequenced RNA (complementary DNA) from brain, ileum, lung, ovary, spleen, and testis using Illumina short-read and Pacific Biosciences long-read sequencing platforms, which were used for annotation. We found 34 autosomes plus Z and W sex chromosomes in the curated genome assembly, with 99.6% of the sequence assigned to chromosomes. Functional annotation revealed 14,099 protein-coding genes that generate 111,934 transcripts, which implies a mean of 7.9 isoforms per gene. We also identified 246 small RNA families.This annotated genome contributes to continuing research into the host response in avian influenza virus infections in a natural reservoir. Our findings from a comparison between short-read and long-read reference transcriptomics contribute to a deeper understanding of these competing options. In this study, both technologies complemented each other. We expect this annotation to be a foundation for further comparative and evolutionary genomic studies, including many waterfowl relatives with differing susceptibilities to avian influenza viruses. Aythya fuligula, NCBI:txid219594) is a non-model organism that has received attention because of its role in the zoonotic ecology of avian influenza A viruses (AIVs). As a member of the Anatidae family of ducks, it is closely related to the mallard (Anas platyrhynchos), the primary natural host of AIV  nt). However, the mean number of reads was almost 600-fold higher with short-read data  compared to long-read data , implying a 60\u00a0times higher sequencing depth with the short reads. Not surprisingly, StringTie2 (in the short-read pipeline) assembled more transcript models and inferred more genes and exons than the long-read pipeline. This was true for all tissues except in the lung; more transcript models were inferred in the long-read pipeline. Lung RNA (cDNA) was sequenced on 2 Zero-mode waveguides (ZMW) and produced the highest numbers of subreads and FLNC after processing. The highest number of genes in each pipeline was predicted for ovary (Illumina) and brain (PacBio). The highest number of transcript models was predicted for ovary (Illumina) and lung (PacBio). The same pattern applies to predicted exons , 345,870 transcripts with 2,381,662 exons were predicted in 49,746 genes Table\u00a0. ConservOf 78,860 full-length transcripts with no UniRef50 hit, 62,147 were flagged as potentially protein-coding (and the remainder as nonsense-mediated decay), and 26,489 as single-exonic while the remainder as composed of \u22652 exons.RIG-I/DDX58 is annotated on chromosome Z (NC_051804.1), position 69,037,671\u201369,061,400 , and consists of 18 exons. Searching the protein sequence in the tufted duck genome assembly produced 1 significant alignment with the predicted gene XM_032205362.1, also on chromosome Z  matched with ORFs of 6 transcripts while G24916.1 contained the same translated amino acid sequence as G24916.2 and G24916.4. Gene G46857 on chromosome Z (NC_045593.1) matched with ORFs of 3 transcripts while G46857.2 contained the same translated amino acid sequence as G46857.3. ORFs G24916.7/8 and G46857.2/3/4 were flagged with \u201c5prime_degrade,\" which means that the transcript might be incomplete on the 5\u2032 end. Both genes were predicted in the short-read and long-read pipeline. A detailed list of reconstructed transcript models by pipeline and tissue for these 2 genes can be found in Searching the predicted open reading frames (ORFs) from the functional annotation in the 2 mallard G24916 sequences relate to the IFIH1 gene  while G46857 sequences relate to the RIG-I/DDX58 gene. An alignment of the mallard and tufted duck RIG-I/DDX58 amino acid sequences revealed 15 variants , which were predicted to have no effect on the biological function of the protein  in each species , 4,766 were exclusively supported by short reads and 432 by long reads. In the short-read pipeline, 11,165 genes intersected across all tissues. The highest number of exclusively expressed genes was in testis (988), followed by ovary, brain, spleen, ileum, and lung  of 93.91% (5.28%) of these reads to the reference genome, which divides into 67.99% (13.75%) uniquely mapped reads and 25.92% (12.08%) multi-mapped reads (\u226410 loci). Cufflinks predicted the highest number of genes in the spleen  followed by testis . The remaining tissues had much lower numbers of genes, ranging from 8,441 (ileum) to 13,606 (brain). The same pattern applies to the number of predicted transcripts and exons  of 95.81% (3.51%) of the reads were retained after adapter and quality trimming Table\u00a0. OverallEach transcript was composed of a mean (SD) of 1.3 (0.2) exons. The distribution of single-exon and multi-exon transcripts shows a clear trend towards single-exon transcripts and a quickly diminishing number of multi-exon transcripts. However, this was less pronounced in spleen but even more so in testis Fig.\u00a0. The gen(in silico) for Rfam\u2019s covariance models of RNAs resulted in 1,234 hits. The same scan on the assembled small RNA transcripts (in vitro) revealed a mean (SD) of 346.5 (26.9) hits across all tissues. After removing lower-scoring overlaps and hits with E-value >5.0E\u22124 from the cmscan result, 1,076 distinct features were predicted in the tufted duck genome. In the tissue\u2019s small RNA assemblies, a mean of 327.5 (26.5) features were annotated, with the same filtering  of 22.2 (2.3) features were annotated that were not predicted in silico (Table\u00a0Scanning the genome After further filtering for unique RNA families (Rfam accession numbers/covariance models), 306 distinct RNA families were predicted in the genome, with 246 annotated in all tissues (pooled). The number of predicted and annotated covariance models overlapped for 237 RNA families, while 69 were only identified in the genome scan and 9 were only identified in the pooled tissue annotations.In this study, we present the first chromosome-level reference genome assembly of the tufted duck. The genome contiguity is on par with other reference bird genome assemblies that used long reads such as mallard ,47, chicThe higher numbers of recovered genes, transcripts, and exons in the short-read transcript model reconstruction can be mainly explained by the higher sequencing depth and further reinforced by the different RNA preparation protocol. With Illumina, virtually all trimmed, paired-end reads were kept for mapping to the genome while with PacBio, only 5\u2032 cap-selected and FLNC reads were kept. However, the transcript model reconstruction in the long-read pipeline often almost matched or even exceeded (lung) the mean number of transcripts in the short-read pipeline. Furthermore, the long-read pipeline recovered more transcripts per gene (isoforms) on average. Taken together, this is remarkable considering the 60-fold higher sequencing depth in the short-read pipeline. It also corroborates the strength of PacBio Iso-Seq, which seems to better reflect the complexity of the transcriptome, considering that transcripts did not need to be assembled but were sequenced full-length. However, this result is not reflected in the functional annotation and, together with the 27,787 putatively intact genes without a hit in the UniRef50 database, may indicate potentially undescribed genes.In the short-read transcript model reconstruction, more 2-exon genes than single-exon genes were predicted for all tissues, and it seems as if some transcripts could not be assembled entirely or StringTie2 tried to \u201cavoid\u201d single-exon genes. Transcript model reconstruction in StringTie2 is based on the concept of extending short reads to create so-called super-reads , which sin silico predictions of coding potential; however, RNA (cDNA) sequencing adds evidence for expressed genes. Based on the inferred transcripts in this study, a total of 14,099 protein-coding genes could be identified in the UniRef50 database after conservative filtering (\u226590% match), which is comparable to NCBI\u2019s in silico prediction of 15,578 protein-coding genes [CPC2 predicted 84.2% of the potentially protein-coding genes found in UniRef50, which would serve as a conservative estimate of the protein-coding potential by just looking at the reference genome. However, beyond the 17,911 genes annotated by UniRef50, the annotation contains an additional 27,787 genes with protein-coding potential according to the TAMA ORF/NMD prediction pipeline, with these being potential candidates for further analyses. Gene and transcript identification in non-model species relies on annotations of preferably closely related model organisms. However, protein-coding genes are mainly described by a single transcript and predominantly built on short-read and comparative data [While both the number and quality of published vertebrate genome assemblies are increasing, hardly any are complemented by a transcriptome of multiple tissues from the same individual ,51. Autong genes . The numng genes ), chickeng genes ), or zebng genes ). CPC2 pive data .RIG-I/DDX58 is intact and expressed in the tufted duck, and almost identical and at the same position as in mallard. The differences in amino acid sequence are tolerated, and transcription factor binding sites are identical with those in mallard. Taken together, there are no obvious differences in the RIG-I gene that can account for the difference in susceptibility to influenza seen in each species. This is remarkable considering that tufted ducks are highly susceptible to AIV and indicates that the host response is complex and depends on more than an intact and expressed RIG-I/DDX58 gene [We could confirm that the gene X58 gene ,39.Besides a high-quality transcriptome for the tufted duck, this study also provides a tissue-specific expression atlas. In the short-read pipeline, there is a large decrease in numbers of genes expressed in all tissues to genes exclusively expressed in a single tissue or a few tissues. This distribution is much more balanced in the long-read pipeline and may indicate that the coverage in PacBio was too low to fully recover all genes in all tissues.Gallus gallus, version 5) in Rfam 14.4 [in silico scan of small RNA in the genome could predict almost all small RNAs in the assembled transcripts. More importantly, however, 22.2 additional small RNAs were discovered on average in the in vitro scan that would otherwise have been unnoticed. Small (non-coding) RNAs play an essential role in gene regulation, translation, and chromosome structure [in vitro in the genome and transcriptome annotation literature. However, small RNA studies have been continuously increasing over the past 20\u00a0years from 1,966 publications in 1999 to 8,034 in 2019  for the tufted duck in this study is comparable to 352 families predicted in chicken . Relyinnotation . We stroThis study presents the first high-quality reference genome assembly of the non-model tufted duck species. It is complemented by coding and small non-coding RNA transcriptome annotation from 6 different tissues. The genome assembly contributes to the VGP\u2019s ongoing mission to generate near error-free and complete genome assemblies of all extant vertebrate species. By utilizing, comparing, and combining the strengths of low error rates and high sequencing depth in Illumina RNA sequencing, and the full-length transcript sequencing in PacBio\u2019s Iso-Seq, this annotation culminates in a merged transcriptome with functional annotation and an expression atlas. Evidence from small RNA of the same tissues sequenced using the Illumina platform revealed small RNAs that would have otherwise remained undetected. Our findings from a comparison between short-read and long-read reference transcriptomics contribute to a deeper understanding of these competing options. In this study, both technologies complemented each other. While short-read data were sufficient to annotate protein-coding genes, long-read data recovered more transcripts per gene and potentially further protein-coding genes that could not be annotated. With the ongoing improvement of base call quality in long-read sequencing, short-read transcriptome sequencing might become expendable, and we recommend reconstructing transcriptomes using long-read sequencing with high coverage. Together, the genome and transcriptome annotation of the tufted duck is an excellent resource for public omics databases and a foundation for downstream studies, e.g., regarding disease response. The data set\u2019s high quality for a non-model species allows for a much finer resolution of genetic differences and commonalities in closely related species, which is crucial while studying the reservoirs of zoonotic pathogens.Captive-bred, wild tufted ducks were kept at the animal breeding facility . The ducks were obtained from Snavelhof breeding farm, Veeningen, the Netherlands, in May 2017. Tissue samples were obtained from 5 females and 5 males (12 months old) after euthanasia with an injection of 1\u00a0mL of pentobarbital (100\u00a0mg) in the wing vein. The following tissues were collected from the birds: brain, ileum, spleen, lung, and gonads (ovary or testis). Tissues were immediately snap-frozen in liquid nitrogen and stored at \u221280\u00b0C until shipment on dry ice to the Roslin Institute, Edinburgh, UK. All animal experiments were carried out in strict accordance with a protocol legally approved by the regional board of the Uppsala animal ethics committee, Sweden (permission No. 5.8.18-07998/2017). The animal experiments were conducted in biosafety level 2 animal facilities at the Swedish National Veterinary Institute.falcon and its haplotype-resolving tool FALCON-Unzip [RRID:SCR_010761) [To obtain both sex chromosomes, DNA was extracted from lung tissue of a female tufted duck. Library preparation and sequencing was conducted as in , using 4ON-Unzip . The resON-Unzip  to identON-Unzip . FinallyON-Unzip  and subs_010761) . The gen_010761) .For disruption and homogenization of tissues, snap-frozen samples were ground to a fine powder under liquid nitrogen using a mortar and pestle. Samples were transferred to 1.5\u00a0mL frozen tubes and kept on dry ice until further processing. Total RNA was obtained following a standard TRIzol protocol with DNase treatment and column purification. Small RNA was prepared according to the miRNeasy kit protocol 217004 . Integrity and quality of the RNA were confirmed by electrophoresis on an Agilent 2200 Tapestation using appropriate screen tapes. The concentration was determined using the Nanodrop ND-1000  . For DNARRID:SCR_016386) [RRID:SCR_016387) [RNA was sent to Edinburgh Genomics, Edinburgh, UK, for library preparation and sequencing on an Illumina HiSeq 4000 platform   with 2 \u00d7_016387)  with 2 \u00d7RRID:SCR_012954) [RRID:SCR_021168) [RRID:SCR_021169) [Aythya fuligula\u201d as the query species (-species \u2018Aythya fuligula\u2019). This was followed by a second round of repeat masking, which was carried out using a novel repeat sequence library obtained by RepeatModeler v2.0.1  [Repeat content in the tufted duck genome assembly was defined using RepeatMasker v4.1.0   with duc_021168)  and RepB_021169)  repeat l_015027) . To gene_015027) ,47) we uTelomeric repeats were identified by searching for known vertebrate-specific repeat hexamers of \u201cTTAGGG\u201d and \u201cCCCTAA,\u201d while known Anseriformes-specific centromeric repeat sequences  were mapRRID:SCR_018550) [Orthologous chromosome pairs were identified by searching for synteny between the tufted duck and mallard genomes. The 2 genomes were aligned with Minimap2   using op_018550)  in SupplTwo micrograms of total RNA from each sample in 4 parallel reactions were converted to cDNA using the Teloprime full-length cDNA amplification kit  according to the manufacturer\u2019s instructions . After end-point PCRs, all samples were tested for quality and quantity. The product size distribution was visualized using an Agilent 2200 Tapestation using D5000 screen tape. The library concentration was measured on a Qubit 3  with high-sensitivity DNA reagents . TechnicRRID:SCR_014583) [RRID:SCR_011848) [HISAT2 v2.2.0  [RRID:SCR_016323) [Illumina raw RNA-Seq reads were quality checked and filtered using FastQC v0.11.8   and Trim_011848) , respect_015530) ,87 and t_016323) . The res_016323) , except _016323) .RRID:SCR_015987) [PacBio raw Iso-Seq reads were pre-processed using the IsoSeq3 pipeline to obtain full-length, non-chimeric reads   and polyRRID:SCR_006646) [CPC2 v0.1  [RRID:SCR_010646) [RRID:SCR_001010) [Transcript models from all 6 tissues inferred by the short-read and long-read pipelines were merged on the basis of similarity using tama_merge.py (options -a 100 -z 100 -d merge_dup) with different priorities for splice junctions and transcript end sites. Short-read inferred transcript models were given higher priority on splice junctions, whereas long-read inferred transcript models were given higher priority on transcript end sites. Nucleotide sequences based on coordinates in the merged transcriptome were extracted from the reference genome using Bedtools v2.29.0  . The pro_002764)  based on_010646)  using Bl_001010) . The resAnas platyrhynchos, version NP_001297309.1) was used to search the tufted duck genome assembly using default Tblastn  [RRID:SCR_017055) [provean  [sift  [RIG-I/DDX58 in each species and compared by searching the transfac database  [RRID:SCR_007787) [The nucleotide sequence of the antiviral innate immune response receptor RIG-I/DDX58 in mallard (_011822)  settings_002182)  and sift_012813) . Transcr_005620)  with the_007787) .In addition to merging transcript models, tama_merge.py also creates gene and transcript reports that trace the source (in this case: pipeline and tissue) of each gene and transcript, respectively. The gene report was parsed with tama_merge_report_parser.pl  and filtRRID:SCR_011841) [star v2.7.3a  [RRID:SCR_014597) [RRID:SCR_002105) [RRID:SCR_014601) [RRID:SCR_000432) [Illumina raw reads were quality checked with FastQC v0.11.8 and adapters removed with Cutadapt v2.10  . Correct_004463)  and asse_014597) . Nucleot_002105) . All plo_014601)  in RStud_000432) ,112.RRID:SCR_011809) [(in silico) and to annotate assembled small RNA transcripts (in vitro) based on Rfam  [The tool cmscan v1.1.3 from the software suite Infernal   was used_007891) ,115 cova_007891) .in silico and in vitro annotations were identified with the intersect option of Bedtools v2.29.2 [The output of cmscan (tblout) was converted to gff3 annotation files using tblout2gff3.pl . Shared  v2.29.2 .https://gitlab.com/rcmueller/tufted_duck_annotation under MIT license.Perl and R scripts used in this study are available on GitLab at GigaScience GigaDB database [The data sets supporting the results of this article are available in NCBI and Figshare. The curated assembly of the tufted duck genome has been deposited in NCBI under accession No. GCF_009819795.1 . The Illdatabase .giab081_GIGA-D-21-00072_Original_SubmissionClick here for additional data file.giab081_GIGA-D-21-00072_Revision_1Click here for additional data file.giab081_GIGA-D-21-00072_Revision_2Click here for additional data file.giab081_GIGA-D-21-00072_Revision_3Click here for additional data file.giab081_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giab081_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giab081_Response_to_Reviewer_Comments_Revision_2Click here for additional data file.giab081_Reviewer_1_Report_Original_SubmissionQi Zhou -- 4/7/2021 ReviewedClick here for additional data file.giab081_Reviewer_1_Report_Revision_1Qi Zhou -- 8/6/2021 ReviewedClick here for additional data file.giab081_Reviewer_2_Report_Original_SubmissionJoshua Pe\u00c3\u00b1alba -- 4/23/2021 ReviewedClick here for additional data file.giab081_Supplemental_FilesClick here for additional data file."}
+{"text": "A series of Hirshfeld atom refinements (HARs) has been performed for an organo-gold(I) compound at different levels of theory. The influences of the relativistic effect, electron correlation and anharmonic thermal motion have been studied based on aspherical charge density models obtained with HAR. Tonto for high-resolution X-ray diffraction datasets of an organo-gold(I) compound. The influence of the relativistic effects on statistical parameters, geometries and electron density properties was analyzed and compared with the influence of electron correlation and anharmonic atomic motions. Recent work in this field has indicated the importance of relativistic effects in the static electron density distribution of organo-mercury compounds. This study confirms that differences in electron density due to relativistic effects are also of significant magnitude for organo-gold compounds. Relativistic effects dominate not only the core region of the gold atom, but also influence the electron density in the valence and bonding region, which has measurable consequences for the HAR refinement model parameters. To study the effects of anharmonic motion on the electron density distribution, dynamic electron density difference maps were constructed. Unlike relativistic and electron correlation effects, the effects of anharmonic nuclear motion are mostly observed in the core area of the gold atom.The main goal of this study is the validation of relativistic Hirshfeld atom refinement (HAR) as implemented in  The theoretical molecular electron density is then divided (Stockholder partitioning) , which allows non-spherical atomic form factor calculations using quantum-mechanical methods  and tri\u00adphenyl bis\u00admuth (BiPh3) at the BLYP level of theory. They found that relativistic effects are important not only in the core electron density of metal atoms, but are also significant in the outer core and bonding regions. In 2019, Bu\u010dinsk\u00fd et al.  which could be accounted for by the more accurate HAR. They summarized the size of tested effects as follows: relativity >> electron correlation > ADP model > basis set \u223c crystalline environment.The above mentioned quantum crystallography method (HAR) has been implemented in the  al. 2016 used the al. 2019 validateet al.  of an organo-gold(I) crystal structure in terms of data quality and crystallographic statistical indicators. The final charge density models are used to examine changes in the electron density arising from relativistic effects, electron correlation and anharmonic motions of the gold atom. The comparison to anharmonic motion required some method development to be able to output and subtract dynamic electron density grid files. Hence, the examination of the magnitude of the effect of relativistic and electron correlation against anharmonic motion effects is a new feature presented here.2.2.1.K\u03b1 (\u03bb = 0.56087\u2005\u00c5) and Mo K\u03b1 (\u03bb = 0.71073\u2005\u00c5) radiation at 90 and 93\u2005K, respectively, hereafter referred to for simplicity as Ag and Mo data. The lattice parameters were obtained by least-squares fit to the optimized setting angles of the reflections collected using the CrysAlis CCD software  were also analyzed, using MoleCoolQT  and the minimum data resolution required for meaningful refinement of the anharmonic displacement parameters  , S9(a) and S11(a)]. For all datasets, almost all GC coefficients were more significant than three standard uncertainties (Tables S9\u2013S14). The derived total probability density functions for refinements with anharmonic nuclear motion of Au up to the fourth order showed only positive integrated probability and, therefore, no visible negative region around Au in the graphical representation , when compared with IAM , we employed topological analysis in the framework of the QTAIM , \u22072\u03c1(r), the kinetic G(r) and potential V(r) energy densities as well as the local energy density H(r) at the BCPs.To analyze the local impacts of various effects on the resulting electron density \u03c1 type were computed with the 3.3.1.r) at the BCPs of the above mentioned bonds, we find that the electron density increases on consideration of relativistic effects. The difference in \u03c1(r) between rks-anh_nr and rks-anh_rel refinements is larger for the Au\u2014P than the Au\u2014C bond with deviations of ca 5.2 and 2.7%, respectively (Table 52\u03c1(r) is a very sensitive quantity. Non-relativistic calculations (rks-anh_nr) result in a difference of 12.6 and 20.6% for Au\u2014P and Au\u2014C bonds, respectively, when compared with the rks-anh_rel refinements. The resulting differences in the energy densities suggest a slight stabilization of the investigated bonds on inclusion of the relativistic effects. The decrease in Hr is relatively small for the Au\u2014P bond, however, it decreases rapidly for the Au\u2014C bond (Table 5a)], whereas in case of the lighter atoms changes are barely observable .At the geometry level, inclusion of relativistic effects yields no significant differences in the Au\u2014P and Au\u2014C bond distances Table 5. This mey Table 5. Changesd Table 5. Changesa), left]. Electron density increases in BCPs on inclusion of relativistic effects as previously shown in Table 4b), left]. Both maps show that the distributions of the electron density further along the Au\u2014P and Au\u2014C bonds are different to each other as the electron density and Laplacian appear to be more reduced in the direction of the Au\u2014C bond  and \u22072\u03c1(r) at the BCPs of both the Au\u2014P and Au\u2014C bonds (Table 5r) values for the Au\u2014P bond is smaller with a deviation of only 1.3%  than for \u03c1(r) and seem to be independent of the dataset for the Au\u2014C bond ,(b), righta),(b), rightThe difference maps reveal that electron correlation dominates over the whole molecule [Fig. 6(3.3.3.c) (2D and 3D maps) as the difference between rks_anh_rel and rks_rel dynamic electron densities. The major effect of anharmonicity is found near the atomic position of gold and is most pronounced in the direction perpendicular to the Au\u2014P or Au\u2014C bonds [Fig. 6c)], whereas no extrema are observed in the valence or bonding region.The introduction of anharmonic motion corrections for the gold atom produces very small changes in the topological parameters at the BCPs of the investigated bonds in Table 53.4.The profiles of electron density along the Au\u2014C and Au\u2014P bonds for all considered refinements are presented in Figs. S13\u2013S15 and represent global measures of the tested effects. In Fig. 7et al., 2019et al., 2007As it can be seen from Fig. 7versus rks-anh_rel) and relativistic effects (rks-anh_nr versus rks-anh_rel), which are also visualized in Figs. S15(a) and S15(b). The non-relativistic curve (pink) lies at a lower level than all the relativisitic curves, which illustrates the well known phenomenon of relativistic contraction of electron density , S16(b) and S17(b)] is also detected. The non-relativistic curve (pink) always lies above all other curves which confirms the previously reported reduction of electron density concentration in this region due to relativity. However, this is only true for the metal atom, whereas the effect cannot be detected for lighter atoms . At this stage, it is worth pointing out that there is a further difference between charge depletion and concentration along the Au\u2014P or Au\u2014C bonds. We note that the maximum of the negative Laplacian profile around 1.5\u2005\u00c5 indicates a local concentration of charge, whereas the outer core region of the Au atom is a region of local charge depletion, suggesting polarization of the Au\u2014P and Au\u2014C bonds towards the metal center.The first apparent difference between all refinements considered is a change in the positions of the minima of the non-relativistic and relativistic curves in the outer core region (from 0.2 to 0.3\u2005\u00c5). The electron depletion is shifted by 0.02\u2005\u00c5 in the direction of the metal core, which represents a relativistic contraction. A significant difference between the magnitude of the local maxima in the region around 0.5\u2005\u00c5 from Au . However, the magnitude of this minimum for the Ag data [subplots in Figs. S16(a) and S17(a)] deviates from the others by \u223c340\u2005e\u2005\u00c5\u22125.For the above mentioned bonds, the shape and magnitude of the minima and maxima of the relativistic effects in the negative Laplacian profiles, which are present in the subplots, vary with the datasets analyzed . In contrast, the magnitude of the electron correlation in the negative Laplacian remains the same for all datasets , S12(a) and S13(a)]. This suggests that they can be detected experimentally for such heavy elements; however, in order to confirm this conclusion, a full X-ray wavefunction fitting procedure should be performed for the experimental X-ray dataset. Due to the partial disorder detected in the structure, the full X-ray wavefunction fitting procedure was not feasible because treatment of disordered structure is not possible in Tonto and the existing disorder might obscure the relativistic effects in the experimentally reconstructed electron density.In summary, the negative Laplacian profiles of the models confirm the significance of the relativistic and electron correlation effects in the negative Laplacian distributions, especially at the Au inner core [region from 0.2 to 0.5\u2005\u00c5; subplots in Figs. 84.et al., 2013et al., 2017In this work, we have successfully performed HAR with relativistic Hamiltonians for an organo-gold(I) compound. The quality of the models was significantly better for HAR than for IAM. When comparing the HAR models, the quality of the relativistic refinements proved to be higher than the non-relativistic refinements, indicated by the improved refinement statistics and flatter residual density maps. However, the most significant impact on the refinements resulted from the inclusion of anharmonic vibrations for the gold atom. We also showed that data resolution is the most important factor when an anharmonic model of thermal motion is applied Fig. 4, even ifr) are comparable in magnitude to those found for relativity. The differences considered are much larger in the negative Laplacian at the BCPs, which demonstrates the usefulness of \u22072\u03c1 in the detection of such subtle changes in electron density. These results are in good agreement with earlier studies  is not as inert as a \u2018noble gas\u2019, but is a \u2018volatile metal\u2019  mg14_IAM_Ag, mg14_ag_rhf_anh_rel, mg14_ag_rks_anh_nrel, mg14_ag_rks_anh_rel, mg14_ag_rks_rel, mg14-Mo_IAM, mg14_mo_rhf_anh_rel, mg14_mo_rks_anh_nrel, mg14_mo_rks_anh_rel, mg14_mo_rks_rel, SP8_IAM, mg14_spring8_rhf_anh_rel, mg14_spring8_rks_anh_nrel, mg14_SP8_rks_anh_rel, mg14_spring8_rks_rel. DOI: 10.1107/S2052252521004541/lt5037sup2.fcfStructure factors: contains datablock(s) mg14_Ag_IAM. DOI: 10.1107/S2052252521004541/lt5037sup3.fcfStructure factors: contains datablock(s) mg14_ag_rhf_anh_rel. DOI: 10.1107/S2052252521004541/lt5037sup4.fcfStructure factors: contains datablock(s) mg14_ag_rks_anh_nrel. DOI: 10.1107/S2052252521004541/lt5037sup5.fcfStructure factors: contains datablock(s) mg14_ag_rks_anh_rel. DOI: 10.1107/S2052252521004541/lt5037sup6.fcfStructure factors: contains datablock(s) mg14_ag_rks_rel. DOI: 10.1107/S2052252521004541/lt5037sup7.fcfStructure factors: contains datablock(s) mg14_Mo_IAM. DOI: 10.1107/S2052252521004541/lt5037sup8.fcfStructure factors: contains datablock(s) mg14_mo_rhf_anh_rel. DOI: 10.1107/S2052252521004541/lt5037sup9.fcfStructure factors: contains datablock(s) mg14_mo_rks_anh_nrel. DOI: 10.1107/S2052252521004541/lt5037sup10.fcfStructure factors: contains datablock(s) mg14_mo_rks_anh_rel. DOI: 10.1107/S2052252521004541/lt5037sup11.fcfStructure factors: contains datablock(s) mg14_mo_rks_rel. DOI: 10.1107/S2052252521004541/lt5037sup12.fcfStructure factors: contains datablock(s) SP8_IAM. DOI: 10.1107/S2052252521004541/lt5037sup13.fcfStructure factors: contains datablock(s) mg14__SP8_rhf_anh_rel. DOI: 10.1107/S2052252521004541/lt5037sup14.fcfStructure factors: contains datablock(s) mg14_SP8_rks_anh_nrel. DOI: 10.1107/S2052252521004541/lt5037sup15.fcfStructure factors: contains datablock(s) mg14_SP8_rks_anh_rel. DOI: 10.1107/S2052252521004541/lt5037sup16.fcfStructure factors: contains datablock(s) mg14_SP8_rks_rel. DOI: 10.1107/S2052252521004541/lt5037sup17.pdfSupporting figures and tables. DOI: 2043573, 2043574, 2043575, 2043576, 2043577, 2043578, 2043579, 2043580, 2043581, 2043582, 2043583, 2043584, 2043585, 2043586, 2043587CCDC references:"}
+{"text": "Hsa_circ_0076931 was up-regulated by overexpression and an mRNA profile compared with wild-type was identified by RNA-seq. The relationship between miR-6760-3p and hsa_circ_0076931 or CCBE1 was confirmed via luciferase reporter or AGO2-RIP assays. A total of 507 circRNAs were identified in glioma tissues that were differentially expressed compared with that in NBT, and the sequencing data were deposited in BioProject (ID: PRJNA746438). Hsa_circ_0007694 and hsa_circ_0008016 were memorably increased whereas hsa_circ_0076931 and hsa_circ_0076948 decreased in glioma compared with those in NBT. Additionally, hsa_circ_0076931 expression was negatively correlated with histological grade. Overexpression of hsa_circ_0076931 inhibited proliferation, migration, and invasion while promoting apoptosis of glioma cells. A total of 4383 and 537 aberrantly expressed genes were identified between the hsa_circ_0076931-overexpressed and control groups in H4 and U118-MG cells, respectively; the sequencing data were deposited in BioProject (ID: PRJNA746438). These differentially expressed genes were mainly enriched in cancer-related pathways. In addition, elevated hsa_circ_0076931 levels induced the expression of CCBE1 while suppressing miR-6760-3p expression. miR-6760-3p can bind to hsa_circ_0076931. The experimental evidence supports using hsa_circ_0076931 as a marker for glioma and to help prevent malignant progression. The mechanism might be relevant to miR-6760-3p and CCBE1.The function of circular RNAs (circRNAs) in gliomas is as yet unknown. The present study explored role of hsa_circ_0076931 in glioma. circRNA expression profiles were identified via RNA-seq followed by qRT-PCR validation in three pairs of glioma and normal brain tissues (NBT). The function of hsa_circ_0076931 was investigated  Gliomas are tumors in the central nervous system that originate from glial or neural stem cells and are the most common primary central nervous system tumor, representing approximately half of all primary intracranial tumors ,2. UnforFOXO3, is a promising biomarker for glioma diagnosis and prognosis [Circular RNAs (circRNAs) are a kind of endogenous RNA that lack free 3\u2032 and 5\u2032 ends and can be stably present in cells and tissues . Accumulrognosis . Overexprognosis . Additiorognosis . AlthougCircRNAs are thought to act as competitive endogenous RNAs (ceRNAs) that sequester target microRNAs (miRNAs) and diminish the repressive effects on downstream miRNAs molecules . Some evTo identify the circRNA profile in gliomas, the present study conducted high-throughput sequencing on glioma samples . We firsA total of 41 glioma samples and 37 normal brain tissues (NBT) were obtained for the present study. The study was approved by the Institutional Review Board of the Second Affiliated Hospital of Guangzhou Medical University , and written informed consent was obtained from the guardians of all subjects.P<0.05 were considered differentially expressed, and differently expressed circRNAs were selected to conduct heatmap and hierarchical clustering analyses. The sequencing data were deposited in BioProject (ID: PRJNA746438).Total RNA was isolated from samples using TRIzol Reagent . The concentration and quality of RNA were determined using an ND-2000 Spectrophotometer. For circRNA sequencing, linear RNA was removed from each sample using RnaseR. Subsequently, total RNA was digested using RNase R . Then, 1 \u03bcg RNA and the VAHTS mRNA-seq v2 Library Prep Kit for Illumina  were used for library preparation. Libraries were subjected to deep sequencing with an Illumina HiSeq 3000 at Guangzhou Forevergen Biotechnology Co., Ltd. circRNAs with |log2 Ratio| > 0.6 and Divergent and convergent primers were run on a 1.5% agarose gel at 100 V for 20 min with 1\u00d7 TAE (Tris Acetate EDTA) buffer.The circling hsa_circ_0076931 bands were visualized by staining the gel with ethidium bromide. Images were captured using a gel documentation unit. The product of divergent primers was sent for sequencing to Sangon Biotech Co., Ltd. .\u2212\u0394\u0394CT method was used for qRT-PCR data analysis [Total RNA was isolated from glioma samples, NBT, and glioma cells  using Column Animal RNAOUT . RNA concentration was determined using an ND-2000 Spectrophotometer (Thermo Fisher Scientific), and quantitative real-time polymerase chain reaction (qRT-PCR) was performed using the KAPA SYBR FAST qPCR Kit (Kapa Biosystems) and a 7300 Real-Time PCR System (Applied Biosystems). mRNA primer pairs are listed in analysis .The H4 and SK-N-MC cell lines were purchased from the American Type Culture Collection. U118-MG and U251 cell lines were purchased from the National Collection of Authenticated Cell Cultures. SK-N-MC cells were expanded in minimum essential medium  containing 10% fetal bovine serum . U251 cell lines were grown in streptomycin  at 37\u00b0C with 10% FBS. In addition, U118-MG, H4, and U251 cell lines were maintained in Dulbecco\u2019s phosphate-buffered saline with 10% FBS .To overexpress hsa_circ_0076931 in U118-MG and H4 cells, the front wing of the cyclization sequence of hsa_circ_0076931 (5\u2032-AGTGCTGAGATTACAGGCGTGAGCCACCACCCCCGGCCCACTTTTTGTAAAGGTACGTACTAATGACTTTTTTTTTATACTTCAG-3\u2032) and the posterior wing of the cyclization site (GTAAGAAGC AAGGAAAAGAATTAGGCTCGGCACGGTAGCTCACACCTGTAATCCCAGCA) were cloned into the LV003 vector . When U118-MG and H4 cells were cultured to approximately 80% confluence, empty vector (LV003) and LV003-hsa_circ_0076931 were transfected into the cells using Lipofectamine 3000 reagent and cultured for 48 h. Transfection efficiency was determined via qRT-PCR.3 cells/well in a 200 \u03bcl of complete medium. Then 20 \u03bcl MTS liquid (#ab197010) was added to each well at 24, 48, and 72 h. After incubation for 4 h, the supernatant was discarded and 150 \u03bcl DMSO was added to each well and the samples were subjected to low-speed oscillation on a shaker for 10 min after. Absorbance values were measured at 490 nm using an automated microplate reader . The growth curve was plotted with time as the X-axis and OD as the Y-axis.Cell viability was evaluated using the MTS test. The treated U118-MG and H4 cells were seeded in 96-well plates at a concentration of 5 \u00d7 106 cells/ml. Next, 100 \u03bcl of the cell suspension was transferred to a 5-ml flow tube into which 5 \u03bcl FITC and 5 \u03bcl PI were added. The contents of the flow tube were mixed and incubated for 15 min in the dark. A flow cytometer  was used to examine the cells, and FlowJo 7.0.1 software  was used to analyze the data.The Annexin V-FITC/PI kit  was used to detect cell apoptosis as per the manufacturer\u2019s instructions. After transfection, U118-MG and H4 cells growing at the logarithmic growth phase were harvested and digested with 0.25% trypsin for 2 min. The cells were centrifuged and then washed twice with cold PBS. The cells were resuspended in 500 \u03bcl of binding buffer to a final concentration of 106/ml cells were resuspended in 100 \u03bcl of serum-free medium and seeded in the inner chamber. Next, the bottom chamber was incubated with three replicates of 600 \u03bcl medium containing 20% FBS for 24 h at 37\u00b0C in a humidified atmosphere of 5% CO2. The cells on the basolateral chamber were fixed with 4% paraformaldehyde for 10 min, stained with 1% Crystal Violet for another 10 min, washed with cold PBS once, and then photographed using a light microscope (OPTEC CCD TP510).Cell migration assays were used to determine the motility of U118-MG and H4 cells. First, 1 \u00d7 106/ml cells were resuspended in 100 \u03bcl serum-free medium and seeded in the inner chamber. The bottom chamber was incubated with 600 \u03bcl DMEM supplemented with 20% FBS for 24 or 48 h. The cells on the basolateral chamber were fixed with 4% paraformaldehyde for 15 min, washed with PBS, and stained with 1% Crystal Violet for 10 min, and then rewashed with cold PBS. A microscope was used to observe the cells that passed the small hole to the bottom chamber, take pictures, and count the number of cells that passed.Matrigel (BD 356234) was dissolved overnight at 4\u00b0C. Next, 40 \u03bcl precooled DMEM supplemented with 1/7 matrigel was added to the precooled Transwell chamber. Then, the matrigel-covered Transwell filters were incubated at 37\u00b0C for 2 h to solidify the matrigel. Next, 100 and 600 \u03bcl DMEM were added to the top and bottom chamber, respectively. The DMEM was dropped after 24 h. Then, 1 \u00d7 10Lentivirus (oe-hsa_corc_0076931 and oe-NC) was provided by Genepharma  and used to infect U118-MG cells according to the manufacturer\u2019s protocol.6 cells in 0.1 ml PBS) into the right lower flank of the mice. The tumor volume was tested for 3 d until the tumor length reached 8 mm. Thereafter, the mice were sacrificed using the CO2 method, and the tumor was collected for further study. Our animal experiment was conducted in Forevergen  and was approved by the Forevergen Biosciences Experimental Animal Ethics Committee .Twelve BALB/c mice (6 weeks old) were purchased from the Guangdong Medical Experimental Animal Center. They were randomly divided into two groups: oe-NC and oe-hsa_corc_0076931. The oe-hsa_corc_0076931 U118-MG cells and oe-NC U118-MG cells were subcutaneously injected  at 4\u00b0C overnight and then sequentially incubated with a biotin-labeled secondary antibody. The sections were then stained with 3,3\u2032-diaminobenzidine. Finally, the sections were counterstained using hematoxylin and fixed. For each section, three fields of view were randomly selected and photographed under 200\u00d7 magnification.Immunohistochemistry (IHC) detected the expression of Ki67 in tumor tissue. The tumor tissue paraffin sections were deparaffinized, and endogenous peroxidase activity was blocked by incubation with 3% HP (P<0.05 and FC > 1.5 or P<0.05 and FC < 0.67). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to identify potential biological processes of the differentially expressed mRNAs based on the GO  and KEGG pathways (http://www.genome.jp/kegg/pathway.html) databases. The P-value of each GO term was calculated from right-sided hypergeometric tests. Benjamini-Hochberg adjustment was used for multiple test correction [P-value < 0.05 were considered notably enriched. The KEGG pathway enrichment analysis was conducted by DAVID. All sequencing data were deposited in BioProject (ID: PRJNA746438).To detect changes in mRNA expression profiles after the overexpression of hsa_circ_0076931 in H4 and U118-MG cells, we collected cells and extracted RNA for mRNA high-throughput sequencing. Briefly, polyA mRNA was purified via hybridization to Dynaloligo beads, the RNA was fragmented, and double-stranded complementary DNA (cDNA) was synthesized. End repair and A-addition were performed to ligate the cDNA fragments to adapters. The ligated cDNA was subjected to PCR amplification, and library quality was assessed using an Agilent Bioanalyzer 2100 (Agilent Technologies). RNA sequencing (RNA-seq) was performed using the Illumina Hiseq 3000  at Guangzhou Forevergen Biotechnology Co., Ltd. R software was used for quantile normalization and subsequent data processing. Differentially expressed mRNAs were screened according to fold change (FC) and rrection ,18. TermFirst, we chose altered mRNAs, which mediated by hsa_circ_0076931 in U118-MG and H4 cells. Then, miRNAs targeting the above mRNAs were predicted by TargetScan (release v.7.1) and miRanda (v.3.3a) ,20. FinaTreated cells were washed thrice with cold PBS, and proteins were extracted with the RIPA Lysis and Extraction Buffer . Protein concentration was quantified using the BCA Protein Assay Kit . Then, Western blotting was performed as previously described . The folDual fluorescein reporter gene detection was conducted using a Dual-Luciferase Assay System Kit  according to the manufacturer\u2019s instructions. Bioinformatic analysis predicts that hsa_circ_0076931 and miR-6760-3p have two potential binding sites at 432-452 bp and 504-524 bp, respectively . Wild-tyThe binding of hsa_circ_0076931, miR-6760-3, or CCBE1 to AGO2 proteins was examined using the RIP kit . U118-MG cells were cleaned, collected, and resuspended with an equal volume of RIPA lysate . After centrifugation, some samples were removed as input and some were incubated with antibodies for co-precipitation. Magnetic beads (50 \u03bcl) were suspended in RIP wash buffer (100 \u03bcl) from each co-precipitation reaction system and combined with AGO2 , and IgG . The magnetic bead-antibody complex was suspended in 900 \u03bcl RIP wash buffer and then incubated with 100 \u03bcl cell extracts at 4\u00b0C overnight. Then, the complex was collected and digested using proteinase K. Finally, the levels of hsa_circ_0076931, miR-6760-3, and CCBE1 were tested by qRT-PCR.in vitro, and all data were analyzed using GraphPad Prism v.7. Also, ImageJ software was used to perform cell-count statistics on cell migration and invasion results and protein grayscale analysis on western blot. The data are presented as mean values \u00b1 standard deviation (SD). Differences were analyzed for significance (P<0.05) by one-way or two-way ANOVA using SPASS 18.0 , which was followed by the Tukey\u2019s post-hoc test.All experiments were repeated at least thrice P<0.05) between glioma and NBT, with 100 circRNAs up-regulated and 407 circRNAs down-regulated markedly. The identified circRNA transcripts were mainly 100\u2013500 bp in length as confirmed by sequence length analysis (P<0.05). We confirmed the expression levels of hsa_circ_0007694, hsa_circ_0008016, hsa_circ_0076931, and hsa_circ_0076948 by qRT-PCR on the same samples used for the RNA-seq analysis. The qRT-PCR showed that the level of hsa_circ_0007694 and hsa_circ_0008016 was higher in glioma compared with NBT, whereas that of hsa_circ_0076931 and hsa_circ_0076948 was lower in glioma compared with NBT. Furthermore, hsa_circ_0007694 was up-regulated prominently, and hsa_circ_0076931 was down-regulated notably, in agreement with the RNA-seq analysis  of circRNAs were from protein-coding exons, 6.99% (747) were from intronic regions, 8.49% (907) were from 5\u2032 UTRs, 1.57% (168) were from 3\u2032 UTRs, and 4.20% (448) were from intergenic regions A. A totaanalysis B. In addanalysis C. Up-reganalysis D,E. The analysis F.n=41) and NBT (n=37) using qRT-PCR. The level of hsa_circ_0076931 was memorably decreased in glioma compared with NBT samples , with a junction of 768 nt A. The jun=17, 0.7549 \u00b1 0.193) compared with male glioma tissues . In addition, hsa_circ_0076931 expression was negatively correlated with pathological gradation (P=0.022). Glioma patients with high-grade, advanced pathological gradation (\u2265III) had lower expression of hsa_circ_0076931  compared with patients with low pathological graduation . However, hsa_circ_0076931 expression had no obvious correlation with age and overall survival . The AUC of hsa_circ_0076931 was 0.882 in glioma patients  and corresponding control cells (oe-NC) and confirmed expression by qRT-PCR  and has a junction of 768 nt. The abundance of hsa_circ_0076931 in glioma samples was lower than in the NBT samples. In addition, hsa_circ_0076931 expression was negatively correlated with histological grade. Additionally, we found that elevated hsa_circ_0076931 inhibited proliferation, migration, and invasion while promoting apoptosis of glioma cells n tumors . We founCircRNAs are thought to act as miRNA sponges that diminish the repressive effects of miRNAs on downstream molecules . MiRNAs in vitro. Additionally, we showed that hsa_circ_0076931 can down-regulate miR-6760-3p through direct binding and can upregulate CCBE1. Therefore, miR-6760-3p and CCBE1 might be the regulatory mechanism of hsa_circ_0076931 in glioma.In summary, we presented differentially expressed circRNAs between glioma and NBT. The level of hsa_circ_0076931 markedly decreased in glioma samples compared with NBT samples. Furthermore, elevated hsa_circ_0076931 inhibited proliferation, migration, and invasion of glioma cells Written informed consent was obtained from the guardians of all subjects.Click here for additional data file."}
+{"text": "The barnacles are a group of >2,000 species that have fascinated biologists, including Darwin, for centuries. Their lifestyles are extremely diverse, from free-swimming larvae to sessile adults, and even root-like endoparasites. Barnacles also cause hundreds of millions of dollars of losses annually due to biofouling. However, genomic resources for crustaceans, and barnacles in particular, are lacking.Pollicipes pollicipes. The P. pollicipes genome is 770 Mb long and its assembly is one of the most contiguous and complete crustacean genomes available, with a scaffold N50 of 47 Mb and 90.5% of the BUSCO Arthropoda gene set. Using the genome annotation produced here along with transcriptomes of 13 other barnacle species, we completed phylogenomic analyses on a nearly 2 million amino acid alignment. Contrary to previous studies, our phylogenies suggest that the Pollicipedomorpha is monophyletic and sister to the Balanomorpha, which alters our understanding of barnacle larval evolution and suggests homoplasy in a number of naupliar characters. We also compared transcriptomes of P. pollicipes nauplius larvae and adults and found that nearly one-half of the genes in the genome are differentially expressed, highlighting the vastly different transcriptomes of larvae and adult gooseneck barnacles. Annotation of the genes with KEGG and GO terms reveals that these stages exhibit many differences including cuticle binding, chitin binding, microtubule motor activity, and membrane adhesion.Using 62\u00d7 Pacific Biosciences coverage, 189\u00d7 Illumina whole-genome sequencing coverage, 203\u00d7 HiC coverage, and 69\u00d7 CHi-C coverage, we produced a chromosome-level genome assembly of the gooseneck barnacle P. pollicipes plays in European fisheries, as a sentinel species for coastal ecosystems, and as a model for studying barnacle adhesion as well as its key position in the barnacle tree of life. A combination of genomic, phylogenetic, and transcriptomic analyses here provides valuable insights into the evolution and development of barnacles.This study provides high-quality genomic resources for a key group of crustaceans. This is especially valuable given the roles  The Earth BioGenome Project (EBP) has the ambitious goal of sequencing a high-quality genome from each described eukaryotic species on the planet . This goThe Thecostraca is a pancrustacean taxon containing the familiar and ubiquitous barnacles and a number of parasitic lineages comprising the Ascothoracida , RhizocePollicipes pollicipes  is a member of the Pollicipedomorpha , a new order . Re. ReP. po_015530)  v2.1 [10_015530) . A GTF f_018965)  v0.12.7 _018965)  and the  package  v2.0.1 ( package . Differe_015687)  v1.34 [1_015687)  with defdatasets ,108.RRID:SCR_005829) [RRID:SCR_014798) [q <\u00a00.05). To further identify functional categories and pathways, DEGs were mapped to KEGG orthologs and pathways [To classify the DEGs into functional categories, the AA sequences of all genes were mapped to GO terms , 110 by _005829)  5.46\u201381._005829) . Because_014798)  v2.44 [1_014798)   hatches from the egg and is nonfeeding (lecithotrophic), relying on yolk stores  coverage , indicating that our assembly covers the majority of the genome well. However, there was a double peak in the distribution of k-mers in the Jellyfish estimate  and the 3 other available barnacle genome assemblies  had a mean length of 13,244\u00a0bp and a median length of 6,980\u00a0bp. A mean of 1.3 transcripts were identified for each gene, with a mean of 7.48 exons per transcript. Exons had a mean length of 241\u00a0bp while introns averaged 2,077\u00a0bp. RepeatMasker identified 3.2% of the genome as repetitive sequences, but a comprehensive repeat library is not available for barnacles, especially not for gooseneck barnacles, and nearly all repeats were classified as simple repeats or low-complexity repeats. To avoid reliance on a repeat library, WindowMasker was used and masked 18.5% of the genome prior to annotation.P. pollicipes genome and transcriptomes from 13 other barnacle species  to filter out transcriptional noise, we observed 2,083 genes expressed only in the nauplius stage, 2,337 unique to the adult stage, and 13,352 genes were expressed in both stages . A similar proportion of the DEGs were overexpressed in each stage . To further filter the DEGs, a log2 fold-change >\u00a02 cut-off was applied, which resulted in 5,189 DEGs . Of these DEGs, 91 and 112 in nauplii and adults, respectively, were classified as pseudogenes in the genome annotation, while 332 genes in nauplii and 148 genes in adults were long non-coding RNAs (lncRNAs).On average, 76.4% of RNA-Seq reads per sample aligned to the ges Fig.\u00a0. HoweverP = 5.6E\u221210) in adults but not in nauplii.To explore the functions of DEGs, they were further mapped to GO terms and KEGG orthologs and pathways. We attempted to map all expressed protein-coding genes to GO terms with pfam and annotated 51%  of all genes with GO terms, including 51.5%  of the most highly DEGs. Figure\u00a0Functions of DEGs were also examined using KEGG orthologs and KEGG pathways. Of the protein-coding DEGs, 81.8%  were assigned to KEGG orthologs using KofamKOALA and these mapped to 335 KEGG pathways . The mosq <\u00a01E\u221210, log2 fold-change >\u00a07)  Fig.\u00a0. The mosP. pollicipes. More than 92% of the assembly length was composed of 17 large scaffolds, which likely represent 16 or 17 chromosomes or chromosome arms .Pollicipes and balamorphan taxa that are lacking in Capitulum are the result of homoplasy. Still, questions remain regarding the interrelationships of the 4 pollicipedomorphan genera. To further resolve the situation, Analesma and Lithotrya must be included in future phylogenomic analyses. Sampling the 8 remaining species in the Pollicipedomorpha is thus within reach and is crucial to understanding the evolution of key larval characters in this morphologically diverse order. Taken together, this work supports the validity of the Pollicipedomorpha and highlights the fact that larval character analyses should be coupled with robust molecular phylogenetic hypotheses to understand barnacle evolution.Here, we resolved part of the Pollicipedomorpha conundrum with phylogenomic analyses of nearly 2 million AA positions from 14 barnacles. We found robust support for the independence of the order and its sister relationship with the Balanomorpha. Reinterpreting the larval characters in light of this phylogeny suggests that the shared naupliar features in P. pollicipes are striking. Nearly half of all genes  undergo significant differential expression between these stages. These transcriptional differences reflect the vastly different biology of larval and adult barnacles. For example, among the 100 most differentially expressed genes, cuticle proteins were highly upregulated in the nauplius, a stage in which cuticle is rapidly being modified as individuals molt 6 times within 10\u201325 days [Pollicipes; adults provide large yolk stores that the non-feeding (lecithotrophic) first nauplius stage relies on. Adults' DEGs were also enriched for heme proteins  that exhibited stage-specific expression , and samples of the cyprid stage (Fig.\u00a0Overall, the differences in larval and adult transcriptomes of nopheles , Apis [1es [Apis , Drosophosophila , and othosophila , typicalion Fig.\u00a0. A notewP. pollicipes. This is one of the most contiguous crustacean genomes to date and, to our knowledge, the most complete assembly for a barnacle species. Using the genome annotation and transcriptomic data from 13 other barnacles, we completed phylogenetic analyses with the greatest number of orthologs and AA positions to date for barnacles and showed that the Pollicipedomorpha is a monophyletic order sister to Balanomorpha (Fig.\u00a0P. pollicipes, underlying the vast difference in lifestyle between these 2 stages. This study hence provides a valuable example of good genomic practices, high-quality genomic resources for a key group of crustaceans, and valuable insights into the evolution and development of barnacles.By combining Illumina short reads, PacBio long reads, and Hi-C and CHi-C chromatin-conformation capture data, we produced a high-quality genome assembly and annotation for the gooseneck barnacle pha Fig.\u00a0. Our DEGgiac021_Supplemental_FileClick here for additional data file.giac021_GIGA-D-21-00365_Original_SubmissionClick here for additional data file.giac021_GIGA-D-21-00365_Revision_1Click here for additional data file.giac021_GIGA-D-21-00365_Revision_2Click here for additional data file.giac021_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giac021_Response_to_Reviewer_Comments_Revision_2Click here for additional data file.giac021_Reviewer_1_Report_Original_SubmissionChao Bian -- 11/23/2021 ReviewedClick here for additional data file.giac021_Reviewer_1_Report_Revision_1Chao Bian -- 1/18/2022 ReviewedClick here for additional data file.giac021_Reviewer_1_Report_Revision_2Chao Bian -- 1/20/2022 ReviewedClick here for additional data file.giac021_Reviewer_2_Report_Original_SubmissionRafael Zardoya, PhD -- 12/7/2021 ReviewedClick here for additional data file."}
+{"text": "However, only very few of these reports find their way into databases or data repositories. One of the major reasons is the absence of digital standard to represent glycoproteins and the challenging annotations with glycans. Depending on the experimental method, such a standard must be able to represent glycans as complete structures or as compositions, store not just single glycans but also represent glycoforms on a specific glycosylation side, deal with partially missing site information if no site mapping was performed, and store abundances or ratios of glycans within a glycoform of a specific site. To support the above, we have developed the GlycoConjugate Ontology (GlycoCoO) as a standard semantic framework to describe and represent glycoproteomics data. GlycoCoO can be used to represent glycoproteomics data in triplestores and can serve as a basis for data exchange formats. The ontology, database providers and supporting documentation are available online ( Glycobiology is the study of saccharides  that are widely distributed in nature. The importance of glycobiology can be understood by considering the fact that they encompass some of the major posttranslational modifications of proteins, as carbohydrates help explain how the relatively small number of genes in the typical genome can generate the enormous biological complexities inherent in the development, growth and functioning of diverse organisms .The biological roles of carbohydrates are particularly prominent in the assembly of complex multicellular organs and organisms, which requires interactions between cells and the surrounding matrix. Without any known exception, all cells and numerous macromolecules in nature carry a repertoire of covalently attached glycans . Glycoproteins are frequently located on the cell membrane or secreted; therefore, modulating or mediating a variety of events in cell\u2013cell, cell\u2013matrix and cell\u2013molecule interactions critical to the development and function of a complex multicellular organism including cellular activation, embryonic development, differentiation and malignancy. They can also mediate interactions between organisms . Consequently, understanding the roles of glycans, changes in glycoforms/abundance of glycans, and site-occupancy are essential for improving our understanding of cellular systems. In the last few years improvements to bioinformatics tools and databases including data standardization and interoperability have helped glycobiologists better understand their functions.Over the last few decades several initiatives have cataloged and organized glycan-related information in databases. These activities started with CarbBank a database project for glycan structures which was initiated in 1987 but ceased operation in 1997 due to lack of funding support . The finIn brief, KEGG Glycan is an integrated knowledge base of protein networks with genomic and chemical information and provides access to glycan structures through the manually drawn pathway maps representing the current knowledge of glycan biosynthesis and metabolism for various species. EUROCarbDB established the technical requirements for developing a centralized and standardized database architecture for carbohydrate-related structure data and analytical data from liquid chromatography, mass spectrometry and nuclear magnetic resonance (NMR) experiments. Several resources were developed under EUROCarbDB including, MonosaccharideDB , and theMore recent developments include the CSDB, which stores structural, bibliographic, taxonomic, NMR spectroscopic and other data on natural carbohydrates and their derivatives comprising the Bacterial CSDB and the Plant/Fungal CSDB . UniCarbSemantic Web technologies, which involve the development of ontologies, controlled vocabularies and Resource Description Framework (RDF) data available from SPARQL endpoints, enables efficient integration of disparate data resources . We haveGlycoRDF was a first step to integrate glycan data across disparate databases. Glycan structures are now linked across various databases by GlyTouCan which has also be implementing Semantic Web technologies by utilizing GlycoRDF. However, glycans function together with other molecules such as proteins and lipids, forming glycoconjugates, which is a term used for glycans that are linked to proteins or lipids, otherwise known as glycoproteins or glycolipids, respectively. With the progress of glycoscience research, studies targeting glycoconjugates have accelerated, and various research results have been reported in the literature.The adoption of GlycoRDF by various databases including GlyTouCan, UniCarbKB, and CSDB, has improved data interoperability in the glycosciences and made it clear that an ontology for glycoconjugates was needed. Several lipid databases exist which contain glycolipids in part, including LIPID MAPS , LipidBaHere we present a glycoconjugate ontology, named GlycoCoO, for describing glycoconjugate structures and their functions, an ontology which will promote integration of data within the related fields of glycoscience, protein and lipid sciences. GlycoCoO can express not only the chemical structural information of a glycoconjugate but also its linked data and annotation such as glycan abundance ratio, disease, bibliographic information, sample information, etc. By integrating data constructed using GlycoCoO through Semantic Web technology, not only can life science researchers improve convenience when using these databases, but also more users across other fields can be expected to take advantage of this information. The role of data science is expected to become more important in life science research. The interest of many researchers in converting research results into data can be expected to help the development of the field.GlycoRDF was originally developed to encapsulate metadata that most pertained to glycan structures. This included publications, the sample from which the glycan was obtained  and the experimental method used to obtain or analyze the glycan , lectin binding, or nuclear magnetic resonance (NMR)). Because the same glycan could be found using different means and published in different papers, a new concept of \u201cReferencedCompound\u201d was created to keep sets of these metadata independent from one another for the same glycan see . In thisSince we wanted to reuse the GlycoRDF ontology to represent glycans in GlycoCoO, subclasses of ReferencedCompound were created, including ReferencedGlycoconjugate, ReferencedProtein and ReferencedLipid. By making these subclasses of \u201cReferencedCompound,\u201d it became possible to describe the relationship of these biomolecules with their related metadata such as disease, publications and species using the same mechanism already implemented in GlycoRDF. GlycoCoO makes it easier to integrate data from other resources. Following the ontology definition as described above three databases containing glycoconjugate data have implemented this ontology to represent their respective datasets. Each of these databases and their available RDFized datasets are as follows:http://sparql.unicarbkb.org) provides access to approximately 1530 glycoprotein entries with over 4000 annotated glycosylation sites, and 4000 glycan structures . UniCarbKB also provides information on the biological source  data that denotes glycan structures characterized for a single purified glycoprotein with knowledge of the site of the glycosylation and (ii) site-specific data describing the glycan structures at specific sites of the protein. For site-specific annotations the UniCarbKB SPARQL endpoint (CBI MeSH  and UberCBI MeSH ), diseasCBI MeSH , and expCBI MeSH . For updhttps://glyconnect.expasy.org/rdf) is being prepared and will be release by the end of 2019.GlyConnect is a glycoprotein and glycopeptide database providing curated experimental glycosylation data and the related contextual information like taxonomy, expression tissue or disease state. The dataset is built with 22,600 glycosylation sites on roughly 2,200 UniProtKB referenced glycoproteins, almost 4,000 glycans and 3,400 glycosylation sites. The curated data is supported by 900 articles. This collection includes several large-scale glycoproteomics studies that span 3,300 human N- and O-glycopeptides. It also makes references to biological context using Uberon , Cell Onhttps://glyconavi.org) is a web portal providing tools and datasets for glycoscientists. The GlycoAbun dataset of GlycoNAVI (https://glyconavi.org/GlycoAbun/) stores information of glycan abundance ratios of glycoforms on glycoconjugates. This dataset was manually curated from the literature and is also integrated in the GlyCosmos project. The GlycoNAVI SPARQL endpoint (https://sparql.glyconavi.org/sparql) provides to access to 1,297 glycans, 178 abundance ratio data, 102 disease states, 9 tissues and 178 articles.GlycoNAVI (https://github.com/glycoinfo/GlycoCoO/tree/master/RDF_Sample).As a proof of concept, the RDF data for a glycoprotein (UniProt ID: P00738) was extracted from all three major glycoprotein data resources  containing metadata from their respective resources. All of these data files are available on the GlycoCoO GitHub Wiki under RDF_Sample  were annotated in GlyConnect, while GlycoNAVI reported 184, 207, 211 and UniCarbKB reported 184, 187, 207, 211 and 241. The following are the SPARQL queries that were used to obtain this data about glycosylation sites (query 1) and glycan structures (query 2) for haptoglobin.http://purl.jp/bio/12/glyco/conjugate#>prefix gco:<http://purl.org/dc/terms/>prefix dcterms:<http://biohackathon.org/resource/faldo#>prefix faldo:<http://purl.org/dc/terms/>prefix dcterms:<select distinct ?g ?uniprot_id (str(?position) AS ?site)where{{graph ?g {http://glycoinfo.org/glycocoo/glyconnect> <http://glycoinfo.org/glycocoo/unicarbkb>}VALUES ?g { <# GlyConnect and UniCarbKB?ref_conjugate gco:has_protein_part ?ref_protein.?ref_protein gco:glycosylated_at ?region .?region faldo:location ?location .?location faldo:position ?position .?ref_protein gco:has_protein ?protein .?protein rdfs:seeAlso ?uniprot .?uniprot dcterms:identifier ?uniprot_id .}}UNION{# GlycoNAVIhttps://sparql.glyconavi.org/sparql> {SERVICE <graph ?g {http://glycoinfo.org/glycocoo/glyconavi> }VALUES ?g { <?ref_conjugate gco:has_protein_part ?ref_protein .?ref_protein gco:glycosylated_at ?region .?region faldo:location ?location .?location faldo:position ?position .?ref_protein gco:has_protein ?protein .?protein rdfs:seeAlso ?uniprot .?uniprot dcterms:identifier ?uniprot_id .}}}}order by ?g ?position# Glycan Parthttp://purl.jp/bio/12/glyco/glycan#>prefix glycan:<http://purl.jp/bio/12/glyco/conjugate#>prefix gco:<http://purl.org/dc/terms/>prefix dcterms:<http://biohackathon.org/resource/faldo#>prefix faldo:<http://semanticscience.org/resource/>prefix sio:<http://xmlns.com/foaf/0.1/>prefix foaf:<select distinct ?g ?uniprot_id ?glytoucan_idwhere{{graph ?g{http://glycoinfo.org/glycocoo/glyconnect> <http://glycoinfo.org/glycocoo/unicarbkb>}VALUES ?g { <# GlyConnect & UniCarbKB?glycoconjugate_ref gco:has_protein_part ?protein_part.?protein_part gco:has_protein ?protein.?protein rdfs:seeAlso ?uniprot.?uniprot dcterms:identifier ?uniprot_id.?glycoconjugate_ref gco:has_saccharide_part ?ref_sac.?ref_sac glycan:has_glycan ?saccharide.?saccharide foaf:primaryTopicOf ?glytoucan.?glytoucan dcterms:identifier ?glytoucan_id.}}UNION{# GlycoNAVIhttps://sparql.glyconavi.org/sparql> {SERVICE <graph ?g {http://glycoinfo.org/glycocoo/glyconavi>}VALUES ?g{<?glycoconjugate_ref gco:has_protein_part ?protein_part.?protein_part gco:has_protein ?protein.?protein rdfs:seeAlso ?uniprot.?uniprot dcterms:identifier ?uniprot_id.?glycoconjugate_ref gco:has_saccharide_part ?ref_sac.?ref_sac glycan:has_glycan ?saccharide.?saccharide foaf:primaryTopicOf ?glytoucan.?glytoucan dcterms:identifier ?glytoucan_id.}}}}order by ?g ?uniprot_id ?glytoucan_idesophageal cancer, while GlycoNAVI and GlyConnect both reported hepatocellular carcinoma. However, GlycoNAVI and GlyConnect both reported additional cancers that were not reported by any of the others. All three databases reported Homo sapiens as the organism, and only GlycoNAVI provides Cell Line information for this protein. Citations only overlapped between UniCarbKB and GlyConnect, most likely because both have data derived from GlycoSuiteDB. Finally, only GlyConnect contained data regarding Tissues.The next example illustrates the SPARQL query used to find all disease annotations, their citations, source and tissue information for this protein . RegardiThe SPARQL queries to obtain disease (query 3), publication (query 4) and source information (query 5) are as follows.# Disease Association parthttp://purl.jp/bio/12/glyco/glycan#>PREFIX glycan:<http://purl.jp/bio/12/glyco/conjugate#>PREFIX gco:<http://www.w3.org/2008/05/skos#>PREFIX skos:<http://purl.org/dc/terms/>PREFIX dcterms:<http://biohackathon.org/resource/faldo#>PREFIX faldo:<http://semanticscience.org/resource/>PREFIX sio:<http://www.w3.org/2000/01/rdf-schema#>PREFIX rdfs: <SELECT DISTINCT ?g ?disease_label ?notation ?uniprot_idWHERE{{graph ?g {http://glycoinfo.org/glycocoo/glyconnect> <http://glycoinfo.org/glycocoo/unicarbkb>}VALUES ?g {<# GlyConnect & UniCarbKB?glycoconjugate_ref glycan:has_association ?association;gco:has_protein_part ?protein_part.?association sio:SIO_000628 ?disease.?disease rdfs:label ?disease_label.?disease skos:notation ?notation.?protein_part gco:has_protein ?protein .?protein rdfs:seeAlso ?uniprot.?uniprot dcterms:identifier ?uniprot_id.VALUES ?uniprot_id {\"P00738\"}}}UNION{# GlycoNAVIhttps://sparql.glyconavi.org/sparql> {SERVICE <graph ?g {http://glycoinfo.org/glycocoo/glyconavi>}VALUES ?g {<?glycoconjugate_ref glycan:has_association ?association;gco:has_protein_part ?protein_part.?association sio:SIO_000628 ?disease.?disease rdfs:label ?disease_label.?disease skos:notation ?notation.?protein_part gco:has_protein ?protein.?protein rdfs:seeAlso ?uniprot.?uniprot dcterms:identifier ?uniprot_id.VALUES ?uniprot_id {\"P00738\"}}}}}ORDER BY ?ghttp://purl.jp/bio/12/glyco/glycan#>prefix glycan:<http://purl.jp/bio/12/glyco/conjugate#>prefix gco:<http://purl.org/dc/terms/>prefix dcterms:<www.w3.org/2000/01/rdf-schema#>prefix rdfs: <http://select distinct ?g ?uniprot_id (str (?pmid) AS ?PMID)where{{graph ?g {http://glycoinfo.org/glycocoo/glyconnect> <http://glycoinfo.org/glycocoo/unicarbkb>}VALUES ?g { <?glycoconjugate_ref glycan:published_in ?citation.?glycoconjugate_ref gco:has_protein_part ?protein_part.?protein_part gco:has_protein ?protein.?protein rdfs:seeAlso ?uniprot.?uniprot dcterms:identifier ?uniprot_id.VALUES ?uniprot_id {\"P00738\"}# glyconnect & unicarbkb?citation dcterms:references ?pubmed.?citation glycan:has_pmid ?pmid.}}UNION{# GlycoNAVIhttps://sparql.glyconavi.org/sparql> {SERVICE <graph ?g {http://glycoinfo.org/glycocoo/glyconavi>}VALUES ?g {<?glycoconjugate_ref glycan:published_in ?citation.?glycoconjugate_ref gco:has_protein_part ?protein_part.?protein_part gco:has_protein ?protein.?protein rdfs:seeAlso ?uniprot.?uniprot dcterms:identifier ?uniprot_id.VALUES ?uniprot_id {\"P00738\"}?citation dcterms:references ?pubmed.?citation glycan:has_pmid ?pmid.}}}}order by ?g ?pmidExample SPARQL Query 5 http://purl.jp/bio/12/glyco/glycan#>prefix glycan:<http://purl.jp/bio/12/glyco/conjugate#>prefix gco:<http://purl.org/dc/terms/>prefix dcterms:<http://biohackathon.org/resource/faldo#>prefix faldo:<http://semanticscience.org/resource/>prefix sio:<http://purl.org/dc/terms/>prefix dcterms:<http://purl.uniprot.org/core/>prefix up: <select distinct ?g ?uniprot_id ?tissue ?cell_line ?organismwhere{{graph ?g {http://glycoinfo.org/glycocoo/glyconnect> <http://glycoinfo.org/glycocoo/unicarbkb>}VALUES ?g {<# glyconnect & unicarbkb?glycoconjugate_ref glycan:is_from_source ?source; gco:has_protein_part ?protein_part.optional {?source glycan:has_tissue ?tissue.}optional {?source glycan:has_cell_line ?cell_line.}?protein_part gco:has_protein ?protein.?protein rdfs:seeAlso ?uniprot.?uniprot dcterms:identifier ?uniprot_id.VALUES ?uniprot_id {\"P00738\"}optional {?source glycan:has_taxon ?taxon.OPTIONAL {?taxon up:scientificName ?organism.}}}}UNION{# GlycoNAVIhttps://sparql.glyconavi.org/sparql> {SERVICE <graph ?g {http://glycoinfo.org/glycocoo/glyconavi>}VALUES ?g {<?glycoconjugate_ref glycan:is_from_source ?source;gco:has_protein_part ?protein_part.optional{?source glycan:has_tissue ?tissue.}optional{?source glycan:has_cell_line ?cell_line.}optional{?source glycan:has_taxon ?taxon.optional {?taxon up:scientificName ?organism.}}?ref_conjugate gco:has_protein_part ?ref_protein.?ref_protein gco:has_protein ?protein .?protein rdfs:seeAlso ?uniprot.?uniprot dcterms:identifier ?uniprot_id.VALUES ?uniprot_id {\"P00738\"}}}}}order by ?g ?tissue ?cell_line ?taxonhttps://bioportal.bioontology.org/ontologies/GLYCOCOO as well as on GitHub https://github.com/glycoinfo/GlycoCoO where the Wiki page illustrates examples of usage and provides the RDF data described in this manuscript.GlycoCoO is a novel compact ontology for describing protein and lipid glycosylation in a consistent manner that can be easily adopted by the broader omics community. It is a dynamic ontology that can be used to describe known glycosylation features, site-specific glycoforms, abundance data, and where available descriptions of experimental conditions and methods. It is available in BioPortal at As illustrated with the SPARQL queries described in the Results, multiple databases could be queried using a single query to retrieve integrated information regarding a single glycoprotein. Diverse information ranging from disease associations to tissues and cell lines could be retrieved from a large number of publications. We note that all of these databases are continuously being updated, therefore, the current data is only a reflection of the data at the time of this writing. Regarding the glycan data, as shown in https://glygen.org), which is adopting GlycoCoO concepts to support data interoperability. We are also planning on contacting lipid ontology and database developers to discuss where concepts could be combined or mapped with one another. Eventually, all of these integrated data will be available from the members of the GlySpace Alliance  provides a robust and scalable ontological research infrastructure  for protWith these developments of ontologies and databases based on an agreed standard for glycoconjugates, a large proportion of life science data can be integrated. However, this will require the adoption of these standards by all parties involved, which may entail much promotion and discussion with various communities. Eventually, GlycoCoO can serve as the basis of a glycoconjugate repository, whereby accession numbers can be assigned to such molecules.GlycoCoO_Supp_cwab013Click here for additional data file."}
+{"text": "CDK1, CDC20, MCM5, MCM7, MCM4, CCNA2, AURKB, KIF2C, ESPL1, BUB1B) and DNA replication . Gene set enrichment analysis (GSEA) reveals that TNF-\u03b1/NF-\u03baB and KRAS pathways may play a critical role in regulating expression levels of transporter-related genes. To further perform qRT-PCR assays, we find that TNF-\u03b1/NF-\u03baB and KRAS pathways were substantially up-regulated, consistent with GSEA analysis. The current findings suggested that a low dosage of PDT intervention may be detrimental to the homeostasis of blood-brain barrier (BBB) by inducing the inflammatory response and affecting the expression of surface biomarkers.Blood vessels in the brain tissue form a compact vessel structure and play an essential role in maintaining the homeostasis of the neurovascular system. The low dosage of photodynamic intervention (PDT) significantly affects the expression of cellular biomarkers. To understand the impact of photodynamic interventions on cerebrovascular endothelial cells, we evaluated the dosage-dependent impact of porfimer sodium-mediated PDT on B.END3 cells using flow cytometer, comet assay, RNA sequencing, and bioinformatics analysis. To examine whether PDT can induce disorder of intracellular organelles, we did not observe any significance damage of DNA and cellular skeleton. Moreover, expression levels of cellular transporters-related genes were significantly altered, implying the drawbacks of PDT on cerebrovascular functions. To address the potential molecular mechanisms of these phenotypes, RNA sequencing and bioinformatics analysis were employed to identify critical genes and pathways among these processes. The gene ontology (GO) analysis and protein-protein interaction (PPI) identified 15 hub genes, highly associated with cellular mitosis process ( Blood circulation system plays a critical role in maintaining the homeostasis physiology and supplying the essential nutrients to the targeted organisms or tissue . In the Photodynamic therapy (PDT) has been widely utilized in the glioblastoma multiforme (GBM) therapy, including the interstitial PDT (iPDT) and post-PDT . PDT reqThe interventional effects of PDT on the endothelial cells are decided by types of photosensitizers and specific cell lines , 18. HitHerein, we explored the biological impact of the photodynamic intervention on endothelial cells, including apoptosis, DNA damage, cellular skeleton, and expression levels of critical transporter-related genes. Then, we utilized RNA-seq to analyze the biological impact of porfimer sodium-driven photodynamic intervention on the cerebrovascular endothelial cells of the mouse (B.END3 cells). We identified 187 and 2976 differentially expressed gene (DEG) depending on two different interventional dosages, respectively. Bioinformatics analysis using gene ontology (GO), gene set enrichment analysis (GSEA), and KEGG gene sets identified critical pathways that were confirmed by quantitative real-time PCR. Thus, the current study provides additional information about the transcriptome changes in the cerebrovascular endothelial cells during the PDT process and the scope to further evaluate the impact of the photodynamic intervention on BBB homeostasis.2 at 37\u00b0C. When the B.END3 cells formed the monolayer, the photosensitizer Porfimer sodium (300 \u03bcg/mL) was added to the medium and incubated for 90 min. Then, the B.END3 cells received the laser exposure (635 nm with 100 mW/cm2) and the light doses at 10 J/cm2 and 20 J/cm2, respectively.Rat endothelial B.END3 cells were obtained from the Cell Bank of the Chinese Academy of Sciences and cultured in DMEM medium  with 10% fetal bovine serum  and 100 U/mL penicillin-streptomycin  under 5% COAfter PDT intervention, the cells were lysed, collected, and stored at -80\u00b0C until delivered to the Tianjin Novogene Bioinformatic Technology Co., Ltd for further analysis. The whole-genome transcriptome profiling was examined by RNA sequencing process: sequencing on the Illumina Hiseq2500 using 150 bp paired-end reads (6.0 G of throughput). The RNA sequencing data have been deposited to NCBI GEO database (GEO accession cat. GSE172198).\u00ae Premix Ex Taq\u2122 (Tli RNaseH Plus) (TaKaRa) with gene-specific primers, and the data were normalized against \u03b2-actin as the control. PCR primers are listed in Total RNA was extracted from the cell lysis samples using Total RNA Extraction Kit  and then reverse-transcribed into cDNA using the PrimeScript\u2122 RT MasterMix  . The cDNA was used for qPCR using TB Greenhttps://biit.cs.ut.ee/gprofiler/gost), in which the \u201cordered query\u201d was selected and other parameters were set as default. The protein-protein interaction (PPI) network was assessed using the STRING website (https://string-db.org/), while the minimum required interaction score was set as the highest confidence (0.900) and kmeans clustering was set as 5. The PPI network was re-generated using Cytoscape (version 3.6.0) with a circular layout. GSEA was performed using GSEA software (v4.1.0) with the molecular signature database obtained from the GSEA website (http://www.gsea-msigdb.org/gsea/msigdb/index.jsp); number of permutations was set as 10000; no collapse was aligned to gene symbols; permutation type was set as gene set, and other parameters were set as default. The function-related information of above-mentioned genes in this study was obtained from the GENE section of NCBI (https://www.ncbi.nlm.nih.gov/). Transcription factors of DEGs were obtained from TRRUST database (https://www.grnpedia.org/trrust/).Before further analysis, the RNA data were aligned against the mouse genome  and deposited in NCBI GEO database. The GO function analysis was performed using g:profilter website (http://bioinformatics.psb.ugent.be/webtools/Venn/). For GSEA analysis, the significance of enriched pathways was set as |Normal Enrichment Score|>1.0 and NORM p-value < 0.05 and FDR q-value <0.05. For other analyses, p-value < 0.05 was considered statistically significant.All the statistical results and figures were generated using GraphPad_Prism 5.0, and the Venn diagram was obtained from the Van der Peer Lab bioinformatics website . Then, this cell solution was dropped on the glass slide to form a thin film and cooled for 10 min using ice to allow solidification. Then, an additional 75 \u03bcL of LMPA (1% DMEM solution) was dropped on this glass slice as the top layer, and the process was repeated. These samples were dipped in the lysis solution  overnight. The DNA sample was unwound for 20 min in the alkaline electrophoresis solution and electrophoresis performed for 20 min (voltage 1 V/cm and current 300 mA). Finally, these samples were stained using ethidium bromide . The images of DNA damage were obtained under Zeiss 880 confocal microscopy.After PDT intervention, 1\u00d710For p53 translocation assay and actin staining assay, B.END3 cells were seeded on glass coverslips and then fixed in 4% paraformaldehyde at room temperature for 10 min. The preparation protocol was performed using standard processes described previously , 28. p53t-test was performed: *, p < 0.05; **, p < 0.01; ***, p < 0.001; ****, p < 0.0001; ns, no significance.Statistical examination and image preparation of assays were performed using GraphPad Prism 5.0 software (GraphPad Software Inc.). Student\u2019s The therapeutic products of PDT are reactive oxygen species (ROS)  that can2 laser dose and 300 \u03bcg/mL porfimer sodium could not directly damage the DNA in cerebrovascular endothelial cells.Next, we investigated the effects of PDT on DNA and cellular skeleton. To explore the effect of PDT, we performed the comet assay to examine whether PDT intervention caused DNA damage. As shown in The cellular skeleton plays an essential role in supporting cellular structure and further affecting the cellular processes . Herein,SLC2A3 (GLUT-3), SLC35F6, ABCC1 (MRP1), SLC22A18 (efflux transporter-like protein), and SLC17A5 (acidic Sugar Transporter) are significantly inhibited, while ABCC4 (MRP4) is significantly up-regulated. The down-regulation of SLC2A3 and SLC17A5 implied that PDT might affect the cellular uptake of glucose into brain parenchyma tissue, owing to that these genes are highly associated with glucose uptake |>0.6. Herein, we identified 187 and 2976 DEGs in Ep1 and Ep2 groups compared to the control group, respectively. The Venn overlapping diagrams of DEGs in both groups are shown in Firstly, principal component analysis (PCA) was conducted to analyze the quality of RNA sequencing. As shown in 2(foldchange|>1.0). Herein, all the DEGs were uploaded to the STRING website, and 267 interaction nodes were obtained for further analysis. As shown in CDK1, CDC20, MCM5, MCM7, MCM4, CCNA2, AURKB, KIF2C, ESPL1, BUB1B) and DNA replication .To elucidate the stimulus response and identify the hub genes of endothelial cells after PDT treatment, PPI analysis of DEGs was employed  analysis of DEGs was employed. GO analysis of DEGs can provide the scope of molecular mechanism affected by external stimulus, especially for identifying specific pathway to explain how to affect molecular network. Among these GO analysis tools, GO:profiler is a robust tool for functional enrichment analysis using DEGs . Herein,For DEGs in Ep1 group compared to control group, the top 15 enriched annotations are listed in 2, number of differentially expressed genes were increased to 2976, and these genes could be divided into up- and down-regulated groups  can be obtained as shown in GO and PPI analysis of DEGs could predict the potential impact on the cellular biological processes. However, it is difficult to identify the attribution of PDT to specific pathways. Conversely, GSEA, which ranked all genes based on the expression level, can be employed to evaluate roles of DEGs on targeted pathways . Herein,via_NFKB, Reactome_nuclear_receptor_trascritpion_pathway, Hallmark_glycolysis, Hallmark_myogenesis, Reactome_neutrophil_degranulation, WP_nuclear_receptor_metapathway, WP_phyochemical_activity_on_nrf2_transcription_activation, Reactome_signaling_by_receptor_tyrosine_kinases, and WP_hair_follicle_development_cytodiffe_rentiation_part_3_of_3. For down-regulated DEGs, top 10 ranked pathways were Hall_E2F_targets, Reactme_cell_cycle, Reactome_cll_cycle_mitotic, Reactome_cell_cycle_checkpoints, WP_DNA_repair_pathways_full_network, Reactome_DNA_repair, Reactome_DNA_strand_elongation, Reactome_S_phase, Reactome_DNA_replication and Reactome_mitotic_prometaphase. These results suggested that major pathways affected by PDT may focus on inflammation response and cell cycle regulation, which is consistent with GO analysis.Before the GSEA scoring analysis, we firstly analyzed the overlaps between DEGs and pathway gene sets, which can be divided into up- and down-regulated DEGs, and top 10 ranked pathways are listed in via NFKB. The activation of KRAS signaling in endothelial cells induces ERK activity and promotes the expression of angiogenesis and notch signaling, which enhances the cell migration  through ROS/NF-\u03baB pathway . However, we did not obtain any GSEA terms in Ep1 group compared to control group. Subsequently, only GSEA results of Ep2 group compared to control group were analyzed. As shown in igration . Coagulalatelets . Hypoxia disease . As aboveceptors . Moreove pathway . Moreove pathway  or Cdc25 pathway . As a reTNFA signaling via NF-\u03baB and KRAS pathways, we utilized qRT-PCR to examine expression levels of critical genes in these pathways. Firstly, we identified the critical genes by overlapping DEGs and pathway gene sets, i.e. TNF-\u03b1 signaling via NF-\u03baB and KRAS signaling pathways  can be found below: YKH and TXL: Conceptualization, Methodology, Funding\u00a0acquisition, Supervision. YYH: Investigation, Conceptualization, Methodology, Funding acquisition, Writing \u2013 original draft preparation, Resources. LD and HW: Investigation, Writing \u2013 original draft preparation, Writing \u2013 review & editing. SC and TYL: Methodology, Writing \u2013 review & editing, Resources.This study was supported by funding from Henan Province Excellent Young Talents Training Project (YXKC2020041) and the Key Scientific And Technological Project of Henan Province (202102310037).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher."}
+{"text": "Ischemic stroke is a disease with high rate of death and disability worldwide. CircRNAs, as a novel type of non-coding RNAs, lacking 5\u2019 caps and 3\u2019 poly-A tails, has been associated with ischemic stroke. This study aimed to investigate key circRNAs related to ischemic stroke.RNA sequencing was performed obtain the circRNA expression profiles from peripheral whole blood of three ischemic stroke patients and three healthy individuals. Through bioinformatic analysis, differentially expressed circRNAs (DEcircRNAs) were identified, and GO and pathway analyses for the host genes of DEcircRNAs were conducted. The expression levels of selected circRNAs were analyzed with qRT-PCR. To further explore the functions of key circRNAs, a DEcircRNA-miRNA interaction network was constructed.A total of 736 DEcircRNAs were detected in ischemic stroke. Functional annotation of host genes of DEcircRNAs revealed several significantly enriched pathways, including Fc epsilon RI signaling pathway, B cell receptor signaling pathway, and T cell receptor signaling pathway. The qRT-PCR results were largely in keeping with our RNA-seq data. The ROC curve analyses indicated that hsa_circ_0000745, hsa_circ_0001459, hsa_circ_0003694 and hsa_circ_0007706 with relatively high diagnostic value. A circRNA-miRNA network, including 1544 circRNA-miRNA pairs, 456 circRNAs and 4 miRNAs, was obtained.The results of our study may help to elucidate the specific mechanism underlying ischemic stroke.The online version contains supplementary material available at 10.1186/s12883-021-02397-0. Stroke is a leading cause of long-term disability and life-threatening disease . IschemiLacking 5\u2019 caps and 3\u2019 poly-A tails, circular RNAs (circRNAs) are resistant to RNaseR treatment , which makes circRNAs more stable than linear RNAs . In addiCircRNAs have been reported to confer functions in multiple pathogenic processes, including cancers and stroke . Bazan eIn the current study, we employed high-throughput RNA sequencing (RNA-seq) to investigate the circRNA expression profiles of ischemic stroke patients. Bioinformatic analysis was applied to identify differentially expressed circRNAs (DEcircRNAs) and DEcircRNA-miRNA interaction networks. In addition, the expression levels of selected circRNAs were validated with quantitative real-time polymerase chain reaction (qRT-PCR). By doing this, the results of our study may help to elucidate the pathogenesis and underlying mechanisms of ischemic stroke.The cohort subjected to RNA-Seq consisted 3 ischemic stroke patients and 3 healthy individuals. The etiology of stroke was classified according to Trial of Org 10,172 in Acute Stroke Treatment (TOAST) criteria . Three phttp://bowtie-bio.sourceforge.net/index.shtml) with default parameters to detect circRNAs, respectively. In this study, circRNA expression was calculated according to the junction reads count at both ends of the circRNA, and the final junction reads count takes the average value of the two software results. The junction reads per billion mapped reads were applied to normalize all samples. DEGseq, an R package to identify differentially expressed genes or isoforms for RNA-seq data from different samples, takes uniquely mapped reads from RNA-seq data for the two samples with a gene annotation as input [p-value\u2009<\u20090.05 & |log2 FoldChange| > 2. Hierarchical clustering analysis of DEcircRNAs was performed with R package \u201cpheatmap\u201d. Then, enrichment analysis for host genes of DEcircRNAs was performed by GeneCodis3 with R package ggplot2.Using TRIzol reagent, total RNA was extracted from samples. RNA integrity and concentration were evaluated with an Agilent 2100 Bioanalyzer. Total RNA samples used in subsequent experiments fulfilled the following requirements: RNA integrity number (RIN)\u2009>\u20097.0 and 28\u00a0S/18S\u2009\u2265\u20091. In brief, total RNA was subjected to ribosomal RNA (rRNA) removal using the Ribo-Zero. To remove linear RNAs, total RNA was digested with RNase R. A total amount of 3\u00a0\u00b5g RNA was used for library preparation. Libraries for sequencing were constructed according to the manufacturer\u2019s protocol. The quality of the libraries was determined using an Agilent 2100 Bioanalyzer and ABI StepOnePlus Real-Time PCR System. Libraries measuring 100\u2013200\u00a0bp were selected. RNA sequencing was performed based on HiSeq 10X-150PE and 10 GB RNA-seq data per sample was generated. Low-quality data, adapter sequences and sequences with N base rate of raw reads\u2009>\u20091\u2009% were filtered using SOAPnukev1.5.2 (parameters: -l 15 -q 0.2 -n 0.01 \u2013i) . Then, t\u2212\u0394\u0394CT method. Statistical significance was assessed by t-test. GAPDH was utilized as an internal control. The characteristics of these individuals were presented in Table SFollowing the manufacturer\u2019s protocol, total RNA was isolated from blood samples of 15 ischemic stroke patients and 15 healthy controls with the TRIzol reagent. RNA integrity and concentration were evaluated by NanoVue Plus. By using FastQuant cDNA , we generated cDNA from 1\u00a0\u00b5g extracted RNA. The qRT-PCR analyses were performed in an ABI 7300 Real-time PCR Detection System with SuperReal PreMix Plus . The qRT-PCR thermal cycling parameters were as follows: an initial denaturation step of 15\u00a0min at 95\u2103, followed by 40 cycles of 10\u00a0s at 95\u2103 and 30\u00a0s at 55\u2103, 32\u00a0s at 72\u2103, and 15\u00a0s at 95\u2103, 60\u00a0s at 60\u2103, 15\u00a0s at 95\u2103. Relative gene expression was calculated with the 2p_adj\u2009<\u20090.05 were defined as significantly enriched pathways. To further investigate the functions of circRNAs, the target miRNAs of circRNAs were predicted based on the RNAhybrid database (https://bibiserv.cebitec.uni-bielefeld.de/rnahybrid) with -sc\u2009>\u2009150 and \u2013en\u2009<\u20097. The circRNA-miRNA interaction network was visualized with Cytoscape (http://www.cytoscape.org).Pathways with p-value\u2009<\u20090.05 and |log2 FoldChange| > 2 .A total of 22,434 circRNAs were detected in this study. Compared with normal controls, 736 DEcircRNAs (307 up-regulated and 429 down-regulated DEcircRNAs) were detected in ischemic stroke with > 2 Fig.\u00a0. These D> 2 Fig.\u00a0. Among tp\u2009=\u20092.65E-14), ubiquitin-dependent protein catabolic process (p\u2009=\u20097.03E-10), cytoplasm (p\u2009=\u20093.74E-55) and protein binding (p\u2009=\u20093.08E-43) , Fc epsilon RI signaling pathway (p\u2009=\u20095.72E-06), B cell receptor signaling pathway (p\u2009=\u20093.72E-05) and Pathways in cancer (p\u2009=\u20093.93E-05)  and two down-regulated DEcircRNAs (hsa_circ_0003694 and hsa_circ_0037852), were selected randomly from the top 10 dysregulated circRNAs  for qRT-PCR analysis. Three of these circRNAs at the level of significance and a trend in the same direction was observed for another. In other words, except for hsa_circ_0003694, the expression of the others in the qRT-PCR results generally exhibited the same pattern as that in our RNA-seq results  has been shown to be highly expressed in immune cells, including B lymphocytes, dendritic cells and natural killer cells . FujimakHsa_circ_0003694 was the second significantly down-regulated circRNA in ischemic stroke. In response to stroke, astrocytes convert to a reactive phenotype (known as reactive astrogliosis) , 36. ReaHsa_circ_0000745 has been linked with various types of cancer. It was suggested that hsa_circ_0000745 was down-regulated in gastric cancer and considered as a diagnostic marker for gastric cancer . Jiao etin vitro study indicated that miR-140-5p inhibits angiogenesis after cerebral ischemia [The DEcircRNA-miRNA interaction network demonstrated that there was a shared miR-140-5p target in hsa_circ_0001459, hsa_circ_0007706, hsa_circ_0000745, hsa_circ_0037852 and hsa_circ_0003694. It has been suggested that angiogenesis is implicated in neurological functional recovery . An in vischemia . S\u00f8renseIn this study, GO and KEGG analyses predicted and analyzed the potential circRNA function and biological pathways. The stroke-related biological processes included the immune process  and signal transduction pathways . It is known that immunity is integral parts of the pathogenic processes provoked by ischemia and reperfusion . ConsistIn conclusion, to the best of our knowledge, these five DEcircRNAs were reported for the first time that may be associated with ischemic stroke by high-throughput sequencing in this study. In addition, these five DEcircRNAs are all intron-type, suggesting they regulate their host genes through the first mechanism described above. Inevitably, the current study has some limitations. First, the sample size for RNA-seq and qRT-PCR validation was small. Second, there are differences in the distribution of hypertension and hyperlipemia between these ischemic stroke groups and healthy control groups. Third, no significant differences in the expression levels of hsa_circ_0037852 in qRT-PCR were observed between ischemic stroke groups and healthy control groups and the opposite results between RNA-seq and qRT-PCR of circ-0003694 were observed. This discrepancy probably arose because of the relatively small sample size, technical bias, the differential distribution of hypertension, hyperlipemia between these two groups, and the heterogeneity among samples. More samples that strictly meet the requirements need to be involved in our research for further verification and functional experiments of the significance of circRNAs in ischemic stroke. In addition, the ratio of the circRNA to linear expression, the effect of polymorphism on circRNA, and the specific mechanism of circRNA regulation of its host genes would be included in our future work plan.Additional file 1.Additional file 2."}
+{"text": "CSNK1G1) in the development and proliferation of HCC. We investigated the expression of Hsa_circ_101555 in HCC and normal tissues using bioinformatics. The expression level of hsa_circ_101555 was further detected by fluorescence in situ hybridization and qRT-PCR in ten HCC patients. Transwell, migration, WST-1 assays, and colony formation assays were used to evaluate the role of hsa_circ_101555 in HCC development and proliferation. The regulatory mechanisms of hsa_circ_101555 in miR-145-5p and CDCA3 were determined by dual luciferase reporter assay. A mouse xenograft model was also used to determine the effect of hsa_circ_101555 on HCC growth in vivo. hsa_circ_101555 showed greater stability than the linear RNA; while in vitro and in vivo results demonstrated that hsa_circ_101555 silencing significantly suppressed cell proliferation, migration, and invasion of HCC cells. Rescue experiments further demonstrated that suppression of miR-145-5p significantly attenuated the biological effects of hsa_circ_101555 knockdown in HCC cells. We also identified a putative oncogene CDCA3 as a potential miR-145-5p target. Thus, our results demonstrated that hsa_circ_101555 might function as a competing endogenous RNA of miR-145-5p to upregulate CDCA3 expression in HCC. These findings suggest that hsa_circ_101555 may be a potential therapeutic target for patients with HCC.Circular RNAs have been reported to play significant roles in regulating pathophysiological processes while also guiding clinical diagnosis and treatment of hepatocellular carcinoma (HCC). However, only a few circRNAs have been identified thus far. Herein, we investigated the role of a specific closed-loop structure of hsa_circ_101555 that was generated by back-splicing of the host gene casein kinase 1\u2009gamma 1 ( HCC is an aggressive disease with dismal prognosis, constituting the third leading cause of cancer-related deaths worldwide2. Despite advances in the clinical understanding of the underlying mechanisms in HCC development and progression, its 5-year survival rate remains low3. Additionally, the molecular pathogenesis and therapeutic targets in HCC remain largely unknown. Therefore, understanding the pathogenic process of HCC and its regulatory mechanisms would significantly aid its management.Hepatocellular carcinoma (HCC), the most prevalent form of primary liver cancer, represents one of the most common malignant tumors globally5. Unlike canonical linear RNAs, circRNAs form a covalently closed continuous loop structure that lack 5\u2032 caps and 3\u2032 polyadenylated tails, making them more stable than linear RNAs6. circRNAs reportedly function as molecular sponges for microRNAs (miRNAs)8 and RNA-binding proteins9, while also serving as vital regulators of gene transcription and expression11. Furthermore, circRNAs are highly conserved across multiple species and exhibit tissue-specific and development stage-dependent expression patterns13. These features imply that circRNAs possess significant functions in biological and pathological processes.Circular RNAs (circRNAs) are a newly discovered noncoding RNA (ncRNA) that ubiquitously exist in several species20. Thus, circRNAs may represent promising diagnostic markers and therapeutic targets for HCC. However, compared with other ncRNAs, such as miRNAs and long noncoding RNAs, the research on circRNA in HCC remains in its infancy. To date, only a few functional circRNAs have been discovered and characterized in HCC21, while a large number remain to be explored or identified.Recent studies have reported that circRNAs are differentially expressed in HCC and serve a central role in its carcinogenesis and progressionIn the present study, we analyzed the expression profiles of three circRNA in human HCC tissues and identified hsa_circ_101555 to be conserved and significantly upregulated. Therefore, we focused on investigating the role of hsa_circ_101555 in the development and proliferation of HCC with respect to the miR-145-5p/CDCA3 signaling axis.http://www.circbase.org), we found that hsa_circ101555 was derived from the host gene casein kinase 1\u2009gamma 1 (CSNK1G1), consisting of six exons  cyclized by the head-to-tail splicing of exon 1 and exon 6. The existence of a back-spliced junction was confirmed by Sanger sequencing  revealed that high CSNK1G1 expression in HCC was not associated with overall survival  database. We then visualized the differentially expressed circRNAs (DEcircRNAs) in HCC and normal tissue samples using the \u201climma\u201d package of R software. A false discovery rate\u2009<\u20090.05 and |log2fold-change\u2009>\u20091 were set as the cutoff criteria for screening the DEcircRNAs Fig. . Among ting Fig. . We furtIn general, the subcellular localization of circRNA determines its primary mode of action. Fluorescent in situ hybridization (FISH) analysis revealed that hsa_circ_101555 expression was higher in tumor tissues than in matched nontumor sections Fig. . FurtherWe also analyzed hsa_circ_101555 expression within the serum of HCC patients and healthy controls and found the expression to be significantly higher in HCC patients compared to healthy controls  to specifically target different binding sites on the back-splice junction sequence of hsa_circ_101555. As si-hsa_circ_101555_001 and si-hsa_circ_101555_003 effectively silenced hsa_circ_101555 expression in HCCLM3 and HepG2 cell lines, they were used for subsequent experiments Fig. . We alsoSubsequently, we detected the effect of hsa_circ_101555 knockdown and overexpression on HCC tumor progression in vitro. WST-1 assay results showed that hsa_circ_101555 silencing reduced HCCLM3 and HepG2 cell proliferation Fig. , whereasHCCLM3 and HepG2 cell migration and invasion were also suppressed by hsa_circ_101555 silencing Fig. , whereas22, while also inducing circRNA expression via binding to the upstream or downstream regions of host gene mRNA and inducing circular RNA formation25. In fact, we discovered four putative binding sites for EIF4A3 in the upstream and downstream region of the CSNK1G1 mRNA transcript (CSNK1G1 pre-mRNA) via CircInteractome (https://circinteractome.nia.nih.gov/index.html)  between hsa_circ_101555 and miR-145-5p expression levels  for miRNAs and regulate mRNA expression, we assessed the potential hsa_circ_101555 targets via a ceRNA-dependent mechanism. First, we determined the expression profiles of miRNAs from GSE115016 and GSE4187 datasets in HCC and normal tissue samples using miRNA microarray Fig. . Based oels Fig. . AdditioWe further investigated whether hsa_circ_101555 affects the function of HCC cells via miR-145-5p by determining its expression levels in vitro. qRT-PCR revealed that hsa_circ_101555 silencing increased miR-145-5p levels in HepG2 and HCCLM3 cells Fig. . We alsoNTN4, CDCA3, SLC25A25, and SLC1A2) by Venn analysis between HCC-related and miR-145-5p predicted genes  revealed that high CDCA3 levels were associated with poorer overall survival  between miR-145-5p and CDCA3 expression levels  between hsa_circ_101555 and CDCA3 expression levels . However, such reduction was not observed following mutation of the miR-145-5p binding sites Fig. . These rCDCA3 expression, while overexpression of hsa_circ_101555 had the opposite effect  and was derived from the host gene 29. hsa_circ_101555 was specifically reported as upregulated in tumor tissues and as associated with the prognosis of colorectal cancer patients, while its silencing significantly suppresses cell proliferation, induces apoptosis, and impairs the DNA repair capacity of CRC cells30. Herein, we found that hsa_circ_101555 was highly expressed in HCC cell lines (most markedly in HCCLM3) as well as patient tissues compared to adjacent nontumor tissues. More importantly, in a murine xenograft model, hsa_circ_101555 silencing significantly reduced HCC growth. We also provided evidence that the ectopic expression of hsa_circ_101555 is likely required to sustain cell proliferation. Meanwhile, we previously demonstrated that circMAST1 silencing inhibits HCC cell migration and invasion, which are important determinants of tumor metastasis31. Our results are consistent with those of previous studies that showed a regulatory role for circRNAs in cancer proliferation, migration, and invasion36. Thus, our research confirmed the stable role of hsa_circ_101555 in promoting HCC progression. Although the mechanisms through which circRNAs regulate carcinogenesis and cancer progression have not been fully elucidated, the \u201ccircRNA\u2013miRNA\u2013mRNA\u201d axis, also known as the \u201cmiRNA sponge,\u201d is frequently cited37. In the present study, we confirmed that hsa_circ_101555 is an miR-145-5p sponge, evidenced by the significant increase in miR-145-5p expression following silencing of hsa_circ_101555, which in turn inhibited the proliferation, migration, and invasion of HCC cell lines. We also confirmed a direct correlation between miR-145-5p and hsa_circ_101555 expression. Consistent with our findings, several other studies have shown that circRNAs act as miRNA sponges during the development and progression of HCC. Hu et al. reported that circASAP1 acts as a ceRNA for miR-326 and miR-532-5p, which are tumor suppressors that regulating cancer cell proliferation, colony formation, migration, and invasion38. Further, many miRNAs have been shown to play critical roles in HCC initiation, development, and progression43.miR-145-5p is a tumor suppressor that is downregulated in several cancer types including glioma44, upper tract urothelial carcinoma45, and gastric cancer46. Consistent with these reports, our findings indicate that miR-145-5p serves as a tumor suppressor miRNA in HCC. As hsa_circ_101555 sponges miR-145-5p, the increased expression of hsa_circ_101555 in HCC cells leads to a decrease in miR-145-5p expression, thereby promoting cancer cell proliferation, migration, and invasion. Conversely, inhibiting hsa_circ_101555 expression increased miR-145-5p, which consequently suppressed the proliferation, migration, and invasion of HCC cells. However, we found that simultaneous inhibition of both hsa_circ_101555 and miR-145-5p expression, resulted in increased tumorigenic properties in HCC cells compared to that in cells with only hsa_circ_101555 inhibition. Thus, our results provide evidence that hsa_circ_101555 regulates HCC progression via miR-145-5p sponging, and that hsa_circ_101555 is an upstream target of miR-145-5p.Previous studies have shown that circRNAs play an essential role in cell cycle progression and proliferation47. We confirmed, via bioinformatics and luciferase reporter gene analyses, that hsa_circ_101555\u2013miR-145-5p targets the oncogene CDCA3. Although several studies have implicated CDCA3 in the regulation of cancer development and progression50, to our knowledge, this is the first study to confirm CDCA3 expression in liver cancer tissues and report its positive correlation with hsa_circ_101555 expression and negative correlation with miR-145-5p expression. Meanwhile, a previous study in colorectal cancer demonstrated that miR-145-5p suppressed proliferation, metastasis, and epithelial\u2013mesenchymal transition by targeting CDCA351. Our results were consistent with these reports. In addition, we found that the miR-145-5p/CDCA3 axis is regulated by hsa_circ_101555 via a sponging mechanism. Further, we demonstrated that miR-145-5p suppression promotes CDCA3 expression, which in turn increases the proliferation, migration, and invasion of HCC cells. The simultaneous inhibition of miR-145-5p and CDCA3 attenuates the tumorigenic features of HCC cells to a greater extent than miR-145-5p inhibition alone. To our knowledge, our study is the first to demonstrate hsa_circ_101555 involvement in regulation of CDCA3 expression. Moreover, we demonstrated that hsa_circ_101555 silencing significantly reduces CDCA3 expression in vivo. These findings suggest that hsa_circ_101555 protects CDCA3 from miR-145-5p-mediated degradation in a ceRNA-mediated manner.The role of miRNA sponging in tumor progression has previously been described52. RNA-binding protein EIF4A3 is the core component of exon junction complex (EJC), which is considered as an important regulator of post-transcriptional regulation processes including mRNA splicing, transport, translation, and surveillance53. Through bioinformatic analysis and experiments, we predicted and screened that EIF4A3 could bind to a flanking sequence of hsa_circ_101555. Our research reveals that EIF4A3-mediated reverse splicing of exons can be a potential mechanism to induce high expression of hsa_circ_101555.The biogenesis of circRNAs is regulated by specific cis-acting elements and trans-acting factors. It has been shown that certain RNA-binding proteins promote circRNA expression54. Thus, additional research is required to further explore the role of hsa_circ_101555 in HCC. In terms of clinical diagnosis and treatment, we additional studies are required, including expanding the sample size and expression stability of hsa_circ_101555 in the peripheral blood of HCC patients, as well as evaluating the initiation of its high expression among the stages of HCC.We acknowledge that our research has certain limitations. Although this study clarifies that hsa_circ_101555 functions as a sponge of miR-145-5p to promote CDCA3-induced HCC cancer cell proliferation and invasion, circRNAs may regulate the development and progression of HCC via other mechanisms. For example, circRNAs have been shown to regulate parental gene expression and the expression of peptides/proteins in other cancersTo summarize, we found that: (1) hsa_circ_101555 is highly expressed in HCC tissues and HCC cell lines (HepG2 and HCCLM3); (2) silencing hsa_circ_101555 in a murine xenograft model significantly reduces the growth of HCC; (3) hsa_circ_101555 is likely required to sustain proliferation, migration, and invasion in HCC cell lines; (4) hsa_circ_101555 acts as an miR-145-5p sponge, while silencing hsa_circ_101555 significantly inhibits cell growth; (5) hsa_circ_101555 sponges miR-145-5p to promote CDCA3 expression; and (6) eIF4A3 induces hsa_circ_101555 cyclization and increases hsa_circ_101555 expression. Thus, our study identified a previously unrecognized role for hsa_circ_101555 in sustaining HCC progression.Our study demonstrated that hsa_circ_101555 is upregulated in HCC cell lines and patient tissues, and its high expression is associated with HCC progression. Moreover, hsa_circ_101555 functions as a tumor promotor and is required to sustain the proliferation and invasion of HCC by directly binding to miR-145-5p and impeding its suppression of CDCA3. Furthermore, we also demonstrate that EIF4A3 could mediate the biogenesis of hsa_circ_101555, but the detailed mechanism needs further study. Based on its role in regulating the miR-145-5p/CDCA3 axis, our findings suggest that hsa_circ_101555 may represent a potential novel biomarker and therapeutic target for HCC.http://ncbi.nlm.nih.gov/geo/). Detailed expression profiles are provided in Additional file 1\u20137.We downloaded the expression microarray data (CEL data) from the GSE7852, GSE94508, GSE97322, GSE115016, GSE41874, GSE115018, and GSE84402 dataset of the GEO . Inclusion criteria for patient selection was curative hepatectomy performed between 2017 and 2018. All patients were pathologically diagnosed with hepatocellular carcinoma and liver specimens were evaluated by pathologists to determine clinical staging according to the TNM classification. HCC patients with the following conditions were excluded: (1) patients \u226418 or \u226570 years of age or without full civil capacity; (2) patients with a history of preoperative anticancer radiotherapy or chemotherapy, biological, immune, or traditional Chinese medicine administration; (3) patients with incomplete postoperative follow-up data; (4) patients with a history of another organ malignancy, or systemic immune disease. All specimens were collected within 15\u2009min of removal from the body and were immediately snap-frozen in liquid nitrogen before storage at \u221280\u2009\u00b0C. Ten paired samples were used to compare the expression levels of the genes of interest between HCC and paired nontumorous tissues. The detailed clinicopathological features, as well as the correlations between hsa_circ_101555 expression and the clinical characteristics, are described in Additional files 8\u20139: Tables S2.Cell lines used in this study  were purchased from the Cell Bank of Type Culture Collection . Huh-7 and L02 were purchased from the Procell . All cells were cultured in DMEM/high glucose medium  supplemented with 10% fetal bovine serum  and 1% penicillin-streptomycin (Hyclone) in a humidified atmosphere at 37\u2009\u00b0C containing 5% CO6 cells per mouse) for 15 days, after subcutaneous incubation of HCC tumor mass. Next, 10\u2009nmol cholesterol-modified hsa_circ_101555 shRNA or control shRNA RiboBio  were intratumorally injected every 3 days for 18 days. the last day of the injections, which would account for day 33 of the experiment, the mice were sacrificed and tumor tissues were collected for examination.Six-week-old male BALB/C nude mice purchased from Vital river  were maintained under specific pathogen-free conditions with a 12-h light/dark cycle. All animal experiments were performed in accordance with a statement of compliance with ethical regulations and approved by the Biomedical Ethics Committee of the Harbin medical university. Animals are grouped randomly during the experiment. HCCLM3 cells were subcutaneously injected into the right upper back of the nude mice  in a 10\u2009\u03bcL total volume at 37\u2009\u00b0C for 45\u2009min, followed by incubation at 70\u2009\u00b0C for 10\u2009min to deactivate RNase R. The treated RNAs were used for qRT-PCR27.The circular structure of hsa_circ_101555 was confirmed by Sanger sequencing, divergent primer PCR and RNase R treatment. PCR products, amplified by divergent primers of hsa_circ_101555, were inserted into the T vector and delivered to SinoGENE  for Sanger sequencing. The results were crosschecked with the back-spliced region of hsa_circ_101555 supplied by circBASEGADPH, while miRNA expression levels were normalized to that of U6. Each sample was tested in triplicate. The relative expression was analyzed by the comparative cycle threshold (Ct) method, according to the equation 2\u2212\u0394\u0394Ct [\u0394Ct\u2009=\u2009Ct-Ct (GAPDH)]. The primer sequences of hsa_circ_101555 and U6 were designed by RiboBio . The linear101555, CDCA3, miR-145-5p, and GAPDH sequences were designed by Genscript . All experiments were performed in triplicate. The primers used in this study are listed in Additional file 10: Table STotal RNA was extracted from HCC cell lines and tissue using Trizol solution, and complement DNA was generated using the Golden 1st cDNA Synthesis kit  following RNA quantification. qRT-PCR assays were performed using Power SYBB Green PCR Master Mix . The circRNA and gene expression levels were normalized to that of For western blotting, the total protein extracts from cells were separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), transferred onto polyvinylidene difluoride membranes, and incubated with the corresponding antibodies. The membranes were developed using the enhanced chemiluminescence method . All antibodies used in this study are listed in Additional file 11: Table SFresh samples were cut to an appropriate size and fixed in 4% paraformaldehyde for 24\u2009h. The fixed specimens were dehydrated in a graded series of ethanol solutions, embedded in paraffin and cut at a thickness of 4\u2009\u03bcm. The sections were dewaxed and rehydrated using xylene and ethanol, and high-pressure heat was applied for antigen retrieval. The sections were incubated with the primary antibodies overnight at 4\u2009\u00b0C. Finally, all sections were dehydrated, cleared, mounted, and visualized with a diaminobenzidine-based colorimetric method. The antibodies used in this study are listed in Additional file 11: Table SIn situ hybridization was performed with a FISH kit . Cells, frozen HCC sections, and paired adjacent liver tissues were briefly rinsed in PBS and fixed in 4% formaldehyde for 10\u2009min. The cells were then permeabilized in PBS containing 0.5% Triton X 100 at 4\u2009\u00b0C for 5\u2009min, washed with PBS three times for 5\u2009min, and prehybridized at 37\u2009\u00b0C for 30\u2009min before hybridization. Next, an anti- hsa_circ_101555, anti-U6, or anti-18S oligodeoxynucleotide probe  was used in the hybridization solution at 37\u2009\u00b0C overnight in the dark. The next day, cells were counterstained with DAPI and imaged using a NA1.4 inverted Leica DMI6000 microscope . Images were captured using a Hamamatsu ORCA-R2 camera  and recorded using LAS AF software (Leica). The experiments were conducted in triplicate.3) were seeded into each well of 96-well plates and 10\u2009\u03bcL of WST-1 solution was added to each well at four timepoints . After 4\u2009h of incubation at 37\u2009\u00b0C, the absorbance at 450\u2009nM was measured using a Spectra Max 250 spectrophotometer . Experiments were independently performed in triplicate.Cell proliferation was assessed using the WST-1 assay . Cells (2\u2009\u00d7\u2009102) were suspended and plated into each well of 6-well plates. After 14 days incubation at 37\u2009\u00b0C in a chamber with an atmosphere of 5% CO2, colonies were fixed with 1\u2009mL of 4% paraformaldehyde  for 30\u2009min and stained with crystal violet  for 25\u2009min. Colonies were then counted after photographing the sample .For the colony formation assays, cells  of a companion plate  with prewarmed culture medium containing 20% fetal bovine serum. The cells were incubated for 24\u2009h at 37\u2009\u00b0C in 5% CO2 and subsequently fixed with 4% paraformaldehyde in PBS.Cell migration and invasion were measured using a transwell migration assay and a Matrigel invasion assay. For the transwell migration assay, 2\u20134\u2009\u00d7\u2009105 cells were suspended in 200\u2009\u00b5L of DMEM without serum and were placed in the cell culture insert precoated with 50\u2009\u00b5L Matrigel . A prewarmed culture medium containing 20% fetal bovine serum was added to the well. The cells were incubated for 48\u2009h at 37\u2009\u00b0C in 5% CO2 and were then fixed with 4% paraformaldehyde in PBS. The nonmigrated or invaded cells on the top of the membrane were gently removed with a cotton swab. Cell migration or invasion was determined by staining cells with 0.1% crystal violet  and the cells were counted under a light microscope (\u00d7200 magnification) in eight randomly selected areas.For the Matrigel invasion assay, 2\u20134\u2009\u00d7\u200910siRNA specific for hsa_circ_101555 was synthesized by RiboBio , while the inhibitor of the miR-145-5p mimics and negative control, miRNA-145-5p, siRNA of CDCA3 were synthesized by Gene Pharma . HepG2 and HCCLM3 cells were transfected with siRNA of hsa_circ_101555 or Huh-7 and SK-hep1 cells were transfected with lv-hsa_circ_101555 using the Lipofectamine 2000\u00ae siRNA transfection reagent  following the manufacturer\u2019s protocol. The target sequences of siRNAs are listed in Additional file 12: Table STargeted binding of hsa_circ_101555 to miR-145-5p was predicted using bioinformatics websites, including CircInteractome, and miRanda; whereas targeted binding of miR-145-5p to CDCA3 was predicted using TargetScan, and miRDB. The full-length sequences of hsa_circ_101555, with and without mutated predicted miR-145-5p binding sites, were subcloned into pmirGLO reporter vector . The full-length sequences of CDCA3, with and without mutated predicted miR-145-5p binding sites, were subcloned into pmirGLO reporter vector . Lipo2000 was then used for transfection of the vectors into 293\u2009T cells. Finally, luciferase activity was measured using the dual luciferase assay kit.CSNK1G1 mRNA and streptavidin-labeled magnetic beads to efficiently enrich and identify the RNA-binding protein, EIF4A3. RNA probes were labeled with biotin by in vitro transcription, and then incubated with cytoplasmic protein extract to form RNA-protein complexes. This complex was then allowed to bind streptavidin-labeled magnetic beads to separate them from the other components in the solution. After elution, western blotting was used to detect the binding proteins of EIF4A3 that interacted with CSNK1G1 mRNA.RNA pull-down technology used desulphurized biotin-labeled RNA of RIP (RNA-binding protein immunoprecipitation) assay was performed with the BersinBio\u2122 RNA Immunoprecipitation kit . Cells were collected and cultivated in RIP lysis buffer, followed by immunoprecipitation with EIF4A3 antibody . The final retrieved RNA was subjected to quantitative real-time PCR analysis. Normal mouse immunoglobulin G (IgG) served as negative controls.t-test, one-way analysis of variance (ANOVA) test, or Mann\u2013Whitney U-test, as appropriate. Correlations were calculated using Pearson correlation analysis. P\u2009<\u20090.05 was considered statistically significant.All statistical analyses were performed using SPSS version 21.0  and GraphPad Prism version 6.0  software. Categorical variables are expressed as a count or percentage and tested using Chi-squared or Fisher\u2019s exact tests, as appropriate. Continuous data are reported as mean\u2009\u00b1\u2009standard deviation (SD) and compared using Student\u2019s Supplement Materials and Methods-Additional file 1 GSE78520Supplement Materials and Methods-Additional file 2 GSE94508Supplement Materials and Methods-Additional file 3 GSE97332Supplement Materials and Methods-Additional file 4 GSE115016Supplement Materials and Methods-Additional file 5 GSE41874Supplement Materials and Methods-Additional file 6 GSE115018Supplement Materials and Methods-Additional file 7 GSE84402Supplement Materials and Methods-Additional file 8 Table S1Supplement Materials and Methods-Additional file 9 Table S2Supplement Materials and Methods-Additional file 10 Table S3Supplement Materials and Methods-Additional file 11 Table S4Supplement Materials and Methods-Additional file 12 Table S5SUPPLEMENTAL MATERIALSupplementary Figure LegendsSupplemental Figure 1Supplemental Figure 2Supplemental Figure 3Supplemental Figure 4Supplemental Figure 5Supplemental Figure 6Supplemental Figure 7Supplemental Figure 8Supplemental Figure 9"}
+{"text": "Aspergillus fumigatus is one of the most important life-threatening infections in immunocompromised patients. The alarming increase of isolates resistant to the first-line recommended antifungal therapy urges more insights into triazole-resistant A. fumigatus infections. In this study, we systematically optimized a longitudinal multimodal imaging-compatible neutropenic mouse model of IPA. Reproducible rates of pulmonary infection were achieved through immunosuppression (sustained neutropenia) with 150\u2005mg/kg cyclophosphamide at day \u22124, \u22121 and 2, and an orotracheal inoculation route in both sexes. Furthermore, increased sensitivity of in vivo bioluminescence imaging for fungal burden detection, as early as the day after infection, was achieved by optimizing luciferin dosing and through engineering isogenic red-shifted bioluminescent A. fumigatus strains, one wild type and two triazole-resistant mutants. We successfully tested appropriate and inappropriate antifungal treatment scenarios in vivo with our optimized multimodal imaging strategy, according to the in vitro susceptibility of our luminescent fungal strains. Therefore, we provide novel essential mouse models with sensitive imaging tools for investigating IPA development and therapy in triazole-susceptible and triazole-resistant scenarios.Invasive pulmonary aspergillosis (IPA) caused by the mold  Summary: A novel reproducible longitudinal multimodal imaging-compatible neutropenic mouse model of invasive pulmonary aspergillosis provides increased early fungal detection through novel red-shifted luciferase-expressing triazole-susceptible and -resistant Aspergillus fumigatus strains, and boosted bioluminescence. Aspergillus fumigatus causes a spectrum of diseases in humans, ranging from allergic-bronchopulmonary infections to acute invasive pulmonary aspergillosis (IPA)  . IPA conis (IPA) . Despiteis (IPA) . Triazoldiseases . Howeverfections . Therefoin vivo studies are indispensable to characterize fungal virulence, host-pathogen interactions, dynamics of onset and progression of IPA infections and for testing novel or current antifungal therapies under controlled conditions. By far, murine animal models are the most commonly used models for studying IPA , with immune responses and disease development comparable to those observed in humans  and micro-computed tomography (micro-CT). These techniques are non-invasive tools that provide in vivo longitudinal, dynamic, visual and quantitative information on fungal burden and lung lesion formation during the onset and progression of disease in individual infected animals. These implementations decrease the variability and number of animals needed per experiment  and the triazole drug posaconazole. Altogether, our study offers a powerful resource set for the investigation of IPA and drug efficacy studies in both triazole-susceptible and triazole-resistant scenarios.This study delivers, in a stepwise approach, a reproducible longitudinal multimodal neutropenic mouse model of IPA with increased fungal burden detection capabilities. We engineered and thoroughly characterized three bioluminescent in vivo detection of A. fumigatus, especially at the onset of disease development, we genetically engineered a triazole-sensitive and two isogenic triazole-resistant A. fumigatus strains (TRAF) that express a thermostable-red-shifted firefly luciferase . A red-shifted version was selected as it had already shown excellent performance when monitoring infections caused by C. neoformans  and its terminator (TgpdA) sequence, and was assembled with the pyrithiamine resistance gene (ptrA) as selection marker in fungal transformation. This entire cassette was flanked by the upstream and downstream flanking regions of the akuB gene to target the integration of the luciferase construct into the akuB locus accompanied by the deletion of the akuB gene. The akuB locus was selected to (1) express the luciferase from a defined locus and (2) to ease the subsequent generation of cyp51A mutant versions because the deletion of the akuB gene results in an increased frequency of homologous recombination without affecting pathogenicity in a murine infection model , and pyrithiamine was used as a selection marker. After confirmation of single-copy integration of the reporter construct into the akuB locus by Southern blot analysis  and initial BLI screening, the bioluminescent \u0394akuB strain No. 5, subsequently named Af_lucOPT_red_WT, was selected for further experiments and served as parental strain to generate the isogenic TRAF strains. To generate TRAF strains, the promoter and partial coding sequences of the wild-type cyp51A gene from Af_lucOPT_red_WT was replaced by cyp51A gene sequences harboring either a TR34/L98H or TR46/Y121F/T289A mutation, the two most commonly reported mutations conferring triazole-resistance in A. fumigatus , and mutations were confirmed by gene sequencing. Transformants meeting the required criteria were named Af_lucOPT_red_TR46 (TR46/Y121F/T289A) and Af_lucOPT_red_TR34 (TR34/L98H), and were selected for further analysis. Lastly, to confirm that the introduced cyp51A gene mutations indeed conferred triazole resistance in the transformants, the wild-type cyp51A gene was restored in selected TRAF strains through transformation with a construct containing the entire wild-type cyp51A gene of the triazole-susceptible parental strain and using the hygromycin resistance cassette  as selection marker.To improve the sensitivity of BLI for oformans . To induumigatus . At firson model . The A. umigatus . Transfoin vitro conditions, we characterized the selected strains for their triazole susceptibility, growth, sporulation, viability and bioluminescence emissions . The red-shifted luciferase expressing Af_lucOPT_red_WT, _TR34 and _TR46 strains showed comparable susceptibility phenotypes (\u00b11 dilution difference) to the non-bioluminescent A. fumigatus wild-type CBS144.89 , the bioluminescent A. fumigatus strain Af2/7/1 expressing a codon-optimized wild-type luciferase (cyp51A mutation strains V-052-35 (TR34/L98H) and CYP-15-7 (TR46/Y121F/T289A), respectively (OPT_red_TR34 harboring the TR34/L98H mutation [itraconazole minimum inhibitory concentration (MIC) >16\u2005mg/l] matched with the reported characteristic elevated itraconazole MIC (>4\u2005mg/l) conferred by this mutation , with variable itraconazole and posaconazole susceptibility , confirming that the resistance of the engineered TRAF strains was solely due to the cyp51A mutations.To confirm that the genetic manipulations of the transformants conferred the expected triazole resistance but had no negative impact on the general performance of the strains under ciferase , and theectively . The resmutation . Similartibility . The com9\u00b18.54\u00d7107 (mean\u00b1s.d.) total spores per cultured flask; P=0.1271] after 4-day incubation or in spore viability from initial inoculum  between our transformants and the wild-type CBS 144.89 reference strain  and a large strength of association of growth between strains . This brings the limit of detection (LOD) down to 610 in all bioluminescence strains that can be detected without prior activation in growth medium above background when compared to control measurements (luciferin plus PBS without spores). Spectral imaging confirmed the red-shifted spectrum of the emitted light from the newly generated strains with maximum peak total flux emissions in the 620-nm red-spectrum emission filter  in our longitudinal non-invasive tracking of IPA development, we correlated the acquired biomarker data from this study section with CFU counts, which are currently used as the gold standard for determining fungal burden and disease development. Bioluminescence imaging  showed a strong correlation (P=0.0492) with lung CFU/g . Likewise, a good correlation between CFU counts/g and micro-CT non-aerated volume , and cumulative clinical score , was observed. Thus, the correlation between our imaging biomarker readouts and CFU counts provides further support for the use of BLI and micro-CT for the longitudinal monitoring of IPA development.To further validate the use of our imaging biomarkers of bioluminescence signal  and micro-CT lung lesion development [non-aerated volume  and Af_lucOPT_red_TR46 (P=0.0004)] showed significantly increased bioluminescence emission compared to baseline scans (3 and percentage) in both TRAF stains was likewise significantly detectable on day 3 , with increased total volume mean density and decreased aerated (%) lung volume . In addition, neither red-shifted TRAF-infected mice showed significant differences in other micro-CT-derived lung biomarkers , clinical visual lesion development  with comparable disease development.To confirm the ability of the newly generated Af_lucculation . As withne scans C,D from ne scans A,B. Compelopment F,G and Celopment H, reflecin vivo luciferin application on the sensitivity of detection of pulmonary aspergillosis. An increase was of particular interest for the early stages of disease development (i.e. the very first 48\u2005h post-infection) in our murine IPA model, during which the bioluminescence signals were only slightly above background levels. Neutropenic mice were orotracheally inoculated with 5\u00d7105 Af_lucOPT_red_WT spores and followed up for disease development by longitudinal multimodal imaging. Fungal burden detection by BLI was longitudinally assessed in mice using three different luciferin doses (i.p. injection): 126\u2005mg/kg (standard reported dose) . Importantly, fungal infections were significantly detectable as early as day 1 after infection at doses of 250\u2005mg/kg (P=0.0071) and 500\u2005mg/kg (P=0.0455) compared to day 2 with the 126\u2005mg/kg dose (P=0.0166). Bioluminescence signals from the 500-mg/kg regimen were significantly higher than those from the 250-mg/kg dose (P=0.0003).To determine whether the fungal burden detection sensitivity can be increased as with other non-fungal microorganisms , we nexted dose) , 250\u2005mg/P=0.0003). Likewise, complementing the initial dose of the 250-mg/kg group to a 500-mg/kg dose resulted in a significantly increased BLI signal (1.8-fold increase) compared to the initial dose (P=0.008), but never reached the signal intensities from the direct application of 500\u2005mg/kg, which may be due to rapid uptake and elimination of luciferin from the first injection by the intestine after i.p. injection . Furthermore, there appeared to be no associated luciferin dose toxicity, as weight loss and survival were comparable among all luciferin-dose groups . This study indicates that a dose of 500\u2005mg/kg luciferin results in significantly increased total photon fluxes in lung-infected mice compared to other doses, with no indications of toxicity, enabling sensitive detection of lung infection as early as the first day after inoculation.Boosting the sensitivity of BLI for detecting aspergillosis was further confirmed by topping-up doses to a total concentration of 500\u2005mg/kg in both the 126\u2005mg/kg and 250\u2005mg/kg luciferin dose-groups on day 4 D,E. In tnjection . We verin=3 per group) were followed longitudinally using our multimodal imaging approach after orotracheal inoculation with the red-shifted luciferase-expressing strains: Af_lucOPT_red_WT, _TR34 and _TR46 . No significant sex-related differences in weight loss and survival were observed in any of the inoculated groups . Likewise, no significant differences in fungal burden were detected in infected mice, as similar bioluminescence emissions between male and female mice were observed over the time course of the experiment . Furthermore, all inoculated groups presented significantly increased bioluminescence signals compared to baseline scans from day 1 post-infection regardless of sex . Similar to infected male mice, lung lesion development  in female mice increased over time compared to baseline scans , with no significant differences in \u00b5CT-derived scores  or CFU counts  between infected mice. In summary, the development of IPA does not significantly differ in our model between female and male mice.We next applied our model to determine the possibility of sex-based differences in the development of IPA in cyclophosphamide-immunosuppressed infected mice. For this, male and female mice  and TRAF strains (Af_lucOPT_red_TR34 and Af_lucOPT_red_TR46).Finally, we assessed the potential and added value for A. fumigatus strain used for infection. IPA development in all placebo-treated mice was characterized by comparable significantly increasing bioluminescence signals from day 1 post-infection compared to baseline scans  and equivalent CFU counts on day 3 , demonstrating altogether that placebo treatment  does not modify the previously observed development of IPA after infection with either strain.The triazole posaconazole is recommended for prophylaxis and therapy of IPA , and wason day 3 C-E, leftOPT_red_TR34 and Af_lucOPT_red_TR46), in which weight loss continued over time compared to wild-type-infected mice . TRAF mice presented with significantly increased bioluminescence signals  compared to WT-infected mice at day 3. CFU counts were likewise increased in the triazole-resistant infected groups compared to wild-type-infected mice , weight loss , fungal burden , CFU counts  and lung lesion development . Thus, the observed therapeutic failure of posaconazole treatment is directly associated with the triazole-resistant phenotype of the triazole-resistant strains.With respect to posaconazole treatment, mice infected with the triazole-susceptible bioluminescent strain (wild type) revealed an initial weight loss followed by subsequent weight gain starting from day 3 after infection A, middled groups E, left oP=0.0253; Fig.\u00a0S9, right). These increases were followed by a subsequent gradual recovery of weight and a decrease in bioluminescence emission and lung lesions, with no significant differences between susceptible and triazole-resistant infected groups at day 6 [P=0.9999 (TR34) and P\u22650.9999 (TR46)]. Likewise, CFU counts were significantly decreased in all L-AmB-treated mice compared to placebo-treated mice [P=0.0415 (wild type), P=0.0500 (TR34) and P=0.0305 (TR46); Treatment with L-AmB of susceptible and TRAF-infected groups increased survival in most mice  from each infected group B, right A. fumigatus strains are suitable for drug treatment efficacy studies for IPA, not only in susceptible infections but also in triazole-resistant scenarios, as implied by the observed therapeutic failure after posaconazole treatment in triazole-resistant infected mice and the beneficial effects of L-AmB in all infected mice groups, enabling the tracking of fungal burden (BLI) as early as from the first day of infection.Overall, our results demonstrate that our newly developed red luciferase-expressing A. fumigatus strains to study triazole-susceptible and triazole-resistant infection scenarios and their treatment.In this study, we established and optimized, using a stepwise approach, a multimodal imaging-compatible reproducible neutropenic mouse model of IPA, with increased fungal burden detection using newly engineered red-shifted luciferase expressing C. albicans and C. neoformans (yeasts) but not in filamentous fungi, such as A. fumigatus  are known to have less light scattering and absorption in tissues compared to luciferases emitting in the \u2018green\u2019 spectrum, and have been successfully implemented to study superficial and deep-seated infections in microorganisms, such as umigatus . We succe models , we usedutations . We specin vitro bioluminescence emissions from our newly developed strains matched those of the Af2/7/1 strain expressing a luciferase in the green spectrum, as light scattering secondary to adjacent tissue-light absorption is absent under in vitro conditions. However, although the bioluminescent Af2/7/1 A. fumigatus strain has been successfully used to detect deep-seated Aspergillus tissue infections in neutropenic mouse models in vivo  and caused no repercussions in the fungal cell physiology, as determined by sporulation efficiencies, spore viability and growth kinetics that were comparable to the parental reference strain. Furthermore,  in vivo , biolumi in vivo . This liin vivo, it is essential to apply an immunosuppressive regimen that renders all mice neutropenic and confers the least compromise to the overall health of the animals (<15% weight loss). However, among the limited in vivo murine studies that have monitored the effects of cyclophosphamide-dose regimens on neutrophil population kinetics, variable dosing regimens between 500\u2005mg/kg and 100\u2005mg/kg have been used with variable outcomes. Moreover, none determined the severity of the effects of these regimes on the overall health of the mice   . Hence, A cyclophosphamide regimen of 150\u2005mg/kg administered i.p. at day \u22124 and \u22121 rendered all mice neutropenic for \u223c3 days, as reported by A. fumigatus infection in cyclophosphamide-immunosuppressed mice should be avoided as the health of mice, based on weight loss and survival, was severely compromised in all tested cyclophosphamide regimens; alternatives should be further investigated. Other triazoles, such as posaconazole therapy, which is also recommended for prophylaxis and therapy of IPA . Therefore, we propose an orotracheal route of inoculation. Although inoculum application by this route requires more training, results demonstrated that it favored a more efficient conidia deposition in the lower respiratory tract and more homogenous IPA development, and, as such, allowed the reduction of mice in individual groups. Compared to the intranasal route of infection, the orotracheal route significantly increased lung lesion development (micro-CT scans) and fungal lung burden , confirming that this method is a more suitable route of inoculation for IPA studies.A reproducible lung infection is crucial, especially when aiming to keep the number of animals in individual groups small. The intranasal route of infection is widely used in murine models of infection, as it allows an easy administration of the inoculum . Howeverin vivo imaging biomarker readouts given by bioluminescence and micro-CT provided a good correlation with the fungal load obtained in the lungs of infected mice. Good correlation of CFUs with BLI and micro-CT, and of bioluminescence signals with kidney CFUs, have previously been reported in a mouse model of Cryptococcosis , which is required for the optimal function of firefly luciferases (OPT_red_WT). The effects of increased luciferin substrate concentration on in vivo bioluminescence production by diverse luciferases have been described in other microorganisms, such as Mycobacterium smegmatis and mammalian cells   . Andreu igh dose . Dose-deigh dose . IncreasA. fumigatus infections. In vivo and clinical studies have reported that IPA caused by TRAF is more likely to have no therapeutic response to triazole antifungal therapy with fatal outcomes  in triazole-susceptible and -resistant outcomes . In our OPT_redt_TR34 or _TR46 strains succumbed at the end due to therapeutic failure, a trend of prolonged survival between day 3 and day 6 in both infected groups, and a trend of decreased CFU counts in the TR34 infected group, was observed compared to placebo-treated mice infected with the same triazole-resistant strains. Although these observed differences did not achieve significance, they indicate that triazole antifungals may still improve infection outcome even in TRAF infections, albeit to a limited extent. The susceptibility of triazole antifungals in vitro cannot fully predict how these drugs will perform in the in vivo context  from as early as the first day of infection, unlike previous therapeutic assessment studies  as template and introducing the mutations S284T, F295L and E354K ; as described for the monitoring of infections caused by C. neoformans (Table\u00a0S2. An 820-bp promoter of the Aspergillus nidulans PgpdA and a 322-bp gpdA terminator region (TgpdA) were amplified from genomic DNA of the A. nidulans FGSC A4 wild-type strain. PCR fragments were fused up and downstream of the luciferase to generate the luciferase reporter. The construct was cloned into the ptrA-pJET1 plasmid containing the pyrithiamine resistance gene . All three PCR products were assembled by in vitro recombination in a SmaI-restricted pUC19 plasmid using an InFusion HD cloning kit  and amplified in Escherichia coli DH5\u03b1. Plasmid DNA was isolated using a NucleoSpin plasmid kit according to the manufacturer's instructions . The \u0394akuB::lucOPT_red_ptrA cassette  was excised from the plasmid backbone through SmaI restriction and used for protoplast transformation of the A. fumigatus wild-type strain CBS144.89 using 0.1\u2005\u03bcg pyrithiamine/ml  as a selection marker. Transformants were cultivated on Aspergillus minimal medium containing 0.2\u2005mM D-luciferin (Promega) and pre-screened by imaging (chemiluminescence setting) using a Chemi-Doc XRS system . Selected transformants were analyzed for single-copy integration of the reporter construct into the akuB locus by Southern blotting using a digoxygenin-dUTP labeled probe against the akuB downstream region . The strain Af_lucOPT_red_WT (Af_\u0394akuB::lucOPT_red_ptrA No. 5) was selected for further in vitro and in vivo characterization, and served as parental strain for generating the isogenic triazole-resistant strains.To generate bioluminescent oformans . All PCRnce gene . This reA. fumigatus triazole-resistant strains, we substituted the wild-type cyp51A gene of the Af_lucOPT_red_WT A. fumigatus strain with a cyp51A gene version harboring either the TR34/L98H or the TR46/Y121F/T289A mutation. Briefly, the promoter sequence of the cyp51A gene, containing the tandem repeat and a part of the coding region comprising the point mutations of the respective cyp51A gene, was amplified from the clinical isolates V-052-35 (TR34/L98H) and CYP-15-7 (TR46/Y121F/T289A) using the oligonucleotides pCYP51A_f and tCYP51A_r. The PCR products were gel-purified and directly used for PEG-mediated transformation of the Af_lucOPT_red_WT strain. The transformants were selected by the addition of 4\u2005\u00b5g/ml of itraconazole to the transformation medium (GG10 with 1.2\u2005M sorbitol) . The resistant phenotype of selected transformants was determined and confirmed using the European Committee on Antimicrobial Susceptibility Testing (EUCAST) broth microdilution reference methodology for filamentous fungi and clinical breakpoints for A. fumigatus  . Mutations in the cyp51A gene were confirmed by sequencing as described previously (OPT_red_TR46 (Af_\u0394akuB::lucOPT_red_TS_ptrA_4003-new7) harboring the TR46/Y121F/T289A cyp51A gene mutation and the Af_lucOPT_red_TR34 (Af_\u0394akuB::lucOPT_red_TS_ptrA_3216-1) harboring the TR34/L98H were selected for further analysis.To develop isogenic orbitol) . The preeviously . The tracyp51A gene, we replaced their mutated cyp51A gene with the wild-type cyp51A gene (CBS144.89). Briefly, the oligonucleotides CypAfHind_up_f and CypAfNotTer_r were used for amplifying a fragment containing the promoter region (986\u2005bp), CDS (1619\u2005bp) and terminator region (228\u2005bp) of the wild-type cyp51A gene. A 792-bp fragment of the downstream region was also amplified using the CypAfNotDown_f and CypAfHindDown_r oligonucleotides. The two fragments were assembled by in vitro recombination in a HindIII-digested pUC19 plasmid, and the construct was amplified in E. coli DH5\u03b1. The hygromycin B resistance marker was cloned into the complementation construct using a NotI site introduced between the two downstream fragments. To perform the transformation of Af_\u0394akuB::lucOPT_red_ptrA_4003-new7 and Af_\u0394akuB::lucOPT_red_ptrA_3216-1, the complementation construct  was released from the vector backbone by HindIII restriction and then gel-purified. Selection of transformants was performed using hygromycin B (180\u2005\u00b5g/ml) as a selection marker. Single-copy integration into the cyp51A locus was verified by Southern blotting , and restoration of the wild-type sequence by cyp51A gene sequencing. The sensitivity of the complemented strains Af_lucOPT_red_TR34_comp_3216-1_No.23_hygR and Af_lucOPT_red_TR46_comp_4003 No.15_hygR against triazole antifungals  was tested using the EUCAST broth microdilution methodology.To corroborate that the observed resistance phenotype in our transformants was conferred by the introduced mutations in the 8\u00a0spores/ml) were made and stored for further experiments. Briefly, strains were cultured for 3 days at 37\u00b0C on Sabouraud agar tubes and harvested by adding 5\u2005ml of distilled water-0.1% Tween 80  and gently scraping off colonies from the surface with a disposable loop. The collected suspension was vigorously vortexed and filtered  to remove hyphae or spore clumps. Spore suspensions were subsequently centrifuged, washed and reconstituted in saline solution for Tween 80 removal. Using a Neubauer hemocytometer, spores were counted, aliquoted and stored (\u221280\u00b0C). For experiments, conidia were thawed, enumerated and diluted based on required experimental setting amounts  were inoculated with 4\u00d7106 spores per flask (n=3) were determined. Statistical significance was determined by one-way ANOVA with multiple comparison analysis with Bonferroni correction.For each tested strain, three small tissue culture flasks containing Sabouraud agar (25 cmer flask . After i8 resting conidia) and subsequent CFU counts (10-fold series dilution) after incubation for 48\u2005h at 37\u00b0C. A ratio of 1 was considered as 100% viability . This experiment was performed in triplicate and significance was calculated by one-way ANOVA with multiple comparison analysis with Bonferroni correction.Spore viability was determined as the ratio from an initial spore inoculum (1\u00d71034/L98H) and CYP-15-7 (TR46/Y121F/T289A) equivalents were determined and compared to the wild-type CBS 144.89 strain. Briefly, a 100-\u00b5l suspension containing 2.5\u00d7105 spores was added to 100\u2005\u00b5l of 2\u00d7 RPMI 1640  with 2% glucose medium in 96-well tissue-treated microdilution plates   Microdilution plates were incubated without agitation at 37\u00b0C for 24\u2005h. Optical density (OD)405\u00a0nm was measured at 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 22 and 24\u2005h using a Wallac Victor 1420 multilabel counter reader . Sequential OD measurements were used to generate growth curves for each strain (8 wells per strain). Simple linear regression analysis and growth curves correlation (Pearson r) to the CBS 144.89 wild-type strain were determined and used as estimates of the growth rates of each strain analyzed.Growth of the genetically modified red-shifted luciferase-expressing strains and of the Af2/7/1  resuspended in 200\u2005\u00b5l of PBS and D-luciferin  were transferred to a 96-well plate ; a well without conidia (PBS+luciferin) was used as a control. BLI signal in samples was measured using an IVIS Spectrum imaging system . Consecutive images were acquired for 10\u2005min using an exposure time of 30\u2005s . Peak total flux was quantified using Living Image Software (version 4.5.4) from a circular region of interest (ROI) of 0.8\u2005cm diameter covering each well. Red-emission spectra  characteristics of the red-shifted luciferase were determined using a range of emission filters from 520 to 740\u2005nm, and compared to the BL Af2/7/1 strain (green spectrum). The LOD of ungerminated conidia of each bioluminescent strain was determined as the last significant measurement  above background levels of the serial 4-fold dilutions of fungal inoculums  compared to control well measurements.To confirm the bioluminescence emission capabilities of red-shifted bioluminescent strains, serial 4-fold dilutions of fungal inoculums  were housed in individually ventilated cages with free access to food and water. To reduce the risk of bacterial infection, an antibiotic  was added to the drinking water at the initiation of the immunosuppressive regimen. Animals were randomly assigned to experimental groups. Mice were monitored daily for body weight, general condition and presence of respiratory distress during experiments, until a predefined experimental or humane endpoint was reached . All animal experiments were approved by the animal ethics committee of KU Leuven (ECD project P227/2018) in accordance with national and European regulations.In all our Mice were rendered neutropenic by i.p. injections of cyclophosphamide  according to pre-assigned immunosuppressive regimens. The following regimens, in which day 0 represents the day of intended inoculation, were used: (A) 150\u2005mg/kg on days \u22124 and \u22121; (B) 150\u2005mg/kg on days \u22124 and \u22121 with a booster  of 150-mg/kg dose on day 2; (C) 100\u2005mg/kg on days \u22124, \u22123, \u22122 and \u22121; and (D) 100\u2005mg/kg on days \u22124, \u22123, \u22122, and \u22121 with a booster of 100\u2005mg/kg dose on day 3. In addition, grapefruit juice was used as a substitute for water  to determine its effects on immunosuppression in two groups based on the regimen doses A (regimens E) and dose C (regimens F). Each regimen consisted of three mice per timepoint . Mice were monitored daily for weight loss and survival. Blood was drawn by cardiac puncture and anticoagulated using 3.8% trisodium citrate (1 unit per 9 parts of blood) for analysis. Blood cells counts were measured at predefined timepoints using an Advia 2120i hematology system . Severe neutropenia was considered as <100\u2005neutrophils/\u00b5l.The human clinical oral formulation of posaconazole  was used in this study. For administration, a 10-fold dilution was performed using sterile water to achieve a concentration of 4\u2005mg/l  to reach a concentration of 8\u2005mg/kg for oral gavage administration. L-AmB was prepared as described previously . Briefly5 spores in PBS) of the corresponding strain through either an intranasal or orotracheal  route as described previously , or by i.p. injection with ketamine and medetomidine followed by atipamezole reversal  and inoculated with 20\u2005\u00b5l of fungal inoculum suspension , a posaconazole serum concentration between 1.2 to 2.9\u2005mg/l at 24\u2005h was determined as target (www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/AFST/Files/EUCAST_E_Def_9.3.2_Mould_testing_definitive_revised_2020.pdf).For treatment experiments, therapy was initiated 1 h after infection. The triazole posaconazole was administered daily via oral gavage (8\u2005mg/kg dose). L-AmB was administered every day by i.p. injections according to stated concentration. Placebo control mice were likewise treated daily by i.p. injections of saline solution (50\u2005\u00b5l). As the posaconazole threshold (MICs) between resistance and susceptibility is much lower than those observed for other triazoles , drug moBioluminescence images were acquired daily (baseline day \u22121 and from day 1 onwards) using an IVIS Spectrum system . Animals first received an i.p. injection with D-luciferin . Mice were subsequently anesthetized with gas anesthesia as described previously and placed in a heated flow chamber in a supine position for image acquisition . AcquisiMicro-CT data were acquired using a small animal micro-CT scanner  and the following scan parameters: 50\u2005kV X-ray source, 1\u2005mm aluminum X-ray filter, 350\u2005\u00b5A current, isotropic reconstructed voxel size 50\u2005\u00b5m and 150\u2005ms exposure time per projection with 0.9\u00b0 increments, resulting in a total scanning time of \u223c3\u2005min . For ima10\u00a0CFU counts per gram of lung tissue.For CFU counts determination, lungs were homogenized in 600\u2005\u00b5l saline solution. A 10-fold dilution series  was prepared from homogenized suspension and plated (100\u2005\u00b5l) on Sabouraud agar plates (plus chloramphenicol). Plates were incubated at 37\u00b0C and counted at 48\u2005h. CFU counts were expressed in logP-value) indication is depicted above the graph when comparing timepoint per group to its own baseline. Significance was depicted next to the graph (right side) when comparing multiple groups over time in a figure.All statistical analyses were performed using GraphPad Prism version 9.0.1. . Survival analysis was performed using the log rank (Mantel\u2013Cox) test. Simple linear regression analysis with Pearson correlation analysis was used for growth curves evaluation. Repeated measurement ANOVA testing with multiple correction comparison were performed for statistical hypothesis testing between groups. Statistical analyses were performed with a two-sided alternative hypothesis at the 5% significance level. In graphs, significance ("}
+{"text": "Dear Editor,2.In transplant-ineligible patients with newly diagnosed multiple myeloma (NDMM), impaired organ function and reduced physiological reserves may lead to a frail phenotype limiting the safe use of drugs and worsening patient outcome3. Briefly, patients are stratified according to an additive score (range 0\u20135) evaluating age , CCI , ADL , and IADL . Patients are classified as \u201cfit\u201d if the additive score is 0, \u201cintermediate fit\u201d if the additive score is 1, and \u201cfrail\u201d if the additive score is \u22652. According to this score, patients aged >80 years are determined to be frail independently from the presence of geriatric impairments (defined as CCI\u2009\u2264\u20091 and/or ADL\u2009>\u20094 and/or IADL\u2009>\u20095).In 2015, the International Myeloma Working Group (IMWG) has developed an index to identify frail patients based on age, Charlson Comorbidity Index (CCI), Activities of Daily Living (ADL), and Instrumental ADL (IADL)Since age in itself does not necessarily define biological frailty, the aim of our analysis was to describe the outcome of NDMM patients aged >80 years without geriatric impairments.6.We analyzed the original cohort that was used to define the IMWG frailty score, consisting of 869 transplant-ineligible NDMM patients enrolled in three prospective trials Frail patients were divided into two groups: patients who were determined to be frail by age only  vs. patients who were determined to be frail for any other reason (Frail_by_other).n\u2009=\u2009609, 70%).The median follow-up was 65 months. Fit and intermediate-fit patients were used as reference population , only 70 patients were Frail_by_age (8.1%). The remaining 190 frail patients (21.9%) showed alterations in CCI (\u22652 in 43% of cases), ADL (\u22644 in 47% of cases), or IADL (\u22645 in 58% of cases) scores and were classified as Frail_by_other. Baseline characteristics are shown in Table Among frail patients (by_age 8.%. The rep < 0.001).As expected, Frail_by_age patients were older (median age 83) than Frail_by_other (median age 78) and No_frail patients  and less advanced disease , similarly to No_frail patients.At diagnosis, Frail_by_age patients compared to Frail_by_other patients showed a better Eastern Cooperative Oncology Group Performance Status  and treatment received Table  were foup\u2009=\u20090.021) and Frail_by_other patients , as compared to No_frail patients  was observed in both Frail_by_age . Progression-free survival (PFS) and PFS2 data showed no significant differences between the two frail groups as well  and Frail_by_other groups  and Frail_by_other group , as compared to the No_frail group . Nevertheless, the cumulative incidence of grade \u22653 non-hematologic and hematologic toxicities was not significantly different between Frail_by_age and Frail_by_other patients , suggesting that patients aged >80 years were more likely to receive a suboptimal therapy after the first line.At the current follow-up, a second therapy was started in 61% vs. 66% vs. 72% of patients in Frail_by_age vs. Frail_by_other vs. No_frail groups, respectively. Among second therapies, low-dose conventional chemotherapy without novel agents was used in 37% vs. 24% vs. 7% of Frail_by_age vs. Frail_by_other vs. No_frail patients , and late deaths p\u2009=\u20090.909) and the No_frail groups, whereas a higher risk of early death was observed in the Frail_by_other group . Within the first 2 months, 21/869 patients died overall (2%), while this percentage was significantly higher in the Frail_by_other group . The main cause of death in this time frame was death due to toxicity (62%).No significant differences in terms of early deaths were found between the Frail_by_age  and Frail_by_other  patients showed a significantly higher risk of death, as compared to No_frail patients. In this time frame, the main cause of death was progressive disease (65%), followed by toxicity (24%).Between 2 and 24 months from diagnosis, both Frail_by_age , followed by toxicity (22%).p\u2009<\u20090.001), but not in the population aged >80 years , thus supporting the hypothesis that octogenarian NDMM patients are frail independently from the presence of geriatric impairments.To exclude an OS bias due to the older age of Frail_by_age patients, we explored the impact of geriatric impairments on patients aged \u226480 years and >80 years Fig. . The preTo summarize our findings, octogenarian patients without geriatric impairments usually present with a low disease burden and a good performance status. However, the high rate of drug discontinuations and the difficulty to deliver effective treatments after the first line of treatment may lead to the observed poor survival.8. Thus, in the near future, physicians are expected to face a growing percentage of octogenarian NDMM patients without geriatric impairments9.To date, this patient population is rare, accounting for <10% of NDMM patients in clinical trials. Nevertheless, the life expectancy and health conditions of the general population are improving11. In a randomized phase III trial in intermediate-fit patients, 9 cycles of lenalidomide-dexamethasone induction followed by low-dose lenalidomide maintenance without steroids produced similar outcomes compared to continuous lenalidomide-dexamethasone (median PFS 20.2 vs. 18.3 months), thus showing that, in this patient subgroup, therapy could be de-intensified after induction without affecting patient outcome. Another trial enrolling both intermediate-fit and frail patients explored daratumumab-ixazomib-dexamethasone induction followed by daratumumab-ixazomib maintenance. A total of 70% of intermediate-fit and 61% of frail patients completed induction treatment (9 months) and PFS rates were 78% and 61%, respectively. The early death rate (\u22643 months after study entry) was higher in frail patients than in intermediate-fit patients (12% vs. 0%).New treatments  that can be safely delivered continuously for a long period of time may be better tolerated and have a lower discontinuation risk, potentially improving the outcome of this patient population. Indeed, dedicated trials selectively enrolling intermediate-fit and/or frail patients are beginning to emergeInterestingly, in our work, we observed an excess of early toxic deaths (<2 months from diagnosis) in patients who were frail due to geriatric impairments. This observation may support the exploration of dose-escalation strategies in the first months after diagnosis in frail NDMM patients presenting with geriatric impairments.In conclusion, in this work, we showed that NDMM patients who were frail by age >80 years but who did not present with any geriatric impairments had a similar OS compared to patients who were determined to be frail for any other reason. These data further support that NDMM patients aged >80 years should be classified as frail regardless of the presence/absence of any comorbidities and ADL/IADL limitations.Supplementary Appendixaj-checklist - Frail by age letter"}
+{"text": "Circular RNAs (circRNAs) play a pivotal regulatory role in bladder cancer (BC) occurrence and progression. The expression level, role and mechanism of circ_0000326 in BC remain unknown. In the present study, quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was conducted to evaluate the expressions of circ_0000326, microRNA-338-3p (miR-338-3p) and ETS Proto-Oncogene 1(ETS1) mRNA in BC tissues and cell lines. Cell counting kit-8 (CCK-8) assay, wound healing assay and flow cytometry were used to detect the impacts of circ_0000326 on BC cell growth, migration and apoptosis. Western blot was used to detect the expressions of ETS1, phospho-phosphoinositide-3 kinase (p-PI3K), phospho-AKT, PI3K and AKT protein. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed to analyze the biological function of ETS1 in BC. Here, we found that circ_0000326 expression was significantly elevated in BC cell lines and tissues, and circ_0000326 could promote BC cell growth and migration, and inhibit apoptosis. Dual-luciferase reporter gene assay confirmed that circ_0000326 and ETS1 could bind directly to miR-338-3p. Furthermore, circ_0000326 sponged miR-338-3p and up-regulated ETS1 expression. ETS1 was associated with the activation of PI3K/AKT pathway. Moreover, circ_0000326 could activate PI3K/AKT pathway by miR-338-3p/ETS1 axis. Collectively, circ_0000326/miR-338-3p/ETS1/PI3K/AKT pathway is involved in regulating BC progression. Bladder cancer (BC) is known as a common urological malignancy. Reportedly, there were 80,500 new cases and 32,900 death cases in China in 2015, and the morbidity and mortality have been increasing year by year  at 4\u00b0C. Next, the membranes were incubated with horseradish peroxidase-conjugated secondary antibody  for 2\u00a0h at room temperature. Ultimately, protein bands were visualized by the ECL luminescence reagent , with GAPDH as the internal reference.The apoptosis of BC cells was detected by annexin V and propidium iodide (PI) staining assay. To be specific, the cells were treated with 0.25% trypsin, and then resuspended in a binding buffer. Then, for each sample, the cell suspension was incubated with 5\u00a0\u00b5L of fluorochrome-conjugated annexin V staining solution  for 30\u00a0min in the dark. Subsequently, 5\u00a0\u00b5L of PI staining solution  was added to stain the cells for 30\u00a0min in the darkness. The percentage of apoptotic cells was detected with a flow cytometer .http://www.ncbi.nlm.nih.gov/geo/). The differentially expressed circRNAs were screened by GEO2R, and P value <0.05 and|log2\u2009fold\u2009change\u2009(FC) | >2 were set as the thresholds. Gene expression profiling interactive analysis (GEPIA) (http://gepia.cancer-pku.cn/) is a web tool for analyzing RNA sequencing data from the TCGA database in the standard processing pipeline. Genes that were correlated with ETS1 in BC were obtained using GEPIA. The thresholds are defined as the Pearson\u2019s correlation coefficient R \u2265\u00a00.5. Then, gene Ontology (GO) classification and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using the DAVID database (https://david.ncifcrf.gov/).The circRNA expression profile dataset GSE92675 was obtained from the GEO database  was a statistical analysis tool. Comparison between two groups and among multiple groups was performed via Student\u2019s In vitro experiments showed that circ_0000326 could promote BC cell growth and migration, and inhibit cell apoptosis. Mechanistically, circ_0000326 activated the PI3K/AKT signal pathway by sponging miR-338-3p and upregulating the expression of ETS1 (Graphic Abstract).This study was performed to explore the function and mechanism of circ_0000326 in BC development, and it was demonstrated that that circ_0000326 expression was upregulated in BC tissues and cell lines. P <\u00a00.05), it was revealed that circ_000543 (circ_0000326) expression in BC tissues was markedly higher than that in para-cancerous tissues . Additi tissues ). Subseq tissues . Hence, To detect the circular structure of circ_0000326, RNA was extracted from BC cells, and then treated with RNase R, and the results of qRT-PCR confirmed that linear MALAT1 was degraded, while circ_0000326 could not be degraded by RNase R ). Next, The cytoplasm and nuclei of BC cells were then separated to determine the distribution of circ_0000326. qRT-PCR indicated that circ_0000326 was mainly distributed in the cytoplasm of 5637 and T24 cell lines . So, cirNext, TargetScan, PicTar, PITA miRmap, microT and miRDB databases were applied to pinpoint the downstream targets of miR-338-3p, and ETS1 was among the downstream targets predicted by all the four databases . Dual-luTo verify that circ_0000326 participated in the BC development via modulating the miR-338-3p/ETS1 axis, circ_0000326 siRNA and miR-338-3p inhibitor, or circ_0000326 siRNA and ETS1 overexpression plasmid was co-transfected into 5637 and T24 cell lines, respectively. CCK-8 assay and wound healing assay confirmed that compared with the si-circ_0000326 group, the co-transfection of circ_0000326 siRNA and miR-338-3p inhibitor, or of circ_0000326 siRNA and ETS1 overexpression plasmid, could significantly enhance cell proliferation and migration . Flow cyTo elucidate the potential mechanism underlying ETS1 in BC progression, a total of 20 similar genes of ETS1 were utilized to perform the GO analysis  and KEGG pathway enrichment analysis. The most enriched biological process terms were \u2018intracellular signal transduction\u2019, \u2018cell migration\u2019 and \u2018cytokine production\u2019; the most enriched cellular component terms were \u2018cytosol\u2019 and \u2018cell surface\u2019; the most enriched molecular function terms were \u2018coreceptor activity\u2019 and \u2018chemokine receptor activity\u2019 ). In addPrevious studies report that circRNAs are closely associated with BC occurrence and development . Circ_00It is well known that circRNAs can function as ceRNAs to regulate genes\u2019 expression by targeting miRNAs . Our finRecognized as a transcription factor belonging to the ETS family, ETS1 can regulate immunity and angiogenesis . ETS1 dyThe PI3K/AKT pathway plays an important role in cancer development and is involved in the regulation of a variety of phenotypes, including proliferation, migration, differentiation and apoptosis . The actin vivo experiments.To sum up, circ_0000326 is highly expressed in BC tissues, and circ_0000326/miR-338-3p/ETS1/PI3K/AKT axis was involved in BC progression, and circ_0000326 may be a new target for BC diagnosis and treatment. However, the present study has certain limitations that need to be mentioned. First of all, the relationship between circ_0000326 expression and the prognosis of BC patients is still unclear. Moreover, the function of circ_0000326 in BC has not been confirmed via Click here for additional data file."}
+{"text": "The pathogenesis of GC involves the complex networking of multiple signaling pathways; however, the detailed mechanisms of tumorigenesis of GC remains largely unknown. Therefore, it is necessary to explore novel diagnostic/prognostic biomarkers for GC. In this study, the levels of hsa_circRNA_100269 in gastric cancer (GC) samples and cells were examined, and its effects on the biological functions of GC cells were elucidated. The levels of hsa_circRNA_100269 in specimens/cell lines were examined using RT-qPCR. Cell models with hsa_circRNA_100269 overexpression or knockdown were generated using lentiviral vectors. Cell viability was determined by MTT assay; cell migratory/invasive activity was evaluated using wound healing/Transwell assay. Cell cycle arrest and apoptosis were assessed by flow cytometry; expression of associated markers involved in cell apoptosis, EMT and the PI3K/Akt signaling were determined by RT-qPCR/immunoblotting. In vivo study was also performed using hsa_circRNA_100269 knockout mice. Our findings revealed downregulation of hsa_circRNA_100269 in GC tissues compared to non-cancerous control. Additionally, the levels of PI3K were remarkably elevated in GC tissues, where hsa_circRNA_100269 and PI3K was negatively correlated. Moreover, the expression of hsa_circRNA_100269 was associated with histology grade and occurrence of metastasis in GC patients. In addition, hsa_circRNA_100269 was downregulated in GC cells compared to normal gastric epithelial cells. Overexpressed hsa_circRNA_100269 notably inhibited the proliferation, migration, invasion and EMT of GC cells, whereas cell cycle arrest at G0/G1 phase was promoted and cell apoptosis was enhanced. Moreover, the PI3K/Akt signaling was involved in hsa_circRNA_100269-regulated GC cell proliferation, migration, invasion, EMT and apoptosis. Knockdown of hsa_circRNA_100269 also remarkably induced tumor growth in mouse model. In summary, our findings indicated that the levels of hsa_circRNA_100269 were reduced in GC. Furthermore, hsa_circRNA_100269 could suppress the development of GC by inactivating the PI3K/Akt pathway. More importantly, hsa_circRNA_100269/PI3K/Akt axis may be a novel therapeutic candidate for GC treatment. Gastric cancer (GC) is a type of aggressive tumor and one leading cause of cancer-related mortality, and the global incidence of GC are rising . The patCircular RNAs (circRNAs) are a group of new non-coding RNAs. Not like their linear counterparts, they can form a continuous circle which is characterized with a more stable structure \u20138. Due tRecent studies have revealed that circRNAs could be able to exert their function as miRNA \u2018sponges\u2019 which competitively suppress the activity of corresponding miRNAs . MoreovePrevious studies have suggested that miR-630 is associated with the regulation of various biological processes including EMT through numerous pathways such as the PI3K/Akt signaling ,17. For 56 matched GC and non-tumour samples were collected during surgery at the First Affiliated Hospital of Jinzhou Medical University from June 2017-July 2018. The tissues were sectioned and snap-frozen using liquid nitrogen post-surgery, and stored at -80\u02daC. The patients were divided into hsa_circRNA_100269 high- or low-expression group according to the mean hsa_circRNA_100269 expression value. The clinic-pathological features of recruited patients were listed in 2.Four human GC cell lines  and one normal human gastric epithelial cell line (GES-1) were obtained from the American Type Culture Collection . The cells were maintained using DMEM containing 10% fetal bovine serum (FBS), 100 \u03bcg/ml streptomycin and 100 U/ml penicillin , and cultured at 37\u02daC in a incubator supplied with 5% CO\u00ae2000 . Eight hours following transfection, culture media were replenished with fresh DMEM supplemented with 10% FBS. For the inhibition of PI3K signalling, cells were treated with LY294002 .In order to establish the cell model overexpressing hsa_circRNA_100269, wildtype (o/e-hsa_circRNA_100269) or mutant (o/e-NC) hsa_circRNA_100269 sequence was amplified using PCR, then subcloned into pcDNA3.1 His C vector . In hsa_circRNA_100269 knockdown model, shRNA sequences against hsa_circRNA_100269 (sh-hsa_circRNA_100269) or negative control (sh-NC) were obtained from Genepharm Co. Ltd. . Following annealing, shRNA were inserted in lentiviral pU6-Luc-Puro vector (Genepharm Co. Ltd.). Cells without any shRNA treatment were used as the control group. Up- or downregulation of hsa_circRNA_100269 was examined by RT-qPCR. All the transfections were performed using Lipofectamine\u00ae reagent . Quality of isolated RNA was evaluated using Bioanalyzer . RNA was then reverse transcribed into cDNA by a PrimeScript\u2122 RT kit . The target cDNA was amplified using SYBR Green PCR Master Mix , which was carried out using an ABI 7500 Real-Time PCR system  Endogenous GAPDH was used as internal control. The sequences of forward and reverse primer were as follows: hsa_circRNA_100269, 5\u2019-CTAACTATGGTCGGACGGATGA-3\u2019 and 5\u2019-CAATGATAAACCACAGACTTCGC-3\u2019; PI3K, 5\u2019-AACACAGAAGACCAATACTC-3\u2019 and 5\u2019-TTCGCCATCTACCACTAC-3\u2019; E-cad, 5\u2019-AAGAAGCTGGCTGACATGTACGGA-3\u2019 and 5\u2019-CCACCAGCAACGTGATTTCTGCAT-3\u2019; vimentin, 5\u2019-AGAACCTGCAGGAGGCAGAAGAAT-3\u2019 and 5\u2019-TTCCATTTCACGCATCTGGCGTT-3\u2019; snail, 5\u2019-TTTCTGGTTCTGTGTCCTCTGCCT-3\u2019 and 5\u2019-TGAGTCTGTCAGCCTTTGTCCTGT-3\u2019; Bax, 5\u2019-TAATCCCAGCGCTTTGGAA-3\u2019 and 5\u2019- TGCAGAGACCTGGATCTAGCAA-3\u2019; cas-9, 5\u2019-CATTTCATGGTGGAGGTGAAG-3\u2019 and 5\u2019-GGGAACTGCAGGTGGCTG-3\u2019; MMP9, 5\u2019-CAGAGATGCGTGGAGAGT-3\u2019 and 5\u2019-TCTTCCGAGTAGTTTTGG-3\u2019; GAPDH, 5\u2019-GCAAGAGCACAAGAGGAAGA-3\u2032 and 5\u2019-ACTGTGAGGAGGGGAGATTC-3\u2019. PCR program was 95\u02daC for 5 min, followed by 45 cycles of 95\u02daC for 15s, 60\u02daC for 20s and 72\u02daC for 10s.Total RNA from clinical samples or cells was extracted by TRIzolS473 , p53 , Bcl-2 , cyclin D1 , E-cad , vimentin , snail , Bax , cas-9 , MMP9  or GAPDH  at 4\u02daC overnight. The membranes were subsequently incubated with corresponding horseradish peroxidase-conjugated anti-mouse  or anti-rabbit IgG  at room temperature for 1h. Protein bands were visualized by an enhanced ECL protein detection kit . Signals were quantified using densitometric method by Image J software .Total protein was extracted by radioimmunoprecipitation assay buffer . The concentration of extracted protein was determined using bicinchoninic acid assay (Beyotime Institute of Biotechnology). Equal amount (30 \u03bcg) of samples were loaded on SDS-PAGE gel and subsequently transferred onto a PVDF membrane . Membranes were blocked using tris-buffered saline (TBS) with 5% skimmed milk at room temperature for 2 h and incubated using correspondent primary antibodies: PI3K , Akt , p-Akt4 cells were placed in 96-well plates. The proliferation of cells was examined using MTT assay  at day 1, 2, 3 and 4. Briefly, 20 \u03bcl of MTT solution was added into each well and incubated at 37\u02daC for 4 h, the absorbance at 450 nm was detected using a microplate reader .Cells were harvested 24 h post-transfection, and 1x105 cells/well and transfected with corresponding vectors. After the cells reached the confluency of 80\u2013100%, and they were pre-treated with 10ug/mL mitomycin C (Thermo Fisher Scientific) for two hours prior to wound healing assay. Then, cell monolayer was scratched in a straight line with a sterile micropipette tip and washed three times with PBS, which was replaced with fresh DMEM. Subsequently, the scratch width changes were observed immediately following the scratch and at 6, 12 and 24 h. The images were captured using a fluorescence microscope . The migration of cells was determined by ImageJ 6.0 using the following formula: Migration area ratio = proportion of closed wound area/entire field of view area.Cells were seeded onto 6-well plates at a density of 4x105 cells were suspended using FBS-free culture medium and seeded onto the Matrigel\u00ae-pre-coated  upper chamber . Subsequently, 500 \u03bcl of culture medium containing 10% FBS was added into the lower chamber. After overnight incubation, non-invasive cells were detached using a cotton swab, while invaded cells in the lower chamber were fixed using 4% paraformaldehyde and stained by 0.5% crystal violet. The numbers of invasive cells were counted in five randomly selected fields using an inverted microscope .Cells were pre-treated with 10ug/mL mitomycin C (Thermo Fisher Scientific) for two hours prior to assay. A total of 1x105 cells per well following the treatments with o/e-hsa_circRNA_100269 or o/e-NC, respectively. Then, cells were collected using low-speed centrifugation (1000rpm) at 4\u02daC for 5 mins. Cell pellets were rinsed and re-suspended in PBS, subsequently fixed with 70% pre-chilled ethanol and stored at 4\u02daC for two days. Cells were lysed prior to flow cytometry, centrifuged and then re-suspended using propidium iodide  staining buffer containing 50 \u03bcl/ml of PI with 250 \u03bcl/ml RNase A. Cell cycle distributions were determined by a flow cytometer  and then analysed using Flowjo version 7.6 software . To evaluate cell apoptosis, the suspended cells was incubated in dark at 4\u02daC for 30 mins and stained with 5 \u03bcl annexin V-FITC , and apoptosis was examined using a a flow cytometer  and subsequently analysed using Flowjo version 7.6 software .Cells were seeded onto 6-well plates at a density of 4x103) = (length x width2)/2. To initiate metastasis, 1x105 cells were suspended in 20 \u03bcl PBS and then injected in the lateral tail vein of mice. A total of five mice were included in each experimental group. The protocol of animal experiment was approved by the Ethics Committee of the First Affiliated Hospital of Jinzhou Medical University.Doxycycline-inducible constructs were produced by inserting hsa_circRNA_100269 into a tet-on circRNA expression vector (Addgene #92351). The CMV promoter was replaced with a tet-on promoter to activate transcription in the presence of doxycycline (Dox). Genomic region of hsa_circRNA_100269 was amplified and cloned into the NheI/MluI-digested vector. The construct was then transfected into MNK-45 cells, which were further used for inoculation into nude mice. BALB/C nude mice were purchased from the Laboratory Animal Research Centre of Jinzhou Medical University. The mice were routinely housed in a temperature-controlled environment (22\u00b12\u02daC) with 60% relative humidity, under a 12-h dark/light cycle with libitum access to food and water for at least three days before the experiments. Mice were randomly grouped (n = 5 in each group) and injected with MNK-45 cells. Briefly, a total of 1x107 cells were suspended in 200\u03bcl PBS and injected into the back subcutaneously. Mice with developing tumors were monitored four times a week. For the dox-induction group, the cells were pre-treated with 1\u2009\u03bcg/mL doxycycline to prime circRNA expression one day before harvesting for injection. Dox-induction animals were given 1\u2009mg/mL doxycycline water, which was changed every 2 days for the duration of the experiment. Six weeks post-injection, the mice were sacrificed, and the tumor tissues were removed and examined. Tumor volume was calculated as follows: V . The significance of differences was analysed using one-way analysis of variance (ANOVA) or the Student\u2019s t-test. A student-Newman-Keuls test was carried out after ANOVA. The association between RNA expression was determined using Spearman\u2019s correlation analysis. All the experiments were performed in triplicate. P<0.05 was considered to indicate a statistically significant difference.The levels of hsa_circRNA_100269 were examined in 56 matched GC and non-tumour samples by RT-qPCR. The data suggested that hsa_circRNA_100269 was significantly downregulated in GC samples compared to para-carcinoma controls . AdditioIn order to investigate the effects of hsa_circRNA_100269 on the progression of GC, hsa_circRNA_100269 was overexpressed in AGS and MKN-45 cells. The transfection efficiencies were evaluated by RT-qPCR . FurtherAccording to the abovementioned results, hsa_circRNA_100269 was able to affect the proliferation and metastasis of GC cells. Furthermore, to investigate the influences of overexpressed hsa_circRNA_100269, the distribution of cell cycle and apoptosis in GC cells transfected with o/e-hsa_circRNA_100269 were evaluated compared with the control. The results revealed that GC cell cycle was dramatically shifted from S and G2/M phase to G0/G1 phase, and the cell percentage in G0/G1 phase was significantly increased, whereas those in S phase was notably reduced . FurtherAs part of gain- and loss-of-function study, hsa_circRNA_100269 was knockdown in GC cells. The efficiencies of shRNA were confirmed by RT-qPCR . MTT assTo study the effects of downregulated hsa_circRNA_100269 on cell cycle distribution and apoptosis, further experiments were performed. The results suggested that cell percentage in G0/G1 phase was remarkably reduced, whereas that in S phase was notably increased . AdditioFurther experiments were conducted to investigate the mechanisms of hsa_circRNA_100269-modulated cell growth and apoptosis in GC. Western blot analysis indicated that overexpressed hsa_circRNA_100269 was able to downregulate the expression levels of PI3K and phosphorylated Akt (p-Akt), while knockdown of hsa_circRNA_100269 enhanced the expression of PI3K and p-Akt . MoreoveTo study whether the influences of hsa_circRNA_100269 on the progression of GC cells was modulated through the PI3K/Akt pathway, GC cells were transfected by sh-NC, sh-hsa_circRNA_100269 or co-treated with LY294002. The results indicated that the effects caused by downregulated hsa_circRNA_100269 in GC cells were abolished by inactivation of the PI3K/Akt signaling . These fin vitro assays following the induced expression of hsa_circRNA_100269 Click here for additional data file."}
+{"text": "Phrynosoma platyrhinos, an iguanid lizard occupying extreme desert conditions of the American southwest. We conduct analysis of the chromosomal structure and composition of this species and compare these features across genomes of 12 other reptiles .The increasing number of chromosome-level genome assemblies has advanced our knowledge and understanding of macroevolutionary processes. Here, we introduce the genome of the desert horned lizard, The desert horned lizard genome was sequenced using Illumina paired-end reads and assembled and scaffolded using Dovetail Genomics Hi-C and Chicago long-range contact data. The resulting genome assembly has a total length of 1,901.85 Mb, scaffold N50 length of 273.213 Mb, and includes 5,294 scaffolds. The chromosome-level assembly is composed of 6 macrochromosomes and 11 microchromosomes. A total of 20,764 genes were annotated in the assembly. GC content and gene density are higher for microchromosomes than macrochromosomes, while repeat element distributions show the opposite trend. Pathway analyses provide preliminary evidence that microchromosome and macrochromosome gene content are functionally distinct. Synteny analysis indicates that large microchromosome blocks are conserved among closely related species, whereas macrochromosomes show evidence of frequent fusion and fission events among reptiles, even between closely related species.Our results demonstrate dynamic karyotypic evolution across Reptilia, with frequent inferred splits, fusions, and rearrangements that have resulted in shuffling of chromosomal blocks between macrochromosomes and microchromosomes. Our analyses also provide new evidence for distinct gene content and chromosomal structure between microchromosomes and macrochromosomes within reptiles. The increasing number of available chromosome-level genome assemblies of non-traditional model organisms has advanced our understanding of genome evolution over large time scales, including intra- and inter-chromosomal rearrangements and karyotype evolution across amniote vertebrates. A major gap in our understanding of amniote genome structure, composition, and evolution has been due to the lack of representative reptilian genomes of high enough quality to compare chromosome composition and structure. From data that are available, reptiles (the clade of Sauropsida) seem to exhibit particularly high levels of karyotypic variation Fig.\u00a0 1, 2]. , 2. 1, 2Anolis carolinensis, with 6 chromosomes and 7 microchromosomal linkage groups [Zootoca vivipara, with 19 chromosomal linkage groups [Lacerta agilis, with 18 autosomes and Z and W sex chromosomes [Podarcis muralis, with 18 autosomes and a Z sex chromosome [Salvator merianae, with chromosome-scale scaffolds that have not been fully ascribed to specific chromosomes [Although microchromosome organization in avian species is relatively conserved at a karyotypic level , microche groups ; the vive groups ; the sanomosomes ; the comromosome ; and thePhrynosoma platyrhinos; NCBI:txid52577) and use this genome to conduct comparative analysis of chromosome content and evolution across reptiles. This species is widely distributed across the southwestern deserts of north America, including some of the hottest and driest places on Earth , snakes , which  viridis , Thamnop elegans , and Najaja naja ), 1 birds gallus ), and tu scripta , Gopherucoriacea ). Our fiP. platyrhinos was sequenced at 21,053.74-fold physical coverage using the Dovetail Genomics HiRise\u2122 [A. carolinensis and Leiolepis reevesii [The genome of  HiRise\u2122  sequenci HiRise\u2122 , 28, andreevesii , the 7 lP. platyrhinos microchromosomes by descending length and numbered them microchromosomes 1\u201311  was used to assemble the transcriptome of 4 0413p1 . The finP. platyrhinos genome assembly (JAIPUX010000000) using the gene prediction software MAKER v. 2.31.10 [P. platyrhinos genome annotation from the total 5,310 BUSCO markers present in the library \u201ctetrapoda_odb10.2019\u201311-20\u201d  than microchromosomes .Chromosomal composition analyses indicate that overall gene density (GD) and GC content tended to be lower on .9%\u00a0SD 1., median .9%\u00a0SD 1., median We assessed whether macrochromosomes and microchromosomes contain distinct functional classes of genes using pathway analyses. From the total of 16,384 protein-coding genes that were identified by homology search, 9,590 gene IDs on macrochromosomes and 3,129 on microchromosomes were identifiable by PANTHER16.0 , 38 usinP. platyrhinos genome and 12 species  for which chromosome-level genome assemblies were available . We performed synteny analyses using a \u201cchromosome painting\u201d technique (see Methods), which established homology between sets of 100-bp in silico \u201cmarkers\u201d from the P. platyrhinos chromosome scaffolds and regions of the genomes of the other reptile species , so in  species . We quanA. carolinensis shows the highest values for SR in microchromosomes , SR \u223c 1 for all microchromosomes (except microchromosome 6). In G. gallus, SR \u223c 1 for all microchromosomes except microchromosome 1. In turtles, mean SR values for microchromosomes are >1, but this is largely driven by higher SR values on microchromosomes 1, 4, and 6 , except for macrochromosome 6  generally possess a greater number of smaller macrochromosomes than P. platyrhinos and associated higher SR values. At greater phylogenetic distances, the breakdown of chromosomal synteny from lizards to other reptilian lineages becomes more apparent (cumulative SR \u223c 30 in turtles) and showing greater rearrangements and partitions of syntenic blocks in macrochromosomes than in microchromosomes , and the only member of this family with well-assembled microchromosomes, thereby contributing a new valuable resource for comparative genomics of reptiles. For P. platyrhinos, we identified scaffolds representing the 6 macrochromosomes and 11 microchromosomes that comprise the known karyotype for the genus Phrynosoma [P. platyrhinos genome relative to that of A. carolinensis does enable some of the first comparisons of chromosome evolution in lizards that incorporates patterns distinct to macro- versus microchromosomes. Our analyses of this and other comparative reptilian genomes highlight distinct functional classes of genes, chromosomal structure, and rearrangement patterns in microchromosomes compared with macrochromosomes.The rynosoma , 28, 41.P. platyrhinos, GC content, GD, and repeat element density differ between macrochromosomes and microchromosomes, with GD and GC content being higher on microchromosomes and repeat elements being more densely distributed on macrochromosomes. Patterns of high GD on microchromosomes have been hypothesized to be an evolutionary solution to reduce overall DNA mass and increase recombination rates between coding regions, predominantly by reducing repeat element content [P. platyrhinos genome . Among macrochromosomes, fusion, splitting, and translocation to other chromosomes in more distantly related species such as turtles and chicken are common, whereas microchromosomes of P. platyrhinos typically remain in single homologous blocks in these other reptilian lineages, although there seem to be exceptions based on our analysis  and resulted in 859.9 million read pairs from paired-end libraries  [RRID:SCR_010700) [\u202fk-mer\u202fsize of 49-mers, which produced an assembly with a scaffold N50 of 0.013 Mb.We sequenced and assembled the reference genome from a female desert horned lizard collected in Dry Lake Valley, Nevada (NCBI accession SAMN17187150). This specimen was collected and killed according to Miami University Institutional Animal Care and Use Committee protocol 992_2021_Apr. Liver tissue was snap frozen in liquid nitrogen and sent to Dovetail Genomics  for extraction of DNA and construction of shotgun, Chicago, and Dovetail Hi-C paired-end libraries. DNA was extracted using buffer G2, and Qiagen protease. Three initial shotgun sequencing libraries were constructed by fragmenting DNA extracts to 475\u00a0bp and using a TruSeq PCR-free library prep kit to ligate sequencing adapters and amplify each library. The resulting libraries were sequenced on an Illumina HiSeqX  . Using tin vitro and fixed with formaldehyde. Fixed chromatin was digested with DpnII,\u202fthe 5\u2032 overhangs filled in with biotinylated nucleotides, and then free blunt ends were ligated. After ligation, crosslinks were\u202freversed,\u202fand the DNA purified from protein. Purified DNA was treated to remove biotin that was not internal to ligated fragments. The DNA was then sheared to \u223c350\u00a0bp mean fragment size and sequencing libraries were generated using\u202fNEBNext\u202fUltra enzymes and Illumina-compatible adapters. Biotin-containing fragments were isolated using streptavidin beads before PCR enrichment of each library.\u202fThe libraries were sequenced on an Illumina\u202fHiSeqX. The number and length of read pairs produced for all libraries was 528 million 2 \u00d7 150\u00a0bp\u00a0paired-end reads  using a paired-end 150\u00a0bp run by Novogene Corporation, Inc.  from a male lizard collected and killed according to Miami University Institutional Animal Care and Use Committee protocol 992_2021_Apr at the same locality as the genome animal. For each library, total RNA was extracted using Trizol reagent, and unstranded mRNAseq libraries were individually prepared using a NEBNext Ultra RNA Library Prep kit with library insert sizes of 250\u2013300\u00a0bp and sequenced on an Illumina Hiseq4000 platform  [A. carolinensis [S. merianae, 3 microchromosome account for this scaffold, while the rest of the scaffolds were linked to a specific microchromosome. Given that Chicago libraries reconstitute chromatin in vitro, interactions between distinct chromosomes are significantly reduced compared with in vivo Hi-C libraries [According to the karyotype for phrynosomatid  and P. ptyrhinos , 54 (2n st+2.8.0  using preevesii)  and X-lilinensis  downloadibraries . Also, mibraries  than expRRID:SCR_015027) [de novo prediction of repeat families. To annotate genome-wide complex repeats, we used RepeatMasker v. 4.0.8  [Repeat elements were first identified using RepeatModeler v. 1.0.11   for de n_012954)  with def_012954) . We thende novo P. platyrhinos transcriptome assembly and protein datasets consisting of all annotated proteins for A. carolinensis [RRID:SCR_008417) [RRID:SCR_015008), which has an internal pipeline to automate the training of Augustus based on a set of conserved, single-copy orthologs for Tetrapoda (Tetrapoda odb9 dataset) [ab initio gene prediction (\"est2genome = 0\u201d and \u201cprotein2genome = 0\u201d options set) using transcripts, proteins, and repeat elements resulting from the first MAKER round as the empirical evidence (in GFF format) to produce gene models using the AUGUSTUS within the MAKER. For all MAKER analyses, we used default settings, except for \u201ctrna\u201d (set to 1), \u201cmax_dna_len\u201d , and \u201csplit_hit\u201d . We used the gene models from our second round of MAKER annotation to re-optimize AUGUSTUS as described above before running 1 final MAKER analysis (round 3) with the re-optimized AUGUSTUS settings . We compared annotation edit distance (AED) distributions, gene numbers, and average gene lengths across each round of Maker annotation to assess quality and used our final MAKER round  as our final gene annotation.We used MAKER v. 2.31.10  as a conlinensis  from NCB_008417) . To do sdataset) . We ran A. carolinensis, Pogona vitticeps [, P. muralis [Gekko japonicus [Python molurus [Pseudonaja textilis [Notechis scutatus [Protobothrops mucrosquamatus [, Thamnophis sirtalis [, Alligator mississippiensis [, Alligator sinensis [, Crocodylus porosus [, Chrysemys picta [, Terrapene carolina [, Chelonia mydas [, Pelodiscus sinensis [, G. gallus, Homo sapiens [, Mus musculus [We ascribed gene IDs based on homology using reciprocal best-blast  and stringent 1-way blast  searches against protein sequences from NCBI for itticeps , P. mura muralis , Gekko japonicus , Python  molurus , Pseudontextilis , Notechiscutatus , Protoboquamatus , Thamnopsirtalis , Alligatppiensis , Alligatsinensis , 67, Cro porosus , Chrysemys picta , Terrapecarolina , Cheloniia mydas , Pelodissinensis , G. gall sapiens , Mus musmusculus , and Swimusculus  using a musculus . We alsomusculus .A. carolinensis, G. gallus, M. musculus, and H. sapiens) were selected as the reference for gene IDs. PANTHER assigned each gene to \u22651 of the 164 pathways identified for P. platyrhinos genome annotation  classification system. Four model organisms , 3 snakes , 1 bird , and 3 turtles .We explored broad-scale structural evolution across reptilian genomes using synteny analyses. We obtained chromosome-level genome assemblies from the NCBI database for 5 lizards . Following Schield et al. [Salvator merianae was the only species in our analysis without assembled chromosomes, so we analyzed the 19 longest scaffolds  containing the majority of confirmed markers [We used a previously established method for painting , 78 to pP. platyrhinos across scaffolds from the 12 target species, we calculated Simpson Dominance Index (D) and its reciprocal, which, in this context, can be considered the effective number of target chromosomes (C) containing homologies from a given P. platyrhinos chromosome:To assess the distribution of syntenic blocks of i represents a P. platyrhinos chromosome, j represents a target species, m is the number of scaffolds in the target species j containing homologies from the ith P. platyrhinos chromosome, and k represents a specific target scaffold. Values of D can range between 0  and 1 . Values of C can range between 1 (full dominance) and m .where GigaScience\u00a0database (GigaDB) [The chromosome-level genome assembly, annotation files, and other supporting datasets are available in the\u00a0(GigaDB) . The rawFigure S1: Repeat elements, GC content, and gene density calculated in 1-Mb windows for each chromosome of P. platyrhinos (2 scaffolds for macrochromosome 3 are concatenated).Figure S2: Proportion of identified gene IDs from protein-coding annotation to unidentified gene IDs by PANTHER (a) across the chromosomes  and (b) between 2 groups of chromosomes .Figure S3: Investigating potential misassembled point on a final scaffold. (a) Chicago scaffolds assembled to a final scaffold \u201cSc4326_4427\u201d were used to investigate a possible misassembled point. (b) Repeat elements, GC content, and gene density calculated in 1-Mb windows were used as evidence to find breakpoint on this final scaffold. Outlined cells are where the breakpoint was placed. Then microchromosomes were numbered on the basis of size, so these 2 scaffolds were numbered as microchromosome 10 (left portion) and microchromosome 6 (right portion).Figure S4: Distribution of P. platyrhinos total annotated protein-coding genes with identified IDs in PANTHER database. Among 164 PANTHER pathways assigned to P. platyrhinos protein-coding genes, each pathway accounts for a different number of genes (2 < genes per pathway < 759)\u00a0that may belong to a specific chromosome  or group of chromosomes (13 pathways only in macrochromosomes group).Supplementary Table S1: The corresponding scaffolds (first column) for each chromosome ofP. platyrhinos (second column) and scaffold length (third column) in base pairs. *This scaffold was broken down into 2 microchromosomes (6 and 10).Supplementary Table S2: Best blast hits of complementary DNA [A. carolinensis and L. reevesii against the genome of P. platyrhinos.tary DNA  and * intary DNA  from A. Supplementary Table S3: Number, length, and percentage of annotated repeat elements identified.Supplementary Table S4: Comparison of molecular pathways analysis on macrochromosomes and microchromosomes. Second column shows the specific pathways identified on each chromosome. Third column shows the pathways that belong to specific group of chromosomes.Supplementary Table S5: Genome assemblies and number of markers used for in silico painting. All assemblies are available through NCBI under the appropriate accession.AED: annotation edit distance; BLAST: Basic Local Alignment Search Tool; bp: base pairs; BUSCO: Benchmarking Universal Single-Copy Orthologs; C: effective number of target chromosomes; D: Simpson Dominance index; GD: gene density; kb: kilobase pairs; Mb: megabase pairs; NCBI: National Center for Biotechnology Information; SR: Simpson Reciprocal.All animals were collected and killed according to Miami University Institutional Animal Care and Use Committee protocol 992_2021_Apr.The authors declare that they have no competing interests.This work was supported by startup funds from Miami University to Tereza Jezkova. Keaka Farleigh was supported by the National Science Foundation Graduate Research Fellowship Program (Award No. 2037786).N.K. and T.J. designed the project and wrote the first draft of the manuscript. N.K., A.A., K.F., D.C.C., and D.R.S. performed bioinformatics and data analyses. All authors contributed to writing and approved the final manuscript.giab098_GIGA-D-21-00044_Original_SubmissionClick here for additional data file.giab098_GIGA-D-21-00044_Revision_1Click here for additional data file.giab098_GIGA-D-21-00044_Revision_2Click here for additional data file.giab098_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giab098_Reviewer_1_Report_Original_SubmissionHardip Patel -- 4/22/2021 ReviewedClick here for additional data file.giab098_Reviewer_1_Report_Revision_1Hardip Patel -- 10/4/2021 ReviewedClick here for additional data file.giab098_Reviewer_2_Report_Original_SubmissionTonia Schwartz -- 5/11/2021 ReviewedClick here for additional data file.giab098_Reviewer_2_Report_Revision_1Tonia Schwartz -- 10/14/2021 ReviewedClick here for additional data file.giab098_Supplemental_Tables_and_FiguresClick here for additional data file."}
+{"text": "Objective: An increasing number of studies have demonstrated that circular RNAs (circRNAs) are involved in tumor progression. However, the role of hsa_circ_0000073 in osteosarcoma (OS) is still not fully elucidated.Methods: Quantitative reverse transcription-polymerase chain reaction or Western blot was used to detect the gene expression. GeneChip analysis, bioinformatics, luciferase reporter, and RNA immunoprecipitation assays were adopted to predict and verify the relationships between genes. Counting Kit-8 Assay, clone formation assay, wound-healing assay, transwell assays, cell cycle assays, and in vivo tumorigenesis were used to evaluate cell function.Results: hsa_circ_0000073 was highly expressed in OS cell lines and could promote OS progression, including proliferation, migration, invasion, and cell cycle in vitro as well as tumorigenesis in vivo. Mechanically, hsa_circ_0000073 could readily downregulate the expression of CCNE2 and MDM2 through miR-1252-5p. Rescue experiments validated miR-1252-5p mimics, or CCNE2/MDM2 short hairpin RNA could reverse the hsa_circ_0000073 overexpressing-induced impairment of malignant tumor behavior.Conclusion: hsa_circ_0000073 functions as a tumor promoter in OS to increase malignant tumor behavior through sponging miR-1252-5p and regulating CCNE2 and MDM2 expression, which could be a novel target for OS therapy. Osteosarcoma (OS), the most frequent bone tumor from malignant mesenchymal cells, is the leading cause of cancer mortality in children and teenagers. Unfortunately, although advanced surgery combined with chemotherapy has been practiced in clinical, the patients with OS have been shown to only approximately 65\u201370% in 5-year survival rate, and many patients suffer from potential distant metastasis .It is widely accepted that patients with OS may benefit from the novel and efficacious treatment methods established, such as molecule-targeted therapies. However, little progress has been made in recent decades. Therefore, there is a pressing need to profoundly investigate the molecular mechanism underlying OS progress, especially complex gene regulation axes, which could help us develop robust interventions and therapies .Circular RNAs (circRNAs) are a subclass of endogenous non-coding RNAs with no polyadenylated tail, which have a closed circular structure joined by the 3\u2032 and 5\u2032 ends . IncreasA normal human osteoblast cell line (hFOB1.19), three OS cell lines , were obtained from the Chinese Academy of Sciences  and cultured under standard conditions . For a complete medium, 100 U/ml penicillin G, streptomycins, and 10% fetal bovine serum were mixed in the Dulbecco\u2019s modified Eagle\u2019s medium .For transfection experiments, the empty vector (pcDNA3.1), overexpressing plasmids (hsa_circ_0000073), short hairpin RNAs (shRNAs) , miRNA mimics, and sponge  were all designed and synthesized by General Biosystems . Lipofectamine 3000  was chosen for cell transfection. The shRNAs used are shown in \u2013\u0394\u0394Ct. The primers used are displayed in TRIzol  was taken for total RNA extraction. At 37\u00b0C, 2,000 ng of total RNA was incubated with or without RNase R  for 15 min. The reverse transcription kit  and an SYBR Green PCR kit  were used for quantitative reverse transcription-polymerase chain reaction (qRT-PCR). The expression levels were normalized with glyceraldehyde 3-phosphate dehydrogenase or U6 and calculated with the 2Counting Kit-8 (CCK-8) reagent  was chosen to test cell proliferation. The transfected cells were cultured in 96-well plates. At 0, 24, 48, and 72 h, 10 \u03bcl of CCK-8 reagent was added to each well. After 2 h of incubation at 37\u00b0C, a microplate reader was taken to measure the optical density value at 450 nm.Transfected cells were plated in 12-well plates for 1-week culture before being fixed in 4% paraformaldehyde. Crystal violet solution  was used to stain. The images were captured for counting.Cells in the different groups were cultured in 12-well plates for 24 h. A 10-\u03bcl pipette tip was taken to scratch the cell surface. At 0 and 48 h after injury, the images were captured by a microscope. A relative migration rate was analyzed by measuring the migratory distance normalized to the 0-h control.A transwell chamber coating matrigel on the upper side was taken to examine the cell invasion. The transfected cells with 200-\u03bcl serum-free media were added into the transwell chambers, whereas the outer chambers were packed with the complete medium. After 48 h of culture, the bottom surface cells were fixed with 4% paraformaldehyde and stained with 0.1% crystal violet . The images were captured for counting.The flow cytometer and a cell cycle analysis kit  were used to detect the cell cycle stages. The cells were immobilized with 75% alcohol at \u221220\u00b0C for 24 h and added 500-\u03bcl propidium iodide solution [buffer:propidium iodide (20\u00d7):RNase A (50\u00d7) = 100:5:2] and incubated 30 min for the test.P < 0.05. Gene Ontology (GO)  network and competing for endogenous RNA (ceRNA) network were created by STRING 11.0 . Then, a GeneChip WT Pico Reagent Kit  was taken to analyze the differentially expressed messenger RNAs with the conditions: fold change > 1.5 and ogy (GO)  and Kyotogy (GO)  were takING 11.0  and CytoING 11.0 .Radioimmunoprecipitation assay buffer and BCA Protein assay kit  were used for protein extraction and protein concentration evaluation. Proteins were separated with a sodium dodecyl sulfate\u2013polyacrylamide gel electrophoresis gel. The polyvinylidene fluoride membrane containing proteins was blocked with 5% milk. Then, specific primary antibodies  were applied to the membrane at 4\u00b0C overnight. After incubating with secondary antibodies, an ECL Western Blotting Substrate  was used to detect the protein blots.An EZ-Magna RNA Immunoprecipitation (RIP) Kit  was taken to AGO2-RIP experiments. The HEK-293 cells were lysed and incubated with human anti-Ago2 or mouse IgG-coated beads . qRT-PCR was used to analyze the immunoprecipitated RNAs.via Circbank . On week 5, mice were killed. The dissected tumors were weighed and collected. Formalin-fixed paraffin sections (4\u20136 \u03bcm) of tumor tissues were carried out for immunohistochemistry assay. The primary antibody  and EnVision Detection System  were used according to the manufacturer\u2019s protocol. A microscope captured images. The Animal Ethics Committee of Guizhou Provincial People\u2019s Hospital approved this work.BALB/C-nu mice  were used to study tumor formation ability, in which 2 \u00d7 10t-test, or one-factor analysis was taken to analyze the group comparison. Through SPSS 22.0  analysis, statistical significance was recording as P < 0.05.The data were represented as means \u00b1 SD and the Student\u2019s We analyzed the most upregulated circRNAs in OS cell lines from GSE96964 . The hsaThen, we designed two shRNAs that targeted the specific junction sites of hsa_circ_0000073. By qRT-PCR, we found that the sh-hsa_circ_0000073-1 had a better knockdown efficiency . Thus, wTo explore the complex underlying mechanisms, we first analyzed the gene expression profiling after transfected sh-hsa_circ_0000073 in MG-63 and Saos-2 cells. After hsa_circ_0000073 was silenced, the scatter plot showed there were 1,859 upregulated and 1,848 downregulated genes in MG-63 cells, as well as 2,339 upregulated and 1,255 downregulated genes in Saos-2 cells . SubsequConsidering circRNAs are important in competing endogenous RNA networks, we searched the databases and found four miRNAs with several potential binding sites of hsa_circ_0000073 in circBank and ENCORI . The ceRSubsequently, we checked the expression of the four miRNAs after the knockdown of hsa_circ_0000073. The results of qRT-PCR uncovered that miR-1252-5p was significantly upregulated by hsa_circ_0000073 repression and was lowly expressed in OS cells . MoreoveNext, based on the ceRNA network predicted by bioinformatics, the five genes related to miR-1252-5p were selected for further investigation, which found CCNE2 and MDM2 were the most dramatically downregulated genes by hsa_circ_0000073 silencing . The furFunctionally, rescue experiments were performed to explore whether hsa_circ_0000073 could participate in the progress of OS by sponging miR-1252-5p and regulating CCNE2 or MDM2 expression. The data of CCK-8, colony formation, transwell, wound-healing, and cell cycle assays verified that miR-1252-5p mimics, sh-CCNE2, or sh-MDM2 could reverse the malignant behavior caused by hsa_circ_0000073 overexpression in OS cells . OverallA xenograft tumor model showed that the tumors derived from the cells transfected sh-hsa_circ_0000073 weighed less and grew more slowly than the control group . Next, qin vitro study of 60 circRNAs confirmed that most circRNAs have longer half-lives than linear RNAs  can be found below: GEO database and assigned GEO accession numbers as This work was approved by the Animal Ethics Committee of the Guizhou Provincial People\u2019s Hospital.XT, ZY, and ZR conceived and designed the experiments. ZR, QY, and JG performed the experiments. BL and ZY performed the statistical analysis. ZR and HH wrote the manuscript. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher."}
+{"text": "Ralstonia solanacearum is an extremely destructive phytopathogenic bacterium for which there is no effective control method. Though many pathogenic factors have been identified, the survival strategies of R. solanacearum in host plants remain unclear. Transposon insertion sequencing (Tn-seq) is a high-throughput genetic screening technology. This study conducted a Tn-seq analysis using the in planta environment as selective pressure to identify R. solanacearum genes required for survival in tomato plants. One hundred thirty genes were identified as putative genes required for survival in tomato plants. Sixty-three of these genes were classified into four Clusters of Orthologous Groups categories. The absence of genes that encode the outer membrane lipoprotein LolB (RS_RS01965) or the membrane protein RS_RS04475 severely decreased the in planta fitness of R. solanacearum. RS_RS09970 and RS_RS04490 are involved in tryptophan and serine biosynthesis, respectively. Mutants that lack RS_RS09970 or RS_RS04490 did not cause any wilt symptoms in susceptible tomato plants. These results confirmed the importance of genes related to \u201ccell wall/membrane/envelope biogenesis\u201d and \u201camino acid transport and metabolism\u201d for survival in plants. The gene encoding NADH-quinone oxidoreductase subunit B (RS_RS10340) is one of the 13 identified genes involved in \u201cenergy production and conversion,\u201d and the Clp protease gene (RS_RS08645) is one of the 11 identified genes assigned to \u201cposttranslational modification, protein turnover, and chaperones.\u201d Both genes were confirmed to be required for survival in plants. In conclusion, this study globally identified and validated R. solanacearum genes required for survival in tomato plants and provided essential information for a more complete view of the pathogenic mechanism of R. solanacearum.IMPORTANCE Tomato plant xylem is a nutritionally limiting and dynamically changing habitat. Studies on how R. solanacearum survives in this hostile environment are important for our full understanding of the pathogenic mechanism of this bacterium. Though many omics approaches have been employed to study in planta survival strategies, the direct genome-wide identification of R. solanacearum genes required for survival in plants is still lacking. This study performed a Tn-seq analysis in R. solanacearum and revealed that genes in the categories \u201ccell wall/membrane/envelope biogenesis,\u201d \u201camino acid transport and metabolism,\u201d \u201cenergy production and conversion,\u201d \u201cposttranslational modification, protein turnover, chaperones\u201d and others play important roles in the survival of R. solanacearum in tomato plants. Ralstonia solanacearum is an aerobic, motile Gram-negative bacterium with a polar flagellar tuft. This soilborne bacterium is probably the most destructive plant-pathogenic bacterium, infecting more than 200 plant species in over 50 families over a broad geographical range  is a high-throughput approach that couples genome-wide transposon mutagenesis with next-generation sequencing , 16. Tn-y plants . A totalanalysis .R. solanacearum in tomato plants was conducted in the present study to acquire a more complete view of the pathogenic mechanism of R. solanacearum. The transposon insertion library was injected into the tomato plant stem and recovered 5\u2009days postinoculation. The transposon interruption of 130 genes reduced the relative fitness of R. solanacearum within tomato plants, providing putative genes required for R. solanacearum survival in tomato plants. Furthermore, targeted gene deletion, pathogenicity assay, in vivo colonization assay, and competition index determination were performed to validate the results of Tn-seq.A Tn-seq analysis of R. solanacearum GMI1000 with approximately 240,000 individual insertion mutants, covering 70.44% to 80.96% of all potential insertion sites  and after (in vivo) infection were extracted and split into two groups for technical replicates. The DNA samples were then subjected to Tn-seq to identify the relative abundance of each insertion mutant under the stress of the in planta environment.We previously constructed a near-saturated transposon insertion library of on sites . An in pant stem . The libin vivo and in vitro replicates were 1.00 and 0.99, respectively, which indicate the reliability and repeatability of this analysis. As shown in in-vivo1 were mapped to the chromosome (NC_003295.1) of R. solanacearum strain GMI1000, and 4,636,990 reads of in-vivo1 were mapped to the megaplasmid (NC_003296.1). These reads hit 135,231 unique locations with 101,663 locations within genes. The output parameters of in-vivo2 were similar to that of in-vivo1. However, more reads were mapped to the chromosome, and fewer reads were mapped to the megaplasmid for the in vitro treatments than for the in vivo treatments. The transposon interruption of a gene essential for survival in plants would reduce the relative fitness of a mutant within tomato plants and result in fewer reads mapped to this gene. As shown in R. solanacearum strain GMI1000 were identified as putative genes required for survival in tomato plants when the threshold value was set to a ratio_reads (in vivo/in vitro) value of <0.5 with an adjusted P (proportions_reads) value of <0.01. The interruption of two genes (RS_RS05450 and RS_RS05405) increased the ratio_reads (in vivo/in vitro) value by more than 2-fold  and all Tn-seq samples (heat map). Download Copyright \u00a9 2021 Su et al.2021Su et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the R. solanacearum in tomato plants.We classified these genes into Clusters of Orthologous Groups of proteins (COG) categories to obtain an overview of the genes required for survival in tomato plants. As shown in R. solanacearum in tomato plants by Tn-seq. RS_RS01965 encodes the outer membrane lipoprotein LolB, which is a component of the LolABCDE system, responsible for sorting and localizing lipoprotein. The absence of LolA in the plant pathogen Xanthomonas campestris pv. campestris reduced the pathogen\u2019s attachment, extracellular enzyme production, and virulence  and RS_RS04475 (\u0394RS_RS04475) deletion mutants exhibited impaired growth in rich BG medium  were injected into the tomato stem to assay the colonization of these mutants. As shown in 9.9 CFU R. solanacearum were detected in 1 g tomato plant stem 5\u2009days after inoculation of the wild-type strain. However, this number was 109.0 and 108.2 for the \u0394RS_RS01965 and \u0394RS_RS04475 mutants, respectively. Moreover, the competitive index was measured to confirm the results of Tn-seq. The \u0394RS_RS01965 and \u0394RS_RS04475 strains were outcompeted by GMI1000Kanr with competitive index values of 0.19 and 0.10, respectively  of these 10 genes are involved in the shikimate pathway, which synthesizes chorismite, an important biochemical intermediate for amino acid biosynthesis. Four genes are responsible for tryptophan biosynthesis, including tryptophan synthase subunit alpha , tryptophan synthase subunit beta , anthranilate synthase component I , and phosphoribosylanthranilate isomerase . Two genes (RS_RS14785 and RS_RS05000) are involved in the biosynthesis of phenylalanine and tyrosine (RS_RS08265 and RS_RS04490), cysteine (RS_RS05790), methionine (RS_RS00135), and lysine (RS_RS05700) were also identified as essential for the survival of R. solanacearum in tomato plants.Nineteen amino acid transport and metabolism genes were identified as essential for survival in tomato plants by Tn-seq . Ten of tyrosine . In addi10.1128/mSystems.00838-21.4FIG\u00a0S2FIG\u00a0S2, TIF file, 0.7 MB.Genes involved in the \u201cphenylalanine, tyrosine, and tryptophan biosynthesis\u201d pathway are required for survival in tomato plants. Download Copyright \u00a9 2021 Su et al.2021Su et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the R. solanacearum. As shown in RS_RS09970 were mapped by 52 weighted reads in average before infection, whereas the transposon insertion mutant of RS_RS09970 was hardly detected from the library after tomato plant infection. The relative abundance of the RS_RS04490 mutant was also sharply reduced after infection  Growth of the \u0394RS_RS09970 mutant, the \u0394RS_RS04490 mutant, and wild-type GMI1000 in liquid minimal Fahraeus medium with or without tryptophan (Trp) or serine (Ser). The growth (A600) of each R. solanacearum strain in a given medium was monitored every hour via Bioscreen C Pro. The growth was indicated by the means of three biological replicates. The error bars indicates standard deviations. (B) Growth of the \u0394RS_RS09970 mutant, the \u0394RS_RS04490 mutant, and wild-type strain GMI1000 on Fahraeus agar medium with or without tryptophan (Trp) or serine (Ser). Gradient-diluted R. solanacearum strains were cultured on Fahraeus medium and photographed 24 h postinoculation. Download FIG\u00a0S3, TIF file, 0.8 MB.The \u0394Copyright \u00a9 2021 Su et al.2021Su et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the R. solanacearum to survive in tomato plants than in rich medium. Seven of these 13 genes are NADH-quinone oxidoreductase subunit-encoding genes (RS_RS10340) mutant in in vivo libraries was 0.06 times higher than that in in vitro libraries , protease modulator HflC (RS_RS06125), ATP-dependent Clp protease proteolytic subunit , and ATP-dependent Clp protease ATP-binding subunit ClpX (RS_RS08650), are involved in proteolysis. ClpP protease plays an important role in the proteostasis of prokaryotic cells and eukaryotic organelles  of XRE family transcriptional regulator RS_RS05450 was 2.4, which means that the environmental stress within tomato plants improved the relative abundance of the RS_RS05450 mutant by 2.4 times  and RS_RS05405 resulted in improved relative fitness in vivo compared with in vitro. (A) Transposon insertion distribution within efpR and RS_RS05405 of transposon insertion libraries in vivo and in vitro. (B) Growth of the \u0394RS_RS05405 mutant in BG medium. (C) Competitive index of the \u0394RS_RS05405 mutant in vitro and in vivo. The \u0394RS_RS05405 mutant and GMI1000Kanr containing a kanamycin resistance gene were coinoculated into the stems of tomato plants, and the in vivo competitive index was measured 5 days postinoculation. The \u0394RS_RS05405 mutant and GMI1000Kanr were coinoculated into BG medium, and the in vitro competitive index was measured 12 h (log phase) and 36 h (stationary phase) postinoculation. Asterisks indicate significant differences . Download FIG\u00a0S4, TIF file, 0.3 MB.Interruption of Copyright \u00a9 2021 Su et al.2021Su et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the efpR, the transposon insertion in RS_RS05405, which encodes nonheme iron oxygenase ferredoxin subunit, improved the relative fitness of R. solanacearum in tomato plants compared with that in vitro. Nonheme-iron-dependent oxygenases catalyze various reactions in the biodegradation of xenobiotics and the biosynthesis of bioactive natural products , LPS O-antigen biogenesis , LPS O-antigen ligation (RS_RS11060), and mannose metabolism (RS_RS15365). Our targeted deletion also verified that the sorting and localization system of lipoprotein RS_RS01965, as well as the functional known membrane protein RS_RS04475, is required for in vivo survival. These results highlighted the importance of the cell membrane for the pathogenicity and survival of R. solanacearum.Lipopolysaccharide (LPS) is a vital component of Gram-negative bacterial outer membrane and protects bacteria from harsh environmental conditions . The LPSo plants . ConsistR. solanacearum needs to regulate its metabolism and alter xylem sap biochemistry to adapt to this niche \u201d by COG, were associated with cysteine biosynthesis. RS_RS12160 and RS_RS06745 were also previously identified  or on BG agar medium at 28\u00b0C, except where noted otherwise. Escherichia coli strains were cultured in Luria-Bertani (LB) medium or on LB agar medium at 37\u00b0C. A final concentration of 25\u2009\u03bcg/ml kanamycin was added to the medium when needed. The growth (A600) of R. solanacearum strains with three biological repeats was monitored every hour via Bioscreen C Pro  to obtain growth curves. The \u0394RS_RS04490 mutant was cultured in Fahraeus medium  by using Bowtie tolerating a 0-bp mismatch. The bam files from mapping were subjected to sample correlation coefficient computation and visualization via deepTools (in vitro) as the control, and gene essentiality in vivo was analyzed by TSAS, a Tn-seq analysis software (in vivo/in vitro) values of <0.5 with adjusted P (proportions_reads) values of <0.01 were set as the threshold values to identify genes required for survival in tomato plants  to 4 (complete wilting) once per day. The 32 plants in a tray were inoculated for each R. solanacearum strain. Kaplan-Meier survival analysis with the Gehan-Breslow-Wilcoxon method was used to compare the pathogenicity between the mutant and wild-type strains ]/[tested strain CFU/GMI1000Kanr CFU (before inoculation)]. Five biological replicates were performed for each R. solanacearum strain. The competitive index differences between mutant/GMI1000Kanr and GMI1000/GMI1000Kanr were analyzed with an unpaired t test.Each PRJNA766096). The wig files from TSAS were deposited in Figshare (https://doi.org/10.6084/m9.figshare.14220053).The processed reads and raw reads are available in the SRA database of NCBI ("}
+{"text": "This study provides insight into the molecular events regulating cervical carcinogenesis, identifies functional circRNAs in CSCC, and improves the understanding of the pathogenesis and molecular biomarkers of CSCC and HSIL.Circular RNAs (circRNAs) are regulatory molecules that participate in the occurrence, development and progression of tumors. To obtain a complete blueprint of cervical carcinogenesis, we analyzed the temporal transcriptomic landscapes of mRNAs and circRNAs. Microarrays were performed to identify the circRNA and mRNA expression profiles of cervical squamous cell carcinoma (CSCC) and high-grade squamous intraepithelial lesion (HSIL) patients compared with normal controls (NC). Short time-series expression miner (STEM) was utilized to characterize the time-course expression patterns of circRNAs and mRNAs from NC to HSIL and CSCC. A total of 3 circRNA profiles and 3 mRNA profiles with continuous upregulated patterns were identified and selected for further analysis. Furthermore, functional annotation showed that the mRNAs were associated with DNA repair and cell division. The protein-protein interaction (PPI) network analysis revealed that the ten highest-degree genes were considered to be hub genes. Subsequently, a competing endogenous RNA (ceRNA) network analysis and real-time PCR validation indicated that hsa_circ_0001955/hsa-miR-6719-3p/CDK1, hsa_circ_0001955/hsa-miR-1277-5p/NEDD4L and hsa_circ_0003954/hsa-miR-15a-3p/SYCP2 were highly correlated with cervical carcinogenesis. Silencing of hsa_circ_0003954 inhibited SiHa cell proliferation and perturb the cell cycle  Cervical cancer is one of the most prevalent gynecological malignancies and the fourth most fatal cancer . CervicaCircular RNAs (circRNAs), a kind of noncoding RNA without a 3\u2032 tail or 5\u2032 cap, are naturally occurring endogenous molecules , 7. The In this study, we recruited not only CSCC patients and controls but also HSIL individuals. Due to the stepwise progression of CSCC, the expression of circRNAs and mRNAs could be dysregulated at any specific tumorigenesis stage. To characterize the changes in circRNA and mRNA expression, we performed trend analysis to identify the predominant circRNAs and mRNAs in the control, HSIL and CSCC groups. This study provides a starting point for further research into the molecular mechanism of circRNAs in cervical carcinogenesis, which provides new insight into the multiple and complex factors in early cervical carcinogenesis.\u00ae Pap Test (TCT) and pathology  results. Based on the International Federation of Gynecology and Obstetrics (FIGO) criteria, the clinical stage of the patients with CSCC was determined  and HSIL , 11 with only HPV16-positive HSIL and 12 with only HPV16-positive stage IA-IIA CSCC, who were collected at the Second Hospital of Shanxi Medical University between December 2019 and October 2020. We collected all patients\u2019 clinical information, including age, HPV testing, the ThinPrep2 and 37\u00b0C.SiHa  and HcerEpic  cells  were cultured with DMEM  with 10% fetal bovine serum , 100 mg/mL streptomycin, and 100 U/mL penicillin in a humidified atmosphere of 5% COTotal RNA was extracted from tissues using TRIzol Reagent  and purified with an RNeasy Mini Kit . Assaying the purity and integrity of the RNA was achieved using agarose gel electrophoresis and a UV/vis spectrophotometer .www.affymetrix.com). The data were analyzed using the robust multichip analysis (RMA) algorithm using default Affymetrix settings. The values presented are the log2 RMA signal intensity. Differentially expressed mRNAs (DEMs) and DECs among the three groups were filtered according to the following criteria: p < 0.05 and fold change > 1.2. The microarray data were uploaded to a public database (accession number: GES166466).Microarray hybridization was performed in accordance with the Affymetrix GeneChip Expression Analysis Technical Manual (http://metascape.org/) was used to analyze DEMs (The online tool Metascape (yze DEMs . Three tyze DEMs . Kyoto Ehttp://www.cs.cmu.edu/~jernst/stem) was utilized to cluster and view probable circRNA and mRNA expression patterns over time (p < 0.05).STEM software version 1.3.13 (ver time . The STEhttp://string-db.org) online database (https://cytoscape.org/). MCODE (molecular complex detection) version 3.7.1 The Cytoscape plugin was used to screen the potential hub modules.The PPI network of DEMs was constructed using the STRING  online database (http://www.targetscan.org/vert_72/)   and miRavia the 2\u2013\u0394\u0394Ct method. The top five circRNAs with the highest degree in the ceRNA network and the top five miRNAs and mRNAs for the potential circRNAs were validated by qRT-PCR. The primers for these RNAs are presented in http://gb.whu.edu.cn/CSCD/)  was performed using Q SYBR Green Supermix , and PCR-specific amplification was conducted in the 7900 HT Sequence Detection System . The expression was determined by using the threshold cycle (Ct) method, and relative expression levels were calculated n/CSCD/) , an onliTo transfect with siRNA, we used custom-designed siRNAs targeting hsa_circ_0003954 . Experiments were repeated three times. Cell cycle assays were conducted using propidium iodide stained SiHa cells by a Beckman Coulter FC500 flow cytometer  and analyzed using Modfit software.Cell proliferation was detected through the CCK-8 assay . For transient transfection experiments, 1\u00d710P < 0.05 was considered statistically significant. Figures were drawn using R Studio version 3.3.4 .Experimental data are presented as the mean \u00b1 standard deviation (SD) of at least three experiments. Significant differences were assessed by Student\u2019s t-test. p < 0.05). Thirty-two circRNAs revealed a fold change \u2265 10. Hsa_circ_0066984 (fold change ~ 24) was the most dysregulated circRNA. The candidate DECs were distributed on 46 human chromosomes, including the chromosomes 1, 2, 3 and X chromosome, which contained more circRNAs than the other chromosomes (p < 0.05). Summarization of the coding gene expression profile showed that 46 mRNAs displayed a fold change \u2265 10. CircRNA and mRNA expression patterns among CSCC, HSIL and NC samples were significantly differentially expressed, as shown by hierarchical clustering , including profiles 10, 11, 12, 13, 14 and 15 , which was consistent with the above microarray analysis results  were also validated by qRT-PCR . The expression patterns of hsa_circ_0001955/hsa-miR-6719-3p/CDK1, hsa_circ_0001955/hsa-miR-1277-5p/NEDD4L and hsa_circ_0003954/hsa-miR-15a-3p/SYCP2 fit with ceRNA network by qRT-PCR (p < 0.01). The structures of hsa_circ_0001955 and hsa_circ_0003954 are presented  based on the data from CSCD, which indicated that both circRNAs contained MREs.qRT-PCR analysis of CSCC, HSIL and NC samples was performed to validate the top 5 circRNAs, namely, hsa_circ_0016456 (degree=64), hsa_circ_0008617 (degree=30), hsa_circ_0001955 (degree=26), hsa_circ_0003954 (degree=21) and hsa_circ_0076726 (degree=14) . The cell cycle analysis showed that more SiHa cells were distributed in G1 phase and less in S phase after silencing hsa_circ_0003954, which suggested that SiHa cells were arrested at G1 phase by silencing hsa_circ_0003954  can be found below: The studies involving human participants were reviewed and approved by Ethics Committee of Second Hospital of Shanxi Medical University. The patients/participants provided their written informed consent to participate in this study.WW designed and supervised this project. HL, YL, and WW analyzed the data, and wrote the manuscript. HL and YL performed the experiments, YZ, JC, XZ, BZ, LG, and WW revised the manuscript. HL and YL contributed to data interpretation. All authors contributed to the article and approved the submitted version.This work was supported by the grant from the Key Research and Development Program of Shanxi (201903D321152).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher."}
+{"text": "Mechanistically, in lung cancer and using bioinformatics, we demonstrated that circ_0044516 sponges miR-136 targeting MAT2A. Furthermore, rescue assays were carried out to identify that circ_0044516 modulates cell proliferation, invasion, and stemness by regulating miR-136 and MAT2A in lung cancer. In summary, our study revealed that the circ_0044516/miR-136/MAT2A axis is involved in lung cancer progression. Our findings may provide novel targets for diagnosis and therapeutic intervention in lung cancer patients.Circular RNA (circRNA) is a type of noncoding RNA that can interact with miRNAs to regulate gene expression. However, little is known concerning circRNA, which is crucial in the pathogenesis of lung cancer. To date, limited studies have explored the role of circ_0044516 in lung cancer progression. Recently, we observed that circ_0044516 expression levels were obviously elevated in lung cancer tissues and cells. A549 and SPCA1 cells were transfected with circ_0044516 siRNA. We observed that knockdown of circ_0044516 dramatically repressed cell proliferation, increased cell apoptosis, and repressed the cell cycle. Moreover, A549 and SPCA1 cell migration and invasion abilities were greatly repressed by circ_0044516 siRNA. Due to accumulating evidence demonstrating the vital role of cancer stem cells, their mechanism of involvement has drawn increasing attention in tumor progression and metastasis research. We also found that cancer stem cell properties were restrained by silencing circ_0044516 in A549 and SPC-A1 cells. Moreover,  Lung cancer is a prevalent malignancy and is becoming an important factor in tumor-related deaths worldwide . NonsmalBack-splicing with no 5\u2032-3\u2032 polarity or a polyadenylated tail also contributes to the generation of circular RNAs (circRNAs) . circRNAMAT2A, which indicated that the circ_0044516/miR-136/MAT2A axis could be a crucial therapeutic target in lung cancer.Increasingly, circRNAs are involved in lung cancer, and in our research, we explored the effects of circ_0044516. Furthermore, we reported that circ_0044516 contributes to lung cancer by modulating miR-136 and Lung cancer tissue samples and normal tissues were collected from lung cancer patients at The First Affiliated Hospital of Soochow University from 2012 to 2018. Adjacent normal tissues were >5\u2009cm from tumor tissues. All patients were diagnosed with primary lung cancer and received no preoperative radiotherapy, chemotherapy, targeted therapy, or immunotherapy. General clinical information and detailed pathological records were collected. All participants involved in this study provided informed consent before the study. This study was approved by the Medical Ethics Committee of the First Affiliated Hospital of Soochow University.2 and 95% air at 37\u00b0C.Human lung bronchial epithelial BEAS-2B cells and five lung cancer cell lines  were obtained from the Type Culture Collection of the Chinese Academy of Sciences . Cells were maintained in RPMI-1640 medium with 10% FBS and 1% penicillin/streptomycin  with 5% COsiRNAs of circ_0044516, miR-136 mimics, circ_0044516, MAT2A overexpression plasmid, and negative controls (NCs) were purchased from GenePharma . Transfections  were carried out using the Lipofectamine 3000 reagent  based on the protocols provided by the manufacturer.Lung cancer cells were grown in 96-well plates and after transfection for 48\u2009h, and CCK-8 solution  was added to the cells to measure cell proliferation. A microplate reader was used to test the optical absorbance values at 450\u2009nm to assess cell proliferation.5) were collected in 1.5\u2009mL EP tubes. The supernatant was discarded after centrifugation at 2,000\u2009\u00d7\u2009g at 4\u00b0C. 500\u2009\u03bcL binding buffer was used to resuspend the cells. Annexin V-FITC (5\u2009\u03bcL) was added and incubated at 4\u00b0C for 30\u2009min in the dark. Next, 5\u2009\u03bcL propidium iodide (PI) was gently mixed and incubated at room temperature for 5\u2009min. The cell apoptosis rate was detected using an Annexin-V-FITC detection kit .Cells  at 1,000 cells/mL in 500\u2009\u03bcL pipette was employed to scratch a wound across the middle part of the well. Images of the wound were captured after 48\u2009h.Wound healing assays were performed to evaluate the migratory capacity of the cells. The wound healing capacity of lung cancer cells was tested. Cells were cultured in 6-well plates at up to ~100% confluence. Then, a 10\u20095/ml) were collected and resuspended in serum-free culture medium. Next, the upper chamber was loaded with 200\u2009\u03bcL of the cell suspension. The lower chamber was filled with 500\u2009\u03bcL of culture medium. After 24\u2009h, the invading cells were stained with 0.5% crystal violet.Transwell chambers with Matrigel matrix were used to determine the invasive capacity of lung cancer cells. Briefly, cells (1 \u00d7 10http://www.targetscan.org).Potential target miRNAs of circ_0044516 were predicted using the bioinformatics database tool CircNet and then further predicted by Shanghai Kangcheng Biotech, China. Potential target genes of miR-136 were predicted using TargetScanHuman  supplemented with protease inhibitors  and centrifuged. Protein concentrations were determined using a BCA kit . After the separation by 10% SDS-PAGE, the proteins were transferred onto nitrocellulose membranes . Nonfat milk (5%) was used to block the membranes. The membrane was then incubated with primary antibodies overnight. The primary antibodies included anti-MAT2A  and anti-GAPDH  antibodies. The next day, secondary antibodies were used. Protein bands were visualized using an enhanced ECL kit (Millipore).\u0394\u0394Ct\u2212 method.Total RNA was extracted using TRIzol reagent (Invitrogen). A NanoDrop 2000c  instrument was used to assess RNA quality. A Bestar RT-qPCR Kit  was used to generate cDNA. Bestar qPCR MasterMix was used to perform RT-qPCR on an ABI 7300 system. The primer sequences are shown in Cells were seeded into 6-well plates and transfected as follows: pMIR-Reporter luciferase reporter plasmid containing wild-type (Luc-circ_0044516/MAT2A-WT) or mutated circ_0044516 3\u2032UTR (Luc-circ_0044516/MAT2A-MUT) via Lipofectamine 3000 (Invitrogen), and transfected with miR-136 mimics. Luc-c/MAT2A-MUT with a mutated miR-136 binding site was constructed using the Site-directed Gene Mutagenesis Kit (Vazyme Biotech). After 72\u2009h, luciferase activity was measured using a dual-luciferase reporter gene assay .7 lung cancer cells were lysed and incubated with a biotin-labeled miR-136 probe. The Pierce RNA 3\u2032End Desthiobiotinylation Kit was used to label RNAs using biotin. Biotin-labeled wild-type miR-136 or NC was treated with cell lysates using magnetic beads. Streptavidin-coated magnetic beads were washed with lysis buffer, and Trizol (Takara) was used to purify the RNA complexes. The abundance of circ_0044516 was detected using RT-qPCR.To pull down circ_0044516, Biotin-labeled wild-type miR-136 or NC was used to pull down circ_0044516. Approximately 1 \u00d7 10in vivo. Animal procedures were performed according to the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. Animal assays were approved by the Animal Care and Use Committee of the First Affiliated Hospital of Soochow University. 5-week-old female BALB/c nude mice were used for xenograft experiments and maintained under specific pathogen-free conditions. 5 \u00d7 106 A549 cells transfected with circ_0044516 siRNA or NC were injected into 5-week-old female BALB/c nude mice. Tumor volumes were calculated using the formula (length \u00d7 width2/2) and recorded weekly. After 28 days, the mice were sacrificed and tumor weights were determined. Tumor tissues were collected for further studies.Xenograft assays were performed to analyze the role of circ_0044516 t-test was performed. Multiple groups were compared using one- or two-way ANOVA. Statistical significance was set at P < 0.05.SPSS v.19.0 software was used to carry out statistical analysis. All results are expressed as means \u00b1 SD. For comparison of two groups, two-tailed Student's First, to identify circ_0044516 expression in lung cancer, a total of 20 paired clinical lung cancer tissues and adjacent normal tissues were examined for circ_0044516 expression using RT-qPCR. We observed that circ_0044516 was upregulated in lung cancer tissues . Next, wCirc_0044516 expression was highest in A549 and SPCA1 cells among the five lung cancer cell lines in our study. These two types of cells were transfected with two circ_0044516 siRNAs. In addition, circ_0044516 siRNA-02 exhibited a superior knockdown function; therefore, it was used for subsequent experiments . ThroughA positive association between circ_0044516 expression and metastasis was observed. To confirm this, we performed wound healing and transwell invasion assays using A549 and SPCA1 cells. Circ_0044516 siRNA suppressed cell migration and invasion, as demonstrated in Figures Sox, Nanog, Oct4, and CD133 mRNA expression were reduced by circ_0044516 siRNA in vitro (Figures To study the function of circ_0044516 on lung cancer stem cell properties, spheroid formation assays were carried out and circ_0044516 siRNA decreased sphere numbers in A549 and SPCA1 cells Figures . Moreove Figures . These fhttps://circinteractome.nia.nih.gov/. miR-136 was predicted to be a target of circ_0044516. miR-136 levels were downregulated in lung cancer cells (in vitro (The miRNA targets of circ_0044516 were analyzed using er cells . RNA puler cells . Next, Wer cells . The finin vitro . In addiin vitro . Togethehttp://starbase.sysu.edu.cn/. We identified MAT2A as a possible target of miR-136. MAT2A mRNA levels were enhanced in lung cancer (MAT2A was enriched by biotin-miR-136 (MAT2A expression by sponging miR-136, as shown in MAT2A served as a direct target of miR-136.We then searched for potential targets of miR-136 using g cancer . We foun-miR-136 . WT-MAT2-miR-136 . Transfe-miR-136 . AdditioMAT2A, rescue assays were performed by transfection with miR-136 mimics or MAT2A OE plasmid inA549 and SPCA1 cells. Transfection efficiency was confirmed by analyzing MAT2A levels (Figures MAT2A overexpression promoted the same. Moreover, circ_0044516 functions by modulating miR-136 and MAT2A. Subsequently, to evaluate whether circ_0044516 affected tumor growth in vivo, a human lung cancer xenograft model was established. We injected circ_0044516 siRNA or control-transfected A549 cells into nude mice. The results indicated that circ_0044516 siRNA suppressed lung cancer tumor volume in a time-dependent manner (MAT2A levels were reduced in circ_0044516 siRNA-transfected tumor tissues, whereas miR-136 was increased (Figures MAT2A expression.To determine whether circ_0044516 functions by modulating miR-136 and  Figures . As show Figures . Howevert manner . As showt manner . Circ_00 Figures . In summcircRNAs are important master regulators involved in multiple processes . In receHuR [Dysregulated circRNAs are closely correlated with tumorigenesis in many cancers \u201322. PrevHuR . Here, wc-MYC to reduce cell proliferation and migration [\u03b2-catenin [circRNAs can participate in various processes in multiple diseases \u201327. For igration . Circ-IT-catenin . Current-catenin . In our BCL2 [Based on the ceRNA hypothesis, circRNA acts as a ceRNA to modulate miRNA target gene expression. To focus on the role of circ_0044516 in lung cancer, potential target miRNAs were predicted and miR-136 was identified. Circ_0014130 can reduce lung cancer cell apoptosis by sponging miR-136-5p and enhancing BCL2 . miR-136BCL2 . AdditioBCL2 . The assMAT2A was predicted and confirmed as a target of miR-136 in lung cancer. Bioinformatic analysis using TargetScan revealed that the biosynthesis of S-adenosylmethionine is a unique metabolic property of CSCs. Inhibition of MAT2A contributes to the repression of drug-resistant CSCs [MAT2A has been shown in gastric cancer, and silencing of the MAT2A gene induces apoptosis and blocks cell cycle progression [MAT2A on lung cancer progression remains poorly understood. We observed that MAT2A was negatively modulated by miR-136 and positively regulated by circ_0044516. MAT2A reversed the effects of miR-136 on lung cancer cell growth, invasion, and cancer stem cell characteristics. In future studies, we would like to overexpress MAT2A alone to investigate whether it can induce a lung tumor model. These results further confirmed our hypothesis that circ_0044516 serves as a ceRNA for miR-136 to enhance MAT2A expression during lung cancer progression. However, the detailed mechanisms involved in the regulatory process of lung cancer require further elucidation.ant CSCs . OverexpMAT2A expression, leading to lung cancer progression. These data indicate a potential mechanism of action in lung cancer. However, the detailed mechanisms involved require further study.Taken together, we identified a novel circRNA, circ_0044516, which plays an oncogenic role in lung cancer. We reported that circ_0044516 might sponge miR-136 to regulate"}
+{"text": "In many embedded systems, we face the problem of correlating signals characterising device operation  with events describing internal device activities. This leads to the investigation of two types of data: time series, representing signal periodic samples in a background of noise, and sporadic event logs. The correlation process must take into account clock inconsistencies between the data acquisition and monitored devices, which provide time series signals and event logs, respectively. The idea of the presented solution is to classify event logs based on the introduced similarity metric and deriving their distribution in time. The identified event log sequences are matched with time intervals corresponding to specified sample patterns (objects) in the registered signal time series. The matching (correlation) process involves iterative time offset adjustment. The paper presents original algorithms to investigate correlation problems using the object-oriented data models corresponding to two monitoring sources. The effectiveness of this approach has been verified in power consumption analysis using real data collected from the developed Holter device. It is quite universal and can be easily adapted to other device optimisation problems. Various sensors are widely used in diverse domains and the collected data need quite sophisticated processing for cognitive or reactive activities. This triggered the development of tiny and low-cost devices installed in the field. They are based on microcontrollers including a system on chip with memory and communication circuitry . The available functional block resources are limited, which is opposed to increasing demands of advanced data processing and interaction with the environment. Hence, in developing practical application systems, we face the problem of optimizing data processing algorithms, device resource usage, dependability, performance, and power consumption. An important issue is testing and validation of relevant device prototypes in simulation or production environments. In practice, this process needs efficient real-time monitoring of the device\u2019s operation. It involves observation of selected physical signals and device and environment states  related to diverse observed signals and events correlated with internal or external state/behaviour changes. In the literature, there are a lot of studies focused on either time series or event logs separately. Within time series analysis, we distinguish four research goals: (i) TS decomposition ,8, whichTypically, sensors generate data samples at regular intervals, and they can be treated as time series (TS). TS represent some variable values of observed device or environment behaviour, e.g., temperature and memory and processor usage. Logged events carry useful information on the device activities, their context, device state changes, etc. In practice, TS and events are collected separately for different purposes, which creates some difficulty in the correlation analysis. Correlating time series with event logs provides the additional context of the underlying device activities and anomalies.The multitude of collected data in monitoring processes arises the problem of their correlation ranges. This can be studied from global or local perspectives with fine- or coarse-grained views considering diverse individual or aggregated features, respectively. For example, we can trace factors impacting the total power consumption of the device or its relevant functional blocks, respectively. In many papers, authors study correlations between diverse signals described by relevant TS ,24. In cIn , correlaThe interpretation of acquired TS data from the monitoring system needs referring to event logs registered by the monitored device. Dealing with data provided by different sources requires a common notion of time. Hence, the problem of data synchronisation arises. In the literature, various synchronisation algorithms have been proposed, especially for distributed and IoT systems. They are based on exchanging synchronisation messages  or on tiAdapting time scales of TS and event logs.Correlating event logs with specified TS objects, including their sequences (instances).Selecting dominating events over the studied object instance series.As opposed to classical approaches dealing with TS at sample level, we consider TS objects aggregating samples, e.g., pulses, series of pulses, snippets, states, and state sequences. We showed the usefulness of aggregating TS samples into objects in ,28 whileThe developed correlation approach has been verified on real data collected from Holter device. Nevertheless, it can be easily adapted to other projects consistent with the quite universal data model presented in 1, s2, \u2026, sr} is composed of data sample values  collected with sampling time T defined by the local clock of the monitoring (data acquisition) device. Within TS, we can distinguish objects defined as subsets of samples with specified properties, e.g., higher average values within a predefined window time and pulses of specified shapes. Decomposing TS into objects can be performed manually or using special algorithms targeted at pulses or more complex object features, as illustrated in our previous papers  targetword_weights, (2) eps_s, (3) eps_time, (4) noise parameters: noise_0 and noise_1, (5) time_increment, and (6) eps_tolerance. The values of parameters (1) and (2) are crucial for bags of words clustering into sets of similar bags. The word_weights influence the similarity metrics results. The weights for word types such as PID or source should be greater because it is common that similar logs or even identical logs are generated by a single service. Moreover, if the specific log format is known, adjusting weight values can increase the accuracy of detecting similar bags. Parameter (2) allows us to discriminate similar bags from others. It can take values between 0.0 and 1.0. The lower it is, the more false positive cases it generates. The specific value depends on a variety of logs and can be selected experimentally by analysing similar bags of words sets. Parameter (3) impacts the number and all statistical features of generated sequences. For higher values, the single sequence can be potentially longer (in time and the number of bags). If the value is too high, the algorithm generates one sequence for each similar set of bags. The proper value is related to the time characteristics of the interval objects, especially the average time of the single object instance and the maximal or minimal time interval between consecutive instances. The noise parameters are used to detect periodical sequences. The value for both noise_0 and noise_1 is set to 0.2 but can be increased for periodic sequences with some jitter. Parameter (5), time_increment value, influences the number of matching candidates and should be dependent on event log density. Reducing this value increases the accuracy of the algorithm up to some level. For example, if the shortest time interval between two event logs is 5 s, setting time_increment to 5 s and parameter (6) eps_tolerance to about 2.5 s will generate the best results in the context of accuracy. Moreover, for lower values of parameter (5), the algorithm execution time increases. Increasing or decreasing parameter (6) can increase or decrease the number of the final set of candidates. The assumed values should not exceed the value of parameter (5). Most of parameters can be set experimentally once for specified event logs and then the algorithm can process other input data with similar format and timing characteristics.The configuration parameters of the algorithms are: (1) foreach logic structure is used to create loops. The foreach loop iterates through all elements in the collection. The collection name is inside the foreach parameter section specified in brackets. Instructions inside the loop (between keywords do and end) are executed the same number of times as the number of elements in the container. Instructions executed in successive iterations can use successive elements of the container. The conditional instructions are consistent with classical If statements. All presented algorithms use an object-oriented approach. Complex data structures such as lists or objects that aggregate other data types are presented as objects of the specified class, and object instances are created with the new operation. The objects provide properties and methods (functions). The property can refer to an object internal collection. Referencing to object properties and methods is denoted with dot operator (.). Construction object.method(arguments) invokes some method (function) on the object that can return some data type of the object or change internal state of the object. Most used names of objects and methods are self-explanatory, others are additionally commented. For example, filtered_sequences.add_sequence(s) denotes adding sequence s (with the specified method/function) to the object filtered_sequences. The presented algorithms use pseudocodes based on C/C++/Pascal notation with bolded keywords. The function keyword starts the function definition that can be identified as a single procedure of the algorithm. The log_text and intervals equivalent to specifications EL and OI in create_bags_of_logs returns a list of a bag of words corresponding to considered logs. It partitions log records into words and generates objects of a bag of words as classified words . The created bag of words list is used by function cluster_into_ssb which provides a classified similar bag of words (using similarity metric specified in xSBS) is partitioned into sequences. A single sequences object is composed of one or more consecutive (in time) sequences. Objects sequences and intervals are the arguments of procedure candidates_from_one_sequences. It returns candidates for matching object intervals with a single object of sequences. This is performed via the iterative checking of the matching result or subsequent time corrections with offset value. Candidates are aggregated in the list candidates. This list is used by the find_best_matching procedure, which returns the candidates list of candidates with a common offset (with tolerance defined by eps_tolerance parameter) that assures the maximal number of matched candidates. This list and the adjusted offset value are the result of the algorithm, which allows us to investigate and filter text logs matched with TS objects  set defined in Algorithm 1: Data correlation.Input: log_text- the log records in text format, objects- the list of TS object intervalsOutput: logs in format bags of words matched with intervals1: function match_events_with_logs 2: logs_as_bags = create_bags_of_logs(log_text)3: ssb = cluster_into_ssb(logs_as_bags)4: candidates = new list5: foreach (similar_bags in ssb) do6: sequences = create_sequences(similar_bags)7: candidates.add)8: end foreach9: matched_logs = find_best_matching(candidates)10: return matched_logs11: end functionThe inputs to Algorithm 1 are two sets: _bags_of_logs(log_text) invoked in line 2 of Algorithm 1 is relatively simple, so we skip the relevant pseudocode. It analyses log entry texts, performs tokenisation to identify words, and attributes appropriate word class  taking into account word contents and context resulting from assumed log formats. The result of this processing is the list bags with elements corresponding to subsequent log records. Each element comprises a relevant bag of words and the normalised timestamp. Timestamp normalisation is calculated by taking the timestamp of the first bag of words (corresponding to the first log record) and subtracting it from the timestamps of subsequent bags of words.Function createunmatched_bags. In the while loop (line 4), a new element of the object (bag of words) is taken with the take_first method. It is used as a seed for the new cluster created with procedure create_similar_bags_list (Algorithm 3). This procedure searches the list unmatched_bags to find bags of words similar to at least one cluster element. Two bags of words a and b are similar if the similarity metric ) is higher than the specified threshold by parameter eps_s. The added new element to the created cluster similar_bags is removed from the collection unmatched_bags. After finding all bags of words similar to the currently created cluster, this cluster is added as a list to the set ssb. Having assigned all bags of words to appropriate clusters, Algorithm 2 returns set ssb.Algorithm 2: Clustering bags into sets of similar bags.Input:bags \u2013 the list of the bags of wordsOutput: list of clustered bags into sets of similar bags1: function cluster_into_ssb(bags)2: ssb = new set3: unmatched_bags = bags.copy4: while (not unmatched_bags.empty) do5: seed = unmatched_bags.take_first6: ssb.add )7: end while8: return ssb9: end functionAlgorithm 2 partitions bags of words into sets of similar bags of words 2: similar_bags = new list3: similar_bags.add(seed)4: umatched_bags.remove(seed)5: foreach (b in similar_bags) do6: foreach (a in umatched_bags) do7: if  > eps_s)8: similar_bags.add(a)9: unmatched_bags.remove(a)10: end if11: end foreach12: end foreach13: return similar_bags14: end functionforeach loop (line 6). The created object, current_sequence, represents the currently created sequence. The sequence is composed of subsequent bags of words within the time interval equal to or lower than eps_time. Subsequent bags of words are compared; in the case of timestamp difference (between the last and the current bag of words) higher than eps_time, the currently created sequence is terminated and added to the object sequences (line 8). Moreover, the time interval causing sequence termination is added to this sequence . In the other case, the processed bag of words is added to current_sequences. Finally, operation calculate_satistics_from_intervals provides statistical parameters  of the created sequences using time intervals derived during their creation.Algorithm 4: Divides a similar bags list into sequences of bags.Input:\u00a0sbags - the list of similar bagsOutput: sequences object that contains bag sequences list with interval timebetween successive sequences1: function create_sequences (sbags)2: sequences = new\u00a0sequences3: sbags.sort4: current_sequence = new list5: last = sbags.first_bag6: foreach (b in s_bags) do7: if (b.timestamp \u2013 last.timestamp > eps_time)8: sequences.add(current_sequence)9: sequences.add_timeRange10: sequences.add_interval(b.timestamp \u2013 current_sequence[0].timestamp)11: current_sequence = new list12: end if13: current_sequence.add(b)14: last = b15: end foreach16: sequences.calculate_satistics_from_intervals17: return sequences18: end functionAlgorithm 4 creates sequences from the list of similar bags of words. For this purpose, the considered list is sorted in ascending order according to the timestamps of bags of words. Subsequent bags of words are processed in candidates\u2019 creation based on the object sequences and ts_intervals. In the first step, objects qualified as noise (noise_0 and noise_1 (compare while loop (line 7), the sequence matches are tested for each offset within the range: < sequences.first_sequence.timestamp , sequences.last_sequence.timestamp >. The offset value is incremented by time_increment. For each offset value procedure, create_candidate is invoked (line 8). If it returns a result different from NONE, then the generated candidate is added to the list candidates (lines 9 and 10). Procedure create_candidate is specified in Algorithm 6. It generates a candidate for matching using offset and sequence objects. In the first step, a new evs object is created as a copy of the original ts_intervals but with appended offset value. This is performed with the copy_and_add_time_offset method (line 2) applied to ts.objects. In line 6, foreach loop checks matching of the single sequence with a single interval in ts_intervals. If for every interval , it is possible to match at least one sequence from the object sequences, then such a sequence object is treated as a candidate. Matching is verified by checking the inclusion of time ranges of the considered sequence and interval. Time ranges comprising initial and final timestamps are represented by timeRanges objects. The inclusion relation is tested with the procedure includes (line 7). Non-matched sequences are returned via the object filtered_sequences (line 9) using the add_sequence(s) method. In the case of unsuccessful matching, the algorithm returns the value NONE.Algorithm 5: Creates candidates list from one sequences object instance.Input: sequences- the sequences object, events- the object container that contains eventobject (each event is defined by start and end timestamps).Output: a list of candidates. Each candidate contains offset value and a sequences instance1: function candidates_from_one_sequences 2: candidates = new list3: if (abs(s.avg \u2013 s.med)/s.avg < noise_0\u00a0and s.std/s.avg < noise_1)4: return candidates5: end if6: offset = sequences.first_sequence.timestamp7: while (offset < sequences.last_sequence.timestamp) do8: matched_sequences = create_candidate9: if (matched_sequences != NONE)10: candidates.add)11: end if12: offset = offset + time_increment13: end while14: return candidates15: end functionAlgorithm 5 describes the method of as noise  are filt compare . The noiAlgorithm 6: Creates a candidate for matching with TS object with a given offset value, basing on the timestamp ranges of the sequences.Input:\u00a0i_sequences- the set of sequences object, ts_objects- the object container that containsevents object (each event is defined by start and end timestamps), offset- the time valuein secondsOutput: sequences object with sequence items that match with ets_intervals1: function create_candidate 2: evs = ts_objects.copy_and_add_time_offset(offset)3: current_interval = evs[0]4: last_match = false5: filtered_sequences = new sequences6: foreach (s in i_sequences) do7: if (current_event.timeRanges.includes(s.timeRanges))8: last_match = true9: filtered_sequences.add_sequence(s)10: else11: if (last_match)12: current_interval = current_interval.next13: last_match = false14: if 15: break16: end if17: end if18: if )19: return filtered_sequences20: end if21: end foreach22: return NONE23: end functionoffset, matched sequences>. It searches for an offset value with tolerance eps_tolerance to maximise the number of sequence objects with intervals in events. For each candidate c from the list candidates, the algorithm creates a list of candidates, for which the offset is in the range <offset c, offset c + eps_tolerance>. The length of this list is used to verify whether the new list is longer than the previous maximum value (line 11). In the case of satisfying this condition, the maximum value is updated, and the considered list is stored as the best list of candidates (line 12). The derived maximal list is returned by the algorithm. It corresponds to the RES set defined in Algorithm 7: Finds the best offset value .Input:candidates - the list of candidate object (defined by an offset and sequences)Output: a list of candidates with similar offset value creates the best matching with events1: function find_best_matching (candidates)2: max_matched_candidates = new list3: foreach (c in candidates) do4: matched_candidates = new list5: matched_candidates.add(c)6: foreach (d in candidates) do7: if (d.offset >= c.offset and d.offset < c.offset + eps_tolerance and d is not in matched_candidates)8: matched_candidates.add(d)9: end if10: end foreach11: if (matched_candidates.count > max_matched_candidates.count)12: max_matched_candidates = matched_candidates13: end if14: end foreach15: return max_matched_candidates16: end functionAlgorithm 7 takes as the input the list of candidates in the form <The developed algorithms have been used in practical analysis of some embedded devices. For better understanding of the proposed analysis and algorithms, we present practical examples. The developed methodology is targeted at embedded and IoT devices. Various operation features of such devices typically comprise some short periods of high activities on a background of less important processing or idle periods. This is illustrated in the time series of timestamp], , , . Algorithm 2 combines logs into sets of similar bags of words. This algorithm uses the function create_similar_bags_list  specified in Algorithm 3. The lists of created similar bags of words are given in the second column of the The event correlation process is illustrated for an excerpt of event logs presented in eps_time = 7 in Algorithm 4, we create object sequences from each list, which comprises single sequences denoted as sequence_0, sequence_2, etc. The created three objects of sequences  are specified as follows:Assuming parameter for list 1-> object sequences1:sequence_0 = {<2> [PER] checking it}interval_0 = 8sequence_1 = {<10> [PER] checking it}interval_1 = 8sequence_2 = {<18> [PER] checking it}interval_2 = 8sequence_3 = {<26> [PER] checking it}interval_3 = 8sequence_4 = {<34> [PER] checking it}interval_4 = 8sequence_5 = {<42> [PER] checking it}interval_5 = 8sequence_6 = {<50> [PER] checking it}interval_6 = 8sequence_7 = {<58> [PER] checking it}For list 2-> object sequences2:sequence_0 = {<5> [DHCP] activity one, <6> [DHCP] activity two}interval_0 = 20sequence_1 = {<25> [DHCP] activity three}interval_1 = 26sequence_2 = {<51> [DHCP] activity four,<53> [DHCP] activity five}For list 3-> object sequences3:sequence_0 = {<9> [MANAGER] task one}interval_0 = 14sequence_1 = {<23> [MANAGER] task two, <24> [MANAGER] task three,<27> [MANAGER] task four}interval_1 = 29sequence_3 = {<52> [MANAGER] task five, <54> [MANAGER] task six}eps_time = 7). For list 3, the algorithm provides three sequences. The time difference between bags of words 4 and 7 is 14 s > eps_time. The difference between bags 7 and 8 is lower than the assumed eps_time. This results in two sequences: {<9> [MANAGER] task one} and {<23> [MANAGER] task two, <24> [MANAGER] task three, <27> [MANAGER] task four}.Most derived sequences comprise single bags of words; three involve two bags of words (lists 2 and 3) and one involves three bags of words (list 3). For example, bags of words \u201c<2> [PER] checking it\u201d and \u201c<10> [PER] checking it\u201d from list 1 create two separate sequences because the relevant timestamp difference is 8 s  between sequences. These values are needed to calculate relevant median, average, and standard deviation metrics, which are used (Algorithm 7) to select a list of bags of words representing periodic , we consider three object instances specified by time intervals (pairs of timestamps): {<7,13>, <25,28>, <53,58>}. Further processing needs timestamp normalisation in bags of words and TS intervals in reference to the first elements. For each bag of words, we have to subtract value 2 (the first timestamp in bags of words\u2014compare source type in the event record). Analysing these modules, we identified some deficiency in one of them, which caused power problems and needed correction. The correlation analysis significantly reduced the number of event logs needed for interpretation. This facilitated the tracing for problem sources in the monitored device.Optimizing power consumption of developed commercial Holter devices, we analysed time series related to the battery power supply current covering a longer period of typical device operation. It was provided by KeysigtData Acquisition Instrument . Within the collected time series (covering time period of 2 h and 30 min), we identified several object instances . These intervals are domain-dependent and can be defined manually or derived using time series decomposition algorithms, e.g., given in  or speciThe developed algorithms use specified parameters. They should fit the features of the time series. It is assumed that the analysed time series include some repetitive activities with diverse distribution in time and low activity background. Such properties are quite typical for many signals characterising various operation properties of embedded and IoT devices. These properties impact selection of the algorithm parameters. They can be also refined experimentally for a given log format and characteristic class.Monitoring device signals with independent data acquisition equipment  assures no impact on device operation, so the results are more accurate, and no hardware or software instrumentation are needed in the monitored device. This is in contrast to synchronised monitoring schemes that interact with the monitored device and can additionally limit the accuracy of monitoring fast processes.The presented algorithms are consistent with data models specified in The data analysis algorithms derive correlated events with pointed objects in time series. The effectiveness of this process depends to some extent on the assumed parameters related to the features of the object  and their properties can be verified by checking the consistency of the result. Another issue is filtering logs that are not interesting, which depends on the used similarity function and the threshold parameter, leading to lower or higher levels of reduction of selected events. This can be trimmed by repeating algorithms for diverse threshold values and assessing results by the users . In the performed power consumption analysis of the Holter device, we correlated event logs correctly for the considered several object sequences; moreover, log reduction was quite significant.The usefulness of the presented algorithms has been positively verified for some devices including developed commercial Holter devices. This allowed us to optimise device power consumption for longer operation times. The derived correlated logs facilitated pointing out deficiencies in hardware and software. Here, a question arises about the scope of application of the presented methodology. It is quite universal due to the object-oriented specifications. Time series intervals are specified in a natural way, and log event features can be easily adjusted, including other similarity metrics and noise specification.The presented original algorithms extend the capabilities of analysing embedded and IoT device operation properties considering time series and event log repositories collected from internal and external monitoring processes. It is assumed that the time series study is targeted at specified time intervals (objects) pointed out by the investigator. For this purpose, other algorithms can be used, including those proposed in , or the The developed algorithms are specified in object-oriented pseudocode, which is quite natural for the time series and event log processing. Moreover, this facilitates introducing some modifications or extensions for better adaptation to diverse studied problems.In future works, the following issues are worth investigating: (1) testing other similarity metrics and including event logs based on diverse log parsing patterns, and (2) verifying the impact of selecting parameters on algorithm results. Another interesting issue is to combine the introduced analysis with other time series decomposition and correlation schemes, e.g., involving deterministic, stochastic, seasonable, and trend components ,18, and"}
+{"text": "Thus, our study determined the role of hsa_circ_0091581/miR-1243-5p/RMI1 in glioma and suggests that this axis may be a novel therapeutic target in glioma.Glioma is a pervasive malignancy and the main cause of cancer-related deaths worldwide. Circular RNA is an important subject of cancer research, and its role and function in glioma are poorly understood. This study demonstrated that hsa_circ_0091581 is upregulated in glioma tissues and cells. The results of the CCK-8, EdU, and transwell assays indicated that hsa_circ_0091581 promotes proliferation, migration, and invasion of glioma cells. The results of the luciferase reporter and RNA immunoprecipitation assays indicated that the mechanism of the effects of hsa_circ_0091581 on glioma cells involves sponging miR-1243-5p to regulate RMI1. The results of the rescue experiments indicated that hsa_circ_0091581 regulates proliferation, migration, and invasion of glioma cells by targeting RMI1 in a miR-1243-5p dependent manner. The results of the nude mice xenograft assays showed that knockdown of hsa_circ_0091581 inhibits glioma growth  Glioma is a pervasive malignancy and the main cause of cancer-related deaths worldwide Circular RNA (circRNA) is a specific type of RNA molecule and has been extensively investigated in the fields of RNA and disease research In this study, we report that hsa_circ_0091581 is upregulated in glioma tissues and glioma cells. Function assays showed that hsa_circ_0091581 promotes proliferation, migration, and invasion of glioma cells. Using online public databases and RNA pull-down, RNA immunoprecipitation, and luciferase reporter assays, we determined the mechanism of the effect of hsa_circ_0091581 on glioma progression. Overall, our study is the first to report that hsa_circ_0091581 promotes glioma proliferation, migration, and invasion by targeting the miR-1243-5p/RMI1 axis that may be a potential target for glioma treatment.Glioma and corresponding adjacent nonneoplastic tissue samples (n=20) were obtained from patients diagnosed with glioma and admitted to the People's Hospital of Xuancheng City from June 2014 to September 2018. The samples were stored in liquid nitrogen immediately after resection. The pathological grade of the tumors was independently determined by two senior pathologists. Written informed consent was obtained from all patients. This study was approved by the Ethics Committee of People's Hospital of Xuancheng City.2 and 37\u00b0C. The synthetic nucleotides and constructs used in this study were provided by GenePharma , and the transfection was performed using Lipofectamine 3000 .Glioma cell lines  were obtained from the American Type Culture Collection . Normal astrocytes were purchased from Procell . All cell lines were grown in Dulbecco's modified Eagle's medium  containing 10% fetal bovine serum  and maintained at 5%CO-\u0394\u0394Ct method. The primers used in this study were shown in Table 1.Total RNA was isolated from the cells or tissues by using TRIzol reagent . Qualified RNA was reverse transcribed into complementary deoxyribonucleic acid (cDNA) using a PrimeScript RT kit ; qRT-PCR was performed using SYBR\u00ae Premix Ex Taq\u2122  at 92\u00b0C for 10 min and 40 cycles at 92\u00b0C for 10 s and 60\u00b0C for 1 min. GAPDH was used as an internal control. The relative level of circ_0091581, miR-1243-5p, and RMI1 was calculated by the 2Isolated RNA (2 \u00b5g) was incubated with RNase R (3 U/\u00b5g) or digestion buffer at 37\u00b0C for 30 min. After the solution was purified, qPCR was performed to determine the RNA levels.Glioma cells were incubated with actinomycin D  to block the transcription of mRNAs for 0 h, 8 h, 16 h, and 24 h. After cells were harvested, circular GPC3 (hsa_circ_0091581) and linear GPC3 RNAs were quantified by qRT-PCR to determine the half-life of RNA.Total protein was isolated by using RIPA lysis buffer . After separation by SDS-PAGE, the proteins were transferred to PVDF membranes . Then, the membrane was incubated with primary and secondary antibodies . Finally, the signals were detected by an Image Quant LAS 4000 system .The synthetic nucleotide and constructs used in this assay were provided by GenePharma . The pmirGLO-circ_0091581-WT or pmirGLO-circ_0091581-MUT vectors were cotransfected with NC mimics or miR-1243-5p mimics into U87 and U251 cells. The pmirGLO-RMI1-WT or pmirGLO-RMI1-MUT vectors were cotransfected with NC mimics or miR-1243-5p mimics into U87 and U251 cells. Transfection was carried out by using Lipofectamine 3000 . After 48 h, final luciferase activity was assessed using a luciferase reporter assay system .RIP assay was performed using a Magna RIP RNA-binding protein immunoprecipitation kit . The treated U87 and U251 cells were lysed in a lysis buffer containing protease and RNase inhibitors. Then, the cell lysates were incubated in a RIP buffer with magnetic beads conjugated with an anti-human Ago2 antibody (Millipore), and normal IgG (Millipore) were used as a negative control. Finally, the coprecipitated RNAs were eluted from the beads and assayed by qRT-PCR.Cells  were inoculated in a 96-well plate. At 12 h, 24 h, 48 h, and 72 h, the optical density at 450 nm at the indicated time was recorded using a CCK-8 kit , and viability curves were constructed.Cells  were plated in a 96-well plate overnight. Then, the cells were incubated in 4% methanol for 30 min followed by permeabilization in 0.5% Triton X-100  for 10 min. Then, the cells were incubated with 1\u00d7ApollorR  for 30 min. Cells were stained by 4',6-diamidino-2-phenylindole (DAPI) for another 30 min in the dark. Finally, EdU-positive cells were counted.Cells  were suspended in DMEM without FBS and added to the upper section of a transwell chamber . DMEM containing FBS (10%) (600 \u00b5l) was added to the bottom of a 24-well plate with inserts. After culture for 48 h, the cells migrated or invaded to the bottom chamber were fixed and stained. In invasion assay, the chambers were precoated with Matrigel , and in migration assay, the precoating was not performed.2\u00d70.5).Eight female nude mice aged 5-6 weeks were purchased from Beijing Laboratory Animal Center . U87 cells  transfected with sh-circ_0091581 or a negative control were injected subcutaneously into mice. Three weeks later, the tumors formed in mice were evaluated . Figures were edited using GraphPad Prism . Two-paired independent t-test was performed to assess the differences between the groups. Differences were considered significant at Fig. A. Moreover, compared with normal astrocytes (NAs), hsa_circ_0091581 was considerably upregulated in glioma cells Fig. B. To validate the circular characteristics of hsa_circ_0091581, U87 and U251 cells were treated with RNase R. The results showed that RNase R did not digest hsa_circ_0091581 Fig. C, D. Furthermore, the results of the actinomycin D assay indicated that the circular transcript (hsa_circ_0091581) is more stable than the linear transcript (GPC3) in U87 and U251 cells Fig. E, F. There results suggest that hsa_circ_0091581 may have important functions in glioma.The expression of hsa_circ_0091581 in glioma and the corresponding adjacent nonneoplastic tissue (ANTs) was assayed by qRT-PCR. Compared with ANTs, hsa_circ_0091581 was significantly upregulated in glioma tissues Fig. A. CCK-8 assay showed that hsa_circ_0091581 knockdown inhibits the proliferation of U87 and U251 cells Fig. B, C. The results of the EdU assay are similar to that of the CCK-8 assay Fig. D, E. The results of the transwell assay indicated that hsa_circ_0091581 downregulation suppresses the migration and invasion of U87 and U251 cells Fig. F-G.To test the functions of hsa_circ_0091581 in glioma cells, short hairpin RNAs (shRNAs) and corresponding negative control were used to construct three cell models with various expression levels of hsa_circ_0091581 https://circinteractome.nia.nih.gov/) was searched to selected 28 miRNAs as the sponge targets of hsa_circ_0091581. miRNAs with the top ten scores were selected for further screening, and miR-1243-5p was of interest. Initially, we detected the expression of miR-1243-5p in the clinical samples and found that miR-1243-5p was downregulated in glioma versus ANTs Fig. A. Pearson correlation analysis indicated an inverse correlation between hsa_circ_0091581 and miR-1243-5p Fig. B. Predictably, comparison with NAs indicated that miR-1243-5p is downregulated in glioma cells Fig. C. Additionally, qRT-PCR showed that knockdown of hsa_circ_0091581 can upregulate miR-1243-5p in U87 and U251 cells Fig. D. Moreover, the results of the luciferase reporter assays indicated that miR-1243-5p can decrease the luciferase activity in the case of hsa_circ_0091581-WT and has no effect on the expression of hsa_circ_0091581-MUT Fig. E. Finally, the results of the RIP assay indicated that miR-1243-5p is enriched in the Bio-hsa_circ_0091581 group Fig. F. These results demonstrated that hsa_circ_0091581 can function as a sponge of miR-1243-5p.A number of studies have shown that circRNAs can regulate the progress of glioma by adsorbing miRNAs, which called competing endogenous RNAs (ceRNA) mechanism http://www.targetscan.org/vert_72/), and RMI1 was selected for further investigations because of its high score. The expression of RMI1 in clinical samples indicated that RMI1 was upregulated in glioma tissues compared with the level in ANTs Fig. A. The result of Pearson correlation analysis showed that RMI1 is negatively associated with miR-1243-5p Fig. B. Additionally, the expression of RMI1 was higher in glioma cells than that in NAs Fig. C. Furthermore, the results of the qRT-PCR and western blot assays indicated that miR-1243-5p can repress RMI1 expression in U87 and U251 cells Fig. D, E. Finally, the results of the luciferase reporter assay indicated that miR-1243-5p can decrease the luciferase activity in the case of RMI1-WT and has no effect on the expression of RMI1-MUT Fig. F. Overall, these results indicated that RMI1 is the function target of miR-1243-5p.The targets of miR-1243-5p were identified by screening an online database  transfected with sh-circ_0091581 or negative control were injected into the right shoulder of mice. After 21 days, the tumors formed in mice were evaluated. The results showed that tumors formation in the sh-circ_0091581 group had lower weight and volume compared with those in the negative control group Fig. A, B. Moreover, the results of qRT-PCR and/or western blot indicated that hsa_circ_0091581 and RMI1 were downregulated and miR-1243-5p was upregulated in sh-circ_0091581 group compared with those in the negative control group Fig. C-F. Thus, knockdown of hsa_circ_0091581 inhibits glioma growth in vivo, and this effect is associated with miR-1243-5p and RMI1.To determine whether hsa_circ_0091581 can suppress glioma growth in vitro.Glioma has always been an important subject of research and a challenge for neurosurgery in vivo assays suggested that hsa_circ_0091581 can inhibit glioma growth, and this effect was mediated by miR-1243-5p and RMI1.To determine the mechanism of action of hsa_circ_0091581 on glioma proliferation, migration, and invasion, we used the results described in the literature that suggested that circRNAs can play a regulatory role by adsorbing miRNAs In summary, hsa_circ_0091581 can promote glioma proliferation, migration, and invasion via the hsa_circ_0091581/miR-1243-5p/RMI1 axis and may be a novel therapeutic target in glioma."}
+{"text": "Further functional analyses confirmed that knockdown of hsa_circ_0008896 decreased proliferation, migration, and invasion of VSMCs. In addition, we conducted bioinformatics analysis and found that hsa-miR-633 could directly bind to hsa_circ_0008896, which was confirmed by RNA immune-precipitation (RIP) assays. Results of proliferation, migration, and invasion assays showed that hsa-miR-633 inhibitor reversed the si-circ_0008896 phenotypes, indicating that hsa_circ_0008896 functionally bound to hsa-miR-633. At last, combining bioinformatics and experimental analyses, we found the protein target of hsa_circ_0008896/hsa-miR-633, CDC20B (cell division cycle 20B). The expression level of CDC20B was regulated by hsa-miR-633, and knockdown of CDC20B decreased proliferation, migration, and invasion of VSMCs. Taken together, hsa_circ_0008896 regulated the expression of CDC20B by sponging hsa-miR-633, and then enhanced proliferation, migration, and invasion of VSMCs to promote the progression of atherosclerosis.Circular RNAs, a class of circularly closed non-coding RNAs, play essential roles in the formation of atherosclerosis, which is a frequent cause of cardiovascular and cerebrovascular diseases. Although many circular RNAs are found to be involved in the progression of atherosclerosis, more circular RNA regulators still need to be identified, to improve the understanding of the regulatory networks of atherosclerosis. Here, we found that hsa_circ_0008896 was significantly up-regulated in both   Atherosclerosis is a chronic progressive inflammatory disorder in which plaques form inside the arteries, and is the primary cause of morbidity and mortality in the world ,2. AtherNon-coding RNAs, including long non-coding RNAs, circular RNAs and microRNAs, play important roles in regulating the proliferation and migration of VSMCs. For instance, miR-146b-3p represses the proliferation and migration of VSMCs induced by PDGF-BB, and long non-coding RNA PVT1/microRNA miR-3127-5p/NCK-associated protein 1-like axis regulates the proliferation of VSMCs ,11. CircIn this study, we hypothesized that hsa_circ_0008896 could be a biomarker and regulator of atherosclerosis progression. The present study aims to identify the potential role of hsa_circ_0008896 in regulating the proliferation, migration and invasion of VSMCs. The purpose of this study is to discover novel molecular targets with the potential to be used therapeutically. Here, we reported that hsa_circ_0008896 was up-regulated in the atherosclerosis cellular and mice models, and hsa_circ_0008896 increased the expression of CDC20B by competitively binding to hsa-miR-633 to increase the proliferation, migration and invasion of VSMCs.2. To construct the cellular model of atherosclerosis, human VSMCs were stimulated with oxidized low density lipoprotein  [Human vascular smooth muscle cells (VSMCs) were obtained from Bena Culture Collection, China . Human VSMCs were cultured using Dulbecco\u2019s Modified Eagle Medium  containing 10% fetal bovine serum  and 1% penicillin/streptomycin . Human VSMCs were cultured in a 37\u00b0C incubator supplied with 5% CO, China) . Si-NC, \u2212\u0394\u0394Ct methods [Total RNA of VSMCs was extracted using the RNA Easy Fast Tissue/Cell Kit  according to the manufacturer\u2019s instructions. TaqMan\u2122 MicroRNA Reverse Transcription Kit  was used for the reverse transcription of microRNAs. The relative changes of gene expression were analyzed using 2 methods . PrimersVSMCs RNA was extracted as the previous description, and 10\u00a0\u03bcg total RNA was incubated with 50\u00a0U RNase R  at 30\u00b0C for 15\u00a0min. After RNase R digestion, the digested products were reverse transcribed and the expression level of hsa_circ_0008896 was determined using qPCR methods as the previous description.3 VSMCs were seeded into a 96-well plate and then cultured for 2\u00a0hours. 10\u00a0\u03bcl CCK-8 solution  was pipetted into each well with VSMCs. Cells were incubated with CCK-8 solution for 2\u00a0hours, and the optional density (OD) value was determined using Multiskan FC with Incubator.The proliferation ability of VSMCs could be detected using cell counting kit-8 (CCK-8) . 2\u00a0\u00d7\u00a0103RIPA lysis buffer  was used for the extraction of total VSMCs protein. The protein concentration was determined using BCA Protein Assay Kit . Proteins were separated by electrophoresis and then transferred onto poly-vinylidene fluoride (PVDF) membrane. These membranes were incubated with 5% BSA for blocking for 1 hour, and then incubated in the primary antibody solution overnight. After washing, the PVDF membranes were incubated with the secondary antibody solution for 2\u00a0hours. The protein levels were detected with Pierce\u2122 ECL Western blotting Substrate  according to the instruction. Antibodies: Anti-Ki67 ; Anti-PCNA ; Anti-GAPDH ; Anti- CDC20B .3 transfected VSMCs were seeded onto 6-well plates and treated with 50\u00a0\u03bcg/ml ox-LDL for 48\u00a0hours. VSMCs were incubated in the complete medium for 14\u00a0days. Then the cell colonies were fixed using 4% paraformaldehyde (PFA) for 2\u00a0hours at room temperature, and stained using 0.1% crystal violet for 2\u00a0hours. The colonies were imaged and counted using Image J software.10Cell migration and invasion were determined using transwell chambers . For the assessment of cell migration, VSMCs were re-suspended in FBS free culture medium, seeded in top chambers, and treated with 50\u00a0\u03bcg/ml ox-LDL. The culture medium with 10% FBS was pipetted to the well under the chamber. Subsequently, cells were cultured for 24\u00a0hours, stained with 0.1% crystal violet for 30\u00a0min, and imaged using a light microscope. For the assessment of cell invasion, the inserts were pre-coated with Matrigel .The luciferase reporter plasmids containing hsa_circ_0008896\u00a0WT or Mut sequences were constructed and co-transfected into cells with hsa-miR-633. After incubation of 48\u00a0hours, Dual-Luciferase\u00ae Reporter Assay System  was used for the detection of luciferase activities. Also, the luciferase reporter plasmids containing CDC20B 3\u02b9UTR WT or CDC20B 3\u02b9UTR Mut sequence were constructed and co-transfected into cells with hsa-miR-633, and the luciferase activities were measured using the Dual-Luciferase\u00ae Reporter Assay System.Hsa-miR-633 and corresponding control were transfected into VSMCs. To confirm whether hsa-miR-633 bound to hsa_circ_0008896 in an AGO2 manner, the anti-AGO2 antibody  was incubated in the VSMCs lysate. Then, the expression level of hsa_circ_0008896 was determined by RT quantitative PCR.t test. The significance was determined by p values. *P <\u00a00.05, **P <\u00a00.01, ***P <\u00a00.001.All experiments were repeated three times. All data were presented with mean \u00b1 SEM, and all statistical analyses were conducted with Prism GraphPad 7.0 using the Students\u2019 in vitro and in vivo atherosclerosis models. Thus, we hypothesized hsa_circ_0008896 could be a biomarker and regulator of atherosclerosis progression. Hopefully, hsa_circ_0008896 could be identified as the regulator of atherosclerosis, and potential therapeutic target. Next, we explored the role of hsa_circ_0008896 in the proliferation, migration and invasion of VSMCs, and found that hsa_circ_0008896 indeed regulated the progression of atherosclerosis. Moreover, using bioinformatics analyses and further experiments, we found hsa_circ_0008896 promoted the expression of CDC20B via competitively binding to hsa-miR-633. Taken together, these results suggest that hsa_circ_0008896 could regulate the proliferation, migration and invasion of VSMCs via hsa-miR-633/CDC20B axis, and these molecules could be the potential therapeutic targets.Accumulating evidence suggests that multiple circular RNAs participate in the progression of atherosclerosis; however, more non-coding RNAs involved in regulating atherosclerosis should be identified. In this study, we found that hsa_circ_0008896 was highly expressed in both in vitro atherosclerosis cellular model stimulated with ox-LDL [in vivo, we examined the expression of hsa_circ_0008896 using the femoral artery wire injury mice model. The results have shown that hsa_circ_0008896 expression was significantly up-regulated in the injured artery  [Circular RNAs could serve as sponges for microRNAs to play essential roles in diverse diseases. We then predicted the potential binding microRNAs of circ_0008896 using CircInteractome (nih.gov) , and fouTo explore whether hsa-miR-633 functionally correlated with circ_0008896, we up-regulated hsa-miR-633 level using hsa-miR-633 mimics. The results have shown that after up-regulation of hsa-miR-633, the proliferation ability of VSMCs was significantly decreased . And hsaMicroRNAs could affect the stability and translation of RNAs at the post-transcriptional level by directly binding to the 3\u02b9UTR of mRNAs . To idenIn the pathological progress of atherosclerosis, endothelial cells, leukocytes, and intimal smooth muscle cells are all involved , especiain vitro and in vivo atherosclerosis models, suggesting that hsa-circ_0008896 may serve as an atherosclerosis marker in the early diagnosis.Increasing studies have confirmed that circular RNAs, a kind of endogenous non-coding RNAs, play essential roles during multiple pathological processes with cell type and tissue specificities, indicating the potential biological functions of circular RNAs . PreviouNowadays, it is widely accepted that circular RNAs are involved in competitive regulatory interactions, known as competing endogenous RNA (ceRNA) networks ,33,34. CIn order to identify the gene regulated hsa-circ_0008896, we then conducted bioinformatics search and found CDC20B could be the potential target. We detected the expression level of CDC20B in the cellular model of atherosclerosis, and results showed CDC20B was up-regulated in VSMCs treated with ox-LDL, indicating CDC20B expression level was regulated by hsa-circ_0008896. CDC20B, a member of the cell division cycle 20 (CDC20) family, is required during the nuclear movement prior to anaphase in the cell cycle . AberranRhodiolacrenulata, prevented ox LDL treated endothelial cell senescence by promoting cell cycle progression by the phosphorylation of the retinoblastoma (Rb) protein [The regulated target of hsa-circ_0008896, CDC20B, participates in the nuclear movement prior to anaphase in the cell cycle , indicat protein . Accumulin vitro cellular model and in vivo animal model. The role of hsa-circ_0008896 in atherosclerosis should be further confirmed using RNA sequencing data from clinical atherosclerosis samples. Second, we need to testify the safety of manipulation of hsa-circ_0008896 level in vivo using mice before further clinical trials. Third, cell cycle is precisely controlled via a network of many genes and non-coding RNAs. The role of hsa-circ_0008896 in the regulatory network should be further studied.However, numerous issues need to be further clarified. First, data in our study were collected using in vitro and in vivo atherosclerosis, increased the expression of CDC20B via binding to hsa-miR-633. Our results identify the role of hsa_circ_0008896 in the proliferation, migration and invasion of VSMCs, and suggest hsa-miR-633 and CDC20B could be potential therapeutic targets; however, further clinical data should be analyzed to confirm these findings.Hsa_circ_0008896, which was highly expressed in both Y Z conceived and designed the analysis; XM H and HD D collected the data; XM H and HD D performed the analysis; XM Hand HD D contributed equallyto this work; Y Z wrote the manuscript with inputs from all authors."}
+{"text": "Circular RNAs (circRNAs) are a novel class of noncoding RNAs that play important roles in human diseases. However, the regulation of circRNAs in glucocorticoid-induced osteoporosis (GIOP) has not been reported. In this study, we performed high-throughput sequencing to identify altered circRNAs in the vertebrae from GIOP patients. A total of 65 clinical samples were collected in this study. Bioinformatics algorithms were employed to predict the target relationship between circRNAs and miRNAs and the circRNAs-miRNAs regulatory network. We focused on the top 10 significantly up-/downregulated circRNAs  and measured their expression by qRT-PCR in clinical samples. Bioinformatics analyses demonstrated that 87 miRNAs were predicted in upregulated circRNAs and 104 miRNAs were predicted in downregulated circRNAs. The functional enrichment analysis showed these targeted miRNAs were significantly enriched in bone metabolism-related biological processes and pathways, including the MAPK signaling pathway, positive regulation of the metabolic process and metabolic pathways, etc. Collectively, our study revealed circRNA regulation and circRNAs-miRNAs regulatory network in GIOP for the first time, which provides a new perspective on the molecular mechanism of GIOP and lays a foundation for GIOP treatment. Glucocorticoid-induced osteoporosis (GIOP) is the most common form of secondary osteoporosis induced by the long-term use of glucocorticoids (GCs) \u20133. EmergCircular RNAs (circRNAs) represent a novel type of noncoding RNAs that are formed by the circularization of back-splicing events , 7. circTo better understand the molecular mechanisms of GIOP, we performed high-throughput sequencing and qRT-PCR in GIOP vertebrae to identify circRNA expression profiles for the first time. Moreover, we generated the target miRNAs based on bioinformatics algorithms and built a circRNAs-miRNAs regulatory network. Furthermore, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to elucidate the biological significance of notably altered circRNAs, which provide a new perspective on the molecular mechanism of GIOP and lay a foundation for GIOP treatment.A total of 65 clinical samples were collected from thirty-seven GIOP patients and twenty-eight healthy volunteers between October 2015 and January 2019 at the Orthopedics Department of The Affiliated Nanping First Hospital of Fujian Medical University. Six pairs of GIOP samples and control samples were used for high-throughput sequencing, while another thirty-one GIOP samples and twenty-two control samples were used for experimental validation. The diagnostic criteria for GIOP patients have characterized the long-term GC use (more than 6\u2009months) and osteoporosis/osteopenia (T-score\u2009\u2264\u2009\u22122.5/T-score\u2009>\u2009\u22122.5 and <\u22121.0). Postmenopausal women, patients suffering from other diseases that may cause osteoporosis , and patients receiving any antiosteoporosis treatment were not included in this study. The inclusion criteria for the control group were as follows: (a) No metabolism diseases that might affect bone metabolism. (b) No osteoporosis and history of GC use. (c) Non-postmenopausal women. The clinical information  was collected from all participants. The vertebral bone samples were collected by bone biopsy from two groups.This study was approved by the Ethics Committee of The Affiliated Nanping First Hospital of Fujian Medical University and was in accordance with the Helsinki Declaration. All participants were well informed of the study protocol, and written informed consent was signed by all participants.After grinding the vertebral bone tissue in liquid nitrogen condition, the bone powder was collected for RNA isolation. Total RNA was extracted by TRIzol . RNA concentration and integrity were evaluated by using the Qubit RNA Assay Kit  and Agilent 2100 Bioanalyzer , respectively, with acceptance criteria of 28S\u2009:\u200918S ratio\u2009\u2265\u20091.5 and RNA integrity number (RIN)\u2009\u2265 \u20097.0.Subsequently, total RNA was digested with RNaseR to remove linear RNAs. The cDNA library was constructed according to the manufacturer's guidance, and then, the library was subjected to paired-end Illumina sequencing .P value < 0.05.The raw sequencing data were filtered by Fast QC and NGSQC software to obtain high-quality clean reads . SubsequThe potential target miRNAs for circRNAs were predicted by RNAhybrid, miRanda, and TargetScan software with default parameters . The VenP value represents the enrichment significance of GO terms and pathways, and P value < 0.05 was considered as statistically significant.Gene Ontology and KEGG pathway analyses enable us to describe gene attributes and regulatory mechanisms in humans. The function of miRNAs targeted by circRNAs was investigated by the miRPathv.3.0 tool . The P v\u2212\u0394\u0394Ct method.QRT-PCR experiments were performed to verify the expression of circRNAs in GIOP samples and control samples. Specific primers were designed by Sangon Biotech , which span the back-splice junction region of circRNAs. Total RNAs were transcribed to cDNAs using the PrimeScripRT reagent Kit  following the instruction. PCR analyses were performed using a real-time PCR system using the SYBR Green PCR kit . GAPDH was used as internal control, and the relative expression level was calculated by the 2hsa_circ_0001451: sense, 5\u2032-CAACAAAAGAUUACUUCCUTT-3\u2032, antisense, 5\u2032- AGGAAGUAAUCUUUUGUUGTT-3\u2032hsa_circ_0007662: sense, 5\u2032-TGTGGGGGAAAAACAGGGTT-3\u2032, antisense, 5\u2032- ACGAGAAATGACAAGAGTAGCTGA-3\u2032hsa_circ_0006173: sense, 5\u2032-CCAGACAGGACTTTCTTCTGCT-3\u2032, antisense, 5\u2032-TGTGAGATCTCCATGGGCTGA-3\u2032hsa_circ_0001564: sense, 5\u2032- CATCCTTTGCGCTCAGAGGA-3\u2032, antisense, 5\u2032- GATTGGCCTGACCACAGTCTA-3\u2032hsa_circ_0108735: sense, 5\u2032- GCTTCTCCAGGCCAGACATT-3\u2032, antisense, 5\u2032- GCTGCTGTGGTTGTTTCTGG3\u2032hsa_circ_0004276: sense, 5\u2032-GCTCACAGCTGATCCTAAGGT-3\u2032, antisense, 5\u2032-GACGTTGGTTCCTTCAAGCC-3\u2032hsa_circ_0001172: sense, 5\u2032- ACAAAGCCCAGATCCAGGTG-3\u2032, antisense, 5\u2032- GTATCGACAGTCTGGGCTCG-3\u2032hsa_circ_0005729: sense, 5\u2032- CAATGCCAAGACAGAGCTGC-3\u2032, antisense, 5\u2032- GCTTTCCTCGAGCTTCCTGT3\u2032hsa_circ_0005778: sense, 5\u2032- CATCCTTTGCGCTCAGAGGA-3\u2032, antisense, 5\u2032- GATTGGCCTGACCACAGTCTA-3\u2032hsa_circ_0004906: sense, 5\u2032- AGTTGCGCTCCCAATCTCTC-3\u2032, antisense, 5\u2032- GTCTCGGTCCGTTACACCAG-3\u2032GAPDH: sense, 5\u2032-CATGGGTGTGAACCATGAGA-3\u2032, antisense, 5\u2032-CAGTGATGGCATGGACTG-TG-3\u2032The sequences of primers were as follows:P value < 0.05).Thirty-seven clinically stable patients with GIOP  and twenty-eight healthy control subjects  were enrolled in this study. As shown in P value , we identified 338 circRNAs that are differentially expressed between two groups. A volcano plot of the differentially expressed circRNAs is shown in The high-quality clean reads were obtained from six pairs of GIOP samples and control samples by using the Illumina Hiseq sequencer. A total of 17,348 circRNAs were detected by sequence alignment in the Hg38 genome and circbase database. These circRNAs were unevenly distributed in human chromosomes except for the sex chromosome Y . By filtStudies have shown that circRNAs may act as sponges of miRNAs to regulate gene expression. Therefore, we predicted the target miRNAs of the top 10 dysregulated circRNAs by bioinformatics tools. There were 679, 984, and 742 miRNAs predicted by TargetScan, miRanda, and RNAhybrid algorithms, respectively. The intersection results of the three bioinformatics algorithms demonstrated that 87 miRNAs were predicted in upregulated circRNAs and 104 miRNAs were predicted in downregulated circRNAs Figures .As shown in To further investigate the biological roles of circRNAs-miRNAs interaction in GIOP, we performed GO and KEGG analyses of target miRNAs using DIANA-miRPathv.3.0 software, which systematically collects the experimentally validated miRNA target genes and their functions. The results showed miRNAs targeted by upregulated circRNAs were mainly enriched in the regulation of signaling/transport, metabolic process, N-glycan biosynthesis, MAPK signaling pathway, and cytokine-cytokine receptor interaction Figures . The miRP value < 0.05), consistent with high-throughput sequencing data. However, the expression of hsa_circ_0005778 did not show an obvious decrease in GIOP samples, which is different from sequencing data .To verify the expression of the top 10 dysregulated circRNAs, we performed qRT-PCR experiments in another thirty-one GIOP samples and twenty-two healthy control samples. The results showed that hsa_circ_0004906, hsa_circ_0001172, hsa_circ_0004276, and hsa_circ_0005729 were significantly upregulated in GIOP samples while hsa_circ_0006173, hsa_circ_0007662, hsa_circ_0001451, hsa_circ_0001564, and hsa_circ_0108735 were significantly downregulated (Figures In recent years, circRNAs have emerged as a novel class of endogenous RNAs dysregulated in human tissues . circRNAIn the present study, we first analyzed the circRNA expression profiles in GIOP patients by high-throughput sequencing and bioinformatics analyses. Our results showed that 338 circRNAs were significantly differentially expressed between the GIOP group and control group, suggesting their important roles in GIOP pathophysiology. We also found that these differentially expressed circRNAs were widely distributed on each chromosome except for the Y chromosome.The top 10 significantly up-/downregulated circRNAs in GIOP were further investigated, including hsa_circ_0004906, hsa_circ_0001172, hsa_circ_0005778, hsa_circ_0004276, hsa_circ_0005729, hsa_circ_0006173, hsa_circ_0007662, hsa_circ_0001451, hsa_circ_0001564, and hsa_circ_0108735. By means of bioinformatics tools, we identified several miRNAs that have binding sites with circRNAs in their sequences. It is worth noting that many of the targeted miRNAs were revealed to participate in bone-related diseases, including hsa-miR-125b-2-3p, which is a key regulator in mediating chemotaxis and survival of bone marrow-derived granulocytes . hsa-miRWe next performed GO and KEGG analyses for the miRNAs in the network and found that the enriched functional terms are related to GIOP, for example, metabolic pathways and N-glycan biosynthesis, which are important pathways in bone metabolism . Other iIn summary, our study revealed the expression profiles of circRNAs in GIOP by high-throughput sequencing and qRT-PCR validation for the first time and identified several significantly up-/downregulated circRNAs that may act as candidate regulatory molecules for GIOP development. Moreover, the circRNAs-miRNAs regulatory network and related functional enrichment were systematically investigated, new perspectives on the molecular mechanism of GIOP were provided, and the base for GIOP treatment was established."}
+{"text": "Logit) and the Lyman\u2013Kutcher\u2013Burman model (NTCPLKB) were also evaluated. We found that the Hybrid IMRT/VMAT plan significantly improved the CN for clinical target volume (CTV) and planning treatment volume (PTV) compared with the nc-VMAT plan. In general, sparing of organs at risk (OARs) is similar with the three techniques, although the Hybrid IMRT/VMAT plan resulted in a significantly reduced Dmax to contralateral (C/L) optic nerve compared with the nc-IMRT plan. The Hybrid IMRT/VMAT plan significantly reduce EUD to the ipsilateral (I/L) and C/L optic nerve in comparison with the nc-IMRT plan and nc-VMAT plan, but the difference in NTCP between the three technique was <1%. We concluded that the Hybrid IMRT/VMAT technique can offer improvement in terms of target conformity and EUD for optic nerves, while achieving equal or better OAR sparing compared with nc-IMRT and nc-VMAT, and can be a viable radiation technique for treating unresectable ONB. However, the clinical benefit of these small differences in dosimetric data, EUD and NTCP of optic nerves may be minimal.The purpose of this study was to compare hybrid intensity-modulated radiotherapy (IMRT) and volumetric-modulated arc therapy (Hybrid IMRT/VMAT), with non-coplanar (nc) IMRT and nc-VMAT treatment plans for unresectable olfactory neuroblastoma (ONB). Hybrid IMRT/VMAT, nc-IMRT and nc-VMAT plans were optimized for 12 patients with modified Kadish C stage ONB. Dose prescription was 65\u00a0Gy in 26 fractions. Dose\u2013volume histogram parameters, conformation number (CN), homogeneity index (HI), integral dose and monitor units (MUs) delivered per fraction were assessed. Equivalent uniform dose (EUD) and normal tissue complication probability (NTCP) based on the EUD model (NTCP Sinonasal cancers account for ~3\u20135% of all head and neck cancers . InvestiOne of the most characteristic imaging findings of ONB is a \u2018dumbbell-shaped\u2019 mass extending across the cribriform plate, and extension to and erosion of the cribriform plate occurs during the early stage of the disease . Involveet\u00a0al. showed that intensity-modulated radiotherapy (IMRT) plans produce better tumor coverage and sparing of organs at risk (OARs) compared with three-dimensional conformal radiotherapy (3D-CRT)  and 179 MU (P\u00a0\u2264\u20090.001), respectively), but there were no significant differences in their integral doses. The Hybrid IMRT/VMAT plan average beam-on time was 2.04\u00a0min, and was slightly shorter than that of nc-VMAT by 7\u00a0s, but this was statistically non-significant. The nc-IMRT plan gave the shortest beam-on treatment time, with an average of 1.25\u00a0min.The MUs delivered per fraction were significantly higher with the nc-IMRT plan and Hybrid IMRT/VMAT in comparison with the nc-VMAT plan  and the \u2018National Cancer Center Research and Development Fund\u2019 [31-A-17].All authors declares that they have no conflict of interest.Supplementary_Figure_1_rrab010Click here for additional data file.Supplementary_Figure_2_rrab010Click here for additional data file.Revised_Supplementary_Figure_3_rrab010Click here for additional data file.Revised_Supplementary_Figure_4_rrab010Click here for additional data file.Revised_Supplementary_Table_1_rrab010Click here for additional data file."}
+{"text": "Various circular RNAs (circRNAs) are dysregulated in the placenta of fetal growth restriction (FGR) fetuses, but their roles and regulatory mechanisms have not been fully elucidated. Herein, we aimed to elucidate the role of hsa_circ_0081343 in regulating the migration, invasion, and apoptosis of human extravillous trophoblast HTR-8 cells.CircRNA and miRNA levels were examined by quantitative reverse transcription PCR (qRT-PCR). Overexpression plasmid constructs and siRNAs were used to overexpress and knockdown hsa_circ_0081343, respectively. Transwell assays and flow cytometry analyses were performed to evaluate the effects of hsa_circ_0081343 or miR-210-5p on migration, invasion, and apoptosis. Protein levels were analyzed by western blotting. Dual luciferase activity and anti-AGO2 RNA immunoprecipitation (RIP) assays were performed to identify the relationship between miR-210-5p and hsa_circ_0081343.Hsa_circ_0081343 expression was significantly downregulated in 37 FGR placental tissues compared to healthy placental control tissues. Hsa_circ_0081343 overexpression may inhibit apoptosis by downregulating the expression of cleaved caspase 3 and caspase 9 and alleviating the migration and invasion of HTR-8 cells by inducing the expression of MMP2 and MMP9. The dual luciferase activity and anti-AGO2 RIP assay results showed that hsa_circ_0081343 binds to miR-210-5p. miR-210-5p overexpression eliminated the effect of hsa_circ_0081343 overexpression in HTR-8 cells. Finally, DLX3 was identified as a direct target of miR-210-5p.hsa_circ_0081343 expression levels are significantly downregulated in FGR placental tissues. Hsa_circ_0081343 regulates the migration, invasion, and apoptosis of HTR-8 cells via the hsa-miR-210-5p/DLX3 axis. This condition further results in the fetal birth weight being less than 2500\u00a0g after 37\u00a0weeks of gestation, which is below the 10Circular RNAs (circRNAs) are a class of single-stranded endogenous molecules . Their rIn our previous study, we identified differentially expressed circRNAs in FGR placenta using circRNA microarrays . We founThe 37 pregnant women with FGR and 37 healthy pregnant women enrolled in this study were the same as those in our previous study . Their d\u2212\u0394\u0394Ct method to represent the relative expression levels of the RNAs. The primers used for qRT-PCR are shown below. The forward and reverse primer sequences for hsa_circ_0081343 were AACGAGAACAAGTTTGCTGTG and AGTCGATGCCAGTCATTCTC, respectively. The forward and reverse primer sequences for GAPDH were GGGAAACTGTGGCGTGAT and GAGTGGGTGTCGCTGTTGA, respectively. The forward primers for miR-210-5p, miR-545-3p, and miR-597-3p were ACACTCCAGCTGGGAGCCCCTGCCCACCGCACAC, ACACTCCAGCTGGGTCAGCAAACATTTATTGTG, and ACACTCCAGCTGGGTGGTTCTCTTGTGGCTCA, respectively. The universal reverse primer for all miRNAs was CTCAACTGGTGTCGTGGA. The forward and reverse primer sequences for U6  were CTCGCTTCGGCAGCACA and AACGCTTCACGAATTTGCGT, respectively.Total RNA from tissue samples or cells was extracted using TRIzol reagent  and quantified using the NanoDrop ND-1000. Reverse transcription (RT) was performed to obtain cDNA using the ImProm-IITM Reverse Transcription System . Random primers were used as the RT primers for detecting circRNA. The RT primer for detecting miRNAs was a special stem-loop primer based on the principle of the stem-loop method. Quantitative PCR analysis was performed using the SYBR GREEN qPCR Super Mix (Promega). GAPDH was used as the internal control for circRNA and mRNA. U6 was used as the internal control for miRNA. All assays were performed with three independent experiments. The data were calculated using the 22. The full-length hsa_circ_0081343  was cloned into the pLCD5H-ciR plasmid  by in vitro DNA synthesis to construct the hsa_circ_0081343 overexpression vector (ov-circ_0081343). Empty pLCD5H-ciR plasmid was used as a negative control (NC). Two small interfering RNAs (siRNAs) targeting hsa_circ_0081343 and named siRNA-1 (sense sequence: GGAGAAUGACUGGCAUCGATT) and siRNA-2 (sense sequence: GACUGGCAUCGACUGGGCCTT) were designed to include splice junctions to avoid degrading linear mRNA, which is then processed into circRNA. The sense sequence of the negative control siRNA (si-NC) was UUCUCCGAACGUGUCACGUTT. The negative control miRNA , miR-210-5p mimics (AGCCCCUGCCCACCGCACACUG), miR-210-5p inhibitor (CAGUGUGCGGUGGGCAGGGGCU), and miR-NC inhibitor (UCUACUCUUUCUAGGAGGUUGUGA) were synthesized by GenePharma Co. .HTR-8/SVneo cells were purchased from the American Type Culture Collection  and cultured in RPMI 1640  supplemented with 10% fetal bovine serum (Gibco), 1% penicillin/streptomycin, and 1% L-glutamine at 37\u00a0\u00b0C in a humidified incubator with 5% COThe HTR-8 cells were seeded in six-well plates and transfected using Lipofectamine 2000 reagent (Invitrogen) according to the manufacturer's protocol. To investigate the effect of hsa_circ_0081343, the HTR-8 cells were divided into four groups: si-NC group transfected with si-NC, si-circ_0081343 group transfected with siRNA-2, NC group transfected with empty pLCD5H-ciR plasmid, and ov-circ_0081343 group transfected with ov-circ_0081343. To investigate whether miR-210-5p overexpression alleviates the effect of hsa_circ_0081343 overexpression, the HTR-8 cells were divided into three groups and transfected with one of the following schemes: ov-circ_0081343\u2009+\u2009miR-NC, ov-circ_0081343\u2009+\u2009miR-210-5p, or NC\u2009+\u2009miR-NC.After 24\u00a0h of transfection, Transwell assays were performed to examine the migration and invasion capabilities of the indicated groups using the same method as described in our previous study . The migAfter 24\u00a0h of transfection, the cells were analyzed for apoptosis using flow cytometry. Cell staining was performed using an Annexin V-FITC/PI Apoptosis Detection Kit , and apoptosis levels were analyzed by flow cytometry using the same method as described previously .The concentrations of total proteins extracted using RIPA strong buffer  were quantified using the Bio-Rad Protein Assay Kit . Western blot analysis was performed using the method described in our previous study . The priRenilla luciferase activities were measured using the Dual-Luciferase Reporter Assay System (Promega) according to the manufacturer's instructions. Three independent experiments were conducted.The fragments of wild-type linear hsa_circ_0081343 and the DLX3 3\u2032 untranslated region (UTR) were amplified and cloned into the dual-luciferase miRNA target expression vector GP-mirGLO (Promega) and named as wild type circ_0081343 and wild type 3\u2032 UTR, respectively. The binding sequence of miR-210-5p on the wild-type circ_0081343 and wild-type 3\u2032 UTR plasmids were mutated by site-directed mutagenesis using one-step overlap extension PCR, and named as mutant circ_0081343 and mutant 3\u2032 UTR, respectively. HTR-8 cells were plated on 24-well plates and co-transfected with 100\u00a0ng of the indicated recombinant plasmids and 50\u00a0nM of miR-210-5p mimic or miR-NC. After 48\u00a0h of transfection, firefly and 7 cells were harvested after transfection with the miR-210-5p mimic or miR-NC. The cell pellets were lysed in polysome lysis buffer supplemented with protease inhibitor cocktail and RNase inhibitor. Partial cell lysate (20 \u03bcL), termed input, was collected for use as a positive control. Subsequently, 100 \u00b5L of cell lysates was incubated with magnetic bead-IgG or Ago2 antibody complex at 4\u00a0\u00b0C overnight. The next day, the complex was washed according to the manufacturer's instructions. RNA was then extracted and purified. The level of hsa_circ_0081343 in the purified RNA was detected by qRT-PCR.RIP was performed according to the instructions included with the Magna RIP RNA-Binding Protein Immunoprecipitation Kit . Briefly, approximately 1\u2009\u00d7\u200910t-tests when the data were normally distributed or non-parametric t-tests when the data were not normally distributed. Differences between more than two groups were analyzed using a one-way analysis of variance. P values\u2009<\u20090.05 were considered statistically significant.Statistical analyses were performed using SPSS 18.0 and GraphPad Prism version 7.0 software. Data are expressed as means\u2009\u00b1\u2009standard deviations based on three independent experiments. Differences between two groups were analyzed using unpaired In our previous study , the cirConsidering the dysregulated expression of hsa_circ_0081343 in FGR placental tissues, we further explored its function by overexpressing or silencing hsa_circ_0081343 in HTR-8 cells. Our results showed that hsa_circ_0081343 expression levels were increased in HTR-8 cells upon transfection with ov-circ_0081343 as compared to the cells transfected with the empty vector or NC group Fig.\u00a0A. FurtheSubsequently, we examined the effect of hsa_circ_0081343 overexpression or knockdown on the migration, invasion, and apoptosis of HTR-8 cells. Our immunoblotting studies revealed that both MMP2 and MMP9 levels increased upon circ_0081343 overexpression in HTR-8 cells (ov-circ_0081343). However, their levels were downregulated when circ_0081343 expression was silenced (si-circ_0081343) as compared to the negative control siRNA (si-NC)  mechanism , 23. To According to the ceRNA mechanism, the circRNA/miRNA axis plays a crucial role in biological phenomena by affecting the translation of target mRNAs. Therefore, it is vital to identify the target of hsa_circ_0081343/miR-210-5p to fully elucidate the molecular mechanisms involved. DLX3 is a member of the homeodomain transcription factor and vertebrate-free distant homeobox gene family. Its function and regulatory mechanisms in the placenta have been previously reported , 25. DLXThe role of hsa_circ_0081343 in HTR-8 cells is similar to that of hsa_circ_0000848, which was reported in our previous study . TherefoWhile our study conclusively shows that hsa_circ_0081343 regulates the cellular function of HTR-8 cells, it also has certain limitations. First, numerous miRNA response elements were found to be present in the hsa_circ_0081343 sequence. Hence, the existence of other miRNAs as potential downstream targets cannot be ruled out. In addition, the function of hsa_circ_0081343 in HTR-8 cells does not conclusively demonstrate that hsa_circ_0081343 regulates the pathogenesis of FGR. In future experiments, it will be necessary to construct an FGR animal model to study the effects of hsa_circ_0081343 in vivo.In conclusion, our study demonstrates that hsa_circ_0081343 expression levels are significantly downregulated in FGR placental tissues. In vitro assays have shown that hsa_circ_0081343 promotes migration and invasion and inhibits apoptosis via the hsa-miR-210-5p/DLX3 axis in HTR-8 cells. Our results suggest that circ_0081343 plays a role in regulating placental trophoblast cell function."}
+{"text": "Scientific Reports 10.1038/s41598-017-04378-1, published online 27 June 2017Correction to: The original version of this Article contained errors in Table 1, where a GenBank accession number was incorrect for Evasin \u2018P1181_AMBMA\u2019 and a GenBank accession number was missing for Evasin \u2018P983_AMBCA\u2019.The correct and incorrect values appear below.Incorrect:Correct:The original Article has been corrected."}
+{"text": "Arabidopsis thaliana, using publicly available Affymetrix CEL microarray data. Because the computational analysis described here is highly dependent on sample quality, we detail an automatic quality control approach.Coexpressed genes tend to participate in related biological processes. Gene coexpression analysis allows the discovery of functional gene partners or the assignment of biological roles to genes of unknown function. In this protocol, we describe the steps necessary to create a gene coexpression tree for For complete details on the use and execution of this protocol, please refer to   \u2022Download and quality control of raw microarray data from multiple public repositories\u2022Normalization of microarray samples using SCAN algorithm and the latest BrainArray CDF\u2022Creation of a gene coexpression tree using UPGMA hierarchical clustering\u2022Biological term enrichment analysis in gene coexpression tree subclades Arabidopsis thaliana, using publicly available Affymetrix CEL microarray data. Because the computational analysis described here is highly dependent on sample quality, we detail an automatic quality control approach.Coexpressed genes tend to participate in related biological processes. Gene coexpression analysis allows the discovery of functional gene partners or the assignment of biological roles to genes of unknown function. In this protocol, we describe the steps necessary to create a gene coexpression tree for  Arabidopsis thaliana Affymetrix microarray data. The same protocol can be used for any species, provided that at least 20 samples of the same Affymetrix chip are available  is required.Ubuntu 20.04 LTS Linux operating system was installed on a 16-core, 64 GB RAM machine. Ubuntu 20.04 and all necessary software can run on a minimum of 2 GHz dual core CPU, 4 GB RAM and 100\u00a0GB hard drive computer setup. However, RAM requirements for the calculation of sample or gene pairwise correlations and hierarchical clustering, depends on the number of available samples and studied genes. At least 64 GB of RAM is recommended for both those steps and that amount should also speed up sample normalization step. The required disk space is proportional to the number of samples and genes. In our case, where 19887 samples and 20430 genes were studied, around 300 GB were required.CRITICAL: All commands listed correspond to a Ubuntu 20.04 installation.After this step, users can proceed either with a Docker installation or by performing a full manual installation.2.Note: This installation is for Ubuntu 20.04. For different operating systems users can refer to: https://www.docker.com/get-starteda.sudo docker pull imichalop/act:latestInstall the Docker image of the protocol by typing in Ubuntu:b.sudo docker run -it imichalop/act:latestThe container can be run in Ubuntu using:c.service mysql startInside the docker container, MySQL needs to be started before starting the analysis:d.mysql -u root -pEnter MySQL as root:e.ALTER USER 'root'@'localhost' IDENTIFIED WITHmysql_native_password BY '<password>';default root password is \u201c1234\u201d and can be changed using:f.SET GLOBAL local_infile=1;In order to enable local file loading the following command must be typed in MySQL as root:g.ALTER USER 'user'@'localhost' IDENTIFIED BY '<new_password>';FLUSH PRIVILEGES;Note: If the user password is changed, then the password field inside /home/ACT/Parsers/config.ini must also be changedLocal Athaliana MySQL database is already created. The local username is \u201cuser\u201d with password \u201c1234\u201d. User password can be changed in MySQL as root using:h.exitExit MySQL by typing:i.The system setup is complete and the users can begin the execution of the protocol.Install Docker by typing the following commands in Ubuntu:3.Install system updates, git, unzip and gunzip:sudo apt-get updatesudo apt-get dist-upgradesudo apt-get install git unzip gzipTo perform a full installation, the following commands must be typed in Ubuntu terminal:Timing: 1\u00a0min4.Download the necessary codes for this protocol from GitHub:https://github.com/imichalop/ACT.gitgit cloneNote: ACT folder is created automatically through git and all custom programs, scripts and files described in this protocol are included or produced inside.Timing: 15\u00a0min5.a.sudo apt install --no-install-recommends software-properties-commondirmngrwget -qO- https://cloud.r-project.org/bin/linux/ubuntu/marutter_pubkey.asc | sudo tee -a/etc/apt/trusted.gpg.d/cran_ubuntu_key.ascsudo add-apt-repository \"deb https://cloud.r-project.org/bin/linux/ubuntu $(lsb_release -cs)-cran40/\"sudo apt-get install r-base gcc make perl libclang-dev libpq5libcurl4-openssl-dev libssl-dev libxml2-devInstall latest version of R and necessary dependencies:b.i.https://www.rstudio.com/products/rstudio/downloadDownload the latest version of RStudio for Ubuntu: ii.sudo dpkg -i <rstudio-\u2217-amd64.deb>Install the .deb file using:Install RStudio:R and optionally RStudio, need to be installed on the Ubuntu machine.Timing: 10\u00a0min6.a.sudo apt-get install mysql-server mysql-clienti.sudo mysql -u root -pNote: Press \u201cEnter\u201d when asked for MySQL root password, unless a root password is already set up.Access MySQL as root after installation using the following command in Ubuntu terminal:The following commands must be typed in MySQL environment:ii.ALTER USER 'root'@'localhost' IDENTIFIED WITH mysql_native_password BY'<password>';Set up MySQL root password using:iii.SET GLOBAL local_infile=1;Enable local file loading using:iv.CREATE USER '<user>'@'localhost' IDENTIFIED BY '<password>';Create a MySQL user account using:v.CREATE DATABASE <database name>;GRANT ALL PRIVILEGES ON <database name>.\u2217 TO '<user>'@'localhost';Note: In our example, the <database name> is \u201cAthaliana\u201d.Create the database using:Setup MySQL client and server:MySQL database management system (DBMS) needs to be installed on the Ubuntu machine, to store and access data, using relevant SQL queries.7.a.sudo apt-get install php-cli php-mysqlInstall PHP and MySQL module for PHP:b.Get data from MySQL using PHP:username\u00a0= <user>password\u00a0= \"<password>\"dbname\u00a0= <database>ACT/Parsers/config.ini includes the local MySQL credentials and should be changed accordingly.PHP is used in this protocol to run all scripts necessary for data file parsing, format conversion and database access.Timing: 30\u00a0min8.a.sudo apt-get install proj-bin libatkmm-1.6-1v5 libcairomm-1.0-1v5 libglibmm-2.4-1v5libgtkmm-3.0-1v5 libopengl0 libpangomm-1.4-1v5 libsigc++-2.0-0v5Install necessary packages:b.https://dev.mysql.com/downloads/workbench/Download MySQL Workbench for Ubuntu 20.04: c.sudo dpkg -i\u00a0< mysql-workbench-community_\u2217ubuntu20.04_amd64.deb>Install the .deb file using:Install MySQL Workbench:9.Arabidopsis thaliana genes. \u201cSelected_Genes\u201d contains the AGI codes of genes that will be studied. \u201cSample\u201d contains details of all samples. \u201cSamples\u201d contains the same details as \u201cSample\u201d, except there is a unique numeric ID associated with each sample. \u201cSelected_Samples\u201d contains the unique numeric ID of the representative samples after quality control is over. Athaliana.mwb is the MySQL workbench file of the proposed ERD in this protocol.a.mysql \u2013u <user> \u2013p <database name> --local-infileCRITICAL: All commands requiring MySQL will be performed from this distinct terminal instance.Open a new Ubuntu terminal instance and access local MySQL database from using:b.Create the tables by copying and pasting the SQL table creation commands from the MySQL Workbench ERD (Right click an ERD table > Copy SQL to Clipboard).The local MySQL database ERD is designed using MySQL Workbench and consists of 8 tables . \u201cExpresMySQL Workbench is a visual database design tool to create the Entity-Relation Diagram (ERD) and the tables of the database.Timing: 5\u00a0min10.a.https://www.thermofisher.com/us/en/home/life-science/microarray-analysis/microarray-analysis-partners-programs/affymetrix-developers-network/affymetrix-power-tools.htmlDownload the APT package for Linux: b.sudo mv apt_\u2217_linux_64_bit_x86_binaries.zip /usr/local/bin/cd /usr/local/bin/sudo unzip apt_\u2217_linux_64_bit_x86_binaries.zipsudo rm apt_\u2217_linux_64_bit_x86_binaries.zipcd apt_\u2217_linux_64_bit_x86_binaries/bin/sudo chmod 755 \u2217sudo chmod 644 axiom_param_conversion.txt apt-annotation-converter.configInstall Array Power Tools using the following commands in Ubuntu terminal:c.pico \u223c/.bashrcIn Ubuntu terminal, type:exportPATH=\"/usr/local/bin/apt_\u2217_linux_64_bit_x86_binaries/bin:$PATH\"to append the following line:Note: Replace \u2217 in the previous line with the downloaded version of APT.Save and exit the filed.source \u223c/.bashrcTo add the bin directory to the path, type:When you relogin, there is no need to execute this command again.Install Array Power Tools :a.DownloTiming: 1 h11.Execute RStudio in Ubuntu terminal as root:sudo rstudio &12.a.if )\u00a0install.packages(\"BiocManager\")BiocManager::installInstall latest version of Bioconductor:b.i.if )\u00a0install.packages(\"BiocManager\")BiocManager::install(\"SCAN.UPC\")SCAN  and oligii.if )\u00a0install.packages(\"BiocManager\")BiocManager::install(\"InterMineR\")InterMineR :if Install Phangorn , using:iExecute the following commands in RStudio environment:Timing: 5\u00a0min13.Install Newick Utilities , typing sudo apt-get install flex bisonwgethttps://web.archive.org/web/20190914014444/http://cegg.unige.ch/pub/newick-utils-1.6-Linux-x86_64-disabled-extra.tar.gztar -zxvf newick-utils-1.6-Linux-x86_64-disabled-extra.tar.gzcd newick-utils-1.6/src/sudo cp nw_\u2217 /usr/local/bin/.Timing: 5\u00a0min14.a.https://software-ab.informatik.uni-tuebingen.de/download/dendroscope3/welcome.html and download Dendroscope installation script for LinuxVisit b.chmod 755 Dendroscope_unix_\u2217shType the following command in Ubuntu terminal:c.sudo ./Dendroscope_unix_\u2217.shRun the installation using:d.During the installation, select the \u201cCheck for updates: On every start\u201d option and set max memory usage to at least 16,384 Megabytes.Install Dendroscope  using thTiming: 2\u00a0days1.Arabidopsis thaliana model plant organism), we performed a search in GEO  keywords.Search ArrayExpress  for \u2018Arab.Arabidopsis thaliana\u2019 and \u2018GPL198\u2019 (the platform code of ATH1-121501 in GEO) keywords.Search Gene Expression Omnibus (GEO) for \u2018c.https://uniofnottm-my.sharepoint.com/:f:/g/personal/sean_may_nottingham_ac_uk/Ep5b_GCihv1Nu0EYxWpkZggBK-6kAgZjMfk-9JQWJPyXUg?e=nXhIrhDownload all experiments from Nottingham Arabidopsis Stock Centre (NASC)  repositoSearch public repositories for \u2018CRITICAL: Arrange the series in different directories. Each series directory should contain the raw data (CEL files) of the samples of the series. <Series Main Directory> is the path of the directory that contains the directories of all series.Pause point: Depending on the total number, study download might take a considerable amount of time.3.a.php Parsers/Uncompress_and_Convert.php <Series Main Directory>Unzip any zipped and/or gzipped CEL files, delete folders of studies which do not contain any CEL files and convert binary CEL files to text files using the following command in Ubuntu bash:b.php Parsers/Find_Non_ATH1.php <Series Main Directory>Check if platform is \u201cATH1-121501\u201d in each CEL file and delete those CEL files which are of different platforms, using:c.php Parsers/Find_CEL_duplicates.php <Series Main Directory>Check and auto-delete duplicate CEL files, using:th June 2018), 19887 unique CEL files belonging to 1391 studies, were stored.Checkpoint: When our analysis was performed (13Data Integrity Check.This identified [ATH1-121501] Affymetrix Arabidopsis ATH1 Genome Array as the Affymetrix chip with the most available samples.Timing: 1d to 2\u00a0months (Varies depending on the sample number)vice versa.4.a.http://brainarray.mbni.med.umich.edu/Brainarray/Database/CustomCDF/CDF_download.aspVisit Brainarray Custom CDF download page: b.Visit the latest ENSG-based CDF page and copy the URL of the appropriate CDF packageArabidopsis thaliana ATH1121501 row. in c.Download the CDF in RStudio, using:Download the latest version of Brainarray CDF.install.packages5.library(SCAN.UPC)#Custom CDF from Brainarraylibrary(ath1121501atensgprobe)#Get each series directorydirseries<-list.dirs#Run SCAN on each series directoryfor(dirhave in dirseries [2:length(dirseries)]){\u00a0#Set working directory\u00a0setwd(dirhave)\u00a0#Run SCAN\u00a0normalised=SCAN\u00a0#Show finished series\u00a0print(dirhave)}Note: In our computer setup, SCAN needs about 2\u20133\u00a0min to run for each sample of Affymetrix ATH1-121501 chip. As such, SCAN total execution time depends on the total number of samples. A matrix containing all \u223c20,000 genes of the CDF and their expression values in each sample of a series is produced as SCAN_matrix.txt inside each series directory.Pause point: Depending on the total number of samples, SCAN might be running for days, weeks or more than a month. The users should leave the computer running during this time, thus, the use of Uninterruptible Power Supply (UPS) is highly advisable.a.cd ACTpwdNavigate to ACT directory in Ubuntu terminal:Record the path of the ACT directory on your system. Replace it for <ACT path> in thefollowing MySQL queries.b.php Parsers/SCAN_Compile.php <Series Main Directory> >data/expression.txtParse all SCAN_matrix.txt files to a single txt file (expression.txt) in a 4-column format, using:n\u2219m lines and 4 columns, where n is the number of genes and m in the number of downloaded samples.Checkpoint: expression.txt is a tab-delimited file of c.chmod 755 db_files.sh./db_files.shProduce the .txt files for the tables of the database using:d.LOAD DATA LOCAL INFILE '<ACT path>/data/sample.txt' INTO TABLESample;LOAD DATA LOCAL INFILE '<ACT path>/data/samples.txt' INTO TABLESamples;LOAD DATA LOCAL INFILE '<ACT path>/data/ENSG.txt' INTO TABLEENSG;LOAD DATA LOCAL INFILE '<ACT path>/data/probeset.txt' INTOTABLE ProbeSet;LOAD DATA LOCAL INFILE '<ACT path>/data/probesets.txt' INTOTABLE ProbeSets;LOAD DATA LOCAL INFILE '<ACT path>/data/selected_genes.txt'INTO TABLE Selected_Genes;LOAD DATA LOCAL INFILE '<ACT path>/data/expression.txt' INTOTABLE Expression;Insert the produced .txt files into local MySQL database in MySQL terminal using, the following commands in this order:n lines, where n is the number of genes which are described by the BrainArray CDF.Checkpoint: selected_genes.txt is a single column file of Execute SCAN on the samples with the newly downloaded CDF in RStudio, using:The downloaded samples are normalized with Single Channel Array Normalization (SCAN). As the default Affymetrix CDF is outdated, the latest version of Brainarray  CDF is uTiming: 1\u20132\u00a0weeks6.library(oligo)dirseries<-list.dirsfor(dirhave in dirseries[2:length(dirseries)]){\u00a0setwd(dirhave)\u00a0celFiles <- list.celfiles\u00a0rawData <- read.celfiles(celFiles)\u00a0fit1\u00a0<- fitProbeLevelModel(rawData)\u00a0boxplot,outline\u00a0= FALSE,col=\"lightblue\", las=3, whisklty=0, staplelty=0, cex.axis=0.5)\u00a0#export boxplot\u00a0dev.copy\u00a0dev.offa.In each series directory, PDF files containing the RLE and NUSE boxplot  for eachb.Samples whose RLE boxplot has an interquartile range (IQR) >0.4 or median >|0.2| (deviates from 0), are considered low-quality.c.Samples whose NUSE boxplot has a median >1.1 (deviates from 1), are considered low-quality.Acquire quality control metrics for each series in RStudio, using:7.A php script was created to automatically delete samples based on the aforementioned out-of-range values. Perform automatic quality control and low-quality sample deletion in Ubuntu terminal, using:A series of quality controls with different metrics needs to be performed to guarantee high-quality samples. In addition, samples which come from whole plant experiments or are from infected or mutated samples need to be deleted from their directory. However, in most of the cases there is no way to parse programmatically the metadata for each sample, as their format may vary significantly, which can lead to loss of important sample details. Thus, extensive manual curation is required.CRITICAL: Manual quality control is still possible, by examining the RLE .Note 1: Metadata files are available in .txt (tab delimited) format. There are certain fields such as \u201cTissue\u201d, \u201cOrganism Part\u201d or \u201cDisease State\u201d which are used to determine if a sample should be kept or deleted.Note 2: Since the aim of this protocol is to study the condition independent coexpression landscape of a species, the same procedure can be used for other organisms, keeping only healthy samples of distinct tissues.Pause point: The users can pause and continue the metadata examination step at any point.9.After quality control is complete, only healthy single-tissue samples remain.php Parsers/oligo_QC_R.php <Series Main Directory>10.Produce sample names using:php Parsers/Get_Sample_Names.php <Series Main Directory> >data/Sample_Names.txt11.php Parsers/Create_Selected_Samples.php data/Sample_Names.txt>data/Selected_Samples.txta.LOAD DATA LOCAL INFILE '<ACT path>/data/Selected_Samples.txt' INTOTABLE Selected_Samples;Insert Selected_Samples.txt into Selected_Samples table in MySQL terminal, using:Produce selected sample index list using:Checkpoint: A large amount of samples might be deleted during the quality control process. In our case, only 6933 samples out of the 19887 remained.Timing: 1\u00a0dayr-values)   between -values)  and a sa13.a.php Parsers/Create_Expression_R.php Samples >data/sample_expression.txtProduce the Sample expression file with the current Selected Samples and Selected Genes in Ubuntu terminal, using:n+1 lines and m+1 columns, where n is the number of genes and m in the number of samples that have remained after quality control.Checkpoint: sample_expression.txt is a tab-delimited file of b.r-values and convert them to a distance value with d\u00a0= 1 \u2013 r formula )fastcor_sample <- 1 - cor(expr_sample)Calculate sample pairwise  formula  in RStudCalculate PCCs between sample pairs:14.Create the sample correlation tree with UPGMA hierarchical clustering algorithm, using the following commands in RStudio:library(\"phangorn\")upgma_tree <- upgma(fastcor_sample)write.tree15.Sort the produced sample correlation tree using Newick Utilities  in Ubuntnw_order data/samples_upgma.new > data/samples_upgma_sorted.new16.Prune the tree to a desired number of representative leaves (in our case the <leaf number to remain> is 3500 samples), using an in-house iterative phylogenetic algorithm pruning adjacent leaves:php TreePrune/upgma_prune.php data/samples_upgma_sorted.new <leaf number toremain> > data/Representative_samples.newThose samples constitute the representative samples for the coexpression analysis.17.Obtain the leaf-sample names using:php TreePrune/Tree_Names.php data/Representative_samples.new >data/Representative_Sample_Names.txtCheckpoint: Representative_samples.new is a Newick formatted file that contains as many leaves as the number of remaining samples (in our case 3500).php Parsers/Create_Selected_Samples_from_Leaf_names.phpdata/Representative_Sample_Names.txt >data/Representative_Selected_Samples.txt18.Empty Selected_Samples table in MySQL terminal, using:truncate table Selected_Samples;and produce Selected Samples indexes using:LOAD DATA LOCAL INFILE '<ACTpath>/data/Representative_Selected_Samples.txt' INTO TABLESelected_Samples;Pause point: The users can pause before performing the final step of the protocol.then insert Representative_Selected_Samples.txt into Selected_Samples table of local MySQL database using:Timing: 2\u00a0days19.PCC-based distances between all pairs of selected genes are calculated with the previously mentioned formula . HoweverBy calculating the pairwise Pearson Correlation coefficients between all gene pairs from the representative samples, we can create a gene coexpression distance matrix which will be used as input for the construction of the gene coexpression tree.20.a.php Parsers/Create_Expression_R.php Genes > data/gene_expression.txtProduce the Gene expression file with the current Selected Samples and Selected Genes in Ubuntu terminal, using:m+1 lines and n+1 columns, where n is the number of genes and m in the number of representative samples.Checkpoint: gene_expression.txt is a tab-delimited file of b.r-values and convert them to a distance value in RStudio, using:expr_gene <- )fastcor_gene <- 1 - cor(expr_gene)Calculate gene pair Calculate PCCs between gene pairs:21.Create the gene coexpression tree with UPGMA hierarchical clustering algorithm, using the following commands in the same session of RStudio:library(\"phangorn\")upgma_tree <- upgma(fastcor_gene)write.tree22.Sort the tree using Newick Utilities in Ubuntu terminal:nw_order data/genes_upgma.new > data/genes_upgma_sorted.new23.a.Open <ACT path>/data/genes_upgma_sorted.new in Dendroscopeb.Select Layout > Draw tree or network as rectangular phylogramc.Untick View > Sparse Labelsd.Press Ctrl+F or click on the Binoculars icon in the Dendroscope toolbar to search for an AGI code of a gene of intereste.Paste the AGI code on the search field and press Enter. The leaf that corresponds to the gene of interest will be highlighted in yellowf.Using the mouse wheel, zoom to a subtree node containing the highlighted gene of interestg.Left-click the last common ancestral nodeh.Select Select > Advanced Selection > Select Subnetworki.Select Select > Invert Selectionj.Select Edit > Delete Taxak.Export the subtree as Newick by clicking File > Export > Newick and saving it as a .new filel.The gene list can be extracted from the subtree file using:Visualize the tree in Dendroscope:php <ACT>TreePrune/Tree_Names.php <subtree newick file> >subtree_gene_list.txtCheckpoint: genes_upgma_sorted.new is a Newick-formatted file that contains as many leaves as the number of genes.s lines, where s is the number of leaves of the subtree Newick-formatted file exported by Dendroscope.Checkpoint: subtree_gene_list.txt is a single column file of Arabidopsis thaliana genes which are represented as leaves. Coexpressed genes that may constitute functional partners and, thus, share similar biological functions and metabolic pathways, are grouped together in the same subclade. The tree itself can be viewed by various phylogenetic software supporting Newick-formatted trees.The outcome of the protocol is a gene coexpression phylogenetic tree A as a NeThe list of the genes of a subtree can be used as input in downstream analyses, such as functional network analysis or enrichment analysis. By examining the enriched gene terms of that subtree, new biological functions for those gene sets may be discovered. In addition, it is possible to attribute biological roles to neighboring genes of unknown function.CTL2 (shown by its AGI code: AT3G16920), is grouped with genes which are associated with plant-type secondary wall biogenesis.Functional partners of a gene of interest may be found in its neighboring leaves. In the example coexpression subtree B, CTL2 (1.a.https://string-db.org/) and click on \u201cSearch\u201dVisit the String website Paste the contents of subtree_gene_list.txt file that was created previously to \u201cList Of Names\u201d field and select the Organism Visit the WebGestalt website Select \u201cOrganism of Interest\u201d \u201d as \u201cMethod of Interest\u201dIn Basic Parameters:c.Select all available or a certain combination of \u201cFunctional Databases\u201d by clicking on the plus mark symbol on the left of the field.d.i.Select \u201cGene Symbol\u201d as GeneID typeii.Paste the contents of the subtree_gene_list.txt file that was created previously.In Gene List:e.i.Select \u201cGene Symbol\u201c as GeneID typeii.Select \u201cUpload Gene List\u201d and use ACT/data/selected_genes.txt file as inputIn Reference Gene List:f.i.Click on \u201cTable\u201d tab for table view. The enriched biological terms of the coexpressed genes to the gene interest included in the subtree, are shown.Click on \u201cSubmit\u201dWebGestalt  performsEnrichment analysis can discover the predominant biological processes of a given coexpressed gene list:The main limitation of this protocol originates from the transcriptomic technology of microarrays which is not able to study the expression of genes for which no probe is available. Furthermore, cross-hybridization may make false estimations of the gene expression, distorting correlation between members of the same family of genes and other genes. Thus, RNA-seq, having greatly advanced in the latest years, has replaced microarrays as transcriptomic technology of choice, to a large extent. RNA-seq has higher sensitivity and there is a growing amount of data available in public repositories. Nevertheless, it is shown that microarray and RNA-seq-based coexpression analyses produce comparable gene coexpression networks . Consider-values are transformed to non-negative distance values, anti-correlated genes are not inferred. Finally, this depiction assumes that one gene may only participate in a single group of functional partners. This limitation contradicts the already known fact that genes may interact with different gene subgroups which are related to different functions.A limitation of gene coexpression tree depiction, used in this protocol, is the fact that it cannot portray anti-coexpressed genes. As gene pairwise As far as the execution of this protocol is concerned, advanced programming and database management knowledge is required.RStudio crashes/displays errors during the creation of the coexpression tree of genes (step 21).First, make sure that the matrix is formatted correctly for Phangorn and that there are no missing values in the matrix.When trying to calculate the correlations or produce a tree using a large number of genes , R requires a lot of RAM (possibly more than the recommended 64 GB). We recommend closing all other applications that might use memory resources. If the problem persists, the only solution would be to increase the available RAM of the machine.Error during the installation of Bioconductor packages.This protocol assumes that the latest available R version is used. However, at some point in time, certain packages may stop being supported. In such case, we recommend installing a (older) version of R that supports the installation of those packages.Oligo package does not support the creation of additional quality control metrics (apart from RLE and NUSE boxplots) for downloaded samples (step 6).We propose installing the following packages to produce NUSE and RLE boxplots and Quality Control reports (saved as AffyQCReport.pdf). However, those are only available for non-exon arrays microarray platforms and for older versions of R (<4.0.3).if )\u00a0install.packages(\"BiocManager\")BiocManager::install(\"simpleaffy\")simpleaffy :if )\u00a0install.packages(\"BiocManager\")BiocManager::install(\"affyPLM\")affyPLM :if )\u00a0install.packages(\"BiocManager\")BiocManager::install(\"affyQCReport\")affyQCReport :if (!reqlibrary(affyQCReport)library(simpleaffy)library(affyPLM)#Get each series directorydirseries<-list.dirsfor(dirhave in dirseries[2:length(dirseries)]){\u00a0setwd(dirhave)\u00a0#read all CEL files from current working directory\u00a0readdata <- ReadAffy(compress\u00a0= FALSE)\u00a0#first Quality Assessment\u00a0Saqc <- QCReport(readdata)\u00a0dataPLM <- fitPLM)\u00a0par\u00a0+ 0.1)\u00a0boxplot,\u00a0outline\u00a0= FALSE, col=\"lightblue\", las=3,\u00a0whisklty=0, staplelty=0, cex.axis=0.5)\u00a0#export boxplot\u00a0dev.copy\u00a0dev.off\u00a0Mbox,\u00a0outline\u00a0= FALSE, col=\"mistyrose\", las=3,\u00a0whisklty=0, staplelty=0, cex.axis=0.5)\u00a0#export Mbox\u00a0dev.copy\u00a0dev.off\u00a0print(dirhave)}Quality control is performed using the following commands:Newick Utilities cannot be installed/run on my system.https://github.com/tjunier/newick_utils). Alternatively, other software, such as Dendroscope, can be used for tree sorting.In this protocol, we suggest downloading an already compiled version of Newick Utilities, which is, however, available only through the Wayback Machine. If the link becomes dead, or any other problem occurs, we recommend visiting the official GitHub page of the software (The coexpression tree cannot load in Dendroscope (step 23).Phylogenetic trees with more than 30,000 leaves, require larger amounts of RAM to open in Dendroscope. We suggest increasing the available RAM of Dendroscope or using another tree visualization software.There is an issue with one or more PHP scripts that are included in the ACT GitHub folder.The user can report the issue through GitHub.imichalop@bioacademy.gr).Further information and requests for resources should be directed to and will be fulfilled by the lead contact, Ioannis Michalopoulos (This study did not generate new unique reagents."}
+{"text": "From our results, we showed that hsa_circ_0000751 may serve as a miRNA sponge to suppress the activity of miR-488, thereby increasing the expression of the miR-488-target gene, UQCRC2, and limiting GC progression. Given its negative regulation of oncogenic miRNAs, the hsa_circ_0000751/miR-488/UQCRC2 axis may be crucial in the development of novel GC therapies.Circular RNAs (circRNAs) are RNA molecules that do not encode proteins but are known to regulate tumor progression. This study was designed to explore the underlying mechanism driving circRNA-mediated modulation of gastric cancer (GC). Bioinformatics analysis of gene chip GSE83521 was used to identify multiple circRNAs that were differentially regulated in matched GC and adjacent normal tissues. The circRNA with the largest variation in expression (hsa_circ_0000751) was selected for further examination. The expression profile of hsa_circ_0000751 and its target-specific interactions with microRNAs (miRNAs) and downstream gene transcripts were determined using quantitative real-time polymerase chain reaction, luciferase reporter assays, and rescue assays in human tissues and cells. The relationship between hsa_circ_0000751 expression and the clinicopathological parameters of 25 GC patients was analyzed. Furthermore, ubiquinol-cytochrome c reductase core protein 2 (UQCRC2), a GC suppressor, was detected via western blot analysis. The results showed that hsa_circ_0000751 levels were markedly downregulated in GC tissues and cell lines, which were also inversely proportional to the stage of tumor-node-metastasis (TNM) classification, tumor volume, and lymph node metastasis in GC patients. Conversely, hsa_circ_0000751 overexpression suppressed tumor progression, migration, and invasion  Gastric cancer (GC) ranks fifth among all cancers in terms of occurrence and is the fourth leading cause of cancer-related deaths. In 2020, approximately 1,089,000 newly diagnosed cases and a whopping 769,000 mortalities were noted worldwide . TherefoCircular RNAs (circRNAs) were initially observed, using electron microscopy, in the Sendai virus in 1976 . CircRNAAccording to current consensus, miRNAs are a highly conserved network of small regulatory ncRNAs that are known to modulate multiple biological functions . PreviouUbiquinol-cytochrome c reductase core protein 2 (UQCRC2) is a pivotal mitochondrial respiratory complex III subunit that plays an important role in the mitochondrial oxidative respiratory chain . Recent Here, we aimed to explore the roles and potential mechanisms of hsa_circ_0000751 in the development of GC. Our research demonstrated a functional loop between hsa_circ_0000751, miR-488, and UQCRC2. In brief, we demonstrated that hsa_circ_0000751 is markedly suppressed in both GC tissue samples and cells, which positively affected the clinical stage, tumor volume, and lymph node metastasis. We also demonstrated that hsa_circ_0000751 inhibits GC progression by sponging miR-488, thereby regulating the expression of the tumor suppressor gene UQCRC2. Taken together, hsa_circ_0000751 has the potential to be an independent diagnostic marker and a likely target for GC therapy.For the study, 25 samples of GC tumors and paracancerous tissues were retrieved from patients who underwent surgery at the Zhongnan Hospital  between December 2016 and December 2019. After collection, all tissue samples were immediately stored at \u221280\u00b0C. All patients selected for sample collection were screened according to a postoperative pathological diagnosis. None of these patients had received radiotherapy, chemotherapy, or any other form of treatment prior to surgery. The clinicopathological factors are summarized in 2 humidified environment and in RPMI-1640 , 10% fetal bovine serum , and 1% penicillin\u2013streptomycin [Human GC cell lines  and normal human gastric epithelial cells-1 (GES-1) were obtained from the American Type Culture Collection. All cells were cultured in a 37\u00b0C and 5% COptomycin .Full-length hsa_circ_0000751 cDNA was amplified in 293\u00a0T cells before cloning into the pLCDH-ciR vector  with a front and back circular frame. The negative control was constructed without the addition of the hsa_circ_0000751 sequence. The following plasmids were purchased from Genechem : UQCRC2, UQCRC2 siRNA, hsa_circ_0000751 siRNA, miR-488 mimic and inhibitor, and two scrambled negative control miRNAs . The siRNA sequences used were as follows: si-UQCRC2: 5\u02b9-ATCCTCGACGCGATGAGA-3\u02b9 and si-hsa_circ_0000751: 5\u02b9-CCGCAGGCTCCCAGTCCCAAT-3\u02b9. Lipofectamine 3000  was used for plasmid incorporation into cells, following the manufacturer\u2019s guidelines. Finally, total RNA and protein were harvested 48\u00a0h post-transfection, following standard protocols .https://www.r-project.org/) and packages. Differentially expressed circRNAs were identified based on the following criteria: fold alterations >2 and p values < 0.05.The circRNA expression profile (source: GSE83521) was downloaded from the National Center for Biotechnology Information Gene Expression Omnibus (GEO). GSE83521 was compiled using data from six GC tissues and six matched non-tumor tissue samples, based on GPL19978 (Agilent-069978 Arraystar Human CircRNA microarray V1). RNA sequencing and microarray data were preprocessed using R software  was used to harvest total RNA from cultured cells and human tissues, and the NanoDrop was used for quantification of the total extracted RNA. Next, 10\u00a0\u03bcg of the total extracted RNA was treated with 40\u00a0U of RNase R  by incubating at 37\u00b0C for 15\u00a0min. CircRNA and mRNA analyses were performed by reverse transcribing RNA into complementary DNA (cDNA) using a PrimeScript\u2122 RT Master Mix reagent kit (Takara). Genomic DNA (gDNA) was isolated using the QIAamp DNA Mini Kit . Transcript levels were assessed using qRT-PCR with SYBR Premix Ex Taq\u2122 (Takara). Endogenous GAPDH and U6 were used to normalize the expression of the relevant transcripts. Finally, gene expression was quantified using the 2t method . The uniProteins were isolated from stably transfected cells at 90% confluence using the RIPA lysis buffer . Their concentrations were quantified using a BCA protein assay kit , and the proteins (30\u00a0\u00b5g/lane) were separated via SDS-PAGE before transfer to a polyvinylidene fluoride membrane . Following this, the membrane with the proteins was subjected to the following incubations and washes: 5% lipid-free milk solution at 37\u00b0C for 1.5\u00a0h, primary antibody at 4\u00b0C overnight (ON), HRP-conjugated secondary antibody  for 2\u00a0h at 37\u00b0C, and three TBST buffer washes. Finally, the protein signals were visualized using an enhanced chemiluminescence detection system with a chemiluminescent HRP substrate . The antibodies used for protein detection were as follows: anti-UQCRC2 , anti-GAPDH , anti-cleaved caspase-3 , anti-Bcl-2 , anti-Bax , and anti-Bak .Dual fluorescein reporter gene analysis was performed using a Dual Luciferase Assay System Kit , following the manufacturer\u2019s guidelines. Wild-type hsa_circ_0000751 3\u02b9 UTRs (WT hsa_circ_0000751 3\u02b9 UTRs), mutant hsa_circ_0000751 3\u02b9 UTRs (MUT hsa_circ_0000751 3\u02b9 UTRs), and UQCRC2 3\u02b9UTRs (MUT UQCRC2 3\u02b9UTRs) were amplified and cloned into the pmirGLO luciferase reporter vector . Next, HEK-293\u00a0T cells were plated in 24-well plates in triplicate and co-incorporated with the corresponding plasmids and miR-488 mimics. Following a 48-h incubation, luciferase activity was assessed using the dual-luciferase reporter assay system (Promega). The data were normalized to the Renilla internal control.The proliferative capacity of GC was measured using the Cell-Light\u2122 EdU DNA Cell Proliferation Kit  and Cell Counting Kit (CCK)-8 , according to the manufacturer\u2019s guidelines . To perf3 cells per well) were grown in 96-well plates for 24\u00a0h. Next, the cells were exposed to 10\u00a0\u03bcL CCK-8 solution  and incubated at 37\u00b0C for 90\u00a0min [To perform the CCK-8 assay, gastric cells  chamber of 24-well plates . Medium with 10% FBS was introduced into the bottom chamber as a chemoattractant. Following a 24\u00a0h incubation, cells that managed to migrate to the bottom surface were fixed in 4% paraformaldehyde, stained with 0.1% crystal violet, imaged, and quantified using at least five random fields of view . All experiments were independently performed thrice.For the transwell invasion assay, 1\u00a0\u00d7\u00a0106 cells per mouse. Next, the tumor volume was monitored every week (volume\u00a0=\u00a0width2\u00d7\u00a0length\u00d71/2). All animal protocols were approved by the Committee on Animal Research of Wuhan University.Xenograft studies were performed in six-week\u2013old BALB/c nude mice obtained from the Chinese Science Academy . For experimentation, 10 random mice were assigned to 2 groups of 5 mice each. Meanwhile, MKN-45 cells were stably incorporated with lentivirus-hsa_circ_0000751-overexpression vector or a negative control before injecting the transfected cells into the mice subcutaneously. In all cases, the forelimbs axilla received 5\u00a0\u00d7\u00a010Tumor tissue samples from mice were collected for IHC staining, as previously reported . The excApoptosis was analyzed using a TUNEL assay. A TUNEL assay was used to calculate the level of fragmented DNA using the TUNEL apoptosis detection kit  following the manufacturer\u2019s guidelines . Brieflyp <\u00a00.05) was assessed using the Student\u2019s t-test (comparing two different groups) and one-way analysis of variance (ANOVA) (comparing multiple groups). Correlations between hsa_circRNA_0000751 levels and clinical manifestations were assessed using the \u03c72 test. Pearson\u2019s correlation coefficient analysis was employed to ascertain relationships between various factors.Data are presented as means \u00b1 standard deviations and analyzed with SPSS 20.0  and GraphPad Prism version 7.0. Statistical significance (http://www.circbase.org/), showed the most significant downregulation in GC. To verify the above results, we also confirmed low hsa_circ_0000751 levels in 25 GC tissues and 25 adjacent tissues . We identified 40 circRNAs with abnormal expression in GC tissues, carrying a fold change > 2 and p <\u00a00.05, as shown in  tissues ) using q tissues ). TherefSince hsa_circ_0000751\u00a0has been shown to originate from the mRNA splicing of NUFIP2 (nuclear FMR1 interacting protein 2), it was imperative to determine whether the hsa_circ_0000751 in our cells originated from a gene rearrangement event. Therefore, we constructed convergent and divergent primers to amplify the linear and circular RNAs, respectively, based on complementary DNA (cDNA) and genomic DNA (gDNA) from two randomly selected GC tissues. Using agarose gel electrophoresis, we demonstrated that hsa_circ_0000751 could be amplified from cDNA using divergent primers, but no amplification occurred from gDNA ). FurtheTo evaluate the effect of hsa_circ_0000751 in GC cells, we exogenously incorporated hsa_circ_0000751 plasmid or relative negative control into MKN-45 and MGC-803 cells. As depicted in in vivo, we subcutaneously administered MKN-45 cells containing either hsa_circ_0000751 plasmid or negative control into the forelimb axilla of nude mice (n\u00a0=\u00a05) and monitored the development of tumors by observing luciferase intensities on a fortnightly basis. By week five, the mice carrying hsa_circ_0000751-overexpressed cells exhibited substantially small tumors, as evidenced by their growth rates, weights, and volumes, compared to mice carrying cells with the negative control . Taken To elucidate the underlying pathways involved in hsa_circ_0000751-mediated suppression of tumorigenesis, we scanned for potential targets of hsa_circ_0000751 using the miRNA target-prediction software . We discovered that hsa_circ_0000751 shared sequence homology with miR-488 ). Next, Given that hsa_circ_0000751 reduces GC cell growth and invasion, we next explored whether the cellular activities of hsa_circ_0000751 are mediated through the sponging of miR-488. Therefore, we conducted rescue experiments via co-transfection of MKN-45 and MGC-803 cells with miR-488 mimics and hsa_circ_0000751 expression vectors. Using CCK-8, colony-formation, and EdU assays, we demonstrated that GC cells incorporated with both hsa_circ_0000751 plasmids and miR-488 mimics exhibited higher cell proliferation than cells transfected with hsa_circ_0000751 plasmids alone . This suTo identify genes targeted by hsa_circ_0000751 via its negative regulation of miR-488, we used TargetScan to predict potential targets of miR-488. Based on our analysis, UQCRC2 was predicted to bind to miR-488 with a high affinity score ). AdditiNext, we investigated whether the hsa_circ_0000751-mediated regulation of GC cell proliferation and invasion occurs via the miR-488/UQCRC2 axis. For this, we employed CCK-8, colony-formation, and invasion assays, which showed that miR-488\u2013overexpressing MKN-45 and MGC-803 cells exhibited substantially higher viability than cells with negative controls. Additionally, cells incorporated with both the miR-488 mimic and UQCRC2 had low viability . We alsoCircRNAs are a group of circular ncRNAs that possess no 5\u02b9\u20133\u02b9 polarity or polyA tails . They haThe competitive endogenous RNA (ceRNA) hypothesis states that miRNAs with sequence homology with target transcripts regulate the transcription of their target transcripts . CircRNABased on the ceRNA hypothesis, circRNA can serve as a ceRNA in the modulation of miRNA target gene expression. Our TargetScan analysis projected UQCRC2 to have binding sites for miR-488. UQCRC2 encodes core protein 2, which is one of the 11 structural subunits of mitochondrial complex III. Multiple studies have shown that UQCRC2 plays a crucial role in the progression and metastasis of numerous cancers, including colorectal cancer , breast In summary, we discovered the miR-488 sponging activity of hsa_circ_0000751, which upregulates UQCRC2 expression and suppresses the proliferation and invasion of GC cells. This study provides insight into the novel hsa_circ_0000751-miR-488-UQCRC2 axis involved in GC progression. However, there are certain limitations of this research. The potential signaling pathways and molecular mechanisms related to circRNAs in the regulation of GC cell proliferation remain to be investigated. Future in-depth clinical investigations involving a larger sample size are both urgent and crucial to the advancement of GC early detection and therapy."}
+{"text": "Artifact chimeric reads are enriched in next-generation sequencing data generated from formalin-fixed paraffin-embedded (FFPE) samples. Previous work indicated that these reads are characterized by erroneous split-read support that is interpreted as evidence of structural variants. Thus, a large number of false-positive structural variants are detected. To our knowledge, no tool is currently available to specifically call or filter structural variants in FFPE samples. To overcome this gap, we developed 2 R packages: SimFFPE and FilterFFPE.SimFFPE is a read simulator, specifically designed for next-generation sequencing data from FFPE samples. A mixture of characteristic artifact chimeric reads, as well as normal reads, is generated. FilterFFPE is a filtration algorithm, removing artifact chimeric reads from sequencing data while keeping real chimeric reads. To evaluate the performance of FilterFFPE, we performed structural variant calling with 3 common tools  with and without prior filtration with FilterFFPE. After applying FilterFFPE, the mean positive predictive value improved from 0.27 to 0.48 in simulated samples and from 0.11 to 0.27 in real samples, while sensitivity remained basically unchanged or even slightly increased.FilterFFPE improves the performance of SV calling in FFPE samples. It was validated by analysis of simulated and real data. For decades, formalin fixation and paraffin embedding (FFPE) has been widely used to prepare and preserve biopsy specimens . FFPE tiNext-generation sequencing (NGS) plays an important role in medical research. It allows us to investigate entire genomes, uncover the molecular characteristics of diseases, and provide insights into therapies. However, formalin fixation can result in fragmented, degraded, protein cross-linked DNA, introducing false-positive results to NGS data analysis . The intNGS can be used to detect genomic variants of different scales: single-nucleotide variants (SNVs), short insertions/deletions, and structural variants (SVs), including copy number variants (CNVs). So far, studies on FFPE-specific artifacts have been focusing on false-positive SNVs and only a few on CNVs . When peTo evaluate and improve the performance of SV calling algorithms in FFPE samples, ground truth data are needed. However, publicly available real-world FFPE data sets with matched FF samples are scarce. Furthermore, to our knowledge, no experimental validation of SV candidates is available for these data sets. Therefore, we simulated data with known biological truth and performed expert-based validation of SV calls for 2 real data sets with FFPE and matched FF samples.Aiming at improving SV calling performance in FFPE samples, we defined the following research objectives: (i) To develop an NGS read simulator that can specifically simulate ACRs in FFPE samples; the simulated reads should be as realistic as possible. (ii) To develop a tool that successfully removes ACRs while keeping non-artifact chimeric reads resulting from real SVs. (iii) To benchmark existing SV callers by using simulated as well as real NGS data sets resulting from FFPE samples, and to evaluate the effect of ACR removal on SV calling.Two real-world data sets were analyzed in this study. Both contain whole-exome sequencing (WES) data of FFPE and matched FF samples publicly available at the European Nucleotide Archive . The firThe real data available to us are all WES data; however, the ideal data for SV calling are whole-genome sequencing (WGS) data with sufficient read length. Therefore, to complement the available real data, we generated simulated data that are more optimal for SV calling (mimicking WGS data with 150\u00a0bp read length). To generate simulated data sets, we first simulated 400 non-overlapping SVs with varying lengths  on chromosome 12 of genome assembly hg19 using RSVSim . Next, wThe general workflow of SimFFPE is shown in Fig.\u00a0The whole simulation can be split into 2 parts\u2014the simulation of normal fragments and the simulation of artifact chimeric fragments (ACFs). While normal fragments are simulated directly from the reference genome, the simulation of ACFs is more complex. Details of ACF simulation are described in the subsection \u201cSimulating ACFs.\u201d We observed normally distributed fragment lengths in real data; therefore, we used a normal distribution to simulate fragment lengths. This observation is in line with several other publications on NGS simulators e.g., , 8)..8]).Simulations for WGS, as well as WES and targeted sequencing data, are supported. For WES and targeted data, we uniformly model the capture efficiency. Simulated read sequences are generated from one end (single-end sequencing) or both ends of the fragments (paired-end sequencing). We refer to the reads generated from ACFs as ACRs. It should be noted that well-known errors in NGS data, such as base substitutions and indels, are not the focus of this work; therefore, SimFFPE performs only simple random error simulations.Phred quality scores are correlated with base position in the reads . AccordiTo simulate ACFs, the essential task is to find genome sequence pairs with SRC regions and combine them to form double-stranded fragments. A graphic representation of this process is available in To locate candidate SRC pairs for binding, we randomly select short (on average 6\u00a0bp) genome sequences (referred to as \u201cseed sequences\u201d) and find their reverse complementary sequences (referred to as \u201ctarget sequences\u201d). The obvious match\u2014target sequences at the same genomic location on the reverse strand\u2014are excluded.For a given seed sequence, there can be millions of candidate target sequences. If 1 target sequence were randomly selected, this could result in simulated data widely deviating from real data. To simulate data as realistically as possible, an elaborate set of characteristics is considered when simulating SRC pairs. Among others, these characteristics include SRC region length distribution, location , distance, and strand Fig.\u00a0. All defBecause only 1 target sequence of a seed is finally selected, computational costs are greatly reduced by SimFFPE identifying target sequences of a seed only within a small region. More specifically, we partition the genome into small windows (5\u00a0kb). Target sequences are searched in a random window or within the same window of the seed. The resulting SRC pairs and ACFs are called between-window SRC pairs and distant ACFs, and within-window SRC pairs and adjacent ACFs, respectively.The reason to differentiate between these 2 cases is as follows: we observed that in real FFPE samples, \u223c27%  of ACFs are derived from the binding of adjacent (within 5\u00a0kb) SRC pairs . Owing tThe most important considerations for adjacent ACF simulation are as follows  Analysis of the real data sets indicates that strand usage for the formation of distant ACFs is almost equal. (ii) In real data sets, we observed a common feature across the whole genome: within some small genomic regions (1-2\u00a0kb), there were more ACRs originating from distant ACFs compared to other regions. These are referred to as \u201cspikes\u201d . To simuA summary of the differences in simulating adjacent and distant ACFs is provided in Table\u00a0The workflow of FilterFFPE is shown in Fig.\u00a0The detection of an SRC region is based on the main characteristics of ACRs. ACRs contain 2 genome segments: 1 from the seed sequence and 1 from the complementary target. Thus, there exist (at least) 2 alignments, both containing soft-clipped bases. In an ACR, towards the end of the mapped sequences, a short region should be mapped in both alignments. This region can be identified as the SRC region that links 2 ssDNAs forming the ACF. First, FilterFFPE identifies potential ACRs. Second, the presence and lengths of SRC regions within these ACR candidates is analyzed. Only reads with plausible SRC regions  are removed by FilterFFPE. This step helps not to exclude real chimeric reads resulting from low-coverage regions or low-frequency SVs by mistake, i.e., preserving sensitivity. However, sequencing noise in ACRs may harm the correct detection of SRC regions. Thus, it is possible that some ACRs are falsely categorized as real chimeric reads, i.e., positive predictive value (PPV) is decreased. Therefore, this second filtration step is optional.After determining the reads to be excluded, FilterFFPE generates a filtered and indexed BAM file, as well as a text file containing the names of the excluded reads.The steps taken to evaluate SV calling performance in real and simulated FFPE samples with and without application of FilterFFPE are shown in Fig.\u00a0For real data sets, each pair of matched FFPE and FF samples was downsampled to the same size. Furthermore, only reads within exonic regions with sufficient coverage were used for SV detection (exonic regions with mean coverage \u226530\u00d7 in both samples of the pair). PPV, sensitivity, and F1-score were used to evaluate each tool's SV calling performance with and without application of FilterFFPE.Different SV callers can detect the same breakpoint with minor shifts in the genomic location. To determine whether an SV call indicates a true-positive SV and whether it is shared between 2 samples, a maximum shift of\u00a0\u00b15\u00a0bp is allowed to identify consistent breakpoints. This threshold was determined on the basis of a previous evaluation on different SV callers\u2019 breakpoint resolution by Gong et\u00a0al. .Because data on experimental validation of SV candidates in real samples were not available, we performed expert-based validation by 2 independent experts in the field of SV detection  and counted separately . As a reIn real data sets, we applied FilterFFPE to both FFPE and matched FF samples. The percentage of filtered reads ranged from 0.33% to 9.2% in FFPE samples (median: 2.5%). In contrast, only 0.015\u20130.33% (median: 0.10%) were filtered in FF samples. These results match our previous observation that ACRs are enriched in FFPE samples compared to FF samples. It should be noted that FF samples are expected to contain some ACRs because any heating step during sequencing can result in DNA denaturation and thus ACR generation. Nevertheless, because the percentage of ACRs in FF samples is low, the effect of these ACRs on SV calling is usually negligible .Figure\u00a0As simulated coverage or ACF proportion increases, the number of ACRs increases; therefore, we expected and also observe an increasing number of false-positive SV calls and decreasing PPV. SV frequency has no effect on the number of ACRs, and thus, we did not expect any effect on the number of false-positive SV calls. It can be observed that both Manta and Delly are characterized by stable PPV at different SV frequencies. Interestingly, Lumpy shows a decrease in PPV with increasing SV frequency. Detailed evaluation of the SV calling results revealed that Lumpy generated several SV candidates for real SVs with different breakpoints. Some of these SV candidates were recognized as false-positive calls because the detected breakpoints were not close enough to the real ones (\u00b15\u00a0bp).After removing ACRs with FilterFFPE, PPVs of all 3 tools increase in all our simulated data sets: Manta shows the largest increase (on average from 0.06\u00a0\u00b1\u00a00.15 [mean\u00a0\u00b1\u00a0SD] to 0.45\u00a0\u00b1\u00a00.21), followed by Delly (0.10\u00a0\u00b1\u00a00.18 to 0.29\u00a0\u00b1\u00a00.22) and Lumpy (0.65\u00a0\u00b1\u00a00.13 to 0.71\u00a0\u00b1\u00a00.12).Sensitivity of the 3 tools is stable across all simulated data sets, except for low coverage (\u226430\u00d7) or low SV frequencies (\u226430%). In these extreme cases, it is difficult to distinguish between real chimeric reads and ACRs. Therefore, application of FilterFFPE slightly reduces sensitivity . For all other samples, sensitivity even increases marginally after using FilterFFPE (on average from 0.94\u00a0\u00b1\u00a00.05 to 0.95\u00a0\u00b1\u00a00.05). Compared to the other tools, Delly is characterized by lowest sensitivity\u2014before and after filtration with FilterFFPE. This is due to the fact that Delly did not detect translocations with precise genomic location: 61 of 100 simulated translocations could not be detected accurately by Delly (often with a deviation of 30\u2013300\u00a0bp at the breakpoint).It should be mentioned that these results are based on all reported SV calls. In addition, every tool has diverse internal categories to characterize SV calls of different qualities, including \"precise\" vs \"imprecise\" calls (whether breakpoints can be precisely located) and/or \"pass\" vs \"non-pass\" calls . Interestingly, with the combined use of these categories and FilterFFPE, the best performance is observed in case of FilterFFPE+Delly, considering only precise calls. Delly\u2019s precise calls have a mean F1-score of 0.71\u00a0\u00b1\u00a00.14 across the 3 simulated data sets and reach 0.91\u00a0\u00b1\u00a00.06 with FilterFFPE. More details can be found in Figure\u00a0To further validate the performance of FilterFFPE, we also calculated the number of reported SV calls in FF samples before and after FilterFFPE\u2019s application . Over alIn this article, we introduce 2 R packages for improved handling of sequencing data generated from FFPE samples: SimFFPE and FilterFFPE. SimFFPE is a novel tool simulating realistic sequencing data from FFPE samples. Simulated data with known biological truth are the prerequisite for, e.g., optimization of variant calling pipelines. Based on the output of SimFFPE we developed and tested a new filtration algorithm for SV calling: FilterFFPE. Results on both simulated and real data show that our filtration algorithm is able to improve PPV without compromising the sensitivity of 3 established SV calling algorithms.Despite developing a tool for realistic simulation of FFPE samples, it can be observed that the sensitivity of the 3 SV calling tools Manta, Delly, and Lumpy differed between simulated and real data. These discrepancies were mainly due to technical differences between these data sets: our simulated samples were whole-chromosome sequencing data  while real samples contained WES data and had a shorter read length .The sensitivity of Lumpy and Manta was much lower for real data than for simulated data. Lumpy uses not only read-pair and split-read support but also read-depth support to identify SV candidates. However, regional coverage fluctuates heavily in WES data. Thus, it can harm read-depth support detection in Lumpy and lead to lower sensitivity. The reduced sensitivity of Manta is likely due to inaccurately detected SV positions. The accuracy of Manta\u2019s local assembly might have been affected by the shorter read length of the real data. Delly showed the lowest sensitivity in simulated data sets but featured the highest in real data. It could be observed that Delly\u2019s imprecise positioning of translocations leads to false-negative calls. In our simulated data, 25% (100 of 400) of all SVs were translocations, but only 2% (7 of 296) in real data.Because the purpose of SimFFPE and the type of its simulated noise are different from those of existing simulators, it is difficult to compare SimFFPE with other simulation tools. However, exemplary comparison of simulated and real data in the IGV shows that reads generated by SimFFPE resemble real FFPE samples, while reads generated by other simulation tools resemble real FF samples.It can be argued that for real data we do not know biological truth based on validation experiments but just by expert-based review. It is possible that our data contain misclassified variants, i.e., false-negative and false-positive calls. Nevertheless, the classification was based on a detailed scheme and criteria, and we performed careful manual inspection on >5,000 SV calls. Therefore, the effect of misclassified variants on our overall results can be assumed to be negligible.Regarding FilterFFPE, the first filtration step may seem very similar to filtering out SV calls with split-read support \u22642. However, these 2 strategies are fundamentally different. Many true SV calls in real samples lack split-read support. For example, in the 18 real FFPE samples, 41%  of the true-positive SV calls had no split-read support. This can be related to the fact that real SVs often overlap with homologous sequences and/or sequence repeats [In real FFPE samples, we observed different levels of artifacts and, thus, variable levels of PPV for SV detection. One possible reason for sample-wise artifact level variation may be different time at high-temperature steps during processing of FFPE samples in the laboratory. For instance, larger paraffin blocks require longer time for deparaffinization, thus leading to a higher proportion of denatured DNA and a higher number of ACRs. Besides, long-term storage of FFPE samples leads to more fragmented DNA, which is more vulnerable to denaturation at high temperature. We also observed varying sensitivities for real samples, which could be explained by different levels of sample coverage.The mechanism of the ACR generation in FFPE samples was first described in detail by Haile et\u00a0al. . They usSV calling in FFPE samples is challenging owing to the presence of ACRs leading to a large number of false-positive calls. To facilitate future development of FFPE-specific algorithms, we developed SimFFPE. It is the first simulation tool generating realistic NGS data from FFPE samples, simulating ACRs as well as normal reads. In addition, we developed the filtration algorithm FilterFFPE. Analyses on simulated as well as real data show that our algorithm successfully removes ACRs while keeping real chimeric reads. Thus, FilterFFPE improves PPV considerably without affecting sensitivity.Project name: SimFFPE and FilterFFPEhttps://bioconductor.org/packages/release/bioc/html/SimFFPE.html; https://bioconductor.org/packages/release/bioc/html/FilterFFPE.htmlProject home page: Operating system: Platform independentProgramming language: ROther requirements: NoneLicense: LGPL-3RRID:SCR_021085; RRID:SCR_021086https://www.ebi.ac.uk/ena/browser/home and can be accessed with accession Nos. SRP044740 and PRJNA301548.The 2 real data sets analyzed during the present study are available in the European Nucleotide Archive repository at The 3 simulated data sets can be generated with SimFFPE and RSVSim\u00a0as described.GigaScience GigaDB database [Additional supporting files including FilterFFPE\u2019s outputs and tabular data are available from the database .Supplementary Section S1. Mechanism of ACF formation.Supplementary Section S2. Real data sets.Supplementary Section S3. Distributions and proportions for simulation.Supplementary Section S4. BAM file processing.Supplementary Section S5. Manual inspection.Supplementary Section S6. IGV view of NGS data.Supplementary Section S7. Proportion of abnormally paired reads.Supplementary Section S8. FilterFFPE excludes FFPE-specific ACRs.Supplementary Section S9. Evaluation of SV calling in simulated data sets.Supplementary Section S10. Evaluation of SV calling in real data sets.Supplementary Section S11. Increased sensitivity after application of FilterFFPE.Supplementary Figure S1. Example of seed- and target sequences used to generate an SRC region in an ACF.Supplementary Figure S2. SRC region length distribution in real FF samples.Supplementary Figure S3. SRC region length distribution in real FFPE samples.Supplementary Figure S4. SRC region length distribution in an exemplary sample simulated with SimFFPE.Supplementary Figure S5. Proportion of SRC pairs with two ssDNA molecules originating from the same chromosome.Supplementary Figure S6. Proportion of adjacent SRC pairs among same chromosomal SRC pairs.Supplementary Figure S7. Proportion of same strand SRC pairs among adjacent SRC pairs.Supplementary Figure S8. Cumulative distribution of the original genomic distance between two ssDNA molecules of adjacent SRC pairs.Supplementary Figure S9. Exemplary alignment of reads simulated by SimFFPE and ART in comparison to real reads in an FFPE sampleSupplementary Figure S10. FilterFFPE removes artifact chimeric reads while keeping real chimeric reads.Supplementary Figure S11. Proportion of improperly paired reads.Supplementary Figure S12. Proportion of read pairs mapping to different chromosomes.Supplementary Figure S13. FilterFFPE excludes FFPE-specific ACRs.Supplementary Figure S14. Proportion of ACRs in excluded reads.Supplementary Figure S15. Examples of chimeric and non-chimeric reads deriving from artifact chimeric fragments.Supplementary Figure S16. Sensitivity of FilterFFPE excluding ACRs based on chimeric reads  from ACFs.Supplementary Figure S17. Sensitivity of FilterFFPE excluding ACRs based on all reads (chimeric + non-chimeric) from ACFs.Supplementary Figure S18. SV calling performance for each SV call category in simulated data sets with and without FilterFFPE\u2019s application (two filtration steps applied).Supplementary Figure S19. SV calling performance for each SV call category in simulated data sets with and without applying FilterFFPE\u2019s first filtering step only.Supplementary Figure S20. SV calling performance for 6 simulated samples with low coverage or low SV frequency.Supplementary Figure S21. SV calling performance for 35 simulated samples with high coverage and high SV frequency.Supplementary Figure S22. SV calling performance for real data with (one- or two-step filtration) and without FilterFFPE\u2019s application.Supplementary Figure S23. Relative change in the number of SV calls in real data after application of FilterFFPE (one-step and two-step filtration).Supplementary Table S1. Information on real data sets.Supplementary Table S2. Number of SV calls with information on initial category and final judgment.Supplementary Table S3. Number of true calls that are exclusively called before and after the application of FilterFFPE with one-step filtration.Supplementary Table S4. Number of true calls that are exclusively called before and after the application of FilterFFPE with two-step filtration.ACF: artifact chimeric fragment; ACR: artifact chimeric read; bp: base pairs; CNV: copy number variant; dsDNA: double-stranded DNA; FF: fresh frozen; FFPE: formalin-fixed and paraffin-embedded; Gb: gigabase pairs; IGV: Integrative Genomics Viewer; indel: short insertion/deletion; kb: kilobase pairs; NGS: next-generation sequencing; PCR: polymerase chain reaction; PPV: positive predictive value; SNV: single-nucleotide variant; SD: standard deviation; SRC: short reverse complementary; ssDNA: single-stranded DNA; SV: structural variant; WES: whole-exome sequencing; WGS: whole-genome sequencing.The authors declare that they have no competing interests.L.W. analyzed the data, developed SimFFPE and FilterFFPE, and wrote the manuscript. S.S. and M.D. guided the project and provided ideas for improvement during the development of the 2 tools, as well as suggestions on the manuscript. All authors read and approved the final version of the manuscript.giab065_GIGA-D-21-00120_Original_SubmissionClick here for additional data file.giab065_GIGA-D-21-00120_Revision_1Click here for additional data file.giab065_GIGA-D-21-00120_Revision_2Click here for additional data file.giab065_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giab065_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giab065_Reviewer_1_Report_Original_SubmissionMichael Linderman -- 5/19/2021 ReviewedClick here for additional data file.giab065_Reviewer_1_Report_Revision_1Michael Linderman -- 8/4/2021 ReviewedClick here for additional data file.giab065_Reviewer_2_Report_Original_SubmissionBinay Panda -- 6/16/2021 ReviewedClick here for additional data file.giab065_Reviewer_2_Report_Revision_1Binay Panda -- 8/12/2021 ReviewedClick here for additional data file.giab065_Supplemental_FileClick here for additional data file."}
+{"text": "CDK6 gene was remarkably upregulated in the tissues of colorectal cancer (CRC) patients and in the CRC cell lines. Moreover, high expression level of hsa_circ_000984 was significantly associated with advanced colorectal cancer. Further analysis revealed that hsa_circ_000984 knockdown could inhibit cell proliferation, migration, invasion in vitro and tumor formation in vivo in CRC cell lines. Mechanically, we found that hsa_circ_000984 may act as a competing endogenous RNA (ceRNA) by competitively binding miR-106b and effectively upregulate the expression of CDK6, thereby inducing a series of malignant phenotypes of tumor cells. Taken together, these observations suggest that the hsa_circ_000984 could mediate the expression of gene CDK6 by acting as a ceRNA, which may contribute to a better understanding of between the regulatory miRNA network and CRC pathogenesis.Circular RNAs (circRNAs) as a novel type of noncoding RNAs (ncRNAs) are widely studied in the development of human various diseases, including cancer. Here, we found circular RNA hsa_circ_000984 encoded by the  Colorectal cancer (CRC) is the common type of malignant tumor with the third occurrence and the fourth leading cause of related cancer death worldwide . NowadayIn the past decades, whole genome and transcriptome sequencing technologies analysis revealed that a majority of mammalian genomes are transcribed to yield a large proportion of short or long RNAs transcripts with non-coding protein ability \u201310. AmonCDK6 gene. As a core member of the cyclin-dependent kinases (CDKs) family, CDK6 is necessary to drive progression of cell cycle passing from G1 to S phase by suppressing cell cycle inhibitors [CDK6 has also been reported to be associated with development and prognosis of various cancers [Here, we focus on the circular RNA hsa_circ_000984 encoded by the hibitors , 24. Bes cancers \u201327. In tGAPDH was used as a linear RNA control.Based on circRNA databases , 31, theP<0.05)   and was correlated with the advanced TNM stage (III+IV) (P<0.05)  Figure . The res) Figure . To stre(P<0.05) .GAPDH and nuclear-localized U6: as expected, they were enriched in the cytoplasmic fraction and nuclear fraction, respectively  and human intestinal epithelial cells (InEpC), hsa_circ_0067934 was also highly expressed in the CRC cell lines Figure , implyiny Figure .We performed CCK-8 assay and colony formation assay to analyze the effect of hsa_circ_000984expression on CRC cell growth. We knocked down hsa_circ_000984 expression in CRC cells using shRNAs, and the shRNA#3 was choose to use in the following investigation because the knockdown efficiency of hsa_circ_000984 by qPCR Figure . The knoversus 49.9%, P<0.05 for SW480 cells and 64.5% versus 54.8%, P<0.05 for SW620 cells), compared with the negative controls.We speculated that the growth inhibition of CRC cells induced by hsa_circ_000984 silencing might be due to the disorder of cell cycle. To validate the possible roles of hsa_circ_000984 in cell cycle progression, we analyzed cell cycle phase distribution in CRC cells with stable expressing hsa_circ_000984 shRNA. In both tested cell types, we found that the cell cycle was mainly arrested in G0/G1 phase Figure  and 2F iWe further performed migration and invasion assay to investigate whether the inhibition of hsa_circ_000984 suppresses the migration and invasion of CRC cells. Migration of cells was reduced >30% in CRC cells with stable expressing hsa_circ_000984 shRNA, compared with negative control cells Figure . SimilarP<0.01 for SW480 cells; and 742.81\u00b148.40 mm3 versus 1314.067\u00b184.67 mm3, P<0.01 for SW620 cells)  Figure  and 2I.P<0.01), and its expression was positively correlated with hsa_circ_000984 expression in CRC samples , suggesting the co-expression of miR-106b and hsa_circ_000984 in CRC. Additionally, miR-106b levels were also higher in CRC cell lines than in control cells    Figure . Intrigu) Figure . Next, wCircRNAs, largely disregarded as novel members of noncoding RNA family that are formed with out of order splice junctions precisely at their canonical linear counterparts . Althougin vitro and tumor formation in vivo, possibly by retarding cell cycle progression (G1 phase to S phase). In consistence with previous reports [Our study first demonstrated that hsa_circ_000984 was significantly up-regulated in CRC tissues and CRC cell lines, compared with adjacent normal tissues. Moreover, we found that high expression levels of hsa_circ_000984 in CRC patients were significantly related to advanced TNM stage (III+IV). We then explored the potential biological role of hsa_circ_000984 by shRNA mediated silencing. Through our series of experiments, we found that knockdown of hsa_circ_000984 inhibited the proliferation, migration, invasion of CRC cells  reports , 43, the reports , 45. Hen reports , 46. Alt reports , 47.In summary, our data showed that expression levels of hsa_circ_000984 is upregulated in CRC tissues and cell lines. Besides, high hsa_circ_000984 expression is positively correlated with advanced CRC. Moreover, hsa_circ_000984 affected CRC cell growth, migration and invasion by competing with cell cycle-associated proteins for binding by miR-106b, indicating an essential role of hsa_circ_000984 in tumor and progression. The hsa_circ_000984 may be as a potential molecular markers for promising application perspectives of CRC.76 paired CRC tumors and matched adjacent normal tissues were retrospectively selected from patients who undergoing surgical procedures at Zhejiang Provincial People's Hospital of Hangzhou Medical College. All patients agreed to sign informed consent in the study provided the detail clinic pathologic parameters including age, sex, histologic classification and tumor size. This study was approved by the Ethics Committee from the Zhejiang Provincial People's Hospital of Hangzhou Medical College.2 humidified atmosphere. These cells were cultured in RPMI Medium 1640  supplemented with 10% fetal bovine serum  and antibiotics/antimycotics .Human CRC cell lines  and normal colon-derived cell (CCD-18Co) and human intestinal epithelial cells (InEpC) included in this study were obtained from the Type Culture Collection of China Academic Science  and maintained at 37\u00b0C in 5% COProtein analysis was performed as previously described . ProteinCDK6 were detected by quantitative RT\u2013PCR using a SYBR green PCR kit . Total RNA was extracted using the TRIzol reagent  according to the manufacturer's protocol. MiRNA expression was carried out using the TaqMan miRNA assay . Relative quantification of gene expression was normalized by the 2\u2212\u0394\u0394Ct method relative to GAPDH and RNU6B which were used as qRT\u2013PCR controls for genes and miRNAs. All experiments were performed in triplicate.The mRNA levels of hsa_circ_000984 and The anti-circ-RNA short hairpin RNAs (shRNAs) were synthesized and cloned into lentiviral vector (LV3) and were then packaged with Lentivector Packaging Plasmid mix  to establish stable cell lines as previously described . BrieflyCDK6 3\u2019-UTR containing miR-106b target site and the full-length of CDK6 3\u2019-UTR deleted miR-106b-binding sequence were inserted downstream of the firefly luciferase gene in psiCHECK2 to create the psiCHECK2-CDK6 3\u2019UTR-WT plasmid (WT) and psiCHECK2-CDK6 3\u2019UTR-MU plasmid (MU), respectively. The WT and MU plasmids subsequently were co-transfected into CRC cells with miRNAs mimics, or inhibitors along with control Renilla luciferase expression plasmid (phRL-TK) using Lipofectamine 2000. After 24h, luciferase and renilla signals were assayed using the Dual-luciferase reporter Assay System  according to the manufacturer's protocol.The full-length of A total of 2000 stably transfected cells in 100\u03bcl medium were seeded into each well of a 96-well plate. On days 0, 1, 2 and 3, 10\u03bcl of CCK-8 reagent  were added each well and proliferation was assayed by a microplate reader (Thermo Scientific Multiskan FC) to detected the absorbance value at 450 nm.For colony formation assay, a total of 200 stably transfected cells were seeded into six-well plates and cultured for 2 weeks in standard conditions. Then the colonies were washed with PBS, fixed with methanol and then stained with crystal violet. Cell number was counted by a coulter counter.5 stably transfected cells were washed in phosphate-buffered saline (PBS). Cellular DNA was stained with PI/RNase Staining Buffer  and were then analyzed using flow cytometry (BD Biosciences).For cell cycle assay 1\u00d7106/ml diluted in 0.1 ml PBS were injected subcutaneously on the back flank of each mouse at day 0, respectively. Tumor size was measured with a caliper every 3 days. The tumor volume was calculated using the relationship (Volume=length\u00d7width2\u00d70.5). Tumor growth rate was determined by averaging the volume of tumors of eight nude mice. All experiment procedures were carried out in accordance with the ethical standards under a protocol approved by the Committee on Animal Welfare of Zhejiang Provincial People's Hospital of Hangzhou Medical College.Negative control cells or treated cells with the indicated lentivirus vector with a concentration of 1\u00d710http://www.targetscan.org/) and miRanda (http://www.microrna.org/) was used to predict the potential miRNAs binding sites in the hsa_circ_000984 and corresponding gene CDK6 3\u2019UTR to study the possible crossing network among circRNA, miRNA and gene.TargetScan  was used to analyze the statistical data, where appropriate. The correlation of hsa_circ_000984 expression level and clinicopathological parameters were evaluated using t \u2013test. The significance of hsa_circ_000984 expression level between CRC samples and matched colorectal nontumorous tissues were determined by the Student\u2019 test, and statistical significance was set at"}
+{"text": "Thiodictyon syntrophicum\u201d sp. nov. strain Cad16T is a photoautotrophic purple sulfur bacterium belonging to the family of Chromatiaceae in the class of Gammaproteobacteria. The type strain Cad16T was isolated from the chemocline of the alpine meromictic Lake Cadagno in Switzerland. Strain Cad16T represents a key species within this sulfur-driven bacterial ecosystem with respect to carbon fixation. The 7.74-Mbp genome of strain Cad16T has been sequenced and annotated. It encodes 6237 predicted protein sequences and 59 RNA sequences. Phylogenetic comparison based on 16S rRNA revealed that Thiodictyon elegans strain DSM 232T the most closely related species. Genes involved in sulfur oxidation, central carbon metabolism and transmembrane transport were found. Noteworthy, clusters of genes encoding the photosynthetic machinery and pigment biosynthesis are found on the 0.48\u00a0Mb plasmid pTs485. We provide a detailed insight into the Cad16T genome and analyze it in the context of the microbial ecosystem of Lake Cadagno.\u201cThe online version of this article (10.1186/s40793-018-0317-z) contains supplementary material, which is available to authorized users. Chromatiaceae are generally found at the interface of aerobic and sulfidic-anaerobic zones that are exposed to sunlight such as stagnant, hypertrophic water bodies, littoral zones and bacterial mats . Th. Th23]. eviously , 25. To 2 assimilation in PSB, electrons derived from the oxidation of reduced sulfur compounds, are transferred to electron carriers NAD(P)+ and ferredoxin through light energy. During photolithoautotrophic growth under anaerobic conditions, strain Cad16T uses electrons from the oxidation of sulfide, thiosulfate and elemental sulfur as reducing equivalents -hydrogenases of the Hox and Hup type  are found in the sequence that could mediate light-dependent Hersicina , 40.T genome also harbors cys genes (THSYN_05020\u201305035) that are probably involved in sulfate assimilation under sulfur-limiting conditions. Furthermore, the genome also encompasses genes encoding the CydDC (THSYN_18930 and THSYN_18935) ATP-driven cysteine transport proteins [The Cad16proteins .2 fixation is essentially achieved through the reductive pentose phosphate also known as the CBB cycle. In accordance, the strain Cad16T genome harbors the complete CBB enzymatic pathway. On the chromosome, the dimeric RuBis-CO form II (THSYN_13250) clusters with RuBis-CO activation protein subunits CbbR, CbbQ and CbbO, . Interestingly, small and large RuBis-CO subunits form I (THSYN_29475 and THSYN_29480) cluster together with carboxysome shell and auxiliary proteins on plasmid pTs417 . The carboxysome may allow efficient photoassimilation across varying CO2 concentrations as proposed for A. vinosumDSM 180T [T suggesting that only the type II is involved in the process of CO2 fixation [In PSB, CODSM 180T . PreviougltA citrate synthase (THSYN_12620), fumA fumarate hydratase (THSYN_24360) and sucCD succinyl-CoA ligase (THSYN_00880 and THSYN_00885) that are essential for the TCA cycle, and isocitrate lyase (THSYN_16275) and malate synthase (THSYN_15655) that are essential for the glyoxylate cycle, respectively, are identified in the strain Cad16T sequence. Recently a proteomic study about the capacity of Cad16T to fix CO2 in the dark suggested the presence of a particular archael DC/HB cycle [T genome.The missing sedoheptulose-1,7-bisphosphatase SBP is possibly bypassed by via the fructose-1,6-bisphosphatase (THSYN_25630). The genes HB cycle . HoweverT additionally encodes genes necessary for glycogen polymerisation. The glucose 1-phosphate adenylyltransferase GlgC (THSYN_00810), the glycogen synthase GlgA (THSYN_11615) and the 1,4-alpha-glucan branching enzyme GlgB (THSYN_00805) allow the synthesis of glycogen.Strain Cad16T also has the potential to produce the storage compound cyanophycin normally found in caynobacteria [Interestingely, strain Cad16bacteria , since tT in the dark [Togheter, these finding provide genetic evidence for the high carbon fixation potential of strain Cad16the dark , 44.Thiodictyon strains [cbb3 type terminal cytochrome C oxidases (THSYN_06760\u201308775) possibly involved in Fe(II) driven carbon fixation in strain Cad16T genome.Anaerobic Fe(II)-oxidation was described for other  strains , 46 and  strains . In accoT grows chemoautotrophically under microaerobic conditions (5% O2) with sulfide, thiosulfate, or sulfide only [Lamprocystis purpurea [Thiocystis violacea and A. vinosum [. In situ, strain Cad16T is possibly exposed to low concentration of oxygen produced by oxygenic microbiota at the mixolimnion-chemocline interface [sod-type superoxide dismutases (THSYN_20405 and THSYN_22720), as well as fnr and fur-type transcriptional regulators involved in peroxide stress response. In situ, strain Cad16T is possibly exposed to oxygen produced by oxygenic microbiota at the mixolimnion-chemocline interface [Strain Cad16ide only , as alsopurpurea , 48, Thi vinosum . In situT could possibly fix nitrogen. Genes encoding the multisubunit urease UreDEFG  and the urea transporter UrtABCDE  indicate the possible utilisation of urea.Furthermore, with the genes encoding NifB (THSYN_03975), NifD (THSYN_08880), NifH (THSYN_08885), NifK (THSYN_08875), NifT (THSYN_08870) NifW, NifZ and NifM , NifX (THSYN_21435) and NifL (THSYN_24590) strain Cad16T genome, including protein secretion system Type II, genes encoding the TAT pathway and several TRAP transporter genes, as well as genes encoding Ton-Tol type and ABC-type transporter complexes. Additionally, a complete TSS4 pilus machinery is encoded in six clusters dispersed on the strain Cad16T chromosome. Notably, also structural components of TSS6 secretion system are found in two clusters on the chromosome  and on pTs485 (THSYN_32540-THSYN_32580). Two effector proteins of the VrgG family were identified. THSYN_15360 belongs to the vgr_GE type Rhs family proteins similar sequences found in \u03b2-proteobacterial family of the Burkholderiaceae whereas THSYN_32425 is conserved in \u03b3-proteobacteria and contains a type IV Rhs element. Togheter, the secretion machinery allows strain Cad16T to interact within the highly populated chemocline with up to 107 bacterial cells per milliliter. The secretion and uptake mechanism may also play a key role in the cell-to-cell contact with Desulfocapsa thiozymogenes.Several membrane transport genes were found in the strain Cad16T can possibly regulate buyoncy by gas vesicles that are formed with the encoded structural gas vesicle proteins. Whereas GvpA proteins forms the vesicle core , GvpFL (THSYN_11800 and THSYN_18685), GvpK (THSYN_11785) and GvpN (THSYN_11815 and THSYN_18695) further stabilize the structure. Proteins homologoues to the transcriptional regulatory factors GvrA (THSYN_11850) and GvrC (THSYN_11830) from the enterobacterium Serratia sp. ATCC 39006 are also found in Cad16T.Strain Cad16Chromatiaceae has been described for different lakes [T a diguanylate cyclase (THSYN_19835) is found upstream the circadian clock genes kaiCBB . These genes act togheter [The diurnal and sesonal behavior of vacuolated nt lakes , 51. In togheter  and may cas genes encode protein that are co-transcribed with the CRISPR locus and interfere with invading DNA guided by the specific spacers [Bacterial CRISPR-Cas systems provide a mechanism against bacteriophage infection and plasmid transformation . A CRISP spacers , 57.T, four being located on the chromosome and one on the plasmid pTs485  were identified in the genome of strain Cad16485 Fig. . The numT. mobilis\u201d 8321  and \u201cThioalkalivibrio sulfidophilus\u201d HL-EbGr7 . The DRs found in CRR3 are similar to the ones in Halothiobacillus neapolitanus c2 , whereas the DRs in CRR5 are similar to the ones found in Vibrio alginolyticusNBRC 15630 .BLASTn analysis of the CRISPR DRsusing the CRISPRfinder platform revealed similarities in CRR1, CRR2 and CRR4 to sequences of \u201cT sequence, containing cas3 genes that are characteristic for type I CRIPSR-Cas systems [GSU0054 family  with C-terminal HD domain (TIGR01596) [cas8c gene and the lack of a cas6 sequence. Additionally, an incomplete CRISPR-Cas locus (CRR5) is identified on plasmid pTs485, encoding for Cas2, Cas1, .Furthermore, three CRISPR-Cas loci were identified in the strain Cad16 systems . A complGR01596) . AnotherThiodictyon syntrophicum\u201d sp. nov. strain Cad16T and the metabolic versatility of this environmentally relevant organism. The observed carbon fixation potential can be explained by the highly developed photosynthesis machinery that is coupled to the sulfur and carbon metabolism. Within the changing conditions in the chemocline, strain Cad16T is able to optimally use light, different organic and inorganic carbon compounds, reduced sulfur, nitrogen and oxygen. The two 0.4\u00a0Mb plasmids found in Cad16T are unique for known PSB species and we report structural similarity to sequences from \u03b1- and \u03b3-proteobacterial phototrophs. The availability of the complete genome sequence of strain Cad16T will facilitate further studies that elucidate its role as key species of the chemocline and the tight association with the Desulfocapsa sp. and the interaction with different PSB and GSB species present in the anoxic part of Lake Cadagno. Due to the limited molecular data on other Thiodictyon strains and no reference strains available, no  DNA-DNA hybridization experiments could be performed. However, the result from phylogenetic analyses on 16S rRNA sequence level, comparative genomic analyses as well a morphological and physiological differences (see above) indicate a novel species within the genus Thiodictyon.We report on the first complete genome sequence of\u00a0\u201c\u201cThiodictyon syntrophicum\u201d sp. nov. strain Cad16T, a novel species within the genus Thiodictyon.The decribed isolate is therefore proposed as A formal description of the proposed novel species follow below:Description of \u201cThiodictyon syntrophicum\u201dsp. nov.\u201cThiodictyon syntrophicum\u201d .a and okeneone. Growth as single cells, as well as in aggregates with up to 100 cells in a EPS layer. Assimilation of elemental sulfur in intracellular sulfur globules. Grow photoautotrophically in Pfennig's minimal medium with a doubling time of 121\u00a0h at 20\u201323\u00a0\u00b0C, a pH of 6.8\u20137.2, at 1\u00a0mM sulfide and a photoperiode of 12\u00a0h dark/ 12\u00a0h light. Dense cultures show a milky purple-red and milky color. Carbon assimilation via Calvin cycle. Following carbon substrates were utilized at a concentration of 5\u00a0mM: acetate, fructose and pyruvate. No growth was observed with 5\u00a0mM butyrate, ethanol, formate, fumarate, glucose, glycerol, lactate, malate, propanol, propionate and succinate, respectively. Chemolitoautotrophic growth was observed with 5% Oxygen and 0.02% hydrogen sulfid and 0.07% thiosulfate, or with 0.07% sulfide only, respectively.Gram-negative, cells are oval-round shaped and 1.4\u20132.4\u00a0\u03bcm in diameter, non-motile, vacuolated and contain BChl T (=JCM 15483T =KCTC5955T) was isolated from a sulfidic chemocline in the alpine Lake Cadagno in Switzerland. The genome size of the type strain is 6.84\u00a0Mb (chromosome), contains two plasmids, pTs485 (0.49\u00a0Mb) and pTs417 (0.42\u00a0Mb) and the G\u2009+\u2009C content of the genome is 66.22%. The 16S RNA gene sequence of strain Cad16T is deposited under the GenBank/EMBL/DDBJ accession number AJ511274. The complete genome sequence of the type strain Cad16T is deposited under the GenBank ID CP020370, CP020371 and CP020372. The type strain has been deposited both at the Japan Collection of Microorganisms (JCM 15483T) and at the Korean Collection for Type Cultures (KCTC 5955T).The type strain Cad16Additional file 1:Figure S1. Phylogenetic placement of \u201cT. syntrophicum\u201d strain Cad16T within the other 12 Chromatiaceae species with a publicly available whole genome sequences. Additionally, the closely related phylogenetic lineages Nitrosococcus, Rheinheimera and Arsukibacterium are also included. Strain Cad16T is most closely related to L. purpurea DSM 4197. The maximum likelihood tree was inferred from 100 concatenated single-copy orthologues sequences\u00a0[IQ-TREE software [equences\u00a0 and a toequences\u00a0, was useequences\u00a0\u00a0. The besoftware . Nodes w"}
+{"text": "Focal dystonia has been associated with deficient processing of sense of effort cues. However, corresponding studies are lacking in cervical dystonia (CD). We hypothesized that dystonic muscle activity would perturb neck force control based on sense of effort cues.with visual force feedback, ii) without visual feedback (requiring use of sense of effort), iii) without visual feedback, but with neck extensor muscle vibration (modifying muscle afferent cues). Trapezius muscle activity was recorded using electromyography (EMG).Neck extension force control was investigated in 18 CD patients with different clinical features (7 with and 11 without retrocollis) and in 19 control subjects. Subjects performed force-matching and force-maintaining tasks at 5% and 20% of maximum voluntary contraction (MVC). Three task conditions were tested: i) CD patients did not differ in task performance from healthy subjects when using visual feedback . In contrast, when relying on sense of effort cues , force control was impaired in patients without retrocollis (p = 0.006), but not in patients with retrocollis (p>0.2). Compared to controls, muscle vibration without visual feedback significantly affected performance in patients with retrocollis (p<0.001), but not in patients without retrocollis. Extensor EMG during rest, included as covariate in ANOVA, explained these group differences.This study shows that muscle afferent feedback biases sense of effort cues when controlling neck forces in patients with CD. The bias acts on peripheral or central sense of effort cues depending on whether the task involves dystonic muscles. This may explain why patients with retrocollis more accurately matched isometric neck extension forces. This highlights the need to consider clinical features (pattern of dystonic muscles) when evaluating sensorimotor integration in CD. Cervical dystonia (CD) is clinically characterized by involuntary neck muscle contraction leading to abnormal movement or posture . IntegraKinematic studies have shown that in CD neck extension amplitude and velocity are reduced toward the non-dystonic side (anti-dystonic) in voluntary movements compared to movements toward the dystonic side (pro-dystonic) ,6. TheseFurthermore, even though anti-dystonic movements are impaired in all planes, movement control in the sagittal plane, i.e. in flexion-extension, may be more severely affected ,6.Together these previous studies suggest that control of neck extension movements and forces will differ depending on clinical features, i.e. on whether task-related muscles are dystonic or not. This leads to the prediction that neck control involving dystonic muscles will be less affected than neck control involving non-dystonic muscles in patients with CD. Particularly, in CD flexion-extension afferent feedback will be differentially affected by the presence or absence of retrocollis. The underlying rationale is that the reliability of sensorimotor information processing  depends We hypothesized that multisensory integration  during voluntary isometric neck force control would be differentially affected in CD patients with varying clinical features .Eighteen patients with primary focal CD were recruited and categorized according to clinical features, i.e. presence  or absence  of a retrocollis. Patients with tardive/drug-induced dystonia were excluded. None of the patients received botulinum toxin injections for at least 3 months prior to this study. Nineteen healthy (age- and gender-matched) control subjects were also recruited  . Maximumhttp://ced.co.uk) to investigate multi-modal sensory processing during isometric force control.Two tasks were developed , to a visually indicated target level. In each trial, force had to be increased to target force level, then maintained (for 3s), and finally released.Condition_noVis: subjects had to reproduce the previous trial without visual feedback (noVis). This required the use of sense of effort cues to match performance between trials.Condition_noVis+Vib: this condition was similar to condition b, but with muscle vibration (+Vib) to modify muscle afferent feedback [\u00ae VB115, www.technoconcept.fr).feedback    and statistics performed under Statistica10 . Forces and EMGs were averaged across all trials in each task condition. Group differences were analyzed using a general linear model repeated measures ANOVA with one GROUP factor (CD_R-/CD_R+/Controls) and two within-group factors: FORCE (5%/20% MVC) and CONDITION (a/b/c). We used Fisher LSD to test for post-hoc differences. The level of significance was set to p<0.05 and adjusted in order to correct for multiple comparisons with the Benjamini and Hochberg method . All repThe two tasks were completed successfully by all subjects. Patient groups did not differ in total TWSTRS (Mann-Whitney U Test p = 0.23). condition_Vis, p>0.7) and GROUP differences were specific to 5% MVC-level (p<0.001 between CD groups).In the force-matching task  the ANOVcondition_noVis (5% MVC), force was significantly increased in CD_R- patients (1.31\u00b10.47N) compared to control subjects , but not in CD_R+ patients . Thus, CD_R- patients applied significantly higher forces than CD_R+ patients (p = 0.002). Control subjects showed no significant force difference between condition_Vis (0.92\u00b10.09N) and condition_noVis .However, performance of CD patients differed in conditions requiring sense of effort cues: in condition_noVis+Vib, 5% MVC), CD_R+ patients showed significantly reduced mean force (0.74\u00b10.33N) compared to CD_R- patients  and to control subjects . Hence, compared to control subjects, CD_R- patients tended to overshoot, whereas CD_R+ patients undershot target forces.During muscle vibration (The above ANOVA was repeated with baseline EMG activity (during rest) as covariate. This cancelled the statistical main  and post-hoc differences between CD patients and control subjects.condition_Vis: no significant difference (p>0.2); (ii) condition_noVis: force CD_R- > force controls (p = 0.003); (iii) condition_noVis +Vib: force CD_R- > force CD_R+, (p = 0.001).In the force-maintaining task, the performance of CD_R- and CD_R+ patients were qualitatively similar to those seen during force-matching. Similar between group differences were found: (i) EMG activity during MVC was similar between groups . However, during force-matching, the ANOVA of EMG activity showed significant effects of GROUP  and FORCE , but not of CONDITION . Post-hoc analyses showed increased EMG activity in CD patients compared to control subjects (p<0.01). EMG activity was higher in CD_R+ than in CD_R- patients (p = 0.04).We have shown that clinical features of CD (presence/absence of a retrocollis) differentially affect multisensory integration of visual and sense of effort signals during voluntary neck force control. We have also shown that the type of force control deficit depends on the characteristics and sources of sensory information . Our results are in line with the optimal multisensory integration theory,  which poWith visual force feedback, performance of CD patients was similar to that of control subjects. CD patients presumably used the most reliable sensory modality (vision) and gated less reliable feedback. This is consistent with previous findings ,16 and wWithout visual feedback, subjects were required to match forces  by using sense of effort cues exclusively. The variability around the average force increased for all subjects. However, force control was impaired only in CD patients without retrocollis, since CD_R- patients overshot, whereas CD_R+ patients and control subjects showed no change. These results suggest that CD_R- and CD_R+ patients optimized their use of sense of effort cues differently. Presumably, CD_R+ patients favoured peripheral cues since voluntary activation of dystonic task-related muscles helped keeping agonist afferent feedback reliable. CD_R- patients may have chosen central cues since non-agonist dystonic muscles may have produced sensory afferent crosstalk, rendering the efferent copy more reliable. Baseline EMG explained group differences suggesting that spontaneous (dystonic) neck muscle activity at rest can account for modified sense of effort.Modifying peripheral sensory cues (by vibration of neck extensor muscles) clearly affected CD_R+, but not CD_R- patients. The fact that muscle vibration acts directly on peripheral cues corroborates our assumption that CD_R- patients relied more on central sense of effort cues (not or less affected by vibration), and that CD_R+ patients relied more on peripheral sense of effort cues (strongly affected by vibration). Moreover, target forces were overshot by CD_R- patients and undershot by CD_R+ patients. This is consistent with vibration acting on dystonic agonist muscles in CD_R+ and on non-dystonic agonists in CD_R- patients. Dystonic muscles are more sensitive to vibration than non-dystonic muscles  and provLastly, we confirm that in focal dystonia, modulating voluntary force according to sense of effort cues is affected . CD patiFurther studies in CD are needed to investigate whether our findings can be generalized to movements other than flexion-extension, such as rotational neck movements. Although this study is limited by a relatively small sample size and lack of antagonist EMG, our findings suggest that modifying sense of effort through training or neuromodulation may be a useful therapeutic approach in CD ,23,24.In conclusion, we found impaired voluntary neck force control in CD patients, when successful task completion required the use of sense of effort cues. Our results showed that this impaired control may be explained by altered muscle afferent feedback related to dystonic muscle contractions, which in turn may hamper optimal use of multi-modal sensory information , includi"}
+{"text": "Bacteroides and Porphyromonadacese_uc_g revealed a strong positive correlation with the levels of phenols and SCFAs. Populations of AC160630_g, Acholeplasmatales_uc_g, Mollicutes_uc_g and Cloacamonas_f_uc_g positively correlated with indole and BCFAs content. Taken together, levels of odorous compounds were increased after two weeks of storage, possibly because of changes in the predominant bacterial groups to those that use protein as a carbon source in the hypo-carbohydrate conditions.Odor from buildings where pigs are housed is generated by anaerobic fermentation of undigested materials in pig slurry stored for several weeks in pit. The objective of this study was to investigate the effect of storage period on the level of odorous compounds in pig slurry and on its bacterial community. A slurry sample (15 L) was taken from the pit of a finisher pig building and incubated in acryl chambers for six- weeks. Slurry for analysis was sampled every two-week. Levels of odorous compounds in the slurry sample were drastically changed after two weeks of storage period; levels of phenols and short chain fatty acids (SCFAs) were decreased (P<0.05), whereas indoles and branched-chain fatty acids (BCFAs) were increased (P<0.05). Among dominant bacteria,  Large amounts of pig slurry are generated by intensive animal farming and industrial livestock production (factory farming); in South Korea, the amount increased from 4,370 million tons in 2009 to 4,724 million tons in 2013 .Pig slurry is usually stored for a couple of weeks to months inside a pit under the pig building before being cleaned out. During this storage period, anaerobic fermentation triggered by microbes using the undigested nutrients and endogenous materials in the slurry is the main cause of malodor generation . SurfaceMost of the odor-causing materials are produced by protein degradation. If carbohydrates are scarce in pig slurry during the storage period, protein becomes a primary source of fermentable carbon . The nutThe objectives of this study were to identify the cause of odor from pig houses with regard to the effect of the slurry storage period on the changes in concentration of odorous compounds and the composition of bacterial communities.Fresh slurry was collected from the pit under a pen housing finisher pigs [total of 60 {(Landrace \u00d7 Yorkshire) \u00d7 Duroc}] with body weight (BW) of 80~110 kg in National Institute of Animal Science, Wanju-Gun, Jeollabuk-Do . The finisher pigs were fed a basal diet formulated according to the Korean Feeding Standard . Fifteen\u0261 for 20 min at 20\u00b0C. One milliliter of supernatant was subsequently centrifuged for 10 min at 13,800 \u00d7 \u0261 and filtered through a 0.2 \u03bcm filter . Filtrates were transferred to 2.0 mL GC vials . The concentration of VFAs was analyzed using a GC  equipped with a HP-INNOWax column  and a flame ionization detector (FID). The sample injection volume was 0.2 \u03bcL with a 10:1 split ratio. The oven temperature was initially temperature of 80\u00b0C for 2 min, increasing to 120\u00b0C at 20\u00b0C/min, then to 205\u00b0C at 10\u00b0C/min, and finally held at 205\u00b0C for 2 min. The injection and detection ports were maintained at 250\u00b0C.Five milliliters of slurry were mixed with 1 mL of 25% meta-phosphoric acid solution  and 0.05 mL of saturated mercury (II) chloride solution  in a 15 mL plastic tube. The mixed solution was then centrifuged at 3,134 \u00d7 \u0261 for 20 min at 20\u00b0C, and then 4 mL of supernatant was mixed with 4 mL of chloroform  and 60 \u03bcL of 4M sodium hydroxide solution  in a 20 mL glass vial. The mixture was centrifuged at 3,134 \u00d7 \u0261 for 20 min at 20\u00b0C, and the chloroform layer was transferred to a 2.0 mL GC vial . Phenols and indoles were analyzed using a GC  equipped with a DB-1 column  and a FID. The sample injection volume was 2.0 \u03bcL with a 5:1 split ratio. The oven temperature was initially 40\u00b0C for 5 min, increasing to 230\u00b0C at 10\u00b0C/min, which was then held at 230\u00b0C for 2 min. The injection and detection ports were maintained at 250\u00b0C.Slurry samples were centrifuged at 3,134 \u00d7 2-AC-GAG TTT GAT CMT GGC TCA G-3') and 518R (5'-adaptor 1-AC-X-WTT ACC GCG GCT GCT GG-3') where \u201cX\u201d denotes unique 7 to 11 bar-code sequences inserted between the 454 Life Sciences adaptor A sequence and the common linker, AC [Total genomic DNA from slurry was extracted using a Fast-DNA Spin Kit  according to the manufacturer\u2019s instructions. Humic acid interferes with PCR amplification was removed using a Power-Clean DNA Clean-Up Kit . Bacterial 16S ribosomal RNA (16S rRNA) genes around 500\u2013700 bp long containing V1 to V3 of the variable region were amplified using primer set 27F . Sequences that could be matched to the EzTaxon-e database at the species level (>97%) were subjected to a secondary process to check for chimeric sequences using the UCHIME program [Pyrosequencing was performed by ChunLab  using a 454 FLX Titanium System . Sequencing reads were assigned to specific samples based on their unique barcodes. Then barcode, linker and PCR primer sequences at both ends were removed from the original sequencing reads. The final pyrosequencing reads for subsequent analysis were selected by a filtering process including only reads containing >300 base pairs and an average quality score >25. Taxonomic assignment of the bacterial high quality reads was performed using the EzTaxon-e database  and a ro program . Operati program .All experimental data including those concerning odorous compounds and bacterial communities were subjected to analysis of variance for a completely randomized design using the general linear model procedures of SAS software . SignifiThe effects of a storage period up to 6 weeks on phenols, indoles and VFAs concentration in pig slurry are shown in Changes in the bacterial community structure during 6 weeks slurry storage period were analyzed by the multiplex bar-coded pyrosequencing technique based on 16S rRNA gene sequences . After rFirmicutes, Spirochaetes, Bacteroidetes, Tenericutes, Cloacamonas_p, Proteobacteria, Lentisphaerae and Actinobacteria (Firmicutes and Lentisphaerae was reduced (P<0.05) after 2 weeks of storage. Bacteroidetes consistently decreased (P<0.05) during the 6 weeks storage period. However, Cloacamonas_p was drastically increased (P<0.05) after 2 weeks of storage.Taxonomic pyrosequencing profiles of bacterial communities in pig slurry are shown in Figs bacteria . The relFirmicutes, 70 Proteobacteria, 64 Bacteroidetes, 29 Actinobacteria, 23 Tenericutes, 21 Lentisphaerae, 7 Cloacamonas_p and 7 Spirochaetes. Altogether, there were 305 Gram-positive bacterial genera and 169 Gram-negative genera. Among the dominant genera, Clostridiales_uc_g, EU470107_g, Ruminococcaceae_uc, Lactobacillus, Turicibacter and HQ716403_g of the phylum Firmicutes, and Bacteroidales_uc_g, Bacteroides, Anaerocella_f_uc and Porphyromonadaceae_uc of the phylum Bacteroidetes were decreased (P<0.05) for 6 weeks of storage. Within these genera, Clostridiales_uc_g, Bacteroides and Porphyromonadaceae_uc were decreased (P<0.05) by 2 weeks. Sphaerochaeta of the phylum Spirochaetes, Cloacamonas_f_uc of Cloacaminas_p, GU454936_g of Firmicutes, AC160630_g of Bacteroidetes, Acholeplasmatales_uc_g and Mollicutes_uc_g of Tenericutes, and Advenella of Proteobacteria were increased (P<0.05) for 6 weeks of storage. Within these genera, Cloacamonas_f_uc, GU454936_g, AC160630_g, Acholeplasmatales_uc_g and Mollicutes_uc_g were drastically increased (P<0.05) after 2 weeks.At the genus level , a totalChanges in the bacterial compositions and concentrations of odorous compounds during the 6 weeks storage were graphically summarized using hierarchical clustering, PCA and PCoA in Figs Bacteroides, Porphyromonadaceae_uc_g, AC160630_g, Acholeplasmatales_uc_g, Mollicutes_uc_g and Cloacamonas_f_uc showed relatively greater (P<0.05) correlation coefficient values with odorous compounds. There was a positive correlation between phenol, p-cresol, acetic acid, propionic acid and butyric acid with Bacteroides and Porphyromonadaceae_uc_g, whereas there was a negative correlation with AC160630_g, Acholeplasmatales_uc_g, Mollicutes_uc_g and Cloacamonas_f_uc. Indole, skatole, iso-butyric acid and iso-valeric acid were shown to have the opposite correlations with these genera.The interrelationships of various bacterial genera associated with odorous compounds analyzed in the current study is shown in The generation of odorous compounds from pig house is contributed to bacterial fermentation within the gastrointestinal tract of the pigs and the slurry in pit under the floor of the pig pen. Bacteria utilize undigested dietary materials, endogenous compounds and dead bacterial cells in slurry , and thePhenols and indoles are produced during bacterial metabolism of tyrosine and tryptophan, respectively, in stored manure . They ar2 by anaerobic bacteria in stored pig waste [In this study, dramatic changes in the contents of phenols and indoles were detected at 2 weeks in stored slurry. Results from others have shown that a decrease in the concentration and emission rate of phenols started at about 36-day, but the indoles level was remained constant over 71-day storage period . Ziemer,ig waste . BacteriSCFAs are a product of carbohydrate fermentation, whereas BCFAs are a product of protein fermentation . SCFAs aAnalyzing the relationship between bacteria and their biotopes is an important step to understanding the mechanism of accumulation of odorous compounds produced by bacterial fermentation. The bacterial community in pig slurry was previously characterized by culture methods \u201351. ReceFirmicutes, Bacteroidetes and Lentisphaerae decreased but Cloacamonas_p increased after 2 weeks storage of pig slurry. Firmicutes and Bacteroidetes constituted the majority of gut bacteria. Bacterial strains belonging to these two phyla are used as a phylogenetic markers because their relative abundance is easily influenced by fermentation conditions [Clostridiales_uc_g, Bacteroides and Porphyromonadaceae_uc decreased but Cloacamonas_f_uc_g, GU454936_g, AC160630_g, Acholeplasmatales_uc_g and Mollicutes_uc_g increased after 2 weeks storage of pig slurry  and discovered by molecular inventories of an anaerobic digester [Cloacamonas spp. is syntrophic amino acid metabolizer, which could ferment a highly enriched hydrolysis product resulting from the hydrolytic activity of other bacteria [Tenericutes and Cloacamonas in stored slurry can be explain the protein availability for growth in the hypo-carbohydrate condition and then the high indoles and BCFA levels, which would be derived mainly from amino acid fermentation.Indole, skatole, iso-butyric acid and iso-valeric acid showed a positive correlation with naerobic . The claradation , 73. Lac skatole . The Moltic acid . In addibutyrate . Mollicun source . The phydigester . WWE1 mebacteria  and prodbacteria . TherefoTenericutes and Cloacamonas. Tenericutes and Cloacamonas strains use proteins as a carbon source for growth and predominate in various environmental conditions. Our current study has significant value in identifying the causes of odor from pig houses. Based on our results, it is desirable that pig slurry needs to be discharged every 2 weeks to reduce the odor in pig house. Further investigation is necessary to control bacterial growth and identify fermentation patterns to increase the efficiency of odor reduction.Our results demonstrate that the storage period of pig slurry in a pit significantly affects the composition of odorous compounds produced as well as the bacterial community. Levels of odorous compounds were dramatically changed after 2 weeks of slurry storage. Phenols and SCFAs decreased, whereas indoles and BCFAs increased in the pig slurry. Accumulation of indoles and BCFAs is associated with increased pH during slurry storage and showed a strong positive correlation with members of the taxa"}
+{"text": "Corynebacterium pseudotuberculosis is a Gram-positive facultative intracellular pathogen of the Corynebacterium, Mycobacterium, Nocardia, and Rhodococcus (CMNR) group. The CMNR group of pathogens has high G\u00a0+\u00a0C content in their genomes and shows a specific cell wall organization composed of peptidoglycan, arabinogalactan, and mycolic acids  at 60\u00a0\u00b0C for 15\u00a0min, reduced with DTT [(10\u00a0mM) ], and alkylated with iodoacetamide [(10\u00a0mM) ]. For enzymatic digestion, trypsin [(0.5\u00a0\u03bcg/\u03bcL) ] was added and placed in a thermomixer at 37\u00a0\u00b0C overnight. The digestion process was stopped by the addition of 10\u00a0\u03bcL of 5% TFA  and glycogen phosphorilase (Sigma-Aldrich) was added to the digests to give 20 fmol.uL\u22121 as an internal standard for scouting normalization prior to each replicate injection into label-free quantitation 2+\u00a0=\u00a0785.8426) was used for initial single-point calibration and MS/MS fragment ions of Glu-Fib were used to obtain the final instrument calibration. Multiplexed data-independent (DIA) scanning with added specificity and selectivity of a non-linear \u2018T-wave\u2019 ion mobility (HDMSE) experiments were performed with a Synapt G2-S HDMS mass spectrometer (Waters), which was automatically planned to switch between standard MS (3\u00a0eV) and elevated collision energies HDMSE (19\u201345\u00a0eV) applied to the transfer \u2018T-wave\u2019 CID (collision-induced dissociation) cell with argon gas. The trap collision cell was adjusted for 1\u00a0eV, using a mili-seconds scan time previously adjusted based on the linear velocity of the chromatography peak delivered through nanoACQUITY UPLC to get a minimum of 20 scan points for each single peak, both in low energy and at high-energy transmission at an orthogonal acceleration time-of-flight (oa-TOF) from m/z 50 to 2000. The RF offset (MS profile) was adjusted is such a way that the nanoUPLC-HDMSE data are effectively acquired from m/z 400 to 2000, which ensured that any masses observed in the high energy spectra with less than m/z 400 arise from dissociations in the collision cell.The lock mass channel was sampled every 30\u00a0s. The mass spectrometer was calibrated with a MS/MS spectrum of [Glu1]-Fibrinopeptide B human (Glu-Fib) solution (100 fmol.uLE and ExpressionE informatics v.2.5.2 (Waters) were used. UniProtKB (release 2013_01) with manually reviewed annotations was used, and the search conditions were based on taxonomy (Corynebacterium pseudotuberculosis). We have utilized a database from genome annotation of 1002_ovis CP001809.2 version and 258_equi CP003540.2 version. These databases were randomized within PLGS v.2.5.2 for generate a concatenated database from both genomes. Thus, the measured MS/MS spectra from proteomic datasets of 1002_ovis and 258_equi were searched against this concatenated database. The maximum allowed missed cleavages by trypsin were up to one, and variable modifications by carbamidomethyl (C), acetyl N-terminal, phosphoryl (STY) and oxidation (M) were allowed and peptide mass tolerance value of 10\u00a0ppm was used [2+ and the absence of decoys were the factors we considered to increase the data quality. The collected proteins were organized by the PLGS ExpressionE tool algorithm into a statistically significant list that corresponded to higher or lower regulation ratios among the different groups. For protein quantitation, the PLGS v2.5.2 software was used with the IdentityE algorithm using the Hi3 methodology. The search threshold to accept each spectrum was the default value in the program with a false discovery rate value of 4%. The quantitative values were averaged over all samples, and the standard deviations at p\u00a0<\u00a00.05 were determined using the Expression software. Only proteins with a differential expression log2 ratio between the two conditions greater than or equal to 1.2 were considered [Following the identification of proteins, the quantitative data were packaged using dedicated algorithms , 24 and was used . Peptidensidered .ovis and 258_equi were subjected to the bioinformatics analysis using the various prediction tools. SurfG+ v1.0 [The identified proteins in 1002_fG+ v1.0  was usedfG+ v1.0  to predifG+ v1.0  to identfG+ v1.0  to deterfG+ v1.0  and COG fG+ v1.0  were usefG+ v1.0  with a sE approach to characterize the proteome of the strains 1002_ovis and 258_equi. Both strains were grown in BHI media, subsequently proteins were extracted and digested in solution, and then the peptides were analyzed by LC/MSE. Our proteomic analysis identified a total of 1227 non-redundant proteins in 1002_ovis  in 258_equi. Considering proteins with LPxTG motif which are involved in covalent linkage with peptidoglycan, we identified 6 proteins in 1002_ovis and 4 proteins in 258_equi that correspond to approximately 38% and 34% of the LPxTG proteins predicted in each strain, respectively.In this study, we applied the 2D nanoUPLC-HDMSnce Fig.  and charig. ovis   that encodes these proteins are part of the core-genome  of the proteins that constitute the core-proteome are shared by at least one of the 15 strains used in the core-genome study. According to Gene Ontology analysis [C. pseudotuberculosis [The core-proteome, between 258_analysis , 32, theequi and 1002_ovis. The ProteinLynx Global Server (PLGS) v2.5.2 software with ExpressionE algorithm tool was used to identify proteins with p\u00a0\u2264\u00a00.05  [equi:1002_ovis), 49 proteins were more abundant and 71 less abundant (Table 2 ratio of 258_equi/1002_ovis versus Log (e) Variance was generated    are related to cellular metabolism. On other hand, the majority of the less abundant proteins (258_equi:1002_ovis) are classified as poorly characterized or of unknown function. However, when proteins of known or predicted function are evaluated the majority of the less abundant proteins are related to cellular processes and signaling.The 120 differential proteins were organized by cluster of orthologous groups, and when evaluated the different biological processes that comprise each category listed above, we observed that 19 process were differentials between 258_ovis and 258_equi respectively, were mapped onto different metabolic pathways  . A totalrculosis , 17, 19,pathogen .equi, than in 1002_ovis  related to carbohydrate metabolism was more abundant in 258_e strain . This inn in BHI . Glutamanfection .C. pseudotuberculosis, it was demonstrated that genes related the iron-acquisition are involved in the virulence of this pathogen [ovis and 258_equi, we detected proteins involved in this process, like CiuA, FagC and FagD; however, all these proteins were not differentially regulated between the two strains , which is normally present in pathogenic Corynebacterium [C. ulcerans, HmuT is required for normal hemin utilization [In pathogen . In the ui Table . Additioacterium , 44. In equi  and reactive nitrogen species (RNS), which are generally found in macrophage. The three major thiol-dependent antioxidant systems in prokaryotic pathogens are the thioredoxin system (Trx), the glutathione system (GSH-system) and the catalase system [equi  biosynthesis, which plays an important role in anaerobic respiration in bacteria and also are required to activation of nitrate reductase (NAR) [M. tuberculosis several studies have showed the great importance of molybdenum cofactor in its virulence and pathogenic process, mainly macrophage intracellular environmental [equi strains. Other protein that also could contribute to resistance of 258_equi macrophage is NADPH dependent nitro/flavin reductase (NfrA), a pseudogene in 1002_ovis. In addition, studies performed in Bacillus subtilis showed that NfrA is involved in both oxidative stress [The distinction between the biovar positive . Howeverse (NAR) . In the onmental . Therefoe stress  and heate stress .ovis, only the ORF that encodes a DNA methylase was not found in the 258_equi genome  , associated to cobalt metabolism, glutamate dehydrogenase (gdh) involved in the L-glutamate metabolism, the PTS system fructose specific EIIABC related to fructose metabolism and the Phosphoribosylglycinamide formyltransferase involved in the purine biosynthesis were all detected in 258_equi. Proteins involved in DNA processes, such as Uracil DNA glycosylase in 258_equi; and Exodeoxyribonuclease 7 small subunit in 1002_ovis were also detected in both strains. Proteins with general function prediction only and unknown function were also identified in both strains.In our proteomic analysis, the measured MS/MS spectra from the proteomic datasets of 1002_C. pseudotuberculosis strains belonging to both ovis and equi biovars. Taken together, the findings reported here show a set of shared and exclusive factors of 1002_ovis and 258_equi at the protein level, which can contribute to understanding both the physiology and the virulence of these strains. In addition, the functional analysis of the genome of 1002_ovis and 258_equi allows the in silico validation of data of the genome of these strains. Thus, the proteins identified here may be used as potential new targets for the development of vaccines against ovis and equi C. pseudotuberculosis in future investigations.In conclusion, we used a label-free quantitative approach to compare, for the first time, the proteome of The datasets supporting the results of this article were then concatenated into a *xlsx file at peptide and protein level to fulfill the requirements and is available at supplemental material including sequence coverage and a number of identified peptides for each protein sequence identified. It also includes the native peptide information.Additional file 1: Figure S1.ovis (blue circles) and 258_equi (red triangles). (JPEG 278\u00a0kb) Growth rates in BHI media of 1002_Additional file 2: Table S1.ovis and 258_equi. (XLSX 215\u00a0kb) Total list of proteins identified in the core-proteome of 1002_Additional file 3: Table S2.ovis. (XLSX 20\u00a0kb) Total list of proteins identified in the exclusive proteome of 1002_Additional file 4: Table S3.equi. (XLSX 21\u00a0kb) Total list of proteins identified in the exclusive proteome of 258_Additional file 5: Table S4.ovis. (XLSB 31769\u00a0kb) Total list of peptide and proteins identified 1002_Additional file 6: Table S5.equi. (XLSB 33204\u00a0kb) Total list of peptide and proteins identified 258_Additional file 7: Figure S2.ovis. (A) General interactome of differentially regulated proteins, identified in the exclusive proteome of 1002_ovis. The proteins are marked with different shapes: exclusive proteome, circle; more abundant, square; less abundant, rhombus. The biological processes were marked with different colors: amino acid transport and metabolism, yellow; secondary metabolites biosynthesis, transport and catabolism, aquamarine; inorganic ion transport and metabolism, orange; coenzyme metabolism, brown; carbohydrate transport and metabolism, chartreuse green; nucleotide metabolism, cerulean; energy metabolism, olive; lipid transport and metabolism, viridian; adhesion and motility cell, crimson; iuntracellular trafficking secretion and vesicular transport, persian blue; signal transduction mechanisms, maroon; cell wall/membrane and envelope, gray; defense mechanism, red; post-translational modification, protein turnover, chaperones, electric blue; DNA metabolism, replication, recombination and repair, violet; translation, ribosomal structure and biogenesis, amber; transcription, regulation, degradation and RNA processing, salmon; poorly characterized, white. (JPEG 3310\u00a0kb) The protein-protein interaction network of 1002_Additional file 8: Figure S3.equi. (A) General interactome of the differentially regulated proteins, identified in the exclusive proteome of 258_equi. The proteins are marked with different shapes: exclusive proteome, circle; more abundant, square; less abundant, rhombus. The biological processes are marked with different colors: amino acid transport and metabolism, yellow; secondary metabolites biosynthesis, transport and catabolism, aquamarine; inorganic ion transport and metabolism, orange; coenzyme metabolism, brown; carbohydrate transport and metabolism, chartreuse green; nucleotide metabolism, cerulean; energy metabolism, olive; lipid transport and metabolism, viridian; adhesion and motility cell, crimson; intracellular trafficking secretion and vesicular transport, persian blue; signal transduction mechanisms, maroon; cell wall/membrane and envelope, gray; defense mechanism, red; post-translational modification, protein turnover, chaperones, electric blue; DNA metabolism, replication, recombination and repair, violet; translation, ribosomal structure and biogenesis, amber; transcription, regulation, degradation and RNA processing, salmon; poorly characterized, white. (JPEG 4178\u00a0kb)The protein-protein interaction network of 258_Additional file 9: Figure S4.ovis. Red line, proteins identified in the proteomic analysis, other colors represent proteins not identified in this study. (JPEG 8633\u00a0kb) Metabolic network of 1002_Additional file 10: Figure S5.equi. Red line, proteins identified in the proteomic analysis, other colors represent proteins not identified in this study. (JPEG 1267\u00a0kb) Metabolic network of 258_Additional file 11: Table S6.ovis by Proteogenomics. (XLSX 216\u00a0kb) Proteins identified in 1002_Additional file 12: Table S7.equi by Proteogenomics. (XLSX 266\u00a0kb) Proteins identified in 258_"}
+{"text": "Staphylococcus aureus lineage USA300.The Oxford Nanopore Technologies MinION(TM) is a mobile DNA sequencer that can produce long read sequences with a short turn-around time. Here we report the first demonstration of single contig genome assembly using Oxford Nanopore native barcoding when applied to a multiplexed library of 12 samples and combined with existing Illumina short read data. This paves the way for the closure of multiple bacterial genomes from a single MinION(TM) sequencing run, given the availability of existing short read data. The strain we used, MHO_001, represents the important community-acquired methicillin-resistant S. aureus USA300 strain MHO_001. The long read data represented only \u223c5% to 10% of an average MinION(TM) run (\u223c7x genomic coverage), but, using standard tools, this was sufficient to complete the circular chromosome of S. aureus strain MHO_001 (2.86 Mb) and two complete plasmids (27 Kb and 3 Kb). Minor differences were noted when compared to USA300 reference genome, USA300_FPR3757, including the translocation, loss, and gain of mobile genetic elements.Using a hybrid assembly of existing short read and barcoded long read sequences from multiplexed data, we completed a genome of the Here we demonstrate that MinION(TM) reads, multiplexed using native barcoding, can be used in combination with short read data to fully complete a bacterial genome. The ability to complete multiple genomes, for which short read data is already available, from a single MinION(TM) run is set to impact our understanding of accessory genome content, plasmid diversity, and genome rearrangements. Staphylococcus aureus represents a significant burden in both the health-care setting and the community. The USA300 clone is a particular cause for concern, being responsible for an increasing number of skin and soft-tissue infections within the community, particularly in North America [Escherichia coli using the Oxford Nanopore Technologies (ONT) MinION(TM) reads alone and on a range of bacteria including Bacteriodes fragilis, Acinetobacter baylyi, and Francisella spp. using a hybrid approach combining error-prone long reads with low error rate short reads [S. aureus of the USA300 lineage as an example.The spread of methicillin-resistant  America . The advrt reads . Here weS. aureus strain MHO_001 was recovered in 2015 from asymptomatic nasal carriage via a standard nasal swab of a healthy individual with informed consent. DNA from an overnight culture was extracted using the Qiagen Genomic Tip 500/G Kit, following the manufacturer's instructions, except lysozyme was replaced with lysostaphin to a final concentration of 200\u2009\u03bcg/ml. Sequencing library preparation was carried out with Nanopore Genomic Sequencing Kit SQK-MAP006  and a PCR-free \u2018native barcoding\u2019 kit provided by ONT. The NEBNext Ultra II End Repair/dA Tailing kit  was used to prepare 1000 ng of sheared genomic DNA . The reaction was incubated for 5\u2009minutes at 20\u00b0C and heat inactivated for 5 minutes at 65\u00b0C. The DNA was purified using a 1:1 volume of Agencourt AMPure XP beads  according to manufacturer's instructions and eluted in 31\u2009\u03bcl of nuclease free water. Blunt/TA Ligase Master Mix  was used to ligate native barcode adapters to 22.5\u2009\u03bcl of 500 ng end prepared DNA for 10 minutes at room temperature. The barcoded DNA was purified using a 1:1 volume of AMPure XP beads and eluted in 26\u2009\u03bcl nuclease free water. Twelve barcoded samples from diverse sources including other bacterial samples were pooled, 58 ng of each sample was added to give 700 ng of pooled library DNA. Hairpin adapters were ligated using 10\u2009\u03bcl Native Barcoding Adapter Mix, 50\u2009\u03bcl Blunt/TA Ligase Master Mix, and 2\u2009\u03bcl Native Barcoding Hairpin Adapter added to 38\u2009\u03bcl of the pooled library DNA to give a final reaction volume of 100\u2009\u03bcl. The reaction mixture was incubated for 10 minutes at room temperature before the addition of 1\u2009\u03bcl of HP tether and a further 10 minutes incubation. The final reaction was cleaned using prewashed Dynabeads MyOne Streptavidin C1 beads . DNA concentrations at each step were measured using a Qubit Fluorometer. Then 6\u2009\u03bcl of the pooled, barcoded library was mixed with 65\u2009\u03bcl nuclease free water, 75\u2009\u03bcl 2x Running Buffer, and 4\u2009\u03bcl Fuel Mix  and immediately loaded onto a MinION(TM) Flow Cell Mk I R7.3 on a MinION(TM) MkI controlled by MinKNOW version 0.50.2.15 software (ONT). Base calling was performed using Metrichor ONT Sequencing Workflow Software v1.19.0 with the Basecall_Barcoding workflow (ONT). The additional DNA samples included in the pooled library were a diverse assemblage of bacterial and eukaryotic DNA samples provided by attendees during the PoreCamp Workshop 2015 at the University of Birmingham. The additional pooled library samples are being prepared for separate publication. Details on the PoreCamp Workshop and associated publications can be found at http://porecamp.github.io/. MinION reads were deposited in the European Nucleotide Archive under study accession PRJEB14152.http://microbesng.uk). A single 250-bp paired end library was constructed and sequenced on both MiSeq and HiSeq Illumina platforms. The reads from both sequencing runs were combined before downstream analysis. The sequenced strain is stored in the MicrobesNG indexed repository as strain 2998-174. Reads were deposited in the European Nucleotide Archive under study accession PRJEB14152.An overnight culture was grown on TSB agar from a 15% glycerol stock maintained at \u221280\u00b0C. An aliquot of the culture was added to tubes containing DNA beads and library preparation was carried out by MicrobesNG, University of Birmingham  and SAP046B (Genbank:GQ900403.1). The smallest plasmid was also identical to USA300_FPR3757 plasmid pUSA01 (CP000256). The complete genome of MHO_001 was annotated using Prokka 1.11 [The full informatics analysis and associated data are available as a step-by-step walk-through at tic-0.33 . Reads wtic-0.33 ,\u00a07. Thestic-0.33 . MinION reads (6\u20138\u00d7) combined with moderate coverage Illumina reads (\u223c50\u00d7) was used to generate a complete genome. The assembly resolved regions of the genome that were problematic for short read assembly alone, such as chromosomal rRNA operons. The generation of a complete genome from only \u223c5% of the possible current yield of a MinION(TM) run using a multiplexed library should represent a cost-effective means to complete multiple genomes during a single MinION(TM) sequencing run, although the approach also requires matching short read Illumina data. Larger or more complex bacterial genomes may require higher coverage read data alongside additional bioinformatics analyses to generate comparably polished, complete genomes .By demultiplexing the 2D fail reads, we were able to double the number of nanopore reads for assembly from 1324 to 2823 reads. The nanopore reads were aligned to the complete MHO_001 genome using BLASR  is intact, it is possible that MHO_001 has never acquired this phage. MHO_001 contained a 42,297-bp tyrosine recombinase bacteriophage integrated at position 867,385. This bacteriophage contained a beta-lactamase and a putative Panton-Valentine-like leuckocidin and several hypothetical genes. The position of an insertion sequence containing ftsK translocase differs between MHO_001 and the reference genome, consistent with a translocation event . The location of this element in MHO_001 truncates a gene of unknown function. There is a short 1282-bp deletion of a gene encoding an exotoxin at position 448,767 in MHO_001. MHO_001 also has an extended tRNA cluster at 554,826 containing 7 additional tRNAs  relative to USA300_FPR3757, representing either gene expansion or reduction of this gene cluster in USA300_FPR3757.There was minor sequence dissimilarity, including a small deletion, in ribosomal RNA operons. This could either reflect evolutionary changes in these highly conserved sequences or minor misassembly; these regions are typically difficult to assemble. MHO_001 lacked Staphylococcal pathogenicity island 5 (SAPI5), a 13,960-bp exotoxin encoding transposon observed at position 881,852 in the reference. MHO_ also lacked the prophage phiSA3USA, which harbours the important virulence factor staphylokinase. As the integration site of this phage  with contributions from the Biotechnology and Biological Sciences Research Council, the National Institute for Health Research on behalf of the Department of Health, and the Chief Scientist Office of the Scottish Government Health Directorate. The authors are grateful for travel funds provided by NERC (NE/N000501/1) for SB and Medical Research Council Cloud Infrastructure for Microbial Bioinformatics for VH to attend.SB and VH were responsible for the conception and design of study and data acquisition. SB performed the analysis and interpretation of data and manuscript drafting. MY carried out the GigaScience GigaDB repository [The dataset supporting the conclusions of this article is available in the European Nucleotide Archive repository under project number PRJEB14152. Further supporting data is also available from the pository .Project name: MHO_001 hybrid read assembly and analysishttps://github.com/SionBayliss/MHO_analysisProject home page: Operating system: UnixProgramming language: R, perlOther requirements: Dependencies include Samtools (> = 1.18), Trimmomatic, SPAdes v3.6.1, BWA (0.7.5a-r405), BioPerl, MAUVE, BLASR, prokka, Tablet/ArtemisLicense: GNU GPL v3GIGA-D-16-00028_Original_Submission.pdfClick here for additional data file.GIGA-D-16-00028_Reviewer_3.pdfClick here for additional data file.GIGA-D-16-00028_Revision_1.pdfClick here for additional data file.GIGA-D-16-00028_Revision_2.pdfClick here for additional data file.Response_to_reviewers_Orginal_Submission.pdfClick here for additional data file.Response_to_reviewer_comments_Revision_1.pdfClick here for additional data file.Response_to_reviewer_comments_Revision_2.pdfClick here for additional data file.Reviewer_1_Report_Original_Submission.pdfClick here for additional data file.Reviewer_1_Report_Revision_1.pdfClick here for additional data file.Reviewer_2_Report_Original_Submission.pdfClick here for additional data file.Reviewer_2_Report_Revision_1.pdfClick here for additional data file.Supplement FilesClick here for additional data file."}
+{"text": "Riemerella anatipestifer is an important waterfowl pathogen, causing major economic losses to the duck-producing industry. However, little is known of the virulence factors that mediate pathogenesis during R. anatipestifer infection. In this study, RAYM_RS09735 and RAYM_RS09740 were predicted to form a two-component signaling system (TCS) through bioinformatics analysis. This TCS was highly conserved across the Flavobacteriaceae. A mutant YM\u0394RS09735/RS09740 strain was constructed to investigate the role of the RAYM_RS09735/RAYM_RS09740 TCS in R. anatipestifer virulence and gene regulation. The median lethal dose (LD50) of YM\u0394RS09735/RS09740 was found to be >1011 CFU, equivalent to that of avirulent bacterial strains. The bacterial abundances of the YM\u0394RS09735/RS09740 strain in the heart, brain, liver, blood, and spleen were significantly lower than that of the wild-type R. anatipestifer YM strain. Pathological analysis using hematoxylin and eosin staining showed that, compared to the wild-type, the mutant YM\u0394RS09735/RS09740 strain caused significantly less virulence in infected ducklings. RNAseq and real-time PCR analysis indicated that the RAYM_RS09735/RAYM_RS09740 TCS is a PhoP/PhoR system. This is a novel type of TCS for Gram-negative bacteria. The TCS was also found to be a global regulator of expression in R. anatipestifer, with 112 genes up-regulated and 693 genes down-regulated in the YM\u0394RS09735/RS09740 strain . In summary, we have reported the first PhoP/PhoR TCS identified in a Gram-negative bacterium and demonstrated that it is involved in virulence and gene regulation in R. anatipestifer.The Gram-negative bacterium  Riemerella anatipestifer, occurs primarily in 1\u20138-week-old ducks but is most common in more susceptible 2\u20133-week-old ducklings. It is currently the most economically damaging bacterial infection affecting the global duck industry. Symptoms are characterized by fibrinous pericarditis, glissonitis, airbag inflammation, and meningitis . A paired TCS typically has a sensing histidine kinase (HK) coupled to a response regulator (RR). The sensing of a signal by the HK leads to autophosphorylation on a histidine residue. Subsequent transfer of the phosphate to an aspartate residue on the cognate RR facilitates the binding of the RR to its specific DNA. Each phosphorylated RR regulates specific genes that enable individual bacteria to sense environmental factors and respond to stresses , subsequently regulating virulence and resistance to oxidative stress in Streptococcus pneumonia  to identify in vivo-induced protein antigens from R. anatipestifer. This predicted the involvement of a putative TCS. To research the function of this TCS in R. anatipestifer, we constructed a mutant with the putative TCS genes, RAYM_RS09735 and RAYM_RS09740, deleted to investigate their biological characteristics. We found that RAYM_RS09735/RAYM_RS09740 form a PhoP/PhoR TCS, the first reported such TCS in Gram-negative bacteria. We confirmed that the RAYM_RS09735/RAYM_RS09740 TCS is an important global transcription regulator and regulates the expression of virulence-associated genes in R. anatipestifer. This may provide the theoretical basis for further study into the molecular pathogenesis of R. anatipestifer and facilitate the design of genetically engineered vaccines against R. anatipestifer.The bacterium In vivo-induced antigen technology (IVIAT) was then used to characterize potential virulence factors that are expressed in ducks during infection with R. anatipestifer. A genomic DNA library of R. anatipestifer was screened, demonstrating in vivo-induced increased expression of genes with two ORFs, RAYM_RS09735 and RAYM_RS09740 (data not shown). RAYM_RS09735 and RAYM_RS09740 were predicted to be a histidine protein kinase (HK) and a response regulator (RR), respectively. To investigate RAYM_RS09735 and RAYM_RS09740 homology among different R. anatipestifer strains, the open reading frames of RAYM_RS09735 and RAYM_RS09740 were amplified from the wild-type R. anatipestifer YM strain. The PCR product was cloned into pMD-18T vectors and sequenced. Homologous amino acid sequences were identified by searching the GenBank database using BLASTX. The resulting alignments were used for the construction of a phylogenetic tree using neighbor-joining (NJ) and were further analyzed using MEGA v6.06 software.The bacterial strains and plasmids used in this study are listed in Table R. anatipestifer RA-YM was grown in trypticase soy broth (TSB) or on agar plates  at 37\u00b0C with 5% CO2. Escherichia coli X7213 was cultured in Luria Bertani (LB) broth containing 50 \u03bcg/mL diaminopimelic acid (DAP), with shaking at 37\u00b0C overnight  cassette  was PCR amplified from plasmid pIC333 using the primers SpecR F and SpecR R. These three fragments were then purified from an agarose gel and used as a PCR template at a 1:1:2 M ratio to join overlapping PCR products with the primers RAYM_RS09735L-F and RAYM_RS09740R-R. The final product was digested with KpnI and SacI enzymes and ligated into the pRE112 plasmid  to isolate the putative R. anatipestifer YM conjugants from the mixed strains. Single colonies were re-purified on TSA plates supplemented with 50 \u03bcg/mL Spec. The R. anatipestifer RAYM_RS09735/RAYM_RS09740 mutant strain was screened and validated by PCR.The R. anatipestifer or the mutant strain YM\u0394RS09735/RS09740 was grown in TSB at 37\u00b0C for 12 h with shaking, respectively. Equal amounts of YM culture were transferred into fresh TSB (without serum) at a ratio of 1:100 (vol/vol) and incubated at 37\u00b0C with shaking at 200 rpm. Bacterial growth was measured as described previously  of the mutant strain YM\u0394RS09735/RS09740 was determined  and infected with either 5 \u00d7 10The mutant strain YM\u0394RS09735/RS09740 or wild-type strain YM was grown in TSB to log phase, respectively, and then harvested in no more than 3 mL culture by centrifugation at 4000\u20135000 \u00d7 g for 5\u201310 min at 4\u00b0C. Total RNA was extracted using a Bacterial RNA Kit (Omega). A NANODROP 2000c (Nanodrop) was used to measure the concentration and quality of bacterial RNA.A total of 1 \u03bcg RNA per sample was used for RNA sample preparations. Sequencing libraries were generated using a NEBNext\u00ae Ultra\u2122 RNA Library Prep Kit for Illumina\u00ae (NEB), following the manufacturer's recommendations. Briefly, mRNA was purified from total RNA using poly-T oligo-attached magnetic beads. Fragmentation was carried out using divalent cations under elevated temperature in NEBNext First Strand Synthesis Reaction Buffer (5X). The first strand of cDNA was synthesized using a random hexamer primer and M-MuLV Reverse Transcriptase (RNase H\u2013). Second strand cDNA synthesis was then subsequently performed using DNA Polymerase I and RNase H. Any remaining overhangs were converted into blunt ends via exonuclease/polymerase. After adenylation of the 3\u2032 ends of DNA fragments, adaptors with a hairpin loop structure were ligated in preparation for hybridization. To select cDNA fragments by length, library fragments were purified with a AMPure XP system (Beckman Coulter). Next, 3 \u03bcL USER Enzyme (NEB) was incubated with size-selected, adaptor-ligated cDNA at 37\u00b0C for 15 min followed by 5 min at 95\u00b0C before PCR. PCR was performed using a Phusion High-Fidelity DNA polymerase, a universal PCR primer set and an Index (X) primer. Finally, PCR products were purified and the library quality assessed using an Agilent Bioanalyzer 2100 system (Agilent).2 fold-change >1 were set as the thresholds for determining significantly differential expression.HTSeq v0.6.1 was used to count the number of reads that mapped to each gene and then gene expression was calculated using an RPKM method (Reads Per kb per Million reads). Differential expression analysis was performed using DESeq. A q-value (or FDR) <0.001& and a loghttp://www.geneontology.org/.Gene Ontology (GO) enrichment analysis of differentially expressed genes was performed using the Bioconductor package GOseq, with a gene length bias correction. GO functional analysis provided GO functional classification annotation for DEGs, as well as GO functional enrichment analysis. The Gene Ontology database used can be found at http://www.genome.jp/kegg/).As different genes cooperate with each other to exercise biological functions, pathway-based analysis can help further understand the biological functions of genes. We used KOBAS to test for the statistical enrichment of differential expression genes in the KEGG pathway data set  validation experiments, 10 genes were randomly selected to assess the RNAseq data Table . For thit-tests were used to compare gene expression data. P-values of \u22640.05 were considered significant. RNAseq original data were uploaded to the NCBI Short Read Archive (SRA) with study accession number SRP096616 (http://www.ncbi.nlm.nih.gov/sra).Student's R. anatipestifer strains. In addition, RAYM_RS09735 and RAYM_RS09740 shared 70% identity with species of Flavobacteriaceae, including Cloacibacterium, Epilithonimonas, and Chryseobacterium. Our results demonstrated that RAYM_RS09735 and RAYM_RS09740 are not only highly conserved in R. anatipestifer, but also conserved across the Flavobacteriaceae in general. Functional assessment predicted RAYM_RS09735 and RAYM_RS09740 to be elements of a two-component signaling system (TCS). TCS are typically composed of a sensor with histidine kinase activity and a cytoplasmic transcriptional regulator. RAYM_RS09735 was identified as a BaeS family histidine kinase, while RAYM_RS09740 was predicted to be an OmpR family transcriptional regulator .The s Figure . Real-ti5 Figure . The RAYs Figure . Transmi50) in 12-day-old Cherry Valley ducks. The LD50 for the mutant strain was greater than 1011 CFU, which was more than a 103-fold attenuation in virulence compared to the wild-type YM strain (4 \u00d7 107 CFU). These LD50 values demonstrate that the strain is almost avirulent in ducklings  between the mutant YM\u0394RS09735/RS09740 and wild-type strains were identified using RNAseq. In total, 805 genes were found to be differentially expressed, with 112 genes upregulated (13.9%), and 693 genes downregulated (86.1%) in the mutant YM\u0394RS09735/RS09740 strain compared to the wild-type strain Figure . Of the S Figure . Gene OnS Figure . Linear S Figure .We found that 11 genes had more than 4-fold higher expression in the mutant strain compared to the wild-type, eight of which encode hypothetical proteins. The three other genes encoded a carbohydrate-binding protein, a glycan metabolism protein (RagB), and a phosphate subunit transfer protein (PstS), respectively. We also found that several transcription factors, components of the CRISPR system, and additional putative proteins were also upregulated, with most upregulated genes involved in bacterial metabolism. In addition, 20 genes were found to be downregulated more than 4-fold. Of these, 13 genes encoded hypothetical proteins, while the other seven encoded RAYM_RS09735, RAYM_RS09740, a lipoprotein, a peptidoglycan hydrate (Nlp/P60), a DNA-binding protein, von Willebrand factor A, ATPase AAA, and an uncharacterized conserved protein. Other downregulated genes included several hypothetical proteins, transcription factors, and metabolic genes, in addition to multiple molecular chaperones and TonB-dependent receptors.pstS, BLP, and Nlp/P60). We found that the mRNA expression of pstS was 8-fold higher  and hydrolase Nlp/P60 were found to be significantly downregulated  to examine ducks infected with R. anatipestifer, we have identified a putative TCS involving the genes RAYM_RS09740 and RAYM_RS09735. Bioinformatics analysis of these two genes, and their proteins, revealed that RAYM_RS09740 and RAYM_RS09735 encode a histidine kinase and response regulator, respectively. We also found that, like most TCSs, RAYM_RS09735 and RAYM_RS09740 have the same promoter and co-transcribe as an operon , consisting of a sensing histidine kinase and a response regulator, mediate gene expression in response to environmental stimuli  and 693 genes that were down-regulated (86.1%) relative to the wild-type strain . To validate the RNAseq results, we randomly assessed the differential expression of 10 genes using qRT-PCR, confirming the accuracy of the RNAseq data  levels in bacteria through the ABC-type phosphate-specific transport (Pst) system and the protein PhoU. PstS is a periplasmic protein that binds Pi with high affinity and PhoP induces the promoter activity of pstS. We found with RNAseq and qRT-PCR that the mRNA expression of pstS was 8-fold higher  stress-response in r Figure  in the mWalters, . We aim R. anatipestifer. As more than one third of genes demonstrated differential expression, it is unlikely that the PhoP/PhoR TCS directly regulates all of these DEGs. However, the TCS we identified may affect wider gene expression by influencing the expression of other transcription factors in a variety of signal transduction systems.Further, DEG analysis showed that the expression of several other signal transduction system genes was altered in the YM\u0394RS09735/RS09740 strain. It is likely that PhoP can influence the expression of transcription factors in other signal transduction system and there is cross-talk or cross regulation between PhoP/PhoR and other TCSs in R. anatipestifer  and the hydrolase Nlp/P60 were both found to be significant down-regulated in the mutant YM\u0394RS09735/RS09740 strain  and Natural Science Foundation of Hubei Province (No. 2015CFB268 to ZL).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest."}
+{"text": "The effectiveness of hsa_circ_0021001 in the diagnosis of IA was assessed by ROC curve. Multivariate Cox proportional hazards regression analysis was used to analyze the prognosis. Hsa_circ_0021001 level in the peripheral blood of IA patients was relatively lower than that in the control group (P=0.002). The area under ROC (AUC) was 0.87, indicating that hsa_circ_0021001 was highly effective in the diagnosis of IA. In addition, hsa_circ_0021001 expression was correlated with aneurysm rupture, Hunt, Hess level, and timing of surgery . Patients with high expression of hsa_circ_0021001 had longer disease-free survival (DFS) and overall survival (OS) (P < 0.05). We found for the first time that hsa_circ_0021001 decreased significantly in the peripheral blood of IA patients, which suggested that hsa_circ_0021001 might be used as a potential novel marker for the diagnosis of IA.Circular RNAs (circRNAs) in the peripheral blood have been reported to be associated with cancer. However, there are few studies about circRNAs in intracranial aneurysms (IA). The purpose of the current study was to investigate the characteristic expression of circular RNA hsa_circ_0021001 in the peripheral blood of patients with intracranial aneurysms and its potential as a diagnostic biomarker for IA. In this study, a cohort of 223 cases of IA patients who were admitted in the department of neurosurgery in the First People\u2019s Hospital of Wenling from January 2009 to July 2012 were collected as the experimental group, and 131 healthy volunteers over the same period served as the control group. Peripheral blood of each subject in both groups was collected on an empty stomach. The expression of hsa_circ_0021001 in peripheral blood was detected by real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR) and the difference was analyzed by paired  Intracranial aneurysm (IA) is characterized by pathological dilatation intracranial artery as a pouch. Till now, IA rupture has been one of the most serious neurological diseases . Early dCircular RNAs (circRNAs) are endogenous RNA with stable structure and highly tissue specific expression . circRNAcircRNAs end to end without free ends and can tolerate nuclease RNase R. Their structures are more stable than miRNA . circRNASanger sequencing confirmed the presence of hsa_circ_0021001 back-splicing site, demonstrating that hsa_circ_0021001 exists in a ring structure in nature  Figure . The AUCThe results showed that the expression of hsa_circ_0021001 was significantly decreased in blood samples of IA, so we analyzed the relationship between hsa_circ_0021001 expression and the clinicopathological features of IA. As shown in Table According to our data, GOS was not significantly correlated with age, gender, tumor size, coronary heart disease, smoking, alcohol consumption, blood glucose and timing of surgery (P>0.05); while GOS was significantly associated with hypertension history, aneurysm location, rupture, Hunt Hess levels (P<0.05) Table .As shown by the survival curve, 8 cases were lost follow-up during the five years. DFS and OS were significantly longer in IA patients with higher expression of hsa_circ_0021001 than those with low expression  . Moreover, we found that IA patients with high expression of hsa_circ_0021001 had longer DFS and OS than patients with low hsa_circ_0021001 expression. Our results further suggested that downregulation of hsa_circ_0021001 was associated with shorter survival of IA patients. ROC analysis proved that hsa_circ_0021001 can be used as a biomarker for IA with high accuracy, sensitivity, and specificity.in vivo. The second circRNA is a testis specific transcript which can determine the expression of male determining gene SRY. It contains 16 binding sites of miR-138. Many miRNA binding sites on circRNAs are predicted to have function. These results imply that hsa_circ_0021001 may play an important role in tumorigenesis and metastasis of IA by interaction with miRNA.Circular RNAs were found to act as competitive endogenous RNAs binding proteins (protein sponges) , 18. cirIn conclusion, we first identified a significant decrease in the expression of hsa_circ_0021001 in IA patients, which may serve as a potential novel biomarker for IA. Moreover, our findings suggest that hsa_circ_0021001 may be involved in the genesis and metastasis of IA.A cohort of 223 cases of IA patients who were admitted in the department of neurosurgery in the First People\u2019s Hospital of Wenling from January 2009 to July 2012 were collected as the experimental group, and 131 healthy volunteers over the same period served as the control group. After admission, all patients underwent computed tomography (CT) examinations and diagnosed by three-dimensional computed tomography angiography (3D.CTA) or digital subtraction angiography (DSA) before surgery. Exclusion criteria: patients suffering from other serious cardiovascular or cerebrovascular diseases; patients with other serious organs diseases; patients with incomplete clinical data. 223 cases of IA patients included 141 males and 82 females. The age range was 20-77 years, and the average age was 52 years. The imaging data of IA patients in this study showed that the diameter of the aneurysm <5 mm in 38 cases, between 5 mm-10 mm in124 cases, more than 10 mm in 61 cases, anterior circulation aneurysms in 204 cases, posterior circulation aneurysms in 19 cases; ruptured aneurysm in 130 cases, unruptured aneurysm in 80 cases. There was no significant difference in age, gender, blood pressure, blood sugar, blood lipid, and smoking between the experimental group and the control group . Then the optical density (OD) 260/280 was determined by ultraviolet spectrophotometry. After calculating the concentration, RNA was frozen in -80\u00b0C refrigerator.\u2212\u0394\u0394Ct. All data were expressed as mean \u00b1 SD of three independent experiments. The experiments were performed by blind method.qRT-PCR experiment was performed using GoTaq qPCR Master Mix kit and Mx3005P real-time PCR operating system. According to the procedures of the kit, 25 \u03bcL reaction systems consisted of 5 \u03bcL cDNA products, 5.5 \u03bcL DEPC water, 1 \u03bcL upstream primers, 1 \u03bcL downstream primers, and 12.5 \u03bcL qPCR mixtures. Hsa_circ_0021001 primers and GAPDH primers were synthesized by Shanghai Sangon biotechnology Inc. The primer sequences of hsa_circ_0021001 were as follows: 5\u2032-GAAACTCGAGCCGCGCTGCGATATGTG-3\u2032 (upstream); 5\u2032-CACAGCCAGCAAAGTTACTCGCTTTAAA-3\u2032 (downstream). GAPDH was used as an internal standard. The primer sequences of GAPDH were as follows: 5\u2032-CCCGATAACACAAGTGCAGC-3\u2032 (upstream); 5\u2032-CCCGATAACACAAGTGCAGC -3\u2032 (downstream). The relative expression of hsa_circ_0021001 was calculated using 2The target fragment was inserted into a T vector and the length was determined by Sanger. The following primers were designed by Shanghai Sangon biotechnology Inc to test the back-splicing site of hsa_circ_0021001: 5\u2032- CAATGCTGAAAACTGCTGAGAGAAG-3\u2032 (upstream); 5\u2032-CCTGCATTCTCTTTTCTGTTGTATCTTTAA-3\u2032 (downstream).The follow-up was conducted by telephone and clinical for 3 years. The prognosis after surgery was determined by Glasgow prognostic score (GOS). The follow-up was from the hospital discharge time after the patient was treated to January 2015. For survival patients at the end of the follow-up visit, the follow-up data were the last contact state. For patients lost follow-up, the follow-up data were the last census state. The survival time was expressed by survival months. Overall survival (OS) was the duration from aneurysm neck clipping to the death of the patient, and the disease-free survival (DFS) was the period at which the operation started without recurrence or death due to IA.Http://www.r-project.org/). The difference in hsa_circ_0021001 expression between IA patients and normal healthy controls were analyzed by paired t-test. The relationship between hsa_circ_0021001 and clinicopathological factors was evaluated by one-way ANOVA. ROC curve was drawn to access the diagnosis value. Cutoff value of hsa_circ_0021001 was analyzed by SigmaPlot 12.3. The survival curve was drawn by Kaplan\u2013Meier method and analyzed by Long-rank test. Multivariate Cox proportional hazards regression analysis was used to analyze the prognosis. P<0.05 was considered statistically significant. The experiment repeatability was determined by Pearson correlation test.All data were analyzed by R software (V, 2.15.0,"}
+{"text": "RNAs (circRNAs) are a new class of noncoding RNAs. However, the expression profile and clinical significance of circRNAs in human gastric cancer is unclear. The global circRNA expression profile in human gastric cancer was measured by circRNA microarray. Hsa_circ_0014717, one of the most downregulated circRNAs in microarray, was selected as a targeted circRNA to explore its levels in gastric tissues and gastric juice. Freeze\u2010thaw experiment and incubation experiment confirmed the stability of gastric juice circRNAs. A total of 308 circRNAs, including 107 (34.74%) upregulated and 201 (65.26%) downregulated circRNAs, were found significantly aberrantly expressed in gastric cancer tissues. The top ten upregulated in gastric cancer tissues were hsa_circ_0035445, hsa_circ_0003789, hsa_circ_0063809, hsa_circ_0074362, hsa_circ_0006282, hsa_circ_0011107, hsa_circ_0084606, hsa_circ_0005556, hsa_circ_0050547, and hsa_circ_0006470, while the top ten downregulated ones were hsa_circ_0007099, hsa_circ_0001897, hsa_circ_0007707, hsa_circ_0008832, hsa_circ_0001546, hsa_circ_0002089, hsa_circ_0004680, hsa_circ_0000154, hsa_circ_0004458, and hsa_circ_0008394. The hot\u2010point chromosomes were chr1, chr2, chr3, chr9, and chr17. Hsa_circ_0014717 was significantly downregulated in 77.2% (74/96) gastric cancer tissues. Its levels in gastric cancer tissues were related to tumor stage (P\u00a0=\u00a00.037), distal metastasis (P\u00a0=\u00a00.048), tissue carcinoembryonic antigen (P\u00a0=\u00a00.001), and carbohydrate antigen 19\u20109 expression (P\u00a0=\u00a00.021). More importantly, hsa_circ_0014717 can stably exist in human gastric juice; and its nature meets the requirements of clinical detection. Our study uncovered the circRNA expression profile in human gastric cancer. Moreover, some circRNAs can stably exist in human body fluid, and has the potential to be used as novel biomarkers for the screening of high\u2010risk gastric cancer patients.Circular  Gastric cancer is the fifth most common malignancy and the third leading cause of global cancer death of people in the world Circular RNAs (circRNAs) are special class of endogenous noncoding RNAs that are formed by back\u2010splicing events via exon or intron circularization Since the global circRNAs expression profile in human gastric cancer has not been uncovered, in this study, we used circRNA microarray to investigate the differential expression profiles of circRNAs between gastric cancer tissues and paired noncancerous tissues. We then selected hsa_circ_0014717, one of the middle downregulated circRNAs in microarray screening, as a targeted circRNA to explore its clinical significance and application in gastric cancer. Its gene is located at chr1:156290629\u2010156304709 with a spliced length of 516 nt. Our results showed that some circRNAs, such as hsa_circ_0014717, can stably exist in human body fluid, and has the potential application in the screening of gastric cancer.Patients were collected from the center for gastroenterology of the Affiliated Hospital of Medical School of Ningbo University (China) between February 2011 and February 2016. Gastric cancer tissues and their matched adjacent nontumorous tissues were obtained from 96 surgical patients. Gastric juice samples were collected from 38\u00a0healthy volunteers, 30 gastric ulcer patients, 15 chronic atrophic gastritis patients, and 39 gastric cancer patients during endoscopic examination. No patient received medical treatment before endoscopy examination or surgery. The final diagnosis of each patient was confirmed histopathologically. All specimens collection and preprocessed were according to previously described protocol and preserved at \u221280\u00b0C condition until RNA isolation Tumors were classified following the tumor\u2010node\u2010metastasis (TNM) staging system (7th ed.). Histologic grade was assessed following the National Comprehensive Cancer Network (NCCN) Clinical Practice Guideline of Oncology (V.1.2012). This study was approved by the Human Research Ethics Committee of Ningbo University; and informed consent was obtained from all participants. Double\u2010blind manner was used through the entire process of all clinical samples and data collection.Tissue total RNA was extracted using TRIzol reagent , whereas gastric juice samples were processed using TRIzol LS reagent (Ambion). All steps of RNA extraction were followed as per the manufacturer's instructions. The concentrations of total RNA were then determined using a DS\u201011+ Spectrophotometer . The integrity of RNA was assessed by 1% agarose gel electrophoresis. Finally, total RNA was reverse transcribed to cDNA by GoScript Reverse Transcription (RT) System  following the manufacturer's protocol.Three gastric cancer tissues and their matched adjacent nontumorous tissues 5\u00a0cm away from the edge of tumor were selected to analyze circRNA expression profile using Arraystar Human circRNA Array . Total RNA from six samples were amplified and transcribed into fluorescent cRNA utilizing random primer according to Arraystar's Super RNA Labeling protocol (Arraystar). The labeled cRNAs were hybridized onto the Arraystar Human circRNA Array , and incubated for 17\u00a0h at 65\u00b0C in an Agilent Hybridization Oven . After having washed the slides, the arrays were scanned by the Axon GenePix 4000B microarray scanner .Scanned images were then imported into GenePix Pro 6.0 software (Axon) for grid alignment and data extraction. Quantile normalization and subsequent data processing were performed using the R software package. Differentially expressed circRNAs with statistical significance between two groups were identified through Fold Change filtering or Volcano Plot filtering. Hierarchical clustering was performed to show the distinguishable circRNA expression pattern among samples.. The sequences of hsa_circ_0014717 and GAPDH were as follows: 5\u2032\u2010TTGCCCTGGATGCTGTCAAG\u20103\u2032 and 5\u2032\u2010GGTCATCACAATGCCTCCCAT\u20103\u2032 for hsa_circ_0014717; 5\u2032\u2010ACCCACTCCTCCACCTTTGAC\u20103\u2032 and 5\u2032\u2010TGTTGCTGTAGCCAAATTCGTT\u20103\u2032 for GAPDH. Targeted circRNA expression levels were calculated using the \u0394Ct method with GAPDH as the control Ct values indicate higher expression levels.The real\u2010time quantitative reverse transcription\u2010polymerase chain reaction (qRT\u2010PCR) was performed using GoTaq qPCR Master Mix (Promega) on an Mx3005P Real\u2010Time PCR System  were designed and synthesized by Sangon Biotech , respectively. The use of divergent primers can only amplify circRNA and differentiates the contamination from its linear isoformsqRT\u2010PCR products of hsa_circ_0014717 in gastric juice were purified using the UNIQ\u201010 PCR Product Purification Kit (Sangon), and then cloned into the pUCm\u2010T vector (Sangon) following the manufacturer's instructions. DNA sequencing was performed by Sangon Biotech Company, Ltd.t\u2010test and one way analysis of variance (ANOVA) test were flexibly used according to actual conditions. P\u00a0<\u00a00.05 was considered as statistically significant.Statistical analyses were performed using Statistical Program for Social Sciences (SPSS) 20.0 software . Student's https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE89143). Differentially expressed circRNAs with statistical significance between cancer group and nontumorous group were identified through Fold Change filtering . Downregulated circRNAs (65.26%) were more common than upregulated circRNAs (34.74%) in microarray data. The top 10 upregulated  and\u00a0downregulated  circRNAs in gastric cancer tissues are listed in Table\u00a0High\u2010throughput human circRNA microarray was used to assess the differences of circRNA expression profiles between gastric cancer tissues and paired adjacent nontumorous tissues. A total of 5396 circRNAs were detected , distal metastasis (P\u00a0=\u00a00.048), tissue carcinoembryonic antigen (CEA) (P\u00a0=\u00a00.001), and carbohydrate antigen 19\u20109 (CA19\u20109) expression (P\u00a0=\u00a00.021). However, they were not associated with other clinic pathologic factors such as tumor diameter, lymphatic metastasis, invasion, and cell differentiation.Then, analyses were performed to assess the relationship between hsa_circ_0014717 expression levels and gastric cancer patients' clinical features. As shown in Table\u00a0Gastric juice has significant advantage in reflecting gastric cancer for its high specificity of gastric organ; and the use of gastric juice in disease diagnosis is a non\u2010invasion method CircRNAs are a special class of endogenous RNAs. Recent studies have indicated that dysregulated circRNAs are associated with several human diseases, such as nervous system, cardiovascular system diseases and cancers https://www.ncbi.nlm.nih.gov/pubmed/), before November 26, 2016, all the top 10 upregulated and top 10 downregulated circRNAs were not found aberrantly expressed in cancer tissues. Moreover, the distributions of differentially expressed circRNAs in human chromosomes showed that most circRNAs are transcribed from chr1, chr2, chr3, chr9, and chr17 .The authors made no disclosures."}
+{"text": "Background. It has been reported that circRNAs are differentially expressed in a wide range of cancers and could be used as a new biomarker for diagnosis. However, the correlation between circRNAs and gastric cancer (GC) it is still unclear. Materials and Methods. In this study, by using real-time quantitative reverse transcription-polymerase chain reactions (qRT-PCRs), we detected the expression level of hsa_circ_0001649 in tissue and serum samples from GC patients. Results. We found that hsa_circ_0001649 expression was significantly downregulated in GC tissue compared with their paired paracancerous histological normal tissues (PCHNTs) (P < 0.01). We next analyzed the expression level of hsa_circ_0001649 in serum samples between preoperative and postoperative GC patients. We found that its level in serum was significantly upregulated after surgery (P < 0.01). The area under the receiver operating characteristic (ROC) curve was 0.834. Moreover, the expression level of hsa_circ_0001649 was significantly correlated with pathological differentiation (P = 0.039). Conclusion. Our test suggested that hsa_circ_0001649 was significantly downregulated in GC and may become a novel potential biomarker in the diagnosis of GC. Although the incidence of gastric cancer (GC) has declined in recent years, it is still one of the common malignancies worldwide, accounting for 841,000 deaths in 2013 [Circular RNAs (circRNAs) are a large class of endogenous noncoding RNAs that attract increasing attention in the field of RNA recently. Compared with linear RNAs that are terminated with 5\u2032caps and 3\u2032tails, circRNAs exhibited a remarkable characteristic of undergoing \u201cbacksplicing\u201d without a free 3\u2032 or 5\u2032 end , 6. It wP < 0.01). Its associated gene symbol is a tumor suppressor gene named SHPRH. Depletion of SHPRH could be observed in a variety of cancer types, such as prostate cancer, ovarian cancer, and liver cancer [By analyzing bioinformatics information in two circRNA databases (CircBase and circ2Traits), we predicted that hsa_circ_0001649, which is located at chr6:146209155-146216113, has a strong association with GC  which were obtained during curative surgery. In the meantime, 20 patients' whole blood samples were collected preoperatively and postoperatively (more than 20 days after surgery). None of the experimental subjects had received prior gastric resection or preoperative chemotherapy/radiation therapy. All samples were immediately frozen and stored at \u221280\u00b0C until total RNA was extracted. In order to reduce bias, samples were randomly coded before processing. All patients voluntarily joined this study with written informed consent to have their biologic specimens analyzed. This study was announced by the Ethical Committee of the First Affiliated Hospital of Xi'an Jiaotong University.2.Immortalized human gastric cancer cell lines, SGC-7901, were used in this study. We purchased the cell line from the Type Culture Collection of the Chinese Academy of Sciences . All cells were routinely cultured in RPMI-1640 medium (Gibco) supplemented with 10% fetal bovine serum (Hyclone) at 37\u00b0C in a humidified atmosphere containing 5% COTRIzol reagent  was used to extract RNA from cells and tissues according to the manufacturer's instructions, and total RNA in plasma was extracted using TRIzol LS Reagent (Invitrogen), following the manufacturer's instructions. Then, concentration of RNA was measured by ultraviolet spectrophotography.cDNA was synthesized by reverse transcription (RT) using a Primescript RT reagent kit with random primers according to manufacturer-provided protocols (TaKaRa).The qRT-PCR was achieved using SYBR Premix Ex Taq\u2122 II (Tli RNaseH Plus) (TaKaRa) on CFX96 Real-Time PCR Detection System  following the manufacturer's instructions. Divergent primers, rather than convergent primers, were synthesized by Sangon Biotech . We use GAPDH as an internal control. The primers used for qRT-PCR are summarized in Normal levels of CEA, CA19-9, and CA-724 were defined as <3.4\u2009ng/mL, <39\u2009U/mL, and <9.8\u2009U/mL, respectively. The tests were done independently at the clinical laboratory in the First Affiliated Hospital of Xi'an Jiaotong University College of Medicine.\u2212\u0394\u0394Ct method. The correlation of hsa_circ_0001649 expression level between GC and their matched gastric nontumorous tissues or serum samples were calculated using paired t-test. The correlations between hsa_circ_0001649 levels and clinicopathological factors were further analyzed by one-way analysis of variance (ANOVA). Receiver operating characteristic (ROC) curve was constructed using SPSS 13.0 to evaluate the diagnostic values. P values < 0.05 (two-sided) were considered statistically significant.Statistical analysis was performed with the SPSS 13.0 software  and GraphPad Prism 5.0 . The qPCR results were analyzed using 2In order to explore the expression level of hsa_circ_0001649 in GC, 76 paired GC and PCHNTs tissue samples  were enrolled in this study. The mean age of GC patients was 57.9 \u00b1 11.6. Besides, we analyzed hsa_circ_0001649 expression level in 20 paired preoperative and postoperative serum samples.We used divergent primers to amplify hsa_circ_0001649 in SGC-7901 cell line. The amplified product yielded a single peak in a melting curve analysis. The qRT-PCR products were then sequenced and the result showed that the sequence was completely consistent with that from CircBase . So we cn = 76, P < 0.01)  .P < 0.01) and, therefore, may be considered as a panel for the early detection of GC.To further investigate whether hsa_circ_0001649 could be used as a biomarker for GC, we detected its expression level in serum samples. Our data suggested that comparing with preoperatively collected samples, hsa_circ_0001649 expression level was significantly upregulated in those serum samples collected postoperatively ( < 0.01) . The aboP = 0.039). On the contrary, no correlation was found of hsa_circ_0001649 expression level with other clinicopathological factors, including age, gender, TNM stage, lymphatic metastasis, CEA, CA19-9, and CA-724 levels. Then we built a ROC curve to estimate the diagnostic values of this circRNA in gastric cancer. The sensitivity and specificity were 0.711 and 0.816, respectively. The cutoff value was 0.22692250 and the area under the curve was 0.834 . The analysis between circRNA expression level and clinicopathological data demonstrated that the expression level of hsa_circ_0001649 was more significantly decreased in poor and undifferentiated tumors than in well differentiated ones (P = 0.039). This phenomenon indicates that hsa_circ_0001649 level may have a negative correlation with GC pathological differentiation. However, detailed molecular mechanisms of hsa_circ_0001649 involved in GC progression are still mysterious. We next estimated the diagnostic value of hsa_circ_0001649 in GC. A comparatively satisfactory result was obtained by using ROC curve analysis . Our preliminary results indicate that hsa_circ_0001649 expression level was downregulated in GC tissue sample compared with normal ones and has the potential to be used as a novel biomarker for GC with high degrees of accuracy, specificity, and sensitivity.In this study, based on previous research and two circRNA databases (circ2traits and CircBase), we found that the expression level of hsa_circ_0001649 is significantly downregulated in GC tissues when compared to the PCHNTs (P < 0.01). However, our study just validated the dysregulation of hsa_circ_0001649 in GC tissue and serum samples. Further experiments still need to be done to elucidate the role of hsa_circ_0001649 in the generation and progression of GC.Recently, some articles reported that changes of circRNAs expression level in fluids paralleled other somatic tissues and are thought to be connected with certain cancers. Li et al.  examinedIn summary, by comparing the expression level of hsa_circ_0001649 in tissue and serum samples, we found that detecting hsa_circ_0001649 between GC and normal ones has a relatively high sensitivity and specificity and, therefore, may be used as a biomarker for noninvasive screening of GC."}
+{"text": "Caenorhabditis elegans separase leads to a temperature sensitive hypomorphic protein. Conservation of this motif in organisms ranging from C. elegans to humans suggests its functional importance.We report a protein sequence analysis of the cell cycle regulatory protease, separase. The sequence and structural conservation of the C-terminal protease domain has long been recognized, whereas the N-terminal regulatory domain of separase was reported to lack detectable sequence similarity. Here we reveal significant sequence conservation of the separase regulatory domain and report a discovery of a cysteine motif (CxCxxC) conserved in major lineages of Metazoa including nematodes and vertebrates. This motif is found in a solvent exposed linker region connecting two TPR-like helical motifs. Mutation of this motif in This article was reviewed by Lakshminarayan Iyer and Michael Galperin.The online version of this article (10.1186/s13062-018-0210-0) contains supplementary material, which is available to authorized users. C. elegans separases are structurally different [C. elegans separase, the N-terminal \u03b1-solenoid domain consists of 25 \u03b1-helices, arranged as atypically compact TPR-like repeats [Separase is a CD clan cysteine protease that regulates cell division. Separase proteolytic activity is regulated mainly by the binding of an inhibitory chaperone, securin , 2, whicifferent , 20. In  repeats . Various repeats \u201326; thusC. elegans separase [C. elegans separase  were aligned to the 25 \u03b1-helices predicted in the N-terminal region of the human separase . The very low percentage of identity between the two sequences and the fact that they were aligned manually, guided by structural information, prompted us to further explore potential sequence conservation in the separase regulatory domain.No multiple sequence alignments of the separase N-terminal domains are available in the current literature; however, a pairwise sequence alignment between the human and nematode homologs was produced based on the recently solved three-dimensional structure of the separase . Twenty-In this study, we reveal significant sequence conservation of the separase regulatory domain and report a discovery of a cysteine motif (CxCxxC) conserved in major lineages of Metazoa including nematodes and vertebrates.C. elegans separase (accession NP_491160.1) as a query. The search confidently retrieved similar proteins from distantly related nematode species and reciprocal BLAST searches validated their orthology. Next, we performed exhaustive PSI-BLAST [Toxicara canis separase (KHN86283.1) retrieved a separase from a vertebrate Danio rerio (XP_001337869.1) with E value 9e-04 in the third iteration and a human separase (NP_036423.4) with E value 2e-11 in the fourth iteration, among many other separase sequences from vertebrates. We then constructed a multiple sequence alignment of representative nematode and vertebrate sequences using MAFFT [C. elegans and H. sapiens sequences published by [C. elegans and H. sapiens sequences were identified by Boland et al. , our al in ref. .C. elegans/H. sapiens), L158/L827, W584/W1294, and R685/R1629. The remaining three identical residues, C448/C1146, C450/C1148, and C453/C1151, appear to form a motif, which is located at the border of Insert 1 and helix H16  and the C-terminal domain (Sse) [Drosophila  . We founD. melanogaster, C. elegans is an excellent model to study the role of the separase regulatory domain, because it is orthologous to that in humans. As expected, multiple residues, distributed throughout this domain ) results in a temperature sensitive phenotype that leads to exocytosis defects [sep-1(e2406) that exclusively introduce mutations to the regulatory domain [C. elegans in understanding separase regulation in humans.Our analysis shows that, in contrast to  defects , 33. We y domain . These p (sep-1e206) resulMelesse and colleagues report the presence of a conserved cysteine cluster in the N-terminal region of the separases. Per se the work is reproducible and the details of the motif and the N-terminal domain are well described. This study is of importance to researchers in the cell cycle/separase field. It is also very curious that a motif is retained between nematodes and vertebrates and not in other metazoan clades, as genome comparisons show that nematodes are usually fast evolving and often lose proteins and domains/motifs observed in vertebrates and other metazoaon clades. I do have a couple of minor comments and suggestions. 1. One of the earliest computational studies on the caspases noticed the TPR-like repeats at the N-terminus, and this might be worth citing (PMID: 11835511).Indeed, this is a notable finding, which predates the knowledge obtained by solving the 3D structure. This paper is cited in the revised manuscript.Author\u2019s response: 2. There is a histidine about 4\u20136 residues upstream of the triple cysteine motif in the nematode separases. Could they possibly align with the conserved Histidine 4 residues upstream of the vertebrate triple Cysteine motif? These might suggest a neomorphic metal-binding motif. This might also be confirmable by available structures.This is an interesting observation. Indeed, even in some unedited MAFFT alignments the histidines were matched at the expense of introducing a gap. Furthermore, in newly identified sequences from other metazoan phyla, a histidine is present in the same location (4\u20136 residues upstream of the triple cysteine motif). We now acknowledge this fact in the text and following the Reviewer\u2019s suggestion propose that one potential role for this motif could be metal binding, e.g., as seen in various cysteine and histidine containing Zn-binding motifs (Pace & Weerapana 2014). Unfortunately, both the histidine and the first cysteine residue of this motif are not resolved in the crystal structure.Author\u2019s response: 3. Was the cysteine cluster motif used to search a limited database of separases  to check if other metazoans might possess it in a comparable location?Following this question and suggestions from Reviewer 2, we identified N-terminal separase domains in representatives of other metazoan phyla and identified the three cysteine motif in those using a simple CxCxxC string search. Satisfactorily, (i) there was only one such motif in each of these sequences, (ii) in most  cases, a histidine residue was located 4\u20136 residues upstream of the motif, and (iii) these motifs were perfectly aligned by BLAST in pairwise comparisons and by MAFFT in multiple sequence alignments of full-length domains prior to any editing. Based on these results, we expanded our description of this motif and its occurrence in Metazoa.Author\u2019s response: C. elegans. This work would benefit from addressing the following points. 1. The paper claims that the described CxCxxC motif of separase is conserved in nematodes and vertebrates. That is true but separase appears to have a much wider phylogenetic distribution. Separin-like proteins have been annotated in Lingula anatine (Brachiopoda), Hydra vulgaris and Exaiptasia pallida (Cnidaria), Crassostrea gigas (Mollusca), Apostichopus japonicas (Echinodermata), Hymenolepis microstoma (Platyhelminthes) and other organisms. Further, a simple BLAST search retrieves separase-like sequences in Anoplophora glabripennis (Insecta), Centruroides sculpturatus (Arachnida), Acanthaster planci (Echinodermata), and other invertebrates. The question then becomes, when did separase first evolve?This paper describes an interesting attempt to investigate sequence conservation within vertebrate separases by comparing them to the recently studied protein from Beside vertebrates and nematodes, separases have been previously described and experimentally studied in such diverse phyla as fungi  and plants , although to our knowledge there was no study specifically addressing their evolutionary history. This was not a goal of our investigation either, but we agree with the Reviewer that it is important to place our specific motif discovery into a broader context of the separase phyletic distribution and our current understanding of its evolution. We have added more background information and our own observations related to this question. In brief, we identified separases in many other invertebrates, obtained evidence for their orthologous relationships and showed that in the majority of cases the N-terminal separase domain is recognizable and it contains the conserved CxCxxC motif at the same location.Author\u2019s response: , et al. , Funabik, et al. ) and pla, et al.\u00a0), althouIs the described CxCxxC motif conserved in all invertebrate separases? If not, why?As our additional analysis shows, the CxCxxC motif is found in the vast majority of separase regulatory domains from most of the metazoan phyla. It is missing from all non-metazoan separases as well as from Hemichordata, Tunicata, Placozoa, Porifera, and Platyhelminthes, although only the latter phyla is represented by more than one genome, From this phyletic distribution, we can safely conclude that the separase N-terminal domain exemplified by vertebrate and nematode sequences originated fairly early in the metazoan evolution. It is likely impossible to answer the question why is this motif missing from some of the homologous N-terminal separase domains, especially because we do not know its function. Our best guess is that its function (whatever it is) can be either achieved or substituted by other means. Non-orthologous gene displacement of the entire separase N-terminal domain in a fly lineage is in line with this proposition.Author\u2019s response: 2. What is the importance of the described CxCxxC motif? Is it located at the separin interacting interface? Is there any evidence of metal binding or disulfide formation by any separases that have this motif?This is obviously the first report on the identification of this motif, so its function is yet to be determined. Its importance, however, is illustrated by the fact that mutation in this motif (C450Y) is highly damaging in C. elegans . The cysteine motif is located away from the known securin interacting and C-terminus interacting interfaces. As suggested by both reviewers, potentially this could be a metal binding motif; however, there is no evidence for this (or for disulfide bond formation) in the literature and no insight from the crystal structure, because half of this motif is not resolved. We hope that our finding will motivate the search for this motif function.Author\u2019s response: 3. The Additional file We agree that showing the separase domain architecture with mapped conserved residues would be helpful to the reader. In the light of our new findings of a broader distribution of the cysteine motif, we now show a comparison of domain architectures for separases from several lineages as Fig. .Author\u2019s response: Additional file 1:C. elegans separase (PDB accession 5MZ6) are shown above the alignment. Identical residues in each group are highlighted: negatively charged, red; positively charged, blue; aromatic, green; aliphatic, yellow; alcohol, magenta; small, grey. Universally conserved residues are highlighted with black boxes. NCBI accession numbers: Caenorhabditis_elegans_1, NP_491160.1; Caenorhabditis_brenneri_1, EGT38506; Caenorhabditis_briggsae_1, CAP33358; Caenorhabditis_remanei_1, XP_003114963.1; Loa_loa_1, XP_003140515.1; Wuchereria_bancrofti_1, EJW80934; Brugia_malayi_1, XP_001894870.1; Dictyocaulus_viviparus_1, KJH53363.1; Dictyocaulus_viviparus_2, KJH53362.1; Haemonchus_contortus_1, CDJ83415.1; Ancylostoma_duodenale_1, KIH65515.1; Ancylostoma_ceylanicum_1, EYC45610.1; Toxocara_canis_1, KHN86283.1; Homo_sapiens_1, NP_036423.4; Monodelphis_domestica_1, XP_007506592.1; Chelonia_mydas_1, XP_007058605.1; Gallus_gallus_1, XP_015128534.1; Xenopus_tropicalis_1, XP_004912005.1; Latimeria_chalumnae_1, XP_014347491.1; Maylandia_zebra_1, XP_014264400.1;; Acanthaster_planci_1, XP_022084422.1; Branchiostoma_floridae_1, XP_002607627.1; Priapulus_caudatus_1, XP_014674242.1; Lingula_anatina_1, XP_013410481.1; Crassostrea_gigas_1; XP_011423994.1; Lottia_gigantea_1, XP_009046347.1; Limulus_polyphemus_1, XP_022249257.1; Nematostella_vectensis_1, XP_001635666.1; Cephus_cinctus_1, XP_015599868.1. \u201cIdentical residues\u201d* show positions defined as identical in a pairwise comparison of C. elegans and H. sapiens sequences by Boland et al., 2017. (PDF 64 kb)Figure: Multiple sequence alignment of separases from representative genomes. Sequences from 11 nematode species (top portion of each panel), from seven representative vertebrate species  (middle portion of each panel), and from nine invertebrate species representing several other metazoan phyla (bottom portion of each panel) are shown. Twenty-five alpha helices (labeled H1 to H25) comprising 11 TPR-like repeats  in the Additional file 2:Table: Separases in representatives of major metazoan phyla. Products of two genes corresponding to N-terminal and C-terminal separase domains are highlighted in yellow and green, respectively. Truncated sequences are highlighted in grey. (XLSX 14 kb)Additional file 3:Table: Proteins corresponding to the separase N-terminal domain in Insecta. Truncated sequence is highlighted in grey; sequences missing the CxCxxC motif are highlighted in yellow. (XLSX 15 kb)Additional file 4:C. elegans separase Cryo-EM structure (PDB 5MZ6) illustrating N-terminal residues conserved among nematodes found in the interior (A) and on the surface (B) of the TPR-like N-terminal domain. Intragenic suppressors of SEP-1(e2406) are shown (C) and are not among the conserved residues. The structures are oriented with the N-terminus to the left with a perspective that best illustrates the distribution of each highlighted residue. (TIF 926 kb)Figure: Separase N-terminal residues conserved among nematodes are distributed throughout the structure. Additional file 5:Table: Conserved residues found within the N-terminal domain helices. Residues that are within interacting distance (as assayed by measuring a distance less than 6\u00a0\u00c5 (\u00c5) between \u03b2-Carbons) are indicated. These residues are generally located within the same helix and don\u2019t appear to be important for stabilizing inter-helix interactions. (PDF 29 kb)Additional file 6:http://exac.broadinstitute.org) and the ICGC (https://icgc.org/) which collects genomic sequences of various cancers. The frequency of each missense mutation is indicated. (TIF 413 kb)Figure: Known mutations in human separase (ESPL-1). The collection of separase allelic variants of human Separase from the ExAC exome collection ("}
+{"text": "Using target capture of viral nucleic acid and next-generation sequencing, we generated the genome sequences of three novel human parainfluenza virus 2 isolates. Isolates ACRI_0185 (GenBank accession number MF077311), ACRI_0230 (MF077312), and ACRI_0248 (MF077313) were collected in October 2016, February 2017, and March 2017, respectively, from pediatric patients with acute respiratory infection in Arkansas. Human parainfluenza viruses (hPIVs) are a major cause of acute respiratory infection (ARI) in children; collectively, they are second only to respiratory syncytial viruses as causes of hospitalization \u20133. Each \u20132 of 96% on room air. He required three doses of nebulized albuterol/ipratropium and oral steroids to improve wheezing, and he was discharged home with a steroid burst. Patient ACRI_0230 was a 5-year-old healthy Caucasian male who presented in February 2017 with persistent cough for 2 weeks. He was afebrile with normal vital signs. Auscultation of the chest revealed bilaterally equal coarse breath sounds without wheezing. The patient was discharged home with supportive measures. Patient ACRI_0248 was a 4-year-old healthy Caucasian male who was seen in March 2017 with a 2-day history of barky cough. He was afebrile with normal vital signs and an SpO2 of 97%. He was diagnosed with croup and given dexamethasone intramuscularly. He was discharged home with continued supportive measures.Here, we present three novel genome sequences of hPIV2 isolates from patients who presented with cold symptoms to the emergency department (ED) at the Arkansas Children\u2019s Hospital in Little Rock, Arkansas, USA. Patient ACRI_0185 was a 15-year-old African American male with severe persistent asthma, seen October 2016 with worsening asthma symptoms over the previous 10\u00a0days. In the ED, the patient had normal vital signs with an SpONasopharyngeal swabs were collected after consent for participation in an ongoing study approved by the institutional review board. An Illumina stranded-RNA library was created from isolated RNA, and hybridization-based enrichment was performed using the University of New Mexico\u2019s ResVir respiratory viral panel probe set, which contains 5,683 hybridization probes designed to be complementary to coding sequence regions of 24 human respiratory viruses. Next-generation sequencing was performed on an Illumina MiSeq platform using V3 chemistry and paired 75-bp reads.Sample ACRI_0185 had 13,757 sequencing reads align to the hPIV2 RefSeq genome (NC_003443), which resulted in a mean coverage of 65\u00d7. Samples ACRI_0230 and ACRI_0238 had 94,858 sequencing reads with a 274\u00d7 mean coverage and 93,696 sequencing reads with a 217\u00d7 mean coverage, respectively. Alignment-guided assembly was used to generate isolate genome sequences (CLC Genomics Workbench version 9), which were annotated using the ViPR Genome Annotator (MF077311, MF077312, and MF077313, respectively.The whole-genome sequences of isolates ACRI_0185, ACRI_0230, and ACRI_0248 have been deposited in GenBank under the accession numbers"}
+{"text": "Escherichia coli MG1655 as a host. With a genome size of 348 kb, vB_Eco_slurp01 is one of the largest bacteriophages isolated to date.Bacteriophage vB_Eco_slurp01 was isolated from porcine feces using  Escherichia coli MG1655.There are approximately 4.4 million pigs in the United Kingdom, with an estimated \u00a3212 million export value  (Bacteriophage genomic DNA was prepared from cultures using a phenol:chloroform extraction method . One nan95538.1)  and 121Q54 modulation protein, RpoD, GyrA, GyrB, and ribonucleoside-diphosphate reductase subunits. Although homologous to E.\u00a0coli genes, many of the coding sequences had greater similarity to genes from other bacteria; for example, gyrB had higher similarity to Bacteriovorax sp. DB6_IX than E.\u00a0coli. In addition, a gene encoding a putative tellurite resistance protein (TelA) was also observed. This is a feature that is also common to the phages PBECO4 and 121Q. Intriguingly, the resistance protein is associated with Gram-positive bacteria (E.\u00a0coli (Furthermore, these phages all share a high degree of synteny across their genomes. Comparison of gene content between the three strains revealed a core gene set of 405 genes. While conserved between isolates, the majority of these genes encode hypothetical proteins. As with previous jumbo phages, a number of bacterial host homologue genes were detected, including genes coding for a \u03c3bacteria  and is n(E.\u00a0coli .The genome of vB_Eco_slurp01 provides further insights into the small number of bacteriophages that have genomes greater than 300\u00a0kb in size. Furthermore, this study demonstrates that double-stranded DNA bacteriophages from distant geographical regions have highly conserved genomes.LT603033.The draft genome sequence of bacteriophage vB_Eco_slurp01 has been deposited in DDBJ/ENA/GenBank under the accession number"}
+{"text": "Metagenome analysis has become a common source of information about microbial communitiesthat occupy a wide range of niches, including archaeological specimens. It has been shownthat the vast majority of DNA extracted from ancient samples come from bacteria(presumably modern contaminants). However, characterization of microbial DNA accompanyinghuman remains has never been done systematically for a wide range of different samples. Weused metagenomic approaches to perform comparative analyses of microorganism communitiespresent in 161 archaeological human remains. DNA samples were isolated from the teeth ofhuman skeletons dated from 100 AD to 1200 AD. The skeletons were collected from 7archaeological sites in Central Europe and stored under different conditions. The majorityof identified microbes were ubiquitous environmental bacteria that most likelycontaminated the host remains not long ago. We observed that the composition of microbialcommunities was sample-specific and not correlated with its temporal or geographicalorigin. Additionally, traces of bacteria and archaea typical for human oral/gut flora, aswell as potential pathogens, were identified in two-thirds of the samples. The geneticmaterial of human-related species, in contrast to the environmental species that accountedfor the majority of identified bacteria, displayed DNA damage patterns comparable withendogenous human ancient DNA, which suggested that these microbes might have accompaniedthe individual before death. Our study showed that the microbiome observed in anindividual sample is not reliant on the method or duration of sample storage. Moreover,shallow sequencing of DNA extracted from ancient specimens and subsequent bioinformaticsanalysis allowed both the identification of ancient microbial species, including potentialpathogens, and their differentiation from contemporary species that colonized humanremains more recently. During the last 2 decades, a number of methods that permit isolation and sequencing ofancient DNA (aDNA) extracted from archaeological specimens have been elaborated. As aresult, several complete genome sequences of long-dead organisms have been determined \u20135. TypicSome target enrichment procedures have been proposed to increase the amount of endogenousaDNA \u201324, and Metagenome analysis has become a common source of information about microbial communitiesthat occupy a wide range of ecosystems. Until today, environmental components  as well The current study was performed to characterize microorganisms associated with humanarchaeological remains. We used shotgun sequencing of DNA isolated from 161 human teethcollected from 7 archaeological sites dated from 100 AD to 1200 AD and stored underdifferent conditions . For each individual sample, the microbiome wasdetermined using Metagenomic Phylogenetic Analysis (MetaPhlAn2) based on multiple specificmarker sequences derived from the genomes of microorganisms , 46. WitWe analyzed 161 human bone samples collected from 7 archaeological sites in Central Europe, but not between types of sample storage (Wilcoxon: P = 0.2685) orage (Wilcoxon: P = 0.5607)  with the usage of an Illumina single-end standard protocol (includingblunt-end DNA repair) and 75 bp sequencing run. Altogether, 846.5 million reads wereobtained. On average, 98.6% of reads passed trimming and quality filtration. Afterfiltration, for 161 samples, the average number of reads per sample was 5 143 975 . In further analysis, we removed 8 samples that didnot meet the arbitrary criterion of minimal raw reads number (<1 million). The averagenumbers of reads differ between archaeological sites , but not for freshly recovered and storedin museum samples (Wilcoxon: P = 0.3160). Marginal statistical significancewas observed between older  and younger  samples (Wilcoxon:P = 0.0467), with a higher share of endogenous human DNA in older samples.All reads were mapped to the reference human genome, and the percentage of human reads wasdetermined for each sample. As shown in Fig. To characterize the microbiomes of analyzed archaeological samples, we used MetaPhlAn2.The program identifies bacteria/archaea, viruses/viroids, and unicellular eukaryotes usinghomology-based classification of NGS reads by alignment with predefined taxa-specificmarker sequences . The numP = 0.0532) , or storageconditions (MANOVA: P = 0.7672).For the remaining 151 samples, our analyses Fig. A showed Dasheen mosaic virus  andVicia cryptic virus (26.7%), are both known to infect plants.Subsequently, 5 viruses and 1 viroid constituted less than 2.5% each of all identifiedviruses/viroids, and also all were found to be associated with plant genera. Theremaining viruses were of low abundance (<1%) and were usually present in no more thana single sample. It is also noteworthy that we identified within our samplesPropionibacterium phage\u2014a double-stranded DNA (dsDNA) virus that isassociated with oral microbiome  and the To assess the amount of endogenous DNA, reads were mapped against human nuclear (hg19) and compRRID:SCR_001240)[RRID:SCR_014601).To investigate aDNA damage patterns, we employed mapDamage2.0 with the default settings. All plok-mers of exogenous reads mightsegregate samples according to their age, storage, or archeological site, we followed theapproach described in Dubinkina et al. [Shannon diversity, PCA, and PCoA on 4 taxonomic levels were run in R  for allidentified microorganisms and for bacteria/archaea only. PCoA was run on the Jaccard, andBray-Curtis distance tables were calculated from the taxon abundance. To determine whetherlow-abundance taxa (<1%) may have influenced the analysis, we also ran PCoA withoutthem (data not shown). To determine if a et al. .To test if certain groups displayed statistically significant differences, we applied a1-way ANOVA, followed by a Tukey HSD and a t-test , as well as the following non-parametric tests: Kruskal-Wallis and Wilcoxon .R was calculated as a Pearson correlation coefficient.Correlation Supplementary Table S1. Summarized information on NGS datasets used within this study. Thefirst column from the left lists sample IDs. Column 2 comprises information on C14 dating ofselected samples. Column 3 and column 4 describe the depth of sequencing (number of raw andfiltered reads). Columns 5 and 6 describe the reads that map to the human genome . Columns 7 and 8 describe reads mapping to the Metaphlan2 markers DB . Column 9 describes the number of reads that mapped to the prokaryotic markersonly. Columns 10\u201316 describe the percentage (within a sample) of viruses/viroids, eukaryote,all prokaryote, environmental prokaryote, oral prokaryote, other human-related prokaryote,and potential pathogens, respectively. Columns 17\u201322 describe the number of identifiedbacterial/archaeal taxa and Shannon index on class, family, and species level,respectively.Supplementary Table S2. Summarized information on bacterial/archaeal taxa (column 1)identified within samples (columns 5\u2013165). Column 2\u20134 describe taxon gram stain type,respiratory type, and its typical habitat, respectively.Supplementary Table S3. The information on 11 samples used for the validation of resultsobtained in a shallow sequencing experiment. The first column from the left lists sampleIDs. Column 2 describes the total number of filtered reads. Columns 3 and 4 describe thereads that mapped to the Metaphlan2 markers DB . Column 5 describes thepercentage of prokaryote identified in a sample. Columns 6 and 7 describe the number ofbacterial/archaeal classes and the Shannon index.Supplementary Table S4. Summarized information on 77 bacterial and archaeal species (column1) selected for aDNA damage analysis. Columns 2\u20134 describe species gram stain type,respiratory type, and its habitat, respectively. Columns 5\u201312 describe the number of samplesin which the species were present in more % than the threshold . Column 13 describes the maximal percentage of a speciesobserved. Column 14 describes the overall percentage of a species in all samples. Columns15\u2013154 describe the species percentage in an individual sample. A) Table summarizes thenumber of samples with species present in more than the threshold and their percentage withrespect to the all the identified species.aDNA_microorganisms_Figlerowicz_Supplementary_ Figures.pdfaDNA_microorganisms_Figlerowicz_Supplementary_ Tables.xlsxaDNA_microorganisms_Figlerowicz_Supplementary_Tables_ leg.docxaDNA ancient DNAdsDNA double-stranded DNANGS next-generation sequencing.GIGA-D-17-00056_Original_Submission.pdfClick here for additional data file.GIGA-D-17-00056_Revision_2.pdfClick here for additional data file.GIGA-D-17-00056_Revision_1.pdfClick here for additional data file.Response_to_Reviewer_Comments_Original_Submission.pdfClick here for additional data file.Response_to_Reviewer_Comments_Revision_1.pdfClick here for additional data file.Reviewer_1_Report_.pdfClick here for additional data file.Reviewer_1_Report_(Revision_1).pdfClick here for additional data file.Reviewer_2_Report_.pdfClick here for additional data file.Reviewer_2_Report_(Revision_1).pdfClick here for additional data file.aDNA_microorganisms_Figlerowicz_Supplementary_Figures.pdfClick here for additional data file.aDNA_microorganisms_Figlerowicz_Supplementary_Tables.xlsxClick here for additional data file.aDNA_microorganisms_Figlerowicz_Supplementary_Tables_leg.docxClick here for additional data file."}
+{"text": "Endogenous noncoding circular RNAs (circRNAs) have gained attention for their involvement in carcinogenesis, but their expression pattern in breast cancer has remained largely unknown. In this two-stage study, we first used an Arraystar Human circRNA Array to construct a genome-wide circRNA profile. We then selected candidate circRNAs for validation using a quantitative real-time polymerase chain reaction system. CircRNA/miRNA interactions were predicted and sequence analyses were performed. Among 1155 differentially expressed circRNAs, 715 were upregulated and 440 were downregulated in breast cancer tissues. The validation study demonstrated that hsa_circ_103110, hsa_circ_104689 and hsa_circ_104821 levels were elevated in breast cancer tissues, whereas hsa_circ_006054, hsa_circ_100219 and hsa_circ_406697 were downregulated. These circRNAs targeted complementary miRNA response elements. The area under the receiver operating characteristic curve for distinguishing breast cancer was 0.82 (95% CI: 0.73-0.90) when hsa_circ_006054, hsa_circ_100219 and hsa_circ_406697 were used in combination. This study provides evidence that circRNAs are differentially expressed in breast cancer and are important in carcinogenesis because they participate in cancer-related pathways and sequester miRNAs. Breast cancer is the most frequently occurring cancer and the leading cause of cancer-related death among women worldwide, with an estimated 1.7 million incident cases and 521,900 deaths in 2012 , 7.Besides genetic mutations, epigenetic mechanisms are also important in the tumorigenesis of breast cancer \u201310. EpigCircular RNA (circRNA) is another novel class of endogenous ncRNA molecules . CircRNAAberrant expression of circRNAs has been shown to occur in colorectal, basal cell and bladder carcinoma \u201328. ReceHere, we performed a molecular epidemiological study in a Chinese population to establish the circRNA expression profile and identify deregulated circRNAs in the carcinogenesis of human breast cancer.In the first stage, we used the Arraystar Human circRNA Array to sequence four paired breast cancer samples from patients with invasive ductal breast cancer . In the Differentially expressed circRNAs were detected in the four matched tissue samples. When we set the filter criteria as a fold-change \u22652 and a P-value <0.05, we found that a total of 715 circRNAs were significantly upregulated and 440 circRNAs were significantly downregulated in the breast cancer lesions compared with adjacent normal-appearing tissues. Considering that false positives can be caused by multi-comparisons, we used the false discovery rate (FDR) method to adjust the P values. After FDR correction, a total of 16 circRNAs were found to be significantly upregulated, while 5 circRNAs were significantly downregulated. Figure To test whether the differentially expressed circRNAs discovered through the microarray were bona fide, we selected six potentially significant circRNAs for validation by the quantitative real-time reverse transcription PCR (qRT-PCR). We used the following criteria: (1) length around 200 to 3000 bp; (2) fold-change >2; (3) p-value <0.01; 4) raw intensity >200; 5) exonic-related circRNAs; and (6) conservative. These six circRNAs are highlighted in the volcano plot  enrichment analysis for the genes targeted by the circRNAs that were found to be differentially expressed in our qRT-PCR results. Target genes of upregulated circRNAs in breast cancer were involved in the developmental process, positive regulation of gene expression and positive regulation of biological processes , whereasAs shown in Table CircRNAs may be generated from exonic or intronic sequences  and funcNRIP1 (nuclear receptor interacting protein 1) gene, the protein product of which stimulates the transcriptional activity of the estrogen receptor and is critical for promoting the progression and development of breast cancer [ASAP1 , which encodes an oncoprotein associated with colorectal cancer, laryngeal squamous cell cancer and epithelial ovarian cancer [FAM120A (family with sequence similarity 120A), and its encoded protein is a signaling partner that activates the FAK and PI3K pathways in colon cancer metastasis [KIAA0355, which encodes an uncharacterized protein that may be involved in colorectal carcinogenesis [FAF1 (Fas associated factor 1), which encodes a protein that binds to FAS antigen and initiates or enhances apoptosis. FAF1 also functions as a tumor suppressor, and ectopic FAF1 expression reduces the migration of cancer cells in vitro and invasion/metastasis in vivo [RBM22 (RNA binding motif protein 22). RBM22 encodes an RNA binding protein which plays a role in cell division and may be involved in pre-mRNA splicing [In this study, we sequenced six circRNAs of interest following a microarray screening. Among them, hsa_circ_103110, hsa_circ_104689 and hsa_circ_104821 were upregulated in breast cancer tissues. Hsa_circ_103110 is encoded by the t cancer , 43. Hsan cancer \u201346. Hsa_tastasis . On the  in vivo . Hsa_cir in vivo , while h in vivo . This cisplicing , 53.GO terms provide proofs of concept for target genes that may regulate crucial biological processes during the development of human diseases. The Hippo signaling pathway has been reported to activate microprocessor which is necessary in mediating the genesis of miRNAs from the primary miRNA transcript, and link cell-density-dependent miRNA biogenesis to cancer . The WNTThe function of circRNAs remains unclear. An intriguing possibility is that circRNAs act as microRNA sponges. Oncogenic miRNAs like hsa-miR-339-5p, hsa-miR-143-5p, hsa-miR-409-3p, hsa-miR-153-3p and hsa-miR-145-5p have been reported to be downregulated in breast cancer \u201335. ThesSome limitations must be considered in the interpretation of our results. First, due to the relatively low levels of circRNA and the minimum detection thresholds of current methods, the possibility of obtaining false negatives when evaluating circRNA expression cannot be avoided. Second, the sample size was limited and the associations need to be further confirmed. The molecules associated with the present circRNAs, such as miRNAs or proteins, should be experimentally identified and characterized in the future. Third, circulating biomarkers are more acceptable than tissue biomarkers and have greater value in clinical applications. Further studies will be needed to evaluate the diagnostic value of circRNA levels in peripheral blood samples.In summary, our study provided a profile of circRNAs in breast cancer and adjacent normal-appearing tissues. We discovered that hsa_circ_103110, hsa_circ_104689 and hsa_circ_104821 were upregulated, while hsa_circ_006054, hsa_circ_100219 and hsa_circ_406697 were downregulated in breast cancer tissues. Specific circRNAs are important promoters of carcinogenesis, as they participate in cancer-related pathways and sequester miRNAs, and thus may be useful biomarkers of breast cancer.The Institutional Review Board of Nanjing Medical University  approved this study. Written informed consent was obtained from all participants included in the study.We designed a two-stage study. First, we used the Arraystar Human circRNA Array V2  to construct a genome-wide circRNA profile. Then, we selected candidate circRNAs for validation using qRT-PCR with a relatively large sample size.We recruited breast cancer patients from the Affiliated Hospital of Jiangsu University, the People's Hospital of Yixing and the First Affiliated Hospital of Suzhou University from March to May 2016. Patients were included if they were: (1) women; (2) with a pathologic diagnosis of breast cancer; (3) without previous cancer history; (4) without HIV/AIDS; (5) >18 years old; (6) having undergone mastectomy; (7) with informed consent. Breast cancer lesions and adjacent normal-appearing tissues were collected from patients who underwent surgical breast resection. The corresponding adjacent normal-appearing tissues were located >5 cm from the edge of the tumors. All patients had no history of radiotherapy or chemotherapy before specimen collection. The specimens were placed in RNA storage solutions  and stored at \u221280 \u00b0C in an ultra-low temperature refrigerator. Tumor stage was determined according to the Classification of Malignant Tumors Staging System (TNM) by the American Joint Committee on Cancer .Total RNA was isolated with an RNeasy Mini Kit  according to the manufacturer's protocol. The quality and quantity of RNA were measured with a NanoDrop ND-2000 . Additionally, RNA integrity was assessed through standard denaturing agarose gel electrophoresis.The Arraystar Human Circular RNA Microarray V2  was used to identify circRNAs with differential expression between breast cancer lesions and adjacent normal-appearing tissues. The array covers 13,617 human circRNAs with stringent experimental support, carefully and comprehensively collected from circRNA studies and landmark publications. Sample labeling and array hybridization were performed according to the manufacturer's protocol.GAPDH as an internal control. The primers were designed through Primer3 web (http://primer3.ut.ee/), verified through primer-BLAST (https://www.ncbi.nlm.nih.gov/tools/primer-blast/), and synthesized by Realgene . The appearance of a single peak in the melting curve of the qRT\u2013PCR indicated the specificity of the PCR results  from 500 ng of total RNA. Subsequently, we performed a SYBR method-based qRT-PCR reaction in a total volume of 10 \u03bcL, including 0.2 \u03bcL/10 \u03bcM forward/reverse primers, 0.2 \u03bcL 50\u00d7 ROX reference dye I, 1 \u03bcL cDNA, 5 \u03bcL 2\u00d7 SYBR Premix Ex Taq II and 3.4 \u03bcL double-distilled water. The cycling program entailed initiation at 95\u00b0C for 30 sec, followed by 40 cycles of 95\u00b0C for 5 sec and a pre-selected annealing temperature for 30 sec. The best annealing temperatures were 63\u00b0C for hsa_circ_103110, hsa_circ_104689, hsa_circ_104821 and hsa_circ_100219, and 65\u00b0C for hsa_circ_006054 and hsa_circ_406697. Results were obtained from three independent wells. The relative expression of each circRNA was calculated from the \u0394Ct. Divergent primers were designed to amplify the circRNA-specific back-splice junctions (Table  results .http://www.targetscan.org/) and miRanda (http://www.microrna.org/).We performed GO analysis to annotate genes meaningfully in terms of their biological processes, cellular components and molecular functions. The -log10  yields an enrichment score representing the significance of GO term enrichment among differentially expressed genes. KEGG analysis was performed to determine the involvement of target genes in different biological pathways. Here, the -log10  yields an enrichment score indicating the significance of pathway correlations. To further elucidate the correlations between circRNAs and miRNAs, we predicted circRNA/miRNA interactions using miRNA target prediction software from Arraystar, which refers to TargetScan (https://www.r-project.org/) and GraphPad Prism 5 .The fold-change of each circRNA was computed from the profile difference between the cancer and control groups, and the significance was analyzed with a t-test. A receiver operating characteristic curve was plotted, and the AUC, sensitivity and specificity were calculated to assess the ability of circRNAs to differentiate between breast cancer and adjacent normal-appearing tissues. Statistical analyses were performed with R software version 3.3.1 ("}
+{"text": "Gordonia species was cultured from soil of a red alder (Alnus rubra) plant. Here we present the assembled and annotated genome sequence to aid investigations into the potential of this organism as a symbiont and comparative studies of the genus Gordonia.A  Gordonia is gaining attention due to its relevance in a variety of realms because its diverse metabolic activity has important implications for bioremediation; several Gordonia species degrade rubber, organic pollutants, and other xenobiotics. Gordonia species also have notable roles in agriculture and as opportunistic human pathogens  Murashige and Skoog medium with full Gamborg\u2019s vitamins. Genomic DNA was isolated with a GenElute bacterial genomic DNA kit (Sigma) following the protocol for Gram-positive bacteria used in conjunction with Lysozyme (Sigma).The genus athogens . A filamhttps://github.com/PacificBiosciences/FALCON). The assembly was further polished using PacBio\u2019s quiver algorithm to increase genomic consensus accuracy (https://github.com/PacificBiosciences/GenomicConsensus). The assembly came together into 3 contigs covering 6,180,512\u00a0bp, with a maximum length of 5,136,039\u00a0bp and 67% GC content. To identify the assembled species, a BLAST search was done against NCBI\u2019s nonredundant database, which showed the highest identity with Gordonia polyisoprenivorans (http://www.pacb.com/wp-content/uploads/2015/09/WP_Detecting_DNA_Base_Modifications_Using_SMRT_Sequencing.pdf) (The microbe sample was prepared for sequencing using the standard Pacific Biosciences 10-kb library prep protocol and sequenced on the PacBio RSII system. The resulting PacBio reads were assembled using the Falcon0.4 software (NQOE00000000. The version described in this paper is version NQOE01000000.This whole-genome shotgun project was deposited at DDBJ/ENA/GenBank under the accession number"}
+{"text": "Dactylis glomerata L. during six different growth periods.Vernalization and the transition from vegetative to reproductive growth involve multiple pathways, vital for controlling floral organ formation and flowering time. However, little transcription information is available about the mechanisms behind environmental adaption and growth regulation. Here, we used high-throughput sequencing to analyze the comprehensive transcriptome of During vernalization, 4689 differentially expressed genes (DEGs) significantly increased in abundance, while 3841 decreased. Furthermore, 12,967 DEGs were identified during booting stage and flowering stage, including 7750 up-regulated and 5219 down-regulated DEGs. Pathway analysis indicated that transcripts related to circadian rhythm, photoperiod, photosynthesis, flavonoid biosynthesis, starch, and sucrose metabolism changed significantly at different stages. Coexpression and weighted correlation network analysis (WGCNA) analysis linked different stages to transcriptional changes and provided evidence of inner relation modules associated with signal transduction, stress responses, cell division, and hormonal transport.WRKY, NAC, AP2/EREBP, AUX/IAA, MADS-BOX, ABI3/VP1, bHLH, and the CCAAT family during vernalization and floral bud development. TFs expression patterns revealed intricate temporal variations, suggesting relatively separate regulatory programs of TF modules. Further study will unlock insights into the ability of the circadian rhythm and photoperiod to regulate vernalization and flowering time in perennial grass.We found enrichment in transcription factors (TFs) related to The online version of this article (10.1186/s12870-017-1170-8) contains supplementary material, which is available to authorized users. Oryza sativa, Triticum aestivum, Hordeum vulgare, and especially in model plants Arabidopsis thaliana, have been identified, which mainly belong to four pathways interacting to each other including photoperiod pathway [Flowering is a critical developmental stage of most higher plants, which makes the plants produce seeds, thus to pass the genetic information from one to the next generation. The timing of flowering is regulated by multiple genetic and environmental factors. Specifically, the plant genes controlling flowering are induced via synchronization of climatic and environmental conditions. Thus, flowering time varies extensively according to climate and latitudinal or altitudinal gradients. For crop production, it is important to coordinate the flowering time with changes of environment to avoiding adverse natural conditions during flower differentiation . With th pathway , GA path pathway , vernali pathway , 4. For VRN) since they are extensively involved in the vernalization response. The latter demonstrated that VRN3 is completely linked to a gene similar to the FLOWERING LOCUS T (FT) in A.thaliana [VRN1, VRN2, and the FT genes in response to vernalization. Prior to cold exposure, VRN2 represses the expression of FT. During cold exposure, VRN1 expression increases, resulting in the repression of VRN2, which in turn allows activation of FT during long days, thus inducing flowering [A. thaliana, the vernalization pathway converge on FLOWERING LOCUS C (FLC), a MAD-box transcription regulator; furthermore, its activator FRIGIDA represses flowering via increase of FLC mRNA abundance [WRKY34-induced and CULLIN3A (CUL3A)-dependent on FRIGIDA, modulate flowering in response to vernalization [VRN1 binds the promoter of FLOWERING LOCUS T-like, and also targets VRN2 and ODDSOC2 [Vernalization or jarovization was first defined by Lysenko et al. in 1986 when it was observed that wheat varieties required cold for stem elongation and flowering . Vernalithaliana . A modellowering . In A. tbundance , 14. Thelization . A lates ODDSOC2 . This reA. thaliana is an excellent tool in studying the complexity of vernalization regulation. Currently, the full flowering network can only be approached in A. thaliana. Studies on Oryza sativa, Triticum aestivum, and Hordeum vulgare have led to the identification of components within individual signaling pathways that affect flowering as well as their positioning within molecular hierarchies. Even more importantly, the current study concentrates on annuals. In contrast to annuals, perennials require a long vegetative phase to accumulate and achieve the transition to the reproductive stage [With the known genetic background and natural variations, ve stage . In addive stage . ObviousDactylis glomerata L.) is a winter perennial gramineae grass, which is native to northern Africa, western and central Europe, and temperate Asia [Orchardgrass  was grown in a greenhouse of the Sichuan Agricultural University located in Chengdu  under natural light conditions, then was transferred to field in Chongzhou farm  prior to temperature reduction in the year of 2016. For sampling, mixed young leaf samples were collected from three biological replicates at six different stages and immediately frozen in liquid nitrogen and stored in \u221280\u00a0\u00b0C freezers before being used. The six sampling stages included before vernalization January 4th, vernalization February 2nd, after vernalization March 2nd, vegetative growth stage March 24th, before heading April 9th, and heading stage May 5th. We defined these six sampling stages as before vernalization stage (BV_DON), vernalization stage (V_DON), after vernalization stage (AV_DON), vegetative growth stage (VG_DON), before heading stage (BH_DON), and heading stage (H_DON) using a sampling timeline . RNA samples were treated with DNaseIto remove DNA, then RNA samples were monitored on 1% agarose gels. RNA purity was verified using the NanoPhotometer\u00ae spectrophotometer  and the concentration was measured via the Qubit\u00ae RNA Assay Kit in Qubit\u00ae 2.0 Fluorimeter . RNA integrity was assessed using the RNA Nano 6000 Assay Kit of the Agilent Bioanalyzer 2100 system . RNA samples with 260/280 ratio between 1.8 and 2.0, 260/230 ratio between 2.0 and 2.5, and RNA integrity number above 8.0, were chosen for further library construction and sequencing.Approximately, 3\u00a0\u03bcg RNA for each sample was used for the RNA sample preparations and a total of eighteen sequencing libraries were generated using NEBNext\u00ae Ultra\u2122 Directional RNA Library Prep Kit for Illumina\u00ae  following manufacturer\u2019s recommendations. Library construction and transcriptome sequencing were conducted by the Novogene Bioinformatics Institute  on Illumina Hiseq 4000 platform . Without reference genome, the clean reads were assembled as a reference genome via Trinity software, according to standard parameters for next analysis. Read counts per gene were expressed as the expected number of Fragments Per Kilobase of transcript sequence per Million base pairs sequenced (FPKM). To depict the global abundance of gene expression, the FPKM values of transcripts were accessed via box-plot. All the assembled unigenes were searched and annotated against the publicly available protein databases including Nr, Nt, Pfam, KOG/COG, and GO (Gene Ontology), using BLASTx analysis with an E-value cut-off of 1.0E-05.p-value below 0.05 [DEGs were identified via pairwise sample comparisons in adjacent sampling points  by using DESeq R package (1.18.0) . The exp\u2009<\u20090.01) .P-value below 0.05 found by DESeq were assigned as differentially expressed and employed for GO and KEGG analyses. GO enrichment analysis of DEGs was implemented with the GOseq R package, in which the gene length bias was corrected. GO terms with corrected P-values below 0.05 were considered significantly enriched by DEGs [http://www.genome.jp/kegg/) and KOBAS software was used to test the statistical enrichment of DEGs in KEGG pathways [To further and systematically predict the complex biological functions of genes and to identify active biological pathways among developmental phases, the assembled unigenes were mapped against both GO and KEGG databases. Genes with an adjusted  by DEGs . KEGG enpathways .The WGCNA package was used for a weighted gene coexpression network analysis in R (v3.3.0) as described by Langfelder et al. . A totalwww.omicshare.com/tools).Principal component analysis (PCA) and hierarchical clustering were performed to assess transcriptome similarity among samples and to evaluate sampling between biological replicates. PCA was performed based on expressed genes in different samples by using the R program with the default parameter. Hierarchical clustering of samples via the complete linkage method showed the change of gene expression levels across different stages. Self-organizing map (SOM) neural network was used for clustering analysis in search of co-expression DEGs groups . Both ofhttp://bioinfo.bti.cornell.edu/cgi-bin/itak/index.cgi) [Unigenes were examined from the iTAK database for families of transcription factors or regulatory motifs (dex.cgi) , 28.The dex.cgi) .FRI (c149523_g1), LHP1 (c143664_g4), VRN1(c147469_g1), VIP1(c137956_g2), LHY (c146679_g3), COL1 (c151793_g1), WNK1 (c148831_g2), CDF2 (c127155_g1), GI (c151406_g1), COL16(c140653_g3), CRY1 (c137241_g1), FD (c128431_g1), GAI (c140748_g1), FVE (c137063_g1), SPL (c131707_g1), SPL3 (c134262_g2), SPL9 (c150483_g1) and FLC-like (c147268_g1). First-strand cDNAs were synthesized using Prime Script\u2122 RT Master Mix Kit (RR036A) . The qRT-PCR reaction was performed using Bio-Rad CFX96 following the instructions for the SsoFast\u2122 EvaGreen\u00ae Supermix Kit (SYBR Green) (#1725200AP) . Three biological replicates were sampled were performed on each sample. The primers for unigenes were designed using online Primer BLAST program(https://www.ncbi.nlm.nih.gov/tools/primer-blast/index.cgi?LINK_LOC=BlastHome). Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) was selected as reference gene [-\u0394\u0394CT [The validity of RNA-seq were verified by quantitative real-time PCR(qRT-PCR). Eighteen flowering related genes of interest were validated using qRT-PCR, including nce gene . RelativNK1 c1488_g2, CDF2v/v) anhydrous ethanol and 25% (v/v) glacial acetic acid prior to microscopic examination. Pictures were obtained with an Olymus SZX12 stereo-microscope system .Anatomic and microscopic examinations were conducted, using an Olymus CX41 microscope to distinguish the different stages and to accurately identify the floral initiation and development. The tissue was fixed with 75% , 5292 in AV_DON-V_DON (AV vs V), 4410 in VG_DON-AV_DON (VG vs AV), 7859 in BH_DON-VG_DON (BH vs VG), and 700 in H_DON-BH_DON (H vs BH), respectively. Moreover, the number of up\u2212/down-regulated genes were also presented Fig.\u00a0. The numThe GO and KEGG analysis indicated, the pathways of plant hormone signal transduction, circadian rhythm-plant, and flavonoid biosynthesis were significantly enriched in P1, relevant to hormone signal, cold response, light signaling, and vernalization induction in up/down-regulated DEGs. After more than a month of vernalization, the cold induction was disappeared as temperatures rose up during P2. The pathways of starch and sucrose metabolism were significantly enriched both in up or down-regulated DEGs. Furthermore, genes related to phenylpropanoid biosynthesis expressed abundantly in the group of up-regulated DEGs, and the result was consistent with morphological observations and gene expression profiling  and AUXIN RESPONSE FACTOR 22 (ARF22), which encode transcription factors involved in auxin signaling. In addition, the auxin responsive protein INDOLE 3 ACETIC ACID 16 (IAA16), IAA18, IAA28, ethylene responsive transcription factor 061(ERF061) and ERF073 were also found in this cluster.. Compared to subcluster_1_2, the pathways of photosynthesis and plant hormone signal transduction enriched more significantly in this cluster, as a response to light stimulus and abiotic stress.Subcluster_1_2 had an overrepresentation function category of ATP/ADP binding, protein binding, oxidative phosphorylation, and transcription factor activity mostly involved in DNA-templated transcription with the energy-related metabolism. Large numbers of DEGs in subcluster_1_3 were significantly enriched in plant hormone signal transduction, photosynthesis-antenna proteins, and circadian rhythm-plant pathways. Unigenes for hormone like auxin and ethylene were present in this cluster for functional action, including AUXIN RESPONSE FACTOR 18 For subcluster_1_4, a large proportion of genes in the sucrose metabolic process, starch metabolic process, and lipid metabolic process were linked. Many pathways such as cytoskeleton organization, cell wall macromolecule catabolic process, and acyl-carrier-protein biosynthetic process were involved in cell replication. In this cluster, several genes associated with calcium ion binding were expressed, which constituted part of the calcium signaling system involved in both the photosystem and late embryogenesis. Subcluster_1_5 comprised genes that expressed in molecular function of ATP binding, zinc ion binding, and DNA binding were involved in transferase activity, protein kinase activity, oxidoreductase activity, and hydrolase activity. The minimum expression peak of subcluster_4_2, subcluster_4_4, subcluster_4_5, subcluster_5_3, and subcluster_5_4 were also appeared in phase V_DON. After this time point, the gene expression demonstrates an increasing tendency in the subsequent developmental phases. These clusters including unigenes category are similar to supercluster 1, but have a completely opposite expression tendency in phase V_DON.psbN). In subcluster_2_6 and subcluster_3_6, the genes showed the minimum expression in biological pathways such as oxidation-reduction processes. The genes of oxidation-reduction process, transport, sucrose metabolic process, carbohydrate metabolic process, and starch metabolic process were greatly changed relative to phase V_DON. Genes involved in cell morphogenesis and embryonic morphogenesis had minimal expression levels in subcluster_4_6 and genes response to auxin, reduction process, and nitrogen compound metabolic process had a maximum fold change compared to phase V_DON.Subcluster_2_2, subcluster_2_6, subcluster_3_1, subcluster_3_6, and subcluster_4_6 showed an expression peak in phase AV_DON, but relatively low expression during other developmental phases. Genes in these clusters encode proteins with similar functions in signal transduction, protein phosphorylation, photosynthesis, oxidation-reduction process, and defense responses. While investigating the profile in subcluster_2_2, we identified several genes, encoding photosystem II reaction centre N protein  and WRKY70. Ethylene-responsive transcription factor 2(ERF2) and the MADS-box transcription factor 34 were also highly expressed in stage BH_DON.Prior to the heading phase, we focused on subcluster_1_6, subcluster_5_1, subcluster_5_2, subcluster_5_3, and subcluster_5_4, which all showed drastic expression changes. 290 genes in subcluster_5_2 followed an ascending expression trend overall, but with three expression peaks in both phases V_DON and BH_DON. Interestingly, the genes in V_DON showed an apparent polarization. For example, the expression transcription factor 70) and sigma-54(\u03c354). Photomorphogenesis and photosystem regulated genes from the cytochrome family were also found in abundance in this cluster, including cytochrome P450, cytochrome oxidase C, cytochrome B6-F, and cytochrome b562.. In addition, subcluster_5_1 also expressed several genes of the cytochrome family, encoding cytochrome b and cytochrome b245.In subcluster_1_6, the genes relevant to stress responses, cell proliferation, morphological development, and iron uptake were detected,such as sigma-70(\u03c3Hsp) responding to stress were Hsp 70, Hsp 71, Hsp 83, Hsp 90, and Hsp 98 which were also presented in this cluster, as well as the identified salt stress-induced protein. In subcluster_1_6, several genes in abiotic stress categories contained multiple genes encoding cold shock proteins, dehydrin, and G-protein, which may be induced by various stresses during development. Furthermore, a large proportion of elongation factors (EF) such as EF1-alpha, EF1-beta, EF1-delta, EF1-gamma, EF2, elongation factor G, and elongation factor-Tu were contained in this cluster.These clusters comprised several genes encoding lipases, containing the GDSL motif and lipid-transfer protein, which is involved in the lipid metabolic process. Many genes in the cell organization category were related to the cytoskeleton, including several tubulin alpha-1/\u22122, tubulin beta-1, and kinesin genes. In addition, genes encoding cell division cycle protein 48, cell division control protein 6, and cell division control protein 45 were identified in this cluster. Furthermore, a number of genes associated with cellulose synthase, cell wall, and cell cycle were expressed in this phase, possibly indicating a major shift in the regulation of cell morphogenesis.Subcluster_5_1 and subcluster_5_2 showed peak expression in phase BH_DON with an increasing tendency from phase BV_DON. Functional categories of unique genes associated with genes encoding heat shock protein (IAA) family genes IAA6, IAA13, IAA21, and IAA31, the AUXIN RESPONSE FACTOR1(ARF1), and WRKY transcription factor family genes WRKY 27, WRKY 30, WRKY 40, WRKY 46, and WRKY 50. Otherwise, the primordia in embryos and flower development related transcription factors, no apical meristem (NAM) also involved in this cluster.Subcluster_5_3 and subcluster_5_4 showed low expression peaks in phase V_DON, followed by rapid increases from phase VG_DON to phase BH_DON. To evaluate the transcriptional changes associated with the morphologic changes after vernalization, we examined a number of genes in these clusters via enrichment in GO and KEGG. Genes in these clusters were predominantly enriched in phenylpropanoid and starch and sucrose metabolism in KEGG; also, the pathways of plant hormone signal transduction were involved. The functional category protein binding, transferase activity, and catalytic activity were revealed via GO enrichment. An overrepresentation of transcription factors were enriched, including AUXIN(AUX)/INDOLE-3-ACETIC ACID (ERF11), ethylene-responsive transcription factor34 (ERF34), and WAX INDUCER1 (WIN1).Subcluster_5_5 and subcluster_5_6 showed unique expression patterns, which have expression peaks in phases V_DON and VG_DON by negative regulation, and were also strongly expressed in phase BH_DON via positive regulation. These clusters contained a large number of genes involved in the protein process in the endoplasmic reticulum and phenylpropanoid biosynthesis, which played a role in cell morphogenesis and structural molecule activities. Subcluster_5_5 contained an overrepresentation of transcription factor induced by various abiotic factors, including ethylene-responsive transcription factor11  was adopted. Based on pairwise correlations and gene expression trends in all samples, coexpression networks were constructed using the normalized microarray expression data of these 25,071 probes from all 18 samples  via R library. Different color represents a specific module, containing a cluster of highly correlated genes. This analysis resulted in 14 distinct modules Fig.\u00a0. RemarkaIn addition, the correlation in different modules was also considered, and 7 broad clades were identified in 14 modules Fig.\u00a0. A heat bHLH) family, comprising transcription factors that are known to be active during flower development in A. thaliana, peaked in the phase of H_DON [NF-YA, NF-YB, and NF-YC that bind with highly specific CCAAT motifs in a variety of genes. We found these transcription factors overrepresented in phase H_DON  than any other phases, and more remarkably, phase 5 only had the least DEGs. This demonstrated that P4 involves far molecular processes and most of these processes have had a regulatory effect before heading. This result agrees with the markedly morphological alterations during P4 (inflorescence formation) and P5 (heading). The plant perception of vernalization and outgrowth of floral buds, involved numerous processes, both temporal and spatial. GO and KEGG analysis of DEGs throughout all five phases showed that the processes focused differently, which also supported this notion. The protein related processes, such as protein phosphorylation and kinases, and protein modification, were highlighted during P1, while the functional hormone related categories were also overrepresented. Protein phosphorylation plays a central role in regulating many cellular processes in eukaryotes. In particular, protein phosphorylation is a major currency of signal transduction pathways . This reWe found various pathways that focus differently throughout developmental phases. In P1 and P2, plant hormone signal transduction, NOD-like receptor signaling, and MAPK signaling were emphasized. Plant hormones exhibit strong functions in response to endogenous signals and environmental cues. Furthermore, the ubiquitin biosynthesis pathways were also identified in this phase, and ubiquitin-mediated proteolysis has been shown to regulate the different steps of plant hormone signal transduction . Ethylen2 assimilation may play a regulatory role during early reproductive stages [Coexpressed gene cluster analysis illustrated the temporal distribution of core molecular events during vernalization and floral bud formation. Our results suggested that transcriptome specialization is established at a specific stage for several biological processes. At vernalization stage, genes related to signal transduction and stress response were overrepresented. When temperature dropped, several mechanisms have been described to enhance the freeze tolerance, including changes in lipid composition, increases in active-oxygen-scavenging enzymes, anthocyanin accumulation, and carbohydrate metabolism, which are consistent with our transcriptome sequencing results . Signal e stages .To identify genes associated with flower formation, we examined genes with expression peak in BH_DON and H_DON. Several clusters highlighted a number of specialized features of transcriptome data. Subclusters_1_6, subcluster_5_1, and subcluster_5_3 revealed the importance of cell wall remodeling by the active cellulose metabolism in the BH_DON stage, which agrees with reports of cellulose as a cell wall polymer as found in eukaryotes . Plant cThis is exemplified by subclusters_5_5 and subcluster_5_6, which indicate a functional partition of different developmental stages by the expression pattern. These subclusters had higher expression during stages V_DON and VG_DON compared to other stages. The subclusters were characterized by genes related to signal transduction and stress responses, likely reflecting the fact that the external environment significantly influenced plant growth during these stages. A distinct gene set associated with cell proliferation and differentiation during BH_DON and H_DON provides evidence that genes with specific function express during a specific period. In other cases, expression preference exists even though gene clusters were expressed throughout the growth stage.WGCNA analysis enabled the identification of specific modules during critical developmental stages. The skyblue module and darkmagenta module connect with vernalization induction in stages V_DON and AV_DON. Furthermore, the grey60 module, red module, and brown module, may contain numerous genes that are associated with growth transduction, floral organ development, and flowering control, respectively. Furthermore, WGCNA also provided evidence in the interaction of different modules during certain stages. This clearly revealed a network of genes instead of individual genes, which may help to uncover the molecular mechanisms underlying vernalization and flowering. Furthermore, the conjoint analysis of WGCNA and clustering analysis supplied a more efficient method to search core regulators in diverse and abundant data.Miscanthus, including WRKY 12, which participates in cell wall formation and promotes flowering [WRKY20, WRKY23, WRKY30, WRKY40, WRKY50, WRKY53, WRKY57, and WRKY70 were involved in ABA and jasmonic acid signaling pathways in moderating flowering [An analysis of genes that are associated with transcription regulation showed that TFs play an important role during vernalization and floral bud formation. Expression profiles during different developmental stages suggest temporal specialization of TFs and the data set can be used to identify key TFs relevant for flowering time control. For example, WRKY, MADS-BOX, and ABI3/VP1 transcription factor families were found to preferentially accumulate during vernalization, floral organ formation, and heading, which could be part of the transcriptional regulatory complex regulating stress response and flower development. Several WRKY transcription factors expressed in phase BH_DON and H_DON, which is consistent with their reported role in lowering . WRKY20,lowering . The ABIlowering . In a prlowering , includiSOC1) signals from the GA-dependent pathway, which influences the flowering time during short days [The MADS-box family has been defined on the basis of primary sequence similarity amongst numerous proteins . In a prort days . The preVIN3) and vernalization 5 (VRN5) [NF-YA, NF-YB, and NF-YC that bind with highly specific CCAAT motifs in a variety of genes. The NF-YA regulate transcriptionally and post transcriptionally to promote drought resistance [NF-YB plays a role in the regulation of the flowering time in A. thaliana [NF-YC can physically interact with constans (CO), and are genetically required for CO-mediated floral promotion.In addition, Alfine-like and CCAAT transcription factor family were overrepresented in stages BH_DON and H_DON. Alfin-like transcription factors encoded several PHD finger proteins, including vernalization insensitive 3 (5 (VRN5) , 62, whisistance , 64. NF-thaliana . FurtherNAM, ATAF, and CUC2) were described during recently years [Previous research demonstrated basic helix-loop-helix (bHLH) transcription factor family to participate in controlling cell proliferation and development of specific cell lineages . Our stuly years , only a ly years . We founERF) is an important member of AP2/EREBP and encoded a type of AP2 containing protein. Many ERFs have been reported to be involved in the regulation of floral development and stress response [AUX/IAA and AP2/EREBP were found in abundance at stage V_DON, VG_DON, BH_DON and H_DON, this suggested that ethylene biosynthesis and signaling have great function in stress response and are particularly associated with flower development [ARF) family comprises transcription factors that are known to act during different phases in floral organ formation and are operated by a complex transcriptional network [ARF7 and ARF19. ARF is an important regulator of auxin activity, and these genes have maximal expression during stage H_DON, suggesting common centers for auxin biosynthesis and transduction during heading time. The diverse expression profiles of gene related hormone signaling may constitute the basis for this stage-specific response, also supplying evidence for the importance of hormones in vernalization and flower development. Timing of CAB expression 1(TOC1) is a widespread study circadian clock gene belongs to AP2/EREBP family, which has been suggested to be a component of the central oscillator of controlling flowering time in A. thaliana. TOC1 is designated as Arabidopsis pseudo-response regulators (APRR1/TOC1), a circadian-associated transcription factor family. Other than APRR1/TOC1, most APRR family members have been implicated in the mechanisms underlying the circadian rhythm; in particular APRR5, APRR7, ARR4, ARR3, and ARR9 in A. thaliana [OsPRR73 and OsPRR95 in rice [SbPRR37 in sorghum [The ethylen-responsive transcription factor  was identified in the transcriptome data and has been reported to affect several circadian-regulated processes [STO (BBX24) that belongs to B-Box Family can affect the key flowering time genes FLC and FT/SOC1, thus regulating the flowering time [COL6) and the SBP-box gene family squamosa promoter binding protein-like3 (SPL3) [Most MYB proteins function in a variety of plant-specific processes especial in controlling plant development, responses to stresses . The MYBrocesses . In addirocesses , 79. Othing time . The zin3 (SPL3) , which aA. thaliana and other species uncovered several critical regulators for the integration of multiple flowering pathways, including FT and SOC [FLC in pathways of vernalization, autonomous, and aging. The previous study indicated the upstream gene FLC have negative regulation on FT. We found the FLC have low expression in vernalization stage and then increase gradually until heading stage. On the contrary, the FT have high expression in vernalization.while lowly expressed in other stages.This results were consistent with the existing conclusion that low temperature inhibit the expression of FLC and release the suppression for FT, which cause flowering transition [CO positively regulate the FT [CO and FT highly expressed in vernalization stage, which may provide support that the CO promote the expression of FT in flowering regulation. Identified these key regulators in RNA-seq data of D.glomerata demonstrated that different species may share a highly conservative flowering genetic network and several homologous critical candidate genes likely have a similar function. Furthermore, we present these flowering pathway related genes via expression dynamics, providing an intuitive display of when the genes are active during developmental phases. Furthermore, our results provide more essential information for functional analysis of flowering regulatory pathways in perennial grasses.Studies on the flowering network of  and SOC . These ransition . Otherwie the FT . Our resIn conclusion, an RNA-seq approach was utilized to investigate the patterns of gene expression during six key flowering developmental stages involved in vernalization and flower development, revealing novel networks and key regulators. Stage-specific profiling provided biological information of molecular events. This evidence included the process of signal transduction, stimulation of the vernalization response, of hormone control, cell proliferation, and differentiation in floral organ formation. Furthermore, this study added insight into the vital function of transcriptional factors in plant growth as well as valuable information for plant biology in the area of flower development. The WGCNA approach revealed a tightly co-expressed gene clusters and highly ordered gene expression networks that control plant growth. Our work highlighted the effectiveness of the clustering analysis intersected with the WGCNA analysis tool in multi-sample and high-volume data analysis.Additional file 1: Figure S1.The photo of orchardgrass in different stages. Including stage before vernalization (BV_DON); vernalization (V_DON); after vernalization (AV_DON); vegetative growth (VG_DON); before heading (BH_DON); heading (H_DON). (TIFF 8381 kb)Additional file 2:The primers information for qRT-PCR. (XLSX 16 kb)Additional file 3: Figure S2.The crown at the bottom of stem. A, showed the overall view. B, showed the anatomical structure of the plant. C, showed the magnified structure. The white arrow point the crown. (TIFF 7662 kb)Additional file 4: Table S1.RNA-seq statistics. (DOCX 17 kb)Additional file 5: Figure S3.Statistics of de novo assembly of transcriptome. A, Transcript length distribution. B, Unigene length distribution. (TIFF 8933 kb)Additional file 6: Table S2.Statistics of annotation analysis of unigenes. (DOCX 16 kb)Additional file 7: Figure S4.The box-plot describing the FPKM distribution of expressed transcripts after filtering in different samples. Sample labels are as follows: BV, before vernalization; V, vernalization; AV, after vernalization; VG, vegetative growth; BH, before heading; H, heading. DON refers to the orchardgrass cultivated varity DONATA (Registered No.398). (TIFF 9497 kb)Additional file 8: Figure S5.GO functional classification of DEGs in five pairwise sampling stages. Including stage V_DON vs stage BV_DON(A), stage AV_DON vs stage V_DON(B), stage VG_DON vs stage AV_DON(C), stage BH_DON vs stage VG_DON(D) and stage H_DON vs stage BH_DON(E). (TIFF 10209 kb)Additional file 9: Figure S6.KEGG functional classification of DEGs in five pairwise sampling stages. Including stage V_DON vs stage BV_DON(A), stage AV_DON vs stage V_DON(B), stage VG_DON vs stage AV_DON(C), stage BH_DON vs stage VG_DON(D) and stage H_DON vs stage BH_DON(E). (TIFF 9769 kb)Additional file 10:Data S1 SOM-clustering results. (RAR 14661 kb)Additional file 11: Table S3.Identified flowering-related gene in orchardgrass. (DOCX 19 kb)Additional file 12:The annotation information for flowering-related genes identified in orchardgrass base on RNA-seq data. (XLSX 39 kb)"}
+{"text": "In the absence of preexisting antibodies, LAIV boosted preexisting T-cell responses to genetically diverse, wild-type IAVs to which the children were not previously exposed.This pediatric live attenuated influenza vaccine (LAIV) study is the first to show long-term, cross-reactive CD8 Live attenuated influenza vaccines (LAIVs) stimulate a multifaceted immune response including cellular immunity, which may provide protection against newly emerging strains. This study shows proof of concept that LAIVs boost preexisting, cross-reactive T cells in children to genetically diverse influenza A virus (IAV) strains to which the children had not been exposed.+ peptides from the internal proteins . Serum antibody responses were determined by means of hemagglutination inhibition assay. Blood samples were collected before vaccination and up to 1 year after vaccination.We studied the long-term cross-reactive T-cell response in 14 trivalent LAIV\u2013vaccinated children using the fluorescent immunospot assay (FluoroSpot) with heterologous H1N1 and H3N2 IAVs and CD8+ T cells, mainly dominated by NP-specific responses. After vaccination with LAIV, the youngest children showed the highest increase in T-cell responses.Preexisting cross-reactive T cells to genetically diverse IAV strains were found in the majority of the children, which were further boosted in 50% of them after receipt of LAIV. Further analyses of these T cells showed significant increases in CD8LAIV boosts durable, cross-reactive T-cell responses in children and may have a clinically protective effect at the population level. LAIV may be a first step toward the desired universal influenza vaccine. Owing to limited blood volume, we tested only 1 heterologous virus per subtype. Synthetic, influenza-specific major histocompatibility complex class 1\u2013restricted matrix protein 1 (M1), nucleoprotein (NP), and polymerase basic protein 1 (PB1) peptide pools were obtained from the BEI Resources, VA, USA.Plasma samples were analyzed in duplicate  with 0.7% turkey red blood cells and 8 hemagglutinating units of the H1N1 and H3N2 homologous vaccine and heterologous wild-type viruses (50 \u03bcL per well). Negative samples were assigned a hemagglutination inhibition (HI) titer of 5 for calculation purposes . Norway does not have effectiveness data, but surveillance data for the study year indicated that approximately 60% and 30% of influenza infections were due to H1N1 and H3N2 strains, respectively [+ T cells were detectable in young children in the absence of antibodies, which is proof of concept that the LAIV boosts CD8+ T-cell responses to conserved antigens. The responses were durable, indicating that a cellular immune response could possibly last through a whole influenza season. Although our findings were limited by small numbers, they support our hypothesis that the LAIV has the potential to provide cross-protective immunity to drifted and potentially heterovariant strains. Hence, it could possibly be a step toward the desired universal influenza vaccine.In conclusion, our unique trial is the first to show long-term cross-reactive T cells elicited by LAIV in children. Importantly, our study found that these preexisting and protection- associated CD8The Journal of Infectious Diseases online. Consisting of data provided by the authors to benefit the reader, the posted materials are not copyedited and are the sole responsibility of the authors, so questions or comments should be addressed to the corresponding author.Supplementary materials are available at Suppl_fig2_foldchange_HIClick here for additional data file.Suppl_fig_2_T_cellfoldchange_H1_H3Click here for additional data file.Supplementary_fig_3_CD8resp_by_HIrespondere_non_respondersClick here for additional data file.Suppl_fig_4_correlation_HI_CD8Click here for additional data file.Figure_legends_w_supplementaryClick here for additional data file."}
+{"text": "Listeria monocytogenes is a foodborne pathogen that causes listeriosis, which is a major public health concern due to the high fatality rate. LMOf2365_0442, 0443, and 0444 encode for fructose-specific EIIABC components of phosphotransferase transport system (PTS) permease that is responsible for sugar transport. In previous studies, in-frame deletion mutants of a putative fructose-specific PTS permease  were constructed and analyzed. However, the virulence potential of these deletion mutants has not been studied. In this study, two in vitro methods were used to analyze the virulence potential of these L. monocytogenes deletion mutants. First, invasion assays were used to measure the invasion efficiencies to host cells using the human HT-29 cell line. Second, plaque forming assays were used to measure cell-to-cell spread in host cells. Our results showed that the deletion mutant \u0394LMOf2365_0442 had reduced invasion and cell-to-cell spread efficiencies in human cell line compared to the parental strain LMOf2365, indicating that LMOf2365_0442 encoding for a fructose specific PTS permease IIA may be required for virulence in L. monocytogenes strain F2365. In addition, the gene expression levels of 15 virulence and stress-related genes were analyzed in the stationary phase cells of the deletion mutants using RT-PCR assays. Virulence-related gene expression levels were elevated in the deletion mutants \u0394LMOf2365_0442-0444 compared to the wild type parental strain LMOf2365, indicating the down-regulation of virulence genes by this PTS permease in L. monocytogenes. Finally, stress-related gene clpC expression levels were also increased in all of the deletion mutants, suggesting the involvement of this PTS permease in stress response. Furthermore, these deletion mutants displayed the same pressure tolerance and the same capacity for biofilm formation compared to the wild-type parental strain LMOf2365. In summary, our findings suggest that the LMOf2365_0442 gene can be used as a potential target to develop inhibitors for new therapeutic and pathogen control strategies for public health. Listeria monocytogenes is a Gram-positive intracellular human pathogen that can cause listeriosis with a high mortality rate (20\u201330% in immuno-compromised groups). It is widely distributed in soil and food environments. L. monocytogenes is also a foodborne pathogen that is often associated with a variety of raw and processed food products, including milk, meat, and vegetables. It can persist in food processing environments for years since it can form biofilms and survive under harsh conditions such as low pH and low temperature  mediate invasion into mammalian cells , LMOf2365_0443 , and LMOf2365_0444  were highly induced under high hydrostatic pressure treatment  serves as a sugar transport system in bacteria. There are approximately 30 copies of different PTS systems present in the genome of ytogenes . A typicytogenes . PTSs noytogenes . Some PTytogenes . LMOf236reatment  and inhireatment  as testereatment . Since aytogenes , we hypoL. monocytogenes deletion mutants (\u0394LMOf2365_0442-0444) were tested for their virulence potential using invasion and plaque forming assays. The virulence and stress-related gene expression levels of deletion mutants were also examined in stationary-phased cells using RT-PCR assays. Finally, the pressure tolerance and the biofilm formation ability of these deletion mutants were also determined.In this study, the three in-frame Listeria monocytogenes strain F2365 (LMOf2365) isolated from Mexican-style soft cheese was implicated in an outbreak of listeriosis in California in 1985 (L. monocytogenes F2365 (serotype 4b), L. innocua (ATCC\u00ae 51742TM), and three isogenic deletion mutants \u0394LMOf2365_0442-0444 of the parent strain L. monocytogenes F2365  in Dulbecco\u2019s Modified Eagle\u2019s Medium (DMEM) with glucose (4.5 g/L)  supplemented with 10% (v/v) fetal bovine serum (Invitrogen) and 1 mM sodium pyruvate (Invitrogen). Antibiotics (100 IU/ml penicillin and 100 \u03bcg/ml streptomycin) were routinely added to the culture medium except for the medium used 24 h prior to the invasion and plaque forming assays. Cells were maintained in a humidified incubator at 37\u00b0C under 5% (v/v) CO2.The human adenocarcinoma cell line HT-29 purchased from ATCC  was grown in 75-cmListeria strains according to L. monocytogenes  and L. innocua grown to log-phase at 37\u00b0C were used for invasion assays. HT-29 cell monolayers incubated in DMEM medium without antibiotics for 24 h were infected for 1 h at 37\u00b0C with 107 bacterial cells in 300 \u03bcl BHI liquid medium [Multiplicity of Infection (MOI) = 60]. The cell monolayers were washed with DMEM and incubated in DMEM containing gentamicin (100 \u03bcg/ml) for 1.5 h at 37\u00b0C. The cell monolayers were gently washed three times with phosphate buffered saline (pH 7.4) and then disrupted with 1 ml cold sterile water (4\u00b0C). Viable intracellular bacteria were counted after plating serial dilutions on BHI agar plates. The results were expressed as log numbers of CFU recovered relative to the number of bacteria (107) deposited per well. Each experiment was conducted in duplicate and repeated three times for each strain.Invasion assays were performed to assess the virulence of L. monocytogenes  grown to log-phase at 37\u00b0C were used to infect HT-29 cell monolayers with a dilution series of 102 to 107 cells per well, and they were incubated for 2 h at 37\u00b0C. L. innocua was used as a negative control. After removal of the bacterial suspensions, cell monolayers were washed with DMEM and incubated in DMEM containing 100 \u03bcg/ml of gentamicin for 1.5 h. Each well was covered with 400 \u03bcl DMEM with 10 \u03bcg/ml gentamicin containing 0.5% agarose. After solidification, the same liquid medium (400 \u03bcl) was added to the top of the agar to prevent starvation. Tissue culture plates were incubated overnight at 37\u00b0C under 5% (v/v) CO2. The cells were stained with 0.01% neutral red solution in DMEM medium with 0.5% agarose and were incubated at 37\u00b0C for overnight. Enumeration of formed plaques was performed using an inverted microscope. The results were expressed as log numbers of plaques per 107 bacteria deposited per well. Experiments were carried out in duplicate and repeated three times for each strain.Plaque forming assays were performed using HT-29 cells according to LMOf2365_0442,\u0394LMOf2365_0443, \u0394LMOf2365_0444) strains together with parental strain LMOf2365 were grown to stationary phase at 37\u00b0C. Total RNA was isolated from stationary phase  for bacterial cells of the deletion mutant strains, as well as the wild-type L. monocytogenes F2365 parent strain (Table 1) as previously described (Table 1) were designed using Primer3 (v.0.4.0) software1 based on the gene sequences of L. monocytogenes strain F2365 (GenBank accession#AE017262). The specificity of the primer sequences was further determined using the NCBI BLASTN program against the non-redundant (nr) database, and analyses revealed that the primer sequences showed 100% homology only to L. monocytogenes strain F2365 (GenBank accession#AE017262). Primers were also designed to the spoG housekeeping gene used as an internal control (Table 1). cDNA synthesis and real-time PCR analysis were performed as described previously  at 30\u00b0C. Prior to the high pressure treatments, 2 ml of bacterial cultures were taken for plate counts. Twenty milliliters of bacterial cultures were vacuum-sealed in two plastic bags and subject to high pressure treatments  at 4\u00b0C for 3 min. HPP was performed in a laboratory scale pressure unit , comprised of a double-jacketed thick-wall stainless steel cylinder (approximate volume of 0.3 L) having an internal stainless steel sample holder of 25.4 mm \u00d7 254 mm (diameter \u00d7 length). The thick-wall cylinder was maintained at a set-point temperature in which heat transfer fluid continuously circulated from a refrigerated liquid chiller . The refrigerated chiller was set at 4\u00b0C, which indirectly cooled the pressure transmitting medium . The pressure come-up rate was 100 MPa per 15 s (or 6.67 MPa/s), and the release rate was 100 MPa per 9 s (or 11.11 MPa/s) (The deletion mutant \u03941 MPa/s) . This teL. monocytogenes F2365, \u0394LMOf2365_0442-0444, and L. innocua were inoculated into 5 ml of BHI broth and incubated at 37\u00b0C overnight with agitation at 200 rpm. The bacterial overnight cultures were diluted 100-fold in Modified Welshimer\u2019s Broth (MWB)  with glucose as the sole carbon source. Two hundred microliters of diluted bacterial cultures were added to a 96-well PVC microtiter plate previously rinsed with 70% ethanol. For each strain, 200 \u03bcL of the freshly diluted culture were placed in eight different wells. Two hundred microliters of MWB (eight wells) was used as a negative control. The 96-well PVC microtiter plate was incubated at 30\u00b0C in a humidified container for 48 h. After removal of the medium, the plate was washed five times with distilled water and air dried for 45 min. The plate was stained with 200 \u03bcL of 0.1% crystal violet for 45 min and washed five times with distilled water. After 30 min of destaining with 200 \u03bcL of 95% ethanol for 30 min, the absorbance at OD595nm was measured using a Tecan Safire 2 microplate reader . The absorbance readings at OD595nm were normalized by subtracting the medium only OD595nm numbers. Any absorbance at OD595nm above 0 indicated some biofilm formation. Three independent experiments were performed.Biofilm assays were performed as described  with thet-test of the Statistical Analysis Software  for paired comparison with P < 0.05 considered significant.Data collected from the study were analyzed using the Student\u2019s LMOf2365_0442-0444 was involved in host infection, two in vitro assays (cell invasion and plaque forming assays) using HT-29 cell monolayers were used to test the virulence potential for each deletion mutant. As shown in Figure 1, L. monocytogenes F2365 (LMOf2365), which was used as a positive control, had high invasion efficiency (0.36 log10 cfu/well). The deletion mutant strain \u0394LMOf2365_0442 showed deficiency in invasion (14%) whereas \u0394LMOf2365_0443 and\u0394LMOf2365_0444 had higher invasion efficiencies  compared to the wild-type strain LMOf2365 (100%). Non-pathogenic strain L. innocua that served as a negative control had no invasion. The second in vitro assay for virulence was based on the ability of Listeria strains to form plaques on HT-29 cell monolayers. As shown in Figure 2, L. monocytogenes F2365 formed a higher number of plaques (approximately 3.9 log10 pfu/well) compared to non-pathogenic strain L. innocua, which did not form any plaques. \u0394LMOf2365_0442 formed a lower number of plaques (2.8 log10 pfu/well) whereas \u0394LMOf2365_0443 and \u0394LMOf2365_0444 had similar plaque forming abilities  as the wild type LMOf2365 (3.9 log10 pfu/well). Taken together, results from invasion and plaque forming assays demonstrated that deletion mutant \u0394LMOf2365_0442 showed a deficiency in both invasion and intracellular cell-to-cell spread in HT-29 cell monolayers, suggesting that LMOf2365_0442 is required for virulence in L. monocytogenes.To understand if the PTS EII complex LMOf2365 strain, the majority of the virulence genes were up-regulated in the deletion mutants (Table 2), indicating that the PTS permease (LMOf2365_0442-0444) negatively regulated virulence gene expression. Of the virulence genes, the expression level of pfrA, the major virulence regulator in L. monocytogenes, was moderately high (3.4 to 4.6-folds) compared to the wild-type parental strain LMOf2365. Genes  regulated by pfrA were also elevated in \u0394LMOf2365_0442 and \u0394LMOf2365_0444. Interestingly, the gene expression levels of hly, lap, actA, and plcB had little change in \u0394LMOf2365_0443. Our previous studies indicated that the deletion mutants were more resistant to multiple stress conditions . As shown in Table 2, the expression levels of clpC were elevated in all of the three deletion mutants. The expression levels of clpE and sigB were moderately elevated (3.5- and 4.8-folds) in \u0394LMOf2365_0444 and\u0394LMOf2365_0442, respectively. The increased levels of stress-related gene expression confirmed our previous observation that these deletion mutants may contribute to general stress response . As shown in Figure 3, the wild-type L. monocytogenes F2365 showed \u223c3 and 4 log reduction under 400 and 450 MPa, respectively. The deletion mutants showed similar log reductions compared to the wild-type strain.Since pressure , we woulStreptococcus gordonii was involved in biofilm formation (LMOf2365_0442-0444) is involved in biofilm formation, the deletion mutants (\u0394LMOf2365_0442-0444) were subject to a biofilm assay. As shown in Figure 4, the wild-type parental LMOf2365 strain formed biofilm (OD595nm \u223c0.4) under experimental condition, whereas L. innocua used as a negative control hardly formed any biofilm (OD595nm < 0.1). The deletion mutant \u0394LMOf2365_0443 had a similar ability for biofilm formation compared to the wild-type LMOf2365 strain. Although \u0394LMOf2365_0442 and\u0394LMOf2365_0444 had a slightly increased ability for biofilm formation, the differences were not statistically significant.A fructose PTS required for virulence in ormation . To detein vitro assays were used to test the virulence of L. monocytogenes deletion mutants. \u0394LMOf2365-0442 was defective in both assays, indicating that this gene is required for virulence. Although we did not test the virulence using the in vivo mouse model, previous studies showed that the results from plaque forming and invasion assays in L. monocytogenes correlated well with bacterial virulence in vivo  might be used to repress virulence gene expression in L. monocytogenes, and deletion of PTS could cause inefficient fructose intake, therefore, deactivating CCR. As a result of CCR deactivation, the virulence gene expression is de-repressed.In our study, fifteen genes related to virulence and stress previously used to study acid and salt stress  were chon mutant . Since gn mutant . Glucoseytogenes . A mannod in CCR . The actd in CCR . Fructosand plcA . It is lL. monocytogenes . The biofilm forming abilities of these deletion mutants were also tested, our results showed that deletion of LMOf2365_0442, 0443, and 0444 genes did not alter biofilm formation in L. monocytogenes . Although our data suggest that LMOf2365_0442, 0443 encoding for the EIIABC components of PTS are not involved in biofilm formation in L. monocytogenes, the EIIAGlc component of PTS has been shown to play a role in biofilm formation in Vibrio cholera  had reduced virulence, inhibitors to the PTS system, fructose-specific, IIA component would attenuate the virulence in L. monocytogenes. In addition, PTSs are highly conserved among the prokaryotic bacteria; therefore it is easier to develop inhibitors for PTSs. In fact, inhibitors were developed through in silico and library screening approaches (The fact that PTSs are uniquely present only in prokaryotic bacteria but not in eukaryotes may allow PTSs to be a useful drug target. Targeting bacterial virulence is an alternative approach to kill pathogens in the host. We have shown that deletion mutant of proaches .YL designed the experiments and wrote the manuscript. BY, C-AH, YS, SS, PK, and LH did the experiments. All of the authors have made a substantial, direct, and intellectual contribution to the work and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest."}
+{"text": "Echiurida is one of the most intriguing major subgroups of annelida because, unlike most other annelids, echiurids lack metameric body segmentation as adults. For this reason, transcriptome analyses from various developmental stages of echiurid species can be of substantial value for understanding precise expression levels and the complex regulatory networks during early and larval development.Urechis unicinctus and were de novo assembled into contigs spanning 63,928,225 bp with an N50 length of 2700 bp. The resulting comprehensive transcriptome database of the early developmental stages of U. unicinctus consists of 20,305 representative functional protein-coding transcripts. Approximately 66% of unigenes were assigned to superphylum-level taxa, including Lophotrochozoa (40%). The completeness of the transcriptome assembly was assessed using benchmarking universal single-copy orthologs; 75.7% of the single-copy orthologs were presented in our transcriptome database. We observed 3 distinct patterns of global transcriptome profiles from 14 developmental stages and identified 12,705 genes that showed dynamic regulation patterns during the differentiation and maturation of U. unicinctus cells.A total of 914 million raw RNA-Seq reads were produced from 14 developmental stages of U. unicinctus and provide a general overview of the dynamics of global gene expression changes during its early developmental stages. The analysis of time-course gene expression data is a first step toward understanding the complex developmental gene regulatory networks in U. unicinctus and will furnish a valuable resource for analyzing the functions of gene repertoires in various developmental phases.We present the first large-scale developmental transcriptome dataset of  Aplysia californica and Platynereis dumerilii) have highlighted insights into molecular mechanisms underlying early development and metamorphosis [Within the major annelid groups, Echiurida  is represented by a morphologically and ontogenetically unique assemblage that includes approximately 165 species, most of which lack segmentation as adults. However, they possess annelid-like morphological and developmental features, including the organization of the larval nervous system . They weorphosis , 9.Urechis unicinctus is an echiuran species that inhabits burrows in soft sediments in intertidal areas , a lophotrochozoan phylum that is represented by a diverse group of segmented worms [U. unicinctus. The goal of this study is to enhance our understanding of gene expression during embryonic development. Here, we report the transcriptome profiles (generated with the Illumina HiSeq platform) of developing embryos of U. unicinctus. Transcriptome sequencing data assist in the discovery of the roles of genes involved in various embryological and larval development processes. As the first large-scale transcriptomic dataset for U. unicinctus, this resource will help in the validation of development-specific gene features predicted by the genome.eas Fig.\u00a0. The Ureed worms , 7. HoweU. unicinctus were collected from intertidal mud flats on the southern coast of South Korea. We extracted eggs and sperms from 1 adult female and 1 male. To obtain U. unicinctus embryos, artificial fertilization was performed by mixing the appropriate ratio of sperms and eggs.Adults of Isochrysis galbana. Reared embryo samples were collected at each of the following stages: 0 hour (unfertilized egg), 0.5 hours post-fertilization (fertilized egg), polar body cell, 2 cell, 4 cell, 8 cell, 16 cell, 32 cell, blastula, emerged cilia, early trochophore (day 1), middle trochophore (day 2), late trochophore (day 5), and segmentation stage (day 30\u201345). Diagnostic features for each of the 3 trochophore stages are as follows. The early trochophore is a nonfeeding stage. In the middle trochophore, the gastrointestinal valve opens and the anus appears. In the late trochophore, the longer cilia of the apical tufts are replaced by shorter cilia that cover a greater area, and the prototroch cilia are longer. These developmental stages follow Newby's classification [Embryos were reared in artificial seawater  in a plastic case at room temperature (18\u00b0C\u201320\u00b0C). The late trochophore, a typical larval stage in which the intestinal tract is formed, was fed with a microalgae called fication .Total RNA was isolated from the embryos of the above samples using TRIZOL reagent  following the manufacturer's instructions. The purity and integrity of the total RNA isolated from each embryo sample were examined using a Nanodrop 2000C spectrophotometer  and Bioanalyzer 2100 . Adult images were taken on a Canon EOS 550D, and embryo bright-field images were taken on a Leica DM6 B microscope using differential interference contrast (DIC) optics.Total RNA concentration was calculated using Quant-IT RiboGreen . To assess the integrity of the total RNA, samples were run on TapeStation RNA screentape . Only high-quality RNA preparations, with a RNA Integrity Number greater than 7.0, were used for RNA library construction. A library was independently prepared with 1 \u03bcg of total RNA for each sample using an Illumina TruSeq Stranded Total RNA Sample Prep Kit . The rRNA in total RNA was depleted using a Ribo-Zero kit. After the rRNA was depleted, the remaining RNA was purified, fragmented, and primed for cDNA synthesis. The cleaved RNA fragments were copied into first-strand cDNA using reverse transcriptase and random hexamers. This was followed by second-strand cDNA synthesis using DNA Polymerase I, RNase H, and dUTP. These cDNA fragments then underwent an end repair process, the addition of a single \u201cA\u201d base, and ligation of the adapters. The products were then purified and enriched with polymerase chain reaction (PCR) to create the final cDNA library. The libraries were quantified using quantitative PCR according to the qPCR Quantification Protocol Guide (KAPA Library Quantification kits for Illumina Sequencing platforms) and qualified using the TapeStation D1000 ScreenTape . The resulting samples were sequenced on the Illumina HiSeq 2000 system with a paired-end read with 101 cycles or the Illumina HiSeq 4000 system with a paired-end read with 151 cycles (Table\u00a0RRID:SCR_011848) [After completion of the sequencing run, to obtain high-quality clean reads from the raw data , we performed quality-based trimming and filtering using Trimmomatic, version 0.33  [http://transdecoder.sourceforge.net). To maximize sensitivity for capturing ORFs, all transcripts were aligned against the Uniprot/Swiss-Prot database (http://www.uniprot.org) via BLASTP search with an E-value cutoff of 10\u22125. Next, ORF lengths <100 amino acids were discarded to avoid maintaining transcripts with poor evidence for protein-coding regions. Finally, redundant transcripts with more than 99% sequence identity were removed using CD-HIT (version 4.6.5) [Before _013048)  with defU. unicinctus transcriptome sequences using Bowtie, version 2.2.6  [RRID:SCR_013027) [To quantify expression levels, the reads for each library were mapped independently to the reference _005476) ; express_013027) . The uniE-value cutoff of 10\u221210 and the best BLAST hit. About 66%  of the CDS were assigned to superphylum-level taxa, including Lophotrochozoa (40%), Deuterostomia (8%), and Panarthropoda (2%)  [To annotate coding sequences (CDSs), the resulting 60,472 CDSs were compared against the NCBI nonredundant protein (NR) database (downloaded on 11 April 2017) using BLASTP with an 2%) Fig.\u00a0, which w_015008) . A totalU. unicinctus developmental stages into 3 phases. The oocyte; polar body; fertilized; 2-, 4-, 8-, 16-, 32-cell embryo; and blastula stages make up phase I. The emerged cilia and early-, middle-, and late-trochophore stages make up phase II. The segmentation stage makes up phase III. These 3 distinct phases of global transcriptome profiles covering 14 developmental stages were supported by principal component analysis, which was performed using the \u201cprcomp\u201d function in the \u201cstats\u201d package in R (version 3.2.4)  Fig.\u00a0. These rP value \u2264 0.1%) in at least one comparison was defined as a developmentally regulated gene. We identified 12,705 genes that showed dynamic regulation patterns during the differentiation and maturation of U. unicinctus cells , the global landscape of its developmental transcriptome is not yet complete due to the lack of biological replicates and reference genome sequences.Although this study presents the first large-scale developmental transcriptome dataset for a developmentally interesting animal group, U. unicinctus and provide a general overview of the dynamics of global gene expression changes at different developmental stages. These data will fill an important gap in annelid-wide comparisons of gene expression patterns and will lead to a better understanding of gene repertoires involved in different developmental stages and of complex developmental gene regulatory networks.In summary, we present the first large-scale, developmental, stage-specific transcriptome dataset for GigaScience repository, GigaDB [All raw sequencing data used for assembly have been deposited in the NCBI database under the accession numbers SRX2999418\u2013SRX2999431, associated with BioProject PRJNA394029. Additional data further supporting the results of this article, including the transcriptome assembly, annotations, and BUSCO results, can be found in the BUSCO: benchmarking universal single-copy orthologs; CDS: coding sequence; ORF: open reading frame.All authors report no competing interests.C.P. and S.J.C. designed the study; J.K.P. contributed to the project coordination; Y.H.H., K.B.R., and S.J.C. performed the experiments; S.G.L., J.O., and C.P. analyzed the data and evaluated the conclusions; C.P., S.J.C., J.K.P., S.G.L., and E.M.A.K. wrote the paper; all authors read and approved the final manuscript.GIGA-D-17-00202_Original_Submission.pdfClick here for additional data file.GIGA-D-17-00202_Revision_1.pdfClick here for additional data file.GIGA-D-17-00202_Revision_2.pdfClick here for additional data file.Response_to_Reviewer_Comments_Original_Submission.pdfClick here for additional data file.Response_to_Reviewer_Comments_Revision_1.pdfClick here for additional data file.Reviewer_1_Report_ -- Gaspar Jekely06 Sep 2017 ReviewedClick here for additional data file.Reviewer_2_Report_ -- Nathan Kenny11 Sep 2017 ReviewedClick here for additional data file.Reviewer_2_Report_(Revision_1) -- Nathan Kenny27 Nov 2017 ReviewedClick here for additional data file.Reviewer_3_Report_ -- Torsten Struck20 Sep 2017 ReviewedClick here for additional data file.Reviewer_3_Report_(Revision_1) -- Torsten Struck04 Dec 2017 ReviewedClick here for additional data file."}
+{"text": "Cells have evoked mechanisms, globally named the DNA damage response (DDR), to detect, signals and repair DNA lesions, which are tightly coordinated with apoptotic or cell cycle arrest responses . DefectsAtm\u2212/\u2212) mice develop spontaneous thymic lymphomas. Likewise, ATM germline mutations predispose to cancer in humans, while somatic ATM mutations are the most common abnormalities found in chronic lymphocytic leukemia patients and are also present in about 5% of solid tumours [Atm\u2212/\u2212 mice [Consistent with ATM playing a key role in DDR, ATM-null ( tumours . A major tumours . In of t tumours , while p\u2212/\u2212 mice .Atm\u2212/\u2212 mice restores the DNA damage-signaling pathway in thymocytes, leading to H2AX phosphorylation, G2/M arrest, and increased apoptosis of thymocytes. The final result is a reduction of DNA instability that probably contributes to the delay of thymic lymphoma in the double-knockout mice [Using mouse models, Granados-Ja\u00e9n et al.  have nowout mice . Questioout mice .In the meantime, the discoveries of Granados-Ja\u00e9n et al.  also off"}
+{"text": "Riemerella anatipestifer is a Gram-negative, non-motile, non-spore-forming, rod-shaped bacterium, which causes fibrinous pericarditis, perihepatitis, and meningitis in infected ducks. We previously constructed a random transposon mutant library using Riemerella anatipestifer strain CH3, in present study, we described that Riemerella anatipestifer M949_0459 gene is responsible for the bacterial resistance to tigecycline. Using the minimum inhibitory concentration assay, a mutant strain showed significantly increased (about six-fold) tigecycline susceptibility. Subsequently, the knocked-down gene was identified as M949_0459, a putative flavin adenine dinucleotide-dependent oxidoreductase. To confirm the resistance function, M949_0459 gene was overexpressed in Escherichia coli strain BL21, and the minimum inhibitory concentration analysis showed that the gene product conferred resistance to tigecycline. Additionally, expression of the M949_0459 gene under treatment with tigecycline was measured with quantitative real-time PCR. Results showed that the mRNA expression of M949_0459 gene was elevated under tigecycline treatment with dose range of 1-10 mg/L, and peaked at 4 mg/L. Moreover, two kinds of efflux pump inhibitors, carbonyl cyanide m-chlorophenyl hydrazone and phenylalanine arginyl \u03b2-naphthylamide were tested, which showed no function on tigecycline resistance in the strain CH3. Our results may provide insights into molecular mechanisms for chemotherapy in combating Riemerella anatipestifer infections.Based on its important role in last-line therapeutics against multidrug-resistant bacteria, tigecycline has been increasingly important in treating infections. However, mounting reports on tigecycline-resistant bacterial strains isolated from different sources are of concern, and molecular mechanisms regarding tigecycline resistance are poorly understood.  In brief, 20 \u00b5L of the alamarBlue reagent was directly added to 200 \u00b5L of the bacterial culture medium and blank medium (negative control); then, these were incubated for 1 h at 37\u00b0C while protected from direct light. Next, the absorbance of alamarBlue was monitored at 570 nm, using 600 nm as a reference wavelength . The proliferation rate (r) was calculated according to the following formula: [117, 216\u00d7AM949_0459 gene was amplified from the R. anatipestifer strain CH3 using the primers listed in Table E. coli strain BL21 was subsequently used for transformation. Using Luria-Bertani agar  containing kanamycin (50 mg/L), the transformants were selected and sequenced. The confirmed transformants were subjected to tigecycline susceptibility testing. E. coli BL21 with Pet-28a(+) vector without the DNA insertion was used as a control.The DNA fragment carrying the An efflux pump inhibition assay was performed as described previously : broth mt test. Mean values are shown in the figures. Statistical significance was established at p < 0.05.Statistical analysis was performed with Student\u2019s"}
+{"text": "A gene expression atlas of pigeonpea revealed spatio-temporal gene expression, co-expressed gene clusters and an important gene network critical for normal pollen and seed development. Cajanus cajan) is an important grain legume of the semi-arid tropics, mainly used for its protein rich seeds. To link the genome sequence information with agronomic traits resulting from specific developmental processes, a Cajanus cajan gene expression atlas (CcGEA) was developed using the Asha genotype. Thirty tissues/organs representing developmental stages from germination to senescence were used to generate 590.84 million paired-end RNA-Seq data. The CcGEA revealed a compendium of 28 793 genes with differential, specific, spatio-temporal and constitutive expression during various stages of development in different tissues. As an example to demonstrate the application of the CcGEA, a network of 28 flower-related genes analysed for cis-regulatory elements and splicing variants has been identified. In addition, expression analysis of these candidate genes in male sterile and male fertile genotypes suggested their critical role in normal pollen development leading to seed formation. Gene network analysis also identified two regulatory genes, a pollen-specific SF3 and a sucrose\u2013proton symporter, that could have implications for improvement of agronomic traits such as seed production and yield. In conclusion, the CcGEA provides a valuable resource for pigeonpea to identify candidate genes involved in specific developmental processes and to understand the well-orchestrated growth and developmental process in this resilient crop.Pigeonpea ( The Asha pigeonpea genotype is a widely cultivated, high yielding, medium duration inbred line resistant to several important diseases (fusarium wilt and sterility mosaic disease), for which a number of genetic and genomic resources including a draft genome have been developed. In the present study, a gene expression atlas has been developed for pigeonpea that reports the identification and quantification of genes exhibiting spatio-temporal expression using 30 diverse tissues. Further, an example has been provided to elucidate the efficacy of this comprehensive dataset to identify a co-expressed gene network exclusive to floral tissues . The targeted tissues are highly specialized and their development is tightly controlled and coordinated for effective fertilization and production of viable seeds. The candidate genes identified were associated to pollen fertility and seed setting, which have specific implications for the key agronomic trait of yield for their possible deployment in GAB in pigeonpea.The advent of the next generation sequencing technology has made sequencing of many non-model crops feasible in recent years. Pigeonpea is one of the few crops for which the technology was adopted early on to develop a draft genome sequence using the \u2018Asha\u2019 genotype. The genome sequence of pigeonpea has provided useful insights into the protein coding regions and gene functions, and clues to biological processes. However, this information was mainly based on the homology and JXB online). Tissues from the germinal stages  and other flower/seed tissues  were carefully dissected on ice and immediately frozen in liquid nitrogen. Root tissues and nodules from all the stages were excised after brief washes in diethyl pyrocarbonate-treated water followed by flash freezing in liquid nitrogen. All other aerial tissues including leaves, stem, petioles, pods, shoot apical meristem, flowers and buds were excised from plants and directly frozen in liquid nitrogen. All the tissues were stored at \u221280 \u00b0C until total RNA isolation.Seeds of the \u2018Asha\u2019 genotype (ICPL 87119) were sown in three different sets under glasshouse conditions by maintaining 26 \u00b0C day/22 \u00b0C night temperature with a photoperiod of 13 h day/11 h night. These sets included seeds germinated in (i) Petri plates with filter paper, (ii) paper cups containing sterile sand, and (iii) pots containing sterile black soil and sand (1:1). All these three sets of experiments were set up in three biological replicates. Set (i) was used for harvesting tissues from the germinal stage, set (ii) for the seedling stage, and set (iii) for the vegetative, reproductive and senescence stages  as per the manufacturer\u2019s instructions. The qualitative and quantitative assessments of these total RNA samples were conducted using an Agilent 2100 Bioanalyzer . RNA samples with RNA integrity (RIN) value \u22658 were pooled in equimolar amounts from three biological replicates prior to library preparation and subsequent sequencing. The cDNA libraries were prepared using an Illumina TruSeq RNA Sample Preparation Kit  following the manufacturer\u2019s instructions. Pair-end sequencing was performed in two sets: a set of 20 samples (nos 1\u201320) was sequenced using an Illumina HiSeq 2000 at Genotypic Technology Pvt. Ltd, India and a second set of 10 samples (nos 21\u201330) was sequenced in-house using an Illumina HiSeq 2500 sequencing system. The raw sequencing data were subjected to quality check to ensure high quality reads for downstream analyses. Reads with Phred score <20, read length <50 bases, and consisting of any uncalled bases using NGSQC Box (http://www.icrisat.org/gt-bt/iipg/genomedata.zip) using a splice-aware alignment algorithm, TopHat (v 2.1.0)  and P-value \u22640.05 were identified using Cuffdiff (http://compbio.mit.edu/cummeRbund/index.htmland), was used to visualize the differential gene expressions between tissues as scatter plots, also called volcano plots. The identified DEGs were annotated using Blast2GO v 3.3  with logcis-acting regulatory elements in selected genes.The co-expressed gene modules were identified and a topological overlap matrix (TOM) plot was generated using Weighted Gene Co-expressed Network Analysis WGCNA; , 2008. TCt value was calculated for each of the genes with respect to the housekeeping genes and was converted to fold change (2Ct\u2212\u0394) value  was performed to validate selected genes in four genotypes  differing in their ability to develop fertile pollen. qPCR analysis was carried out using Applied Biosystems 7500 Real Time PCR System with SYBR Green chemistry . The actin gene was used as an endogenous control and reactions were performed with two technical replicates and two biological replicates. The relative expression of the genes in each of the four genotypes  was calculated using a modified Livak method. The \u0394t) value .cis-acting elements were identified by scanning 1500 bp upstream regions of the transcription start site of the selected genes using the PlantCARE database (The Cajanus cajan gene expression atlas (CcGEA), 30 samples were collected representing all the major tissues encompassing the plant\u2019s complete lifecycle (To generate the ifecycle . These 3ifecycle . Germina2-transformed (Supplementary Table S1).Using Illumina sequencing platform, a total of 590.84 million paired-end reads were generated from 30 samples . The lowTo evaluate the quality of the generated gene expression atlas, a multi-dimensional scaling (MDS) analysis was first performed using the global expression dataset across the 30 samples. A clear repartition of the dataset in four major groups was observed, which represented the origin of the tissues, with aerial, underground, floral and embryo groups . SimultaC.cajan_18478, C.cajan_31580, C.cajan_19893, C.cajan_16707), cell cycle machinery factors  and actin-related protein (C.cajan_31922). Based on the large diversity of analysed tissues in this study, it was difficult to identify genes displaying a CV lower than 10%. However, based on our comparative gene expression experiment, this catalogue of stably expressed genes could be refined to study specific tissues and/or stages. For instance, in the case of an experiment based on specific plant organs, 50 potential reference genes displaying a CV below 10% were identified for the study focused on underground tissues, 43 genes for the study of pod and seed tissues, and three genes for the study of aerial tissues (Supplementary Table S2).From the transcriptomic dataset, a total of 1044 stably expressed genes have been identified within all tissues . The catalogue of stably expressed genes represents a resource for identification of reference or housekeeping genes, which are necessary for comparative gene expression analysis to normalize transcript expressions due to developmental or environmental fluctuations. In the dataset, 62 stably expressed genes within all the tissues with a coefficient of variation (CV) below 20% were identified (Supplementary Table S2). These genes were mainly annotated as involved in basic cell functions such as RNA machinery  based on the  , EN133, EN67, DIVARICATA, HY5 homolog, SCREAM, MYB86, and RADIALIS (Supplementary Table S5). Cl-X .Similarly, Cl-IV , followed by Rep_Bud vs Immature_Pod (79 genes) and Sen_Stem vs Sen_Petiole (53 genes) and Veg_Root vs Seedling_Root (49 genes). Differentially expressed genes showed up to 10- to 14-fold expression differences in different pairwise comparisons, which include beta-conglycinin (C.cajan_28781) and late embryogenesis abundant protein  in mature seeds, sugar-binding proteins (C.cajan_34645) in mature pods, and glycerol-3-phosphate acyltransferase 6 (C.cajan_45202), acid beta-fructofuranosidase (C.cajan_01573), and cinnamoyl-CoA reductase 1 (C.cajan_29356) in petals. Further, GO annotations of 1076 DEGs identified more than 50% of genes mainly involved in metabolic process , binding, catalytic and cellular process (molecular functions), and constituting cell and cell parts (cellular components). Few genes were also found to be involved in other biological processes such as cellular processes, response to stimuli, and pigmentation . A unique set of 1076 DEGs showed significant differential expression, either induced or repressed depending on the tissue (Supplementary Table S7). For conciseness, tissue samples were represented as Stage_tissue name, \u2018Veg\u2019 for vegetative, \u2018Rep\u2019 for reproductive, \u2018Sen\u2019 for senescence, \u2018Mat\u2019 for mature and \u2018Imm\u2019 for immature tissues. Comparisons were named as the sample name followed by entation .vs Sen_Leaf), and similarly for root tissues (Veg_Root vs Sen_Root), in order to identify senescence-related genes. In the senescing leaf (Sen_Leaf), 29 genes were identified encoding plant U-box protein 18 (C.cajan_02471), small chloroplastic heat shock protein (C.cajan_03228), desiccation-responsive protein 29B (C.cajan_07296), universal stress protein Slr1101 (C.cajan_07683), calcium permeable stress-gated cation channel 1 (C.cajan_09319), galactinol synthase 2 (C.cajan_10278), transcription factor PIF3 (C.cajan_10677), MYB48 (C.cajan_13565), homeobox-leucine zipper protein ATHB-12 (C.cajan_10940), MUD21-2 protein (C.cajan_12676), endoglucanase (C.cajan_13234), and senescence-related gene 1 (C.cajan_14735). These genes showed negligible expression in the Veg_Leaf. Similarly, 39 genes were identified exclusively in Sen_Root, including cation transport regulator-like protein 2 (C.cajan_09112), DnaJ homolog subfamily B (C.cajan_10265), PRA1 family protein B4 (C.cajan_17674), glutathione S-transferase (C.cajan_19173), signal recognition particle receptor subunit \u03b1 homolog (C.cajan_22178), ERF096 (C.cajan_22554), remorin (C.cajan_26491), probable aquaporin NIP7-1 (C.cajan_28406), vignain (C.cajan_28469), squamosa promoter-binding protein 1 (C.cajan_31328), \u03b2-galactosidase 13 (C.cajan_32927), agamous-like MADS-box protein AGL11 (C.cajan_36667), probable LRR receptor-like serine/threonine-protein kinase (C.cajan_38944), signal peptidase complex subunit 3B (C.cajan_39695), and heat stress transcription factor B-2b (C.cajan_44684).The dynamics of gene expression during the course of development in pigeonpea were studied to show the potential of the CcGEA to answer biological questions related to temporally or spatially regulated genes. We focused on DEGs between leaf at the vegetative and senescence stages  and cell-wall modifying enzymes (C.cajan_42632), along with other proteins such as late embryogenesis abundant protein D-29 (C.cajan_03928), basic 7S globulin (C.cajan_10207), and oxygen-evolving enhancer protein 3-2 (C.cajan_37794). Other enzymes involved in photorespiratory carbon metabolism and wax biosynthesis have also been identified in aerial tissues, such as serine-glyoxylate aminotransferase (C.cajan_22602) (C.cajan_28231) (C.cajan_22417), sugar transport protein 13 (C.cajan_07532), subtilisin-like protease SDD (C.cajan_02637), probable 2-oxoglutarate-dependent dioxygenase AOP1.2 (C.cajan_00158), and cytochrome P450 78A5 (C.cajan_19316) were highly expressed in nodules, while cytokinin dehydrogenase 3 was over-expressed in the radicles. Subtilisin-like proteases and leghemoglobins have been suggested to have widespread function during early stages of nodule symbioses   and 3-keC.cajan_48299) from bud, RADIALIS (C.cajan_31277) from leaf (reproductive stage), and GATA transcription factor 27 (C.cajan_17218) from immature seeds (5 days after anthesis). Several other genes were also identified, such as those encoding BOBBLER 2, PHD finger protein ALFIN-LIKE 1, and plantacyanin in bud, superoxide dismutase and auxin-induced protein 6B in nodules (vegetative stage), FAR-RED IMPAIRED RESPONSE 1 protein in stamen, polygalacturonase in pistil, and sucrose synthase 2 in mature seeds (30 days after anthesis), apart from several retrovirus-related proteins. Defense-related proteins such as defensin-like protein 19 and defensin-like protein 244 were specifically expressed in root (reproductive stage) and immature pods (5 days after anthesis), respectively. A complete list of tissue-specific genes is provided in Supplementary Table S8.Apart from DEGs, 220 genes were identified with specific expression in exclusively one tissue . VariousP-values, the strongest expression association was measured in the brown module, especially in the floral tissues , blue (208 genes) and brown (197 genes). Here, modules are referred to the distinct groups formed by the clustering of genes, and each module has been designated by an arbitrary color to distinguish between them . Module C.cajan_35396), a pollen specific SF3 protein (C.cajan_07765) and an uncharacterized protein (C.cajan_28171). C.cajan_35396 gene encoding a putative H+ symporting sucrose transporter protein 2 has been suggested to be involved during pollen maturation in mediating sucrose uptake in pollen grains (C.cajan_35396 and C.cajan_28171) was connected to all the other 27 genes, which encoded serine threonine protein kinases , pectinesterase inhibitors , pectate lyase 3 , pollen-specific proteins , Olee1-like , L-ascorbate oxidase homolog , ATPase 8 (C.cajan_45656), \u03b2-galactosidase 13 (C.cajan_32927), polygalacturonase (C.cajan_04312), phosphatidylinositol transfer protein (C.cajan_35458), boron transporter 6 (C.cajan_04911), formin-like protein 5 (C.cajan_32517), aldose 1-epimerase (C.cajan_31220), and uncharacterized proteins . Among these genes, L-ascorbate oxidase, \u03b2-galactosidase, polygalacturonase homolog and sucrose transporter were reported to be pollen-specific genes  to identify three highly connected genes, referred to as \u2018hub\u2019 genes. WGCNA defines co-expression networks as weighted gene network, where the nodes correspond to gene expression profiles, and edges are determined by pair-wise correlations between gene expressions. Genes within the co-expression module that display high connectivity form the \u2018highly connected genes\u2019 referred to as \u2018hub genes\u2019 . The huben genes . All theen genes , which v4-based hybrid breeding system in pigeonpea  while in the sterile genotypes the expression was found negligible (0.5-fold).Expression of 25 genes (Supplementary Table S9) belonging to the floral gene network , salicylic acid (SA), gibberellin (GA) and abscisic acid (ABA) responsive elements. Genes that were validated using qPCR also showed the presence of light responsive, circadian control, MeJA, SA, auxin, ABA, and endosperm-responsive sequence elements (Supplementary Table S10).Sequence analyses of the promoter regions of these co-expressed genes identified a majority of C.cajan_11513, C.cajan_31667, C.cajan_35458, C.cajan_45656, C.cajan_35396, C.cajan_18757, C.cajan_27022 and C.cajan_24722. These AS events consisted of an alternative 3\u2032 acceptor site, an alternative 5\u2032 donor site, exon skipping, and alternative 3\u2032 and 5\u2032 splice sites. All the eight genes showed splicing events in floral tissues , while C.cajan_18757 also showed an alternative 5\u2032 donor site in Immature seed, Immature pod, Sen_Petiole, and Rep_SAM (Supplementary Table S11). The \u2018hub\u2019 gene encoding sucrose-proton symporter 2 (C.cajan_35396) and two other genes encoding a pollen-specific SF3 protein  events were studied in all the 28 genes belonging to a floral gene network across all the 30 samples. Overall, 18 AS events were identified among eight genes, namely d stamen , whereasp_Pistil . The AS p_Pistil , alternap_Pistil  and exonp_Pistil , have beC.cajan_20802), FIP-37 (C.cajan_00080), YTH domain-containing family protein 1 (C.cajan_17267), YTH domain-containing family protein 2 (C.cajan_43994), \u03b1-ketoglutarate-dependent dioxygenase alkB (C.cajan_19310), \u03b1-ketoglutarate-dependent dioxygenase alkB homolog 6 (C.cajan_06509), alkylated DNA repair protein alkB homolog 8 (C.cajan_08198) and \u03b1-ketoglutarate-dependent dioxygenase AlkB homolog (C.cajan_11002). All these genes displayed a similar pattern of expression within the 30 tissues and belonged to cluster VI (2 transformed FPKM \u22653), which suggested the involvement of post-transcriptional regulation in these developing tissues , FKBP12-interacting protein of 37 kDa (FIP37) and YT521-B homology (YTH)-domain containing protein in pigeonpea. These genes were previously reported to be involved in mRNA methylation, recognition and demethylation in Arabidopsis  has been developed, which catalogued more than 28 000 genes that were expressed in 30 diverse tissues of the plant and at five different developmental stages. This comprehensive dataset will enhance the present understanding of the genes involved in various regulatory and metabolic processes, which could directly impact important agronomic traits. With the recent advances in genomics research, GAB has accelerated precision and efficiency of breeding in many crops  was further used to identify functional modules based on the assumption that each module contains genes involved in similar biological function  was a pollen-specific SF3 gene, which is a developmentally regulated TF, well documented in Arabidopsis to play a role in expression of late pollen genes, pollen tube formation and fertilization events , important for maintaining boron homeostasis, is critical for pollen viability and ability to accumulate starch, as boron deficiency could lead to impaired pollen viability (C.cajan_32517) has also been identified and is known to be involved in pollen\u2013pistil interaction  identified by gene network analysis has been studied. The brown module revealed pigeonpea genes involved in late pollen maturation, pollen tube formation and fertilization. The \u2018hub\u2019 gene of the network , probable pectate lyase 3 (C.cajan_44741), serine/threonine-protein kinase (C.cajan_07067), sucrose-proton symporter 2 (C.cajan_35390) and an uncharacterized protein (C.cajan_27282) have been shown to have important roles in development of pollen. Further, the sequence analysis of the promoter regions of these genes has suggested their stimulus-dependent expression in response to light and phytohormones such as abscisic acid, auxin, salicylic acid, and methyl jasmonic acid. In addition, the preferential splicing events in seven of the genes exclusively in the floral tissues including bud, flower, stamen, pistil, sepal, and petal have suggested their critical role in normal pollen and seed development. Additionally, gene clusters represent interconnected and highly correlated genes that would be helpful in interpreting the biological role of those that are novel or uncharacterized. That is, clustering and visualization of the co-expressed gene network allows understanding of the basic function of genes that were annotated or unannotated genes forming a module in performing a specific function , universal stress protein-A, heat shock proteins, protein EARLY RESPONSIVE TO DEHYDRATION 15 (ERD15), and many stress-related proteins (SRP). This cluster could be analysed for identifying candidate genes that would be crucial for bolstering hardiness to the crop. Furthermore, this resource could also be utilized to look into the baseline expression of genes studied in other legumes/crops in different tissues of pigeonpea that could be traced at different developmental stages. Thus, this resource could be valuable for the scientific community not only working in pigeonpea but also in related legume crops.A gene expression atlas has been developed in different legumes such as The gene expression atlas (CcGEA) developed in pigeonpea complements the genome sequence of pigeonpea and other genomic resources in understanding gene functions and their biological role. The CcGEA represents a comprehensive dataset of genes expressed in 30 diverse tissues across five developmental stages from embryo to senescence. The dataset has been analysed using pairwise comparison, clustering and correlation network analysis. The efficacy of the CcGEA has been demonstrated by identifying a gene network of 28 genes putatively regulated by a pollen-specific SF3 and a sucrose\u2013proton symporter. Gene expression studies using two sets of male sterile and fertile genotypes revealed 20 genes crucial for pollen development. The role of these genes could also be ascertained in floral tissues with exclusive splicing variants identified in these tissues. This study also provide genes that would be excellent candidates for a reverse genetics approach to determine their roles in pollen fertility and seed formation. Likewise, this dataset could be further analysed to identify candidate genes for various agronomic traits such as abiotic stress tolerance, especially for drought and heat stress. The CcGEA would also be useful in looking at the basal expression of genes when investigating mutant genotypes or any candidate gene expression for a specific agronomic trait. This resource will be valuable for studying the genes expressed in specialized tissues or organ systems such as nodules, flowers and pods in pigeonpea or related legumes. With further refinement of the existing draft genome assembly or the development of a pan genome, the CcGEA could be improved further and in that scenario, it will provide more and comprehensive insights into gene expression.Supplementary DataClick here for additional data file."}
+{"text": "Significant differences in survival following i.m. inoculation with low doses as well as i.n. inoculation were observed. Also, striking variations in incubation periods following i.c. inoculation and i.m. inoculation with high doses were seen. Hereby, the clinical picture differed between general symptoms, spasms and aggressiveness depending on the inoculation route. Immunohistochemistry of mouse brains showed that the virus distribution in the brain depended on the inoculation route. In conclusion, different EBLV-1 isolates differ in pathogenicity indicating variation which is not reflected in studies of single isolates.European bat lyssavirus 1 is responsible for most bat rabies cases in Europe. Although EBLV-1 isolates display a high degree of sequence identity, different sublineages exist. In individual isolates various insertions and deletions have been identified, with unknown impact on viral replication and pathogenicity. In order to assess whether different genetic features of EBLV-1 isolates correlate with phenotypic changes, different EBLV-1 variants were compared for pathogenicity in the mouse model. Groups of three mice were infected intracranially (i.c.) with 10 European bat lyssavirus 1 (EBLV-1) is one of fourteen officially recognized lyssavirus species causing rabies, a zoonosis resulting inevitably in death once clinical signs appear. EBLV-1 is responsible for most bat rabies cases detected in Europe, and spill-over infections in humans highlight its zoonotic potential. In our study, we compared eight genetically diverse EBLV-1 isolates in the mouse model using various routes of inoculation. Although EBLV-1 isolates displayed very high sequence conservation, significant differences in pathogenicity, i.e. in incubation periods and mouse survival, were observed. Furthermore, depending on the inoculation route the clinical picture as well as the virus antigen distribution within the brain varied. Thus, transfer of results obtained with single isolates to the whole lyssavirus species can be misleading, and results indicating reduced pathogenicity obtained with single EBLV-1 isolates in previous studies have to be carefully interpreted. Chiroptera) are the reservoir leading to the assumption that bats are the true ancestral host of all lyssaviruses  and submitted to the European Nucleotide Archive under study number PRJEB20390  together with the RNeasy Mini Kit  and DNase (Qiagen) treatment as recommended by the supplier. The RNA was further concentrated using Agencourt RNAclean XP beads (Beckman Coulter) and used as input for the preparation of cDNA sequencing libraries as described elsewhere . SequencJEB20390 . For seq6.5 and 109 TCID50/ml with the highest titres at different time points observed for 35009_RABV_CVS for MOI 0.01 while 5006_EBLV-1b_ins had the lowest titres for MOI 3  .Groups of mice inoculated i.m. with high doses of the lyssavirus isolates started to show clinical sings between 5 and 10 dpi. Mean incubation periods varied between the groups from 6 to 13 dpi with significant differences between EBLV-1 isolates . 5006_EBLV-1b_ins had significant longer incubation periods compared to all other EBLV-1 isolates  with the exception of 5776_EBLV-1a_(ins) . For the latter isolate, a significant difference in the mean incubation period could be observed compared to 13027_EBLV-1a_Yuli  . After RABV infection 83% (35009_RABV_CVS) and 0% (5989_RABV_dog azerb) of mice survived, respectively . Only isolates 5989_RABV_dog_azerb and 5782_EBLV-1a_del were pathogenic following i.m. inoculation with a low dose, resulting in a significant difference in survival between the RABV isolates  as well as between 5782_EBLV-1a_del and the other EBLV-1 isolates . Compared to isolates 13454_EBLV-1a_ref and 976_EBLV-1a_dist, isolate 13027_EBLV-1a_Yuli displayed a significant lower survival . No mice survived following inoculation with isolate 20174_EBLV-1b which resulted in a significant difference in survival compared to isolates 5006_EBLV-1b_ins and 13424_EBLV-1c  as well as compared to isolate 13454_EBLV-1a_ref, 976_EBLV-1a_dist  and isolates 5776_EBLV-1a_ins and 5782_EBLV-1a_del . Survival following inoculation with the RABV isolates was similar in both groups  All mock infected mice did not show clinical sings and survived until the end of the observation period.All mice which were euthanized or died during the experimental stage were positive while all animals that were killed at the end of the observation period were negative using FAT. With IHC, the amount of antigen in the brain varied depending on the inoculation route, with a lower antigen content in the paramedian cross sections and the olfactory bulb following i.m. inoculation compared to i.c.  and i.n. inoculation  compared to low doses (19% for EBLV-1 and 33% for RABV) . FollowiSequence analysis of the EBLV-1 full genome sequences revealed nucleotide identities within the lineages above 98.8% for EBLV-1a and above 97.4% for both EBLV-1b and EBLV-1c. Also, the heterogeneity between the groups was below 5%, as visualized in the branching pattern of the phylogenetic tree . For isoPathogenicity studies are essential e.g. to characterize individual viruses and to understand virus-host interactions. The latter studies are preferentially performed in the respective reservoir host. Unfortunately, most lyssaviruses including EBLV-1 have their reservoir in bats, with evident challenges in performing studies in those bat species. Although initial studies were performed with EBLV-2 in Daubenton\u2019s bats  and withMost pathogenicity studies were performed using RABV , demonstWe observed significant differences in the pathogenicity between the EBLV-1 isolates, with isolate 5782_EBLV-1_del displaying a higher pathogenicity following i.m. inoculation with a low dose compared to all other EBLV-1 isolates. This is remarkable, considering that the nucleotide sequence is 99.6% identical with isolate 5776_EBLV-1a_ins which was not pathogenic after i.m. low dose application. Overall, there is a high nucleotide identity among the EBLV-1 isolates and the only distinctive feature of 5782_EBLV-1a_del on nucleotide level is the 35nt deletion in the pseudogene region as described before . On protWhile deletions or insertions in the pseudogene region of fixed RABV strains did not change their pathogenicity after intracranial inoculation , 53, expFollowing i.n. inoculation, interestingly, the majority of EBLV-1 isolates displayed a higher pathogenicity compared to i.m. low-dose inoculation, although the same viral dose was used. Within i.n. inoculated mice significant differences were observed, with survival rates of the isolates varying between 0% and 100% . HoweverPrevious studies investigating intranasal or aerosol infection used the fixed RABV strain CVS and EBLV-2 with different results regarding pathogenicity \u201358. But Aside from differences in pathogenicity, significantly longer incubation periods were observed following i.m. inoculation with two particular isolates when high doses were used . InteresThe incubation periods between the different inoculation routes and doses varied for the same isolates. In several cases the incubation periods following i.m. inoculation with high doses were even shorter than following i.c. inoculation. This is interesting since after i.m. inoculation the virus needs to travel from hind limb to the central nervous system in order to reach its main replication site. An explanation may be the dose of infection, whereby a thousand-fold higher dose was used for i.m. compared to i.c. inoculation. This is corroborated by the fact that incubation periods for 5989_RABV_dog_azerb and 5782_EBLV-1a_del following i.m. inoculation with low doses were significantly longer compared to i.c. inoculation  two step and b) one step replication kinetics of the isolates used in the study.(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 FigPercentage seroconversion for the different inoculation routes following inoculation a) with EBLV-1 isolates and b) with RABV isolates. Percentage of seroconverted mice for the individual isolates can be seen following i.m. inoculation with c) high doses and d) low doses.(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table13454* is identical to 13454_EBLV-1a_ref used in this study.(PDF)Click here for additional data file."}
+{"text": "Thus, circRNA_100338 functions as an endogenous sponge for miR-141-3p in HCC. In addition, we identified the crucial antagonistic roles of circRNA_100338 and miR-141-3p in the regulation of invasive potential in liver cancer cells. Overall, the differential expression of multiple circRNAs in HCC tissues and their clinical significance in hepatitis B-related HCC patients as revealed by our study suggests that circRNA_100338 is a potentially valuable biomarker for HCC diagnosis and target for HCC therapeutics.Circular RNAs (circRNAs) represent a class of endogenous noncoding RNAs that have recently been recognized as important regulators of gene expression and pathological networks. However, their transcriptional activities and functional mechanisms in cancer remain largely unknown. Here, we present results from a global circRNA expression and functional analysis of patients with hepatocellular carcinoma (HCC). Using a circRNA microarray, we identified 226 differentially expressed circRNAs, of which 189 were significantly upregulated and 37 were downregulated. High expression of circRNA_100338, one of the upregulated circRNAs in HCC, is closely correlated with a low cumulative survival rate and metastatic progression in HCC patients with hepatitis B. Furthermore, our  Evidence from computational analyses of expression data in multiple organisms suggests that circRNAs are created by RNA splicing events that occur at a characteristic \u201chead to tail\u201d splice junction, where an acceptor splice site at the 5\u2032 end of an exon and a donor site at the 3\u2032 end of a downstream exon are joined4. In humans, most (~85%) circRNAs are transcribed from the sense strand of known protein coding genes, spanning across exons 1\u201352. Furthermore, sequences of circRNAs are conserved to some degree, suggesting their potential functions in biological processes6.Circular RNAs (circRNAs) represent a class of naturally occurring endogenous noncoding RNAs that have recently been recognized as important regulators of gene expression networks. The widespread presence of circRNAs with highly regulated temporal-specific or tissue-specific expression patterns has been identified in a variety of animals10, little is known about circRNAs, and even less is understood. Recent studies from two independent groups showed that one human circRNA derived from the antisense strand of the human Cerebellar Degeneration-Related protein 1 (CDR1) locus contains more than 70 endogenous miR-7 target recognition sites, thereby serving as a miR-7 sponge to \u201csponge up\u201d or sequester the biological impacts of endogenous miR-7. This striking feature enables this circRNA, named CiRS-7 (Circular RNA Sponge for miR-7) or CDR1as (antisense), to function as a negative regulator of miRNA4. Consistently, perturbation of CiRS-7 levels in both cell culture and neuronal tissues leads to an inverse change in endogenous miR-7 and dramatic changes in transcriptome profiles or developmental processes4. Similarly, the testis-specific circRNA Sry functions as a miR-138 sponge4. These findings suggest that circRNAs play a crucial role in regulating gene expression and that alteration of circRNA expression may contribute to the pathogenesis of many diseases, including cancer.In contrast to other linear RNAs, such as mRNAs or microRNAs (miRNAs), whose functions have been intensively studied in the past decades12. Given that circRNAs are potential ceRNAs, understanding circRNA transcriptional activities in cancer would greatly facilitate the study of cancer pathogenesis and provide potential novel targets for cancer therapeutics.In recent years, the impacts of ceRNA (competing endogenous RNA) interplay on the course of cancer initiation and progression have gradually emerged, and ceRNAs have been documented in various types of cancer, including prostate, liver and breast cancers15. The development of HCC is a complex process that involves accumulation of gene regulation alteration at multiple levels, and molecules such as transcriptional factors, histone modifiers, microRNAs, lncRNAs and ceRNAs have been identified to play adominant role20. However, the exact roles of circRNAs in cancer and the underlying molecular mechanism of circRNA-mediated gene regulation during HCC development remain elusive.As one of the most malignant and common cancers worldwide, hepatocellular carcinoma (HCC) is the third leading cause of cancer mortality and has steadily spread from the eastern to western countriesThe development of a circRNA microarray has greatly facilitated the understanding of circRNA expression in diverse biological contexts. In this study, we present a large-scale circRNA expression analysis of human HCC tissues. We found that multiple circRNAs exhibit differential expression in HCC tissues, suggesting their crucial roles in cancer development. The clinical significance of circRNA_100338 was also studied and its potential as a biomarker for HCC diagnostics is proposed. Computational analyses followed by experimental verification revealed that hsa_circRNA_100338 directly interacts with miR-141-3p in the context of HCC, thus sponging miR-141-3p for downstream gene regulation in HCC. In addition, we demonstrate the crucial antagonistic roles of circRNA_100338 and miR-141-3p in regulation of metastatic potential in liver cancer cells. Our data indicate that circRNAs potentially mediate gene expression in HCC, and provide one of the first circRNA biomarkers for HCC clinical studies.P\u2009<\u20090.05). Overall, analyses from the microarray resulted in the identification of a total of 226 differentially expressed circRNAs in HCC tissues, of which 189 were significantly upregulated and 37 were downregulated  and performed circRNA microarrays to examine their expression profiles in each tissue. To increase the reliability of differential expression detected in the microarray between HCC and paired pericancerous tissues, circRNAs with low signal intensities or not expressed in all samples in the array were first filtered out (see Methods for details). Thus, among the remaining circRNAs, only the circRNAs that showed at least 2-fold expression change were considered . Therefore, we removed this circRNA from further investigation in this study. To further confirm that the remaining three circRNAs are subjected to specific regulation in HCC, we performed qRT-PCR to determine their expression levels in HCC and paired pericancerous tissue samples derived from an additional six HCC patients   and circRNA_100338-high group  and evaluated the patient cumulative survival rate in these two groups. Survival and metastasis data for two of the patients were missing. The overall survival rate was 61.5% within the remaining 78 cases. Strikingly, the cumulative survival rate (72.0%) of HCC patients in the circRNA_100338-low group was significantly higher than that (42.9%) of HCC patients in the circRNA_100338-high group (P\u2009<\u20090.02), suggesting that circRNA_100338 may serve as a marker for malignancy diagnosis in HCC. The survival of the circRNA_100338-low group (low group) was significantly longer than that of the circRNA_100338-high group (high group)  with increased metastatic potential  revealed progressively increased expression of circRNA_100338  Fig.\u00a0. Within up) Fig.\u00a0, but the338 Fig.\u00a0, consist4. Therefore, we performed in silico analyses to predict miRNAs targeted by these two circRNAs. For each of the circRNAs, many miRNAs have been predicted as potential targets  miR-141-3p has previously been implicated as a tumour or metastasis suppressor in various types of cancer cells26. These lines of evidence suggest that circRNA_100338 may interact with miR-141-3p to regulate the gene expression necessary for HCC carcinogenesis.Despite the broad expression of circRNAs in diverse cells and tissues, their cellular and biological functions remain largely elusive. Given the cases where circRNAs \u201csponge up\u201d miRNAs to promote expression of miRNA target genes, we speculated that these two upregulated circRNAs likely regulate gene expressions by interacting with endogenous miRNAsets Fig.\u00a0 as expecets Fig.\u00a0; (2) miRvia the predicted binding site in a human cellular context and provided direct evidence of sponging of miR-141-3p by circRNA_100338 in vivo.We next sought to test whether or not circRNA_100338 is capable of sponging miR-141-3p in a cellular context. To validate our hypothesis, we fused the linearized sequence of circRNA_100338 (with a wild-type (WT) or mutant miR-141-3p binding site) into the 3\u2032 UTR of the reporter gene Renilla luciferase and performed a Dual-Luciferase Reporter Assay in human embryonic kidney 293T (HEK293T) cells Fig.\u00a0. As expeP\u2009<\u20090.001)  Fig.\u00a0. Taken tin vitro invasion assays to test the metastatic potential of miR-141-3p-overexpressing MHCC97H cells. The principle of this assay is based on two medium-containing chambers separated by a porous membrane through which cells can transmigrate. Generally, cells seeded in medium in the upper chamber migrate vertically through the membrane pores into the lower compartment, in which medium containing an attractant or simply a higher serum level is present. The migratory and invasive capacities of tumour cells are determined by the number of cells that invade the membrane after 24\u2009h of incubation. Invasive cells were fixed and stained with cytological dyes for counting. As indicated in Fig.\u00a0Given that circRNA_100338 interacts with miR-141-3p and that circRNA_100338 is positively correlated with metastasis in HCC patients, we next determined whether miR-141-3p can counteract circRNA_100338 and inhibit cell metastatic progression in HCC. We first overexpressed miR-141-3p in the liver cancer cell line MHCC97H, and then performed 31. However, very little is known regarding their roles in cancer. In this study, we investigated the circRNA expression profile in HCC and paired pericancerous tissues and found that 189 of 226 differentially expressed circRNAs were significantly upregulated and 37 were downregulated in HCC. Specifically, we showed that circRNA_100338 is upregulated in HCC compared with paired pericancerous tissues, which significantly affects the cumulative survival rate and cancer metastasis in HCC patients. The follow-up period in this study was at least 5 years; thus, we think we can derive a firm conclusion even though the sample number is relatively low. In addition, our study indicated that circRNA_100338 sequesters miR-141-3p in the context of HCC tissue. Despite the small number of circRNA reports in HCC studies, to our knowledge, this study is one of the first few differential expression analyses of circRNAs reported for HCC. More importantly, we identified a novel circRNA biomarker for HCC clinical diagnosis and patient survival estimates.As a novel gene regulator, circRNAs are potentially involved in multiple biological and pathological processes38. Therefore, as a circular miR-7 inhibitor, the newly identified CiRS-7 potentially plays an important role as a putative oncogene in cancer. However, knowledge regarding a direct connection between circRNAs and cancer is still very limited. Currently, the correlation between individual circRNAs and cancer has been explored for very few cancer types, such as colorectal cancer, ovarian cancer, bladder cancer and gastric cancer41. Nonetheless, the downstream interacting miRNAs as well as their regulated protein coding genes in those cancers are still missing, although individual circRNAs, such as hsa_circ_002059 have been reported to be significantly downregulated in gastric cancer. Recently, two circRNA studies also indicated that hsa_circ_0005075 and hsa_circ_0001649 are differentially regulated in HCC tissues43. In our study, we further expanded the scope of exploration in HCC and performed a more comprehensive, large scale circRNA expression analysis. We identified at least two differentially expressed circRNAs (hsa_circRNA_104075 and hsa_circRNA_100338) in HCC. Importantly, our clinical evidence from 80 HCC patients further indicated that ectopic expression of circRNA_100338 was always accompanied by a decreased cumulative survival rate, elevated vascular invasion and lung metastasis in HCC patients, providing a potential circRNA biomarker for HCC diagnosis and patient survival rate estimation. Moreover, our study identified miR-141-3p as part of the underlying mechanism of hsa_circRNA_100338-mediated HCC carcinogenesis, presenting the first functional model of a circRNA during carcinogenesis in the liver. Given that the interaction between circRNA and miRNA may not be exclusive, these two upregulated HCC-associated circRNAs may target other cancer-associated miRNAs in HCC tissues. Alternatively, because each miRNA may have multiple mRNA targets, hsa_circRNA_100338 is also likely to target more oncogenes by interacting with miR-141-3p.Since the first report of circRNA functioning as a miRNA sponge, the potential of circRNAs in regulating cancer-related genes through fine-tuning miRNAs has recently been recognized. To date, it has been well established that miR-7 directly targets many oncogenic factors and is involved into multiple cancer-related signalling pathways, including EGFR, IRS-1/2, Raf1, Pak1, Ack1, PA28-gamma, IGF1R, PIK3CD and mTORSry, circRNAs have shown huge potential for interaction with endogenous miRNAs. In our study, we showed that hsa_circ_100338 is upregulated in HCC tissues and can target miR-141-3p; thus, hsa_circ_100338 may serve as an important gene regulator in HCC tissues. Currently, many miRNAs are associated with cancer-related signalling pathways; by contrast, studies of the circRNA-miRNA-mRNA network are still lacking. Using computational analyses followed by experimental verification, we provide the first identification of a miRNA that potentially directly interacts with circRNA_100338. Furthermore, in vitro invasion assays in MHCC97H cells, a metastatic liver cancer cell line, provided direct evidence of the involvement of circRNA_100338 and miR-141-3p in regulation of metastasis in liver cancers. Given than each miRNA targets multiple downstream genes, it would be very interesting to identify the downstream miR-141-3p target genes that are responsible for the regulation of cancer cell metastasis. In fact, our in silico analyses of miR-141-3p target genes have revealed that metastasis suppressor 1 (MTSS1) is very likely a potential target of miR-141-3p, as indicated by highly conserved miRNA recognition elements (MREs) at the 3\u2032 UTR of MTSS1. Even though MTSS1 is widely known as a metastasis suppressor gene that is involved in regulation of cell mobility and consequently cancer metastasis49, recent studies surprisingly indicated that MTSS1 also acts as an oncogene and a driver of metastasis in melanoma tumours and breast cancers51. This evidence indicates that MTSS1 may also serve as a metastasis driver in HCC patients and that circRNA_100338 regulates HCC metastasis though a potential circRNA_100338-miR141-3p-MTSS1 interaction pathway. Indeed, qRT-PCR performed in HCC and paired pericancerous tissues in this study also indicated that the expression of MTSS1 is significantly upregulated in HCC tissues compared with paired pericancerous tissues, an inverse expression pattern to that of miR-141-3p. Therefore, it is very likely that miR-141-3p can function as a novel tumour suppressor in HCC. This speculation is also specifically supported by serum miRNA analysis in hepatitis B virus-related HCC52.With the discovery of CiRS-7 and Using a circRNA microarray, we determined that out of a total of 226 differentially expressed circRNAs in HCC tissues, 189 were significantly upregulated and 37 were downregulated. Specifically, circRNA_100338 is upregulated in HCC compared with paired pericancerous tissues and highly correlated with the cumulative survival rate and cancer metastasis in HCC patients. In addition, our study identified miR-141-3p as a direct downstream target of circRNA_100338 in the context of HCC tissue, and functions antagonistically with circRNA_100338 to regulate cell metastasis in liver cancers. To our knowledge, this study is one of the first two global circRNA differential expression analyses in HCC. In addition, our study provided a novel circRNA biomarker for hepatitis B-related HCC clinical diagnosis and patient survival estimation.Four pairs of snap-frozen HCC tissue and matched para-carcinoma tissue were obtained from the Hospital Clinic for circRNA microarray analysis. Subsequently, a total of tenpaired samples, including the samples for microarray analysis, were used for circRNA validation using reverse transcriptase quantitative (RT-q) PCR. All the experimental subjects were consecutive patients and did not receive any other treatment prior to operation. All HCC cases were confirmed by experienced pathologists. Four T2 stage HCC samples were used for the circRNA microarray assay, and then six T1\u2013T4 stage HCC samples were applied for circRNA validation using qPCR. Clinical and pathological characteristics of patients were determined according to WHO/ISUP classification and UICC TNM classification (2010) and are presented in Table\u00a0Between January 2006 and December 2010, 80 HCC patients underwent open hepatectomy by the same surgical team in our centre. All specimens were collected in the operating room immediately (\u226415\u2009min) after tissue removal and were snap frozen in liquid nitrogen and stored at \u221280\u2009\u00b0C. For the 80 para-carcinoma controls, tissues adjacent to carcinoma, which were diagnosed as normal tissue using pathological methods, were taken from tissue \u22652\u2009cm away from the tumour in HCC patients. HCC and pericancerous tissues were differentiated via haematoxylin and eosin (H&E) staining.Total RNA from either HCC or paired paracancerous tissues from each patient was extracted using TRIzol and quantified using a NanoDrop ND-1000. An aliquot of the RNA from each sample was reserved for downstream qRT-PCR analysis. Sample labelling and array hybridization were performed according to the manufacturer\u2019s protocol (Arraystar Inc.). Briefly, circRNA was treated with Rnase R  to remove linear RNAs. Then, each sample was amplified and transcribed into fluorescent cRNA utilizing a random priming method . The labelled cRNAs were purified using an RNeasy Mini Kit (Qiagen). The concentration and specific activity of the labelled cRNAs (pmol Cy3/\u03bcg cRNA) were measured using the NanoDrop ND-1000. One \u03bcg of each labelled cRNA was fragmented by adding 5\u2009\u03bcl of 10\u00d7 Blocking Agent and 1\u2009\u03bcl of 25\u00d7 Fragmentation Buffer. Then, the mixture was heated 60\u2009\u00b0C for 30\u2009min, and finally, 25\u2009\u03bcl of 2\u00d7 Hybridization buffer was added to dilute the labelled cRNA. Subsequently, 50\u2009\u03bcl of hybridization solution was dispensed into the gasket slide and assembled onto the circRNA expression microarray slide. The slides were incubated for 17\u2009h at 65\u2009\u00b0C in an Agilent Hybridization Oven. The hybridized arrays were washed, fixed and scanned using an Axon GenePix 4000B microarray scanner .t-test P-values of less than 0.05 were identified as differentially expressed circRNAs. All experiments were performed and analysed in triplicate.Scanned images were imported into GenePix Pro 6.0 software (Axon) for grid alignment and raw data extraction. Quantile normalization of raw data and subsequent data processing were performed using the R software package. After quantile normalization of the raw data, low intensity filtering was performed, and the circRNAs associated with at least four of eight samples with an \u201cexpressed\u201d flag (greater than two times background standard deviation) were retained for further analyses. Then, by comparing two groups of profile differences, the \u201cFC\u201d between the groups for each circRNA was computed. Only the circRNAs that exhibited FCs greater than 2.0 and Student\u2019s 53. Specifically, expression values in HCC and paired pericancerous tissues from each patient were first assessed by qRT-PCR independently. A histogram or box and whisker plots were then generated based on the values from the independent measurements of all patients. Target cDNAs were amplified using the following probe set:Total RNA extracted from cells of each patient was reverse transcribed using random primers, and quantitative PCR assays of cDNA were performed using a CFX96 Real-time PCR system (Bio-Rad) to evaluate the abundance of target transcripts relative to the house-keeping genes U6 or GAPDHGAPDH_F: 5\u2032-GGGAAACTGTGGCGTGAT-3\u2032GAPDH_R: 5\u2032-GAGTGGGTGTCGCTGTTGA-3\u2032hsa_circRNA_100338_F: 5\u2032-AAAAGCAAGCAGTGCCCATA-3\u2032hsa_circRNA_100338_R: 5\u2032-GCTCGAATCAGGTCCACCA-3\u2032hsa_circRNA_102922_F: 5\u2032-GCCTTCACCCTCCTTATCTCTA-3\u2032hsa_circRNA_102922_R: 5\u2032-TGGCATTCCATATTCAGCGA-3\u2032hsa_circRNA_104075_F: 5\u2032-GAAGATGTCAAGCCCTTTAGC3\u2032hsa_circRNA_104075_R: 5\u2032GAGTTGCTTAGCTTTCATTTGTC-3\u2032hsa_circRNA_101139_F: 5\u2032-CATCCGCTACCTCATCTCGT-3\u2032hsa_circRNA_101139_R: 5\u2032-GTTGCTACCACCACTCCCATA-3\u2032hsa_circRNA_102049_F: 5\u2032-GAAGCATTTCATCAATAACCCTC-3\u2032hsa_circRNA_102049_R: 5\u2032 \u2013CAAAGCCACAGTCCATCACAG-3\u2032hsa_circRNA_102533_F: 5\u2032-GCTGCCAAAAGCATAACCAA-3\u2032hsa_circRNA_102533_R: 5\u2032-CCCCTTTTCTGCTAAATGAACTCT-3\u20324 cells/well) were added to the upper chamber (in 100\u2009\u03bcl of DMEM), and 600\u2009\u03bcl of conditioned medium was added to the lower chamber; chambers were separated by a porous membrane. The pore size of the membranes was determined by the size of the cells. After 24\u2009h of incubation, the cells in the lower chamber were fixed with methanol and stained with crystal violet solution. The results were expressed as the number of penetrated cells as assessed using a microscope at 200x magnification and analysing five random fields. Results are presented as the means\u2009\u00b1\u2009SD of three assays.The untreated control or treated MHCC97H cells  and their respective sequences are as follows:miR-141-3p inhibitor: 5\u2032-CCAUCUUUACCAGACAGUGUUA-3\u2032miR-141-3p mimics: 5\u2032-UAACACUGUCUGGUAAAGAUGG-3\u2032Cells were co-transfected with circRNA-100338 plasmids or their mutant fragments andmiR-141-3P mimic by using Lipofectamine 2000  according to the manufacturer\u2019s protocol. Firefly and Renilla luciferase activities were measured consecutively using a Dual-Luciferase Reporter Assay System  after transfection for 48\u2009h. Each assay was repeated in six independent experiments.The following is the sequence of circRNA_100338: GAACCACGUGAAUGUUGAGGGGGCGACACACAAGCAGGUGGUGGACCUGAUUCGAGCAGGCGAGAAGGAAUUGAUCUUGACAGUGUUAUCUGUACCUCCUCAUGAGGCAGAUAACCUAGAUCCCAGUGACGACUCGUUGGGACAAUCAUUUUAUGAUUACACAGAAAAGCAAGCAGUGCCCAUAUCGGUCCCCAGAUACAAACAUGUGGAGCAGAAUGGUGAGAAGUUUGUG. To transcribe the circRNA-100338 transcript, a circRNA-100338 overexpression vector was constructed. The specially designed front and back circular frames were synthesized and added to pCDH-CMV-MCS-EF1-copGFP for circulation of the transcripts. The front circular frame contains the endogenous flanking genomic sequence with an EcoRI restriction enzyme site, and the back circular frame contains part of the inverted upstream sequence with a BamHI site. The cDNA encoding circTCF25 in HeLa cells was amplified using the following primers:5\u2032-cgGAATTCTGAAATATGCTATCTTACAGGAACCACGTGAATGTTGAGG-3\u20325\u2032-cgGGATCCTCAAGAAAAAATATATTCACCACAAACTTCTCACCATTCTG-3\u2032As a result, the 232\u2009bp target fragment (in order) contains an EcoRI site, splice acceptor AG, the circRNA-100338 sequence, splice donor GT and a BamHI site. Then, the amplified fragment was cloned into the vector between the two frames. In addition, we also established a mock vector containing a nonsense stuffer between the two circular frames rather than the circRNA-0000130-encoding cDNA. The vector construction was verified by direct sequencing. The vectors were constructed with help from Guangzhou Geneseed Biotech Co, Guangzhou, China.Between January 2006 and December 2010, 80 HCC patients who underwent open hepatectomy by the same surgical team in our centre were recruited based on the diagnosis of HCC. These HCC patients were also infected with hepatitis B virus. The inclusion criteria for patients in this study were as follows: (a) patients with hepatitis B from 2006 to 2010; (b) no anticancer treatment prior to hepatectomy; (c) pathologically proven HCC based on WHO criteria; (d) availability of frozen resected HCC tissues and follow-up data. This study was approved by the Research Ethics Committee of Shanghai Jiaotong University affiliated Sixth People\u2019s Hospital, and informed consent was obtained from each patient. All methods were performed in accordance with the relevant guidelines and regulations. The determination of HCC patient grouping was derived from the average value of hsa_circRNA_100338/GAPDH from all HCC patients (0.005), and the cutoff of the circRNA_100338-high group was set as 0.015, which is 3 times more than average value. Patients with hepatectomy were followed up with every two months during the first postoperative year and at least every four months subsequently until December 2015 via monitoring abdominal ultrasonography, chest X-ray or computed tomography depending on the patient\u2019s condition. General data, metastatic characteristics, pathological characteristics and survival were compared between the two groups. The cumulative survival rate presented in Fig.\u00a0http://www.microrna.org/microrna/home.do)54, TargetScan 55 and MicroCosm . Only the genes that overlapped in the results of at least two algorithm predictions were considered.Target genes of hsa-miR-141-3p were predicted independently using three different algorithms: miRanda .Quantile normalization and subsequent data processing were performed using the R software package. All other statistical data were analysed and visualized with GraphPad Prism 6.0 software . The qPCR validation of all samples tested by a paired This study was approved by the Research Ethics Committee of Shanghai Jiaotong University affiliated Sixth People\u2019s Hospital, and informed consent was obtained from each patient. All specimens were collected in the operating room immediately (\u226415\u2009min) after tissue removal and were snap frozen in liquid nitrogen and stored at \u221280\u2009\u00b0C. All methods were performed in accordance with the relevant guidelines and regulations."}
+{"text": "P < 0.001) and markedly associated with the number of tumor foci (P = 0.014). Furthermore, in vitro approaches showed that overexpression of hsa_circ_0001445 promoted apoptosis and inhibited proliferation, migration, and invasion of HCC-derived cells, suggesting that hsa_circ_0001445 might be involved in the development of HCC. In addition, we found that the plasma hsa_circ_0001445 transcription levels in HCC patients were lower than those in cirrhosis (P < 0.001) and hepatitis B (P < 0.001) patients as well as in healthy controls (P < 0.001). In fact, receiver operating characteristic curve analysis indicated that plasma hsa_circ_0001445 could be a fairly accurate marker to distinguish HCC cases from healthy controls as well as patients with cirrhosis or hepatitis B.Circular RNAs (circRNA), a class of noncoding RNAs, have been found to be involved in various diseases. Here, the expression levels of the circRNA hsa_circ_0001445 in 73 pairs of hepatocellular carcinoma (HCC) and adjacent nontumor tissues were investigated by quantitative real-time polymerase chain reaction (qRT-PCR). Our data demonstrate that the hsa_circ_0001445 levels were significantly decreased in HCC tissues ( Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related mortality worldwide . HCC usuCircular RNAs (circRNAs) are a class of endogenous noncoding RNAs that result from a noncanonical form of alternative splicing . Unlike in vitro experiments were performed to explore the biological function of hsa_circ_0001445. Finally, we analyzed the plasma levels of hsa_circ_0001445 in HCC, cirrhosis, hepatitis B patients, and healthy controls to determine its diagnostic value for the detection of HCC.Recently, Conn et al.  have shog for 5\u2009min at 4\u00b0C. The supernatants obtained were transferred to microcentrifuge tubes and centrifuged at 12,000g for 5\u2009min at 4\u00b0C for a complete removal of the cell debris. The obtained plasma samples were stored at \u221280\u00b0C until used.A total of 73 pairs of HCC and adjacent nontumor tissues were obtained from HCC patients  who underwent surgery without preoperative chemotherapy or radiotherapy in Zhongnan Hospital of Wuhan University from 2011 to 2015. All patients were selected based on their pathology reports. Tumor staging  was defined according to the seventh edition of the AJCC Cancer Staging Manual. The number of tumor foci was determined by computed tomography and pathology reports. Tumor specimens and paired adjacent nontumor tissues were stored at \u221280\u00b0C in RNAlater\u00ae RNA Stabilization Solution . Blood samples from 104 HCC patients , 57 cirrhosis patients , 44 hepatitis B patients , and 52 healthy subjects  were obtained from Zhongnan Hospital of Wuhan University, between 2016 and 2017. All the healthy subjects chosen for the study were free of hepatitis, hepatic diseases, or abnormal liver biochemical outcomes. The blood samples were collected in EDTA-anticoagulant tubes and centrifuged at 2000Total RNA content of tissues and plasma were extracted using the Trizol reagent  and blood total RNA isolation kit , respectively, according to the manufacturers' instructions. The concentration and purity of the obtained RNA samples were quantified using the NanoDrop ND2000 . The RNA samples were reverse-transcribed to cDNA using the PrimeScript\u2122 RT reagent kit with gDNA Eraser  according to the manufacturer's instructions.The expression levels of hsa_circ_0001445 were detected via qRT-PCR with the Bio-Rad CFX96  according to the manufacturer's instructions. The reactions were started with an initial denaturation at 95\u00b0C for 5\u2009min, followed by denaturation at 95\u00b0C for 30\u2009s, annealing at 63.3\u00b0C for 30\u2009s, and extension at 72\u00b0C for 30\u2009s. The denaturation, annealing, and extension steps were repeated for 40 cycles. The glyceraldehyde 3-phosphate dehydrogenase (GAPDH) gene was used as an internal control. The primers used for the PCR reactions were hsa_circ_0001445 (forward: 5\u2032-CAAGATGGGCGAAAGTTCACT-3\u2032 and reverse: 5\u2032-TGTGTTGCTCCATGTCTAATCATT-3\u2032) and GAPDH (forward: 5\u2032-AGAAGGCTGGGGCTCATTTG-3\u2032 and reverse: 5\u2032-GCAGGAGGCATTGCTGATGAT-3\u2032). All experiments were carried out in duplicate for each data point.We used the PcDNA3.1(+) circRNA (Addgene plasmid number 60648), a mini vector that is a circRNA-forming plasmid, a kind gift from Jeremy Wilusz  to subcl2. A six-well plate was seeded with 5\u00a0\u00d7\u00a0105 cells and incubated for 24\u2009h, the cells were then transfected with pcDNA3.1(+)-circRNA-hsa_circ_0001445 or pcDNA3.1(+) circRNA mini vector using Lipofectamine\u2122 2000  according to the manufacturer's instructions.The HCC cell lines HepG2, HCCLM9, Hep3B, HCCLM3, and MHCC97L as well as the immortalized human hepatic cell line L02 were obtained from the Cell Bank of Type Culture Collection of Chinese Academy of Sciences . Cells were cultured in DMEM  with 10% fetal bovine serum  in a humidified incubator at 37\u00b0C with 5% CO\u03bcl of CCK8 solution was added to each well, and the plates were incubated at 37\u00b0C for additional 2\u2009h. Finally, the solution was measured using a 450\u2009nm spectrophotometer .Cell proliferation assays were conducted using the Cell Counting Kit-8 (CCK-8)  according to the manufacturer's instructions. In brief, transfected cells were seeded into 96-well plates (2000 cells/well) and cultured for 0\u2009h, 24\u2009h, 48\u2009h, and 96\u2009h. Then, 10\u2009Transfected HepG2 cells were harvested after transfection for 24\u2009h and stained using an Annexin V-FITC/PI apoptosis detection kit  according to the manufacturer's instructions. The cells were then analyzed with a Cytomics\u2122FC500 flow cytometer .\u03bcl of serum-free DMEM to quantify cell migration. Similarly, transfected HepG2 cells were seeded into the upper chambers  of transwell plates with the Matrigel-coated membrane  in 200\u00a0\u03bcl serum-free DMEM to quantify cell invasion. The lower chambers were filled with DMEM containing 10% FBS. After an incubation period of 24\u2009h, the medium was removed, and cells were fixed with methanol for 20\u2009min. The cells were then stained with crystal violet for 20\u2009min. Air-dried and photographed with a digital microscope. The number of cells was calculated from five random fields for each chamber.Transfected HepG2 cells were harvested after transfection for 24\u2009h and seeded into the upper chambers  of transwell assay plates  with 200\u00a0P < 0.05. The normality of distribution for each data set was tested by the Shapiro-Wilk test. Normally distributed data sets were analyzed by Student's t-tests, while nonnormally distributed data were analyzed by Kruskal-Wallis variance analyses. Correlations were analyzed by the Spearman correlation method. The combined diagnosis of hsa_circ_0001445 and AFP was analyzed using binary logistic regression. Finally, receiver operating characteristic (ROC) curves were generated to assess the diagnostic value of hsa_circ_0001445.All statistical analyses were carried out with SPSS version 21.0  and GraphPad Prism 5.0 . Normally distributed data are presented as mean\u2009\u00b1\u2009standard error of mean (M\u2009\u00b1\u2009S.E.M.). Results were considered statistically significant for P < 0.001). The analysis of the relationship between hsa_circ_0001445 expression and clinical characteristics of HCC was performed (P = 0.014), while no statistically significant relationship was found between hsa_circ_0001445 expression and gender, age, smoking, alcoholism, tumor size, TNM stages, differentiation, AFP, or other biochemical indices.The expression of hsa_circ_0001445 was measured in 73 pairs of HCC and adjacent nontumor tissues by qRT-PCR . The reserformed . The resNext, we analyzed hsa_circ_0001445 expression in the HCC-derived cell lines and the hepatic cell line L02. The native expression of hsa_circ_0001445 in the HCC-derived cell lines HepG2, HCCLM9, Hep3B, and MHCC97L, but not HCCLM3, was significantly lower, compared to that in the hepatic cell line L02 . To inveThe CCK8 assay showed that overexpression of hsa_circ_0001445 in HepG2 cells significantly inhibited their proliferation . SubsequP < 0.001), cirrhosis patients (P < 0.001), or hepatitis B patients (P < 0.001). The expression of plasma hsa_circ_0001445 in cirrhosis (P < 0.001) and hepatitis B patients (P < 0.001) was also lower than that in the healthy controls. No significant difference in plasma hsa_circ_0001445 levels was found between cirrhosis and hepatitis B patients. Correlation analysis results (P = 0.009)  , while nROC curves were constructed to assess the diagnostic value of plasma hsa_circ_0001445 levels for HCC detection. The results indicated that the levels of plasma hsa_circ_0001445 can serve well as an indicator to determine HCC. To distinguish HCC patients from healthy controls , the specificity and sensitivity of using plasma hsa_circ_0001445 levels as diagnostic index were 94.2% and 71.2%, respectively . FurtherPrevious studies have revealed that circRNAs may be involved in the development of variety of cancers . In thisin vitro experiments showed that overexpressed hsa_circ_0001445 promoted the apoptosis and inhibited the proliferation in HepG2 cells. In addition, we found that overexpressed hsa_circ_0001445 inhibited the migration and invasion of HCC cells, indicating that hsa_circ_0001445 might inhibit the metastasis of HCC. Multifocal HCC is known to mainly resulted from intrahepatic metastasis [Consistent with our findings in paired HCC and adjacent nontumor tissue samples, we found that HCC-derived cell lines HepG2, Hep3B, HCCLM9, and MHCC97L exhibited lower expression levels of hsa_circ_0001445 compared to the hepatic cell line L02. Furthermore, our tastasis , 31. Thetastasis , regulattastasis , and modtastasis . HoweverCurrent studies indicated that noncoding RNAs (ncRNAs), including miRNAs , long noin vitro studies indicated that hsa_circ_0001445 promoted apoptosis and inhibited proliferation, migration, and invasion in these cells. In addition, plasma levels of hsa_circ_0001445 could be a good diagnostic marker for differentiating HCC patients from healthy controls as well as from patients with cirrhosis or hepatitis B. Furthermore, plasma hsa_circ_0001445 and serum AFP levels, when used in combination, served as a remarkably sensitive diagnostic method for the detection of HCC. Collectively, our data support that hsa_circ_0001445 levels regulate HCC development and could serve as a potential diagnostic biomarker for HCC.Hsa_circ_0001445 levels were lower in HCC tissues than in adjacent nontumor tissues. Furthermore,"}
+{"text": "Most investigators of brain\u2013computer interface (BCI) research believe that BCI can be achieved through induced neuronal activity from the cortex, but not by evoked neuronal activity. Motor imagery (MI)\u2013based BCI is one of the standard concepts of BCI, in that the user can generate induced activity by imagining motor movements. However, variations in performance over sessions and subjects are too severe to overcome easily; therefore, a basic understanding and investigation of BCI performance variation is necessary to find critical evidence of performance variation. Here we present not only EEG datasets for MI BCI from 52 subjects, but also the results of a psychological and physiological questionnaire, EMG datasets, the locations of 3D EEG electrodes, and EEGs for non-task-related states.We validated our EEG datasets by using the percentage of bad trials, event-related desynchronization/synchronization (ERD/ERS) analysis, and classification analysis. After conventional rejection of bad trials, we showed contralateral ERD and ipsilateral ERS in the somatosensory area, which are well-known patterns of MI. Finally, we showed that 73.08% of datasets (38 subjects) included reasonably discriminative information.Our EEG datasets included the information necessary to determine statistical significance; they consisted of well-discriminated datasets (38 subjects) and less-discriminative datasets. These may provide researchers with opportunities to investigate human factors related to MI BCI performance variation, and may also achieve subject-to-subject transfer by using metadata, including a questionnaire, EEG coordinates, and EEGs for non-task-related states. Motor imagery (MI)\u2013based brain\u2013computer interface (BCI) has attracted great interest recently. Compared with other BCI paradigms, MI BCI can provide users with direct communication without any limb movement or external stimulus . MI BCI uses \u201cinduced\u201d brain activity  from theGigaScience database, GigaDB [In this, GigaDB .We conducted a BCI experiment for motor imagery movement (MI movement) of the left and right hands with 52 subjects ; the experiment was approved by the Institutional Review Board of Gwangju Institute of Science and Technology. Each subject took part in the same experiment, and subject ID was denoted and indexed as s1, s2, \u2026, s52. Subjects s20 and s33 were both-handed, and the other 50 subjects were right-handed. All subjects gave written informed consent to collect information on brain signals and were paid for their participation. The data collected were used only for research purposes.EEG data were collected using 64 Ag/AgCl active electrodes. As shown in Fig.\u00a0For each subject, EEG channel locations (3D coordinates) were collected with a 3D coordinate digitizer (Polhemus Fastrak). Electrode location was measured as the average of three measurements of the digitizer to obtain a stabilized position and prevent hand shaking.All experiments were conducted at our laboratory during one of four time slots: T1 (9:30\u201312:00), T2 (12:30\u201315:00), T3 (15:30\u201318:00), or T4 (19:00\u201321:30). The experiments began in August 2011 and ended in September 2011. The background noise level was 37\u201339 decibels.Six types of non-task-related data: We recorded 6 types of noise data  for 52 subjects. Each type of noise was collected twice for 5 seconds, except the resting state, which was recorded for 60 seconds.Real hand movement: Before beginning the motor imagery experiment, we asked subjects to conduct real hand movements. Subjects sat in a chair with armrests and watched a monitor. At the beginning of each trial, the monitor showed a black screen with a fixation cross for 2 seconds; the subject was then ready to perform hand movements (once the black screen gave a ready sign to the subject). As shown in Fig.\u00a0MI experiment: The MI experiment was conducted with the same paradigm as the real hand movement experiment. Subjects were asked to imagine the hand movement depending on the instruction given. Five or six runs were performed during the MI experiment. After each run, we calculated the classification accuracy over one run and gave the subject feedback to increase motivation. Between each run, a maximum 4-minute break was given depending on the subject's demands.For each subject, we recorded data for non-task-related and task (MI)-related states, as follows:The entire procedure of the experiment is presented in Table\u00a0Before the MI experiment began, we asked each subject to move his/her fingers, starting from the index finger and proceeding to the little finger  and EMG (65th to 68th channel) data (\u201c*.mat\u201d) for each subject is shown below:First, we checked the number of bad trials in each subject's data. If a band-passed (8\u201330 Hz) trial had an amplitude greater than \u00b1100 \u03bcV  within 5- High-pass filtering of all EMG trials above 0.5 Hz to remove drifts;- Common average reference;- Band-pass filtering of all trials with 50\u2013250 Hz;- Hilbert transform;- Take absolute and squared magnitudes for each complex value of all trials;- Extract data in resting window (\u22121000\u20130 msec) and task-related window (0\u20133000 msec) for each trial;* Tag \u201c\u22121\u201d value for time points in resting window;* Tag \u201c+1\u201d value for time points in task-related window;- Prepare labels for each time point within a trial:- Both squared EMG magnitudes and label of time points are decimated (averaged) by a factor of 8. Then calculate Pearson correlation between ranked squared EMG magnitudes and label of time points;- Execute permutation test over time points within a trial:- Calculate Pearson correlation between ranked permuted features and labels;- Repeat 100 times;- Make probability density function (PDF) of the values of Pearson correlation;P-values (one right-tailed test) over all trials and four EMG channels;- Calculate P-value is smaller than 0.01 and the correlation value is greater than 0.8 , then it is declared a bad trial correlated with EMG.- If Bonferroni-corrected Second, we investigated whether each trial is correlated with EMG  adopting Vaughan and colleagues\u2019  1998 ideFinally, the EMG-correlated EEG trial indices were added for each subject dataset, as shown in the \u201cData format and structure\u201d section.- High-pass filtering of all EEG trials above 0.5 Hz to remove drifts;- Laplacian filtering;- Band-pass filtering of all trials with 8\u201314 Hz;- Hilbert transform of all trials;- Absolute magnitude taken for each complex value of all trials;- Magnitude of Hilbert-transformed samples averaged across all trials;- Baseline correction for each trial to obtain a percentage value for ERD/ERS per the formula Third, we checked event-related desynchronization/synchronization (ERD/ERS) of SMR for each subject . To calcLast, we validated the discriminability of the left versus right hand MI EEG data as classification accuracy. All trials for each subject were pre-processed by high-pass filtering and common average reference, and then filtered both spectrally (8\u201330 Hz) and temporally (0.5\u20132.5 seconds after stimulus onset). For the feature extraction algorithm, we used 2 spatial filters of the common spatial pattern (CSP) for each class , 13. ForFor preprocessing, we used Butterworth filtering with fourth order for high-pass and band-pass filtering. We validated the EEG datasets in three different ways:P-value threshold at 0.05, a few trials were not correlated with the labels of resting or task-related states. Thus, our threshold of P-values was set at 0.01. Furthermore, according to the observed correlation distributions of real hand movement data, we set 0.8 as a correlation threshold. Finally, if the correlation value is greater than the 0.8 threshold and the P-value is smaller than 0.01 in MI datasets, the trial was classified as a bad trial correlated with real hand movement.Percentage of bad trials. We calculated the percentage of bad trials for each subject, as shown in Fig.\u00a0Most existing studies detected EMG activity through manual monitoring. They recorded EMG and EEG simultaneously and monitored EMG burst during the experiment. On the other hand, in the published literature , the resERD/ERS. The ERD/ERS results of mu rhythm 8\u201314 Hz) are depicted in Fig.\u00a0 Hz are dClassification. The mean accuracy of all BCI performances Fig.\u00a0C over thBCI: brain\u2013computer interface; CSP: common spatial pattern; EEG: electroencephalography; EMG: electromyography; ERD/ERS: event-related desynchronization/synchronization; FLDA: Fisher's linear discriminant analysis; MI: motor imagery; SMR: somatosensory rhythm.This work was supported by GIST Research Institute (GRI) grant funded by the GIST in 2017, and Institute for Information & Communication Technology Promotion (IITP) grant funded by the Korea government (No. 2017-0-00451).GigaScience database, GigaDB [The data supporting this paper, including EEG datasets and questionnaire results, are available in the , GigaDB .The authors declare that they have no competing interests.GIGA-D-16-00104_Original_Submission.pdfClick here for additional data file.GIGA-D-16-00104_Revision_1.pdfClick here for additional data file.GIGA-D-16-00104_Revision_2.pdfClick here for additional data file.GIGA-D-16-00104_Revision_3.pdfClick here for additional data file.Response_to_reveiwer_comments_Original_Submission.pdfClick here for additional data file.Response_to_reviewer_comments_revision_1.pdfClick here for additional data file.Response_to_reviewer_comments_revision_2.pdfClick here for additional data file.Reviewer_1_Report_.pdfClick here for additional data file.Reviewer_1_Report_(revision_1).pdfClick here for additional data file.Reviewer_2_Report_.pdfClick here for additional data file."}
+{"text": "Bacillus velezensis JTYP2 was isolated from the leaves of Echeveria laui in Qingzhou, China, and may control some of the fungal pathogens of the plant. Here, we present the complete genome sequence of B.\u00a0velezensis JTYP2. Several gene clusters related to its biosynthesis of antimicrobial compounds were predicted. Bacillus velezensis is reported to be one of the plant growth-promoting bacteria. It has been reclassi\ufb01ed as a synonym of B.\u00a0methylotrophicus, B.\u00a0amyloliquefaciens subsp. plantarum, and B.\u00a0oryzicola  DNA sequencing of 10 kb was carried out (de novo assembled by SmrtLink (B.\u00a0velezensis JTYP2 reached 980\u00d7. The genome was annotated by the NCBI Prokaryotic Genome Annotation Pipeline (PGAP) (https://www.ncbi.nlm.nih.gov/genome/annotation_prok/). The repeated sequences were detected by RepeatModeler (http://antismash.secondarymetabolites.org/).Recently, ried out . The seqSmrtLink  (v3.1.1)tModeler   genes. Meanwhile, 115 pseudo genes were annotated. There were 7 short interspersed nuclear elements (SINEs), 25 long interspersed nuclear elements (LINEs), 3 long terminal repeats (LTRs), and 13 transposable elements. A total of 12 gene clusters were predicted to code antagonistic substances on plant pathogens, and half of them present high similarity with the known gene clusters. Two gene clusters (BAJT_07230-BAJT_07470 and BAJT_11035-BAJT_11300), which belong to type transAT polyketide synthase (PKS), were similar to the biosynthetic genes of macrolactin and difficidin, respectively. Two gene clusters (BAJT_08585-BAJT_08825 and BAJT_09175-BAJT_09505) were classified as nonribosomal peptide synthetase (NRPS) type transAT PKSs. The first one showed 100% similarity with the biosynthetic genes of bacillaene. The other one showed 100% similarity with the gene cluster of fengycin. An NRPS-bacteriocin type gene cluster (BAJT_14725-BAJT_15045) was related to bacillibactin biosynthesis. A gene cluster (BAJT_17730-BAJT_17945) was detected to be relevant to bacilysin production. The other 6 clusters of genes might be involved in biosynthesis of new antimicrobial compounds. The complete genome data will be helpful to understand the molecular mechanisms of biocontrol in B.\u00a0velezensis JTYP2.The circular chromosome ofBacillus velezensis JTYP2 has been deposited in GenBank under the accession number CP020375. The version described in this paper is the first version, CP020375.1.The genome sequence of"}
+{"text": "Bacillus anthracis phages Negev_SA, Carmel_SA, and Tavor_SA were isolated from soil samples, and their complete genomes were sequenced and analyzed. The isolated phages have potential use in future phage therapy treatment against anthrax.The new highly effective  Bacillus anthracis is a Gram-positive spore-forming bacterium that causes the anthrax disease  was added to 3.5\u00a0ml of 0.5% agarose, which was poured onto an LB plate. Three microliters of each sample was spotted on the bacterial lawn, and the plates were incubated overnight at 37\u00b0C.The phages were isolated from soil samples from various places in Israel, mainly the Golan Heights region, where several outbreaks of anthrax disease were reported. Purification was conducted using the phage titration method, as previously described , with a https://www.bioinformatics.babraham.ac.uk/projects/fastqc). The de novo assembly with the trimmed paired-end reads was performed using Geneious version 10 (Biomatters). The mean coverages are 30.7\u00d7 (\u00b110.4) for Negev_SA, 35.2\u00d7 (\u00b111.4) for Carmel_SA, and 31.3\u00d7 (\u00b111.6) for Tavor_SA. Annotation was performed with PHAST (PHAge Search Tool). Analysis of the open reading frames and phylogenetic tree generation were performed with Geneious version 10 and its plugins.The phages\u2019 DNA was purified using a phage DNA isolation kit (Norgen Biotek), libraries were prepared with an Illumina Nextera XT DNA kit , and sequencing was performed using the Illumina MiSeq platform. The quality of the 150-bp paired-end reads was assessed with FastQC (B.\u00a0anthracis (35.1%).The genomes of Negev_SA, Carmel_SA, and Tavor_SA are linear and contain 40,375\u00a0bp, 40,165\u00a0bp, and 40,397\u00a0bp, respectively. The G+C contents of Carmel_SA and Tavor_SA (both 35.2%) and Negev_SA (34.9%) are similar to that of Bacillus phages Gamma (GenBank accession number NC_007458) and Fah (DQ222851) belonging to the Siphoviridae family of the order Caudovirales. There are 57 coding sequences in Negev_SA, 56 in Carmel_SA, and 61 in Tavor_SA. The genes of the three phages are similar, except for a few exceptions; Carmel_SA is the only genome which contains beta-galactosidase and does not code for a phage terminase small subunit. Only Negev_SA has a flagellar hook-length control protein (FliK) and a phage antirepressor.Carmel_SA, Tavor_SA, and Negev_SA are similar to The phages contain repressor proteins, site-specific recombinases, and antirepressor proteins, which indicates that these phages have lysogenic capabilities that might impair the use of these phages for therapy. However, the fact that they are genetically related to phage Gamma, which is lytic , and thaB. anthracis phages Negev_SA, Carmel_SA, and Tavor_SA are available in GenBank under the accession numbers KY963370, KY963371, and KY963369, respectively.The complete genome sequences of"}
+{"text": "Since PGAP  was published in 2012, it has been widely employed in bacterial genomics research. Though PGAP has integrated several modules for pan-genomics analysis, how to properly and effectively interpret and visualize the results data is still a challenge.Streptococcus pneumonia strains and 14 Chlamydia trachomatis. The results show that, S. pneumonia strains have higher diversity on genome structure and gene contents than C. trachomatis strains. In addition, S. pneumonia strains might have suffered many evolutionary events, such genomic rearrangements, frequent horizontal gene transfer, homologous recombination, and other evolutionary process.To well present bacterial genomic characteristics, a novel cross-platform software was developed, named PGAP-X. Four kinds of data analysis modules were developed and integrated: whole genome sequences alignment, orthologous genes clustering, pan-genome profile analysis, and genetic variants analysis. The results from these analyses can be directly visualized in PGAP-X. The modules for data visualization in PGAP-X include: comparison of genome structure, gene distribution by conservation, pan-genome profile curve and variation on genic and genomic region. Meanwhile, result data produced by other programs with similar function can be imported to be further analyzed and visualized in PGAP-X. To test the performance of PGAP-X, we comprehensively analyzed 14\u00a0http://pgapx.ybzhao.com.Briefly, PGAP-X directly presents the characteristics of bacterial genomic diversity with different visualization methods, which could help us to intuitively understand dynamics and evolution in bacterial genomes. The source code and the pre-complied executable programs are freely available from The online version of this article (doi: 10.1186/s12864-017-4337-7) contains supplementary material, which is available to authorized users. Mycobacterium [Bifidobacterium [Lactococcus [Since the pan-genome concept was proposed in 2005 , 2, it hacterium , Bifidobacterium , Lactocotococcus  and so otococcus .However, a never-ending improvement of pan-genomic tools is data interpretation and visualization, which would provide better data mining results and quality graphics for research and publication. In the past years, several standalone programs and web-based servers have been developed to visualize data from pan-genome sight. However, these programs and servers provided very limited functions. Moreover, they cannot present orthologous relationship and genetic variation inside both genomes and genes from genomic structure sight. To address this question, we developed a genome-oriented software, PGAP-X, which will perform pan-genome analysis from genome structure sight. PGAP-X does not only perform data analysis independently, but also directly visualize and interpret result data. Results data generated by other programs with similar function could also be imported to PGAP-X for further analysis and visualization, after being converted to compatible data format. PGAP-X can be used to well analyze and present the diversity of genome structure and gene content for those strains from the same specie or closely related species, which have high similarity in genome structure.In PGAP-X, analytical processes are divided into three layers logically Parse whole genome alignment result and descending sort those homologs genomic regions by their conservation . For those genomic regions with the same conservation, they will be sorted by the average fragment size in descending order.ii)Cluster genes by sequence similarity and genome synteny on the same homologs genomic regions.iii)Merge different gene clusters from step ii) by gene sequence similarities.In the data analysis module, a new in-house algorithm was developed to identify orthologous gene clusters among all genes across strains. The workflow for this algorithm contains three steps N gradient colors will be pre-defined for those values ranging from 1 to N. and each gene will have one kind color based on its conservation value.Gradient color mode: ii)N-1) and core genes .Three color mode: three colors will be pre-defined, and blocks with these colors represent strain specific genes , dispensable genes N-1 share the third one.Four color mode: four colors will be pre-defined. Strain specific genes and core genes will use the first and the fourth colors, respectively. Dispensable genes with conservation\u00a0value from 2 to Orthologous gene clusters are required to visualize the gene distribution on their genomes. Each genome will be laid out horizontally, and genes are shown as colored blocks on their corresponding genomes. The color for each gene will be decided by the conservation value of the orthologous gene cluster, to which this gene was assigned. Three color models are provided in the visualization module: gradient color mode, three-color mode and four-color mode. For an analyzed bacterial population with In the data analysis module, pan-genome profile will be calculated based on orthologous gene clusters from all strains, and computational methods were the same as those in PGAP. In the visualization module, the curves for pan-genome size and core gene size will be viewed in the same window. A graphic interactive interface was provided to adjust the graph.f) and substitution number (m) in 1\u00a0kb regions, are employed to filter out genomic regions or genic region with high substitutions. These genomic region or genic region would be displayed on the genome structure in the genome scale or gene scale models respectively.Genetic variants in bacterial genomes will be analyzed on both genome scale and gene scale. In the data analysis module, two key parameters, substitution frequency  substitution frequency in the region is no less than f, 3) the interval between any two substitution sites is no more than 1/ f.Genetic variants analysis among pairwise or multiple genomes are also included on genome scale. For pairwise variation analysis, a reference genome should be selected first, and the remaining genomes will be taken as query genomes. All variation sites will be detected based on the whole genome alignment result. Regions with the following criterions will be outputted and displayed as high substitution regions: 1) no less than f and substitution sites no less than m are taken as high substitution genes. All high substitution genes will be visualized by their coordinates on the genomes.For variation analysis on gene scale, MUSCLE program is utilized to align those protein sequences for all genes from the same cluster , and theStreptococcus pneumonia strains were NC_003028, NC_003098, NC_008533, NC_010380, NC_010582, NC_011072, NC_011900, NC_012466, NC_012467, NC_012468, NC_012469, NC_014251, NC_014494 and NC_014498. The accession number for the genomic data from 14 Chlamydia trachomatis strains were NC_007429, NC_010287, NC_017430, NC_017431, NC_017436, NC_017437, NC_020511, NC_020977, NC_021050, NC_021888, NC_021892, NC_021898, NC_022548, NC_023060. All these genomic data were downloaded from NCBI FTP.The accession number for the genomic data from 14 Based on C++/Qt, we developed a microbial comparative genomic analysis platform with a user-friendly graphic interface, which could be run on Windows, Linux and Mac OSX platform. The snapshot for the graphic user interface, and example results are shown as Fig.\u00a0Streptococcus pneumonia strains and 14 Chlamydia trachomatis strains from NCBI FTP. With the default mode and parameters, we comprehensively analyzed S. pneumonia strains and C. trachomatis strains genome sequences with PGAP-X. The analysis results present the genome diversity of these two kinds of bacteria from several sights, including genome structure, orthologous gene clusters, pan-genome profile, gene distribution, and genetic variation from both gene and genome scales.To test the performance of PGAP-X, we downloaded genomic data of 14 S. pneumonia strains with the default mode, and results show that genomic fragment inversion events frequently occurred among genome regions range from 640\u00a0kb to 1.4\u00a0Mb  are core genes clusters , and 24.4% (964) are specific genes clusters (those genes present in only one strain)  core genes clusters and 98 (8.8%) strain specific genes clusters Additional file 2:Supplementary document. (DOCX 22\u00a0kb)Additional file 3: Figure S2.The whole genome alignment result among 14 C. trachomatis strains. (DOCX 809\u00a0kb)Additional file 4: Table S1.Comparison of identical orthologous clusters from PGAP-X and PGAP. (DOCX 12\u00a0kb)Additional file 5: Figure S8.The algorithm details for orthologous gene clustering. (DOCX 567\u00a0kb)Additional file 6: Figure S3.Percentage of orthologous clusters with paralogs among orthologous clusters from PGAP-X and PAGP (MP and GF). (DOCX 198\u00a0kb)Additional file 7: Figure S4.Example for distinguishing paralogs by their location on the genome. (DOCX 1836\u00a0kb)Additional file 8: Figure S5.The location distribution of all genes by their conservation in 14\u00a0S. pneumonia strains genomes and 14 C. trachomatis strains genomes. (DOCX 6111\u00a0kb)Additional file 9: Figure S6The diversity of gene contents in 14 C. trachomatis strains genomes. (DOCX 793\u00a0kb)Additional file 10: Figure S7.Genetic variation in 14 C. trachomatis strains genome from both genome and gene scale. (DOCX 623\u00a0kb)"}
+{"text": "Fig 2 is incorrect. The axis labels Severe TB and TB free are inappropriately switched. The proper label order should read: TB free, Mild TB, Severe TB. The authors have provided a corrected version of"}
+{"text": "Enterococcus faecium (assigned the multi locus sequence type ST796) was simultaneously isolated from geographically separate hospitals in south eastern Australia and New Zealand. Here we describe the complete genome sequence of Ef_aus0233, a representative ST796 E. faecium isolate. We used PacBio single molecule real-time sequencing to establish a high quality, fully assembled genome comprising a circular chromosome of 2,888,087 bp and five plasmids. Comparison of Ef_aus0233 to other E. faecium genomes shows Ef_aus0233 is a member of the epidemic hospital-adapted lineage and has evolved from an ST555-like ancestral progenitor by the accumulation or modification of five mosaic plasmids and five putative prophage, acquisition of two cryptic genomic islands, accrued chromosomal single nucleotide polymorphisms and a 80 kb region of recombination, also gaining Tn1549 and Tn916, transposons conferring resistance to vancomycin and tetracycline respectively. The genomic dissection of this new clone presented here underscores the propensity of the hospital E. faecium lineage to change, presumably in response to the specific conditions of hospital and healthcare environments.From early 2012, a novel clone of vancomycin resistant  Enterococcus faecium is a human and animal gastrointestinal tract (GIT) commensal but a lineage within the species has rapidly evolved to become a significant opportunistic pathogen  has adapted to the hospital environment and is adept at GIT colonization with the potential to cause invasive disease . Membersronments .E. faecium clones within the clade A1 hospital lineage which spread rapidly and displace previously endemic clones. For example, from 1994 to 2005, Australian hospital acquired E. faecium VRE was uncommon and mostly caused by ST17 strains. The situation changed suddenly from 2005 when there was a nationwide wave of by E. faecium ST203 blood stream infections (BSI), a significant and rising proportion of which are vanB VRE  of all patient episodes of all E. faecium bacteraemia in Melbourne Hospitals, compared with 10 of 117 (8.5%) for ST203.At the Austin Hospital in Melbourne, improved cleaning protocols following our local ST203 outbreak were associated with a reduction in VRE BSI between 2009 and 2011 . Howevert method  but havet method . ST796 wive Care  and in tE. faecium isolate Ef_aus0233, a representative of this emerging clone and then employed population based comparative genomics to better understand the genetic changes that have accompanied the emergence.In the current study, we used single molecule real-time sequencing to establish a high quality, fully assembled genome sequence of ST796 E. faecium were cultured as previously described . The final chromosome assembly was validated by reference to a high-resolution NcoI optical map using MapSolver . Common bacterial DNA base modifications and methyltransferase motifs were assessed using the protocol, RS_Modification_and_Motif_Analysis in the SMRT Analysis System v2.3.0.140936 (Pacific Biosciences).Short fragment DNA libraries were generated using the Illumina NexteraXT DNA preparation kit and fragment sequencing was undertaken with the Illumina NextSeq 500 platform using 2 \u00d7\u00a0150 bp chemistry. Highly intact and high quality genomic DNA was extracted from Ef_aus0233 and subjected to Pacific Biosciences SMRT sequencing according to the manufacturer\u2019s instructions and sequenced with two SMRT cells on the RS II platform (Pacific Biosciences) using P5-C3 chemistry. Genome assembly was performed using the SMRT Analysis System v2.3.0.140936 (Pacific Biosciences). Raw sequence data were The approximate number of plasmid copies per cell for the Ef_aus0233 genome was inferred using differences in Illumina sequence read depth. The read depth of plasmid sequences was compared to the average chromosomal coverage to estimate copy number multiplicity.E. faecium genomes. The Ef_aus0233 chromosome was compared against other fully assembled E. faecium chromosomes using BLASTn DNA:DNA comparisons that were undertaken and visualized using Blast Ring Image Generator (Artemis Comparison Tool  was usedenerator .de novo assembled into contigs using Spades v3.6.1 (https://github.com/tseemann/prokka/blob/master/db/genus/Enterococcus) as well as manually annotated protein files derived from two fully assembled E. faecium genomes (in silico tool (https://github.com/tseemann/mlst). CRISPR databases were used to search for CRISPR sequences (http://crispi.genouest.org and http://crispr.u-psud.fr/Server/) (accessed 19th of May 2016). Sequence files were uploaded to the web based ISsaga  differences. Hierarchical Bayesian clustering was performed upon a core SNP alignment to assign genomes into discrete populations using hierBAPS with BAPS6 (a prior of 10 depth levels and a maximum of 20 clusters were specified). Nested Recombination within the core genome was inferred using ClonalFrameML v1.7  using thhttps://github.com/drpowell/FriPan) (downloaded on the 28th of April 2016). The General Feature Format files have been deposited in Figshare (https://figshare.com/articles/Evolutionary_origins_of_the_emergent_ST796_clone_of_vancomycin_resistant_Enterococcus_faecium/4007760).Orthologous proteins were identified through reciprocal blast using Proteinortho5 v5.11 . A blastwww.geneious.com)).The alignment of homologous sequences was undertaken using Mauve . AlignmeNcoI optical map  resulted in reconstruction of a 3,272,427 bp genome, comprising a circular chromosome and five circular plasmids . Remainiical map . DNA basE. faecium in the clinical environment is its ability to acquire genes conferring antibiotic resistance   have been isolated to date, one of which (Ef_aus1016) is included in our study and is discussed later.One of the major drivers behind the success of sistance . In silincomycin . Vancomy genomes . Of partaus0085) , shared E. faecium strains and are thought to enhance fitness in the hospital environment  of the Ef_aus0233 genome revealed the presence of several genes associated with virulence including collagen-binding adhesin  shared 83.3% and 82.9% nucleotide identity with the ortholog in Ef_aus0004 , a fully assembled ST17 genome  and Ef_aCarnobacterium and Lysinibacillus, reflecting the easy by which E. faecium can acquire exogenous DNA (The Ef_aus0233 chromosome was found to contain 80 distinct elements (9 families) while Ef_aus0233_p1 had 41 (6 families), Ef_aus0233_p3 had 8 (5 families), Ef_aus0233_p4 had 8 (2 families) and no IS elements were detected on Ef_aus0233_p2 or Ef_aus0233_p5. Several of these IS families have been found not only in enterococci but additionally in species of other genera, including nous DNA .E. faecium lineage, genomes belonging to the CC17 E. faecium have been found to lack CRISPR systems  systems of prokaryotes function as a sequence-specific security to defend genomes against viral predation and exposure to invading nucleic acid . Unlike  systems . Given t systems . Despite systems , it is uE. faecium genomes is discussed below.The Ef_aus0233 genome was found to contain five putative prophages. Prophages Ef_aus0233_chr_phage-1 , Ef_aus0233_chr_phage-2 , Ef_aus0233_chr_phage-3  and Ef_aus0233_chr_phage-4  were located on the chromosome  while EfE. faecium phenotypic differences  . WithoutRecombination analyses indicated that both the ST555-796 and ST796 MRCAs have evolved in part by recombination. Inspection of the inferred recombining segments for these two ancestors revealed a single hotspot of 170 kb that contained two overlapping clusters of increased SNP density  . The spaIn order to assess the evolutionary divergence between the ST555 and ST796 clades, a ST555-796 specific core genome was established and pairwise SNP distances were calculated. As we were primarily interested in SNPs derived through clonal evolution, again we removed SNPs inferred to have arisen through recombination. Inspection of SNP distribution in VSE Ef_aus1016 revealed several dense clusters, indicating substantial recombination, which was subsequently removed by a second round of recombination detection. Consistent with two distinct groups, within clade comparisons revealed smaller mean SNP differences  than that between clades (between ST555 and ST796: 330 SNPs) .Forty-one core-genome SNPs differentiated the ST555\u2013ST796 MRCA from its predecessors, while only two core-genome SNPs were predicted in the ST796 MRCA compared to ST555 . AnalysiE. faecium pan genome \u2014representing the n genome . Using tThe acquisition of genomic islands has been reported in previous studies that compared the genomes of hospitalized and non-hospital derived isolates, suggesting that such novel elements may offer possessing strains a competitive advantage . In this\u2032\u00a0end of this element and replication proteins at the 3\u2032\u00a0extreme were identical to a previously described Enterococcus faecalis pathogenicity island . The phage CDS content and their predicted products is provided , suggesting that these elements are likely to have been acquired by the ST555\u2013ST796 MRCA. Assessment of the CDS annotation for ST555\u2013ST796_GI-1 (56 kb) suggests it is a mosaic integrative element. Two 3 kb regions spanning a site-specific tyrosine recombinase and excisonase at the 5y island . A 13 kby island . The funprovided .916-like transposon , carrying tetracycline resistance , carrying vancomycin resistance (1549, suggesting that this lineage was originally VSE. The horizontal acquisition of Tn1549 has been demonstrated (E. faecium clone to precede the VRE version has been previously documented with the emergence of ST17, ST203 and ST252 (Two other elements were found to be exclusively present among ST796 genomes in this comparison. One of these was the Tnsistance  and the sistance . Given tsistance . The excnstrated  and evidnd ST252 .E. faecium population (Alignment of the orthologs found within the prophages that were identified in the Ef_aus0233 genome revealed the extent to which these elements are conserved among the greater pulation . The majpulation .E. faecium accessory genome that can spread horizontally through a population and carry genetic elements that may confer enhanced fitness (E. faecium population, the presence and absence of plasmid genes within the ortholog clusters were inspected (Plasmids form an important component of the  fitness . Approxinspected . Patternnspected .Given these gene content patterns and the aforementioned phylogenomic relationships between ST555 and ST796 genomes, it appears likely that the ST555\u2013ST796 MRCA acquired these plasmids, as they are not observed in their entirety in surrounding clades. Interestingly, Ef_aus0233_p2 was not only scarce among non-ST796 genomes but lacked conservation among the ST796 genomes. Overall, no single plasmid ortholog was specific to ST796, however given the set of isolates analyzed in this study, such plasmids, in their entirety, appear to be diagnostic of the ST555\u2013ST796 lineage. Furthermore, the intra-ST796 differences in plasmid gene content, particularly in Ef_aus0233_p2, indicate there are appreciable amounts of diversity within the ST796 accessory genome, variation that might be useful during outbreak investigations involving this clone .E. faecalis was the leading causative agent of enterococcal nosocomial infections, however E. faecium infections have escalated in the last decade (E. faecium STs in hospitals (E. faecium ST early in its evolutionary history. The preparation of a fully assembled ST796 genome facilitated a comprehensive genomic analysis of this lineage and enabled detailed comparisons among other clinically relevant draft and fully assembled E. faecium genomes.The hospital environment presents a challenging ecological niche for the adaptation of bacterial pathogens. Historically, t decade . Followiospitals . Here weE. faecium core and accessory genome that may have been important drivers for the evolution of the ST555\u2013ST796 and ST796 lineages. Given the likely significance of genomic island acquisition for the emergence of CC17 (We demonstrate that the emergence of ST796 was preceded by several genomic events including the acquisition of two genomic islands, plasmid and phage activity, modest SNV accumulation and recombination. These analyses highlight genetic elements within the  of CC17 , the GIs of CC17 .This analysis focused upon providing an overview of the first fully assembled ST796 genome and genomic differences that were assessed at the inter-ST population level. In order to explore specific diversity within the ST796 lineage, an intra-ST population study focusing upon diversity among a large collection ST796 genomes is currently underway. Our observation of substantial variation within the ST796 accessory genome, in particular plasmid presence and absence, suggest a means for effective intra-ST796 genotyping that could potentially be more useful than core genome analysis in the tracking of outbreaks.E. faecium clone. With our current state of knowledge, we know that there is rapid genomic change occurring however we don\u2019t fully understand the consequences of such changes, as a large percentage of identified E. faecium genes have unknown functions. The promise of genomics will only be realized when we can combine our insights on genomic evolution with the functional consequences of such changes on pathogen phenotypes. The research presented here incrementally builds our understanding and provides a solid basis for future studies, as both clinical and public health microbiology transition into the genomic era.Hospitals are controlled but nonetheless dynamic environments. Examining pathogen genomic changes in these environments is important to understanding the bacterial response to human interventions, such as changing infection control or antibiotic stewardship practices. In this study we deconstructed the genomic events that have shaped the evolution of a highly successful 10.7717/peerj.2916/supp-1Figure S1MUSCLE alignment of Esp orthologs among Ef_aus004, Ef_aus0085 and Ef_aus0233.Click here for additional data file.10.7717/peerj.2916/supp-2Figure S2Mauve alignment of prophages detected in the Ef_aus0233 genomeClick here for additional data file.10.7717/peerj.2916/supp-3Table S1Click here for additional data file.10.7717/peerj.2916/supp-4Table S2Click here for additional data file.10.7717/peerj.2916/supp-5Table S3Click here for additional data file.10.7717/peerj.2916/supp-6Table S4Click here for additional data file.10.7717/peerj.2916/supp-7Table S5Click here for additional data file."}
+{"text": "Accurate structural annotation depends on well-trained gene prediction programs. Training data for gene prediction programs are often chosen randomly from a subset of high-quality genes that ideally represent the variation found within a genome. One aspect of gene variation is GC content, which differs across species and is bimodal in grass genomes. When gene prediction programs are trained on a subset of grass genes with random GC content, they are effectively being trained on two classes of genes at once, and this can be expected to result in poor results when genes are predicted in new genome sequences.Oryza sativa genome compared to using the standard MAKER annotation protocol. Gene structure was improved in over 13% of genes, and 651 novel genes were predicted by the GC-specific MAKER protocol.We find that gene prediction programs trained on grass genes with random GC content do not completely predict all grass genes with extreme GC content. We show that gene prediction programs that are trained with grass genes with high or low GC content can make both better and unique gene predictions compared to gene prediction programs that are trained on genes with random GC content. By separately training gene prediction programs with genes from multiple GC ranges and using the programs within the MAKER genome annotation pipeline, we were able to improve the annotation of the Oryza sativa. We expect that this protocol will also be beneficial for gene prediction in any organism with bimodal or other unusual gene GC content.We present a new GC-specific MAKER annotation protocol to predict new and improved gene models and assess the biological significance of this method in The online version of this article (doi:10.1186/s12859-017-1942-z) contains supplementary material, which is available to authorized users. Most widely used gene prediction programs depend on Hidden Markov Models (HMMs) to predict gene structure within genomic sequence \u20133. TypicWe perceived that two factors might limit the accuracy of gene prediction in grass genomes. First, in many species including most plants, the GC content of genes has a relatively narrow and unimodal distribution, but in the grasses (Poaceae), the GC content of genes has a broad bimodal distribution  relative to available transcript and protein evidence. Furthermore, we identified novel genes with high and low GC content that had not been predicted by the standard MAKER protocol. Comparisons to the AUGUSTUS isochore-based prediction method as well as to the standard MAKER protocol showed that this GC-specific MAKER protocol shifts the overall GC content of predicted gene models both higher and lower than the standard MAKER protocol. This new GC-specific MAKER annotation method will be of interest to anyone working on structural annotation of genomes with bimodal GC content but will likely improve the annotation of any genome.MAKER is a commonly used structural annotation engine that has been used to annotate numerous plant genome assemblies , 14\u201318. O. sativa. In order to compare gene models within the O. sativa ssp. Nipponbare genome  based ossembly; .Table 1NThe distribution of GC content of the gene predictions varied greatly Fig.\u00a0. The staO. sativa while 7004 gene models found in the MSU annotation were missing from the six HMMs annotation  scores were plotted for each set of predicted gene models and visualized as heatmaps  can be used for genome wide assessment of annotation quality. The percentages of AED0.5 genes were similar for all three annotations  in which a gene prediction is made, we also trained AUGUSTUS in its isochore-sensitive mode and used it to make gene predictions within MAKER. Overall, the MAKER six HMMs annotation produced more genes than any other annotation strategy tested here  to NCBI\u2019s non-redundant protein database. Second, the homology and orthology of these genes was evaluated relative to other MAKER six HMM predictions and Brachypodium distachyon, Sorghum bicolor and Zea mays using OrthoMCL [Using the total predictions generated through the MAKER six HMMs annotation, additional support was given to the novel predictions made by the high GC and low GC HMMs by first assessing sequence homology of the novel gene predictions to the NCBI non-redundant protein database . Of the OrthoMCL \u201332 There are 7004 genes in the MSU-RGAP Release 7 data set that were not predicted by the six HMMs annotation. Of these genes, 4635 are characterized as \u201cexpressed\u201d meaning that they only have transcript support. An additional 1324 MSU-RGAP genes missing from the six HMMs annotation are described as \u201chypothetical\u201d, which indicates that they have no transcript or protein support, but they may contain a conserved protein domain  , and all O. sativa protein sequences were removed. The remaining protein sequences not from O. sativa were used as protein evidence during the MAKER annotation.Thirty-one paired end RNA-seq datasets for O. sativa using a method described previously [http://weatherby.genetics.utah.edu/MAKER/wiki/index.php/Repeat_Library_Construction-Advanced), and the custom repeat library was used by RepeatMasker within the MAKER pipeline to mask repetitive elements. Transcript assemblies and protein sequences described above were used as evidence to aid gene predictions.The MAKER-P (r1128) genome annotation pipeline was used to annotate the Os-Nipponbare-Reference-IGRSP-1.0 v7 genome assembly. A custom repeat library was created for eviously ; . A GFF3 file of TE-related genes was derived from the MSU-RGAP gene annotation GFF3 file  and was compared to the MAKER standard GFF3 file using gffcompare [Despite our use of a custom repeat library that was used for masking repeat elements in the genome, some TE-related genes remain unmasked, and we performed additional analyses to identify and remove any TE-related predictions from our MAKER standard gene set. Predicted proteins were compared to a database of Gypsy transposable elements (3.1.b2) . Predictfcompare .hmmscan The create_no_TE_genelist.py script use the data derived above, the Pfam hmmscan results file and a list of TE-related Pfam domains  to create a list of MAKER standard genes with no TE-related predictions.create_no_TE_genelist.py --input_file_TEpfam <TE_Pfam_domains.txt> --input_file_maxPfam <MAKER max predictions hmmscan output file> --input_file_geneList_toKeep <path to MAKER standard gene list> --input_file_TEhmm <Gypsy HMM analysis output> --input_file_TEblast <path to Tpases blast output> --input_file_TErefmap <TE comparison output refmap file> --output_file <path to TE filtered MAKER standard gene list>create_maker_standard_gff.pl --input_gff <MAKER standard GFF3> --output_gff <TE filtered MAKER standard GFF3> --maker_standard_gene_list <path to TE filtered MAKER standard gene list>get_subset_of_fastas.pl -l <path to TE filtered MAKER standard gene list> -f <fasta_merge output transcript/protein fasta> -o <TE filtered MAKER standard transcript/protein fasta>This high-quality gene set without TE-related genes was used for all analyses presented in the Results section. In addition to this standard MAKER annotation, two additional annotations were created using either the SNAP HMM alone or the AUGUSTUS HMM alone, and high-quality gene sets without TE-related genes were identified for each of these annotations, which were used for comparisons to the final GC-specific six HMMs annotation described below.In order to train high and low GC-specific HMMs for SNAP and AUGUSTUS, it was necessary to use training data that consisted of gene models with CDS (coding DNA sequence) GC content within specific ranges. The transcript-based gene predictions from the initial MAKER run (when the est2genome parameter was used) served as the starting point for GC-specific HMM training Fig. . After gThe MAKER_GC_cutoff_determination.pl script helps to identify the GC values of the peaks in a bimodal grass gene GC content distribution. The script pulls out the CDS FASTA sequences for the transcript-based gene predictions from the GFF3 and calculates the GC content for each gene prediction. The script assigns the gene GC values to integer bins based on the --smooth parameter, which helps to smooth the calculation of the distribution by using a moving window average and writes the results to a file. This output file can be used in R to plot the distribution of gene GC content. A FASTA file of the CDS sequences and a GC content file (showing nucleotide composition and GC content of each prediction) are also produced. In addition, a text file is created with the high and low peak values of the bimodal gene GC distribution that were used here as set points in creating the high and low GC HMM training sets. These peaks are determined by taking each GC bin and looking at a set number of bins on either side (set by --peak). A peak is identified when the ((peak - 1) / 2) bins on each side of a GC bin have lower calculated GC values than the middle GC bin. However, users may pick their own high-GC and low-GC cutoff values, and the gene GC content distribution graph may aid in picking those cutoff values. The MAKER_GC_training_set_create.py script relies on two files produced from the MAKER_GC_cutoff_determination.pl script: BASE_NAME_gc_content.txt and BASE_NAME_cutoff.txt. The MAKER_GC_training_set_create.py script will create high-GC and low-GC GFF3 files that can be used for training SNAP and AUGUSTUS.MAKER_GC_training_set_create.py --input_file_gff <path to MAKER GFF file> --input_file_GC_content <BASE_NAME_gc_content.txt file> --input_file_GC_cutoff <BASE_NAME_cutoff.txt file> --output_file_low <path to the low GC GFF file> --output_file_high <path to the high GC GFF file> --genome_fasta <path to the genome fasta file>As detailed in Fig.\u00a0After the creation of high and low GC SNAP and AUGUSTUS HMMs, a final MAKER run is performed using the standard, high and low GC HMMs at the same time. When using multiple SNAP and AUGUSTUS HMMs for this six HMMs annotation, predictions from the different HMMs can be identified by providing the path to a specific HMM, a colon, and an HMM-specific identifier (see below). Providing a comma-separated list to the snaphmm and augustus_species parameters allows the designation of multiple HMMs. To create this new six HMMs structural annotation, the following parameters are set in the MAKER maker_opts.ctl file:#-----Re-annotation Using MAKER Derived GFF3maker_gff = path to MAKER alignment GFF3est_pass=1protein_pass=1rm_pass=1#-----Gene Predictionsnaphmm= path to standard SNAP HMM:orig_snap, path to high GC HMM:high_snap, path to low GC HMM:low_snapAUGUSTUS_species= path to standard AUGUSTUS directory:orig_aug, path to high GC AUGUSTUS directory:high_aug, path to low GC AUGUSTUS directory:low_augkeep_preds=1Once the six HMMs MAKER annotation is finished, a final high-quality MAKER gene set composed of gene models with transcript, protein or Pfam domain support was created using the same protocol that was used above for the standard annotation.O. sativa, three MAKER annotations were created using HMMs trained with transcripts with randomized GC content from the standard annotation. The following Perl scripts were used, which create the training GFF3 files based on a random seed instead of percentage GC content. The random_dataset_generate.pl script takes as input the MAKER standard transcript FASTA and outputs three transcript FASTAs to be used for downstream GFF3 creation and HMM training.random_dataset_generate.pl --transcript < name of transcript FASTA file > --random1 <name of output random file1> --random2 <name of output random file2> --random3 <name of output random file3>To assess the impact of GC specific HMM training on the structural annotation of The seq_name.pl script was run for each of the three random output FASTA files, and generates a list of MAKER standard transcript names from each transcript FASTA.seq_name.pl--fastafile <path to an output file from random_dataset_generate.pl>--output <name of text file with ID names for each FASTA sequence>Finally, the random_gff3_create.pl script requires as inputs the MAKER standard GFF3 with the genome FASTA included and the gene IDs from each of the random FASTAs, and the script generates the final randomized GFF3s that were used for SNAP and AUGUSTUS HMM training.random_gff3_create.pl--align_gff <path to MAKER GFF3 with FASTA included>--rand_1 <path to random 1 IDs>--rand_2 <path to random 2 IDs>--rand_3 <path to random 3 IDs>The outputs of these steps are three GFF3 files containing the coordinates of randomly selected gene predictions. Each of the GFF3 files created by random_gff3_create.pl was then used for SNAP and AUGUSTUS training.To compare the MAKER GC specific HMM training protocol to the isochore-specific AUGUSTUS method, we trained AUGUSTUS in its isochore-sensitive mode as detailed below . After iAfter one round of traditional AUGUSTUS training  which crThe BEDtools v 2.23.0  intersecO. sativa and orthologs in other grass species were identified using OrthoMCL (v1.4) [Brachypodium distachyon  Zea mays , Oryza sativa ssp. Nipponbare  and Sorghum bicolor  were used for comparison.Paralogs of novel high or low GC gene predictions in L (v1.4)  using deBUSCO  (v2.0) was used to assess completeness for the six HMMs protocol compared to the MSU-RGAP annotation. BUSCO was run in protein mode using the plant reference dataset (embryophyta_odb9) containing 1440 protein sequences and orthologous group annotations for major clades. Predicted proteins from the six HMMs annotation and representative proteins from the MSU-RGAP annotation were separately used as input to BUSCO.http://codonw.sourceforge.net). The data was then plotted alongside a curve showing the relationship between the effective number of codons and GC3s under the hypothesis of no selection.The effective number of codons (Nc) was determined and plotted against the GC content of synonymous sites (GC3s) for coding regions of the novel genes predicted by the six HMMs protocol and the MSU-RGAP annotation. CodonW was used to determine the effective number of codons and the GC content of the synonymous sites .Figures were created in R (v. 3.1.1)  using thAdditional file 1:Venn diagram depicting the overlap between the rice GC-specific sixHMM annotation and IGRSP v7 annotation. Of the 7004 genes that are only present in the IGRSPv7 annotation, 1365 (19.5%) are designated as \u201chypothetical\u201d, while 4327 (61.8%) are designated as \u201cexpressed\u201d. (PDF 27\u00a0kb)Additional file 2:Oryza sativa and additional information from gene predictions generated from alternative MAKER methods. Table S1. Number of predictions, average transcript length and AED0.5 of gene predictions generated by alternative MAKER approaches. Table S2. Number of predictions, average transcript length and AED0.5 of the three randomly replicated standard MAKER annotations. Table S3. RNA-seq transcript evidence used in the reannotation of the Oryza sativa genome. (XLSX 40\u00a0kb)Transcript evidence used to reannotate Additional file 3:Figure S1. AED curves of MAKER annotations of Oryza sativa using various ab initio prediction methods. Figure S2. Distribution of GC content of MAKER annotations of Oryza sativa using various ab initio prediction methods. Figure S3. AED curves of MAKER annotations of Oryza sativa using HMMs trained with randomized training data. (PDF 56\u00a0kb)AED curves from various MAKER annotation methods. Additional file 4:Oryza sativa genome. A) Genomic GC content in 300\u00a0Mb bins. Warmer colors indicate higher than average GC content while cooler colors indicate lower than average GC content. B) Heatmap visualization of the density of MAKER six HMMs gene models. C) Genomic location of novel genes predicted by the high GC HMMs. D) Genomic location of novel genes predicted by the low GC HMMs. (PDF 1342\u00a0kb)Distribution of GC content, MAKER six HMMs gene predictions and novel genes predicted by the high and low GC HMMs in the Additional file 5:Gene length distributions of MAKER original, six HMMs, low GC, high GC, novel low and novel high GC predictions. Distribution of gene lengths from each of the MAKER annotation methods and from the novel low and high GC gene sets. (PDF 27\u00a0kb)Additional file 6:Codon usage of novel high and low GC genes compared to MSU-RGAP annotation. The solid line represents the expected number of codons (Nc) values under a null model where there is no selection on codon usage. (PDF 3224\u00a0kb)Additional file 7:OrthoMCL orthogroups containing novel high and low GC gene predictions. OrthoMCL output listing the number of genes, taxa and gene names for each orthogroup that contains at least one novel high or low GC prediction. Novel genes are indicated by bold text. (TXT 55\u00a0kb)Additional file 8:RNA-sequencing analysis of novel high and low GC genes. Heatmap of transcripts per million (TPM) of A) novel genes predicted by low GC HMMs and B) novel genes predicted by high GC HMMs. RNA-sequencing data used for the TPM calculations were obtained from caryopsis, root, leaves inoculated with Xanthomonas oryzae pv. Oryzae, well watered leaves, drought stressed leaves, mesocotyl under light and dark conditions, coleoptile under light and shade conditions, anther and spikelet tissues. Values are scaled by row to a sum of one for visualization purposes. (PDF 128\u00a0kb)Additional file 9:RNA-sequencing data used to assess tissue and treatment specificity of the novel high and low GC gene predictions. (XLSX 34\u00a0kb)Additional file 10:List of MSU-RGAP genes that are not in the MAKER six HMMs annotation, functional descriptions and the lengths of the genes and their CDSes. (XLSX 221\u00a0kb)"}
+{"text": "Bioinformatics research for finding biological mechanisms can be done by analysis of transcriptome data with pathway based interpretation. Therefore, researchers have tried to develop tools to analyze transcriptome data with pathway based interpretation. Over the years, the amount of omics data has become huge, e.g., TCGA, and the data types to be analyzed have come in many varieties, including mutations, copy number variations, and transcriptome. We also need to consider a complex relationship with regulators of genes, particularly Transcription Factors(TF). However, there has not been a system for pathway based exploration and analysis of TCGA multi-omics data. In this reason, We have developed a web based system BRCA-Pathway to fulfill the need for pathway based analysis of TCGA multi-omics data.BRCA-Pathway is a structured integration and visual exploration system of TCGA breast cancer data on KEGG pathways. For data integration, a relational database is designed and used to integrate multi-omics data of TCGA-BRCA, KEGG pathway data, Hallmark gene sets, transcription factors, driver genes, and PAM50 subtypes. For data exploration, multi-omics data such as SNV, CNV and gene expression can be visualized simultaneously in KEGG pathway maps, together with transcription factors-target genes (TF-TG) correlation and relationships among cancer driver genes. In addition, \u2019Pathways summary\u2019 and \u2019Oncoprint\u2019 with mutual exclusivity sort can be generated dynamically with a request by the user. Data in BRCA-Pathway can be downloaded by REST API for further analysis.BRCA-Pathway helps researchers navigate omics data towards potentially important genes, regulators, and discover complex patterns involving mutations, CNV, and gene expression data of various patient groups in the biological pathway context. In addition, mutually exclusive genomic alteration patterns in a specific pathway can be generated. BRCA-Pathway can provide an integrative perspective on the breast cancer omics data, which can help researchers discover new insights on the biological mechanisms of breast cancer. Multi-omics data such as SNV, CNV, and gene expression can be visualized simultaneously on KEGG pathway maps, together with TF-TG correlation and relationships among cancer driver genes.Users can perform comparative analysis of BRCA data, including selection of differentially expressed genes (DEGs) in arbitrary patient groups, mutual exclusivity module (MEMo) summary of genomic alterations (SNV and CNV).Data can be downloaded by REpresentational State Transfer Application Programming Interface (REST API).Transcriptome data measured at the whole genome level requires interpretation at a higher level. For this reason, biological pathway analysis of transcriptome data has become a standard approach, e.g., Pathview , PathwayBRCA-Pathway consists of three components: Database system, REST API, and Web front-end. Overall system design is described in Fig.\u00a0BRCA-Pathway stores and utilizes data such as TCGA breast cancer data, breast cancer subtype by PAM50, TF-TG regulation data, Hallmark gene sets, driver gene list and KEGG pathway data. Data is accessible by web-server system or REST API. Data source and status is described in the Table\u00a0TCGA breast cancer data was obtained from FIREHOSE . ClinicaBreast cancers are classified into subtypes based on gene expression data. PAM50 breast cancer subtyping is widely used method to classify breast cancer into four subtype: Luminal A, Luminal B, Basal-like, HER2-enriched. Breast cancer subtype data is generated using PAM50 predictor bioclassifier R script . The genBRCA-Pathway stored KEGG pathway data by accessing KEGG API  and KEGGKEGG pathway includes information about the gene-gene relation . However, relationships between TFs and potential target genes are not provided by the KEGG pathway. To supplement the KEGG pathway, we provide Pearson correlation coefficient between TF-TG with the KEGG pathway. TF-TG relationships were obtained from two different databases. Molecular Signatures Database provides gene sets that share a transcription factor binding site defined in TRANSFAC database . Human THallmark gene sets summarize and represent well-defined biological states or processes and display coherent expression. Fifty Hallmark gene sets were obtained from MSigDB . When reDriver gene is a gene that contains driver gene mutations or is expressed aberrantly in a fashion that confers a selective growth advantage . We obtaIn order to start with BRCA-Pathway, users need to select pathways of their interest. There are several ways to select pathways Fig.\u00a0. First, BRCA data can be explored in different ways. For example, when users specify subpopulation of TCGA breast cancer patients, the system loads multi-omics data of the selected subpopulation into the web-browser. By clicking \u2018Data overlap option\u2019, users can change the type of data  mapped onto the pathway. This enables users to see the same pathway by three different points of view. BRCA-Pathway colorizes KEGG Pathway entries according to the user-controllable classification criteria. The system provides a function that highlights specific patterns of omics data such as gene expression level up, mutation free and copy number deletion Fig.\u00a0. In addiFigure\u00a033BRCA-Pathway provides visualization of user data to extend the usability of the system. After switching to \u2018User data mode\u2019, input a text file consisting of Entrez geneId and fold change value, and the gene expression level is shown in color. Adjusting color or threshold value helps to customize pathway visualization. However, unlike \u2018TCGA mode\u2019, only a single gene selected by the entry label is considered and all the other genes belonging to the entry are ignored.REST API separates data extraction from the developmental environment so that users can easily extract data without understanding the internal system . Given ptcga-brca.bhi2.snu.ac.kr/api/landscapetcga-brca.bhi2.snu.ac.kr/api/search?keyword=erbb1tcga-brca.bhi2.snu.ac.kr/api/genes/hsa00010+hsa00030tcga-brca.bhi2.snu.ac.kr/api/pathways/hsa00010/related_pathwaystcga-brca.bhi2.snu.ac.kr/api/TCGA-BRCA/hsa00010/CNV?gender=maleThe domain address is the server URL that BRCA-Pathway is configured on. After the slash(/) mark, at least one argument should be given. The 1st argument specifies the data to retrieve and the argument can be landscape, search, genes, pathways, TCGA-BRCA. In case of tcga-brca.bhi2.snu.ac.kr/api/landscape, \u2018landscape\u2019 represents the current status of TCGA data and KEGG pathway. \u2018search\u2019 means that the pathway list will be provided by searching gene names or pathway names, and \u2018genes\u2019 provides the gene list in pathways specified by argument 2. Furthermore, \u2018pathways\u2019 returns pathway information specified by argument 3\u2019s endpoint filtered by argument 2. REST API examples are listed below. The last example means that it will provide the result of aggregating CNV data from TCGA-BRCA data given the patients option is male and the genes filtered by the pathway \u2018hsa00010\u2019. For more customized use, reference Table\u00a0Basal|Her2 |LumA|LumB|Normalsubtype_BHI_RNASeq_Log2 : all |years_to_birth_from : integer & years_to_birth_to : integerindeterminate|negative|positiveer_status : all |indeterminate|negative|positivepr_status : all |indeterminate|negative|positive|equivocalher2_status : all |vital_status : all |0|1 *0: alive, 1:deadpathologic_stage : all |stage_i |stage_ii |stage_iii |stage_iv |stage_iv |stage_tis |stage_xpathologic_T_stage : all |t1 |t2 |t3 |t4 |txpathologic_N_stage : all |n0 |n1 |n2 |n3 |nxpathologic_M_stage : all |cm0_ |m0 |m1 |mxfemale|malegender : all |no|yesradiation_therapy : all |histological_type : all |infiltrating_carcinoma_nos |infiltrating_ductal_carcinoma |in-filtrating_lobular_carcinoma |medullary_carcinoma |metaplastic_carcinoma |mixed_history(please_specify) |mucinous_carcinoma |other__specifynumber_of_lymph_nodes : all |0|1|2 *1: #of node less than or equal to 10, 2: greater thane 10asian|black_or_african_american |whiterace : all |american_indian_or_alaska_native |ethnicity : all |hispanic_or_latino |not_hispanic_or_latinoindeterminate|peri|post|premenopause_status : all |Patient_options are listed below: P-value by the logrank test is provided for the comparison of two groups, and all breast cancer patients are depicted as blue line for the convenience. Figure\u00a05p-value of 0.05, which indicates a significant difference between the survival curves. Figure\u00a05p-value of 0.02, the survival curve of \u2018Her2\u2019 patients having mutation in \u2018Oocyte meiosis\u2019 and \u2018Her2\u2019 patients having no mutation in \u2018Oocyte meiosis\u2019 pathway.BRCA-Pathway provides survival analysis of selected patients. It divides the selected patients into two groups according to the presence of mutation in genes belong to a particular pathway and provides survival analysis for the patient population. For example, if user selected \u2018Basal\u2019 subtype and \u2018Cell cycle\u2019 pathway then patients of \u2018Basal\u2019 subtype are divided into two groups, mutation group and mutation free group. Patients having at least one mutation in the genes involved in the \u2018Cell cycle\u2019 pathway belong to the mutation group. On the other hand, patients without a mutation in \u2018Cell cycle\u2019 pathway belong to the mutation free group. Two hundred twenty-eight patients of \u2018Basal\u2019 subtype are divided into mutation group (168 patients) and mutation free group (60 patients). The green and red line represent the survival curves of mutation group and mutation free group, respectively. p-value is over 0.05, correlation coefficient in \u2018Basal\u2019 subtype samples (-0.07) is shaded. Although we could not identify the responsible genes that promoted transcription of CCNA1 and CCNA2, we found that positive correlation between FOXP3 as TF and CCNA2 as TG is disrupted in tumor samples.If the user wants to prioritize genes that is significant in \u2018Basal\u2019 subtype breast cancer, the exploration can start from DEGs in \u2018Basal\u2019 subtype. By selecting patient subpopulation as \u2018Basal\u2019 and comparison condition as normal pool, then DEGs and related pathways are shown. Since the \u2018Cell cycle\u2019 pathway is listed at the top rank, it is natural to select and load \u2018Cell cycle\u2019 pathway for the next step. After loading a pathway, the user can explore multi-omics data on the \u2018Cell cycle\u2019 pathway of \u2018Basal\u2019 subtype patients. Figure\u00a033BRCA-Pathway helps researchers navigate multi-omics data towards potentially important genes, regulators, and discover complex patterns involving mutations, CNV, and gene expression data of various patient groups in the biological pathway context. In addition, mutually exclusive genomic alteration patterns in a specific pathway can be generated. BRCA-Pathway can provide an integrative perspective on the breast cancer omics data, which can help researchers discover new insights on the biological mechanisms of breast cancer. In the future, BRCA-Pathway could include other omics data sets such as miRNA expression and DNA promoter methylation profiles to support more extensive research. And besides breast cancer data of TCGA, other cancer dataset availability is also needed."}
+{"text": "To determine precision of magnetic resonance imaging (MRI) based fat and muscle quantification in a group of postmenopausal women. Furthermore, to extend the method to individual muscles relevant to upper-body exercise.This was a sub-study to a randomized control trial investigating effects of resistance training to decrease hot flushes in postmenopausal women. Thirty-six women were included, mean age 56 \u00b1 6 years. Each subject was scanned twice with a 3.0T MR-scanner using a whole-body Dixon protocol. Water and fat images were calculated using a 6-peak lipid model including R2*-correction. Body composition analyses were performed to measure visceral and subcutaneous fat volumes, lean volumes and muscle fat infiltration (MFI) of the muscle groups\u2019 thigh muscles, lower leg muscles, and abdominal muscles, as well as the three individual muscles pectoralis, latissimus, and rhomboideus. Analysis was performed using a multi-atlas, calibrated water-fat separated quantification method. Liver-fat was measured as average proton density fat-fraction (PDFF) of three regions-of-interest. Precision was determined with Bland-Altman analysis, repeatability, and coefficient of variation.All of the 36 included women were successfully scanned and analysed. The coefficient of variation was 1.1% to 1.5% for abdominal fat compartments , 0.8% to 1.9% for volumes of muscle groups , and 2.3% to 7.0% for individual muscle volumes . Limits of agreement for MFI was within \u00b1 2.06% for muscle groups and within \u00b1 5.13% for individual muscles. The limits of agreement for liver PDFF was within \u00b1 1.9%.Whole-body Dixon MRI could characterize a range of different fat and muscle compartments with high precision, including individual muscles, in the study-group of postmenopausal women. The inclusion of individual muscles, calculated from the same scan, enables analysis for specific intervention programs and studies. Body composition measurements are increasingly important for diagnosis and monitoring of metabolic diseases ,2, musclMagnetic resonance imaging provides tomographic images with high soft tissue contrast, which enables quantification of fat and muscle compartmental volumes. Especially Dixon methods , that prRecently, methods have been suggested that automatically or semi-automatically identify and quantify fat and muscle tissue volumes using MRI \u201316. ThesWhile these studies show high reproducibility and accuracy for whole body measurements and groups of muscles, such as thigh muscles, it is challenging to combine this with smaller individual muscles based on the same MR-acquisition. To our knowledge, no study has previously reported on the reproducibility of individual muscle volumes combined with the more common whole body and compartmental measurements.e.g. due to hot flushes and sleep disturbances. A recent review concluded that increased weight in middle-aged women is mainly due to chronological aging while changed body composition and fat distribution after menopause are related to ovarian aging [One subject group of particular interest is postmenopausal women, where fat-accumulation tend to shift toward increasing visceral adiposity. As a consequence of this, postmenopausal women display variability in phenotypes depending on their specific fat-accumulation pattern. Also, many women experience decreased quality of life an aging . Increasan aging . Increasan aging  and is can aging . Obese pan aging . Hot fluan aging . Exercisan aging . MRI-basan aging . It will(1) to determine precision for MRI-based fat and muscle measurements in the study-group at baseline, and (2) to extend the method to measure volume of specific individual muscles relevant to the main RCT training-program.This was a sub-study of the larger randomized control trial (RCT) investigating effects of resistance training in postmenopausal women where the primary outcome is hot flush frequency. The aim of this study was i.e. more than 12 months since last menstrual bleeding) were invited to the study by means of advertisements in the local newspaper. Exclusion criteria were e.g. use of therapy that may influence hot flushes and physical activity more than 225 minutes per week of any intensity (including a maximum of 75 minutes per week of moderate to vigorous intensity). The study was performed according to the Declaration of Helsinki and Good Clinical Practice, and the study protocol was approved by The Regional Ethical Review Board in Link\u00f6ping, Sweden (No: 2013/285-31). Written informed consent was obtained from all subjects prior to study entry.The main RCT (registered as ID: NCT01987778) is an open, parallel group, randomized controlled intervention study conducted in Link\u00f6ping, Sweden. From the main RCT 36 postmenopausal women were enrolled and included in this precision sub-study. Subjects were enrolled on a voluntary basis, and all subjects that volunteered for MRI and had been included in the main RCT were included in this sub-study. Full details on inclusion/exclusion and the rationale for using resistance training as intervention have been reported in Berin et al. ; in summ3. The first and last four slabs consisted of 66 slices; slabs two to six consisted of 39 slices accelerated using a SENSE factor of 1.6, acquired during 17-seconds expiration breath-holds. Additional abdominal slabs with a flip angle of 5\u00b0, but otherwise identical, were acquired over the liver for liver-fat quantification. Water and fat images were calculated from the multi echo images [In-vivo imaging was performed using a Philip Ingenia 3.0 T MR-scanner . Each subject was scanned twice on one occasion, where the subject was removed from the scanner room in between acquisitions. The protocol was a four-point 3D spoiled gradient multi-echo protocol with real and imaginary image reconstruction, acquired using a dStream WholeBody coil array. Total head-feet coverage was 1.76 m, divided over ten overlapping slabs of axial image with 25 mm overlap. Common parameters for all slabs were; flip-angle 10\u00b0, repetition time TR = 6.69 ms, echo times TE = 1.15/2.30/3.45/4.60 ms and voxel size 2.5\u00d72.5\u00d74 mmo images , using ao images . Phase s\u00ae Profiler . The methods used in AMRA\u00ae Profiler have been thoroughly described in earlier publications [(1) image calibration to fat referenced images, (2) labels of fat and muscle compartments registered to the acquired volumes, (3) quality control of labels performed by trained analysis engineers at Advanced MR Analytics , and (4) quantification of fat and muscle volumes based on the calibrated images by integrating over the quality controlled labels. This process was described in detail in [Body composition analyses for abdominal fat and muscles were performed from the reconstructed water and fat images, using the commercially available service AMRAications , 15, 33 etail in . The inci.e. muscle tissue with an adipose tissue concentration of less than 50%. As the calibrated fat images are T1-corrected [Muscle fat infiltration was measured for each muscle. The MFI measurements were defined as the average PDFF of the muscle tissue, orrected , and rep3 regions of interest (ROI) manually placed in right liver lobe, avoiding major vessels and bile ducts. The liver test and re-test scans were pooled and analysed in randomized order.Based on water-fat images acquired with a 5\u00b0 flip angle, the liver-fat was measured as the average PDFF of three 22x22x28 mmsw, was estimated as the square root of the mean within-subject variance. The repeatability was calculated as 2.77 * sw, as suggested by Bland and Altman [Descriptive statistics (mean \u00b1 SD) were calculated for all volumes. Precision was calculated as repeatability, defined in , using Bd Altman . This dee.g. that no slabs were missing and no severe swaps were present. The mean age was 56 \u00b1 6 years (range 45 to 70 years), the mean BMI was 26.5 \u00b1 3.6 kg/m2 (range 18.9 to 33.5 kg/m2). Acquisition time for each scan was 8:00 minutes. Representative results for the compartmental fat and muscle segmentations are shown in All of the 36 included women were scanned and analysed with approved quality control, as defined in , includiWithin the group, the mean VAT was 2.50 \u00b1 1.30 L (range 0.73 L to 5.93 L), mean ASAT was 8.30 \u00b1 2.74 L (range 2.61 L to 12.84 L), mean total thigh volume was 8.92 \u00b1 1.18 L (range 6.80 L to 10.94 L), and mean total thigh MFI was 9.02 \u00b1 1.98% (range 6.02 to 13.00%). Complete volumes and liver fat details are reported in The repeatability was 0.116 L for VAT, 0.346 L for ASAT, and 0.246 L for total thigh volume, and 1.32% for total thigh MFI. Furthermore, the repeatability was 1.69% for liver fat, 0.0477 L to 0.186 L for the larger muscle regions, and 0.0054 L to 0.0220 L for the individual muscles. Complete volumes and liver fat precision statistics are reported in Bland-Altman plots for fat compartments are shown in \u00ae Profiler are available in the supporting information file The complete body composition measurements results as provided by AMRAThis study was a baseline sub-study to an on-going RCT, with the purpose of determining precision for MRI-based compartmental fat and muscle measurements, and to extend the measurements to specific individual muscles that are relevant to the main RCT training-program. Body composition measurements were calculated from a single rapid Dixon-based MR acquisition including the volume of VAT, ASAT, and PDFF in the liver, as well as the fat-free muscle volume and MFI in the posterior thigh, anterior thigh, lower leg, and muscles in the abdomen. Furthermore the individual muscles latissimus dorsi, pectoralis major, and rhomboideus were included.3. The protocol was similar to previously published results from the vast population study in UK Biobank [In this study, whole-body coverage was achieved using an MR protocol with ten slabs and a voxel size of 2.5\u00d72.5\u00d74 mm Biobank . In that Biobank  , pectorialis is normally smaller , and rhomboideus is the smallest muscle . This encompasses a representative sample of muscle sizes and this may be extrapolated to measurements of other muscles in different intervention programs, and studies. Furthermore, the women presented a diversity of phenotypes, especially concerning the visceral adipose tissue (range 0.73\u20135.97 L) and abdominal subcutaneous adipose tissue (range 2.58\u201313.00 L), as can be expected due to the shift in fat-accumulation pattern during menopause.The measurements in this sub-study were made at baseline of the main RCT, In this MR-study we used a standardized MR-protocol for whole-body coverage and body composition analysis. The protocol was optimized to balance scan-time and acquired volume in terms of coverage, voxel size and signal-to-noise-ratio (SNR). In this study, the acquisition resolution and SNR was too low to achieve high precision for the smallest muscle, rhomboideus. As a consequence of this the results showed higher CV for this muscle. One method to increase the resolution and SNR could be to add additional breath-hold slabs, but this would also increase the scan-time. Furthermore, all individual muscles that were measured in this study were in the abdominal region that is affected by breathing artefacts, with a 17-second breath-hold time it is possible that the reported precision was somewhat affected by this. While sufficient for all subjects in this particular study, the maximum height for head-feet coverage was 1.76 m. In a different study population adding an additional slab, at the expense of scan-time, could increase this.A further limitation of this study was that all data was collected at baseline. Although this gives a comprehensive view of the baseline precision, in terms of repeatability, it is not possible to assess the effects of daily variations, that are likely to affect the precision and required power in the main RCT. Also, it was not possible to assess reproducibility where the experimental conditions change, such as a different MR-scanner or different operators.In conclusion, this study verifies that whole-body Dixon MRI can characterize a range of different fat and muscle compartments with high precision in the study-group of postmenopausal women. Furthermore, the method was successfully extended to allow precise measurements of individual muscles. The results support the use of combined fat and muscle measurements on a compartmental level and for individual muscles, based on a single rapid MR acquisition. The addition of individual muscle measurements, calculated from the same scan, opens the possibility to tailor the analysis for specific intervention programs and studies.S1 TableColumns in the file are, for the first scan: liver PDFF (liver_fat_t), visceral adipose tissue (vat_t), abdominal subcutaneous adipose tissue (asat_t), left posterior thigh muscle volume (lp_thigh_t), right posterior thigh muscle volume (rp_thigh_t), left anterior thigh muscle volume (la_thigh_t), right anterior thigh muscle volume (ra_thigh_t), total thigh muscle volume , left lower leg muscle volume (l_lower_leg_t), right lower leg muscle volume (r_lower_leg_t), left abdominal muscle volume (l_abd_t), right abdominal muscle volume (r_abd_t), left latissimus muscle volume (l_lat_t), right latissimus muscle volume (r_lat_t), left pectoralis muscle volume (l_pec_t), right pectoralis muscle volume (r_pec_t), left rhomboideus muscle volume (l_rho_t), right rhomboideus muscle volume (r_rho_t), and for the second scan: liver PDFF (liver_fat_r), visceral adipose tissue (vat_r), abdominal subcutaneous adipose tissue (asat_r), left posterior thigh muscle volume (lp_rhigh_r), right posterior thigh muscle volume (rp_rhigh_r), left anterior thigh muscle volume (la_rhigh_r), right anterior thigh muscle volume (ra_rhigh_r), total thigh muscle volume , left lower leg muscle volume (l_lower_leg_r), right lower leg muscle volume (r_lower_leg_r), left abdominal muscle volume (l_abd_r), right abdominal muscle volume (r_abd_r), left latissimus muscle volume (l_lat_r), right latissimus muscle volume (r_lat_r), left pectoralis muscle volume (l_pec_r), right pectoralis muscle volume (r_pec_r), left rhomboideus muscle volume (l_rho_r), right rhomboideus muscle volume (r_rho_r). MFI measurements are prefixed by \u201cmfi_\u201d followed by the muscle or muscle group and test/retest as defined above.(XLSX)Click here for additional data file."}
+{"text": "Although CircRNA_100269 is a biomarker used to predict cancer recurrence, its expression and function in gastric cancer (GC) remain unknown. In this study, the expression of circRNA_100269 and its potential downstream miRNA targets were investigated. The molecular function and regulatory mechanism of circRNA_100269 in GC cell lines were also elucidated. The expression levels of circRNA_100269 and its linear isomer LPHN2 mRNA were found to be downregulated (p<0.01) in GC tissues. The target miRNA was predicted to be miR-630, whose expression was upregulated (p<0.01) and found to be negatively correlated with that of circRNA_100269 (r = \u22120.688) in GC tissues. Moreover, direct interaction of circRNA_100269 and miR-630 was confirmed through dual-luciferase assays. Overexpressing the circRNA_100269 plasmid inhibited cell proliferation (p<0.05). Furthermore, transfection of miR-630 mimics into cell lines overexpressing circRNA_100269 blocked the function of circRNA_100269 (p<0.05). Thus, circRNA_100269 level was downregulated in GC and correlated negatively with that of miR-630. Taken together, our results suggest that circRNA_100269 and miR-630 comprise a novel pathway that regulates proliferation of GC cells. Gastric cancer (GC) is a common malignant tumor with the fourth highest occurrence among different cancers and is the third leading cause of death worldwide . In 2013Circular RNAs (circRNAs) are closed-loop RNAs produced through end-to-end joining of RNA transcription fragments during transcription . AlthougIn our previous study, we found that circRNA_100269 is an independent predictor of early recurrence of stage III GC . HoweverqRT-PCR analysis was performed in 112 pairs of human GC specimens and their adjacent non-cancerous tissue samples to confirm circRNA_100269 expression in GC. CircRNA_100269 expression was reduced in 70.5% (79/112) of GC tissues Fig.  and was We detected expression of linear LPHN2 mRNA, which is the linear isomer of circRNA_100269, in 67 randomly selected GC and adjacent tissues Fig. . ExpressThe potential targets of circRNA_100269 were searched in bioinformatics databases via Target-Scan and miRanda to explore the underlying molecular mechanism. In addition, the potential binding sites of miR-605-3p and miR-630 in circRNA_100269 were predicted Fig. . To explTo determine whether miR-630 directly targets circRNA_100269, we constructed dual-luciferase reporter plasmids carrying a fragment of the mutant or wild-type circRNA_100269 sequence and the predicted miR-630 recognition site. A Dual-Luciferase Reporter Assay System was then adopted in random selected AGS and SGC7901 cells. Normalized fluorescence intensity of the reporter was significantly lower in cancer cells co-transfected with the circRNA_100269 segment and miR-630 mimics compared to controls Fig. . By contWe analyzed circRNA_100269 expression in human GC cell lines. Expression level of circRNA_100269 in AGS, MKN28, MKN45, BGC823, MGC803, and SGC7901 cells was significantly lower than that in the normal gastric mucosa cell line GES1 Fig. . We thenIf the effect of circRNA_100269 is specific, then the effect of overexpressing circRNA_100269 must be suppressed by co-expression of miR-630. Following this reasoning, miR-630 mimics, which can increase the levels of miR-630, were co-transfected with pcDNA3.1- circRNA_100269 into AGS and MKN28 cells. CCK-8 and cell formation assays were then performed. Growth rates significantly increased in groups with co-expressed circRNA_100269 and miR-630 mimics compared to that with co-expressed circRNA_100269 and NC mimics in both cell lines Fig. .CircRNAs exist widely in various organisms, including human cells . CircRNACircRNA_100269 is an exon circRNA transcript from the GRCh38.p7 fragment of chromosome 1, which is homologous to the protein coding gene LPHN2. LPHN2 encodes the latrophilin protein, a cell surface receptor, containing seven transmembrane segments .LPHN2 is considered to be a downstream target of p53 and is aberrantly expressed in some tumors , 26. In A plethora of studies have been conducted on the roles of miRNAs in cancer. Various aberrantly expressed miRNAs have been found to be related to the progression and prognosis of GC , 28. MosWe sought to elucidate the direct biological function of the circRNA_100269-miR-630 axis in GC cell lines. First, two cell lines with the lowest expression of circRNA_100269 were chosen for overexpression experiments. We found that the level of miR-630 decreased significantly after overexpressing circRNA_100269. This suggested that circRNA_100269 may negatively regulate miR-630.Next, the function of circRNA_100269 in proliferation of CRC cells was explored. Overexpressing circRNA_100269 in GC cells decreased the rate of cancer cell proliferation. Moreover, miR-630 decreased the function of circRNA_100269. These results strongly suggest that the circRNA_100269-miR-630 axis plays an important role in GC cell growth.MiR-630 is one of the newly discovered miRNAs, and its role in cancer has gained increasing attention. MiR-630 is overexpressed in a variety of tumors , 32. Furin vivo. Besides, although several miR-630 target genes were predicted by bioinformatics, it remains to be verified whether these represent bona fide targets in the context of GC. Further experimental analyses should be carried out to elucidate this.Our study has several limitations. Our studies were done primarily in cell-based assays. As such, the function of the circRNA_100269-miR-630 axis remains to be validated In summary, we found that the expression levels of circRNA_100269 and its linear isomer were downregulated in GC tissues. Additionally, expression of the downstream target miR-630 was negatively correlated with circRNA_100269 expression. These results uncover a novel circRNA_100269-miR-630 signaling pathway involved in GC cell growth. Our findings highlight the diagnostic and therapeutic potential of these molecules in GC treatment.A total of 112 patients diagnosed with GC were recruited in this study. All fresh tissues were collected between December 2012 and May 2015 during radical surgery at Nanfang Hospital of Southern Medical University. The samples were frozen in liquid nitrogen for 5 min and stored at \u221280\u00b0C. None of enrolled patients received chemotherapy, radiotherapy, or target therapy before radical surgery. The study protocol was approved by Institutional Review Board of Nanfang Hospital Southern Medical University. Informed consent was obtained from all patients involved in this study. All methods were performed in accordance with the relevant guidelines and regulations.Frozen tissues were homogenized using Trizol reagent  to extract total RNA following the manufacturer\u2019s instruction.cDNA was synthesized by reverse transcription using GoScript RT System  and All-in-One miRNA Reverse Transcription Kit . qRT-PCR analysis was performed using GoTaq qPCR Master Mix  and SYBR Green Human miRNA Assay Kit . The thermocycler programs were as follows: 95\u00b0C for 10 min and 40 cycles of 95\u00b0C for 30 s, 55\u00b0C annealing temperature for primer pairs for 30 s, and 72\u00b0C for 30 s. Each reaction was performed in triplicate. Reverse primers were designed to ensure the amplification of the head-to-tail splicing of circRNA.http://www.targetscan.org) and miRanda (http://www.microRNA.org) were used to predict potential circRNA_100269 targets. Although five miRNA targets were predicted, only two  with expression in human tissues were followed up on. The binding site for circRNA_100269 was predicted to be at position 325\u2013346 in miR-630 and at position 351\u2013371 in miR-605-3p.TargetScan (5 cells/well) were transiently transfected with circRNA_100269 segment vector. Co-transfection with 20 nmol/L miR-630 mimics or control was then performed. Cells were harvested 48 h after transfection. The Dual Luciferase Reporter Assay System  was used to detect luciferase activity.A circRNA_100269 segment (100bp) was synthesized with either mutant or wild-type seed region and cloned into the psiCHECK-2 vector . Five nucleotides in the seed region were mutated to obtain the mutant circRNA_100269 sequences. All cell lines were transfected using Lipofectamine 3000 . Cells  to overexpress circRNA_100269.Human colorectal cancer cell lines, namely, AGS, MKN28, MKN45, BGC823, MGC803, SGC7901, and GES1, were purchased from the Cell Bank of Type Culture Collection . All of these cell lines were maintained in RPMI 1640 medium containing 10% fetal bovine serum  in a humidified incubator at 37\u00b0C under 5% CO2.Cell proliferation was assayed using the Cell Counting Kit-8 (CCK8) assay and clone formation assay.For CCK8 assay, the transfected cells were plated in 96-well plates (1000 cells/well). Cell proliferation was detected every 24 h according to the manufacturer\u2019s protocol. Briefly, 10 \u03bcL of CCK 8 solution  was added to each well and incubated at 37\u00b0C for 2 h. The solution was measured spectrophotometrically at 450 nm. Each group was analyzed three times.For clone formation assay, the transfected cells were plated in six-well plates (200 cells/well). Cells were cultured for 2 weeks and stained with Giemsa after fixing with paraformaldehyde. The number of clones formed was counted, and the rate of colony formation ratio in each plate was calculated. Each group was analyzed three times.All statistical analyses were performed using SPSS 20.0 software . Data were expressed as mean \u00b1 SD from at least three separate experiments. Differences between groups were analyzed using Student\u2019s t test and Kruskal\u2013Wallis test. The correlation between circRNA_100269 and miRNAs was analyzed using Pearson correlation test. P-value less than 0.05 was considered statistically significant."}
+{"text": "P<0.001). Our data further showed that lower expression of hsa_circ_0004018 was correlated with serum alpha-fetoprotein (AFP) level, tumor diameters, differentiation, Barcelona Clinic Liver Cancer stage and Tumor-node-metastasis stage. More importantly, we detected liver tissues from chronic hepatitis, cirrhosis and HCC patients; and found that hsa_circ_0004018 harbored HCC-stage-specific expression features in diverse chronic liver diseases (P<0.001). The area under receiver operating characteristic curve was up to 0.848 . The sensitivity and specificity were 0.716 and 0.815, respectively. Finally, hsa_circ_0004018 might be involved in cancer-related pathways via interactions with miRNAs.Circular RNAs (circRNAs) have been emerged as an indispensable part of endogenous RNA network. However, the expression significance of circRNAs in hepatocellular carcinoma (HCC) is rarely revealed. The aim of this study was to determine the circRNA expression profile in HCC, and to investigate their clinical significances and relevant mechanisms for cancer progression. The global circRNA expression profile in HCC was measured by circRNA microarray. Levels of one representative circRNAs, hsa_circ_0004018, were confirmed by real-time reverse transcription-polymerase chain reaction. The expression levels of hsa_circ_0004018 in HCC were significantly lower compared with para-tumorous tissue ( Globally, hepatocellular carcinoma (HCC) is the most common type of hepatic malignancies, accounting for approximately 90% of primary liver cancer. It ranks as the second most significant cause of cancer-related deaths in men, 50% of the cases and deaths occurred in China . It is dIn recent years, circular RNAs (circRNAs) have emerged as a new star in noncoding RNA (ncRNA) world, representing a class of endogenous RNAs existing in mammalian cells and featuring stable structure and high cell-type-specific, tissue-specific and developmental-specific expression . By inteP value=4.7164E-07). It is transcribed from SMYD4 (SET and MYND domain containing 4) on chromosome 17.Since the global circRNA expression profile in HCC is not fully uncovered, in the present study, we explored the circRNA expression profile in HCC. We identified 527 differentially expressed circRNAs (including 174 upregulated and 353 downregulated genes) in HCC tissues compared with para-tumorous tissues. And then, we focused on hsa_circ_0004018, one of the most downregulated circRNAs in microarray detection . The box plot is a direct way to rapidly visualize the distributions of a dataset for the circRNAs profiles. After normalization, the distributions of log2 ratios among ten samples are nearly the same  between groups were identified by volcano plot filtering . As is shown in Figure P<0.001). As shown in Figure P<0.001), SMMC7721 (P<0.05), Huh7 (P<0.01), MHCC97H (P<0.01), and HCCLM3 (P<0.01).We used qRT-PCR method to measure the hsa_circ_0004018 expression levels in liver tissues from chronic hepatitis, cirrhosis, HCC and para-tumorous tissues. The head-to-tail splicing junction of hsa_circ_0004018 was confirmed by sequencing of the product of qRT-PCR Figure , which wThen, we analyzed the relationship between the expression levels of hsa_circ_0004018 and clinicopathological factors of patients with HCC. As Table P<0.001); and its levels in cirrhosis tissues were significantly lower than those in chronic hepatitis tissues (F=0-3) (P<0.001). Hsa_circ_0004018 expression levels exhibited HCC-stage-specific characteristics  was 0.848 , one of representative circRNAs, was recently shown to harbor 76 miR-7 binding sites and to influence many diseases including HCC, diabetes, prion disorders and cancers [Myrip and Pax6, which may become a new target for improving \u03b2 cell function in diabetes [The function and mechanism of most circRNAs are not completely known , 33. Rec cancers \u201342. Xu ediabetes . In benzdiabetes . With bidiabetes , 45. AndIn conclusion, as one of circRNAs, hsa_circ_0004018 was lowly expressed in HCC. At the same time, hsa_circ_0004018 showed HCC-stage-expressive characteristics from chronic hepatitis to cirrhosis and to HCC. These indicate that hsa_circ_0004018 not only might be a potential biomarker for the diagnosis of HCC, but also play a role in the carcinogenesis and metastasis of HCC. And further detailed mechanism studies underlying hsa_circ_0004018 are being carried out in our laboratory.The total of 102 HCC patients, who underwent surgeries at three medical centers  from March 2013 to December 2016, were included in this study. The para-tumorous tissues were obtained from 1 cm away from the edge of the HCC; and there were no obvious tumor cells. The diagnosis of HCC was confirmed by histological examination. Staging was determined by the BCLC staging system  and AmerOther liver tissues were collected from 55 cases of chronic hepatitis patients from September 2013 to December 2016 in Ningbo No. 2 Hospital through liver biopsy under guided ultrasound. Fibrosis stage was assessed by the METAVIR scoring system  . In all After being obtained, tissue samples were immediately soaked in RNA fixer Reagent  and stored at \u221280 \u00b0C until used. Histology was independently assessed by two experienced pathologists who were blinded to the clinical data. This study was approved by the Human Research Ethics Committee from Ningbo University. Informed consent was obtained from all patients.2.HCC cell lines, HepG2, Huh7, SMMC-7721, MHCC97H and HCCLM3, and human normal hepatic cell line L02 were cultured with RPMI 1640 Medium  containing 10% fetal bovine serum in a humidified atmosphere of 5% COTotal RNA was extracted by TRIzol reagent  in accordance with the manufacturer's instructions. Concentration and purity of total RNA samples were measured by the Smart Spec Plus spectrophotometer . If the ratio of A260/A280 was 1.8\u20132.0, RNA was used for further experiments.The microarray detection was performed by KangChen Bio-tech  under the guidance of the experiment workflow . The cir2, 4\u03bcl GoScript 5\u00d7reaction buffer, 1\u03bcl nucleotide mix, 0.5\u03bcl recombinant RNasin ribonuclease, and 1\u03bcl GoScript reverse transcriptase were added in the system and then incubated at 42 \u00b0C for 1h. RT reaction and no-template control were run at the same time.The cDNA was generated using the GoScript Reverse Transcription (RT) System  following the manufacturer's instructions. Briefly, 2\u03bcg total RNA, 1\u03bcl random primer, 1\u03bcl oligo(dT)15 primer, 2\u03bcl MgClhttp://www-genome.wi.mit.edu) and synthesized by Sangon Biotech . Their sequences were as follows: for hsa_circ_0004018 (target gene) 5\u2019- TCAACCTTTTGCCCCACACT-3\u2019 and 5\u2019- AAGACACGTCTGTGTGTTGT-3\u2019; and for glyceraldehyde 3-phosphate dehydrogenase , 5\u2019-TCGACAGTCAGCCGCATCTTC TTT-3\u2019 and 5\u2019-ACCAAATCCGTTGACTCCGACCTT-3\u2019. Real-time PCR was done in triplicate. The amplification specific was confirmed by melting curve analysis. The data from qRT-PCR were analyzed by the \u0394Ct method and the 2Ct\u2212\u0394\u0394 method. All results are expressed as the means\u00b1SD. All of assays were performed in a blinded fashion.Quantitative polymerase chain reaction (qPCR) was performed using the GoTaq qPCR Master Mix (Promega) on an Mx3005P real-time PCR System  in the light of the protocol. Outward facing Primers were designed with Primer3 . After that, DNA sequencing was performed by Sangon Biotech Co., Ltd.Liver function including total protein (TP), albumin, aspartate transaminase (AST), alanine aminotransferase (ALT), alkaline phosphatase (AKP), gamma glutamyl transferase (GGT), and total bilirubin was measured by Olympus AU 2700 automatic biochemical analyzer with original kits . AFP was measured with an Elecsys 2010 machine .P<0.05 was used as the criterion for statistical significance.The miRNA pathway was carried out based on DIANA-miRPath . All of t test, independent t test and one way analysis of variance (ANOVA) were used in this study correctly. A receiver operating characteristic (ROC) curve was established to value the diagnostic power. P value of 0.05 or less was considered statistically significant.All statistical analysis in this study were performed by the Statistical Product and Service Solutions (SPSS) 16.0 software package  and GraphPad Prism 6.0 . Paired"}
+{"text": "Mycobacterium tuberculosis has the ability to survive inside macrophages under acid-nitrosative stress. M. tuberculosis Rv1685c and its ortholog in M. smegmatis, MSMEG_3765, are induced on exposure to acid-nitrosative stress. Both genes are annotated as TetR transcriptional regulators, a family of proteins that regulate a wide range of cellular activities, including multidrug resistance, carbon catabolism and virulence. Here, we demonstrate that MSMEG_3765 is co-transcribed with the upstream genes MSMEG_3762 and MSMEG_3763, encoding efflux pump components. RTq-PCR and GFP-reporter assays showed that the MSMEG_3762/63/65 gene cluster, and the orthologous region in M. tuberculosis (Rv1687c/86c/85c), was up-regulated in a MSMEG_3765 null mutant, suggesting that MSMEG_3765 acts as a repressor, typical of this family of regulators. We further defined the MSMEG_3765 regulon using genome-wide transcriptional profiling and used reporter assays to confirm that the MSMEG_3762/63/65 promoter was induced under acid-nitrosative stress. A putative 36 bp regulatory motif was identified upstream of the gene clusters in both M. smegmatis and M. tuberculosis and purified recombinant MSMEG_3765 protein was found to bind to DNA fragments containing this motif from both M. smegmatis and M. tuberculosis upstream regulatory regions. These results suggest that the TetR repressor MSMEG_3765/Rv1685c controls expression of an efflux pump with an, as yet, undefined role in the mycobacterial response to acid-nitrosative stress. Mycobacterium tuberculosis multi-drug resistant strains continues to plague the control of TB worldwide  is still endemic in many low and middle-income countries and the high incidence of orldwide . Indeed,lication . Bacillilication . This islication . Model sc stress .M. tuberculosis and M. smegmatis to acid-nitrosative multi-stress, simulating a macrophage-like environment. In these conditions, Rv1685c in M. tuberculosis and its ortholog MSMEG_3765 in M. smegmatis were found to be up-regulated. Both genes are annotated as transcriptional regulators of the TetR family, sharing a high percentage of identity between their deduced amino acid sequences. Members of the TetR family of transcriptional regulators are widespread among bacteria  using a combination of mutagenesis, local and global gene expression analyses, and DNA binding studies to show that it regulates the MSMEG_3762/63/65 (and Rv1687c/86c/85c) operon encoding an efflux pump. This system is conserved in M. tuberculosis and our experiments suggest that this TetR regulator plays a novel role in the mycobacterial response to the intracellular environment.Here, we characterize the TetR regulator Escherichia coli TOP10 and DH5\u03b1 were used as strains for cloning and E. coli BL21 (DE3) was used as a host for protein expression. M. smegmatis mc2155 and M. tuberculosis H37Rv were used throughout this work. The E. coli strains were grown in Luria-Bertani (LB) broth, while the mycobacterial strains were cultured in Middlebrook 7H9 broth (Difco) containing 10% oleic acid-albumin-dextrose-catalase supplement (Becton Dickinson) and 0.05% Tween 80. All strains were grown at 37\u00b0C with shaking. Hygromycin (200 \u03bcg ml-1 for E. coli and 100 \u03bcg ml-1 for M. smegmatis), kanamycin (50 \u03bcg ml-1 for E. coli and 25 \u03bcg ml-1 for M. smegmatis), 5-bromo-4-chloro-3-indolyl-\u03b2-D-galactopyranoside  and sucrose (2% w/v) were used for selection or screening as appropriate. Acid-nitrosative multi-stress was induced in 7H9 buffered medium at pH 5.3 by the addition of NaNO2 up to a final concentration of 5 mM for 5 h. Plasmids used throughout this work are listed in Supplementary Table MSMEG_3765 locus was conducted using BLAST and Clustal Omega. The analysis to identify putative TetR binding sites was conducted using MEME1 , was PCR-amplified from M. smegmatis mc2155 genomic DNA using the forward upMS3765f and reverse upMS3765r primers, with HindIII-BamHI sites respectively, to clone the up fragment into p2NIL, yielding the pFP2 plasmid. A 906 bp fragment (dw), containing the downstream flanking regions of MSMEG_3765 (from 3830395 to 3831300), was PCR-amplified from M. smegmatis mc2155 genomic DNA using the forward dwMS3765f and reverse dwMS3765r primers, with BamHI-PacI sites respectively, to clone the dw fragment into pFP2, yielding the pFP3 plasmid. To obtain the suicide delivery vector (pFP4), the PacI cassette from pGOAL19 was cloned into pFP3. pFP4 was electroporated into M. smegmatis mc2155 and single crossovers were selected using kanamycin, hygromycin and Xgal. A single blue kanamycin and hygromycin-resistant colony was streaked onto fresh media without selection, and incubated at 37\u00b0C for 3\u20135 days to allow for second recombination events, before selection on plates containing sucrose and Xgal. The white sucrose-resistant colonies were screened for kanamycin and hygromycin sensitivity, then analyzed by PCR to confirm the deletion in MSMEG_3765. The deletion event in M. smegmatis \u0394MSMEG_3765 was verified by sequencing.The strategy . A 804 bM. smegmatis \u0394MSMEG_3765, a DNA fragment containing the 636 bp coding sequence of MSMEG_3765 (including start and stop codons) was amplified from M. smegmatis mc2155 with forward cMS3765f and reverse cMS3765r primers. The forward primer included an optimized Shine\u2013Dalgarno sequence , and vortexed using glass beads. Cell lysates were recovered by centrifugation and RNAs was extracted using RNeasy kit (Qiagen), according to the manufacturer\u2019s instructions. RNA samples were treated with RQ1 DNase (Promega) for 30 min at 37\u00b0C, followed by heat inactivation. Finally, the quality and quantity of RNA was assessed using NanoDrop spectrophotometer analysis  and gel electrophoresis. Reverse transcription was performed for 15 min at 42\u00b0C in a total volume of 20 \u03bcl containing 1 \u03bcg total RNA (QuantiTect Reverse Transcription kit-Qiagen). As negative controls, samples without the reverse transcription step were used as template.RNA for RT-PCR and RTq-PCR analyses were extracted from wild-type mcMSMEG_3765 locus. Oligonucleotides were designed to span the intergenic regions from MSMEG_3760 to MSMEG_3765 . The sigA gene was used as an internal standard for expression analysis. The PCR conditions included an initial denaturation at 95\u00b0C for 10 min, followed by 40 cycles of amplification of 15 s at 95\u00b0C, 1 min at 60\u00b0C, and 30 s at 72\u00b0C. RT-qPCR analysis was performed in triplicate, and each assay included standard curves for both internal control and target genes, obtained by amplifying serial dilutions (ratio 1:10) of the samples. Relative expression levels were normalized using sigA and calculated using the 2-\u0394\u0394Ct method  designed by the Bacterial Microarray Group at St. George\u2019s (ArrayExpress accession number A-BUGS-39). To define the \u0394MSMEG_3765 regulon, significantly differentially expressed genes were identified comparing \u0394MSMEG_3765 to both wt and complemented strains using a moderated t-test  and a >2.5-fold change. Fully annotated microarray data have been deposited in ArrayExpress (accession number E-MTAB-5869).Total RNA was extracted from mid-log phase l method . Samplesl method  using anMSMEG_3760, using MS13f and MS13r primers; (2) a 218 bp DNA fragment containing 106 bp of MSMEG_3761 coding sequence, 36 bp of MSMEG_3762 coding sequence and 76 bp of intergenic non-coding region, using MS14f and MS14r primers. The PCR products, obtained with iProof high-fidelity Taq (Bio-Rad), were cloned into the BamHI and ApaI sites of the E. coli\u2013mycobacteria shuttle vector pFPV27  a 277 bp DNA fragment containing 43 bp coding sequence and 234 bp upstream from the ATG codon of r pFPV27 , yieldinM. smegmatis wt and M. smegmatis \u0394MSMEG_3765 cells. GFP fluorescence was measured as described previously  log-phase cells were incubated for 5 h in 7H9 buffered medium at pH 5.3 and supplemented with 5 mM NaNO2 before measuring fluorescence.The resulting recombinant plasmids, together with the pFPV27 negative control plasmid (empty vector) and the pFPV27hsp positive control plasmid  , were eleviously , with exMSMEG_3765 coding region (630 bp) was PCR-amplified from M. smegmatis mc2155 genomic DNA using eMS3765f and eMS3765r primers, and the product was cloned into the NdeI-XhoI sites of the pET-22b(+) expression vector. The recombinant plasmid, pFP5, was sequence verified and used to express and purify the C-terminally His-tagged TetR3765 protein. For expression, E. coli BL21 (DE3) cultures containing pFP5 were grown at 37\u00b0C to mid-exponential phase. Cultures were induced with 1 mM IPTG for 2 h at 37\u00b0C and harvested by centrifugation. The recombinant protein was purified using Profinity IMAC resins (Bio-Rad) following manufacturer\u2019s instructions and concentrated using Centricon ultra-filtration spin columns  in 20 mM Tris HCl pH 8, 500 mM NaCl. The purified protein was correctly folded, as evaluated by circular dichroism spectroscopy (data not shown).The 2 20 mM, 10% glycerol with 0.5 pmol of MSMEG_3762 (218 bp) or Rv1687c (200 bp) upstream fragments (used in the GFP assays). The binding reaction was performed at 37\u00b0C for 20 min. The resulting DNA/protein complexes were resolved on native PAGE gels (8% acrylamide:bisacrylamide 30:1) and stained with SYBR Safe DNA Gel Stain (Invitrogen). To probe the specificity of MSMEG_3765 binding activity to MSMEG_3762 and Rv1687c upstream regions, reactions were incubated in the presence of 0.5 pmol MSMEG_3760 upstream fragment  as non-specific competitor DNA. The MSMEG_3762 upstream fragment of 218 bp was refined into four shorter fragments for further EMSA analysis: (a) 133 bp containing 21 bp of the MSMEG_3761 coding sequence, 36 bp of MSMEG_3762 coding sequence and 76 bp of intergenic non-coding region (primers mot3762f1/MS14r); (b) 182 bp containing 106 bp of the MSMEG_3761 coding sequence and 76 bp of intergenic non-coding region (primers MS14f/mot3762r1); (c) 97 bp containing 21 bp of the MSMEG_3761 coding sequence and 76 bp of intergenic non-coding region (primers mot3762f1/mot3762r1); (d) 36 bp putative TetR binding motif.Binding assays were performed incubating increasing concentrations of purified recombinant MSMEG_3765 (from 1.5 to 10 pmol) in HEPES 40 mM (pH 8.0), NaCl 150 mM, MgClMycobacterium tuberculosis H37Rv Rv1685c and M. smegmatis MSMEG_3765 TetR transcriptional regulators share 71% amino acid identity. In order to identify their distribution among other mycobacteria, we searched for orthologs in other species. The regulator is conserved in mycobacteria with amino acid identities ranging from 62 to 73% . In all species, the regulator is preceded by two genes in the same transcriptional orientation, annotated as an ABC transporter ATP-binding protein and an ABC transporter, that also share high percentage of identity between species (68\u201379%).MSMEG_3765 locus, an RT-PCR analysis was performed on total RNA extracted from log phase M. smegmatis mc2155 using oligonucleotide pairs designed to detect transcripts of individual or co-transcribed genes. MSMEG_3762, MSMEG_3763 and MSMEG_3765 were found to be co-transcribed defining the MSMEG_3762/63/65 operon  in the MSMEG_3765 on the expression of the surrounding genes. A M. smegmatis mutant strain carrying a 495 bp deletion in MSMEG_3765 was generated, \u0394MSMEG_3765. The deletion was also complemented using the integrative recombinant pFP6 plasmid, harboring the MSMEG_3765 coding sequence under the control of the hsp60 promoter. Deletion or complementation of MSMEG_3765 did not affect growth in standard conditions, with a doubling time of \u223c3 h for wt, mutant and complemented strains. Transcription of the MSMEG_3762/63/65 operon (as assessed by measuring the first gene in the operon MSMEG_3762) was upregulated by 55-fold in the mutant strain versus the wt , whereas the expression of MSMEG_3760 and MSMEG_3761 was unchanged. The expression of the MSMEG_3762/63/65 operon was restored to wt levels in the complemented strain . These data strongly suggest that MSMEG_3765 acts as a repressor of its own operon but does not control the expression of MSMEG_3760 or MSMEG_3761.Many TetR-like proteins have been shown to regulate adjacent genes . RTq-PCRMSMEG_3765 regulon. The MSMEG_3765 regulon was defined as genes significantly differentially expressed in M. smegmatis \u0394MSMEG_3765 compared to both wt and complemented strains to control for the possibility of polar effects of gene manipulation. Microarray analysis confirmed that MSMEG_3765 acts as a repressor of MSMEG_3762 and MSMEG_3763 with both genes induced in the \u0394MSMEG_3765 genetic background (Table 1).Genome-wide transcriptional profiling was applied as an unbiased approach to further describe the MSMEG_3762/63/65 operon. A conserved 36 bp region, containing a 34 bp palindrome, at the 5\u2032 intergenic region extending into the coding sequence of M. tuberculosis Rv1687c, M. smegmatis MSMEG_3762, M. marinum MMAR_2486, M. avium subsp. paratuberculosis MAP_1393c, M. avium MAV_3085 genes was found . This motif overlaps with a putative TATA box found 10 bp upstream of the translation start site for each gene.TetR proteins often bind palindromic regulatory sequences, therefore we searched for palindromic motifs upstream of the mpg/amt1 in M. avium paratuberculosis and M. avium and mpg/MSMEG_3760 in M. smegmatis. Two copies of a 16 bp motif containing a 6 bp palindrome were found in this region in the M. smegmatis genome but not in the other genomes . The RTq-PCR data suggested that this motif was not recognized by MSMEG_3765, however, reporter assays were performed to determine the functionality of both motifs.TetR proteins often control divergently oriented genes, therefore we searched for additional motifs in the intergenic region upstream of MSMEG_3760, MSMEG_3762/63/65, and Rv1687c/85c/85c upstream of GFP to assay promoter activity. Promoterless GFP (empty vector) and GFP under the control of the hsp60 promoter were used as negative and positive controls, respectively. Expression of GFP from the MSMEG_3760 promoter resulted in a small but significant increase in fluorescence compared to empty vector (p < 0.01), but no significant difference was observed between wt and \u0394MSMEG_3765 backgrounds . Conversely, expression of GFP from the MSMEG_3762/63/65 promoter showed a significant increase  in the \u0394MSMEG_3765 compared to wt . These results suggest that the promoter region upstream of MSMEG_3760 is active but not controlled by MSMEG_3765, while the MSMEG_3762/63/65 promoter is active and negatively regulated by MSMEG_3765. Since the motif identified in this region was also present in the M. tuberculosis Rv1687c/86c/85c gene cluster, a 200 bp fragment, containing the Rv1687c upstream sequence was assayed in M. smegmatis wt and \u0394MSMEG_3765 strains. Promoter activity was detected in both genetic backgrounds with a fivefold increase (p < 0.0001) in expression in the mutant strain , suggesting that the regulation of this gene cluster is conserved in M. tuberculosis.Several reporter strains were made by cloning the promoter regions (including the motif regions) from Rv1687c/86c/85c gene cluster is induced by acid-nitrosative stress , further evidence that this TetR-regulator is involved in the response to acid-nitrosative stress in M. smegmatis and M. tuberculosis.Previous reports indicated that the e stress . TherefoMSMEG_3762/63/65 cluster but not to the motif identified upstream of MSMEG_3760. To verify the binding activity of MSMEG_3765, we conducted an electrophoretic mobility shift assay (EMSA) with the purified recombinant protein. A preliminary experiment using the three DNA fragments upstream of the MSMEG_3760, MSMEG_3762/63/65, and Rv1687c86c/85c genes (from the GFP assays) was performed. The upstream MSMEG_3760 fragment did not show a shift of electrophoretic mobility in the presence of the MSMEG_3765 recombinant protein, while the other two DNA fragments were shifted in the presence of the protein  up to saturation , while no shift was detected for the negative control fragment. In addition, a MSMEG_3765 concentration-dependent shift in electrophoretic mobility was observed for the Rv1687c upstream fragment (200 bp), also up to saturation with the highest concentration of MSMEG_3765 tested . The EMSA analysis shows specific binding activity of MSMEG_3765 at the upstream regulatory regions of the M. smegmatis MSMEG_3762/63/65 and homologous M. tuberculosis Rv1687c/86c/85c operons.Electrophoretic mobility shift assay experiments were then performed using the 127 bp upstream MSMEG_3762 upstream fragment was truncated for further EMSA. Three DNA fragments were analyzed: (a) a 133 bp DNA fragment depleted of 85 bp MSMEG_3761 coding sequence (truncated at the 5\u2032 end); (b) a 182 bp DNA fragment depleted of 36 bp MSMEG_3762 coding sequence (truncated at the 3\u2032 end); (c) a 97 bp DNA fragment depleted of 85 bp MSMEG_3761 coding sequence and of 36 bp MSMEG_3762 coding sequence (truncated at the 5\u2032 and 3\u2032 ends) . Only the 133 bp fragment, extending up to the 12th codon into the MSMEG_3762 coding region, retained binding activity for the MSMEG_3765 protein , showing that truncation of the motif at the 3\u2032 end destroyed binding.The 218 bp MSMEG_3762 upstream fragment  and a 40 bp MSMEG_3760 upstream fragment, containing the palindromic motif shown in Figure 3B, were tested by EMSA. The former fragment showed a MSMEG_3765 concentration-dependent shift in electrophoretic mobility, while the latter fragment was not affected by the presence of the protein . These data correlate with the MEME analysis  and demonstrate that MSMEG_3765, acting as transcriptional repressor, binds to a 36 bp TetR-like motif extending into the coding sequence of MSMEG_3762 and M. tuberculosis ortholog Rv1687c operons, controlling expression.To further characterize the DNA motif for MSMEG_3765 binding, the 36 bp Mycobacterium tuberculosis is a well-adapted intracellular pathogen, employing multiple strategies to survive within macrophages. Many of the mechanisms that enable M. tuberculosis to survive stresses encountered in macrophages are still poorly understood. Experimental strategies involving in vitro-simulated phagosomal environments have been widely used to highlight differential gene expression of M. tuberculosis to the changing microenvironment , and that the MSMEG_3762/63/65 operon is regulated by MSMEG_3765. Given that this region and regulatory motif is conserved in M. tuberculosis, it is likely that the equivalent regulator in M. tuberculosis (Rv1685c) also controls the orthologous region in M. tuberculosis (Rv1687c/86c/85c). This is supported by the observation that the Rv1687c promoter is de-repressed in \u0394MSMEG_3765 genetic background . Understanding the function of MSMEG_3762/63/65 and Rv1687c/86c/85c is crucial to identifying the physiological role of this tightly regulated system.In this study, we show that Rv1687c/86c/85c have been shown to be induced upon exposure to triclosan and lupulone, compounds that show potential as anti-mycobacterial agents, therefore it is feasible to speculate that the TetR-regulated ABC transporter is involved in drug efflux . Short (16 bp) palindromic motifs are frequently described for this family of regulators, although there are exceptions. EthR, a TetR regulator involved in ethionamide bio-activation binds to a 55 bp operator containing imperfect direct repeats (Of the 52 TetR regulators in the cterized . In this repeats . We haveFP, BDS, and LM performed the experimental work under the supervision of MS, and FP performed the microarrays and GFP assays under the supervision of SW, at the University of Sussex. SK and FP performed the bioinformatics work. MS and SK wrote the manuscript with a consistent contribution of FP, LM, and SW.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest."}
+{"text": "Phytophthora infestans, potentially secrete many RxLR effector proteins into plant cells to modulate plant immune responses and promote colonization. However, the molecular mechanisms by which these RxLR effectors suppress plant immune responses are largely unknown. Here we describe an RxLR effector PITG_22798 (Gene accession: XM_002998349) that was upregulated during early infection of potato by P. infestans. By employment of agroinfiltration, we observed that PITG_22798 triggers cell death in Nicotiana benthamiana. Confocal microscopic examination showed that PITG_22798-GFP (Green Fluorescent Protein) located in the host nucleus when expressed transiently in N. benthamiana leaves. A nuclear localization signal (NLS) domain of PITG_22798 is important for nuclear localization and cell death-inducing activity. Sequence alignment and transient expression showed that PITG_22798 from diverse P. infestans isolates are conserved, and transient expression of PITG_22798 enhances P. infestans colonization of N. benthamiana leaves, which suggests that PITG_22798 contributes to P. infestans infection. PITG_22798-triggered cell death is dependent on SGT1-mediated signaling and is suppressed by the P. infestans avirulence effector 3b (AVR3b). The present research provides a clue for further investigation of how P. infestans effector PITG_22798 associates with and modulates host immunity.Phytopathogenic oomycetes, such as  Plants are attacked by various pathogens and defend themselves via multiple resistance mechanisms ,2. GenerP. infestans, P. sojae, P. ramorum, and Hyaloperonospora arabidopsidis have more than 550, 390, 370, and 130 genes, respectively, encoding potential effector proteins with RxLR-dEER motifs [P. infestans RXLR effector PITG_04314 targets plant PP1c isoforms by re-localizing PP1c isoforms from the nucleolus to the host nucleoplasm to promote late blight disease [Two classes of oomycetes effectors target distinct sites in the host plant: apoplastic effectors, such as GIP1, EPI1, and EPICI, are secreted into plant extracellular space, whereas cytoplasmic effectors, such as CRNs (crinkling and necrosis induced protein) and RxLR effectors, are translocated inside the plant cell, where they target different subcellular compartments . CRNs arR motifs ,10. SomeR motifs ,15,16,17 disease .P. infestans RxLR effector PexRD2 interacts with MAPKKKe in the plant cytosol and specifically inhibits the MAPKKKe-dependent resistance [P. infestans [Increasing evidence indicates that effectors traffic to a range of subcellular localizations in plant cells and target diverse host proteins to execute their functions . For exasistance . Many panfestans .Agrobacterium-mediated expression studies of P. infestans RxLR effectors in Nicotiana benthamiana, we found that one RxLR-like effector, PITG_22798 (Gene accession: XM_002998349), could induce cell death. In this work, we found that PITG_22798 was localized in the host nucleus and its induction of cell death in N. benthamiana required nuclear localization. We also discovered that this induced cell death was dependent on the defense regulator SGT1  and was suppressed by the RxLR effector, avirulence 3b (AVR3b). The work described herein provides important foundations for further dissection of the roles of P. infestans RxLR effector PITG_22798 regulation in plant immunity.During transient PITG_22798 during P. infestans infection, we designed gene-specific primers  control, indicating that PITG_22798 enhances P. infestans leaf colonization.To test whether PITG_22798 in N. benthamiana by agroinfiltration, cell death occurred at 6 days post-infiltration (dpi). The cell death observed was associated with accumulation of autofluorescent compounds. To better visualize the accumulation of such compounds, we examined the inoculated sites under UV light. Similar to INF1 (positive control), PITG_22798 resulted in increased autofluorescence in dead and dying cells, whereas GFP (negative control) did not lead to necrosis or autofluorescence in N. benthamiana -agroinfection assay, and in N. tabacum leaves by agroinfiltration. The results revealed that expression of PITG_22798 induced cell death in N. tabacum but not in potato species  with a signal peptide from 1\u201322 aa. It contains the RXLR-like motifs: LFLR-DER. To study the diversity of PITG_22798, its orthologues were cloned from 11 diverse P. infestans isolates, namely EC1, Ljx18, IPO-C, UK3928A, HB09-23, HB09-16-2, HB09-14-2, HB09-21, HB09-41, 88069, and PIC99183 (PIC99183 . Among 1PIC99183 A.PITG_22798 orthologues from P. infestans isolates 88069 (PITG_2279888069), 99183 (PITG_2279899183), and Ljx18 (PITG_22798Ljx18) were transiently expressed in N. benthamiana. The results showed that all of them triggered cell death, while a delayed symptom was observed with the orthologue from Ljx18  was used for agroinfiltration. The results showed that PITG_22798-GFP localizes to the nucleus  and transiently expressed in  nucleus A. Meanwh nucleus , indicatA predicted nuclear localization signal (NLS) motif (157-KKRLAKLKRKR-167) was located in the C-terminus of PITG_22798. We mutated the NLS of PITG_22798 (nls-PITG_22798) and found that the NLS mutant  and \u0394NLS-PITG_22798  mainly accumulated in the cytoplasm rather than nucleus B. It wasN. benthamiana . Three weeks after infiltration of silencing constructs, newly grown developed leaves were infiltrated with A. tumefaciens strains containing PITG_22798 (SGT1 and HSP90 transcripts were reduced in the silenced plants compared with negative control infiltrated with tobacco rattle virus (TRV)-GFP  and SGT1 are considered components of signal-transduction pathways leading to cell death mediated by many NB-LRRs (nucleotide-binding site leucine-rich repeats) cell death. Here, we tested whether SGT1 and HSP90 are required for TG_22798 A. We conTRV)-GFP B. PercenPITG_22798-mediated cell death, we selected eight RxLR effectors which were co-agroinfiltrated with PITG_22798 in N. benthamiana. The results obtained at 6 dpi demonstrated that PITG_22798-induced cell death was significantly inhibited when it was coexpressed with AVR3b, whereas other effectors did not interfere with PITG_22798-triggered cell death  factors, triggering the ETI pathway due to recognition by cognate R proteins [N. benthamiana. This is supported by the fact that PITG_22798-triggered cell death is dependent on SGT1, which is required for many R proteins to function. Even if this is in accordance with the view that N. benthamiana possibly possesses a broad-spectrum R protein against P. infestans [Cell death plays a ubiquitous role in plant\u2013microbe interactions and can be associated with both susceptible and resistance responses . Howeverimmunity ,20. In tthamiana  and tobaproteins ,21. The The nucleus is an activity center for both PTI and ETI, and many critical regulators are trafficked there from various subcellular locations following pathogen perception ,24. By fR gene-mediated resistance in several plant species against various plant pathogens, including fungi and bacteria [NbSGT1 was silenced in N. benthamiana, indicating that SGT1 function is not only limited to the NB-LRR proteins but also required for the immune response that is triggered by non-NB-LRR-type proteins [N. benthamiana  medium and used for cloning and propagation of recombinant plasmids. Agrobacterium tumefaciens strain GV3101 used for transient expression was cultured at 28 \u00b0C in YEB (Yeast Extract Broth) medium using appropriate antibiotics. P. infestans isolates used in this study are shown in N. benthamiana, N. tabacum, S. chacoense, S. hjertingii, and a Chinese potato variety \u201cE-potato 3\u201d (S. tuberosum cv.) were grown at 25 \u00b0C in greenhouse under 16/8 h light/dark photoperiod. PITG_22798 was inserted into pK7FWG2 vector to produce C-terminal GFP fusion vector, which was then transformed into the A. tumefaciens strain GV3101.The deletion mutants were obtained by PCR amplifications using appropriate primers . For proP. infestans included AVR3aKI [To clarify whether other RxLR effectors could interfere with PITG_22798-triggered cell death, eight RxLR effector genes were cloned and constructed into pGR106 vector . Eight R AVR3aKI , AVR2 [3 AVR3aKI , AVR3b [ AVR3aKI , PITG_21 AVR3aKI , PITG_14 AVR3aKI , and PITP. infestans isolate HB0914-2 at 5 \u00d7 104 mL\u22121 using the method of Vleeshouwers et al. [PITG_22798 expression at different time points. The constitutively expressed PiEF2 (Gene accession: XM_002901697.1) was used as a reference for equalizing cDNA amounts in each reaction. The RT-PCR primers used are listed in Detached leaves of potato (\u201cE-potato-3\u201d) of 8-week-old plants were inoculated with 10 \u00b5L zoospores from s et al. . Leaf dis et al. . ReverseAgrobacterium tumefaciens transient assays (ATTA) in combination with P. infestans infection were carried out as described [Agrobacterium cultures were resuspended in agroinfiltration medium at a final concentration of OD600 = 0.05 and used for transient expression in N. benthamiana by agroinfiltration. After 1 day, each infiltration site was inoculated with 10 \u00b5L zoospores from P. infestans isolate 88069 at 5 \u00d7 104 mL\u22121. Lesions were measured and photographed at 5 days postinfection and data was collected from three biological replicates (each replicate with at least nine leaves). escribed . BrieflyPITG_22798, P. infestans genomic DNA was used as templates. P. infestans mycelia were scraped from the rye agar medium surface after cultured for about 14 d for DNA isolation using the DNA isolation kit . High-fidelity Pfu polymerase  was used for amplification of PITG_22798 orthologues  and the primers are shown in ClaI and NotI and ligated into a binary vector pGR106 vector (potato virus X vector), which was then transformed into A. tumefaciens strain GV3101 for Agrobacterium-mediated transient expression as described by Du et al. [For cloning of u et al. . All thePITG_22798 was conducted with ClustalW  to create multiple sequence alignments, which were then manually adjusted to minimize the number of implied mutations. SignalP 4.1 server  was used for discriminating signal peptides. Sequence translation was done at http://web.expasy.org/translate/. NLS sequence domain of PITG_22798 was predicted by NLStradamus with a prediction cutoff value of 0.6. NLStradamus uses hidden Markov models (HMMs) (http://www.moseslab.csb.utoronto.ca/NLStradamus/) to predict novel NLSs in proteins [The sequence analysis of proteins .N. benthamiana and PVX (potato virus X vector)-agroinfection in potato were performed as described elsewhere [Agroinfiltration in lsewhere . At leasPITG_22798 homologs in N. benthamiana plants, three independent tests were conducted, each contained 24 leaves of 6 plants. The cell death was measured at 4 and 6 dpi and t-test was used for comparison with PITG_22798Ljx18. The number of the positive cell deaths  were counted as described previously [To check the different induction of eviously  and exprPITG_22798-induced cell death in N. benthamiana leaves, A. tumefaciens cells carrying the cell death-inducing gene (PITG_22798) and each other RxLR effector genes were mixed in a 1:1 ratio and then infiltrated by following the method mentioned above. Long-wave UV lamp  was used to monitor the autofluorescence of dead cells where phenolic compounds were released. To look into the effects of other RxLR type effectors on Nicotiana benthamiana leaves infiltrated with A. tumefaciens containing GFP (Green Fluorescent Protein gene) and PITG_22798-GFP were harvested at 36 h post-inoculation (hpi). The stability of GFP fusion protein was tested by Western blot as previously described [escribed . BrieflyA. tumefaciens (OD600 = 0.01) containing GFP was pressure infiltrated in the left half of a leaf of 4-week-old N. benthamiana, while PITG_22798 was infiltrated in the right half. The infiltrated cells were observed using a LSM510 Meta confocal microscope  at 36 hpi. The excitation wavelengths used for GFP was 488 nm.Subcellular localization of PITG_22798 was detected by confocal microscopy PDS was used as a positive control and TRV-GFP was used as a negative control. At four-leaf stage, N. benthamiana leaves were infiltrated by A. tumefaciens containing TRV-GFP, TRV-PDS, TRV-SGT1, and TRV-HSP90. Three weeks later, when positive control leaves became totally white, RNA was extracted from the leaves using Trizol Reagent  according to the manufacturer recommendations. Primers RT-NbSGT1 and RT-NbHSP90 were used for RT-PCR (EF-1\u03b1 was used as a control for equalizing cDNA amounts in each reaction. VIGS was performed as previously described ,43. TRV-r RT-PCR . EF-1\u03b1 wIon leakage was measured as previously described . After iP. infestans RxLR effector PITG_22798 which could trigger cell death in N. benthamiana and enhance P. infestans colonization. PITG_22798-triggered cell death requires its nuclear localization and can be suppressed by the P. infestans effector AVR3b. PITG_22798-triggered cell death is dependent on the SGT1-mediated signaling pathway. Although the exact mechanisms of signaling crosstalk remain unclear, our results provide useful information for in-depth investigation of how PITG_22798 modulates defense of N. benthamiana. The next challenge is to better understand the role of PITG_22798 and its targets in the progression of disease in important host crop potato. Up to now, we have identified several interacting proteins in potato, which will help us further investigate its function involved in manipulating of potato immunity.In summary, we report a nuclear-localized"}
+{"text": "Gene functionality is closely connected to its expression specificity across tissues and cell types. RNA-Seq is a powerful quantitative tool to explore genome wide expression. The aim of this study is to provide a comprehensive RNA-Seq dataset across the same 13 tissues for mouse and rat, two of the most relevant species for biomedical research. The dataset provides the transcriptome across tissues from three male C57BL6 mice and three male Han Wistar rats. We also describe our bioinformatics pipeline to process and technically validate the data. Principal component analysis shows that tissue samples from both species cluster similarly. We show by comparative genomics that many genes with high sequence identity with respect to their human orthologues also have a highly correlated tissue distribution profile and are in agreement with manually curated literature data for human. In summary, the present study provides a unique resource for comparative genomics and will facilitate the analysis of tissue specificity and cross-species conservation in higher organisms. Factors include sensitivity  and scope . This is reflected by the observation that public repositories of proteomics data are underused by the scientific community3 compared to the RNA field. In contrast, RNA resources, for example the Gene Expression Omnibus (GEO) and ArrayExpress, are widely used resources for end users as well as for some powerful public tools like the Expression Atlas (https://www.ebi.ac.uk/gxa/home), or commercial tools like the nextbio BaseSpace Correlation Engine (https://www.nextbio.com), Genevestigator (https://www.genevestigator.com) or Genestack (https://www.genestack.com).Biological cells have multiple functions within the body: They may act as small reactors transforming and exchanging energy and organic compounds within their compartments and tissue environment. They transmit or modulate biochemical and physical signals and provide structural integrity. These functions are determined by the abundance and activity of co-expression networks. Despite the progress of protein quantification techniques including mass spectrometry and other methods4.Although DNA microarrays are still widely used, RNA-Seq by next generation sequencing (NGS) is now the technology of choice for \u2018transcriptome wide\u2019 gene expression quantification. A wide range of protocols allows RNA-Seq of dissected samples from complex tissues, body fluids, cell-type enriched biosamples and single cells. Depending on the type of RNA preparation , sequencing protocol and sequencing depth, this method allows a full spectrum of transcriptome related read-outs and bioinformatics applications beyond gene expression i.e., inferring strand-, isoform- and sequence variant-specific information which is a unique feature of this technology compared to DNA microarrays or real-time polymerase chain reaction (rPCR) based methods5\u20137. However, to our knowledge there is no homogenous RNA-Seq dataset for both mouse and rat. Existing studies that are published rather focus on a single species on specific aspects such as ageing and development8, on a few organs9 or are based on alternative technologies e.g., genome wide microarrays10. Thus a scientist wanting to compare in depth features of genes across species in the same tissue would be only left with the option of performing a meta-analysis across datasets generated in different labs under different conditions.A number of important RNA-Seq projects for human tissues have been established which allow in depth exploration of the human transcriptome across a wide range of tissues and cell typesThe present study provides access to a normal tissue gene expression atlas for male C57BL6 mice and male Han Wistar rats. Each tissue atlas is represented by 13 aligned normal tissues see . Samplesn=3 for each species) were sacrificed thereafter by intraperitoneal injection of pentobarbital (rats) or cervical dislocation (mice) and tissues  were harvested and transferred immediately to RNA Later at 4\u2009\u00b0C.Male Wistar Han rats (Crl:WI(Han)) and male BL/6J mice (C57BL/6J) were obtained from Charles River Laboratories (Germany). Experimental protocols concerning the use of laboratory animals were reviewed by a German Federal Ethics Committee and approved by German governmental authorities. Animals were housed in groups of three on a 12-h light/dark cycle and fed ad libitum a standard pelleted rodent diet  with free access to water. Rats with a body weight of 160\u2013180\u2009g and mice at the age of 7\u20138 weeks were used for tissue sampling. Animals  according to the manufacturer\u2019s instructions. Briefly, 5\u2009mg of tissue was placed in the lysis solution and homogenized in Qiagen Tissuelyzer\u2122 for a period of 30\u2009s. Nucleic acids were captured onto magnetic beads, washed and treated with DNase. Total RNA was then eluted in 50\u2009\u03bcl elution buffer. RNA quality and concentration was measured using an RNA Pico chip on an Agilent Bioanalyzer.The Sequencing library preparation has been done using 200\u2009ng of total RNA input with the TrueSeq RNA Sample Prep Kit v2-Set B  producing a 275\u2009bp fragment including adapters in average size. In the final step before sequencing, eight individual libraries were normalized and pooled together using the adapter indices supplied by the manufacturer. Pooled libraries have then been clustered on the cBot Instrument from Illumina using the TruSeq SR Cluster Kit v3\u2014cBot\u2014HS sequencing was then performed as 50\u2009bp, single reads and 7 bases index read on an Illumina HiSeq2000 instrument using the TruSeq SBS Kit HS- v3 (50-cycle) .11 with their corresponding Ensembl 84 reference genomes (http://www.ensembl.org). Sequenced read quality was checked with FastQC v0.11.2 (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and alignment quality metrics were calculated using the RNASeQC v1.1812. Following read alignment, duplication rates of the RNA-Seq samples were computed with bamUtil v1.0.11 to mark duplicate reads and the dupRadar v1.4 Bioconductor R package for assessment13. The gene expression profiles were quantified using Cufflinks software version 2.2.114 to get the Reads Per Kilobase of transcript per Million mapped reads (RPKM) as well as read counts from the feature counts software package15. The matrix of read counts and the design file were imported to R, normalization factors calculated using trimmed mean of M-values (TMM) and subsequently voom normalized, before subjected to downstream descriptive statistics analysis.The processing pipeline is described in detail below. One sample could not be processed due to technical issues (mouse_11_heart). For all remaining samples, RNA-Seq reads from rat and mouse samples were aligned to the rat and mouse genomes respectively using the STAR Aligner v2.5.2aBefore running the execution steps mentioned above, one has to prepare target organism alignment indices for the STAR aligner. For mouse this is done as follows:STAR --runMode genomeGenerate \\--genomeDir mouse84.STARIndex/ \\--genomeFastaFiles Mus_musculus.GRCm38.dna.primary_assembly.fa \\--sjdbGTFfile Mus_musculus.GRCm38.84.gtf \\--sjdbOverhang 49 \\--runThreadN 16For rat this has to be adopted accordingly. After the genome index is prepared, all samples from each species are processed individually. In all subsequent commands <sample_id> corresponds to the sample name (for example 199_1 for the first mouse sample from the pancreas).Make a sample output directory, where all the outputs from each step will be stored:mkdir <sample_id>Check sequenced read qualities with FastQC v0.11.2:fastqc --outdir=<sample_id>/ <sample_id> &> <sample_id>/<sample_id>.fastqc.logAlign reads using the STAR aligner v2.5.2a:STAR --genomeDir mouse84.STARIndex/ \\--readFilesIn <sample_id>.fastq.gz \\--outFileNamePrefix <sample_id>/<sample_id>.fastq.gz. \\--runThreadN 8 \\--limitBAMsortRAM 60000000000 \\--outSAMattrRGline ID:<sample_id>.fastq.gz SM:<sample_id>.fastq.gz \\--outBAMsortingThreadN 8 \\--outSAMtype BAM SortedByCoordinate \\--outSAMunmapped Within \\--outSAMstrandField intronMotif \\--readFilesCommand zcat \\--chimSegmentMin 20 \\--genomeLoad NoSharedMemoryCreate BAM file index (*.bai) using samtools v0.1.18:samtools index <sample_id>/<sample_id>.fastq.gz.Aligned.sortedByCoord.out.bamMark Duplicates using BamUtils v1.0.11 \u2018dedup\u2019 step:bam dedup --in <sample_id>/<sample_id>.fastq.gz.Aligned.sortedByCoord.out.bam \\--log <sample_id>/<sample_id>.fastq.gz.Aligned.out.dupmark.log \\--out <sample_id>/<sample_id>.fastq.gz.Aligned.out.dupmark.bam \\--noPhoneHomesamtools index <sample_id>/<sample_id>.fastq.gz.Aligned.out.dupmark.bamRun DupRadar v1.4 on the duplicate marked bam:mkdir <sample_id>/dupradardupRadar.sh --bam=<sample_id>/<sample_id>.fastq.gz.Aligned.out.dupmark.bam \\--gtf=Mus_musculus.GRCm38.84.gtf \\--stranded=no \\--paired=no \\--outdir=<sample_id>/dupradar \\--threads=16Gene/Transcript quantification with Cufflinks v.2.2.1 to get RPKMs:cufflinks -u -p 8 -o <sample_id>/cufflinks \\--max-bundle-frags 1000000000 \\--no-effective-length-correction \\--compatible-hits-norm \\-G Mus_musculus.GRCm38.84.gtf \\<sample_id>/<sample_id>.fastq.gz.out.dupmark.bamRun featureCounts to generate read counts:featureCounts -a Mus_musculus.GRCm38.84.gtf \\-o <sample_id>/<sample_id>.fastq.gz.featureCounts.ensembl.txt \\-T 3 <sample_id>/<sample_id>.fastq.gz.Aligned.out.markdup.bamRNA quality control:java -Xmx20g -jar RNA-SeQC_v1.1.8.jar \\-t Mus_musculus.GRCm38.84.gtf \\-r Mus_musculus.GRCm38.dna.primary_assembly.fa \\-o <sample_id>/rnaqc -singleEnd -ttype 2 \\-s \u2018<sample_id>|<sample_id>.fastq.gz.Aligned.out.dupmark.bam|Notes\u2019All per sample output is finally merged into the read count (<species>_counts.txt), RPKM (<species>_rpkm.txt), and technical QC (<species>_rnaqc.txt) tabular output files. The graphics in 16 voom-transformed log(counts per million). Intra- and inter-tissue variation was assessed based on RPKM expression values  versus the mean log10 tissue RPKM . Consequently, cell type specific dissection, purification and sequencing protocols can largely improve our understanding of cell type specific expression.The tissue samples investigated in the present study correspond to dissected samples from complex organs. Consequently, the gene expression signal from each gene and sample is superimposed by signals that originate from the individual cell types which make up the corresponding organ. Eventually this might explain the observed inverse bell shape distribution of the variation coefficient versus absolute expression. Some cell types express a significant amount of very specific genes because this is an inherent feature of their function. The pancreas, for example, is composed of functionally different cell types implying distinct sets of highly active genes. Insulin is exclusively expressed by beta cells at very high levels. However, pancreatic beta cells only represent a minor fraction of the whole pancreas. Consequently, bulk samples from the pancreas contain a variable composition of different cell types which will contribute to a high expression variability of cell type specific genes like Insulin. INS1 gene expression, for example, shows a relatively wide spread of 389.7, 1449.0 and 4020.8 RPKM in the three rat samples of the present study. Although this is a very strong signal (there are less than 50 genes with a higher median expression in the pancreas samples) it is at least two orders of magnitude lower compared to levels in isolated pancreatic islets  for further examination (see 20 (GSE) using canonical pathways from BIOCARTA, KEGG, and REACTOME revealed a highly significant enrichment of genes involved in transcript splicing and RNA processing (FDR<10\u221215) as well as neuronal genes (FDR<10\u22127) and genes involved in the immune system (FDR<10\u22127). Although those gene categories are also found enriched in studies of evolutionary divergence21 one should keep in mind that the underlying biological processes are essential for development and homeostasis. Thus, mutations observed in the corresponding human genes are often pathologic23. Consequently, there is a strong enrichment of genes involved in the immune system (FDR<10\u221252) and neuronal genes (FDR<10\u221236) among the set of known human disease genes according to the Online Mendelian Inheritance in Man (OMIM).By combining the two approaches described in the previous section we selected ultra-conserved genes (sequence identity versus human\u226590%) which are highly correlated .Publisher\u2019s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations."}
+{"text": "Chlamydia trachomatis imposes serious health problems and causes infertility. Because of asymptomatic onset, it often escapes antibiotic treatment. Therefore, vaccines offer a better option for the prevention of unwanted inflammatory sequelae. The existence of serologically distinct serovars of C. trachomatis suggests that a vaccine will need to provide protection against multiple serovars. Chlamydia spp. use a highly conserved type III secretion system (T3SS) composed of structural and effector proteins which is an essential virulence factor. In this study, we expressed the T3SS needle protein of Chlamydia muridarum, TC_0037, an ortholog of C. trachomatis CdsF, in a replication-defective adenoviral vector (AdTC_0037) and evaluated its protective efficacy in an intravaginal Chlamydia muridarum model. For better immune responses, we employed a heterologous prime-boost immunization protocol in which mice were intranasally primed with AdTC_0037 and subcutaneously boosted with recombinant TC_0037 and Toll-like receptor 4 agonist monophosphoryl lipid A mixed in a squalene nanoscale emulsion. We found that immunization with TC_0037 antigen induced specific humoral and T cell responses, decreased Chlamydia loads in the genital tract, and abrogated pathology of upper genital organs. Together, our results suggest that TC_0037, a highly conserved chlamydial T3SS protein, is a good candidate for inclusion in a Chlamydia vaccine. Chlamydia trachomatis is the most common sexually transmitted bacterial pathogen. It imposes serious health problems in humans and can cause severe complications such as pelvic inflammatory disease and ectopic pregnancy and infertility in women [ Chlamydia muridarum has been extensively used to study the mechanisms of C. trachomatis pathogenesis and immunity in a mouse model [ C. muridarum can lead to infection in the lower and upper genital tract, which closely mimics the pathology induced by C. trachomatis in humans [\u03b3-producing CD4+ T cells, and the complementary role of humoral immunity in host resistance to chlamydial infection [+ T cells have been shown to protect against infection when cultured ex vivo and transferred to naive animals, and immunization with recombinant vaccinia viruses expressing CD8+ T cell antigens from C. trachomatis conferred protection in mice [in women . Althougse model . Intravan humans . Both annfection . CD8+ T  in mice . Chlamyd in mice \u20139. Major in mice . Chlamydia. It is required for cell invasion and is active at all life stages [ C. trachomatis infection in highly exposed women [ C. trachomatis T3SS filament protein, CdsF, and its orthologs in other bacteria form the needles of injectisomes and are believed to facilitate the insertion of translocators into the host cell membrane [ Chlamydia. It is abundant on bacterial surfaces, raising the possibility that a CdsF-based vaccine may induce a wide range of protection against all medically significant strains.The type three secretion system (T3SS) is the predominant virulence factor ine stages , 12. Somed women , and T3Sed women \u201317. The membrane , 12, 18. via mucosal surfaces [The ability of human adenoviruses to induce strong innate and adaptive immune responses makes them a powerful delivery system to induce an immune response against an encoded antigen. Adenovirus has a natural tropism for the mucosal epithelium, which makes it an ideal vector for vaccination against infections acquiredsurfaces . Intranasurfaces , 20\u201322. surfaces \u201327. Thersurfaces \u201331. Combsurfaces \u201338. Tollsurfaces . C. muridarum, TC_0037, a CdsF ortholog, in a replication-defective adenoviral vector (AdTC_0037) and evaluated its protective efficacy in an intravaginal Chlamydia infection mouse model. To study the protective immunity of our vaccine candidate, we utilized a prime-boost immunization protocol with AdTC_0037 intranasal priming and subcutaneous boosting with recombinant TC_0037 and TLR4 agonist monophosphoryl lipid A (MPLA), mixed in a squalene nanoscale emulsion. We found that immunization with TC_0037 antigen induced specific humoral and T cell responses, decreased Chlamydia loads in both the lower and upper genital tract, and reduced the pathology of upper genital organs.In this study, we expressed the T3SS needle protein of C. muridarum TC_0037 gene was obtained from UniProtKB (Q9PLQ8). We performed an in silico analysis of the TC_0037 gene on the presence of a bacterial signal sequence  and no bacterial signal sequence was found. We further optimized TC_0037 gene sequences for expression in mouse cells by modifying its codons with the two most frequent amino acid triplets. Frequent Mus musculus codons were defined using the http://www.kazusa.or.jp/codon/ database. As TC_0037 is abundant in bacterial cells and small in size , the N-terminal portion of this protein was bound to a 128-amino acid-sized N-terminal portion of mouse mannose-binding lectin (MBL) to obtain TC_0037 hexamers crosslinked with a collagen-like domain. The nucleotide sequence of mouse MBL was obtained from UniProtKB (P41317). The MBL-TC_0037 gene was synthesized by Evrogen  in plasmid pAL-TA-MBL-TC_0037.The nucleotide sequence of the NotI and HindIII sites of the MBL-TC_0037 fragment were cloned into shuttle vector, pShuttle-CMV , to obtain the shuttle plasmid, pShuttle-CMV-MBL-TC_0037. Homologous recombination was used to generate the replication-defective adenovirus, Ad-MBL-TC_0037 , and then cotransformed into Escherichia coli (BJ5183 strain). The obtained recombinant clones were used to extract plasmid DNA, whose molecular weight was later assessed. E. coli strain DH5alpha was transformed with plasmids larger than 20\u2009kbp because this strain, unlike BJ5183, allows one to produce a sufficient amount of recombinant plasmids. The purified plasmid clones were analyzed by both cleavage with the HindIII restriction endonuclease and polymerase chain reaction (PCR). Next, we studied the infectivity of the described plasmids in permissive cells. HEK-293 cells were transfected with plasmid pAd-MBL-TC_0037 and linearized by PacI. Transfection was performed in a 24-well plate using Lipofectamine 2000 . Ten days after the transfection, the cells were collected and subjected to a freeze-thaw cycle; the obtained lysate containing recombinant adenoviruses was used to infect HEK-293 cells in a 35\u2009mm dish. After 5 days, specific lysis caused by cytopathic effect of the recombinant viruses was detected. The lysate was used to extract DNA and perform a PCR analysis. The cell lysate was shown to contain DNA of recombinant human adenovirus serotype 5 (rAd5) carrying insertions that encode the protective antigen.The-TC_0037 . For thi7\u2009PFU (plaque-forming units) of rAd5 per 15\u2009cm plate. After 48\u2009h, infected cells were collected, concentrated by low-speed centrifugation, resuspended in Tris HCl buffer  and disrupted by a triple freeze-thaw. The obtained suspension was centrifuged and purified by cesium chloride equilibrium density gradient centrifugation. The concentration of adenovirus was determined by a plaque-forming assay on HEK-293 cells.Recombinant adenovirus serotype 5 (rAd5) was grown in HEK-293 cells as described elsewhere . Cell mo NdeI/XhoI restriction sites. E. coli BL21 (DE3), transformed with the designed plasmid, was grown in Luria-Bertani broth supplemented with ampicillin or kanamycin on a shaker at 37\u00b0C to an OD600 of 0.5. Protein expression from the pET29b-based plasmid was induced by 1\u2009M isopropyl-\u03b2-D-thiogalactopyranoside  for 3\u2009h at 25\u00b0C. The cleared lysate was subjected to chromatography on a nickel-equilibrated chelating Sepharose Fast Flow column according to the manufacturer's instructions . Protein TC_0037 production was analyzed by western blotting with a monoclonal antibody targeting the histidine tag.The gene encoding the full-length TC_0037 protein, along with six histidine tags (6x His), was cloned into the pET29b  expression vector at\u03bcL per dose) consisting of rTC_0037 protein (10\u2009\u03bcg/dose), immunostimulatory molecule MPLA (1\u2009\u03bcg/dose) , 50% squalene , 0.5% Tween 80, and 0.5% Span 85 in an isotonic phosphate buffer  was prepared by homogenization at 12,000\u2009psi with a microfluidizer  and passed through a polysulfone filter  for sterilization. The average diameter (108.3 +/\u2212 8.7\u2009nm) of the emulsion droplets was determined by Nanoparticle Tracking Analysis (NTA) using a NanoSight NS300 .The boosting squalene oil-in-water emulsion was prepared as described previously . BrieflyC. muridarum (strain Nigg) ATCC VR-123 was grown in cycloheximide-treated McCoy cells  as described previously [eviously , 42. ChlAll animal work was undertaken in strict accordance with the recommendations of the National Standard of the Russian Federation (GOST R 53434-2009). The procedures used were approved by the Gamaleya Research Center of Epidemiology and Microbiology Institutional Animal Care. Female 6\u20138-week-old BALB/c mice obtained from the Animal Resource Center , accredited by the Association for Assessment and Accreditation of Laboratory Animal Care , were maintained at the central animal facility of the Gamaleya Research Center of Epidemiology and Microbiology.n = 20 per group) were intranasally (i.n.) primed with 108\u2009PFU of adenoviral vector-expressed TC_0037 (AdTC_0037) in 100\u2009\u03bcL of sterile phosphate-buffered saline (PBS) or empty vector (Ad-null control). Prime-boost experimental groups of mice were further subcutaneously (s.c.) boosted with 200\u2009\u03bcL of rTC_0037-MPLA two weeks after priming (n = 10 per group) were intravaginally (i.vag.) infected with 106\u2009IFU (inclusion-forming units) of C. muridarum in 40\u2009\u03bcL of PBS as described previously [Mice  was collected from tails and centrifuged 2 weeks after boost immunization, and serum was harvested and stored at\u201320\u00b0C. To assess IFN-\u03b3 T cell responses, lymphocyte cultures from spleens (n = 5 per group) were prepared as described previously [ C. muridarum obtained at 14 days after infection were used as a positive control. Spleen cells (2 \u00d7 106 per mL) were incubated with UV-killed C. muridarum or rTC_0037 for 24\u2009h in complete RPMI-1640  containing 5% fetal bovine serum, 2\u2009mM l-glutamine, and 1% penicillin-streptomycin . Vaginal swabs were obtained at 3, 6, 9, 15, and 20 days after infection. Uterine samples for chlamydial DNA detection were collected in PBS and frozen at \u221270\u00b0C. Gross uterine pathology presenting as a hydrosalpinx was examined at day 30 after intravaginal C. muridarum challenge of immunized and naive mice.Blood  and coated onto microtiter plates . Nonspecific binding was blocked by using 0.1% bovine serum albumin (BSA) in PBS for 30\u2009min at 37\u00b0C. Serum was diluted and titrated in 0.1% BSA in PBS and then incubated overnight at 4\u00b0C. Biotinylated anti-mouse IgG1 and IgG2a monoclonal antibodies , used at a dilution of 1\u2009:\u20091000, were added to plates and incubated for 1\u2009h at room temperature (20\u00b0C), followed by the addition of streptavidin-HPR and TMB substrate . The reaction was stopped 20\u2009min later by adding 50\u2009\u03bcL/well of 1\u2009M H2SO4. Absorbances at 450\u2009nm were determined using a Multiskan EX microplate reader .The antibody (IgG1 and IgG2a subclasses) response in serum was analyzed using an enzyme-linked immunosorbent assay (ELISA) with plates coated with rTC_0037 protein. Briefly, rTC_0037 protein was diluted to 10\u2009 C. muridarum neutralization assay was performed in McCoy cell cultures [\u03bcL of C. muridarum EB suspension (1.4 \u00d7 105\u2009IFU/mL) in Dulbecco's modified Eagle's medium  was added to 100\u2009\u03bcL of serial dilutions (from 1\u2009:\u200916 to 1\u2009:\u2009256) of immune sera from vaccinated mice and incubated for 30\u2009min at 37\u00b0C on a slowly rocking platform. Samples incubated with EB but without serum were used as an infection control. One hundred microliters of each dilution were then inoculated in duplicate into McCoy cells. DMEM containing 10% fetal bovine serum and 1\u2009mg/mL of cycloheximide were added up to 1\u2009mL to infected cells in 24-well plates with coverslips (12\u2009mm in diameter). After centrifugation at 800\u2009\u00d7g for 1\u2009h, infected cultures were incubated at 37\u00b0C for 48\u2009h. After this, ethanol-fixed monolayers were stained with fluorescein isothiocyanate- (FITC-) conjugated monoclonal antibodies against chlamydial lipopolysaccharide . Inclusion-containing cells were examined using a Nikon Eclipse 50i fluorescent microscope  at 1350x magnification and counted to determine the percent infected cells in the monolayer.Acultures . Briefly\u03b3-producing T cell enzyme-linked immunospot (ELISPOT) assays were performed on mouse splenocytes using a mouse IFN-gamma ELISPOT Ready-SET-Go! Kit  according to manufacturer's instructions. Briefly, IFN-\u03b3 capture antibody was coated onto ethanol-activated MultiScreen-IP Filter Plates , incubated overnight at 4\u00b0C, and then washed with sterile PBS. CD4+ and CD8+ T cells were isolated from the spleens of vaccinated mice using CD4+ and CD8+ T cell separation columns  according to manufacturer's protocols. The final concentration of enriched T cells was approximately 85%, as determined by flow cytometry; therefore, we hypothesized that there were enough antigen-presenting cells left to present antigens in the study. This assumption was confirmed in a separate set of experiments. Enriched cell suspensions were added to the coated plates at 2 \u00d7 105 cells per well (in 100\u2009\u03bcL of culture medium) in the presence of UV-treated C. muridarum EB (positive control) at a final concentration of 104\u2009IFU/mL and rTC_0037 at a final concentration of 10\u2009\u03bcg/mL. The plates were incubated for 24\u2009h before further washing. After this, plates were incubated with biotinylated anti-IFN-\u03b3 monoclonal antibodies. The spots were visualized with Avidin-HRP reagent and freshly prepared 3-amino-9-ethyl carbazole (AEC) substrate solution. Spots were calculated using an AID ELISpot Reader .Chlamydia and TC_0037-specific IFN-2 at 37\u00b0C for 48\u2009h. Coverslips were incubated with FITC-conjugated monoclonal antibodies against chlamydial lipopolysaccharide . Inclusion-containing cells were determined using a Nikon Eclipse 50i fluorescent microscope  at 1350x magnification. The results were expressed as log10 IFU per vaginal swab.Vaginal swabs were obtained at 3, 6, 9, 15, and 20 days after infection. The canal and exocervix were vigorously scraped, and swabbed material was transferred to DMEM and frozen immediately at \u221270\u00b0C. Bacterial loads were determined as previously described . Thawed,\u03bcL of proteinase K  at 60\u00b0C for 1\u2009h. DNA was extracted with automated nucleic acid extractor NucliSENS easyMAG . DNA contamination controls were included. The primers and TaqMan probe targeting the cryptic plasmid region were selected using Primer 3 (http://simgene.com/Primer3) and Oligo7 programs and are presented in  C. muridarum DNA was amplified using a Real-Time PCR Cycler CFX96 . The results are presented as percent positive mice in each group.Chlamydial DNA in uteruses was evaluated using a quantitative real-time PCR that allowed enumeration of the parasite's genome equivalents in infected tissue. Uterine samples were collected at day 30 after infection, homogenized in 1\u2009mL of physiological solution, and frozen at \u221270\u00b0C. The prepared suspensions were lysed in 1\u2009mL of lysis buffer  and incubated with 40\u2009 C. muridarum challenge as described previously [The percent of mice with hydrosalpinges in each group was assessed at day 30 aftereviously .U test was used to evaluate differences in antibody titers in ELISA, specific IFN-\u03b3-producing CD4+ and CD8+ T cells, and Chlamydia loads. An analysis of variance (ANOVA) test was used to compare neutralizing antibodies in experimental groups.The results were analyzed with the aid of GraphPad Prism 7.0 software . Shapiro\u2013Wilk test was used to determine normality of combined datasets from several similar experiments. The Mann\u2013Whitney P < 0.05) , followed by subcutaneous immunization with recombinant TC_0037 and MPLA in a squalene nanoscale emulsion (rTC_0037-MPLA). Sera from mice immunized with an empty vector, with or without MPLA, and sera from intact mice were used as controls. AdTC_0037 immunization induced TC_0037-specific IgG2a and IgG1 antibodies with titers of 1\u2009:\u2009800 for both isotypes ( < 0.05) . Prime-b in vitro. C. muridarum EBs were preincubated with serum from immunized mice and then used to infect McCoy cell monolayers. Chlamydial inclusions were counted under the microscope 48\u2009h after incubation. The results are presented in  in vitro up to 90% at a dilution of 1\u2009:\u2009512. In the group immunized with AdTC_0037, the reduction in infectivity reached 65% (dilution 1\u2009:\u2009512). Overall, our results suggest that immunization with AdTC_0037 induces antibodies with high potential to neutralize C. muridarum infection in vitro. Interestingly, the neutralizing potential of specific antibodies from mice immunized with AdTC_0037/rTC_0037-MPLA was higher than that of antibodies in mice immunized with AdTC_0037 alone (P \u2264 0.01). This might contribute to higher protection with the prime-boost approach.In the experiments described above, we demonstrated that immunization with AdTC_0037/rTC_0037-MPLA or AdTC_0037 alone induced TC_0037-specific antibodies of IgG2a and IgG1 isotypes. Next, we assessed the ability of serum antibodies to neutralize pathogen infectivity\u03b3 has been found to play a major role in mediating control of Chlamydia infection, we measured IFN-\u03b3 responses to rTC_0037 and UV-killed C. muridarum in immunized and infected mice to evaluate the potential of TC_0037 to prime T cell responses to Chlamydia. TC_0037 and C. muridarum-specific IFN-\u03b3+ CD4+ and CD8+ T cell responses were assessed by ELISPOT 2 weeks after final immunization or infection. As shown in  C. muridarum stimulation triggered robust IFN-\u03b3 production in CD4+ and CD8+ T cells derived from mice immunized with AdTC_0037/rTC_0037-MPLA or AdTC_0037 alone, but not with the control adenovirus . UV-killed C. muridarum stimulation induced IFN-\u03b3 CD4+ and CD8+ production in immunized mice at the same level as that in C. muridarum-infected mice . Almost complete eradication of infection (9 of 10 mice cleared infection in AdTC_0037/rTC_0037-MPLA group) was observed at day 20 after infection (\u2217\u2217P < 0.01), whereas eradication following immunization with AdTC_0037 and in the infection control was not observed. These results show that prime-boost immunization with AdTC_0037/rTC_0037-MPLA reduced the magnitude and duration of C. muridarum vaginal shedding after infection.BALB/c mice were i.vag. infected withrom mice  on days rom mice . There wrom mice  after ch C. muridarum DNA in the upper genital tract of mice, and this effect was more pronounced in the mice immunized with AdTC_0037/rTC_0037-MPLA  have been reported [ Chlamydia antigen TC_0037 and evaluated its protective potential in a C. muridarum mouse model. To enhance protection by our vaccine candidate, we utilized a heterologous prime-boost immunization regimen, using a combination of intranasal priming with adenovirus-expressing TC_0037 and subcutaneous boost with recombinant TC_0037 and MPLA mixed in squalene. Prime-boost immunization of mice with adenovirus-expressed TC_0037 and rTC_0037/MPLA elicited serum antibodies against TC_0037 of isotypes IgG2a and IgG1 ( in vitro ( C. muridarum showed a reduction in both bacterial shedding and Chlamydia-induced fallopian tube pathology.Despite decades of research and considerable progress made in recent years, an efficacious elusive . As prevreported \u201348, in tand IgG1 . In addiin vitro . Mice va Chlamydia vaccine [ C. muridarum, represents a good vaccine candidate, as it is able to induce specific antibody and T cell responses and decrease bacterial loads and pathology of the upper genital tract.TC_0037 protein forms a conserved and abundant TTSS needle structure and may be a strong candidate protein for inclusion in a pan-serovar vaccine . We show Chlamydia. Recently, intranasal immunization with a mixture of three T3SS components, CopB, CopD, and CT584, with CpG resulted in the production of specific neutralizing antibodies and decreased chlamydial infection and Chlamydia-induced pathology [ in vivo challenge with Shigella has been demonstrated after vaccination with a Shigella CopB ortholog in combination with other T3SS antigens [ Yersinia spp. and PcrV in Pseudomonas aeruginosa have been shown to block these infections [ Chlamydia challenge.To date, few studies have examined the use of T3SS proteins as antigens to vaccinate againstathology . In contantigens . Antibodfections , 48. In  Chlamydia infection. A large amount of data has been accumulated on the role of CD4+ T cells in protection against genital infection in previous Chlamydia vaccine studies [\u03b3 to Chlamydia control in vivo has also been demonstrated [\u03b3 CD4+ and CD8+ T cell responses (+ and CD8+ enriched T cell populations with purity of 85%. It should be noted that the remaining 15% of non-T cells could also contain cells that produce IFN-gamma. Besides, differences in the remaining subpopulations of antigen-presenting cells in different groups of mice could also affect the resulting IFN-gamma production. Future studies employing more rigorous protocols of CD4+ and CD8+ T cells isolation will help to elucidate the role of CD4+ and CD8+ T cells in the response to TC_0037. Prime-boost immunization also coinduced significant levels of TC_0037-specific IgG2a and IgG1 antibodies that had higher neutralizing activity than those in response to AdTC_0037 immunization, and they afforded better protection.Cell-mediated immune responses have been documented as critical for protection and clearance of studies , 51. Thenstrated , 53. In esponses . In thes Chlamydia is dependent upon CD4+ T cell responses. However, antibodies play a contributing role in the resolution of primary infections and protect from reinfection [+ and CD8+ T cells have been reported to act cooperatively with antibodies [ Chlamydia genital infection only in the presence of CD4+ T cells [+ T cell responses [ Chlamydia challenge, but we observed that a higher level of neutralizing antibody activity in AdTC_0037/rTC_0037-MPLA-immunized mice was associated with better protection. This is in agreement with previous findings in mice regarding the role of neutralizing antibodies in protection against Chlamydia [ in vitro, suggesting that antibodies directed at these proteins block an essential component of T3SS virulence [As reported in other studies , 55, enhnfection \u201358. CD4+tibodies \u201361. For  T cells \u201363. Antiesponses . In our irulence . Serum c Chlamydia vaccine is its ability to decrease bacterial shedding for reduced transmission and to produce an immune response that prevents Chlamydia-induced immunopathology. During the course of a chlamydial infection in mice, bacterial shedding occurs for approximately 14\u201335 days before being cleared from the lower genital tract [An important characteristic of aal tract . Based o Bacillus anthracis [ Chlamydia vaccine candidate TC_0037. However, we did not compare the efficacy of our delivery system to other possible variants such as virus-like particles. This question requires additional studies in future.Previously our group has successfully used Ad5 for design of vaccines against influenza , Bacillunthracis , Ebola vnthracis , 31, andnthracis . They alnthracis . Overall Chlamydia infection. Recently, our group published the results of successful clinical trial of Ad5-based Ebola vaccine candidate that confirmed safety of this vaccine [It should be noted that we used adenovirus as a delivery system for our antigen only once in our protocol avoiding possible complications due to preexisting adenoviral immunity. However, taking into account the fact that our adenoviral delivery system is intended as a new platform for a range of novel vaccines to different pathogens, the possibility of preexisting immunity should be taken into consideration. In our study, we employed Ad5 genome modification with mannose-binding lectin to improve the efficacy of our vaccine and to escape limitations connected with preexisting adenoviral immunity. However, the practical efficacy of this approach expects confirmation in preclinical and clinical trials. The safety of adenoviral vaccines is another question that should be addressed, especially for widespread diseases like vaccine , 31. Ade vaccine . Besides C. trachomatis, we examined upper urogenital tracts following pathogen challenge to determine whether vaccination could prevent Chlamydia-induced pathology. Indeed, it completely eradicated pathology in the upper genital tract in mice vaccinated with AdTC_0037/rTC_0037-MPLA or AdTC_0037 alone (Because the development of oviduct pathology and complications are major concerns in individuals infected with37 alone . C. muridarum infection model in mice demonstrates a potentially effective vaccine candidate, which provides B and T cell immune responses and protects from genital tract infection and pathology. However, to proceed closer to a clinical trial, there is a need for testing of our vaccine candidate in a model employing C. trachomatis. Besides, nonrodent animal models with better resemblance to Chlamydia infection in humans can afford higher level of immunological and physiological relevance. Finally, long-term protection studies are also highly urgent to complete the profile of protection for our vaccine candidate.Overall, evaluation of AdTC_0037/rTC_0037-MPLA vaccination in C. muridarum infection coupled with protection against Chlamydia-induced pathology suggests that prime-boost immunization with AdTC_0037/rTC_0037-MPLA affords a significant degree of protection and TC_0037 can be considered for further evaluation as a vaccine candidate. We are currently investigating the potential of our vaccine candidate to protect against intravaginal C. trachomatis infection in mice in our recently developed murine model [The resolution of genitalne model ."}
+{"text": "Porphyromonas gingivalis, a periodontopathic gram-negative anaerobic bacterium, generally expresses two types of fimbriae, FimA and Mfa1. However, a novel potential fimbrilin, PGN_1808, in P. gingivalis strain ATCC 33277 was recently identified by an in silico structural homology search. In this study, we experimentally examined whether the protein formed a fimbrial structure. Anion-exchange chromatography showed that the elution peak of the protein was not identical to those of the major fimbrilins of FimA and Mfa1, indicating that PGN_1808 is not a component of these fimbriae. Electrophoretic analyses showed that PGN_1808 formed a polymer, although it was detergent and heat labile compared to FimA and Mfa1. Transmission electron microscopy showed filamentous structures (2\u20123 nm \u00d7 200\u2012400 nm) on the cell surfaces of a PGN_1808-overexpressing P. gingivalis mutant (deficient in both FimA and Mfa1 fimbriae) and in the PGN_1808 fraction. PGN_1808 was detected in 81 of 84 wild-type strains of P. gingivalis by western blotting, suggesting that the protein is generally present in P. gingivalis. Porphyromonas gingivalis, a gram-negative anaerobic coccobacillus, is a member of the \u201cred complex\u201d of bacteria, which are pathogens primarily responsible for human periodontal diseases , and disrupted using a French press at 7.3 MPa. Unbroken cells and large debris were removed by centrifugation. The supernatant was subjected to precipitation with 50% ammonium sulfate saturation. The precipitate was dialyzed with the Tris buffer, and then applied to DEAE Sepharose Fast Flow chromatography  with 50 ml of bed volume. After washing thoroughly with the buffer, sample was fractionated by a linear gradient elution with 400 ml of NaCl (0 to 0.5 M) in the buffer.Fractionation were performed largely according to a previously described method . BrieflyIntact bacterial cells or the PGN_1808 fraction as described above was placed on a grid with an elastic carbon supporting film , negatively stained with 10 mM ammonium molybdate, pH 7.0, and observed by transmission electron microscopy .P. gingivalis strains were washed with PBS, pH 7.4, and the optical density at 600 nm was adjusted to 0.5, 1.0 and 2.0. The bacterial suspensions were mixed with the antisera to PGN_1808 or whole cells of P. gingivalis cells [The slide agglutination assay was performed following a standard protocol. is cells  on a glaSamples were mixed with a loading buffer consisting of 50 mM Tris/HCl, pH 6.8, 1% (w/v) SDS, 0.5 M 2-mercaptoethanol, 10% (w/v) glycerol, and 0.01% (w/v) bromophenol blue , and denatured by heating at the indicated temperatures for 10 min. Then, the samples were loaded onto an SDS-PAGE gel consisting of 11% or 5\u201220% gradient polyacrylamide. After electrophoresis, protein bands were visualized by staining with Coomassie Brilliant Blue (CBB) R-250. For the western blot analysis, proteins separated in the SDS-PAGE gel were transferred to a nitrocellulose membrane. The membrane was blocked with 5% skim milk in 20 mM Tris/HCl, pH 7.5, supplemented with 300 mM NaCl and 0.05% (w/v) Tween 20, and incubated with the primary anti-FimA , anti-MfFractionated PGN_1808 was applied to blue native-PAGE . BrieflyXL  at a flow rate of 1.0 ml/min in PBS, pH 7.4, supplemented with 0.02% NaN3. The protein elution was monitored by optical absorbance at 280 nm.The molecular size of the fractionated PGN_1808 was examined by using HPLC with a gel filtration chromatography column TSKgel G3000SWMass spectrometry analysis was performed as described previously . N-termipgn_1808 was examined by reverse transcription (RT)-PCR. Total RNA was isolated using ZR Fungal/Bacterial RNA MiniPrep  and treated with DNase I (Zymo Research Corp.) to remove residual DNA strands. No genomic DNA contaminants were detected in the total RNA samples (data not shown). The pure RNA was used to generate cDNA with a PrimeScript RT-PCR Kit and random 6 mers . Transcription unit was examined by a standard PCR for intergenic regions using the cDNA as a template and primers listed in Transcription unit comprising http://www.cbs.dtu.dk/services/LipoP/) was used for predictions of lipoprotein signal peptides [In silico structure homology-modeling of PGN_1808 was performed using the SWISS-MODEL online program (https://swissmodel.expasy.org/) [The LipoP 1.0 online program (peptides . In silisy.org/) .P. gingivalis strain TDC60 for the fractionation assay because TDC60 normally expresses both FimA and Mfa1 fimbriae [P. gingivalis ATCC 33277 expresses aberrant FimA fimbriae due to a nonsense mutation in fimB [Previously, we detected PGN_1808 concomitantly with FimA and Mfa1 fimbriae when the fimbriae were fractionated by ammonium sulfate precipitation and anion-exchange chromatography [ and unpufimbriae , whereasThe elution diagram showed a major peak at fractions 36 and 37 , which wDenaturation of FimA and Mfa1 fimbriae by heating at low temperatures  resulted in a smear and/or ladder-like pattern, due to partial dissociation of the FimA and Mfa1 polymers, respectively, in the SDS-PAGE, whereas denaturation by heating at 100\u00b0C resulted in a single band at the sizes corresponding to their monomers (FimA at 40 kDa and Mfa1 at 70 kDa), although degraded products of these proteins were also detected , as prevN-terminal sequencing of the PGN_1808 monomer band shown in  revealedP. gingivalis mutant deficient in both FimA and Mfa1 fimbriae , indicating that there might be variants of PGN_1808.PGN_1808 was detected in 81 of 84 wild-type strains of blotting , suggestBacteroides ovatus (PDB ID: 4rfj), that was resolved by Xu et al [We submitted PGN_1808, excluding the predicted signal peptide in the N-terminal 20 amino acids, to structure homology-modeling in the SWISS-MODEL program. The program found that PGN_1808 shared significant homology with a putative cell adhesion protein BACOVA_01548) from 48 from BXu et al . The PGNWe have not yet identified any accessory components of PGN_1808 fimbriae, because no proteins were concomitantly fractionated with PGN_1808. However, this possibility should be addressed in future research using a wild-type strain, rather than the PGN_1808-overexpressing mutant.pgn_1808 was examined in P. gingivalis wild-type strains ATCC 33277 and TDC60 by RT-PCR. We found that pgn_1808 was co-transcribed with the upstream genes including pgn_1805, pgn_1806, and pgn_1807, but not with pgn_1804 and the downstream gene pgn_1811 (note that pgn_1809 and pgn_1810 are absent). The pgn_1805, pgn_1806, and pgn_1807 genes are annotated as cysteine-tRNA ligase, patatin (a lipase), and glycosyl transferase, respectively [Transcription unit comprising ectively . FurtherP. gingivalis. However, PGN_1808 was detergent and heat labile compared to FimA and Mfa1. The biological function of PGN_1808 fimbriae should be examined in the future.We showed that PGN_1808 polymerized to form a fimbria and that it was generally expressed in S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file."}
+{"text": "The structure and function of bacterial nucleoid are controlled by histone-like proteins of HU/IHF family, omnipresent in bacteria and also founding archaea and some eukaryotes.HU protein binds dsDNA without sequence specificity and avidly binds DNA structures with propensity to be inclined such as forks, three/four-way junctions, nicks, overhangs and DNA bulges. Sequence comparison of thousands of known histone-like proteins from diverse bacteria phyla reveals relation between HU/IHF sequence, DNA\u2013binding properties and other protein features.N. gonorrhoeae, which sequence is similar to one of E.coli HU, and HU from M. gallisepticum and S. melliferum which sequences are distant from E.coli protein. We found that in respect to dsDNA binding, only S. melliferum HU essentially differs from E.coli HU. In respect to binding of distorted DNA structures, S. melliferum HU and E.coli HU have similar properties but essentially different from M. gallisepticum HU and N. gonorrhea HU. We found that in respect to dsDNA binding, only S. melliferum HU binds DNA in non-cooperative manner and both mycoplasma HU bend dsDNA stronger than E.coli and N. gonorrhoeae. In respect to binding to distorted DNA structures, each HU protein has its individual profile of affinities to various DNA-structures with the increased specificity to DNA junction.Performed alignment and clusterization of the protein sequences show that HU/IHF family proteins can be unambiguously divided into three groups, HU proteins, IHF_A and IHF_B proteins. HU proteins, IHF_A and IHF_B proteins are further partitioned into several clades for IHF and HU; such a subdivision is in good agreement with bacterial taxonomy. We also analyzed a hundred of 3D fold comparative models built for HU sequences from all revealed HU clades. It appears that HU fold remains similar in spite of the HU sequence variations. We studied DNA\u2013binding properties of HU from N. gonorrhoeae, M. gallisepticum and S. melliferum are studied. Here we provide detailed analysis of the similarity and variability of DNA-recognizing and bending of four HU proteins from closely and distantly related HU clades.HU/IHF family proteins sequence alignment and classification are updated. Comparative modeling demonstrates that HU protein 3D folding\u2019s even more conservative than HU sequence. For the first time, DNA binding characteristics of HU from  Members of HU/IHF family proteins were identified by InterPro ID IPR000119, which represents bacterial histone-like proteins. Multiple sequence alignment (MSA) was performed by the rate matrix of residue substitution search using an algorithm described in supporting materials  and the https://www.R-project.org). The protein-protein distances were estimated by the dist.alignment function of the seqinr package with Fitch matrix as parameter; totally 2000 species (25 phyla) with annotated HU/IHF family protein are listed. We hope that this easy-to-use table will help researchers to itemize HU/IHF of interest.HU/IHF family protein sequences and annotation were acquired from InterPro ID IPR000119, which represents bacterial histone-like proteins. Results of multiple sequence alignment (MSA) of HU/IHF family proteins sequences are presented in . This tahttps://www.ebi.ac.uk/interpro/]. Comparison of our alignment and clustering results with InterPro annotation shows that actual annotation of HU/IHF proteins is rarely inaccurate:2% of HU sequences are erroneously annotated as IHF, and 1% of IHF_A or HF_Bare annotated as HU. Although, one third of HU/IHF proteins are annotated as \u201cDNA-binding protein\u201d; without further attributing the proteins to HU, IHF_A or IHF_B. We show here that any HU/IHF protein can be unambiguously attributed to HU, IHF_A or IHF_B: for each sequence its scores are significantly different for these three groups. This result is well correlated with phylogenetic analysis performed previously [Each aligned protein sequence belongs to one of three major groups: HU proteins, IHF_A or IHF_B proteins. Supporting For visualization of the results of HU/IHF family MSA and clusterization we employed principal component analysis (PCA), a powerful method for the dimensional reduction and analysis of large data sets  to serve as a basis for the whole family HU/IHF sequences cauterization  is ambiguous as reference sequences, which historically were rization . We beliE. coli IHF sequences are, by chance, well placed to entitle IHF proteins: E. coli IHF\u03b1 and IHF\u03b2 have only 24 identities of 90 amino acid residues within the core sequence in contrast to E. coli HU\u03b1 and HU\u03b2.IHF_A sequences are further subdivided to three clades , it contains three HU/IHF family proteins, one HU and two IHFs, IHF_A_dand IHF_A_d,both characteristic for delta-proteobacteria. Among Eukaryotes containing IHF proteins we note Capitellateleta and Castor bean; see Note some interesting exceptions when IHF can be found outside of proteobacteria. Subdivision of HU sequences is not as unambiguous as it is for IHF where clusterization is in very good agreement with taxonomy data. Nevertheless, we present results of clusterization 1) to demonstrate HU variability and 2) to describe the most obvious HU clades which identity is apparent. HU clade represents a totality of HU sequences which are similar to each other and, hence, to the core consensus motif. Consensus sequences that represent revealed HU and IHF clades are shown in Mycobacterium, Gordonia and other genera) we could not observe any HU homologues which do not belong to HU_acti_C clade.All of them contain also C-terminal extension of 36\u2013158 amino acids. Obviously, the only form of HU protein of Corynebacterialesorderis HU that belongs to the HU_acti_C clade. Properties of Mycobacterium HU C-termini are described in details [Streptomyces genus this HU from HU_acti_C clade, also with C-terminal extension (88\u2013150 long), is expressed during spore maturation [Bacillus phage SP01 and Cellvibriogilvus). Similarly, HU proteins of HU_acti_C clade were found exclusively in Actinobacteria phylum, even if only core sequence  is taken into account. Thus, Actinobacteria HU/IHF sequences classification on two HU clades is exemplary classification of the proteins: each Actinobacteria HU/IHF is unambiguously attributed to HU_acti_C or to HU_acti_0clade and all HUs from these clades belong to Actinobacteria.Actinobacteria phylum is a perfect example of HU clusterization. All HU proteins consist of HU 90 amino acid core, some HU have N- and C- terminal extensions out of core sequence. Mycobacterium genera HU proteins consist of HU core with characteristic consensus motif \u201cHU_acti_C\u201d  and a lo details ,22. In Sturation ,24. Thesturation . HU_actiWe analyzed how Actinobacteria HU variants are distributed among Actinobacteria species. Three situations were observed:Bacteria of orders: Bifidobacteriales, Actinomycetales, Geodermatophilales, Acidimicrobiia, Coriobacteriia, contain only HU proteins of clade HU_acti_0.Bacteria of orders: Acidothermales, Catenulisporales, Corynebacteriales (including Mycobacteria), Glycomycetales, Micromonosporales, Nakamurella, and Pseudonocardiales contain only HU proteins of clade of HU_acti_C with long C-terminal extension. Interesting that HU_acti_C HU protein can be functional without C-terminal extension: Frankie\u2019s contain only one HU/IHF polypeptide, HU_acti_C, while their HU C-terminal extension has just 3\u20134 residues. Similarly, the only one HU/IHF polypeptide observed in Streptosporangiales is HU_acti_C with a short C-terminal extension.In orders: Kineosporiales, Micrococcales, Propionibacteriales, and Streptomycetales we found HU polypeptides of both clades, HU_acti_0 and HU_acti_C.We entitle HU clades according to the taxonomy group which is the most present in this clade. Two large HU clades are HU_Firmicutesand HU_ecoB see . Clade HBacteria of Pseudomonas genera (class gamma-proteobacteria)carry four HU/IHF proteins, IHF-A, IHF-B and two HU proteins, one HU belongs to HU_ecoB clade and is similar to HU from many other classes, another belongs to HU_pseudomonas clade and is specific for Pseudomonas. Only one such protein was found elsewhere (in Fungi). Consensus sequence of HU_pseudomonas clade is presented in Spirochaetales order (mainly Borrelia) carries HU of HU_Spirochaetales clade as a single HU/IHF protein specific exclusively for this order. HU_Spirochaetales clade members distances to other HU are similar to their distances to IHFs. See Spiroplasma melliferum HU protein (HU_ecoB clade) and Mycoplasma gallisepticum HU protein (HU_mycoplasma clade) is presented below.Species from Mollicutes class have only one HU polypeptide that belongs to HU_mycoplasma clade; HU of this clade are observed exclusively in Mollicutes. Of note, one order among Mollicutes, Entomoplasmatales, which includes Spiroplasmas, possess more common HU from HU_ecoB clade. The comparative analysis of DNA\u2014binding properties of Paulinellachromatophora (Cercozoa) and Rhodomonassalina (Cryptomonads).Species of class Cyanobacteria have only one HU/IHF protein, HU_cyano that essentially differs from other HU and IHF. Vice versa, HU_cyano motif is found exclusively in Cyanobacteria. Two interesting exceptions are eukaryotes Helicobacter and all Helicobacteraceae family species possess only one HU/IHF polypeptide. All these proteins belong to HU_helicobacter clade. This clade is specific for Helicobacteraceae family. Other Campylobacterales bacteria possess HUs that does not belong to HU_helicobacter clade.Rhodobacterales  possess both IHF subunits, IHF_A_a and IHF_B_a, and only one HU sequence, mainly it has HU_ Rhodobacterales motif that essentially differs from other HU and IHF . InteresHU_insclade is populated by alpha-, beta-, and gamma-proteobacteria, mainly Rhizobiales, Burkholderiales, and Xanthomonadales. Its core sequence is the most remote from all other HU Figs  and 3.HU/IHF family proteins from phylum Bacteroidetes gives good MSA only in 60% cases. Further analysis of these sequences, reveals some clades, HU_Parabac, HU_Prevotella, and HU_bacL, specific only for Bacteroidetes .The most described HU/IHF terminal extension is \u0421-termini of Actinobacteria, clade HU_acti_C. In average, it contains 111 residues, including 29 lysines and 5 arginines, and only 0.4 and 0.7 aspartic and glutamic acids, respectively. HU_acti_C\u0421-termini are associated with a lysine rich \u2018\u2018PAKKA\u201d repeat, this repeat is implicated in protection of DNA from adverse conditions . This PABesides Actinobacteria, only Bacteroideteshave C-termini longer than 48 residues . Bacteroidetes HU core sequence is essentially different from HU_acti_C. Most of Bacteroidetes HUs with long C-terminal extension constitute a clade HU_bacL characterized by long, 238 residues in average, C-termini with very high content of charged residues: 19 lysine sand 9 arginines, as well as 13 aspartic and 31 glutamic acids. Often it contains proline\u2019s and two consecutive lysine\u2019s that make them similar to PAKKA motif.HU_bacL HU proteins have extended N-termini, which are 1 to 4 residues or around 30 residues long. Most of HU_insclade proteins possess long N-terminal extension, 40 residues in average; with high lysine content . Other HU proteins with long N-termini are found among Bacteroidetes, Mollicutes and Deinococci. IHF_A of Burkholderiales and Rhizobiales also possess long N-termini. Usually long IHF/HU N-termini have several negatively and positively charged residues.Charged residues, especially positively charged, at C- and N- terminal extensions of the HU/IHF proteins are able to modulate protein-DNA interactions .HU/IHF sequences can contain amino acid inserts and deletions compared to consensus sequences. About 10% of HU proteins have an insert, usually of one amino acid (80% of inserts). Insert position distribution along the HU sequence is far not uniform. The most frequent position for insertion is a loop between alpha helixes 1 and 2 (34%). Turn between helix 2 and beta strand 1 (18%) as well as DNA\u2013interacting tip (15%) of HU are also hotspots for amino acid insertions .Most frequent position for insertion is a loop between alpha helixes 1 and 2 (\u03b11-loop-\u03b12 in the bottom of figure). Turn between helix 2 and beta strand 1 is the second hotspot for insertions. We believe that indels at these positions does not change essentially HU fold.Though average insertion rate is low, all HU sequences of several clades have amino acid insertions: in majority of phytoplasma HU one amino acid residue is inserted between beta strands 1 and 2. All sequences of HU_Spirochaetales clade contain one amino acid insertion in beta-strand 3. Most Dinoflagellata (Eukaryota) HU sequences  also contain one amino acid insert in beta-strand 3. All sequences of HU_insclade HU contain 5 or 3 amino acid insert in beta strand 2.About 3.4% of HU proteins have a deletion of one amino acid; longer deletions are rare. Majority of deletions (70%) are localized within the loop between alpha helixes 1 and 2. Turn between helixes 2 and beta strand 1 also can contain deletions (7%).About a half of HU sequences of clade HU_acti_Chave an amino acid deletion within the loop between helixes 1 and 2, they are responsible for 67% of deletions observed in HU proteins.IHF_A and IHF_B also contain insertions, insertion rates are 3,8% and 2.4%, respectively. Again, most insertions are localized in the loop between helixes 1 and 2. Deletions in IHF_A are rare (0.5%), all within the loop between helix 1 and 2; among IHF_B proteins only IHFs of the clade IHF_B_d contain deletions (6% sequences), all are within the loop between helix 1 and 2.S. melliferum HU protein, RMSD between model and crystal structure was 0.1348 nm.To estimate how the sequence differences between HU proteins influence their 3D folding we performed comparative modeling (CM) using the known structure of HU protein from Anabaena sp. (PDB ID 1P71) as a template. Because of broad spectrum of HU sequences we built and analyzed 103 models in overall (at least four models for each of the 14 HU clades). All models and their validations are available from supporting materials  Files. TComparative analysis of the models was aimed to determine several parameters of both alpha-helical body and beta-stranded DNA-binding arms of HU dimer. We measured angles between either alpha helixes 1 and 2 or 2 and 3 of the same monomer as well as an angle between alpha helixes 2 of opposite monomers. We also measured distances between C-terminal ends of alpha helixes 2, C-terminal ends of beta strands 2 and N-terminal ends of beta strands 5 of opposite monomers. See Alpha helixes 1 and 2  include more than two thirds of HU monomer residues; angle between alpha helix 1 and 2 determine the architecture of the HU protein body. The angle between the long alpha helixes 2 of two HU monomers that form a dimer determines reciprocal orientation of HU subunits.Angle between alpha helix 1 and alpha helix 2 constitutes 61.5 to 63.6 degrees (62.7 degrees in average) and is equal for all the HU models analyzed with few exceptions . In HU pAngle between alpha helix 2 and alpha helix 3 of the same monomer constitutes from 61.8 to 64.4degrees (63.7 degrees in average) and is equal for all the HU clades analyzed with one exception . An impoAngle between alpha helixes 2 of two HU subunits that form a homodimer constitutes from 82.0 to 85.0 degrees (83.5 degrees in average) and is equal for all the HU clades analyzed without exceptions.Distance between C-terminal amino acid residue of alpha helix 2 and corresponding amino acid of the second HU monomer within a homodimer  can be uHU arms are flexible and are able to adopt DNA minor grove independently of DNA sequence. HU has a capacity to bind DNA not only in B-form, but also dsRNA and RNA-DNA hybrids in A-form . HU armsFor the beta strands 2 ends, the distances between three corresponding C-alpha atoms of consecutive residues 54, 55, 56  and DNA-recognizing tips) [High genome plasticity of Mollicutes  leads tooB clade . It has nterface , and a tng tips) ,41.E. coli HU\u03b1 binding to dsDNA is noticeable for all tested DNA lengths, from 21 to 36 bp  binding [It suggests that binding of the second HU dimer to DNA molecule is decreased when one HU dimer is already bound to this DNA molecule. Cooperativity parameter(\u03c9) specifying the relative affinity of the second bound HU dimer for a contiguous site versus an isolated binding site, \u03c9, can be calculated from the cooperative McGhee\u2013von Hippel equation. Cooperativity parameter for st dimer . This reing site . Similarorrhoeae . M. gallst dimer . Coperat (\u03c9 = 60 ). Non-co binding . Non-cooN. gonorrhoeae and E. coli HUs  of observed complexes and its reciprocal\u2014the association constants were calculated. The Kd values are available from supporting materials . The assS. melliferum HU), DNA junctions and DNA overhang (with the exception of S. melliferum HU), DNA invasion and DNA fork . DNA junctions and DNA invasion as well as fork and A7 bulge (in lesser degree) are more preferable HU substrates, perhaps as they carry more sites for HU binding than smaller DNA structures. At the same time, all studied HU proteins have individual characteristics of DNA binding. E. coli HU\u03b1 has relatively low level of discrimination between the DNA-substrates comparing to other three proteins. S. melliferum HU has highest affinity to A7 bulge DNA and similar affinities to nick, junction and overhang DNAs, while affinity M. gallisepticum HU to nicked DNA is about 10 and 20 times less than to DNA junctions and invasion, respectively. N. gonorrhoeae HU also binds DNA junction and invasion 4 and 5 times stronger than nick. Such better recognition of DNA junction compared to nick was already shown for H. pylori HU [Comparison of individual profiles of specificity indicates that all HU proteins has lowest affinity to small A1 and A3 bulges and nicked DNA and high affinity to A7 bulge  according to primary structure each representative of HU/IHF protein family (InterPro ID IPR000119) can be unambiguously attributed to one of three group: HU, IHF_A or IHF_B; 2) HU proteins 3D folding is more conservative than HU sequence; 3) comparison of DNA-binding features of four HU representatives closely or distantly related to each other show that in respect to DNA recognition, each HU protein has its individual profile of affinities to various DNA-structures with the increased specificity to the most complex structures. At the same time, the most dissimilar mycoplasma\u2019 HUs bend dsDNA stronger than S1 File(DOCX)Click here for additional data file.S2 File(DOCX)Click here for additional data file.S3 File(ZIP)Click here for additional data file.S4 File(ZIP)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S5 File(DOCX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file."}
+{"text": "Via bioinformatics prediction program and luciferase reporter assays, hsa_circ_0018289 was observed to directly bind to miR-497. Taken together, the results indicate that hsa_circ_0018289 plays important role in cervical cancer proliferation, migration and invasion, suggesting the miRNA \u2018sponge\u2019 of hsa_circ_0018289 and its oncogenic role on cervical cancer tumorigenesis.Circular RNAs (circRNAs) are a type of non-coding RNAs that have been identified as critical regulators in various diseases, especially in cancers. However, the expression profiles and functions of circRNAs in cervical cancer are still unclear. In present study, human circRNAs microarray were performed to screen the circRNAs expression in cervical cancer tissue. Microarray analysis revealed 45 significantly expressed circRNAs with 4 fold change. Among these up-regulated circRNAs, hsa_circ_0018289 was validated to be significantly up-regulated in 35 pairs of cervical cancer tissue compared with adjacent normal tissue and cell lines. Loss-of-function experiments revealed that,  Cervical cancer is one of the most common gynecologic tumors and accounts for large percentage of tumor associated death worldwide , 2. EverCircular RNAs (circRNAs) is an emerging type of ncRNAs without protein translation capacity \u20138. CircRMicroRNAs (miRNAs) are a kind of noncoding RNA with about 18-22 nucleotides. Usually, miRNAs participate in post-transcriptional regulation by targeting the 3\u2019-UTR region of target genes. Plentiful researches have revealed the important role of miRNAs in various diseases. In cervical cancer, hundreds of miRNAs have been testified to regulate the proliferation, migration, invasion, and apoptosis. MiR-497 has been identified as tumor suppressor and inhibits the proliferation, migration and invasion of retinoblastoma, cervical cancer cells , 14.In present study, our team screened the circRNAs expression profiles in cervical cancer tissue using human circRNA microarray assay, and ultimately identified a significantly overexpressed hsa_circ_0018289. Hsa_circ_0018289 is located at chr10:46968580-46969453 with 348 spliced length. Series of functional experiments revealed the important role of hsa_circ_0018289 on cervical cancer tumorigenesis. Besides, we also detected the interaction of hsa_circ_0018289 with miR-497. These finding provides valuable assistance for the prevention and treatment of cervical cancer.In initial stage of experiments, 4 pairs of cervical cancer tissue and adjacent noncancerous tissue were performed for circRNA expression profile. Scatter plot and volcano plot revealed that total 393 dysregulated circRNAs with 2 fold change (P<0.01) were screened Figure . Heat maCircRNA microarray assay revealed the expression profiles of aberrantly expressed circRNAs in cervical cancer tissue compared to normal tissue. Among these up-regulated circRNAs, 6 circRNAs were randomly selected and validated using RT-PCR, showing the significant overexpression of candidate circRNAs Figure . Hsa_cirin vitro. Specially designed interfering oligonucleotides targeting hsa_circ_0018289 were synthesized to knock down hsa_circ_0018289 expression in HeLa and SiHa cells  and functioning as miRNA sponges. Until now, the major canonical function of circRNAs is miRNAs \u2018sponge\u2019, as well as lncRNAs. Because circRNAs have a specific covalently closed circular construction, it might harbor numerous miRNAs binding sites, acting as a huge \u2018sponge\u2019 to consume target miRNAs. For example, CDR1as (ciRS-7) comes from \u201ccircular RNA sponge for miR-7\u201d with near 70 miR-7 binding site in the loop . MoreoveAnother important role of circRNAs is to act as biomarker for early detection in series of tumors. Due to the conservative covalently closed circular structure, circRNAs could resist the digestion of RNA enzyme, making its enrichment in peripheral blood or body fluid. For instance, the aberrant expression of hsa_circRNA_103636 in peripheral blood mononuclear cells is tested to be a potential novel biomarker for the diagnosis and treatment of major depressive disorder . For cerIn summary, our study reveals the circRNAs expression profiles in cervical cancer tissue and identifies the functional candidate hsa_circhsa_circ_0018289 for cervical cancer tumorigenesis, suggesting the important suppressive role of hsa_circhsa_circ_0018289 knockdown on proliferation. These results provide a novel insight of circRNAs for cervical cancer carcinogenesis.A total of 35 pairs of cervical cancer tissue and matched non-tumor tissue were collected for the study in the Cangzhou Central Hospital and Zibo Central Hospital between Dec 2015 and Aug 2016. None of the cervical cancer patients received chemotherapy or radiotherapy before surgery or biopsy. All tissue samples were rapidly stored at \u221280\u00b0C after resection. This study was approved by the Ethics Committee of Cangzhou Central Hospital and Zibo Central Hospital. All the enrolled patients have signed the informed consent.Four pairs of cervical cancer tissue and matched non-tumor tissue samples were selected for microarray studies. RNA extraction and microarray hybridization were performed based on the Arraystar\u2019s standard protocols. In briefly, total RNA was digested with Rnase R  to remove linear RNA and enrich circular RNA. Then, RNAs were amplified for cRNA and labeled with an Arraystar Super RNA Labeling Kit . Finally, these labeled RNAs were hybridized using Arraystar mouse circRNA Array , and scanned by the Agilent Scanner G2505C.2. Cells were transfected with indicated nucleotides or plasmid using Lipofectamine 2000  according to manufacturer\u2019s instructions.Cervical cancer cells , human epidermal cell (HaCaT) were purchased from the American Type Culture Collection . Cervical cancer cells were cultured in Dulbecco's modified Eagle medium  supplemented with 10% FBS, L-glutamine (2 mM), 100 mg/ml penicillin and 100 mg/ml streptomycin . All cells were grown at 37\u00b0C in a cell incubator with a humidified atmosphere containing 5% CO-\u0394\u0394Ct method.Total RNA were isolated from cervical cancer tissues and cells using Trizol reagent . Then, cDNA were synthesized using RevertAid First Strand cDNA Synthesis kit . Quantitative RT-PCR was performed using the SYBR-Green PCR Master Mix kit . GAPDH acted as the endogenous control. All specific primers for circRNAs and miRNA were purchased from Sangon Biotech . The primer sequences were shown as follows: hsa_circ_0018289 (outward facing primers): 5\u2019-TCACCAACCTTTGCCCTTCACACCT-3\u2019, and 5\u2019-AAGACTTACGTCTGTGTGCGTTGT-3\u2019; miR-497, forward, 5\u2019-CTCTTGAACTGCAGACTCA-3, reverse, 5\u2019-TATGACATTTCAAGAATT-3\u2019; GAPDH, forward, 5\u2019-TCGACAGTCAGCCGCATCTTCTTT-3\u2019, reverse, 5\u2019-ACCAAATCCGTTGACTCCGACCTT-3\u2019. Relative levels of gene expression were normalized to GAPDH housekeeping genes and calculated using the 24 cervical cancer cells (HeLa and SiHa) were seeded into 96-well plates and 10 \u03bcl CCK-8 solution was added to each well. Then, the cells were incubated at 37\u00b0C for 90 minutes. At the indicated time points, the absorbance at 450 nm was measured using a spectrophotometer. The data are representative of three individual experiments carried out in triplicate.Cell count kit-8  was used to detect cell proliferation. Briefly, 3\u00d7104) were suspended in 100 \u03bcl serum-free medium and then seeded on the upper floor of Transwell chambers . The lower chamber was added 500 \u03bcl serum with 20% FBS. After 48 h of incubating at 37\u00b0C with 5% CO2, the un-invaded cells were wiped with a cotton swab, and invaded cells were fixed in methanol and stained with 0.1% crystal violet. The number was counted under microscope. Each experiment was performed in triplicate.Transwell assay was performed for cervical cells (HeLa and SiHa) migration and invasion. In briefly, the inserts were coated with 50 \u03bcL Matrigel . Cells . Then, HEK293T cells were co-transfected with wild type vector (150 ng) or mutant vector (150 ng). Besides, miR-497 mimics or miR-NC (2 ng) were also transfected into HEK293T cells using Lipofectamie 2000 (Invitrogen). After 48 h of transfection, the luciferase activities were detected using dual-luciferase reporter assay kit (Promega) normalized to Renilla luciferase activity. All the experiments were performed in triplicate.6 cells in 100 \u03bcl) transfected with si-hsa_circhsa_circ_0018289 were subcutaneously injected into the back of nude mice. The tumour size was measured every 3 days. At indicated times, the mice were sacrificed and tumor weight were measured.The xenograft mouse models were performed in nude mouse to determine the tumorigenicity. The animal assay was approved by the Institutional Committee of Cangzhou Central Hospital and Zibo Central Hospital and carried out based on the Institutional Animal Care and Use Committee\u2019s guidelines. Male BALB/c nude mice (6 weeks) were purchased from Slac Laboratory Animal Center  and maintained under specific pathogen-free conditions. HeLa cells  16.0 software package  and GraphPad Prism 6.0 . Paired t test, independent t test and one way analysis of variance (ANOVA) were used in this study correctly. P value of 0.05 or less was considered statistically significant."}
+{"text": "Somatic X dosage compensation requires two mechanisms: X inactivation balances X gene output between males (XY) and females (XX), while X upregulation, hypothesized by Ohno and documented in\u00a0vivo, balances X gene with autosomal gene output. Whether X dosage compensation occurs in germ cells is unclear. We show that mouse and human germ cells exhibit non-canonical X dosage states that differ from the soma and between the sexes. Prior to genome-wide reprogramming, X upregulation is present, consistent with Ohno's hypothesis. Subsequently, however, it is erased. In females, erasure follows loss of X inactivation, causing X\u00a0dosage excess. Conversely, in males, erasure leads\u00a0to permanent X dosage decompensation. Sex chromosomally abnormal models exhibit a \u201csex-reversed\u201d X dosage state: XX males, like XX females, develop X dosage excess, while XO females, like XY males, develop X dosage decompensation. Thus, germline X dosage compensation states are determined by X chromosome number, not phenotypic sex. These unexpected differences in X dosage compensation states between germline and soma offer unique perspectives on sex chromosome infertility. \u2022X dosage compensation in germ cells is reset during GWR\u2022PGCs exhibit X upregulation before GWR, in keeping with Ohno's hypothesis\u2022X upregulation is lost during GWR\u2022Mouse and human germ cells exhibit X dosage states that are sexually dimorphic Germ cells reset their epigenome and transcriptome prior to meiosis. Sangrithi et\u00a0al. show that unique X chromosome dosage compensation states prevail in germ cells. These states are determined by the number of X chromosomes present rather than phenotypic sex, providing a different perspective on infertility associated with sex chromosome aneuploidy. Sry on the proto-Y chromosome. The subsequent appearance of\u00a0sexually antagonistic alleles near Sry caused progressive suppression of X-Y recombination  while males have one X\u00a0chromosome and one Y chromosome (XY). The X and Y chromosomes evolved from a pair of ancestral autosomes following the acquisition of the male-determining locus bination . The X cbination .Evolutionary loss of genes from the Y chromosome led to a disparity in the dosage of X chromosome versus autosomal genes, with males becoming monosomic for X-linked gene products. Susumo Ohno proposed that to rectify this imbalance, expression of X chromosome genes was increased 2-fold to match the output of the diploid autosomal complement, i.e., giving an X-to-autosome ratio (X:A) of 1 (termed Ohno's hypothesis) . This prDrosophila melanogaster  arise from the post-implantation epiblast and migrate along the hindgut endoderm before colonizing the gonad. During this time, they undergo genome-wide reprogramming in which the pluripotency gene network is reactivated, somatic genes are repressed, and genomic imprints are removed . In femaTo address this point, we have generated extensive RNA-seq datasets from wild-type XY male and XX female, as well as sex chromosomally abnormal XO female (Turner syndrome) and XX\u00a0male (Klinefelter syndrome variant) mouse germ cells before,\u00a0during, and after reprogramming. Consistent with Ohno's hypothesis, early male and female germ cells exhibit upregulation of the active X chromosome. Later, however, they display unusual and sexually dimorphic dosage compensation patterns. Female germ cells exhibit a phase of X dosage excess, during which X:A ratios exceed 1, while male germ cells, conversely, exhibit X dosage decompensation, with X:A ratios falling below\u00a01. These X dosage compensation patterns are conserved in human germ cells. Intriguingly, sex chromosome variant mice manifest a \u201csex-reversed\u201d dosage compensation state: XO female germ cells become dosage decompensated like XY males, while XX male germ cells exhibit X dosage excess like XX females. Our studies reveal important differences in X dosage compensation states between the germline and soma and provide fresh insight into the etiology of subfertility caused by sex chromosome abnormalities.0 phase of the cell cycle  cells formed three distinct branches (59 of our 60 conditions), suggesting that our transcriptomic data recapitulated the ontology of germ cell development A. We furNext, we ascertained whether X upregulation occurs in somatic cells. We analyzed X chromosome activity in XX and XY non-gonadal somatic cells (E14.5 liver and tail) and gonadal somatic\u00a0cells (E9.5\u2013E18.5). In both males and females, these cells carry one active X chromosome, as females undergo somatic X\u00a0chromosome inactivation. In order to assay expression at a\u00a0chromosome-wide level, we charted median X chromosome expression in relation to that of median expression from the autosomes as a comparison. This X:A ratio was calculated as the ratio of the respective medians, with 95% confidence intervals of the ratio computed using the bootstrap method, which involves random sampling from a distribution with replacement .Consistent with earlier studies , when alWhile our data supported Ohno's prediction that expression from the single active X chromosome is upregulated in male and female somatic tissues , two poiA second consideration was that the X chromosome is over-represented in genes expressed in reproductive tissues, including gonadal somatic cells and germ cells . Such geNext, we analyzed X dosage compensation patterns during and after reprogramming in the XX female germline. Previous work has shown that one of the two X chromosomes is already inactive in the epiblast prior to PGC specification , and subXist RNA fluorescence in\u00a0situ hybridization (FISH)  , and fouulations . Subsequulations A and 3B.Our findings demonstrated that during germline reprogramming\u00a0in XX female germ cells, expression of X genes undergoes dynamic changes relative to those of the autosomes, resulting in a period of excess X chromosome dosage. However, we could not decipher the relative contribution of the two X chromosomes to this unusual X dosage compensation state. To better understand this phenomenon, we repeated our analysis using germ cells from Turner syndrome female (XO) mice, which carry one\u00a0rather than two X chromosomes C and 3D.\u22124). Thus, in the female germline, upregulation of the active X chromosome is maintained at E14.5. By deduction, the state of X dosage excess in XX females at E14.5 . Germ cell loss is first evident in Klinefelter male mice from E15.5  males. These mice have two X chromosomes, and thus undergo X chromosome inactivation, but they are male due to presence of a sex reversing ransgene . XX maleransgene C and 4D.ransgene A and 3B,ransgene C and 4D,om E15.5 ; we therPgk1, Pdha1, have duplicate copies known as retrogenes. These arise by reverse transcription of X-derived RNAs and subsequent integration at autosomal sites, and thus differ from their parental copies in being intronless , or/and the continued effects of other Y-encoded genes that could modulate X dosage compensation states. It is already well established that the X chromosome is silenced during pachynema by the process of meiotic sex chromosome inactivation (MSCI)  , but itsFinally, our studies are informative with respect to understanding the etiology of infertility in sex chromosome aneuploidies. XO females exhibit X chromosome decompensation reminiscent of that seen in wild-type males, while XX males exhibit X dosage excess like that in wild-type females. We suggest that this sex-reversed X dosage compensation pattern could contribute to the infertility phenotypes. For example, while it is accepted that\u00a0infertility in XX males is due to reactivation of the inactive X chromosome , one modjames.turner@crick.ac.uk.Further information and requests for reagents may be directed to, and will be fulfilled by the Lead Contact, Dr. James Turner, \u2212\u00a0+Sry (C57B6) Oct4-EGFP males were crossed with XX Oct4-EGFP females to yield XX female, XY male and XX male embryos used in the study.Oct4-EGFP mice were obtained from the Reik lab  and maintained on a B6 background. The reporter strain was used to isolate fluorescently marked germ cells through FACs. Homozygous XYYO male , which enabled the timely isolation of these germ cells during the first-wave of male meiosis through FACS.Leptotene/zygotene stage male germ cells were isolated at postnatal Day 11, by crossing a Stra8-Sry transgenic) by a combination of gonadal inspection, conventional PCR genotyping and the presence of Y chromosome-derived transcripts from RNA-Seq data. Each condition comprised of at least two replicates. All animal procedures were in accordance with the United Kingdom Animal Scientific Procedures Act 1986 and were subject to local ethical review.Samples were sexed as female (XX or XO) or male  and associated somatic cells (GFP-negative) were isolated separately from individual embryos using FACs sorting on the MoFLo XDP or FACS Aria platforms. Live cells, i.e. only those staining negative for propidium iodide, were collected, and typically purity checking on the GFP negative populations was >99%.RNA was isolated from FACs sorted purified cell populations using the Ambion RNA isolation kit (Ambion #AM1931). Eluted RNA was used to obtain double-stranded-cDNA using the Clontech (SMARTER) Ultralow input RNA-Seq Kit according to the manufacturer's protocol, or the Smart-Seq2 protocol . cDNA waXist RNA FISH probes using an established protocol  compound and transferred to appropriate molds, quick-frozen and then stored at \u221280\u00b0C until the time of cryosectioning. 5\u00a0\u03bcm cryosections were collected and placed on coverglasses, and subsequently processed with protocol . Germ ceReads were aligned to the mouse genome (mm10) using Tophat2 v2.0.13. Transcript abundances were calculated using Cufflinks2 and Cuffdiff . At a mith centile FPKM value of expression across conditions. ES Cell RNA seq datasets were accessed from the European Nucleotide Archive database.We imposed an upper FPKM threshold that corresponded to the lowest 99Relevant samples from Study accession numbers PRJNA342888 and PRJNA253304 were accessed  and is publicly available under accession number ArrayExpress: Conceptualization: M.S. and J.T.; Methodology: M.S. and J.T.; Investigation: M.S., S.K.M., and J.T.; Validation: H.R., M.B.S., and A.H.F.M.P.; Writing: M.S. and J.T.; Reviewing and Editing: M.S. and J.T.; Funding Acquisition: J.T. and M.S.; Resources: O.O., L.B., and A.S.; Supervision: J.T."}
+{"text": "DNA methylation plays a key role in the regulation of gene expression and carcinogenesis. Bisulfite sequencing studies mainly focus on calling single nucleotide polymorphism, different methylation region, and find allele-specific DNA methylation. Until now, only a few software tools have focused on virus integration using bisulfite sequencing data.RRID:SCR_015727), to detect viral integration breakpoints in whole human genomes. The tool is hosted at https://github.com/BGI-SZ/BSVF.We have developed a new and easy-to-use software tool, named BS-virus-finder (BSVF, BS-virus-finder demonstrates high sensitivity and specificity. It is useful in epigenetic studies and to reveal the relationship between viral integration and DNA methylation. BS-virus-finder is the first software tool to detect virus integration loci by using bisulfite sequencing data. DNA methylation plays a crucial role in many areas including development , 2 and XWhole-genome-based bisulfite sequencing (WGBS) has been developed to detect DNA methylation. Recent clinical studies showed that DNA methylation is associated with viral integration , 9. WholDifferent types of paired-end (PE) reads  that include 700 breakpoints in chromosome 1 (chr 1) of GRCh38 were simulated in our study. Input fragments of 50 to 400 bp were randomly selected from chr 1 in the GRCh38 assembly of the human genome. The hepatitis B virus (HBV) genome (GenBank: X04615.1) was used in our simulation. Its integration length was between 45 bp and 180 bp. We cut HBV-containing segments with given PE insert size at all possible positions on every integration event. After alignment, mapping accuracy of each of the 17 different types of read mappings was calculated Fig.\u00a0. MappingBisulfite sequencing is a sophisticated technique to study DNA cytosine methylation. Bisulfite treatment followed by polymerase chain reaction (PCR) amplification specifically converts unmethylated cytosine to thymine. By cooperating with next-generation sequencing technology, it is able to detect the methylation status of every cytosine in the whole genome. Moreover, longer reads make it possible to achieve higher accuracy. Besides simulated data, the PLC/PRF/5 hepatocellular carcinoma cell lines  were cultured as previously described . The celPLC/PRF/5 hepatocellular carcinoma cell line was obtained from ATCC  and was cultured as previously described  and valiAbout 1.5 \u03bcg of gDNA was sonicated to 100\u2013300-bp fragment genome DNA by Sonication (Covaris) and purified with QIAquick PCR Purification Kit (Qiagen). Adapter ligation and target insert size fragment recovering and quantifying library by real-time quantitative PCR  were then performed. The qualified library was sequenced on an Illumina Hiseq X Ten platform, and 150 bp of PE reads were obtained. In total, around 90 G of clean data were generated.\u0384 ends of the blunt fragments. Methylated adapters were then purified and added to the 5\u0384 and 3\u0384 ends of each strand in the genomic fragment. Sizes 300\u2013400 bp were selected. DNA was then purified with QIAquick Gel Extraction Kit (QIAGEN) and bisulfite treated with Methylation-Gold Kit (ZYMO). Finally, PCR was conducted and sizes 350\u2013400 bp were selected and purified with QIAquick Gel Extraction kit (QIAGEN). Qualified library was amplified on cBot to generate the cluster on the flowcells . The flowcells were sequenced for 150 bp of PE reads on the HiSeq X Ten platform, and more than 90G of clean data were generated.About 3 \u03bcg of gDNA were sonicated to 100\u2013300 bp by Sonication (Covaris) and purified with MiniElute PCR Purification Kit (QIAGEN). A single \u201cA\u201d nucleotide was added to the 3Alignment: We use bwa-meth  to alignClustering: After alignment, the result was filtered. We select read pairs with 1 read match by the following criterion: The Phred-scaled mapping quality is larger than 30 (\u226530), and at least 1 soft clipping is longer than 5 bp (\u22655). The mapped parts of reads, which is marked as \u201cM\u201d by its CIGAR string, cover the human reference genome. For paired reads, we also add the gap between 2 mapped reads to their covered region, making read 1 and read 2 continuously covered on the human reference. Each continuous region with at least 1 bp of overlap is defined as a cluster. All reads involved are selected to form the cluster. The remaining soft clippings are viral junction candidates. Read pairs with 1 read mapped on the virus also indicate a potential virus junction between the read pairs.posteriori probability estimation for A, C, G, T as:Assembling: Within 1 cluster, all soft clipping start sites are collected. The position with the most abundance of start sites is identified as the most likely candidate breakpoint. All clipping sequences in the cluster are extracted and aligned together. A restore algorithm was used to calculate the most possible base in each position based on the aligned bases and their sequencing quality. The algorithm is based on a Bayesian model, where we compute the i. DW is a realization (or observation) of the NGS reads in the Watson strand. DC is a realization (or observation) of the NGS reads in the Crick strand. P(TWi|D) is the likelihood component, which can be interpreted as the probability of observing D when the true genotype is TWi. P(TCi|D) is the likelihood component, which can be interpreted as the probability of observing D when the true genotype is TCi. At each virus location, prior probability P(Ti) of each genotype Ti was set according to Table S5. The likelihood P(D|Ti) for the assumed genotype Ti was calculated from the observed allele types in the sequencing reads in formula 2. Thus, on the Watson strand, it is P(DW|Ti), and on the Crick strand it is P(DC|Ti). We defined the likelihood of observing allele dk in a read for a possible haploid genotype T as P(dk|T), on the Watson strand it is P(dWk|T), and on the Crick strand it is P(dCk|T). So, for a set of n observed alleles at a locus, D = {d1, d2, \u2026, dn} on each strand, these probabilities are computed as shown by formulas (3) and (4), where Q stands for the base quality from the fastaq file.Here, D is the observation of the next-generation sequencing (NGS) reads on given position. P(Ti|D) is the likelihood component, which can be interpreted as the probability of observing D when the true genotype is TWe used \u201cY\u201d and \u201cR\u201d to represent C/T and G/A, respectively (IUPAC nucleotide code). If a region is covered by both the Watson strand and the Crick strand, we were able to deduce the original base from Y or R by calculation.Detection of viral integrations: The assembled clipping regions above were mapped to the given virus reference sequence with a Smith-Waterman local alignment tool from the EMBOSS package , which sThe read coverage situation for 1 integration is shown in Fig.\u00a0In summary, we have implemented the first software tool to detect virus integration using BS data. Our software is based on bwa-meth, and by assembling and aligning soft-clip regions, it can find the virus breakpoints. However, accuracy of reads surrounding the breakpoints needs to be further improved. A virus usually integrates into regions that are homologous to both human and virus (micro-homologous) . TherefoProject Name: BS-virus-finder: virus integration calling using bisulfite-sequencing datahttps://github.com/BGI-SZ/BSVF [Project home page: -SZ/BSVF Operating system: LinuxProgramming language: Perl, Python, CLicense: LGPL v3RRID:SCR_015727Research Resource Identifier: BSVF, GigaScience repository, GigaDB [Data used in this paper are simulated based on random insertion of the HBV sequence into the human chromosome 1 sequence. A Perl script named \u201csimVirusInserts.pl\u201d is included, and our simulation schema is coded within. We have run the simulation several times, and the result shows no significant difference. The PLC/PRF/5 hepatocellular carcinoma cell lines were from American Type Culture Collection  and sequenced by HiSeq X Ten System from Novogene company. WGS and WGBA data have been submitted to NCBI SRA project PRJNA400455. Supporting data, an archival copy of the code, and the Perl script \u201csimVirusInserts.pl\u201d are also available via the , GigaDB .in silico with different read lengths and insert sizes.bp: base pair; BS: bisulfite sequencing; DMR: different methylation region; HBV: hepatitis B virus; IUPAC: International Union of Pure and Applied Chemistry; NGS: next-generation sequencing; PCR: polymerase chain reaction; PE: paired-end; SNP: single nucleotide polymorphism; WGBS: Whole-genome-based bisulfite sequencing.The authors declare that they have no competing interests.This work was funded by the National Natural Science Foundation of China (81602477) and Shenzhen Municipal Government of China (ZDSYS201507301424148).C.P., L.B., and H.Y. conceptualized the project. S.G., X.H., S.L., and J.W. designed BSVF and developed its accompanying utilities. S.G., X.H., C.G., X.Z., M.W., and S.Z. developed the protocol. F.X., D.F., H.C., and J.B. conducted experiments. S.G., X.H., B.L., and S.W. undertook the analysis. K.X., L.M., S.G., X.H., L.B., and C.P. wrote and approved the final version of the manuscript. All authors read and approved the final manuscript.GIGA-D-17-00032_Original_Submission.pdfClick here for additional data file.GIGA-D-17-00032_Revision_1.pdfClick here for additional data file.GIGA-D-17-00032_Revision_2.pdfClick here for additional data file.GIGA-D-17-00032_Revision_3.pdfClick here for additional data file.Response_to_Reviewer_Comments_Original_Submission.pdfClick here for additional data file.Response_to_Reviewer_Comments_Revision_1.pdfClick here for additional data file.Response_to_Reviewer_Comments_Revision_2.pdfClick here for additional data file.Reviewer_1_Report_ -- Lada Koneva07 Mar 2017 ReviewedClick here for additional data file.Reviewer_1_Report_(Revision_1) -- Lada Koneva22 Sep 2017 ReviewedClick here for additional data file.Reviewer_1_Revision_1_(Attachment).pdfClick here for additional data file.Reviewer_2_Report_ -- Thomas Mikeska07 Mar 2017 ReviewedClick here for additional data file.Reviewer_2_Report_(Revision_1) -- Thomas Mikeska12 Sep 2017 ReviewedClick here for additional data file.Supplemental materialClick here for additional data file."}
+{"text": "Acetobacter pasteurianus SKU1108 is a typical thermotolerant acetic acid bacterium. In this study, the complete genome sequence of the SKU1108 strain was elucidated, and information on genomic modifications due to the thermal adaptation of SKU1108 was updated. In order to obtain a clearer understanding of the genetic background responsible for thermotolerance, the SKU1108 genome was compared with those of two closely related complete genome strains, thermotolerant A. pasteurianus 386B and mesophilic A. pasteurianus NBRC 3283. All 24 \u201cthermotolerant genes\u201d required for growth at higher temperatures in the thermotolerant Acetobacter tropicalis SKU1100 strain were conserved in all three strains. However, these thermotolerant genes accumulated amino acid mutations. Some biased mutations, particularly those that occurred in xanthine dehydrogenase XdhA, may be related to thermotolerance. By aligning whole genome sequences, we identified ten SKU1108 strain-specific regions, three of which were conserved in the genomes of the two thermotolerant A. pasteurianus strains. One of the regions contained a unique paralog of the thermotolerant gene xdhA, which may also be responsible for conferring thermotolerance. Thus, comparative genomics of complete genome sequences may provide novel insights into the phenotypes of these thermotolerant strains. Acetobacteraceae family in the class Alphaproteobacteria. AAB strongly oxidize various sugars, alcohols, and sugar alcohols. Vinegar is industrially produced by oxidative fermentation using AAB, particularly those in Acetobacter and Komagataeibacter (2 via the TCA cycle (Strictly aerobic acetic acid bacteria (AAB) are classified as a sub-group of the eibacter . In ethaeibacter , 19. In eibacter . In the CA cycle , 19.Acetobacter pasteurianus SKU1108, isolated from fruits in Thailand, has been shown to efficiently perform acetic acid fermentation at higher temperatures than the required temperature range for other A. pasteurianus strains, such as NBRC 3283 and IFO 3191 (=NBRC 3191) (A. pasteurianus NBRC 3283 (A. pasteurianus 386B has recently been published (A. pasteurianus SKU1108. Comparisons of this sequence with those of other thermotolerant and mesophilic strains enabled us to identify the genomic regions conserved in thermotolerant bacteria.The RC 3191) , 28. We ublished . TherefoGenomic DNA from SKU1108 for genome sequencing was prepared as previously reported . Genomicde novo assembly was performed using SPAdes 3.0.0  sequencing technology . The sequence data obtained from 2 SMRT cells were used for the subsequent sequence assembly. Sequencing reads were assembled using Hierarchical Genome Assembly Process 3 (HGAP3) in PacBio SMRT portal version 2.3.0. Three large contigs were assembled with a mean coverage of 387-fold. The assembly was corrected with the Quiver consensus algorithm to obtain a high-accuracy genome assembly , 22. UnmAcetobacter pasteurianus genome and the NCBI non-redundant (NR) database . All signal peptide genes encoded by the SKU1108 genome were predicted by SignalP 4.1  and A. pasteurianus IFO 3283-01 (=NBRC 3283) (AP011121\u2013AP011127) were downloaded from the NCBI FTP website at ftp.ncbi.nlm.nih.gov. The chromosome sequences of the two A. pasteurianus strains, 386B and NBRC 3283, were independently aligned against that of the strain SKU1108 using NUCmer  and their homologous sequences were also searched for in the three A. pasteurianus complete genomes by BLASTP with an E-value cut-off of 10\u221210 and sequence overlap (query and subject) \u226570% (The previously reported mutated sites in the genomes of the TI and TH-3 strains associated with thermotolerance were re-confirmed using a previously reported method . The 24 ct) \u226570% .Acetobacter and 1 Gluconacetobacter diazotrophicus Pal 5 genomes was performed using the amino acid sequences of AarC and AarC1 in A. pasteurianus SKU1108 as a query. The resulting hits were aligned using MUSCLE v.3.8.31 at the amino acid sequence level  genome sequence was deposited in DDBJ/EMBL/GenBank under the accession numbers AP014881 to AP014885. The versions described here are the first versions. The BioProject ID is PRJDA65545.The A. pasteurianus SKU1108 consisted of a 2,902,389-bp circular chromosome and four plasmids: plasmid 1 , plasmid 2 , plasmid 3 , and plasmid 4 , with a GC content of 52.75% (rrn) were predicted in the chromosome sequence , adhAB operon (locus_tag: APT_00084\u2013APT_00083) and its subunit III, adhS (APT_00654), and other PQQ enzymes, such as the membrane-bound glucose dehydrogenase (APT_00581), PQQ-dependent dehydrogenase 1 (APT_02219), and PQQ-dependent dehydrogenase 4 (APT_01465). In addition, genes encoding two membrane-bound aldehyde dehydrogenase operons (APT_00973\u2013APT_00975 and APT_00426\u2013APT_00428) were identified. These genes were also conserved in complete genomes of the strains 386B and NBRC 3283. The respiratory chains of Acetobacter species are known to play crucial roles in energy metabolism (ba3 ubiquinol oxidase cyaBACD operon (APT_00087\u2013APT_00090), cytochrome bd ubiquinol oxidase cydBACD operon (APT_01543\u2013APT_01546), cyanide-insensitive ubiquinol oxidase (CIO) cioBA operon (APT_02213\u2013APT_02214), type I NADH-quinone oxidoreductase, nuoA-nuoN operon (APT_01737\u2013APT_01725) and nuoM (APT_00690), two type II NADH dehydrogenase ndh (APT_00547 and APT_02111), heme A synthase ctaA (APT_00075), heme O synthase ctaB (APT_01774), and cytochrome c oxidase subunit ctaD (APT_01775) were conserved in the complete genomes of the strains SKU1108, 386B, and NBRC 3283. In contrast, the cytochrome b subunit petB (APT_01924) of the ubiquinol-cytochrome c reductase (bc1 complex) operon (APT_01924-APT_01926) was disrupted with a stop codon insertion around the start codon site in SKU1108, suggesting that this bc1 complex is not functional in SKU1108. However, a paralog sequence of this gene was also found in the SKU1108 genome (APT_00754). Therefore, a combination of these remaining subunits may construct an active bc1 complex.The genomes of tabolism , 27. TheA. pasteurianus 386B . Therefore, together with the result that all thermotolerant genes were encoded in the conserved regions of all three complete genomes, we concluded that all known thermotolerant genes are conserved in the three genomes, even in the mesophilic strain NBRC 3283 (rhnB (APT_00195), minE (APT_00952), minD (APT_00953), rpoE (APT_01243), xdhC (APT_01265), and xdhB (APT_01266)  are shown in amiA (APT_02041), and cysG (APT_02252), nucleotide mutations accumulated in all three strains. Non-synonymous mutations in these thermotolerant genes were also investigated and amino acid mutations were detected in 15 (xdhABC operon (APT_01265\u2013APT_01267) accumulated 6 amino acid mutations. Of these, the T220 residue (T206 in Rhodobacter capsulatus) of xdhA (APT_01267), shown to be in the active site in the crystal structure of xdhAB from R. capsulatus, is particularly important because it mediates hydrogen bonding with the co-factor FAD  encoding xanthine dehydrogenase XdhA (APT_01267) was only conserved in the two thermotolerant strains. This is a part of the xdhAB operon (APT_01390\u2013APT_01389) that is encoded in specific region 4 found in the genomes of both thermotolerant strains  sequence in strain-specific region 7. Acetobacter species have a specialized citric acid cycle in which the aarC gene encoding acetate CoA-transferase plays a crucial role by regulating acetate assimilation (Acetobacter genome sequences, produced the phylogenetic tree that was divided into two large clades: genes belonging to the aarC clade are conserved in all Acetobacter species, whereas those of the aarC1 clade are only conserved in genomes of the three A. pasteurianus strains studied including SKU1108 (aarC1 gene in acetate assimilation.Incidentally, related to the acetic acid resistance or acetic acid assimilation ability of milation . A phylo SKU1108 . Thus, iA. pasteurianus SKU1108, and compared it with those of other thermotolerant and mesophilic A. pasteurianus strains. By using a comparative genomic analysis of closely related strains, we revealed several candidate genes that underpin the thermotolerance phenotype. Further investigations of these closely related strains may provide novel insights into genetic causes of their specific phenotypes.In the present study, we elucidated the complete genome sequence of the thermotolerant AAB,"}
+{"text": "Two versions are provided: (I) Midori-UNIQUE that contains all unique haplotypes associated with each species and (II) Midori-LONGEST that contains a single sequence, the longest, for each species. Overall, the mitochondrial Cytochrome oxidase subunit I gene was the most sequence-rich gene. However, sequences of the mitochondrial large ribosomal subunit RNA and Cytochrome b apoenzyme genes were observed for a large number of species in some phyla. The Midori reference is compatible with some taxonomic assignment software. Therefore, automated high-throughput sequence taxonomic assignments can be particularly effective using these datasets.Mitochondrial-encoded genes are increasingly targeted in studies using high-throughput sequencing approaches for characterizing metazoan communities from environmental samples . Yet, unlike nuclear ribosomal RNA markers, there is to date no high-quality reference dataset available for taxonomic assignments. Here, we retrieved all metazoan mitochondrial gene sequences from GenBank, and then quality filtered and formatted the datasets for taxonomic assignments using taxonomic assignment tools. The reference datasets\u2014\u2018Midori references\u2019\u2014are available for download at  Massively parallel sequencing technologies have revolutionized our ability to survey and monitor biological diversity. Samples containing multiple species are collected directly from the environment  and variants of one or several sets of genes are inventoried using PCR-based  or PCR-free  approaches.2) and mitochondrial-encoded genes4. Nuclear-encoded ribosomal RNA fragments, especially hypervariable regions of the 18S rRNA gene, were prime targets in early metagenetic analyses because broad-range primers well conserved across the eukaryotic domain were available6. As a result, considerable efforts have already been made to build quality filtered and formatted reference sequence datasets of nuclear-encoded ribosomal RNA genes for taxonomic assignments8. Mitochondrial genes, which provide higher taxonomic resolution for most metazoan groups9, have been increasingly used following the design of highly degenerate primer sets10\u201312 and the development of bioinformatics tools to facilitate the assembly of mitogenomes from environmental samples13. However, high quality reference datasets that are compatible with taxonomic assignment software are not yet available for metazoan mitochondrial genes. Therefore, at the moment, most of the metazoan metagenetic studies target low-resolution nuclear ribosomal RNA gene as a marker . Some exceptions, which target high-resolution mitochondrial genes, used Blastn searches against sequences from GenBank for taxonomic assignments12 without explicit taxonomic quality control of the database. This means that high-throughput sequence taxonomic assignment with quality controlled mitochondrial gene reference dataset is currently not feasible. Here, we constructed quality-controlled reference datasets \u2018Midori\u2019 for thirteen protein-coding and two ribosomal RNA genes sequences encoded in the mitochondrial genome.Two types of gene sequences have been widely used as phylogenetic and taxonomic markers in metazoans: nuclear-encoded ribosomal RNA genes  extlrRNA] and Small [srRNA] ribosomal subunit RNA) and thirteen protein  gene sequences . In both datasets, COI had the largest number of sequences overall  Midori-UNIQUE, which contains for each species a representative sequence of each unique haplotype and (II) Midori-LONGEST, which contains for each species the single longest sequence. Each dataset is composed of two ribosomal RNA  that are compatible with taxonomic assignment software such as RDP Classifierftp://ftp.ncbi.nih.gov/blast/db/FASTA) on 18 September 2015 (www.reference-midori.info/download.php#) along with following scripts described below to build the reference datasets. Next, GenBank flat files of all the mitochondria-related gene sequences were downloaded using NCBI Edirect (efetch -db nucleotide -id gene_id -format gb), and metazoan flat files were extracted using a custom perl script (06_ext_fasta_seq.pl). Next, CDS and rRNA features were extracted from the metazoan flat files using a custom Perl script (09_ext_cds_rna.pl), each combination  was counted using MySQL, and the CDS and rRNA feature table  was created. The table with feature combinations was manually examined to assign each gene. Sequences that could not be assigned unambiguously to one of the thirteen protein-coding genes or one of the two ribosomal RNA genes were discarded. Accession number, feature, position, gene, product and gene abbreviations of those assigned sequences were extracted using MySQL. The mitochondrial fasta file that was prepared as described above was partitioned in 15 individual fasta files with sequences of each mitochondrial-encoded gene.The nt fasta file was downloaded from the National Centre for Biotechnology Information (NCBI) server (ber 2015 . Mitochohttps://github.com/spond/gb_taxonomy_tools). These two output files were combined into a taxonomy file using a custom perl script (19_rdp_train_3.pl). RDP classifier15 utilizes only eight taxonomic rankings ; therefore, we extracted only those ranks. At this stage, we performed the following quality controls: (I) removal of sequences that did not have species name in the species rank; (II) removal of sequences containing the following text in the taxonomy ranks: \u2018cf.\u2019, \u2018aff.\u2019, \u2018sp.\u2019, \u2018environment\u2019, \u2018undescribed\u2019, \u2018uncultured\u2019, \u2018complex\u2019, \u2018unclassified\u2019, \u2018nom.\u2019, \u2018nud.\u2019 and \u2018unidentif\u2019 (because these terms indicate uncertainty of species identity); (III) removal of sequences with the following identifiers \u2018sp0936BC\u2019, \u2018MG98.09\u2019, \u2018sp0942A\u2019 and \u2018EEG-2007\u2019 (since these are obviously not Latin names). This quality filtration was performed using a combination of Perl, MySQL and Unix commands.We extracted the GI number from the mitochondrial fasta file using a custom perl script (15_ext_gi.pl). Then, taxonomy ID and taxonomy ranking were extracted using the gb_taxonomy_tool  for srRNA: 200\u20132,000, lrRNA: 100\u20132,500, A6: 100\u20131,000, A8: 100\u2013500, COI: 100\u20132,000, COII: 100\u20131,500, COIII: 100\u20131,300, Cytb: 100\u20131,500, ND1: 50\u20131,200, ND2: 150\u20131,500, ND3: 100\u2013600, ND4: 150\u20132,000, ND4L: 100\u2013700, ND5: 150\u20132,000 and ND6: 150\u20131,500.Each mitochondrial sequence prepared previously was added onto the taxonomy rank file using a custom perl script (23_rdp_train_hash.pl). Each sequence was separated into single files using a custom perl script (25_fna_split.pl), and the target region of each gene was excised and separated into different gene regions using a custom perl script (27_gbk_ext_target.pl). In some cases using RDP ClassifierAutomolus to Automolus01).First, we counted the number of occurrences of each taxonomy rank. Next, we extracted the eight taxonomic rankings. Then, the taxonomy rank file was formatted in two steps using two custom Perl scripts (trainset_db_taxid.pl and trainset_db_taxid_parent_2.pl). On some occasions, a conflict of taxonomic names was observed, such as the same genus name for animals in different higher taxonomic groups. Such cases, caused by duplicated taxonomic names above the species level (which are prohibited within the Metazoa but occur through error) caused the taxonomic assignment software to report error messages and abort analysis. In those cases, we made some modifications to the taxonomic name, such as addition of a distinguishing number to one of them . The numbers of sequences included in the datasets are listed in All reference datasets are freely available from the Midori reference web site (www.reference-midori.info) and also Dryad Digital Repository (Data Citation 1). Midori-UNIQUE and Midori-LONGEST (see usage notes for more information) are available in two formats, one compatible with the RDP Classifier18 and clusters were flagged if they contained sequences of multiple phyla, classes or orders. To identify which sequence was mislabeled in each flagged cluster, we performed a similarity search using the BLAST server19 (blastn with \u2018low complexity region filter\u2019 and \u2018mask for lookup table only\u2019 function disabled). The distance tree functionality on the BLAST server was used to explore phylogenetic relationships with 100 close matches to each query. Sequences confidently identified as mislabelled were deleted from the datasets. Whenever we could not confidently determine which sequence of the cluster was mislabelled we retained all sequences. Overall, we found that the number of such cases was very low . Here, we assume that species-level identifications of metazoan are more likely to be performed by well-trained taxonomic specialists, although this step does not ensure the absence of mislabelled sequences, it increases taxonomic accuracy, particularly at higher taxonomic levels. For example, a specimen identified at the species level is more likely to have a correct genus name. Second, we also performed systematic sequence length restrictions by removing extremely long or short gene sequences in the original nucleotide datasets . We observed that sequences at the extremities of the length distribution of each gene were more likely the result of mis-annotations. We also observed that the RDP Classifier consistently provided erroneous sequence taxonomic assignments if very long or very short sequences were included in the reference datasets. Third, we attempted to detect and remove taxonomically mislabelled sequences in the datasets. To do so, we performed a high similarity (99%) clustering of the reference datasets  within tvery low  which inIn some cases, we observed missing taxonomic information, such as class, order or family name . In those cases, estimation of statistical support for the missing taxonomic level is not feasible. Therefore, we recommend including all standard levels of taxonomic names in the GenBank taxonomy.15. An example of RDP Classifier usage, which required two steps, is as follows. The first step consists of training the reference dataset: $ java -Xmx64g (available memory) -jar /path-to-the-file/classifier.jar train -o /path-to-outfolder/out -s one_of_the_MIDORI_Reference.fasta -t./TaxonomyFile.txt. The second step is the actual taxonomic assignment: $ java -Xmx64g (available memory) -jar /path-to-the-file/classifier.jar classify -t /path-to-the-outfile-from-training/rRNAClassifier.properties -o./outfile_name.txt./query.fasta.Midori reference datasets are compatible with the RDP Classifier16. An example of SPINGO usage is as follows: $ spingo -b 100 -k 13 -d /database/ one_of_the_MIDORI_Reference.fasta -i /SPINGO-master/infile/query.fasta>outfile.txt.We also prepared the Midori reference datasets in a format compatible with SPINGO17. An example of usage, which required two steps, is as follows. The first step consists of building a local database: $ makeblastdb -in DB.fasta -dbtype nucl. The second step is the actual search: $ blastn -query example.fasta -db DB.fasta. Refer to http://www.ncbi.nlm.nih.gov/books/NBK279668/ for more detailed information.Both formats are compatible with BLAST+All three taxonomic assignment approaches can be used with Midori-UNIQUE and Midori-LONGEST.How to cite this article: Machida, R. J. et al. Metazoan mitochondrial gene sequence reference datasets for taxonomic assignment of environmental samples. Sci. Data 4:170027 doi: 10.1038/sdata.2017.27 (2017).Publisher\u2019s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations."}
+{"text": "Mycobacterium tuberculosis (Mtb). Recent studies have demonstrated circular RNAs (circRNAs) are implicated in a variety of physiological and pathological processes; however, the role of circRNAs in macrophages response to Mtb infection remain unknown. To address this issue, here we characterized circRNAs expression profiles in human monocyte derived macrophages (MDMs) response to Mtb infection using microarray assay. Our results revealed that many circRNAs were differentially expressed in human MDMs after Mtb infection; of these, 32 circRNAs were up-regulated and 110 were down-regulated. Real time PCR results were generally consistent with the microarray data. Furthermore, we found that hsa_circ_0043497 and hsa_circ_0001204 may be effective diagnostic biomarkers for TB. This study provides the first evidence that circRNAs alterations are involved in human MDMs response to TB infection and reveal potential targets for diagnostics and the treatment of TB.Macrophages act as the first line of host immune defense against  Mycobacterium tuberculosis (Mtb) infection, remains a leading cause of morbidity and mortality worldwide1. In 2015, World Health Organization (WHO) reported that an estimated 10.3 million cases of TB occurred and that 1.4 million died of TB2. Despite the high rate of Mtb infection in humans, especially in developing countries, only 5\u201310% of infected people develop active TB in their lifetime3. Interactions between Mtb and the host largely determine the development and outcome of TB infection.Tuberculosis (TB), an infectious disease caused by 4. They serve as the major host cell niche for intracellular growth and persistence of Mtb during all phases of TB, from primary infection with bacillary dissemination, through latency and reactivation TB5. Despite several studies that have been conducted on Mtb-macrophages interaction, however, the underlying molecular regulation is not fully understood.Macrophages play a critical role in the host immune response against mycobacterial infection6. Recent evidence has suggested that circRNAs play essential roles in various physiological and pathological processes8. Many circRNAs are abundant, stable, conserved and potentially function as competing endogenous RNAs9. Due to a lack of 3\u2032 and 5\u2032 ends and resistance to RNases, circRNAs might be used as potential biomarkers and treatment targets for human diseases10. With the increasing studies about circRNAs, researchers have reported that circRNAs are involved in the development of several types of diseases, such as cancer11, cardiovascular disease12, and neurological disorders13. However, the role of circRNAs within macrophages innative response to TB infection has yet to be explored.Circular RNAs (circRNAs) are a special type of non-coding RNA that are formed from the covalent linkage of the 3\u2032 and 5\u2032 ends to form a closed loopIn this study, we analysed the expression patterns of circRNAs in Mtb-infected human monocyte derived macrophages (MDMs) using microarray assay. Our data revealed that a number of circRNAs were consistently altered under Mtb infection. And then, we demonstrated that hsa_circ_0043497 and hsa_circ_0001204 may be effective diagnostic biomarkers for TB. These findings provided novel insight into the pathogenesis of TB and provide a basis for the diagnosis and therapy of TB.This study was approved by the ethical committee of the First Affiliated Hospital of Nanchang University and conducted in accordance with the Declaration of Helsinki. All participants provided informed consent before commencement of the study.The peripheral blood samples (5\u2009mL) used to validate candidate biomarkers were collected from 96 patients with active pulmonary TB and 85 healthy controls. Patients with pulmonary TB were recruited from the Jiangxi Chest Hospital and the First Affiliated Hospital of Nanchang University from January 2016 to July 2017. All of these patients were diagnosed with TB on the basis of the typical TB clinical symptoms, bacterial culture, and imaging examinations, and in accordance with the Health criteria in People\u2019s Republic of China: The legal diagnostic criteria of infectious diseases (WS288\u20132008) and the revised international definitions in TB control of the WHO. Individuals with malignant tumor, HIV infections were excluded. Subsequently 12 active pulmonary TB (TB naive) inpatients were treated according to prevailing China National Tuberculosis Program guidelines (2HRZE/6HE). peripheral blood samples were obtained at diagnosis prior to treatment initiation and after completion of treatment (six months). After sample collection, peripheral blood mononuclear cells (PBMCs) were freshly isolated by density gradient centrifugation on Ficoll-Paque  according to the manufacturer\u2019s protocol. Then, the PBMCs samples were lysed with TRIzol reagent  and stored at \u221280\u2009\u00b0C.2 and 37\u2009\u00b0C14. The MDMs were identified by morphologic observation and flow cytometric analysis followed by anti-CD68 staining.The peripheral blood samples (5\u2009mL) were collected from 22 healthy donors. Individuals with cancer, allergic diseases, immune-compromised conditions, diabetes or other infectious diseases such as HBV, HCV and HIV infection were excluded. After sample collection, PBMCs were isolated by density gradient centrifugation. To develop MDMs, the monocytes were purified using anti-CD14 magnetic beads  and the purified monocytes were cultured in RPMI 1640 medium contains 10% Human serum and 0.05% Glutamine  for 7 days at 5% COMtb H37Rv strain 27294 was purchased from the American Type Culture Collection (ATCC). They were grown in Middlebrook 7H9 broth containing 10% oleic acid-albumin-dextrose-catalase (OADC) and 0.05% Tween-80 at 37\u2009\u00b0C. They were washed with PBS and passed through a syringe, to disrupt the clumps, before infecting human MDMs.2. After 24\u2009h, cells were harvested and RNA was isolated.The human MDMs were plated in 12-well plates, and incubated overnight. The cells were infected at a multiplicity of infection (MOI) of 5. Uninfected cells which received only PBS served as controls. After 4\u20136\u2009h incubation at 37\u2009\u00b0C, non-phagocytosed bacteria were washed off using PBS. MDMs were replenished with fresh RPMI 1640 and incubated for 24\u2009h at 37\u2009\u00b0C with 5% COTotal RNA was extracted using TRIzol reagent  according to the manufacturer\u2019s protocol. The integrity of the RNA was assessed by electrophoresis on a denaturing agarose gel. A NanoDrop ND-1000 spectrophotometer was used for the accurate measurement of RNA concentration.P\u2009<\u20090.05) between groups were identified using fold change cut-off or volcano plot filtering, respectively. The circRNAs/microRNAs (miRNAs) interaction was predicted using Arraystar\u2019s home-made miRNA target prediction software based on TargetScan and miRanda.Human CircRNA Array V2.0 (8\u2009\u00d7\u200915\u2009K) is manufactured by Arraystar Technologies . Six MDMs samples, including three infected samples and three uninfected samples, were sent to KangChen Bio-tech  for the Arraystar circRNA microarray analysis. Microarray hybridization were performed according to the protocols of Arraystar. The scatter plot is a visualization method used for assessing the circRNA expression variation. Differentially expressed circRNAs with statistical significance  LE agarose (Seakem) stained with ethidium bromide.Total RNA (2\u2009\u03bcg) was reversely transcribed into cDNA using the Reverse Transcription System Kit . The expression levels of circRNAs and miRNAs were determined by quantitative real-time PCR (RT-qPCR) using SYBR Master Mix  and mirVanaTM RT-qPCR miRNA Detection Kit  on Applied Biosystems 7500 Real-Time PCR System , respectively. Primers used in this study were listed in Supplementary Table\u00a0P\u2009<\u20090.05 was considered statistically significant.Numerical data were shown as the mean\u2009\u00b1\u2009standard error of the mean (SEM). A one-way ANOVA test, Mann-Whitney test or Student t-test was used for statistical analysis. Receiver operating characteristic (ROC) analysis was used to evaluate the power of candidate circRNAs. All statistical tests were performed with GraphPad Prism 5.0 . P\u2009<\u20090.05), among which 32 circRNAs were up-regulated while 110 circRNAs were down-regulated  were measured in PBMCs samples from 96 patients with active pulmonary TB and 85 healthy controls using RT-qPCR. The demographic characteristics of the participants are showed in Table\u00a0Furthermore, we compared the expression of hsa_circ_0043497 and hsa_circ_0001204 in 12 patients before and after TB therapy. As compared to their pre-treatment, the levels of hsa_circ_0043497 decreased after anti-TB treatment. As compared to controls, the mean levels of hsa_circ_0043497 which was higher in TB infected group came to near normal after therapy. The mean hsa_circ_0043497 levels did not differ significantly between healthy control and TB treated group. As compared to healthy controls, the mean levels of hsa_circ_0001204 were significantly lower in TB infected group; the levels increase post therapy. The mean hsa_circ_0001204 levels did not differ significantly between healthy control and TB treated group Fig.\u00a0.Figure 5P\u2009<\u20090.05) to predict their miRNA response elements (MREs), including 9 up-regulated circRNAs and 9 down-regulated circRNAs. Five MREs with good mirSVR scores for each circRNA are shown in Table\u00a0To evaluate circRNAs potential functions, we investigated potential miRNAs binding with circRNAs using Arraystar\u2019s home-made miRNA target prediction software. We selected 18 differentially expressed circRNAs with the highest fold-change  negative in sputum. However, a larger sample size is needed to confirm our results.Early diagnosis of TB infection is essential for controlling the spread of the disease and providing early therapy for TB epidemic8. To evaluate hsa_circ_0043497 and hsa_circ_0001204 potential functions, we investigated potential miRNAs binding with hsa_circ_0043497 and hsa_circ_0001204. The potential miRNAs targets of hsa_circ_0043497 include miR-335-3p, miR-186-5p, miR-380-5p, miR-296-3p and miR-522-3p. For hsa_circ_0001204, the potential miRNAs targets include miR-612, miR-657, miR-362-3p, miR-377-3p and miR-136-5p. These potential target miRNAs were verified in MDMs with or without Mtb infection using RT-qPCR. And the data of RT-qPCR showed that the expression levels of miR-377-3p were markedly elevated and the expression levels of miR-186-5p were significantly reduced in Mtb-infected MDMs. However, due to the limited known function of circRNAs and miRNAs, a lot of circRNAs/miRNAs interactions should be analyzed in the future.Some studies have revealed that circRNAs could function as miRNAs sponges, modulate alternative splicing or transcription, and regulate the expression of parental genesIn conclusion, in the study, we described differentially expressed circRNAs in human MDMs response to TB infection. Furthermore, hsa_circ_0043497 and hsa_circ_0001204 were identified as potential non-invasive molecular markers for rapid diagnosis of TB. Our findings shed a novel light on our understandings of the pathogenesis of TB infection. To our knowledge, this is the first research addressing circRNAs expression profiles in macrophages response to TB infection. Further studies should focus on the function of circRNAs involved in TB infection, which may lead to new theories for TB pathogenesis and give new potentially therapeutic targets in active TB.Table s1Dataset 1"}
+{"text": "Paramecium aurelia cryptic species complex emerged after a whole genome duplication that occurred tens of millions of years ago. Given extensive knowledge of the genetics and epigenetics of Paramecium acquired over the last century, this species complex offers a uniquely powerful system to investigate the consequences of whole genome duplication in a unicellular eukaryote as well as the genetic and epigenetic mechanisms that drive speciation. High quality Paramecium gene models are important for research using this system. The major aim of the work reported here was to build an improved gene annotation pipeline for the Paramecium lineage.The 15 sibling species of the Paramecium tetraurelia. We determined, for the first time in a ciliate, candidate P. tetraurelia transcription start sites using an adapted Cap-Seq protocol. We developed TrUC, multi-threaded Perl software that in conjunction with TopHat mapping of RNA-Seq data to a reference genome, predicts transcription units for the annotation pipeline. We used EuGene software to combine annotation evidence. The high quality gene structural annotations obtained for P. tetraurelia were used as evidence to improve published annotations for 3 other Paramecium species. The RNA-Seq data were also used for differential gene expression analysis, providing a gene expression atlas that is more sensitive than the previously established microarray resource.We generated oriented RNA-Seq transcriptome data across the sexual process of autogamy for the model species Paramecium species. A novel component of this pipeline, TrUC, predicts transcription units using Cap-Seq and oriented RNA-Seq data. TrUC could prove useful beyond Paramecium, especially in the case of high gene density. Accurate predictions of 3\u2032 and 5\u2032 UTR will be particularly valuable for studies of gene expression . The P. tetraurelia improved transcriptome resource, gene annotations for P. tetraurelia, P. biaurelia, P. sexaurelia and P. caudatum, and Paramecium-trained EuGene configuration are available through ParameciumDB (http://paramecium.i2bc.paris-saclay.fr). TrUC software is freely distributed under a GNU GPL v3 licence (https://github.com/oarnaiz/TrUC).We have developed a gene annotation pipeline tailored for the compact genomes and tiny introns of The online version of this article (doi:10.1186/s12864-017-3887-z) contains supplementary material, which is available to authorized users. Ciliates are unique among unicellular eukaryotes in making a germ/soma distinction. The germline and somatic functions of chromosomes are respectively ensured by a germline micronucleus (MIC) which undergoes meiosis and fertilization and a somatic macronucleus (MAC) that contains a version of the germline genome stripped of parasitic sequences and optimized for gene expression. The MAC is lost at each sexual cycle and a new one differentiates from a copy of the zygotic nucleus, by reproducible DNA elimination under the control of meiosis-specific RNA interference pathways .Paramecium was pioneered nearly a century ago  -tts [truc TTS GFF3 output file].truc TSS -min_coverage 15 -nb_replicates 2 -min_score 500; truc TTS -min_coverage 5 -nb_replicates 2 -min_score 10 -nb_min_A 5; truc transcript -min_splicing_rate 0.7 -no_overlap -min_coverage 15 \\P. biaurelia, P. sexaurelia and P. caudatum, TrUC was used with unoriented RNA-Seq data reported in [truc transcript -not_stranded -min_splicing_rate 0.7 -min_coverage 10 -intron_consensus -min_intron_length 10\u00a0\\ -max_intron_length 100 -min_intron_coverage 3 -min_length 300 -min_score 10.https://github.com/oarnaiz/TrUC.TrUC is distributed under a GNU GPL v3 license at For orted in , 8  of expression greater than 2. We filtered out genes if there was not at least one time point with more than 20 normalized counts. The genes were classed as induced (FC\u00a0>\u00a02) or repressed (FC\u00a0<\u00a0\u00bd) before hierarchical clustering.Paired-end RNA-Seq reads were mapped to the reference 1 genome  using To1 genome , an R Bip-value <0.01). GO biological process terms were electronically inferred using InterProScan (v5.7.48) domain annotation of the corresponding proteins. If more than one protein domain was associated with a protein, the domain with the lowest InterProScan P-value was retained. All words in the terms were counted for all the protein-coding genes in the genome and for the protein-coding genes in each co-expression group. After removing non-discriminatory words , a Fisher exact test was used to determine the word enrichment ratio  in each co-expression group with respect to the word frequency for the whole genome. A score determined for each word ) was used as weight to draw each word cloud (R wordcloud v2.5). The protein domains and GO terms used for this analysis can be found in the gene expression atlas Additional file 2:Figure S1. Comparison of the sizes of P. tetraurelia transcription units and genes. Figure S2. Intron size distributions. Figure S3. Autogamy time-course experiments. Figure S4. Anti-sense transcription. Figure S5. Hierarchical clustering of differentially expressed genes. Figure S6. Autogamy co-expression clusters. Figure S7. Paralog discrimination by microarrays and RNA-Seq. Figure S8. Word cloud analysis of biological processes in clusters. (PDF 8271\u00a0kb)Additional file 3:P. tetraurelia v2 genes (\u2018ID\u2019) with their normalized RNA-Seq counts  are given. The mean value for biological replicates are given in the columns VEG, MEI, FRG, DEV1, DEV2/3, DEV4. The \u2018P-value\u2019 \u2018Significant\u2019 and \u2018Expression profile\u2019 refer to the differential gene expression analysis (cf. Methods). \u2018Note\u2019 is the description of the best SwissProt BLASTP match. The GO ID and GO description were inferred electronically using InterProScan. The Biological Process GO term associated with the highest scoring protein domain is given. (TSV 12379\u00a0kb)Gene expression atlas. All"}
+{"text": "The inflammatory intestinal disorder Crohn's disease (CD) has become a health challengeworldwide. The gut microbiota closely interacts with the host immune system, but itsfunctional impact in CD is unclear. Except for studies on a small number of CD patients,analyses of the gut microbiota in CD have used 16S rDNA amplicon sequencing. Here weemployed metagenomic shotgun sequencing to provide a detailed characterization of thecompositional and functional features of the CD microbiota, comprising also unannotatedbacteria, and investigated its modulation by exclusive enteral nutrition. Based onsignature taxa, CD microbiotas clustered into 2 distinct metacommunities, indicatingindividual variability in CD microbiome structure. Metacommunity-specific functionalshifts in CD showed enrichment in producers of the pro-inflammatory hexa-acylatedlipopolysaccharide variant and a reduction in the potential to synthesize short-chainfatty acids. Disruption of ecological networks was evident in CD, coupled with reductionin growth rates of many bacterial species. Short-term exclusive enteral nutrition elicitedlimited impact on the overall composition of the CD microbiota, although functionalchanges occurred following treatment. The microbiotas in CD patients can be stratifiedinto 2 distinct metacommunities, with the most severely perturbed metacommunity exhibitingfunctional potentials that deviate markedly from that of the healthy individuals, withpossible implication in relation to CD pathogenesis. Escherichia coli ,identity > 35%, score > 60, E < 1e-3), and their relative abundances could thenbe determined accordingly.Sequences of SCFA-producing enzymes were retrieved as previously described . Genes i\u03b1-Diversity (within-sample diversity) was calculated on the basis of the gene profile ofeach sample according to the Shannon index, as described previously . The totRRID:SCR_001905)[Permutational multivariate analysis of variance (PERMANOVA)  was perf_001905).n = 30) logarithmic linear discriminant analysis scores ofat least 2. The open source R code is available at [Differential abundance analyses were performed using a less stringent LEfSe algorithm toidentify feature microbes whose abundances differed at least in 1 comparison . Metacomlable at .bioenv function in the vegan Rpackage, which selects the combination of covariates with the strongest correlation tomicrobiota variation (Pearson correlation between Gower distances of covariates andmicrobiome Bray-Curtis dissimilarity) .Twenty-four metadata covariates and their combined effect size when pooled into thebroader predefined categories  were estimated with the P-values ofless than 0.01 were retained, which was calculated via a total of 10 000 simulated datasets. This set of iterative procedures was applied separately to data from CTs and CDpatients, and to patients\u2019 data before and after EEN to infer the correlation values.Correlation coefficients with magnitude of 0.3 or greater were selected for visualizationin Cytoscape .Eighty-five MGS, which were previously selected via the detection of microbial communityclusters through DMM modeling, were subjected to compositionality data analysis using theSparCC algorithm . Taxon\u2013tSupplementary Table\u00a0S1: Clinical characteristics of participants and the dysbiosis indexfor their gut micorbiome.Supplementary Table\u00a0S2: MGS of the IBD cohort. Clusters containing >700 genes wereannotated according to available bacteria and archaea genomes, as was described previously.P-values from Fisher's exact tests were adjusted by Benjamini-Hochbergstep-up procedure.Supplementary Table\u00a0S3: Association between metacommunity and CD status.Supplementary Table\u00a0S4: Results of differential abundance analysis on signature MGS formetacommunities. An adapted version of the LDA effect size method was applied for selectingdifferential MGS. Those with an LDA score over 2 were visualized in Fig.\u00a0Supplementary Table\u00a0S5: Summary of differential abundance analysis on KEGG pathways betweensubgroups. Differentially enriched KO pathways were identified according to their reporterscores.Supplementary Table\u00a0S6: Summary of differential abundance analysis on KEGG modules betweensubgroups. Differentially enriched KO pathways were identified according to their reporterscores.q < 0.2). Statisticalcomparison by Wilcoxon test followed by a Benjamini-Hochberg correction for significancelevel.Supplementary Table\u00a0S7: A list of LPS/SCFA-producing bacteria that had differentialabundance/growth rate between subgroups .pdfClick here for additional data file.Reviewer_1_Report_(Revision_1).pdfClick here for additional data file.Reviewer_2_Report_.pdfClick here for additional data file.SI-CD-paper-gigascience.docxClick here for additional data file.Table_&_Supplementory_Table.CD.xlsxClick here for additional data file."}
+{"text": "Staphylococcus aureus, Escherichia coli and Vibrio parahaemolyticus, and can cause severe gastroenteritis symptoms. In this study, we completed the genome sequence of a foodborne pathogen V. parahaemolyticus FORC_014, which was isolated from suspected contaminated toothfish from South Korea. Additionally, we extended our knowledge of genomic characteristics of the FORC_014 strain through comparative analysis using the complete sequences of other V. parahaemolyticus strains whose complete genomes have previously been reported.Foodborne illness can occur due to various pathogenic bacteria such as V. parahaemolyticus FORC_014 was generated using the PacBio RS platform with single molecule, real-time (SMRT) sequencing. The FORC_014 strain consists of two circular chromosomes , one plasmid , and one putative phage sequence . The genome contains a total of 4274 putative protein coding sequences, 126 tRNA genes and 34 rRNA genes. Furthermore, we found 33 type III secretion system 1 (T3SS1) related proteins and 15 type III secretion system 2 (T3SS2) related proteins on chromosome 1. This is the first reported result of Type III secretion system 2 located on chromosome 1 of V. parahaemolyticus without thermostable direct hemolysin (tdh) and thermostable direct hemolysin-related hemolysin (trh).The complete genome sequence of V. parahaemolyticus FORC_014, which differs from previously reported strains, we revealed two type III secretion systems  located on chromosome 1 which do not include tdh and trh genes. We also identified several virulence factors carried by our strain, including iron uptake system, hemolysin and secretion system. This result suggests that the FORC_014 strain may be one pathogen responsible for foodborne illness outbreak. Our results provide significant genomic clues which will assist in future understanding of virulence at the genomic level and help distinguish between clinical and non-clinical isolates.Through investigation of the complete genome sequence of The online version of this article (doi:10.1186/s13099-016-0134-0) contains supplementary material, which is available to authorized users. Vibrio parahaemolyticus is an important gastrointestinal pathogen which is characterized by a gram-negative, rod shaped, and halophilic organism which causes food borne illness. When people eat oysters, shrimps, fish and other seafood contaminated with V. parahaemolyticus, they may develop a foodborne illness with serious gastroenteritis symptoms such as acute gastroenteritis, vomiting and even death [en death .V. parahaemolyticus caused an outbreak of foodborne illness in Japan in the early 1950s [V. parahaemolyticus began to occur frequently worldwide [V. parahaemolyticus, particularly focusing on how its toxins associate with food poisoning. While environmental strains rarely contain pathogenic genes thermostable direct hemolysin (tdh) and thermostable direct hemolysin-related hemolysin (trh), clinical strains which create foodborne illness, possess virulence factor including tdh, and trh. Therefore, tdh, and trh have been considered as the indicators of V. parahaemolyticus pathogenicity, which has an enterotoxic effect on the intestinal cells of the affected mammal [tdh and trh genes [V. parahaemolyticus pathogenicity [V. parahaemolyticus at the genomic level are still unclear despite the many studies which have been performed which attempted to identify them.The initial spread of ly 1950s . From thorldwide . With thd mammal , 5. Recerh genes , 5. In agenicity . HoweverV. parahaemolyticus FORC_014, which was isolated from toothfish which was suspected to have caused a spread of foodborne illness in South Korea. The whole genome sequences of V. parahaemolyticus will help to understand genetic variation between non-pathogenic strain and pathogenic strains. In addition, we performed comparative analysis on the FORC_014 strain with eight other complete genome sequences from public databases to gain genomic level information and greater understanding of this strain.In this study, we sequenced the putative clinical strain Vibrio parahaemolyticus FORC_014, a strain of V. parahaemolyticus which was isolated from contaminated fried toothfish in Busan, South Korea, was received from the Ministry of Food and Drug Safety. Total genomic DNA preparation was performed using a Qiagen blood and tissue kit following manufacturer\u2019s protocol.Approximately 5\u00a0\u03bcg of DNA was fragmented to 8\u201312 kbp using the Hydroshear system and assembly of DNA was performed at a shearing speed of 9 for 20 cycles. PacBio DNA Template Prep Kit 2.0 (3\u201310 kbps), used for SMRT Sequencing with C2 chemistry on PacBio RS, was used for SMRTbell library preparation following manufacturer\u2019s instructions. The size distribution of the purified DNA template was measured using an Agilent 12,000 DNA kit and the concentration of the template was measured using Invitrogen Qubit. Primers were annealed to the template and DNA polymerase C2 was added following the manufacturer\u2019s recommendations. Enzyme-template complexes were set up with DNA/Polymerase Binding Kit P4 (PacBio) on the 75,000 zero-mode waveguides (ZMWs). DNA sequencing Reagent 2.0 kit (Pacific Bioscience) was used for SMRTbell library sequencing with a long (1\u00a0\u00d7\u00a0120\u00a0min) sequence capture protocol for maximizing read length with PacBio RS II. The summary of sequencing result is included Additional file V. parahaemolyticus CDC_K4557 [Sequencing reads were assembled within the SMRT portal system . The whoDC_K4557 . The polDC_K4557 .V. parahaemolyticus FORC_014 [www.mgc.ac.cn/VFs/) were used for defining virulence factors in all strains, except for the well-defined strain RIMD2210633, using BLASTn method .Rapid Annotation of Prokaryotic Genomes(PROKKA), which includes prediction tools such as Prodigal , RNAmmerFORC_014 . We alsoFORC_014 . After gFORC_014 , SEED anV. parahaemolyticus strains: RIMD2210633, CDC_K4557, BB22OP, FORC_008, UCM-V493, FORC_006, FORC_004, and FDA_R31 were downloaded from NCBI (http://www.ncbi.nlm.nih.gov/genome/genomes/691) and used for comparative analysis.In this study, the complete genome sequences of eight For calculation of the Average Nucleotide identity (ANI) value among 9 strains, the Jspecies tool based on the BLAST algorithm was used . Each ofwww.mgc.ac.cn/VFs/) was used as subject sequence database and FORC_14 strain sequence used as query sequence.Also, BLAST search was used to predict virulence factors of FORC_014 strain. The Virulence Factors Database  were calculated with these 8 strains and a dendrogram was constructed using ANI values. All of values among strains are higher than 95% identity which known as criteria of the same species. As a result, the FORC_014 strain was found to be clustered with FORC_006 and UCM_V493 strain. The FORC_006 strain was isolated from South Korea and UCM_V493 strain was environmentally isolated in Spain [Genome tree analysis was performed on 8 complete genomes of in Spain . This coV. parahaemolyticus [V. parahaemolyticus strain, RIMD2210633, using ACT. Moreover, we defined T3SS1 and T3SS2 genes in our strain using the BLAST method, which produced the same result. T3SS2 has been described as a major essential factor for enterotoxicity and intestinal colonization [vopB2(FORC14_1152) gene was detected in this T3SS2 region on chromosome 1. Previous studies have suggested the vopB2 gene as a possible indicator of strain virulence substitute for tdh or trh [In addition, we performed a comparison with the UCM_V493 sequence to determine the difference between the two strains using ACT. From the comparison, we identified a noticeable unmatched region on chromosome 1 . This region of FORC_014 contains Type III secretion system2 (T3SS2) proteins  using the BLAST method , and hemolysin . Additionally, we performed LDH release assay using the INT-407 cells for testing cytotoxicity activity (Additional file tdh and trh negative [Our results also revealed that the FORC_014 strain does not encode negative , 28.V. parahaemolyticus FORC_014, which is considered a leading cause of foodborne illness from comparative studies with already published strains. As a result, we found pathogenic island regions of FORC_014 that clustered T3SS1 related genes and T3SS2 related genes on chromosome 1. Our findings provide not only new information about virulence related genes, especially T3SS2 on Chromosome 1 of V. parahaemolyticus, but also could support results of previous studies on the pathogenicity of tdh and trh negative clinical strains. Further comparative genome studies of clinical and environmental isolates with our V. parahaemolyticus strain will provide information crucial to revealing the major pathogenic mechanism.In conclusion, we completed genomic sequencing of"}
+{"text": "Amphiprion ocellaris) genome utilizing Illumina and Nanopore reads, further demonstrating the substantial impact of modest long read sequencing data sets on improving genome assembly statistics.Some of the most widely recognized coral reef fishes are clownfish or anemonefish, members of the family Pomacentridae (subfamily: Amphiprioninae). They are popular aquarium species due to their bright colours, adaptability to captivity, and fascinating behavior. Their breeding biology  and symbiotic mutualism with sea anemones have attracted much scientific interest. Moreover, there are some curious geographic-based phenotypes that warrant investigation. Leveraging on the advancement in Nanopore long read technology, we report the first hybrid assembly of the clown anemonefish (50 length (401 kb) and increased the genome completeness by an additional 16%. A total of 27 240 high-quality protein-coding genes were predicted from the clown anemonefish, 26 211 (96%) of which were annotated functionally with information from either sequence homology or protein signature searches.We generated 43 Gb of short Illumina reads and 9 Gb of long Nanopore reads, representing approximate genome coverage of 54\u00d7 and 11\u00d7, respectively, based on the range of estimated k-mer-predicted genome sizes of between 791 and 967 Mbp. The final assembled genome is contained in 6404 scaffolds with an accumulated length of 880 Mb . Compared with the Illumina-only assembly, the hybrid approach generated 94% fewer scaffolds with an 18-fold increase in NA. ocellaris genome will be an invaluable molecular resource for supporting a range of genetic, genomic, and phylogenetic studies specifically for clownfish and more generally for other related fish species of the family Pomacentridae.We present the first genome of any anemonefish and demonstrate the value of low coverage (\u223c11\u00d7) long Nanopore read sequencing in improving both genome assembly contiguity and completeness. The near-complete assembly of the  Amphiprion ocellaris .Tissues for genome assembly and as reference material were sourced from the collection of the Museum and Art Gallery of the Northern Territory (NTM). The samples used for DNA extraction and subsequent whole-genome sequencing were from freshly vouchered captive bred Genomic DNA was extracted from multiple fin clip and muscle samples using the E.Z.N.A. Tissue DNA Kit . For Illumina library prep, approximately 1 \u03bcg of gDNA from isolate A3764 was sheared to 300 bp using a Covaris Focused-Ultrasonicator  and subsequently processed using the TruSeq DNA Sample Prep Kit  according to the manufacturer's instructions. Paired-end sequencing was performed on a single lane of HiSeq 2000  located at the Malaysian Genomics Resource Centre Berhad. Two additional libraries were constructed from specimen NTM A3764, and both libraries were sequenced on the MiSeq (2 \u00d7 300 bp setting), located at the Monash University Malaysia Genomics Facility.To generate Oxford Nanopore long reads, approximately 5 \u03bcg of gDNA was extracted from isolates NTM A4496 and A4497, size-selected (8\u201330 kb) with a BluePippin , and processed using the Ligation Sequencing 1D Kit  according to the manufacturer's instructions. Three libraries were prepared and sequenced on 3 different R9.4 flowcells using the MinION portable DNA sequencer  for 48 hours.ILLUMINACLIP:2:30:10, MINLEN:100; Trimmomatic, RRID:SCR_011848) [RRID:SCR_005484) [50: 12.7 kb). Sequencing statistics are available in Raw Illumina short reads were adapter-trimmed with Trimmomatic v.0.36 (_011848) , followe_005484)  based onRRID:SCR_005491) [max kmer coverage: -1). A separate estimation performed by BBMap [Amphiprion species (792 Mb\u20131.2 Gb) as reported on the Animal Genome Size Database [K-mer counting with the \u201cclean\u201d Illumina reads was performed with Jellyfish v.2.2.6  , generat_005491) , which eby BBMap  estimateDatabase .de novo assemblies were performed with the Maryland Super-Read Celera Assembler v.3.2.2  [RRID:SCR_010750). To overcome this, given that the CA assembler is no longer maintained, we disabled the frgcorr step based on one of the developer's recommendations, and the hybrid assembly was subsequently improved with 10 iterations of Pilon v.1.22  [RRID:SCR_015008) [Short reads used for assemblies described in this study were only trimmed for adapters, but not for quality. Both short-read-only and hybrid _010691) . During _014731) , using s_015008)  was used50 length from 21 802 bp to 401 715 bp  were individually assembled using Scallop v0.10.2 [A. ocellaris genome. The transcriptome assemblies were subsequently merged using the tr2aacds pipeline from the EvidentialGene [A. ocellaris genome, contains 25 264 contigs/isotigs (putative transcripts) with an accumulated length of 68.4 Mb and BUSCO-calculated completeness of 92.8%  according to the manufacturer's protocols. After assessing total RNA intactness on the Tapestation2100 (Agilent), mRNA was enriched using NEBNext Poly(A) mRNA Magnetic Isolation Kit  and processed with NEBNext Ultra RNA Library Prep Kit for Illumina . Libraries from both whole-body and muscle tissues were sequenced on a fraction of MiSeq V3 flowcell (1 \u00d7 150 bp). Single-end reads from both libraries in addition to 2 publicly available  v0.10.2  based on v0.10.2  alignmentialGene . The finRRID:SCR_005309) [est2genome) and protein sequences from 11 fish species downloaded from Ensembl  [protein2genome), whereas the second and third passes included gene models trained from the first (and then second) passes with ab initio gene predictors SNAP  [RRID:SCR_008417) [Protein-coding genes were predicted with the MAKER v.2.31.9 genome annotation pipeline  . A total_002344)  (protein_008417) . In the blastp v.2.6.0  [RRID:SCR_005829) [Further, to infer the putative function of these predicted proteins, NCBI\u2019s _001010)  was used_005829)  was usedA. ocellaris individuals from known localities (RRID:SCR_015056) [A. ocellaris (GenBank: NC009065.1) as the bait for read mapping. The assembled mitogenomes were subsequently annotated with MitoAnnotator [A. ocellaris NTM A3764 exhibits strikingly high whole-mitogenome nucleotide identity (99.98%) to sample NTM A3708 as a wild collection from Darwin Harbour, Australia. In addition, the overall high pair-wise nucleotide identity (>98%) of NTM A3764 to newly generated and publicly available A. ocellaris whole mitogenomes further supports its morphological identification as A. ocellaris  , using tnnotator . Consistcellaris .cyp19a1a enzyme of Danio rerio (Uniprot: O42145) was used as the query  for blastp search against the predicted A. ocellaris proteins. The top blast hit, AMPOCE_00\u00a0012675-RA (71.5% protein identity to O42145), was searched (tblastn) against the NCBI TSA database (Taxon: Amphirion) and showed strikingly high protein identity (99%) to a translated RNA transcript from Amphiprion bicinctus (c183337_g1_i2: GDCV01327693) [cyp19a1a gene codes for a steroidogenic enzyme that converts androgens into estrogens [Amphiprion bicinctus, as evidenced by significant correlation and differential expression of this gene between males and mature females [A. ocellaris to the cyp19a1a gene region as visualized using the Integrative Genomics Viewer  Nanopore long reads. Hybrid assembly of Illumina and Nanopore reads is one of the new features of the MaSuRCA assembler, version 3.2.2, which works by constructing long and accurate mega-reads from the combination of long and short read data. Although this is a relatively computationally intensive strategy with long run times, we observed substantial improvement in the genome statistics when compared with Illumina-only assembly. As Nanopore technology becomes more mature, it is likely that future de GigaDB repository [Data supporting the results of this article are available in the pository . Raw Illbp: base pair; CDS: coding sequence; Gb: giga base; kb: kilo base; Mb: mega base; SRA: Sequence read archive; TE: transposable elements; TSA: transcriptome shotgun assembly.A. ocellaris based on Illumina short reads.Additional file 1: Figure S1: Genome profiling of Additional file 1: Table S1: Summary of raw reads generated from genome and transcriptome sequencing.Additional file 1: Table S2: Assembly details after each pilon iteration.Amphiprion ocellaris between the target sample (NTM A3764) and other isolates with known locality; body-colour phenotype is marked where known.Additional file 1: Table S3: Mitogenome similarity of The authors declare that they have no competing interests.GIGA-D-17-00310_Original_Submission.pdfClick here for additional data file.GIGA-D-17-00310_Revision_1.pdfClick here for additional data file.Response_to_Reviewer_Comments_Original_Submission.pdfClick here for additional data file.Reviewer_1_Report_ -- Christiaan Henkel04 Dec 2017 ReviewedClick here for additional data file.Reviewer_2_Report_ -- Ole K T\u00f8rresen06 Dec 2017 ReviewedClick here for additional data file.Supplemental materialClick here for additional data file."}
+{"text": "Klebsiella pneumoniae (XDR-KP) infections cause high mortality and are disseminating globally. Identifying the genetic basis underpinning resistance allows for rapid diagnosis and treatment. XDR isolates sourced from Greece and Brazil, including 19 polymyxin-resistant and five polymyxin-susceptible strains, were subjected to whole genome sequencing. Seventeen of the 19 polymyxin-resistant isolates harboured variations upstream or within mgrB. The most common mutation identified was an insertion at nucleotide position 75 in mgrB via an ISKpn26-like element in the ST258 lineage and ISKpn13 in one ST11 isolate. Three strains acquired an IS1 element upstream of mgrB and another strain had an ISKpn25 insertion at 133\u2009bp. Other isolates had truncations  or a missense mutation (D29E) affecting mgrB. Complementation assays revealed all mgrB perturbations contributed to resistance. Missense mutations in phoQ  were also found to facilitate resistance. Several variants in phoPQ co-segregating with the ISKpn26-like insertion were identified as potential partial suppressor mutations. Three ST258 samples were found to contain subpopulations with different resistance-conferring mutations, including the ISKpn26-like insertion colonizing with a novel mutation in pmrB (P158R), both confirmed via complementation assays. These findings highlight the broad spectrum of chromosomal modifications which can facilitate and regulate resistance against polymyxins in K. pneumoniae.Extensively drug-resistant  Assembly revealed 23_GR_12 harboured an ISKpn26-like disrupted mgrB alongside the intact version with mutations in phoP and phoQ in 57\u200a% of the samples. Furthermore, assemblies for mgrB, pmrAB and phoPQ were aligned to ATCC 700603 (Table S4). Several non-synonymous mutations were detected, but the majority were not predicted to be deleterious. Various mutations were unique to KP compared to KQ. ST11, 147, 258 and 437 remained conserved across these genes with the exception of mutations predicted to be deleterious. ST383 harboured several dissimilarities including the lack of pmrA (D131N) and pmrB (S105N) and gain of pmrA  and pmrB . Only subtle differences were observed in KQ isolate 21_GR_ 13, which included pmrA  and pmrB (G358A). Predicted deleterious mutations detected both in polymyxin-susceptible and in polymyxin-resistant isolates included pmrA (Q140L) and pmrB (R256G).Mutations in genes commonly identified to confer polymyxin resistance in KP include nd pmrAB . SeveralmgrB restored susceptibility in all resistant isolates with mgrB coding mutations or upstream disruptions, with the exception of two strains heterogeneous for the mgrB disruption and a pmrB coding mutation (13_GR_14 and 14_GR_14) . Colonies which were reverted on complementation were further passaged three times with no antibiotic pressure in order to remove the plasmid and discern if these mutations were contributing to resistance. After passaging, pTOPO-mgrB isolates harboured an MIC of \u226564\u2009mg\u2009l\u22121 whilst pTOPO-pmrB colonies had an MIC of 16\u2009mg\u2009l\u22121, confirming two resistant populations in these samples. 23_GR_12 was also observed to have a heterogeneous mgrB disruption but did not carry a corresponding pmrB mutation, although it harboured similar mutations to 2_GR_12 in phoPQ. Amplification of mgrB identified two of three 23_GR_12 transformed colonies contained the IS element disruption and reverted to being susceptible upon complementation with pTOPO-mgrB.Complementation of the WT gene elucidated the role of these mutations in resistance . MICs we4_GR_14) . For thetibility  and mgrBphoQ potentially conferring resistance , although several wells containing high polymyxin B concentrations exhibited growth (n\u22654) and therefore the mutated gene was introduced into a polymyxin-susceptible isolate, 20_GR_12 , phoQ  and pmrB (T140P). Complementation of WT genes in these isolates facilitated a \u22652-fold increase in MIC with the exception of 10_GR_13, which had an additional predicted neutral mutation in phoQ (A225T)  into 7_GR_13 did not lead to an observable corresponding reduction in MIC, but once transformed into 20_GR_12, a twofold increase in MIC was apparent (phoQ (N253T and V446G) exhibited a twofold reduction in MIC (phoQ (V446G) mutation was anticipated to segregate with the mgrB-disrupted population in 13_GR_14 and 14_GR_14, but when phoQ was amplified from a colony reverted to susceptible via pTOPO-mgrB complementation, the WT phoQ was observed . The phoQ (V446G) mutation was successfully amplified from a 14_GR_14 colony containing the pmrB (T158R) mutation. Although this mutation did not segregate with disrupted mgrB, it may act as a partial suppressor mutation when a resistance-conferring mutation is present in pmrB. Interestingly, a \u22654-fold reduction in MIC was witnessed for phoP mutations P47L and A95S, indicating partial suppressor mutations  which were reverted upon complementation indicating an impact on the promoter region.Inactivation of Colombia . The ISKescribed . We idenescribed . DisruptmgrB have been previously detected, although these were identified in differing STs, indicating mutations potentially have arisen independently in Greece [mgrB is prevalent in polymyxin-resistant KP and may arise owing to its capacity to promote virulence and further attenuate the early host defence response, with little or no fitness cost [Truncations identified at positions 28 and 30 of n Greece . Complemess cost .phoQ histidine kinase region, critical for phosphorylation and interaction with phoP, in 8_GR_13 (G385C) and 9_GR_12 (T281M). The G385C mutation had previously been reported, [phoQ has recently been highlighted and these results may imply the inability of pTOPO-phoQ to override the resistance caused by these mutations [phoQ in the pCR-Blunt II-TOPO vector and warrants further investigation.Single predicted detrimental mutations were observed in the eported, , but in utations . FurtherpmrA (Q140L) and pmrB (R256G). These mutations represent lineage-specific mutations, but this does not negate the possibility of previously resistance-conferring variants being acquired in these loci with subsequent reversion mutations to give rise to a susceptible phenotype.Several non-synonymous changes were identified to be not deleterious according to PROVEAN analysis. Notably, these were abundant in KQ strains, including 21_GR_13 and KP ST383 isolates. When these clinical isolates were aligned to ATCC 700603, multiple coding changes were identified, with the majority detected as neural changes with the exception of Kpn26-like mgrB disruption and a new mutation conferring resistance in pmrB, P158R, as determined by complementation. 23_GR_12 contained approximately half the reads mapping to the undisrupted genes and the other to the ISKpn26-like strain, with several additional predicted deleterious mutations. This heterogeneity may explain the initial clinical detection for this isolate to be polymyxin-susceptible.Heterogeneity was apparent in several isolates. In near equal ratios, 13_GR_14 and 14_GR_14 possessed the ISKpn26-like element-disrupted mgrB were accompanied by mutations in phoPQ and/or\u2009pmrB. These changes were present in \u226598\u200a% of reads, making the involvement of heterogeneity unlikely. Once complemented, an increase in resistance was commonly recorded. This potentially reflects partial suppressor mutations as strains which solely possessed this IS element disruption commonly exhibited a heightened MIC of \u226564\u2009mg\u2009l\u22121. One variant segregating with this disruption included pmrB T140P. This had formerly been identified in an ST258 lineage but even when the resistant gene was complemented, the MIC increased by twofold but was not defined as clinically resistant [Several isolates harbouring ISesistant .phoP or phoQ were introduced into the mgrB-disrupted isolate, a reduction in MIC was apparent. The involvement of additional mutations in PhoPQ causing a suppressing effect on the level of resistance in a background where the disrupted mgrB is lacking has yet to be reported in KP. Previous research by Miller et al. [Pseudomonas aeruginosa. Their study describes phoP mutations with the capacity to partially or fully suppress resistance-causing mutations in phoQ. These mutations in phoP were near or within the DNA binding site, which differs from our results, where the mutations are impacting the response regulatory region that interacts with PhoQ. Conversely, all mutations partially suppressing the MIC were identified to be targeting the HAMP  domain and histidine kinase component of PhoQ. These were in regions similar to revertant P. aeruginosa strains identified by Lee and Ko [pmrD expression. Whether these mutations constitute a fitness advantage due to the reduction of metabolism required for the production of lipopolysaccharide modifications is yet to be discerned. Furthermore, due to variability in some of the complementation data, a knockout phoPQ background and introduction of genes that are potential suppressor mutations is required.When mutated r et al.  determine and Ko . We postmgrB ISKpn26-like disruption (nucleotide 75), truncations in mgrB (nucleotides 28 and 30) and a missense mutation in phoQ (G385C). The study provides the first potential report of suppressor mutations for polymyxin resistance. Through complementation assays, we have discerned the role of these modifications and have identified resistance-causing variants that can be monitored in future genome-based diagnostics.Rapid and accurate detection of mutations attributed to polymyxin resistance remains a long-standing problem. Our research has contributed to the current understanding of the dissemination and evolution of this resistance in KP. Although our sample size is limited, this study highlights several issues arising from solely interrogating genomes for resistance detection, including ST-specific non-synonymous changes, and heterogeneity. Our study reveals several mutations causing polymyxin resistance across various STs in comparison with prior literature. These include the NCBI Bioproject PRJNA307517 (2016).et al.Liu P, Li P, Jiang X, Bi D, Xie Y, Tai C,  Complete genome sequence of Klebsiella pneumoniae subsp. pneumoniae HS11286, a multidrug-resistant strain isolated from human sputum. J Bacteriol 2012;194:1841\u20131842. NCBI BioProject PRJNA78789.et al.Zowawi HM, Forde BM, Alfaresi M, Alzarouni A, Farahat Y,  Stepwise evolution of pandrug-resistance in Klebsiella pneumoniae. Sci Rep 2015;5:15082. European Nucleotide PRJEB7538.et al.Deleo FR, Chen L, Porcella SF, Martens CA, Kobayashi SD, Porter AR,  Molecular dissection of the evolution of carbapenemresistant multilocus sequence type 258 Klebsiella pneumoniae. Proc Natl Acad Sci USA 2014;111:4988\u20134993. European Nucleotide PRJNA237670.Elliott AG, Ganesamoorthy D, Coin L, Cooper MA, Cao MD. Complete Genome Sequence of Klebsiella quasipneumoniae subsp. similipneumoniae Strain ATCC 700603. Genome Announc 2016;4:e00438-16.Liu L, Ahmad AH, Leung FC. Klebsiella quasipneumoniae strain HKUOPA4, complete genome. NCBI Bioproject PRJNA224116 (2017).et alPinto-Tom\u00e1s AA, Anderson MA, Suen G, Stevenson DM, Chu FS, . Symbiotic nitrogen fixation in the fungus gardens of leaf-cutter ants. Science 2009;326:1120\u20131123.Di DY, Jang J, Unno T, Hur HG. Emergence of Klebsiella variicola positive for NDM-9, a variant of New Delhi metallo-\u03b2-lactamase, in an urban river in South Korea. J Antimicrob Chemother 2017;72:1063\u20131067."}
+{"text": "Background: Virus discovery using high-throughput next-generation sequencing has become more commonplace. However, although analysis of deep next-generation sequencing data allows us to identity potential pathogens, the entire analytical procedure requires competency in the bioinformatics domain, which includes implementing proper software packages and preparing prerequisite databases. Simple and user-friendly bioinformatics pipelines are urgently required to obtain complete viral genome sequences from metagenomic data.Results: This manuscript presents a pipeline, drVM , for rapid viral read identification, genus-level read partition, read normalization, de novo assembly, sequence annotation, and coverage profiling. The first two procedures and sequence annotation rely on known viral genomes as a reference database. drVM was validated via the analysis of over 300 sequencing runs generated by Illumina and Ion Torrent platforms to provide complete viral genome assemblies for a variety of virus types including DNA viruses, RNA viruses, and retroviruses. drVM is available for free download at: https://sourceforge.net/projects/sb2nhri/files/drVM/ and is also assembled as a Docker container, an Amazon machine image, and a virtual machine to facilitate seamless deployment.Conclusions: drVM was compared with other viral detection tools to demonstrate its merits in terms of viral genome completeness and reduced computation time. This substantiates the platform's potential to produce prompt and accurate viral genome sequences from clinical samples. Viruses are the most abundant biological entities on Earth and are found among all cellular forms of life including animals, plants, bacteria, and fungi. More than 4500 viral species have been discovered; their sequence information has been collected by researchers \u20133. VirusSURPI  and Taxode novo assembles viral genomes from the corresponding genus-level reads. For ease of deployment, a Docker container ) AND (viridae[organism])\u201d; this query resulted in 642\u2009079 hits (as of 16 March 2016) and increased to 705\u2009577 hits (as of 20 October 2016). Based on each sequence identifier (accession number) of the viral sequence, its taxonomic ID, scientific name, and genus-level annotation were separately obtained from the NCBI Taxonomy database. Viral sequences with taxonomy ID not included in the virus division (division id = 9), those with taxonomic information absent, and those lacking genus-level annotation were labeled \u201cnonViral, \u201cnoTax,\u201d and \u201cnoGenus,\u201d respectively. The noGenus sequences can be used by adding \u201c-kn on\u201d option in the creation of databases. With the exception of noGenus sequences, those sequences with the nonViral or noTax labels were excluded from database construction. The remaining viral sequences were utilized as rawDB. Three hundred and seventy-seven viral genomes were obtained by filtering with host \u201chuman\u201d from the NCBI viral genome resource to retrieve 684 sequences (1 March 2016) [ 0.15.4) , possess 0.15.4)  from rawde novo assembly via SPAdes (v.3.6.1). Prior to assembly, digital normalization [The drVM pipeline is implemented in Python and incorporates several open-source tools, including BLAST, SNAP, and SPAdes . The paclization  in khmerlization  is emplolization  and the A reference genome of the hepatitis C virus  was used to simulate metagenomes with 10\u00d7, 15\u00d7, 20\u00d7, 30\u00d7, 40\u00d7, and 50\u00d7 viral reads using VirtualNextGenSequencer . The simA total of 349 sequencing runs in the SRA were downloaded see . The metde novo assembly of classified reads. Each genus-level assembly was then annotated with a close reference in refDB or rawDB to produce corresponding coverage plots. The drVM pipeline was implemented, in Python, to incorporate open-source tools including BLAST, khmer, SNAP, SOAP2, and SPAdes. The software is open-source and available for download: https://sourceforge.net/projects/sb2nhri/files/drVM/. In addition to the drVM script, the module is distributed as a Docker image in the Docker Hub (https://hub.docker.com/r/990210oliver/drvm/) repository, an Amazon Machine Image (AMI) in Community AMIs, and as a virtual machine image (drVM.ova) on the sourceforge website. The instructions for users can be found in de novo, by SPAdes using reads within the same genus, and each assembled contig , drVM first placed all the HCV (485\u20132425) along with 105 liver and 274 HMP paired-end reads into fastq files classified under the genus \u201cHepacivirus.\u201d It proceeded to assemble the reads into a HCV genome (length ranging from 9631 to 9644 bp) with 100% sequence identity to the reference genome (NC_004102.1) as the depth of HCV reads was \u226720\u00d7. Hence, based on this simulation study, the minimum read depth for drVM to reconstruct the viral genome was 20\u00d7. To assess the capability of assembling mixed viral genomes, two simulation datasets have been used: 20\u00d7-40\u00d7-80\u00d7 and 20\u00d7-20\u00d7-20\u00d7 for a mixture of reads of CA16, HRV, and HRSV combined with 1-Gbp reads from an influenza-negative respiratory sample (ERR690488). The results generated by drVM for 20\u00d7-40\u00d7-80\u00d7 and 20\u00d7-20\u00d7-20\u00d7 are shown in Fig.\u00a0http://sb.nhri.org.tw/drVM/, and the assembled viral sequences , respiratory syncytial virus (RSV), and West Nile virus (WNV); 31 consensus genome assemblies have been submitted to NCBI (JX503071-JX503101) [http://sb.nhri.org.tw/drVM/). drVM reconstructed complete viral genomes in 25 runs, thus the remaining 9 runs are labeled as \u201cdetection\u201d in Table\u00a0http://sb.nhri.org.tw/drVM/) and 10 H3N2 virus genomes  in 12 influenza-positive samples and one human respiratory syncytial virus in a negative control sample (ERR690491). Taken together, various viral genomes including DNA, RNA, and retro-transcribing viruses  . These vhttps://sourceforge.net/projects/sb2nhri/files/drVM/Comparison/. Please also note that the results are reproducible even when running a drVM virtual machine with a configured memory size of 8 GB on a Windows system with Intel Xeon E31248 CPU and 32 GB RAM (see Supplementary information). To simulate the disturbance of host reads, homo sapiens raw reads from liver cancer cells (SRR3031107) were concatenated to sequencing reads (SRR544883) from the hepatitis C virus infection [de novo assembly of the classified reads into viral genomes.Unlike SURPI , VIP 1212, and Vnfection . Before de novo assembly, digital normalization was then employed to correct uneven read depth distribution so as to eliminate redundant reads while retaining sufficient information. For example, drVM produced a 7455-bp contig of porcine kobuvirus for ERR1097471 (Table\u00a0http://sb.nhri.org.tw/drVM/) from run to run. The normalized reads were assembled, in a de novo fashion, by SPAdes with multiple k-mer sizes . Since SPAdes operates with Illumina and Ion Torrent reads, drVM is able to handle sequencing reads produced by these two platforms . Moreover, drVM annotates each genus-level assembly with a close reference to produce the corresponding coverage plots. If multiple contigs are present in one coverage plot, reads aligned to the contigs are extracted in pairs for subsequent re-assembly; such a process is able to improve genome completeness. An example can be seen in the run corresponding to SRR527705 \u2013 fragmented contigs were annotated with the WNV in the pre.run routine; drVM produced a 11\u2009017-bp contig of the complete genome (see http://sb.nhri.org.tw/drVM/ for the details). Over 99.98% of the target sequence identity was obtained in the drVM-produced contig when referenced against the submitted assembly (JX503096.1) [As NGS technology is becoming a more common means to detect pathogens in clinical samples, our goal is to establish a simple and effective pipeline that allows accurate and rapid viral genome reconstruction from metagenomic NGS data generated from complex clinical samples. As described in SURPI, SNAP executes 10\u2013100 \u00d7 more rapidly than existing alignment tools including Bowtie 2  and BWA 71 Table\u00a0 but prod03096.1) , as showIn the comparative study of VIP and VirusTAP, the authors have compared their assemblies with direct metagenomic assemblies via Ensemble Assembler, IDBA_UD, A5-miseq, and CLC workbench. The results have led the authors to conclude that \u201cclassification to assembly\u201d outperformed direct assembly in terms of assembly continuity and execution time , 13, sughomo sapiens notch 2 (100% identity). Nevertheless, the provirus that integrated into a host genome is partial and it can be easily distinguished from the complete viral genome  by length. We therefore recommend that conclusions drawn from drVM should be made with caution, especially with regards to retrovirus detection. We have applied drVM in the analyses of over 300 sequencing runs retrieved from NCBI's SRA to demonstrate that drVM is indeed able to efficiently produce genome assemblies for known eukaryotic viruses from metagenomic data.Although drVM operates in such a manner that the subtraction of the host read is neglected, which may produce valid host sequences annotated with viral references, this error can be easily identified via inspection of the assembled contigs. For example, analyzing SRR3031107 concatenated with SRR544883, a 1872-bp contig annotated with Feline leukemia virus gene for viral Notch2 (92% identity) actually corresponded to Project name: drVMhttps://sourceforge.net/projects/sb2nhri/files/drVM/Project home page: Operating system(s): OS X, Linux, WindowsProgramming language: PythonRequirements: Amazon machine image, Docker, or Virtual machine; 8 GB RAMLicense: GNU General Public License, version 3.0 (GPL-3.0)Snapshots of the code and further supporting data is available in the GigaScience repository GigaDB .AMI: Amazon machine image; HCV: hepatitis C virus; HIV: immunodeficiency virus; NGS: next-generation sequencing; RSV: respiratory syncytial virus; SRA: sequencing read archive; WNV: West Nile virus.GIGSCI online.Supplementary data are available at Supplementary Table\u00a0S1: list of 349 sequencing datasets from 18 research studies.Supplementary File 2: supplementary information.GIGA-D-16-00060_Original_Submission.pdfGIGA-D-16-00060_Revision_1.pdfGIGA-D-16-00060_Revision_2.pdfResponses_to_reviewer_comments_Orginal_Submission.pdfResponse_to_Reviewer_Comments_Revision_1.pdfReviewer_1_Report_(Revision_1).pdfReviewer_1_Report_Original_Submission.pdfReviewer_2_Report_(Revision_1).pdfReviewer_2_Report_Original_Submission.pdfReviewer_3_Report_(Revision_1).pdfReviewer_3_Report_Original_Submission.pdfSupplemental materialSupplementary Table\u00a0S1: list of 349 sequencing datasets from 18 research studies.Supplementary File 2: supplementary information.The authors declare that they have no competing interests.YCL conceived the project. HHL implemented the pipeline. YCL and HHL prepared, read, and approved the final manuscript."}
+{"text": "A comprehensive benchmark is presented, including open-source tools and the popular USEARCH suite. Simulated, mock, and environmental communities were used to analyze sensitivity, selectivity, species diversity , and taxonomic composition. The results demonstrate that recent clustering algorithms can significantly improve accuracy and preserve estimated diversity without the application of aggressive filtering. Moreover, these tools are all open source, apply multiple levels of multithreading, and scale to the demands of modern next-generation sequencing data, which is essential for the analysis of massive multidisciplinary studies such as the Earth Microbiome Project (EMP)  to reduce the run time of subsequent analysis steps. Here, we evaluated the performance of recently released state-of-the-art open-source clustering software products, namely, OTUCLUST, Swarm, SUMACLUST, and SortMeRNA, against current principal options (UCLUST and USEARCH) in QIIME, hierarchical clustering methods in mothur, and USEARCH\u2019s most recent clustering algorithm, UPARSE. All the latest open-source tools showed promising results, reporting up to 60% fewer spurious OTUs than UCLUST, indicating that the underlying clustering algorithm can vastly reduce the number of these derived OTUs. Furthermore, we observed that stringent quality filtering, such as is done in UPARSE, can cause a significant underestimation of species abundance and diversity, leading to incorrect biological results. Swarm, SUMACLUST, and SortMeRNA have been included in the QIIME 1.9.0 release.IMPORTANCE Massive collections of next-generation sequencing data call for fast, accurate, and easily accessible bioinformatics algorithms to perform sequence clustering. A comprehensive benchmark is presented, including open-source tools and the popular USEARCH suite. Simulated, mock, and environmental communities were used to analyze sensitivity, selectivity, species diversity , and taxonomic composition. The results demonstrate that recent clustering algorithms can significantly improve accuracy and preserve estimated diversity without the application of aggressive filtering. Moreover, these tools are all open source, apply multiple levels of multithreading, and scale to the demands of modern next-generation sequencing data, which is essential for the analysis of massive multidisciplinary studies such as the Earth Microbiome Project (EMP) . Moreover, based on UCLUST documentation (http://www.drive5.com/uclust/uclust_userguide_1_1_579.pdf), the allegedly serial implementation is impractical for massive high-throughput sequencing data. More accurate, faster, community-accessible tools are needed to overcome these challenges.Current DNA sequencing technologies generate hundreds of gigabytes of data in a single run and have enabled new detailed investigations into the human microbiome \u20133 and in\u20139\u2013Within the previous 2\u00a0years, four new sequence-clustering tools have emerged: OTUCLUST from the Micca package , Swarm , 14, SUMde novo, and open reference. In the closed-reference approach, the input sequences are clustered against a reference sequence database. In de novo clustering, the input sequences are grouped based on pairwise similarity among all sequences in the data set. The open-reference approach  an initial set of OTUs is constructed by iteratively agglomerating similar amplicons, and (ii) amplicon abundance values are used to reveal OTUs\u2019 internal structures and to break them into sub-OTUs, if necessary.Swarm , 14 is ade novo clustering algorithms; both are based on a greedy strategy in which the clusters are constructed incrementally by comparing an abundance-ordered list of input sequences against the representative set of already-chosen sequences  (OTUCLUST  and SUMAy empty) . A similde novo clustering algorithms  which cluster sequences based on genomic distance. In nearest neighbor (single linkage), a sequence is linked to an OTU if it is similar to any other sequence in that OTU, in furthest neighbor (complete linkage), a sequence is linked to an OTU if it is similar to all other sequences in that OTU, and in average neighbor, a sequence is linked to an OTU if it is similar to the averaged differences between all other sequences in that OTU. More details on these algorithms are available in references 8 and 22.mothur  implemenE\u00a0value threshold is applied to evaluate the quality of resulting alignments. In SortMeRNA 2.0, the reference sequence achieving the lowest E\u00a0value when aligned with a query sequence is chosen as the OTU centroid for that query. In addition to passing the E\u00a0value threshold, the query must also have sufficient percent identity and coverage (both set to 97% by default). Contrary to UCLUST, the run time of SortMeRNA is not affected by reducing these thresholds .SortMeRNA  is suitede novo, closed-reference, and open-reference (except for USEARCH 5.2) clustering. In QIIME\u2019s implementation, USEARCH 5.2 is executed via a pipeline closely shadowing otupipe  are supported in QIIME (v1.8.0). Both tools can perform  otupipe  to clust otupipe  is the lSwarm 1.2.19, SUMACLUST 1.0.00, and SortMeRNA 2.0 have been integrated into QIIME 1.9.0 and can be used through QIIME\u2019s three different OTU clustering commands : pick_clA variety of datasets were chosen to evaluate the performance of these open-source OTU clustering approaches relative to QIIME\u2019s UCLUST/USEARCH-based OTU clustering approaches as well as UPARSE see  for extrAll tools were run with default parameters. Input FASTA files for Swarm, SUMACLUST, and SortMeRNA were generated using QIIME\u2019s demultiplexing and quality filtering workflow. Input FASTA files for OTUCLUST, mothur, and UPARSE were demultiplexed using QIIME and quality filtered using each tool\u2019s recommended standard operation procedure (SOP) . Sequence filtering for OTUCLUST was performed with default quality score cutoffs of 20 (labeled as OTUCLUST_q20) and 3 . UPARSE was run using the recommended settings with truncation lengths of 150\u00a0bp and 250\u00a0bp; similarly to OTUCLUST, runs were performed with a default quality score cutoff of 16 (labeled as UPARSE_q16) and additionally with a quality score cutoff of 3 (labeled as UPARSE_q3). Biological observation matrix (BIOM) format  tables wde novo clustering, most tools report F measures  of 0.82 to 0.84 (sim_even) and 0.81 to 0.83 (sim_staggered) at the genus level  and 0.81 to 0.83 (sim_staggered) for all tools, which is in agreement with Results for Bokulich_2 (and Bokulich_3 for UPARSE) are unavailable for UPARSE, OTUCLUST, and mothur due to significant memory, run time, and disk space requirements, respectively. All other methods were compared against the expected taxonomic composition for each data set. In addition, Pearson\u2019s correlation coefficient was computed to measure the relatedness of taxonomic assignment between all pairs of tools , OTUCLUST_, and mothur_nearest frequently reported the lowest number of OTUs, the lowest number of observed taxa, and the highest F measure , Bokulich_2. Download Figure\u00a0S1, PDF file, 0.1 MB.Read abundance for true-positive and false-positive assigned taxonomies , Bokulich_3. Download Figure\u00a0S2, PDF file, 0.1 MB.Read abundance for true-positive and false-positive assigned taxonomies , Bokulich_6. Download Figure\u00a0S3, PDF file, 0.1 MB.Read abundance for true-positive and false-positive assigned taxonomies  to 0.97 to 1 (Bokulich_3) to 0.92 to 0.99 (Bokulich_6), showing a strong relationship between all algorithms. The coefficient was lower in the cases where the taxonomy could not be assigned  or significant filtering of sequences .Results for UPARSE_q4 and UPARSE_q16 are unavailable for the global_soil data set due to memory limitations in the 32-bit version of UPARSE and for OTUCLUST due to significant run time (limited to one thread).In contrast to mock communities, the Pearson correlation for natural communities was much more variable  , highlig2 metric for body_sites and canadian_soil was <0.3 for most software , body_sites. For de novo graphs, the legend represents various sampling depths. Download Figure\u00a0S4, PDF file, 0.2 MB.Alpha diversity for tools at different sampling depths , global_soil. For de novo graphs, the legend represents various sampling depths. Download Figure\u00a0S5, PDF file, 0.2 MB.Alpha diversity for tools at different sampling depths . If the user\u2019s primary goal is to focus on the most abundant microbial profiles, low-abundance OTUs may be filtered out postclustering, but care should be taken, as such low-abundance OTUs can be important members of communities  performed equally well on simulated datasets where the ground truth was well established, with mothur_average and OTUCLUST closely behind. Despite this controlled chimera-free environment, UPARSE with recommended parameters reported the lowest accuracy for the sim_staggered data set, implying that stringent quality filtering can cause a significant underestimation of species abundance and diversity and lead to incorrect biological results. For the mock communities, most tools were able to correctly detect the expected number and identity of genera, but only UPARSE reported significantly fewer false-positive taxa (followed by OTUCLUST and USEARCH). For UPARSE, this was expected, as a large proportion of reads was filtered out prior to clustering, leaving evidence of only the most abundant taxa (OTUs comprised of hundreds of thousands of reads). The majority of false-positive taxa reported by other tools were low-abundance OTUs that could be mapped to BLAST\u2019s NT database with very high similarity , or a relatively small memory limit in the case of UPARSE. Regarding UPARSE, the small memory limit makes it necessary to purchase the 64-bit license in order to process large projects  or use open-source alternatives. QIIME\u2019s current open-source, open-reference pipeline (based on SortMeRNA and SUMACLUST) was able to process this quantity of data within 24\u00a0h using 64 threads on Intel Xeon CPU E5-4620 v2 at 2.60GHz or within 3\u00a0days using 64 threads on AMD Opteron Processor 6276.In terms of accurately predicted taxonomic composition for Although most open-source tools report an increased run time in comparison to UCLUST and USEARCH , they prde novo clustering to remove (prior to clustering) any sequences not matching a specific gene model  and a refined reference database for targeted hypervariable regions  to improve alignment quality . Swarm 2 was released in reference 14 and reported to be faster and more memory efficient than Swarm 1; however, as of this writing, only Swarm 1 has been integrated into QIIME. Ongoing work to improve the QIIME OTU clustering workflows that use these tools includes adding a targeted gene prefilter for  quality . Furtherhttps://github.com/ekopylova/OTU-clustering.All steps taken to generate the analyses presented in this article are documented and implemented as shell or python scripts, available at http://www.drive5.com/usearch/manual/uparse_cmds.html) was run. For OTUCLUST, the script micca-preproc was used for sequence filtering and the command otuclust for clustering. For mothur, the MiSeq SOP  was used for measuring their run time performance. All run time performance tests were performed using 1 to 32 threads on Intel Xeon CPU E5-2640 v3 at 2.60\u00a0GHz.Open-source with multilevel parallelization tools tested in this paper\u2014Swarm, SUMACLUST, and SortMeRNA\u2014have been integrated into QIIME 1.9.0. For these tools, all benchmarks were launched through QIIME. For UPARSE, the recommended workflow , false-negative , and true-positive  measures were computed between the pickers\u2019 results (observed) and the ground truth or expected taxonomic composition (expected). The following definitions were used: precision = TP/(TP + FP); recall = TP/(TP + FN); F measure = 2 \u00d7 precision \u00d7 recall/.The python script run_compute_precision_recall.py was used to compute TP, FP, FN, precision, recall, F measure, the number of false-positive taxa whose complete set of OTUs are identified as chimeric (FP-chimeric) by UCHIME, the number of false-positive taxa whose complete set of OTUs map with \u226597% identity and coverage to BLAST\u2019s NT database (FP-known), and the number of false-positive taxa whose complete set of OTUs map with <97% identity and coverage to BLAST\u2019s NT database (FP-other). The script plot_tp_fp_distribution.py was used to generate All of the following steps can be executed using the shell script simulate_reads.sh.Reads were simulated using PrimerProspector  and the Amplicon sequencing simulation in ART (version VanillaIceCream-03-11-2014) could generate only evenly distributed communities. To simulate the staggered data set, a staggered distribution of template sequences was passed . To simulate the staggered data set, the following steps were taken. (i) Generate a random staggered distribution FASTA file of template V4 sequences using the list of OTU identifications from the even data set and the V4 subsampled sequences and (ii) simulate staggered abundance reads with ART using the staggered subsampled V4 sequences.For both even and staggered reads, QIIME\u2019s split_libraries_fastq.py script was run to filter simulated reads based on quality scores and format FASTA labels to be compatible with QIIME .Ground-truth OTU maps and BIOM tables were constructed using the simulate_reads.sh script that was used for simulating reads. OTU maps were generated using the reads\u2019 origin information stored in the FASTA labels of ART-simulated reads. BIOM tables were generated using QIIME\u2019s make_otu_table.py script together with Greengenes 97% taxonomy strings.A eukaryotic/18S rRNA sequence set tree was built using QIIME\u2019s filter_alignment.py and make_phylogeny.py scripts:filter_alignment.py -i Silva_111_post/rep_set_aligned/97_Silva_111_rep_set.fasta -e 0.0005 -g 0.80 -o Silva_111_post/trees; make_phylogeny.py -i Silva_111_post/trees/97_Silva_111_rep_set_pfiltered.fasta -o Silva_111_post/trees/97_Silva_111_rep_set_pfiltered.tre.Customs scripts iterating over all benchmarking results were used to launch QIIME\u2019s alpha and beta diversity analyses. The script run_single_rarefaction_and_plot.py was used to compute and plot alpha diversity as shown in 10.1128/mSystems.00003-15.6Table\u00a0S1\u00a0Table\u00a0S1, PDF file, 0.04 MB.URLs for software and studies used in this analysis. Download Copyright \u00a9 2016 Kopylova et al.2016Kopylova et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00003-15.7Table\u00a0S2\u00a0Table\u00a0S2, PDF file, 0.02 MB.Expected genera for Bokulich_2 and Bokulich_3. Download Copyright \u00a9 2016 Kopylova et al.2016Kopylova et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00003-15.8Table\u00a0S3\u00a0Table\u00a0S3, PDF file, 0.02 MB.Expected families for mock_nematodes. Download Copyright \u00a9 2016 Kopylova et al.2016Kopylova et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00003-15.9Table\u00a0S4\u00a0Table\u00a0S4, PDF file, 0.02 MB.Expected genera for Bokulich_6. Download Copyright \u00a9 2016 Kopylova et al.2016Kopylova et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00003-15.10Table\u00a0S5\u00a0Table\u00a0S5, PDF file, 0.04 MB.Sensitivity and selectivity statistics for assigned taxonomies at genus level, mock_nematodes. After software, columns represent OTU count (excluding singleton OTUs), precision (P), recall (R), F measure, TP (number of true-positive taxonomies), FN , FP . The remaining three FP columns represent a refined breakdown of the FP column, including false-positive taxonomies for which all comprising OTUs were classified chimeric (using UCHIME) (FP chimeric), mapped to BLAST\u2019s NT database with \u226597% similarity (FP-known), or mapped to BLAST\u2019s nt database with <97% similarity (FP-other). Download Copyright \u00a9 2016 Kopylova et al.2016Kopylova et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the"}
+{"text": "Contiguous genome assemblies are a highly valued biological resource because of the higher number of completely annotated genes and genomic elements that are usable compared to fragmented draft genomes. Nonetheless, contiguity is difficult to obtain if only low coverage data and/or only distantly related reference genome assemblies are available.de novo assembly process by constructing mate-pair libraries in silico.In order to improve genome contiguity, we have developed Cross-Species Scaffolding\u2014a new pipeline that imports long-range distance information directly into the We show how genome assembly metrics and gene prediction dramatically improve with our pipeline by assembling two primate genomes solely based on \u223c30x coverage of shotgun sequencing data. Accurate, complete, and well-annotated genomes provide a wealth of information about the past, present, and future of species and individuals and, therefore, constitute highly valuable resources for medical and biological research . Thanks Despite recent advances in sequencing technologies and genome assembly approaches, obtaining a contiguous assembly of a large genome from short reads remains challenging. For this reason, sequencing technologies that are providing new means for contiguous assembly of large genomes are of great interest to the genomics community. Third-generation long-read sequencing technologies such as PacBio  and Nanode novo assemblies because the many highly similar copies scattered across the genome lead to a multitude of ambiguous and often unresolvable paths in the underlying assembly graph. As a result, the obtained genome assemblies are fragmented, limiting their use for further analysis.Among the largest obstacles for assembling contiguous genomes, especially when using only short-reads, are low-complexity regions and transposable elements ; in the To increase contiguity, syntenic information may be imported from a closely related species for which a chromosome-level genome assembly is available . While rde novo genome assemblers today can make use of the long-range information of mate-pairs, and the use of large insert size libraries (20\u201325 kb) can greatly increase contiguity. Altogether, a more contiguous assembly with larger scaffolds is easily obtained if provided with adequate and sufficient mate-pair information  and SRP007603 for the chimpanzee and aye-aye, respectively. Supporting data, including assemblies, BUSCO results, and an archival copy of the code, are available via the , GigaDB .Project name: Cross-species scaffoldinghttps://github.com/thackl/cross-species-scaffoldingProject home page: Operating system(s): UnixProgramming language: Perl, BashOther requirements: Perl v5.10.1 or higher, Bash v4.2 or higherLicense: MITRRID:SCR_015932.Research Resource Identifier: Cross-species-scaffolding, Additional file 1: Text S1, Tables S1 to S4, Figure S1.Additional file 2: QUAST pdf reports for yeast dataset.Additional file 3: QUAST pdf reports for tapeworm dataset.Additional file 4: QUAST pdf reports for chimp dataset.BUSCO, Benchmarking Universal Single-Copy Orthologs; Mya, million years ago; NCBI, National Center for Biotechnology Information.The authors declare that they have no competing interests.GIGA-D-17-00092_Original_Submission.pdfClick here for additional data file.GIGA-D-17-00092_Revision_1.pdfClick here for additional data file.GIGA-D-17-00092_Revision_2.pdfClick here for additional data file.GIGA-D-17-00092_Revision_3.pdfClick here for additional data file.Response_to_Reviewer_Comments_Original_Submission.pdfClick here for additional data file.Response_to_Reviewer_Comments_Revision_1.pdfClick here for additional data file.Response_to_Reviewer_Comments_Revision_2.pdfClick here for additional data file.Reviewer_1_Report_ -- Kristoffer Sahlin12 May 2017 ReviewedClick here for additional data file.Reviewer_1_Report_(Revision_1) -- Kristoffer Sahlin22 Aug 2017 ReviewedClick here for additional data file.Reviewer_1_Report_(Revision_2) -- Kristoffer Sahlin06 Dec 2017 ReviewedClick here for additional data file.Reviewer_2_Report_ -- Mohammed-Amin Madoui15 May 2017 ReviewedClick here for additional data file.Reviewer_2_Report_(Revision_1) -- Mohammed-Amin Madoui24 Aug 2017 ReviewedClick here for additional data file.Additional FilesClick here for additional data file."}
+{"text": "Nitrosomonas cryotolerans ATCC 49181 is a cold-tolerant marine ammonia-oxidizing bacterium isolated from seawater collected in the Gulf of Alaska. The high-quality complete genome contains a 2.87-Mbp chromosome and a 56.6-kbp plasmid. Chemolithoautotrophic modules encoding ammonia oxidation and CO2 fixation were identified. Nitrosomonas cryotolerans ATCC 49181 was isolated from surface seawater at 59\u00b047\u203267\u2033N, 151\u00b055\u203208\u2033W in 1980 and described in 1988  Joint Genome Institute (JGI) using Pacific Biosciences (PacBio) technology . A PacBiGenes were identified using Prodigal , using tN.\u00a0cryotolerans contains one ribosomal operon (SAMN02743940_1059 to SAMN02743940_1062). Metabolic modules encoding chemolithotrophic ammonia catabolism include three clusters each of ammonia-monooxygenase and hydroxylamine-oxidoreductase genes. The amo gene clusters have unique arrangements of neighboring genes, including the amoCABEDcopCD cluster (SAMN02743940_0955 to SAMN02743940_0961), amoCABED cluster (SAMN02743940_1533 to SAMN02743940_1537), and amoCAB cluster (SAMN02743940_2438 to SAMN02743940_2436). The genome contains 2 standalone amoC genes (SAMN02743940_2312 and 2350). Three haoABcycAB clusters are located at SAMN02743940_0346 to SAMN02743940_0349, SAMN02743940_0686 to SAMN02743940_0689, and SAMN02743940_1793 to SAMN02743940_1796. The N.\u00a0cryotolerans genome contains a single gene cluster encoding the Calvin-Benson-Bassham cycle for carbon assimilation  with highest nucleic acid sequence identity to homologous genes in Nitrosomonas ureae Nm10 (Nitrosomonas sp. AL212 (Nitrosomonas cryotolerans contains a cluster encoding urease (SAMN02743940_1408 to SAMN02743940_1413) and a urea transporter (SAMN02743940_1414). We identified an nirK gene at SAMN02743940_0821, putatively involved in nitrite processing, and a nitric oxide reductase norCBQD cluster at SAMN02743940_1672 to SAMN02743940_1675 suggestive of nitrifier denitrification. The nitrosocyanin protein, unique to ammonia-oxidizing bacteria (AOB), was encoded by SAMN02743940_2670. The evolutionary relationships in the Nitrosomonadaceae are currently under reconsideration.The genome of eae Nm10  and NitrFSRO01000001 to FSRO01000002.This complete genome sequence is deposited in ENA under accession numbers"}
+{"text": "In this study, we characterized the functional homolog of Hsp104 from Schizosaccharomyces pombe (Sp_Hsp104). As its S. cerevisiae counterpart, +Sp_hsp104 is heat-inducible and required for thermotolerance in S. pombe. Sp_Hsp104 displays low disaggregase activity and cannot propagate the [+PSI] prion in S. cerevisiae. When overexpressed in S. cerevisiae, Sp_Hsp104 confers thermotolerance to \u0394hsp104 cells and reactivates heat-aggregated proteins. However, overexpression of Sp_Hsp104 does not propagate nor eliminate [+PSI]. Strikingly, [+PSI] was cured by overexpression of a chimeric chaperone bearing the C-terminal domain (CTD) of the S. cerevisiae Hsp104 protein. Our study demonstrates that the ability to untangle aggregated proteins is conserved between the S. pombe and S. cerevisiae Hsp104 homologs, and points to a role of the CTD in the propagation of the S. cerevisiae [+PSI] prion.The molecular chaperone Hsp104 is a crucial factor in the acquisition of thermotolerance in yeast. Under stress conditions, the disaggregase activity of Hsp104 facilitates the reactivation of misfolded proteins. Hsp104 is also involved in the propagation of fungal prions. For instance, the well-characterized [ ATPase associated with various activities) involved in the acquisition of thermotolerance in yeasts Saccharomyces cerevisiae Hsp104 protein contains two ATPase domains that are both involved in disaggregation. The first nucleotide-binding domain (NBD1) is crucial for substrate binding, while the second NBD2 domain is involved in the processing of protein aggregates and in the oligomerization of Hsp104 In vitro experiments demonstrated that Hsp104 binds and untangles aggregated proteins, releasing them in a folding-competent state Yeasts have the ability to survive a broad spectrum of stress conditions. When exposed to mild stresses, cells trigger an adaptive response to enhance their protection. This adaptation endows the cells with the ability to survive more severe stresses. In the case of heat shock, this adaptive response is called thermotolerance. Hsp104 is an AAA+ protein  showing a high level of sequence identity with the S. cerevisiae Hsp104 protein. Our results demonstrate that Sp_Hsp104 is a heat-inducible disaggregase and a crucial factor in the acquisition of thermotolerance in fission yeast. Heterologous expression of Sp_Hsp104 in S. cerevisiae confirmed that this protein is a functional homolog of Hsp104 for thermotolerance. However, unlike the budding yeast Hsp104, Sp_Hsp104 did not support the propagation of the [+PSI] prion under basal expression levels. Furthermore, overexpression of Sp_Hsp104 did not cure [+PSI]. Remarkably, a chimeric Sp_Hsp104 bearing the CTD of S. cerevisiae Hsp104 gained the ability to cure the [+PSI] prion. These observations suggest that the CTD region of Hsp104 plays an important role in the propagation of prions.Whereas protein folding has been extensively studied in budding yeast, less is known about the function of molecular chaperones in S. pombe homolog of Hsp104, we performed a protein-protein BLAST search (http://www.ncbi.nlm.nih.gov/BLAST/) against the S. pombe proteome. A single S. pombe ORF, SPBC16D10.08c S. cerevisiae Hsp104 protein , in this article the hsp104+ gene will be referred to as +Sp_hsp104, and the synthesized protein Sp_Hsp104. Accordingly, proteins from S. pombe and S. cerevisiae will be referred to as Sp_Hsp104 and Sc_Hsp104, respectively.To identify a putative  protein . This prS. pombe and S. cerevisiae . All the critical residues required for the ATPase activity of those domains are perfectly conserved between these two proteins Like all members of the ClpB/Hsp100 family, Sp_Hsp104 contains two putative nucleotide-binding domains (NBD1 and NBD2) that are well conserved between S. pombe genome-wide microarray study that the SPBC16D10.08c gene (+Sp_hsp104) is overexpressed 54 fold by a mild heat shock of 15 minutes at 39\u00b0C +Sp_hsp104 is greatly enhanced by environmental stresses, similarly to Sc_HSP104Sc_Hsp104 is a major factor involved in the acquisition of thermotolerance in the budding yeast Sp_hsp104 strain (SP12422); thus confirming the specificity of the immunodetection . In contrast, most WT and \u0394Sp_hsp104 cells did not survive a severe heat shock of 20 minutes at 50\u00b0C. When WT cells were pre-treated at 37\u00b0C for 1 hour before being submitted to a severe heat shock, they acquired thermotolerance and survived . However, after a pre-treatment of 1 hour at 37\u00b0C, cells expressing either the fission yeast or the budding yeast Hsp104 survived as well as the untreated cells  complemented by an episomal copy of Sc_HSP104+PSI], suggesting that Sp_Hsp104 does not have a dominant negative effect on the endogenous protein (not shown). Next, we shuffled the Sc_HSP104-encoding plasmid with 5-FOA. Loss of Sc_Hsp104 was confirmed by auxotrophy on selective media and Western blotting using monoclonal antibodies specifically recognizing Sc_Hsp104 (not shown). After streaking on YPD\u00bc medium, the control strain expressing Sc_Hsp104 efficiently propagated the [+PSI] prion, as observed by the white color of the colonies. In contrast, all the cells bearing the +Sp_hsp104-encoding plasmid were undistinguishable from the [\u2212psi] strain  were expressed by Western blotting . Since one of the 24 prions reported in S. cerevisiae is independent of Sc_Hsp104 for replication Whereas Sp_Hsp104 is unable to propagate the [PSIS. cerevisiae but unable to propagate [+PSI]. Hence, the fission yeast Hsp104 could be used in further studies to discriminate the molecular requirements of prion propagation from those responsible of disaggregation of heat-aggregated proteins.In conclusion, our research demonstrates that Sp_Hsp104 is the first wild-type yeast AAA+ protein able to complement thermotolerance in S. pombe strains were grown at 30\u00b0C in Edinburgh minimal medium (MM) supplemented with the required nutrients S. cerevisiae strains, standard growth media were used, and cells were routinely cultured at 30\u00b0C as previously described +PSI]-mediated suppression of the ade1-14 marker was routinely assessed by the color of colonies formed on YPD\u00bc medium (YEPD with 2.5 g/L of yeast extract rather than 10 g/L) and confirmed on SD-adenine defined medium supplemented with 2.5% (vol/vol) YEPD. The GAL1 promoter was induced by incubating cells on SDGal  after preliminary growth in SDLG (YEPD containing 3% of lactate and 3% of glycerol instead of glucose) to eliminate all glucose from the liquid medium Yeast strains used in this study are listed in S. cerevisiae Hsp104 (YLL026W) amino acid sequence, the S. pombe genome database was searched with the BLASTP alignment program _ Hsp104 protein sequence. The deletion of +Sp_hsp104 was carried out by the method described in Krawchuk and Wahls +Sp_hsp104 was amplified with its flanking regions from fission yeast genomic DNA using the primers Sp_Hsp104_FL_FW and Sp_Hsp104_FL_REV and cloned into the pCR-XL-TOPO vector. The coding region of Sp_hsp104+ was extracted by a AgeI/PacI digestion and replaced with the neomycin resistance gene. The +Sp_hsp104 knockout cassette was extracted from pCR-XL-TOPO by XhoI digestion and transformed into strain SP3220. Southern blot analyses were performed to confirm the correct and unique insertion of the cassette in the S. pombe genome using standard methods Using the S. cerevisiae HSP104 gene are from the Susan Lindquist lab. Plasmids expressing the S. pombe hsp104+ gene were created by in vivo recombination in S. cerevisiae. First, the +Sp_hsp104 gene was amplified using the Sp_Hsp104_REC_FW and Sp_Hsp104_REC_REV primers to add 50 bp corresponding to the flanking regions of the pYSGAL-Sc_HSP104 plasmid on either side of the coding sequence. The BglII-linearized pYSGAL-Sc_HSP104 vector and the PCR amplification of +Sp_hsp104 were then transformed into W303a competent S. cerevisiae cells with the specifications described in Knop et al. (1999) URA3, the pYSGAL-+Sp_hsp104 plasmid was extracted using the lyticase extraction protocol of Ling et al. (1995) GAL1 promoter of the pYSGAL-+Sp_hsp104 plasmid was replaced by 640 pb of the genomic 5\u2032UTR of S. cerevisiae HSP104. The 5\u2032UTR was amplified using the Sc_HSP104_UTR_FW and Sc_HSP104_UTR_REV primers. The +Sp_hsp104 gene under the control of the endogenous S. cerevisiae promoter was then extracted by NotI digestion and cloned into pRS315 and pRS316. The chimeric genes were constructed using the same recombination approach using PCR amplification of the CTDs of each gene. The CTD from S. pombe Hsp104 was amplified using the Sp_Hsp104_CTD_FW and Sp_Hsp104_CTD_REV primers, while the CTD from S. cerevisiae Hsp104 was amplified using the Sc_Hsp104_CTD_FW and Sc_Hsp104_CTD_REV primers. All PCR amplifications were performed with the Phusion\u2122 High-Fidelity DNA Polymerase . All plasmid constructions were verified by standard sequencing methods . In addition, all plasmids were tested for protein expression by immunoblotting with appropriate antibodies. DNA transformations into S. pombe and S. cerevisiae cells were performed by the polyethylenglycol (PEG)-lithium acetate procedure Plasmids used in this study are described in S. cerevisiae, we used the commercially available polyclonal rabbit antibodies directed against the last residues of Sc_Hsp104 (Stressgen) or the monoclonal antibodies described in Cashikar et al. (2002) et al. (2004) Sp_hsp104 strain, as described in Sambrook et al. (1989) For the specific detection of Hsp104 from 595 of 0.5, serially diluted (10\u22121 to 10\u22124), spotted on solid media and grown for 5 days at 30\u00b0C. Heat shock was performed on exponentially growing cells adjusted to an OD595 of 0.5. Cells were pre-treated or not at 37\u00b0C for 1 hour, and then incubated with slight agitation at 50\u00b0C for 20 minutes and cooled on ice for 5 minutes. Cells were mixed by vortexing, serially diluted and subsequently spotted on the corresponding solid media.Exponentially growing cells were adjusted to an ODet al. (2006) luxAB(HIS) (AddGene #1106), which expresses a temperature-sensitive Vibrio harveyi luciferase 595 of approximately 0.5. The luciferase activity was determined before treatment as a control. The culture was then transferred to 46\u00b0C, and after 30 minutes of incubation at this temperature, cycloheximide was added to a final concentration of 10 \u00b5g/mL. The culture was then incubated for further 15 minutes, after which the cell culture was transferred back to 25\u00b0C to allow the cells to recover. Cell samples were taken immediately to determine the level of luciferase activity and then collected every 30 to 45 minutes for up to 4 hours. The luciferase activity was determined by using 200 \u00b5L of cells plus 5 \u00b5L decylaldehyde (Sigma), and the resulting luminescence was immediately quantified using a Lumat LB 9507 luminometer (EG&G Berthold). Three independent samples were taken per time point.The luciferase reactivation assay was essentially performed as described in Zenthon Figure S1Expression and overexpression of Hsp104 homologs and chimeras(A) Expression of Sc_Hsp104 and Sp_Hsp104 was verified by immunoblotting. Protein extracts from S. cerevisiae \u0394hsp104 strains bearing an empty vector or expressing Sc_HSP104 or +Sp_hsp104 under the control of the endogenous Sc_HSP104 promoter were separated by SDS-PAGE and immunoblotted using monoclonal anti-Hsp104 antibodies (left panel) or polyclonal antibodies raised against the full-length protein (right panel). The monoclonal antibodies specifically recognized the Sc_Hsp104 protein, while the polyclonal antibodies from Tkach and Gover (2004) were the only ones able to detect Sp_Hsp104, when concentrated at a dilution of 1\u22365000. Immunoblotting of Pgk1p (phosphoglycerate kinase) is shown as a loading control. (B) Overexpression of Sc_Hsp104 and Sp_Hsp104 was verified by immunoblotting. Protein extracts from S. cerevisiae \u0394hsp104 strains bearing an empty vector or overexpressing Sc_HSP104 or +Sp_hsp104 under the control of the GAL1 promoter were separated by SDS-PAGE and immunoblotted using monoclonal anti-Hsp104 antibodies (left panel) or polyclonal antibodies raised against the full-length protein (right panel). Immunoblotting of Pgk1p is shown as a loading control. (C) Overexpression of Hsp104 chimeras was verified by immunoblotting. Protein extracts from S. cerevisiae \u0394hsp104 strains bearing an empty vector or overexpressing either Sc_HSP104, +Sp_hsp104, Sc_HSP104/CTDSp or Sp_hsp104/CTDSc under the control of the GAL1 promoter were separated by SDS-PAGE and immunoblotted using polyclonal antibodies directed against the CTD of Sc_Hsp104  or monoclonal anti-Hsp104 antibodies (middle panel). Immunoblotting of Pgk1p is shown as a loading control (lower panel).(0.59 MB TIF)Click here for additional data file.Figure S2Overexpression of Sp_Hsp104 cannot sustain [PSI+] propagation A [+PSI] \u0394Sc_hsp104 strain complemented by a plasmidic Sc_HSP104 gene (YJW532) was transformed with an empty vector or with a plasmid overexpressing +Sp_hsp104 under the control of the GAL1 promoter. After shuffling of the Sc_HSP104-encoding plasmid, cells were streaked on YPG1/4 to test the maintenance of [+PSI]. Control strains show the expected white color of [+PSI] cells (\u03a8-74-D694) and the red color of [\u2212psi] cells (74-D694)(3.84 MB TIF)Click here for additional data file."}
+{"text": "Direct comparisons using two standard data sets show that HMM_RA consistently outperforms HMMTOP and TMHMM in topology prediction. Specifically, on a high-quality data set of 83 proteins, HMM_RA outperforms HMMTOP by up to 7.6% in topology accuracy and 6.4% in \u03b1-helices location accuracy. On the same data set, HMM_RA outperforms TMHMM by up to 6.4% in topology accuracy and 2.9% in location accuracy. Comparison also shows that HMM_RA achieves comparable performance as Phobius, a recently published method.\u03b1-helical transmembrane (TM) proteins play important and diverse functional roles in cells. The ability to predict the topology of these proteins is important for identifying functional sites and inferring function of membrane proteins. This paper presents a Hidden Markov Model (referred to as HMM_RA) that can predict the topology of \u03b1-helical transmembrane proteins with improved performance. HMM_RA adopts the same structure as the HMMTOP method, which has five modules: inside loop, inside helix tail, membrane helix, outside helix tail and outside loop. Each module consists of one or multiple states. HMM_RA allows using reduced alphabets to encode protein sequences. Thus, each state of HMM_RA is associated with  About 20%\u201330% of all genome sequences encode integral membrane proteins . \u03b1-helicTMHP  that can predict the topology of \u03b1-helical TM proteins with improved performance. HMM_RA adopts the same structure as HMMTOP and allows the use of reduced alphabets to represent amino acids. Each state of HMM_RA is associated with a probability distribution over http://www.cbs.dtu.dk/~krogh/TMHMM/) (set_160) contains ~160 proteins, among which 108 are multiple-spanning membrane proteins and 52 are single-spanning. The second data set (referred to as set_83) is a subset of set_160. It contains 83 proteins (38 multi-spanning and 45 single-spanning) whose topologies have been experimentally determined.Two well-annotated sets of \u03b1-helical TM proteins were obtained from the TMHMM website (/TMHMM/) . The firhttp://www.cbs.dtu.dk/krogh/TMHMM/). Briefly, the data set was divided into ten even subsets. The sequence identity between any two proteins from different subsets is less than 25%. Methods were trained using nine subsets and tested using the remaining subset. This procedure was repeated ten times with each subset being used as test set once.In There are 20 naturally occurred amino acids. It is well known that some amino acids share similar physicochemical features. Many studies  have clun emission probabilities, where n is the size of the reduced alphabet used.We modified the HMMTOP method  and deveHMMTOP can make prediction in either single sequence mode or multiple sequence mode. In single mode, the topology of a protein is predicted using only the primary sequence of the protein as input. In multiple sequence mode, the topology of a protein is predicted using its sequence and its homologous sequences as input. HMM_RA can also run in single sequence mode and multiple sequence mode. When multiple sequence mode was chosen, the predictions were carried out as described in One issue in the evaluation of topology prediction is the minimal overlap required between the predicted and observed helices. A minimal overlap of 3 residues has been used in most of the previous studies . Moller Topology Accuracy = NT/N, where NT is the number of proteins whose topology is correctly predicted and N is total number of proteins.Location accuracy = NL/N, where NL is the number of proteins whose TM helices are all correctly localized and N is defined as above.Set_160 is used to evaluate HMM_RA using multiple sequence mode. First, we encode protein sequences using the various sets of reduced alphabets developed by Set_83 is a subset of Set_160. The topology of proteins in Set_83 have been experimentally confirmed. We evaluate HMM_RA using set_83, and compare the results with those obtained using set_160. The results  show thahttp://www.enzim.hu/hmmtop/. HMM_RA and HMMTOP are evaluated on set_160 and set_83 using ten-fold cross-validations as described in Materials and Methods. The cross-validations are carried out using the same data partition as in Many methods have been developed to predict the topology of \u03b1-helical membrane proteins. TMHMM  and HMMTResults from previous sections show that HMM_RA can achieve one of the best results in both set_83 and set_160 when reduced alphabet Li_8 is used. Thus, in the comparisons, Li_8 is used to encode protein sequences for HMM_RA. First, we use set_83 to compare the performance of the three methods because set_83 is a high-quality data set.The results  show thaTMHMM only works in single sequence mode. When single sequence mode is used for HMM_RA, HMM_RA outperforms TMHMM by 5.4% in topology accuracy and 0.7% in location accuracy. When multiple sequence mode is used for HMM_ RA, the improvement is increased to 6.4% in topology accuracy and 2.9% in location accuracy.In additional to set_83, set_160 is also used to evaluate and compare the three methods. The results  show thaset_160. Therefore, the accuracy of Phobius may have been overestimated. On the other hand, HMM_RA is evaluated using a stringent ten-fold cross-validation. Remarkably, HMM_RA still achieves the same accuracy as Phobius.We also compare HMM_RA with a recently published method Phobius . Set_160In summary, we present a method, HMM_RA, that can predict the topology of \u03b1-helical TM proteins with improved performance. Direct comparison shows that HMM_RA can outperform HMMTOP by up to 7.6% in topology accuracy and 6.4% in \u03b1-helices location accuracy and outperform TMHMM by up to 6.4% in topology accuracy and 2.9% in location accuracy.Using reduced alphabets to encode amino acids can reduce the complexity of protein sequence. In this study, using reduced alphabets has the additional benefit of reducing the number of parameters (emission probabilities) in the models. Different amino acids can perform a similar function because they have similar physiochemical properties or they are close in the evolution. Clustering the amino acids based on these properties can produce reduced alphabets without losing information for function or structure identification. Using reduced alphabets to represent amino acids help to identify the features essential for the function. In this study, as the alphabet size decreases from 20, the performance of HMM_RA first increases, reaching a maximal value, and then decreases. Ongoing research in our group analyzes the characteristics of the reduced alphabet on which the best performance is achieved to search for physical-chemical properties that are indicative of TM locations and topology.TMMTOP_RA work in either single sequence mode or multiple sequence mode. On both data sets used in this study, TMMTOP_RA achieves better performance when multiple sequence mode is used as input. Another factor that affects the performance is data quality. On high-quality data set, TMMTOP_RA can achieve better performance."}
+{"text": "Throughout the paper it was written that the test E_OUV gives a measure of how GC content varies within genomes. This is not accurate, since the formula (E_OUV) is concerned with all nucleotides . A more fitting description is \"Variation of base composition within genomes.\" Thus, the equation:YE_OUV=exp(-10.7-16.7XGC+14.3X2GC),actually describes how base composition, measured as varying 4-tuples of mononucleotide frequencies, varies within genomes."}
+{"text": "The CoIII cation has site symmetry m, and is coordinated by four oxygen atoms from four bridging pivalate groups, one central O anion and a methanol oxygen atom, forming a distorted octa\u00adhedral geometry. The coordinated methanol mol\u00adecule is located on a crystallographic special position, the C and O atoms being located on the mirror plane. The central O anion lies in the crystallographic \u03bc               3-O bridge, linking three equivalent CoIII cations and generating the oxo-centered trinuclear CoIII complex. The chloride anion, which acts as the counter-ion, is located on crystallographic The crystal structure of the title compound, [Co DOI: 10.1107/S1600536809041907/xu2614Isup2.hkl            Structure factors: contains datablocks I. DOI:  crystallographic information; 3D view; checkCIF report            Additional supplementary materials:"}
+{"text": "Until now the only known biological process involved in H2 metabolism in marine environments is nitrogen fixation.Surface waters of aquatic environments have been shown to both evolve and consume hydrogen and the ocean is estimated to be the principal natural source. In some marine habitats, H2 uptake hydrogenase being the most widespread. A clear bias of hydrogenases to environments with terrestrial influence was found. This is exemplified by the cyanobacterial bidirectional NAD(P)-linked hydrogenase that was found in freshwater and coastal areas but not in the open ocean.We analyzed marine and freshwater environments for the presence and distribution of genes of all known hydrogenases, the enzymes involved in biological hydrogen turnover. The total genomes and the available marine metagenome datasets were searched for hydrogenase sequences. Furthermore, we isolated DNA from samples from the North Atlantic, Mediterranean Sea, North Sea, Baltic Sea, and two fresh water lakes and amplified and sequenced part of the gene encoding the bidirectional NAD(P)-linked hydrogenase. In 21% of all marine heterotrophic bacterial genomes from surface waters, one or several hydrogenase genes were found, with the membrane-bound H2-evolving hydrogenases might be useful as marker for bacteria living inside of marine snow particles.This study shows that hydrogenases are surprisingly abundant in marine environments. Due to its ecological distribution the primary function of the bidirectional NAD(P)-linked hydrogenase seems to be fermentative hydrogen evolution. Moreover, our data suggests that marine surface waters could be an interesting source of oxygen-resistant uptake hydrogenases. The respective genes occur in coastal as well as open ocean habitats and we presume that they are used as additional energy scavenging devices in otherwise nutrient limited environments. The membrane-bound H The composition of earth's atmosphere is the result of a number of concurring processes and a matter of continuous change. Especially the amount of trace gases governs important aspects of the gas cover of our planet, such as its retention capacity of heat or the amount of ozone present. After methane, hydrogen is the second most abundant trace gas in the atmosphere, making up around 0.5 ppm to 0.6 ppm Approximately 90% of hydrogen evolution is due to photochemical oxidation of hydrocarbons such as methane in the atmosphere, the combustion of fossil fuels and biomass burning. Natural evolution results from volcanic activity, the nitrogen fixation process in legumes and an uncharacterized source in the oceans. The latter comprises the majority with around 6% \u22121 escapes to the environment Trichodesmium thiebautii (former Oscillatoria thiebautii), which is one of the major oceanic N-fixing strains, questioned whether its hydrogen evolution is actually sufficient to explain the concentrations found Marine hydrogen uptake has been attributed to particulate fractions of 0.2 \u00b5m to 5 \u00b5m in size Recently it was shown that photochemical production of hydrogen from chromogenic dissolved organic matter can contribute, at least in part, to hydrogen production in fresh water lakes as well as coastal seawater In the microbial world hydrogen is a valuable energy source that is exchanged efficiently between different prokaryotes and anaerobic eukaryotes. Some produce hydrogen while fermenting whereas others capture it to drive anaerobic or aerobic respiration and make use of its energy. A wealth of different enzymes called hydrogenases have been found in microorganisms that are able to split or form hydrogen 2-oxidizing bacteria that are able to oxidize hydrogen at ambient oxygen concentrations Hydrogenases are classified according to their metal content into the Fe-, FeFe-, and NiFe-varieties. Fe-hydrogenases are confined to the methanogenic archaea and FeFe-hydrogenases occur in bacteria and anaerobic eukaryotes. NiFe-hydrogenases are separated into 4 different groups and are widespread in archaea and bacteria E. coli belongs to the group 1 H2-uptake hydrogenases and was originally described as H2-oxidizing enzyme 2-evolving hydrogenase. In many cases these enzymes seem to be used under fermentative conditions to generate a proton gradient To this end we analyzed the distribution of hydrogenases in cyanobacteria since they are one of the largest prokaryotic groups that occur in aquatic surface waters. The search was then expanded to the complete genomes of bacteria isolated from marine surface waters  since it can produce or take up hydrogen, depending on the physiological conditions and the other is an uptake hydrogenase (group 2a) that is linked to the nitrogen fixation process and seems to be confined to diazotrophic strains http://www.ncbi.nlm.nih.gov/) and cyanobase (http://bacteria.kazusa.or.jp/cyanobase/) for all available cyanobacterial sequences revealed the presence of the bidirectional NAD(P)-linked hydrogenase , this gene is either part of the hyp-gene cluster or in close proximity to the hox-genes, suggesting that the birdirectional hydrogenase is used to dispose of electrons during fermentation via a PFOR-like enzyme , e enzyme .Synechcococcus strains isolated from a hot spring and Cyanothece sp. PCC 7425 harbor the nitrogenase genes but no uptake hydrogenase. This confirms the previous finding of a marine nitrogen-fixing Synechococcus strain without an uptake hydrogenase The occurrence of the uptake hydrogenase  in cyanobacteria does not correlate with a specific habitat but with the diazotrophy of the respective strains, as indicated by the presence of the nitrogenase genes (e.g. NifD). Of the Cyanothece sp. PCC 7425 is the only strain containing the genes of the bifunctional NAD(P) linked hydrogenase (group 3b)(group 3b) but exprRepresentatives of each of the hydrogenase classes were used to search the completely sequenced prokaryotic genomes in the genebank . Of the Shewanella strains (ANA-3 and MR-4) have all the genes necessary for the expression of a FeFe-hydrogenase. Since this type of hydrogenase is extremely sensitive against and irreversibly inactivated by oxygen The genomes of two 2-uptake hydrogenase (group 1), two genomes with a cyanobacterial-type uptake hydrogenase (group 2a), six genomes with a sensor hydrogenase (group 2b), seven genomes with a bifunctional hydrogenase (group 3b), four genomes with a bidirectional NAD(P)-linked hydrogenase (group 3d), and three genomes with a membrane-bound H2-evolving hydrogenase (group 4) similar to hydrogenase 3 of E. coli.Concerning the NiFe-hydrogenases, there are 24 genomes with a membrane-bound HRoseovarius group contain large gene clusters with the membrane-bound hydrogenase in conjunction with a sensor hydrogenase and the whole complement of the two-component system , which accounted for 4 sequences all of the other 44 were exclusively from Punta Cormorant. This confirms the presence of hoxH in shallow coastal environments and ponds in a variety of different bacterial groups.These findings are also corroborated when looking at the rogenase , whereasThe largest group of sequences in the metagenome database were those of the membrane-bound NiFe-hydrogenases. Again most of the 51 sequences were found at Punta Cormorant, although 11 sequences were detected in the datasets of coastal stations  and two were found in the open ocean (outside Seychelles and 250 miles of Panama) .Cyanobacterial-like uptake hydrogenases could also be found in the metagenomic dataset . Because+-linked hydrogenases, 37 of the membrane bound H2 uptake hydrogenases and 18 of the cyanobacterial-like uptake hydrogenases. In all these cases the numbers are close to the expected number when comparing the gene sizes of the respective large and small hydrogenase genes Sequences of the oxygen sensitive FeFe-hydrogenases retrieved from the GOS database were from a Mangrove (Isabella Island) and the hypersaline pond at Punta Cormorant. In all other samples no FeFe-hydrogenase was found  and none2-uptake hydrogenases. One transcript was most similar to a cyanobacterial uptake hydrogenase, one to the Flavobacteriaceae and one to the Bradyrhizobiaceae. In this dataset only samples from the open ocean are available.Recently large amounts of metatranscriptomics data became available e.g. . A searcChloroflexaceae and some proteobacteria. In cyanobacteria this enzyme is known as the bidirectional hydrogenase. It is closely related to the soluble hydrogenase of Ralstonia eutropha and the respiratory complex I Although all NiFe-hydrogenases share two characteristic motifs with altogether four cysteins at the N- and C-terminus for the binding of the NiFe active site, it is impossible to design degenerated primers that bind to the genes of all different classes of these enzymes. Therefore, we limited our effort to a single class and constructed degenerated primers specific for the bidirectional NAD(P)-linked hydrogenases of cyanobacteria, the hoxH. In We collected surface water from Stollergrundrinne outside the Kielfjord , in the Norderpiep west of B\u00fcsum (North Sea) and two freshwater lakes in northern Germany, Westensee and Selenter See. These samples were sequentially filtered on 10 \u00b5m and 0.2 \u00b5m filters and DNA isolated from the retained material. In samples from all these locations we could detect hoxH that are most similar to the Chroococcales  or the filamentous, heterocystous Nostocaceae. In the North Sea the \u03b1\u2013proteobacterial group Rhodobacteraceae made up the same proportion as all the cyanobacterial sequences taken together. From the freshwater mesotrophic lakes Westensee and Selenter See we could only amplify cyanobacterial hoxH  and in each case some sequences of methylotrophic bacteria and Dictyoglomaceae.From the Baltic Sea as well as the fresh water lakes we could amplify a large number of cyanobacterial In contrast to this, all attempts to amplify sequences of the bidirectional NAD(P)-linked hydrogenases from the samples taken in the North Atlantic off the west African coast and the Ionian Sea (Mediterranean Sea) were negative. This corroborates that the open ocean and marine oligotrophic waters are devoid of this hydrogenase type.Any conclusion concerning the activity of a gene from its environmental distribution is hampered by the fact that it is not necessarily expressed in a specific environment. Genomes might have genes in store that are not necessary to survive under the present-day conditions, but can be used to invade other niches or to prepare the organism for a drastic change. In the case of the distribution of hydrogenases found in this work, this scenario seems highly unlikely. For several reasons described in detail below, we think that biological hydrogen production and consumption, as depicted in nifJ, in the same cyanobacteria  linked hydrogenase. Neither the cyanobacterial genomes nor all of the heterotrophic bacteria  was isolated from the Sargasso Sea, but all of the others were from coastal areas. In these regions, this \u03b1-proteobacterial subclass makes up as much as 24% of the bacterioplankton The membrane-bound hydrogenase gene clusters found in the 2, hydrogen uptake could add to the ability to survive in a variety of these habitats. Similar suggestions have already been made for hydrogen uptake for long-term survival of bacteria Mycobacteria, known to colonize aquatic ecosystems, take up hydrogen in the same concentration range under aerobic conditions Vibrionaceae  linked hydrogenase), or the membrane-bound HThis study intends to deliver a first key to the elucidation of the underlying biological processes of hydrogen turnover in aquatic ecosystems. Whether a specific body of water is a hydrogen sink or source will depend on a number of factors such as primary production, nitrogen fixation, the concentration of photodegradable organic compounds and organic particles, and the availability of electron acceptors. This is the first evidence that microorganisms can be an integral part of hydrogen turnover in marine waters, but much more remains to be learned. This is especially true when considering oxygen minimum zones Samples were collected from the surface. In the North Sea water was collected in the Norderpiep (54\u00b013\u2032N/8\u00b027\u2032E), in the Baltic Sea it was collected in the Stollergundrinne (54\u00b029\u2032N/10\u00b013\u2032E) and from the freshwater lakes Selenter See (54\u00b018\u203225N/10\u00b028\u203253E) and Westensee (54\u00b017\u203253N/9\u00b057\u203209E) at least four times a year from every season. These samples were sequentially filtered on 10 \u00b5m and 0.2 \u00b5m filters with a peristaltic pump 620 S (Watson-Marlow Bredel).Samples from the Mediterranean Sea were taken from the Ionian Sea at station 2 (36\u00b041\u2032N/21\u00b039\u2032E), station 3 (36\u00b050\u2032N/21\u00b031\u2032E), station 5.2 (36\u00b037\u2032N/21\u00b017\u2032E) and station 6 (36\u00b042\u2032N/21\u00b004\u2032E). In this case 5 l water from a depth of 5 m was filtered on 5 \u00b5m and then on 0.2 \u00b5m.The samples from the North Atlantic were taken during the Poseidon 284 cruise at 18\u00b0N/30\u00b0W, 25\u00b0N/30\u00b0W and 29\u00b0/30\u00b0W in April 2002.For DNA isolation the UltraClean\u2122 Soil DNA Kit  was used.2, 0.025 U/\u00b5l Taq polymerase  and 10x buffer as recommended by the manufacturer in a total volume of 50 \u00b5l. Of each sample different amounts of DNA between 2 and 100 ng were tested as template. If no PCR product was detected DNA concentrations were increased at least 10 times. Positive controls were run in parallel to prove the efficiency of the PCR. The approximate size of the product is around 1190 bp and covers close to 84% of the hoxH gene.Sequences of the bidirectonal NAD(P)-linked hydrogenase were amplified with the primers HoxH-f GTATYTGYGGYATTTGTCCTGT and HoxH-r GGCATTTGTCCTRCTGYATGTGT were used. Prior to 40 cycles of the program the DNA was denatured for 5 min at 95\u00b0C. The temperature program was as follows: 30 sec at 95\u00b0C, 40 sec at 50\u00b0C, 2 min at 72\u00b0C. In a final step the temperature was kept at 72\u00b0C for 10 min. The reaction contained 0.5 \u00b5M of the two primers, 0.2 mM of dNTPs, 2.5 mM MgClThe resulting PCR products were ligated into the pCRII-topo (Invitrogen), sequenced with the Big-Dye Kit, and applied on a 96 capillary sequencer .hoxH sequences of Aphanothece halophytica and Mastigocladus laminosus SAG 4.84 (Accession numbers GQ454444 and GQ454445).If possible contigs were assembled from the obtained sequence data and the respective sequences deposited in the genebank (Accession numbers GQ454414 to GQ454443 and GU238237 to GU238258) including two additional cyanobacterial The genebank, cyanobase, and the GOS database were searched for hydrogenase specific sequences by using the hydrogenase sequences given in In the case of critical candidates or unclear phylogenetic affiliation phylogenetic trees were used. Sequence alignments were made with ClustalW Table S1Complete list of all marine bacteria searched for hydrogenase genes(0.05 MB XLS)Click here for additional data file.Figure S1Rhodobacteraceae and the delta-proteobacterium Neptuniibacter caesariensis. The structural genes of the hydrogenase  are shown in blue. Red genes (hoxAJBC) are involved in the regulation of the hydrogenase. HoxJ encodes a histidine kinase that is known to interact with a hydrogen sensor encoded by hoxB and hoxC and regulates the activity of the response regulator encoded by hoxA. HupK might encode a protein necessary to express an oxygen-tolerant hydrogenase. Accessory genes known to be necessary for this type of membrane hydrogenase are shown in grey, whereas grey patterned genes are general accessory genes for all NiFe-hydrogenases. Genes depicted in green are putative proteases that cleave the C-terminus of the hydrogenase. HypX of Ralstonia eutropha is known to render its soluble hydrogenase oxygen tolerant.Structure of the gene cluster of the membrane bound hydrogen uptake NiFe-hydrogenase of marine (0.06 MB DOC)Click here for additional data file.Figure S2Vibrioanceae isolated from marine environments that are most similar to the energy converting H2-evolving NiFe-hydrogenases. The color code is the same as in Structure of three hydrogenase gene clusters of (0.05 MB DOC)Click here for additional data file.Figure S3Phylogenetic tree of HypX. Representatives of enoyl-CoA hydratase/crotonase have been used as outgroup. The abbreviations and the respective accession numbers are as follows: Aaeoli, Aquifex aeolicus VF5 NP_213788; Aehrli, Alkalilimnicola ehrlichei MLHE-1 YP_742845; Amarin, Acaryochloris marina MBIC11017 YP_001520946; BjapUSDA, Bradyrhizobium japonicum USDA 110 NP_773566; Cviola, Chromobacterium violaceum ATCC 12472 NP_903812; Daroma, Dechloromonas aromatica RCB YP_287160; Frankia Cc Frankia sp. CcI3 YP_482743; Frankia EA Frankia sp. EAN1pec YP_001505433; MmagAMB, Magnetospirillum magneticum AMB-1 YP_420998; MmagMS-1, Magnetospirillum magnetotacticum MS-1 ZP_00055441; Mmarina Microscilla marina ATCC 23134 ZP_01691397; Mpetro, Methylibium petroleiphilum PM1 YP_001021998; Ncaesar, Neptuniibacter caesariensis ZP_01166042; Nitrati, Nitratiruptor sp. SB155-2 YP_001356445; Pedobac Pedobacter sp. BAL39 ZP_01883353; Pnapht, Polaromonas naphthalenivorans CJ2 YP_982187, PfluPF-5, Pseudomonas fluorescens Pf-5 YP_260772; Pfluore, Pseudomonas fluorescens PfO-1 YP_348856; Reutro, Ralstonia eutropha H16 NP_942660; Rferri, Rhodoferax ferrireducens T118 YP_525330; Rmetalli, Ralstonia metallidurans CH34 YP_583693; Saverm, Streptomyces avermitilis MA-4680 NP_828541; Savermi Streptomyces avermitilis MA-4680 NP_823962; Scoelic Streptomyces coelicolor A3(2) NP_629596; Sdegra, Saccharophagus degradans 2-40 YP_526001; Smalto Stenotrophomonas maltophilia R551-3 YP_002027502; Ssedimi, Shewanella sediminis HAW-EB3 YP_001475080; Sulfuro, Sulfurovum sp. NBC37-1 YP_001358952; Xcamp Xanthomonas campestris pv. vesicatoria str. 85-10 YP_363011(0.50 MB DOC)Click here for additional data file.Figure S4Phylogenetic tree of HupL sequences. Representatives of the 49 kDa subunit of the complex I have been used as outgroup. The used abbreviations and their respective accession numbers are as follows: Abac345 Candidatus Koribacter versatilis Ellin345 YP_593314; Abut4018 Arcobacter butzleri RM4018 YP_001490358; Afer53993 Acidithiobacillus ferrooxidans ATCC 53993 YP_002219307; Ahyd7966 Aeromonas hydrophila subsp. hydrophila ATCC 7966 YP_857036; AmacDE Alteromonas macleodii \u2018Deep ecotype\u2019 YP_002124659; Aple4074 Actinobacillus pleuropneumoniae serovar 1 str. 4074 ZP_00134404; AsalA449 Aeromonas salmonicida subsp. salmonicida A449 YP_001141617; Asiam Anabaena siamensis TISTR 8012 AAN65266; Avar Anabaena variabilis ATCC 29413 YP_325087; Bac Ellin bacterium Ellin514 ZP_03626632; BBTAi1-2 Bradyrhizobium sp. BTAi1 YP_001220511; BBTAi1-3 Bradyrhizobium sp. BTAi1 YP_001236652; Bjap110 Bradyrhizobium japonicum USDA 110 NP_773581; Bphy815 Burkholderia phymatum STM815 YP_001863308; C.fer13031 Chlorobium ferrooxidans DSM 13031 ZP_01386726; C511412 Cyanothece sp. ATCC 51142 YP_001802481; C7424 Cyanothece sp. PCC 7424 YP_002377118; C7822 Cyanothece sp. PCC 7822 ZP_03153783; C8802 Cyanothece sp. PCC 8802 ZP_03142797; Cagg Chloroflexus aggregans DSM 9485 YP_002461848; Caur10-fl Chloroflexus aurantiacus J-10-fl YP_001636362; CCY0110 Cyanothece sp. CCY 0110 ZP_01728928; Chyd Carboxydothermus hydrogenoformans Z-2901 YP_360377; Cjej1221 Campylobacter jejuni RM1221 YP_179388; Ckos895 Citrobacter koseri ATCC BAA-895 YP_001455880; Clim245 Chlorobium limicola DSM 245 YP_001942914; CmedTB-2 Caminibacter mediatlanticus TB-2 ZP_01871651; Cpha Chlorobium phaeobacteroides DSM 266 YP_911445; CtepTLS Chlorobium tepidum TLS NP_661672; Cwat8501 Crocosphaera watsonii WH 8501 ZP_00519188; Dbac Desulfomicrobium baculatum 1CC1_L; DBAV1 Dehalococcoides sp. BAV1 YP_001213724; Deth Dehalococcoides ethenogenes 195 YP_180861; DvulDP4 Desulfovibrio vulgaris DP4 YP_966691; Ecar1043 Pectobacterium atrosepticum SCRI1043 YP_049334; EcolK12 Escherichia coli str. K-12 substr. MG1655 NP_415492; EcolNuoD Escherichia coli CAA48363; FACN14a Frankia alni ACN14a YP_712616; FACN14a-2 Frankia alni ACN14a YP_712064; Fbac Flavobacteria bacterium MS024-2A ZP_03702421; FCci3 Frankia sp. CcI3 YP_481046; FEAN Frankia sp. EAN1pec YP_001506830; FEAN2 Frankia sp. EAN1pec YP_001507712; Gaur Gemmatimonas aurantiaca T-27 YP_002759924; Gloeo Gloeothece sp. PCC 6909 AAP04005; GlovSZ Geobacter lovleyi SZ YP_001952291; GlovSZ-2 Geobacter lovleyi SZ YP_001950403; HpylJ99 Helicobacter pylori J99 NP_223293; L8106 Lyngbya sp. PCC 8106 ZP_01619041; Laes Lyngbya aestuarii ABD34838; Lint Lawsonia intracellularis PHE/MN1-00 YP_594816; Lmaj Lyngbya majuscula CCAP 1446/4 AAO66476; Mavi Mycobacterium avium 104 YP_881873; MJLS Mycobacterium sp. JLS YP_00107040; Mkan Mycobacterium kansasii ATCC 12478 ZP_04750138; Mmag-1-3 Magnetospirillum magneticum AMB-1 YP_421305; MmagMS-1 Magnetospirillum magnetotacticum MS-1 ZP_00052632; Mmar Mycobacterium marinum M YP_001850173; MMCS Mycobacterium sp. MCS YP_639307; Msil Methylocella silvestris BL2 YP_002364007; Msme Mycobacterium smegmatis str. MC2 155 YP_887053; N7120 Nostoc sp. PCC 7120 NP_484720; N7422 Nostoc sp. PCC 7422 BAE46791; Nazo \u2018Nostoc azollae\u2019 0708 ZP_03768004; Neptuni2 Neptuniibacter caesariensis ZP_01167270; Neptuni 1 Neptuniibacter caesariensis ZP_01166595; Npun Nostoc punctiforme PCC 73102 AAC16277; Nspu Nodularia spumigena CCY 9414 ZP_01628406; Paes Prosthecochloris aestuarii DSM 271 YP_002015547; Pars Pyrobaculum arsenaticum DSM 13514 YP_001153513; Pdis8503 Parabacteroides distasonis ATCC 8503 YP_001303173; Photob34 Photobacterium sp. SKA34 ZP_01160131; Pisl Pyrobaculum islandicum DSM 4184 YP_929722; Plut Pelodictyon luteolum DSM 273 YP_375349; PMED4NdH Prochlorococcus marinus subsp. pastoris str. CCMP1986 NP_892293; Ppha Pelodictyon phaeoclathratiforme BU-1 YP_002018704; Rcap Rhodobacter capsulatus AAA69668; Rcas Roseiflexus castenholzii DSM 13941 YP_001433219; Rcas2 Roseiflexus castenholzii DSM 13941 YP_001433862; ReryPR4 Rhodococcus erythropolis PR4 YP_002766098; RerySK121 Rhodococcus erythropolis SK121 ZP_04384689; Reut Ralstonia eutropha H16 NP_942704; ReutC Ralstonia eutropha H16 NP_942663; ReutG Ralstonia eutropha H16 AAA16462; Rgel Methylibium petroleiphilum PM1 YP_001022015; RHTCC2501 Robiginitalea biformata HTCC2501 ZP_01119574; Rhtcc2601 Roseovarius sp. HTCC2601 ZP_01443057; RHTCC2601-Sens Roseovarius sp. HTCC2601 ZP_01443054; Rjos Rhodococcus jostii RHA1 YP_704548; Ropa Rhodococcus opacus B4 YP_002781742; Rpal009 Rhodopseudomonas palustris CGA009 NP_946314; RpalA53 Rhodopseudomonas palustris BisA53 YP_780164; RpalB5 Rhodopseudomonas palustris BisB5 YP_568300; RRS-1 Roseiflexus sp. RS-1 YP_001276649; Rrub Rhodospirillum rubrum ATCC 11170 YP_426250; Rsph17029 Rhodobacter sphaeroides ATCC 17029 YP_001044019; Rsph2.4.1 Rhodobacter sphaeroides 2.4.1 YP_353568; Rtm1035 Roseovarius sp. TM1035 ZP_01881109; Sag12614 Stappia aggregata IAM 12614 ZP_01550392; Sag12614-2 Stappia aggregata IAM 12614 ZP_01550270; Sala2256 Sphingopyxis alaskensis RB2256 YP_611130; Sama Shewanella amazonensis SB2B YP_927554; Save Streptomyces avermitilis MA-4680 NP_828543; SbalOS155 Shewanella baltica OS155 YP_001050263; Sdys197 Shigella dysenteriae Sd197 YP_402612; SentATCC Salmonella enterica subsp. enterica serovar Paratyphi A str. ATCC 9150 YP_152163; SentCT18 Salmonella enterica subsp. enterica serovar Typhi str. CT18 NP_456296; SfumMPOB Syntrophobacter fumaroxidans MPOB YP_847061; Slin Spirosoma linguale DSM 74 ZP_04492490; SoneMR-1 Shewanella oneidensis MR-1 NP_717701; SoneMR-4 Shewanella sp. MR-4 YP_733952; SoneMR-7 Shewanella sp. MR-7 YP_738201; Sros Streptosporangium roseum DSM 43021 ZP_04474993; Sste37 Sagittula stellata E-37 ZP_01748533; Ssvi Streptomyces sviceus ATCC 29083 YP_002204206; Susi Solibacter usitatus Ellin6076 YP_827763; Svir Saccharomonospora viridis DSM 43017 ZP_04507584; TcarNor1 Thermosinus carboxydivorans Nor1 ZP_01667576; Tden25259 Thiobacillus denitrificans ATCC 25259 YP_315133; Tden33889 Sulfurimonas denitrificans DSM 1251 YP_393947; Tery Trichodesmium erythraeum IMS101 YP_722943; TM1035-Sens Roseovarius sp. TM1035 ZP_01881113; Tros 5159 Thermomicrobium roseum DSM 5159 YP_002523076; Tros2 Thiocapsa roseopersicina AAA27410; Tros Thiocapsa roseopersicina AAC38282; Ucyn-A Cyanothece sp. CCY 0110 ZP_01728928; VangS14 Vibrio angustum S14 ZP_01234606; Wsuc1740 Wolinella succinogenes DSM 1740 NP_907813; Yent8081 Yersinia enterocolitica subsp. enterocolitica 8081 YP_001007767. The sequence of the marine unicellular group A cyanobacteria has been generated from the available short reads (0.67 MB DOC)Click here for additional data file.Figure S5Distribution of small subunits of the bidirectional NAD(P)+ linked hydrogenase found in the GOS database of the different prokaryotic groups. The small subunit gene, hoxY, of Synechocystis has been used for the search. All genes have been retrieved form Punta Comorant, a hypersaline pond on the Galapagos Islands.(0.06 MB DOC)Click here for additional data file.Figure S6Distribution of small subunits of the membrane bound H2 uptake hydrogenasses found in the GOS database of the different prokaryotic groups. The hupS sequence of Desulfovibrio vulgaris was used for the search. On the right the number of sequences from the different sampling stations is shown.(0.08 MB DOC)Click here for additional data file.Figure S7Distribution of small subunits of the cyanobacterial-like uptake hydrogenase found in the GOS database of the different prokaryotic groups. The small subunit gene, hupS, of Nostoc sp. PCC 7120 has been used for the search.(0.05 MB DOC)Click here for additional data file.Figure S8Phylogenetic tree of HoxH sequences. Representatives of the 49 kDa subunit of the complex I have been used as outgroup. The used abbreviations and their respective accession numbers are as follows: Afla Acetomicrobium flavidum CAA56464; Ahalo Aphanothce halophytica GQ454444; Amar Acaryochloris marina MBIC11017 YP_001521996; Amax Arthrospira maxima FACHBSM AAQ63961; Apla1 Arthrospira platensis FACHB341 AAQ63964; Apla2 Arthrospira platensis FACHBOUQDS6 AAQ63959; Apla3 Arthrospira platensis FACHB439 AAQ63960; Apla4 Arthrospira platensis FACHB791 AAQ91344; Avar Anabaena variabilis ATCC 29413 YP_325153; Bxen Burkholderia xenovorans LB400 YP_555781; Cagg Chloroflexus aggregans DSM 9485 YP_002463784; CaggL Chlorobium chlorochromatii CaD3 YP_378564; Caur Chloroflexus aurantiacus J-10-fl YP_001634807; CCY0110 Cyanothece sp. CCY 0110 ZP_01727423; ClimL Chlorobium limicola DSM 245 YP_001944104; Cnec Ralstonia eutropha H16 NP_942730; CphaL Chlorobium phaeobacteroides DSM 266 YP_912598;CtepL Chlorobium tepidum TLS NP_662771; Daro Dechloromonas aromatica RCB YP_284208; DethV Dehalococcoides ethenogenes 195 YP_181357; Dpsy Desulfotalea psychrophila LSv54 YP_065948; DpsyV Desulfotalea psychrophila LSv54 YP_064749;Ecol Escherichia coli CAA48363; Galp Gloeocapsa alpicola str. CALU 743 AAO85440; Gmet1 Geobacter metallireducens GS-15 YP_384078; Gmet2 Geobacter metallireducens GS-15 YP_386258; GOS1 and GOS2 are the two consenus sequeces retrieved from the GOS database; Gsul1 Geobacter sulfurreducens PCA NP_953465; Gsul2 Geobacter sulfurreducens PCA NP_953763; Lyng Lyngbya majuscula CCAP 1446/4 AAT07678; Magneto Magnetococcus sp. MC-1 YP_864809; Mastigo Mastigocladus laminosus SAG 4.84 GQ454445; Mcap Methylococcus capsulatus str. Bath YP_112653; MferV Methanothermus fervidus Q49179; MjanV Methanocaldococcus jannaschii DSM 2661 NP_248187; Mkan Methanopyrus kandleri AV19 NP_613553; Mmag Magnetospirillum magnetotacticum MS-1 ZP_00053777; MmarV Methanococcus maripaludis S2 NP_987943;MvolV1 Methanococcus voltae Q00404; MvolV2 Methanococcus voltae Q00407;N7120 Nostoc sp. PCC 7120 NP_484809; N7422 Nostoc sp. PCC 7422 BAE46796; Neptuni Oceanospirillum sp. MED92 ZP_01164927; Nitrococcus Nitrococcus mobilis Nb-231 ZP_01126922; Nspu Nodularia spumigena CCY 9414 ZP_01629499; Nspu Nodularia spumigena CCY 9414 ZP_01629499; PaesL Prosthecochloris aestuarii DSM 271 YP_002016588; PfurL1 Pyrococcus furiosus DSM 3638 NP_578623; PfurL2 Pyrococcus furiosus DSM 3638 NP_579061; Phol Prochlorothrix hollandica AAB53705;Plancto Planctomyces maris DSM 8797 ZP_01852867; PMED4 Prochlorococcus marinus subsp. pastoris str. CCMP1986 NP_892293; PphaL Pelodictyon phaeoclathratiforme BU-1 YP_002019299; Rcas Roseiflexus castenholzii DSM 13941 YP_001431482; Rmet Ralstonia metallidurans CH34 YP_583677; Ropa Rhodococcus opacus AAB57892; RRS-1 Roseiflexus sp. RS-1 YP_001277847; S6301 Synechococcus elongatus PCC 6301 YP_172265; S6803 Synechocystis sp. PCC 6803 NP_441259; S6803 Synechocystis sp. PCC 6803 NP_441411;S7002 Synechococcus sp. PCC 7002 YP_001733469; S7942 Synechococcus elongatus PCC 7942 YP_401572; Spla Arthrospira platensis FACHB440 AAQ63963; Ssub Spirulina subsalsa FACHB351 AAQ63962; Susi Solibacter usitatus Ellin6076 YP_826256;Tros Thiocapsa roseopersicina AAP50523; WH5701 Synechococcus sp. WH 5701 ZP_01085930.(0.13 MB DOC)Click here for additional data file."}
+{"text": "Pseudoautosomal regions (PAR1 and PAR2) in eutherians retain homologous regions between the X and Y chromosomes that play a critical role in the obligatory X-Y crossover during male meiosis. Genes that reside in the PAR1 are exceptional in that they are rich in repetitive sequences and undergo a very high rate of recombination. Remarkably, murine PAR1 homologs have translocated to various autosomes, reflecting the complex recombination history during the evolution of the mammalian X chromosome.We now report that the SNF2-type chromatin remodeling protein ATRX controls the expression of eutherian ancestral PAR1 genes that have translocated to autosomes in the mouse. In addition, we have identified two potentially novel mouse PAR1 orthologs.We propose that the ancestral PAR1 genes share a common epigenetic environment that allows ATRX to control their expression. The sex chromosomes in modern placental mammals (eutherians) are highly dimorphic but initially evolved from a homologous pair of autosomes . Over miCsf2ra) is located on mouse chromosome 19 , [GenBank:BI202412], [GenBank:BE457721], [GenBank:AK007409] and [GenBank:BI320076] respectively). To further investigate these probe sets, their NCBI nucleotide sequences were used for a Basic Local Alignment Search Tool nucleotide (BLASTn) search of the nr database. The expressed sequence tag (EST) [GenBank:W45978] has similarity to Mus musculus Dhrsxy . The EST [GenBank:BI202412] displayed similarity to several unidentified mouse cDNA clones. Interestingly, a BLAST-like Alignment Tool (BLAT) search of this clone showed similarity to intron 1 of mouse Dhrsxy and it could represent an unknown splice variant of Dhrsxy. The EST [GenBank:BE457721] is annotated as similar to human Arse and a BLASTn search revealed high similarity to Rattus norvegicus Arse . BLASTn of [GenBank:AK007409] showed high similarity to Asmtl in cow  as well as dog, human, the putative rat Asmtl, and numerous other species. The EST [GenBank:BI320076] displayed no significant hits to any sequences by either BLASTn or BLAT. Interestingly, while Dhrsxy, Arse and Asmtl do not display an obvious connection, they do share a common link in that they are all pseudoautosomal genes in eutherians. In addition, the microarray data showed decreased expression of Cd99, Shox2 and Csf2ra, that also lie within the pseudoautosomal region in most eutherians. Therefore, while GO analysis identified a subset of downregulated genes involved in brain development at both timepoints, a more in depth analysis of downregulated targets revealed that many are orthologs of PAR1 genes residing on the tip of the X and Y chromosomes in most placental mammals. Overall, our transcriptional screen identified six of these genes, constituting approximately half of all PAR1 orthologs discovered in the mouse genome so far. The more intriguing aspect of this finding is that in the mouse, these genes no longer reside within the PAR1 region but have translocated to autosomes   Table . These rDhrsxy, Cd99, Csf2ra, Shox2 and also the putative new orthologs of Asmtl and Arse in Atrx-null and control E13.5 and P0.5 forebrain (n = 3 at each time point). Since Arse and Asmtl have not yet been identified in the mouse, we sequenced the PCR products to ensure they corresponded to the transcripts identified on the microarray, and not to other contaminating sequences. The qRT-PCR results confirmed that five of the six genes exhibit decreased expression in the Atrx-null forebrain at E13.5, and that these genes remain downregulated at P0.5  analysis of 5 Figure . In addi5 Figure . One exce Figure . TherefoOur discovery that the expression of several ancestral PAR1 genes is controlled by ATRX throughout the early developmental period of the mouse brain reveals an unexpected association between the levels of ATRX protein and the expression of these ancestral PAR1 genes.ARS genes are located approximately 115 kilobases centromeric to the PAR1 region on the X chromosome, but still possess the ability to escape XCI in females  suggested that it is a fragment of the full length ARSE protein, aligning in the middle of the approximately 600 amino acid ARSE protein of multiple other species , indicates that we have likely identified the mouse homologue of a previously unidentified mouse Ars gene rather then a gene fragment from a known mouse family member.Comparisons to available mouse BE457721], we assessed the outcome of ATRX depletion on Arsd/e expression by RNA interference in the Neuro-2a cultured neuroblastoma cell line. Small interfering RNAs (siRNAs) were used to transiently deplete ATRX, as was done previously [Atrx expression levels using primers that simultaneously amplify both the full length isoform and the reported truncated isoform [Atrx transcript levels were depleted by approximately 5 fold  domain. A multiple sequence alignment of amino acid sequences was used to further determine the identity of [GenBank:AK007409]  with the bacterial maf gene [AK007409] contains a MAF domain, it lacks the ASMT domain. However, this is similar to the putative rat ASMTL (Accession [GenBank:NP_001099385]) which also lacks the ASMT domain. The putative mouse ASMTL is 54% identical to rat, and 51% identical to the human protein., ARSD [GenBank:NM_001669] and ASMTL [GenBank:NM_004192], respectively, which is similar to what was reported for other PAR1 genes. For example, DHRSXY exhibits 59% protein identity between humans and mice *100, where a desirable slope is -3.32 and r2 > 0.99. Samples were normalized to \u03b2-actin expression and relative gene expression levels were calculated using GeneX software .Total RNA was isolated using the RNeasy Mini kit (QIAGEN). First-strand cDNA was synthesized from 3 \u03bcg of total RNA using the SuperScript\u2122 II Reverse Transcriptase kit (Invitrogen) with 25 mM dNTPs , 1 \u03bcL porcine RNAguard  and 3 \u03bcL random primers . PCR reactions were performed on a Chromo4 Continuous Fluorescence Detector in the presence of iQ\u2122 SYBR Green Supermix and recorded using the Opticon Monitor 3 software . Samples were amplified as follows: 95\u00b0C for 10 seconds, annealed for 20 seconds, 72\u00b0C for 30 seconds  according to the manufacturer's instructions and sequenced at the DNA Sequencing Facility at Robarts Research Institute .For  and used for BLASTn searches . For calculation of interspecies similarity, sequences were obtained from NCBI RefSeq  or Ensemble  where RefSeq sequences were not available, and pairwise comparisons made using Jalview , cow [GenBank:ABS45001], dog [GenBank:NP_001041587], horse [GenBank:XP_001495573], macaque [GenBank:Q60HH5], human [GenBank:CAA58556], platypus [GenBank:XP_001514429], opossum [GenBank:XP_001362844], pufferfish [GenBank:CAG09268], rat [GenBank:CAI84983]. ARSD: dog [GenBank:XP_548838], horse [GenBank:XP_001495553], human [GenBank:CAA58555], macaque [GenBank:XP_001092405], opossum, [GenBank:XP_001362931], platypus [GenBank:XP_001507106], chicken [GenBank:XP_416855], zebrafish [GenBank:XP_700386]. Mouse Arsd/e translated from [GenBank:BE457721].fee 5.56  using defee 5.56  and shadfee 5.56 . Mouse AClick here for fileComparisons of Ars family members. The transcript identified as a putative mouse Arse ortholog is more similar to rat Arse then to any other Ars family members. Pairwise percent identities were calculated using Jalview [Arse [GenBank:BE45772], Arsa [GenBank:NM_009713], Arsb [GenBank:NM_009712.3], Arsc/Sts [GenBank:NM_009293.1], Arsg [GenBank:NM_028710.2], Arsi [GenBank:NM_001038499.1], Arsj [GenBank:NM_173451.2], Arsk [GenBank:NM_029847.4], rat Arse [GenBank:NM_001047885.1], rat Arsc/Sts [GenBank:NM_012661.1]. Jalview . AccessiClick here for fileAmino acid alignment of the N terminal of ASMTL between multiple species. Sequences were aligned using T-Coffee 5.56 [XP_001133965], orangutan [GenBank:CAH90398], chimpanzee [XP_001137696], cow [GenBank:AAI03000], dog [GenBank:XP_851655], frog [GenBank:NP_001085814], chicken [GenBank:XP_001231914], zebrafish [GenBank:NP_998676], platypus [GenBank:XP_001506357], mouse [GenBank:NP_081215].fee 5.56  using defee 5.56  and shadfee 5.56 . The putClick here for fileConditions for quantitative real-time PCR. Primer sequences and annealing temperatures used for quantitative real-time PCR confirmation of downregulated ancestral PAR genes.Click here for file"}
+{"text": "In the asymmetric unit, there are one dysprosium ion, one and a half malonate ligands, and three water mol\u00adecules. Each DyIII atom is coordinated by six O atoms from four malonate ligands and by three water mol\u00adecules, and displays a tricapped trigonal\u2013prismatic coordination geometry. The malonate ligands adopt two types of coordination mode, linking dysprosium centres to form a three-dimensional coordination polymer. The extensive network of hydrogen bonds in this polymer enhances the structural stability.The title compound, [Dy DOI: 10.1107/S1600536808015961/dn2344Isup2.hkl            Structure factors: contains datablocks I. DOI:  crystallographic information; 3D view; checkCIF report            Additional supplementary materials:"}
+{"text": "Fusarium oxysporum.An insertional mutagenesis screen identifies pathogenicity-related genes in the plant fungal pathogen  Fusarium oxysporum f. sp. lycopersici is the causal agent of vascular wilt disease in tomato. In order to gain more insight into the molecular processes in F. oxysporum necessary for pathogenesis and to uncover the genes involved, we used Agrobacterium-mediated insertional mutagenesis to generate 10,290 transformants and screened the transformants for loss or reduction of pathogenicity.This led to the identification of 106 pathogenicity mutants. Southern analysis revealed that the average T-DNA insertion is 1.4 and that 66% of the mutants carry a single T-DNA. Using TAIL-PCR, chromosomal T-DNA flanking regions were isolated and 111 potential pathogenicity genes were identified.F. oxysporum. Several known pathogenicity genes were identified, such as those encoding chitin synthase V, developmental regulator FlbA and phosphomannose isomerase. In addition, complementation and gene knock-out experiments confirmed that a glycosylphosphatidylinositol-anchored protein, thought to be involved in cell wall integrity, a transcriptional regulator, a protein with unknown function and peroxisome biogenesis are required for full pathogenicity of F. oxysporum.Functional categorization of the potential pathogenicity genes indicates that certain cellular processes, such as amino acid and lipid metabolism, cell wall remodeling, protein translocation and protein degradation, seem to be important for full pathogenicity of  Fusarium oxysporum, a soil-borne facultative pathogen with a worldwide distribution, causes vascular wilt and foot-, root-, and bulbrot diseases in a wide variety of economically important crops , an enzyme involved in the GABA-shunt and found to be up-regulated in F. graminearum when grown on hop cell wall dATP. Hybridization was done overnight at 65\u00b0C in 0.5 M sodium phosphate buffer, pH 7.2, containing 7% SDS and 1 mM EDTA. Blots were washed with 0.2 \u00d7 SSC, 0.1% SDS. Hybridization signals were visualized by phosphorimaging .For Southern analysis, 10 \u03bcg genomic DNA of each transformant was digested with 20 U k et al. . The proPZP201BK  was usedPZP201BK  (AdditioCDA: Czapek-Dox agar; GFP: green fluorescent protein; GPI: glycosylphosphatidylinositol; LB: left border; ORF: open reading frame; PDA: potato dextrose agar; RB: right border; TAIL-PCR: thermal asymmetric interlaced PCR; T-DNA: transfer DNA.MR and CM designed the study; CM, RvW and LR carried out the experiments and performed data processing; CM interpreted the data and wrote the manuscript; BC provided guidance and review.pex mutants is disturbed on minimal medium and fatty acids. Additional data file FOXG_02084, FOXG_08300 or FOXG_05013.The following additional data are available with the online version of this paper. Additional data file FOXG_08602. Additional data file FOXG_03318. Additional data file FOXG_09487.Additional data file FOXG_02054. Additional data file Additional data file Pathogenicity mutants with a T-DNA insertion in an ORF.Click here for filePathogenicity mutants with a T-DNA insertion within 500 bp up- or 200 bp downstream of an ORF.Click here for filePathogenicity mutants with a T-DNA insertion within 1,000-500 bp up- or 200-1,000 bp downstream of an ORF.Click here for fileIintergenic regions are defined as 3,000-1,000 bp up- or downstream of an ORF.Click here for filepex mutants is disturbed on minimal medium and fatty acids.Growth of the Click here for fileFOXG_02084, FOXG_08300 or FOXG_05013.Method and analysis of transformants complemented with Click here for fileFOXG_08602.Method and analysis of transformants deleted for Click here for fileFOXG_03318.Method and analysis of transformants deleted for Click here for fileFOXG_09487.Method and analysis of transformants deleted for Click here for fileFOXG_02054.Method and analysis of transformants deleted for Click here for filePrimer sequences used for PCR and sequencing.Click here for fileConditions used for TAIL-PCR.Click here for file"}
+{"text": "The Ru atom has a distorted octa\u00adhedral coordination with two cis-oriented chloride ligands and four dimethyl sulfoxide ligands. Three of the sulfoxide ligands are S-bonded in a fac configuration, while the fourth is O-bonded. The title compound represents a new, and fourth, polymorph of the complex. Two other monoclinic forms and an ortho\u00adrhom\u00adbic modification have been reported previously.The title compound,  DOI: 10.1107/S160053680801996X/fj2125Isup2.hkl            Structure factors: contains datablocks I. DOI:  crystallographic information; 3D view; checkCIF report            Additional supplementary materials:"}
+{"text": "D values of the interactions calculated from SPR experiments fall in the 10\u22128 M\u201310\u22127 M range. In reporter assays Agrin-Nterm inhibited the activities of BMP2 and BMP4, half maximal inhibition being achieved at \u223c5\u00d710\u22127 M. Paradoxically, in the case of TGF\u03b21 Agrin N-term caused a slight increase in activity in reporter assays. Our finding that agrin binds members of the TGF\u03b2 family may have important implications for the role of these growth factors in the regulation of synaptogenesis as well as for the role of agrin isoforms that are unable to induce clustering of acetylcholine receptors. We suggest that binding of these TGF\u03b2 family members to agrin may have a dual function: agrin may serve as a reservoir for these growth factors and may also inhibit their growth promoting activity. Based on analysis of the evolutionary history of agrin we suggest that agrin's growth factor binding function is more ancient than its involvement in acetylcholine receptor clustering.The C-terminal 95 kDa fragment of some isoforms of vertebrate agrins is sufficient to induce clustering of acetylcholine receptors but despite two decades of intense agrin research very little is known about the function of the other isoforms and the function of the larger, N-terminal part of agrins that is common to all isoforms. Since the N-terminal part of agrins contains several follistatin-domains, a domain type that is frequently implicated in binding TGF\u03b2s, we have explored the interaction of the N-terminal part of rat agrin (Agrin-Nterm) with members of the TGF\u03b2 family using surface plasmon resonance spectroscopy and reporter assays. Here we show that agrin binds BMP2, BMP4 and TGF\u03b21 with relatively high affinity, the K The proteoglycan agrin is crucial for development and maintenance of the neuromuscular junction (NMJ) in vertebrates The N-terminal part of all forms of vertebrate agrins consist of nine follistatin-related and two laminin EGF-like modules In vertebrates, alternative splicing at a conserved site in the C-terminal part  gives riLittle is known about the function of agrin's N-terminal region. Based on homology with follistatin, we have suggested previously that this region, common to all agrin isoforms, might bind growth factors of the TGF\u03b2 family Escherichia coli JM109 bacterial strain was used for DNA propagation during DNA manipulation steps. Mature human BMP2, BMP4, TGF\u03b21 and TGF-\u03b2sRII (corresponding to the Extracellular domain of TGF-\u03b2RII) were purchased from R&D Systems . CM5 sensorchips and the reagents for protein coupling to the chips were from Biacore AB . The extracellular domain of human BMPR1A (ECD-BMPR1A) was produced as described in a separate publication .Restriction enzymes, T4 DNA Ligase and Klenow polymerase were New England Biolabs products . PCR primers were obtained from Integrated DNA Technologies . For amplification reactions we used Taq polymerase from Fermentas  or the proof-reading thermostable polymerase Accuzyme . DNA purification was performed with Nucleospin Extract PCR purification kit . The firefly luciferase kit was from Biotium . Mink lung epithelial cells stably transfected with a truncated PAI-1 promoter/firefly luciferase construct (MLEC-clone32) 5\u2032-GCAGATCTGATGTATGCAGGGGAATGTTATGTGG -3\u2032 sense and 5\u2032-GCTCTAGACTGGCAGGGACCAAGACTCTG-3\u2032 antisense primers using rat agrin cDNA (NCBI Reference Sequence: NM_175754.1).The cDNA segment encoding residues Asp65-Gln865 of rat agrin (Agrin-Nterm) was amplified with the Drosophila expression vector pMT/BiP/V5-His A digested with the same enzymes. The ligation mixture was transformed into Escherichia coli JM109 cells and the recombinants were selected on LB medium with 100 mg/ml Ampicillin. Plasmids from transformants were isolated and analysed for the presence of insert. The sequence of the cloned DNA was verified on both strands.The amplified DNA was digested with BglII and XbaI restriction endonucleases and ligated into Drosophila melanogaster S2 cells  were transfected with 4 \u00b5g pMT/BiP/V5-His A expression plasmid containing the cDNA encoding Agrin-Nterm and 16 \u00b5g pCoHygro selection vector using Cellfectin reagent  according to the protocol recommended by the manufacturer. For selection of stable transfectants, cells were suspended and cultured in Schneider's Drosophila medium  supplemented with 10% Fetal bovine serum  and 300 \u00b5g/ml hygromycin B . Stably transformed polyclonal lines were established after 5 weeks of selection with hygromycin B. Except for propagation in serum-free medium hygromycin B was always included in the media.6/ml and protein expression was induced by adding CuSO4 at 400 \u00b5mol final concentration. After 1 week of induction the culture was centrifuged, the conditioned medium was harvested and the cells were suspended in fresh induction medium to start another round of induction. Usually three rounds of induction were performed with the same cells. The medium collected from three rounds of induction was dialyzed against 25 mM Tris pH 7.5 buffer.For protein induction stable transfectants were grown in serum free medium  to a cell density of 2\u20133\u00d710Dialyzed culture fluid was applied onto a Ni affinity column (Amersham Biosciences UK). The column was washed with 10 column volumes of 20 mM Tris-HCl buffer, pH 7.9 containing 500 mM NaCl and 5 mM imidazole, then with 5 column volumes of 20 mM Tris-HCl buffer, pH 7.9 containing 500 mM NaCl and 30 mM imidazole and the bound protein was eluted with 20 mM Tris-HCl buffer, pH 7.9 containing 300 mM imidazole . The elur>90 kDa) characteristic of proteoglycans. The calculated molecular mass of recombinant Agrin-Nterm is 88,054 Da; the difference of predicted and observed molecular mass of Agrin-Nterm is due to glycosylation at multiple sites in this part of agrin The composition of protein samples was analysed by SDS\u2013PAGE using 6\u201316% linear polyacrylamide gradient slab gels under both reducing and non-reducing conditions. The gels were stained with Coomassie Brilliant Blue G-250. On SDS-PAGE recombinant Agrin-Nterm appeared as a broad, high molecular smear (Mhttp://us.expasy.org/tools/protparam.html).The concentration of recombinant protein was determined using the extinction coefficient of 45475 M\u22121 cm\u22121. The extinction coefficient was determined with ExPASy's ProtParam tool (DVCRGMLCGF (the residues in bold underline correspond to residues 65\u201374 of rat agrin).N-terminal sequencing was performed on an Applied Biosystems 471A protein sequencer with an online ABI120A PTH Amino Acid Analyser. The N-terminal sequence of Agrin-Nterm was RSSurface plasmon resonance measurements were performed on a BIAcore X instrument  essentially as described previously Recombinant human proteins  were dissolved according to the instructions of the manufacturer . The proteins were diluted in 50 mM sodium acetate buffer, pH 4.5 at a final concentration of 0.04 \u00b5g/ml (TGF\u03b21 and BMPs) and 50 \u00b5l of these solutions were injected with a 5 \u00b5l/min flow rate for 10 min on a CM5 sensor chip  activated by the amine coupling method according to the instructions of the manufacturer. ECD-BMPRIA-sensor chips were prepared in a similar way, except that the protein was dissolved in 50 mM sodium acetate, pH 4.2 . For interaction measurements, 70 \u00b5l samples containing different concentrations of the analyte were injected on the sensor-chips at a flow rate of 20 \u00b5l/min, followed by wash with buffer at a flow rate of 20 \u00b5l/min.Binding and washes were performed in 20 mM HEPES, 150 mM NaCl, 5 mM EDTA, 0.005% Tween 20, pH 7.5 buffer. Regeneration of the chip surface after each cycle was performed with injection of 40 \u00b5l 20 mM HEPES, 150 mM NaCl, 5 mM EDTA, 0.005% Tween 20, pH 7.5 buffer containing 8 M urea over the sensor chip. All experiments were repeated at least twice. Reference cells were used to obtain control sensorgrams showing non-specific binding to the surface as well as refractive index changes resulting from changes in bulk properties of the solution. Reference flow cells were prepared by executing the coupling reaction in the presence of coupling buffer alone. Reference sensorgrams were subtracted from sensorgrams obtained with immobilized ligand. To correct for differences between the reaction and reference surfaces we have also subtracted the average of sensorgrams obtained with blank running buffer injections.a \u2013 association rate constant; kd \u2013 dissociation rate constant; KD\u200a=\u200akd/ka \u2013 equilibrium dissociation constant) for each interaction were determined by globally fitting the experimental data with BIAevaluation software 4.1 and the closeness of the fits was characterized by the \u03c72 values. Only fits with \u03c72 values lower than 5% of the Rmax were accepted. Data were fitted to a model of 1\u22361 Langmuir interaction.The kinetic parameters  streptomycin (100 \u00b5g/ml) and geneticin at a concentration of 200 \u00b5g/ml (MLEC-clone32) or 700 \u00b5g/ml (HEPG2-BRA) at 37\u00b0C, 5%CO4 cells/well) or HEPG2-BRA cells (5\u00d7103 cells/well) were allowed to attach for 3 hours or 24 hours respectively, then the medium was changed to DMEM supplemented with 0.1% BSA, penicillin (100 U/ml) streptomycin (100 \u00b5g/ml) containing 16 pM TGF\u03b21, 250 pM BMP2 or 250 pM BMP4 preincubated for 30 minutes with different concentrations of Agrin-Nterm. Control experiments were performed similarly, except that no growth factor was added.TGF\u03b21 activity was measured with MLEC-clone32 cells, whereas the activities of BMP2 and BMP4 were monitored with HEPG2-BRA cells, using 96-well tissue culture dishes. In these reporter assays MLEC-clone32 cells . The protein content of the samples was determined with the Bio-Rad protein assay  and the luciferase activity was normalized to the protein content of the wells.After incubation for 17 hours at 37\u00b0C, 5%COhttp://www.ncbi.nlm.nih.gov/sites/entrez) and UniProt (http://www.uniprot.org/) websites and were used as queries in BLAST searches (http://blast.ncbi.nlm.nih.gov/Blast.cgi) of protein, nucleotide and genomic databases to identify agrin orthologs of invertebrate species. In these analyses we focused on species with completely sequenced genomes representing major groups of Metazoa: the Placozoan Trichoplax adhaerens (http://genome.jgi-psf.org/Triad1/Triad1.home.html), the Nematodes Caenorhabditis elegans and Caenorhabditis elegans, the Arthropods Drosophila melanogaster, Drosophila pseudoobscura, Apis mellifera, Tribolium castaneum), the Echinoderm Strongylocentrotus purpuratus and the Urochordate Ciona intestinalis.Protein sequences of vertebrate agrins were retrieved from the NCBI (Invertebrate proteins identified in these searches were considered to be orthologs of vertebrate agrins if they satisfied the following criteria: 1) in reciprocal searches of vertebrate sections of protein sequence databases they gave the lowest E-scores with agrins; 2) in reciprocal searches of vertebrate sections of protein sequence databases the individual constituent domains of the candidate sequences gave the lowest E-scores with agrins.http://pfam.sanger.ac.uk/) and PfamA domains were identified with the search strategy \u2018global and local (merged)\u2019 using an E-value cut-off of 1.0. Sequences that contained NtA-, follistatin-, laminin EGF-, SEA or laminin G-domains were retained for further analysis .Accordingly, appropriate invertebrate taxonomic sections of databases were first queried with vertebrate agrin sequences to identify proteins that gave the lowest E-scores then the ten top-scoring sequences were used in reciprocal searches of vertebrate sections of protein sequence databases. Sequences that gave the lowest E-scores with agrins were subjected to protein domain analyses using Pfam 23.0 , GenomeScan (http://genes.mit.edu/genomescan.html), Augustus (http://augustus.gobics.de/), Wise2 (http://www.ebi.ac.uk/Tools/Wise2/index.html) and predictions that corrected the error(s) identified by MisPred were selected.Invertebrate agrin sequences were analysed by the Mispred Procedure \u22125<E value<10\u22122) or regions (>100 residues) with no significant PfamA hits were analyzed with the consensus sequence procedure The domain architectures of invertebrate agrins were compared with those of vertebrate agrins and the validity of deviations (e.g. presence/absence of domains) was checked using the consensus sequence procedure The results of these analyses are summarized in D\u200a=\u200a5,15\u00d710\u22128 M; BMP2, KD\u200a=\u200a2,62\u00d710\u22127 M; BMP4, KD\u200a=\u200a2,57\u00d710\u22127 M, respectively  of BMP2 and BMP4 to the ECD of BMPR1A at 12 nM and 345 nM, respectively. In the case of TGF\u03b21  used; interestingly, Agrin-Nterm caused a slight increase in TGF\u03b21 activity .Trichoplax adhaerens), nematodes , some Arthropods , Echonoderms (Strongylocentrotus purpuratus) and Urochordates ; see Drosophila melanogaster and Drosophila pseudobscura.We have shown that true orthologs of vertebrate agrins are present in the genomes of Placozoa  bind and inhibit activin and GDF8/myostatin Proteins that bind growth factors may control their action in multiple ways: they may act as inhibitors if they prevent their association with cellular receptors, they may serve as a reservoir for growth factors, they may localize their action in the vicinity of the binding proteins. The interplay between these effects is determined by the affinity and concentrations of the various interacting partners. Accordingly, we suggest that binding of growth factors by vertebrate agrins may have multiple functions: agrin may serve as a reservoir of these growth factors, may localize their action and may also inhibit their growth promoting activity.Xenopus, Schwann cells were shown to promote synaptogenesis at the neuromuscular junction via TGF\u03b21 Obviously, the growth factor-binding activity of agrin has relevance for its role in development and maintenance of the neuromuscular junction only if growth factors of the TGF\u03b2 family also have a role in the control of synaptogenesis. Although relatively little is known about the role of TGF\u03b2s in synaptogenesis in vertebrates, it is noteworthy that in Drosophila, is triggered by Glass bottom boat (Gbb), an Arthropod protein related to BMPs of vertebrates. Gbb acts as a muscle-derived retrograde signal that activates the TGF\u03b2-pathway presynaptically; this pathway includes the type II receptor Wishful thinking, type I receptors Thick veins and Saxophone. Mutations that block this pathway result in small synapses that are morphologically aberrant and severely impaired functionally.In the case of some invertebrates, there is clear evidence that signaling by TGF\u03b2 family members plays a pivotal role in formation of NMJ Our results showing that the N-terminal part of agrin binds growth factors may be of particular interest in the context of the recently reported role of this region of agrin for the promotion of dendritic and axonal filopodia, which are considered as precursors of new synapses. Studies with cultured neurons and non-neuronal cells revealed that transmembrane anchored agrin promotes the formation of filopodial protrusions Our finding that the N-terminal part of agrin binds growth factors may also have important functional consequences for the cleavage of vertebrate agrins by neurotrypsin Non-neuronal tissues of vertebrates, such as muscle, heart, kidney also express agrin (isoforms inactive in AchR clustering) but very little is known about the function of agrin in these tissues. We suggest that these agrin isoforms may function as growth factor-binding proteins.Drosophila melanogaster and Drosophila pseudoobscura lack agrin genes  suggests that insect agrin is dispensable for the synaptogenetic process.Our finding that the N-terminal part of agrin binds growth factors also has important implications for the biological role of agrin in invertebrates. Despite the ancient origin of agrin practically nothing is known about its function in invertebrates. The fact that the completely sequenced genomes of C. elegans agrin is not involved in synaptogenesis The recent conclusion that Trichoplax adhaerens, a simple organism that does not have nerve and muscle cells Trichoplax adhaerens has multiple members of the TGF\u03b2 family and all essential components of the TGF\u03b2 signalling pathway are also present in the Trichoplax genome The fact that there is an agrin ortholog in the Placozoan Table S1Trichoplax adhaerens, Caenorhabditis elegans, Apis mellifera, Tribolium castaneum, Strongylocentrotus purpuratus, Ciona intestinalis that - according to the criteria described in the main text - are orthologs of vertebrate agrins.Invertebrate agrins. The file contains analyses of sequences from the invertebrate species (0.28 MB PDF)Click here for additional data file.Figure S1Trichoplax adhaerens; agrin_caeel_nta - NtA domain of the agrin of Caenorhabditis elegans; agrin_strpu_nta - NtA domain of the agrin of Strongylocentrotus purpuratus; agrin_cioin_nta - NtA domain of the agrin of Ciona intestinalis; agrin_danre_nta - NtA domain of the agrin of Danio rerio; agrin_chicken_nta - NtA domain of the agrin of Gallus gallus; agrin_mouse_nta - NtA domain of the agrin of Mus musculus; agrin_human_nta - NtA domain of the agrin of Homo sapiens.Multiple alignment of NtA-domains of agrins. The abbreviations are: agrin_triad_nta - NtA domain of the agrin of (0.02 MB PDF)Click here for additional data file.Figure S2Trichoplax adhaerens; agrin_caeel_fs1, agrin_caeel_fs2 - the first two follistatin-domains of Caenorhabditis elegans; agrin_apime_fs1, agrin_apime_fs2 - the first two follistatin-domains of the agrin of Apis mellifera; agrin_strpu_fs1, agrin_strpu_fs2 - the first two follistatin-domains of the agrin of Strongylocentrotus purpuratus; agrin_cioin_fs1, agrin_cioin_fs2 - the first two follistatin-domains of the agrin of Ciona intestinalis; agrin_chick_fs1, agrin_chick_fs2 - the first two follistatin-domains of the agrin of Gallus gallus; agrin_rat_fs1, agrin_rat_fs2 - the first two follistatin-domains of the agrin of Rattus norvegicus.Multiple alignment of Follistatin domains of agrins. The abbreviations are: agrin_triad_fs1, agrin_triad_fs2 - the two follistatin-domains of the agrin of (0.02 MB PDF)Click here for additional data file.Figure S3Trichoplax adhaerens; agrin_strpu_egf1, agrin_strpu_egf2, agrin_strpu_egf3 - EGF domains of the agrin of Strongylocentrotus purpuratus; agrin_cioin_egf1, agrin_cioin_egf2, agrin_cioin_egf3 - EGF domains of the agrin of Ciona intestinalis; agrin_rat_egf1, agrin_rat_egf2, agrin_rat_egf3, agrin_rat_egf4 - EGF domains of the agrin of Rattus norvegicus.Multiple alignment of EGF-domains of agrins. The abbreviations are: agrin_triad_egf1, agrin_triad_egf2, agrin_triad_egf3, agrin_triad_egf4 - EGF domains of the agrin of (0.01 MB PDF)Click here for additional data file.Figure S4Trichoplax adhaerens; agrin_apime_lamg1, agrin_apime_lamg2, agrin_apime_lamg3 - laminin G domains of the agrin of Apis mellifera; agrin_strpu_lamg1, agrin_strpu_lamg2, agrin_strpu_lamg3 - laminin G domains of the agrin of Strongylocentrotus purpuratus; agrin_human_lamg1, agrin_human_lamg2, agrin_human_lamg3 - laminin G domains of the agrin of Homo sapiens.Multiple alignment of laminin G domains of agrins. The abbreviations are: agrin_triad_lamg1, agrin_triad_lamg2, agrin_triad_lamg3, agrin_triad_lamg4, agrin_triad_lamg5, agrin_triad_lamg6 - Laminin G domains of the agrin of (0.04 MB PDF)Click here for additional data file.Figure S5Caenorhabditis elegans; agrin_apime_lamegf1, agrin_apime_lamegf2 - laminin EGF-domains of the agrin of Apis mellifera; agrin_strpu_lamegf1, agrin_strpu_lamegf2 - laminin EGF-domains of the agrin of Strongylocentrotus purpuratus; agrin_cioin_lamegf1, agrin_cioin_lamegf2 - laminin EGF-domains of the agrin of Ciona intestinalis; agrin_chick_lamegf1, agrin_chick_lamegf2 - laminin EGF-domains of the agrin of Gallus gallus; agrin_rat_lamegf1, agrin_rat_lamegf2- laminin EGF-domains of the agrin of Rattus norvegicus.Multiple alignment of Laminin EGF-domains of agrins. The abbreviations are: agrin_caeel_lamegf1, agrin_caeel_lamegf2 - laminin EGF-domains of the agrin of (0.01 MB PDF)Click here for additional data file.Figure S6Ciona intestinalis, agrin_disom_sea - SEA domain of the agrin of Discopyge ommata; agrin_danre_sea - agrin of the SEA domain of Danio rerio; agrin_chick_sea - SEA domain of the agrin of Gallus gallus; agrin_human_sea - SEA domain of the agrin of Homo sapiens.Multiple alignment of SEA domains of agrins. The abbreviations are: agrin_cioin_sea - SEA domain of the agrin of (0.01 MB PDF)Click here for additional data file.Figure S7Trichoplax adhaerens; agrin_caeel - the agrin of Caenorhabditis elegans; agrin_caebr - the agrin of Caenorhabditis briggsae; agrin_apime - the agrin of Apis mellifera; agrin_trica - the agrin of Tribolium castaneum; agrin_strpu - the agrin of Strongylocentrotus purpuratus; agrin_cioin - the agrin of Ciona intestinalis; agrin_disom - the agrin of Discopyge ommata; agrin_chick - the agrin of Gallus gallus; agrin_rat - the agrin of Rattus norvegicus; agrin_human - the agrin of Homo sapiens. Note that vertebrate agrins contain a conserved four-residue insert, KSRK, at the A/y splice site (positions underlined); analysis of genomic sequences revealed that this motif is missing in invertebrate agrins.Multiple alignment showing a region of the second LamG domain affected by alternative splicing in vertebrate agrins: the A/y splice site see . The abb(0.02 MB PDF)Click here for additional data file.Figure S8Trichoplax adhaerens; agrin_trica - the agrin of Tribolium castaneum; agrin_apime - the agrin of Apis mellifera; agrin_cioin - the agrin of Ciona intestinalis; agrin_disom - the agrin of Discopyge ommata; agrin_chick - the agrin of Gallus gallus; agrin_rat - the agrin of Rattus norvegicus; agrin_human - the agrin of Homo sapiens. Note that vertebrate agrins contain a conserved eight-residue insert, xLxNEIPx, at the B/z splice site (positions underlined); analysis of genomic sequences revealed that this motif is missing in invertebrate agrins. The alignment also includes the \u03b2 neurotrypsin cleavage site (arrow) of vertebrate agrins Click here for additional data file.Figure S9Discopyge ommata; agrin_danre - the agrin of Danio rerio; agrin_chick - the agrin of Gallus gallus; agrin_rat - the agrin of Rattus norvegicus; agrin_human - the agrin of Homo sapiens. Note that in vertebrate agrins the \u03b1 neurotrypsin cleavage site is conserved (positions double-underlined); analysis of genomic sequences revealed that this motif is missing in invertebrate agrins.Multiple alignment of regions affected by neurotrypsin cleavage (arrow) in vertebrate agrins: the \u03b1 site see . The abb(0.48 MB PDF)Click here for additional data file."}
+{"text": "The peptide is treated as fully flexible, while the protein backbone undergoes small fluctuations and, optionally, large-scale rearrangements. Here, we present a specific CABS-dock protocol that enhances the docking procedure using fragmentary information about protein\u2013peptide contacts. The contact information is used to narrow down the search for the binding peptide pose to the proximity of the binding site. We used information on a single-chosen and randomly chosen native protein\u2013peptide contact to validate the protocol on the peptiDB benchmark. The contact information significantly improved CABS-dock performance. The protocol has been made available as a new feature of the CABS-dock web server . Using information on individual protein\u2013peptide contacts allows to improve the accuracy of CABS-dock docking. Peptides have an enormous potential as future therapeutics . RationaProtein\u2013peptide docking methods face two major issues : samplinProtein\u2013peptide docking approaches can be divided into three categories : (i) temIn general, global docking protocols do not use any knowledge about the binding site, although it is possible to obtain significant enhancement of the quality of global docking by using additional information (even very fragmentary) about the interaction interface , 26. TheCABS-dock uses a CABS coarse-grained model as an efficient simulation engine . In a nThe CABS-dock protocol for protein\u2013peptide docking  consistsdocking simulation of a fully flexible peptide and a flexible protein receptor using the CABS model: docking simulation starts from random conformation of a peptide placed in a random position around the protein receptor structure;filtering of the models based on CABS protein\u2013peptide interaction energy values ;clustering and scoring of the final models https://bitbucket.org/lcbio/cabsdock).reconstruction of the final models to all-atom representation . Note that any model selected by a user can be reconstructed to all-atom representation using Modeller   modifying the filtering step preceding the clustering and scoring.Protein\u2013peptide contact information is introduced into the CABS-energy function as an additional, relatively weak, contact energy term , given bained SC , \\documeIn addition to the new term in the CABS energy function, the filtering step of the CABS-dock docking protocol has been modified. The structures that do not satisfy the user-provided contact criterion are filtered out from the trajectories and excluded before the clustering and scoring step of the protocol.We tested the contact information-driven CABS-dock protocol on the peptiDB benchmark set  of 103 bhttp://biocomp.chem.uw.edu.pl/CABSdock/). To submit docking tasks with contact information defines a restraint in the \u2018contact information\u2019 field using the following format:The contact information-driven docking protocol has been made available as a new feature in the CABS-dock web server , the following two lines are typed in the \u2018contact information\u2019 field:1060:C 6:PEP 5.0 1.01066:C 7:PEP 5.0 1.0If the parameters are omitted, the default values will be used. This way all the following three commands will result in the same docking settings:1060:C 6:PEP 5.0 1.01060:C 6:PEP 5.01060:C 6:PEPNote that the contact information used in the docking will be provided under the \u2018project information\u2019 tab available from the unique job page , 24.A docking job with contact information may be also submitted to CABS-dock server via command line using the RESTful service. A detailed tutorial for running CABS-dock from the command line or command line scripts, has been recently provided in the book section . The RESFor example, to introduce a restraint with a cut-off distance of 5.0\u00a0\u00c5 and restraint weight of 1.0 on residue 1060 of chain C (PDB ID of protein receptor: 1AWR:C) and residue 6 of the peptide (peptide sequence: HAGPIA), enter the following string in the command line:curl -H \"Content-Type: application/json\" -X POST -d '{\"receptor_pdb_code\":\"1AWR:C\", \"ligand_seq\":\"HAGPIA\", \"contact_information\":\"1060:C 6:PEP 5.0 1.0\"}'http://biocomp.chem.uw.edu.pl/CABSdock/REST/add_job/To introduce multiple contacts use semicolon as a line separator. For example, to use the previous restraint together with a second one, imposed on residue 1066 of chain C and residue 7 of the peptide (using the same parameters), type the following command in the command line:curl -H \"Content-Type: application/json\" -X POST -d '{\"receptor_pdb_code\":\"1AWR:C\", \"ligand_seq\":\"HAGPIA\", \"contact_information\":\"1060:C 6:PEP 5.0 1.0;1066:C 7:PEP 5.0 1.0\"}'http://biocomp.chem.uw.edu.pl/CABSdock/REST/add_job/https://bitbucket.org/lcbio/cabsdock).CABS-dock is also available as a standalone application. CABS-dock standalone combines several tools  into a software package that can be freely customized. CABS-dock standalone uses a similar definition of distance restraints as a web server version  accessible binding sites in the input protein structure: the binding site was either localized in a deep pocket (sometimes even inside the protein structure) or covered by a flexible part of the protein . The coIn this work, we demonstrated that the incorporation of the contact information into the CABS-dock protein\u2013peptide docking leads to a significant increase of prediction quality. The contact information can be deduced from experimental data  (for exaIt is important to note that the CABS-dock input of contact information can take into account various levels of accuracy (controlled by the restraint parameters in Formula 1). The restraint can pull the peptide to the vicinity of the binding site, where the generic CABS force field can take over. Therefore, even approximate data can be used in CABS-dock modeling procedures that include predictions of protein\u2013peptide complexes , 24, proThe presented protocol for CABS-dock docking with contact information can be accessed via a graphical user interface within the CABS-dock web server, command line execution using the CABS-dock RESTful web service or CABS-dock standalone application. The RESTful service and CABS-dock standalone application enables easy incorporation of the CABS-dock protocol within high-throughput modeling pipelines that integrate different tools.CABS-dock is a tool for flexible docking of peptides to proteins.In this article, we present a protocol for CABS-dock docking driven by information about protein\u2013peptide contact(s). Using information on individual protein\u2013peptide contacts allows improving the accuracy of CABS-dock docking.The protocol for protein\u2013peptide docking using CABS-dock and contact information is available within the CABS-dock web server.Brief_in_Bioinfo_Suppl_FINAL_11_bby080Click here for additional data file."}
+{"text": "In situ detoxification of lignocellulose-derived microbial inhibitory compounds is an economical strategy for the fermentation of lignocellulose-derived sugars to fuels and chemicals. In this study, we investigated homologous integration and constitutive expression of Cbei_3974 and Cbei_3904, which encode aldo-keto reductase and previously annotated short chain dehydrogenase/reductase, respectively, in Clostridium beijerinckii NCIMB 8052 (Cb), resulting in two strains: Cb_3974 and Cb_3904. Expression of Cbei_3974 led to 2-fold increase in furfural detoxification relative to Cb_3904 and Cb_wild type. Correspondingly, butanol production was up to 1.2-fold greater in furfural-challenged cultures of Cb_3974 relative to Cb_3904 and Cb_wild type. With 4-hydroxybezaldehyde and syringaldehyde supplementation, Cb_3974 showed up to 2.4-fold increase in butanol concentration when compared to Cb_3904 and Cb_wild type. Syringic and vanillic acids were considerably less deleterious to all three strains of Cb tested. Overall, Cb_3974 showed greater tolerance to furfural, 4-hydroxybezaldehyde, and syringaldehyde with improved capacity for butanol production. Hence, development of Cb_3974 represents a significant progress towards engineering solventogenic Clostridium species that are tolerant to lignocellulosic biomass hydrolysates as substrates for ABE fermentation. Clostridium species are strict anaerobes capable of converting a wide range of substrates including the major LB-derived sugars\u2014namely, glucose, xylose and arabinose\u2014to acetone, butanol and ethanol (ABE) during ABE fermentation1. LB, which is composed of cellulose, hemicellulose and lignin is recalcitrant to mild biochemical deconstruction, hence, requires a pretreatment process to render it amenable to enzymatic hydrolysis. However, while LB pretreatment releases fermentable sugars, it also generates lignocellulose-derived microbial inhibitory compounds (LDMICs) that are deleterious to fermenting microorganisms.Renewable feedstocks such as lignocellulosic biomass (LB) and organic municipal wastes are sources of cheap sugars with potential to lower the overall cost of fuels and chemicals production. For instance, large-scale bio-butanol production is currently not economically viable, in part, due to the higher cost of traditional feedstocks such as corn and sugarcane. Solventogenic 2. These inhibitors significantly affect microbial growth and metabolism by damaging membranes, inhibiting enzymes, and damaging DNA, in addition to disrupting cellular redox balance, often with concomitant decreases in cellular ATP levels5. Consequently, LB-derived inhibitors impede industrial-scale utilization of LB-derived sugars as substrates in large-scale fermentation. Considerable research efforts have pursued development of strategies and techniques for inhibitor removal prior to fermentation. These techniques include the use of chemical additives such as dithionite, dithiothreitol, sulfite and calcium hydroxide (over-liming), enzymatic treatments with laccases and peroxidases, liquid-liquid extraction with ethyl acetate or trialkyl amine, liquid-solid extraction with activated carbon or ion exchange resins for inhibitor removal15. Although effective, these techniques introduce additional detoxification steps, with the attendant increase in overall cost, which diminishes the economic competitiveness of ABE fermentation for bio-butanol production. Additionally, a considerable percentage of fermentable sugars is lost during inhibitor removal, which further affects the economics of the overall process. A cheap and economical strategy for improving large-scale microbial fermentation of LB-derived sugars to fuels and chemicals is to metabolically fortify fermenting microbes with the genetic repertoire to detoxify LB-derived inhibitors in situ during fermentation. Towards achieving this goal, our group has focused on identifying genes whose protein products are central to cellular detoxification of LB-derived inhibitors during ABE fermentation1. An extensive study of genome-wide transcriptional response of Clostridium beijerinckii NCIMB 8052 (hereafter referred to as Cb) to furfural stress during ABE fermentation revealed that, of the 721 genes that were differentially expressed, aldo/keto reductase  and short-chain dehydrogenase/reductase  were among the most strongly upregulated genes16. This, coupled with the annotated functions of both genes suggest that they likely play a critical\u00a0role in LDMICs detoxification by Cb.The LDMICs generated during LB pretreatment and hydrolysis include furfural, 5-hydroxymethyl furfural (HMF) and a collection of lignin-derived phenolic compoundsAKR and SDR genes in furfural-challenged Cb, we cloned (in Escherichia coli Rosetta-gami\u2122), overexpressed, purified and characterized the protein products of both genes1. Our results showed that the enzyme encoded by each gene (Cbei_3974 and Cbei_3904) convert furfural to the less toxic furfuryl alcohol using NADPH as cofactor1. Furthermore, both enzymes were found to be active on HMF and the phenolic compound, benzaldehyde, which is also co-generated during LB pretreatment. Based on the above findings, we hypothesized that overexpression of Cbei_3974 and Cbei_3904 in Cb would likely expedite inhibitor detoxification, hence; increase the ability of the resulting strains to tolerate higher concentrations of furanic aldehydes. Such increase in furanic aldehyde tolerance would ultimately enhance solvent production\u2014particularly, butanol\u2014during ABE fermentation in furanic aldehyde-challenged cultures. Whereas initial attempts to clone and express both genes in Cb were successful, the combined effect of antibiotic (erythromycin) as a selectable marker for maintaining the plasmid-borne inserts (Cbei_3974 and Cbei_3904) in Cb and furfural hampered phenotypic characterization of the resulting strains in furfural-challenged cultures (unpublished data). To circumvent this bottleneck, we explored genomic integration of both genes in Cb to eliminate the need for antibiotic supplementation, thereby allowing characterization of the resulting recombinant strains in furanic aldehyde- and phenolic compound-challenged cultures.AKR and SDR are NADPH-dependent oxidoreductases that participate in redox reactions that utilize aldehydes  as substrates. To establish and delineate the roles of the upregulated Cbei_3974 and Cbei_3904 were integrated into Cb genome and expressed under the control of a constitutive promoter (thiolase). Both genes were chromosomally integrated into Cb genome via double-cross homologous recombination to generate Cb_3974 (AKR) and Cb_3904 (SDR). Both strains were tested for the capacity to detoxify furfural and select lignin-derived microbial inhibitory compounds during ABE fermentation. Development of Cb_3974 and Cb_3904 represents a significant step towards fermentation of LB-derived sugars to biobutanol.Cbei_3974 (AKR) and Cbei_3904 (SDR), both of which have been shown to play a role in furfural detoxification by Cb in our previous studies16, into the genome of Cb for improved detoxification of furfural and other LDMICs generated during pretreatment and hydrolysis of lignocellulosic biomass. To achieve this goal, we used the Clostridium integrative plasmid, pMTL-JH16, which targets CA_C2872 (membrane protein) and atpB (F0/F1 ATP synthase subunit A) for replacement by homologous recombination17. Both Cbei_3974 and Cbei_3904 were placed under the control of a constitutive thiolase promoter from Cb to ensure expression of both genes from the inception of cell growth, which is critical for early and efficient detoxification of LDMICs in the culture broth. Upon successful integration of Cbei_3974 (AKR) and Cbei_3904 (SDR) in the Cb genome, both strains were characterized extensively relative to wild type Cb to test for stable expression of the integrated genes, cell growth, ABE production and detoxification of LDMICs. The growth profiles of Cb_3974 and _3904 were compared to the wild type. Interestingly, both strains (Cb_3974 and _3904) showed 1.2- and 1.3-fold increases in cell optical density, respectively, when compared to the wild type  between generation zero (G0) and generation 50 (G50), following several sub-culturing. This confirms that, (a) the protein products of additional copies of both genes do not exert a deleterious effect on the growth of Cb, and (b) both enzymes do not disrupt cellular metabolism, particularly, the ABE fermentation pathway. Fifty generations were chosen to exceed the number of generations typically achieved in industrial-scale fermentations. Similarly, ethanol and acetone production were not affected in both strains after 50 generations  and Cbei_3904 (SDR) were expressed in Cb after integration, we conducted a quantitative real-time polymerase chain reaction (qRT-PCR) using specific primers for Cbei_3974 and Cbei_3904 (Table\u00a0Cbei_3974 (AKR) and Cbei_3904 (SDR) increased 4.7- and 3-fold, respectively in Cb_3974 and _3904 relative to the wild type  and Cb_3904 (SDR)] were plated out on erythromycin-un-supplemented and erythromycin-supplemented plates from G0 to G50  in fermentation medium and optical density and the concentrations of butanol, acetone, ethanol, and ABE were measured. In addition, the rate of furfural detoxification was monitored by measuring the concentrations of furfural and furfuryl alcohol\u2014the less toxic product of furfural reduction\u2014in the fermentation broth. In each case, furfural was added to the cultures after an optical density of 2.0 had been attained (10\u201312\u2009hours fermentation). Overall, Cb_3974 showed greater capacity to detoxify furfural when compared to Cb_3904 and Cb_wild type. With 0\u2009g/L furfural, the optical densities of Cb_3974 and Cb_3904 were 1.2- and 1.3-fold greater than that of Cb_wild type  gene, Cbei_3974] to reduce furfural to the less toxic furfuryl alcohol, when compared to Cb_3904 and Cb_wild type. With 4\u2009g/L furfural, Cb_3974 exhibited a furfural detoxification rate of 2.0\u2009g/L/h, whereas the detoxification rates for Cb_3904 and Cb_wild type were ~1.1\u2009g/L/h and 1.0\u2009g/l/h, respectively  consumed relatively the same amount of glucose (~56\u2009g/L) in cultures un-supplemented with furfural (0\u2009g/L). The rate of glucose utilization in the 0\u2009g/L furfural-supplemented cultures was 0.8\u2009g/L/h for all the three strains studied. With 4\u2009g/L furfural challenge however, glucose utilization reduced considerably for all the strains studied. Notably, reduction in glucose utilization was more pronounced in cultures of Cb_3904. Following 4\u2009g/L furfural challenge, Cb_3974, _3904 and _wild type consumed ~54\u2009g/L, ~41\u2009g/L, and ~52\u2009g/L glucose, respectively, which translates to ~1.4-, and ~1.1-fold decreases in glucose utilization by Cb_3904 and _wild type, respectively . When 6\u2009g/L furfural was pulse-fed into the culture medium, Cb_3974, _3904, and _wild type consumed 25.7\u2009g/L, 14.9\u2009g/L and 17.0\u2009g/L of glucose, respectively, which represent ~2.1-, 3.7-, and 3.3-fold decreases in glucose consumption, respectively  and Cb_wild type (0.24\u2009g/L/h) did not decrease any further following 6\u2009g/L furfural challenge, when compared to 5\u2009g/L furfural-challenged cultures in which the rates of glucose utilization were 0.20\u2009g/L/h and 0.24\u2009g/L, respectively. Furfural toxicity appeared to have plateaued at 5\u2009g/L for both strains (Cb_3904 and Cb_wild type), such that when furfural concentration was increased to 6\u2009g/L, no further decrease in glucose utilization occurred.As depicted in Table\u00a0Cb_3974 produced considerably more butanol than Cb_3904 and Cb_wild type  and Cb_wild type  and Cb_wild type , when compared to Cb_3904  and Cb_wild type . Nitrogen was used as the carrier gas, and the inlet and detector temperatures were maintained at 250 and 300\u2009\u00b0C, respectively. The oven temperature was set to span from 60 to 200\u2009\u00b0C with increments of 20\u2009\u00b0C/min, and a 5-min hold at 200\u2009\u00b0C. One microliter of each sample was injected into the gas chromatography with a split ratio of 10:1. Residual glucose was analyzed using a Waters 2796 Bioseparations HPLC Module  equipped with evaporative light scattering detector  and a 9-mm Aminex HPX-87P, 300\u2009mm (length)\u2009\u00d7\u20097.8\u2009mm  column and a 30\u2009mm (length)\u2009\u00d7\u20094.6\u2009mm  Aminex deashing guard column . The analysis was performed at 65\u2009\u00b0C using HPLC-grade water as the mobile phase, operated at a flow rate of 0.6\u2009mL/min. Residual furfural and furfuryl alcohol concentrations were determined by measuring maximum absorption at 275 and 220\u2009nm, respectively, using a DU\u00ae 800 spectrophotometer. The concentrations of furfural and furfuryl alcohol were confirmed by HPLC equipped with a photodiode array detector  and a 3.5\u2009\u00b5m Xbridge C18, 150\u2009mm (length)\u2009\u00d7\u20094.6\u2009mm  column  as described previously37. ABE yield and productivity were calculated as total grams of ABE produced per total grams of glucose utilized and total concentration (g/L) of ABE divided by fermentation time (h), respectively38.Cell growth was determined by measuring optical density at 600\u2009nm (ODSupplementary Materials"}
+{"text": "Non-obstructive azoospermia (NOA) is a multifactorial disorder whose molecular basis remains largely unknown. Circular RNAs (CircRNAs), a novel class of endogenous RNAs, have been recognized to play important roles in many biological processes. However, little is known about the expression patterns and functions of circRNAs in human testes involved in NOA.In this study, the testicular circRNA expression profile were explored in NOA patients and the controls by high-throughput circRNA microarray. Real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR) was performed to confirm the microarray data. Bioinformatics analyses including the circRNA/miRNA/mRNA interaction network, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were used to predict the functions of differentially expressed circRNAs.A total of 368 differentially down-regulated and 526 up-regulated circRNAs were detected in NOA patients. These findings have been verified by qRT-PCR on 6 selected circRNAs. Among these differentially expressed circRNAs, the hsa_circRNA_0023313 was obviously up-regulated in testicular tissue of NOA patients. The most likely potential target miRNA for hsa_circRNA_0023313 include hsa-miR-520d-3p, hsa-miR-373-3p, hsa-miR-372-3p, hsa-miR-302c-3p and hsa-miR-130b-5p. Function analysis indicated that hsa_circRNA_0023313 was ubiquitin-protein transferase activity and chromatin binding. KEGG analysis revealed that the top five pathways related to hsa_circRNA_0023313 were endocytosis, meiosis, FoxO signaling pathway, ubiquitin mediated proteolysis and AMPK signaling pathway.This is the first report that the testicular circRNA expression profile is altered in NOA patients indicating circRNAs might play important roles in regulating spermatogenesis and be potential biomarkers for the diagnosis and treatment of NOA. Infertility is a worldwide reproductive health problem that affects an estimated 70 million people globally . The worCircular RNAs (CircRNAs) are a novel type of endogenous RNAs featuring stable structure and high tissue-specific expression . Unlike Therefore, the current study aimed to investigate the expression profile and functions of circRNAs in NOA patients. Bioinformatics analysis were also used to identify the circRNA/miRNA/mRNA interaction network, biological process and signal pathways. These results may provide potential targets for the development of novel diagnostic and therapeutic strategies against NOA.The protocol was fully approved by the Institutional Medical Ethics Committee of Xi\u2019an Jiaotong University. The purpose of this study was explained to all subjects, and written informed consent forms were obtained from all subjects. NOA patients were selected from couples attending the infertility clinic in reproductive center of Northwest women and children Hospital who had a history of infertility of \u226512\u2009months. Three times semen analyses were carried out after 3\u20137\u2009days of sexual abstinence. Patients with chronic diseases, hypoandrogenism, hypogonadism, history of pelvic/spinal injuries, karyotype abnormalities and microdelections of AZF region on Y chromosome were excluded. According to the World Health Organization (WHO) 2010 guidelines, all NOA patients were diagnosed by detecting three times semen samples without spermatozoa in the ejaculate including high-speed centrifugation of the entire pellet .Finally, testicular samples were obtained from 50 patients with NOA (ages 25\u201346\u2009years). An ideal normal control should consist of volunteers of known fertility, but difficulties in acquiring testicular samples makes it impractical. Therefore, 50 patients (ages 25\u201340\u2009years) with obstructive azoospermia (OA) whose testicular histopathological examination demonstrated normal spermatogenesis were used as controls. Of which, 3 NOA patients whose testicular histopathological examination showed early maturation arrest and 3 controls were further used for circRNA microarray labeling and hybridization.Total RNA was extracted from testicular biopsy tissues with TRIzol reagent according to the manufacturer\u2019s instructions . In order to reduce the inter-group difference, we mixed the three testicular tissue samples in NOA and the control group respectively for subsequent circRNA microarray labeling and hybridization. The RNA quantification and quality was examined by using the Nanodrop ND-1000 spectrophotometer. RNA integrity and gDNA contamination was tested by denaturing agarose gel electrophoresis.The sample preparation and microarray hybridization were performed based on the Arraystar\u2019s standard protocols provide by KANGCHENG Inc. . Firstly, total RNAs of 2 groups were digested with Rnase R  to remove linear RNAs and enrich circular RNAs respectively. Secondly, the enriched circular RNAs were amplified and transcribed into fluorescent cRNA utilizing a random priming method . Thirdly, the labeled cRNAs were hybridized onto the Arraystar Human circRNA Array . Finally, after having washed the slides, the arrays were scanned by the Agilent Scanner G2505C.Briefly, acquired array images were analyzed by using Agilent Feature Extraction software (version 11.0.1.1). Quantile normalization and subsequent data processing were performed using the R software package. Differentially expressed circRNAs with statistical significance between two groups were explored by Scatter Plot filtering. Differentially expressed circRNAs between samples were identified through Fold Change filtering. Hierarchical Clustering was performed to show the distinguishable circRNAs expression pattern among samples.Real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR) was performed to confirm the circRNA microarray data.\u00a06 differentially expressed circRNAs (including 3 up-regulated and 3 down-regulated) were selected for qRT-PCR experiments in 50 pairs of fresh frozen testicular tissue samples (50 from NOA and 50 from OA). Specific primers designed for circRNAs were listed in Table\u00a0-\u25b3\u25b3Ct method.Firstly, total RNA from testicular samples was prepared using MiniBEST Universal RNA Extraction Kit  according to the manufacturer\u2019s protocol. Secondly, total RNA was reverse transcribed into cDNA by using the HiFiScript cDNA Synthesis Kit  in a 20\u2009\u03bcl reaction volume. Thirdly, real time PCR was performed on the Bio CFX Connect real-time PCR analyzer  by using the UltraSYBR Mixture (High ROX) . In brief, the total volume of 10\u2009\u03bcl PCR reactions was prepared by mixing 5\u2009\u03bcl UltraSYBR Mixture (2\u00d7), 0.3\u2009\u03bcl each forward and reverse primer and 10\u2009ng cDNA. The reaction conditions were as follows: initial incubation at 95\u2009\u00b0C for 10\u2009min, followed by 40\u2009cycles of 10s denaturation at 95\u2009\u00b0C, 30s annealing at 57\u2009\u00b0C and 32\u2009s extension at 72\u2009\u00b0C. All of the experiments performed in triplicate, and the average Ct value was used to calculate the relative expression of circRNA through the comparative 2http://www.targetscan.org) [http://starbase.sysu.edu.cn/) [http://mirdb.org) [To identify the potential functions of selected circRNAs, the circRNA/miRNA interaction was predicted using Arraystar\u2019s home-made miRNA target prediction software based on miRanda  and Targcan.org) . The difedu.cn/)  and miRDrdb.org) .https://david.ncifcrf.gov/home.jsp), we conducted the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. GO analysis was used to identify the functional roles of circRNA-targeting genes in terms of cellular components, biological processes and molecular functions. KEGG analysis was performed to explore the pathways related to circRNA-targeting genes.Based on DAVID 6.8  were selected as being significantly differentially expression, and the false discovery rate (FDR) was calculated to correct the P value of microarray analysis results. Correlations between the relative expression of circRNAs and their ceRNA were evaluated by Pearson\u2019s correlation method.All data is described as mean\u2009\u00b1\u2009standard deviation (SD). All statistical analyses were carried out using SPSS statistical software version 18.0 , and Hierarchical clustering picture revealed the circRNA expression profile in testicular tissues of NOA patients and the control Fig.\u00a0a. Box plP\u2009<\u20090.05). According to the genomic origin of human circRNAs, the classification of the differentially expressed circRNAs was summarized in pie chart , hsa_circ_0008045  and hsa_circ_0058058  was up-regulated, and hsa_circ_0061817 , hsa_circ_0002023 , and hsa_circ_0008533  was down-regulated in NOA patients, compared with the control group.To confirm the circRNA microarray results, qRT-PCR analysis was performed on 6 randomly selected differentially expressed circRNAs, including 3 up-regulated circRNAs  and 3 down-regulated circRNAs  in control and NOA group testicular tissue samples. The results indicated that the expression patterns of selected circRNAs were in consistent with microarray data  was selected for further bioinformatics analysis and prediction.For hsa_circRNA_0023313, the most likely potential target miRNAs are hsa-miR-520d-3p, hsa-miR-373-3p, hsa-miR-372-3p, hsa-miR-302c-3p and hsa-miR-130b-5p. The sequence analysis of miRNA response elements (MREs) are shown in Fig.\u00a0https://cytoscape.org/) [The circRNA/microRNA/mRNA interaction network diagram Fig.\u00a0 based onpe.org/) .Fig. 4CGo analysis and KEGG pathway analysis were used to predict the potential biological functions of hsa_circRNA_0023313.As shown in Fig. KEGG analysis revealed that the top five pathways related to hsa_circRNA_0023313 were endocytosis, meiosis, FoxO signaling pathway, Ubiquitin mediated proteolysis and AMPK signaling pathway Fig. d.P\u2009<\u20090.05). These findings have been confirmed by qRT-PCR assays on randomly selected circRNAs, including hsa_circ_0023313, hsa_circ_0058058, hsa_circ_0008045, hsa_circ_0061817, hsa_circ_0002023, and hsa_circ_0008533. Further systemic bioinformatics analyses including the circRNA/miRNA/mRNA interaction network, GO and KEGG pathway analysis were used to predict the functions of differentially expressed circRNAs suggesting a potential important role of circRNAs in regulating spermatogenesis.As far as we know, this is the first study to identify the comprehensive circRNAs expression pattern in testicular tissues of NOA patients. The microarray data revealed that 368 circRNAs were down-regulated and 526 circRNAs were up-regulated  of normal human testis circRNA deep sequence, the all of 6 circRNAs we selected were included in this database, and each of circRNA had changed . Among tCircRNA/miRNA/mRNA interaction network prediction provides a comprehensive understanding of the biological functions of hsa_circRNA_0023313. Our circRNA/miRNA interaction analysis demonstrated that the most likely potential target miRNA for hsa_circRNA_0023313 include hsa-miR-373-3p, hsa-miR-372-3p, hsa-miR-520d-3p, hsa-miR-302c-3p and hsa-miR-130b-5p. The study of Liu et al. showed that hsa-miR-373 and hsa-miR-372 were dysregulated in the semen of infertile males with semen abnormalities, which might be associated with semen abnormalities in infertile males . In addiCircRNAs may compete with linear RNAs by binding miRNAs with miRNAs response elements (MREs), which strongly suppress miRNA activity and result in increased levels of miRNA target genes . In our At the same time, Go analysis and KEGG pathway analysis were used to predict the potential biological functions of hsa_circRNA_0023313. The cellular component analysis revealed that the target genes of hsa_circRNA_0023313 were mainly involved in cytoplasm, cytosol and autophagosome. The biological process analysis showed its target genes were mainly take part in positive regulation of transcription, DNA-templated and positive regulation of transcription from RNA polymerase II promoter. The molecular function analysis indicated that it mainly focuses on ubiquitin-protein transferase activity, chromatin binding and ATP-binding and so on. KEGG analysis revealed that the top five pathways related to hsa_circRNA_0023313 were endocytosis, meiosis, FoxO signaling pathway, Ubiquitin mediated proteolysis and AMPK signaling pathway. All these data strongly indicate that hsa_circRNA_0023313 might be closely related to the initiation and progression of spermatogenesis.In conclusion, this work illustrates for the first time that the comprehensive expression pattern of circRNAs in testicular tissues of NOA patients, indicating that circRNAs might play important roles in regulating spermatogenesis and it might be potential molecular targets for diagnosis and treatment of NOA. However, the exploration of molecular mechanism about the detailed role of circRNAs on spermatogenesis are still needed in the future."}
+{"text": "Background: The morbidity and mortality of gastric cancer (GC) remain high worldwide. With the advent of the Human Genome Sequencing Project, circular RNAs (circRNAs) have attracted widespread attention in cancer research due to their stable ring structure. Our aim was to identify differentially expressed circRNAs in GC and explore their potential roles in GC diagnosis, treatment, and prognostic prediction.Methods: Large-scale gene screening was performed in three pairs of GC tissues and adjacent noncancerous tissues using high-throughput sequencing. The expression of hsa_circ_0001821 was detected in 80 pairs of tissue samples by quantitative real-time PCR (qRT-PCR). Stability of the ring structure of hsa_circ_0001821 RNA was verified by exonuclease digestion assay, and its diagnostic value was evaluated by receiver operating characteristic (ROC) analysis. In addition, the location of hsa_circ_0001821 in GC cells was detected by nucleoplasm separation assay.Results: A total of 25,303 circRNAs were identified, among which 2,007 circRNAs were differentially expressed . Further validation disclosed that hsa_circ_0001821 was significantly downregulated in the 80 pairs of GC tissues and 30 whole-blood specimens obtained from the GC patients. The specificity of hsa_circ_0001821 in GC was higher than that in other solid tumors. In addition, hsa_circ_0001821 was relatively stable after RNA exonuclease digestion. Clinicopathological parameter analysis showed that hsa_circ_0001821 was negatively correlated with tumor depth  and lymph node metastasis . Area under the curve (AUC) analysis showed that the diagnostic efficiency of circulating hsa_circ_0001821 in distinguishing GC patients was higher than that in GC tissues . Combined use of circulating hsa_circ_0001821 with the existing tumor markers yielded the largest AUC of 0.933. Finally, hsa_circ_0001821 was demonstrated to mainly locate in the cytoplasm, implying that it played a potential regulatory role in GC at the posttranscriptional level.Conclusion: Hsa_circ_0001821 may prove to be a new and promising potential biomarker for GC diagnosis. Gastric cancer (GC) remains one of the most common malignant tumors worldwide. According to the latest statistics released by the World Health Organization (WHO) Cancer Control Program, over seven million people die of cancer worldwide each year, with about 700,000 of them suffering from GC . MeanwhiUsing the Human Genome Sequencing Project, scientists have found that the proportion of protein-coding genes in the transcriptome is much lower than that in noncoding RNAs (ncRNAs), and about 80% transcription products are ncRNAs . Initialvia binding miRNAs as a molecule sponge. On the other hand, circRNAs might bind to RNA-binding proteins or other RNA translation proteins through complementary base pairs, interfering with the normal function of genes at the posttranscriptional level. These findings provide a new direction for the exploration of circRNAs as targets for disease diagnosis and prognostic prediction.circRNAs are a group of endogenous ncRNA molecules that widely exist in human cells. Current studies have demonstrated that circRNA is produced by special variable shear, and its 3\u2032 and 5\u2032 ends are joined together by covalent bonding to form a closed circular structure. Compared with other types of ncRNAs, circRNA is well tolerable by RNA exonuclease, relatively stable, and not easily degradable, making it a highly variable competitive endogenous RNA (ceRNA) . A singlvia circRNA sequencing. Subsequently, we chose hsa_circ_0001821 as our study object to further our investigation in 80 pairs of GC tissues and 30 whole-blood samples from GC patients and evaluate the clinical utility of hsa_circ_0001821 in GC diagnosis by receiver operating characteristic (ROC) analysis in an attempt to provide a novel biomarker for GC research.To find differentially expressed circRNAs, we detected circRNA expression in three pairs of GC tissues by high-throughput sequencing in the present study and identified 2,007 significantly differentially expressed circRNAs vs. three matched noncancerous tissues and identified a total of 25,303 circRNA targets, including 20,036 known circRNAs and 5,267 undefined circRNAs. The heatmap was depicted as a direct approach to visualize the distributions of the dataset for circRNA profiles (P < 0.5) (P < 0.05). The details regarding these circRNAs are presented in To investigate the expression profiles of circRNAs in GC tissues, we conducted high-throughput sequencing in three GC tissues http://www.ensembl.org), hsa_circ_0001821 is located at chr8_128902834_128903244_+, and the length of its mature transcript is 410 bp (According to the human reference genome (GRCh37/hg19) from the Ensembl genome database (P = 0.0030) and lymph node metastasis (P = 0.0072). However, we did not find any association between the hsa_circ_0001821 expression and other clinicopathological parameters, such as gender (P = 0.8285), age (P = 0.1887), histological differentiation (P = 0.0696), tumor size (P = 0.8900), CEA (P = 0.0977), CA199 (P = 0.0864), and CA125 (P = 0.7259). Furthermore, the Spearman correlation analysis also indicated that hsa_circ_0001821 expression was negatively correlated with tumor depth  and lymph node metastasis  (As shown in P < 0.0001)   based on the data obtained from the 80 pairs of GC tissues. The AUC of hsa_circ_0001821 in differentiating GC tissues from noncancerous ones was 0.792   were detected, using the normal gastric mucosal epithelial GES-1 cells as the control. Similarly, hsa_circ_0001821 showed a significantly lower expression level in the five GC cell lines  splicing . circRNAsplicing . Then insplicing . It was splicing . Howeversplicing .via high-throughput sequencing was contrary to that detected by qRT-PCR. A similar situation also appeared in Li\u2019s article  but are excessively depleted in the serum samples, we chose whole-blood samples to isolate circulating circRNAs. ROC analysis proved that the AUC of circulating hsa_circ_0001821 in distinguishing GC patients from the healthy donors was 0.872, which is higher than that in GC tissues and other laboratory markers of CEA, CA199, and CA125. More importantly, combining circulating hsa_circ_0001821 with other existing tumor markers yielded a maximum AUC of 0.933. These results suggest that hsa_circ_0001821 could be utilized as a biomarker with favorable sensitivity and specificity in GC.via activating the STAT3/VEGFA axis in GC . The tissue samples were added to an RNA fixative agent  immediately after excision and stored at \u221280\u00b0C. In addition, a total of 60 peripheral blood samples (stored in EDTA tubes), including 30 GC patients and 30 healthy controls, were also included in this study. All the included patients were diagnosed by professional pathologists and clinicians and did not receive preoperative chemotherapy or radiotherapy. All the samples described above were collected in accordance with the Code of Ethics of the World Medical Association, and informed consent was obtained for experimentation with human subjects. The study was approved by the ethics committee of the local hospital .2).Human GC cell lines  were purchased from the Stem Cell Bank of the Chinese Academy of Sciences . Human normal gastric epithelial GES-1 cells were used as the normal control. All cell lines were cultured in RPMI 1640 medium  supplemented with 10% fetal bovine serum , 1% penicillin and streptomycin in a humidified incubator  following the protocol and subjected to qRT-PCR analysis. Up to 107 fresh cultured cells were collected for the experiment. After one wash with phosphate-buffered saline (PBS), cells were resuspended in 300-\u03bcl ice-cold cell fractionation buffer, incubated on ice for 5\u201310 min, and centrifuged at 4\u00b0C, 500 \u00d7 g, for 3 min. Then the cytoplasmic fraction was carefully aspirated away from the nuclear pellet. Subsequently, approximately 400-\u03bcl ice-cold cell disruption buffer and an equivalent volume of 2\u00d7 lysis/binding solution were added to the nuclear pellet. After mixing upside down, 400-\u03bcl 100% ethanol was added to the mixture. Then the sample mixture was drawn through a filter cartridge. Following orderly washing, centrifugation, and filtration, RNA was eluted twice with elution solution at 95\u00b0C. Finally, the isolated nuclear/cytoplasmic RNA was stored at \u221280\u00b0C for later use.The nuclear/cytoplasmic RNA was isolated from SGC-7901 cells using a PARISRibonuclease R (RNase R) was purchased from Geneseed Biotech Co., Ltd . About 3\u20134 U/\u03bcg of RNase R was added to 10-\u03bcg total RNA extracted from SGC-7901 and BGC-823 cells. Subsequently, we configured a total of 50-\u03bcl digestion reaction system containing 5-\u03bcl 10\u00d7 reaction buffer and then added RNase-free water to make up the total volume. Next, the reaction mixture was incubated at 37\u00b0C for 30 min and kept at 70\u00b0C for 10 min to inactivate the enzyme before reverse-transcription reaction was performed.\u2122 One . RNA integrity and gDNA contamination were verified by standard denaturalized agarose gel electrophoresis, and purity was determined by spectrophotometry at 260\u2013280 nm. cDNA was synthesized using reverse-transcription reagent (Thermo Fisher Scientific). The relative expression of hsa_circ_0001821 was normalized by the housekeeping gene GAPDH. All primers used in this study were synthesized by RiboBio Corporation . The sequences of the target gene are as follows: hsa_circ_0001821: 5\u2032-tggaatgtaagaccccgact-3\u2032 (forward) and 5\u2032-ccatcttgaggggcatcttt-3\u2032 (reverse); PVT1: 5\u2032-gcatggagcttcgttcaagt-3\u2032 (forward) and 5\u2032-gccacagcctcccttaaaac-3\u2032 (reverse); GAPDH: 5\u2032-gaacgggaagctcactgg-3\u2032 (forward) and 5\u2032-gcctgcttcaccaccttct-3\u2032 (reverse). All qRT-PCR assays were performed on the LightCycler 480 system for a total of 20 \u03bcl. The 2\u2212\u0394\u0394CT method was used to calculate the relative expression level, and the \u0394\u0394Ct value was presented as the \u2005difference between the experimental group (Cttarget \u2212 Ctreference) and the calibrator group (Cttarget \u2212 Ctreference). All experiments were performed independently three times.Total cell and tissue RNA were extracted using TRIzol reagent , while the peripheral blood samples were pretreated with erythrocyte lysate , and then RNA was extracted with TRIzol reagent. Total RNA in each sample was quantified as indicated by NanoDrop\u00ae . Briefly, total RNA was incubated at 37\u00b0C for 30 min with 10 units RNase R  after removal of ribosomal RNA. Next, the RiboMinus RNase R (+) RNA was fragmented, and then first-strand and directional second-strand syntheses were performed. Subsequently, a tailing/adapter ligation approach was performed with the purified cDNA. Finally, the purified, adapter-ligated DNA was amplified. Each library was diluted to 10 nM and pooled equimolar prior to clustering. Paired-end (PE150) sequencing was performed on all samples.Total RNA was isolated from the tissues using HiPure Total RNA Mini Kit . The RNA concentration was determined using the Qubit 3.0 fluorometer , and RNA integrity assays were performed using the Agilent 2100 Bioanalyzer . A RIN value over 7.0 was considered eligible. RNA-seq library was prepared with approximately 2-\u03bcg total RNA using KAPA RNA HyperPrep Kit with RiboErase (HMR) for IlluminaM-value (TMM) was used to normalize the gene expression. Differentially expressed genes were identified using the edgeR program  was in the circBase/circBank database, and if so, the corresponding ID was given; if not, it was represented by NA.As for the screening of differentially expressed circRNAs, the reads were first mapped to the latest UCSC transcript set using Bowtie 2 version 2.1.0  and the  program . Genes shttps://circinteractome.nia.nih.gov) and found that the context + score percentile of seven miRNAs was greater than 85. Secondly, we searched the miRDB database (http://mirdb.org/miRDB/index.html) for the downstream target genes of the above seven miRNAs and selected the top 10 genes for network mapping.Based on the circRNA-seq data, we firstly searched for hsa_circ_0001821-targeted miRNAs in the CircInteractome database . Student\u2019s t test was performed on data of two groups, and paired t test was used for comparison of cancerous tissues and adjacent noncancerous tissues. When there were more than two groups of data to compare, we used one-way ANOVA. The ROC curve was established to evaluate the diagnostic value. Youden index  was calculated to assess the authenticity of the screening test. The correlation between hsa_circ_0001821 and the clinicopathological parameters was evaluated by chi-square test and Spearman correlation test. A P value of less than 0.05 was considered statistically significant.The statistical analysis was conducted by GraphPad Prism 7.0  and SPSS 20.0 . The clustered heatmap and volcano plots were generated The datasets generated for this study can be found in GEO database, GSE131414.The study was approved by the ethics committee of the Affiliated Hospital of Nantong University.SK wrote the manuscript and performed the experiences; QY helped write the manuscript and perform the experiences; CT helped collect the data; TW interpreted the results; XS and SJ conceived and designed the project, gave vital suggestions and approved the final version.This project was supported by grants from the National Natural Science Foundation of China (81871720).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest."}
+{"text": "One study suggested a correction formula for femoral venous access that markedly reduced the bias for GEDVI. Therefore, the last PiCCO-algorithm requires information about the CVC-position which suggests a correction of GEDV for femoral access. However, a recent study demonstrated inconsistencies of the last PiCCO algorithm using incorrected GEDV to calculate CFI despite obvious correction of GEDV. Nevertheless, this study was based on mathematical analyses of data displayed in a total of 15 patients equipped with only a femoral, but not with a jugular CVC. Therefore, this study compared CFI derived from the femoral indicator injection TPTD to data derived from jugular indicator injection in 28 patients with both a jugular and a femoral CVC.Cardiac function index (CFI) is a trans-pulmonary thermodilution (TPTD)-derived estimate of systolic function. CFI is defined as the ratio of cardiac output divided by global end-diastolic volume GEDV (CFI = CO/GEDV). Several studies demonstrated that the use of jugular venous gold standard access and the femoral access with and without information about the femoral indicator injection to evaluate, if correction for femoral GEDV also pertains to CFI. ).28 ICU-patients with PiCCO-monitoring were included. Each dataset consisted of three triplicate TPTDs using the -1; p<0.001). Similarly, CFI_fem_cor was significantly lower than CFI_jug . This is explained by the finding that CFI_fem_uncor was not different to CFI_fem_cor . This suggests that correction for femoral CVC does not pertain to CFI. Calculative correction of CFI_fem_uncor by multiplying CFI_fem_uncor by the ratio GEDVI_fem_uncor/GEDVI_jug resulted in CFI_fem_uncor_form which was slightly, but significantly different from the gold standard CFI_jug . The agreement of measurements classified in the same category of CFI (decreased (<4.5), normal (4.5\u20136.5) and increased (>6.5 min-1)) was high for CFI_jug and CFI_fem_uncor_form . By contrast, the agreement with CFI_jug was significantly lower for CFI_fem_cor  and CFI_fem_uncor .CFI_fem_uncor was significantly lower than CFI_jug (4.28\u00b11.70 vs. 5.21\u00b11.91 minWhile the last PiCCO algorithm obviously corrects GEDVI for femoral indicator injection, this correction is not applied to CFI. Therefore, femoral TPTD indicator injection results in substantially lower values for CFI compared to TPTD using a jugular CVC. Necessarily, uncorrected CFI-values derived from femoral TPTD are misleading and have to be corrected. Accurate haemodynamic monitoring is essential for the diagnosis and therapeutic management of critically ill patients with circulatory failure . DiffereBased on their mathematical derivation, CFI (CO/GEDV) and GEF (4*SV/GEDV) necessarily depend on an accurate determination of GEDV. However, several studies demonstrated that GEDV is markedly overestimated in case of using a femoral venous access for TPTD-indicator injection instead of a jugular or subclavian access \u201316. ConsAnother recent study in patients equipped with both jugular and femoral CVCs demonstrated that the last PiCCO-algorithm corrects GEF, but not PVPI which resulted in a substantial underestimation of PVPI in case of femoral indicator injection . HoweverTherefore, we have compared in the present study CFI values of 28 patients equipped with both jugular and femoral CVCs: Two triplicate measurements with femoral indicator injection with and without giving the information of femoral indicator injection were compared to the gold-standard of CFI derived from jugular indicator injection.This prospective observational study was conducted in a ten-bed general ICU at a university hospital between October 6, 2016 and March 31, 2017. The study was approved by institutional review board approved . Written informed consent was obtained by all patients (18 years of age or older) or their legal representatives. All patients had to be equipped with PiCCO device and with both jugular and femoral catheter. The indication for PiCCO monitoring was made independently from the study by the ICU physician in charge None of the patients had been included in one of the previous studies or databases comparing TPTD-parameters derived from jugular to femoral indicator injection , 17\u201319. The abbreviations and nomenclature of haemodynamic parameters used in this paper are summarized in with (TPTD_fem_cor) or without (TPTD_fem_uncor) the information about the femoral indicator injection. This was done to evaluate, if correction for femoral GEDV pertains to CFI_fem.28 datasets including triplicate TPTD with 15 ml cold saline solution were recorded in 28 patients equipped with both jugular and femoral CVC. The jugular venous access was used as the gold standard TPTD_jug. Furthermore, two triplicate TPTDs were performed via the femoral access The three TPTDs were performed in a random order with the intention to avoid a systematic bias by repeated triplicate TPTDs with a total volume of 9*15 ml.All measurements were performed in patients equipped either with conventional jugular and femoral CVC or conventional CVC and a dialysis catheter irrespective of the study. According to the local standard CVCs or dialysis catheters were inserted in different positions (one in the superior vena cava and the other one in the inferior vena cava).st indicator injection, since the larger volume of the dialysis catheters (up to 1.6 ml) might result in a loss of indicator  and in a consecutive overestimation of volumetric parameters for the 1st of TPTD-measurement. Femoral venous catheters were completely  inserted under ultrasound guidance. The position of the tip was controlled (and corrected) according to X-ray for all jugular, but not for the femoral venous catheters.A 5-lumen CVC  with a maximum intravascular length of 20 cm and a diameter of 3.15 mm (9.5 French (Fr)) or a Gambro Gam Cath Dolphin dialysis-catheter  was used for TPTD indicator injections. For femoral access dialysis catheters with a length of 250 mm and a diameter of 13 Fr were used. For jugular RRT access we used catheters with a length of 150\u2013175 mm and a diameter of 13 F. The dialysis catheters were prefilled with ice cold saline immediately before the 1TPTD was performed as previously described , 20, 21 All data were controlled for input data error. Continuous variables are expressed as mean\u00b1standard deviation. Categorical variables are expressed as percentages. Wilcoxon-test for paired samples was used to compare continuous variables.Bland-Altman analysis was used for the analysis of the agreement between CFI derived from jugular vs. femoral venous catheter sites for CFI and to compute the percentage error (PE).-1), normal (4.5\u20136.5 min-1) and increased (>6.5 min-1)) derived from different measurements was primarily analyzed using Fisher\u00b4s exact test (\u201cagreement yes or no\u201d). Additionally, we calculated kappa-statistics and Kendall\u00b4s coefficient of correlation.The agreement of classification of CFI .-1 and significantly lower values of 3.8\u00b11.6 min-1 derived from uncorrected femoral indicator injection, using an online statistical power calculator  Sample sizes of n = 12 and n = 16 would provide statistical powers of 80% and 90% respectively [-1 instead of 1.6 min-1 in the study by Beitz et al.. This resulted in a sample size of n = 28 to provide a statistical power of 90% and an alpha-error of 5% (two-tailed test).The sample size calculation based on the finding of the previous study by Beitz et al. with CFI-values calculated for jugular indicator-injection of 5.1\u00b11.8 minectively . Howeverectively . A large-1; p<0.001; CFI_fem_uncor was significantly lower than CFI_jug  was in the same range as the ratio of GEDVI_fem_uncor/GEDVI_fem_cor .The relation of CFI_jug/CFI_fem_uncor  [Therefore, CFI_fem_form was calculated by correcting CFI_fem_uncor by multiplication of CFI_fem_uncor with the ratio GEDVI_fem_uncor/GEDVI_fem_cor using the recently suggested correction formula for GEDVI_fem based on uncorrected GEDVI and cardiac index (CI) derived from femoral indicator injection (GEDV_fem_uncor and CI_fem_uncor) as well as on ideal bodyweight  :GEDVI_fConsequently, for ex-post-correction of CFI_fem_uncor we calculated CFI_fem_uncor_form by multiplying CFI_fem_uncor with the ratio GEDVI_fem_uncor/GEDVI_fem_cor:-1; p = 0.024; -1 and a percentage error of 29.6%. The CV-values were comparable for CFI_fem_uncor_form and CFI_jug .CFI_fem_uncor_form was slightly, but significantly different from the gold standard CFI_jug .Despite a slightly significant difference between CFI_fem_uncor_form and CFI_jug with questionable clinical relevance, ex-post correction of CFI_uncor resulted in significantly lower amount of the bias |CFI_fem_uncor_form\u2013CFI_jug| vs. |CFI_fem_uncor\u2013CFI_jug|  for the \u201cgold-standard\u201d CFI_jug vs. the classifications according to CFI_fem_cor, CFI_fem_uncor and CFI_fem_uncor_form, respectively , which was not significant different to CFI_jug , but significantly higher compared to CFI_fem_uncor (p = 0.0019) and CFI_fem_cor (p<0.001).Furthermore, kappa-statistics and Kendall\u00b4s coefficient tau-b confirm a markedly better agreement of CFI_fem_uncor_form with CFI_jug  compared to CFI_fem_uncor  and CFI_fem_cor .TPTD-derived CFI is a bedside surrogate of LV systolic function. CFI is strongly associated with echocardiography-derived LVEF and facilitates guidance of inotropic therapy and fluid management. Repeated CFI measurement is readily available and independent of the examiner. Changes in CFI over time provide dynamic information that might be superior to single measurements, particularly when interpreted in the light of a clinical situation. According studies with their findings are summarised in femoral CVC for indicator injection, compared to the gold standard of jugular or subclavian injection. Interestingly, a similar phenomenon was found in case of misplacement of subclavian central venous catheter tip into the jugular vein [However, the validity of CFI calculation relies on the accurate determination of CO and GEDV. Several recent studies suggest marked overestimation of GEDV/GEDVI and CFI in case of using a lar vein .One study suggested a correction formula for GEDVI derived from femoral indicator injection. This correction is based on GEDVI and CI obtained from femoral access and on ideal bodyweight . SeveralThis study demonstrates that the correction formula for femoral venous access is not applied to correct CFI. The resulting underestimation of this value would have had a consequence for around 50% of our patients as demonstrated by the wrong classification of CFI in 14 out of 28 measurements. Therefore, measurement of CFI in patients with femoral venous access for indicator injection at present is misleading and has to be replaced by echocardiography as long as the correction formula is not implemented in the TPTD-algorithm. This problem might also apply to the other commercially available TPTD-device EV-1000 , since at least one study suggests that this device does not correct GEDVI, PVPI and GEF for femoral indicator injection [femoral central venous access echocardiography should be performed to assess left ventricular contractility. Irrespective of the central venous access, echocardiography enables to exclude isolated right heart failure which might impede the use of CFI and GEF also in case of a jugular or subclavian central venous access [Unless an appropriate correction is implemented in the PiCCO and the EV-1000, in patients with However, repeated echocardiography is time consuming and requires the continuous availability of experienced investigators. On the other hand, TPTD is straightforward and reliable even when performed by different investigators. Furthermore, it provides additional extra-cardiac parameters such as EVLWI and calibrates continuous measurement of CI, GEF and CFI.femoral venous access CFI has to be classified as misleading and may result in wrong therapeutic interventions due to substantial underestimation and wrong categorization of CFI.Although a correction formula for femoral venous access markedly reducing the bias for GEDVI has been published 7 years ago, and despite several studies gave hints for inconsistencies of the correction of GEDVI, PVPI, GEG and CFI, our data demonstrate that the most recent algorithm of the PICCO still does not apply this correction to CFI. Therefore, in patients with From a practical viewpoint, there are two options to overcome this dilemma in addition to the use of echocardiography:As demonstrated by this study, ex post correction by the previously suggested formula to correct GEDVI appropriately corrects CFI_fem with acceptable bias, percentage error and categorization according to clinical thresholds.Since this mathematical correction maybe cumbersome in clinical routine, GEF can be used instead of CFI. At least two studies suggest that GEF is appropriately corrected for femoral indicator injection by the most recent PiCCO algorithm , 19.This study included a small number of patients and has been conducted as a single-centre study. Furthermore, all measurements were performed in critically ill patients and not in healthy persons.femoral TPTD indicator injection results in substantially lower values for CFI compared to TPTD using a jugular CVC. Necessarily, uncorrected CFI-values derived from femoral TPTD are misleading and have to be corrected.While the last PiCCO algorithm obviously corrects GEDVI for femoral indicator injection, this correction is not applied to CFI. Therefore,"}
+{"text": "Cataloguing the distribution of genes within natural bacterial populations is essential for understanding evolutionary processes and the genetic basis of adaptation. Advances in whole genome sequencing technologies have led to a vast expansion in the amount of bacterial genomes deposited in public databases. There is a pressing need for software solutions which are able to cluster, catalogue and characterise genes, or other features, in increasingly large genomic datasets.Here we present a pangenomics toolbox, PIRATE , which identifies and classifies orthologous gene families in bacterial pangenomes over a wide range of sequence similarity thresholds. PIRATE builds upon recent scalable software developments to allow for the rapid interrogation of thousands of isolates. PIRATE clusters genes (or other annotated features) over a wide range of amino acid or nucleotide identity thresholds and uses the clustering information to rapidly identify paralogous gene families and putative fission/fusion events. Furthermore, PIRATE orders the pangenome using a directed graph, provides a measure of allelic variation, and estimates sequence divergence for each gene family.We demonstrate that PIRATE scales linearly with both number of samples and computation resources, allowing for analysis of large genomic datasets, and compares favorably to other popular tools. PIRATE provides a robust framework for analysing bacterial pangenomes, from largely clonal to panmictic species. For most bacteria the complement of genes for a given species is far greater than the number of genes in any single strain. Comprising core genes shared by all individuals in a species and accessory genes that are variously present or absent, the pangenome represents a pool of genetic variation that underlies the enormous phenotypic variation observed in many bacterial species. Through horizontal gene transfer, bacteria can acquire genes from this pangenome pool that bestow important traits such as virulence or antimicrobial resistance .Over the past decade, advances in whole-genome sequencing technologies and bioinformatic analyses have allowed the cataloguing of genes and intergenic regions that make up the pangenomes of many species .Current approaches define genes on the basis of strict sequence identity thresholds ,3, 7,8, In order to address these considerations we have created the Pangenome Iterative Refinement and Threshold Evaluation (PIRATE) toolbox, which evaluates and classifies genetic diversity within the pangenome. PIRATE provides the means to create pangenomes from any annotated features  over a user-defined range of amino acid or nucleotide identity thresholds. PIRATE provides measures of sequence divergence and allelic diversity within the sample. PIRATE also categorizes paralogs into duplication and/or fission loci, loci disrupted by an insertion, deletion, or nonsense mutation. A consistent nomenclature is applied to allow for the user to identify gene clusters that are the product of duplication or fission events, providing additional context on both methodological and evolutionary gene provenance. This rapid, scalable method allows for a comprehensive overview of gene content and allelic diversity within the pangenome.The PIRATE pipeline has been summarized as a schematic in Fig.\u00a0Clusters that contain >1 sequence per individual genome are putative paralogs and undergo an additional post-processing step . All locAfter paralog classification, fission loci are treated as a single locus. Gene families that contain genomes with multiple loci, after accounting for fission loci, potentially represent 2 or more related gene families that have been over-clustered. In these cases the gene family is checked against the presence of MCL clusters , which contain a single copy of the loci in all constituent genomes . These aSyntenic connections between gene families in their source genomes are used to create a pangenome graph. Parsimonious paths between gene families contained in the same number of genomes are used to identify co-localized gene families. This information is used to order the resulting tabular pangenome file on syntenic blocks of genes in descending order of number of genomes that those blocks were present in. Gene-by-gene alignments are produced using MAFFT to generate a core gene alignment . InstallA number of supplementary tools are provided to extract, align, and subset sequences and to compare and visualize outputs. To facilitate integration with existing pipeline, scripts have been provided to convert the outputs of PIRATE into common formats, which allows for them to be used as inputs to software used for downstream analysis, such as the PanX user interface, SCOARY, Microreact, or Phandango ,16\u201318. ACampylobacter jejuni, S. aureus, and Escherichia coli, representing both a range of pangenome sizes  and guanine-cytosine content . The screctively) and 3. Pectively).S. aureus complete genomes downloaded from the RefSeq database   were classified as core (>95% genomes) and 1,817 (42.75%) as accessory  between core and accessory genomes; 21.83% of accessory genes  clustered at <95% homology compared with only 15.40% of core genes    21]. PI. PIS. auer 2018) . The panory Fig.\u00a0. Gene fa18) Fig.\u00a0. A possiagr locus exhibited a range of sequence identity clustering thresholds; agrA clusters at 91%, agrB and agrC at 65%, and agrD at 45% amino acid identity, each with a copy number of 1. We identified that another gene, arlR, which is known to interact with the agr locus, has a similarly low amino acid similarity of 45%, perhaps implying that the linked genes have undergone similar patterns of diversifying selection. This example highlights how diversification may lead to over-splitting of genes if only a single sequence identity threshold were used, even if this threshold were applicable to the vast majority of genes in the pangenome. Expansion of families of mobile genetic elements or individual genes within the population can also be identified from the outputs. For example, the transposase for IS256, known to play a role in biofilm formation and resistance to various antimicrobial agents, is present in 35 genomes and has a conserved amino acid sequence (<2% divergence) but a variable copy number of between 1 and 32 copies within the genomes in which it is present. Using these data it is possible to identify the strains that have an increased copy number of IS256.PIRATE can quickly be used to identify genes with both highly conserved or divergent sequence similarity or variable copy number. The biological ramifications of these genes will vary between applications. For example the core \u201caccessory regulator\u201d S. aureus dataset (default settings). Roary was run at a range of percentage identity thresholds matching those used by PIRATE (-i option) to facilitate comparison. Paralog splitting in Roary was also switched off (-s option) to assess the influence of paralog splitting on the resulting pangenome size estimates. The number of core and accessory genes (<95% isolates) estimated by both tools was compared with those estimated using PIRATE  of the sample was observed at thresholds >90% Fig.\u00a0. At thesATE Fig.\u00a0. All tooS. aureus collection the estimated number of core genes remains fairly constant at thresholds <90% and decreases sharply at thresholds >95%  using 12 threads, an MCL inflation value of 6, and a high-scoring pair (HSP) query length threshold of 0.9. The pangenome comprised 2,858,820 loci clustered into 102,425 gene clusters of which 1,841 (1.8%) were considered core (present in >95% of isolates) , which was reflected in the number of genes present per isolate  Fig.\u00a0. An incres) Fig.\u00a0. This ines) Fig.\u00a0. Pseudomate Fig.\u00a0. There wsed Fig.\u00a0.Prochlorococcus marinus, a marine cyanobacterium with extremely diverse gene complement, from the NCBI database  using 8 threads, an MCL inflation value of 6, and a range of sequence similarity thresholds from 0 to 95% . This relaxed range of sequence similarity thresholds allowed us to test the lower limits of BLAST/DIAMOND for detecting homology in these data. The pangenome comprised 91,593 loci clustered into 8,325 gene clusters of which 867 (10.41%) were considered core (present in >95% of isolates)  Fig.\u00a0. There wes) Fig.\u00a0. The majnes Fig.\u00a0. Interesnes Fig.\u00a0. Observanes Fig.\u00a0.Here we present PIRATE, a toolbox for pangenomic analysis of bacterial genomes, which provides a framework for exploring gene diversity by defining genes using relaxed sequence similarity thresholds. This pipeline builds upon existing tools using a novel methodology that can be applied to any annotated genomic features. PIRATE identifies and categorizes duplicated and disrupted genes, estimates allelic diversity, scores gene divergence, and contextualizes genes using a pangenome graph. We demonstrate that it compares favourably with other commonly used tools for pangenomic analysis, in both execution time and computational resources, and is fully compatible with software for downstream analysis and visualization. Furthermore, it is scalable to multiprocessor environments and can be applied to large numbers of genomes on modest hardware. Together the enhanced core and accessory genome characterization capability, and the practical implementation advantages, make PIRATE a potentially powerful tool in bacterial genomics - a field in which there is an urgent need for tools that are applicable to increasingly large and complex datasets.Project name: PIRATE: A fast and scalable pangenomics toolbox for clustering diverged orthologues in bacteriahttps://github.com/SionBayliss/PIRATEProject home page: Operating system(s): Ubuntu 16.04/18.04, MacOSProgramming language: Perl, ROther requirements: mcl, mafft, cd-hit, fasttree, ncbi-blast+, bioperl, GNU parallel, diamondLicense: GNU GPL v3.0SCR_017625RRID: An archival copy of the code, scripts, and other supporting data are also available via the GigaScience database GigaDB .Supplementary Information. Benchmarking analysis and expanded details on the methods used in the PIRATE pipeline.Supplementary Table 2. Accession numbers for samples used in the benchmarking analysis.Supplementary Table 3. Parameters used in the analysis of comparisons between PIRATE, Roary and PanX.BLAST: Basic Local Alignment Search Tool; GFF: General Feature Format; HSP: High-Scoring Pair; MAFFT: Multiple Alignment using Fast Fourier Transform; Mb: Megabase Pairs; MCL: Markov Cluster; NCBI: National Center for Biotechnology Information; PIRATE: Pangenome Iterative Refinement and Threshold Evaluation.The authors declare that they have no competing interests.giz119_GIGA-D-19-00122_Original_SubmissionClick here for additional data file.giz119_GIGA-D-19-00122_Revision_1Click here for additional data file.giz119_GIGA-D-19-00122_Revision_2Click here for additional data file.giz119_GIGA-D-19-00122_Revision_3Click here for additional data file.giz119_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giz119_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giz119_Response_to_Reviewer_Comments_Revision_2Click here for additional data file.giz119_Reviewer_1_Report_Original_SubmissionAndrew Page -- 5/7/2019 ReviewedClick here for additional data file.giz119_Reviewer_2_Report_Original_SubmissionRichard Neher -- 5/11/2019 ReviewedClick here for additional data file.giz119_Reviewer_2_Report_Revision_1Richard Neher -- 8/7/2019 ReviewedClick here for additional data file.giz119_Reviewer_3_Report_Original_SubmissionJason Sahl -- 5/16/2019 ReviewedClick here for additional data file.giz119_Reviewer_3_Report_Revision_1Jason Sahl -- 7/31/2019 ReviewedClick here for additional data file.giz119_Supplemental_FilesClick here for additional data file."}
+{"text": "Streptomyces. In this review, \u03b2-lactamases and penicillin-binding proteins (PBPs) in Streptomyces are explored mainly by phylogenetic analyses from the viewpoint of self-resistance. Although PBPs are more important than \u03b2-lactamases in self-resistance, phylogenetically diverse \u03b2-lactamases exist in Streptomyces. While class A \u03b2-lactamases are mostly detected in their enzyme activity, over two to five times more classes B and C \u03b2-lactamase genes are identified at the whole genomic level. These genes can subsequently be transferred to pathogenic bacteria. As for PBPs, two pairs of low affinity PBPs protect Streptomyces from the attack of self-producing and other environmental \u03b2-lactam antibiotics. PBPs with PASTA domains are detectable only in class A PBPs in Actinobacteria with the exception of Streptomyces. None of the Streptomyces has PBPs with PASTA domains. However, one of class B PBPs without PASTA domain and a serine/threonine protein kinase with four PASTA domains are located in adjacent positions in most Streptomyces. These class B type PBPs are involved in the spore wall synthesizing complex and probably in self-resistance. Lastly, this paper emphasizes that the resistance mechanisms in Streptomyces are very hard to deal with, despite great efforts in finding new antibiotics.Antibiotic resistance is one of the most serious public health problems. Among bacterial resistance, \u03b2-lactam antibiotic resistance is the most prevailing and threatening area. Antibiotic resistance is thought to originate in antibiotic-producing bacteria such as  Recently, however, D\u2019Costa et al. demonstrated that genes conferring resistance to tetracyclines, vancomycin, aminoglycosides, \u03b2-lactams and macrolides existed in 30,000-year old Beringian permafrost and that the amino acid sequences of the \u03b2-lactamases showed identities between 53% and 84% with those of known determinants, suggesting that antibiotic resistance is a natural phenomenon that predates the modern selective pressure of clinical antibiotic use [The first antibiotic, penicillin, was discovered in 1929 by Fleming [ [et al. . Surpris [et al.  and onlys aureus ,6 and Steumoniae  were alreumoniae . Gram-nengitides  and Eschhia coli  were inthia coli . That th in 1978 . If the otic use . Streptomyces [et al. cultured clonal isolates from 11 different soils that could use one of 18 different antibiotics as their sole carbon source, phylogenetic profiling of the clonal isolates revealed a diverse set of species consisting of orders Burkholderiales (41%), Pseudomonadales (24%) and Actinomycetales (7%) and others and, indicated that pathogenic microbes can more readily use resistance genes originating from bacteria subsisting on antibiotics than the resistance gene from more distantly related antibiotic producer bacteria [Burkholderiales and Pseudomonadales belong to the proteobacteria and are known to function as scavengers, capable of using a large variety of single carbon sources as food. However, horizontal gene transfer is possible from proteobacteria such as Burkholderiales and Pseudomonadales to Actinomycetales including antibiotic-producing Streptomyces in soil and then to pathogenic bacteria. From the result of a metagenomic analysis to isolate antibiotic resistance genes from soil, Riesenfeld et al. concluded that soil bacteria are a reservoir of antibiotic resistance genes with greater diversity than previously recognized [et al. described that soil dwelling bacteria produce and encounter a myriad of antibiotics and are a reservoir of resistance determinants (resistome) [et al. isolated 110 resistance genes. Of the 110 resistance genes, 18 had 100% amino acid identity to entries in GenBank and another 32 were highly similar. Interestingly, of 110 resistance genes, 55 were \u03b2-lactamase genes highly divergent from those of the antibiotic-producing Streptomyces. From these results they indicated ancient evolutionary relationships between \u03b2-lactamases from the soil bacteria and those of the antibiotic-producing bacteria [Streptomyces are class A \u03b2-lactamases [Drug resistance is one of the greatest challenges in modern medicine. Among drug resistance, antibiotic resistance is the most prevailing and threatening topic in public health. According to the information of the Centers for Disease Control and Prevention (CDC), at least 2 million people become infected with bacteria that are resistant to antibiotics and at least 23,000 people die each year as a direct result of these infections in the United States each year , and in ptomyces ,18,19. Sbacteria . Burkholcognized . Howeversistome) , althougonadales % and Actctamases ,27,28,29Actinobacteria constitute one of the largest phyla within the bacteria [Streptomyces species belonging to the Actinobacteria dwell in the soil and are high guanine + cytosine (G + C)-content Gram positive bacteria. They are characterized by their peculiar morphology to undergo complex cellular differentiation like filamentous fungi [Streptomyces species are prokaryotic microorganisms so they must protect themselves from the attack of antibiotics to avoid suicide, that is, they have to have self-resistance mechanisms. In addition, \u03b2-lactam antibiotics have been most widely used for chemotherapy of infectious diseases even about 90 years after penicillin\u2019s discovery, even though the chemical structures were extensively modified. \u03b2-Lactam antibiotic resistance is caused mainly by two mechanisms: antibiotic-degrading enzymes, \u03b2-lactamases and modification of target sites, penicillin-binding proteins (PBPs). Streptomyces species are known to be highly resistant to benzyl penicillin, although they are Gram-positive bacteria, so it is interesting to know the self-resistance mechanisms in Streptomyces and the relationships between PBPs, \u03b2-lactams and \u03b2-lactamases [Streptomyces, focusing primarily on \u03b2-lactam antibiotics. The phylum bacteria ,31. Streus fungi  and by tus fungi ,34 such us fungi  and clavus fungi . Howeverctamases ,37. ThisStreptomyces is at least one of the origins of multi-drug resistance prevailing and threatening in various environments at the present time. Major biochemical mechanisms of drug resistance are detoxication or inactivation of the drug, changing the target site, blocking the transport of the drug into the cell and the efflux pump system [Streptomyces is supposed to be penicillin-binding proteins [As described above, the self-resistance in antibiotic-producing bacteria such as p system ,38. Thesproteins . A numbeproteins ,41,42,43Streptomyces [Bacillus [Meiothermus ruber DSM 1279) , a green sulfur bacterium , a green non-sulfur bacterium , a methylotrophic bacterium [Methylovorus glucosetrophus SIP3-4, ACT50532, a metallo-\u03b2-lactamase), a fungus  and even a plant [Arabidopsis thaliana, AAC49866, a glyoxalase II mitochondrial enzyme) and mammals [Rattus norvegicus and Homo sapiens, EDL84248 \u201ca serine \u03b2-lactamase-like protein\u201d and NP_116246 \u201ca serine \u03b2-lactamase-like mitochondria protein\u201d, respectively). Furthermore, an alkalophilic and halotolerant Gram-positive bacterium Oceanobacillus iheyensis isolated from the bottom of the Pacific Ocean at a depth of 1050 m [Pseudomonas fluorescens isolated from a remote Antarctic coastal area [Mycobacterium tuberculosis H37Rv, an Actinobacterium [\u03b2-Lactamases are the enzymes which catalyze the hydrolysis of the \u03b2-lactam antibiotics to produce antimicrobiologically inactive compounds. Because of this, \u03b2-lactamases are responsible for \u03b2-lactam resistance in many pathogenic bacteria . These eptomyces ,45, BaciBacillus  and the Bacillus ,48. In aBacillus ,50, \u03b2-laBacillus  and RNA-Bacillus ,53. Consacterium   were aligned by using the Muscle and T-coffee programs [70XXK73 and S130DN132. Furthermore, SCAT_1418, SCAT_4581 and F750_2387 do not carry K234T/SG236, indicating that these candidates do not function as \u03b2-lactamases , Amycolicicoccus subflavus DQS3-9A1 (AEF33802) and Rhodococcus sp. B7740 (AJW42765) were identified.Although some other sequences are also deficient or possess incompletely identical signature amino acid sequences, they are supposed to operate as \u03b2-lactamases even if the catalytic efficiency is not perfect. GWGStreptomyces violaceusniger (WP_014060235) and metallo-dependent hydrolase of Streptomyces himastatinicus (WP_00971837) were identified. Actually, SCAT_1418 possesses some class B \u03b2-lactamase signature residues. Likewise, SCAT_4581 is highly similar to Zn-dependent hydrolases from Actinobacteria, so that they may be class B \u03b2-lactamases.In addition blast analysis using SCAT_1418 as a query, metallo-dependent hydrolase of Streptomyces albus (WP_060729068) and Rhodococcus rhodnii (WP_037260801). F750_2387 is a large membrane protein and belongs to metallo-\u03b2-lactamase superfamily. The fact that SCAT_1418, SCAT_4581 and F750_2387 belong to metallo-\u03b2-lactamases but not to class A reflects on large molecular distances between these \u03b2-lactamases and others such as TEM-1 and SHV-1 (Streptomyces cellulosae (D12653). This is one example of the horizontal gene transfer. Another interesting thing is that the amino acid sequences of B446_02285 and B446_33010 in class A \u03b2-lactamases, and B446_02210 and B446_33085, and B446_02000 and B446_33290 in class B \u03b2-lactamases, and B446_01720 and B446_33570 in \u03b2-lactamase-like sequences and B446_01315 and B446_33975 in class C \u03b2-lactamases are also completely identical, indicating that about 7.7 Mb region including these genes were duplicated in the chromosome in the past. In fact, two transposases exist at both sides in reverse direction from 7,984,885-7,985,573n and 287,352-288,040n in Streptomyces collinus genome, respectively, suggesting that these transposases acted in the transposition or the duplication. SRIM_07318 is too short to function as a \u03b2-lactamase, but may be a part of class A \u03b2-lactamase, as its amino acid sequence of the C-terminal part is very similar to that of class A \u03b2-lactamases from nd SHV-1 . InteresS. aureus (M15526), P. aeruginosa (Q03170), K. pneumoniae , K. pneumoniae , and E. coli  as reference sequences disclosed that although these reference sequences are originated from quite different species, they form only one cluster  is also thought to be involved in clavulanic acid biosynthesis [As for the relationship between class A \u03b2-lactamases and the \u03b2-lactam biosynthetic gene cluster, SSCG_00130  is located in the terminal region of the cephamycin biosynthetic gene cluster and SSCG_00160 (corresponding to SCLAV_4187) is located within the clavulanic acid biosynthetic gene cluster in uligerus . These tcattleya . Howeverynthesis ,94.Streptomyces species. The amino acid sequence alignment using the Muscle and T-coffee programs [120) except SSCG_05130, SHJG_8335, SCAT_4145, and SRIM_16845 , S. silaceus (WP_055701146), S. sclerotialus (WP_030612498), S. corchorusii (WP_059262406), S. hygroscopicus (WP_058083348), S. achromogenes (WP_030611171), S. bingchenggensis (WP_014181511), S. alboniger (WP_055536123), S. rimosus (WP_033031918), S. albus (WP_060732298), S. monomycini (WP_030021421) and others, indicating that these sequences belong to the metallo \u03b2-lactamase superfamily. As in the case of class A, while the class B \u03b2-lactamases of pathogenic bacteria form compact clusters in a phylogenetic tree, those from Streptomyces are dispersed quite extensively  fold hydrolases from ensively . Streptomyces species with each other and those with pathogenic bacteria  of Streptomyces corchorusii (WP_059265188), S. bingchenggensis (WP_043485943) , and Nonomuraea candida (WP_043633066); SHJG_2828 is similar to Zn-dependent hydrolases  of multiple species of Streptomyces , Streptomyces fulvoviolaceus (WP_03061340) , and S. avermitilis (WP_010988349); SSCG_03668 is related to serine hydrolases of Streptomyces aureus (WP_037619730), Actinokineospora spheciospongiae (WP_035277635) and Micromonospora parva (WP_030334201); SRIM_31085 is similar to PBPs of S. rimosus (WP_030645517), S. albus (WP_060729102) and S. yokosukanensis (KUN09412); BN_7932 is associated with serine hydrolases of Streptomyces venezuelae (WP_055639726), Streptomyces vietnamensis (WP_041132293), and Streptomyces antibioticus (WP_059193915); SSQS_02491 is similar to serine hydrolases of Streptomyces olindensis (KDN73989), Streptomyces acidiscabies (WP_059045061), and Streptomyces torulosus (WP_055716632); and SSQG_00225 is related to PBPs of Streptomyces chartreusis (WP_010033556), Streptomyces iakyrus (WP_051814832), and Streptomyces pactum (WP_055421147). Therefore, all of these sequences are members of the \u03b2-lactamase superfamily. The amino acid numbers of SHJG_2828, SSCG_03668, SRIM_31085, SSQG_02491 and SSQG_00225 are too short to function as \u03b2-lactamases, and the C-terminal residues in SSCG_03303 are missing. The amino acid sequence alignment of putative 94 class C \u03b2-lactamases is shown in Rhodobacter sphaeroides (YP_355265), Mycobacterium smegmatis (NC_018289), Acinetobacter baumannii (CAB77444), Aeromonas caviae (AF462690_1), and E. coli (ABM69263 and NP_418574) as reference sequences  is present at the N-terminal region in class B PBPs. However the detailed functional role of the dimer domain is yet to be fully explicated. Class C PBPs are also called low molecular weight PBPs and, having the carboxypeptidase activity, are responsible for the maturation and recycling of the peptidoglycan [Actinobacteria in general was already published [Streptomyces, especially emphasizing their roles in self-resistance.The bacterial cell wall peptidoglycan is a three-dimensional, covalently closed, net-like mesh called sacculus in which glycan strands are cross-linked by peptide chains. It maintains cell shape and provides mechanical strength to resist osmotic pressure ,97. The ptidases ,98. The ptidases ,100,101.s C PBPs ,101,102.doglycan , but theublished , so thisStreptomyces are summarized in Streptomyces species together with a reference sequence  were aligned by the Muscle and T-coffee programs , S. hygroscopicus (WP_060948597) and S. bingchenggensis (WP_014181633) are identified and, that of SSQG_02328 as a query, PBPs of S. afghaniensis (WP_037667886), S. iakyrus (WP_033306427), and S. caeruleatus (KUN93289) are identified, and that of SHJG_3853 as a query, PBPs of S. antibioticus (WP_053211715), S. reticuli (CUW28219), and S. achromogenes (WP_030604457) are identified. In addition, the glycosyltransferase and transpeptidase domains are preserved in these sequences, indicating that these sequences can function as PBPs. A phylogenetic tree was constructed by using SCO4049 (putative penicillin acylase) as outgroup , A2 , A3 , A4 , and A5 . Interesting enough, all class A PBPs from Streptomyces analyzed in this paper form a completely different cluster in the phylogenetic tree from these five subclasses , SAV_3603 and SAV_3604 (3 \u00d7 10\u201368), SCLAV_4178 and SCLAV_4180 (1.5 \u00d7 10\u201311), SMCF_7795 and SMCF_7796 (3.3 \u00d7 10\u201360), SCO3156 and SCO3157 (2.8 \u00d7 10\u201347), BN159_5121 and BN159_5122 (1.4 \u00d7 10\u201362), SSFG_04216 and SSFG_04217 (1.3 \u00d7 10\u201356), SSRG_03705 and SSRG_03706 (2.7 \u00d7 10\u201362), SHJG_4627 and SHJG_4628 (2.8 \u00d7 10\u201369), SLIV_21910 and SLIV_21915 (4.3 \u00d7 10\u201358), SCAB_53611 and SCAB_53621 (6.8 \u00d7 10\u201353), SSEG_00010 and SSEG_00011 (3.8 \u00d7 10\u201363), and SSQG_03242 and SSQG_03243 (5.5 \u00d7 10\u201365). These amino acid sequences are not only very similar to each other, but also all the sequences belong to the same cluster in a phylogenetic tree constructed by using SCO4049 as outgroup (cluster C in S. clavuligerus SCLAV_4179 and SCLAV_4180 is an exception. One hundred sixty one class B PBPs in 24 \u201311). This character may be related to \u03b2-lactam production and these two PBPs seem to behave independently, because these two sequences are aligned in the reverse direction, i.e., SCLAV_4180 (pbpA) acts together with the clavulanic acid biosynthetic gene cluster, while SCLAV_4179 (pbp2) goes on with SCLAV_4178  and SCLAV_4177 (RNA polymerase) or independently [S. clavuligerus is reported to have a low affinity to \u03b2-lactam antibiotics and is essential to the growth [S. cattleya which produces only cephamycin but not clavulanic acid, genes corresponding to SCLAV_4180 and SCLAV_4198 are deleted, but a gene (SCAT_5676) corresponding to SCLAV_4179 and responsible for self-resistance is retained [SCLAV_4179 belong to cluster B and SCLAV_4180 pertain to cluster A in the phylogenetic tree and the similarity of the amino-acid sequences is very low , SCLAV_4198 and SCLAV_2276 (8.1 \u00d7 10\u201327), F750_6320 and F750_2998 (5.6 \u00d7 10\u201329), STRVI_1135 and STRVI_3190 (1.2 \u00d7 10\u201361), and SBI_04376 and SBI_06233 (7.7 \u00d7 10\u201336) are examples. Together with the fact that tandem two genes in most Streptomyces species are associated with cluster C as described above, these results clearly indicate that most Streptomyces species are firmly defended by the presence of two low-affinity PBPs. Moreover, cluster B PBPs, to which SCLAV_4179 belong, reconfirm the self-resistance [Streptomyces from the attack of \u03b2-lactam antibiotics. These characters are moderately similar with each other: SRIM_04191 and SRIM_06646  [Actinobacteria. Interestingly, some PASTA domains in PBPs such as PBP2x of S. pneumoniae bind peptidoglycan and \u03b2-lactam antibiotics [M. tuberculosis do not [Actinobacteria [Protein phosphorylation was first described as a major regulatory mechanism in eukaryotes ,109. Lat domain) . PASTA dibiotics , but oths do not , indicats do not ,117,118.bacteria , so in tB. subtilis, Clostridium perfringens and S. pneumonia, PBPs with PASTA domains are detected only in class A PBPs but not in class B PBPs of Actinobacteria. In addition, none of Streptomyces species has PBPs with PASTA domain. Class A PBPs have both transglycosylase and transpeptidase domains, whereas class B PBPs have only transpeptidase domain. Therefore, it is interesting to know the interaction between transglycosylase and PASTA domains in Actinobacteria. However, it is not interpreted yet. Related to this fact, one of class B PBPs without PASTA domain and a STPK with four PASTA domains are located in adjacent position in most Streptomyces species  is reported to form the Streptomyces spore wall synthesizing complex (SSSC) [i.e., SCO3848) and be involved in the peptidoglycan biosynthesis and/or morphogenesis coupled with PBPs and FtsW/RodA family proteins, Jones et al. reported that SCO3848 (PknB) regulates the timing of development and TCA cycle favoring antibiotic production together with two forkhead-associated proteins  [i.e., SCO3845) inside the operon. It is very interesting to know how self-resistance is implicated in this reaction, because the identity and the similarity of PBPs of this group (cluster Ac in \u2013102). The indication that clusters Aa, Ab, and Ac PBPs in In sharp contrast to other bacteria such as  species . For exa species . The genn operon . In S. cx (SSSC) ,121. AltSCO3844) . In addiS. clavuligerus and S. cattleya was shown in S. clavuligerus was published [S. clavuligerus and S. cattleya indicates clearly that clavulanic acid gene cluster from SCLAV_4180 to SCLAC_4198 in S. clavuligerus was inserted between SCAT_5676 and SCAT_5678 of S. cattleya . SCAT_5677 is missing. Interestingly, both sides of the clavulanic acid gene cluster are occupied by PBPs (SCLAV_4180 and SCLAV_4198), indicating that these PBPs behave together with clavulanic acid/cephamycin gene cluster and are involved in the self-resistance of S. clavuligerus. Similar gene constructs for cephamycin biosynthesis are observed in Nocardia lactamdurans, Lysobacter lactamgenus, Penicillium chrysogenum, and Acremonium chrysogenum [P. chrysogenum, and A. chrysogenum. On the other hand, the organizations of clavulanic acid gene clusters of three Streptomyces species, S. clavuligerus, S. flavogirseus, and S. viridis are also similar with each other. Unfortunately, however, relationship between \u03b2-lactam biosynthesis, \u03b2-lactamases, and PBPs remains to be clarified, although \u03b2-lactamase was reported to be expressed during the active growth phase, prior to the formation of \u03b2-lactam antibiotics [Actinobacteria was published [The relationship between \u03b2-lactam biosynthetic gene clusters, \u03b2-lactamases, and PBPs of ublished ,128. Comysogenum ,131,132.ibiotics . Recentlublished .Streptomyces are diverse in their characteristics, and the PBPs are multiplexed in their guard systems. In addition, as antibiotic resistance mechanisms are supposed to originate and evolve in their producing microbes, and be transferred to pathogenic bacteria by transformation, transduction, transfection and/or conjugation, the public health crisis will be getting worse and worse. \u03b2-Lactamases and PBPs are two major resistance mechanisms in pathogenic bacteria against \u03b2-lactam antibiotics, the most frequently used antibiotics for infectious diseases at the present time. Moreover, from about 40 years ago, the rate of discovery of new antibiotics has declined rapidly and many large pharmaceutical companies have abandoned research and development on antibiotics [Streptomyces species are very hard to deal with as described in this paper.Although antibiotics still play a key role for the prevention of microbial infections as remaining treasures from the twentieth century, antibiotic resistance is prevailing and putting us in a critical situation. Furthermore, it is said that the post-antibiotic era is coming soon . As descibiotics . To avoiibiotics ,140,141."}
+{"text": "Leishmania cause severe human and veterinary diseases worldwide, termed leishmaniases. A hallmark of Leishmania biology is its capacity to adapt to a variety of unpredictable fluctuations inside its human host, notably pharmacological interventions, thus, causing drug resistance. Here we investigated mechanisms of environmental adaptation using a comparative genomics approach by sequencing 10 new clinical isolates of the L. donovani, L. major, and L. tropica complexes that were sampled across eight distinct geographical regions. Our data provide new evidence that parasites adapt to environmental change in the field and in culture through a combination of chromosome and gene amplification that likely causes phenotypic variation and drives parasite fitness gains in response to environmental constraints. This novel form of gene expression regulation through genomic change compensates for the absence of classical transcriptional control in these early-branching eukaryotes and opens new venues for biomarker discovery.Protozoan parasites of the genus  Leishmania adapt to environmental change through chromosome and gene copy number variations. Only little is known about external or intrinsic factors that govern Leishmania genomic adaptation. Here, by conducting longitudinal genome analyses of 10 new Leishmania clinical isolates, we uncovered important differences in gene copy number among genetically highly related strains and revealed gain and loss of gene copies as potential drivers of long-term environmental adaptation in the field. In contrast, chromosome rather than gene amplification was associated with short-term environmental adaptation to in vitro culture. Karyotypic solutions were highly reproducible but unique for a given strain, suggesting that chromosome amplification is under positive selection and dependent on species- and strain-specific intrinsic factors. We revealed a progressive increase in read depth towards the chromosome ends for various Leishmania isolates, which may represent a nonclassical mechanism of telomere maintenance that can preserve integrity of chromosome ends during selection for fast in vitro growth. Together our data draw a complex picture of Leishmania genomic adaptation in the field and in culture, which is driven by a combination of intrinsic genetic factors that generate strain-specific phenotypic variations, which are under environmental selection and allow for fitness gain.Protozoan parasites of the genus  Leishmania are transmitted by female blood-feeding sand flies and can cause severe diseases in infected humans and animals. The success of this pathogen relies on its capacity to sense changes in various host environments that trigger a series of distinct developmental transitions  of individual genes or chromosomes linked to drug resistance  from eight geographical regions. Read depth analysis revealed gene and chromosome CNVs as potential drivers of long-term and short-term adaptation, respectively. Isolates during early and later stages of culture adaptation showed reproducible karyotypic changes for a given strain, providing strong evidence that chromosomal amplification is under positive selection. Significantly, these changes occurred in an individualized manner in even highly related strains, thus implicating for the first time environment-independent intrinsic genetic factors affecting Leishmania karyotypic adaptation.Combining DNA sequencing (DNA-seq) and transcriptome sequencing (RNA-seq) analyses of karyotypically distinct fication , 13\u2014a fofication , 14, 15.specific , suggestspecific , 16. DesLeishmania strains belonging to the L. tropica, L. major, or L. donovani complexes were obtained from different sources and regions , and parasites from early passage (passage 2) and later culture passages  were subjected to sequencing analysis (see Ten 10.1128/mBio.01399-18.1FIG\u00a0S1in vitro. Promastigotes from logarithmic culture at passage 2 (early passage [EP]) or passage 5 (EP\u2009+\u20093) were subjected to sequencing analysis to monitor the dynamics of genomic adaptation to the culture environment. For certain strains, two independent cell cultures were derived for EP\u2009+\u20093 to test for reproducibility of genome adaptation between biological replicates (EP\u2009+\u20093.1 and EP\u2009+\u20093.2). Download FIG\u00a0S1, PDF file, 0.1 MB.Overview of experimental design. Clinical isolates were obtained from infected patients or dogs, placed in culture under standardized conditions, and maintained for a defined number of passages Copyright \u00a9 2018 Bussotti et al.2018Bussotti et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Leishmania species .We first used the EP sequence information to confirm species determination and to characterize strain-specific genetic variations that may inform on mechanisms of adaptation. Principal-component analysis (PCA) and clustering analyses based on the average nucleotide identity (ANI) among strains confirmed the molecular determination of the various analysis \u201319. Base10.1128/mBio.01399-18.2FIG\u00a0S2Leishmania isolates used in this study and the indicated Leishmania reference assemblies is shown by the PCA (A) and clustering analyses (B). In the PCA plot, the L. donovani and the L. major clusters are, respectively, highlighted in green and cyan. Download FIG\u00a0S2, PDF file, 0.5 MB.Species validation. The genomic distance between the Copyright \u00a9 2018 Bussotti et al.2018Bussotti et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the L. infantum isolates  across the isolates  confirmeuggested , 20, or uggested .L. major samples, with 36,726 SNVs shared between the strains compared to the reference genome , revealing a potential role of these structural genome variations in L. donovani adaptation.Finally, the SNV analysis revealed the close genetic relationship between the Tunisian and Algerian e genome . The masanalyses . Differehttps://gitlab.pasteur.fr/gbussott/Leishmania_genome_dynamics_during_environmental_adaptation_reveals_strain_specific_differences/]). Plotting the gene coverage values for the three L. infantum isolates, the three L. donovani isolates, or the two L. major isolates together with the L. tropica sample, resulted in strong, confined signals at the center of the ternary plots that correspond to genes with equal copy number and thus a 33% distribution across the three axes . A selection of annotated genes is shown in Cross-comparison of read depths among the EP samples revealed important intraspecies variations in copy number for single- and multicopy genes  a 2.94-fold amplification in Linf_LLM56 of LinJ.30.2990 encoding a glyceraldehyde 3-phosphate dehydrogenase, (ii) a cluster of seven genes (Linj.29.0050 to Linj.29.0110) located in an \u223c23-kb region delimited by SIDER repetitive elements that showed a 2-fold amplification in Linf_ZK27, and (iii) the amplification (up to 32-fold) of the GP63 leishmanolysin cluster (LinJ.10.0490 to LinJ.10.0530) in Linf_02A. For L. donovani, we identified (i) a 48-fold amplification specific to Ldo_LTB of a cluster of 10 genes (LdBPK_350056400 to LdBPK_350057300), which includes a biopterin transporter, an RNase P, an RNA pseudouridylate synthase, and a putative ribosomal L37e protein, (ii) an up to 26-fold amplification in Ldo_BPK26 of a putative amastin surface glycoprotein (LdBPK_340024100), and (iii) the deletion in Ldo_CH33 and partial depletion in Ldo_LTB of a putative amastin-like surface protein (LdBPK_340015500). Finally, as expected from their phylogenetic relationship, important differences were observed in gene CNVs between the L. tropica and L. major strains, including (i) an amplification on chromosome 35 in both Lmj_1948 and Lmj_A445 , spanning a hypothetical protein (LmjF.35.0250) and the 5\u2032 portion of a putative GTPase-activating protein (LmjF.35.0260), (ii) an up to 6-fold amplification in Ltr_16 of a putative KU80 protein (LmjF.30.0340) flanked by SIDER2 elements, and (iii) an Lmj_A445-specific amplification of a small nucleolar RNA (snoRNA) cluster on chromosome 26.In Leishmania field isolates, causing phenotypic differences with respect to stress resistance, nutrition, and infectivity, as judged by gene CNVs observed in heat shock proteins, transporters, and known virulence factors  and read depth analysis revealed important karyotype differences between the two in vitro passages of a given strain (intrastrain variation) and among different strains (interstrain variation). Aside from an intrachromosomal duplication at both EP and EP\u2009+\u20093 observed in Ldo_LTB spanning nearly half of chromosome 27  affecting 113 genes, changes in read depth were homogenous across all chromosomes, thus revealing frequent aneuploidy . All other isolates showed higher intrastrain karyotype instability with both gain and loss of chromosomes observed between EP and EP\u2009+\u20093. Linf_02A represented the most extreme example showing significant changes in read depth for 21 chromosomes  Circos plot representing the normalized sequencing coverage of the strains indicated. The bar height correlates with sequencing coverage. The coverage is shown on the vertical axis and ranges from 0 to 3. The ticks, scaled to represent 100 kb, show the genomic position. Green, early passage (EP); orange, EP\u2009+\u20093.1 replicate; purple, EP\u2009+\u20093.2 replicate. (B) Enlargement of Lmj_1948 chromosomes 10, 11, 14, 24, 26, 27, and 35. Download Copyright \u00a9 2018 Bussotti et al.2018Bussotti et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the in vitro. While differences in culture conditions certainly account for some of the observed karyotypic variability, the comparison of two closely related Spanish L. infantum isolates, Linf_LLM45 and Linf_LLM56, reveals a culture-independent component implicated in genomic adaptation. Both isolates were adapted to culture at the same time under the same conditions, yet they showed important differences in karyotype dynamics, with only Linf_LLM56 demonstrating changes in somy levels at EP\u2009+\u20093 . These strains are genotypically identical (zymodeme MON-1) (see Table\u00a0S1 at GitLab) and are genetically closely related, with an average nucleotide identity of over 99.95%, suggesting that minor genetic differences may have an important impact on Leishmania karyotypic adaptation to a given environment. Aside from SNVs , L. infantum (Linf_ZK27), L. donovani (Ldo_BPK26), and L. tropica (Ltr_16)  . Thus, ehttps://gitlab.pasteur.fr/gbussott/Leishmania_genome_dynamics_during_environmental_adaptation_reveals_strain_specific_differences/]). Overall, the majority of genes were scattered around a normalized coverage of 1 , suggesting that their copy number matches the one in the reference strains. We nevertheless observed a significant number of genes across all isolates that showed coverage either below 0.5-fold or above 2-fold, independent of culture passage, thus, revealing important differences between the isolates and their corresponding reference genomes. This analysis uncovered a significant increase in coverage at EP\u2009+\u20093 for all chromosomes of strain Linf_02A  versus EP (x axis). The red diagonal lines indicate the bisectors. To show the extent of gene CNV with respect to the reference genomes, the axis limits are not fixed but dynamically assigned for each chromosome to include the maximum and the minimum measured values. Download FIG\u00a0S4, PDF file, 1.6 MB.Chromosome-specific gene coverage variation analysis. For each sample and for each chromosome, the scatter plots show the normalized gene coverage for EP\u2009+\u20093  as they may play important roles in virulence and may qualify as biomarkers with diagnostic or prognostic value.Drawing from newly generated genome sequences of de novo culture as a proxy for short-term environmental adaptation revealed two forms of dynamic genomic changes. First, as judged by the establishment of reproducible aneuploidy profiles in duplicate cultures of a given strain, chromosomal amplification is the result of selection rather than random genetic drift. This result corroborates our previous observations in the L. donovani experimental strain LD1S, where spontaneous karyotypic fluctuations generate genotypically and phenotypically diverse mosaic populations that are substrates for evolutionary adaptation and fitness gain in response to environmental change , as revealed by a progressive increase in sequencing read depth toward the chromosome ends. Nonclassical mechanisms of telomere maintenance have been documented in a variety of eukaryotes, including (i) rolling circle replication in Kluyveromyces lactis, implicating extrachromosomal circular templates  br repeats , or . For environmental adaptation, Leishmania can draw from a vast genetic landscape of spontaneous karyotypic fluctuations, stochastic gene amplifications, and nucleotide polymorphisms. Our comparison of highly related Spanish L. infantum isolates revealed that even small variations in sequence might result in important differences in karyotypic adaptation. Thus, closely related isolates evolving in the same epidemiological niche can attain similar levels of fitness in a highly pleiotropic way using alternative genetic solutions . Some strains were cryopreserved in liquid nitrogen prior to culture adaptation until used for this study (see Table\u00a0S1 at GitLab). Leishmania isolates were first stabilized in vitro in media that were optimized in the various LeiSHield partner laboratories , prior to expansion in classical RPMI culture medium for a defined number of passages (expansion medium). Seven strains belonging to the L. donovani complex were selected for the comparison of intraspecies evolvability in culture. These include the four L. infantum strains Linf_ZK27 from Tunisia, Linf_LLM56 and Linf_LLM45 from Spain, and Lin_02A from Brazil , as well as the three L. donovani strains Ldo_BPK26 from India, Ldo_LTB from Sudan, and Ldo_CH33 from Cyprus. The latter strain belongs to the L. donovani MON-37 zymodeme (\u2013L. donovanisensu lato (s.l.) group  and one L. tropica strain (Ltr_16 from Morocco) (see Table\u00a0S1 at GitLab). Genotyping methodologies were applied to confirm species identity of the strains used in this work (see Table\u00a0S1 at GitLab). Standardized procedures for DNA sample preparation and cell culturing or subculturing were used in all partner laboratories (see Table\u00a0S2 at GitLab). Promastigotes from early cell culture  and derived parasites maintained in culture for three more in vitro passages (EP\u2009+\u20093) were processed for whole-genome sequencing (WGS) using parasites from the late logarithmic growth phase. While different Leishmania strains can show differences in terms of generation time and can reach different population densities, we previously estimated that a single passage in culture corresponds to ca. 10 generations  were generated for the Linf_ZK27, Lmj_1948, Lmj_A445, Ldo_BPK26, and Ltr_16 strains , Ldo_LTB_EP\u2009+\u20093 (5 cycles), Linf_02A_EP (10 cycles), Linf_02A_EP\u2009+\u20093 (5 cycles). No PCR amplification was performed for the other samples.https://research.pasteur.fr/en/team/biomics/) with Hiseq 2,500 rapid runs, resulting in 2\u2009\u00d7\u2009108-bp reads using the NEXTflex PCR-Free kit. All other samples were sequenced with the KAPA Hyper Prep kit (Kapa Biosystems) at Centro Nacional de An\u00e1lisis Gen\u00f3mico (CNAG [http://www.cnag.crg.eu/]) using the TruSeq SBS kit v3-HS . Multiplex sequencing was performed according to standard Illumina procedures, using HiSeq2000 flowcell v3, generating 2\u2009\u00d7\u2009101-bp paired-end reads.Whole-genome, short-insert, paired-end libraries were prepared for each sample. Samples Ltr_16_EP, Ltr_16_EP\u2009+\u20093.1, Ltr_16_EP\u2009+\u20093.2, Ldo_BPK26_EP, Ldo_BPK26_EP\u2009+\u20093.1, Ldo_BPK26_EP\u2009+\u20093.2, Lmj_A445_EP, Lmj_A445_EP\u2009+\u20093.1, and Lmj_A445_EP\u2009+\u20093.2 were sequenced by the Biomics sequencing platform (L. major Friedlin and L. infantum JPCM5 were downloaded from the Sanger FTP server (ftp://ftp.sanger.ac.uk/pub/project/pathogens/gff3/CURRENT/) on 5 September 2017, whereas PacBio L. donovani LDBPK assembly and annotations were downloaded on 5 February 2017 (ftp://ftp.sanger.ac.uk/pub/project/pathogens/Leishmania/donovani/LdBPKPAC2016beta). The reads were aligned to the reference genomes with BWA mem (version 0.7.12) (\u2013https://broadinstitute.github.io/picard/) using the option \u201cVALIDATION_STRINGENCY=LENIENT.\u201d While the reads were aligned against full assemblies, including unsorted contigs, just the canonical 36 chromosomes were considered for downstream analyses of ploidy estimation and copy number alterations. This filter was necessary because of the high content of repetitive elements and the absence of comparable and high-quality annotations in the contigs. Given that the L. tropica reference genome is still unfinished, the sample Ltr_16 was aligned against the L. major Friedlin genome. Overall, starting from a total of 1,011,803,806 short reads, 952,093,114 were successfully aligned to the respective reference genomes . Picard CollectAlignmentSummaryMetrics was used to estimate sequencing and mapping statistics.Gene annotations and reference genomes of P server . (ftp:// 0.7.12) , 29 with 0.7.12)  were use 0.7.12) \u201333 were Leishmania isolates were processed with Trimmomatic (version 0.35)  and reference genomes of L. braziliensis, L. mexicana, and L. panamensis that were retrieved from ENSEMBL Protists release 29 (https://www.r-project.org/]).Whole-genome sequencing data from the EP on 0.35)  to removon 0.35) . The analease 29 . The ANIFor each read alignment file, Samtools view (version 1.3) and BEDTools genomecov (version 2.25.0)  were useP values and the chromosome somy comparisons are reported in Table\u00a0S4 at GitLab .The chromosome sequencing coverage was used to evaluate aneuploidy between EP and EP\u2009+\u20093 samples. For each sample and for each chromosome, the median sequencing coverage was computed for contiguous windows of 2,500 bases. For those strains for which two EP\u2009+\u20093 samples were available, the mean of EP\u2009+\u20093.1 and EP\u2009+\u20093.2 was used to calculate the statistical significance of amplification compared to EP. The distributions of the median window coverage in EP and EP\u2009+\u20093 were compared by one-way analysis of variance (ANOVA). To have an estimate of the chromosome copy number differences, the window coverage was further normalized by chromosome 19 median coverage and multiplied by 2. For each chromosome, the median values in EP and EP\u2009+\u20093 were compared. Both the ANOVA Samtools view (version 1.3) and BEDTools coverage (version 2.25.0) were used to measure the mean sequencing depth of every annotated gene and were run, respectively, with options \u201c-q 50 -F 1028\u201d and \u201c-d -split.\u201d Possible intragenic gap regions were excluded from the calculation of the mean. Then the mean coverage of each gene was normalized by the median coverage of its chromosome. To account for GC content sequencing bias, the coverage values were corrected using a LOESS regression with a 5-fold cross validation to optimize the model span parameter. Genes supported by reads with a mean mapping quality (MAPQ) score of <50 were filtered.To enable CNV analysis of gene arrays and genes sharing high sequence identity, we clustered the nucleotide sequences of the annotated genes into groups with cd-hit (version 4.6) . We usedhttp://www.repeatmasker.org]) using the options \u201c-e crossmatch -gff -xsmall -s\u201d in combination with Repbase . In the genome browser tracks, the repeat elements and low-complexity regions were predicted with RepeatMasker (version 4.0.6)   was usedhttps://gitlab.pasteur.fr/gbussott/Leishmania_genome_dynamics_during_environmental_adaptation_reveals_strain_specific_differences/).To call single nucleotide variants (SNVs), we used Freebayes (version v1.0.1-2-g0cb2697)  with optDELLY (version 0.6.7)  was run Leishmania reference genomes and explore their syntenic relation: L. infantum JPCM5, L. donovani PBQ7IC8, L. major Friedlin, and L. donovani BPK282A1. This new tool hosting Leishmania syntenic data is publicly available at http://genopole.pasteur.fr/SynTView/flash/Leishmania/SynWebLinfantum.html.The synteny analysis was performed with SyntView , a softwhttps://gitlab.pasteur.fr/gbussott/Leishmania_genome_dynamics_during_environmental_adaptation_reveals_strain_specific_differences/.All supplemental tables are publicly available at GitLab at SRP126578.Reads were deposited in the Sequence Read Archive database (SRA) database  and are"}
+{"text": "Esophageal squamous cell cancer (ESCC) is a high incidence and mortality disease worldwide. However, specificity and sensitivity of its diagnostic and prognostic biomarkers are still unsatisfactory. Recently, circular RNAs (circRNAs) as biomarkers have been studied extensively while the expression profile and clinical significance of circRNAs in ESCC have rarely been studied. We performed circular RNA microarray in 3 pairs of ESCC frozen tumor and non-tumor tissues to identify ESCC-related circRNAs and found 1045 up-regulated and 1032 down-regulated circRNAs among which 6 circRNAs  displayed consistency with microarray results by qRT-PCR. 3 circRNAs  were also detected in plasma and 2 of them except hsa_circ_0062459 could be used as diagnostic biomarkers and found in exosome of cell-conditioned culture conditioned media. The AUC, sensitivity and specificity of hsa_circ_0001946 were 0.894, 92, 80%, of hsa_circ_0043603 were 0.836, 64, 92% while a signature combining them were 0.928, 84 and 98%. Hsa_circ_0001946 was confirmed to predict the recurrence, overall survival (OS) and disease-free survival (DFS) in frozen and FFPE tissues, while its overexpression decreased cell proliferation, migration, and invasion.The online version of this article (10.1186/s12943-018-0936-4) contains supplementary material, which is available to authorized users. Esophageal cancer (EC) is a common cancer type with high incidence and mortality rate, which is characterized by the international variance of incidence rate and pathologic patterns . The hig: Figure S1). We demonstrated that circRNAs could be used for ECSS diagnosis or prognostic prediction and as promising targets for ESCC treatment.CircRNAs are could be free from RNA exonuclease and exhibits higher stability than messenger or linear noncoding (NC) RNA . Additio1: Figure S2).To verify the results of microarray and identify the most possible clinical biomarkers, we ranked up-regulated and down-regulated DE circRNAs respectively according to fold changes, P value, processed signal value and the number of miRNA-responsive elements (MREs). 8 circRNAs were picked  and 6 circRNAs  were consistent with the microarray result by qRT-PCR in another 10 pairs of tissues . Then we expanded sample size to 50 pairs to measure the expression level of these 6 DE circRNAs  and explore their relationship with clinicopathological characteristics of ESCC patients .We conducted high-throughput human circRNA microarray to assess the differences of circRNA expression profiles between ESCC frozen tumor and non-tumor tissues.Since blood is the most commonly used sample in laboratory medicine and blood test is a less invasive method in clinical medicine, we decided to found circRNAs secreted to blood among these 6 ones mentioned above and assessed their value as diagnostic biomarkers. It was shown that hsa_circ_0042261, hsa_circ_0072215, and hsa_circ_0076535 were neither found in patients\u2019 nor healthy people\u2019s plasma and serum but existed in both frozen tumor and non-tumor tissues .Then, another 50 pre-operative plasma from patients diagnosed as ESCC by pathological findings were collected while 50 plasma from healthy people were gathered as control. Levels of hsa_circ_0001946, hsa_circ_0043603, and hsa_circ_0062459 were detected in these samples. We found that expression levels of hsa_circ_0001946  and overall survival (OS) prediction, patients in the high hsa_circ_0001946 group (according to the median level) had a much shorter DFS and OS  showed that expression of hsa_circ_0001946 by FISH was associated with DFS and OS and an independent prognostic factor.And then, we used FISH for semi-quantitation and location of hsa_circ_0001946. As Additional\u00a0file\u00a09: Figure S6a). The result was conjoint with predicted miRNAs bond to hsa_circ_0001946 based on miRanda and miRNA-7-5P stood out from the crowd. Then we make target prediction of miRNA-7-5p by starBase and 1597 targeted mRNA were found. We picked 6 genes which were positive in all 5 algorithms . As it was shown in Additional file 11: Figure S6b. Finally, we performed GO and KEGG analysis as well as miRNA cluster analysis by DIANA tool .Increasing studies have revealed the sponge role of circRNA to miRNA by a conserved seed sequence. We also conducted miRNA microarray in the same ESCC frozen samples and performed coexpression analysis . The results also showed that hsa_circ_0001946 overexpression significantly decreased the proliferation, migration, and invasion of TE-1, K-30, K-50 .We next explored the role of hsa_circ_0001946 in pathogenic mechanism of ESCC in vitro. Firstly, stable cell line overexpressing hsa_circ_0001946 was built by lentiviral transduction into Eca-109, TE-1, K-30, K-50 and named Eca-109-V, TE-1-V, K-30-V, K-50-V while the control cell lines were named Eca-109-NC, TE-1-NC, K-30-NC, K-50-NC. The lentiviral transduction elevated hsa_circ_0001946 expression a lot in these cell lines Additional file 2:Figure\u00a0S2. Screening of differentially expressed circRNAs by circRNA microarray and functional annotation of their host genes. (A) Hierarchical clustering results of circRNAs expression profiles among 3 pairs of ESCC tumor tissues and non-tumor tissues. Red represented relatively high expression while green represented relatively low expression. A1~C1 were tumor tissues while A2~C2 were non-tumor tissues. (B) Red dots in the scatter-Plot indicated high expressed circRNAs while green dots here indicated low expressed circRNAs. (C) Go analysis of host genes was performed to obtain three categories . (D) The top 30 signaling pathways potentially involved in the circRNA-mediated regulatory network in ESCC by KEGG analysis of host genes. (PDF 3924 kb)Additional file 3:Table\u00a0S1. Selected DE circRNAs by microarray and validation by qRT-PCR. *number of MRE targeted by the related circRNA; DE: differentially expressed; FC: fold change; MRE: miRNA response elements (DOCX 17 kb)Additional file 4:Figure\u00a0S3. Confirmation of differentially expressed circRNAs by qRT-PCR in frozen tumor and non-tumor tissues. (A)~(H)The expression levels of 8 circRNAs were detected by qRT-PCR in 10 pairs of frozen tissues. Mann-Whitney test was used for the significance test. (PDF 1728 kb)Additional file 5:Figure\u00a0S4. Confirmation of differentially expressed circRNAs by qRT-PCR in frozen tumor and non-tumor tissues. (A)~(F) The expression levels of 6 circRNAs were detected by qRT-PCR in 50 pairs of frozen tissues. Student\u2019s t-test was used for significance test. (PDF 2114 kb)Additional file 6:Table\u00a0S2. Relationships of circRNAs expression levels in ESCC frozen tumor tissues with clinicopathological characteristics by qRT-PCR. (DOCX 21 kb)Additional file 7:Table S3. Average expression of 6 circRNAs in 6 ESCC patients\u2019 samples and 6 healthy people\u2019s samples. *It means hsa_circ_0042261 was detected in one samples among total 6 samples (DOCX 14 kb)Additional file 8:Table\u00a0S4. Relationships of circRNAs expression levels in plasma of ESCC patients with clinicopathological characteristics by qRT-PCR. (DOCX 18 kb)Additional file 9:Figure\u00a0S5. Semi-quantitation and location of hsa_circ_0001946 by FISH in FFPE tissues. (A~B) High and low expression levels of hsa_circ_0001946 in samples by Immunofluorescence Accumulation Optical Density (IOD) analysis under 200X condition. (C) Expression pattern of hsa_circ_0001946 under 400X condition. (D) This image was recorded on a wide-field fluorescence microscope via a 63\u00d7 oil objective. It represented the localization of hsa_circ_0001946 in cells. The blue color was stained nuclei by DAPI, and the green color was stained hsa_circ_0001946 in the cytoplasm. (PDF 32764 kb)Additional file 10:Table\u00a0S5. Univariate and multivariate Cox regression analyze hsa_circ_0001946 for overall survival (OS) and disease-free survival (DFS) of patients in frozen tumor tissue, FFPE tissue and plasma of ESCC patients. (DOCX 17 kb)Additional file 11:Figure\u00a0S6. Prediction an annotation of hsa_circ_0001946 targeted miRNA-mRNA network. (A) The coexpression network of circRNAs and miRNAs obtained by microarray tested in the same three pairs of ESCC tumor tissues and non-tumor tissues. Green ones represent miRNAs while yellow ones represent circRNAs. (B) Hsa_circ_0001946 targeted miRNA-mRNA network combined with the microarray results and predicted by several algorithms. Yellow one represents algorithms hsa_circ_0001946. Pink and green ones represent targeted miRNAs of hsa_circ_0001946. Blue ones represent targeted mRNAs of hsa-miR-7-5P. (C~D) The GO and KEGG analysis of the targeted miRNAs of hsa_circ_0001946. (E~F) The miRNA cluster analysis of the targeted miRNAs of hsa_circ_0001946. (PDF 9920 kb)Additional file 12:Figure\u00a0S7. Hsa_circ_0001946 overexpression affects the proliferation, migration, and invasion of K30, K50, and TE-1. (A)(D)(G) MTT method showed that hsa_circ_0001946 overexpression inhibited K30, K50, and TE-1 proliferation after 24\u2009h. (B)(E)(H) Wound-healing assay indicated that hsa_circ_0001946 overexpression decreased K30, K50 and TE-1 migration after 24\u2009h. (C)(F)(I) Transwell assays showed that hsa_circ_0001946 overexpression decreased K30, K50 and TE-1 migration and invasion after 48\u2009h incubation.  (J)Subcutaneous xenografts excised from nude mice.  (PDF 33853 kb)"}
+{"text": "The canopy leaves including the top three, i.e., the flag, the 2nd and 3rd from the top, are important for photosynthesis and grain yield of wheat. Molecular markers associated with traits of these leaves should be helpful for the high-yielding breeding. In this study, 1366 single nucleotide polymorphisms (SNP) markers covering the whole genome of durum wheat were used to genotype 150 cultivars collected from 46 countries and regions in the world. Leaf length, leaf width and chlorophyll content of the top three leaves were measured, respectively, in three consecutive years. Association analyses were performed on the leaf traits and SNP markers. A total of 120 SNP marker associations were detected on 13 of the 14 chromosomes. Among these markers, 83 were associated with the canopy leaf traits, 10 with 1000-grain weight, and 29 with kernel number per spike. This study is helpful for better understanding the potential and genetic basis of functional leaves, and facilitates pyramiding of the favorable alleles using marker assisted selection for ideal plant-type and high photosynthesis efficiency in durum wheat breeding. Triticum spp.) is one of the major food crops that widely planted in the world [nd and the 3rd leaf from the top) are the most important for the entire life cycle of wheat [Wheat . Xue et al. [TaFLW1 for flag leaf width into the Xzmw482\u2014Xzmw752 interval of 0.2 cM on chromosome 5A. Eleven QTL controlling flag leaf width were detected and mapped to chromosomes 1B, 2A, 2B, 3A, 4D, 5A, 6B and 7D by Wu et al. [At present, many QTL have been identified for the morphological traits of flag leaves in rice and barley ,14, while et al.  mapped tu et al. . At the u et al. . Liu et u et al.  detectedu et al. .Y1718 in common wheat was identified and mapped to chromosome 2BS using molecular markers [The regulation mechanism of chlorophyll content is very complicated. Any variation related to chloroplast differentiation and chlorophyll metabolism can lead to the change of chlorophyll content, and leaf color variation manifested is shown. In addition, some of the genes that indirectly regulate chlorophyll metabolism and the pathway of chloroplast differentiation and development may lead to the change of chlorophyll content. In the model organisms such as Chlamydomonas, Arabidopsis and rice, all enzymes involved in chlorophyll biosynthesis have been identified . However markers .Based on the linkage disequilibrium (LD) of alleles, association mapping analysis can be performed to reveal relationship between molecular markers and target traits \u201327. So fT. aestivum) and tetraploid durum wheat (T. durum) [Cultivated wheat consists of mainly two species, the hexaploid bread wheat (. durum) . The mod. durum) ,38. It i. durum) . In thisOne hundred and fifty durum wheat germplasm accessions collected from 46 countries and regions in the world were used in the study ,39. Durind and the 3rd leaf form the top were measured, respectively. Mean of the 6 plants was calculated as the phenotypic value for the specific leaf traits of a genotype.At flowering stage, 6 plants with uniform vegetative and reproductive growth, and without disease and pests were randomly chosen from each accession. The leaf length, width and chlorophyll content of the canopy leaves, flag leaf, the 2The leaf length was measured as the distance from the leaf ear to the leaf tip. The leaf width referred to width at the widest part of the leaf. The leaf area was calculated as length \u00d7 width \u00d7 0.75, as described previously . Chlorop2); FLCC, the flag leaf chlorophyll content; SLL, the upper second leaf length (cm); SLW, the upper second leaf width (cm); SLA, the upper second leaf area (cm2); SLCC, the upper second leaf chlorophyll content; TLL, the upper third leaf length (cm);TLW, the upper third leaf width (cm); TLA, the upper third flag leaf area (cm2);TLCC, the upper third leaf chlorophyll content; TATL, total area of the top three leaves (cm2); ACTL, average chlorophyll content of top three leaves; KGW, 1000-grain weight (g); and KN, kernel number per spike.In total, 16 traits were measured or calculated: FLL, the flag leaf length (cm); FLW, flag leaf width (cm); FLA, the flag leaf area . The probability threshold for a significant trait-marker association was set as 0.001, equivalent to LOD = 3.0. Both Q-Matrix of the population structure and K matrixes used as covariate in MLM analysis were established as described previously [In total, 14 leaf traits and 2 grain yield trait described above were subjected to association analyses with the SNP markers. The analyses were performed based on the mixed linear model (MLM) with software TASSEL 3.0.124  among genotypes for all the phenotypic traits were calculated. Mean and CV of the 15 examined traits in three consecutive years were shown in Distribution histograms of the 15 traits were presented in Ninety-two SNP markers were found to be significantly associated with the canopy leaf traits and 1000-grain weight in three consecutive years. In the three years from 2015 to 2017, we detected 13, 60 and 42 SNP marker-trait associations, respectively. Among these 115 associations, 23 were repeatedly detected for two years .BE404339_7_B_649 associated with FLL) to 46.39% (BE637485_5_B_Y_219 associated with SLL). Three SNP markers, BE585760_2_A_Y_481 , CD452967_5_B_Y_229 , and BE637485_5_B_Y_219 , could explain over 30% of variation  by marker ranged from 7.48% , CD452967_5_B_Y_229  and BE637485_5_B_Y_219   .BF474023_3_A_Y_425 associated with FLA) and 45.97% (BE637485_5_B_Y_219 associated with SLA). There were three SNP markers with PVE > 30%, namely BE585760_2_A_Y_481 , CD452967_5_B_Y_229 , and BE637485_5_B_Y_219   .BE490384_2_A_Y_544 (associated with FLCC and SLCC in 2016 and 2017), BE585760_2_A_Y_481 (associated with FLCC and SLCC in 2016 and 2017), CD452967_5_B_Y_229 (associated with FLCC and SLCC in 2016 and 2017), and BG274019_2_B_N_260 (associated with FLCC and SLCC in 2016 and 2017) The PVE was in a range between 8.81% (BF482960_4_B_Y_75 associated with SLCC) and 38.01% (CD452967_5_B_Y_229 associated with FLCC). Notably, two markers BE585760_2_A_Y_481  and CD452967_5_B_Y_229  explained more than 30% of the phenotypic variation . We detected 120 SNP markers associated with the canopy leaf and grain yield traits  were detected on all the 14 chromosomes except for 3B, and 18 of these SNP were associated with all the traits examined. However, the genomic distribution of the associations is uneven, mostly on 2A, 3A, 5B, and 6B chromosomes . The numBG274019_2_B_N_260 was associated with nine leaf traits, FLL, FLW, FLA, FLCC, SLL, SLW, SLA, SLCC, TLL, and was located in the region of 2BL6-0.89\u20131.00, a physical interval of about 538.9 Mb  reported by Liu et al [In this study, 538.9 Mb . Wu et a438.3 Mb . Of thesiu et al .BE443540_7_B_N_1397 was associated with all of the 10 morphological traits, and could explain over 11% of the phenotypic variation in the three consecutive years. Physical bin mapping analysis showed that the BE443540 located in the wheat chromosome bin C-7BL2-0.33 (http://wheat.pw.usda.gov/GG2/index.shtml), and had high homology (E = 1e-166) with the putative lipase ROG1 in Sorghum bicolor (http://www.ncbi.nlm.nih.gov/). ROG plays a key role in regulating plant growth and development, stress resistance and morphogenesis of tissues and organs [BE443540 was also found to be associated with the seedling traits of durum wheat, growth rate of fresh weight and number of leaves in our previous study [BE443540_7_B_N_1397 is evidently involved in the regulation of wheat growth and development.The EST-derived SNP marker d organs . BE44354us study . TherefoBE590521 was developed from wheat 20\u201345 DAP spike cDNA library, mapped into the wheat chromosome bin C-6BL3-0.36 (http://wheat.pw.usda.gov/GG2/index.shtml), and had very high homology (E = 6e-108) with adenine phosphoribosyl transferase (http://www.ncbi.nlm.nih.gov/). The derived SNP marker BE590521_6_B_N_331 was found to be significantly associated with leaf length and leaf area in the present study  with DNA damage-inducible protein (http://www.ncbi.nlm.nih.gov/). The SNP marker BE606541_6_B_Y_676 was shown to be associated with the morphological traits of all the three canopy leaves with R2 > 13% in the present study  with the pre-mRNA-splicing factor ATP-dependent RNA helicase (BE445587_7_A_N_347 showed very high homology (E = 0.0) with the ABC transporter C family member. ABCC (MRP) transporter was initially identified as an ion pump for transporting GS conjugates on vacuoles, which also participate in other physiological processes, such as detoxification in cells, transport of chlorophyll metabolites, and regulation of ion channels [PDR1) with a SNP marker BM137384_5_A_444 in this study. Previous study demonstrated that TaPDR1 was associated with gibberellic disease[The EST of helicase . In planchannels . Meanwhic disease. TherefoBE405834_1_B_Y_216 showed very high homology (E = 0.0) (http://www.ncbi.nlm.nih.gov/) with the soluble inorganic pyrophosphatase  with the LIM domain-containing protein. LIM protein family mediates protein-protein interactions and has one or more zinc finger structures in its molecular structure [The EST of SNP marker tructure . The famtructure ,60. In otructure . At the BE490384_2_A_Y_544 and BE585760_2_A_Y_481, in the 2AL1-0.85\u20131.00 region of chromosome 2A and the size and chlorophyll content of the canopy leaf  [BE517711_5_B_49. Given that SNP markers for canopy leaf-related traits co-localized in the same region, the region should contain a major QTL with pleiotropic effects or multiple linked SNP markers.About 80% of wheat yield is accumulated through photosynthesis in canopy leaves . Chloropopy leaf . One SNPWe demonstrated significant positive correlations among morphological traits , and negative correlations between the morphological traits and the chlorophyll content of the canopy leaves in durum wheat . There wS1 Table2).LL, leaf length (cm); LW, leaf width (cm); LA, leaf area (cm(DOCX)Click here for additional data file.S2 TableFLCC, flag leaf chlorophyll content; SLCC, second leaf chlorophyll content; TLCC, third leaf chlorophyll content; ACTL: average chlorophyll content of top three leaves.(DOCX)Click here for additional data file.S3 TableKGW, 1000-grain weight (g).(DOCX)Click here for additional data file.S4 TableKN, kernel number per spike.(DOCX)Click here for additional data file.S5 Tablehttp://www.ensembl.org/; b: Gene function and the homologous EST correspond to the best hit detected by blast from http://www.ncbi.nlm.nih.gov/; c:FLL, flag leaf length (cm); SLL, second leaf length (cm); TLL, third leaf length (cm);FLW, flag leaf width (cm); SLW, second leaf width (cm); TLW, third leaf width(cm); FLA, flag leaf area (cm2); SLA, second leaf area (cm2); TLA, third leaf area (cm2); FLCC, flag leaf chlorophyll content; SLCC, second leaf chlorophyll content; TLCC, third leaf chlorophyll content;TATL, total area of the top three leaves; ACTL, average chlorophyll content of the top three leaves; KGW, 1000-grain weight (g); KN, kernel number per spike.a: Overlapping gene by blast from (XLSX)Click here for additional data file.S6 TableGNP, grain number per plant; GWP, grain weight per plant (g); RLMS, rachis internode length of main spike (cm); KGW, 1000-grain weight (g); SMS, number of spikelets on main spike; FW, fresh weight (g); NL, number of leaves; GRFW, growth rate of fresh weight; GRNL, growth rate for number of leaves; LA, leaf area (cm2); GRNR, growth rate for number of roots; GRLA, growth rate of leaf area.(DOCX)Click here for additional data file."}
+{"text": "This study is aimed at exploring the levels of peripheral blood circular RNAs (circRNAs) as biomarker candidates for the diagnosis of new-onset rheumatoid arthritis (RA). The selected twenty-two circRNAs in peripheral blood from new-onset RA patients and healthy controls (HC) were determined by quantitative reverse transcription-polymerase chain reaction (qRT-PCR). The levels of hsa_circ_0002715, hsa_circ_0001947, hsa_circ_0000367, and hsa_circ_0035197 were significantly increased in the peripheral blood of new-onset RA patients than in the peripheral blood of HC. And, there were obvious differences in the above four peripheral blood circRNAs between new-onset RA patients and systemic lupus erythematosus (SLE) patients and ankylosing spondylitis (AS) patients. Moreover, there were obvious differences in hsa_circ_0001947 and hsa_circ_0035197 between new-onset RA patients and patients with undiagnosed arthritis (UA). Receiver operating characteristic (ROC) curve analysis suggested that the levels of hsa_circ_0002715 and hsa_circ_0000367 in peripheral blood could distinguish new-onset RA patients from the HC, AS patients, and SLE patients, and the levels of hsa_circ_0001947 and hsa_circ_0035197 in peripheral blood could distinguish new-onset RA patients from the HC, AS patients, SLE patients, and UA patients. The logistic regression model showed that the combination of hsa_circ_0002715 and hsa_circ_0035197 could provide the best diagnostic accuracy with an area under the curve (AUC) of 0.758 . Moreover, the levels of peripheral blood hsa_circ_0002715 were correlated with swollen joint count (SJC), tender joint count (TJC), disease duration, rheumatoid factor (RF), anticitrullinated protein antibodies (ACPA), and hematologic disorder. And, the levels of peripheral blood hsa_circ_0035197 were correlated with hematologic disorder. This study suggests that the combination of hsa_circ_0002715 and hsa_circ_0035197 in peripheral blood may be a potential biomarker of patients with new-onset RA and may be associated with disease activity. Rheumatoid arthritis (RA) is the most common chronic and debilitating systemic autoimmune disease characterized by synovitis, destruction of the joints, and systemic immune and inflammatory manifestations. Although the treatment and survival rate of patients with RA have improved, most patients experience long-term joint damage, severe illness, and disability . CurrentCircular RNAs (circRNAs), a unique form of RNA, possess covalently closed continuous loops without free ends , 5. ThisIn one of our previous studies, we found some differentially expressed circRNAs in the peripheral blood from SLE patients by circRNA microarray screening, which suggests that circRNAs might play a role in autoimmune diseases. Moreover, we found some differentially expressed circRNAs between RA patients and healthy controls (HC); we also found that hsa_circ_0044235 can serve as a potential diagnostic biomarker of RA. Therefore, some other dysregulated circRNAs were selected to investigate the possibility of being used as diagnosis biomarkers for distinguishing new-onset RA patients from SLE patients, ankylosing spondylitis (AS) patients, undiagnosed arthritis (UA) patients, and HC in this study. Results showed that the levels of hsa_circ_0002715, hsa_circ_0000367, hsa_circ_0001947, and hsa_circ_0035197 in patients with new-onset RA were significantly increased. And, the levels of hsa_circ_0002715 were correlated with swollen joint counts (SJC), tender joint counts (TJC), and some autoantibodies of new-onset RA patients. Moreover, a logistic regression model showed that a combination of hsa_circ_0002715 and hsa_circ_0035197 could provide the best diagnostic accuracy. In conclusion, hsa_circ_0002715 and hsa_circ_0035197 in peripheral blood were found to have potential to be used as new biomarkers for new-onset RA diagnosis.59 new-onset RA patients receiving clinical care at the Department of Rheumatology, the First Affiliated Hospital of Nanchang University from September 2018 to February 2019 were enrolled in this study. All RA patients fulfilled the revised ACR criteria for RA [Peripheral blood samples (2\u2009ml) were collected into EDTA-2K-containing tubes and total RNA was extracted as soon as possible by using the TRIzol Reagent according to the manufacturer's protocol. The concentration and quality of the RNA were assessed by absorbance spectrometry measuring absorbance ratios of A260/A280 and A 260/A230 using a NanoDrop ND-1000 spectrophotometer . Isolated total RNA was kept at -80\u00b0C or immediately used for reverse transcription.\u03b2-Actin was set as an internal control. The relative expression level of each circRNA was measured through the equation 2\u2212\u0394\u0394Ct. The experiments were repeated at least three times.According to our previous study , qRT-PCRL), lymphocyte percentage (L%), monocyte count (M), monocyte percentage (M%), neutrophil count (N), neutrophil percentage (N%), eosinophil count (E), eosinophil percentage (E%), basophil count (B), basophil percentage (B%), and platelet large cell ratio (P-LCR) were measured using the Sysmex XE-2100 analyzer .Data of SJC, TJC, and visual analogue scale (VAS) for all patients were recorded at the time of recruitment and one month after treatment. RA disease activity was measured according to the disease activity score 28 (DAS28) . The DASt-test and Mann-Whitney's U test were employed to compare normally distributed parameters and those with skewed distribution, respectively. For the evaluation of changes with treatment, paired t-tests or Wilcoxon's matched-pairs test was used. Likewise, the Pearson method or the nonparametric Spearman method was used for correlation analysis. In addition, receiver operating characteristic (ROC) curves were carried out to assess the diagnostic value of dysregulated circRNAs.Baseline characteristics were assessed using descriptive statistics. Student's P values < 0.05 were considered statistically significant. Statistical analysis and graphic presentation were carried out with SPSS version 16.0  and GraphPad Prism version 5.0 .Two-sided P < 0.05)  .P < 0.05), and the levels of other circRNAs showed no significant difference in the peripheral blood of new-onset RA patients and HC (P > 0.05) (data not shown). Furthermore, the levels of these 4 circRNAs were also significantly upregulated in the peripheral blood of new-onset RA patients compared with those in AS patients (P < 0.05). 3 circRNAs  in the peripheral blood of new-onset RA patients were significantly upregulated compared with those in SLE patients (P < 0.05), but hsa_circ_0002715 was significantly lower than that in SLE patients (P < 0.05). In addition, 2 circRNAs (hsa_circ_0001947 and hsa_circ_0035197) in the peripheral blood of new-onset RA patients were significantly upregulated compared with those in UA patients (P < 0.05), but the levels of hsa_circ_0002715 and hsa_circ_0000367 showed no significant difference in the peripheral blood of new-onset RA patients and UA (P > 0.05).Twenty-two differentially expressed circRNAs  between SLE and HC in circRNA microarrays were selected to investigate their levels in peripheral blood from RA patients and HC. As shown in The above results showed that the levels of hsa_circ_0002715, hsa_circ_0000367, hsa_circ_0001947, and hsa_circ_0035197 in peripheral blood of new-onset RA patients were significantly different from those of HC, AS patients, SLE patients, and UA patients. Then, we investigated whether circRNAs could be used as a new diagnostic marker of new-onset RA using the ROC curve analysis. As shown in To evaluate the cumulative performances of these four circRNAs in discriminating new-onset RA from HC, a binary logistic regression was performed. As shown in r = 0.2844, P = 0.0290; r = 0.2679, P = 0.0402)   . There wr = 0.2924, P = 0.0288) (P = 0.0549) (P = 0.0342)  . And, th 0.0549) . Moreove 0.0342) . There wAs shown in To determine the function of candidate biomarker circRNAs, we predicted the target miRNAs by aligning with the MREs of differentially expressed circRNAs using miRanda software. As shown in CircRNAs are ubiquitous and functionally important. As the expression and function of circRNAs during the development of RA are still largely elusive, we examined 22 differentially expressed circRNAs in the peripheral blood to detect circRNAs associated with new-onset RA using qRT-PCR. Four circRNAs  were chosen for further analysis because of their differential expression between new-onset RA patients and HC.Hsa_circ_0002715, hsa_circ_0000367, hsa_circ_0001947, and hsa_circ_0035197 in the peripheral blood of new-onset RA patients were significantly increased compared to those in the peripheral blood of HC. In addition, hsa_circ_0002715 and hsa_circ_0000367 were abnormally expressed between new-onset RA, SLE, and AS. Hsa_circ_0001947 and hsa_circ_0035197 were abnormally expressed between new-onset RA and other autoimmune diseases, such as SLE, UA, and AS. These results indicated that hsa_circ_0002715, hsa_circ_0000367, hsa_circ_0001947, and hsa_circ_0035197 are not only associated with RA but they also have potential to effectively distinguish RA patients from AS patients and SLE patients.circRNAs are very stable in blood circulation. Moreover, the expression of circRNAs has good tissue and developmental stage specificity. These characteristics make circRNAs very suitable to be used as a new serum marker for a variety of diseases . The staOur results also showed that the levels of peripheral blood hsa_circ_0002715 are correlated with SJC, TJC, disease duration, RF, WBC, RBC, HGB, HCT, and L of new-onset RA patients; the levels of peripheral blood hsa_circ_0001947 are correlated with ACPA, L%, M%, and N% of new-onset RA patients; the levels of peripheral blood hsa_circ_0000367 are correlated with PLT, M, and M% of new-onset RA patients; and the levels of peripheral blood hsa_circ_0035197 are correlated with M and M% of new-onset RA patients. These results showed that the levels of peripheral blood hsa_circ_0002715, hsa_circ_0001947, hsa_circ_0000367, and hsa_circ_0035197 are associated with SJC, TJC, disease duration, autoantibodies, and hematological system damage of RA, indicating that these circRNAs may be relevant biomarkers for disease activity. Other reports  and our ATP8B4 was recently identified as a risk factor for systemic sclerosis, which is a rare multisystem autoimmune disease [ATP8B4 in RA has been published until now. The interactions between hsa_circ_0002715, hsa_circ_0001947, hsa_circ_0000367, and hsa_circ_0035197 and the above potential target genes remain largely unknown and require further research.By searching circBase, a database for circRNAs, we found that hsa_circ_0002715, hsa_circ_0001947, hsa_circ_0000367, and hsa_circ_0035197 regulate the expression of the pericentrin (PCNT) gene, the AF4/FMR2 family member 2 (AFF2) gene, the sialic acid acetylesterase (SIAE) gene, and the ATPase phospholipid transporting 8B4 (ATP8B4) gene, respectively. PCNT is an integral component of the pericentriolar material, which binds to calmodulin and is expressed in the centrosome. It was reported that PCNT is associated with hematological abnormalities . Our res disease . HoweverIt was reported that the levels and functions of circRNAs are closely related to the occurrence and development of autoimmune disease, such as RA  and SLE In this study, we aimed to show the possible association of peripheral blood circRNAs with new-onset RA. Our results demonstrated that peripheral blood hsa_circ_0002715, hsa_circ_0001947, hsa_circ_0000367, and hsa_circ_0035197 were differentially expressed between new-onset RA patients, AS patients, SLE patients, and HC. In addition, we found that the combination of hsa_circ_0002715 and hsa_circ_0035197 in the peripheral blood may be a potential biomarker for patients with new-onset RA and the levels of these circRNAs associate with the disease activity of RA."}
+{"text": "Circular RNAs (circRNAs) are a type of covalently closed loop structure of endogenous RNAs. Recent studies have shown that circular RNAs may play an important role in human cancer. However, there is limited information on the function of circRNA in oral squamous cell carcinoma (OSCC).  Hsa_circ_001242 expression levels in 40 paired OSCC tissues and four OSCC cell lines were selected using real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR). A receiver operating characteristic (ROC) curve was used to evaluate the diagnostic value of hsa_circ_001242 in OSCC. P < 0.001). Hsa_circ_001242 expression levels were significantly downregulated in four OSCC cell lines  than in human normal oral keratinocyte (HOK) cell lines. Moreover, the expression level of hsa_circ_001242 was negatively correlated with tumor size and T stage (P < 0.05). The area under the ROC curve was 0.784.  Hsa_circ_001242 was significantly downregulated in OSCC tissues compared to paired adjacent normal tissues ( This study showed that hsa_circ_001242 was significantly downregulated in OSCC and may act as a potential novel biomarker for the diagnosis and treatment of OSCC. Oral squamous cell carcinoma (OSCC) is the third most common cancer in developing countries and ranks sixth among systemic cancers worldwide , 2. AlthCircular RNAs (circRNAs) are a type of covalently closed loop structure of endogenous RNAs, which are characterized by linking the 3\u2032 and 5\u2032 ends generated by backsplicing. Unlike linear RNAs, circRNAs require high conservation, have high stability and tissue specificity, and are not easily degraded by enonuclease , 7. In tSearching OSCC associated circRNAs from circBase . We firsForty oral cancer tissue samples and paired adjacent normal tissues were collected from the Department of Oral and Maxillofacial Surgery, Shenzhen Hospital, Peking University , from December 2016 to May 2017. All samples were verified by histopathology. No patient previously underwent OSCC surgery, chemotherapy, or radiotherapy. Tissue samples were stored at \u221280\u00b0C before use. This study was approved by Ethics Committee of Peking University Health Science Center (IRB00001053-08043).\u03bcl) containing 1\u2009\u03bcg total RNA was reverse transcribed to cDNA using PrimeScript RT-polymerase .Total RNA was extracted from OSCC tissues and paired adjacent normal tissues using the TRIzol reagent  according to the manufacturer's protocol. Total RNA from each specimen was quantified, and quality assurance was conducted using a NanoDrop ND\u00ad2000 spectrophotometer . Reaction mixture . The integrity of the RNA was determined using 1% formaldehyde denaturing gel electrophoresis. A PrimeScript RT Reagent Kit  was used for the production of complementary DNA (cDNA) by reverse transcription, according to the manufacturer's instructions. qRT-PCR was performed using SYBR-Green Premix Ex Taq  and was monitored using the ABI PRISM 7500 Sequence Detection System . The relative expression levels of circRNAs were determined by qRT-PCR. The sequences of the primers used in the qRT-PCR assay are shown in Supplementary \u03b2-actin were 5\u2032-AAACTGGAACGTTGAGAGTG-3\u2032 (forward primer) and 5\u2032-AGTGGTCTGGCTTTTAGGT-3\u2032 (reverse primer). The reaction conditions were as follows: 95\u00b0C at 5\u2009min for a preincubation and 40 cycles of 95\u00b0C for 5\u2009s, annealing temperature of 60\u00b0C for primer pairs for 30\u2009s, and 72\u00b0C for 20\u2009s. RNA levels were normalized using \u03b2-actin as the internal control.The sequences of the hsa_circ_001242 primers were as follow: 5\u2032-GCCCACTTGTAGAAGGTCCG-3\u2032 (forward primer) and reverse 5\u2032-CTGGCAGGGAGGGCTCATTA-3\u2032 (reverse primer). The primer sequences for 2.The human OSCC cell lines, SCC9, SCC15, SCC25, and CAL27, were obtained from the College of Stomatology, Wuhan University . Human oral keratinocytes (HOK) cells were obtained from the cell bank of the Chinese Academy of Sciences . SCC9 cells were cultured in DMED/F12 medium supplemented with 1% penicillin/streptomycin. SCC15, SCC25, CAL27, and HOK cells were cultured in Dulbecco's Modified Eagle Medium  supplemented with 1% penicillin/streptomycin . All cells were cultured at 37\u00b0C under 5% COt-test. A nonpaired t-test was used to analyze the relationship between hsa_circ_001242 expression level and clinicopathological factors in OSCC patients. The ROC curve was constructed to evaluate the diagnostic values. \u2217P < 0.05, \u2217\u2217P < 0.01, and \u2217\u2217\u2217P < 0.001 were considered statistically significant.GraphPad Prism 5.0 Software  was used to analyze the obtained data. Results of hsa_circ_001242 expression for OSCC tissues and paired adjacent normal tissues or between OSCC cell lines and HOK cell lines were compared using a paired n = 40, P < 0.001)  .hsa_circ_001242 in the HOK cell lines and four human OSCC cell lines, SCC-9, SCC-15, SCC-25, and CAL27. Expression levels in the OSCC cell lines were significantly lower than those in the HOK cell lines  and T stage (P = 0.0434) but were not associated with other clinicopathological features, such as age, gender, tumor differentiation, TNM stage, or lymphatic metastasis (P > 0.05).Clinicopathological analysis revealed that hsa_circ_001242 expression level was significantly associated with clinicopathological factors of OSCC patients. As presented in P < 0.001; To estimate the diagnostic value of hsa_circ_001242 in OSCC, an ROC curve was constructed for differentiating OSCC tissues from paired adjacent normal tissues. The area under the ROC curve (AUC) was 0.784 \u2009=\u20090.717\u20130.867; \u03b2-catenin signaling pathway by acting as a sponge for miR-7 and miR-214. Zhu et al. [CircRNAs are a class of noncoding RNAs (ncRNAs) that have been neglected as transcriptional noise in eukaryotes for the past 30 years , 16, 17.u et al.  have repu et al.  have obsIn this study, we first observed that hsa_circ_001242 was downregulated in both OSCC tissues and OSCC cell lines. Furthermore, the study on the clinical features and the expression of hsa_circ_001242 indicated that hsa_circ_001242 expression level was negatively relevant to tumor size and T stage . These dIn conclusion, our study manifested that hsa_circ_001242 was significantly downregulated in OSCC tissues and OSCC cell lines. In addition, the expression of hsa_circ_001242 expression is negatively correlated with tumor size and T stage. Thus, hsa_circ_001242 may serve as a potential diagnostic biomarker for OSCC."}
+{"text": "Advances in RNA sequencing (RNA-seq) technologies that measure the transcriptome of biological samples have revolutionised our ability to understand transcriptional regulatory programs that underpin diseases such as cancer. We recently published singscore - a single sample, rank-based gene set scoring method which quantifies how concordant the transcriptional profile of individual samples are relative to specific gene sets of interest. Here we demonstrate the application of singscore to investigate transcriptional profiles associated with specific mutations or genetic lesions in acute myeloid leukemia. Using matched genomic and transcriptomic data available through the TCGA we show that scoring of appropriate signatures can distinguish samples with corresponding mutations, reflecting the ability of these mutations to drive aberrant transcriptional programs involved in leukemogenesis. We believe the singscore method is particularly useful for studying heterogeneity within a specific subsets of cancers, and as demonstrated, we show the ability of singscore to identify where alternative mutations appear to drive similar transcriptional programs. Transcriptomic analyses have traditionally focused on \u2018differential expression\u2019 of genes between sets of samples, however with the rapid growth of publicly available RNA data there has been increasing usage of \u2018relative approaches\u2019, which quantify the relative concordance of a sample or samples with a specific gene signature1. While sequencing of genomic mutations has been important for classifying different tumour subsets based upon the presence of mutations or fusion genes, and identifying genetic lesions which may act as drivers of cancer progression, transcriptomic profiling can provide further information on the state or phenotype of cells carrying these mutations. Cancers are a heterogeneous set of diseases with a number of clinical and pathological subtypes. In diseases such as breast cancer the primary clinical classifications relate to the expression of hormone receptors  or the overexpression of Erb-B2 receptor tyrosine kinase (HER2), as these features can be directly targeted with therapeutic agents. A common example of transcriptomic or gene expression data informing clinical practice is the use of prediction analysis of microarray 50 (PAM50) signatures for distinguishing the intrinsic breast cancer subtypes [1]. For many other cancers, subtype classification has largely relied upon identifying sets of recurrent mutations across large patient cohorts, with whole genome or whole exome sequencing studies helping to resolve the clinically significant subtypes [et al.4].The development of microarrays and more recently the rapid uptake of RNA-sequencing technologies have provided a platform to examine the transcriptional profile (or transcriptome) of biological samples5, often used through theGenePattern web-tool. Another common approach isgene set variation analysis (GSVA)6 which is available as an R/Bioconductor package that also includes functionality for ssGSEA, an alternative approach known as PLAGE7, and a z-score based approach8. Both ssGSEA and GSVA use a Kolmogorov-Smirnov like random-walk statistic to convert normalised gene ranks to the resulting score, however this normalisation procedure means that the scores are not truly \u2018single-sample\u2019, and variations in the overall sample composition for a study (e.g. variations in the presence or relative frequency of different cancer subtypes) can lead to unexpected changes in sample scores. Furthermore, the resultant scores from these methods can vary in their range and absolute value, making them difficult to interpret without further processing. To overcome this, we have developed a single-sample gene set scoring methodsingscore9 which simply uses the ranks of genes within a given set, normalised relative to the maximum and minimum theoretical scores for a gene set of a given size.Perhaps the most well-known \u2018relative approach\u2019 is single-sample gene set enrichment analysis (ssGSEA)9 can be used to classify TCGA AML samples using transcriptional \u2018gene signatures\u2019 for the NPM1c mutation,KMT2A (MLL) gene fusions, andPML-RARA gene fusions that were derived from independent studies. Without any need for parameter fitting or estimation, we show that gene set scoring with singscore can distinguish samples carrying these mutations. The case studies we present demonstrate the application of gene set scoring to examine not only the differences, but also the relative similarities between established subtypes of AML that impact clinical outcome. This workflow is available as a bioconductor workflow package fromhttps://bioconductor.org/packages/release/workflows/html/SingscoreAMLMutations.html.Through large scale efforts such as The Cancer Genome Atlas (TCGA), transcriptomic data are available for thousands of clinical samples, often together with corresponding genomic or epigenomic (often DNA methylation) data. These transcriptomic data can help to characterise the functional effects of corresponding mutations, and provide a window to study the heterogeneity which arises within different subtypes of cancer due to epigenetic and transcriptional regulatory programs which can also influence cell behaviour. Here, we demonstrate that the single-sample gene set scoring method singscore3. A more recent study which focused primarily on genomic data has further refined the clinically significant AML subtypes4, highlighting a number of co-occurring as well as mutually exclusive mutations. As the identification of putative driver fusions/mutations continues, work has also been directed towards how these lesions interact with each other and other features  to drive transcriptional changes as discussed in a recent review10.As with most cancers, acute myeloid leukemia (AML) is a heterogeneous disease with a number of classified subtypes. Analysis of TCGA AML genomic data identified a number of subtypes based upon the presence or absence of specific \u2018driver mutations\u2019; recapitulating and expanding upon previously identified clinical subsetsNPM1) gene4. This mutation leads to aberrant localisation of nucleophosmin with cytoplasmic accumulation rather than localising to the nucleolus, and accordingly this mutation is often referred to as the NPM1c mutation11. As noted by Verhaaket al.12, the NPM1c mutation is associated with dysregulated activity of the homeobox domain (Hox) family of transcription factors which are essential for developmental patterning. The effects of this mutation in disease progression have been further demonstrated in recent work which showed that loss of NPM1c leads to differentiation of AML cells11.Of note for this work, one of the most common mutations in clinical AML samples is a frameshift mutation within exon 12 of the nucleophosmin  fusion genes, partial tandem duplications withinKMT2A (KMT2A-PTD), and fusion genes between promyelocytic leukemia and retinoic acid receptor alpha (PML-RARA). Given the role of NPM1c in dysregulating the Hox gene family, it is interesting to note that AML samples with MLL fusion genes also show dysregulated expression of Hox family genes [14]; however, samples withMLL-PTD appear to show a relatively distinct phenotype from MLL-fusion samples14. While there is good evidence demonstrating the role of NPM1c mutations and other genetic lesions in blocking AML cell differentiation, thePML-RARA fusion subset is diagnostic for a specific subset of AML known as acute promyelocytic leukemia (APL). This clinically distinct subtype of AML is associated with a specific morphology under the French-American-British (FAB) classification of AML, FAB-M3, with cells showing a distinct morphology due to a differentiation block at the promyelocyte stage15.Further recurrent genetic lesions in AML relevant for this work include lysine methyl transferase 2A 17, we demonstrate that a bi-directional scoring approach can classify TCGA AML samples with corresponding mutations with a good precision and recall. A particularly useful feature of gene set scoring is the ability to project samples onto 2D or higher-order landscapes defined by corresponding phenotypic signatures. Accordingly, by comparing scores for both the NPM1c andKMT2A-/MLL-fusion signatures, we show that this classification likely arises through the shared downstream biological effects of Hox family dysregulation. We also compare the NPM1c mutation signature to thePML-RARA signature and show a clear separation of these subtypes reflecting their divergent phenotypes and the mutually exclusive nature of these mutations.In this workflow we demonstrate the ability of the singscore method for single sample gene set scoringWhile we demonstrate that singscore is capable of inferring mutation status from the transcriptional profile of AML samples, we note that it is best used to supplement alternative data which can provide a more definitive resolution of these lesions. Processing of raw RNA seq data will directly identify the presence of gene fusion products or mutations within protein-coding regions, although for many large data sets the quantified transcript abundance data are much easier to obtain without access agreements. The method can also be applied to legacy microarray data sets where genome and RNA sequencing data are unavailable. As such singscore provides a useful approach to supplement established methods for the study of genetic lesions in cancer. By exploring associations between different genomic and phenotypically relevant signatures, it may also help to better characterise true driver mutations which exert consistent effects on the transcriptome of AML samples and other cancers.cBioPortal andFireBrowse. The GDC data used STAR to perform a two-pass alignment followed by quantification using HTSeq. Data from the GDC can be downloaded using theGDC data transfer tool which allows users to select the specific files of interest using the GDC portal. These files then have to be read, merged, annotated and processed into a data structure that simplifies downstream analysis. Alternatively, all the above mentioned steps, including the download, can be performed using the R packageTCGAbiolinks18. The package supports data download using the GDC API and the GDC client. We will use the TCGAbiolinks package to download, annotate and process the data into a SummarizedExperiment R object.Data from TCGA project is made available through the Genomic Data Commons (GDC). Open access data from the project can be accessed in multiple pre-processed formats. Transcriptomic data can be downloaded either at the count level or as FPKM transformed abundance, before or after upper quantile normalisation. Other pre-processed version can be found from sources such as the1. Create a query to select the files to download2. Execute the query and download the data3. Read the data into R4. Filter out genes with low expression5. 9Normalise the data for compositional bias and transform to account for gene-length biases as outlined in the singscore manuscriptThe following steps need to be performed to prepare the data:getGDCInfo function returns the release date of all data on the GDC along with a version.The first step in any analysis should be to determine and report the data version and the service used to download the data. Thelibrary(SingscoreAMLMutations)library(TCGAbiolinks)         #get GDC version information  gdc_info = getGDCInfo       gdc_info                      ## $commit## [1] \"b18b2385b1e916597856067dc6437f3c20b46bca\"#### $data_release## [1] \"Data Release 19.0 - September 17, 2019\"#### $status## [1] \"OK\"#### $tag## [1] \"1.22.0\"#### $version## [1] 1MANIFEST file using the GDC portal. The first parameter of the query specifies the project - available projects can be accessed usinggetGDCprojects or fromhttps://portal.gdc.cancer.gov/projects. The TCGA acute myeloid leukemia data is part of the TCGA-LAML project. Following this, the data category, data type and workflow type need to be specified. The query formed below selects files containing the count level transcriptomic measurements. Values for different parameters of the query can be identified from \u201csearching arguments\u201d section of the \u201cquery\u201d vignette:vignette. The result of this query will be a dataframe containing filenames and additional annotations related to the files.A query then needs to be run, using the GDC to identify the specific files for download. This step is similar to generating aRead count level data are selected instead of the processed FPKM data as one of the downstream pre-processing analysis results in filtering out of genes. A general recommendation is to compute FPKM values after filtering genes out so as to ensure counts are normalised by the corresponding library sizes. In cases where count-level data is not available, filtering can be performed directly on FPKM values, provided that the library size is large enough.#form a query for the RNAseq data               query_rna = GDCquerydata.category = \u2019Transcriptome Profiling\u2019,    data.type = \u2019Gene Expression Quantification\u2019, workflow.type = \u2019HTSeq - Counts\u2019              )                                               #extract results of the query                   rnaseq_res = getResults(query_rna)              dim(rnaseq_res)                                 ## [1] 151 28colnames(rnaseq_res)                            ## [1] \"data_release\"               \"data_type\"## [3] \"updated_datetime\"           \"file_name\"## [5] \"submitter_id\"               \"file_id\"## [7] \"file_size\"                  \"cases\"## [9] \"id\"                         \"created_datetime\"## [11] \"md5sum\"                    \"data_format\"## [13] \"access\"                    \"state\"## [15] \"version\"                   \"data_category\"## [17] \"type\"                      \"experimental_strategy\"## [19] \"project\"                   \"analysis_id\"## [21] \"analysis_updated_datetime\" \"analysis_created_datetime\"## [23] \"analysis_submitter_id\"     \"analysis_state\"## [25] \"analysis_workflow_link\"    \"analysis_workflow_type\"## [27] \"analysis_workflow_version\" \"tissue.definition\"GDCdownload function then executes the query on the GDC database and begins downloading the data using the GDC API. The download method should be changed to \u201cclient\u201d, if the size of the data is expected to be large, e.g for RNA-seq read data or methylation data. It is good practice to specify the directory for data storage - we store all the data in the \u201cGDCdata\u201d directory in the temporary directory. Users should store their data in a permanent storage to retain the data. The function downloads the data and organises it into the folder based on parameters specified in the query. This allows multiple different levels and types of data to be stored in the same directory structure. Files with counts are stored atTEMPDIR/GDCdata/TCGA-LAML/harmonized/Transcriptome_Profiling/Gene_Expression_Quantification/.Thedatapath = file.path                GDCdownload #(size: 39MB)GDCprepare function reads and processes the downloaded data into aRangedSummarizedExperiment object from theSummarizedExperiment package which allows patient annotations, gene annotations and count data to be stored in one object. Patient annotations are downloaded upon calling this function and subsequently mapped and attached to the resulting object. A RangedSummarizedExperiment object is similar to an ExpressionSet object but provides added functionality such as indexing with genomic coordinates and storing multiple data matrices with the same structure. Feature annotations used to annotate the data are stored in an RDA/RDATA file.Theaml_se = GDCprepare   rowData(se) andcolData(se), respectively, and the counts data can be accessed usingassay(se). TCGA data usually contains some formalin-fixed paraffin-embedded (FFPE) samples which should be discarded from the analysis as the protocol introduces biological artefacts. This procedure is only performed on solid tumours and not leukemias, therefore, no filtering is required for this data set.The object contains data for 56,925 features and 151 samples. The original data files contain 60,483 features, some of which  could not be mapped to ENSEMBL GRCh38.p12. Feature and sample annotations can be accessed usingaml_se                                                 ## class: RangedSummarizedExperiment## dim: 56512 151## metadata(1): data_release## assays(1): HTSeq - Counts## rownames(56512): ENSG00000000003 ENSG00000000005 ...##   ENSG00000281912 ENSG00000281920## rowData names(3): ensembl_gene_id external_gene_name##   original_ensembl_gene_id## colnames(151): TCGA-AB-3001-03A-01T-0736-13##   TCGA-AB-2853-03A-01T-0734-13 ... TCGA-AB-2977-03B-01T-0760-13##   TCGA-AB-2995-03A-01T-0735-13## colData names(61): sample patient ... name is_ffpeedgeR package contains methods that assist in the data normalisation and transformation required for filtering and subsequent steps. The methods require a DGEList object therefore we begin by creating a DGEList for the AML data from the SummarizedExperiment.Thelibrary(SummarizedExperiment)                                     library(edgeR)                                                    aml_dge = DGEList(counts = assay(aml_se), genes = rowData(aml_se))et al.19 and Lawet al.20 filter genes based on the experimental design whereby the proportion of samples with enough read counts are evaluated per experimental group. As the AML data have many samples, filtering is performed across all samples rather than within sub-groups. Group specific filtering would be recommended for the study of rare groups. The distribution of logCPMs is much closer to the expected log-normal distribution after filtering out genes with low counts as seen inGenes with low counts across most samples are discarded from the analysis. This is a standard step in differential expression analysis as inclusion of such genes in the analysis could skew estimates of dispersion. It is also motivated in rank-based analysis, such as with singscore, to avoid rank duplication. Rank duplication reduces the discriminant power of scores as the number of unique ranks is reduced. A commonly used filter is to select only those genes that have CPMs above a certain threshold across a proportion of samples. Filtering is performed on the CPMs rather than raw counts as the former accounts for variation in library sizes, therefore, is unbiased. For instance, a CPM of 1 would equate to read counts between 19 and 50 for samples in the AML data where library sizes vary between 18.6 and 49.7 million reads. Here, we retain genes that have a CPM > 1 across more than 50% of the samples. Other methods to filter out genes with low counts exist and may be preferable in specific applications. Chenprop_expressed = rowMeans(cpm(aml_dge) > 1)                                keep = prop_expressed > 0.5                                                op = par(no.readonly = TRUE)                                               par)                                                       hist, main = \u2019Unfiltered\u2019, xlab = \u2019logCPM\u2019)       abline(v = log(1), lty = 2, col = 2)                                       hist, main = \u2019Filtered\u2019, xlab = \u2019logCPM\u2019) abline(v = log(1), lty = 2, col = 2)                                       par(op)                             #subset the data                                  aml_dge = aml_dge aml_se = aml_se                           21. Transformations such as transcripts per million (TPM) and reads/fragments per kilobase per million (RPKM/FPKM), that normalise by gene length, may be used. Both- TPM and RPKM/FPKM values should produce similar results when applying singscore provided that the library size is large enough, which they are here. RPKM values are generally computed after correcting for compositional biases. ThecalcNormFactors function in edgeR provides three methods to do so, TMM normalisation being the default. Chenet al.19 and Lawet al.20 discuss the implications of normalisation prior to down-stream processing such as differential expression analysis. Normlisation is generally performed for cross-sample analysis where samples need to be comparable. Singscores are invariant to data normalisation as the analysis is contained within the sample of interest. The idea extends to any transformation that preserves the relative ranks of genes within a sample such as a log transformation. Here, we use TMM normalisation solely for visualisation purposes.Singscore requires gene expression measurements to be comparable between genes within a sample, therefore, correction for gene length bias needs to be performedhttps://docs.gdc.cancer.gov/Data/Bioinformatics_Pipelines/Expression_mRNA_Pipeline/). HTSeq quantifies reads mapping to the exons of each gene, therefore, effective gene lengths can be calculated as the sum of all exons spanning the gene. The GENCODE v22 annotation file was used for quantification therefore the same file needs to be used to compute gene lengths.Data transformation to TPM or RPKM/FPKM requires the lengths for all genes to be calculated. Gene lengths need to be computed based on the alignment and quantification parameters. The TCGA transcriptomic data has been aligned using STAR and quantified using HTSeq                                                                          download.file #(size: 39MB) rtracklayer R package provides functions to help parse GTF files.Thelibrary(rtracklayer)                                                                        library(plyr)                                                                               gtf = import.gff #split records by gene to group exons of the same gene                                      grl = reduce$gene_id))                                      gene_lengths = ldply {                                                       #sum up the length of individual exons                                                        return(c(\u2019gene_length\u2019 = sum(width(x))))                                                }, .id = \u2019ensembl_gene_id\u2019)                                                                 Genes are also annotated with their biotype for further analysis. The annotation file uses Ensembl IDs with versions as keys to records, which then need to be converted to Ensembl IDs. This is simply achieved by truncating the trailing version number.#extract information on gene biotype                                               genetype = unique(elementMetadata(gtf))               colnames(genetype)[1] = \u2019ensembl_gene_id\u2019                                          gene_lengths = merge                                       #remove ENSEMBL ID version numbers                                                 gene_lengths$ensembl_gene_id = gsub saveRDS                  gene_lengths                                                                       ## DataFrame with 60483 rows and 3 columns##       ensembl_gene_id      gene_type gene_length##           <character>    <character>   <integer>## 1     ENSG00000000003 protein_coding        4535## 2     ENSG00000000005 protein_coding        1610## 3     ENSG00000000419 protein_coding        1207## 4     ENSG00000000457 protein_coding        6883## 5     ENSG00000000460 protein_coding        5967## ...               ...            ...         ...## 60479 ENSGR0000275287       misc_RNA         290## 60480 ENSGR0000276543          miRNA          68## 60481 ENSGR0000277120          miRNA          64## 60482 ENSGR0000280767        lincRNA         515## 60483 ENSGR0000281849      antisense         484The SummarizedExperiment object allows feature annotations to be stored, therefore, information on gene length and biotypes should be added to the existing annotations. Similarly, annotations need to be added to the DGEList object. The column containing lengths should include \u201clength\u201d in its name.#allocate rownames for ease of indexing                                     rownames(gene_lengths) = gene_lengths$ensembl_gene_id                       rowData(aml_se)$gene_length = gene_lengths rowData(aml_se)$gene_biotype = gene_lengths  #annotate gene lengths for the DGE object                                   aml_dge$genes$length = gene_lengths       RPKM/FPKM values can now be calculated with the computed gene lengths after computing the normalisation factors. The SummarizedExperiment object can store multiple levels of the data simultaneously, provided that the number of features and samples remains the same across measurements. As such, FPKM values are appended to the existing object.aml_dge_tmm = calcNormFactors       #compute FPKM values and append to assays                    assay = rpkm aml_se                                                       ## class: RangedSummarizedExperiment## dim: 17412 151## metadata(1): data_release## assays(2): HTSeq - Counts logFPKM_TMM## rownames(17412): ENSG00000000419 ENSG00000000457 ...##   ENSG00000281772 ENSG00000281896## rowData names(5): ensembl_gene_id external_gene_name##   original_ensembl_gene_id gene_length gene_biotype## colnames(151): TCGA-AB-3001-03A-01T-0736-13##   TCGA-AB-2853-03A-01T-0734-13 ... TCGA-AB-2977-03B-01T-0760-13##   TCGA-AB-2995-03A-01T-0735-13## colData names(61): sample patient ... name is_ffpe3  rather than variant calls from the standard TCGA pipeline  and readers should note that there are some discrepancies between these. For genetic lesions of interest , patients were identified by the following criteria:Patient ID: The \u2018TCGA Patient ID\u2019 column was extracted directlyNPM1c: TRUE if the \u2018NPM1\u2019 column contains the strings \u2018p.W287fs\u2019 or \u2018p.W288fs\u2019KMT2A-fusion: TRUE if the \u2018MLL-partner\u2019 column contains the string \u2018MLL-\u2019 or \u2018-MLL\u2019 KMT2A-PTD: TRUE if the \u2018MLL-PTD\u2019 column contains the string \u2018exons\u2019PML-RARA: TRUE if the \u2018PML-RARA\u2019 column contains the string \u2018PML-RARA\u2019For this analysis we have used the curated mutation list from the original TCGA AML publicationdata(AMLPatientMutationsTCGA)                                            patient_mutations = AMLPatientMutationsTCGA                              patient_mutations = patient_mutations # order samplesaml_mutations = colnames(patient_mutations) # get mutation labels        colData(aml_se) = cbind(colData(aml_se), patient_mutations)              colData(aml_se)                                         ## DataFrame with 151 rows and 4 columns##                              NPM1c.Mut KMT2A.Fusion KMT2A.PTD  PML.RARA##                              <logical>    <logical> <logical> <logical>## TCGA-AB-3001-03A-01T-0736-13     FALSE        FALSE     FALSE      TRUE## TCGA-AB-2853-03A-01T-0734-13      TRUE        FALSE     FALSE     FALSE## TCGA-AB-2929-03A-01T-0735-13     FALSE        FALSE     FALSE     FALSE## TCGA-AB-2939-03A-01T-0740-13     FALSE        FALSE     FALSE     FALSE## TCGA-AB-2982-03B-01T-0748-13     FALSE        FALSE     FALSE      TRUE## ...                                ...          ...       ...       ...## TCGA-AB-2815-03A-01T-0734-13     FALSE        FALSE     FALSE     FALSE## TCGA-AB-3000-03A-01T-0736-13     FALSE        FALSE     FALSE     FALSE## TCGA-AB-2919-03A-01T-0740-13      TRUE        FALSE     FALSE     FALSE## TCGA-AB-2977-03B-01T-0760-13     FALSE        FALSE      TRUE     FALSE## TCGA-AB-2995-03A-01T-0735-13     FALSE        FALSE     FALSE     FALSE22. However, RefSeq annotations (Entrez IDs) may be better suited to RNA-seq analyses which require a stable reference annotation for comparison23. As such, we choose to map Ensembl IDs to Entrez IDs and discard any unmapped features.Ensembl annotations (Ensembl IDs) have higher coverage of the genome which may be useful in applications such as variant calling and similar exploratory analysisorg.Hs.eg.db annotation R package which provides a stable set of annotations, thereby enhancing reproducibility. Mapping is performed with themapIds function in theAnnotationDbi R package.Mapping can be performed using the Ensembl Biomart service, which can be queried using the biomaRt bioconductor package. This would provide the most up to date annotations. Alternatively, mapping could be performed with the bi-annually updatedlibrary(org.Hs.eg.db)               rowData(aml_se)$entrezgene = mapIds,            keytype = \u2019ENSEMBL\u2019,                column = \u2019ENTREZID\u2019,                multiVals = \u2019asNA\u2019                  )                                 gene_annot = rowData(aml_se)        NAs, then discarded to enforce unique mapping. Similarly, Entrez IDs that map to multiple Ensembl IDs are identified from the mapping, and discarded. Only features with unique Ensembl ID to Entrez ID mappings remain.Multimapped Ensembl IDs are replaced by#select genes with mapped Entrez IDs                                           keep = !is.na(gene_annot$entrezgene)                                           #select genes with unique Entrez IDs                                           dup_entrez = gene_annot$entrezgene[duplicated(gene_annot$entrezgene)]          keep = keep & !gene_annot$entrezgene %in% dup_entrez                           #Biotype of discarded genes (due to non-unique mapping)                        head, decreasing = TRUE), n = 10)####         processed_pseudogene                          antisense##                         1017                                903##                      lincRNA                                TEC##                          451                                263##               sense_intronic                     protein_coding##                          249                                138##       unprocessed_pseudogene transcribed_unprocessed_pseudogene##                          128                                 93##         processed_transcript   transcribed_processed_pseudogene##                           72                                 72#subset the data                                                               aml_se = aml_se                                                        et al.12 is now used to predict the mutation status of the NPM1c mutation. This is done by quantifying the concordance of genes in the signature with their expression in each sample. As such, high expression of up-regulated genes and low expression of down-regulated genes would result in higher scores. This single value can then be used to predict the mutation status of individual samples if these data were unavailable.The signature by VerhaakGeneSet objects from theGSEABase R package. We then use thesingscore R/Bioconductor package to quantify each sample for the Verhaak signature. Some of the visualisation and diagnostic tools within thesingscore package are used to interpret the signatures and scores. Finally, we use a simple logistic regression model on the scores to predict the mutation status.The signatures of interest are first downloaded from the MSigDB and read intoet al.12 signature is composed of an up-regulated and a down-regulated gene set. Many signatures are developed in such a manner to improve discrimination of samples. MSigDB stores such signatures using names with suffixes \"_UP\" and \"_DN\" representing the independent components of the signature. Here, we form the download links for the signature with the base name \u201cVERHAAK_AML_WITH_NPM1_MUTATED\u201d.The Verhaak#create signature names                                                          verhaak_names = paste, sep = \u2019_\u2019) verhaak_names                                                                    ## [1] \"VERHAAK_AML_WITH_NPM1_MUTATED_UP\" \"VERHAAK_AML_WITH_NPM1_MUTATED_DN\"mapply function is used to run the download function on all pairs of link-output arguments.The signatures are then downloaded using the links, resulting in an XML file for each component of the signature. The#generate URLs                                                                         verhaak_links = paste0                                                                                  #download files                                                                      verhaak_files = paste0                                        mapply              GSEABase package help with reading, parsing and processing the signatures. Signatures from an MSigDB XML file can be read using thegetBroadSets function which results in aGeneSet object. Gene symbols, Entrez IDs and affymetrix chip IDs from the original experiment (HG-U133A in this case) are stored in the XML file. Entrez IDs are read from the file as these can be mapped directly to our data. Conversions to other identifiers can be achieved using themapIdentifiers function fromGSEABase and an annotation package that contains the mappings. The advantage of using this function instead of themapIds function from theAnnotationDbi package is that the former retains theGeneSet object after conversion of IDs.Functions in thelibraryverhaak_sigs = (GSEABase)                                                                                 getBroadSetsverhaak_sigs                                                          ## GeneSetCollection##   names: VERHAAK_AML_WITH_NPM1_MUTATED_UP, VERHAAK_AML_WITH_NPM1_MUTATED_DN ##   unique identifiers: 10051, 10135, ..., 9828 ##   types in collection:##     geneIdType: SymbolIdentifier ##     collectionType: BroadCollection SummarisedExperiment object are changed to Entrez IDs which are already part of the row annotations.To make data indexing easier during signature scoring, row names of therownames(aml_se) = rowData(aml_se)$entrezgenerankGenes function will compute ranks from expression data in the form of either a numeric matrix, numeric data frame, ExpressionSet object, DGEList object or a SummarizedExperiment object. Users also have to specify what method should be used to break ties. The default is \u2018min\u2019 and we recommend this be used for RNA-seq data which may have many genes with zero counts. This will reduce the effect of zeros in the scores, however, appropriate pre-filtering of genes with low counts will still be required.Singscore is a rank based metric of gene set enrichment in single samples. Scores for multiple signatures make use of the same ranked expression per sample. As such, it makes sense to compute the ranks only once and re-use them for scoring different signatures. The implementation separates these two phases of the analysis to reduce the computational cost of scoring. Thelibrary(singscore)                                                          #apply the rankGenes method to each version of the dataset, excluding countsaml_ranked = rankGenes)                        et al.12 signature come in such pairs. This mode can be invoked by passing the up- and down-regulated gene sets to the argumentsupSet anddownSet respectively. In some cases, only one set of genes forms the signature. If all genes in the gene set are up-regulated or all down-regulated, the second mode of operation applies and is invoked by passing the gene set to theupSet argument. For sets of down-regulated genes, the score would simply be inverted . Finally, if the user is unsure of the composition of genes in the gene-set, such that, the gene set may contain both up- and down- regulated genes, the final mode of singscore applies. The gene set is passed to theupSet argument similar to the previous mode with the additional argumentknownDirection set toFALSE.Singscores can be computed using three modes, depending on the properties of the gene signature. The first mode of operation is applied when two directed gene sets (expected up- and down-regulated gene sets) form the transcriptomic signature. Many signatures in the MSigDB, including the VerhaakBy default, singscores are centered such that the range of scores is  and  for the first two modes respectively. Negative scores indicate an inverse enrichment of signatures, that is, expected up-regulated genes are in fact down-regulated and vice-versa. Scores from the last mode can not be centered and have the range . In this mode, high scores are obtained when ranks of genes are distant from the median and low scores obtained when ranks converge to the median rank. If scores are centered in this scenario, it would lead to the conclusion that a negative score shows inverted enrichment, which is not the case. Score centering only serves the purpose of easing interpretation for users, a simple linear transformation is applied to achieve it.Scores for the NPM1c mutation signature are computed using the default settings, with the first mode of operation being used due to the presence of an up- and down- regulated gene set. The function returns a data frame reporting the score and dispersion of ranks for the up-regulated gene set, down-regulated gene set and the combination of both. Dispersion of the combined gene set in this mode is simply the mean of the independent dispersion estimates. If any gene names/IDs are present in the signature but missing in the expression data, a warning will be reported.#apply the scoring function                              verhaak_scores = simpleScore## Warning in checkGenes): 24 genes missing: 10265,## 108, 10924, 11025, 11026, 1672, 200315, 2212, 2215, 3215, 3216, 3569, 3627,## 3759, 50486, 6346, 6364, 6967, 8337, 861, 8843, 9518, 9627, 9997## Warning in checkGenes): 29 genes missing:## 10232, 10267, 11217, 2122, 221981, 2258, 23532, 24141, 25907, 2697, 28526,## 28638, 3047, 3386, 3848, 3934, 4070, 445, 4680, 4681, 5457, 5790, 6091,## 653067, 653145, 7441, 8277, 862, 8788IQR function as an argument to thedispersionFun argument.It should be noted that singscores are composed of two components, an enrichment score and a dispersion estimate of ranks. The quantity of interest in gene set enrichment is the distribution of the expression or ranks of genes in the signature. In an ideal scenario, all expected up-regulated genes would have high expression therefore higher values of ranks. As such, ranks would be distributed on the higher end of the entire rank spectrum. Singscore aims to quantify this distribution of ranks, therefore, computes and reports the average and dispersion of ranks of genes in the signature relative to all other genes. The first quantity is similar to scores computed from all other single sample scoring methods. We determined a two component score to be a more appropriate and accurate representation of the distribution of ranks of signature genes. The default and recommended measure of dispersion is the median absolute deviation (MAD) due to its non-parametric property. Other appropriate measure of dispersion could be the inter-quartile range (IQR) and can be used by passing thehead(verhaak_scores)##                               TotalScore   TotalDispersion        UpScore## TCGA-AB-3001-03A-01T-0736-13 -0.09161242          5256.929  -0.0768291807## TCGA-AB-2853-03A-01T-0734-13  0.19605308          4882.943   0.0353047490## TCGA-AB-2929-03A-01T-0735-13 -0.07106828          5443.737   0.0006426157## TCGA-AB-2939-03A-01T-0740-13 -0.09795161          5697.632  -0.0599342558## TCGA-AB-2982-03B-01T-0748-13 -0.11011396          5464.122  -0.0986913016## TCGA-AB-2813-03A-01T-0736-13  0.15487900          4694.653   0.1385452964##                              UpDispersion     DownScore  DownDispersion## TCGA-AB-3001-03A-01T-0736-13      4513.034  -0.01478324        6000.823## TCGA-AB-2853-03A-01T-0734-13      5868.872   0.16074833        3897.014## TCGA-AB-2929-03A-01T-0735-13      5152.035  -0.07171089        5735.438## TCGA-AB-2939-03A-01T-0740-13      4977.829  -0.03801735        6417.434## TCGA-AB-2982-03B-01T-0748-13      5024.531  -0.01142266        5903.713## TCGA-AB-2813-03A-01T-0736-13      4332.157   0.01633370        5057.149singscore package provides a set of visualisation tools that enable diagnostics of the gene signature. For instance, these tools may be used to determine the importance of each component for a bidirectional signature (up- and down-regulated gene sets) to the total score, determine the importance of each gene of a signature in discriminating between the classes of interest, and to investigate the relationship between the final score and the dispersion of signature gene ranks. Sample annotations of interest  can be colour coded on each plot. Singscore supports both continuous and categorical annotations, which can either be input as a vector, or as a string specifying a column within the score data frame. We begin by investigating the relationship between the score and dispersion of ranks for the up-regulated gene signature, down-regulated gene signature and the full signature. TheplotDispersion functions generates a diagnostic plot with annotations overlaid. Annotations can be discrete or continuous, and can be passed as independent variables, or as a column name when the data is appended to the score data frame. It should be noted that all plotting functions insingscore can be made interactive by setting theisInteractive argument toTRUE.The#relative size of text in the figure                                         relSize = 0.7                                                                #create annotation                                                           mutated_gene = rep)                                    mutated_gene[aml_se$NPM1c.Mut] = \u2019NPM1c Mut\u2019                                 mutated_gene[aml_se$KMT2A.Fusion | aml_se$KMT2A.PTD] = \u2019MLL Fusion/PTD\u2019      p1 = plotDispersionp1MLL (KMT2A) fusions and PTDs from the other samples. NPM1c mutations produce higher scores for the down-regulated gene set whileMLL fusions and PTDs produce moderate scores. Similar trends are observed with the set of upregulated genes; however, despite the range of scores increasing, the discriminant power drops moderately. In fact, the signature of up-regulated genes is able to discriminate samples without the genomic changes of interest (\u2018Other\u2019) better by producing negative scores for most of these samples. Negative scores for the expected up-regulated gene set indicate that these genes are expressed below median expression, therefore, likely down-regulated within corresponding samples. Another observation from these plots is based on the trend of the dispersion (MAD). The dispersion is generally expected to be higher for scores close to zero. Zero scores are achieved in three possible scenarios: when genes are expressed at median expression level; when genes are evenly distributed at both ends of the expression spectrum; or, the more likely scenario whereby the rank of expression of all genes are uniformly distributed. The last two scenarios would result in a high dispersion. To explain these ideas, we select 3 samples and plot the ranks of genes in both signatures. The sample with the highest total score, lowest total score and highest dispersion are chosen.library(gridExtra)                                                       #select samples with the properties required                             select_samples = c,                \u2019Min Total Score\u2019 = which.min,                \u2019Max Dispersion\u2019 = which.max             )                                                                      #plotRankDensity applied to each sample                                  rank_plots = lapply(names(select_samples), function(x) {                   #get the sample index                                                    aml_sample = select_samples[x]                                           #drop = FALSE is required to ensure the data.frame is intact             p1 = plotRankDensity                                #overwrite title of the plot with the description of the sample          #this is possible because singscore uses ggplot2                         p1 = p1 + ggtitle) +        guides(colour = guide_legend(ncol = 1))                                 return(p1)                                                             })                                                                       #create a multipanel plot                                                grid.arrange                               MLL fusion/PTD samples), and wild-type samples. These diagnostic plots help in determining the importance of genes in signatures with respect to the samples of interest and should be used prior to application of signatures. In some cases, signatures may have been developed in specific contexts due to inherent biases in datasets and yet, described or applied in a generalised setting. These diagnostic plots may help in validating their application in specific contexts and possibly assist in characterising the contexts of all existing signatures.Combined, these plots show that both the up- and down-regulated genes play an important role in discriminating between NPM1c mutated .Mutation status can be predicted from singscores using a logistic regression model with a \u201clogit\u201d link function. The benefits of this model over one where each gene in the signature is used as a term in the model is the simplicity of the model. The VerhaakIn any case, our aim here is not to discuss the various models that can be used to predict mutation status, but to exhibit the discriminant power of singscore and transcriptomic signatures. consequently, the data used to train the model are used to evaluate the basic performance properties. We begin by combining the scores with mutation annotations.#create a dataframe with the data required: scores and mutations scoredf = as.data.frame(colData(aml_se))        scoredf$Score = verhaak_scores$TotalScore                        scoredf$Dispersion = verhaak_scores$TotalDispersion              et al.12 signature separates the mutants from wild-type samples for some genes of interest.MLL (KMT2A) fusions andPML-RARA fusions.Before training a model, we can visualise how well scores resulting from the Verhaak#restructure the data for ploting                             plotdf = melt,                            variable.name = \u2019Mutation\u2019,                                   value.name = \u2019Status\u2019                                         )                                                           #convert TRUE-FALSE values to Mut-WT                          plotdf$Status = factor)p1 = ggplot) +      geom_boxplot +               scale_fill_brewer +                         current_theme +                                               theme)  p1                                                            p-values of each model are listed below.To quantify the above observations, we fit logistic regression models with each genomic lesion as the response variable and the score as the predictor. Coefficient estimates, standard errors, z-statistics, and#fit GLMs to each mutation                                           glms = lapply {                             #generate a formula for the fit                                      form = as.formula)                             glm1 = glm)  return(glm1)                                                       })                                                                   names(glms) = aml_mutations                                          #extract coefficients                                                coefs = lapply coef(summary(x)))                   ldply x, .id = \u2019Mutation\u2019)                   ##       Mutation  Estimate Std. Error    z value     Pr(>|z|)## 1    NPM1c.Mut 13.276945   2.209811  6.0081806 1.876167e-09## 2 KMT2A.Fusion  4.442768   2.151409  2.0650505 3.891822e-02## 3    KMT2A.PTD  1.646183   2.089950  0.7876661 4.308921e-01## 4     PML.RARA -6.780992   2.414752 -2.8081526 4.982661e-03MLL gene fusion are also associated with the score reflecting their shared effects on Hox gene dysregulation, although with a lower significance. Interestingly thePML-RARA fusion carries a negative coefficient, likely reflecting the distinct cellular morphology/phenotype of acute promyelocytic leukemia relative to other subsets of AML, as noted above.NPM1c mutations are significantly associated with the score. Samples carrying adcanr package provides functions to compute these metrics along with other measures of performance.The above statistics give insight on the models trained but not on their performance. Precision and recall provide insight on a models predictive performance. Thelibrary(dcanr)                                                                 #assess sensitivity and specificity                                            prec_rec = ldply {                                          #predict mutations for the data used in training and convert to binary format  prediction = as.numeric(predict(glm1) > 0)                                     observed = glm1$y                                                              prec = performanceMeasure                   recall = performanceMeasure                    f1 = performanceMeasure                                  return)                  }, .id = \u2019Mutation\u2019)                                                           prec_rec                                                                       ##       Mutation Precision    Recall        F1## 1    NPM1c.Mut       0.7 0.5675676 0.6268657## 2 KMT2A.Fusion       NaN 0.0000000 0.0000000## 3    KMT2A.PTD       NaN 0.0000000 0.0000000## 4     PML.RARA       NaN 0.0000000 0.0000000Precision for predictions of all genomic changes other than NPM1c is undefined because all samples are predicted to be wild-types. Consequently, their recall is zero. Precision, recall and the F1 score of the NPM1c mutation model are high as expected. As singscores are two-components scores, performance may be further improved by including the dispersion of ranks. This is observed inAs observed from the model below, using both components of singscores significantly improves the predictive performance.#include dispersion in the model                                      glm_npm1c = glm)                     #evaluate performance of the new model                                prediction = as.numeric(predict(glm_npm1c) > 0)                       observed = glm_npm1c$y                                                c,  \u2019Recall\u2019 = performanceMeasure,        \u2019F1\u2019 = performanceMeasure                       )                                                                   ## Precision    Recall        F1## 0.7631579 0.7837838 0.7733333There may be some cases where sample annotation is not available. In such scenarios, we are unable to build regression models to interpret scores. A higher singscore would provide stronger evidence for the signature but the magnitude is difficult to interpret without a reference. One approach to deal with this situation is to compare scores to those from other datasets where the mutations status is known. An alternative approach would be to compare scores within the dataset using unsupervised learning methods.24 use the adjusted Rand index (ARI) to compare classifications. As expected, supervised (GLM) classification results in the best prediction. This is followed by clustering based on the score using Gaussian mixture decomposition. Any other classification algorithm along with prior knowledge could be used to decompose scores into groups. The important characteristic of singscores is that they maintain the discriminant power of gene signatures therefore can be coupled with supervised, semi-supervised or unsupervised algorithms to perform stratification.Here we demonstrate the use of three clustering methods  to stratify samples, and as we have done previouslylibrary(mclust)                                                   #Gaussian mixture model                                           m1 = Mclust                #k-means clustering                                               m2 = kmeans            #hierarchical clustering                                          m3 = hclust)                                 mutation_inference = cbind                                    )                                                                 apply##       GLM    mclust   k-means    hclust## 0.5712724 0.4451106 0.3298026 0.3725541et al.25 first introduced the idea of molecular signature landscapes to investigate the relationship between signatures related to the epithelial-mesenchymal transition (EMT) and TGF\u03b2-induced EMT. Subsequently, Cursonset al.26 computed epithelial and mesenchymal phenotype signature singscores and demonstrated an epithelial phenotype shift following miR-200c transfection into a mesenchymal cell line using a signature landscape. Foroutanet al.9 used signature landscapes to stratify breast cancer subtypes along the epithelial-mesenchymal axis and included it as part of thesingscore package. Here, we show how such landscapes can be used beyond the current application of the epithelial-mesenchymal axis. We demonstrate how transcriptomic signatures of different mutations can be used to stratify AML samples.Often, we are interested in the relationship between two dependent or independent phenotypes, for instance, the epithelial and mesenchymal phenotypes. The role of most signatures is to estimate an unobservable molecular phenotype so they may be considered as proxies of phenotypes. As such, we could investigate the relationship between two phenotypes using corresponding signatures. Foroutanet al.14 MLL-fusion signatures to score the TCGA AML samples. Unlike the NPM1c signature, this signature is composed of genes that discriminate samples with MLL-fusion genes. We download and parse the signature as demonstrated with the NPM1c signature.We now use the Ross#create signature names                                                              rossmll_name = \u2019ROSS_AML_WITH_MLL_FUSIONS\u2019                                           #generate URLs                                                                       rossmll_link = paste0                                                                                  #download files                                                                      rossmll_file = paste0                                          download.file                        rossmll_sig = getBroadSets                 rossmll_sig                                                                          ## GeneSetCollection##   names: ROSS_AML_WITH_MLL_FUSIONS ##   unique identifiers: 10113, 10479, ..., 9961 ##   types in collection:##     geneIdType: SymbolIdentifier ##     collectionType: BroadCollection knownDirection parameter of thesimpleScore function is set toFALSE to induce this mode. Ranks computed previously can be reused to compute scores with the new signature.The gene set is a composition of both up- and down-regulated genes as genes were selected based on their ability to discriminate mutants from wild-types. We use the third mode of singscore, which does not require the direction of genes in the gene set to be known. Therossmll_scores = simpleScoreplotScoreLandscape function plots a hexbin plot to visualise the two-dimensional distribution of scores. Scores computed using the Verhaaket al.12 signature and the Rosset al.14MLL-fusions signature are passed as arguments. Both scores should have been computed on the same samples with the order of samples retained. Names of the scores should be passed as arguments to thescorenames argument. ThetextSize argument can be used to specify the size of all text relative to the plot size. This may prove useful when plots are being generated for scientific posters, publications and presentations, all of which require different image sizes.Thep_mll_npm1c = plotScoreLandscape,  textSize = relSize                                                             )                                                                            p_mll_npm1c                                                                    \u03c1 = 0.628) despite only 16 genes being shared across the two signatures (signature sizes are 78 and 429 genes). In such an analysis, we may be interested in projecting new data points onto the landscape as done by Cursonset al.26. Alternatively, we may want to overlay some existing data points to investigate sample stratification using scores. Here, we overlayMLL fusions,MLL PTDs,PML-RARA fusions and NPM1c mutations onto the landscape. First, we build the annotation vector.#new annotation - modify previously used annotationsmutated_gene[aml_se$KMT2A.Fusion] = \u2019MLL Fusion\u2019    mutated_gene[aml_se$KMT2A.PTD] = \u2019MLL PTD\u2019          mutated_gene[aml_se$PML.RARA] = \u2019PML-RARA\u2019          projectScoreLandscape function. This functions uses thep_mll_npm1c plot generated using theplotScoreLandscape and overlays new data points onto it. Scores for the new data must be computed with the same signatures that were used to compute the landscape. As we are using existing data, scores computed in earlier sections are re-used. ThesubSamples can be used to select a subset of samples to project. Here, we select samples with the mutations we are interested in annotating.Points are projected onto an existing landscape using theselect_aml = !mutated_gene %in% \u2019Other\u2019                                         #label samples with an mclust NPM1c classification uncertainty of > 0.3         label_samples = substr(rownames(verhaak_scores), 6, 12) #sample ID from barcodeslabel_samples[m1$uncertainty < 0.3] = NA                                        #project mutations onto the landscape                                     p1 = projectScoreLandscape                                                                               p1 + theme                                             MLL fusions andMLL PTDs exhibit variation across different axes.MLL PTDs have a lower score than MLL fusions for theMLL fusion signature as expected. These sets of samples do not cluster along the two axes, consistent with observations by Rosset al.14. Overlaying samples annotations onto the plot assists in interpreting different regions of the landscape.PML-RARA samples differ from all other samples examined here. We repeat the analysis with thePML-RARA signature from Rosset al.14 to verify this distinction. The signature is available on the MSigDB and is named \u201cROSS_AML_WITH_PML_RARA_FUSION\u201d. We download and parse the signature, score all samples against it, plot a landscape with scores from the Verhaaket al.12 signature, and finally, project samples onto the plot. The signature was constructed in a similar manner to theMLL fusions signature, therefore, samples are scored using the same settings.It is evident from the previous analysis thatMLL fusion signature. ThePML-RARA signature forms a clear separation between thePML-RARA and NPM1c samples such thatPML-RARA samples are the only ones with a high score for this signature. Moreover, no association is observed between the two signatures. As discussed above  thePML-RARA fusion is diagnostic of a specific subtype of AML known as acute promyelocytic leukemia, with a highly distinct cell phenotype reflecting a block on differentiation at the promyelocyte stage15. The distinct features of this subtype are correspondingly reflected in the L-shaped landscape for these two signatures and the different mechanisms by which these lesions drive leukemogenesis.The singscore package provides an easy interface to apply gene set scoring methods within the R/Bioconductor environment. The TCGAbiolinks package allows relatively easy access to large clinically relevant data sets such as TCGA, together with appropriate annotation functions for interpreting biological data. Diagnostic and plotting functions included with singscore allow the user to investigate gene sets of interest to determine their power for distinguishing differences between samples. Different gene signatures can then be combined to explore how different cellular phenotypes are associated across a large cohort of cancer samples. As demonstrated, when appropriate gene set signatures are used, metrics calculated by singscore can be used for sample classification and this may be useful for further interrogation of large transcriptomic data sets where no genomic data are availableThis workflow depends on various packages from version 3.9 of the Bioconductor project, running on R version 3.6.1 (2019-07-05) or higher. The complete list of the packages used for this workflow are shown below:sessionInfo                                                              ## R version 3.6.1 (2019-07-05)## Platform: x86_64-pc-linux-gnu (64-bit)## Running under: CentOS Linux 7 (Core)#### Matrix products: default## BLAS:   /stornext/System/data/apps/R/R-3.6.1/lib64/R/lib/libRblas.so## LAPACK: /stornext/System/data/apps/R/R-3.6.1/lib64/R/lib/libRlapack.so#### locale:##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C##  [9] LC_ADDRESS=C               LC_TELEPHONE=C## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C#### attached base packages:## [1] parallel  stats4    stats     graphics  grDevices utils     datasets## [8] methods   base#### other attached packages:##  [1] mclust_5.4.5               dcanr_1.1.4##  [3] gridExtra_2.3              reshape2_1.4.3##  [5] singscore_1.4.0            GSEABase_1.46.0##  [7] graph_1.62.0               annotate_1.62.0##  [9] XML_3.98-1.20              org.Hs.eg.db_3.8.2## [11] AnnotationDbi_1.46.0       plyr_1.8.4## [13] rtracklayer_1.44.0         edgeR_3.26.5## [15] limma_3.40.2               SummarizedExperiment_1.14.1## [17] DelayedArray_0.10.0        BiocParallel_1.18.1## [19] matrixStats_0.55.0         Biobase_2.44.0## [21] GenomicRanges_1.36.1       GenomeInfoDb_1.20.0## [23] IRanges_2.18.2             S4Vectors_0.22.1## [25] BiocGenerics_0.30.0        TCGAbiolinks_2.12.3## [27] ggplot2_3.2.0              SingscoreAMLMutations_1.0.1            #### loaded via a namespace (and not attached):##   [1] backports_1.1.4            circlize_0.4.6##   [3] aroma.light_3.14.0         igraph_1.2.4.1##   [5] selectr_0.4-1              ConsensusClusterPlus_1.48.0##   [7] lazyeval_0.2.2             splines_3.6.1##   [9] usethis_1.5.1              sva_3.32.1##  [11] digest_0.6.20              foreach_1.4.4##  [13] htmltools_0.3.6            magrittr_1.5##  [15] memoise_1.1.0              BSgenome_1.52.0##  [17] cluster_2.1.0              doParallel_1.0.14##  [19] ComplexHeatmap_2.0.0       Biostrings_2.52.0##  [21] readr_1.3.1                R.utils_2.9.0##  [23] prettyunits_1.0.2          colorspace_1.4-1##  [25] blob_1.1.1                 rvest_0.3.4##  [27] ggrepel_0.8.1              BiocWorkflowTools_1.10.0##  [29] xfun_0.8                   dplyr_0.8.3##  [31] hexbin_1.27.3              crayon_1.3.4##  [33] RCurl_1.95-4.12            jsonlite_1.6     ##  [35] genefilter_1.66.0          survival_2.44-1.1##  [37] zoo_1.8-6                  iterators_1.0.10##  [39] glue_1.3.1                 survminer_0.4.4##  [41] registry_0.5-1             gtable_0.3.0##  [43] zlibbioc_1.30.0            XVector_0.24.0##  [45] GetoptLong_0.1.7           shape_1.4.4##  [47] scales_1.0.0               DESeq_1.36.0##  [49] rngtools_1.4               DBI_1.0.0##  [51] bibtex_0.4.2               ggthemes_4.2.0##  [53] Rcpp_1.0.2                 xtable_1.8-4##  [55] progress_1.2.2             cmprsk_2.2-8##  [57] clue_0.3-57                bit_1.1-14##  [59] matlab_1.0.2               km.ci_0.5-2##  [61] httr_1.4.0                 RColorBrewer_1.1-2##  [63] pkgconfig_2.0.2            R.methodsS3_1.7.1##  [65] locfit_1.5-9.1             labeling_0.3##  [67] tidyselect_0.2.5           rlang_0.4.0##  [69] munsell_0.5.0              tools_3.6.1##  [71] downloader_0.4             generics_0.0.2##  [73] RSQLite_2.1.1              broom_0.5.2##  [75] evaluate_0.14              stringr_1.4.0##  [77] yaml_2.2.0                 knitr_1.23##  [79] bit64_0.9-7                fs_1.3.1##  [81] survMisc_0.5.5             purrr_0.3.2##  [83] doRNG_1.7.1                EDASeq_2.18.0##  [85] nlme_3.1-140               R.oo_1.22.0##  [87] xml2_1.2.0                 biomaRt_2.40.1##  [89] compiler_3.6.1             rstudioapi_0.10##  [91] curl_3.3                   png_0.1-7##  [93] ggsignif_0.5.0             tibble_2.1.3##  [95] geneplotter_1.62.0         stringi_1.4.3##  [97] GenomicFeatures_1.36.3     lattice_0.20-38##  [99] Matrix_1.2-17              KMsurv_0.1-5## [101] pillar_1.4.2               BiocManager_1.30.4 ## [103] GlobalOptions_0.1.0        data.table_1.12.2## [105] bitops_1.0-6               R6_2.4.0## [107] latticeExtra_0.6-28        hwriter_1.3.2## [109] bookdown_0.11              ShortRead_1.42.0## [111] codetools_0.2-16           assertthat_0.2.1## [113] pkgmaker_0.27              rjson_0.2.20## [115] withr_2.1.2                GenomicAlignments_1.20.1## [117] Rsamtools_2.0.0            GenomeInfoDbData_1.2.1## [119] mgcv_1.8-28                hms_0.4.2## [121] grid_3.6.1                 tidyr_0.8.3## [123] rmarkdown_1.13             git2r_0.26.1## [125] ggpubr_0.2.1SingscoreAMLMutations (v1.0.0).All data analyzed in the workflow is read automatically from public websites as part of the code. Mutation data for the samples in this study are available as part of the R/Bioconductor packageSoftware available from:https://bioconductor.org/packages/release/workflows/html/SingscoreAMLMutations.htmlSource code available from:https://github.com/DavisLaboratory/SingscoreAMLMutations/Archived source code at time of publication:https://doi.org/10.5281/zenodo.3470443License: Artistic-2.0SingscoreAMLMutations. MF, JC, DDB and MJD conceived the idea for the workflow. YX translated the vignette for the package vignette into chinese. YX and RL reviewed the chinese translation. All authors have tested and reviewed the workflow. This work was supervised by JC and MJD.DDB, JC and MJD wrote the article. DDB wrote the code for the workflow and the associated R/Bioconductor workflow package All my comments were addressed by the authors. Some of the suggested improvements were introduced, other not but the authors explained they did not overlap with the objectives of the article.\"Normlisation\u201d instead of \"Normalisation\u201d occurs once in the chapter \u201cTransformation to FPKM values and normalisation\u201d;\"those those\u201d \u2013 repeated word in the sentence started from \u201cFigure 2 shows that the set of down-regulated genes has more discriminant power\u2026\u201dNPM1 gene. The term \u201cwild-type NPM1 samples\u201d is more appropriate.there is no dot at the end of the last sentence of the Summary chapter. Moreover, I suggest not to use a term \u201cwild-type NPM1c samples\u201d formed as an equivalent of \u201cNPM1c mutated samples\u201d, e.g. in the description of the Figure 3 and 4, as \u201cNPM1c\u201d means \u201ccytoplasmic NPM1\u201d which is typical for samples with the mutated I only suggest to correct a few editorial errors:I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. I don't have many more comments. Figures could be improved, as noted in the first version of this peer review.I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Introduction The article presents a new package, which was developed from a previous one (\u2018singscore\u2019) elaborated by the same authors. The package \u2018singscore\u2019 is a single-sample gene set scoring method which is valuable for analysis of transcriptomes of samples collected through the long time and not sequenced in the same run, experiment, platform or laboratory. Here, the authors apply the method for classification of TCGA acute myeloid (AML) samples using transcriptional \u2018gene signatures\u2019 identified by other authors (Verhaak and Ross) as typical for the NPM1c mutation, KMT2A (MLL) gene fusions, and PML-RARA gene fusions.1). Sometimes, the gene expression profiles can overlap between samples with different genetic lesions. The examples are HOX-gene-based discriminative signatures determined by Verhaak and Ross, not limited to AML with mutated NPM1, but specific also for AML cases with 11q23 abnormalities and KMT2A (MLL) gene rearrangements. The authors are aware of this fact and underline it in the article, too. On the other side, samples with the same mutation can present varying expression profiles because of additional features which also affect the transcriptome. Therefore, interpretation of the results must be done with caution. For me, the authors interpret the results a little bit too optimistically. The presented method is not sufficient to determine the mutation of interest, but can be used as a supplementary approach, a screening tool or (in future) a personalized medicine tool which could classify patients based on transcriptomic profiles, associated with specific treatment response. AML is a heterogeneous and multi-clonal disease. The transcriptome picture of AML is very complex and can result from different mutations, genomic rearrangements, and aberrant regulation of gene expression at different levels (see my last review paperTesting package2). My dataset contains 28 AML samples, including 8 with NPM1 mutation (verified with three independent approaches). The data load was more tricky as I started from the csv table with counts and I had to convert the DESeqDataSet object into the RangedSummarizedExperiment object. The best discrimination was achieved with the Verhaak signature, but only with the down-regulated genes. All samples with NPM1 mutation were clearly separated from others, however, 2 additional samples, without NPM1 mutation, were grouped together with NPM1c. It is possible they have KMT2A (MLL) rearrangements, but I cannot verify it now. Analyzing the plots, up-regulated genes and all genes form the signature are not as efficient, separating only 2 or 3 NPM1c samples from the rest. In the article, the authors also admit that the set of down-regulated genes has more discriminant power, but they claim up-regulated genes also contribute to the discrimination. I have used the package in R with TCGA data and confirm the code works, it is fast and generates the same results and plots as presented in the article. Moreover, I have tested the whole procedure on my own RNA-seq dataset  gene was unknown in my patients, so I could not compare the efficiency of signatures other than NPM1c. APL with PML-RARA is the most distinctive AML subtype which is easily separated from other AML samples based on transcriptome profile, so I would expect good results. This example shows the package works well for samples with very specific gene expression profile.Interpretation of the results Considering the result interpretation, I have some doubts. The singscores are composed of two components, an enrichment score and a dispersion estimate of ranks. I am informed that \u201chigh expression of up-regulated genes and low expression of down-regulated genes would result in higher scores\u201d. This is logical. I also know the maximal ranges . However, which value is really high? For example, is 0.2 enough  or maybe I should expect much more, e.g. 0.7? Similarly, which value of dispersion should I expect? I suppose, the lowest, but which is low enough or which range is optimal? What is difficult to understand is that \u201cdespite the range of scores increasing, the discriminant power drops moderately\u201d . I see the same paradox in my data \u2013 the scores are higher for up-reg. genes, which are much less efficient in discrimination between NPM1c+ and NPM1c- samples. It looks like scores only do not reflect the trends observed from score vs dispersion plots.Possible improvementsInstruction how to prepare and load the data other than those deposited in the GDC database.Annotation of samples on the plots with unique sample IDs  \u2013 it is written in the article that the \u2018singscore\u2019 package supports different types of annotations (\u201cAnnotations of interest can be overlayed on each plot\u201d) but I found only color code annotation whereas I would like to have color code for mutation type and additional text labels with sample IDs on the same plot.A command which lists the samples typed as strong candidates for having particular mutation, ordered according to the calculated metrics.Generating a threshold line between samples with and without mutation. In future, it would be also good to include other signatures, e.g. the signature of 369 genes identified by Alcalayet al. in 20053, discriminating AML patients with NPMc+ from patients with NPMc-, even in cases with rare chromosomal abnormalities. Although the package is generally useful and well described, the following improvements will make it more friendly for less advanced R users, e.g.4). For genes with high and middle expression level, the coverage can be even higher than that obtained from genome- or exome-level data. And in the case when DNA data are unavailable, it would be really fantastic. Because it demands completely different data processing, it may be considered for future versions of the package. What I would expect the most from a package designed to identify mutations in transcriptomes, is a direct identification of mutations in RNAseq data. The results of mutation typing based on gene expression profile will be strongly supported when a particular mutation will be covered by RNAseq reads. From my own experience, I know it is possible  elaborated by the same authors. The package \u2018singscore\u2019 is a single-sample gene set scoring method which is valuable for analysis of transcriptomes of samples collected through the long time and not sequenced in the same run, experiment, platform or laboratory. Here, the authors apply the method for classification of TCGA acute myeloid (AML) samples using transcriptional \u2018gene signatures\u2019 identified by other authors (Verhaak and Ross) as typical for the NPM1c mutation, KMT2A (MLL) gene fusions, and PML-RARA gene fusions.DDB: We note that the purpose of the initial manuscript may have been unclear, in part because it is listed under the F1000 Software Tool Article section. The purpose of this article is to present a workflow demonstrating the use of singscore, and it is a R/Bioconductor workflow illustrating the use of singscore, and so is not intended as a new package or tool.https://www.bioconductor.org/packages/release/workflows/https://www.bioconductor.org/packages/release/workflows/html/SingscoreAMLMutations.htmlSome of the review comments below assume that this manuscript presents a specific software package for detecting NPM1 mutations, so we have clarified the manuscript as outlined below in order to make the purpose and intention more clear (note that the title of the manuscript has changed from \u2018predicting mutations\u2019 to \u2018predicting mutation status\u2019 based upon feedback from reviewer 1). This work arose from an observation in another project that the Verhaak signature scored with singscore appeared to correlate strongly with mutation status, and we thought this would be an interesting example that might also help researchers to investigate the links between genetic lesions and corresponding transcriptional changes, an area in which the reviewer clearly has expertise.LH: AML is a heterogeneous and multi-clonal disease. The transcriptome picture of AML is very complex and can result from different mutations, genomic rearrangements, and aberrant regulation of gene expression at different levels (see my last review paper1). Sometimes, the gene expression profiles can overlap between samples with different genetic lesions. The examples are HOX-gene-based discriminative signatures determined by Verhaak and Ross, not limited to AML with mutated NPM1, but specific also for AML cases with 11q23 abnormalities and KMT2A (MLL) gene rearrangements. The authors are aware of this fact and underline it in the article, too. On the other side, samples with the same mutation can present varying expression profiles because of additional features which also affect the transcriptome.LH: Therefore, interpretation of the results must be done with caution. For me, the authors interpret the results a little bit too optimistically. The presented method is not sufficient to determine the mutation of interest, but can be used as a supplementary approach, a screening tool or (in future) a personalized medicine tool which could classify patients based on transcriptomic profiles, associated with specific treatment response.DDB: We agree with the reviewer that gene set scoring of transcriptomic data should not be the only method used to identify patient samples carrying genetic lesions. The Bioconductor workflow we present is intended to provide an example of applying singscore to study mutation/fusion based gene sets as we believe that singscore provides a relatively flexible and intuitive approach for investigating different gene sets across large data sets. We feel that a particularly useful feature is the ability to combine different signatures/gene sets (including phenotype/cell-cycle signatures etc) to explore how these transcriptional changes are associated over different samples, demonstrated in Figures 5-7.underlined):We have modified the \u201cDescription of biological relevance\u201d section to address the reviewer\u2019s comments around the complexity of AML genomic lesions and corresponding transcriptomic changes. The review highlighted is particularly relevant for this work and accordingly we mention the complexity of AML and direct the reader towards this resource by extending the first paragraph , NEJM], highlighting a number of co-occurring as well as mutually exclusive mutations.As the identification of putative driver fusions/mutations continues, work has also been directed towards how these lesions interact with each other and other features  to drive transcriptional changes as discussed in a recent review .We have also added a paragraph at the end of this section which discusses some of the limitations for our approach and provides more context in which it could be applied:While we demonstrate that singscore is capable of inferring mutation status from the transcriptional profile of AML samples, we note that it is best used to supplement alternative data which can provide a more definitive resolution of these lesions. Processing of raw RNA\u2011seq data will directly identify the presence of gene\u2011fusion products or mutations within protein-coding regions, although for many large data sets the quantified transcript abundance data are much easier to obtain without access agreements. The method can also be applied to legacy microarray data sets where genome and RNA sequencing data are unavailable. As such singscore provides a useful approach to supplement established methods for the study of genetic lesions in cancer. By exploring associations between different genomic and phenotypically relevant signatures, it may also help to better characterise true driver mutations which exert consistent effects on the transcriptome of AML samples and other cancers.Testing packageLH: I have used the package in R with TCGA data and confirm the code works, it is fast and generates the same results and plots as presented in the article. Moreover, I have tested the whole procedure on my own RNA-seq dataset . My dataset contains 28 AML samples, including 8 with NPM1 mutation (verified with three independent approaches). The data load was more tricky as I started from the csv table with counts and I had to convert the DESeqDataSet object into the RangedSummarizedExperiment object. The best discrimination was achieved with the Verhaak signature, but only with the down-regulated genes. All samples with NPM1 mutation were clearly separated from others, however, 2 additional samples, without NPM1 mutation, were grouped together with NPM1c. It is possible they have KMT2A (MLL) rearrangements, but I cannot verify it now. Analyzing the plots, up-regulated genes and all genes form the signature are not as efficient, separating only 2 or 3 NPM1c samples from the rest. In the article, the authors also admit that the set of down-regulated genes has more discriminant power, but they claim up-regulated genes also contribute to the discrimination.LH: I have no samples with PML-RARA fusion in my dataset and the status of KMT2A (MLL) gene was unknown in my patients, so I could not compare the efficiency of signatures other than NPM1c. APL with PML-RARA is the most distinctive AML subtype which is easily separated from other AML samples based on transcriptome profile, so I would expect good results. This example shows the package works well for samples with very specific gene expression profile.DDB: We thank the reviewer for the exceptional effort and time invested to test our workflow on independent data - we hope results from this analysis have been informative for identifying other features within their data. Our workflow includes guidance for users who wish to import data from other sources such as those used by the reviewer.Interpretation of the resultsLH: Considering the result interpretation, I have some doubts. The singscores are composed of two components, an enrichment score and a dispersion estimate of ranks. I am informed that \u201chigh expression of up-regulated genes and low expression of down-regulated genes would result in higher scores\u201d. This is logical. I also know the maximal ranges . However, which value is really high? For example, is 0.2 enough  or maybe I should expect much more, e.g. 0.7? Similarly, which value of dispersion should I expect? I suppose, the lowest, but which is low enough or which range is optimal? What is difficult to understand is that \u201cdespite the range of scores increasing, the discriminant power drops moderately\u201d . I see the same paradox in my data \u2013 the scores are higher for up-reg. genes, which are much less efficient in discrimination between NPM1c+ and NPM1c- samples. It looks like scores only do not reflect the trends observed from score vs dispersion plots.DDB: Interpretation of singscores is intentionally left to be problem specific as it generally requires some domain-specific knowledge of the biological system and corresponding signature genes \u2013 ideally the computational biologist or bioinformaticians working on each project can provide some guidance.The basic interpretation of the score is the normalised mean rank of genes within the signature relative to all other genes in the sample. At the extrema this interpretation is relatively simple - near 1, a higher value would indicate that genes in the signature are expressed at higher levels relative to other genes. For scores towards zero, however, the interpretation can be much more difficult \u2013 a score of zero  could indicate the signature genes are tightly clustered around the sample-wide mean abundance, or it could indicate a highly-dispersed almost uniform distribution across the entire abundance range . By exploring the singscores together with dispersion estimates this information is summarised, assisting in estimation of effect size variability. Interpretation of scores depends on the context of the experiment and the typical behaviour of the gene set. A \u201chigh\u201d score is best determined relative to other samples. This is achieved either by comparing scores from other samples in large datasets such as TCGA, or better, across a set of samples from a given experiment with known conditions. Other methods normalise the data before computing scores, and we note that a recent paper has applied z-score normalisation to results from singscore for comparison to ssGSEA . All singscores for samples remain the same and do not have to be recomputed upon addition of new samples, and interpretation will improve as more samples are added to the study. For example, Gaussian mixture modelling could be used to separate the NPM1c scores based on our expectation that there are two groups. This could be switched with other unsupervised classification algorithms such as hierarchical clustering or k-means clustering. We have added an example analysis to the manuscript to demonstrate how scores can be interpreted in an unsupervised setting, under the section \u201cTranscriptional signatures to predict mutation status/Unsupervised classification of mutations\u201d.There may be some cases where sample annotation is not available. In such scenarios, we are unable to build regression models to interpret scores. A higher singscore would provide stronger evidence for the signature but the magnitude is difficult to interpret without a reference. One approach to deal with this situation is to compare scores to those from other datasets where the mutations status is known. An alternative approach would be to compare scores within the dataset using unsupervised learning methods. Here we demonstrate the use of three clustering methods  to stratify samples, and as we have done previously [wang et al (2012) Journal of clinical bioinformatics] use the adjusted Rand index (ARI) to compare classifications. As expected, supervised (GLM) classification results in the best prediction. This is followed by clustering based on the score using Gaussian mixture decomposition. Any other classification algorithm along with prior knowledge could be used to decompose scores into groups. The important characteristic of singscores is that they maintain the discriminant power of gene signatures therefore can be coupled with supervised, semi-supervised or unsupervised algorithms to perform stratification.```library(mclust)#Gaussian mixture modelm1 = Mclust#k-means clusteringm2 = kmeans#hierarchical clusteringm3 = hclust)mutation_inference = cbind)apply```Possible improvementsLH: Although the package is generally useful and well described, the following improvements will make it more friendly for less advanced R users, e.g.LH: Instruction how to prepare and load the data other than those deposited in the GDC database.DDB: We have noted in text that the rank matrix can be computed using either a SummarizedExperiment object, DGEList object, ExpressionSet object, numeric matrix or a numeric data frame. As such, a numeric matrix with sample names as column names and genes as row names would suffice. Scoring should be performed on length bias corrected measurements such as RPKM/FPKM or TPM and not CPM or raw counts.Transcriptional signatures to predict mutation status/Score TCGA AML samples using the Verhaak signature - extract from text: \u201cThe `rankGenes` function will compute ranks from expression data in the form of either a numeric matrix, numeric data frame, ExpressionSet object, DGEList object or a SummarizedExperiment object\u201dLH: Annotation of samples on the plots with unique sample IDs  \u2013 it is written in the article that the \u2018singscore\u2019 package supports different types of annotations (\u201cAnnotations of interest can be overlayed on each plot\u201d) but I found only color code annotation whereas I would like to have color code for mutation type and additional text labels with sample IDs on the same plot.DDB: Sample labels can be added to landscape plots but were not supported in other visualisations. We have added functionality to the latest version of the singscore package (v1.5.1) to allow labelling of samples in the score vs. dispersion plots. We have modified the text to clarify, and modified Figure 6 to label samples where classification uncertainty (NMP1c vs WT) is high to demonstrate this feature. The changes below have been made to the section: \u201cTranscriptional signatures to predict mutation status/Diagnostics of the Verhaak signature\u201d.Sample annotations of interest  can be colour coded on each plot. \u2026\u2026Figure 6:```select_aml = !mutated_gene %in% 'Other'#label samples with an mclust NPM1c classification uncertainty of > 0.3label_samples = substr(rownames(verhaak_scores), 6, 12) #sample ID from barcodeslabel_samples[m1$uncertainty < 0.3] = NA#project mutations onto the landscapep1 = projectScoreLandscapep1 + theme```LH: A command which lists the samples typed as strong candidates for having particular mutation, ordered according to the calculated metrics.DDB: As discussed in an earlier comment, we recommend such analyses to be problem specific. Generally, a higher score would indicate a stronger effect of genes in the signature relative to WT samples therefore samples with higher scores would be stronger candidates for mutations. Alternatively, the partitioning created using Gaussian mixture modelling could be used as a guide for separation and samples with a score much higher than the threshold would be the strongest candidates for the mutation.LH: Generating a threshold line between samples with and without mutation.DDB: See above discussion/recommendation.LH: In future, it would be also good to include other signatures, e.g. the signature of 369 genes identified by Alcalayet al. in 20053, discriminating AML patients with NPMc+ from patients with NPMc-, even in cases with rare chromosomal abnormalities.LH: What I would expect the most from a package designed to identify mutations in transcriptomes, is a direct identification of mutations in RNAseq data. The results of mutation typing based on gene expression profile will be strongly supported when a particular mutation will be covered by RNAseq reads. From my own experience, I know it is possible . For genes with high and middle expression level, the coverage can be even higher than that obtained from genome- or exome-level data. And in the case when DNA data are unavailable, it would be really fantastic. Because it demands completely different data processing, it may be considered for future versions of the package.DDB: As we have outlined at the start of this review there may have been some misunderstanding around the purpose of this Workflow package/paper. We agree with the reviewer that direct detection of mutations/fusions from RNA-seq data is the best approach, and we now note this in the \u2018Description of biological relevance section\u2019 as stated above. The other gene signatures mentioned above could be incorporated in a workflow as singscore supports analysis and comparison of multiple gene sets.  The authors Bhuva et al. presented the application of a previously published gene set scoring tool, singscore, to discriminate AML subsets based on gene expression signatures, using signatures derived from\u00a0known specific gene mutations or fusions. The authors show that singscore can\u00a0effectively separate AML samples based on such gene signatures. Given the ability of the software to discriminate samples within heterogenous diseases, such as leukemias, its application to other cancers and diseases with similar levels of heterogeneity can be beneficial to improve patients' clinical outcomes. The rationale behind the application of\u00a0the software\u00a0and its\u00a0step-wise descriptions are well explained. So is the interpretation of results. I appreciate the authors' efforts to explain all the steps and suggest alternatives, giving the software description the feel of a tutorial.Title. As far as I have understood, singscore doesn't actually predict mutations. Rather, it uses gene signatures associated to certain mutations in order to identify sub-groups within a population. As it is, the title is misleading.Similar rationale about the title of section \"Predicting mutations using the Verhaak signature\", page 16.In the section \"Filter out genes with low counts\", page 7, the authors describe explicitly how to handle duplicated entries caused by the GDCprepare function. Here I would reorganise and shorten the paragraph by first mentioning the issue introduced by the function, then saying edgeR is applied to the data filtered of duplicate entries.Figure 3. The legend below each graph is identical. I suggest to only use one legend for all three plots.The authors comment on several instances about the similarities between scores for signatures of two\u00a0subgroups of AML samples, NPM1c and KMT2A-/MLL-fusion, and that's due to the presence of Hox genes. I suggest to have\u00a0the\u00a0lists of the two gene signatures directly in the article for immediate lookup. This is not essential, as the authors already refer to the articles they take the data from,\u00a0but I think it will help readers. Minor comments:\u00a0Typo in the caption of figure 1 (approcimately instead of approximately).Typo in figure 2 caption.\u00a0I suppose \"scores are plot against...\" should read \"scores are plotted against...\". I have some small\u00a0comments on some points I'd like to see addressed:I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. We would like to thank Dr Marina Lizio for her very useful review, and we have addressed her comments as outlined below.ML: I have some small comments on some points I'd like to see addressed: 1. Title. As far as I have understood, singscore doesn't actually predict mutations. Rather, it uses gene signatures associated to certain mutations in order to identify sub-groups within a population. As it is, the title is misleading.status in AML from transcriptomic signatures\u201d in order to clarify that we are simply predicting the mutation status of samples, not actual mutations.DDB: This is an excellent point. We have now changed the title to \u201cUsing singscore to predict mutation 2. Similar rationale about the title of section \"Predicting mutations using the Verhaak signature\", page 16.NPM1c mutation status using the Verhaak signature\u201d. DDB: A similar change is made here, the title of this section is now: \u201cPredicting 3. In the section \"Filter out genes with low counts\", page 7, the authors describe explicitly how to handle duplicated entries caused by the GDCprepare function. Here I would reorganise and shorten the paragraph by first mentioning the issue introduced by the function, then saying edgeR is applied to the data filtered of duplicate entries.DDB: The issue of duplicated features with the TCGAbiolinks package has been fixed in a recent update (TCGAbiolinks v2.12.2). Consequently, we will be removing the text describing the identification and removal of duplicated features. The text removed is:------------------------------Creation of a DGEList object is initially unsuccessful due to duplicated row names in the data matrix (107 features). The raw count files for individual samples do not have any duplicated features indicating this is introduced by the `GDCprepare` function. This is further supported by the fact that the counts for a single feature are the same for duplicated entries. An example of such a feature is shown below and we have verified duplicated entries of all other features to be the same.```{r}dup_features = rownames(aml_se)[duplicated(aml_se)]``````{r dup_entries}library(SummarizedExperiment)#example check on one feature IDdup_feature = 'ENSG00000011454'dup_data = assay(aml_se)identical```As such, it is safe to discard duplicated entries.------------------------------We also remove the code that discards duplicate features------------------------------aml_se = aml_se------------------------------ 4. Figure 3. The legend below each graph is identical. I suggest to only use one legend for all three plots.DDB: We appreciate this point, however each plot in this figure is generated by a function in the package which includes the legend. For a normal use case, users would be generating a single plot to diagnose a single sample, which is why we include a legend for the plot. In this workflow, we simply combine the three plots into a single figure. Removing legends from the default plots would overcomplicate the code of this diagnostic procedure in the workflow which is why we decided to leave the legends as they are. If users were interested in manipulating figures in their application, they could add extra options to the ggplot2 object (as we show with adding a new title and modifying the legend to be a single column). 5. The authors comment on several instances about the similarities between scores for signatures of two subgroups of AML samples, NPM1c and KMT2A-/MLL-fusion, and that's due to the presence of Hox genes. I suggest to have the lists of the two gene signatures directly in the article for immediate lookup. This is not essential, as the authors already refer to the articles they take the data from, but I think it will help readers.DDB: The signatures we downloaded from MSigDB have Entrez IDs for genes. The NPM1c and MLL signatures contain 491 unique genes. The reason we did not have the lists in the article was that we would be presenting 491 numbers that would have been difficult to interpret without mapping. Additionally, our intention is for readers to execute the workflow while reading this article. In this scenario, the geneIds function could be used to list the gene identifiers forming the signatures once they have been downloaded and parsed into R. Identifiers could be mapped to symbols using the mapIds function in the AnnotationDbi package and an annotation such those provided by the org.Hs.eg.db R/Bioconductor package. We hope that the ease with which these data can be accessed in the application will make the lists accessible for readers.ML: Minor comments: 1. Typo in the caption of figure 1 (approcimately instead of approximately).DDB: Thanks for helping us identify this, we have fixed it now. 2. Typo in figure 2 caption. I suppose \"scores are plot against...\" should read \"scores are plotted against...\".DDB: This has also been changed. Many thanks for the useful and constructive review. Best wishes, Mr Dharmesh Bhuva Bioinformatics Division The Walter and Eliza Hall Institute Dr Melissa Davis Bioinformatics Division The Walter and Eliza Hall Institute"}
+{"text": "Scope: Maternal obesity leads to glucose intolerance in the offspring. Changes in the gut microbiota are being increasingly implicated in the pathogenesis of diabetes. We hypothesized that inulin intervention during gestation and lactation improves glucose metabolism disorders in mouse offspring from high-fat diet (HD)-fed dams.Procedures: Female C57BL mice were fed a control diet or HD for 4 weeks before mating. After mating, pregnant mice were randomly divided into three groups through gestation and lactation: control diet (CD) group, HD group, and HD treated with inulin (HD-inulin) group. At weaning, glucose metabolic status was assessed. Gut microbial DNA from offspring cecal contents was isolated and processed for metagenomic shotgun sequencing, and taxonomic and functional profiling were performed.Results: Offspring from dams in the HD-inulin groups demonstrated reduced fasting blood glucose, decreased blood glucose area under the curve during the oral glucose tolerance test, and reduced fasting serum insulin and HOMA-IR compared to offspring from dams in the HD group. Nineteen differentially abundant bacterial species were identified between the HD-inulin and HD groups. The HD-inulin group displayed significantly greater abundances of Bacteroides_acidifaciens, Eubacterium_sp_CAG_786, Clostridium_sp_CAG_343, and Bifidobacterium_breve species and lower abundances of Oscillibacter_sp_1_3, Ruminococcus_gnavus_CAG_126, and Bacteroides_massiliensis species. Differentially abundant bacterial species among the three groups were involved in 38 metabolic pathways, including several glucose and lipid metabolism pathways.Conclusion: Our results show that early inulin intervention in HD-fed mouse dams moderates offspring glucose metabolism and gut dysbiosis. Nutrition status during the intrauterine period has been reported to lead to the programming of metabolic disorders in the offspring throughout their whole life span . MaternaDietary inulin-type prebiotic treatment represents a promising strategy for altering the gut microbiota and affecting host metabolism and physiology , 13. As We hypothesized that inulin intervention during gestation and lactation improves glucose metabolism disorder in offspring from HD-fed dams. To identify this alteration in gut microbiota, we used a metagenomic shotgun sequencing approach to analyze the gut microbiota of offspring from inulin-supplemented HD-fed dams. In this study, we used metagenomic shotgun sequencing to sequence the whole set of genes present in the gut microbiome. This sequencing information can provide the relative abundance of genes not only in functional pathways but also at all taxonomical levels . The aimad libitum access to control  or high-fat diets  for 4 weeks. At 9 weeks of age, females were bred with control male mice fed with a control diet. The vaginal plug was checked to confirm pregnancy. During the gestation and lactation period, female mice fed a control diet before gestation remained on a control diet (CD); female mice fed a high-fat diet before gestation went on either a high-fat diet (HD) only or a high-fat diet with 10% wt/wt inulin supplementation . Because of the differential sex phenotype following different maternal nutrition, this project researched only male offspring. At weaning, male offspring (n = 8 per group) were sacrificed. Mice were fasted for 10 h and anesthetized with chloral hydrate, and a blood sample was collected from the intraorbital retrobulbar plexus. The cecal contents were quickly removed, snap frozen on dry ice, and then stored at \u221280\u00b0C for further analysis. All animal experimental protocols were approved by the Animal Care Committee of Peking Union Medical Hospital (Permit Number: MC-07-6004). Five-week-old female C57BL6/J mice (body weight 13.03 \u00b1 0.79 g) were given The body weight of both the mother and pups was measured. Blood was collected from a tail bleed and analyzed to test glucose levels using a Contour TS glucometer .At weaning, an OGTT was performed to assess glucose tolerance in pups. After 10 h of fasting, pups were given a glucose load (2.0 g/kg body weight) by gavage. Before (0 min) and at 30, 60, and 120 min after the gavage, the blood glucose levels were measured. The area under the glucose tolerance curve (AUC) of the OGTT was calculated as previously described .At weaning, pups were fasted for 10 h to measure serum insulin by using an ELISA kit . Insulin sensitivity was assessed using HOMA-IR as previously described .Fifty micrograms of cecal contents was used for metagenomic DNA isolation using a TIANamp Stool DNA Kit . The quality and quantity of DNA was assessed using agarose gel electrophoresis and fluorometry .One microgram of DNA was sheared to 350 bp fragments by sonication. After polishing and ligation with a full-length adaptor, DNA fragments were amplified using an NEBNext\u00ae Ultra\u2122 DNA Library Prep Kit from Illumina . Then, PCR amplification products were purified . DNA libraries underwent size distribution via an Agilent 2100 Bioanalyzer. Finally, DNA libraries were sequenced on the Illumina HiSeq 2000 Platform . Paired-end reads were generated for further processing.Mus musculus reference genome using Bowtie 2.2.4 software  were predicted by MetaGeneMark software . DIAMONDpost hoc test. For sequencing data, statistical analyses were performed using R software (v. 2.15.3). For determination of the abundance of genes, taxonomies, and KEGG ontologies, the Mann-Whitney test was used for statistics between two groups, and the Kruskal-Wallis test was used for comparisons among three groups. Statistical analyses were performed in GraphPad Prism 6 . Statistical significance was defined as P < 0.05.The data are expressed as the mean \u00b1 SD. Differences among the groups were analyzed using one-way ANOVA followed by Tukey's P > 0.05). After eating a HD for 4 weeks, the HD group had a higher body weight than the CD group . Despite decreased food intake in the HD group compared with the CD group (P < 0.01), the energy intake of the HD group was higher than that of the CD group during the pregnancy period (P < 0.01). Accordingly, the body weight change in HD mice was 139% of that in CD mice (P < 0.01). Although no significant difference in food intake and energy intake was observed between the HD and HD-inulin groups, the body weight change in HD-inulin dams was 33.9% lower than that in the HD mice during the pregnancy period (P < 0.01). Similarly, maternal HD feeding increased fasting blood glucose by 24.8% compared with CD mice (P < 0.01). Interestingly, fasting blood glucose was reduced by 11.3% in HD-inulin dams compared with HD mice (P < 0.05).Before consuming a HD, dam body weight between groups did not differ  were used to perform whole-metagenome shotgun sequencing to understand the gut microbial composition. After quality control, we acquired a total of 113.5 Gbp of high-quality metagenomic data (7.56 \u00b1 0.62 Gbp per sample) for further analysis. The sequence data generated in this study were submitted to the NCBI Sequence Read Archive database (accession number PRJNA552163). After de novo assembly and gene data calling, we constructed a non-redundant gene catalog of all cecal contents containing 1,048,576 genes. This gene catalog was qualified for further gut microbial analysis.Cecal contents from 15 mice . Bacteroides_acidifaciens (P < 0.05), Bacteroides_sp_CAG_98 (P < 0.05), Eubacterium_sp_CAG_786 (P < 0.05), Clostridium_sp_CAG_343 (P < 0.01), and Bifidobacterium_breve (P < 0.05) were significantly elevated in pups from HD-inulin dams vs. pups from HD dams. However, Oscillibacter_sp_1_3 (P < 0.01), Firmicutes_bacterium_CAG_534 (P < 0.01), Bacteroides_massiliensis (P < 0.05), Ruminococcus_albus (P < 0.05), Clostridium_sp_CAG_354 (P < 0.05), Ruminococcus_flavefaciens (P < 0.05), Desulfovibrio_vulgaris (P < 0.05), Mycoplasma_sp_CAG_776 (P < 0.05), Ruminiclostridium_Eubacterium_siraeum (P < 0.05), Clostridium_sp_CAG_245 (P < 0.01), Clostridium_sp_CAG_230 (P < 0.05), Ruminococcus_sp_CAG_254 (P < 0.01), Ruminococcus_gnavus_CAG_126 (P < 0.05), and Faecalibacterium_sp_CAG_74 (P < 0.01) were reduced significantly in pups from HD-inulin dams vs. HD dams , a major pro-inflammatory cytokine , fatty acid biosynthesis, adipocytokine signaling pathway, glucagon signaling pathway, type II diabetes mellitus, and carbohydrate digestion and absorption. Previous studies indicated the beneficial effect of prebiotics on metabolic disorders, such as obesity and type 2 diabetes , 81. GivBifidobacterium and several butyrate-producing bacteria. Thus, maternal inulin supplementation is promising for the prevention of metabolic disorders in the offspring.In conclusion, maternal inulin supplementation has beneficial effects on glucose metabolism in offspring, including improvements in glucose intolerance and insulin resistance. Importantly, maternal inulin supplementation increased the abundance of xiaoxh2014@vip.163.com) on reasonable request.The datasets analyzed in this manuscript are not publicly available. Requests to access the datasets should be directed to The datasets supporting the conclusions of this manuscript are available from the corresponding author .XX conceived and designed the experiments. QZ, JZ, TW, and XW performed the experiments. MY, ML, and FP analyzed the data. XX contributed reagents, materials, and analysis tools. QZ wrote the paper.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest."}
+{"text": "This work highlights the importance of the hydrophilicity of a catalyst\u2019s active sites on an oxygen reduction reaction (ORR) through an electrochemical and physico-chemical study on catalysts based on nitrogen-modified carbon doped with different metals . BET, X-ray Powder Diffraction (XRPD), micro-Raman, X-ray Photoelectron Spectroscopy (XPS), Scanning Electron Microscopy (SEM), Scanning Transmission Electron Microscopy (STEM), and hydrophilicity measurements were performed. All synthesized catalysts are characterized not only by a porous structure, with the porosity distribution centered in the mesoporosity range, but also by the presence of carbon nanostructures. In iron-doped materials, these nanostructures are bamboo-like structures typical of nitrogen carbon nanotubes, which are better organized, in a larger amount, and longer than those in the copper-doped material. Electrochemical ORR results highlight that the presence of iron and nitrogen carbon nanotubes is beneficial to the electroactivity of these materials, but also that the hydrophilicity of the active site is an important parameter affecting electrocatalytic properties. The most active material contains a mixture of Fe and Cu. An oxygen reduction reaction (ORR) is a fundamental step in many electrochemical applications, e.g., fuel cells and zinc/air batteries. Being kinetically hindered, an ORR requires efficient catalysts typically based on Platinum Group Metals ,2,3. TheThe aim of this work is to demonstrate the importance of the hydrophilicity of the active sites for ORRs. For this purpose, an electrochemical and physico-chemical study was carried out on catalysts based on nitrogen-modified carbon doped with different metals , having a comparable specific surface area and amount of nitrogen to avoid hiding cumulative effects.The studied catalysts were synthesized by thermal decomposition of a mixture of a sugar, guanidine acetate, metal salts, and silica as templating agent. In the following, catalysts containing Fe, Cu, and Fe\u2013Cu will be respectively labelled S_GA_Fe, S_GA_Cu, and S_GA_FeCu, whereas S_GA will indicate a metal-free (reference) sample.All chemicals/solvents were purchased from Sigma Aldrich  and used as received without further purification. Gelling sugar  was purchased in a market . A Pt-based commercial catalyst  was tested and used as a reference material.2 flow rate = 100 cm3 min\u22121) followed by lixiviation in boiling NaOH (3 mol dm\u22123) to remove silica. After washing, the carbon was dried  and, finally, pyrolysed  to activate catalytic sites.In the synthesis, gelling sugar (3 g), guanidine acetate (2 g), and metal (Me) acetate  were added to silica powder (4.3 g) and mixed. This mixture underwent a first heating step  apparatus and the instrumental software (Version 1.03) and applying Brunauer\u2013Emmett\u2013Teller (BET) and Barrett\u2013Joyner\u2013Halenda analyses, respectively. Prior to measuring, sample powders were heat-treated  to remove adsorbed foreign species.Specific surface area and porosity distribution were obtained from NMicro-Raman spectra were recorded in air at room temperature using a NTEGRA-Spectra SPM spectrometer  . Raman scattering was excited by a thermo-cooled solid-state laser . The scattered light from the sample was collected using a 100x  Mitutoyo objective. Spectra acquired from different random positions on each specimen were averaged to have a reliable picture of the sample bulk. The average spectra were analyzed by using a commercially available software package  to assesX-ray Photoelectron Spectroscopy (XPS) analyses were run on a PHI 5000 Versa Probe II Scanning XPS Microprobe spectrometer . Measurements were done with a monochromatic Al K\u03b1 source (X-ray spot 100 \u03bcm) at a power of 24.8 W. Wide scans and detailed spectra were acquired in Fixed Analyzer Transmission (FAT) mode with a pass energy of 117.40 eV and 29.35 eV, respectively. An electron gun was used for charge compensation (1.0 V 20.0 \u03bcA). Data processing was performed by using the MultiPak software v. 9.5.0.8 .X-ray Powder Diffraction (XRPD) patterns were collected at room temperature at the ID22 beamline of the ESRF with a wavelength \u03bb = 0.399946(4) \u00c5 up to 2\u03b8 = 40\u00b0 and a ~0.5 h/pattern counting time. Data recorded continuously have been then merged using different step sizes spanning from \u03942\u03b8 = 0.005\u00b0 to 0.050\u00b0 to highlight different features in the patterns. Patterns merged with the former step size were analyzed via the Rietveld method using the General Structure Analysis System (GSAS) suite of the program .Scanning Electron Microscopy (SEM) observations were carried out using a Leo 1430 SEM .Scanning Transmission Electron Microscopy (STEM) measurements were performed by using a Zeiss Supra 40 Field Emission Scanning Electron Microscope (FE-SEM)  working in high vacuum conditions and equipped with the GEMINI column.Wettability features of carbons were evaluated by a Kr\u00fcss Easy Drop instrument  using a water drop (V = 7 \u03bcL) gently placed on the surface of the samples compacted as a flat layer. By taking into account the complexity of the system and the relationship between the wetting properties and the physico-chemical features of the materials  only the time necessary to achieve complete spreading was measured .\u22123 KOH by the Thin Film Rotating Disk Electrode method using Cyclic Voltammetry (CV) as in . T. T3/2 coThe low signal-to-noise ratio of the Fe2p high resolution regions  might maThe results of STEM analysis shown in \u22123 KOH are reported in Polarization curves in 0.1 mol dmE1/2 (0.099 V versus SHE), also considering the Pt-based catalyst . S_GA_Fe and S_GA_Cu are more cathodic  The exchanged electron number is about 3.5\u20134.0 for all metal-doped materials. S_GA, with the lowest onset potential (0.044 V versus SHE) and an exchanged electron number of ~2.7 (suggesting a predominant formation of peroxides) is the worst catalyst. Similar results on iron-doped samples have been obtained in a previous work by Liang et al. [Except for S_GA_Cu, all the samples show a well-defined limiting current. The best electrocatalytic performance pertains to S_GA_FeCu, with the most anodic onset potential (0.145 V versus SHE) and the highest g et al.  on Fe\u2013N-g et al.  and of ag et al. ,44,45,46g et al.  and incrg et al. , impartsg et al. ,48,49. HAs reported in , the surSince Fe(0) is more hydrophilic than Cu(0)  and N-CNComing back to the different electrocatalytic activity of S_GA_Fe and S_GA_FeCu carbons, our physico-chemical characterization shows that they have very similar structure and properties but there is a larger hydrophilicity in the S_GA_FeCu sample, which is probably related to the presence of Cu(0) phase. Thus, we propose that the improved performance of S_GA_FeCu has to be attributed to the different hydrophilicity of its active centers. Actually, an increase of the interactions between water and active centers could favor interactions with molecular oxygen and thus, its adsorption and, finally, its reduction. Although the influence of the active center\u2019s hydrophilicity on ORR activity of Pt-free materials should be deeply investigated, the results here presented pave the way to the comprehension of their properties.In this work, catalysts based on nitrogen-modified carbons doped with different metals  were synthesized and characterized by BET, synchrotron radiation XRPD, micro-Raman, XPS, SEM, STEM, and hydrophilicity measurements. The N-modified carbon phases are characterized by porous structures and by the presence of N-CNTs. In the Fe-doped samples (S_GA_Fe and S_GA_FeCu), N-CNTs are more well-organized, in a larger amount, and longer than in the copper-doped ones. In Cu-doped (S_GA_Cu and S_GA_FeCu) samples, copper is present in the crystalline (ccp) phase.In spite of the different nature of the metal-doping agent, catalysts exhibit few differences in terms of surface area, functional surface groups, and graphitization degree of the carbon phase. Electrocatalytic activity toward ORRs increases in the order S_GA_Cu < S_GA_Fe < S_GA_FeCu. The presence of a larger amount of N-CNTs can account for the better performance of iron-doped catalysts with respect to S_GA_Cu. Instead, also the hydrophilicity of the active sites, which decreases in the order S_GA_FeCu > S_GA_Cu > S_GA_Fe, must be considered in order to explain the higher electrocatalytic activity of S_GA_FeCu with respect to S_GA_Fe.The presence of iron, N-CNTs, and an improved hydrophilicity synergically boost the electrocatalytic properties of ORR catalysts.The preliminary results here presented highlight the influence of the hydrophilicity of active sites on ORRs and pave the way to the comprehension of the electrocatalytic properties of Pt-free materials."}
+{"text": "Plasmodium infections increase the likelihood of enteric bacteria causing systemic infections. Currently, it is not known whether Plasmodium infection impacts human gut microbiota as a prelude to bacteremia or whether antimalarials affect gut microbiota. Our goal was to determine to what degree Plasmodium infections and antimalarial treatment affect human gut microbiota.Gut microbiota were recently shown to impact malaria disease progression and outcome, and prior studies have shown that One hundred Kenyan infants underwent active surveillance for malaria from birth to 10 months of age. Each malaria episode was treated with artemether-lumefantrine (AL). Any other treatments, including antibiotics, were recorded. Stool samples were collected on an approximately biweekly basis. Ten children were selected on the basis of stool samples having been collected before (n = 27) or after (n = 17) a malaria episode and without antibiotics having been administered between collections. These samples were subjected to 16S ribosomal ribonucleic acid gene (V3\u2013V4 region) sequencing.Bacterial community network analysis revealed no obvious differences in the before and after malaria/AL samples, which was consistent with no difference in alpha and beta diversity and taxonomic analysis at the family and genus level with one exception. At the sequence variant (SV) level, akin to bacterial species, only 1 of the top 100 SVs was significantly different. In addition, predicted metagenome analysis revealed no significant difference in metagenomic capacity between before and after malaria/AL samples. The number of malaria episodes, 1 versus 2, explained significant variation in gut microbiota composition of the infants.In-depth bioinformatics analysis of stool bacteria has revealed for the first time that human malaria episode/AL treatment have minimal effects on gut microbiota in Kenyan infants. Salmonella infections, this report demonstrates that Plasmodium infections and oral treatment with antimalarial drugs do not alter stool bacteria populations in Kenyan infants.Although malaria-induced dysbiosis of gut microbiota has been postulated to contribute towards increased susceptibility to Non-typhoid  Plasmodium ; P = .234, LMEM; 2 = 0.09, P = .044; 2 = 0.007, P = .6)  using Observed_OTUs (richness/number of species present) and Shannon index (richness and evenness), were analyzed between the before and after malaria/AL episodes. Neither Observed_OTUs ( P = .6) . Similar P = .6)  and D.P > .05, LMEM)  was calculated using (1) non-phylogenetic Bray-Curtis distance and (2) phylogenetic weighted and unweighted UniFrac distance for all samples  and paired samples  . The bac5, LMEM) . These aP < .05, Wilcoxon and Kruskal-Wallis test) . Consistently, analysis of the relative abundance of the top 12 bacteria family among the individual participants revealed no gross pattern between the before and after malaria/AL stool samples   . However samples . Similar samples . When ea t test) . Likewis t test)  and B of2 = 0.948, P < .0001)   . The LEf< .0001) . SV43, S< .0001) . However6, LMEM) . The topt test with the web portal, RNA-Seq 2G (http://rnaseq2g.awsomics.org). Nine and eight pathways were enriched in stools before and after malaria/AL using DeSeq2 and t test, respectively (P < .05)  (The metagenome in the stool samples was predicted using the online tool Piphillin. None of the top 20 most highly abundant KEGG pathways  were morP < .05) . Six patP < .05) . Only N- samples , but thi = .217) .t test using RNA-Seq 2G, resulting in 47 genes common to both tests (N-glycan biosynthesis (N-glycan biosynthesis pathway with Pathview (https://pathview.uncc.edu/), only 3 KO were inferred by Piphillin of 43 KO in the pathway  level was analyzed. The inferred metagenome at gene level were normalized with DeSeq2, and differentially abundant genes were calculated using DeSeq2 and th tests . All werth tests . Among tth tests . Inconsith tests , the patynthesis . Moreove pathway . However pathway  and S9. 2) explained by various covariates in this study was calculated using EnvFit implemented in vegan R package. Earlier, we showed no difference in alpha diversity among groups of various covariates     and C. H9, LMEM) .Plasmodium species (Plasmodium berghei ANKA and P yoelii nigeriensis) induced pathological changes in the intestine including infiltration of inflammatory macrophages, T cells, detachment of epithelia in the small intestine, increased expression of inflammatory cytokines, and intestinal permeability [P berghei ANKA-infected BALB/c mice [P yoelii nigeriensis infection in CBA mice induces anti-inflammatory interleukin (IL)-10 [Macaca mulatta) infected with Plasmodium fragile\u2014the parasite suppressed gut inflammation via the induction of IL-10, which blunted the influx of neutrophils in the gut [Plasmodium species.This report provides the first longitudinal analysis of stool bacteria communities before and after a clinical malaria episode plus AL treatment in humans. The data identified minimal changes in gut microbiota of Kenyan infants due to malaria/AL. In contrast to these data in humans, 2 previous studies in rodent malaria models have reported larger shifts in the gut microbiota of mice before and after malaria infection associated with intestinal inflammation. Infection of mice C57BL/6) with rodent 7BL/6 wit (IL)-10 . Likewis the gut . Finally the gut , 18. TakP falciparum infection have been shown to have increased gastrointestinal permeability lasting for a couple of days depending on the severity of malaria and treatment, which reverts to normal during convalescence [Salmonella during malaria, including macrophage dysfunction due to ingestion of the malaria pigment hemozoin [Salmonella initially breaches the intestinal barrier to cause a systemic infection during malaria. It is well established that dysbiosis of the normal gut flora provides an opportunity for Salmonella to establish infection [P yoelii nigeriensis-induced dysbiosis resulted in increased susceptibility to Salmonella typhimurium enterica infection in C57BL/6 mice [P yoelii nigeriensis infections induce elevated production of intestinal IL-10, which facilitates increased translocation of S typhimurium enterica out of the intestinal tract [Plasmodium-induced changes in the anti-inflammatory status of the intestinal tract may be more likely to contribute to intestinal translocation of Salmonella rather than gut microbiota dysbiosis.Adults with lescence . In spitlescence . Varioushemozoin , impairehemozoin , dysfunchemozoin  among othemozoin . Howevernfection , and it al tract . Taken tP falciparum infection. Where slide is negative but RDT positive, suspicion may be lower. The RDT used detects the P falciparum-specific surface antigen histidine-rich protein 2 (HRP-2) in blood. The HRP2-based RDTs can remain positive for up to 42 days or beyond after the beginning of a treated clinical malaria episode [Of the 16 malaria episodes, all were RDT positive but only 11 were slide positive. Copresence of fever and slide positivity amongst infants in this moderate to high transmission intensity setting is strongly indicative of acute clinical malaria rather than incidental parasitaemia . Suspici episode . Partici episode . For parPrior studies have identified a positive correlation between microbial diversity, measured by alpha diversity, and age of infants . In contOnly the number of malaria episodes explained significant variation in the gut microbiome composition of Kenyan infants. It is important to note that this observation has been made in a small sample size, and that significance is lost when repetitive sampling is taken into consideration. Nevertheless, it raises the exciting possibility that differences in gut bacteria may contribute to differential outcomes of malaria in children. Clearly, this possibility will need to be examined in the context of a larger and more definitive study that determines the ability of gut microbiota to modulate the severity of malaria in African children.Plasmodium infections? Addressing these questions could make substantial improvements in developing novel approaches to prevent malaria-related fatalities.We demonstrate for the first time that human malaria episodes and AL treatment result in a minimal shift in the gut microbiota of Kenyan infants, which is in contrast to what was observed in some murine models of malaria. These results suggest that changes in the inflammatory nature of intestinal tissue, in contrast to gut microbiota dysbiosis, during malaria may contribute to translocation of enteric bacteria and progression to bacteremia. This study also leaves open exciting, yet unanswered, questions regarding interactions between human gut microbiota and malaria. For example, are there different gut microbiota between healthy individuals and patients with severe malaria? Given the increased appreciation of the gut-brain axis, is there an interplay between gut microbiota and cerebral malaria? Can machine learning algorithms predict the gut microbiota composition associated with children prone to The Journal of Infectious Diseases online. Consisting of data provided by the authors to benefit the reader, the posted materials are not copyedited and are the sole responsibility of the authors, so questions or comments should be addressed to the corresponding author.Supplementary materials are available at jiy740_suppl_Supplementary_Table_S1Click here for additional data file.jiy740_suppl_Supplementary_Table_S2Click here for additional data file.jiy740_suppl_Supplementary_Table_S3Click here for additional data file.jiy740_suppl_Supplementary_Table_S4Click here for additional data file.jiy740_suppl_Supplementary_Figure_S1Click here for additional data file.jiy740_suppl_Supplementary_Figure_S2Click here for additional data file.jiy740_suppl_Supplementary_Figure_S3Click here for additional data file.jiy740_suppl_Supplementary_Figure_S4Click here for additional data file.jiy740_suppl_Supplementary_Figure_S5Click here for additional data file.jiy740_suppl_Supplementary_Figure_S6Click here for additional data file.jiy740_suppl_Supplementary_Figure_S7Click here for additional data file.jiy740_suppl_Supplementary_Figure_S8Click here for additional data file.jiy740_suppl_Supplementary_Figure_S9Click here for additional data file.jiy740_suppl_Supplementary_MaterialClick here for additional data file."}
+{"text": "Riemerella anatipestifer is a gram-negative bacterium that mainly infects ducks, turkeys and other birds. In a previous study, we established a markerless mutation system based on the pheS mutant as a counterselectable marker. However, the toxic effect of p-Cl-Phe on the R. anatipestifer strain expressing the pheS mutant was weak on blood agar plates. In this study, we successfully obtained streptomycin-resistant derivative of R. anatipestifer ATCC11845 using 100 \u03bcg/mL streptomycin as a selection pressure. Then, we demonstrate that rpsL can be used as a counterselectable marker in the R. anatipestifer ATCC11845 rpsL mutant strain, namely, R. anatipestifer ATCCs. A suicide vector carrying wild-type rpsL, namely, pORS, was constructed and used for markerless deletion of the gene RA0C_1534, which encodes a putative sigma-70 family RNA polymerase sigma factor. Using rpsL as a counterselectable marker, markerless mutagenesis of RA0C_1534 was also performed based on natural transformation. R. anatipestifer ATCCs\u0394RA0C_1534 was more sensitive to H2O2-generated oxidative stress than R. anatipestifer ATCCs. Moreover, transcription of RA0C_1534 was upregulated under 10 mM H2O2 treatment and upon mutation of fur. These results suggest that RA0C_1534 is involved in oxidative stress response in R. anatipestifer. The markerless gene mutation method developed in this study provides new tools for investigation of the physiology and pathogenic mechanisms of this bacterium. Riemerella anatipestifer (referred to herein as R. anatipestifer or RA) is a gram-negative bacterium belonging to the family Flavobacteriaceae, phylum Bacteroidetes, and genus Riemerella [R. anatipestifer is naturally resistant to various antibiotics [emerella . To dateemerella \u20134. Thus,ibiotics \u20139. The awza-like gene, which is involved in capsule biosynthesis [R. anatipestifer is required.Currently, OmpA ; TonB, wynthesis ; and AS8ynthesis \u201319 have Escherichia coli, the rpsL gene encodes the S12 ribosomal protein of the 30S subunit. Mutations in rpsL confer antibiotic resistance to E. coli and other bacteria, including Streptococcus pneumoniae [Thermus thermophilus [Staphylococcus aureus [rpsL mutant strain becomes sensitive to streptomycin when wild-type rpsL is expressed in trans, indicating that the antibiotic susceptibility phenotype is dominant [rpsL strains in the presence of streptomycin can be used to select for loss of wild-type rpsL, demonstrating the utility of this gene as a counterselectable marker [In eumoniae , Thermusmophilus , Staphyls aureus  and so odominant . Furthere marker .pheS mutant has been used for counterselection, allowing markerless deletion in R. anatipestifer ATCC11845, the toxic effect of p-Cl-Phe on R. anatipestifer ATCC11845 grown on a blood agar plate was weak when the pheS mutant was expressed in trans [R. anatipestifer ATCC11845 using rpsL as a counterselectable marker. RA0C_1534 encodes a putative sigma factor, and as a proof of concept, the homolog of this gene in R. anatipestifer CH-1 was shown to be upregulated under iron-limited conditions [RA0C_1534 was disrupted using the knockout strategy established by this study and its function was investigated.Although the in trans . In thisnditions . RA0C_15E. coli strains were grown on LB agar and in LB broth. R. anatipestifer strains were routinely grown on LB agar supplemented with 5% sheep blood and in GC broth (GCB) [E. coli; cefoxitin (Cfx) at 1 \u03bcg/mL, streptomycin (Str) at 100 \u03bcg/mL and erythromycin (Erm) at 1 \u03bcg/mL for R. anatipestifer ATCC11845. The concentration of Erm used in this study was determined according to the minimum inhibitory concentration of erythromycin on R. anatipestifer ATCC11845 (0.5 \u03bcg/mL).The bacterial strains used in this study are listed in th (GCB) . AntibioR. anatipestifer ATCC11845 cells were obtained by plating 108 wild-type cells on LB agar supplemented with 5% sheep blood containing 100 \u03bcg/mL streptomycin. Streptomycin-resistant clones were streaked for isolation on fresh medium, and rpsL from each clone was amplified by PCR using the primers rpsLP1 and rpsLP2  as well as with streptomycin (0 and 100 \u03bcg/mL). The plates were incubated overnight at 37\u00b0C.To determine whether wild-type r pLMF03 . SpecifipsLexpP2 , which wescribed . R. anatE. coli-Capnocytophaga canimorsus shuttle plasmid pMM47.A [oriT was amplified from the plasmid pEX18GM [rpsL with the native promoter was amplified from R. anatipestifer ATCC11845 using the primers rpsLexpP1  and rpsLexpP2 (introducing a NcoI site) and was cloned into pMM47.C to generate the suicide vector pORS.The  pMM47.A  was dige pMM47.A . To incr pEX18GM  using thRA0C_1534 was amplified using the primers RA0C_1534 upP1 (containing a NcoI site) and RA0C_1534 upP2. The 796-bp downstream sequence of RA0C_1534 was amplified with the primers RA0C_1534 downP1 and RA0C_1534 downP2 (containing a XhoI site). The two PCR products were ligated using the overlap PCR method. The fused fragment was purified and digested with NcoI and XhoI. Then, the fragment was cloned into pORS, which had been digested with the same enzymes, to generate pORS::RA0C_1534 up-down. The plasmid pORS::RA0C_1534 up-down was introduced into streptomycin-resistant strain ATCCs by conjugation according to a previously described method [The process of knockout based on pORS was done as described in previous study with a little modification . Brieflyd method , 24.5 bacterial cells were spread on blood plates with 100 \u03bcg/mL streptomycin to screen the mutants which had lost the plasmid after a second recombination event. Mutation was identified by PCR using the primers RA0C_1534 CompP1 and RA0C_1534 CompP2  and tested by PCR using the primers CfxP1 and CfxP2 . The cor4 CompP2 .ermR-rpsL was as follows. Briefly, the Erm-resistant (ermR) gene cassette was amplified from the genome of R. anatipestifer CH-1 [rpsL gene was amplified from R. anatipestifer ATCC11845 by PCR using the primers rpsLP1* (containing a XbaI site) and rpsLP2* . Two fragments were ligation with the plasmid pBAD24 generating pBAD24::ermR-rpsL. Gene deletion based on natural transformation was accomplished using a similar procedure as that described in a previous study [RA0C_1534 were amplified from ATCC11845 using the primers RA0C_1534 upP1* (containing a NcoI site), RA0C_1534 upP2* (containing a KpnI site); RA0C_1534 downP1*,  and RA0C_1534 downP2* (containing a SphI site), respectively. Two fragments were cloned into the plasmid pBAD24::ermR-rpsL, then the mutagenic PCR fragments RA0C_1534 upstream-ermR-rpsL-RA0C_1534 downstream were amplified from pBAD24::ermR-rpsL using the primers RA0C_1534 upP1* and RA0C_1534 downP2*. The purified fragments were used for the first natural transformation. The transformational bacteria were spread on blood agar plates with 1 \u03bcg/mL Erm to select for the first recombination. The correct Erm-resistant colonies were verified by PCR. For the second natural transformation, the fragments RA0C_1534 upstream and RA0C_1534 downstream were amplified using the primers RA0C_1534 upP1** and RA0C_1534 upP2**; RA0C_1534 downP1** and RA0C_1534 downP2**, respectively. The RA0C_1534 upstream fragments and RA0C_1534 downstream fragments were ligated using overlap PCR. The purified fragments were transformed into the Erm-resistant strain obtained in the first natural transformation. Transformants were plated on blood agar supplemented with 100 \u03bcg/mL streptomycin. Mutation was identified by PCR.Similarly as described in previous study , the profer CH-1  using thus study . The upsRA0C_1534 were constructed using the shuttle vector pLMF03 [RA0C_1534. The plasmids were transferred to various R. anatipestifer strains by conjugation and selected on a blood agar plate containing 1 \u03bcg/mL Cfx as previously described for complementation studies [fur was constructed by ligating pLMF03 with fur fragment which was amplified using the primers fur CompP1 and fur CompP2.Plasmids carrying r pLMF03 . The pri studies . Consist2O2 challenge was performed as described in previous study with a little modification [R. anatipestifer ATCCs pLMF03, ATCCs\u0394RA0C_1534 pLMF03 and ATCCs\u0394RA0C_1534 pLMF03::RA0C_1534 were grown in GCB medium until the exponential phase. Bacteria were collected and washed twice in PBS. The cell suspension was then diluted to an OD600 of 0.5. Before H2O2 challenge, several dilutions of the tested cell suspensions were spread on blood agar plates (T0). For the challenge assay, the bacteria were incubated for 30 min in PBS in the presence of H2O2 (5 or 10 mM) at 37\u00b0C. After exposure to H2O2, the bacteria were washed twice with PBS, and several dilutions were spread onto blood agar plates (T1). After a 1-day incubation at 37\u00b0C, the colonies were counted. The survival rate was calculated as (T1/T0) \u00d7100%. All the experiments were performed in triplicate.The assay of Hfication . BrieflyP<0.05 was considered significant.Statistical analysis was performed using GraphPad Prism 6 software for Windows. Statistical significance was ascertained using Student\u2019s T-test. R. anatipestifer ATCC11845 that were resistant to streptomycin were selected on blood agar plates supplemented with 100 \u03bcg/mL streptomycin. The frequency of the spontaneous mutation was approximately 10\u22128. Ten spontaneous streptomycin-resistant mutants of wild-type R. anatipestifer ATCC11845 were isolated, and the rpsL gene was amplified and sequenced. Seven of the mutants had an adenine-to-guanine point mutation at position 128 of the rpsL coding sequence, resulting in a K43R substitution in RpsL. The mutant strain was named R. anatipestifer ATCCs.Spontaneous mutants of rpsL has been used as a counterselectable marker for Flavobacterium johnsoniae [Flavobacterium columnare [Flavobacterium johnsoniae, wild-type rpsL is dominant compared to the mutant allele, and merodiploids are sensitive to streptomycin [R. anatipestifer ATCCs. The plasmid pLMF03::rpsL, harboring wild-type R. anatipestifer ATCC11845 rpsL, was introduced into R. anatipestifer ATCCs. R. anatipestifer ATCCs pLMF03::rpsL failed to grow on blood agar plates containing 100 \u03bcg/mL streptomycin, whereas R. anatipestifer ATCCs pLMF03 grew well in the presence of streptomycin 3 3 . Transcrild type . Moreoveype  at a frequency of 10\u22125. Streptomycin-resistant colonies were obtained for the second step (plasmid loss) at a frequency of 10\u22123, and approximately 30% of the streptomycin-resistant colonies carried the deletion. It's worth noting that the frequency for each step recombination should be various according the different target genes.The genetic tools were critical for the study on the physiology and pathogenic mechanism of the pathogens, however, it was imperfect for the n marker . Howeverwas weak . Here, w species , 23, 33.R. anatipestifer cells are naturally competent [ermR-rpsL cassette replaced the target gene on the chromosome by homologous recombination. In the second natural transformation, the ermR-rpsL cassette was deleted by homologous recombination. This step restored streptomycin resistance, allowing the generation of mutations without antibiotic markers. In this case, Erm-resistant colonies were obtained for the first natural transformation at a frequency of 10\u22125. Streptomycin-resistant colonies were obtained for the second natural transformation at a frequency of 10\u22125, and approximately 90% of the streptomycin-resistant colonies carried the deletion.Alternatively, we also developed a markerless mutant based on natural transformation because it has been shown that ompetent . For thipheS mutant as a counterselection marker in a previous study [In addition to the construction of in-frame deletion mutants, in principle, the methods described here can also be used to insert any DNA fragment of interest into a desired location on the chromosome or to introduce site-directed point mutations in genes of interest, as described using the us study . HoweverR. anatipestifer can infect ducks, thus, this species must have a specific mechanism for protection against the host-defense-associated oxidative burst. However, the mechanisms by which R. anatipestifer responds to oxidative stress remain unknown. Bacterial sigma factors are subunits of the RNA polymerase that play a fundamental role in the ability of bacteria to adapt to different environments [RA0C_1534 mutant was highly sensitive to H2O2-induced oxidative stress and that transcription of RA0C_1534 was upregulated by H2O2 treatment. This gene is the first factor identified in R. anatipestifer to be involved in the oxidative stress response. It is unclear which genes are regulated by RA0C_1534. The regulation mechanism of RA0C_1534 needs further study.ronments . In thisR. anatipestifer based on rpsL as a counterselection marker. The simple and efficient method presented in this work expands the genetic toolbox for R. anatipestifer. This approach will facilitate future studies on the gene functions of R. anatipestifer and may also be adapted for use with other members of the large and diverse phylum Bacteroidetes.In conclusion, we developed a markerless mutation method in"}
+{"text": "Solenopsis invicta. Using RNA profiling of male and female germline tissues, we found that the majority of TE-containing transcripts in the fire ant germline belong to the IS630-Tc1-Mariner superfamily. Subsequent genomic characterization of fire ant mariner content, molecular evolution analysis, and population comparisons revealed a highly expressed and highly polymorphic mariner element that is rapidly expanding in the fire ant genome. Additionally, using comparative genomics of multiple insect species we showed that this mariner has undergone several recent horizontal transfer events (<5.1 My). Our results document a rare case of a currently active TE originating from horizontal transfer.Transposable elements (TEs) are present in almost all organisms and affect the host in various ways. TE activity can increase genomic variation and thereby affect host evolution. Currently active TEs are particularly interesting because they are likely generating new genomic diversity. These active TEs have been poorly studied outside of model organisms. In this study, we aimed to identify currently active TEs of a notorious invasive species, the red imported fire ant  Transposable elements (TEs) are parasitic genetic elements that can jump to different positions in the genome and, occasionally, into other genomes. Virtually all organisms harbor TEs, with genome occupancy ranging from <1% to \u223c85% in multicellular animals and plants .TE insertions are typically deleterious or neutral in animals and plants. Although infrequent, many instances of adaptive insertions have been documented . TEs canThe life cycle of a TE begins upon entering a host and acquiring activity in the germline . Often, mPing element provides a potentially illustrative case. This TE is currently increasing by \u223c40 inserts per generation in some landraces, and such landraces are more stress resistant   because Solenopsis invicta. This species has established itself in the United States, Australia, China, and Taiwan , genomic characterization of TE content, molecular evolution analysis, population comparisons, and comparative genomics. We found that one mariner element has been and is likely currently active. Furthermore, this element has undergone several recent horizontal transfer events and has continued proliferating in the fire ant. This is the first identification of a currently active TE via RNA profiling in a social or an invasive species.In this study, we examined currently active TEs in Gp-9 gene   and SB/Sb polygyne virgin queens , respectively.To identify TEs expressed in the germline, we extracted RNA from fire ant ovaries and testes. To obtain \u201cmature\u201d ovaries for RNA isolation, we artificially removed the wings from randomly selected virgin queens to stimulate ovary development. We took this approach because fire ant virgin queens usually initiate reproductive development after shedding their wings under natural conditions when no reproductive queen is present , and artr SB/Sb) , and thu-9 locus . To ensun\u2009=\u200963, all from one colony). At the time of the experiment, we did not have males from polygyne colonies, so we used only SB males from monogyne colonies (monogyne testis [MT]).Because the testes degenerate in the adult stage and since germ cells are the ones contributing to all future generations , we deciWe purified total RNA from these germline tissues with the Illustra RNAspin Mini Kit . Total RNA was then submitted to the High Throughput Genomics Core at the BRCAS  who enriched for mRNA by polyA selection. After, polyA-enriched RNA was sequenced on the Illumina HiSeq-2500 and Genome Analyzer platforms, with paired-end read lengths of 101 bp and 96 bp, respectively. In total, we obtained 4.8\u2009Gb (MO), 5.0\u2009Gb (PO), and 5.1\u2009Gb (MT) of RNA sequence data.We assessed the quality of the raw RNA-seq reads with FastQC v0.11.5 , which rhttps://github.com/trinityrnaseq/trinityrnaseq/wiki/RNA-Seq-Read-Representation-by-Trinity-Assembly; last accessed September 21, 2016). Second, we evaluated the completeness of the assembled genes by comparing each transcriptome assembly to the predicted proteins in the fire ant official gene set (GCF_000188075.1_Si_gnG_protein.faa downloaded from NCBI) using analyze_blastPlus_topHit_coverage.pl. For each gene, we considered the assembly adequate if the top BLASTx transcript hit covered \u226580% of the protein length. Finally, we determined the representativeness of 2,675 conserved single-copy orthologs in the arthropod lineage   .S. invicta. We retained the TEs with masked length \u2265200\u2009bp and divergence \u226420%. These criteria should identify germline TEs based on sufficient similarity regardless of annotation state in Repbase and without too many false positives. A caveat is that we may fail to include novel or highly divergent TEs. For estimating transcript expression levels, we first removed rRNA sequences with SortMeRNA  (To detected de novo and polymorphic TE insertions we used a previously generated data set comprising of seven pairs of P017317) . QualityP017317)  revealedP017317)  using thUsing this data set, de novo insertions can only be identified in regions where both brothers inherited the same allele from the mother, for example, regions identical by descent (IBD). To find IBD regions, we first determined the single-nucleotide polymorphism (SNP) densities at 10 kb intervals across the genome between each pair of brothers. Regions with very low SNP density would indicate genomic regions likely IBD. In contrast, genomic regions with high SNP density would indicate genomic regions where the brothers inherited different maternal alleles. Variant calling followed the GATK best practices . In brieSb supergene region because we used SB as the reference genome.To locate the de novo TE insertions, we used ngs_te_mapper . This prFor our reference genome we used a new version generated by Pacific Biosciences sequencing  and then found redundancies among these copies using the script Sequence Dereplicator and Database Curator (SDDC) .We estimated TE copy number conservatively by counting only the number of BLASTn hits (coverage \u226560%) in the fire ant genome for the 17 query TE consensus sequences. We selected coverage \u226560% as the threshold because a large enough deletion within a gene may result in double counting with a lower threshold . Additionally, this approach will underestimate the number of short miniature inverted-repeat transposable elements (MITEs).To estimate mean genetic diversity (\u03c0) for each of the 17 TEs with high germline expression, we extracted all sequences with \u226560% of the full-length TE sequence from the fire ant genome. We then conducted sequence alignments with MUSCLE  in MEGA Mariner-2_DF, we used BLASTn to query against the 52 arthropod genomes in Flybase . We used BLASTn and BLASTp  from the EMBOSS 6.6.0 package  in the genome, we used BLASTn hits \u22651,059\u2009bp (the full length of the transposase ORF) instead. We aligned sequences based on amino acids using MUSCLE  between species, we extracted  package . For D. g MUSCLE  and seleg MUSCLE . We condMariner-2_DF with those from nuclear genes, we downloaded the transcriptome of each insect from NCBI  models. The Akaike information criterion with a correction for finite sample (AICC) analysis was used to select the best substitution model for alignments.We used the KaKs_calculator 2.0  to estimFor reconstructing the species phylogenetic tree, we randomly selected 10 genes from the 1,951 BUSCO genes common to all five genomes see . Next, wMariner-2_DF, because some species contained no intact transposase ORF, we used BLASTn to query Mariner-2_DF ORFs against the corresponding genome assembly and extracted all hits \u2265847\u2009bp (80% of the length of the transposase ORF) instead. For accelerating the calculations, we removed identical copies with SDDC  for visualizing the phylogenetic trees.We used both maximum likelihood (GTRGAMMA model) and Bayesian inference (GTR+I\u2009+\u2009G model) methods with 1,000 Bootstrap replicates to reconstruct the unrooted phylogeny in RAxML v8.2.12  and MrBaMariner-2_DF proliferation time, we used the formula T\u2009=\u2009k/2r  , 91 Mya. For Ks estimation in Mariner-2_DF, we compared the same genome copies as above with their species-specific consensus  sequences  : Dipteraequences . FinallyActive TEs are presumably expressed in germ cells or during early development prior to germ cell specification to ensure their long-term survival . To idenMariner-2_DF matched at 44% [MO], 64% [MT], and 78% [PO], respectively, the Mariner-2_DF in MO was a chimera, IS630-Tc1-mariner elements were the dominant TE in all samples .To identify assembled transcripts containing potential TE sequence, we used RepeatMasker to compare each germline transcriptome against the Repbase repeat database (v20.09) . This apIS630-Tc1-mariner superfamily. Interestingly, only one TE, Mariner-2_DF, was highly expressed relative to the BUSCO genes in all three independent samples  genes. We chose the BUSCO genes because they span a range of expression levels, and thus would be a suitable reference or control gene set. Comparisons within each sample revealed that >84% of the TE-containing transcripts  were expressed at levels below the 25th percentile of the BUSCO genes . Very few TE-containing transcripts were expressed above the 75th percentile of BUSCO genes : 27 (MO), 8 (PO), and 13 (MT) transcripts  online.  samples .Table 1Mariner-2_DF was identified as the most highly expressed TE in all three data sets and much more than the next most expressed TE  in the fire ant genome, more than Mariner-30_SIn (two variants with two copies each), Mariner-5_SIn (two variants with two copies each), and Mariner-4_AEc (one variant with two copies).TE activity can be indirectly inferred from sequence divergence among the elements. Multiple TE insertions with identical sequences in the host genome would suggest that the focal TE likely was recently active. We searched for identical copies for each of the 17  highly expressed autonomous TEs in the fire ant genome and found only E copies . The remMariner-2_DF had the lowest mean genetic diversity . Mariner-2_DF also had the highest copies . Together, these observations suggest that Mariner-2_DF has expanded most recently in the fire ant genome (Recently proliferating elements are predicted to have low genetic diversity (\u03c0) among TE copies and may have many copies in the genome. Examination of the mean genetic diversity of the 17 highly expressed autonomous TEs in the fire ant genome revealed that t genome ; table\u00a01Mariner-2_DF, and possibly Mariner-30_SIn, Mariner-5_Sin, and Mariner-4_AEc, may have been recently active. We next asked if any of them are currently active. Evidence supporting this would be the observation of TE insertion site polymorphisms. We searched for TE insertions in the low-coverage genome sequences from brothers of seven fire ant families from Georgia, USA  or in \u22652 families (\u201ccommon\u201d). We found that 42 insertions (60%) were common, and for these we cannot exclude that they were due to segregation of common alleles. Stringent filtering revealed 28 (40%) robust nonreference data set  online. Mariner-2_DF insertion rate is <\u223c1/9 genomes per generation . Although we did not find evidence for a new insertion, we did find evidence for a somatic excision; for one locus in individual F7_b both reads with and without Mariner-2_DF were observed  reported in Drosophila ficusphila . The eight species included four Drosophila: D. yakuba, D. erecta, D. ficusphila, and D. grimshawi; three Hymenoptera: Megachile rotundata, Acromyrmex echinatior, and S. invicta; and one Hemiptera, Rhodnius prolixus . Thus, Mariner-2_DF clearly has a patchy distribution in insects consistent with HTT events.Based on the above analyses, we found that cusphila . This suMariner-2_DF sequence in the eight species, we reconstructed the species-specific consensus sequences from all Mariner-2_DF sequence fragments \u2265100\u2009bp in each genome. For D. yakuba, D. erecta, and R. prolixus, we could only detect remnants of the Mariner-2_DF sequence.In order to identify the structural features of the D. ficusphila, D. grimshawi, M. rotundata, A. echinatior, and S. invicta), we were able to generate full-length Mariner-2_DF consensus sequences. Pairwise comparisons of the consensus sequences amongst the species revealed high identity  DNA binding motifs, the catalytic domain harboring a DD34D motif, and a C-terminal YSPDLAP amino acid motif  and R. prolixus (Rpmar57)  , 2016.Mariner-2_DF within each of the five species could be the result of recent transposon expansion, another remote alternative explanation could be extremely strong purifying selection to maintain the same sequence. We tested for evidence of strong purifying selection using the codon-based Z-test and found no support . Therefore, the high identity of Mariner-2_DF is most simply explained by recent proliferation within each genome.Although high nucleotide identity of Mariner-2_DF, we conducted interspecies Ks comparisons of Mariner-2_DF and 1,951 orthologous nuclear genes (BUSCO genes) common to all five insect species , to assess nucleotide divergence. In the MA model, the software averages the Ks from 203 time-reversible models, thereby reducing biases arising from model selection. In the MS model, Ks is estimated from the best model based on the Akaike information criterion with a correction for finite sample size (AICC). In our analysis, both methods showed the same result: inter-species Ks values were significantly lower for Mariner-2_DF than for nuclear genes in all species-pair comparisons  and the \u201cmost possible\u201d model  closest to each other  from the average Ks estimates from 1,951 BUSCO genes (above) and species divergence times from the TTOL (\u22129 (MA) and 5.22 \u00d7 10\u22129 (MS) in Drosophila, and 1.02 \u00d7 10\u22128 (MA) and 1.00 \u00d7 10\u22128 (MS) in bees, 3.53 \u00d7 10\u22129 (MA) and 3.27 \u00d7 10\u22129 (MS) in ants (r in the formula T\u2009=\u2009k/2r (Mariner-2_DF entered into D. grimshawi about 0.23 (MA) \u2212 0.18 (MS) Mya (million years ago), D. ficusphila 0.55 (MS) \u2212 0.53 (MA) Mya, M. rotundata 0.62 (MA) \u2212 0.52 (MS) Mya, A. echinatior 5.07 (MA) \u2212 4.96 (MS) Mya, and S. invicta 2.88 (MS) \u2212 2.45 (MA) Mya  Mya . Interes.026 Mya , and theDrosophila and mosquitoes . This copy number is similar to other mariner lineages in Drosophila  that are likely in a recent phase of expansion  in the gaps would be undetected. Similarly, fire ant centromeres occupy a third of the genome  and testes (third and fourth instar), so TEs expressed at other developmental times or during periods of stress e.g.,  would ale genome , and anyMariner-2_DF activity in the fire ant, this transposon may have been horizontally transferred into several other species recently (<5.1 My). With the caveat that the analyzed genome assembly qualities were variable, thereby possibly introducing false negatives in Mariner-2_DF presence and sequence completeness, our investigation of its taxonomic distribution revealed a patchy distribution, being found in eight species among 52 diverse insects. For three of the eight species, only remnants of the Mariner-2_DF transposon sequence were detected, indicating host inactivation of the transposon and possibly suggesting an older horizontal transfer date. For the remaining five species, there was high sequence identity among the species and fewer synonymous substitutions in Mariner-2_DF than in nuclear genes in pairwise comparisons, suggesting at least five independent relatively recent horizontal transfer events , suggesting that Mariner-2_DF may potentially be active in only these two species. Our results match previous studies reporting HTT for Mariner-2_DF in D. ficusphila (Dromar8Mfic), D. grimshawi (Dromar8) (R. prolixus (Rpmar57) (In addition to contemporary r events . Intact Dromar8) , 2016 anRpmar57) .Stomoxys calcitrans), but in fire ants this value is only 0.75%  containing only highly fragmented, and presumably fairly old, copies of Mariner-2_DF, could have been the source for the HTT events into the other five species. Related, and compatible with the first possibility, is that the two ants, which have estimated Mariner-2_DF colonization dates of >2.6 Mya, could have been the source for the three species with more recent insertion dates . Future studies incorporating additional genomes are needed to resolve this issue.The direction of HTT, either direct or indirect, among the eight species examined is not clear from our study. Nevertheless, one possibility is that the three species (Mariner-2_DF and the high likelihood that it is currently active, highly expressed, and highly polymorphic, we suggest that, of all the TEs, Mariner-2_DF has been disproportionately affecting the fire ant genome. An intriguing question would be: Has this transposon generated beneficial mutations in the fire ant genome that have contributed to its adaptation to the invasive ranges? This topic will be the subject of future experiments and analyses.Periods of active transposition may disproportionately shape the host\u2019s genome, leading to increased host genome diversity. Associations between bursts of TE activity and species radiations has been proposed in apes, rodents, and bats . Given tGenome Biology and Evolution online.Supplementary DataClick here for additional data file."}
+{"text": "We present a novel, to our knowledge, comparative perspective on the virulence of all currently known human RNA virus species. The risk factors identified may provide novel perspectives in understanding the evolution of virulence and elucidating molecular virulence mechanisms. These risk factors could also improve planning and preparedness in public health strategies as part of a predictive framework for novel human infections.Novel infectious diseases continue to emerge within human populations. Predictive studies have begun to identify pathogen traits associated with emergence. However, emerging pathogens vary widely in virulence, a key determinant of their ultimate risk to public health. Here, we use structured literature searches to review the virulence of each of the 214 known human-infective RNA virus species. We then use a machine learning framework to determine whether viral virulence can be predicted by ecological traits, including human-to-human transmissibility, transmission routes, tissue tropisms, and host range. Using severity of clinical disease as a measurement of virulence, we identified potential risk factors using predictive classification tree and random forest ensemble models. The random forest approach predicted literature-assigned disease severity of test data with mean accuracy of 89.4% compared to a null accuracy of 74.2%. In addition to viral taxonomy, the ability to cause systemic infection was the strongest predictor of severe disease. Further notable predictors of severe disease included having neural and/or renal tropism, direct contact or respiratory transmission, and limited (0 < R Comparative analysis using machine learning shows that specificity of tissue tropism and transmission biology can act as predictive risk factors for the virulence of human RNA viruses. The emergence of novel infectious diseases continues to represent a threat to global public health. Emerging pathogens have been defined as those newly recognised infections of humans following zoonotic transmission or those increasing in incidence and/or geographic range . High-prEmerging RNA viruses vary widely in their virulence, with some never having been associated with human disease at all. For example, Zaire ebolavirus causes severe haemorrhagic fever with outbreaks, including the 2014 West African outbreak, showing case fatality ratios (CFRs) of approximately 60% or more ,14. In cFew comparative analyses have addressed the risk factors driving human pathogen virulence to date but see \u201319), and, and19])Several studies have suggested a link between host range breadth and virulence, in which higher virulence has been predicted for pathogens with a narrower, specialist host range . VirulenWe aimed to determine patterns of virulence across the breadth of all known human RNA viruses. We then aimed to use predictive machine learning models to ask whether ecological traits of viruses can act as predictive risk factors for virulence in humans. Specifically, we examined hypotheses that viruses would be more highly virulent if they lacked transmissibility within humans, had vector-borne or faecal\u2013oral transmission routes, had a narrow host range or infected nonhuman primates, or had greater breadth of tissue tropisms.p < 0.001), with Arenaviridae, Filoviridae, and Hantaviridae having the highest fractions of severe-rated virus species  and test set (n = 31) partition based on taxonomy and severity to minimise potential biases from trait imbalances between sets. Using this training set, we then constructed a single classification tree that aimed to optimally classify viruses in virulence based on their ecological traits. The final pruned classification tree included variables relating to transmissibility, tissue tropism, and taxonomy . The random forest approach also achieved superior performance when considering sensitivity, specificity, true skill statistic, and the negative predictive value as a performance measure prioritising correct classification of \u2018severe\u2019-rated viruses , no evident improvement on the null model assigning all viruses as nonsevere (null accuracy = 74.2%). The random forest approach gave better predictive performance, correctly predicting virulence with a mean accuracy of 89.4% across all training/test partitions (95% CI: 72.0%\u201397.0%), significantly greater than the null accuracy .Nineteen of 139 viruses featured in test set partitions were misclassified from averaged random forest predictions : seven vThe observed predictor importance and risk factor directions were robust to constructing random forest models for subsets of viruses, removing those with low-certainty data or data from serological evidence only  Figs, an0 \u2264 1). These risk factors were robust to alternative modelling methods, alternative definitions of virulence, and exclusions of poor-quality data.We present the first comparative analysis of virulence across all known human RNA virus species to our knowledge. We find that disease severity is nonrandomly distributed across virus families and that beyond taxonomy, severe disease is predicted by risk factors of tissue tropism and, to a lesser extent, transmission route and level of human-to-human transmissibility. In both classification tree and random forest models, viruses were more likely to be predicted to cause severe disease if they caused systemic infections, had neural or renal tropism, transmitted via direct contact or respiratory routes, or had limited capability to transmit between humans  than self-limited transmissibility (level 3) . This apAlthough cross-species infections incapable of onward transmission (sometimes termed \u2018dead-end\u2019 infections) can result in high virulence because without coevolution, viral phenotypes within the novel host will be nonadapted\u2014i.e., a \u2018coincidental\u2019 by-product ,24\u2014we diTaxonomic family being a highly informative predictor in the random forests implies that there is a broad phylogenetic signal to virulence, but it is also highly likely that the explanatory power represents a proxy for many other phylogenetically conserved viral traits that are challenging to implement in comparative analyses of this scale, such as variation at the proteomic, transcriptomic, or genomic level or further data beyond simple categorisations, e.g., specific arthropod vector species. Untangling these sources of variation from different scales of traits will be a critical next step in predictive modelling of viral virulence.We acknowledge several limitations to the quality of our data, as with any broad comparative analysis. Risk factor data were problematic or missing for certain viruses, e.g., natural transmission route for viruses only known to infect humans by accidental occupational exposure and tissue tropism for viruses only known from serological evidence. However, the consistency of findings between alternative, stricter definitions of virulence and data subsets removing viruses with suspected data quality issues suggests scarcity of data does not bias our analyses.Virulence also exhibits substantial variation at the subspecies level, i.e., between strains or variants. For example, severity of Lassa virus disease superficially varies with infection route and geography, though this appears to be driven by variation between genotypes . ConfirmThe value of predictive modelling as an inexpensive and rapid tool for risk assessments during early emergence is increasingly recognised . InstancHowever, our models have restricted function in predicting the virulence of a newly identified virus, particularly if human infections are not yet recognised. Taxonomy may be easily accessible and applicable to give simple virulence estimates. However, the most informative nontaxonomic predictors, tissue tropism and transmission route, are not likely to be identified with confidence before clinical observations of virulence. One way to address this information gap would be use of available data from animal infections, assuming that tissue tropism and transmission route do not differ between human and nonhuman hosts. Alternatively, predictor data might be imputed from the nearest-related known virus, particularly for traits that appear highly phylogenetically conserved such as tissue tropism .A more powerful future approach lies in the potential predictability of tissue tropism based on cell receptors and, more challengingly, of cell receptors based on viral proteomics or sequence data , an incrMore widely, our analysis brings a novel, to our knowledge, focus that complements comparative models predicting other aspects of the emergence process such as zoonotic transmission ,9,37,41,This work adds to the comparative and predictive modelling efforts surrounding emerging infectious diseases. Here, we contribute a novel, to our knowledge, focus on ecological predictors of virulence of human RNA viruses, which can be combined in holistic frameworks with other models such as those predicting emergence dynamics. As a predictive model, the featured random forests offer valuable inference into the evolutionary determinants of virulence in newly emerging infections. We propose that future predictive studies and preparedness initiatives with respect to emerging diseases should carefully consider potential for human virulence.For each of the 214 recognised human-infective RNA virus species, following standardised data compilation efforts and critical assessment protocols , data onHuman enterovirus C cause mild disease; however, poliovirus, which causes severe paralytic disease, is also classified under this species). These were examined both individually and within a composite six-rank system . Therefore, we also included an additional \u2018viraemia\u2019 category in the primary tissue tropism predictor to indicate only blood presence was known.Data were compiled for four main risk factors: transmission route(s) and tissue tropism(s), sourced from literature search exercises as described, and extent of human-to-human transmissibility and host range, sourced directly from . AlthougSecondly, binary variables were also constructed, denoting whether viruses had ever been observed to utilise a) multiple transmission routes/tissue tropisms and b) each individual transmission route and tropism, including additional categories that were never among the primary routes/tropisms . We accepted isolation of the virus, viral proteins or genetic material, or diagnostic symptoms of the virus  as evidence of infection within an organ system but did not accept generalised symptoms such as inflammation.0 = 0), level 3 denotes a virus with limited human-to-human transmissibility (0 < R0 \u2264 1), and level 4 denotes a virus with sustained human-to-human transmissibility (R0 \u2265 1). Host range was specified as either \u2018narrow\u2019 (infection known only within humans or humans plus nonhuman primates) or \u2018broad\u2019  [Human-to-human transmissibility was specified using infectivity/transmissibility levels, based on previous conceptual models and a systematic compilation and review of evidence ,5,12. Le10.6084/m9.figshare.7406441.v3 (https://figshare.com/articles/Data_and_supporting_R_script_for_Tissue_Tropism_and_Transmission_Ecology_Predict_Virulence_of_Human_RNA_Viruses/7406441/3).To identify potential differences in risk factors between adapted and nonadapted viruses, we also categorised whether each virus was zoonotic. We considered a virus to be zoonotic if it had transmissibility level 2 or 3 or had transmissibility level 4 and was known to infect nonhuman hosts . We also conservatively considered viruses to be zoonotic if zoonotic potential was suspected but data-deficient, e.g., rotavirus A\u2013C. All virulence and risk factor data pertained to natural or unintentional artificially acquired human infection only, and data from intentional human infection, animal infection, and in vitro infection were not considered. Viral taxonomy was included in analyses by specifying both genome type and taxonomic family as predictors. All virulence and risk factor data are available via figshare: Ebolavirus, three were randomly assigned to the training set and the remaining one assigned to the test set. If a genus\u2013severity combination contained less than four viruses, all defaulted to the test set. Comparative risk factor analyses were firstly carried out by constructing a classification tree using the R package \u2018rpart\u2019 v4.1\u201311 [Firstly, the 212 retained virus species were split into a training set for model fitting and a test set for model evaluation. In order to avoid bias from an imbalance between types of viruses assigned to training and test sets, our selection was based on random sampling, stratified by genus\u2013severity rating combinations. We sampled at a ratio of 75:25, i.e., for the four known severe viruses in the genus  v4.1\u201311 . Classifx with n possible ratings and p(xi) denoting proportion of data with rating i, which is equal to zero for perfectly separated data. To prevent overfitting, the tree was pruned back to the optimal branching size, taken as the most common consensus size over 1,000 repeats of 10-fold cross-validation. To validate the predictive power of the classification tree, predictions of virulence rating were generated when applied to the test set. Tree accuracy was then calculated, comparing the proportion of correct predictions compared to literature-assigned ratings (assuming these to be 100% accurate as the \u2018gold standard\u2019 or \u2018ground truth\u2019). Because virulence ratings were imbalanced , accuracy was directly compared to the null model, i.e., a model with no predictors that predicted \u2018nonsevere\u2019 for all viruses. Additional diagnostics of interest  were alp, small n\u2019 data architecture much more easily than traditional regression frameworks [Although classification trees have the advantage of presenting an interpretable schematic of risk factor effects and directions, individual tree structures may be sensitive to particular data points and have no intuitive measures of uncertainty. We therefore generated a further 200 partitions of our data into alternative training/test sets using the random stratified sampling procedure described. Then, for each partition, we constructed a random forest, an ensemble collection of a large number of bootstrapped classification trees . Having ameworks . Missingameworks . Using tameworks , random 10.6084/m9.figshare.7406441.v3 (https://figshare.com/articles/Data_and_supporting_R_script_for_Tissue_Tropism_and_Transmission_Ecology_Predict_Virulence_of_Human_RNA_Viruses/7406441/3).Because of their high structuring, random forest models cannot give a simple parametric predictor effect size and direction . Instead, potential virulence risk factors were evaluated using two metrics: variable importance and partial dependence. Variable importance is calculated as the mean decrease in Gini impurity following tree splits on the predictor and can be considered as how informative the risk factor was towards correctly predicting virulence. Partial dependence is calculated as the mean relative change in log-odds of predicting severe virulence, which were converted to predicted probabilities of severity associated with each risk factor. Partial dependence describes marginal effects averaging across any influence of other predictors, and, as such, point estimates may not reflect any complex risk factor interactions. Therefore, to test hypotheses regarding virulence risk factors, we present both averaged random forest partial dependence and the less robust but more accessible single classification tree for its ease of interpretation in risk factor structure and directly compare the statistical validity of both methods by plotting receiver operating characteristic curves. All modelling was carried out in R v3.4.3  with a sS1 TableVirulence data for 212 human virus species ordered by genome type and taxonomy, including disease severity rating and supporting criteria for viruses rated \u2018severe\u2019, whether virus is known to have caused fatalities in vulnerable individuals and/or otherwise healthy adults, and whether virus is known to have \u2018severe\u2019 strains if species is rated \u2018nonsevere\u2019. Both disease severity rating/supporting criteria following the literature protocol given in the main text and mean predicted probability of severe disease from the random forest models are given. Bold type denotes when predictions do not match literature-based ratings. Dashes indicate predictions were not generated because fewer than four viruses were observed with this genus\u2013severity combination and virus always defaulted to training set. AIDS, acquired immunodeficiency syndrome; CFR, case fatality ratio; HFRS, hantavirus haemorrhagic fever with renal syndrome; HPS, hantavirus pulmonary syndrome; HTLV, human T-lymphotropic virus.(PDF)Click here for additional data file.S2 TablePartial dependence given as mean marginal relative change in log-odds and mean predicted probability of classifying virulence as \u2018severe\u2019 for all predictor variables from random forest models featuring all viruses and models featuring zoonotic viruses only.(PDF)Click here for additional data file.S3 Tablen denotes number of viruses excluded). Diagnostics indicate mean values across 200 training/test partitions sampled separately for each data subset. Otherwise, random forest methodology follows that of Materials and Methods. Supporting data are available via figshare: 10.6084/m9.figshare.7406441.v3 (https://figshare.com/articles/Data_and_supporting_R_script_for_Tissue_Tropism_and_Transmission_Ecology_Predict_Virulence_of_Human_RNA_Viruses/7406441/3).Predictive performance metrics of random forest models applied to data subsets, excluding viruses with low-certainty data ((CSV)Click here for additional data file.S4 TableSix-rank system of classifying human RNA virus virulence with available data , along with example viruses and number of viruses fitting each exclusive rank\u2019s criteria.(PDF)Click here for additional data file.S5 Tablen denotes number of viruses considered \u2018severe\u2019 using that definition). Vulnerable individuals are defined as those age 16 and below, age 60 and above, immunosuppressed, having comorbidities, or otherwise cited as being \u2018at-risk\u2019. Ranks follow those given in Table S5. Diagnostics indicate mean values across 200 training/test partitions sampled separately for each virulence metric. Otherwise, random forest methodology follows that of Materials and Methods. Supporting data are available via figshare: 10.6084/m9.figshare.7406441.v3 (https://figshare.com/articles/Data_and_supporting_R_script_for_Tissue_Tropism_and_Transmission_Ecology_Predict_Virulence_of_Human_RNA_Viruses/7406441/3).Predictive performance metrics of random forest models predicting alternative virulence measures using different two-category definitions of \u2018severe\u2019 ((CSV)Click here for additional data file.S1 Fign = 36), b) viruses with <20 recognised human infections (n = 55), and c) viruses with poor data quality in at least one predictor (n = 71). Variable importance is calculated as the relative mean decrease in Gini impurity scaled against the most informative predictor within each model alongside importance from the main analysis for comparison. Points denote mean values across 200 training/test partitions. Error bars denote \u00b1 1 standard deviation. Colour key denotes type of predictor variable. Supporting data are available via figshare: 10.6084/m9.figshare.7406441.v3 (https://figshare.com/articles/Data_and_supporting_R_script_for_Tissue_Tropism_and_Transmission_Ecology_Predict_Virulence_of_Human_RNA_Viruses/7406441/3).Variable importance for virulence risk factors from random forest models applied to data sets, excluding a) viruses only known to infect humans from serological evidence ((TIF)Click here for additional data file.S2 Fign = 36), b) viruses with <20 recognised human infections (n = 55), and c) viruses with poor data quality in at least one predictor (n = 71) alongside predicted probabilities from the main analysis for comparison. Probabilities given are marginal, i.e., averaging over any effects of other predictors. Because each data subset required resampling of the training and test partitions, note that raw prevalence of \u2018severe\u2019 virulence differed between each model (see 10.6084/m9.figshare.7406441.v3 (https://figshare.com/articles/Data_and_supporting_R_script_for_Tissue_Tropism_and_Transmission_Ecology_Predict_Virulence_of_Human_RNA_Viruses/7406441/3).Predicted probability of classifying virulence as \u2018severe\u2019 for each of the most informative risk factors from random forest models applied to data sets excluding a) viruses only known to infect humans from serological evidence (odel see . Boxes d(TIF)Click here for additional data file.S3 Fig10.6084/m9.figshare.7406441.v3 (https://figshare.com/articles/Data_and_supporting_R_script_for_Tissue_Tropism_and_Transmission_Ecology_Predict_Virulence_of_Human_RNA_Viruses/7406441/3).Variable importance for virulence risk factors from random forest models predicting alternative virulence measures using different two-category definitions of \u2018severe\u2019, calculated as the relative mean decrease in Gini impurity scaled against the most informative predictor within each model alongside importance from the main analysis for comparison. Points denote mean values across 200 training/test partitions. Error bars denote \u00b1 1 standard deviation. Colour key denotes type of predictor variable. Supporting data are available via figshare: (TIF)Click here for additional data file.S4 Fig10.6084/m9.figshare.7406441.v3 (https://figshare.com/articles/Data_and_supporting_R_script_for_Tissue_Tropism_and_Transmission_Ecology_Predict_Virulence_of_Human_RNA_Viruses/7406441/3).Predicted probability of classifying virulence as \u2018severe\u2019 in alternative virulence measures for each of the most informative risk factors from random forest models alongside predicted probabilities from the main analysis for comparison. Probabilities given are marginal, i.e., averaging over any effects of other predictors. Because each measurement used a different two-category definition of \u2018severe\u2019, note that the raw prevalence of \u2018severe\u2019 virulence differed between each model see . Boxes d(TIF)Click here for additional data file."}
+{"text": "Circular RNAs (circRNAs) are a large class of endogenous noncoding RNAs that regulate gene expression and mainly function as microRNA sponges. This study aimed to explore the aberrant expression of circRNAs in colorectal cancer (CRC). Using a circRNA microarray, we identified 892 differentially expressed circRNAs between six pairs of CRC and adjacent paracancerous tissues. Among them, hsa_circ_0007142 was significantly upregulated. Further analysis in 50 CRC clinical samples revealed that hsa_circ_0007142 upregulation was associated with poor differentiation and lymphatic metastasis of CRC. Bioinformatic analysis and luciferase reporter assay showed that hsa_circ_0007142 targeted miR-103a-2-5p in CRC cells. Moreover, the silencing of hsa_circ_0007142 by siRNAs decreased the proliferation, migration, and invasion of HT-29 and HCT-116 cells. Taken together, these findings suggest that hsa_circ_0007142 is upregulated in CRC and targets miR-103a-2-5p to promote CRC. Colorectal cancer (CRC) has the third highest incidence among cancers and is the fourth leading cause of cancer-related mortality worldwide . AlthougCircular RNAs (circRNAs) are noncoding RNAs that play an important role in regulating gene expression and function . CompareThe present study was approved by the Ethics Committee of Huai'an First People's Hospital, Nanjing Medical University , and written informed consent was obtained from each patient. The tissue samples of CRC and paired adjacent paracancerous tissues were obtained from CRC patients at Huai'an First People's Hospital. All patients did not receive prior radiotherapy and chemotherapy, and CRC was confirmed by experienced pathologists. After surgery, the tissues were quickly snap-frozen and stored at -80\u00b0C until further analysis.2.Human colorectal cancer HCT-116, HT-29, and LoVo cells and normal human enteral epithelial (HCO) cells were obtained from the Shanghai Institutes for Biological Sciences  and cultured in Roswell Park Memorial Institute (RPMI) 1640 medium  supplemented with 10% FBS, 100 units/mL of penicillin, and 100 mg/mL of streptomycin at 37\u00b0C in an incubator with 5% CO\u03bcg of total RNA using random primers. For miRNAs, specific primers were used for cDNA synthesis. The expression levels of circRNAs or mRNA were determined using RT-qPCR, with GAPDH as an internal control. For miRNA expression, U6 was used as an internal control for normalization. All experiments were independently conducted in triplicate, and the 2\u2212\u0394\u0394CT method was used to analyze the data. The classification of CRC patient grouping was derived from the value of 2\u2212\u0394\u0394CT and the cut-off of the hsa_circ_0007142-high group was set as 1. The sequences of the primers are listed in Total RNA was isolated from CRC tissues and cells using TRIzol reagent as described previously . For cir5/well and cultured to a confluency of 50-60% and then were transfected with control or gene specific siRNAs using Lipofectamine 2000 .The control and si-hsa_circ_0007142 siRNAs (mixtures of three siRNAs targeting different sites of hsa_circ_0007142) were designed and synthesized by RiboBio .The Cell Counting Kit-8  assay was performed to evaluate CRC cell proliferation. Briefly, 48 h after transfection, cells were seeded in 96-well plates at a density of 5\u00d710Transfected cells were seeded into 6-well plates and cultured for 15 days. Then the cells were fixed with methanol and stained with 0.5% crystal violet (Beyotime Biotechnology) for 30 min. Colonies with more than 10 cells were counted under a light microscope.\u03bcm pore  was used for the cell migration and invasion assays. 48 h after transfection, 1\u00d7105 cells were plated in the upper chamber and 500 \u03bcl of medium was added to the lower chamber for the migration assay. For the invasion assay, the upper layer of membrane inserts was added with 40 \u03bcg of Matrigel per well and incubated at 37\u00b0C for 1 h. After 48 h, cells that migrated to the lower compartment through the coated membrane were fixed with methanol, stained with 1% crystal violet, and quantified under a microscope. All assays were performed in triplicate.A 24-well Transwell insert with 8 The plasmids carrying the fragment of either the wild-type or the mutant hsa_circ_0007142 sequence of the predicted miR-103a-2-5p binding sites were constructed by Geneseed . HT-29 cells were cotransfected with the plasmids and miR-103a-2-5p mimic using Lipofectamine 2000 . After 48 h, cells were collected and luciferase activity was determined using the Dual Luciferase Reporter Assay System . t-test, Wilcoxon test, or X2-test. A two-tailed P-value <0.05 was considered statistically significant.All data were presented as mean \u00b1 standard deviation (SD) and analyzed using SPSS 20.0 software . Differences between groups were analyzed using Student's P<0.05; Figures To assess the expression of circRNAs in CRC, the differential expression of circRNAs between six CRC tissues and six paracancerous tissues was examined by circRNA microarray. A total of 892 differentially expressed circRNAs were found in CRC, including 412 upregulated and 480 downregulated  and lymphatic metastasis of CRC (P=0.037)  . These rTo explore the role of hsa_circ_0007142 in CRC, first we compared its expression level in CRC cell lines HCT-116, HT-29, and LoVo with normal enteral epithelial cell line HCO. The expression of hsa_circ_0007142 was significantly higher in HCT-116 and HT-29 cells than in HCO cells, but there was no significant difference between HCO and LoVo cells . Next, lSince hsa_circ_0007142 upregulation was associated with lymphatic metastasis of CRC, next we investigated the role of hsa_circ_0007142 in the migration and invasion of CRC cells. Chamber assay showed that, compared to control siRNA groups, the knockdown of hsa_circ_0007142 by siRNA effectively decreased the invasion  and migrP<0.01, r = -0.623, Arraystar software revealed that hsa_circ_0007142 might target miR-103a-2-5p . As expe\u03b2-catenin pathway [As endogenous noncoding RNAs, circRNAs exhibit a remarkable organization- and disease-specific characteristic, suggesting that circRNAs may serve as specific biomarkers for disease diagnosis and therapy . A negat pathway . Moreove pathway . Numerou pathway . The wel pathway , 25. For pathway . Further pathway . With thIn present study, we performed circRNA microarray analysis and identified 892 differentially expressed circRNAs in CRC tissues versus paracancerous tissues. We focused on a distinctly upregulated circRNA hsa_circ_0007142, which was confirmed to be upregulated in CRC tissues and cells. The silencing of hsa_circ_0007142 by siRNAs decreased the proliferation, migration, and invasion of CRC cells. Importantly, hsa_circ_0007142 upregulation was correlated to the differentiation and lymphatic metastasis of CRC. These findings suggest that hsa_circ_0007142 might play an oncogenic role in CRC.Furthermore, bioinformatics predicted that miR-103a-2-5p was a target of hsa_circ_0007142. While several studies suggested oncogenic role of miR-103a in CRC , 29, it In summary, the expression of hsa_circ_0007142 is upregulated in CRC tissues and is associated with the differentiation and lymphatic metastasis of CRC. Furthermore, hsa_circ_0007142 promotes the proliferation and invasion of CRC probably by functioning as a sponge for miR-103a-2-5p. These findings may provide new clue for the strategies in the diagnosis and therapy of CRC."}
+{"text": "Circular RNAs (circRNAs) are involved in regulating tumor pathogenesis. The mechanism of circRNAs in gastric cancer (GC) is still unknown. Our study aimed to identify differentially expressed circRNAs and assess a novel circRNA (hsa_circ_0000144) in the proliferation, migration, and invasion in GC.Gene ontology (GO) enrichment and analyses of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, pathway network, and the ceRNA regulatory network of hsa_circ_0000144 targeting miRNAs and mRNAs were performed with the help of bioinformatics using R language and Perl software. hsa_circ_0000144 expression and circRNA knockdown in GC cell lines were detected using quantitative PCR (qPCR) in vitro. Cell proliferation, migration, and invasion after circRNA knockdown were measured using the cell counting kit-8 assay and Transwell assay.The circRNA expression profile GSE78092 downloaded from the Gene Expression Omnibus database included three GC patients and three normal tissues. Thirty-two differentially expressed circRNAs comprised six upregulated circRNAs and 26 downregulated circRNAs. In particular, the ErbB signaling pathway, neurotrophin signaling pathway, cellular senescence, and pathways in bladder cancer and GC played the most important roles in the pathway network. The expression of hsa_circ_0000144 was upregulated in GC cell lines. Hsa_circ_0000144 knockdown suppressed tumor growth in vitro.Hsa_circ_0000144 promotes GC cell proliferation, migration, and invasion, and the ceRNA regulatory network of hsa_circ_0000144 targeting miRNAs and mRNAs might be biomarkers for GC diagnosis and targeted therapy. Gastric cancer (GC) is the fifth most common cancer and the third leading cause of cancer-related deaths . In 2018Non-coding RNAs (ncRNAs) that regulate proliferation and invasion of GC cells have been evaluated in previous studies. MicroRNAs (miRNAs) and long ncRNAs (lncRNAs) play important roles in tumor biology \u20135. MoreoHsa_circ_0000144, which was generated from the back splicing of SLAMF6 first intron, is also known as circSLAMF6. It has been previously detected in bladder cancer. However, the underlying mechanism of hsa_circ_0000144 in GC is still unknown. In this study, we demonstrate that circSLAMF6 promotes tumor proliferation and invasion in GC and explore the regulatory network of circRNA targeting miRNA\u2013mRNA. These data provide evidence for targeting circSLAMF6 to explore therapeutic measures and mechanisms underlying the pathogenesis of GC.https://www.ncbi.nlm.nih.gov/geo/), and the gene chip data sets GSE78092 (https://www.ncbi.nlm.nih.gov/gds/?term=GSE78092), submitted by Huang et al., were downloaded. GSE78092 contained six samples including three GC tissues and three normal tissues based on the GPL21485 platform of the ArrayStar human circular RNA microarray V2.0. The miRNA expression, clinical, and meta- and manifest data on GC were downloaded from The Cancer Genome Atlas (TCGA).We used the keywords \u201cgastric cancer circRNA\u201d to search the National Centre of Biotechnology Information (NCBI) Gene Expression Omnibus database ). P-values of 0.05 and log-fold change of 2 were considered evidence of significant difference. Volcano plots and heat maps were constructed using limma and pheatmap packages, respectively.The circRNA expression data were converted using R language and Perl software. The circRNAs in these platforms were named according to international standard names. Differentially expressed circRNAs were identified by the limma package in the Bioconductor package (source (https://circinteractome.nia.nih.gov/bin/mirnasearch) and built an miRNA txt file. We then, downloaded four files, including miRDB.tsv, miRTarBase.tsv, and TargetScan.tsv, and identified miRNA targeted genes. Next, we extracted the expression and survival time of these miRNAs. The overall survival curves were analyzed using the R language survival package.We obtained the miRNAs targeting hsa_circ_0000144 in the circinteractome database  (\"dose\"), biocLite (\"DOSE\") (\"clusterProfiler\"), and (\"pathview\").2. MGC-803 cells were transfected with plasmids using Lipofectamine 2000 reagent  based on the manufacturer's instructions. The expression of hsa_circ_0000144 in the transfected cells was detected by quantitative PCR (qPCR).The GC cell line MGC-803 and normal cell line GES-1 were purchased from the Chinese Academy of Sciences . MGC-803 cells were cultured in F-12K and DMEM-H medium , respectively. All cells were cultured at 37\u00a0\u00b0C for 18 to 24\u00a0h in a humidified incubator containing 5% COAfter 24\u00a0h of incubation, transfected cells were plated onto 96-well plates and cultured for 48\u00a0h. Every well contained 3000 cells. 3--2,5-diphenyltetrazolium bromide (MTT) solution was added to each well, and cell viability was assessed by measuring the absorbance at 450\u00a0nm.To examine cell migration and invasion ability, we conducted the Transwell assay according to the manufacturer\u2019s instructions. Cells were incubated for 24\u00a0h. Three microscopy fields were randomly selected to acquire images.Total RNA was extracted using TRIzol reagent  and synthesized into cDNA using M-MLV reverse transcriptase  following the manufacturer\u2019s instructions. qRT-PCR was performed using SYBR Green assay . Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) and U6 were used as controls. The primer sequences were:GAPDH-S: 5\u2032-ACACCCACTCCTCCACCTTT-3\u2032GAPDH-AS: 5\u2032-TTACTCCTTGGAGGCCATGT-3\u2032Hsa_circ_0000144-S: 5\u2032-GAGTGTTGGCCTGTCCTCAA-3\u2032Hsa_circ_0000144-AS: 5\u2032-TTGTGCCCAGTTGCCTGTAT-3\u2032circRNA expression data analysis was performed using GraphPad Prism 6.0 software, and survival analysis was performed using the R language survival package. P\u2009<\u20090.05 was considered significant.We analyzed the data GSE78092 from the GEO database containing three GC tissues and three adjacent normal tissues. A total of 32 circRNAs were differentially expressed in the GC samples compared to the normal samples. Among the differentially expressed circRNAs, 26 were downregulated and six were upregulated. These results are presented in the volcano plot and heat map Fig.\u00a0. The tophttps://circinteractome.nia.nih.gov/bin/mirnasearch) and obtained the miRNAs that could bind to the circRNA hsa_circ_0000144. These included hsa-mir-1178, hsa-mir-1276, hsa-mir-197, hsa-mir-217, hsa-mir-485-3p, hsa-mir-502-5p, hsa-mir-526b, hsa-mir-532-3p, hsa-mir-554, hsa-mir-580, hsa-mir-583, hsa-mir-610, hsa-mir-623, and hsa-mir-942. The relationship of miRNAs targeting genes is listed in Table We used the keyword \u201chsa_circ_0000144\u201d in the circinteractome database RNA was lower than with siNC Fig.\u00a0a.Fig. 6CThe CCK8 assay was used to evaluate the effect of hsa_circ_0000144 on proliferation. SiRNA-hsa_circ_0000144 markedly inhibited cell proliferation Fig.\u00a0b.Transwell migration and invasion assays were performed to further examine the effect of hsa_circ_0000144 on GC metastasis. Hsa_circ_0000144 significantly affected cell invasion and migration Fig.\u00a0c, d.SLAF6 gene, in the pathogenesis of GC.Globally, GC is a common disease and is the second leading cause of cancer-related deaths , 13. TheWe analyzed data on GC circRNAs from the GEO database using R software and bioinformatics. Thirty-two differentially expressed circRNAs were identified, including six upregulated circRNAs and 26 downregulated circRNAs. Hsa_circ_0000144, hsa_circ_0005529, hsa_circ_0023642, hsa_circ_0061274, hsa_circ_0008035, and hsa_circ_0032821 were upregulated. Hsa_circ_0000144 was the most significantly different circRNA expressed.From the circinteractome database, we obtained 14 miRNAs targeting hsa_circ_0000144 and tested them using the miRNA expression data of the TCGA database. The expression of hsa-mir-217 and hsa-mir-942 was associated with increased survival in GC (Fig.\u00a0The ceRNA of the lncRNA\u2013miRNA\u2013mRNA network affects tumor regulation. CircRNAs can function as ceRNAs in tumor biology , 21. RecPresently, we analyzed the ceRNA network of circRNA-hsa_circ_0000144 targeting miRNA\u2013mRNA. We found that hsa_circ_0000144 barely interacted with miRNAs and mRNAs Fig.\u00a0. To deteRecent evidence has also demonstrated that circRNAs affect functions in cancer , 31. AltHsa_circ_0000144 promotes GC cell proliferation and invasion. The ceRNA regulatory network of hsa_circ_0000144 targeted miRNAs and mRNAs, indicating the potential value as biomarkers for GC diagnosis and targeted therapy.Additional file 1. KEGG pathway analysis of genes differentially expressed circRNAs targeting."}
+{"text": "Comparative genomics studies are central in identifying the coding and non-coding elements associated with complex traits, and the functional annotation of genomes is a critical step to decipher the genotype-to-phenotype relationships in livestock animals. As part of the Functional Annotation of Animal Genomes (FAANG) action, the FR-AgENCODE project aimed to create reference functional maps of domesticated animals by profiling the landscape of transcription (RNA-seq), chromatin accessibility (ATAC-seq) and conformation (Hi-C) in species representing ruminants , monogastrics (pig) and birds (chicken), using three target samples related to metabolism (liver) and immunity (CD4+ and CD8+ T cells).RNA-seq assays considerably extended the available catalog of annotated transcripts and identified differentially expressed genes with unknown function, including new syntenic lncRNAs. ATAC-seq highlighted an enrichment for transcription factor binding sites in differentially accessible regions of the chromatin. Comparative analyses revealed a core set of conserved regulatory regions across species. Topologically associating domains (TADs) and epigenetic A/B compartments annotated from Hi-C data were consistent with RNA-seq and ATAC-seq data. Multi-species comparisons showed that conserved TAD boundaries had stronger insulation properties than species-specific ones and that the genomic distribution of orthologous genes in A/B compartments was significantly conserved across species.We report the first multi-species and multi-assay genome annotation results obtained by a FAANG project. Beyond the generation of reference annotations and the confirmation of previous findings on model animals, the integrative analysis of data from multiple assays and species sheds a new light on the multi-scale selective pressure shaping genome organization from birds to mammals. Overall, these results emphasize the value of FAANG for research on domesticated animals and reinforces the importance of future meta-analyses of the reference datasets being generated by this community on different species. Most complex trait-associated loci lie outside protein-coding regions, and comparative genomics studies have shown that the majority of mammalian-conserved and recently adapted regions consist of non-coding elements \u20133. This The Functional Annotation of Animal Genomes (FAANG) initiative  aims to Bos taurus , Capra hircus , Gallus gallus , and Sus scrofa , generating a total of 4115 corresponding entries registered at the EMBL-EBI BioSamples database (see \u201cHere we report the main results of a pilot project (FR-AgENCODE ) launchease see \u201c\u201d sectionFor each animal  of the four species, we used RNA-seq to profile the transcriptome of liver, CD4+ and CD8+ T cells see \u201c\u201d sectionFor each species, we explored the similarity among samples using the expression profiles of the reference genes. Principal component analysis (PCA) revealed quite consistent patterns across species, where the first principal component (explaining 84 to 91% of the variability among samples) clearly separated samples according to their tissue of origin (liver vs. T cells). A more moderate yet systematic separation was observed between CD4+ and CD8+ T cells on the second principal component . Among those, 10 and 29 genes showed significant overexpression in CD4+ and CD8+ cells respectively , all genes were found to be expressed in human CD4+ and/or CD8+ \u03b1\u03b2 T cells and 25 of them showed a relative enrichment in CD4+ (or CD8+) human cells consistent with our data across the four species. Out of these 25 genes, six and eight genes, respectively, could be associated with CD4+ and CD8+ T cell differentiation, activation, and function according to the literature ; (2) extension: same as (1), but the FR-AgENCODE transcript extends a reference transcript by at least 1 bp on at least one side; (3) alternative: a FR-AgENCODE transcript that shares at least one intron with a reference transcript but does not belong to the previous categories (only multi-exonic transcripts can be in this class); and (4) novel: a FR-AgENCODE transcript that does not belong to any of the above categories. We found that most FR-AgENCODE transcripts (between 37% for goat and 49% for chicken) were of the alternative class, therefore enriching the reference annotation with new splice variants of known genes , 52 (goat), 74 (chicken), and 26 (pig) genes, of which 12 are common to at least 2 livestock species , and considering their transcription orientation with respect to these reference genes. This analysis revealed an overwhelming majority of intergenic lncRNA genes over intragenic ones  and 950M (pig) ATAC-seq fragments were sequenced per species, and were processed by a standard pipeline \u201c\u201d sectionIn comparison to the reference annotation, about 10\u201315% of the peaks lie at 5 kb or less from a Transcription Start Site (TSS) and can be considered to be promoter peaks. The precise distribution of these promoter peaks showed a clear higher accumulation at the TSS for all species Fig.\u00a0, supportThe vast majority of the peaks, however, were either intronic or intergenic Fig.\u00a0, similarp value <10\u22123, permutation tests). Moreover, this subset of overlapping peaks have significantly higher q-value signal scores than their non-overlapping counterparts , which confirms the existence of a common signal between the datasets.Since active enhancers are expected to be enriched in chromatin regions that are both accessible and tagged with specific histone modification marks, we compared our ATAC-seq peaks to histone ChIP-seq peaks from another functional genomics study . In thatp value <7.1\u00d710\u22124 for goat and p value <2.2\u00d710\u221216 for chicken and pig, Wilcoxon tests, see \u201cTo further characterize functional regulatory sites in our samples, we compared chromatin accessibility between liver and T cells. The ATAC-seq peaks of each species were quantified in each sample and resulting read counts were normalized using a loess correction see \u201c\u201d sectionAccessible promoters are commonly associated with gene activation , 44. GivWithin each sample, genes with highly accessible promoters showed higher expression values globally  conserved ATAC-seq peaks across species. We identified conserved peaks by aligning all the sequences that corresponded to peaks from each species  to the human genome see \u201c\u201d sectionA large majority of the human hits (about 86%) originated from a single livestock species, which is consistent with previous reports about the fast evolution of regulatory elements and the species-specific feature of many enhancers , 42. NevIn addition, shuffling the peak positions within each species did not drastically change the mapping efficiency on the human genome overall but resulted in a much lower proportion of orthologous peaks , supporting a selective pressure on functionally active regulatory regions. Remarkably, this contrast was even stronger after discarding human hits close to a TSS in any of the species  to 2000 (pig) TADs of variable sizes , emphasizing the biological consistency of our results across all molecular assays and species.At a higher organizational level, we identified \u201cactive\u201d (A) and \u201cinactive\u201d (B) epigenetic compartments as defined by  see \u201cMe. We alsoIt has been shown that the general organization in TADs tends to be conserved across species , 56 and As previously done with ATAC-seq peaks see \u201ca. Since To further characterize this link between conservation and TAD strength, we assigned to each boundary a similarity level depending on the number of livestock species with a common hit on the human genome, as we did for ATAC-seq peaks , suggesting that such conservation was not restricted to regions of higher gene expression.Since all these orthologous genes were assigned a compartment type  in each species separately, we tested whether any significant conservation of compartment type across species could be detected. Among the 5728 orthologous genes, 3583 had the same compartment type in all species, which was 49% more than expected by chance assuming independence between species. This cross-species conservation was observed for both A and B compartments  and another just before slaughter, in order to obtain at least 50 ml of whole blood for separation of lymphocytes (PBMC). PBMC were re-suspended in a medium containing 10% FCS, counted, conditioned with 10% DMSO and stored in liquid nitrogen prior to the sorting of specific cell types: CD3+CD4+ (\u201cCD4\u201d) and CD3+CD8+ (\u201cCD8\u201d).For chicken, spleen was sampled after exsanguination. Spleen leucocytes were purified by density-gradient separation to remove nucleated erythrocytes contamination and stored in liquid nitrogen prior to CD4+ and CD8+ T cell sorting.http://ftp.faang.ebi.ac.uk/ftp/protocols/samples/All protocols for liver sampling, PBMC separation, splenocyte purification, and T cell sorting can be found at http://www.faang.org. All detailed protocols used for RNA extraction and libraries production for RNA-seq, ATAC-seq, and Hi-C are available at http://ftp.faang.ebi.ac.uk/ftp/protocols/assays/.All assays were performed according to FAANG guidelines and recommendations, available at Cells and tissues were homogenized in TRIzol reagent (Thermo) using an ULTRA-TURRAX (IKA-Werke) and total RNAs were extracted from the aqueous phase. They were then treated with TURBO DNase (Ambion) to remove remaining genomic DNA and then processed to separate long and small RNAs using the mirVana miRNA Isolation kit. Small and long RNA quality was assessed using an Agilent 2100 Bioanalyzer and RNA 6000 nano kits (Agilent) and quantified on a Nanodrop spectrophotometer.\u03bcg of total long RNA with a RNA Integrity Number (RIN) over 8 following the manufacturer\u2019s instructions. Libraries were PCR amplified for 11 cycles and library quality was assessed using the High Sensitivity NGS Fragment Analysis Kit DNF-474 and the Fragment Analyser system (AATI). Libraries were loaded onto a High-seq 3000 (Illumina) to reach a minimum read numbers of 100M paired reads for each library.Stranded mRNA libraries were prepared using the TruSeq Stranded mRNA Sample Prep Kit -V2 (Illumina) on 200 ng to 1 \u03bcm cell strainer. Cells were then fixed with 1% formaldehyde for 10 min at 37 \u2218C and fixation was stopped by adding Glycine to a final concentration of 0.125M. After two washes with PBS, cells were pelleted and kept at \u221280 \u2218C for long term storage. Subsequently, cells were thawed on ice and 5 million cells were processed for each Hi-C library. Cell membranes were disrupted using a potter-Elvehjem PTFE pestle and nuclei were then permeabilized using 0.5% SDS with digestion overnight with HindIII endonuclease. HindIII-cut restriction sites were then end-filled in the presence of biotin-dCTP using the Klenow large fragment and were religated overnight at 4 \u2218C. Nucleus integrity was checked using DAPI labelling and fluorescence microscopy. Nuclei were then lysed and DNA was precipitated and purified using Agencourt AMPure XP beads (Beckman Coulter) and quantified using the Qubit fluorimetric quantification system (Thermo). Hi-C efficiency was controlled by PCR using specific primers for each species and, if this step was successful, DNA was used for library production. DNA was first treated with T4 DNA polymerase to remove unligated biotinylated ends and sheared by sonication using a M220 Covaris ultra-sonicator with the DNA 550 pb SnapCap microtube program .In situ Hi-C libraries were made according to  with a fSheared DNA was then size-selected using magnetic beads, and biotinylated fragments were purified using M280 Streptavidin Dynabeads (Thermo) and reagents from the Nextera_Mate_Pair Sample preparation kit (Illumina). Purified biotinylated DNA was then processed using the TrueSeq nano DNA kit (Illumina) following the manufacturer\u2019s instructions. Libraries were amplified for 10 cycles and then purified using Agencourt AMPure XP beads. Library quality was assessed on a Fragment Analyser (AATI) and by endonuclease digestion using NheI endonuclease. Once validated, each library was sequenced on an Illumina Hi-Seq 3000 to reach a minimum number of 150M paired reads per library. Libraries from the cattle samples failed the Quality Control steps  and were not included in the analysis.\u2218C. DNA was then purified using the Qiagen MinElute PCR purification kit. Libraries were first amplified for 5 cycles using custom-synthesized index primers and then a second amplification was performed. The appropriate number of additional PCR cycles was determined using real-time PCR, permitting the cessation of amplification prior to saturation. The additional number of cycles needed was determined by plotting the Rn versus Cycle and then selecting the cycle number corresponding to one-third of the maximum fluorescent intensity. After PCR amplification, libraries were purified using a Qiagen MinElute PCR purification kit followed by an additional clean-up and sizing step using AMPure XP beads (160 \u03bcl of bead stock solution was added to 100 \u03bcl of DNA in EB buffer) following the manufacturer\u2019s instructions. Library quality was assessed on a BioAnalyser (Agilent) using Agilent High Sensitivity DNA kit (Agilent), and libraries were quantified using a Qubit Fluorometer (Thermo). Considering that the Hi-C protocol was not successful on the liver samples from cattle, ATAC-seq was not attempted on these samples either.ATAC-seq libraries were prepared according to  with a fCapra hircus was not part of the Ensembl release, we used the NCBI CHIR_ARS1 annotation  was performed using the  package  on the RAnnotated gene orthologsWe used Ensembl Biomart  to definRNA-seq sample hierarchical clustering\u22123). We then represented this sample by sample correlation matrix as a heatmap where the samples were also clustered using a complete linkage hierarchical clustering  were collected from each animal, an animal effect was also included to account for these repeated measures:\u03bcgi represents the mean expression of gene g in sample i, si the TMM normalization factor for sample i, tissue(i)\u2208{liver, CD4, CD8} and animal(i)\u2208{1,2,3,4} the tissue and animal corresponding to sample i, and \u03b2g,tissue(i) and \u03b3g,animal(i) the fixed tissue and animal effects, respectively, of gene g in sample i. Hypothesis tests were performed to identify significantly differentially expressed genes among each pair of tissues, e.g.,In Model 2 is identical to the previous model, where gene expression was modeled using both a tissue and an animal effect, with the exception that the CD4 and CD8 tissues were collapsed into a single group. In this model, the only hypothesis of interest is thus between the liver and global CD cell group:To perform the differential analysis of gene expression, we used the expected read counts provided by RSEM . RNA-seqedgeR . The samedgeR .In ModeAll hypothesis tests were performed using likelihood-ratio tests and were corrected for multiple testing with the Benjamini-Hochberg  proceduGO analysis of differentially expressed genesGOstatR/Bioconductor package .over.chain.gz chain file for each species (created in-house for goat following UCSC instructions) and retained only the best hit for each transcript (according to the pslMap score). We further required each FR-AgENCODE gene to project to a single human gene that did not strandedly overlap any other projected FR-AgENCODE gene.In order to obtain gene orthology relationships, we projected FR-AgENCODE transcripts from the four livestock species to the human GRCh38 genome using the UCSC pslMap program  the lncRNA was located between two orthologous protein-coding genes, (2) the lncRNA was the only one in each species between the two protein-coding genes, and (3) the relative gene order and orientation of the resulting triplet was identical between species. Using these criteria, we found six triplets shared between the four species, 73 triplets shared between cattle, goat, and pig, and 19 triplets shared between cattle, chicken, and pig.ATAC-seq data analysis pipelineATAC-seq reads were trimmed with trimgalore 0.4.0 using the \u2013stringency 3, -q 20, \u2013paired and \u2013nextera options . The highest proportion of filtering was due to the MAPQ 10 and PCR duplicate filters  plots between pairwise samples after normalization using the TMM approach, suggesting that an alternative normalization strategy was needed. In particular, trended biases are problematic as they can potentially inflate variance estimates or log fold-changes for some peaks. To address this issue, a fast loess approach  implemencsaw  was usedAs for RNA-seq, we used two different differential models: Model 1 for tissue pair comparisons, Model 2 for T cell versus liver comparisons (see corresponding \u201cATAC-seq peak TFBS densityIn order to identify Transcription Factor Binding Sites (TFBS) genome-wide, we used the FIMO  softwareComparison between ATAC-seq peaks and ChIP-seq histone mark peakshttps://genome.sph.umich.edu/wiki/LiftOver). About 86.7%  of the H3K4me3 peaks and 91.8%  of the H3K27ac peaks could be lifted over to the 11.1 genome assembly. The median peak size was 1944 bp for H3K4me3 and 2786 bp for H3K27ac, and the peak size distribution was very similar for the initial 10.2 and the lifted over 11.1 peaks. As for genome coverage, the H3K4me3 and H3K27ac peaks covered 0.9% and 4.7% of the 11.1 pig genome, respectively. In comparison, there were 25,885 pig liver ATAC-seq peaks with a median size of 360 bp and covering 0.5% of the pig genome. Consistent with what was expected from the two histone marks, the vast majority (94.9%) of the H3K4me3 peaks (known to be associated to promoter regions) overlapped (bedtools intersect program) with the H3K27ac peaks (known to be associated to both promoter and enhancer regions), and about 30% of the H3K27ac peaks overlapped with the H3K4me3 peaks. Comparing our pig liver ATAC-seq peaks to the histone mark peaks, we found that 27.1%  and 36.4%  of our pig liver ATAC-seq peaks overlapped with the H3K4me3 and H3K27ac peaks, respectively. Reciprocally, 70.3% (6773 out of 9632) and 28.3%  of the H3K4me3 and H3K27ac peaks respectively overlapped with our pig liver ATAC-seq peaks.Pig liver H3K4me3 and H3K27ac ChIP-seq peaks from the Villar et al. study  were dowp value <10\u22123).To assess if these numbers were higher than expected by chance, we shuffled (bedtools shuffle program) the 25,885 pig liver ATAC-seq peaks 1000 times on the pig genome and recomputed their intersection with the two sets of histone mark peaks (H3K4me3 and H3K27ac). After doing so, we never obtained percentages of H3K4me3 and H3K27ac peaks, respectively, overlapping the shuffled ATAC-seq peaks that were equal or higher than the ones obtained with the real ATAC-seq peaks. This means that indeed, 70.3% and 28.3% of the histone mark peaks overlapping our ATAC-seq peaks are percentages that are significantly higher than expected by chance , highlighting a common signal between the two techniques.We also compared the ATAC-seq, H3K4me3 and H3K27ac peak scores  of the common peaks versus the other peaks. In doing so, we found that common peaks had significantly higher scores than non common peaks , we used the lastal program followed by the last-split program (-m1 and \u2013no-split options)  to project the 104,985 cattle, 74,805 goat, 119,894 chicken, and 149,333 pig ATAC-seq peaks onto the human genome. In doing so and consistent with the phylogenetic distance between our species and human, we were able to project 72.6%  cattle, 73.7%  goat, 12.3%  chicken, and 80.1%  pig peaks to the human genome. The percentage of bases of the initial peaks that could be aligned was around 40% for mammals and 14% for chicken. Then, for each peak that could be projected onto the human genome, we retained its best hit  and then merged all these best hits  on the human genome (using bedtools merge). A total of 215,620 human regions were obtained, from which we kept the 212,021 that came from a maximum of 1 peak from each species. Those 212,021 regions were called human hits.In order to investigate the conservation of chromatin accessibility across our 4 livestock species, we used the human GRCh38 genome as a reference. After indexing the softmasked GRCh38 genome (main chromosomes) using lastdb . We then represented this sample-by-sample correlation matrix as a heatmap where the samples were also clustered using a complete linkage hierarchical clustering . We found that 23.1%  of the human hits obtained from the real ATAC-seq peaks overlapped human DNAse I peaks, whereas only 8.5%  of the human hits obtained from shuffled ATAC-seq peaks overlapped human DNAse I peaks. This further supports the biological signal present in these data.We also compared the human hits to the combined set of 519,616 ENCODE human DNAse I peaks from two CD4+, two CD8+ and one \u201cright lobe of liver\u201d samples . For each human hit, we computed its phastcons score using the bigWigAverageOverBed utility from UCSC (https://github.com/ENCODE-DCC/kentUtils).Finally we used the phastcons measure of vertebrate sequence conservation obtained from the multiple alignment of 100 vertebrate species genomes including human  was computed using the original definition introduced by  to indicKij, were corrected for a distance effect with:i and j, d=d and \u03c3d is the standard deviation of the counts over all pairs of bins at distance d. Within-chromosome Pearson correlation matrices were then computed for all pairs of bins based on their distance-corrected counts and a PCA was performed on this matrix. The overall process was performed similarly to the method implemented in the R/Bioconductor package HiTC 52] and sata.html  for the \u03c72 goodness-of-fit test.The number and proportion of genes  in each compartment type was computed using bedtools map (-distinct option on the gene ID field). Orthologous genes were taken from Ensembl as previously described. Under the independence assumption of compartment assignment between species, the expected proportion of orthologous genes with \u201ctriple A\u201d (resp. with \u201ctriple B\u201d) assignments between species is equal to the product of the observed frequencies for A (resp. for B) compartments in the three species. The observed frequencies of \u201ctriple A\u201d and \u201ctriple B\u201d assignments in orthologous genes was compared to this expected proportion using a ATAC-seq vs. RNA-seq correlation: intra- and inter-sample analysisn=10 for pig, Additional file\u00a0For each ATAC-seq peak that overlapped a promoter region  and tables (S1-S15).Additional file 2 Reference genes and transcripts  of the 4 species. Archive content:\u2022 bos_taurus.gtf\u2022 bos_taurus.refgn.tpm.tsv\u2022 capra_hircus.gtf\u2022 capra_hircus.refgn.tpm.tsv\u2022 gallus_gallus.gtf\u2022 gallus_gallus.refgn.tpm.tsv\u2022 sus_scrofa.gtf\u2022sus_scrofa.refgn.tpm.tsvAdditional file 3 Orthologs between the 4 livestock species. We used Biomart to retrieve the 1 to 1 orthology relationships between chicken, pig and cattle and added goat via gene name. The human gene id is given for reference.Additional file 4 Reference DE genes : the archive contains four folders, one for each species . Each folder contains itself two subfolders, one for each model: diffcounts.nominsum (Model 1) and diffcounts.cdvsliver (Model 2). Results of Model 1 are given in:\u2022 refgenes.counts.min2tpm0.1.normcounts.diff.readme.idx\u2022 refgenes.counts.min2tpm0.1.normcounts.diff.cd4.cd8.bed\u2022 refgenes.counts.min2tpm0.1.normcounts.diff.cd4.liver.bed\u2022 refgenes.counts.min2tpm0.1.normcounts.diff.cd8.liver.bedResults of Model 2 are given in:\u2022 refgenes.counts.min2tpm0.1.normcounts.diff.readme.idxrefgenes.counts.min2tpm0.1.normcounts.diff.cd.liver.bedAll bed files contain the coordinates and id of the genes found to be differentially expressed between the two conditions. The file also contains the normalized read counts of those genes in the different samples as well as the adjusted pvalue, logFC and normLogFC (see readme.idx file for more details).Additional file 5 FR-AgENCODE genes and transcripts .\u2022 bos_taurus_cuff_tpm0.1_2sample_complete.gff\u2022 bos_taurus_cuff_tpm0.1_2sample_trid_4posclasses_3codingclasses_booleans.tsv\u2022 bos_taurus.frag.gnid.posclasslist.codclasslist.tsv\u2022 bos_taurus.fraggn.tpm.tsv\u2022 capra_hircus_cuff_tpm0.1_2sample_complete.gff\u2022 capra_hircus_cuff_tpm0.1_2sample_trid_4posclasses_3codingclasses_booleans.tsv\u2022 capra_hircus.frag.gnid.posclasslist.codclasslist.tsv\u2022 capra_hircus.fraggn.tpm.tsv\u2022 gallus_gallus_cuff_tpm0.1_2sample_complete.gff\u2022 gallus_gallus_cuff_tpm0.1_2sample_trid_4posclasses_3codingclasses_booleans.tsv\u2022 gallus_gallus.frag.gnid.posclasslist.codclasslist.tsv\u2022 gallus_gallus.fraggn.tpm.tsv\u2022 sus_scrofa_cuff_tpm0.1_2sample_complete.gff\u2022 sus_scrofa_cuff_tpm0.1_2sample_trid_4posclasses_3codingclasses_booleans.tsv\u2022 sus_scrofa.frag.gnid.posclasslist.codclasslist.tsv\u2022 sus_scrofa.fraggn.tpm.tsvAdditional file 6 Four livestock species FR-AgENCODE gene orthology.Additional file 7 FR-AgENCODE DE genes . The archive has the same structure than de.refgn.tar.gz with names starting with cuffgenes instead of refgenes.Additional file 8 lncRNAs . Archive content:\u2022 bos_taurus.lncrna.TPM0.1in2samples.classif.tsv\u2022 capra_hircus.lncrna.TPM0.1in2samples.classif.tsv\u2022 ConservedLncRNABySynteny_73_19_6.xlsx\u2022 gallus_gallus.lncrna.TPM0.1in2samples.classif.tsv\u2022 sus_scrofa.lncrna.TPM0.1in2samples.classif.tsvAdditional file 9 ATAC-seq peaks : the archive contains four folders, one for each species . Each folder contains the following six files:\u2022 mergedpeaks_allinfo_gn_frag.tsv\u2022 mergedpeaks_allinfo_tr_frag.tsv\u2022 mergedpeaks_allinfo_tr_ref.tsv\u2022 mergedpeaks_allinfo_gn_ref.tsv\u2022 mergedpeaks.peaknb.allexp.readnb.bed.readme.idx\u2022 mergedpeaks.peaknb.allexp.readnb.bedAdditional file 10 DA ATAC-seq peaks . The archive has the same structure as de.refgn.tar.gz with names starting with mergedpeaks.peaknb.allexp.readnb instead of refgenes.counts.min2tpm0.1.Additional file 11 Four livestock species ATAC-seq peak orthology.Additional file 12 Hi-C TADs and A/B compartments: the archive contains three folders, one for each species . Each folder contains the following two files:\u2022 compartments.bed\u2022 mat.40000.longest25chr.tad.consensus.bedAdditional file 13 Three livestock species TAD boundary orthology."}
+{"text": "P < 0.01). Thereafter, a hsa_circ_0000517-related regulatory network was built based on application of databases including CSCD, TargetScan, miRDB, and miRTarBase. We uncovered the potential function of hsa_circ_0000517 through bioinformatics approaches, such as PPI network, GO, and KEGG pathway analyses. Specifically, functional analysis unveiled that hsa_circ_0000517 was likely to regulate the MAPK and Ras pathway through sponging several miRNAs and having an impact on the expression of TP53, MYC, and AKT1. To verify our initial finding, the expression of hsa_circ_0000517 in 60 HCC patients was detected by qRT-PCR and the expression in cancer tissues was higher compared with the paracarcinoma tissues. Survival analysis suggests high hsa_circ_0000517 expression was associated with adverse prognosis in HCC patients. Furthermore, this circRNA was significantly up-regulated in worse TNM stage, consistent with the progressive-stage-specific characteristic of circRNAs. A prognostic nomogram built on AFP and has_circ_0000517 showed significant diagnostic value. In all, we concluded that hsa_circ_0000517, a promising molecular in underlying mechanism of HCC, is a potent valuable biomarker for prognosis prediction.Although huge progress has been made in therapeutics against hepatocellular carcinoma (HCC) over the decades, the prognosis of this lethal disease remains poor. To find out risk factors for HCC-related outcome and better predict the prognosis, there is an unmet need to identify novel biomarkers of HCC. Accumulating evidence suggests that circRNAs play pivotal roles in carcinogenesis of several malignancies. In this study, we analyzed two datasets (GSE 94508 and GSE 97332) to examine differentially expressed circRNAs markedly related to HCC pathogenesis. Using Limma package in R and WGCNA analysis, hsa_circ_0000517 was significantly up-regulated in HCC (adjusted  Hepatocellular carcinoma (HCC) is one of the most fatal carcinomas with relatively high mortality . AlthougIn recent studies, circular RNAs (circRNAs), characterized by stable circular structure and sponge function in most cells, have been identified as one kind of novel and important regulatory non-coding RNAs (ncRNA). Given that circRNA expression shows high heterogeneity among different cells, tissues, and even the stages of disease, this kind of ncRNA has been considered as a latent diagnostic and prognostic biomarker and promising target for developing new therapeutic options against human malignant cancers . Prior sIn the current study, we analyzed GSE 97332 and GSE 94508 from the Gene Expression Omnibus (GEO) database to mine differentially expressed circRNA expression data. We selected differentially expressed circRNAs in HCC tissues compared with adjacent normal specimens. Then, we concentrated on the comprehensive analysis of hsa_circ_0000517, the overlapping and up-regulated circRNA between two datasets. Furthermore, in an attempt to better figure out the relationship between hsa_circ_0000517 and prognosis of HCC, quantitative real time-polymerase chain reaction (qRT-PCR) was performed in 60 HCC patients from our center and corresponding clinicopathological characteristics were analyzed by various statistical analyses, involving cox regression analyses and a Least Absolute Shrinkage and Selector Operation (Lasso) regression algorithm.http://www.ncbi.nlm.nih.gov/geo/) is a public database providing functional genomic information from high-throughout gene expression, chips, and microarray data  >1 or Log (Fold Change)< \u22121\u201d were defined as the thresholds for the screening of differential expression of circRNAs. Probe sets without corresponding circbase ID (http://www.circbase.org) were discarded. The circos plot was used to present the chromosomes, hosting genes, average circRNA expression, and the circRNA name (GEO (ray data . A microray data  (data frray data  (data frray data . The adjRNA name .http://gb.whu.edu.cn/CSCD) is a database of available RNA sequencing datasets from 87 cancer cell line samples, and 272,152 cancer-specific circRNAs are deposited in the CSCD database, including the corresponding number and position of MRE (microRNA response element), RBP (RNA binding protein), and ORF (open reading frame) elements located in cancer-specific circRNAs (http://www.targetscan.org/vert_72/), miRDB (http://www.mirdb.org/), miRTarBase (http://mirtarbase.mbc.nctu.edu.tw/php/index.php), were then used to predict the potential targets of the miRNAs. With an aim to acquire more trustworthy targets, the Venn plot was performed to collect the consensus genes from the three online databases.Cancer-specific circRNA database . The R package \u201cWGCNA\u201d was applied to explore traits-related modules . The dathttps://string-db.org) and a protein network was created. The network edges indicated confidence: the thicker the lines, the more reliabilities they are. Network edges stand for confidence and the custom value of minimum required interaction score was 0.910. Next, a TP53-related network was extracted from the whole PPI network using Cytoscape 3.7.1  is a widely-used tool for annotating genes with functions, especially molecular function (MF), biological pathways (BP), and cellular components (CC) . Kyoto E2 at 37\u00b0C.Specimens of tumor and adjacent normal tissues were obtained from 60 HCC patients undergoing surgery at Department of Hepatobiliary Surgery, Sun Yat-sen Memorial Hospital of Sun Yat-Sen University . The patients did not undergo any radiotherapy or chemotherapy before surgical operation. The normal and tumor tissues of 60 HCC patients were frozen immediately in liquid nitrogen and then stored at \u221280\u00b0C for RNA extraction. Detailed HCC patient information is listed in \u2212\u0394\u0394CT method. The primers were as follows: U6, forward CTCGCTTCGGCAGCACA, reverse AACGCTTCACGAATTTGCGT; GAPDH, forward ACAACTTTGGTATCGTGGAAGG, reverse GCCATCACGCCACAGTTTC; hsa_circ_0000517, forward GGGAGGTGAGTTCCCAGAGA, reverse TGGCCCTAGTCTCAGACCTC.Total RNA from tissues was extracted with Trizol reagent based on the manufacturer's instructions . The mRNA was reverse transcribed using a Prime-Script RT reagent Kit (Takara); cDNA or circRNA were amplified and quantified on CFX96 system . Briefly, qRT-PCR was performed in a final volume of 10 \u03bcL, and the thermal conditions were 95\u00b0C for 30 s, and 45 cycles of 95\u00b0C for 5 s and 60\u00b0C for 20 s. U6 and GAPDH was used as endogenous controls. Expression of hsa_circ_0000517 was calculated using the 2http://www.R-project.org). In our present study, P < 0.05 was considered statistically significant. The Uni-variate cox regression analysis, Lasso regression analysis  and R software , only one circRNA, hsa_circ_0000517, was significantly upregulated in both GSE 94508 and GSE 97332. According to the molecular character of has_circ_0000517 from CSCD database, has_circ_0000517 has numerous microRNA response elements (MRE) and RNA binding protein (RBP) sites . The turquoise module contained a total of 701 circRNAs, including has_circ_0000517 (P = 2.43E-09 and the correlation coefficient = 0.90), highlighting the pivotal role of has_circ_0000517 in HCC.CircRNAs are known to be evolutionally conserved and relatively stable, accounting for the potential as prognostic biomarkers and possible treating targets for precise personalized medicine . In our P) sites . To furtP) sites , we coul_0000517 . As presHsa_circ_0000517, as one of the 27 circRNAs derived from Ribonuclease P RNA component H1 (RPPH1), embodies only one exon and is lack of an open reading frame (ORF). However, it consists of several microRNA response elements (MRE) , indicatP-value was smaller than 1.0e-16, and the analysis proved that the network had significantly more interactions than expected. As presented in So far, studies have demonstrated that circRNAs mainly exert their function by regulating the targeted miRNAs and genes. Therefore, figuring out the biological function of potential circRNA-related genes could predict the corresponding potential function of circRNAs . In the Given that a better comprehension of has_circ_0000517-related genes might shed light on specific molecular function characterized by has_circ_0000517. The GO enrichment analysis revealed that several targets were involved in \u201chistone deacetylase binding,\u201d \u201cAT DNA binding,\u201d \u201ccore promoter binding,\u201d \u201cubiquitin conjugating enzyme activity,\u201d \u201cactin filament binding,\u201d and \u201cubiquitin-like protein conjugating enzyme activity\u201d . ImportaP = 0.0278). Moreover, referring to the criteria , we separated 60 patients into three groups: High-expression group, No-change group, and Low-expression group. Among 60 HCC patients in our center, 41 (68.33%) patients exhibited high hsa_circ_0000517 expression in tumor tissues compared with non-tumor tissues (P = 0.0305) as well as disease-free survival duration   . As showP = 2.794e-02). Moreover, the ROC curve of the prognostic nomogram was plotted in A good prognostic biomarker is able to construct a signature or nomogram with other risk factors . To furtin silico. Furthermore, the qRT-PCR result confirmed its differential expression in cancerous and para cancerous tissues. Finally, we validated the prognostic value of has_circ_0000517, via cox and Lasso regression analyses, in HCC patients.Recent studies have reported that circRNAs could be transcribed and involved in several diseases as the expression of circRNAs varied in neither histologic types nor disease stages , 28. In P < 0.01) and just intersected up-regulated circRNAs between the datasets. Due to the heterogeneity of HCC patients, we figured out that hsa_circ_0000517 was the single significantly up-regulated molecular, further confirmed by WGCNA analysis. Moreover, a consistent result of 60 HCC patients from our center was validated. Given the samples across studies above were small and bias might be unavoidable, future hsa_circ_0000517-related researches urge to enroll participants with a large scale, and gain stronger evidence in the comparison of tumor and non-tumor HCC tissues.Much attention has been paid to investigate the relation between circRNAs and HCC . Qin et In this study, we constructed a quadruple network of hsa_circ_0000517 based on bioinformatics analysis. The hsa_circ_0000517 regulatory network was composed of 34 miRNAs, 78 mRNAs, and eight signaling pathways , and thiP = 0.0312), indicating the potential prognostic role of the serum hsa_circ_0000517  HCC patients in our center. Moreover, the OS and DFS curves illustrated that hsa_circ_0000517 could account for the poor diagnosis and play a carcinogenic role in the development of HCC . In addi_0000517 . HoweverOn the other hand, RPPH1, as the host gene of has_circ_0000517, is the RNA component of the RNase P ribonucleoprotein  and consin silico and in 60 HCC patients of our center. Bioinformatics analysis also suggested that sponging several miRNAs, hsa_circ_0000517 might regulate the expression of TP53, MYC, and AKT1 via MAPK pathway and Ras pathway. Our findings indicated that hsa_circ_0000517 might not only be an undeveloped prognostic biomarker of HCC, but also accountable for hepatocarcinogenesis.In summary, hsa_circ_0000517 was highly expressed in HCC, verified Publicly available datasets were analyzed in this study. These data can be found here: GSE97332; GSE94508.The studies involving human participants were reviewed and approved by the Ethics Committee of Sun Yat-Sen Memorial Hospital. The patients/participants provided their written informed consent to participate in this study.TC and JC designed and supervised the study. XicW and XinW performed the specific procedures and wrote the manuscript. WL analyzed the data and made the pictures and graphs. QZ revised the final manuscript. All the authors have read and approved the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest."}
+{"text": "Dysdera silvatica, a nocturnal ground-dwelling spider from a genus that has undergone a remarkable adaptive radiation in the Canary Islands.We present the draft genome sequence of de novo assembly (1.36 Gb), which represents 80% of the genome size estimated by flow cytometry (1.7 Gb), is constituted by a high fraction of interspersed repetitive elements (53.8%). The assembly completeness, using BUSCO and core eukaryotic genes, ranges from 90% to 96%. Functional annotations based on both ab initio and evidence-based information (including D. silvatica RNA sequencing) yielded a total of 48,619 protein-coding sequences, of which 36,398 (74.9%) have the molecular hallmark of known protein domains, or sequence similarity with Swiss-Prot sequences. The D. silvatica assembly is the first representative of the superfamily Dysderoidea, and just the second available genome of Synspermiata, one of the major evolutionary lineages of the \u201ctrue spiders\u201d (Araneomorphae).The draft assembly was obtained using short (Illumina) and long (PaciBio and Nanopore) sequencing reads. Our Dysderoids, which are known for their numerous instances of adaptation to underground environments, include some of the few examples of trophic specialization within spiders and are excellent models for the study of cryptic female choice. This resource will be therefore useful as a starting point to study fundamental evolutionary and functional questions, including the molecular bases of the adaptation to extreme environments and ecological shifts, as well of the origin and evolution of relevant spider traits, such as the venom and silk. Dysdera Latreille, 1804, includes half of the family diversity (282 species). This genus is remarkable in several aspects. First, it represents one of the few cases of stenophagy, i.e., prey specialization, across spiders   ) or long_005476] ) to the D. silvatica, using either a de novo with RepeatModeler v1.0.11  [RRID:SCR_012954) [D. silvatica\u2013specific repetitive elements generated with RepeatModeler v1.0.11 [D. silvatica account for \u223c7% of the genome and encode 5 unknown, 3 SINEs, and 2 LINEs, with an average length of \u223c193, \u223c161, and \u223c1,040\u00a0bp, respectively  , or a da_012954) . We used v1.0.11 , (ii) th v1.0.11  . We estimated the coverage after mapping the short reads (from the 100PE library) to those contigs. We defined as high-coverage regions (HCRs) those with a coverage \u22652.5\u00d7 or 5\u00d7 the genome-wide average (\u223c30\u00d7), in a region of \u2265150, \u2265500, \u22651,000, or \u22655,000\u00a0bp . We founWe found a strong relationship between the length of the HCR and the type of the included repetitive elements Fig.\u00a0. For insD. silvatica genome draft is very likely to be caused by abundant interspersed repeats preventing genome continuity. Despite the low N50 we estimated that the draft presented here is mostly complete in terms of functional regions (see below).Given that the HCR analysis covers an important fraction of the assembled bases (\u223c82%), the present results can likely be extrapolated to the whole genome. Therefore, the relatively low N50 of the D. silvatica transcriptome with the RNAseq data from Vizueta et al. [RRID:SCR_015530) [RRID:SCR_013048) [RRID:SCR_005309) [D. silvatica genes  [RRID:SCR_002127) [Dysdera species and the 1:1 orthologs among spiders available at OrthoDB v10  [We used the newly generated genome sequence to obtain a reference-guided assembly of the a et al. . We used_015530)  to map t_013048)    and SNAP_011980)  8,792)._011980)  and regt_011980) , and (ii._011980)_011980) , 59. AftRRID:SCR_005829) [RRID:SCR_001010) [\u22125; >75% alignment length) against the Swiss-Prot database to annotate D. silvatica genes. We found that 74.9%  of the predicted protein-coding genes have hits with records of either InterPro   or Swiss-Prot   ,61, whic_001010)   Table\u00a0.D. silvatica genome assembly . We searched for homologs of the functionally annotated peptides  (i) among CEG genes of Drosophila melanogaster [Parasteatoda tepidariorum, a spider with a well-annotated genome [Dysdera species; and (iv) among the 2,198 single-copy genes identified in all spiders and available in OrthoDB v10 [D. silvatica a high fraction of putative homologs  [D. silvatica.We determined the completeness of the ly Table\u00a0 using BLnogaster ; (ii) amd genome ; (iii) ahoDB v10 . We foun_015008) , applyinD. silvatica homologs to a broader taxonomic range . We found that a great majority of D. silvatica genes are shared among arthropods (57.9%), 11,995 of them (32.95%) also being present in Ecdysozoa  identified 1,798 genes, with 1:1 orthologous relationships  from 126,758 reads identified in the 100PE library by the software NOVOPlasty [de novo assembly yielded a unique contig of 14,440\u00a0bp (coverage of 878\u00d7) (RRID:SCR_011779) [We assembled the mitochondrial genome of VOPlasty . Our de of 878\u00d7) . CGVIEW _011779)  was used_011779) , the comD. silvatica genome precluded obtaining a high-continuity draft. The characteristic holocentric chromosomes of Dysderidae [We have reported the assembly and annotation of the nuclear and mitochondrial genomes of the first representative of the spider superfamily Dysderoidea and the second genome of a Synspermiata, one of the main evolutionary lineages within the \u201ctrue spiders\u201d (Araneomorphae) and still sparsely sampled at the genomic level . Despitesderidae  may alsosderidae .Nevertheless, the completeness and the extensive annotations achieved for this genome, as well as the new reference-guided transcriptome, make this draft an excellent source tool for further functional and evolutionary analyses in this and other related species, including the origin and evolution of relevant spider traits, such as venom and silk. Moreover, the availability of new genomic information in a lineage with remarkable evolutionary features such as recurrent colonizations of the underground environment or complex reproductive anatomies indicative of cryptic female choice, to cite 2 examples, will further provide valuable knowledge about relevant scientific questions, such as the molecular basis of adaptation to extreme habitats or the genetic drivers of sexual selection, along with more general aspects related to gene content across main arthropod groups, the consequences of whole-genome duplications, or phylogenetic relationships with the Araneae. Additionally, because this genus experienced a spectacular adaptive radiation in the Canary Islands, the present genome draft could be useful to further studies investigating the genomic basis of island radiations.QLNU00000000 and project ID PRJNA475203. The version described in this article is version QLNU01000000. This project repository includes raw data, sequencing libraries information, and assemblies of the mitochondrial and nuclear genomes. Other relevant datasets such as annotation, reference-guide assembled transcripts, repeat, and HCR data, as well as other data relevant for the reproducibility of results, are available in the GigaDB dataset [The whole-genome shotgun project has been deposited at DDBJ/ENA/GenBank under accession number  dataset .File S1. Supplemental Material SummarySanchezHerrero_Dsilvatica_SupMaterial_Summary.pdfThe scripts employed and developed in this project are available under the github repository:Dysdera silvaticaProject name: Genome assembly of https://github.com/molevol-ub/Dysdera_silvatica_genomeProject home page: Operating system(s): Platform independentProgramming language: Bash, Perl, Python, RLicense: MITDysdera silvatica; Gb: gigabase pairs; GC: guanine cytosine; GO: Gene Ontology; HCR: high-coverage regions; Isca: Ixodes scapularis; kb: kilobase pairs; LINE: long interspersed nuclear element; LTR: long terminal repeats; MaSuRCA: Maryland Super-Read Celera Assembler; Mb: megabase pairs; MP: mate pair; Mya: million years ago; NCBI: National Center for Biotechnology Information; PacBio: Pacific Biosciences; PE: paired-end; PRINSEQ: PReprocessing and INformation of SEQuence data; Ptep: Parasteatoda tepidariorum; RNAseq: RNA sequencing; SINE: short interspersed nuclear element; Smim: Stegodyphus mimosarum; SMRT: Single-Molecule Real Time; tRNA: transfer RNA; Turt: Tetranychus urticae.AED: annotation edit distance; AGOUTI: Annotated Genome Optimization Using Transcriptome Information; BLAST: Basic Local Alignment Tool; bp: base pair; BUSCO: Benchmarking Universal Single Copy Orthologs; CEG: core eukaryotic gene; Cz: Cretaceous period; Dsil: The authors declare that they have no competing interests.This study was supported by the Ministerio de Econom\u00eda y Competitividad of Spain , and by the Comissi\u00f3 Interdepartamental de Recerca I Innovaci\u00f3 Tecnol\u00f2gica of Catalonia, Spain (2014SGR-1055 and 2014SGR1604). J.F.S.-H. was supported by a Formaci\u00f3n del Profesor Universitario (FPU) grant ; C.F.-L. by an IRBio PhD grant; S.H-A by Becas Postdoctorales en el Extranjero CONACyT; A.S.-G. by a Beatriu de Pin\u00f3s grant ; and J.R. and M.A.A. were partially supported by ICREA Academia .J.R., A.S.-G., and M.A.A designed the study. C.F.-L., J.F.S.-H., P.E., and S.H-A. processed the samples and extracted DNA. J.F.S.-H. performed the bioinformatics analysis and drafted the manuscript. J.F.S.-H., A.S.-G., and J.R. interpreted the data. All authors revised and approved the final manuscript.giz099_GIGA-D-19-00156_Original_SubmissionClick here for additional data file.giz099_GIGA-D-19-00156_Revision_1Click here for additional data file.giz099_GIGA-D-19-00156_Revision_2Click here for additional data file.giz099_GIGA-D-19-00156_Revision_3Click here for additional data file.giz099_GIGA-D-19-00156_Revision_4Click here for additional data file.giz099_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giz099_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giz099_Response_to_Reviewer_Comments_Revision_2Click here for additional data file.giz099_Response_to_Reviewer_Comments_Revision_3Click here for additional data file.giz099_Reviewer_1_Report_Original_SubmissionTatsuhiko Kadowaki -- 5/29/2019 ReviewedClick here for additional data file.giz099_Reviewer_2_Report_Original_SubmissionNadia Ayoub -- 6/11/2019 ReviewedClick here for additional data file.giz099_Supplemental_FilesClick here for additional data file."}
+{"text": "Rodentibacter (R.) pneumotropicus colonizes the respiratory and urogenital tracts of laboratory mice with a reported moderate serological prevalence from 4 to 13%. Thus, regular tests to identify this pathogen in mice are recommended for animal facilities. However, a recent study indicated that current serological assays are partly insensitive, as C57BL/6 and BALB/c mice infected with R. pneumotropicus were incorrectly screened as seronegative.R. pneumotropicus and the closely related species R. heylii. Furthermore, the main immunogen, designated as \u2018characteristic antigen for Rodentibacter of laboratory origin 1\u2019 (CARLO-1), was identified by two-dimensional gel electrophoresis followed by immunoblot and tandem mass spectrometry in a preparation of outer membrane proteins. An indirect ELISA relying on the recombinantly expressed protein provided high sensitivity, specificity, and selectivity. The corresponding carlo1 gene was highly conserved (>\u200997%) among 21 isolates of R. pneumotropicus and R. heylii.Here, we report a systematic analysis of protein and lipopolysaccharides antigens by immunoblot and ELISA that allowed establishing a sensitive test system able to differentiate between Rodentibacter infections in mice. Indirect differentiation of R. pneumotropicus and R. heylii infections may be possible using an ELISA based on a whole-cell antigen preparation. All four established ELISA systems using a whole-cell preparation, lipopolysaccharides, outer-membrane proteins and protein CARLO-1 as antigen, respectively, outperformed a commercial ELISA in terms of sensitivity.The newly identified protein CARLO-1 is well suited for the sensitive and specific serological detection of The online version of this article (10.1186/s12866-019-1417-7) contains supplementary material, which is available to authorized users. Rodentibacter (R.) pneumotropicus [Pasteurella pneumotropica biotype Jawetz] [Pasteurellaceae family that frequently colonizes the respiratory and urogenital tracts of laboratory mice and rats. The infection is mostly described as asymptomatic in immunocompetent mice [R. pneumotropicus strains may have been underestimated [R. pneumotropicus infections in mice colonies [ Jawetz]  is a Graent mice , whereasent mice . Howeverstimated . Althougstimated  and transtimated , moderatstimated . Hence, colonies .Pasteurella (P.) pneumotropica biotypes Jawetz and Heyl were recently reclassified into separate species of the genus Rodentibacter, i.e., R. pneumotropicus and R. heylii, respectively  \u00d7\u2009100; with TP: true positives, TN: true negatives, FP: false positives, FN: false negatives, and F: sera positive for other FELASA-listed pathogens. Repeatability of the indirect ELISA was tested by 42 replicates of positive  and negative controls each . Intermediate precision was tested by five replicates of PC and NC each on five different plates. Calculations followed ISO 5725-2  \u00d7\u2009100; DSp\u2009=\u2009[TN/(TN\u2009+\u2009FP)] \u00d7\u2009100; Selectivity\u2009=\u2009: No template was added to the PCR reaction. 100\u2009bp molecular marker is indicated left. (TIF 165 kb)HP-screening in murine isolates of Additional file 12:XmaI and XhoI. (PDF 116 kb)Sequence of pET21b_JF (5406\u2009bp). Nucleotide sequence of vector pET21b_JF encoding Strep-tag II and restrictions sites"}
+{"text": "Citrus limon (SOD_Cl), namely GliSOD_P51 and GliSOD_P61 to increase permeation of SOD_Cl through intestine. In this work, the permeation of fluorescein isothiocyanate (FITC)-Dextran 10 kDa, FD10 and 40 kDa, FD40 as paracellular transport markers across excised rat intestinal wall was investigated with the presence of GliSOD_P51 and GliSOD_P61. A permeability study was performed using non-everted rat intestine by incubating FD10 or FD40 with SOD_Cl, and GliSOD_P61. The presence of SOD_Cl, GliSOD_P51 or GliSOD_P61 inside intestine  and outside intestine  was analyzed by protein electrophoresis. The concentration of FD that penetrated to the basolateral solution was analyzed by spectrofluorometry. Sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) analysis revealed the presence of GliSOD_P51 and GliSOD_P61 but not SOD_Cl in basolateral compartment. The percentage of FD10 but not FD40 and SOD_Cl that penetrated to the basolateral solution significantly increased with the presence of gliadin in GliSOD_P51 and GliSOD_P61. GliSOD_P51 and GliSOD_P61 are able to penetrate the rat intestinal epithelial membrane and the gliadin peptides facilitate FD10 to penetrate the epithelial.Superoxide dismutase (SOD) is an antioxidant protein. When administered orally, it has low bioavailability due to its low permeation. In a previous study we fused gliadin peptide P51 (LGQQQPFPPQQPYPQPQPF) and gliadin peptide P61 (QQPYPQPQPF) with SOD  Oxidative stress due to the higher generation of reactive oxygen species (ROS) is a prevalent condition in various diseases, including cancer, asthma, diabetes, arthritis, atherosclerosis, aging, infertility, neurological disorders, ischemia-reperfusion injury, transplant rejection, autoimmune diseases, rheumatoid arthritis, and septic shock-induced tissue injury ,2. SuperProtein delivery via oral route has been a persistent challenge. Protein should have the ability to be efficiently absorbed from the gastrointestinal tract and to cross biological membranes. However, the low lipophilicity and large molecular mass of proteins limit their absorption ,7,8. OwiCucumis melo or Cu\u2013Zn SOD from Citrus limon was fused with gliadin peptide (QQPYPQPQPF), both proteins interestingly showed high cell viability during cytotoxicity assay in vitro even up to 24 h, suggesting that this fusion protein did not elicit a cytotoxic effect on intestinal epithelial cells [Some evidence suggests that gliadin peptides can cross the intestinal barrier. Despite their history as the factor responsible for the development of intestinal damage in Coeliac Disease (CD) patients, some studies showed that gliadin peptides were not toxic. Gliadin-derived peptide (LGQQQPFPPQQPYPQPQPF) did not cause toxicity in healthy jejunal specimens, showing that the toxicity was specific to CD . When real cells ,11. Anotal cells . These fCitrus limon (SOD_Cl) as a protein model. GliSOD_P61 showed permeation capacity crossed Caco-2 cells monolayer [In order to assess that possibility, we recently developed two gliadin-derived peptides as permeation enhancer, namely gliadin peptide P51 (LGQQQPFPPQQPYPQPQPF) and gliadin peptide P61 (QQPYPQPQPF). In a previous study, we constructed a fusion protein GliSOD_P51 and GliSOD_P61 comprising gliadin peptide P51 or P61 and SOD onolayer , while GFor screening of a permeation enhancer, a high-throughput method for evaluating intestinal permeability is desired . An in vEscherichia coli BL21(DE3)  is maintained at the Laboratory of Pharmaceutical Biotechnology, School of Pharmacy, Institute of Technology Bandung and used for intracellular production of SOD_Cl, GliSOD_P51 and GliSOD_P61, respectively. The plasmids carrying DNA encoding SOD_Cl, GliSOD_P51 and GliSOD_P61 were each constructed in our previous study [Triticum monococcum) .us study . To creaMale Wistar rats (240\u2013270 g), obtained from the School of Pharmacy, Institut Teknologi Bandung are maintained in a controlled environment of 25 \u00b0C with a 12\u201312 h light/dark cycle. The rats were fasted overnight before experimentation and had access to water ad libitum. All protocols and procedures experiments involving animal research plans were approved by the Animal Research Ethics Committee, School of Pharmacy, Institut Teknologi Bandung (Certificate No. 03/KEPHP-ITB/11-2016).E. coli strain BL21(DE3) containing each pET_16b_SOD_Cl, pET_16b_GliSOD_P51 and pJExpress416_GliSOD_P61 plasmids was inoculated into 5 mL Terrific Broth (TB) supplemented with appropriate antibiotics, respectively. An overnight culture (5%) of recombinant E. coli was used to inoculate 200 mL medium and was cultured to final OD600 of 0.6\u20130.8 at 37 \u00b0C with vigorous aeration (200 rpm). For protein overproduction, E. coli pJExpress416_GliSOD_P61 was induced by Isopropyl-\u03b2-d-thiogalactoside (IPTG) final concentration of 0.5 mM for 4 h. While for SOD_Cl and GliSOD_P51 production, the respective recombinant E. coli was cultured overnight (24 h) at 22 \u00b0C with vigorous aeration (200 rpm) without the addition of IPTG. Overproduction and purification of SOD_Cl, GliSOD_P51 and GliSOD_P61 were performed according to a previously described procedure . A singlg, 4 \u00b0C, 20 min), washed and resuspended 1:4 in Lysis-Equilibration-Wash (LEW) buffer  containing 1 mM phenylmethylsulfonyl fluoride. The cell pellet was lysed by sonication using Ultrasonic Homogenizers CY-500 . Afterward, the cell debris and other insoluble fraction were separated from the crude extract by cold centrifugation for 20 min at 4500\u00d7 g. The SOD proteins were individually purified using cOmplete\u2122 His-tag Purification Resin  from crude extract and eluted by 250 mM imidazole. All fractions containing purified protein were each concentrated using a 10 kDa Nanosep devices by cold centrifugation. The concentrated purified proteins were analyzed by a Coomassie brilliant blue stained 10% sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE). The cell pellets were harvested by cold centrifugation (4500\u00d7 2-anesthesia. The intestine was excised (\u00b13 cm) and rinsed with Ringer Lactate Solution at room temperature. The jejunum was knotted at one end, filled with sample solutions and the other end was knotted. This intestine bag  was placed in an acceptor  solution LEW buffer for 2 h at 37 \u00b0C, 50 rpm. After 2 h, the remaining samples in the apical and basolateral compartments were independently collected and their protein contents were each evaluated by an SDS-PAGE analysis with coomasie blue staining. The FD10 or FD40 concentration was quantified from the solutions from basolateral compartment using Spectrofluorometer Shimadzu RF-5301 PC . Samples used in these experiments were SOD_Cl, GliSOD_P51 or GliSOD_P61 (40 or 200 \u00b5g) with or without FD10 or FD40 (100 \u00b5g). The ability of recombinant proteins crossing intestine walls was determined using the non-everted gut sac method followed by the published methods with slight modification . The nonThe purified proteins were each concentrated and dialyzed against LEW Buffer to remove the imidazole. The overproduction system and one step purification using Nickel affinity column was sufficient to obtain a sufficient amount of pure protein .In this study, we further confirmed the permeation capacity of GliSOD_P61, investigated the role of gliadin peptide P51 in GliSOD_P51 as a permeation enhancer and assessed its potential for the oral delivery of SOD_Cl. In a permeability assay, we first used 40 \u00b5g proteins followed the amount of proteins used in our previous publication . HoweverTo investigate whether gliadins in GliSOD_P51 and GliSOD_P61 were responsible for protein transport via the paracelluar route, the cells were co-incubated with both SODs and FITC-Dextran. FITC-Dextran is the marker used to study the paracelluar absorption along the small intestine . To evalp > 0.05). However, the same amount of GliSOD_P51 and GliSOD_P61 did not affect FD40 absorption. The percentage of permeation of both FITC-Dextran after incubation with SOD_Cl, GliSOD_P51 and GliSOD_P61 is depicted in When the amount of protein was increased (200 \u00b5g), GliSOD_P51 and GliSOD_P61 significantly affected the permeability of FD10 compared to the SOD_Cl  as permeation enhancer was also size-selective. Sodium caprate increased the permeation of FITC-Dexran 4 and 20 kDa through Caco-2 cells and C-CPE enhanced that of FITC-Dextran 4 and 10 kDa permeation but not FITC-Dextran 20 and 40 kDa through intestine [In the present study, we demonstrated that, in the presence of gliadin, permeability of FD 10 but not FD40 across intestine was increased. The increase of FD10 transport when co-incubated with GliSOD_P51 and GliSOD_P61 indicated that both gliadin peptides could regulate TJ opening or paracellular pathway. This finding is in agreement with the study by Lammers and coworkers that reported the permeability enhancement by gliadin intact protein through opening of tight junction . Interesntestine . A critical issue in the clinical application of permeation enhancer is safety. This includes the safety of gliadin peptide in itself and that of the modulation of TJ, i.e., the entry of unwanted substances by the opening of TJs . The gliTaken together, the results of this study demonstrate the activity of gliadin peptides, P51 and P61 as permeation enhancer and their potency in oral protein delivery. However, further research in a preclinical setting is necessary to provide evidence for their safety and efficacy and to disclose their precise molecular mechanism in increasing paracellular transport. The use of gliadin-derived peptide opens new perspectives as a biomaterial for permeation enhancer in a drug delivery system. Here, we also report that the use of the non-everted gut sac method in permeability assay for screening of permeation enhancer showed similar results to the Caco-2 cell method."}
+{"text": "P < 0.001) was obtained for detecting TB, with hsa_circ_0001953 and hsa_circ_0009024 used in combination. Additionally, plasma levels of hsa_circ_0001953 and hsa_circ_0009024 were reduced significantly in patients after treatment (P < 0.001). The present findings indicate that the circRNAs hsa_circ_0001953 and hsa_circ_0009024 may represent novel plasma biomarkers for active TB diagnosis.Recent studies have demonstrated that circular RNAs (circRNAs) could serve as potential molecular markers for disease diagnosis; however, little is known about their diagnostic value in active tuberculosis (TB). This study first performed a microarray screening of circRNA changes in plasma samples from 3 patients with active pulmonary TB and 3 healthy controls. Then, candidate circRNAs were selected for validation on a quantitative real-time PCR system. Of the 61 differentially expressed circRNAs recorded, 43 and 18 were upregulated and downregulated in the TB group, respectively. Validation assays demonstrated that plasma levels of 6 circRNAs, including hsa_circ_0009024, hsa_circ_0001953, hsa_circ_0008297, hsa_circ_0003528, hsa_circ_0003524 and hsa_circ_0015879 were remarkably increased in TB patients. Plasma levels of hsa_circ_0001953 and hsa_circ_0009024 were correlated with TB severity. Next, hsa_circ_0001953 and hsa_circ_0009024 were assessed in an independent cohort consisting of 120 TB patients and 100 control individuals. An area under the receiver operating characteristic (ROC) curve of 0.915 (95% confidence interval 0.880-0.951;  Mycobacterium tuberculosis culture  is the ninth leading cause of death worldwide and the leading cause from a single infectious agent, with 1.3 million deaths and 10.4 million new cases worldwide in 2016 WHO, . AccuratCircular RNAs (circRNAs) are a novel class of RNAs that participate in several physiological and pathological processes  with active pulmonary TB were consecutively enrolled from the First Affiliated Hospital of Nanchang University and Jiangxi Chest Hospital, China, between May 2015 and January 2017. All TB cases were clinically diagnosed and confirmed as active pulmonary TB by positive AFB smear staining or sputum culture. The patients were then grouped by case severity, including minimal, moderate, and advanced disease stages based on chest radiology  seeking annual check-up, without clinical diagnosis of any infectious disease, diabetes and malignancy, and no close contact with tuberculosis patients, were randomly enrolled from outpatient clinics of the First Affiliated Hospital of Nanchang University . As diseased controls, 120 patients with lung ailments , confirmed clinically after eliminating pulmonary TB, were enrolled from the First Affiliated Hospital of Nanchang University and Jiangxi Chest Hospital from July 2015 to December 2016. It should be noted that there are 52 TB patients, 21 healthy controls, 9 lung cancer patients, 7 pneumonia patients, and 10 COPD patients who enrolled in this study and overlap with the subjects of one of our previous study . RNA integrity and quantity were assessed on a NanoDrop\u21221000 spectrophotometer .t-test, and P-value correction for False Discovery Rate (FDR) was carried out using the Benjamini-Hochberg (BH) procedure. An absolute fold change value \u22651.5 and FDR P < 0.05 was considered statistically significant. Quantile normalization of raw data and subsequent data processing were performed with the R software limma package (version 2.7.10).Six RNA specimens were assessed by KANGCHEN  using Arraystar circRNA Microarray  analysis as directed by the manufacturer. In this study, human circRNA microarray v1.0 (Arraystar Inc.) containing 5396 circular RNA probes was used. In brief, total RNA was treated with RNase R for linear RNA removal and circRNA enrichment. Upon amplification, the obtained circRNAs were submitted to transcription for fluorescent cRNA production by the random priming method. Then, the fluorescent cRNAs were hybridized onto the Arraystar Human circRNA Array. Subsequently, the arrays were scanned on Agilent G2505C Scanner. The Agilent Feature Extraction software was used for image import and raw data were extracted. Differentially expressed circRNAs between the two groups were evaluated by Ct\u2212\u0394\u0394 method.The cDNAs were obtained by reverse transcription from total RNA with a PrimeScript\u2122 RT kit . SYBR\u00aePremix Ex Taq\u2122 II (TaKaRa) was used for fluorescence quantitative real-time PCR (RT-qPCR), with GAPDH as an internal control  curve was generated to evaluate the diagnostic value of circRNAs. The Spearman method was used for correlation analysis. The validated plasma biomarkers were entered into binary logistic regression models, and model selection was performed to determine the final combinations of biomarkers. P < 0.05 was considered statistically significant.Quantile normalization and subsequent data processing were performed in R. SPSS 17.0 (SPSS Inc.) was utilized for other statistics. Data are mean \u00b1 standard deviation (SD); normality was assessed by the Kolmogorov-Smirnov method. Student's We first analyzed plasma circRNAs of 3 patients with active pulmonary TB and 3 healthy control individuals by circRNA microarrays. In the validation stage, 170 pulmonary TB patients, 40 pneumonia patients, 40 COPD patients, 40 lung cancer patients, and 150 healthy controls were recruited. The clinical features of all participants are presented in Table P < 0.05) , and was the largest among the 6 circRNAs. The other AUC values were 0.808  for hsa_circ_0009024, 0.768  for hsa_circ_0003528, 0.715  for hsa_circ_0003524, 0.692  for hsa_circ_0008297 and 0.676  for hsa_circ_0015879. Sensitivities and specificities of all circRNAs were obtained based on respective cut-off values . Then, associations of circRNA levels with the radiological score (severity index) were determined by the Spearman's rank correlation test. As depicted in Figure P < 0.001), indicating sensitivity and specificity of 69.17 and 89.00%, respectively. For hsa_circ_0009024, an AUC of 0.777  was obtained; sensitivity and specificity were 60.00 and 86.00%, respectively. When hsa_circ_0001953 and hsa_circ_0009024 were combined, the AUC increased to 0.915 , with sensitivity and specificity of 72.50 and 96.00%, respectively , respectively; however, the COPD, pneumonia, and lung cancer groups showed similar values (P > 0.05).As shown in Figure P < 0.001), with sensitivity and specificity of 80.00 and 87.73%, respectively ; sensitivity and specificity were 79.17 and 86.67%, respectively . The AUC for the latter risk score was 0.909 .In this study, hsa_circ_0001953 and hsa_circ_0009024 amounts were assessed in 25 TB cases pre- and post-treatment. In comparison with pre-treatment amounts, hsa_circ_0001953 and hsa_circ_0009024 showed lower values upon anti-TB therapy Figure . Indeed,rs = 0.5449, P = 0.0022; Figure Moreover, in our previous studies, we found that hsa_circ_0009024 was also significantly elevated in PBMCs from TB patients  were remarkably increased in the TB patients analyzed. ROC curve analysis suggested that hsa_circ_0001953 had a significant value for TB diagnosis, followed by hsa_circ_0009024, hsa_circ_0003528, hsa_circ_0003524, hsa_circ_0008297, and hsa_circ_0015879. Furthermore, Spearman's rank correlation analysis revealed that hsa_circ_0001953 and hsa_circ_0009024 amounts were moderately correlated with the radiological score, implying that hsa_circ_0001953 and hsa_circ_0009024 might be involved in TB pathology.P > 0.05). ROC analysis using both targets in combination yielded an increased AUC of 0.915, with 72.50% sensitivity and 96.00% specificity in discriminating TB patients from normal controls, indicating additive effects in diagnostic potential of the two circRNAs. These diagnostic abilities were quite comparable to those reported for blood biomarkers of TB, especially in terms of specificity , we did not assess control subjects for latent TB in this study. In addition, the subjects were limited to the Chinese Han population, and only 25 TB patients completed follow-up. Therefore, the current findings require confirmation in larger and more diverse samples.P-value < 0.05, which was more harsh than the criterion in Qian's study. A P-value < 0.05 were deemed differentially expressed in Qian's study  are different from the candidate circRNAs obtained by Qian et al. . By compOverall, differentially expressed circRNAs were detected in plasma specimens from active TB and normal control patients. Our data indicated that hsa_circ_0001953 and hsa_circ_0009024 levels in plasma could constitute new diagnostic biomarkers for TB diagnosis. Comprehensive studies are warranted to unveil the mechanisms by which circRNAs affect TB infection. And, considering the inconsistency of the findings between different laboratories, a more rigorous, large sample and multicenter study is needed to further confirm the reliability and reproducibility of these circRNAs in TB diagnosis.ZH, QL, and JL designed the study. RS, CQ, and YP collected clinical specimens. ZH, RS, and CQ performed laboratory assays. ZH, QL, and JL performed data analysis. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest."}
+{"text": "The Sequence Distance Graph (SDG) framework works with genome assembly graphs and\u00a0raw data from paired, linked and long reads. It includes a simple deBruijn graph module,\u00a0and can import graphs using the graphical fragment assembly (GFA) format. It also maps\u00a0raw reads onto graphs, and provides a Python application programming interface (API) to navigate the graph, access the mapped and raw data and perform interactive or scripted analyses. Its complete workspace can be dumped to and loaded from disk, decoupling mapping from analysis and supporting multi-stage pipelines. We present the design and implementation of the framework, and example analyses scaffolding a short read graph with long reads, and navigating paths in a heterozygous graph for a simulated parent-offspring trio dataset.https://github.com/bioinfologics/sdg SDG\u00a0 is\u00a0 freely\u00a0 available\u00a0 under\u00a0 the\u00a0 MIT\u00a0 license\u00a0 at This flattening of graph representations within pipelines with multiple steps, that use different types of sequencing in an iterative fashion, produces ever-longer linear genome sequences through an information loss process. As a result, genome assembly projects are prone to error propagation and difficult to reproduce and control. These problems can be addressed developing graph-based frameworks to integrate the analysis of hybrid datasets.Sequence graphs are the core representation of genome assemblersSequenceDistanceGraph representation that defines sequences in nodes and their adjacency in links, and an associatedWorkspace containing raw data and mappings. This provides an integrated working environment to use multiple sources of information to navigate and analyse genome graphs.Datastores allow random access to short, linked, and long read sequences on disk. A mapper on each datastore contains methods to map the reads to the graph and access the mapping data.KmerCounters provide functions to computek-mer coverage over the graph from sequencing data, enabling coverage analyses. AdditionalDistanceGraphs, typically representing longer-range information and different linkage levels, define alternative topologies over theSequenceDistanceGraph nodes. Finally, aNodeView abstraction provides a proxy to a node, with methods to navigate the graph and access its mapped data. This comprehensive framework can be used to explore genome graphs interactively or to create processing methods for assembly or downstream analysis.The Sequence Distance Graph (SDG) framework implements aHere we describe the SDG implementation and basic tools, providing examples of use cases that highlight its analytic flexibility. First, we show how to create a hybrid assembly by integration of long reads linkage into a short-read graph. Then we analyse a simulated parent-child trio and show how the coverage of the parent datasets can be used to navigate the graph topology. These are only two of the multiple ways integrating data and genome graphs can be used to perform simple but powerful analyses.WorkSpaces, graphs, datastores and mappers. Its main goal is to provide a straightforward interface to project information from raw datasets onto graphs, and enable easy access and analysis of the graph-data combination. It uses OpenMP for parallel processing, and SWIG 4.0 to export a Python API to enable interactive data analysis.The C++ core library implements SDG\u2019s data structures and methods forSequenceDistanceGraph class contains a vector of nodes defining DNA sequences, and a vector of links. Every node has a positive and a negative end, and links are defined between these node ends. Links with positive distances represent gaps between linked sequences and negative distances represent overlaps. This representation, shown inTheDistanceGraph class contains a set of links over the nodes of aSequenceDistanceGraph object. It is used to represent alternative sources of linkage information, such as longer range linkage produced by mapped reads for scaffolding.TheWorkSpace contains a singleSequenceDistanceGraph, multipleDistanceGraphs, datastores and mappers, and its structure in memory represents the status of the SDG framework. It can be dumped and loaded from disk, providing persistence and checkpoints between different steps on SDG-based pipelines. Raw reads andk-mer counts are kept in separate files, pointed from theWorkSpace, to avoid duplication when using multipleWorkSpaces around the same dataset.TheDataStores andMappers provide access and management to raw data and its mapping on the graph.Datastores do not load read data into memory, but rather provide random access to the on-disk data. ThePairedRedMapper andLinkedReadMapper classes use a uniquek-mer index to map reads to single nodes, with single reads mapping to multiple nodes not being mapped12 TheLongReadMapper class generates multiple mappings from each read to nodes, using a short non-uniquek-mer index (k=15 by default)14 Long read mapping filtering is left to later stages of the processing.TheKmerCounters creates an index with all thek-mers at a given k up to k=31 and counts occurrences of thesek-mers on the graph, allowing then to count occurrences in datastores or fastq files. These counts, persisted in theKmerCounter with a name, can be then accessed to performk-mer coverage analyses. Projections of rawk-mer coverage in the reads and the assembly over a particular sequence for a node or path, similar to those produce by the \"sect\" tool of K-mer Analysis Toolkit (KAT)15 are valuable for content analysis. Spectra analysis of these frequencies can provide further insight into genome composition and representation on the assembly.TheLinkageUntangler andLinkageMaker, work with alternative linkage configurations. TheLinkageMaker is used to condense information via one of itsmake_linkage* methods, from evidence in theWorkSpace into links in aDistanceGraph. TheLinkageUntangler class works on aDistanceGraph to simplify, condense and/or linearise its linkage. In the second use case below it can be seen how a combination ofLinkageMaker andLinkageUntangler can be used for scaffolding with long reads.Two processing classes,NodeView class, and its associatedLinkViews, provide a single-entry point for node-centric analyses. ANodeView from either aDistanceGraph orSequenceDistanceGraph is a wrapper containing a pointer to the graph and a node id, and will provide access to its nodes\u2019 previous and next linked nodes, mapped reads, ork-mer coverage. A user with good understanding of theNodeView class should be able to access most information in theWorkSpace through it, making it the default choice for analysing the graph.Finally, theRequirements and installation. SDG can be run on Linux and MacOS, and requires enough RAM to hold the WorkSpace completely in memory, which will depend on the dataset. Space to hold the uncompressed sequences on the datastores on disk will also be required.https://github.com/bioinfologics/sdg/releases. The binaries have been built using Python3 and GCC version 6 from the Ubuntu package manager for the Linux version. The MacOS version dependencies were obtained using Homebrew . SDG can be compiled using CMake, Python3, SWIG version 4 and GCC version 6 onwards. Detailed instructions can be found athttps://bioinfologics.github.io/sdg/sdg/README.html#installation.SDG can be installed via pre-compiled binaries fromTypical workflow. Working with SDG typically involves two different stages: creating aWorkSpace with the data and mappings, and analysing thisWorkSpace. SDG includes command line tools to createDataStores,KmerCounts, andWorkSpaces, and map reads within aWorkSpace.sdg-datastore: creates aDatastore from raw reads and can process paired, 10x or long reads. An output prefix is specified as a parameter and a <prefix>.prseq, <prefix>.lrseq or <prefix>.loseq file is generated.sdg-kmercounter: creates aKmerCounter indexing a graph from aWorkSpace or GFA, or works with an already generated one. A count can be added directly from raw reads or from a datastore. TheKmerCounter is persisted on file with extension \u2019sdgkc\u2019.sdg-workspace: creates aWorkSpace from a base graph or works with an already generated one.Datastores andKmerCounters can be added. TheWorkSpace is persisted on file with extension \u2019sdgws\u2019.sdg-dbg: creates aWorkSpace from aPairedReadDatastore by building adeBruijn graph and using this as the base graph. Counts for thek-mers from the graph and raw reads are added too.sdg-mapper: maps reads within aWorkSpace. An updatedWorkSpace is produced and dumped to the specified prefix.WorkSpaces can also be instantiated with an empty graph, and the graph populated through theadd_node andadd_link methods. The following example on a python session shows how the simple graph fromNodeView instance and sequence from its paths extracted.>>> import pysdg as SDGversion 0.1master b4d3f02>>> ws=SDG.WorkSpace>>> ws.sdg.add_node(\"CTACGGA\")1>>> ws.sdg.add_node(\"GACCTTA\")2>>> ws.sdg.add_node(\"AATACGGTCC\")3>>> ws.sdg.add_node(\"TTACGAA\")4>>> ws.sdg.add_node(\"CTGATATGA\")5>>> ws.sdg.add_link>>> ws.sdg.add_link>>> ws.sdg.add_link>>> ws.sdg.add_link>>> ws.sdg.add_link>>> nv=ws.sdg.get_nodeview(1)>>> nv<NodeView: Node 1 in SDG>>>> nv.next<Vector: 2 LinkViews>>>> print(nv.next)>>> nv = nv.next[0].node>>> nv<NodeView: Node -3 in SDG>>>> print(nv.prev)[<LinkView: -3bp to Node 1>]>>> nv.sequence'GGACCGTATT'>>> SDG.SequenceDistanceGraphPath.sequence'CTACGGACCGTATTACGAANNNNNNNNNNCTGATATGA'WorkSpace, with the methods accessing both in-memory and on-disk data, and modifying the status of theWorkSpace.Typically, as shown inhttp://bioinfologics. github.io/sdg_examples.To illustrate the use of SDG, we have reproduced a short version of two examples fromhttps://zenodo.org/record/3363871#.XUwyVy2ZN2416, and the PacBio reads are from NCBI accession PRJNA19443717 For simplicity, we have also made the datasets available onhttps://opendata.earlham.ac.uk/opendata/data/sdg_datasets/ as ready-to-use \u2019fastq.gz\u2019 files.All paired end datasets are available onE. coli dataset combining PacBio reads fromsdg-dbg. Graphs are dumped to GFA files at different stages, and visualised usingBandage v0.8.118This example is based on anWorkSpace containing a DBG assembly:First, we use the command line tools to create datastores for both long and short reads and an initialsdg-datastore make -t paired -o ecoli_pe ../ecoli_pe_r1.fastq.gz -2 ../ecoli_pe_r2.fastq.gzsdg-datastore make -t long -o ecoli_pb -L ../ecoli_pb_all.fastq.gzsdg-dbg -p ecoli_pe.prseq -o ecoli_assmFrom this point on, we use the python SDG library. First, we load the workspace, add a long read datastore and map its reads using a k=11 index.importpysdgasSDG# Load sdg-dbg's workspace from disk, add the pacbio datastorews=SDG.WorkSpace('ecoli_assm.sdgws')lords=ws.add_long_reads_datastore('ecoli_pb.loseq')# Map long readslords.mapper.k= 11lords.mapper.map_readsws.sdg.write_to_gfa1The graph, as shown inmake_longreads_multilinkage method, with alignment filtering parameters of 1000bp and 10% id.We can use the LinkageMaker to create linkage using the long reads datastore. We do this by selecting the nodes between which to analyse possible linkage, in this case all nodes of 1100bp or more, and then calling thelm=SDG.LinkageMaker(ws.sdg)lm.select_by_size(1100)mldg=lm.make_longreads_multilinkagemake_nextselected_linkage method links every selected node to its closest selected neighbours on each direction, aggregating the distances via a simple median calculation:This multi-linkage can be collapsed using the LinkageUntangler. Thelu=SDG.LinkageUntangler(mldg)lu.select_by_size(1100)ns_dg=lu.make_nextselected_linkagens_dg.write_to_gfa1('ns_collapsed.gfa')LinkageUntangler, which will then skip them in the solution.The new graph we dumped, as shown infornvinns_dg.get_all_nodeviews:if len(nv.prev)> 1orlen(nv.next)> 1:lu.selected_nodes[nv.node_id]=Falsens_nr_dg=lu.make_nextselected_linkagens_nr_dg.write_to_gfa1The last graph is now a circle, with all the repeats disconnected from any linkage.Pseudoseq.jl v0.1.019 Chromosomes 4 and 5 of the reference genome of the yeast strain S288C were used as templates to create a diploid, genome for each parent with 1% heterozygous sites. Each homologous pair of chromosomes was crossed over and recombined and the child inherited one chromosome from the first parent at random, and one chromosome from the second parent at random. Simulated paired end reads were generated for each genome, using an average fragment length of 700bp and a read length of 250bp, and an expected coverage of 70x with error rate was set to 0.1%.We created a simulation of a trio dataset for this example using the synthetic genome creation and sequencing packagek-mer counts for both parents into the datastore.First we used the command line tools to create a graph from the child reads using sdg-dbg, and addsdg-datastore make -t paired -1 child/child-pe-reads_R1.fastq.gz -2 child/child-pe-reads_R2.fastq.gz -o child_pesdg-dbg sdg-dbg -p child_pe.prseq -o sdg_childsdg-kmercounter add -c main.sdgkc -n p1 -f p1/p1-pe-reads_R1.fastq.gz -f p1/p1-pe-reads_R2.fastq.gz -o mainsdg-kmercounter add -c main.sdgkc -n p2 -f p2/p2-pe-reads_R1.fastq.gz -f p2/p2-pe-reads_R2.fastq.gz -o mainWorkSpace and use theNodeView::parallels method to look for the largest bubble structure in the graph, which should be formed by two parallel nodes with haplotypes coming from each parent.We now open theimport pysdgas SDGws = SDG.WorkSpace('sdg_child.sdgws')#Largest node with one parallel node, and its parallelmaxbubble = 0for nv in ws.sdg.get_all_nodeviews:if nv.size > maxbubble andlen == 1:maxbubble=nv.sizebubble_nvs=k-mer coverage on the parent that didn\u2019t contribute that haplotype. To check this, we create a plotting function to plot the output from theNodeView::kmer_coverage method.Since each side should be a haplotype from a different parent, we should see a loss ofdefplot_kcov(nv):'''Plot kmer coverage across the three read sets. Requires pylab.'''figure;suptitle(\"Coverage for \"+str(nv));subplot;ylim)plot, label=\"child\"); legend(loc=1);subplot;ylim)plot,\"red\", label=\"parent 1\"); legend(loc=1);subplot;ylim)plot,\"blue\", label=\"parent 2\"); legend(loc=1);plot_kcov(bubble_nvs[0])plot_kcov(bubble_nvs[1])The plots, shown indefextend_parent_covered_path:ifws.sdg.get_nodeview(starting_node).kmer_coverage.count(0)!= 0:returnSDG.SequenceDistanceGraphPathp=SDG.SequenceDistanceGraphPathforxin:nv=ws.sdg.get_nodeview(p.nodes[-1])whilenv.next:next_node= 0fornlinnv.next:ifnl.node.kmer_coverage.count(0)== 0:ifnext_nodeornl.node.node_idinp.nodes:next_node= 0breakelse:next_node=nl.node.node_idifnext_node== 0:breakp.nodes.append(next_node)nv=ws.sdg.get_nodeview(next_node)p.reversereturnppath1=extend_parent_covered_pathpath2=extend_parent_covered_pathAfter using this function, path1 contains 49 nodes yielding 8672bp of sequence inherited from parent 1, and path2 contains 139 nodes yielding 26351bp of sequence inherited from parent 2. It is important to note that the difference in node count and sequence length arises because the extension function is haplotype-specific and its results depend in the topology of each haplotype graph.The Sequence Distance Graph framework provides a unified workspace for different sequencing technologies using the genome graph as the basis of integration. It enables analyses across the graph topology, the raw data and its projections to the graph. We have shown how the NodeView class can be used through the Python API to produce interactive analyses that are both powerful and easy to follow. We expect this will be a useful codebase for all levels of users, not only for the construction of graph-based analysis but also for their teaching and dissemination.E. coli reads are deposited on NCBI accession PRJNA194437 from Korenet al.17The PacBio,E. coli K12 Re-sequencing with PacBio RS and 454: Accession number PRJNA194437,https://identifiers.org/ncbi/bioproject:PRJNA194437https://opendata.earlham.ac.uk/opendata/data/sdg_datasets/ and archived in Zenodo Zenodo: SDG Paper Datasets.http://doi.org/10.5281/zenodo.336387116The datasets used in the examples are available from:Creative Commons Zero \"No rights reserved\" data waiver (CC0 1.0 Public domain dedication).Data are available under the terms of thehttps://bioinfologics.github.io/sdgSoftware documentation:http://github.com/bioinfologics/sdgSource code available from:https://zenodo.org/record/3363165#.XUw1yy2ZN2520Archieved source code at time of publication:License: MIT License The authors describe a software package aimed at construction, storage, and manipulation of sequence graphs. The software is publicly available on Github. While the core functionality is developed in C++, the package also provides Python wrapper library and a command-line tool. The authors outline the software design and provide pipelines and code examples for two different types of data.1);Comparison of (features\u00a0of) the developed software and\u00a0existing software such as VG toolkit based on variation graphs , or estimation of its running time/space complexity. Some minor comments:Datastores allow random\u00a0access...\u201d describing its features, but WHAT is Datastores?The use of bold font is not explained. For example,\u00a0Datastores first appears in the sentence \u201cOrthogonal edge routing in Fig. 1 is somewhat confusing, why not make edges curved? \u00a0 \u00a0Overall, the paper is well-written and describes potentially useful software. At the same time, the paper lacks:We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however we have significant reservations, as outlined above. The authors describe a framework for constructing sequence graphs, aligning reads, manipulating graph structures, and extracting them into standard formats . This framework is available as both a set of command line tools and a python library which wraps much of the underlying functionality. Their implementation unifies the representation of gaps and overlaps as a single linkage type within the graph. This is the primary theoretical advance of the work. This work is scientifically sound but its description as written could benefit from some minor additions. The software is freely available on GitHub and binary releases of the command line tools are provided. These are functional on a modern linux laptop and clear examples with data are provided. The paper includes the outputs of these examples as figures. The python libraries rely on SWIG and are not included in the binaries. While not requisite for publication, providing the python libraries through pip, conda, or another package manager would increase the reach of the framework. This would match the authors' conclusion that the sdg package provides \"a useful codebase for all levels of users.\"E. coli\u00a0data  and the description of genotyping a simulated yeast trio  are both realistic. The examples provided are clear and scientifically relevant. The graph mapping and manipulation example using However, the authors should provide run times and machine details for these examples. Both are relatively fast as the datasets are small. There is no need for extensive benchmarking; a footnote for each example would address this adequately. A brief 1-2 sentence discussion of a larger scale example the authors have attempted should also be included. In addition, the phrasing \"simulated parent-offspring trio\" in the abstract should be modified to make it clear that the data is from yeast. As it is written the phrasing implies the framework may work on human/animal-scale data, though no evidence of this has been provided in this version of the paper. Lastly, a brief description of the similarities and differences between the sequence (distance) graph, the variation graph, and the de Bruijn graph from an assembler such as ABySS should be included in the introduction or provided by a reference. This description need not be longer than two to four sentences in length. This should highlight the similar representations of the graphs  and the different amounts of information content within the graph types. This would strengthen the critical need for the software and is partially highlighted by the example in Figure 3. As it stands the paper is deserving of indexing. These additions would further strengthen what is already an excellent tool description, I hope without adding too much additional work for the authors.I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. The authors demonstrate a new toolchain and data model for working with sequence graphs. This method allows the user to dynamically interact with sequence graphs made in the process of assembly. They provide a number of examples of the use of the method as well as code snippets to demonstrate its functionality. The library is written in C++, but wrapped in python with SWIG, which should make it useful to many researchers for whom C++ is difficult to use. I find only one thing strange about the work. In the beginning, the authors indicate that there are not interoperable methods for working with sequence graphs and alignments to them, but they have in effect created another competing standard. Are there particular limitations with existing data models that they hope to address with the Sequence Distance Graph framework? How is their model different than the variation graph model, in which distances are provided by a collection of paths  embedded within the sequence graph?I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard."}
+{"text": "According to our projections, 52% of the intact forests (IFs) and 62% of the wilderness areas (WAs) are partially devoid of large mammals, and hunting may affect mammal populations in 20% of protected areas (PAs) in the tropics, particularly in West and Central Africa and Southeast Asia. The pervasive effects of overhunting on tropical mammal populations may have profound ramifications for ecosystem functioning and the livelihoods of wild-meat-dependent communities, and underscore that forest coverage alone is not necessarily indicative of ecosystem intactness. We call for a systematic consideration of hunting effects in  biodiversity assessments for more representative estimates of human-induced biodiversity loss.Tropical forests are increasingly degraded by industrial logging, urbanization, agriculture, and infrastructure, with only 20% of the remaining area considered intact. However, this figure does not include other, more cryptic but pervasive forms of degradation, such as overhunting. Here, we quantified and mapped the spatial patterns of mammal defaunation in the tropics using a database of 3,281 mammal abundance declines from local hunting studies. We simultaneously accounted for population abundance declines and the probability of local extirpation of a population as a function of several predictors related to human accessibility to remote areas and species\u2019 vulnerability to hunting. We estimated an average abundance decline of 13% across all tropical mammal species, with medium-sized species being reduced by >27% and large mammals by >40%. Mammal populations are predicted to be partially defaunated  in ca. 50% of the pantropical forest area (14 million km This study estimates that roughly half of the tropics contain defaunated populations of medium and large mammal species as a result of hunting pressure; hunting effects are so pervasive that even intact forests and wilderness areas are becoming empty of mammal populations. Tropical forests are increasingly degraded by industrial logging, urbanization, agriculture, and infrastructure , 2, withOverhunting, as opposed to deforestation, is undetectable by remote-sensing techniques , and reshttps://figshare.com/projects/Intact_but_emtpy_forests_Patterns_of_hunting-induced_mammal_defaunation_in_the_tropics/31118). We related the abundance declines to socioeconomic drivers of hunting, including the distance to hunters\u2019 access points, accessibility of urban markets, protection status (whether hunting occurred inside or outside PAs), human population densities, poverty levels, and access to domestic meat. Species body mass and diet were included as proxies of vulnerability to hunting  and those that were categorized as cities in OSM). We also filtered out location points that referred to the name of the country, county, region, or island (OSM data set). Subsequently, we calculated a raster map of the distance to the nearest settlement across the whole pantropical forest zone at a resolution of 30\u201d  were extracted from each study . When thbrik.de) . SettlemHPD is an indicator of wild meat demand and hunting pressure. We obtained raster maps of HPD (1-km resolution) for the period 1990\u20132015 from SEDAC , 50. Perhttp://www.who.int/nutgrowthdb/about/en/), Demographic and Health Surveys (DHS), UNICEF MICS, and national surveys , producing a new global map of stunting  as a proxy for educational attainment.Education has been shown to correlate with the potential to access the labor market and, thus, alternative livelihoods that are less dependent on wild meat. We used literacy rate per country from the World Bank database (2) based on the average weights of cattle, sheep, pigs, and chicken extracted from the literature  and abundance declines (or increases) compared with control areas in the rest of the data set . Hurdle models use a binomial distribution to specify the probability of getting a 0 or a positive value, and then fit a zero-truncated probability density function to the nonzero data [R2 (fixed effects) and the conditional R2 (fixed and random effects)  see . We alsos models . We evalN = 3,923 mammal species). This area was based on the global \u201cforest zone\u201d following Potapov and colleagues (2017) [s) as the reverse of the exp(RR), i.e., DIs = 1 \u2212 exp(RR). Species-specific defaunation maps were then aggregated to create a composite map of hunting-induced defaunation by averaging the DIs values across all species per grid cell (S being the number of species in a grid cell). We present our results in the form of defaunation gradients that range from 0 (not defaunated) to 1 (fully defaunated) [s > 0.7 to identify hotspots of defaunation caused by hunting. Because hunting is known to be a size-differential pressure [We extracted IUCN species ranges for all mammal species with distributions that overlapped the tropical forest area   as foresaunated) , and conpressure , we genepressure , 28. Finpressure . This anWe then estimated the degree to which intact forest landscapes (IFLs) and WAs are defaunated DI > 0.1) by overlapping our defaunation maps with the IFLs map, as defined by Potapov and colleagues 2017) [ by overl017 [ by R2 of the models, \u201cdata.table\u201d [All analyses were conducted in R 3.4.1 . The paca.table\u201d  was useda.table\u201d  was useda.table\u201d  was useda.table\u201d  and \u201crgda.table\u201d  were usea.table\u201d .S1 Text(DOCX)Click here for additional data file.S1 Fighttps://figshare.com/projects/Intact_but_emtpy_forests_Patterns_of_hunting-induced_mammal_defaunation_in_the_tropics/31118.Available at (TIF)Click here for additional data file.S2 Fig2), (D) HPD (ind/km2), (E) travel time to major cities, (F) prevalence of stunting among children under five by the lowest available subnational administrative unit, varying years. Based primarily on the WHO Global Database on Child Growth and Malnutrition (http://www.who.int/nutgrowthdb/about/en/). Available at https://figshare.com/projects/Intact_but_emtpy_forests_Patterns_of_hunting-induced_mammal_defaunation_in_the_tropics/31118. HPD, human population density.(A) Location of 163 studies (in blue) with 3,281 abundance estimates for mammals in areas under hunting pressure. (B) Distance to the nearest rural settlement (km), (C) livestock biomass (kg/km(TIF)Click here for additional data file.S3 Fighttps://figshare.com/projects/Intact_but_emtpy_forests_Patterns_of_hunting-induced_mammal_defaunation_in_the_tropics/31118. HPD, human population density; PA, protected area.(A) Distance to hunters\u2019 access points, (B) HPD, (C) PA status , (D) body mass, and (E) prevalence of stunting. CIs (95%) are shown in gray. The scale of the y-axis has been adjusted to enhance visualization of the fitted lines. Available at (TIF)Click here for additional data file.S4 FigOryzomys spp.; light blue: 1 kg, e.g., Sylvilagus brasiliensis; yellow: 10 kg, e.g., Alouatta spp.; orange: 100 kg, e.g., Panthera onca; red: 4,000 kg, e.g., Loxodonta africana. Available at https://figshare.com/projects/Intact_but_emtpy_forests_Patterns_of_hunting-induced_mammal_defaunation_in_the_tropics/31118. HPD, human population density; RR, response ratio.The dashed gray line indicates that hunting pressure has no effect on species abundance (RR = 0). Positive values indicate an increase in species abundance, whereas negative values indicate a negative effect on species abundance. (A) Distance to hunters\u2019 access points, (B) body mass, (C) interaction between body mass and distance, and (D) HPD. CIs (95%) are shown in gray. In (C), dark blue: 0.1 kg, e.g., (TIF)Click here for additional data file.S5 Fighttps://figshare.com/projects/Intact_but_emtpy_forests_Patterns_of_hunting-induced_mammal_defaunation_in_the_tropics/31118. BM, body mass; Dist, distance to hunters\u2019 access points; HPD, human population density; PA, protected area; RR, response ratio; Stunt, stunting.Standardized coefficient estimates of the variables retained in the best (A) binomial (extinct/no extinct) and (B) Gaussian models (RR). Explained variance by (C) the random effects and the (D) fixed effects of the binomial and Gaussian models. Available at (TIF)Click here for additional data file.S6 Fighttps://figshare.com/projects/Intact_but_emtpy_forests_Patterns_of_hunting-induced_mammal_defaunation_in_the_tropics/31118. BM, body mass, DI, defaunation index; Dist, distance to hunters\u2019 access points; HPD, human population density; Literacy, literacy rate; LivestockBio, biomass of domestic livestock; Stunt, stunting; TravTime, travel time to major towns.(A) Correlation plot between explanatory variables, (B) predictive performance metrics (mean \u00b1 SD) for three categories of defaunation . (C) Predicted versus observed categories of defaunation intensity obtained with the best hurdle model for the cross-validated data set. Size of the squares relative to the size of the grid indicates the proportion of the observed data of a given DI category (columns) to match with the prediction of a particular DI category (rows). Available at (TIF)Click here for additional data file.S7 Fighttps://figshare.com/projects/Intact_but_emtpy_forests_Patterns_of_hunting-induced_mammal_defaunation_in_the_tropics/31118. CAR, Central African Republic; DI, defaunation index; DRC, Democratic Republic of Congo.Colors denote different regions. Available at (TIF)Click here for additional data file.S8 Fighttps://figshare.com/projects/Intact_but_emtpy_forests_Patterns_of_hunting-induced_mammal_defaunation_in_the_tropics/31118. HPD, human population density; MESS, multivariate environmental similarity surface.(A) Geographic areas inside and outside the socioeconomic domain covered by our data, as estimated by the MESS. The values represent the similarity between each grid cell in pantropical range and those in the reference data set used to fit the models. Values range from positive (green) to negative (red). Positive values represent interpolation areas with similar socioeconomic factors  than those used to fit the models that are covered by our data set. Negative values indicate localities where at least one socioeconomic variable is outside the range of socioeconomic variables in our data set. (B) Main variable that is dissimilar in each grid cell compared with the socioeconomic domain in our data set. Orange, distance to the nearest rural settlement; green, HPD; blue, prevalence of stunting. Available at (TIF)Click here for additional data file.S9 Fighttps://figshare.com/projects/Intact_but_emtpy_forests_Patterns_of_hunting-induced_mammal_defaunation_in_the_tropics/31118. IUCN, International Union for Conservation of Nature; PA, protected area.Available at (TIF)Click here for additional data file.S10 Fighttps://figshare.com/projects/Intact_but_emtpy_forests_Patterns_of_hunting-induced_mammal_defaunation_in_the_tropics/31118. CAR, Central African Republic; DI, defaunation index; DRC, Democratic Republic of Congo; IUCN, International Union for Conservation of Nature; PA, protected area.Colors denote different regions. Available at (TIF)Click here for additional data file.S11 Fighttps://figshare.com/projects/Intact_but_emtpy_forests_Patterns_of_hunting-induced_mammal_defaunation_in_the_tropics/31118.Lines show 10% (dotted), 50% (solid), and 90% (dashed) representations of the predicted species in our data set. Available at (TIF)Click here for additional data file.S1 TableBC, book chapter; DT, doctoral thesis; MT, master thesis; SP, scientific publication; TR, technical report.(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 TableModels were ranked according to BIC. We only show models with a BIC weight >0.01. The best model  was used in the cross-validation analyses and for spatial predictions. BM, body mass; BIC, Bayesian Information Criterion; Dist, distance to hunters\u2019 access points, PA, protected area; PopDens, human population density; Stunt, stunting; TravTime, travel time to major towns.(DOCX)Click here for additional data file."}
+{"text": "Among the novel class of endogenous long non-coding RNAs, circular RNA (circRNA) is known as a key regulator in the development and progression of different cancers. Its function and mechanism in the tumorigenesis of colorectal cancer, however, has not been well studied. This study thus aimed to investigate potential regulation of colorectal cancer by circRNAs and the corresponding regulatory mechanism. We demonstrated that the expression of circRNA hsa_circ_0000523  was down-regulated in different colorectal cancer cell lines. It was also found that interference of hsa_circ_0000523 induced proliferation and suppressed apoptosis of colorectal cancer cells, the proliferation rate of which was reduced by the overexpression of hsa_circ_0000523. In addition, we found that miR-31 could recognize hsa_circ_0000523 sequence and that it acted as a \u201csponge\u201d of miR-31, indirectly regulating Wnt/\u03b2-catenin signaling pathway, which was involved in the progression of colorectal cancer. The results suggested that the expression of hsa_circ_0000523 correlated to the tumorigenesis of colorectal cancer cells. In addition, as a sponge of miR-31, the low level of hsa_circ_0000523 led to activation of Wnt/\u03b2-catenin signaling pathway, inducing the subsequent progress of colorectal cancer. Colorectal cancer has the third highest incidence rate and fourth highest mortality rate among all cancer cases . AlthougCircular RNAs (circRNAs) are a novel class of endogenous long non-coding RNAs with their 3\u2032- and 5\u2032-ends joined together to form a covalently closed loop . Most Based on Bachmayr-Heyda's study, we focused on hsa_circ_0000523 , which was down-regulated in colorectal cancer tissues and cell lines, to further explore the function of circRNA in colorectal cancer. The expression of hsa_circ_0000523 was also negatively correlated with the proliferation rate among different colorectal cell lines  or with the Guidelines laid down by the NIH (USA).5 cells/well for siRNA  or miRNA mimic  transfection. Transfection was performed using Lipofectamine 3000 , following the manufacturer's instructions. Final concentration of transfections was 100 nM. Sequences of siRNAs were (sense strands are reported as 5\u2032- 3\u2032): si-circ_0000523-1: GAGCAAGAAGAUCUACGGAdTdT; siControl-1: GAGCAAGAAGUAGAUGCCUdTdT; si-circ_0000523-2: GAAGAUCUACGGAAUCCAGAdTdT; siControl-2: CUUCAUCUACGGAAUCCAGAdTdT; si-circ_0000523-3: CAACAGAGCAAGAAGAUCUAdTdT; siControl-3: CAACAGAGCAAGAAGUAGAUdTdT (Cells were seeded into 6-well plates at a density of 2\u00d710AGAUdTdT .https://www.ncbi.nlm.nih.gov/tools/primer-blast). Primers were checked for specificity by gel, melting curve analysis, and sequencing. miRNA primers were ordered from Ribobio (China). qPCR cycling was initially performed at 95\u00b0C for 2 min, then 40 cycles at the conditions of 95\u00b0C/15 s, 60\u00b0C/15 s, and 68\u00b0C/20 s. The relative expression of each target gene was determined using the formula 2\u2212\u25b3\u25b3Ct. GAPDH was used as the internal control while parallel negative control experiments were performed in the absence of cDNA. Sequences of primers were: circ-F: CAGCATCGGAACCAGCAAAG; circ-R: CTGGGCTGTCACTACGGAAG; GAPDH-F: GCTCTCTGCTCCTCCTGTTC; GAPDH-R: ACGACCAAATCCGTTGACTC.Total RNA was isolated using TRIzol reagent (Invitrogen). Complementary DNA (cDNA) was reversely transcribed using TransScript reverse transcriptase (Transgene) with random primers. Primer pairs for qPCR were designed using Primer-BLAST , and lysed with lysis buffer . The bicinchoninic acid (BCA) protein assay (Invitrogen) was used to determine the protein concentration of cell lysate. The nucleoprotein was extracted using Nuclear and Cytoplasmic Protein Extraction Kit  following the product specification. The resulting proteins (50 mg) were loaded for electrophoresis on 10% Tris-glycine polyacrylamide gels and transferred onto polyvinylidene fluoride membranes. The membranes were blocked in 2.5% BSA for 1 h and incubated with the primary antibodies , Dkk1 , and c-Myc  or \u03b2-actin ) at 4\u00b0C overnight, and then with the second antibody  at room temperature for 2 h after rinsing. Western blot was detected using a chemiluminescence detection system (CWBIO).3 cells/well and incubated for 24 h, after which the medium was removed, and the cells were washed with PBS. Pre-diluted CCK-8  was added to the cells, which were incubated at 37\u00b0C for 2 h. Intensity of absorbance was determined using a Multimode Reader .Cells were seeded into 96-well plates at a density of 3\u00d710Apoptosis was assessed using an ApoDETECT annexin V-FITC apoptosis detection kit (Sigma-Aldrich) by flow cytometry following the manual. Cells were digested, washed twice with cold PBS, and re-suspended in binding buffer. FITC-labeled annexin V (5 \u03bcL) was added to the cell suspension (190\u03bcL) and mixed, followed by the addition of propidium iodide (PI) solution (5 \u03bcL). Cells were incubated in the dark at room temperature for 10 min and were then analyzed on a flow cytometer .t-tests. Multiple comparisons were analyzed with ANOVA followed by the Bonferroni post hoc test using GraphPad Prism software (USA).Data are reported as means\u00b1SE. Single-factor differences between two sets of data were analyzed for statistical significance by Student's It was previously found that RNA-seq showed a global reduction of circRNA abundance in both colorectal cancer cell lines and tissues . In ordeTo study if hsa_circ_0000523 was involved in the development of colorectal cancer, RNAi assay was performed to inhibit the expression of hsa_circ_0000523. Three siRNAs targeting hsa_circ_0000523 were designed and their silence efficiencies were tested on FHC cells . ResultsTo further explore the function of hsa_circ_0000523, overexpression assay was also performed in colorectal cancer cells. Overexpression plasmids were constructed by inserting hsa_circ_0000523 cDNA into pLVX-IRESneo. hsa_circ_0000523 was up-regulated approximately 4-fold in SW480 and 6-fold in SW620 . In addiTo explore the possible mechanism for hsa_circ_0000523 regulating proliferation of colorectal cancer cells, the effect of hsa_circ_0000523 on apoptosis of colorectal cancer cells was hence measured by flow cytometry. The rate of apoptosis was significantly increased in hsa_circ_0000523-overexpressed SW480 and SW620 cells after 24 h  and bothA major function of circRNAs is sponging miRNAs. It was hence predicted there might be miRNAs that could recognize sequences in hsa_circ_0000523 and interact with it. Based on the results of TargetScan, we found that several miRNAs could potentially recognize targets in hsa_circ_0000523, such as miR-31, miR-558, and miR-1270. After preliminary screening by miRNA mimics transfection, miR-31 was chosen as a candidate for further studies, for the inhibition effect of miR-31 mimics on hsa_circ_0000523 (pre-experiment data not shown). The predicted target sequence of miR-31 in hsa_circ_0000523 is shown in To study the interaction between hsa_circ_0000523 and miR-31, target recognition efficiency of miR-31 was assessed using dual-luciferase system. Both wild type and mutated target sequences in hsa_circ_0000523 were inserted into the coding region of luciferase gene separately, and then co-transfected modified reporter plasmids and miR-31 mimics into HEK293A cells. It was shown that miR-31 significantly down-regulated the expression of luciferase with wild type target, but not with mutated target . The resIn addition, the effect of miR-31 on endogenous hsa_circ_0000523 was assessed using miR-31 mimic and miR-31 inhibitor. Transfection with miR-31 mimic moderately down-regulated hsa_circ_0000523 in both SW480 and SW620 cells, while transfection with miR-31 inhibitor significantly up-regulated hsa_circ_0000523 for 2.0\u20132.3-fold . The resWe further investigated the expression level of miR-31 in SW-480 cells after transfection of si-circ_0000523-3. The expression level of miR-31 doubled that of hsa_circ_0000523 and reduced by 70% by siRNA , compareIt was reported that miR-31 promote breast tumorigenesis by suppressing Wnt signaling antagonists, Dkk1 . TherefoThe function of circRNA remains unclear to this date ,29, howeBy performing loss-of-function and gain-of-function assays, we demonstrated that hsa_circ_0000523 not only inhibited the proliferation of SW480 and SW620 cells but also induced apoptosis of colorectal cancer cells. This finding suggested that the down-regulation of hsa_circ_0000523 contributed to the development of colorectal cancer, both by inducing abnormal proliferation and by suppressing cell apoptosis.It was previously reported \u201333 thatIt was previously reported that hsa_circ_0000523 promotes breast tumorigenesis by suppressing DDK1, an antagonist of Wnt signaling pathway . Thus, wThis study demonstrated that circRNA hsa_circ_0000523 was down-regulated in 12 colorectal cancer cell lines and that it negatively regulated proliferation of colorectal cancer cells via sponging miR-31. In addition, the decrease in the expression of hsa_circ_0000523 was involved in the tumorigenesis of colorectal cancer through releasing miR-31, followed by the subsequent activation of Wnt/\u03b2-catenin signaling pathway."}
+{"text": "Pre-mRNA-splicing and adenosine to inosine (A-to-I) RNA-editing occur mostly cotranscriptionally. During A-to-I editing, a genomically encoded adenosine is deaminated to inosine by adenosine deaminases acting on RNA (ADARs). Editing-competent stems are frequently formed between exons and introns. Consistently, studies using reporter assays have shown that splicing efficiency can affect editing levels. Here, we use Nascent-seq and identify \u223c90,000 novel A-to-I editing events in the mouse brain transcriptome. Most novel sites are located in intronic regions. Unlike previously assumed, we show that both ADAR (ADAR1) and ADARB1 (ADAR2) can edit repeat elements and regular transcripts to the same extent. We find that inhibition of splicing primarily increases editing levels at hundreds of sites, suggesting that reduced splicing efficiency extends the exposure of intronic and exonic sequences to ADAR enzymes. Lack of splicing factors NOVA1 or NOVA2 changes global editing levels, demonstrating that alternative splicing factors can modulate RNA editing. Finally, we show that intron retention rates correlate with editing levels across different brain tissues. We therefore demonstrate that splicing efficiency is a major factor controlling tissue-specific differences in editing levels. Adenosine to inosine editing (A-to-I editing) deaminates adenosines in double-stranded RNAs leading to nucleotide differences between RNA and DNA . InosineAdar die at embryonic day 12.5, accompanied by liver disintegration, hematopoietic defects, and an increase in interferon signaling (Ifih1 or Mavs) are also deleted . In mammals, two catalytically active ADAR enzymes, ADAR (ADAR1) and ADARB1 (ADAR2) are known. Both enzymes have overlapping, yet distinct substrate specificities . Mice laignaling . Lethali deleted . Togetheself RNA . Adarb1 xpressed .Drosophila or human cells shows that the majority of editing takes place cotranscriptionally (Adarb1 transcript leads to the inclusion of a premature termination codon (ADAR and ADARB1 require double-stranded RNA structures (dsRNA) for substrate recognition and editing . The seqtionally . The efftionally . Vice veon codon . Moreoveon codon . Finallyon codon .Editing levels increase during development and vary between tissues, a phenomenon that cannot be explained by differential expression of RNA editing enzymes alone . For insA-to-I editing sites have been mostly identified in different human tissues . Most of\u2212/\u2212Adarb1 mice were rescued by a pre-edited version of the glutamate receptor  . After crossing the heterozygous offspring, we selected for F2 mice of genotype \u2212/\u2212Adarb1, Gria2R/R, Adar\u2212/+, \u2212/\u2212Mavs which are fully viable. Crossing of these mice to each other resulted in 25% mice carrying a homozygous deletion for Adar. These mice are smaller when compared to their heterozygous \u2212/+Adar littermates and have a high mortality around day 15 after birth. For editing site determination, we isolated RNA at postnatal day 14 from editing-positive mice expressing ADAR and ADARB1: +/+, Mavs\u2212/\u2212, Adarb1+/+, Gria2R/RAdar (termed wildtype in this study). Secondly, we isolated RNA from editing-null mice (double-knockout or dko mice) where Adar and Adarb1 have been deleted: \u2212/\u2212, Mavs\u2212/\u2212Adar, \u2212/\u2212Adarb1, R/RGria2. Thirdly, we analyzed RNA from mice where only Adarb1 (Adar2) had been deleted: +/+, Mavs\u2212/\u2212Adar, \u2212/\u2212Adarb1, R/RGria2 .Most mouse A-to-I editing sites known today locate to exonic regions. To explore the intronic editome, we used Nascent-seq . To alloria2R/R)  and crosR) . Editing site detection was done using the RDDpred package  yielding between 113 mio and 247 mio uniquely mapped reads per replicate and mapped to the mm10 RefSeq genome. Comparison with poly(A)-mRNA-seq shows an increased intronic coverage  or that could not be unambiguously aligned to one strand. This resulted in the wild type-only set (\u201cwt/stranded\u201d). Here, we almost exclusively observed A-to-G mismatches, suggesting enrichment for true A-to-I editing events. Using Sanger sequencing, we validated 21 out of 22 editing sites; i.e., 95% . As expected, we did not detect any A-to-G peak in the \u2212/\u2212Adar, \u2212/\u2212Adarb1 set. This supports the notion that ADAR and ADARB1 are the only active editing enzymes. Subsequently, only A-to-G transitions exclusively detected in the wild type were considered for further analysis. Thereby, we identified almost 100,000 A-to-I editing sites, which we submitted to the REDIportal (http://srv00.recas.ba.infn.it/atlas/search_mm.html)  .Supplemental Fig. S3; Supplemental Table S3). Of 97,416 identified editing sites, approximately 50,000 are not edited in the \u2212/\u2212Adarb1 mice, suggesting that they are primarily edited by ADARB1 . Using the Ensembl variant effect predictor (VEP), we predicted the effect of editing events separately for exonic, intronic, and UTR sites . To test the quality and significance of the predicted pairing, several assays were performed. First, we deduced an empirical P-value by comparing hybridization energy of the predicted ECSs to the energies calculated for 1000 input sequences with shuffled dinucleotides. An ECS was accepted at a P-value \u22640.001. As a control, we repeated the analysis with randomly selected adenosines having the same genomic features . In contrast to true editing sites, randomly selected genomic regions did not allow identification of ECSs of similar quality . Next, to independently confirm the predictions, we analyzed the sequence spanning the editing site to the predicted ECS using the RNA folding prediction tool RNAfold and the structure visualization tool forna . Lastly, for experimental validation, we cloned the genomic DNA coding for 10 editing sites with or without the corresponding predicted ECS into pcDNA3.1\u2212 . Following cotransfection with a plasmid expressing FLAG-rADAR2, RT-PCR, and Sanger sequencing, we validated 10 out of 10 ECS predictions .Editing-complementary sequences (ECSs) oppose editing sites and form a dsRNA with the editing region. To identify ECSs regions, we calculated the energetically most favorable hybridization site between the region \u00b115 nt around all editing sites identified in the Nascent-seq data and the extended surrounding region of \u00b12500 nts around the editing sites. Thereby, we predicted \u223c50,000 ECSs .The many intronic editing sites identified by Nascent-seq suggest that the majority of editing happens cotranscriptionally as seen before . When weBy studying a selected set of exonic editing sites, we previously showed that splicing efficiency is a major factor controlling the level of editing . To deteFor isolation of poly(A)-selected RNA and cDNA library preparation, 5 + 5 or 6 + 6 (untreated plus treated) biological replicates of primary neurons and bone marrow, respectively, were used. Libraries were sequenced in a paired-end 125-bp mode, and reads were mapped to the mouse genome (mm10). Each replicate yielded between 63 mio and 279 mio uniquely mapped reads.P-value < 0.05) in bone marrow and neuronal cultures, respectively . Editing generally increased upon treatment, most likely because splicing inhibition leads to a prolonged persistence of editing-competent dsRNA-structures formed within introns or between exons and introns . Under these conditions, a similar shift in editing levels was observed, albeit the changes were stronger for repeat-associated sites. The position of the ECS played only a minor role, but the increase in editing was particularly strong for exonic sites in the bone marrow when the ECS was located in an intron, consistent with the notion that, in this scenario, the impact of splicing on editing levels should be particularly high . We validated the NGS results using Sanger sequencing with a validation rate of 83% . In sum, reduced splicing efficiency is associated with an increase in editing levels.Splicing inhibition works in both systems as evidenced by the larger proportion of reads mapping to intronic regions in the meayamycin-treated compared to control cells A. Next,  introns C. Consis introns D. The ba\u2212/\u2212Nova1 and \u2212/\u2212Nova2 knockout mice . In Nova1\u2212/\u2212 and Nova2\u2212/\u2212 mice, 385 and 520 editing sites, respectively, were differentially edited as compared to wild type  .Next, we determined whether alternative splicing factors can affect editing levels. To this end, we analyzed RNA-seq data from cortices of out mice . Both NOout mice . After m < 0.05) A. Depend < 0.05) . Consequ < 0.05) B. To tes . In sum, we classified 16 sites as intronic and 29 sites as exonic .Alternative splicing and intron retention levels vary between tissues and are important for defining tissue-specificity . Alterna regions . Therefo regions . From th regions . As a me regions A,B. If t regions . SubsequFLNB or GRIK2  is a third member of the ADAR proteins . While tAdarb1 showed an approximately threefold drop in editing in \u2212/\u2212Adarb1 mice. This is consistent with previous findings showing higher expression of ADARB1 in the mouse brain as compared to ADAR . For Nascent-seq, mice of desired genotypes were sacrificed at p14, and nascent-RNA was isolated from brain tissue (n tissue . After tBone marrow cultures were established as described . Bone mahttp://epigenomics.snu.ac.kr/RDDpred/prior_data/Mouse.MES.txt.gz). Annotated editing sites from the databases DARNED (https://github.com/genome/bam-readcount). For follow-up analysis, editing sites supported by less than five reads were omitted. For ADARB1 knockout samples, editing sites were treated as edited if edited reads could be observed in more than one (out of three) samples or had a cumulative editing level greater than 1%. The obtained editing sites were characterized with respect to their genomic context . Each ES was assigned to be either located in an UTR, exon, intron, or intergenic. If more than one classification applied to one ES, the higher ranked (UTR > exon > intron > intergenic) was selected. Subsequently, the potential impact of the observed A \u2192 G substitution was interrogated using Ensembl's variant predictor tool (VEP). If multiple effects for one substitution were predicted, only one variant was reported according to the following ranking: missense_variant > synonymous_variant > stop_lost > stop_ retained_variant > splice_acceptor_variant > splice_donor_variant > splice_region_variant > TF_binding_site_variant > regulatory_ region_variant > mature_miRNA_variant > 5_prime_UTR_variant > 3_prime_UTR_variant > non_coding_transcript_exon_variant > non_coding_transcript_variant > NMD_transcript_variant > intron_ variant > downstream_gene_variant > upstream_gene_variant > intergenic_variant. For a description of the variants see: www.ensembl.org/info/genome/variation/prediction/predicted_data.html#consequences.Sequenced reads were quality-trimmed and adapter-clipped using Trimmomatic (version 0.33)  with defs DARNED  and RADAs DARNED  served as DARNED . Only RNs DARNED . ES cands DARNED , observewww.repeatmasker.org; mm10; Repeat Library 20140131).To characterize the repeat status of an ES, RepeatMasker was used  , the program RNAplex was used . To detehttps://plogo.uconn.edu/)  . To consSupplemental Table S1). Geneious v11 (Biomatters) was used to analyze Sanger chromatograms. The percentage of editing is defined as the height of the G peak divided by the sum of the A + G peaks (in the case of the reverse primer: the height of the T peak divided by the sum of the T + C peaks).For validation of editing events, Sanger sequencing on cDNA libraries and corresponding genomic DNA from the same individual was used. Editing sites with at least 10% editing  were valt-test with the Welch approximation for the degrees of freedom on the log10 values of the observed levels. Sites with an editing level of zero were set to 0.001 before applying the log10. Unmapped reads for Nova1 and Nova2 knockout mice (cortex) and corresponding wild-type controls were downloaded from the Gene Expression Omnibus (GEO) repository (GSE69711). Reads were adapter-trimmed using Trimmomatic  additionally a complementary intron chain. In this step, the parameter \u201c\u2013aggregate no\u201d was set to prevent genes sharing exons from being merged into aggregated genes. Furthermore, transcript parts shorter than 10 bases were removed. From there, the standard DEXSeq workflow was used of counting reads and testing for differential transcript part usage  between the WT and the respective mutant. Eventually, this information was linked with the information on differential editing levels between the two conditions for all editing sites that exhibit significant differential editing . The distribution of the log2FC of editing levels was plotted separately for editing sites located in an up- or down-regulated transcript part, according to the DEXSeq analysis.To determine the effect of NOVA1 or NOVA2 deletion on splicing efficiency for individual introns, DEXSeq  was usedhttps://www.ncbi.nlm.nih.gov/gap; accession number phs000424.v6.p1). For each RNA editing site, genomic coordinates of the exon including the editing event as well as coordinates of the flanking intron were extracted by using RefSeq annotations (downloaded from UCSC) and custom scripts. Such coordinates were provided in input (in SAF format) to the featureCounts tool  under accession number PRJEB27264. A-to-I editing sites identified using Nascent-seq have been submitted to REDIportal (http://srv00.recas.ba.infn.it/atlas/search_mm.html). Sanger sequencing chromatograms and Python scripts are available as Supplemental Material .RNA-seq data from this study have been submitted to the European Nucleotide Archive (ENA;"}
+{"text": "Long-read nanopore sequencing enables direct high-resolution breakpoint mapping on balanced carriers of reciprocal translocation. The mean sequencing depth on the translocated chromosomes to achieve accurate mapping of breakpoints ranged from 2.5-fold to 6.2-fold. To speed up determination of the breakpoints from long-read sequencing data, alignment reads on the translocated chromosomes were extracted before piped into NanoSV. Checking the position of breakpoints on Interactive Genomics Viewer (IGV) was crucial to successful design of breakpoint PCR primers, especially when large deletion was involved at the breakpoints.\u2022Long-read sequencing enables accurate breakpoint mapping with base-pair resolution\u2022Splitting bam files by translocated chromosomes drastically speeded up the breakpoint determination\u2022IGV helps to identify the breakpoint positions and facilitate the design of breakpoint PCR primers Specification TableGenomic DNA of balanced reciprocal translocation carriers was sequenced in a MinION flow cell  with a 48\u202f-h sequencing protocol . In brie1)Local base calling was performed using Albacore 2.3.1 . FastQ files were sorted into pass and fail folders using a default quality score cutoff of 7. Please note that at the time of manuscript revision, FAST5 files generated by the updated version of MinKNOW , are no longer supported by Albacore. Real time basecalling can be performed by the data processing toolkit (Guppy) integrated in MinKNOW, or performed locally using Guppy Basecalling Software (Version 3.3.0).folder_with_fast5read_fast5_basecaller.py --flowcell FLO-MIN106 --kit SQK-LSK108 --output_format fastq --input 1)The FastQ files in the \u201cpass\u201d folder were aligned to GRCh37/hg19 using minimap2(2.11) [--recursive \u2013save_path fastq/ --worker_threads 12reference.fasta fastq/pass/*.fastq > example.samminimap2 \u2013ax map-ont example.sam > example.bamsamtools view \u2013bS example.bam example.sortedsamtools sort example.sorted.bam1)The bam files were split according to the translocated chromosomes. Merge of the 2 split bam files was necessary before breakpoint determination. This could significantly cut down the time for breakpoint determination. Take sample #100648 as an example, the time required was reduced 60\u202f% after splitting the bam file.samtools index example.sorted.bam chrA\u202f>\u202fchrA.bam (where chrA and chrB are the translocated chromosomes)samtools view \u2013hb samtools sort chrA.bam chrA.sortedexample.sorted.bam chrB\u202f>\u202fchrB.bamsamtools view \u2013hb samtools sort chrB.bam chrB.sortedsamtools merge chrA_chrB.sorted.bam chrA.sorted.bam chrB.sorted.bam1)example.vcfBreakpoints determination was performed using NanoSV1.2.0 , and allsamtools index chrA_chrB.sorted.bamreference.bed \u2013o example.vcfNanoSV chrA_chrB.sorted.bam \u2013t 8 \u2013b example.vcf. Coordinates of the predicted breakpoints that were correlated with the cytogenetic reports of the patients were extracted from the In case where a large deletion was suspected at the breakpoints, it is important to visually check the alignment reads by zooming out on IGV. For instance, the aligned reads on chr8 of sample #100648 indicated a deletion of 216 bases at the breakpoint A. This pIn summary, we demonstrate a new method for accurately mapping of the reciprocal translocation breakpoints directly on balanced carriers. Splitting the bam files according to the translocated chromosomes before breakpoint prediction can dramatically cut down the time required for the prediction. Visual inspection of the aligned reads on IGV is crucial to accurate identification of the breakpoints. Nonetheless, this method has a few limitations. First, it relies on the previous cytogenetic diagnosis for splitting bam files, therefore it is not applicable for mapping genome-wide de novo structural variations. Second, the method is not applicable to patients with translocation breakpoints located across the highly repetitive centromeric regions of acrocentric chromosomes, such as Robertsonian translocation carriers.Balanced reciprocal translocation carriers are usually phenotypically normal but are at an increased risk of infertility, recurrent pregnancy loss or having children with physical or mental abnormalities. Preimplantation genetic testing on chromosomal structural rearrangement (PGT-SR) offers a way to screen against unbalanced embryos during in vitro fertilization treatment cycle, but the methods cannot distinguish euploid carrier from noncarrier embryos. Although replacing carrier embryos should result in phenotypically normal live births, the offspring will encounter the same problems as their parent in terms of infertility, recurrent pregnancy loss or having affected children. Distinguishing between carrier and noncarrier embryos is possible by SNP haplotyping ,5 and neThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper."}
+{"text": "Salvia splendens Ker-Gawler, scarlet or tropical sage, is a tender herbaceous perennial widely introduced and seen in public gardens all over the world. With few molecular resources, breeding is still restricted to traditional phenotypic selection, and the genetic mechanisms underlying phenotypic variation remain unknown. Hence, a high-quality reference genome will be very valuable for marker-assisted breeding, genome editing, and molecular genetics.de novo assembly yielded a final genome with a scaffold N50 size of 3.12 Mb and a total length of 808 Mb. The repetitive sequences identified accounted for 57.52% of the genome sequence, and \u200954,008 protein-coding genes were predicted collectively with ab initio and homology-based gene prediction from the masked genome. The divergence time between S. splendens and Salvia miltiorrhiza was estimated at 28.21 million years ago (Mya). Moreover, 3,797 species-specific genes and 1,187 expanded gene families were identified for the scarlet sage genome.We generated 66 Gb and 37 Gb of raw DNA sequences, respectively, from whole-genome sequencing of a largely homozygous scarlet sage inbred line using Pacific Biosciences (PacBio) single-molecule real-time and Illumina HiSeq sequencing platforms. The PacBio We provide the first genome sequence and gene annotation for the scarlet sage. The availability of these resources will be of great importance for further breeding strategies, genome editing, and comparative genomics among related species. Salvia L., with nearly 1,000 species of shrubs, herbaceous perennials, and annuals, is the largest genus in the mint family  [S. officinalis [S. miltiorrhiza (Danshen). Additionally, they are used as ornamental plants valued for their flowers and for their aromatic foliage, such as S. splendens  . The genicinalis  and S. mens Fig. -k.Salvia splendens , scarlet or tropical sage, is a herbaceous perennial species that is native to Brazil. While it is a perennial in warmer climate zones, it grows as an annual in cooler areas. Salvia splendens,characterized by its dense flowers, wide variation of colors , and long-lasting flowering (3\u20139 weeks or longer), is a very popular bedding plant that is widely cultivated in public gardens all over the world [S. splendens can provide outstanding visual effects when grown in beds, borders, and containers; and its long life span ranges from late spring to the occurrence first frost. Furthermore, the flower is easy to maintain and fairly free of pests and diseases due to Lamiaceae's characteristic insect-repellent fragrance content [Salvia splendens is a prolific and durable bloomer, thrives in full sun, and survives in a large range of soil moisture regimes.Traditional breeding activities using phenotypic selection as well as performing targeted variety hybridizations between elite cultivars have resulted in a large number of new cultivars with different performances regarding flowering characteristics , individual growth performance, height, and tolerance to moisture and temperature extremes. However, little is known about the molecular mechanisms underlying such economically important characteristics for ornamental varieties. To date, few genetic markers  are avaiS. splendens with a hybrid assembly strategy using Pacific Biosciences (PacBio) single-molecule real-time (SMRT) and Illumina HiSeq short-read sequencing platforms. The genome assembly, its structural and functional annotation, provide a valuable reference for the genomic dissection of the phenotypic variation in Salvia and new breeding strategies. This reference genome could also be used in comparative genomics with the recently released Salvia genome (S. miltiorrhiza) [Mentha longifolia) [Here, we present the first high-quality genome assembly for orrhiza)  and the gifolia)  to studyS. splendens, \u201cAoyunshenghuo (Olympic flame)\u201d \u201d Fig. -b, for wHigh-quality high-molecular-weight genomic DNA was extracted from leaves of two soil-grown seedlings (huo1 and huo1_1) following \u223c20 kb SMRTbell Libraries\u201d protocol . Plants RRID:SCR_011848) [RRID:SCR_011841) [DNA was extracted from leaf tissue of the same soil-grown seedlings (huo1 and huo1_1) using the Qiagen DNeasy Plant Mini Kit. Two 500-bp paired-end (PE) libraries (huo1 and huo1_1) were prepared using the NEBNext Ultra DNA Library Prep Kit for Illumina sequencing with an Illumina HiSeq X Ten machine. Short reads were processed with Trimmomatic v0.33   and Cuta_011841)  to removRRID:SCR_015880) [RRID:SCR_005491) [All generated PacBio reads were filtered and corrected with Canu v1.5  ; thereaf_005491)  was used_005491)  was usedde novo assembly was conducted as follows in a progressive manner. First, primary assemblies were generated from PacBio long reads of the 31 Gb from the \u201chuo1\u201d sequenced individual by four overlap-layout-consensus\u2013based assemblers, Canu (produced assembly v0.1), MECAT 1.1 (assembly v0.2) [RRID:SCR_016089) [RRID:SCR_015008) [RRID:SCR_005056) [RRID:SCR_015026) [RRID:SCR_014731) , we obtained the final genome assembly (v1.2). Mapping of Illumina reads was done using Bowtie2 v2.3.0  [Salvia genus [Thely v0.2) , FALCON _016089)  after Ca_016089)  after Ca_015008)    was used_015008)  and SSPA_005056)  were use_015026)  (v1.1), _005476) . We deteia genus  and for ia genus , as it wRRID:SCR_015027) [de novo identify and classify repeat families in the genome assembly. Subsequently, the outputs from the RepeatModeler and RepBase [RRID:SCR_012954)  [RepeatModeler v1.0.10   was used RepBase  library -2.2.28)  analysesRNA was extracted from the two cultivated lines with different flower colors (red and purple) using tissue obtained from roots, shoots, leaves, calyxes, and corollas. Frozen tissue from all samples was ground manually using a mortar and pestle, and RNA was isolated using the NEBNext Poly(A) mRNA Magnetic Isolation Module. RNA quality was assessed using an Agilent 2100 BioAnalyzer. Sequencing libraries were prepared using the NEBNext Ultra RNA Library Prep Kit for Illumina; 150 bp PE sequencing was performed using an Illumina HiSeq X Ten.RRID:SCR_015530) [RRID:SCR_014583) [RRID:SCR_014597) [RRID:SCR_016323) [RRID:SCR_013048) [De novo assembly was generated using Trinity. Then, transcriptome assemblies were combined and further refined using CD-HIT v4.6 [A total of 1,344 million raw reads from RNA sequencing were processed by Trimmomatic and Cutadapt and aligned to the genome assembly with HiSat2 v2.1.0  . Base qu_014583)  before a_016323) , and Tri_013048) . De novoHIT v4.6 , and finRRID:SCR_008417) [ab initio gene prediction, using model training based on coding sequences from Arabidopsis thaliana and S. miltiorrhiza (with two sets of proteins from independent genome annotation [RRID:SCR_004870) [A. thaliana and S. miltiorrhiza were aligned to the repeat-masked reference genome assembly with BlastX . After optimization with Exonerate v2.4.0 [RRID:SCR_005309) [RRID:SCR_011919) [RRID:SCR_002380) [RRID:SCR_004726) [RRID:SCR_005829) [\u22125.AUGUSTUS v3.2.3   was usednotation ). Then, _004870)    provided_011919)  search o_002380) . Protein_004726)  and Inte_005829)  ID were A total of 54,008 genes could be predicted, with average lengths of gene regions, genes (exons and introns), coding DNA sequence, and exons of 3,430.43 bp, 1,696.34 bp, 1,293.62 bp, and 265.94 bp, respectively (Supplementary Table S6). The comparisons among genomes from related species regarding lengths of genes, exons, and introns are shown in Fig. The predicted genes were annotated against several functional databases, including the NCBI nonredundant protein database , the SwSalvia miltiorrhiza [Fraxinus excelsior [Olea europaea [Mimulus guttatus [Utricularia gibba [Sesamum indicum [Coffea canephora [Solanum lycopersicum [Daucus carota [Vitis vinifera [Arabidopsis thaliana [Populus trichocarpa [Oryza sativa [Beta vulgaris [To analyze gene families, we downloaded the protein sequences of 15 genome assemblies of 14 additional species   that were specific to the scarlet sage genome when compared with the other 15 genomes (Supplementary Table S10), and we detected \u200910,770 gene families that have expanded in the scarlet sage lineage using CAFE v4.0 [q<0.05) GO terms of three functional categories, i.e., BP , CC (Cellular Component), and MF (Molecular Function)(Supplementary Table S11), and one KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway (amino acid metabolism) (Supplementary Table S12) significant at q <0.05. Also, 3,579 genes and 78 gene families were detected to be contracted and found to have rapidly evolved within the scarlet sage genome  [RRID:SCR_014629) [A. thaliana and V. vinifera (124 Mya) [A. thaliana and P. trichocarpa divergence time (90 Mya) [Salvia genomes; their divergence time was estimated to be about 28.21 Mya   using de_014629)  with the_014629)  and cali124 Mya)  as well (90 Mya) . The phy_011812  The mint family is recognized as providing promising sources of bioactive secondary metabolites . In factS. miltiorrhiza genome [S. miltiorrhiza genome, which could be due, in part, to the draft status of the genome assembly for S. miltiorrhiza. Furthermore, significant gene co-expression across different organs was detected for one TPS gene and two of four P450 genes located in a single gene cluster . Evidence for moderate or significant co-expression among clustered genes was revealed and is shown in Supplementary File 2.The presence of metabolic gene clusters for secondary metabolic pathways is common in bacteria and filamentous fungi and is also widely reported in plants . Using ta genome . The gena genome . InteresS. miltiorrhiza genomes revealed six pairs of gene clusters that share synteny between these two congeneric plants, and two blocks from the scarlet sage share synteny with one block from S. miltiorrhiza . Among the shared synteny blocks, four could be related to saccharide, one to lignan, and another to polyketide biosynthesis. The smaller number of gene clusters detected for S. miltiorrhiza and, subsequently, fewer shared synteny blocks of metabolic gene cluster between these two species may be partially attributed to the present state of the S. miltiorrhiza genome assembly, which is 100 times more fragmented than that of the scarlet sage. Thus, here, we provide a starting point for comparative genomics among plant species within the mint family.Based on the collinearity elucidated by former OrthoMCL analyses, a comparative genomic study between the scarlet sage and Salviasp. and sets the foundation for molecular-informed breeding strategies and genome editing approaches for this valued ornamental flowering plant. Moreover, this genome assembly is useful for comparative genomic studies among related species.In summary, we presented the draft assembly for the scarlet sage genome using a PacBio long-read dominated strategy that was responsible for obtaining the high-quality sequence assembly. Also, the almost complete homozygosity within the sequenced inbred line's genome was a key factor for the high continuity gained in this study. The novel genome data generated in the present study will provide a valuable resource for studying the molecular underpinnings of the various phenotypic variations found within GIGA-D-18-00028_Original_Submission.pdfClick here for additional data file.GIGA-D-18-00028_Revision_1..pdfClick here for additional data file.Response_to_Reviewer_Comments_Original_Submission.pdfClick here for additional data file.Reviewer_1_Report_ -- Fritz Sedlazeck2/21/2018 ReviewedClick here for additional data file.Reviewer_2_Report_ -- Stephen Tsui2/22/2018 ReviewedClick here for additional data file.Reviewer_2_Report_(Revision_1) -- Stephen Tsui5/12/2018 ReviewedClick here for additional data file.Reviewer_3_Report_ -- Robert van Buren2/26/2018 ReviewedClick here for additional data file.Additional FilesClick here for additional data file."}
+{"text": "RNA plays essential roles in all known forms of life. Clustering RNA sequences with common sequence and structure is an essential step towards studying RNA function. With the advent of high-throughput sequencing techniques, experimental and genomic data are expanding to complement the predictive methods. However, the existing methods do not effectively utilize and cope with the immense amount of data becoming available.Hundreds of thousands of non-coding RNAs have been detected; however, their annotation is lagging behind. Here we present GraphClust2, a comprehensive approach for scalable clustering of RNAs based on sequence and structural similarities. GraphClust2 bridges the gap between high-throughput sequencing and structural RNA analysis and provides an integrative solution by incorporating diverse experimental and genomic data in an accessible manner via the Galaxy framework. GraphClust2 can efficiently cluster and annotate large datasets of RNAs and supports structure-probing data. We demonstrate that the annotation performance of clustering functional RNAs can be considerably improved. Furthermore, an off-the-shelf procedure is introduced for identifying locally conserved structure candidates in long RNAs. We suggest the presence and the sparseness of phylogenetically conserved local structures for a collection of long non-coding RNAs.By clustering data from 2 cross-linking immunoprecipitation experiments, we demonstrate the benefits of GraphClust2 for motif discovery under the presence of biological and methodological biases. Finally, we uncover prominent targets of double-stranded RNA binding protein Roquin-1, such as BCOR\u2019s 3\u2032 untranslated region that contains multiple binding stem-loops that are evolutionary conserved. High-throughput RNA sequencing and computational screens have discovered hundreds of thousands of non-coding RNAs (ncRNAs) with putative cellular functionality\u00a0. FunctioFor many ncRNAs and regulatory elements in messenger RNAs (mRNAs), however, it is well known that the secondary structure is better conserved than the sequence, indicating the paramount importance of structure for the functionality. This fact has promoted annotation approaches that try to detect structural homologs in the forms of RNA \u201cfamilies\u201d and \u201cclasses\u201d\u00a0. MembersO(n2) of the sequence length\u00a0 is the expected Rand Index . The ARWe evaluated GraphClust2 using known RNA families from the Rfam database\u00a0. The RfaIn comparison to GraphClust1, GraphClust2 provides alternative approaches for the identification of the secondary structures. Using similar configurations as in GraphClust1\u00a0, i.e., RIn the previous benchmark, the clustering relies on the free energy models for secondary structure prediction. A predicted structure sometimes deviates from the real functional structure owing to the cellular context and folding dynamics. In this case, the SP SHAPE data associated with the real functional structure are expected to improve the quality of structure prediction, which in turn should improve the clustering. We wanted to investigate how an improvement in the structure prediction quality at the early clustering steps influences the final clustering results. To draw a conclusion, however, extensive SHAPE data would be needed for a set of labeled homologous ncRNAs, ideally with different sequence identity and under similar experimental settings. Currently, such collection of data, especially over multiple organisms, is still unavailable. However, because the SP is turning into a standard and common procedure, data of such nature are expected to become available soon.One solution to the mentioned data scarcity is provided in the literature\u00a0, by simuTo validate GraphClust2\u2019s scalability and linearity claims, we used a millions-sequence biological dataset. GraphClust2 is implemented with comprehensive support for parallel computation using the Galaxy framework. The MinHash-based clustering step is the only step in which the entities are evaluated altogether to identify the dense neighborhoods as clusters. Thanks to the MinHash technique, this step has only a linear complexity . Nonetheless, the quality metric is slightly improved by incorporating the SP data (+DMS-seq mode ARI 0.91). We further manually inspected the quality of the produced clusters. Fig.\u00a0in vivo probing data. In the +DMS-seq mode  in mRNAs, only a locally conserved structural motif is expected to suffice to perform a function, independent of the precise global structure. We thus revert to a frequently used strategy in the RNA field, namely, to look for locally conserved structural motifs. We wanted to evaluate whether we can use GraphClust2 for this purpose.Looking for locally conserved secondary structures in lncRNAs is alluring for several reasons. First, with an increase in the base pair span length the prediction quality decreases\u00a0, which iIt should be noted that distinguishing conserved structures from background genomic sequence similarity using base-pair conservation signals is a challenging task. Genome-wide screening studies over genomic alignments require adjusted thresholds for statistical significance discovery and report up to 22%\u00a0 false diAn advantage of this clustering approach over traditional screening methods is its ability as an unsupervised learning method, for not imposing explicit presumption on the depth or number of predicted motifs. This also makes it possible to find the locally conserved structures in the regions where a subset of species do not have a conserved structure. Furthermore, this approach does not require a precise co-location of the conserved elements within the transcript, in contrast to traditional alignment-based screening approaches. A further advantage is the availability of the solution in the Galaxy framework because it provides a rich collection of assets for interactive data collection and analysis of genomic data. We used the 100way vertebrate alignments to extract the orthologous genomic regions for each of the studied RNAs in human and other vertebrates. Each of the orthologous sequences is split into windows, which are then clustered by GraphClust2. The alignment of each cluster has been further annotated with some of the best-practice complementary methods in assessing covariation patterns and structure conservation potentials, namely, RNAz, Evofold, and R-scape . In the following section, some example studies are presented.We investigated clustering of orthologous genomic regions of FTL mRNA and 4 well-studied lncRNAs, using the approach described before. The selected lncRNAs have been previously reported for having loss-of-function phenotypes\u00a0, 78. In cis-acting iron response element (IRE) is a conserved structured element that is located on the 5\u2032 UTR of FTL (ferritin light chain) and several other genes. Mutations that disrupt the hairpin structure of IRE cause disease phenotypes by changing the binding affinity of a regulatory iron response protein\u00a0[FTL mRNA. The IRE element was identified as 1 of the 3 clusters detected by Evofold . We clustered target sites of human SLBP using the publicly available eCLIP data\u00a0. The larThe stem-loop structure of the eCLIP data has a lower covariation level than Rfam\u2019s seed alignment Fig\u00a0 vs B. ThRoquin-1 is a protein with conserved double-stranded RNA binding domains that binds to a constitutive decay element (CDE) in TNF-\u03b1 3\u2032 UTR and several other mRNAs\u00a0, 83. RoqIt should be noted that the union of Roquin-1\u2019s CDE-like motifs have a lower enrichment score based on the PAR-CLIP ranks, in comparison to the SLBP motif based on the eCLIP ranks Figs\u00a0. For exaWe performed a follow-up conservation study over the identified CDE-like motifs from the clustering of Roquin-1 binding sites Fig.\u00a0. By inveWe have presented a method for structural clustering of RNA sequences with a web-based interface within the Galaxy framework. The linear-time alignment-free methodology of GraphClust2, accompanied by cluster refinement and extension using RNA comparative methods and SP data, were shown to improve the detection of ncRNA families and structurally conserved elements. We have demonstrated on real-life and complex application scenarios that GraphClust2 provides an accessible and scalable way to perform RNA structure analysis and discovery.GraphClust2 provides an integrative solution, which can start from raw high-throughput sequencing and genomic data and ends with predicted motifs with extensive visualizations and evaluation metrics. The users can benefit from the vast variety of the bioinformatics tools integrated by the Galaxy community and extend these applications in various ways. Thus, it will be for the first time possible to start from putative ncRNAs in transcriptomic RNA-sequencing studies and immediately cluster the identified transcripts for annotation purposes in a coherent manner.Project name: GraphClust2https://github.com/BackofenLab/GraphClust-2Project repository: https://graphclust.usegalaxy.euProject home page: https://github.com/bgruening/galaxytools/tree/master/tools/GraphClustGalaxy tools repository: Operating system(s): Unix (Platform independent with Docker)https://hub.docker.com/r/backofenlab/docker-galaxy-graphclustGraphClust2 Docker image: License: GNU GPL-v3RRID:SCR_017286https://graphclust.usegalaxy.eu). Archival copies of the GitHub repositories are also available from the GigaScience GigaDB repository\u00a0[The data presented here that illustrate our work are available from Zenodo\u00a0, and allpository\u00a0.Supplementary information: Supplementary Methods and Results are available via the additional file associated with this article.Supplementary Figure S1: Clustering scalibility and runtime evaluation using the marine metatranscriptomic dataset.Supplementary Figure S2: Characteristics of the clusters predicted from the long RNA conservation analysis.Supplementary Figure S3: Locally conserved structured elements predicted in XIST lncRNA.Supplementary Figure S4: Distribution of SLBP motifs over the eCLIP scores.Supplementary Figure S5: Color legend for Supplementary Figures S6-S11.Supplementary Figures S6-11: Sequence-structural alignments of the selected clusters from Figure 4. Supplementary Figure S12: Overview of BCOR's CDE-like alignment for all the available species.Supplementary Table S1: Clustering benchmark performance using Rfam-cliques datasets.Supplementary Table S2: Statistics of the clusters predicted from the marine metatranscriptomic study.Supplementary Table S3: Clustering runtimes of the long RNA conservation and CLIP analyses.Tabular file T1: The genomic coordinates, structure conservation scores and statistics of the GraphClust2 candidates in the long RNA conservation analysis.Tabular file T2: Genomic coordinates, gene names and conservation information of the identified CDE-like motifs from Roquin-1 CLIP data.ARI: adjusted Rand index; bp: base pairs; CDE: constitutive decay element; CLIP: cross-linking immunoprecipitation; CM: covariance model; DMS: dimethyl sulfate; HPC: high-performance computing; lncRNA: long non-coding RNA; MAF: Multiz alignment format; mRNA: messenger RNA; NCBI: National Center for Biotechnology Information; ncRNA: non-coding RNA; RBP: RNA binding protein; SCFG: stochastic context-free grammar; SHAPE: selective 2\u2032-hydroxyl acylation analyzed by primer extension; SP: structure probing; SRA: Sequence Read Archive; UCSC: University of California Santa Cruz; UTR: untranslated region.This work was supported by German Research Foundation Collaborative Research Centre 992 Medical Epigenetics (DFG grant SFB 992/1 2012) and German Research Foundation (DFG grants BA 2168/13-1 and BA 2168/14-1). The article processing charge was funded by the German Research Foundation (DFG) and the Albert Ludwigs University Freiburg in the funding programme Open Access Publishing. R.B. is funded by the Deutsche Forschungsgemeinschaft  under Germany\u00b4s Excellence Strategy - EXC-2189 - Project ID: 390939984. Gef\u00f6rdert durch die Deutsche Forschungsgemeinschaft (DFG) im Rahmen der Exzellenzstrategie des Bundes und der L\u00e4nder - EXC-2189 - Projektnummer 390939984.The authors declare that they have no competing interests.We thank Freiburg Galaxy team for their support. We thank Sean Eddy for the helpful comments and discussions. We also thank Sita J. Saunders and Mehmet Tekman for providing feedback about this manuscript.giz150_GIGA-D-19-00089_Original_SubmissionClick here for additional data file.giz150_GIGA-D-19-00089_Revision_1Click here for additional data file.giz150_GIGA-D-19-00089_Revision_2Click here for additional data file.giz150_GIGA-D-19-00089_Revision_3Click here for additional data file.giz150_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giz150_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giz150_Response_to_Reviewer_Comments_Revision_2Click here for additional data file.giz150_Reviewer_1_Report_Original_SubmissionFabrizio Ferre -- 5/16/2019 ReviewedClick here for additional data file.giz150_Reviewer_2_Report_Original_SubmissionIoanna Kalvari -- 5/27/2019 ReviewedClick here for additional data file.giz150_Reviewer_2_Report_Revision_1Ioanna Kalvari -- 10/14/2019 ReviewedClick here for additional data file.giz150_Supplemental_FilesClick here for additional data file."}
+{"text": "Escherichia coli bacteriophages that may be explored for biocontrol strategies and to expand the understanding of phage-host interactions.Bacteriophages and their proteins have potential applications in biotechnology for the detection and control of bacterial diseases. Here, we describe the sequencing and genome annotations of two strictly virulent  Escherichia coli bacteriophages that may be explored for biocontrol strategies and to expand the understanding of phage-host interactions.Bacteriophages and their proteins have potential applications in biotechnology for the detection and control of bacterial diseases. Here, we describe the sequencing and genome annotations of two strictly virulent  Escherichia coli is a commensal Gram-negative microorganism inhabiting the gastrointestinal tract of humans and animals, but it is also known for causing fatal infections  and propagation in E. coli BL21. Both phages belong to the Myoviridae family and have broad lytic activity (in 15/32 strains for vB_CEB_NBG1 and 12/32 strains for vB_CEB_NBG2). Phage DNA was extracted as previously described  and manually inspected. Annotation was performed using the RAST server  using a multihost approach of 32 escribed . SequencT server  and a PfT server . tRNAs, T server , GeneiouT server , respectE. coli (\u224850%) , 2 (Arg and Met) and 10  tRNAs, 8 promoters each, and 27 and 20 Rho-independent terminators, respectively. Their GC contents of 37.7% and 35.4% are lower than that of i (\u224850%) .Phages vB_EcoM_NBG1 and vB_EcoM_NBG2 have 269 and 261 predicted open reading frames (ORFs), of which 125 (46.5%) and 134 (51.3%) could be assigned a function, respectively. None of the predicted proteins exhibit homology toward virulence factors, integration-related proteins, or antibiotic resistance determinants; no genomic markers were found indicating a temperate lifestyle. These genetic features make both phages suitable candidates for phage therapy. Also, proteins were identified with exploratory interest, such as tail fibers , endolysins (gene 124 of vB_EcoM_NBG1), holins (genes 251 of vB_EcoM_NBG1 and 239 of vB_EcoM_NBG2), spanin complex (genes 228 and 229 of vB_EcoM_NBG1 and genes 212 and 213 of vB_EcoM_NBG2), and tail-associated lysozymes (gene 156 of vB_EcoM_NBG1 and genes 118 and 139 of vB_EcoM_NBG2).Phylogenetic analysis of the large terminase subunit suggests both phages use a T4-like mechanism of headful packaging, with no preferred packaging signal . As thisEscherichia phage APCEc01 (accession number NC_029091), sharing 98% identity over 98% of its sequence, whereas phage vB_CEB_NBG2 is closely related to Escherichia phage PEC04 (accession number KR233165), with 97% identity over 95% of its sequence. The genome comparisons revealed regions with pronounced differences located mainly in the tail fiber proteins, which likely confer the phages a distinct host lytic spectrum.Comparative genomics revealed that phage vB_EcoM_NBG1 is closely related to MH243438 (vB_EcoM_NBG1) and MH243439 (vB_EcoM_NBG2).The genome sequences have been deposited in GenBank under the accession numbers"}
+{"text": "Gaussia (GLuc) and synthetic NanoLuc (NLuc). As fluorescent reporter proteins, mTurqouise2, tdTomato and iRFP720 were chosen. Specificity of pathway activity was validated by co-transfection with pathway activating genes, showing significant response to induction. In addition, multi-gene plasmids were cloned, allowing the detection of all three pathways by one vector. By using the multi-gene vector 3P-Luc , we could unambiguously demonstrate the crosstalk between pathways, while excluding cross reactivity of luciferase substrates. First studies with synthetic compounds confirmed the applicability of the system for future drug screening approaches.Tracking the activity of signalling pathways is a fundamental method for basic science, as well as in cancer- and pharmaceutical research. The developmental pathways Wnt, Hedgehog and Notch are frequently deregulated in cancers and represent a valuable target for the discovery of novel anticancer compounds. Here we present reporter systems for tracking activity of these pathways by using specific promoter elements driving the expression of either sensitive luciferases or fluorescent proteins. A high level of sensitivity was obtained using the luciferase reporter genes for firefly (FLuc), secreted  Up-reguo models . Nevertho models , but also models . To giveo models . Factorso models . Similaro models . In caseo models .in vivo [Renilla luciferase  [RLuc exhibits a flash kinetic, and FLuc a glow type kinetic. An alternative coelenterazine driven, ATP independent luciferase is derived from Gaussia princeps (GLuc), offering a significantly higher signal [Oplophorus gracilirostris, has been introduced [For reporter systems, all of the aforementioned promoter elements have been mostly used in conjunction with firefly luciferase, which usually requires the lysis of cells or analysis of the supernatant. With luciferases, a very high signal to noise ratio is obtained, and they can be utilised both in cell culture, but also in vivo . On the in vivo  enable ain vivo . With thin vivo . The excin vivo . For lucerazine) . Signal r signal . GLuc istroduced . This extroduced . During troduced . Being atroduced . To avoitroduced . However2 and saturated humidity. L Wnt-3A cells  were maintained under the same conditions, with 0.4mg/ml G-418  supplemented in the medium. Conditioned medium (CM) for activation of the Wnt pathway was produced as described [HeLa  and 293T #CRL-3216, ATCC) cells were cultured in DMEM/4.5g/L glucose  with 10% heat inactivated fetal bovine serum , 2% L-Glutamin  and 1% Penicillin/Streptomycin  at 37\u00b0C, 5% CO6, ATCC cThe 12GLI-RETKO-luc reporter was a gift from Peter Zaphiropoulos (GLI-RET) , CBF:H2BE.coli One Shot Mach1  as per manufacturer\u2019s instructions. All other plasmids were propagated either in E.coli 10-beta  or DH5\u03b1 after heat shock transformation. All bacteria were expanded in LB medium with appropriate antibiotics, and plasmids isolated with commercial miniprep or maxiprep kits . All plasmids were characterized and validated by analytical restriction digests, and additional sequencing (Microsynth), if required.Primers were synthesized by Microsynth . All restriction enzymes were fast-digest variants from ThermoFisher Scientific. For preparative PCR, Q5 high-fidelity DNA polymerase  was used. Digests and PCR products were routinely gel purified in 0.7%-1.5% agarose gels  in sodium borate buffer at 80V. For gel extractions, a commercial kit  was used as per the manual. Ligations were performed with T4 DNA ligase from ThermoFisher Scientific (#EL0011) or Blunt/TA ligase from NEB (#M0367S). For LR reactions, LR clonase plus from ThermoFisher Scientific (#12538120) was used as per the manual. Plasmids from LR reactions were transformed into chemically competent pMuLE_ENTR_TOP-NLuc1.1_L5-L4 : pMuLE_ENTR_MCS_L5-L4 was digested with KpnI and BamHI , M50 Super 8x TOPFlash with KpnI and Hind III , and pNL1.1 with HindIII and BamHI . The three target fragments were ligated together to yield the final plasmid.pMuLE_ENTR_12GLI-FLuc_R4-R3 : A PCR of 12GLI-RETKO-luc was performed . The resulting fragment was cut by SacI and SpeI , and cloned into a SacI/SpeI digested pMuLE_ENTR_MCS_R4-R3 .pMuLE_ENTR_CBF-GLuc_L3-L2 : pMuLE_ENTR_MCS_L3-L2 was cut with EcoRI and XhoI . A segment of CBF:H2B-Venus was PCR amplified  and digested with EcoRI and HindIII . pCMV-Gluc was cut with HindIII and XhoI . The three pieces were ligated to yield the final plasmid.pMuLE_EXPR_CMV-eGFP_TOP-NLuc1.1_GLI-FLuc_CBF-GLuc : An LR reaction of pMuLE_Lenti_Dest_Neo, pMuLE_ENTR_CMV-eGFP_L1-R5, and the three abovementioned ENTR luciferase reporter plasmids was performed.pMuLE_ENTR_TOP-iRFP_L5-L4 : pMuLE_ENTR_MCS_L5-L4 was cut with KpnI and SpeI . A PCR of M50 Super 8x TOPFlash was performed  and the fragment digested with KpnI and PvuII . A segment of pMuLE_ENTR_SV40-iRFP_L3-L2 was also PCR amplified  and digested with PvuII and SpeI . The three fragments were then ligated to yield the final plasmid.pMuLE_ENTR_PTCH1-mTurquoise2_R4-R3 : A segment of pmTurquoise2-NES was PCR amplified  and digested with HindIII and BamHI . pGL3b hPtch1 prom wt was digested with KpnI and HindIII . In a first ligation step, the Ptch1 and mTurquoise2 fragments were ligated and then PCR amplified , The amplified fragment was subsequently digested with KpnI and BamHI  and cloned into KpnI/BamHI digested pMuLE_MCS_R4-R3 .pMuLE_ENTR_CBF-tdTomato_L3-L2 : pMuLE_ENTR_SV40_tdtomato_L3-L2 was PCR amplified  to introduce a new NdeI site, and digested with BamHI/NdeI . Similarly, a fragment of CBF:H2B-Venus was obtained by PCR , digested with the same enzymes , and cloned into the backbone.pMuLE_EXPR_CMV-eGFP_TOP-iRFP_PTCH1-mT2_CBF-tdT : pMuLE_Lenti_Dest_Neo, pMuLE_ENTR_CMV-eGFP_L1-R5, and the three abovementioned ENTR fluorophore reporter plasmids were combined in an LR reaction.EF.hICN1 (phICN1): For easy usage in flow cytometry, the CMV.GFP of the plasmid EF.hICN1.CMV.GFP was removed by digestion with EcoRI (9.2kb fragment), after which it was ligated. pUC19 and pDest were used as control or stuffer plasmids in transfections.Addgene.org  for luciferase reporter assays or in transparent well plates  for fluorophore reporter assays. Twenty-four hours after seeding, cells were transfected using linear polyethylenimine (LPEI) in principle as described . Growth All plasmids used for luciferase transfection are summarised as supplementary material . For sinFor GLuc activity measurements, 20 \u03bcl supernatant was transferred to a new white 96-well plate, 50 \u03bcl coelenterazine buffer  added pFor the 3P-Luc based experiments and their comparison with individual plasmids, cells were co-transfected with 50 ng reporter plasmid plus 25 ng of respective inducer plasmid or pUC19. Luciferase activities were measured as described above. All luciferase activities were normalised to total cell viability using the CellTiter Fluor kit (Promega #G6081). For this, supernatant was completely aspirated and, if required, GLuc measured as described above. 100 \u03bcl of PBS and 20 \u03bcl 5x CellTiter-Fluor reagent was added, and plates incubated up to 2.5h at cell culture conditions. The complete supernatant was then transferred to a black 96-well plate  and fluorescence measured as by manufacturers recommendation. Afterwards, the cells were utilised for FLuc or NLuc measurements.Signal normalization was carried out as follows: First, from each reporter and normalizer value, background was subtracted. Background values were obtained from untransfected cells measured with the indicated assay . An average background value was then substracted from each measured value. Per well, RLUs from reporter were divided by RLUs (or fluorescence units) from the normalisation method. For better graphical representation, the result was multiplied to give values of similar magnitude, using the same factor within one parameter comparison. For single reporter experiments, CMV-driven luciferases were used to normalize RLU values per well.All plasmids used for fluorophore transfection are summarised as supplementary material . 400ng oThe following small molecules were evaluated for induction or reduction of pathway activity. Wnt: CHIR99021(#SML1046) , LY20903Transfections and evaluations were performed as described above, with the following changes: For induction of the Wnt-pathway in the respective wells, 100 \u03bcl conditioned medium  was added to the wells four hours after transfection. Compounds were also added four hours after transfection, at a final concentration of 10\u03bcM. An equal amount of appropriate solvent (water or DMSO) was added to the solvent control conditions.https://www.socscistatistics.com/). For fold induction analysis, results from identical experiments were pooled. Statistical analysis consisted of an unpaired t-test, columns show mean values, and error bars denominate standard deviation.If not mentioned otherwise, all experiments were at least performed twice, with each condition at least in triplicate. Statistical analysis was performed using GraphPad Prism, version 7.02  or an on-line tool  transfected cells  and the signal was measured either 24h (pWnt3a co-transfection) or 48h  thereafter. The activity of all three luciferases was determined for each induction, i.e. NLuc and FLuc in the lysate of cells, and GLuc in the supernatant. This ensured that there was negligible signal overspill between the luciferases. The induction of both Hh or Notch did not significantly activate Wnt . In contIn this work, we generated novel reporters for the Wnt, Hh and Notch pathway, with the overarching goal of creating triple pathway reporter constructs. Such reporters should be desirable for drug screening approaches, but also for investigating biological or pathological entities in real time. We have utilised mostly synthetic promoter elements, consisting of a repeat of identical cis-acting elements interspaced with stuffer sequences and placed either up- or downstream of a basic promoter. The TOPFlash promoter contains seven TCF/LEF binding sites next to a minimal TK promoter , 12GLI-RWe assumed that due to the signal amplifying effects of luciferase enzymes, the signal increase would be considerably reduced when pairing the same promoters with fluorophores. Bauer and colleagues directly compared TOP-GFP and TOP-Luc adding Wnt3a protein, estimating a >100-fold increase in both cases, although apparently saturating amounts of protein were added . In anotIn a next step, we generated triple pathway reporter plasmids with our constructs, which were then also evaluated. We used the approach described by Albers et al., based on Gateway cloning . As back+ and tdTomato+ cells, at induced as well as at control conditions. The reduction in iRFP signal, compared to the single pathway reporter plasmid, can be explained as for 3P-Luc. We assume that the strong absolute signal intensity of eGFP made compensation of existing, but faint mTurquoise and tdTomato signal impracticable, as our compensation matrices show the large overspill of eGFP in these channels. In case of tdTomato, the fluorophore was excited at 488 nm with only approximately 25% efficiency. This and the overall lower promoter strength when compared to CMV caused potentially too much signal overspill. The same can be assumed for mTurquoise2: while the 405 nm diode laser excites the fluorophore with approx. 50% efficiency, it also excited eGFP with approx. 17% . Using flow cytometers with better-suited excitation wavelengths and emission filters would be one option. Alternatively, the CMV-EGFP cassette can be either eliminated and selection of transfected/transduced cells be carried out using neomycin. As another approach, either a weaker, constitutive active promoter or a destabilised EGFP version, e.g. D2EGFP [With the ambitious aim of enabling pathway activity research on the live single cell level, we generated a fluorophore based triple reporter named 3P-Fluor. While the Wnt reporter iRFP was clearly inducible , we were unable to detect a clear population of mTurquoise2. D2EGFP , could bWe also evaluated several small molecules to demonstrate the suitability of our single pathway reporters for such an approach. Only substances with reported activities for up- or downregulation were tested on respective pathways indicators. CHIR99021 and LY2090314 both inhibit GSK3  and hencThe SMO-activating compound SAG did not induce our Hh-reporter, but as it was used at lower concentrations and measured within shorter timeframes, our setup might need optimization . SonidegVPA is a histone deacetylase, leading to the activation of Notch1, which in turn induces Notch signalling via CBF1 . We saw We were also interested to determine the utility of 3P-luc for measuring potential cross-activation between the pathways. This interplay can be of key importance in anticancer therapy, and could help to develop appropriate novel treatment regimens . When eFor further studies, we have generated VSV-G pseudotyped lentiviral vectors with 3P-Luc and 3P-Fluor constructs as transfer plasmids. Cells stably transduced with these multi-reporter lentiviral vectors will be utilized for broader screening approaches.in vitro screening approaches as well as in vivo applications using preclinical imaging. The fluorescence based reporter will need further optimization, but offers possibilities for studying the interplay of Wnt, Hh and Notch pathway and their response to treatment approaches on a single cell level. In principle, also 3P-Luc could be used for such microscopy based approaches, e.g. bioluminescence microscopy [Taken together, we report successful generation and evaluation of a multi-gene, luciferase-based three pathway reporter allowing the analysis of pathway specific regulation and cross-induction studies. Additionally, a functioning fluorescence based 3P-Fluor construct has the potential to enable croscopy , althougS1 FigPlasmid maps of (A) CBF-GLuc and (B) 3P-Luc.(TIFF)Click here for additional data file.S2 Fig293T cells were transfected with (A) TOP-NLuc (substrate: furimazine) or (B) GLI-RETKO (substrate: luciferin); HeLa cells were transfected with (C) CBF-GLuc (substrate: coelenterazine). Co-transfections were carried out with the indicated inducer plasmids or pUC19/pDest (= pMuLE_Lenti_Dest_Neo) as control. . Normalised RLUs were multiplied with 100 (TOP-NLuc), 10 (GLI-RET) or 1000 (CBF-GLuc).(TIFF)Click here for additional data file.S3 Fig(A) Representative experiment showing results of a co-transfection of the two plasmids upon induction with the Hh pathway activating plasmid phGli1 or the control plasmid pUC19 . Normalised RLUs were multiplied with 100. (B) Quantification of signal fold increase upon induction over four independent experiments. .(TIFF)Click here for additional data file.S4 Fig+ cells could be observed after 48h (n = 6).Upon induction of the Notch pathway via co-transfection with phICN1, no increase in tdTomato(TIFF)Click here for additional data file.S5 Fig293 T cells were transfected either with TOP-NLuc (bright grey bars) or CBF-GLuc (dark grey bars) for 24h. Thereafter, supernatant was completely removed, 20 \u03bcL of supernatant incubated with coelenterazine assay reagent and bioluminescence measured (supernatant). Remaining cells were incubated with CellTiter Fluor reagent as described in materials and methods to determine total cell viability. After removing the CellTiter Fluor solution, remaining cells were lysed with 1x passive lysis buffer and bioluminescence measured using either furimazine (lysate) or coelenterazine (lysate). All signals are normalized for cell viability; mean values of two independent experiments are shown (n \u2265 6).(TIF)Click here for additional data file.S6 Fig+ population can be easily detected. In contrast, (B) the gating approach of 3P-Fluor and hGli1 co-transfected cells show no clear-cut population, and gating was tentative. Similarly, (C) a small and reproducible tdTomato+ population was discernible in CBF-tdTomato and EF.hICN1 co-transfected HeLa cells, but (D) not present in compensated 3P-Fluor and EF.hICN1 co-transfected samples. Representative samples shown.(A) 293 T cells were co-transfected with PT-mT2 and phGli1, and an mTurquoise2(TIFF)Click here for additional data file.S1 File(GB)Click here for additional data file.S2 File(GB)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file."}
+{"text": "Plasmodium infection in the vector. The first mosquito organ to interact with the parasite is the midgut and its transcriptomic characterization during infection can reveal effective antiplasmodial responses able to limit the survival of the parasite. The vector response to Plasmodium vivax is not fully characterized, and its specificities when compared with other malaria parasites can be of fundamental interest for specific control measures.Elimination of malaria depends on mastering transmission and understanding the biological basis of P. vivax-blood-fed, blood-fed on inactivated gametocytes, and unfed mosquitoes. Twenty-four hours after feeding, the mosquitoes were dissected and the midgut collected for transcriptomic analysis using RNAseq. Nine cDNA libraries were generated and sequenced on an Illumina HiSeq2500. Readings were checked for quality control and analysed using the Trinity platform for de novo transcriptome assembly. Transcript quantification was performed and the transcriptome was functionally annotated. Differential expression gene analysis was carried out. The role of the identified mechanisms was further explored using functional approaches.Experimental infections were performed using a membrane-feeding device. Three groups were used: P. vivax infection: 34 were upregulated and 15 were downregulated. Half of the P. vivax-related differentially expressed genes could be related to autophagy; therefore, the effect of the known inhibitor (wortmannin) and activator (spermidine) was tested on the infection outcome. Autophagic activation significantly reduced the intensity and prevalence of infection. This was associated with transcription alterations of the autophagy regulating genes Beclin, DRAM and Apg8.Forty-nine genes were identified as being differentially expressed with P. vivax invasion of An. aquasalis midgut epithelium triggers an autophagic response and its activation reduces infection. This suggests a novel mechanism that mosquitoes can use to fight Plasmodium infection.Our data indicate that The online version of this article (10.1186/s13071-019-3506-8) contains supplementary material, which is available to authorized users. Plasmodium vivax is the predominant species accounting for 85% of reported cases . Illumina reads from the An. aquasalis mosquitoes were checked for quality control using FastQC v.0.11.5 (https://www.bioinformatics.babraham.ac.uk) and analysed using the Trinity platform for de novo transcriptome assembly v.2.4.0 [The RNA integrity was confirmed using a 2100 Bioanalyzer . The RNA-seq library preparation and sequencing were performed using total RNA and an Illumina HiSeq v.2.4.0 . Trimmom v.2.4.0 . Transcr v.2.4.0 . TransdeedgeR v.3.16.5 package [P-value to control the false discovery rate (Benjamini\u2013Hochberg adjustment) was less than 0.05 and if log fold change was higher than 1. Differentially expressed genes were further analysed for functional classification using gene ontology analysis on PANTHER (http://www.pantherdb.org) [Differential expression (DE) analysis was performed using GLM test in the  package  in R. Pardb.org) . The datP. vivax (Pv) and fed on blood in which gametocytes were inactivated (Bl) were chosen for real-time quantitative PCR analysis which was performed as described in [In order to validate transcriptome analysis, a total of 8 differentially expressed genes between mosquitoes fed on blood with infective ribed in . For thiribed in . The cDN\u2212\u0394\u0394CT method. The ribosomal protein S7 was used as the endogenous control.Real-time quantitative PCR was performed on a Fast 7500 instrument  with SYBR Green Power Master Mix (Applied Biosystems) using 2 \u03bcl of cDNA template in a final volume of 20 \u03bcl reaction mixture. Fold-changes of gene expression were analysed using the 2P. vivax infection revealed a variety of transcripts that play a key role in autophagy. In order to evaluate the effect of the autophagy process in the outcome of infection, we inoculated mosquitoes with wortmannin (an inhibitor of phosphatidylinositol 3-kinase DPI3K) and spermidine (an autophagy activator) [2O Ultra Pure and with 69 nl of a 100 \u03bcM solution of spermidine (Sigma) or DMSO (0.05%) using a Nanoject micro-injector . Twenty-four hours after injection with the solutions, the mosquitoes were fed with a P. vivax-infected blood meal as described above. Three independent biological replicates were performed for each experiment. Mosquitoes were dissected 18\u201324 h after feeding; batches of 20\u201330 midguts were dissected in cold DEPC-treated phosphate-buffered saline (PBS) and processed for RNA preparation and cDNA synthesis using the same protocols mentioned above. Mosquito midguts were also collected on the 8th day post-infection to determine the prevalence and intensity of infection.The transcriptome associated to tivator) , 37. ThrBeclin, DRAM and Apg8) was investigated 18\u201324 h after P. vivax infection and 24 h after inhibition and activation of autophagy (treatment with wortmannin or spermidine) as described above.The expression of genes that regulate autophagy , two-sample comparisons were done using the non-parametric Mann\u2013Whitney test. The differences in the infection rate between the control group and the tested groups were compared using Fisher\u2019s exact one-tailed test (F). Comparisons of mRNA expression levels obtained by RT-qPCR between the control and the tested groups were done using the Mann\u2013Whitney one-tailed test. Statistical analyses were performed using the software GraphPad Prism v.6.00.An. aquasalis midguts were constructed and sequenced, namely three libraries for each of the following groups: (i) P. vivax-blood-fed mosquitoes (Pv: groups Pv1 to Pv3); (ii) mosquitoes fed on P. vivax-blood from which gametocytes were inactivated (non-infected: groups Bl1 to Bl3); and (iii) unfed mosquitoes (unfed groups: Unf1 to Unf3). The obtained mean number of high quality paired-end short reads were: 56,217,833 , 45,546,489  and 46,523,955  for each group, respectively  and mosquitoes fed on non-infective blood (Bl) or unfed mosquitoes, FDR\u2009<\u20090.05 and logFC\u2009>\u20091 (fold change) were used as the threshold to classify differentially expressed genes. The analyses showed a total of 12,942 expressed genes. Of these, 49 genes were identified as differentially expressed genes in the P. vivax infected-blood-fed group (Pv) in relation to non-infected-blood-fed group (Bl); 34 were upregulated and 15 were downregulated, which represents differential expression associated to P. vivax infection  when compared to the unfed group (Unf); of these, 65 were upregulated and 46 were downregulated  of the differentially expressed genes exclusively in this group  of the differentially expressed genes in the son Fig.\u00a0 and invooup Fig.\u00a0.Fig.\u00a03PrPv\u2009\u00d7\u2009Bl  between the qRT-PCR and the RNAseq data.To validate the robustness of RNAseq results, we analysed eight genes by real-time qRT-PCR and compared the expression of these genes in P. vivax infected-blood-fed group (Pv) in relation to non-infected-blood-fed group (Bl); of these, 34 were upregulated and 15 were downregulated. From these, genes involved in cellular process, metabolic process (GO: 0008152), cellular component organization or biogenesis process (GO: 0050896) and biological regulation process (GO: 0065007) were predominant  when compared to the mosquitoes fed on inactivated gametocytes (Bl). LRR-containing protein 58 has been previously associated with the An. gambiae response to Plasmodium berghei infection [Activation of mosquito immunity genes has been traditionally associated with midgut-infected mosquitoes. In the present study, a transcript coding for a leucine-rich repeat protein, orthologue of LRR-containing protein 58 (TRINITY_DN6165_c5_g1_i4), was found upregulated in mosquitoes infected with nfection , 39. LRRnfection . InformaPlasmodium vivax is probably able to modulate detoxification of free radicals while invading the midgut of An. aquasalis, as suggested by the increase of H2O2 following artificial reduction of catalase activity which leads to increased parasite infection in the mosquito midgut. As gene silencing also decreases the midgut microbiome, Bahia et al. [Pv\u2009\u00d7\u2009Bl group, as was its orthologue in deltamethrin-resistant An. gambiae mosquitoes when compared with a sensitive mosquito line from Kenya [a et al.  suggest om Kenya . NAD+ kiom Kenya .Anopheles gambiae midgut response to P. berghei ookinete invasion is characterized by profound alterations in the transcription of genes that modulate the architecture of the cytoskeleton [Plasmodium parasites need to modify the cytoskeleton of mosquito epithelial cells to successfully complete their life-cycle. We found several downregulated genes  that could be associated with cytoskeleton remodelling, which reinforces the prominent role of this cellular mechanism in response to Plasmodium and extends it to P. vivax ookinete invasion of the mosquito midgut.skeleton . PlasmodAn. aquasalis females following a P. vivax infected blood meal. Forty-nine percent of differentially expressed genes during invasion (60.0% of the upregulated and 44.1% of the downregulated genes) could be associated with autophagic processes  was downregulated, as was \u03b2-arrestin (TRINITY_DN5911_c0_g1), which uncouples GPCRs from their G-proteins, and suggests that regulation of free radical production might occur through this molecule. In radicals . Wang etradicals , using aAn. aquasalis midgut increases intracellular trehalose by upregulating trehalose 6-phosphate synthase/phosphatase (TRINITY_DN6177_c2_g2) and downregulating the TreT1-facilitated trehalose transporter (TRINITY_DN5823_c0_g1), suggesting autophagy induction in the An. aquasalis midgut during P. vivax infection. Anopheles gambiae TreT1 RNA silencing reduces the number of P. falciparum oocysts in the mosquito midgut [Trehalose is a natural sugar found in prokaryotes, yeast, fungi, plants and invertebrates, and serves not only as a reserve of carbohydrate, but can also protect organisms and cells against adverse environmental conditions. Some controversy exists on the real effect of trehalose on autophagy. In murine models, trehalose seems to induce autophagy, while in cultured cells it could inhibit fusions of autophagosomes and lysosomes, thus blocking the final stage of autophagy . Our dato midgut , suggestP. vivax invasion of the midgut epithelium, and it is possible that this is associated to autophagy.Microtubules (MT) are important to autophagosome formation and motility. Dynein light chain 1 (TRINITY_DN6473_c3_g4), a motor protein, was upregulated upon infection in our study. In vertebrates, Beclin-1 is sequestered in MT in complexes containing dynein light chain 1. When autophagy is stimulated, Beclin-1 is released from this complex. In parallel, c-Jun N-terminal kinase-1 (JNK1) is activated which allows phosphorylation of Bcl-2 and Bim, which, in turn, releases Beclin 1 and contributes to autophagosome formation . RegardiDrosophila [An. stephensi midgut extended lifespan and enhanced resistance to P. falciparum. In the present study, we found that the AP-1 transcription factor (TRINITY_DN6454_c2_g2), a downstream product of this signalling pathway, was upregulated in mosquitoes fed on P. vivax blood.JNK signalling has been demonstrated to be involved in lifespan control and is required in differentiated cells of the intestinal epithelium in order to prevent excessive sensitivity of these cells to oxidative stress in osophila  and has osophila . Garver osophila  showed tosophila  demonstrAn. aquasalis, An. stephensi and An. gambiae during Plasmodium parasite infection, which limits parasite development within the mosquito [P. vivax. OTU domain-containing proteins are deubiquitinating enzymes and cleave distinct sets of ubiquitin chain types [P. vivax invasion of An. aquasalis midgut epithelium triggers an autophagic response. ATG3, among other enzymes, is involved in the maturation of the growing autophagosome, a process that occurs once autophagy is initiated. Recently, Frudd et al. [An. aquasalis [The ubiquitin machinery regulates fundamental biological processes within eukaryotic cells. The enrichment of functional terms such as ubiquitin-dependent proteasome was also denoted for insects facing dehydration stress . Nitric mosquito \u201357. Ubiqin types . In vertin types . USP10 rin types . Since ud et al.  describequasalis , togethevia inhibition of mTORC1 and mTORC2, while it impairs autophagy via upregulation of mTORC1/2 activities. Prominin (TRINITY_DN6489_c3_g1) was upregulated during parasite invasion of the midgut epithelium, which suggests that autophagy might be activated during this stage of infection.Overexpression of prominin 1 constitutively activates autophagy in the human retinal pigment epithelium P. vivax invasion affects regulation of different stages of the autophagic process and includes autophagosome maturation and degradation.Lipid droplet (LD) homeostasis  also plaPlasmodium invasion of the midgut epithelial cell leads to a number of molecular and morphological changes, including cell death. Vlachou et al. [An. stephensi in response to P. falciparum invasion [Plasmodium vivax infection of the midgut positively regulated the expression of this gene (TRINITY_DN6333_c5_g2).u et al.  proposedinvasion  has beeninvasion  mediatedinvasion . IntegriDrosophila epidermal growth factor receptor (EGFR) pathway has been implicated in the control of delamination and anoikis of damaged enterocytes following oral bacterial infection [Serratia marcescens infection of An. gambiae activates the EGFR pathway by modulating the outcome, possibly through synergistic functions in gut homeostasis [P. vivax infection. This probably contributes to gut hemostasis through autophagy. Subcellular localization of the EGFR seems to be determinant on the effect on autophagy, being either an inhibitor or stimulant [The nfection  and Serreostasis . Spitz \u2009=\u20093.913, P\u2009=\u20090.0021). A reduction of 44.9% (58.6 to 32.3%) in IP and of 47% in II (25.7 to 13.6%) was observed. Wortmanin treatment resulted in a 54.3% reduction in IP and a 65% reduction in II when higher doses were used, while the 0.05 \u00b5M doses resulted in a low reduction (7.9%) of IP and a 5.9% increase in II ; II: t-test: W-5 \u00b5M*control, t(4)\u2009=\u20092,528, P\u2009=\u20090.0648; W-0.05 \u00b5M*control, t(12)\u2009=\u20090.4003, P\u2009=\u20090.6960)  and infection intensity (II) were significantly lower: IP: Mann\u2013Whitney U-test: The differences between the two doses of wortmannin are probably a consequence of the drug mode of action. Wortmannin is a PI3-kinase inhibitor, therefore, as autophagosome formation requires class III PI3-kinase activity, it is normally used to study the effect of autophagy inhibition. Nonetheless, wortmannin can also inhibit class I PI3-kinase activity (which inhibits autophagy) and can also inhibit mTOR (an autophagy-inhibitory molecule) . FurtherP. vivax infection after treatment. Polyamine biosynthesis inhibitors cause growth arrest of P. falciparum blood stages in vitro but show no effect on survival of mice infected with P. berghei . Despitermidine . This inPlasmodium infection, qRT-PCR was used to quantify the changes in gene expression in response to a P. vivax-infected blood meal. A differential expression analysis of several autophagy genes, including DRAM, Apg8 and Beclin, during inhibition and activation of autophagy, was performed. Atg8 protein, formerly known as Apg8/Aut7 is part of a group of proteins that control autophagy, many of which also participate in direct cytoplasm-to-vacuole transport of proteins [DRAM-1), which belongs to an evolutionarily conserved family of proteins that encodes for a lysosomal protein that is required in order to induce autophagy [To further characterize the role of autophagy in the mosquitoes treated with autophagy inhibitor in response to proteins , 81. Amoutophagy , 83, andutophagy .beclin, which was downregulated after mosquitoes were treated with spermidine (P\u2009=\u20090.0635), suggesting that spermidine is downregulating this gene while exerting a negative effect on P. vivax sporogonic development.No major differences were observed in expression of these genes after treatment with both drugs when compared with infection without treatment Fig.\u00a0. This isP. vivax invasion of the mosquito midgut epithelium. A vast number of genes associated with autophagy were regulated by infection of which 60% were upregulated. Furthermore, when autophagy was inhibited by spermidine, we observed a significant reduction of the prevalence and intensity of infection. In view of our results, we propose that when ookinetes invade the midgut cells they trigger host cell morphological rearrangement, with actin and microtubule remodelling and production of nitrogen and oxygen radicals and possible cell death. To counterbalance invaded epithelial cell death/extrusion and other injuries parasites, could trigger an autophagic mechanism that would restrain parasite development, possibly through GPCR signalling Methuselah, the increase of intracellular trehalose, and detachment from the excellular matrix. This effect was apparent by the regulation of genes that could be assigned to the different stages of autophagy  [Plasmodium invasion in epithelial midgut cells is a novel mechanism for mosquitos in order to fight Plasmodium infection.Our results clearly indicate that autophagy is regulated by adation) . AutophaAdditional file 1: Table S1. List of primers used in qRT-PCR analysis.Additional file 2: Table S2. Lists of differentially transcribed genes Pv\u2009\u00d7\u2009Bl_FDR\u2009<\u20090.05. Table S3. Lists of differentially transcribed genes Pv\u2009\u00d7\u2009Unf_FDR\u2009<\u20090.05. Table S4. Lists of differentially transcribed genes Pv\u2009\u00d7\u2009Bl_Total. Table S5. Lists of differentially transcribed genes Pv\u2009\u00d7\u2009Unf_Total.Additional file 3: Figure S1. Validation of RNAseq analysis using qRT-PCR. Gene expression values for eight genes obtained by RNAseq were plotted against the corresponding averages of three qRT-PCR-derived gene expression values from biological replicates. The Pearson\u02bcs correlation coefficient (0.874) and the best-fit linear-regression analysis R2\u2009=\u20090.7663 demonstrated a good degree of correlation between gene expression determined by each assay. Genes used for validation: TRINITY_DN4493_c0_g1_i2, TRINITY_DN5277_c0_g1_i2, TRINITY_DN5911_c0_g1_i4, TRINITY_DN6055_c0_g1_i13, TRINITY_DN6039_c0_g1_i17, TRINITY_DN6296_c2_g1_i5, TRINITY_DN6531_c1_g1_i4, TRINITY_DN6536_c2_g8_i1."}
+{"text": "Coronary heart disease (CHD) is a complex disease caused by multi-factors and a major threat to human health. Circular RNAs (circRNAs) have critical roles in various biological processes and diseases. This study explores the independent role of circRNAs and their interaction with environmental factors in CHD.A case\u2013control study was conducted from March 2015 to September 2017 in Fuzhou, China. A total of 585 CHD patients and 585 gender- and age-matched healthy controls were enrolled. Questionnaire survey, health examination and molecular biology laboratory testing were conducted. Microarray technology and quantitative real-time polymerase chain reaction (PCR) were used to profile the expression levels of circRNAs. The area under the curve (AUC) of the receiver operating characteristic (ROC) was used to determine the diagnostic cut-offs. Multivariate logistic regression and multiplicative analysis were used to analyse the effects of environmental factors and hsa_circ_0008507, hsa_circ_0001946, hsa_circ_0000284 and hsa_circ_0125589 on CHD.P\u2009<\u20090.05, but none pass multiple testing correction. qRT-PCR further confirmed the expression levels of hsa_circ_0008507, hsa_circ_0001946 and hsa_circ_0000284 in peripheral blood leukocytes in CHD cases were higher than those in non-CHD subjects (All p\u2009<\u20090.05). Hsa_circ_0008507 , hsa_circ_0001946  and hsa_circ_0000284  were independent risk factors for CHD after controlling other common environmental risk factors. The AUC for hsa_circ_0008507, hsa_circ_0001946 and hsa_circ_0000284 was 0.75, 0.71 and 0.68, respectively. Compared with non-smoking individuals with low hsa_circ_0008507 expression, the smokers with high hsa_circ_0008507 expression showed the highest magnitude of OR in CHD risk. Additionally, a statistically significant multiplicative interaction was found between hsa_circ_0008507 and smoking for CHD.The expression profile of circRNAs showed that 3423 circRNAs were differentially expressed at Hsa_circ_0008507, hsa_circ_0001946 and hsa_circ_0000284 were closely related to the occurrence and development of CHD. The combination of smoking and high hsa_circ_0008507 expression causes the occurrence and development of CHD.The online version of this article (10.1186/s12872-019-1191-3) contains supplementary material, which is available to authorized users. Coronary heart disease (CHD) is a leading global cause of death with annually increasing mortality and morbidity , 2. FamiCircular RNAs (circRNAs) are currently noticeable in the field of RNA. They form covalently fused loops, where the RNA\u2019s 5\u2032 end fuses to its 3\u2032 end and removes the 5\u2032 Cap and poly(A) tail . Recent In this study, 585 CHD patients and 585 healthy controls were enrolled to evaluate the effects of environmental factors on CHD risk. Microarray and experiments involving these populations were conducted to explore the roles of dysregulated circRNAs in the CHD cases. circRNA have been tested in tissues, serum, exosomes and other body fluids in various diseases , 16. In 0\u2009=\u20090.260 and OR\u2009=\u20091.70. Calculations using PASS revealed N1\u2009=\u2009N2\u2009=\u2009379 people. In this study, 1300 questionnaires, including 650 cases and 650 controls, were distributed, and all were collected between March 2016 and September 2017 from the First Affiliated Hospital of Fujian Medical University and the Affiliated Union Hospital of Fujian Medical University, China. After corresponding the age and gender, 585 CHD patients and 585 controls were enrolled in this study. CHD was defined using the following criteria: (1) significant stenosis (\u2265 50%) of >\u20091 major coronary artery was confirmed by present cardiac catheterisation, (2) documented history of prior myocardial infarction (MI) and a prior coronary revascularisation procedure (percutaneous coronary intervention or coronary artery bypass graft), (3) patients in the stable stage after acute MI and patients with ST-segment elevation/depression on ECG. The subjects without medical history of cardiovascular diseases were selected as the non-CHD subjects. All subjects were long-term residents in Fujian, subjects with other types of heart disease, serious brain organic diseases, malignant tumours, hepatic or renal dysfunction, recent infections and endocrine system diseases were excluded from the study. This study protocol conformed to the ethical guidelines of the Declaration of Helsinki. The protocol was approved by the ethics committee of Fujian Medical University School. All selected patients and controls provided informed consent.A frequency-matched case-control study was conducted in which case groups and controls must have the same gender and age ratio (\u00b13\u2009years). A research factor with a small OR was selected to calculate the appropriate sample size, according to the pre-experimental results and considered the following: \u03b1\u2009=\u20090.05, \u03b2\u2009=\u20090.10, P2 and categorised to three scales as follows: <\u200918.5\u2009kg/m2 underweight, 18.5\u201324.0\u2009kg/m2 normal and\u2009\u2265\u200924.0\u2009kg/m2 overweight and obese. A male waistline cutoff of 85 or higher means abdominal obesity, and a female waistline cutoff of 80 or more means abdominal obesity. According to gender, WC was classified into two categories: A male WC with a cutoff of 85 or higher implies abdominal obesity, whereas a female WC with a cut-off value of 80 or more suggests abdominal obesity.All interviewer who conducted the standard questionnaire were specially trained. The questionnaire included demographic characteristics , lifestyle habits , social psychological factors , Physiological index ) and family history of cardiovascular disease. The questionnaire was listed in Additional\u00a0file\u00a0p\u2009<\u20090.05 according to the t-test. The data were log-2 transformed and median-centred by genes through CLUSTER 3.0 software and then analysed using hierarchical clustering with average linkage.The samples used for microarray analysis include 4\u201312 cases . Therefo1\u2009=\u2009N2\u2009=\u200925, considering \u03b1\u2009=\u20090.05, \u03b2\u2009=\u20090.10, P0\u2009=\u20090.50 and OR\u2009=\u20091.8. Therefore, at least 25 samples were required in each group. Therefore, 30 cases and controls were finally determined for peripheral blood quantitative real-time polymerase chain reaction (qRT-PCR) validation, and 100 cases and controls were used for peripheral blood leukocyte qRT-PCR validation. A fast total RNA extraction kit  was used to extract total RNA from peripheral blood in 30 CHD patients and 30 non-CHD controls. Total RNA from peripheral blood leukocytes in 100 CHD patients and 100 non-CHD controls was extracted by using TRIzol reagent . The examination of RNA concentration, purity, and integrity were as described above.Representative subjects were selected using stratified sampling from the first part of the study. The subjects in the case group were divided into two layers according to age (i.e. <\u200965\u2009years old and\u2009\u2265\u200965\u2009years old) and stratified by gender according to each age group. The sex ratio of all subjects in the case group were determined. According to the random number table, the corresponding number of male and female subjects was finally selected at each age level. The control group were also sampled using the same method. According to the pre-experimental results, the PASS software obtained the following result: N-\u25b3\u25b3CT method was used to calculate the expression levels of circRNAs. The primer sequences are shown in Additional\u00a0file\u00a0Reverse transcription of quantified RNA was performed using PrimeScript RT Reagent Kit  according to the manufacturer\u2019s instructions. qRT-PCR was used to measure the expression levels of circRNAs, Which was performed on the LightCycler 480 Real-Time PCR System  with the SYBR\u00ae Premix Ex Taq\u2122 II kit . The reaction conditions were listed as follows: Amplification curves were obtained by 45\u2009cycles of 95\u2009\u00b0C 30\u2009s, 95\u2009\u00b0C 5\u2009s, 60\u2009\u00b0C 34\u2009s, whereas dissolution curves were obtained by one cycle of 95\u2009\u00b0C 15\u2009s, 60\u2009\u00b0C 1\u2009min, 95\u2009\u00b0C 15\u2009s. GAPDH was used as internal control. 22) test. The diagnostic cut-offs of circRNAs were obtained from the receiver operating characteristic (ROC) curve. Univariate analysis of meaningful variables was included in the multivariate analysis. Crossover analysis was used to assess the association between CHD and risk factors. A p-value <\u20090.05 (two-tailed) was considered significant. All statistical analyses were performed using SPSS 25.0 software.Data with normal distribution were presented as the mean value\u00b1SD and compared using two-tailed Student\u2019s t-test. Skewed data were represented as median (25th\u201375th quartile) and compared using Mann\u2013Whitney U test. Discrete variables were displayed as percentages, distribution differences were examined by the chi-square (\u03c7p\u2009>\u20090.05), thereby indicating that the frequency matching was adequate  and abdominal obesity were significantly different in CHD and non-CHD subjects. However, light diets and physical exercise (one to two times per week) were protected factors for CHD. Unconditional logistic regression analysis was further used to evaluate the associations between the environmental factors  and CHD. Significant increased risk effects for CHD were associated with anxiety {odds ratio (OR)\u2009=\u20092.34; 95% confidence interval (95% CI): 1.67\u20133.28} and being overweight (BMI\u2009\u2265\u200924.00) {OR\u2009=\u20091.47; 95% CI: 1.13\u20131.91}. Physical exercise (one to two times per week) {OR\u2009=\u20090.41; 95% CI: 0.23\u20130.75} was the protective factor for CHD . Four circRNAs were selected for qRT-PCR validation analysis to independently validate our results and determine the roles of circRNAs in CHD. Selection was based on the following: (1) hsa_circ_0008507, hsa_circ_0125589 and hsa_circ_0000284 are among the most abundant and have significantly differentially expression according to microarray analysis, (2) overexpression of hsa_circ_0001946 in cardiac myocyte cells promotes cell apoptosis [The heat map, volcano plot and scatter plot of microarray assay showed abnormal expression of circRNAs in the CHD cases Fig.\u00a0. The micpoptosis  and silepoptosis . In summWe selected 30 CHD cases and 30 non-CHD controls with corresponding age and gender as qRT-PCR subjects. The expression levels of hsa_circ_0001946 and hsa_circ_0000284 in CHD were significantly elevated compared with those in the non-CHD subjects   Fig.\u00a0. These rTwo logistic regression analysis models were further used to analyse the association of factors  with circRNA. CHD was the dependent variable , and the related environmental factors and four verified circRNAs in peripheral blood leukocytes were the independent variables. The results suggested that the high expression levels of hsa_circ_0008507 {, }, hsa_circ_0001946 {, }, hsa_circ_0000284 {, } were the independent risk factors of CHD in the two models . The maximum Youden\u2019s index was 0.46 with a sensitivity of 0.86 and a specificity of 0.60, corresponding to a diagnostic cut-off of 1.975. The ROC curve of hsa_circ_0001946 showed that the AUC was 0.71 (95% CI: 0.64\u20130.79). The maximum Youden\u2019s index was 0.37 with a sensitivity of 0.85 and a specificity of 0.52, corresponding to a diagnostic cut-off of 1.00. The ROC curve of hsa_circ_0000284 showed that the AUC was 0.68 (95% CI: 0.61\u20130.76). The maximum Youden\u2019s index was 0.37 with a sensitivity of 0.66 and a specificity of 0.71, corresponding to a diagnostic cut-off of 1.42  . A statistically significant multiplicative interaction was found between hsa_circ_0008507 and smoking for CHD  in cardiac myocyte cells promotes cell apoptosis . In the Hsa_circ_0000284  mediates retinal vascular dysfunction in diabetes mellitus . RetinalThe qRT-PCR results were incompletely consistent with those of the microarray analysis. The possible causes for this contradiction are as follows: (1) microarray analysis is only a tool for initial screening. It has high sensitivity and poor reliability. Therefore, the final result is based on the PCR verification result. (2) Differences in research objects: the same sample could not satisfy the requirement for microarray analysis and PCR verification at the same time due to the limited number of experimental samples. Therefore, the two groups of subjects tested on the microarray analysis and validated by PCR are different. (3) Sample size: the microarray analysis is costly; only five cases and five controls were selected, and the individual differences were not excluded.Peripheral blood leukocytes can participate in the development of CHD through processes, such as blood vessel adhesion , 28. TheStratified analysis shows that hsa_circ_0008507, hsa_circ_0001946 and hsa_circ_0000284 exhibit significant differences in various populations. A statistically significant multiplicative interaction is found between hsa_circ_0008507 and smoking. Smoking can reduce the bioavailability of nitric oxide, further promote the expression of adhesion molecules and endothelial dysfunction and increase the adhesion of platelets and macrophages, thereby leading to coagulation and inflammation. Smoking can also promote the differentiation of macrophages into foam cells, thereby aggravating the progression of CHD , 30. TheThis study has the following deficiencies. (1) In this case\u2013control study, we collected the subject\u2019s exposure through a structured questionnaire. Although objective data were selected as much as possible, the possibility that the respondent\u2019s memory is distorted or incomplete still exists. Hence, information bias is inevitable. (2) The representative sample is insufficient because the cases and controls were only obtained from two hospitals in Fuzhou, indicating admission rate bias. (3) The expression level of circRNAs cannot be dynamically evaluated due to the lack of detection at multiple time points and the limitation on the causal association between circRNA and CHD.Hsa_circ_0008507, hsa_circ_0001946 and hsa_circ_0000284 are closely related to the occurrence and development of CHD. In addition, the combination of smoking and high hsa_circ_0008507 expression induces the occurrence and development of CHD.Additional file 1:Table S1. the questionnaire used for investigation. The questionnaire included demographic characteristics , lifestyle habits , social psychological factors , Physiological index ) and family history of cardiovascular disease. (DOC 62 kb)Additional file 2:Table S2. Baseline characteristics of subjects used for microarray analysis. Five patients with similar age, disease and disease duration and with no other diseases and five controls with similar general conditions, age and sex was selected for microarray analysis. (DOC 37 kb)Additional file 3:Table S3. List of the primers used for qRT-PCR experiments. The primers used for qRT-PCR experiments was listed in Table S3. (DOC 32 kb)"}
+{"text": "Cassava brown streak virus (CBSV) and Ugandan cassava brown streak virus (UCBSV) infectious clones (IC), which can be used to gain insights into the functions of viral proteins and sequences associated with symptom development. In this study, we perform the first reporter gene tagging of a CBSV IC, with the insertion of green fluorescent protein (GFP) sequence at two different genome positions. Nicotiana benthamiana infections with the CBSV_GFP ICs revealed active CBSV replication in inoculated leaves at 2\u20135\u00a0days post inoculation (dpi) and systemic leaves at 10\u201314 dpi. We also constructed the chimera CBSV_UCP IC, consisting of the CBSV genome with a UCBSV coat protein (CP) sequence replacement. N. benthamiana infections with CBSV_UCP revealed that the CBSV CP may be associated with high levels of viral accumulation and necrosis development during early infection. These initial manipulations pave the way for U/CBSV ICs to be used to understand U/CBSV biology that will inform vital CBSD control strategies.Cassava brown streak disease (CBSD) is a leading cause of cassava yield losses across eastern and central Africa and is having a severe impact on food security across the region. Despite its importance, relatively little is known about the mechanisms behind CBSD viral infections. We have recently reported the construction of The online version of this article (10.1007/s11262-019-01697-5) contains supplementary material, which is available to authorized users. Across consumed . Cassavaconsumed . Unlike consumed  and so iconsumed . CBSD syconsumed .Cassava brown streak virus (CBSV) and Ugandan cassava brown streak virus (UCBSV) collectively termed U/CBSVs, which belong to the family Potyviridae of the Ipomovirus genus [Bemisia tabaci (whitefly) [CBSD is caused by at least two related viral species: us genus \u20138. U/CBShitefly) , howeverhitefly) , 11.CBSV and UCBSV produce distinct symptom types in different cassava cultivars and indicator hosts; CBSV tends to cause more severe necrosis and accumulates to higher titers, compared with UCBSV , 12\u201314. CBSV and UCBSV genomes typically share \u2248\u200970% nucleotide sequence similarity . HoweverEscherichia coli. We have recently constructed three U/CBSV ICs, which can be manipulated to enable characterization of viral sequences involved with symptom development, pathogenicity, host-range, host interactions, movement and vector transmission [Despite the importance of U/CBSVs, relatively little is known about their fundamental molecular biology and sequences associated with symptom development. Progress has been hampered by the lack of U/CBSV infectious clones (IC) due to sequence instability during propagation in smission . In thisNicotiana benthamiana plants were grown in growth cabinets 28\u00a0\u00b0C with a 16\u00a0h/8\u00a0h: light/dark cycle. Plants were agroinfiltrated with CBSV_Tanza IC plasmids according to the protocol described in Duff-Farrier [Infected plant material and viral ICs were used under the DEFRA license No. 51045/197610/2 and handled according to Brewer et al. . Nicotia-Farrier  and mech-Farrier .E. coli TOP10 cells. To confirm construction, the modified CBSV_Tanza ICs were analyzed by restriction digestion, PCR and Sanger sequencing. The genome structures for the modified CBSV_Tanza ICs are provided in Fig.\u00a0Modifications of the CBSV_Tanza IC were performed using homologous yeast recombination according to Duff-Farrier . SchematN. benthamiana 16c line (kindly donated by Professor Sir David Baulcombe), which constitutively expresses GFP [For CBSV_GFP1/2 ICs proteolytic cleavage sites were included flanking GFP to enable cleavage of from the CBSV polyprotein. In CBSV_GFP1, the Ham1-CP cleavage site: I-D-V-Q-/A was added either side of GFP, whereas in CBSV_GFP2, the NIa\u2013NIb cleavage site: I-S-V-Q-/A was added to the 5\u2032 of the GFP sequence and no cleavage site was necessary at the 3\u2032 end, as GFP is the last peptide in the polyprotein . Proteolytic cleavage site sequences were designed so that third base of each codon was modified to a different nucleotide to encode the same amino acid but reduce spurious homology with the corresponding cleavage sequence elsewhere in the genome. This was an attempt to reduce homologous recombination between the two cleavage sequences, which could result in deletion of the GFP sequence. GFP sequence was amplified by reverse transcription PCR (RT-PCR) on the sses GFP .E. coli, prevented the use of the UCBSV_CCP IC, consisting of a UCBSV Kikombe genome with CBSV Tanza CP replacement.For CBSV_UCP, the genome structure was designed to consist of the CBSV Tanza genome with a UCBSV Kikombe CP replacement Fig.\u00a0. In the N. benthamiana [\u2212\u0394\u0394Ct method [To detect viral infections, RT-PCR was performed on 1\u00a0\u03bcg of plant RNA using a First Strand cDNA synthesis kit and an oligo d(T)18 primer. Viral specific primers were then used to amplify RT-PCR fragments from the cDNA, and RT-PCR fragments were then Sanger sequenced. Quantification of viral transcript abundance was performed using quantitative real-time PCR (qPCR) on diluted cDNA. To amplify CBSV transcripts, primers were used which target the CBSV CP and the F-Box gene was used as the endogenous reference gene, as it is reported to show relatively stable expression during viral infections of thamiana . All qPCt method . The abuPlants were assessed for disease symptoms and rated according to the following scoring system, adapted from Ogwok et al. : 1\u2009=\u2009no Green fluorescence was visualized in whole leaves using a Leica CL5 Fluorescence microscope with the GFP2 filter (480/40\u00a0nm excitation and 510\u00a0nm barrier) and a confocal microscope (Leica TCS SP5) was used to visualize GFP in individual cells.N. benthamiana plants were agroinfiltrated with the unmodified CBSV_Tanza IC, the CBSV_GFP1 IC or the CBSV_GFP2 IC. Infections were performed in three repeat experiments, which produced the following consistent results. During both CBSV_GFP1 and CBSV_GFP2 infections, GFP was visible in epithelial and mesophyll cells of agroinfiltrated leaves at 2\u20135\u00a0days post inoculation (dpi), the vascular system of upper leaves at 7 dpi and epithelial and mesophyll cells in the lamina of upper systemic leaves at 10\u201314 dpi  and amplicon sequencing . This demonstrates that GFP can be precisely deleted from modified CBSV during N. benthamiana infections.Compared with the highly necrotic symptoms that developed during infections with the unmodified CBSV_Tanza IC, CBSV_GFP1 and CBSV_GFP2 infections were asymptomatic . QPCR analysis also demonstrated that viral transcripts were dramatically lower in CBSV_GFP1/2 infected plants, compared with unmodified CBSV_Tanza infections . This suggests that the insertion of the GFP sequence has a large effect on CBSV symptom development and viral accumulation. Attempts to use CBSV_GFP1/2 infected leaf material to mechanically back-inoculate To determine whether the CBSV Tanza and UCBSV Kikombe CP sequences are representative of their respective species, a phylogenetic tree was built with 19 CBSV and 23 UCBSV CP amino acid sequences. This revealed that that the CBSV Tanza and UCBSV Kikombe CP sequences cluster within their respective species clades and so should be relatively representative of their respective species .N. benthamiana plants. Infections were performed in three repeat experiments, which produced the following consistent results. N. benthamiana agroinfiltrated with CBSV_UCP developed systemic symptoms , demonstrating that differences in symptom development during CBSV_Tanza and CBSV_UCP infections were not dependent on inoculation method.Differences in symptom development were observed during infections with CBSV_UCP compared with unmodified CBSV_Tanza Fig.\u00a0. At 7\u00a0dpN. benthamiana is consistent with untagged CBSV localization to epidermal and mesophyll cells and phloem, detected using immune-histochemical staining of CBSD infected cassava [N. benthamiana appeared to peak at 14\u00a0dpi, which may correspond to a peak in CBSV replication, as was identified during untagged CBSV infections , green fluorescence was visible in upper systemic leaves at 5\u00a0dpi [N. tabacum infections with GUS tagged Tobacco etch virus (TEV), GUS was detected in roots at 1\u00a0dpi and stems at 2\u00a0dpi [N. benthamiana may be due to inherent differences during CBSV and PVX/TEV infections of Nicotiana spp. Alternatively, marker gene insertion may have a greater detrimental effect on CBSV infection mechanisms compared to PVX and TEV. Compared with necrotic CBSV_Tanza infections, CBSV_GFP1/2 infections were asymptomatic, and titers were dramatically lower. A reduction in symptom expression and viral accumulation during infections with tagged viruses has also been reported for Turnip mosaic virus (TuMV) [Lettuce mosaic virus (LMV) [Zucchini yellow mosaic virus (ZYMV) [Tobacco mosaic virus (TMV) [Plum pox virus (PPV) [In this study, GFP was inserted into the CBSV_Tanza IC at two genome positions: between the Ham1\u2014CP (CBSV_GFP1) and between the CP\u20143\u2032UTR (CBSV_GFP2). This is the first report of a CBSV IC being modified with a marker protein sequence. The localization of CBSV in epidermal and mesophyll cells of  cassava . Green fat 5\u00a0dpi  and duriat 2\u00a0dpi . The dels (TuMV) , PVX [29us (LMV) . This isus (LMV) . In thiss (ZYMV) , TuMV [3s (ZYMV) , Tobaccous (TMV) , Plum pous (PPV)  and TEV us (PPV) . There dN. benthamiana. When used to agroinfiltrate or mechanically inoculate N. benthamiana, CBSV_UCP was able to cause systemic infections. As CPs are required for the systemic movement of nearly all plant viruses [In addition to marker gene insertion, the CBSV_Tanza IC was also used to construct a chimera: CBSV_UCP, containing a UCBSV Kikombe CP replacement. The CP region was selected because CBSV and UCBSV CP sequences share low sequence homology at their N\u2019 ends , and so  viruses , it seem viruses . Mixed C viruses , 14, 39  viruses . The CBSN. benthamiana infections with CBSV_UCP, indicating that additional genome regions other than CP may be associated with necrosis development during CBSV infections. However there were distinct changes in the severity and timing of symptom development. Whereas N. benthamiana infected with CBSV_Tanza develop severe systemic necrosis by 14\u00a0dpi, CBSV_UCP infections only develop mild necrosis by 18\u00a0dpi. Therefore, the CBSV CP may be associated with high levels of necrosis during early infection.In terms of symptom development, systemic necrosis developed during Viral titers are also lower during early CBSV_UCP infections, compared with unmodified CBSV_Tanza. This indicates that, compared with the UCBSV CP, the CBSV CP may enable higher viral accumulation during early infection. Alternatively, lower CBSV_UCP titers during early infection may be due to: (1) a reduced efficiency of proteolytic cleavage of the UCBSV CP from the CBSV polyprotein, (2) a reduced ability for the UCBSV CP to interact with CBSV_Tanza proteins and/or (3) a reduced ability for the UCBSV CP to trans-encapsidate the CBSV genome for movement. It is also possible that any modifications to the CBSV genome results in reduced viral accumulation and so alteres symptom expression.N. benthamiana and cassava and so U/CBSV ICs should ideally be tested in cassava.We performed phylogenetic analysis of U/CBSV CP sequences, which indicated that the CBSV_Tanza and UCBSV Kikombe CP sequences cluster within their separate species clades, and so should be relatively representative of CBSV and UCBSV. However, U/CBSVs are highly diverse  and diffIn conclusion, the CBSV_Tanza IC manipulations reported in this study have enabled visualization of CBSV replication in planta and provided initial insights into the viral sequences associated with symptom development and accumulation. These manipulations represent important progress in understanding the fundamental biology of U/CBSVs and how they cause devastating food insecurity. Ultimately, this understanding should inform vital CBSD control strategies, which are urgently needed.Supplementary material 1 (DOCX 3208\u00a0kb)Below is the link to the electronic supplementary material."}
+{"text": "Sardina pilchardus Walbaum, 1792) is culturally and economically important throughout its distribution. Monitoring studies of sardine populations report an alarming decrease in stocks due to overfishing and environmental change, which has resulted in historically low captures along the Iberian Atlantic coast. Important biological and ecological features such as population diversity, structure, and migratory patterns can be addressed with the development and use of genomics resources.The European sardine  each, and a consensus genome of total size 950 megabase pairs (N50 97 kilobase pairs). The genome completeness assessment captured 84% of Actinopterygii Benchmarking Universal Single-Copy Orthologs. To obtain a more complete analysis, the transcriptomes of 11 tissues were sequenced to aid the functional annotation of the genome, resulting in 40,777 genes predicted. Variant calling on nearly half of the haplotype genome resulted in the identification of >2.3 million phased single-nucleotide polymorphisms with heterozygous loci.A draft genome was obtained, despite a high level of sequence repeats and heterozygosity, which are expected genome characteristics of a wild sardine. The reference sardine genome and respective variant data will be a cornerstone resource of ongoing population genomics studies to be integrated into future sardine stock assessment modelling to better manage this valuable resource. Sardina pilchardus Walbaum, 1792)   and allis shad  [The European sardine  , 5.The sardine is of major economic and social importance throughout its range, with a reported commercial catch for 2016 of 72,183 tonnes in European waters [A number of sardine populations have been identified by morphometric methods, including as many as 5 populations in the northeastern Atlantic (including the Azores), 2 off the Moroccan coast, and 1 in Senegalese waters . Each ofIt is now well established that to fully understand the genetic basis of evolutionarily and ecologically significant traits, the gene and regulatory element composition of different individuals or populations needs to be assessed .Sardines were caught during commercial fishing operations in the coastal waters off Olh\u00e3o, Portugal, and maintained live at the experimental fish culture facilities (EPPO) of the Portuguese Institute for the Sea and Atmosphere (IPMA), Olh\u00e3o, Portugal . A singl\u00ae Poly(A) mRNA Magnetic Isolation Module kit and NEBNext\u00ae Ultra\u2122 Directional RNA Library Prep kit for sequencing using Illumina HiSeq 4000 paired-end 150 base pair (bp) cycle to generate \u223c596 million paired-end reads in total.Total RNA was extracted using a total RNA purification kit  and digested twice with DNase . The total RNA samples were kept at \u221280\u00b0C until shipment to the RNA sequencing service provider Admera Health Co. (USA), which confirmed a RNA integrity number > 8 (Qubit Tapestation) upon arrival. The messenger RNA library preparation was performed with NEBNextThe genomic DNA (gDNA) was isolated from 20 \u00b5l of fresh blood using the DNeasy blood and tissue kit (Qiagen), followed by RNase treatment according to the manufacturer's protocol. The integrity of the gDNA was confirmed using pulsed-field gel electrophoresis and showed fragment sizes largely >50 kilobase pairs (kb). The gDNA was stored at \u221220\u00b0C before shipping to the service provider . Microfluidic partitioned gDNA libraries using the 10x Genomics Chromium System were made using 0.6 ng of gDNA input. Sequencing (150 bp paired-end cycle) was performed in a single lane of the Illumina HiSeq X Ten instrument . Chromium library size range (580\u2013850 bp) was determined with LabChip GX Touch (PerkinElmer) and library yield (6.5\u201340 nM) by quantitative polymerase chain reaction.RRID:SCR_005491) [RRID:SCR_017014) [de novo assembly.A total of 759 million paired-end reads were generated, representing 113.8 gigabase pairs (Gb) of nucleotide sequences with 76.1% bases \u2265 Q30. Raw reads were edited to trim 10x Genomics proprietary barcodes with a Python script \u201cfilter_10xReads.py\u201d  prior to_005491) . A totalde novo genome assembly was performed using the paired-end sequence reads from the partitioned library as input for the Supernova assembly algorithm v2.0.0 (7fba7b4)   [The CA, USA) . Two hap\u00a0499\u00a0035.1) using the parameters anchoring sequence length (\u2212d 100) and minimum sequence identity (\u2212i 0.95). Three scaffolding and gap closure procedures were performed iteratively with 1 haplotype of the initial assembly as the assembly per se, and previous de novo assemblies from Supernova v1.2.2 (315 million/100% and 450 million/80% reads/barcodes). By closing several gaps within scaffolds and merging other scaffolds into longer and fewer scaffolds , this procedure resulted into a slightly longer genome size of 949.62 Mb, which slightly deflated the scaffold N50 length to 96.6 kb (Table\u00a0Further scaffolding and gap closure procedures were performed with Rails v1.2/Cobbler v0.3 pipeline script  to obtaiRRID:SCR_015008) [The genome completeness assessment was estimated with Benchmarking Universal Single-copy Orthologs (BUSCO) v3.0.1  . The genRRID:SCR_006515) (project accession PRJEB27990).The EMBRIC configurator service  was usedde novo repeat library running RepeatModeler v1.0.11  [RRID:SCR_012954) [The SP_G consensus assembly was used as a reference genome to build a _015027)  with def_015027)  and RepB_012954) . The anaRRID:SCR_005309) [de novo transcriptome was mapped to the genome using blastn v2.7.1  [RRID:SCR_008417) [RRID:SCR_011930) [RRID:SCR_001653) (protein2genome parameter in Maker) against the deduced proteomes of herring  (GCF_000\u00a0966335.1), zebrafish  (GCF_0\u00a000002035.6), blind cave fish  (GCF_000\u00a0372685.2), European sardine [RRID:SCR_004426) [ab initio predictions supported by the transcriptome and proteome evidence. Based on the transcriptomic evidence, 12,761 gene models were annotated with untranslated regions (UTR) features, more specifically 9,486 gene models with either 5\u2032 or 3\u2032 UTR and 3,275 gene models with both UTR features.The Maker v2.31.10   pipeline_008417)  was self_011930) , a self-_004426) . After tRRID:SCR_005829) [RRID:SCR_001010) [RRID:SCR_006695) [RRID:SCR_007672), Hamap , PANTHER , Pfam , PIRSF , PRINTS , ProDom , ProSite Patterns , ProSite Profiles, SFLD , SMART , SUPERFAMILY , and TIGRFAM . In total, 38,880 (95.3%) of the predicted proteins received a functional annotation. The annotated genome assembly is published [InterProScan v5.30   and NCBI_006695)  databaseRRID:SCR_016343) [OrthoFinder v2.2.7  was used_016343)  RRID:SCR_001876) [FASTQ files were processed using the 10x Genomics LongRanger v2.2.2 pipeline  with a m_001876)  as the vRRID:SCR_001235). A VCF file was obtained containing 2,369,617 filtered SNPs , resulting in a mean distance between heterozygous phased SNPs of 206 bp. Similar results were obtained in the Supernova input report estimation (Table\u00a0Single-nucleotide polymorphisms (SNPs) were furthered filtered to obtain only phased and heterozygous SNPs between the 2 alleles with a coverage >10-fold using VCFtools v0.1.16  [RRID:SCR_011841) [RRID:SCR_014583) [The 596 million paired-end raw transcriptomic reads were edited for contamination  using TrimGalore v0.4.5 wrapper tool  , low-qua_011841) , and the_014583) .de novo assembled as a multi-tissue assembly using Trinity v2.5.1  [de novo assemblies. The genome and transcriptome assemblies were conducted on the Portuguese National Distributed Computing Infrastructure [The 553 million edited paired-end reads were _013048)  with a mtructure .de novo transcriptome assemblies (Table\u00a0RRID:SCR_005305) [RRID:SCR_012773), and Gene Ontology annotation  is listed in tabular format in The 12 es Table\u00a0 were eac_005305) , resultiWe conducted a phylogenetic analysis of ray-finned fish (Actinopterygii) taxa based on 17 fish species. The sardine protein dataset used in the phylogenetic analysis was obtained by querying the deduced proteins from our sardine genome against the 1-to-1 orthologous cluster dataset (106 proteins from 17 species) obtained from Machado et al. .RRID:SCR_015945) [For the query, gene models were constructed for each protein with hmmbuild (HMMER v3.1b2)  using de_015945)  using de_015945) . Protein4+F).The best-fitting empirical protein model of the concatenated data was evaluated using ModelFinder  in IQ-TRRRID:SCR_006086) [Petromyzon marinus (lamprey) and Latimeria chalumnae (coelacanth).Optimal maximum likelihood tree searches (100 replicates) and bootstrap analyses (300 replicates) were conducted using RAxML v8.2.12   with thede novo genome assembly, a more than adequate draft genome was obtained with the 10x Genomics linked-reads (Chromium) technology. The Chromium technology's ability to tag and cluster the reads to individual DNA molecules has proven advantages for scaffolding, just like long-read technologies such as Nanopore and Pacific Biosciences, but with high coverage and low error rates. The advantage of linked reads for de novo genomic assemblies is evident in comparison with typical short-read data, especially in the case of wild species with highly heterozygous genomes, where the latter often result in many uncaptured genomic regions and with a lower scaffolding yield due to repeated content.Despite the sardine genome having a high level of repeats and heterozygosity, factors that pose a challenge to The high degree of heterozygosity identified here in the sardine genome illustrates future problems for monitoring sardine populations using low-resolution genetic data. However, the phased SNPs obtained in this study can be used to initiate the development of a SNP genetic panel for population monitoring, with SNPs representative of haplotype blocks, allowing insights into the patterns of linkage disequilibrium and the structure of haplotype blocks across populations.The genomic and transcriptomic resources reported here are important tools for future studies to elucidate sardine response at the levels of physiology, population genetics, and ecology of the causal factors responsible for the recruitment and collapse of the sardine stock on the Iberian Atlantic coast. Besides the commercial interest, the sardine plays a crucial role at a key trophic level by bridging energy from the primary producers to the top predators in the marine ecosystem. Therefore, disruption of the sardine population equilibrium is likely to reverberate throughout the food chain via a trophic cascade. Consequently, these genomic and genetic resources are the prerequisites needed to develop tools to monitor the population status of the sardine and thereby provide an important bio-monitoring system for the health of the marine environment.GigaScience GigaDB database [Raw data, assembled transcriptomes, and assembled genomes are available at the European Bioinformatics Institute ENA archive with the project accession PRJEB27990. The annotated genome assembly is published in the wiki-style annotation portal ORCAE . Supportdatabase .Additional file 1. Heterozygous SNPs identified in the phased haploid blocks listed in a VCF file format.Additional file 2. Annotation of all tissues transcriptome assembly in a tabular format.Additional file 3. Sequence alignment statistics of the 97 proteins concatenated for the phylogenetics analyses.GIGA-D-18-00377_Original_Submission.pdfClick here for additional data file.GIGA-D-18-00377_Revision_1.pdfClick here for additional data file.GIGA-D-18-00377_Revision_2.pdfClick here for additional data file.Response_to_Reviewer_Comments_Original_Submission.pdfClick here for additional data file.Response_to_Reviewer_Comments_Revision_1.pdfClick here for additional data file.Reviewer_1_Report_Original_Submission -- Andrew Thompson10/29/2018 ReviewedClick here for additional data file.Reviewer_1_Report_Revision_1 -- Andrew Thompson3/29/2019 ReviewedClick here for additional data file.Reviewer_2_Report_Original_Submission -- Shigehiro Kuraku11/4/2018 ReviewedClick here for additional data file.Reviewer_2_Report_Revision_1 -- Shigehiro Kuraku4/3/2019 ReviewedClick here for additional data file.Supplemental FilesClick here for additional data file.bp: base pair; BUSCO: Benchmarking Universal Single-copy Orthologs; DGAV: Veterinary Medicines Directorate; ENA: European Nucleotide Archive; EPPO: experimental fish culture facilities; Gb: gigabase pairs; gDNA: genomic DNA; GFF: General Feature Format; HMM: hidden Markov model; IPMA: Portuguese Institute for the Sea and Atmosphere; kb: kilobase pairs; KEGG: Kyoto Encyclopedia of Genes and Genomes; Mb: megabase pairs; NCBI: National Center for Biotechnology Information; ORF: open reading frame; SNP: single-nucleotide polymorphism; UTR: untranslated region.The authors declare that they have no competing interests.This research was supported by Portuguese national funds from FCT\u2014Foundation for Science and Technology through project UID/Multi/04326/2016 and by FCT and the European Regional Development Fund (FEDER) under projects 22153-01/SAICT/2016 , ALG-01-0145-FEDER-022121 and ALG-01-0145-FEDER-022231; and co-funds from MAR2020 operational programme of the European Maritime and Fisheries Fund (project SARDINOMICS MAR-01.04.02-FEAMP-0024). The EMBRIC configurator service received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 654008.Writing original draft: B.L., G.D.M., C.J.C.; investigation: B.L., G.D.M., C.G.; review and editing: A.V., S.J.S., A.M.S., A.V.M.C.; conceptualization: B.L., G.D.M., C.J.C., A.V., S.J.S., A.M.S., A.V.M.C."}
+{"text": "Tracking and predicting the growth performance of plants in different environments is critical for predicting the impact of global climate change. Automated approaches for image capture and analysis have allowed for substantial increases in the throughput of quantitative growth trait measurements compared with manual assessments. Recent work has focused on adopting computer vision and machine learning approaches to improve the accuracy of automated plant phenotyping. Here we present PS-Plant, a low-cost and portable 3D plant phenotyping platform based on an imaging technique novel to plant phenotyping called photometric stereo (PS).Arabidopsis thaliana throughout the day-night (diel) cycle and investigated growth architecture under a variety of conditions to illustrate the dramatic effect of the environment on plant phenotype. We developed bespoke computer vision algorithms and assessed available deep neural network architectures to automate the segmentation of rosettes and individual leaves, and extract basic and more advanced traits from PS-derived data, including the tracking of 3D plant growth and diel leaf hyponastic movement. Furthermore, we have produced the first PS training data set, which includes 221 manually annotated Arabidopsis rosettes that were used for training and data analysis . A full protocol is provided, including all software components and an additional test data set.We calibrated PS-Plant to track the model plant PS-Plant is a powerful new phenotyping tool for plant research that provides robust data at high temporal and spatial resolutions. The system is well-suited for small- and large-scale research and will help to accelerate bridging of the phenotype-to-genotype gap. Quantitative and accurate methods are required to aid strategies for predicting plant growth performances in our changeable natural environments. Such tools are critical for calibrating predictive models in the face of a changing global climate and our growing global population . ComputeAbove ground growth is a strong indicator of plant yield, and therefore 3D imaging of vegetative growth is a very active area of phenotyping research . A numbePassive 3D imaging approaches capture plant architecture without introducing new energy  into the environment . MethodsActive 3D imaging approaches emit energy , which can overcome several problems associated with passive approaches. Structured light  and lasex, y, and z) that allows the overall orientation of the object to be determined. Prior work has shown that plant leaf SN data acquired by PS can be captured at high resolutions (4.1 megapixels) and thus has significant advantages in encoding complex 3D morphology to aid challenging automated recognition and quantification tasks, such as the extraction of plant growth data . As. As48]. lectance , and imalectance . The acrArabidopsis rosettes in PS-Plant and observed that Arabidopsis leaves exhibited near-Lambertian reflectance under NIR light . 3D data continued to outperform 2D data at increased inclinations with an MRE of 4.5% and 18.1% for 3D and 2D estimations, respectively. The accuracy of 3D estimations did decrease at angles >30\u00b0 as a result of the increase in leaf (self-) occlusion that occurred when the whole rosette was inclined , the estimated angles were still >0\u00b0 because they were calculated from the varying SN values across each leaf blade surface.Next, we investigated IR light . We hypotes Fig.\u00a0. Even wiinclined . When thArabidopsis plants for 12 days, starting from 11 days after germination (DAG) in standard growth conditions . The automated image capture program resulted in an SN map produced for each plant every 30 minutes that was used to characterize rosette surface curvature  that were typical for wth Fig.\u00a0. Howeverwth Fig.\u00a0. In contwth Fig.\u00a0, such thcillator . PS-PlanRosette architectural parameters derived from 2D data were also obtained from PS-Plant, including circularity (or stockiness), compactness, diameter, and perimeter Fig.\u00a0-H 36, 7, 54. TheArabidopsis plants under 9 conditions that differed in temperature  and light intensity   Fig.\u00a0.\u22122 s\u22121 the PRA was strictly temperature-dependent, with the highest PRA achieved at the highest light and temperature levels  typically correlate with increased CO2 assimilation in C3 plants grown under non-limiting light conditions , followed by Tukey's honest significant difference [HSD] tests). This was not unexpected because the rate of leaf starch turnover during the night is known to be maintained over a wide range of environmental conditions and temperatures in Arabidopsis [2 assimilation. All plants grown in LL had a significantly decreased RER in the light compared to the dark period. Notably, temperature had no impact on RER in the light for LL plants, indicating that photosynthetic growth was primarily limited by the low irradiance. Further studies on carbon allocation and starch turnover should be carried out to complement these observations and hypotheses generated using PS-Plant data.We then compared the relative expansion rate (RER) based on 3D PRA data for different light-temperature conditions over the diel cycle Fig.\u00a0\u2013H. RER dnditions , 43, 83.bidopsis , 85. RERThe internal circadian clock in plants has a periodicity close to 24 h that can be entrained by environmental cues . Thus, wP < 0.05) because all plants were grown in a 12:12 h light:dark cycle , (ii) greyscale, and (iii) albedo images. All data used for training, including the raw PS-Plant data and rosette masks, are available as outlined in PS-Plant produces a range of different data: from greyscale images to SN maps  is the mean x and y coordinates of the leaf blade and petiole , and (ii) the mean surface inclination of the whole leaf blade. Both methods produced similar results .Once we were confident that we could reliably track individual leaves using PS-Plant, we separated leaf blades and petioles by applying a morphological opening function with a predefined radius (3\u201311 pixels based on the leaf size) to the leaf binary mask. The point of differentiation  and immature (3 and 4) leaves Fig.\u00a0. ConsistWe then calculated parameters associated with diurnal movement for individual leaf blades Fig.\u00a0. We targFinally, we used PS-Plant to reveal whether petiole elongation showed a response to temperature similar to that of the leaf blade by comparing the ratio of leaf blade and petiole length from maturing and immature leaves Fig.\u00a0. PetioleArabidopsis plants produced in response to varied environments. This provides credibility that future work with PS-Plant will produce robust data for a wide variety of mutant phenotypes. Additionally, the concomitant quantification of overall growth, leaf traits, and circadian rhythms can facilitate a better understanding of the relationships among environment, plant yield, and internal molecular networks. Previous work has also highlighted that PS can capture high-resolution 3D surface details of leaf surface structures, such as leaf curvature and trichomes, which could be used to investigate dynamic changes in leaf development [Arabidopsis. However, we believe it can also be used during the seedling stage of other eudicot species  to analyse circadian rhythms by observing the rhythmic movements of cotyledons. Future work with PS-Plant will focus on improvements in leaf tracking [In this article, we have introduced an adaptable and low-maintenance platform for affordable, advanced image-based phenotyping. A key goal was to ensure accessibility to the research community. In this regard, PS-Plant can be considered a powerful, alternative solution to 3D systems based on laser scanning and light-field camera technologies , 43, whielopment . Researcelopment , 100. Totracking , integratracking , and incArabidopsis  wild-type seeds were stratified for 2\u20133 days at 4\u00b0C. Each seed was placed in a square pot (50 mm) containing F2+S compost  covered in acrylic black felt fabric with a central hole (5 mm) and germinated at 22\u00b0C under white light (150 \u00b5mol photons m\u22122 s\u22121 at the plant level) in 12:12 h light:dark for 10 days in a Percival growth cabinet . For the plant area validation experiment, the plants were kept in this cabinet for 22 DAG. For imaging with PS-Plant, the seedlings were transferred to a Snijders growth cabinet .PS-Plant consists of a machine vision NIR monochrome camera  with a 16-mm fixed focal length lens  with an NIR filter attached , 4 or 8 NIR LEDs , and an in-house\u2013designed LED controller that allows rapid switching of LEDs using an Arduino platform . The camera and LEDs were fixed on a square acrylic sheet (44 \u00d7 44 cm) and positioned at a height of 40 cm above the imaging plants Fig.\u00a0 \u00a0 and\u00a0C. TThe leaf movement rhythm analysis was performed using the mean inclination angles  as an input for BioDare2 beta . The datProject name: PS-Plant-Frameworkhttps://github.com/g2-bernotas/PS-Plant-FrameworkProject home page: Operating system(s): Platform independent Programming language: PythonOther requirements: Python v3.5, Python interpreter (Miniconda and Pyzo are suggested) and FlyCapture camera software packages (see License: GNU GPLv3 LicenseRRID:SCR_017032SciCrunch https://datashare.is.ed.ac.uk/handle/10283/3280) and outlined in https://datashare.is.ed.ac.uk/handle/10283/3279). Images of our training set and code, including other supporting data, are available in the GigaScience repository, GigaDB [The training data set supporting the results of this article is available in an Edinburgh DataShare repository , and a zoomable, colour version can be found at https://bit.ly/2GXNhLy.Supplementary Data S2. Comparison of Arabidopsis growth from 2D and 3D data. The graph (top) includes standard deviation of PRA data for 3 plants growing under conditions outlined in Fig.\u00a0Supplementary Data S3. Arabidopsis plants grow and move differently under different light and temperature conditions. Examples of (A) surface normal models or (B) greyscale images for plants of the same age under each of the growth conditions tested .Supplementary Data S5. Using PS-Plant for automated tracking of individual Arabidopsis leaf movement in 3 dimensions. Four videos illustrate leaf blade tracking of leaves 1\u20134, respectively, for a plant grown in ML-MT from 15 to 18 DAG. Each video shows a trail of leaf blade centroid movement (red dots) on an albedo 2D video (top left). Blue dots illustrate leaf blade movement on 2D x-y (bottom left) and y-z projections (bottom right), and a 3D x-y-z graph (top right).giz056_GIGA-D-18-00459_Original_SubmissionClick here for additional data file.giz056_GIGA-D-18-00459_Revision_1Click here for additional data file.giz056_GIGA-D-18-00459_Revision_2Click here for additional data file.giz056_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giz056_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giz056_Reviewer_1_Report_Original_Submission -- Chris Armit12/4/2018 ReviewedClick here for additional data file.giz056_Reviewer_1_Report_Revision_1 -- Chris Armit4/2/2019 ReviewedClick here for additional data file.giz056_Reviewer_2_Report_Original_Submission -- Nathan Miller1/18/2019 ReviewedClick here for additional data file.giz056_Reviewer_2_Report_Revision_1 -- Nathan Miller4/10/2019 ReviewedClick here for additional data file.giz056_Reviewer_3_Report_Original_Submission -- Ji Zhou2/6/2019 ReviewedClick here for additional data file.giz056_Reviewer_3_Report_Revision_1 -- Ji Zhou4/4/2019 ReviewedClick here for additional data file.giz056_Reviewer_4_Report_Original_Submission -- Dijun Chen2/7/2019 ReviewedClick here for additional data file.giz056_Reviewer_4_Report_Revision_1 -- Dijun Chen4/1/2019 ReviewedClick here for additional data file.giz056_Supplemental_FilesClick here for additional data file.B: leaf base point, or intersection point between leaf blade and petiole; PO: rosette origin point; PRA: projected rosette area; PS: photometric stereo; PT: leaf tip point; R-CNN: Mask R-CNN NN architecture; RER: relative expansion rate; RGB: red, green, and blue channels, or a colour image; RNN: end-to-end instance segmentation with recurrent attention NN architecture; rubisco: ribulose bisphosphate carboxylase/oxygenase; SBD: symmetric best dice score; SD: standard deviation; SN: surface normal.2D: 2-dimensional; 3D: 3-dimensional; ANOVA: analysis of variance; COCO: Common Objects in Context; DAG: days after germination; FBD: foreground-background dice score; GUI: graphical user interface; HL: high light; HSD: honest significant difference; HT: high temperature; LED: light-emitting diode; LiDAR: light detection and ranging (distance measurement method using pulsed laser light); LL: low light; LR: learning rate; LT: low temperature; ML: medium light; MRE: mean relative error; MT: medium temperature; NIR: near-infrared; NN: neural network; PThis study abides by UK guidelines and legislation for plant science research.The authors declare that they have no competing interests.This work was supported by the UK Biotechnology and Biological Sciences Research Council grants BB/N02334X/1, BB/M025551/1, and BB/N005147/1. G.B. was funded by the University of the West of England (UWE) Partnership Fund.G.B., M.F.H., and I.J.H. designed the hardware and software of the PS-Plant system including the image-processing pipeline. A.J.M., K.J.H., and L.C.T.S. designed the plant experimental setup. G.B. and L.C.T.S. performed and analysed the validation experiments. L.C.T.S. performed and analysed plant growth experiments. G.B. designed the study for NN model generation for leaf segmentation. A.J.M., L.C.T.S., and G.B. wrote the manuscript, with assistance from all authors. A.J.M., L.N.S., and M.L.S. supervised the project."}
+{"text": "This article has been corrected: The following sentence has been added to the first paragraph in the Material and Methods section: \u2018Protein digestion and mass spectrometry analyses were performed by the Proteomics Platform of the CHU de Qu\u00e9bec Research Center .\u2019Proteomics analysesImmunoprecipitation of proteins associated with ADAM12L were performed using extracts from ADAM12L-overexpressing MCF10A cells and immunoprecipitation using Rabbit IgG was included as control. Proteins from immunoprecipitates were denaturated and size-separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). After cutting gel tracks in 10 bands, in-gel digestion of proteins was performed with trypsin and resulting peptides were injected into a capillary HPLC system coupled to a mass spectrometer via a nanospray ionization source (ES MS/MS). All MS/MS samples were analyzed using Mascot  and X! Tandem softwares. Mascot was set up to search the TAX_HomoSapiens_9606_20141128 database. X! Tandem was set up to search a subset of the TAX_HomoSapiens_9606_20141128 database. For protein identification, Scaffold software  was used to validate MS/MS based peptide and protein identifications. Peptide identifications were validated when established at a probability greater than 86,0 % to achieve an FDR less than 1,0 % using the Scaffold Local FalseDiscoveryRate (FDR) algorithm. Protein identifications were validated when established at a probability greater than 99,0 % probability to achieve an FDR less than 1,0 % and contained at least 2 identified peptides. Protein probabilities were assigned by the Protein Prophet algorithm [69]. Protein digestion and mass spectrometry analyses were performed by the Proteomics Platform of the CHU de Qu\u00e9bec Research Center .https://doi.org/10.18632/oncotarget.25106Original article: Oncotarget. 2018; 9:21366-21382."}
+{"text": "New library preparation techniques along with better assembly algorithms result in continued improvements in assemblies for non-model organisms, moving them toward reference-quality genomes. We report on the latest genome assembly of the Atlantic bottlenose dolphin, leveraging Illumina sequencing data coupled with a combination of several library preparation techniques. These include Linked-Reads , mate pairs (MP), long insert paired ends, and standard paired end. Data were assembled with the commercial DeNovoMAGIC assembly software, resulting in two assemblies, a traditional \u201chaploid\u201d assembly (Tur_tru_Illumina_hap_v1) that is a mosaic of the two parental haplotypes and a phased assembly (Tur_tru_Illumina_phased_v1) where each scaffold has sequence from a single homologous chromosome. We show that Tur_tru_Illumina_hap_v1 is more complete and more accurate compared to the current best reference based on the amount and composition of sequence, the consistency of the MP alignments to the assembled scaffolds, and on the analysis of conserved single-copy mammalian orthologs. The phased  Such assemblies are essential for constructing haplotype diversity databases for breeding, comparative biology, medicine, and conservation planning. Even highly complex genomes now benefit from higher contiguity and improved protein coding coverage : txid9739). This genome assembly, like those of the Hawaiian Monk seal and African wild dog, is being published with the goal to facilitate research on comparative genomics, provide structure for cataloging biodiversity, and ultimately support decisions around species conservation and management [Technical advances in the past decade have reduced sequencing costs and improved access to sequencing data. Subsequent improvements in DNA extraction, preparation, and assembly algorithms facilitate low-cost accurate coverage \u20134. Consocoverage \u20137. Here,nagement , 9.Tursiops genus remains unresolved. Numerous species designations have been suggested but not adopted due to a lack of resolution afforded by available data [The bottlenose dolphin is one of the most widely studied marine mammals; however, the taxonomy of the ble data . Even wible data . To usheble data  makes thble data \u201315, protble data , 17, andble data .A preliminary dolphin genome was first submitted to NCBI  using low-coverage (2.82X) Sanger sequencing for the purpose of cross-species comparison , 19, 20.With the collection of data from multiple sources including Linked-Reads , mate pWe generated sequence data for a total coverage of approximately 450X, the majority from PCR Free and Chromium 10X Genomics Linked-Read libraries  assembly where each scaffold represents sequence corresponding to a single haplotype, Tur_tru_Illumina_phased_v1. The quantitative statistics for both assemblies are listed in Table\u00a0Both assemblies were compared to the best available assembly Tur_tru v1 . We didRRID:SCR_015008) tool to show that the extra sequence is meaningful  tool [Same scaffold happy\u2014number of MPs where both mates aligned to the same scaffold in the correct orientation with mate separation within 3 standard deviations of the library mean.Same scaffold short\u2014number of MPs where both mates aligned to the same scaffold in the opposite orientation with mate separation of less than 1,000 bp; these MPs are not indicative of scaffolding misassemblies, they are simply a by-product of the MP library preparation process as they are MPs that are missing the circularization junction site between the mates.Same scaffold long\u2014number of MPs where both mates aligned to the same scaffold in the correct orientation, but the mate separation exceeded 3 standard deviations of the library mean.Same scaffold misoriented\u2014number of MPs where both mates aligned to the same scaffold in the opposite orientation with mate separation of more than 1,000 bp.Mates aligned to different scaffolds\u2014number of MPs where the two mates aligned to different scaffolds.Only one mate in the pair aligned\u2014number of MPs where only one read aligned to the assembly.Since both Tur_tru v1 and Tur_tru_Illumina_hap_v1 reference the same species, we expect few rearrangements between the assemblies. To examine this, we compared the absolute and relative correctness of the scaffolds of Tur_tru_Illumina_hap_v1 assembly by aligning the Illumina data from the 5\u20137 Kbp MP library to the scaffolds of Tur_tru_Illumina_hap_v1, Tur_tru_Illumina_phased_v1, and Tur_tru v1 assemblies using the Bowtie2  tool . We chosThe \u201cSame scaffold mate ALL\u201d category in Table\u00a0Comparing Tur_tru_Illumina_hap_v1 with Tur_tru v1, the total number of reads uniquely aligning to both \u201chaploid\u201d assemblies is similar; 295.2 M reads aligned to Tur_tru_Illumina_hap_v1 vs 293.6 M reads aligned to Tur_tru v1. The total number of MPs aligning to the same scaffold is larger for Tur_tru_Illumina_hap_v1. Of the MPs aligning to the same scaffold, the number of MPs in the \u201dSame scaffold happy\u201d category is similar between the two assemblies. The differences that stand out are the much larger (7.3 times more) number of mates that aligned to the same scaffold in the wrong orientation and the much larger (3.3 times more) number of the same scaffold long pairs in Tur_tru v1 compared to Tur_tru_Illumina_hap_v1 Table\u00a0. Of courThe haplotype phased assembly is much more fragmented compared to both haploid assemblies, resulting in higher relative numbers of MPs mapping to different scaffolds. However, when looking at the \u201cinternal\u201d MPs, i.e., where both mates map at least 10 Kb away from the scaffold ends, we see remarkable consistency, with less than 0.5% of the mates mapped to the wrong scaffold (see next section). Since for this analysis we only used mates mapping uniquely to the assembly, and there are two copies of the genome in the assembly, the total number of mapped mates is much lower.x-axis there are two corresponding alignments on the y-axis. The regions that are covered by a single haplotype (rather than 2) are most probably homozygous regions of this genome. In cases where the homozygous region is long, it is more difficult to phase its heterozygous ends. Thus, in some cases, the homozygous regions are represented only once in the phased assembly (instead of twice). This is the cause for some of the single copy BUSCOs in the phased assembly. In our experience, this issue is more pronounced in mammalian phased assemblies due to the relatively lower heterozygosity level and the way it is distributed along the genome.To Illustrate the resolution of the haplotypes in Tur_tru_Illumina_phased_v1, we aligned it to Tur_tru_Illumina_hap_v1 using the Nucmer tool. In Fig.\u00a0In haplotype phasing, it is easy to phase small regions. For example, a single isolated single-nucleotide polymorphism (SNP) with no haplotype differences within 100 bp in both directions can be trivially phased into two 201 bp (or longer) contigs different by one base in the middle. It gets more difficult for larger contigs/scaffolds, where one must make sure that the contig/scaffold represents single haplotype and not a \u201cmosaic\u201d of haplotypes and that the SNPs and other bigger haplotype differences are correctly \u201cphased.\u201d To do that, we mapped the MPs from the 5\u20137 Kb MP library to all phased scaffolds using Bowtie2  and thenDolphin is a mammal, and currently the best mammalian reference genome is the human genome. To understand similarities between dolphin and human on the DNA level, we aligned the Tur_tru_Illumina_hap_v1 assembly to the primary chromosomes of the current haploid human reference genome GRCh38 . Since hThe sample for this study came from a female Atlantic bottlenose dolphin (sample ID 04329), captive born at SeaWorld of Orlando, Florida, from wild male and female Atlantic bottlenose dolphins. The animal was 36 years old at blood collection with a healthy medical history. Blood was collected using PAXgene Blood DNA Tubes (Qiagen). High-molecular-weight genomic DNA was isolated using the MasterPure DNA Purification Kit (Illumina) and subsequently quantified and qualified using Quant-iT dsDNA Kit and E-Gel EX Agarose Gel (ThermoFisher).We generated the 450 bp and 800 bp paired-end (PE) libraries using the TruSeq PCR-free DNA Sample Prep kit (Illumina). The protocol was slightly modified at fragmentation and double-size selection steps by adjusting the DNA shearing protocols (Covaris) and by empirically titrating the ratios of SPRI magnetic beads over DNA to obtain insert sizes around 450 bp and 800 bp. We then evaluated the libraries for insert size and yield using Bioanalyzer (Agilent) and real-time qPCR assay, using Illumina DNA Standards and primer master mix qPCR kit , then normalized to 2 nM prior to clustering and sequencing. Both the 450 bp and 800 bp libraries were then denatured and diluted to 8 pM and 12 pM, respectively. The 800 bp PE library was clustered and sequenced on the HiSeq 2000, using the HiSeq Cluster and SBS v4 kits for PE 2 \u00d7 160 bp reads (Illumina). The 450 bp PE library was clustered and sequenced on the HiSeq 2500 v2 Rapid Run mode using the HiSeq Rapid Cluster and SBS v2 kits for PE 2 \u00d7 250 bp reads.To maximize sequence diversity and genome coverage, three separate MP libraries were constructed corresponding to 2\u20135 Kb, 5\u20137 Kb, and 7\u201310 Kb insert sizes using the Nextera MP Library Preparation Kit according to the manufacturer's instructions (Illumina). All three libraries were generated from a single input of 4ug of genomic DNA size-selected on a 0.8% E-gel (Invitrogen). Proper sizing of gel-extracted products was confirmed using the Bioanalyzer High Sensitivity chip (Agilent), and 600 ng was subsequently used as input for circularization. Following library preparation, the Bioanalyzer was used to confirm library quality. Each of the three libraries were quantified by qPCR , denatured, and diluted to 200 pM after size-adjustment, according to Bioanalyzer results, and clustered on the cBot according to the manufacturer's instructions (Illumina). Then, 2 \u00d7 150 bp of Illumina PE sequencing was performed on the HiSeq 4000 using the HiSeq 3000/4000 Cluster and SBS kits.10xGenomic DNA quality was assessed by pulsed-field gel electrophoresis to determine suitability for 10x Chromium library preparation (10x Genomics). A total of 1.125 ng of input was used for library preparation according to the manufacturer's instructions without size-selection. Final library concentration was determined by qPCR  and size-adjusted according to Bioanalyzer DNA 100 chip (Agilent) results. Next, 2 \u00d7 150 bp of Illumina PE sequencing with an 8-base index read was performed on the HiSeq 4000 using the HiSeq 3000/4000 Cluster and SBS kits.Aegilops tauschii [Genome assembly was completed using the DeNovoMAGIC software platform (NRGene). This is a proprietary DeBruijn-graph-based assembler that was used to produce assemblies of several challenging plant genomes such as corn  and ancetauschii . The folIn the pre-processing step, we first removed PCR duplicate reads and trimmed Illumina adaptor AGATCGGAAGAGC and Nextera linker (for MP library) sequences. We then merged the PE 450 bp 2 \u00d7 250 bp overlapping reads with minimal required overlap of 10 bp to create stitched reads using the approach similar to the one implemented in the Flash software .k-mers (k = 24) in the reads and looking for low abundance k-mers. We have high coverage data (\u223c450x), with each read yielding 127 (150\u201324+1) to 227 (250\u201324+1) k-mers. Thus, average 24-mer coverage is at least 300x. The 24-mers that only appear fewer than 10 times in the set of reads likely contain errors. We did not use the reads that contain these low abundance k-mers for building initial contigs.We scanned through all merged reads to detect and filter out reads with apparent sequencing errors by examining k-mer = 127 bp) of contigs from all filtered reads. Next, PE and MP reads are used to find reliable paths in the graph between contigs for repeat resolving and contig extension.The 10x barcoded reads were mapped to contigs to ensure that adjacent contigs were connected only when there is evidence that those contigs originate from a single stretch of genomic sequence (reads from the same two or more barcodes were mapped to the same contigs).The first step of the assembly consists of building a DeBruijn graph  is statistically significant with respect to the number of clusters that span the left and the right edge of the window. If there was a statistically significant disagreement in the coverage by the clusters over the window, we broke the scaffold at the two edges of the window. Finally, the barcodes that were mapped to the scaffold edges (first and last 20 kb sequences) were compared to generate a graph of scaffolds. The scaffolds are nodes and the edges are links connecting nodes with more than two common barcodes on the ends. We broke the links to the nodes that had more than two links and output the resulting linear paths in the scaffold graph as final scaffolds.de novo assembly Tur_tru_Illumina_phased_v1 is of the first publicly available, and it provides the community with novel ways to explore the heterozygous nature of the dolphin genome. These findings illustrate the impact of improved sample preparation and improved de novo assembly methods on progress toward more complete and accurate reference quality genomes. Better-quality assemblies will improve our understanding of gene structure, function, and evolution in mammalian species.We show that Tur_tru_Illumina_hap_v1 is more complete and more accurate compared to the current best reference Tur_tru v1, based on the amount and composition of sequence, the consistency of the MP alignments to the assembled scaffolds, and on the analysis of conserved single-copy mammalian orthologs. The additional 12.5% of sequence data identified and assembled here was found to contain 165 additional BUSCO alignments as compared to the latest published assembly Tur_tru v1. The large scaffolds represented by Tur_tru_Illumina_hap_v1 enabled and confirmed expected synteny to human chromosome 1. The phased GigaScience GigaDB repository [The dolphin assembly Tur_tru_Illumina_hap_v1 has been deposited at NCBI under BioProject PRJNA476133, accession QMGA00000000. The dolphin assembly Tur_tru_Illumina_phased_v1 has been deposited at NCBI under BioProject PRJNA478376, accession QUXD00000000. All data are also available from the pository .BUSCO: Benchmarking Universal Single-Copy Orthologs; MP: mate pair; NCBI: National Center for Biotechnology Information; Ns: ambiguous nucleotides; PE: paired end; SNP: single-nucleotide polymorphism.de novo assembly.K.V. and C.T.L. were both full-time employees of Illumina at the time this work was completed. G.B.Z., K.B., and T.B. are employees of NRGene, a company that provides software analysis tools for K.V.M., C.T.L., and M.M.V. designed the project. K.V.M., C.T.L., and A.Z. wrote the manuscript. G.B.Z., T.B., and K.B. generated genome assemblies. A.Z. conducted validation, MP consistency analysis, human chromosome 1 and BUSCO analyses, and submitted the genomes to NCBI. J.S.L. provided the blood sample. J.L., A.N., M.R., M.G., E.J., and B.S. processed samples, generated sequencing, and completed quality checks on sequence data. All authors contributed to editing the manuscript.GIGA-D-18-00268_Original_Submission.pdfClick here for additional data file.GIGA-D-18-00268_Revision_1.pdfClick here for additional data file.Response_to_Reviewer_Comments_Original_Submission.pdfClick here for additional data file.Reviewer_1_Report_Original_Submission -- Granger Gideon Sutton, Ph.D.8/17/2018 ReviewedClick here for additional data file.Reviewer_2_Report_Original_Submission -- Shengfeng Huang8/24/2018 ReviewedClick here for additional data file."}
+{"text": "Oral squamous cell carcinoma (OSCC) is the most common cancer of the head and neck region. The circular RNA (circRNA) is known to serve an important role in the carcinogenesis of different types of cancer. However, the circRNA role of OSCC remains unclear.  8 pairs of OSCC tissues and adjacent normal tissues were obtained to detect circRNAs expression by high-throughput sequencing, and 45 pairs of OSCC tissues were selected to verify the differentially significant circRNAs by reverse transcription-quantitative polymerase chain reaction (RT-qPCR). To further investigate the role of hsa_circ_0008309, the circRNA-microRNA (miR)-mRNA network was predicted using bioinformatics databases. The expression levels of hsa_circ_0008309, miR-1290, miR-136-5P, and miR-382-5P in SCC-15 and CAL27 cell lines were detected by RT-qPCR. Western blotting was performed to detect the protein level of Ataxin 1 (ATXN1).  The high-throughput sequencing results demonstrated that circRNAs were abundantly expressed in OSCC, and 16 circRNAs were significantly differentially expressed. Hsa_circ_0008309 was significantly downregulated in 45 pairs of OSCC tissue samples and was statistically correlated with pathological differentiation. The bioinformatics databases suggested that hsa_circ_0008309 could combine with miR-1290, miR-136-5P, and miR-382-5P, respectively, to regulate the expression of ATXN1. It was subsequently identified that hsa_circ_0008309 may inhibit miR-136-5P and miR-382-5P expression and increase ATXN1 expression in the OSCC cell lines.  In summary, the results of the present study revealed that OSCC tissues have abundant circRNAs and, to the best of our knowledge, we firstly explore the regulatory role of the hsa_circ_0008309-miR-136-5P/hsa-miR-382-5P-ATXN1 network in OSCC. The results indicated that hsa_circ_0008309 may be a potential biomarker for OSCC. Oral squamous cell carcinoma (OSCC), the most frequently occurring oral malignancy, is the sixth most prevalent cancer worldwide and the third most common carcinoma in some developing countries , 2. OSCC\u03b2-catenin pathway [Recently, circular RNAs (circRNAs) have been considered to be a special type of noncoding RNA, which are widespread and diverse in mammals . Unlike  pathway \u201311. HoweA total of 45 pairs of frozen OSCC tissues and adjacent normal tissues were acquired from patients with OSCC and these tissues were prepared for high-throughput sequencing and validation by RT-qPCR. These samples were acquired from the Department of Oral and Maxillofacial Surgery of Peking University Shenzhen Hospital  from June 2015 to June 2016. Patients had not undergone additional treatments prior to surgery and all OSCC tissues were confirmed by strict pathological examination. The age of patients ranged from 29\u201378 years, and the median age at the time of diagnosis was 54 years. The male to female ratio was 34\u2009:\u200911. Tissues were obtained from the tongue, gingiva, bucca, and floor of the mouth. Clinical and pathological characteristics of patients were based on the most recent World Health Organization (WHO) classification and UICC tumor-node-metastasis (TNM) classification . WrittenSamples obtained from surgical specimens were immediately frozen using liquid nitrogen. Total RNA was extracted from frozen tissues using TRIzol\u00ae reagent  according to the manufacturer's protocol. The quality and quantity of the RNA were evaluated at a 260/280 ratio using a NanoDrop spectrophotometer .http://www.rproject.org/) was used to identify differentially expressed circRNAs. Some significant circRNAs were blasted against the circBase for annotation [Following extraction, total RNA was treated with RNase R to degrade the linear RNA and purified with RNeasy MinElute Cleanup Kit . Next, a strand-specific library was constructed with VAHTS Total RNA-seq (H/M/R) Library Prep Kit for Illumina according to the manufacturer's protocol. In brief, ribosomal RNA was removed to retain the circRNAs. The enriched circRNAs were broken into short fragments using a fragmentation buffer and reverse transcribed into cDNA with random primers. Secondly, strand cDNA fragments synthesized by DNA polymerase I were purified with VAHTSTM DNA Clean Beads and liquated to Illumina sequencing adapters. Uracil-N-glycosylase was used to digest the second-strand cDNA. The digested products were purified with VAHTSTM DNA Clean Beads, amplified, and sequenced with Illumina HiSeq\u2122 2500 by Gene Denovo Biotechnology Co. . The edgeR package (notation . The cir\u03bcl) containing 1\u2009\u03bcg of total RNA was reverse transcribed into cDNA with the PrimeScript RT Master Mix . The mixture was incubated at 37\u00b0C for 15\u2009min and 85\u00b0C for 5\u2009sec to acquire cDNA. RT-qPCR was conducted with a Roche Applied Science LightCycler\u00ae 96 Real-Time PCR System  in accordance with the manufacturer's protocol. The reaction mixture comprised of 2\u2009\u03bcl cDNA, 5\u2009\u03bcl SYBR\u00ae Premix Ex Taq\u2122 II  and 1\u2009\u03bcl primers (reverse and forward) with RNase-Free water to a final volume of 10\u2009\u03bcl. The thermocycling conditions were as follows: 95\u00b0C for 3\u2009min and amplified by 40 cycles of denaturing at 95\u00b0C for 10\u2009sec and 60\u00b0C for 30\u2009sec. \u03b2-actin was used as an internal standard. Melting curves were produced to check product purity and the expression levels of circRNAs were detected by using the 2\u0394\u0394Cq- method. The Cq value was the fractional cycle number at which the fluorescence exceeded the given threshold [\u03b2-actin forward primer: 5\u2032-AAACTGGAACGGTGAAGGTG-3\u2032; \u03b2-actin reverse primer: 5\u2032-AGTGGGGTGGCTTTTAGGAT-3\u2032.The reaction mixture (20\u2009hreshold . Primer http://diana.imis.athena-innovation.gr), MiRanda (http://www.microrna.org), and TargetScan (http://www.targetscan.org) databases. According to conserved seed-matching sequence principles, the predicted miRs and potential target genes were chosen by identifying the intersection of three databases [http://www.cytoscape.org).To investigate hsa_circ_0008309 function, the circRNA-miR-mRNA network was theoretically predicted through DIANA  for the circularization of transcripts. The front circular frame contained the endogenous flBiotec genomic sequence with an EcoRI restriction enzyme site, and the back circular frame contained part of the inverted upstream sequence with a BamHI site. The cDNA encoding hsa_circ_0008309 was amplified using primers 5\u2032-CGGAATTCTGAAATATGCTATCTTACAGATGACCATGGATGAAAAATATGTA-3\u2032 and 5\u2032-CGGGATCCTCAAGAAAAAATATATTCACCATGTACATTAGTATGTCTCTA-3\u2032 in the 293T cell line (Geneseed Biotechnology Co.). As a result, the amplified fragment was cloned into the vector between the two reading frames, and the mock vector was confirmed to contain a nonsense sequence between the two circular frames without the hsa_circ_0008309 encoding cDNA. The result of vector construction was verified by direct sequencing. The vectors were constructed with the help of Guangzhou Geenseed Biotech Co.In order to produce the hsa_circ_0008309 transcript formation 2 humidified incubator. The empty vector without the hsa_circ_0008309 encoding cDNA was used as negative control. According to the manufacturer's protocol, hsa_circ_0008309 overexpression vector, miR-136-5P inhibitor plus hsa_circ_0008309 expression plasmid, miR-382-5P inhibitor plus hsa_circ_0008309 expression plasmid, and an empty control vector was, respectively, transfected into cell lines with 4000\u2009ng plasmids using Lipofectamine 3000 , and cell lines were harvested at 24\u2009h following transfection.The 293T cell line (Geneseed Biotechnology Co.) and SCC15 and CAL27  cell lines were cultured in Dulbecco's modified Eagle's medium supplemented with 10% fetal bovine serum , and culture plates were incubated at 37\u00b0C in a 5% CO\u03bcg) were separated via 10% SDS-PAGE and transferred to a polyvinylidene difluoride membrane (Millipore). Bovine serum albumin  was used as a blocking agent to reduce background and nonspecific binding. After blocking for 1\u2009h, the membranes were incubated overnight at 4\u00b0C with rabbit monoclonal anti-human Ataxin 1 (ATXN1) and rabbit monoclonal anti-human GAPDH  antibodies. GAPDH was used as a loading control for normalization. Following intensive washing, the membranes were incubated with anti-rabbit horseradish peroxidase-conjugated secondary antibodies  for 1\u2009h at room temperature. The protein bands were visualized using SignalFire\u2122 ECL Reagent with the ImageQuant LAS4000 system .Cells were lysed with Blue Loading Buffer Pack  with a protease inhibitor cocktail and phenylmethanesulfonyl fluoride . The protein concentrations were quantified using a BCA Protein Assay Kit . Total cellular proteins (20\u2009t-test. The Student's t-test (two-tailed) was performed to analyze the association between the hsa_circ_0008309 expression level and the clinicopathological features of patients with OSCC. A one-way analysis of variance test was used to analyze hsa_circ_0008309 expression between different pathological groups, and the post hoc test was Tukey's multiple comparison test. Correlations between the circRNA expression level and miRNAs were evaluated by one-way ANOVA and Tukey's multiple comparison test. The clinical diagnostic value of hsa_circ_0008309 was verified by receiver operating characteristic (ROC) curve analysis in which an area under the curve (AUC)\u2009=\u20090.5 indicated no diagnostic value. P < 0.05 was considered to indicate a statistically significant difference. All statistical analyses were performed by GraphPad Prism 5.0 .All experiments were repeated three times, and data were presented as the means\u2009\u00b1\u2009standard deviation. Differences and characterizations in circRNA expression profiles between OSCC tissues and adjacent noncarcinoma tissues were assessed by Pearson's correlation test. Hsa_circ_0008309 expression level between OSCC tissues and para-cancerous tissues was evaluated by two-tailed Student's A total of 11,942 circRNA targets, including 1921 known circRNAs and 10,021 novel circRNAs, were detected and defined in 8 pairs of OSCC samples and adjacent normal tissues through high-throughput sequencing . A totalhttp://www.ensembl.org), hsa_circ_0008309 is located at chr2: 225400244-225422573 and the parental gene is Cullin 3 (CUL3). The whole length of the CUL3 gene is 22,329\u2009bp, while the mature transcript of the hsa_circ_0008309 is 312\u2009bp.The 16 significantly differentially expressed circRNAs in 45 pairs of OSCC samples were analyzed by RT-qPCR. The results demonstrated that hsa_circ_0008309 was significantly downregulated in the carcinoma tissues . AccordiTo confirm the potential diagnostic value of hsa_circ_0008309, the clinicopathological characteristics of the OSCC patients were analyzed with respect to the hsa_circ_0008309 expression level in Results from the bioinformatics analysis found that hsa_circ_0008309 could, respectively, combine with miR-1290, miR-136-5P, and miR-382-5P . A largeAfter transfection, hsa_circ_0008309 was identified to be overexpressed in the SCC15 and CAL27 cell lines . CompareCircRNA was previously considered to be a noise from aberrant RNA splicing \u201318. Howen = 8). Furthermore, 16 circRNAs were identified to be significantly differentially expressed in the OSCC samples through bioinformatics analysis. The results suggest that circRNA may serve an important role in OSCC, and these significantly differentially expressed circRNAs were subsequently validated in the 45 pairs of OSCC samples. As a result, hsa_circ_0008309 was demonstrated to be significantly downregulated in OSCC tissues (P < 0.001). Notably, compared with normal tissues, the hsa_circ_0008309 expression level was revealed to be 2.010811 times in OSCC tissues by high-throughput sequencing. The levels of hsa_circ_0008309 in certain samples were upregulated in the 45 pairs of OSCC tissues; however, the levels of hsa_circ_0008309 in the majority of samples were downregulated. These results indicated that hsa_circ_0008309 expression level of each patient was not consistent and varied between individuals. The expression of hsa_circ_0008309 was downregulated in tumor tissues and was, therefore, more likely to act as a tumor suppressor. The ROC analysis indicated that the hsa_circ_0008309 expression level exhibited a diagnostic role in distinguishing OSCC tissues from adjacent normal tissues. Taken together, we hypothesized that hsa_circ_0008309 has a potential effect on OSCC.In the present study, a total of 11,942 circRNAs were identified, demonstrating that circRNAs were in abundant existence in the OSCC samples (Compared with linear RNA, circRNA has been reported to have an increased number of miR binding sites and may regulate gene expression by acting as miR sponges, thereby regulating linear RNA transcription and protein production . ConsequIn summary, the present study revealed that the OSCC tissues have abundant circRNAs and identified that hsa_circ_0008309 was significantly downregulated in OSCC tissues. Herein is an offered novel approach to explore the role of circRNA by various bioinformatics analyses. Furthermore, the regulatory role of hsa_circ_0008309-miR-136-5P/hsa-miR-382-5P-ATXN1 network was identified in OSCC. These results highlighted the possibility that hsa_circ_0008309 could serve as a potential target for OSCC. The functions and mechanisms of hsa_circ_0008309 in OSCC should continue to be extensively investigated."}
+{"text": "Prochlorococcus and Synechococcus are the dominant primary producers in marine ecosystems and perform a significant fraction of ocean carbon fixation. These cyanobacteria interact with a diverse microbial community that coexists with them. Comparative genomics of cultivated isolates has helped address questions regarding patterns of evolution and diversity among microbes, but the fraction that can be cultivated is miniscule compared to the diversity in the wild. To further probe the diversity of these groups and extend the utility of reference sequence databases, we report a data set of single cell genomes for 489 Prochlorococcus, 50 Synechococcus, 9 extracellular virus particles, and 190 additional microorganisms from a diverse range of bacterial, archaeal, and viral groups. Many of these uncultivated single cell genomes are derived from samples obtained on GEOTRACES cruises and at well-studied oceanographic stations, each with extensive suites of physical, chemical, and biological measurements. The genomic data reported here greatly increases the number of available Prochlorococcus genomes and will facilitate studies on evolutionary biology, microbial ecology, and biological oceanography. Prochlorococcus and Synechococcus are estimated to be responsible for roughly 25% of ocean net primary productivity1. Prochlorococcus is the numerically dominant phototroph in oligotrophic subtropical gyres, which are among the largest contiguous biomes on Earth2. In these nutrient poor regimes, Prochlorococcus can account for over half of the chlorophyll4. While Prochlorococcus is generally restricted to open ocean habitats between 45oN and 40oS, Synechococcus has a much broader geographical distribution that extends to subpolar and coastal regions1. This difference in range is thought to be due in part to the greater phenotypic flexibility and regulatory capacity among Synechococcus, enabling acclimation to heterogeneous conditions5. By contrast, Prochlorococcus has a more streamlined genome adapted to less variable but nutrient depleted regions of the open ocean6. Although Prochlorococcus cells have the smallest genomes of known oxygenic phototrophs (~1.6\u20132.7 Mbp and ~2000\u20133000 genes), the global collective of this group harbors an immense diversity of protein encoding genes7. Recent estimates using 41 genomes of cultivated isolates suggested that the Prochlorococcus pan-genome\u2013the complete set of genes harbored by all Prochlorococcus\u2013contains more than 80,000 distinct genes6, many of which presumably play a role in adaptation to local environmental conditions. Only a small fraction of these genes have been catalogued, highlighting the potential for culture-independent single cell genomics to reveal new ecologically relevant functions among Prochlorococcus.Marine cyanobacteria within the genera Prochlorococcus and Synechococcus perform key functions at the base of marine food webs, primarily the supply of fixed carbon to higher trophic levels. Much of this carbon is regenerated through respiration by co-occurring heterotrophic bacteria, such as the highly abundant SAR11 clade of marine Alphaproteobacteria (Candidatus Pelagibacter ubique). Many of these heterotrophic bacteria perform ecosystem services that in turn benefit the cyanobacterial populations. In particular, Prochlorococcus is highly sensitive to reactive oxygen species, such as hydrogen peroxide8, which can be detoxified by some heterotrophic community members. Abundant catalase encoding heterotrophs in oligotrophic environments, such as the SAR86 and SAR116 clades of marine proteobacteria10 and some sub-populations of SAR11 . We expect these data to be useful for a variety of studies related to evolutionary biology, microbial ecology, and ocean biogeochemistry.Single cell genomes of both abundant and rare taxa are useful for phylogenetic anchoring of metagenomic data sets and expanding our knowledge of previously undetected phylogenetic lineages and the functions they harbor. Casting a broad net in order to most effectively capture the diversity of cyanobacterial and sympatric heterotrophic microorganisms, we have obtained samples from 22 geographic locations across the world\u2019s oceans , represe19 . Many ofSamples were collected on 13 cruises in the Pacific and Atlantic oceans and encompass 30 discrete biosamples  from thede novo assembly of SAGs were performed at the Bigelow Laboratory for Ocean Sciences\u2019 Single Cell Genomics Center (scgc.bigelow.org). The cryopreserved samples were thawed and pre-screened through a 40\u2009\u03bcm mesh size cell strainer (Becton Dickinson). Fluorescence-activated cell sorting (FACS) was performed using a BD InFlux Mariner flow cytometer equipped with a 488\u2009nm laser for excitation and a 70\u2009\u03bcm nozzle orifice , as previously described21. The cytometer was triggered on side scatter, and the \u201csingle-1 drop\u201d mode was used for maximal sort purity. For cyanobacteria, the sort gate was defined based on cellular pigment autofluorescence16. In order to discriminate heterotrophic bacteria and extracellular particles, environmental samples were incubated with the SYTO-9 DNA stain  for 10\u201360\u2009min, after which the particle green fluorescence (proxy to nucleic acid content), light side scatter (proxy to size), and the ratio of green versus red fluorescence  were used to define the sort gate21. Individual cells were deposited into 384-well plates (21. The DNA for each cell was amplified using either multiple displacement amplification (MDA) or WGA-X21, with amplification kinetics distributions for each plate available from figshare .The generation, identification, sequencing, and l plates  containi21. Heterotrophic bacteria were screened using 16S rRNA gene primers 27\u2009F and 907\u2009R. Cyanobacteria were analyzed using primers targeting the 16S-23S intergenic transcribed spacer (ITS) sequence16. The obtained PCR amplicons were sequenced from both ends using Sanger technology at GeneWiz . The two reads were automatically aligned and the consensus was manually curated using Sequencher v4.7 . Chimeric 16S rRNA sequences were identified using DECIPHER22 and removed. ITS and 16S rRNA sequences have been deposited with GenBank .Single cell MDA and WGA-X products were diluted 50x in UV-treated, 0.2\u2009\u03bcm filtered water and then used as templates in real-time PCR, as previously described21. Forty-eight additional cyanobacterial SAGs from plates AG-347, AG-355, AG-363, AG-402, AG-418, and AG-459 were selected based on the presence/absence of the narB marker gene as determined by a PCR screen using primer sequences 5\u2019-CANTGGCAYACNATGAC-3\u2019 and 5\u2019-RAANCCCCARTGCATNGG-3\u2019. All other cyanobacterial SAGs were selected based on ITS taxonomy with the aim of obtaining a diverse set of cyanobacterial single cell genomes from multiple geographic locations and depths. All heterotroph SAGs were selected based on the classification of 16S sequences using the Ribosomal Database Project (RDP) Release 11 .A selection of cyanobacterial and extracellular SAGs derived from the BiG-RAPA cruise  and HOT and BATS cruises  were chosen for sequencing based on their fast whole genome amplification, which correlates with good genome recovery in de novo assemblies21. Only contigs longer than 2,000\u2009bp were retained. This workflow was evaluated for assembly errors using three bacterial benchmark cultures with diverse genome complexity and %GC, indicating 60% average genome recovery, no non-target and undefined bases, and average frequencies of misassemblies, indels and mismatches per 100 kbp: 1.5, 3.0 and 5.0  and IMG (https://img.jgi.doe.gov/). Data can also be viewed and analyzed within IMG/ProPortal . A table linking IMG accession numbers with genome assembly statistics is provided to facilitate use of these annotation data .All genome assemblies were also deposited at the Joint Genome Institute\u2019s Integrated Microbial Genomes (IMG) system and annotated using the JGI Microbial Genome Annotation Pipeline (MGAP v. 4)26 to identify and align a collection of core protein coding gene families from the single cell genomes. Briefly, PhyloSift uses LAST27 to identify 37 protein-coding marker genes28. The identified orthologous sequences are then aligned to marker gene HMM profiles using the hmmer software suite29 and concatenated into a reading-frame-aware nucleotide codon alignment. The alignments were then trimmed using the automated heuristic method -automated1 in trimAl v1.2 .In order to facilitate downstream analyses, we have inferred the phylogeny for the cyanobacterial genomes and heterotrophic bacterial genomes  and 3. W1.2 ref. . The rec1.2 ref.  using thProchlorococcus, Synechococcus, cyanophages, and cyanobacterial virocells (genome assemblies containing both bacteria and phage genomes) from our data set. We also included publicly available genome data from IMG for Prochlorococcus, marine Synechococcus in subclusters 5.1, 5.2, and 5.3, and cyanophages isolated using these cyanobacteria as hosts. The following assemblies with likely heterotroph contamination were excluded: AG-418-M21 and scB245a_518D8 . The workflow comprises an initial clustering step performed with MCL35 on all-versus-all alignments generated with DIAMOND36, followed by a phylogeny-aware postprocessing procedure to split paralogous groups34. This analysis yielded a total of 40,295 CyCOGs , of which 23,427 are found in Prochlorococcus, 17,692 are found in Synechococcus, and 3,267 are found in cyanophage.The clustering of the proteins was carried out with panX37\u201340. Version 2 is an unreleased set of CyCOGs developed for testing purposes only. Legacy CyCOG definitions are also available from IMG/ProPortal .This is ProPortal CyCOGs version 6.0 . Prior releases include versions 1, 3, 4, and 5 of ProPortal CyCOGsNo custom code was used in the generation or processing of the data. Software versions and the use of any adjustable variables and parameters are as follows:DECIPHER 2.2.0 ref. Trimmomatic 0.32 ref. : -phred3http://sourceforge.net/projects/kmernorm)kmernorm 1.05: -k 21 -t 30 -c 3  plots associated with the single cell genome assemblies can be found in facs_ssc_fsc_plots.pdf (Data Citation 1).File 2: DNA amplification kinetics summaries associated with the single cell genome assemblies can be found in kinetics_platemap_summary.pdf (Data Citation 1).File 3: DNA amplification kinetics distributions associated with the single cell genome assemblies can be found in kinetics_welltype_distributions_summary.pdf (Data Citation 1).File 4: A complete list of genomes used for CyCOG annotations can be found in cycogs-genomes.tsv (Data Citation 1).IID \u2013 Strain or single cell identifierProchlorococcus, Synechococcus, or VirusGROUP \u2013 IMG_ID \u2013 IMG genome identification numberTYPE \u2013 Single amplified genome (SAG) or cultured reference (ISOLATE)JGI_GENOMEPORTAL_NAME \u2013 Name in the JGI Genome PortalCompleteness \u2013 Percent genome completeness determined by checkMFile 5: CyCOG definitions can be found in cycogs.tsv (Data Citation 1).cycog_iid \u2013 Unique CyCOG identifiercycog_num_taxa \u2013 Number of genomes containing CyCOGcycog_num_genes \u2013 Number of genes encompassed by CyCOGcycog_num_duplications \u2013 Number of paralogous genes within CyCOGProchlorococcus genes within CyCOGcycog_num_pro \u2013 Number of Synechococcus genes within CyCOGcycog_num_syn \u2013 Number of cycog_num_phage \u2013 Number of cyanophage/virus genes within CyCOGcycog_cns_product \u2013 Consensus annotation for genes within CyCOGcycog_genes \u2013 Comma delimited list of all genes found in CyCOG with the format of A_B, where A is the IID of the genome found in File 4 and B is the unique IMG gene ID.File 6: Taxa used for the phylogeny of cyanobacteria can be found in cyanobacteria_phylogeny_taxa.tsv (Data Citation 1).File 7: The reading-frame-aware nucleotide codon alignment used for phylogenetic inference of cyanobacterial taxa can be found in cyanobacteria_phylogeny_alignment.fna (Data Citation 1).File 8: The maximum likelihood phylogenetic tree for cyanobacteria can be found in cyanobacteria_phylogeny_rootedtree.nwk (Data Citation 1).File 9: Taxa used for the phylogeny of heterotrophic bacteria can be found in heterotroph_phylogeny_taxa.tsv (Data Citation 1).File 10: The reading-frame aware nucleotide codon alignment used for phylogenetic inference of heterotrophic bacterial taxa can be found in heterotroph_phylogeny_alignment.fna (Data Citation 1).File 11: The maximum likelihood phylogenetic tree for heterotrophic bacteria can be found in heterotroph_phylogeny_unrootedtree.nwk (Data Citation 1).25 (Data Citation 1).File 12: A bzip2 compressed tar archive containing IMG annotated genome assemblies, gene and protein sequences, and associated annotation files derived from the JGI Microbial Genome Annotation Pipeline (MGAP v. 4).fna \u2013 Nucleic acid file in multi-fasta format.genes.fna \u2013 Gene sequences in multi-fasta format.genes.faa \u2013 Amino Acids file in mult-fasta format.gff \u2013 Gene Information file in GFF3 format.cog.tab.txt \u2013 COG hits in tab-delimited format.intergenic.fna \u2013 Intergenic regions in multi-fasta format.ipr.tab.txt \u2013 IPR hits in tab-delimited format.ko.tab.txt \u2013 KO and EC annotation in tab-delimited format.pfam.tab.txt \u2013 pFam hits in tab-delimited format.signalp.tab.txt \u2013 Signal peptide annotation in tab-delimited format.tigrfam.tab.txt \u2013 TigrFam hits in tab-delimited format.tmhmm.tab.txt \u2013 Transmembrane helices in tab-delimited formatFile 13: Indexed fluorescence activated cell sorting data, estimated cell sizes, and cross-over point (Cp) values for whole genome amplification can be found in indexed_facs_wga_summary.tsv (Data Citation 1).File 14: IMG genome IDs, phylogenetic inference, usage notes, and genome assembly statistics can be found in genome_assembly_summary.tsv (Data Citation 1).ITS and 16S sequences for all SAGs (including those that did not undergo whole genome sequencing) are available from GenBank under the accession numbers MG666579-MG668595 for ITS sequences (Data Citation 2) and MH074888-MH077527 for 16S sequences (Data Citation 3).Paired-end sequencing reads in fastq format are available from the NCBI Sequence Read Archive (Data Citation 4).Genome assemblies are available from GenBank (Data Citations 5\u20137).https://img.jgi.doe.gov/) and IMG/ProPortal .Annotated genome assemblies and geolocation metadata are available from IMG  and thus contain both host and virus genomes. Other single cells may contain multiple genomes due to a close physical association between two cells that resulted in co-sorting and co-amplification of DNA. Given that many of these events are biologically meaningful, these genome assemblies were not removed from the data set or modified to separate multiple genomes. Based on our technical validation, we have identified possible virocells or co-sorted genomes in the data set .While the single cell genomes in this data set were screened for contamination that could have been introduced during cell sorting and DNA amplification, users should be aware that these screening procedures do not eliminate the potential for multiple genomes being present in the same assembly. Some single cell genomes may be derived from cells infected with a bacteriophage (i.e. virocellshttp://hahana.soest.hawaii.edu/cmoreDS/), HOT (http://hahana.soest.hawaii.edu/hot/hot-dogs/), BATS (http://bats.bios.edu/), and GEOTRACES (https://www.bodc.ac.uk/geotraces/data/) using the sample metadata available in https://www.bco-dmo.org/project/2101), BATS (https://www.bco-dmo.org/project/2124), and the U.S. GEOTRACES North Atlantic Transect (https://www.bco-dmo.org/project/2066).Ancillary physical, chemical, and biological data associated with the data set can be accessed from C-MORE .Publisher\u2019s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations."}
+{"text": "Multiple alignments of mammalian genomes have been the basis of many comparative genomic studies aiming at annotating genes, detecting regions under evolutionary constraint, and studying genome evolution. A key factor that affects the power of comparative analyses is the number of species included in a genome alignment.To utilize the increased number of sequenced genomes and to provide an accessible resource for genomic studies, we generated a mammalian genome alignment comprising 120 species. We used this alignment and the CESAR method to provide protein-coding gene annotations for 119 non-human mammals. Furthermore, we illustrate the utility of this alignment by 2 exemplary analyses. First, we quantified how variable ultraconserved elements (UCEs) are among placental mammals. Leveraging the high taxonomic coverage in our alignment, we estimate that UCEs contain on average 4.7%\u201315.6% variable alignment columns. Furthermore, we show that the center regions of UCEs are generally most constrained. Second, we identified enhancer sequences that are only conserved in placental mammals. We found that these enhancers are significantly associated with placenta-related genes, suggesting that some of these enhancers may be involved in the evolution of placental mammal-specific aspects of the placenta.https://genome-public.pks.mpg.de/and for download at https://bds.mpi-cbg.de/hillerlab/120MammalAlignment/.The 120-mammal alignment and all other data are available for analysis and visualization in a genome browser at  These 119 pairwise alignments are the input for MultiZ [4], which computes the multiple sequence alignment of 120 mammals.Supplementary Figure 2: Relationship between the variability and length of UCEs. Scatter plots show that there is a weak negative correlation between the fraction of variable columns and the length of UCEs. (A) For the lower bound value for the fraction of variable columns (only considering shared substitutions), we obtain Kendall \u03c4 of \u20130.11 with P-value <\u00a010\u20133. (B) For the upper bound value for the fraction of variable columns , we obtain Kendall \u03c4 of \u20130.12 with P-value <\u00a010\u20133. This indicates that larger UCEs tend to be slightly less variable than smaller UCEs. Kendall \u03c4 is preferred over Spearman rank correlation if the data contain ties.Supplementary Table 1: Species and genome assemblies used in the alignment. The previous 145-vertebrate alignment in column G refers to Sharma and Hiller (2017) [r (2017) .Supplementary Table 2: Fraction of variable alignment columns per UCE. Coordinates refers to the human hg38 genome assembly.Supplementary Table 3: GREAT enrichments of enhancers conserved across mammals for mouse phenotypes.Supplementary Table 4: GREAT enrichments of enhancers conserved across mammals for Gene Ontology biological processes.Supplementary Table 5: FANTOM enhancers that are placental mammal specific and contain \u22651 conserved 10-mer.Supplementary Table 6: GREAT enrichments of placental mammal enhancers for mouse phenotypes. Placenta-related terms are in boldface.Supplementary Table 7: GREAT enrichments of placental mammal enhancers for Gene Ontology biological processes. Placenta-related terms are in boldface.Supplementary Table 8: Enriched motifs in 10-mers of placental mammal\u2013specific enhancers.Supplementary Table 9: Enriched motifs in 10-mers of mammal conserved enhancers.giz159_GIGA-D-19-00313_Original_SubmissionClick here for additional data file.giz159_GIGA-D-19-00313_Revision_1Click here for additional data file.giz159_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giz159_Reviewer_1_Report_Original_SubmissionHsin-Nan Lin -- 10/30/2019 ReviewedClick here for additional data file.giz159_Reviewer_1_Report_Revision_1Hsin-Nan Lin -- 12/2/2019 ReviewedClick here for additional data file.giz159_Reviewer_2_Report_Original_SubmissionQuan Nguyen -- 11/6/2019 ReviewedClick here for additional data file.giz159_Supplemental_FilesClick here for additional data file.AME: Analysis of Motif Enrichment; bp: base pairs; CESAR: Coding Exon-Structure Aware Realigner; FANTOM: Functional Annotation of the Mammalian Genome; GO: Gene Ontology; GREAT: Genomic Regions Enrichment of Annotations Tool; MGI: Mouse Genome Informatics; UCE: ultraconserved element; UCSC: University of California Santa Cruz.The authors declare that they have no competing interests.This work was supported by the Max Planck Society and the Leibniz Association (SAW-2016-SGN-2).MH and NH conceived the study. NH generated and analyzed all data. MH and NH wrote the manuscript and produced the figures."}
+{"text": "Saccharopolyspora erythraea is an important antibiotic extensively used in human medicine. Dissecting of transcriptional regulators and their target genes associated with erythromycin biosynthesis is crucial to obtain erythromycin overproducer strains through engineering of relevant regulatory elements in S. erythraea.Erythromycin A (Er-A) produced by the actinomycete ery cluster and cannot regulate itself and its adjacent gene SACE_5753. RNA-seq coupled with EMSAs and qRT-PCR was performed to identify the targets of SACE_5754, and confirmed that transcription of SACE_0388 , SACE_3599 (encoding an antibiotic resistance macrolide glycosyltransferase) and SACE_6149 (encoding a FAD-binding monooxygenase) were directly repressed by SACE_5754. A consensus palindromic sequence TYMAGG-n2/n4/n11-KKTKRA  was proved to be essential for SACE_5754 binding using DNase I footprinting and EMSAs. During the three target genes of SACE_5754, SACE_0388 and SACE_6149 exhibited the positive effect on erythromycin production. Overexpression of either SACE_0388 or SACE_6149 in \u2206SACE_5754 further increased the Er-A production. By engineering the industrial strain S. erythraea WB with deletion of SACE_5754 combined with overexpression of either SACE_0388 or SACE_6149, Er-A production in WB\u2206SACE_5754/pIB139\u20130388 and WB\u2206SACE_5754/pIB139\u20136149 was successively increased by 42 and 30% compared to WB. Co-overexpression of SACE_0388 and SACE_6149 in WB\u2206SACE_5754 resulted in enhanced Er-A production by 64% relative to WB. In a 5-L fermenter, WB\u2206SACE_5754/pIB139\u20130388-6149 produced 4998\u2009mg/L Er-A, a 48% increase over WB.Here, we identified a TetR family transcriptional regulator (TFR), SACE_5754, negatively controlling erythromycin production. SACE_5754 indirectly repressed the transcription of SACE_5754 and its target genes, SACE_0388 and SACE_6149, resulted in enhanced erythromycin production in both wild-type and industrial S. erythraea strains. The strategy demonstrated here may be valuable to facilitate the manipulation of transcriptional regulators and their targets for production improvement of antibiotics in industrial actinomycetes.We have identified a TFR, SACE_5754, as a negative regulator of erythromycin biosynthesis, and engineering of The online version of this article (10.1186/s13036-018-0135-2) contains supplementary material, which is available to authorized users. Saccharopolyspora erythraea [ery cluster) in S. erythraea contains 20 genes arranged in four major polycistronic units [Erythromycin A (Er-A) is widely used in clinic against pathogenic Gram-positive bacteria, industrially produced by the actinomycete rythraea . As a morythraea . At firsrythraea . Next, aic units , but lacTraditionally, optimization of medium composition, random mutagenesis and selection have been performed to enhance erythromycin production . NowadayS. erythraea, in which SACE_3986, SACE_7301, SACE_3446 and PccD (SACE_3396) were successively proved to regulate erythromycin biosynthesis [S. erythraea need to be further identified to improve the understanding of regulatory mechanism underlying erythromycin biosynthesis.The TetR family transcriptional regulators (TFRs), a class of regulators commonly found in bacteria, participated in diverse cellular processes . In receynthesis , 15\u201317. SACE_5754 and its target genes significantly increased erythromycin production in both wild-type and industrial S. erythraea strains.Here, we identified a novel TFR, SACE_5754, indirectly repressing the erythromycin biosynthesis. SACE_5754 cannot regulate itself and its adjacent gene, increasing the difficulty to study its targets to understand the molecular mechanism of SACE_5754 for regulating erythromycin biosynthesis. Thereby, this study utilized RNA-seq based transcriptome analysis coupled with qRT-PCR, EMSAs and genetic experiments to identify and characterize SACE_5754\u2019s targets related to erythromycin production. Further engineering S. erythraea. These contained three TFRs  previously published and SACE_5754 currently investigated. SACE_5754 contains 624 nucleotides with approximately 22\u2009kDa molecular mass. The location of SACE_5754 and its adjacent genes on the chromosome were shown in Fig.\u00a0Considering the key role of TFRs in antibiotic biosynthesis in actinomycetes, we performed gene inactivation and identified several TFRs involved in erythromycin production in SACE_5754 was disrupted with a tsr replacement in S. erythraea A226, generating the \u2206SACE_5754 mutant  was 41% higher than that in strain A226 (45\u2009mg/L). The complemented strain \u2206SACE_5754/pIB139\u20135754 restored the original erythromycin level, suggesting that production enhancement of erythromycin in \u2206SACE_5754 was solely due to the SACE_5754 disruption  successively exhibited 31 and 28% reduction in Er-A production relative to A226 and A226/ pIB139 (43\u2009mg/L) , eryBIII , ermE  and eryCI  were respectively increased by 4-, 2.7-, 4- and 3- folds compared with A226\u00a0, and was used to examine its affinity to the five regions containing ery promoters with EMSAs \u00a0in\u00a0\u2206SACE_5754. Herein, promoter regions of 80 genes with a greater than 0.9 probability in the transcriptomic data were tested by EMSAs  in P0388\u20130389 was protected by SACE_5754  and GTTATTCAACATGCGTTGAAAACT (termed site C), respectively .TFRs can be grouped into three types based on their orientation relative to neighboring genes in the genome, which can be used to predict the target genes of TFRs . Type I n 200\u2009bp . The majn 200\u2009bp . The preSACE_0388, encoding a pyruvate, water diknase, catalyze pyruvate into phosphoenol-pyruvate, which may enter into TCA cycle. Methylmalonyl-CoA can either be filled or drained by methylmalonyl-CoA mutase-catalyzed reaction converting intermediate succinyl-CoA and methylmalonyl-CoA of TCA cycle in S. erythraea [SACE_0069; fumarate reductase iron-sulfur subunit, SACE_1171; malate dehydrogenase, SACE_3674; methylmalonyl-CoA mutase subunit beta, SACE_5638; methylmalonyl-CoA mutase, SACE_5639; succinyl-CoA synthetase subunit alpha, SACE_6668; succinyl-CoA synthetase subunit beta, SACE_6669; phosphoenolpyruvate carboxykinase, SACE_7274) were analyzed by qRT-PCR. Compared to A226, SACE_1171 was transcriptionally down-regulated by 2.4- folds while the transcription of SACE_7274 was up-regulated by 2.0- folds, and other genes was not transcriptionally regulated by SACE_5754  were cultured in LB liquid medium or on LB solid plates at 37\u2009\u00b0C, used to propagate plasmids for routine cloning and as the host for heterologous protein production, respectively [The strains and plasmids used in this work were listed in Additional file neration . Liquid neration . The E. ectively .S. erythraea were showed as previously described [SACE_5754 deletion mutant, two 1.5-kb DNA fragments flanking SACE_5754 were prepared from the genomic DNA of strain S. erythraea A226 by PCR using the primer pairs 5754-up-F/R and 5754-down-F/R  in S. erythraea A226. The desired thiostrepton-resistant mutant, named \u2206SACE_5754, was further confirmed by PCR analysis using the primers 5754-C-F/R , SACE_0388 overexpression in A226 (A226/pIB139\u20130388), SACE_3599 deletion mutant \u2206SACE_3599, SACE_6149 deletion mutant \u2206SACE_6149, complemented of SACE_6149 in the \u2206SACE_6149 (\u2206SACE_6149/pIB139\u20136149) and SACE_6149 overexpression in A226 (A226/pIB139\u20136149). All primers used in this work were listed in Additional file In accord with above procedures, we constructed SACE_0388 and SACE_6149 overexpression in the \u2206SACE_5754, pIB139\u20130388 and pIB139\u20136149 were introduced into \u2206SACE_5754 strain to obtain \u2206SACE_5754/pIB139\u20130388 and \u2206SACE_5754/pIB139\u20136149 by PEG-mediated protoplast transformation, respectively. For co-expression of SACE_0388 and SACE_6149 in WB\u2206SACE_5754, the combined DNA fragment containing PermE* and SACE_6149 from the pIB139\u20136149 was amplified using the primer pair ermE-F and 6149-C-R. The PCR product was digested with NotI/EcoRV, ligating into the corresponding sites of pIB1390388 to generate pIB139\u20130388-6149. By PEG-mediated protoplast transformation, pIB139\u20130388-6149 was introduced into the WB\u2206SACE_5754. The co-expression SACE_0388 and SACE_6149 in the WB\u2206SACE_5754 strain WB\u2206SACE_5754/pIB139\u20130388-6149 was obtained by apramycin resistance screening and confirmed by PCR analysis with primers Apr-F/R.For SACE_5754 coding region of 207 amino acids was obtained by PCR using the primers 5754-22b-F and 5754-22b-R , and SACE_5754 expression was induced by 0.5\u2009mM IPTG at 20\u2009\u00b0C for18\u201320\u2009h. The recombinant His6-tagged SACE_5754 was extracted and purified on Ni2+-NTA spin column (Bio-RAD). The quality of the purified protein was estimated by polyacrylamide gel electrophoresis (SDS-PAGE). The purified protein was stored at 4\u2009\u00b0C and used for electrophoretic mobility shift assays (EMSAs) and DNase I footprinting assays.The 6-tagged SACE_5754 in binding-buffer (10\u2009mM Tris (pH\u20097.5), 5\u2009mM MgCl2, 60\u2009mM KCl, 10\u2009mM DTT, 50\u2009mM EDTA and 10% glycerol) at 30\u2009\u00b0C for 10\u2009min in 20\u2009\u03bcL reaction mixture. After incubation, the samples were fractionated on 6% native PAGE gels in 1\u00d7 TAE buffer at 80\u2009mA for 35\u201345\u2009min.The EMSAs were performed in the light of previously reported methods . DNA pro0388\u20130389 (the entire SACE_0388-SACE_0389 intergenic region), P3599 (the entire SACE_3599 intergenic region) and P6148\u20136149 (the entire SACE_6148-SACE_6149 intergenic region), a 261-bp 6-carboxyfluorescein (FAM), a 218-bp 6-carboxyfluorescein (FAM) and a 143-bp 6-carboxyfluorescein (FAM) fluorescence-labeled DNA fragment were amplified by PCR using primers FAM-P0388\u20130389-F/R, FAM-P3599-F/R and FAM-P6148\u20136149-F/R , and incubated at 30\u2009\u00b0C for 10\u2009min in binding buffer. DNase I  treatments with various concentrations were performed for 60\u2009s at 25\u2009\u00b0C, and terminated by addition of DNase I Stop Solution and heating for 10\u2009min at 65\u2009\u00b0C to inactivate the DNase I. DNA samples were analyzed with a 3730 XL DNA Genetic Analyzer (Applied Biosystems) after purification, and data analyses were performed using GeneMarker software program v2.2.An FAM fluorescence labeling capillary electrophoresis method was used for DNase I footprinting . To idenTransZol up , from cultures of S. erythraea A226 and \u2206SACE_5754 grown in R5 liquid medium after 2\u2009days. The quality and quantity of RNAs were examined using a microplate reader (BioTek) and confirmed by electrophoresis. The transcription levels of various genes were determined by quantitative real-time PCR analysis as described previously [hrdB (SACE_1801) gene was used as the internal control, and relative transcription was quantified using a comparative cycle threshold method [Total RNA was isolated, using the d method .S. erythraea and its derivatives, spores were inoculated into 50\u2009ml of TSB seed medium and grown for 2\u2009days. Then, 5\u2009ml seed culture was transferred into 50\u2009ml R5 liquid medium and all fermentation cultures were grown at 220\u2009rpm, 30\u2009\u00b0C for 6\u2009days [S. erythraea WB and its derivatives were cultivated in the industrial medium with a 5-L fermenter . Samples (50\u2009ml) were taken every 24\u2009h. Erythromycin was extracted in fermentation culture of the S. erythraea strains as described previously [For flask fermentation of r 6\u2009days . For bioTransZol up , from cultures of S. erythraea A226 and \u2206SACE_5754 grown in R5 liquid medium after 2\u2009days. The quality and quantity of RNAs were examined using a microplate reader (BioTek) and confirmed by electrophoresis. RNA samples of A226 and \u2206SACE_5754 were used for RNA sequencing. Library construction and sequencing were performed using Illumina Hiseq\u2122 2000 at The Beijing Genomics Institution .The data obtained from the sequencing of the Illumina HiSeq \u2122 2000 is called raw reads or raw data, and then the raw reads were subjected to quality control (QC)-controlled to determine if a resequencing step is needed. After raw reads are filtered, the clean reads were aligned to the reference sequence [https://www.ncbi.nlm.nih.gov/sra/SRP129064) under the accession number SRP129064. For gene expression analysis, the matched reads were calculated and then normalized to probability using Noiseq package method [Total RNA was isolated, using the sequence . The orie method . The sigTo validate the RNA-seq results, 28 genes annotated with biological functions and the probability from high to low were analyzed by qRT-PCR. Our qRT-PCR measurements for these genes showed similar trends of expression changes estimated from the RNA-seq data , and analysis by Student\u2019s Additional file 1:Table S1. Strains and plasmids used in this study. Table S2. Primers used in this study. Table S3. Putative target genes of SACE_5754 were screened by EMSAs. Figure S1. Analyses of SACE_5754 homologs and comparison of growth rates and morphological differentiation in S. erythraea A226 and relevant strains. Figure S2. Inactivation of SACE_0388 in S. erythraea A226. Figure S3. Inactivation of SACE_3599 in S. erythraea A226. Figure S4. Inactivation of SACE_6149 in S. erythraea A226. Figure S5. Effects of SACE_5754 disruption on transcriptional levels of related genes involving in TCA cycle. Figure S6. Possible regulatory pathway of SACE_5754 on erythromycin biosynthesis in S. erythraea. Figure S7. qRT-PCR analysis of the accuracy of transcriptome analysis. (DOCX 968 kb)"}
+{"text": "Despite its prevalent use, the sample preparation methods for Hi-C have not been intensively discussed, especially from the standpoint of genome scaffolding.Hi-C is derived from chromosome conformation capture (3C) and targets chromatin contacts on a genomic scale. This method has also been used frequently in scaffolding nucleotide sequences obtained by To gain insight into the best practice of Hi-C scaffolding, we performed a multifaceted methodological comparison using vertebrate samples and optimized various factors during sample preparation, sequencing, and computation. As a result, we identified several key factors that helped improve Hi-C scaffolding, including the choice and preparation of tissues, library preparation conditions, the choice of restriction enzyme(s), and the choice of scaffolding program and its usage.Pelodiscus sinensis.This study provides the first comparison of multiple sample preparation kits/protocols and computational programs for Hi-C scaffolding by an academic third party. We introduce a customized protocol designated \u201cinexpensive and controllable Hi-C (iconHi-C) protocol,\u201d which incorporates the optimal conditions identified in this study, and demonstrate this technique on chromosome-scale genome sequences of the Chinese softshell turtle  Chromatin, a complex of nucleic acids (DNA and RNA) and proteins, exhibits a complex 3D organization in the nucleus, which enables the intricate regulation of the expression of genome information via spatio-temporal control (reviewed in ). To chade novo genome sequences [de novo genome sequencing, the number of assembled sequences is usually far larger than the number of chromosomes in the karyotype of the species of interest, regardless of the sequencing platform chosen [Analyses of chromatin conformation using Hi-C have revealed more frequent contacts between more closely linked genomic regions, which has recently prompted the use of this method in scaffolding equences . In de nm chosen . The appde novo genome assembly was revolutionized by the release of versatile computational programs for Hi-C scaffolding -based mapping data for 162 protein-coding genes covering almost all chromosomes [In addition to performing protocol optimization using human culture cells, we focused on the softshell turtle sis Fig.\u00a0. This spomosomes , which sA draft sequence assembly of the softshell turtle genome was built using short reads and was released in 2013 . This seIt would be ideal to be able to assess the quality of prepared libraries before engaging in costly sequencing. Based on the literature , 27, we Some of the libraries that we prepared passed the QC steps performed before sequencing but yielded an unfavourably large proportion of invalid read pairs. To identify such libraries, we routinely performed small-scale sequencing for quick and inexpensive QC (designated \u201cQC3\u201d) using the HiC-Pro program  see Fig and guarWe identified overt differences between the sample preparation protocols of published studies and those of commercial kits, especially regarding the duration of fixation and enzymatic reaction, as well as the library preparation method used Fig.\u00a0. TherefoTo evaluate the effect of the degree of cell fixation, we prepared Hi-C libraries from GM12878 cells fixed for 10 and 30 minutes. Our comparison did not detect any marked differences in the quality of the Hi-C DNA  is attributable mainly to shortened restriction and ligation times Fig.\u00a0. To moniOn the basis of the detailed optimization of the sample preparation conditions described above, we built an original protocol, designated the \u201ciconHi-C protocol,\u201d that included a 10-minute-long cell fixation, 16-hour-long restriction, 6-hour-long ligation, and successive QC steps  but a larger number of undetected reference orthologues (141 orthologues) than most of the other assemblies. The largest scaffold (scaffold 5) in this assembly is \u223c703 Mb long, causing a large N50 length, and accounts for approximately one-third of the whole genome in length, as a result of possible chimeric assembly that bridged 14 putative chromosomes see .The choice of restriction enzymes has not been discussed in depth in the context of genome scaffolding. Here, we prepared Hi-C libraries separately with HindIII and DpnII. We did not mix multiple enzymes in the same reaction ; rather, we performed a single scaffolding run with both HindIII-based and DpnII-based reads . This did not show any substantial differences between the assemblies , probablUCHL1 and COX15; Fig.\u00a0RBM5, TKT, WNT7A, and WNT5A, previously shown by FISH, was consistently unconfirmed by all 3 assemblies . This ftruction , uses geIn the preparation of the starting material, it is important to optimize the degree of cell fixation depending on sample choice, to obtain an optimal result in Hi-C scaffolding Fig.\u00a0. AnotherIn this study, we did not test all commercial Hi-C kits available on the market. This was partly because the Dovetail Hi-C kit specifies the non\u2013open source program HiRise as the only supported downstream computation solution and does not allow a direct comparison with other kits, namely, those from Phase Genomics and Arima Genomics.According to our calculations, the preparation of a Hi-C library using the iconHi-C protocol would be \u22653 times cheaper than the use of a commercial kit. Practically, the cost difference would be even larger, either when the purchased kit is not fully consumed or when the post-sequencing computation steps cannot be undertaken in-house, which implies additional outsourcing costs.The genomic regions that are targeted by Hi-C are determined by the choice of restriction enzymes. Theoretically, 4-base cutters e.g., DpnII), which potentially have more frequent restriction sites on the genome, are expected to provide a higher resolution than 6-base cutters  . ObviouspnII, whia priori (15 cycles for the Phase kit and 11 cycles for the Dovetail Hi-C kit)  because their manuals specify the use of a certain number of PCR cycles i-C kit) . In our i-C kit) , as donei-C kit) . The DovCommercial Hi-C kits, which usually advertise ease and speed of use, have largely shortened the protocol down to 2 days, compared with the published non-commercial protocols . Such tThe quantity of Hi-C read pairs to be input for scaffolding is critical because it accounts for the majority of the cost of Hi-C scaffolding. Our protocol introduces a thorough safety system to prevent sequencing unsuccessful libraries, first by performing pre-sequencing QCs for size shift analyses Fig.\u00a0 and secoOur comparison showed a dramatic decrease in assembly quality in cases in which <100,000,000 read pairs were used  Fig.\u00a0\u2013D. On thWe used various parameters of the scaffolding programs Fig.\u00a0. First, Our assessment using cytogenetic data confirmed the continuity of gene linkage over the obtained chromosome-scale sequences Fig.\u00a0. This vaChelonia mydas) released by the DNA Zoo Project [Gopherus evgoodei) released by the Vertebrate Genome Project [For further evaluation of our scaffolding results, we referred to the sequence length distributions of the genome assemblies of other turtle species that are regarded as being chromosome-scale data. This analysis yielded values of the basic metrics that were comparable to those of our Hi-C scaffolds of the softshell turtle, i.e., an N50 length of 127.5 Mb and a maximum sequence length of 344.5 Mb for the genome assembly of the green sea turtle . However, this has a minor effect on the total length of assembled sequences.In this study, we introduced the iconHi-C protocol, which implements successive QC steps. We also assessed potential key factors for improving Hi-C scaffolding. Overall, our study showed that small variations in sample preparation or computation for scaffolding can have a large effect on scaffolding output, and that any scaffolding output should ideally be validated using independent information, such as cytogenetic data, long reads, or genetic linkage maps. The present study aimed to evaluate the output of reproducible computational steps, which in practice should be followed by the modification of the raw scaffolding output by referring to independent information or by analysing chromatin contact maps. The study used limited combinations of species, sample preparation methods, scaffolding programs, and their parameters, and we will continue to test different conditions for kits/programs that did not necessarily perform well here using our specific materials.Pelodiscus sinensis) assembly published previously [GCA_000230535.1), whose gene space completeness and length statistics were assessed by gVolante [The Chinese softshell turtle  from a female purchased from a local farmer in Japan because the previous whole-genome sequencing used the whole blood of a female . All expRRID:CVCL_7526) was purchased from the Coriell Cell Repositories and cultured in RPMI-1640 medium  supplemented with 15% fetal bovine serum, 2\u00a0mM L-glutamine, and a 1\u00d7 antibiotic-antimycotic solution (Thermo Fisher Scientific), at 37\u2009\u00b0C, 5% CO2, as described previously [The human lymphoblastoid cell line GM12878  which uses the restriction enzyme Sau3A1 and transposase-based library preparation  Fig.\u00a01B1B was usRRID:SCR_014583; [RRID:SCR_011847; [Small-scale sequencing for library QC (QC3) was performed in-house to obtain 127\u00a0nt-long paired-end reads on a HiSeq 1500  in the Rapid Run Mode. For evaluating the effects of variable duration of the restriction digestion and ligation reactions, sequencing was performed on a MiSeq (Illumina) using the MiSeq Reagent Kit v3 to obtain 300\u00a0nt-long paired-end reads. Large-scale sequencing for Hi-C scaffolding was performed to obtain 151\u00a0nt-long paired-end reads on a HiSeq X (Illumina). The obtained reads underwent QC using FastQC ver. 0.11.5 , and lo_011847; ) with thFor post-sequencing library QC, 1,000,000 trimmed read pairs for each Hi-C library were sampled using the \u201csubseq\u201d function of the program seqtk ver. 1.2-r94 . The resTo control our comparison with intended input data sizes, a certain number of trimmed read pairs were sampled for each library with seqtk, as described above. Scaffolding was processed with the following methods using 2 program pipelines, 3d-dna and SALSA2.RRID:SCR_017226) [RRID:SCR_010910) [Scaffolding via 3d-dna was performed using Hi-C read mapping onto the genome with Juicer ver. 20180805   using th_010910) . The resRRID:SCR_002105) [RRID:SCR_006525) [RRID:SCR_006646) [Scaffolding via SALSA2 using Hi-C reads was preceded by Hi-C read pair processing with the Arima mapping pipeline ver. 20181207  together_002105) , and Pic_006525) . The map_006646) , the outRRID:SCR_015008) [gVolante ver. 1.2.1  was used_015008) . No cut-RRID:SCR_013048) [Paired-end reads obtained by RNA sequencing of softshell turtle embryos at multiple stages were downloaded from NCBI SRA (DRX001576) and were assembled using Trinity ver. 2.7.0   with def_013048) , and the_013048) .RRID:SCR_011919) [Cytogenetic validation of Hi-C scaffolding results was performed by comparing the gene locations on the scaffold sequences with those provided by previous chromosome FISH for 162 protein-coding genes . The nuc_011919) , followeGigaScience GigaDB database [All sequence data generated in this study have been submitted to the DDBJ Sequence Read Archive (DRA) under accession IDs DRA008313 and DRA008947. The datasets supporting the results of this article are available in FigShare  and the database .Supplementary Figure S1. DNA size distribution of the softshell turtle Hi-C libraries.Supplementary Figure S2. Pre-sequencing quality control of softshell turtle blood Hi-C libraries (Libraries a and b).Supplementary Figure S3. Pre-sequencing quality control (QC2) of the Hi-C libraries generated using the Phase kit (Libraries g and h).Supplementary Figure S4. Structural analysis of the possibly chimeric scaffold in Assembly 8.Supplementary Figure S5. Hi-C contact maps for selected softshell turtle Hi-C scaffolds.Supplementary Figure S6. Pairwise alignment of Hi-C scaffolds.Supplementary Table S1. Statistics of the Chinese softshell turtle draft genome assembly before Hi-C.Supplementary Table S2. HiC-Pro results for the human GM12878 HindIII Hi-C library with reduced reads.Supplementary Table S3. Quality control of the human GM12878 Hi-C libraries.Supplementary Table S4. Effect of the duration of restriction enzyme digestion and ligation.Supplementary Table S5. Quality control of Hi-C libraries.Supplementary Table S6. Scaffolding results with variable input data and computational parameters.Supplementary Table S7. Mapping results of assembled transcript sequences onto Hi-C scaffolds.Supplementary Table S8. Effect of variable degrees of PCR amplification.Supplementary Table S9. HiC-Pro results for the softshell turtle liver libraries  with reduced reads.Supplementary Protocol S1. iconHi-C protocol.Supplementary Protocol S2. Computational protocol to support the use of multiple enzymes.giz158_GIGA-D-19-00211_Original_SubmissionClick here for additional data file.giz158_GIGA-D-19-00211_Revision_1Click here for additional data file.giz158_GIGA-D-19-00211_Revision_2Click here for additional data file.giz158_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giz158_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giz158_Reviewer_1_Report_Original_SubmissionMatthew Zachariah DeMaere, Ph.D -- 7/24/2019 ReviewedClick here for additional data file.giz158_Reviewer_2_Report_Original_SubmissionDerek Bickhart -- 7/29/2019 ReviewedClick here for additional data file.giz158_Reviewer_2_Report_Revision_1Derek Bickhart -- 11/7/2019 ReviewedClick here for additional data file.giz158_Reviewer_3_Report_Original_SubmissionJay Ghurye -- 7/29/2019 ReviewedClick here for additional data file.giz158_ResponseLetter-Rev2-TrackChangeMS2(1)Click here for additional data file.giz158_Revision-report-2Click here for additional data file.giz158_Supplemental_FilesClick here for additional data file.in situ hybridization; Gb: gigabase pairs; GC: guanine-cytosine; Mb: megabase pairs; NCBI: National Center for Biotechnology Information; NGS: next-generation sequencing; QC: quality control; SRA: Sequence Read Archive.3C: chromosome conformation capture; BLAT: BLAST-like alignment tool; bp: base pairs; BUSCO: Benchmarking Universal Single-Copy Orthologs; BWA: Burrows-Wheeler Aligner; FISH: fluorescence This work was supported by intramural grants within RIKEN including the All-RIKEN \u201cEpigenome Manipulation Project\u201d to S.K. and I.H. and by a Grant-in-Aid for Scientific Research on Innovative Areas from the Ministry of Education, Culture, Sports, Science, and Technology (MEXT) to I.H. (18H05530).The authors declare that they have no competing interests.S.K., I.H., H.M., and M.K. conceived the study. M.K. and K.T. performed laboratory work, and O.N. performed bioinformatic analysis. M.K., O.N., and H.M. analysed the data. S.K., M.K., and O.N. drafted the manuscript. All authors contributed to the finalization of the manuscript."}
+{"text": "Pelteobagrus fulvidraco, belonging to the Siluriformes order, is an economically important freshwater aquaculture fish species in Asia, especially in Southern China. The aquaculture industry has recently been facing tremendous challenges in germplasm degeneration and poor disease resistance. As the yellow catfish exhibits notable sex dimorphism in growth, with adult males about two- to three-fold bigger than females, the way in which the aquaculture industry takes advantage of such sex dimorphism is another challenge. To address these issues, a high-quality reference genome of the yellow catfish would be a very useful resource.The yellow catfish, To construct a high-quality reference genome for the yellow catfish, we generated 51.2 Gb short reads and 38.9 Gb long reads using Illumina and Pacific Biosciences (PacBio) sequencing platforms, respectively. The sequencing data were assembled into a 732.8 Mb genome assembly with a contig N50 length of 1.1 Mb. Additionally, we applied Hi-C technology to identify contacts among contigs, which were then used to assemble contigs into scaffolds, resulting in a genome assembly with 26 chromosomes and a scaffold N50 length of 25.8 Mb. Using 24,552 protein-coding genes annotated in the yellow catfish genome, the phylogenetic relationships of the yellow catfish with other teleosts showed that yellow catfish separated from the common ancestor of channel catfish \u223c81.9 million years ago. We identified 1,717 gene families to be expanded in the yellow catfish, and those gene families are mainly enriched in the immune system, signal transduction, glycosphingolipid biosynthesis, and fatty acid biosynthesis.P. fulvidraco. The genomic resources generated in this work not only offer a valuable reference genome for functional genomics studies of yellow catfish to decipher the economic traits and sex determination but also provide important chromosome information for genome comparisons in the wider evolutionary research community.Taking advantage of Illumina, PacBio, and Hi-C technologies, we constructed the first high-quality chromosome-level genome assembly for the yellow catfish  Pelteobagrus fulvidraco  is a teleost fish belonging to the order Siluriformes  gene, a novel PDZ domain-containing gene in whose intron the sex-linked marker was located. The pfpdz1 gene plays an important role in male sex differentiation and maintenance in yellow catfish [The yellow catfish, In spite of the importance of yellow catfish both in sex-determination research and in aquaculture, the genomic resources for the species are still limited. To date, only transcriptome, simple sequence repeat, and single-nucleotide polymorphism (SNP) data have been reported for yellow catfish  and the A XX genotype female yellow catfish Fig.\u00a0, reared The extracted DNA molecules were sequenced with both Illumina HiSeq X Ten platform  and PacBio Sequel platforms. Short reads generated from the Illumina platform were used for the estimation of the genome size, the level of heterozygosity, and repeat content of the genome, and long reads from the PacBio platform were used for genome assembly. To this end, one library with an insertion length of 250 bp was generated for the HiSeq X Ten platform and three 20-kb libraries were constructed for the PacBio platform according to the manufacturers' protocols, resulting in the generation of \u223c51.2 Gb short reads and \u223c38.9 Gb long reads, respectively  [Although the size of the genome assembly from both Falcon and canu was comparable with the estimation based on the er (GPM)  is a tooer (GPM) . Based oer (GPM) . Taking er (GPM)  to mergeer (GPM)  using Paer (GPM)  using Il2, 100 \u03bcg/mL bovine serum albumin (BSA), pH 7.9) and 150 U of MboI and incubated at 37\u00b0C overnight. On the next day, the MboI enzyme was inactivated at 65\u00b0C for 20 minutes. Next, the cohesive ends were filled in by adding 1 \u03bcL 10 mM dTTP, 1 \u03bcL 10 mM dATP, 1 \u03bcL 10 mM dGTP, 2 \u03bcL 5 mM biotin-14-dCTP, 14 \u03bcL water, and 4 \u03bcL (40 U) Klenow and incubated at 37\u00b0C for 2 hours. Subsequently, 663 \u03bcL water,120 \u03bcL 10x blunt-end ligation buffer , 100 \u03bcL 10% Triton X-100, and 20 U T4 DNA ligase were added to start proximity ligation. The ligation reaction was heldat 16\u00b0C for 4 hour. After ligation, the cross-linking was reversed with 200 \u00b5g/mL proteinase K (Thermo) at 65\u00b0C overnight. Subsequent chromatin DNA manipulations were performed using a method similar to the one described in the previous study [Hi-C is a technique that makes it possible to unbiasly identify chromatin interactions across the entire genome . The tecus study . DNA purRRID:SCR_005476) [Pseudobagrus fulvidraco [A total of 487 million raw reads were generated from the Hi-C library and were mapped to the polished yellow catfish genome using Bowtie 1.2.2 (_005476)  with the_005476) . Two end_005476) . The con_005476) , which wlvidraco . Lachesilvidraco  to correRRID:SCR_015008, version 3.0) with the actinopterygii_odb9 database to evaluate the completeness of the genome. Among 4,584 total BUSCO groups searched, 4,179 and 92 BUSCO core genes were completed and partially identified, respectively, leading to a total of 91.2% BUSCO genes in the yellow catfish genome. After aligning short reads from the Illumina platform to the genome, the insertion length distribution for the sequencing library of 250 bp exhibited a single peak around the sequencing library length design (RRID:SCR_010910), we identified 21,143 homozygous SNP loci using the GATK (RRID:SCR_001876) package [First, we compared the genome assembly continuity of the yellow catfish genome to those of other teleost species. We found that both contig and scaffold N50 lengths of the yellow catfish reached considerable continuity Fig.\u00a0, providih design . Paired- package .RRID:SCR_015027) was used to detect transposable elements (TEs) in the genome by a de novo manner. The de novo and known repeats library from Repbase [RRID:SCR_012954) [We first used Tandem Repeat Finder  to ident Repbase  were the_012954) .de novo-, homology-, and RNA-sequencing-based methods were used. Augustus (RRID:SCR_008417) [de novo prediction. For the homology-based method, protein sequences of closely related fish species, including Astyanax mexicanus, Danio rerio, Gadus morhua, Ictalurus punctatus, Oryzias latipes, Takifugu rubripes, Tetraodon nigroviridis, and Oreochromis niloticus, were downloaded from Ensembl [RRID:SCR_011822) software [RRID:SCR_013035) [RRID:SCR_014597) [For protein-coding gene annotation, _008417)  was used Ensembl  and aligsoftware . Short r_013035) , and the_014597) . Finally_014597) . The genRRID:SCR_001653) and BLASTN (RRID:SCR_001598) programs were used to search all predicted gene sequences to NCBI nonredundant protein (nr), no-redundant nucleotide (nt) Swissprot database with a maximal e-value of 1e\u22125 [Local Basic Local Alignment Search Tool (BLAST) X  programs [\u22125. OrthMCL [To cluster families from protein-coding genes, proteins from the longest transcripts of each gene from yellow catfish and other fish species, including programs  with a m OrthMCL  was usedRRID:SCR_011812) [RRID:SCR_014629) [To reveal phylogenetic relationships among yellow catfish and other fish species, the protein sequences of single-copy ortholog gene families were aligned with MUSCLE 3.8.31 (_011812) , and the_014629)  was used_014629)  was empl_014629) . The phyP < 0.05). The functional enrichment on GO and KEGG of those expanded gene families identified 350 and 42 significantly enriched  GO terms  via project ID OEP000129 (http://www.biosino.org/node/project/detail/OEP000129). The genome, annotation, and intermediate files and results are also available via the GigaScience GigaDB repository [The raw sequencing and physical mapping data are available from NCBI via accession numbers SRR7817079, SRR7817060, and SRR7818403 via the project PRJNA489116, as well as the National Omics Data Encyclopedia  and the Fundamental Research Funds for the Central Universities (2662017PY013).J.M., J.-F.G., and N.C. conceived the study; D.C., J.Z., W.G., and P.H. collected the samples and performed sequencing and Hi-C experiments; S.X., G.G., and Y.H. estimated the genome size and assembled the genome; S.X., G.G., and X.L. assessed the assembly quality; G.G., S.X., Y.X., and J.W. carried out the genome annotation and functional genomic analysis; and J.M., N.C., S.X., G.G., and J.-F.G.wrote the manuscript. And all authors read, edited, and approved the final manuscript.Reviewer_1_Report_ -- Geoffrey Waldbieser7/23/2018 ReviewedClick here for additional data file.Reviewer_2_Report_ -- Christiaan Henkel7/29/2018 ReviewedClick here for additional data file.Reviewer_3_Report_ -- Paolo Franchini8/2/2018 ReviewedClick here for additional data file.Response_To_Reviewer_Comments_.pdfClick here for additional data file.GIGA-D-18-00234_Original_Submission.pdfClick here for additional data file.GIGA-D-18-00234_Revision_1.pdfClick here for additional data file.Supplemental FileClick here for additional data file."}
+{"text": "This article provides data on the simulation results of consumer debt default for bank and non-bank lenders in Chile, using the model described in Ref.\u00a0[1]. Furthermore, it provides a summary description of all the codes used for the simulation exercises and how to implement them from publicly available microdata sources. The data is of particular interest for those interested in analyzing the sensitivity of consumer loan default to heterogeneous labor market shocks and aggregate interest rates. All the codes and datasets are in Stata format. In the folder \u201cMain_data_for_figures\u201d the interested researchers can find the code \u201cM_Cdebt_Graphs.do\u201d which calls 10 datasets to create Table 6 plus Figures 1, 3, 4, 5, 6, 7, 8 and 9 in the original article of \u201cFig 1_delinquency_rates_countries.dta\u201d contains the delinquency rates for Chile, Spain and the USA.\u201cFig 1_delinquency_DIR_InterestRates_Unemployment_Chile.dta\u201d contains the rates for Chile (in log differences over the mean) for the Consumer Delinquency, Debt to Income, Interest Rate and Unemployment.\u201cFig 3_Simulated_NPL_ENPL_BankingSystem_1990_to_2012Quarterly.dta\u201d contains the simulated and real NPL and ENPL rates for the Chilean banking system.\u201cFig 4_Simulated_NPL_ENPL_NonBanks_1990_to_2012Quarterly.dta\u201d contains the simulated NPL and ENPL rates for the Chilean Non Bank Lenders.\u201cFig 4_Simulated_NPL_ENPL_AllLenders_1990_to_2012Quarterly.dta\u201d contains the simulated NPL and ENPL rates for all Chilean Lenders.\u201cFig 5_ConsumptionCost_IncomeHouseholdWeights.dta\u201d shows the consumption reduction that financially stressed households suffer as a percentage of the average household income and as a percentage of the income for the entire economy.\u201cFig 6_Simulated_NPL_ENPL_Banks_Quintiles_1990_2012.dta\u201d contains the simulated NPL and ENPL rates by household income quintile (with quintile 1 being the 20% poorest households and 5 being the 20% richest households) for the Chilean Bank borrowers.\u201cFig 7_Simulated_NPL_ENPL_NonBanks_Quintiles_1990_2012.dta\u201d contains the simulated NPL and ENPL rates by household income quintile (with quintile 1 being the 20% poorest households and 5 being the 20% richest households) for the Chilean Non Bank borrowers.\u201cFig 8_Simulated_NPL_ENPL_Banks_RiskScenarios_1990_2012.dta\u201d contains the simulated NPL and ENPL rates for the Chilean Bank borrowers across 4 stress scenarios.\u201cFig 9_Simulated_NPL_ENPL_NonBanks_RiskScenarios_1990_2012.dta\u201d contains the simulated NPL and ENPL rates for the Chilean Non Bank borrowers across 4 stress scenarios.22.1The data consists of simulations of the Non-Performing Loans (NPL) and Expenses with Non-Performing Loans (ENPL) rates for Chilean households, using a structural model where households use behavioral rules to decide between consumption and defaulting on their loan commitments 2.2The folder \u201cSimulationCodes\u201d contains all the simulation codes and algorithms that create the 10 datasets listed above from the original source data. See the file \u201cSimulation Codes Summary.docx\u201d for a brief explanation of all codes.http://www.bcentral.cl. The Employment Survey , plus its Income Module (Encuesta Suplementaria de Ingresos), and the Household Expenditure Survey  are available from the website of the Chilean Institute of National Statistics : http://www.ine.cl/ene/base-de-datos-ene.php; http://www.ine.cl/epf/VII/base-de-datos.php.The model is calibrated using several sources of publicly available microdata, including the Chilean Household Finance Survey , the Employment Survey , and the Household Expenditure Survey . Users can apply for all the waves of the Chilean Household Finance Survey  by filling a form on the website of the Central Bank of Chile: http://observatorio.ministeriodesarrollosocial.gob.cl/casen/casen_usuarios.php.Also, some users can opt to calibrate the model by using the Chilean Income and Participation survey , since the codes are prepared to do this automatically. The CASEN 2006 has less detailed financial information than the EFH, but it has a similar measure of income and a much larger sample : 2.31)The EFH dataset (waves 2007 to 2011) is used as the original 12,264 households' sample and households were randomly selected with replacement to form a 135,000 household population before the statistical simulation process.2)Each of the labor force members of every household had a sequence of dynamic labor earnings' simulated with an industry earnings\u2019 drift increase, plus idiosyncratic permanent and temporary wage shocks and flows into and out of unemployment 3)Expenditure and consumption decisions by the households were calibrated using the EPF dataset (wave 2007) and a semi-parametric model that is linear on the log of the household's permanent income plus a continuous flexible function of its demographic characteristics .4)Access to new loans from the credit market was calibrated using risk-adjusted interest rates (adequate for a competitive lender market) with a loan delinquency model estimated from the EFH dataset (2007\u20132011) with a probit function, using current income (in log), the debt over permanent income ratio, the debt service over current income ratio, the households' weighted unemployment risk, and demographic risk factors . A Maximum Legal Interest Rate and debt ceilings based on permanent income were applied for each households\u2019 loan access decision 5)Finally, the default decisions were calibrated based on the households' inability to keep both debt commitments and required expenditures and consumption within their budget constraint. The structural equations used for this decision and its budget constraint are detailed in Ref.\u00a06)50 Bootstrap replicas of this procedure were repeated to calculate standard-errors and other dispersion statistics.The sequence for the final data creation (which was simulated for the entire period between 1990 and 2012 at a quarterly frequency) can be described as follows:"}
+{"text": "Single-cell RNA sequencing is essential for investigating cellular heterogeneity and highlighting cell subpopulation-specific signatures. Single-cell sequencing applications have spread from conventional RNA sequencing to epigenomics, e.g., ATAC-seq. Many related algorithms and tools have been developed, but few computational workflows provide analysis flexibility while also achieving functional  and computational reproducibility  through a user-friendly environment.rCASC is a modular workflow providing an integrated analysis environment (from count generation to cell subpopulation identification) exploiting Docker containerization to achieve both functional and computational reproducibility in data analysis. Hence, rCASC provides preprocessing tools to remove low-quality cells and/or specific bias, e.g., cell cycle. Subpopulation discovery can instead be achieved using different clustering techniques based on different distance metrics. Cluster quality is then estimated through the new metric \"cell stability score\" (CSS), which describes the stability of a cell in a cluster as a consequence of a perturbation induced by removing a random set of cells from the cell population. CSS provides better cluster robustness information than the silhouette metric. Moreover, rCASC's tools can identify cluster-specific gene signatures.rCASC is a modular workflow with new features that could help researchers define cell subpopulations and detect subpopulation-specific markers. It uses Docker for ease of installation and to achieve a computation-reproducible analysis. A Java GUI is provided to welcome users without computational skills in R. Since the end of the 90s omics high-throughput technologies have generated an enormous amount of data, reaching today an exponential growth phase. The analysis of omics big data is a revolutionary means of understanding the molecular basis of disease regulation and susceptibility, and this resource is made accessible to the biological/medical community via bioinformatics frameworks. However, owing to the increasing complexity and the fast evolution of omics methods, the reproducibility crisis , 2 demanSingle-cell analysis is instrumental to understanding the functional differences among cells within a tissue. Individual cells of the same phenotype are commonly viewed as identical functional units of a tissue or an organ. However, single-cell sequencing results  suggest To the best of our knowledge, rCASC is the only computational framework that provides both computational and functional reproducibility for an integrated analysis of single-cell data, from count generation to cell subpopulation identification. It is one of the tools developed under the umbrella of the Reproducible Bioinformatics project , 8, an oAll the computational tools in rCASC are embedded in Docker images stored in a public repository on the Docker hub. Parameters are delivered to Docker containers via a set of R functions, part of the rCASC R github package . To simpThe overall characteristics of rCASC were compared with 4 other workflows for single-cell analysis Fig.\u00a0: (i) simrCASC is the only workflow providing support at the fastq level because all the other packages require as input the processed count table. Cell quality control and outlier identification is available in all the workflows but Granatum. Association of ENSEMBL gene IDs to gene symbols is only provided by rCASC. All the workflows provide gene-filtering tools but simpleSingleCell. All packages provide normalization procedures to be applied to raw count data. However, rCASC is the only tool providing both Seurat specific normalization  and coun+ T cells show sustained survival and increased long-term memory reprogramming capacity. Our re-analysis extends the information described by Pace et al. [Finally, rCASC was used to re-analyze the single-cell dataset from Pace et al. . In thise et al. , suggestThe inDrop single-cell sequencing approach was originally published by Klein et al. . The autrCASC provides various data inspection and preprocessing tools.The \"genesUmi\" function generates a plot where the number of detected genes is plotted for each cell with respect to the number of UMI Fig.\u00a0 and\u00a0C.mitoRiboUmi calculates the percentage of mitochondrial/ribosomal genes with respect to the total number of detected genes in each cell and plots the percentage of mitochondrial genes with respect to percentage of ribosomal genes. Cell color indicates the number of detected genes Fig.\u00a0 and\u00a0D. mThe function \"scannobyGtf\" uses ENSEMBL gtf and the R package refGenome to associate gene symbol with the ENSEMBL gene ID. Furthermore, scannobyGtf allows one to remove mitochondrial/ribosomal genes Fig.\u00a0 and\u00a0C anThe function \"lorenzFilter\" embeds the Lorenz statistics developed by Diaz et al. , a cell As count table preprocessing steps, we implemented the functions \"checkCountDepth/scnorm\" to detect the presence of sample-specific count\u2013depth relationship  i.e., t. Scnorm,The \"ccRemove\" function is instead based on the work of Barron and Li  and embeRRID:SCR_016341) [k-mean clustering, where the number of clusters  is taken as input. SIMLR requires as input raw counts that are log10 transformed. SIMLR is capable of learning an appropriate cell-to-cell similarity metric from the input single-cell data and can exploit it for the clustering task. In the learning phase SIMLR identifies a distance metric that better fits the structure of the data by combining multiple Gaussian kernels [k\u201d of clusters. Griph clustering [For the identification of cell subpopulations we implemented 4 approaches: Seurat (_016341) , SIMLR [_016341) , griph [_016341) , and sca_016341) . Seurat  kernels . Thus, t kernels . The fun kernels , is explustering  is basedustering  uses forWe developed, for Seurat, SIMLR, griph, and scanpy, a procedure to measure the cluster quality on the basis of data structure. The rationale of our approach is that cells belonging to a specific cluster should be little affected by changes in the size of the dataset, e.g., removal of 10% of the total number of cells used for clustering. Thus, we developed a metric called CSS, which describes the persistence of a cell in a specific cluster upon jackknife resampling and therefore offers a peculiar way of describing cluster stability. A detailed description of the CSS metric is available in To select the most important features of each cluster we implemented in the \"anovaLike\" function the edgeR ANOVA-like method for single cells  and in tTo estimate the scalability of rCASC clustering we used the GSE106264 dataset made of 10,035 cells and published by Pace and coworkers in 2018  and the All the above samples were preprocessed removing ribosomal/mitochondrial protein genes and cells with a total count of UMIs <100.The computing time as a function of increasing number of genes had a quite limited effect on the overall computing time Fig.\u00a0.The definition of the computing time for an analysis depends on multiple parameters: (i) the number of permutations performed in parallel, (ii) the number of cells under analysis, (iii) the clustering tool in use, and (iv) the hardware used for the analysis. Concerning the amount of RAM required for each permutation run in parallel, for up to 5,000 cells the maximum amount of RAM required is \u223c4 GB; from 10,000 to 100,000 cells, the maximum RAM required is \u223c20 GB. Independently by the clustering approach and the size of the dataset, we suggest running \u2265100 permutations to correctly estimate CSS.GigaScience GigaDB repository [https://hub.docker.com/u/repbioinfo.Snapshots of the code and test data are available from the pository . All theProject name: rCASC: reproducible Classification Analysis of Single Cell sequencing datahttps://github.com/kendomaniac/rCASC; https://github.com/mbeccuti/4SeqGUIProject home page: Operating system: LinuxProgramming language: R and JAVAOther requirements: NoneLicense: GNU Lesser General Public License, version 3.0 (LGPL-3.0)RRID:SCR_017005Supplementary Methods: Details about the implemented methods.giz105_GIGA-D-18-00522_Original_SubmissionClick here for additional data file.giz105_GIGA-D-18-00522_Revision_1Click here for additional data file.giz105_GIGA-D-18-00522_Revision_2Click here for additional data file.giz105_GIGA-D-18-00522_Revision_3Click here for additional data file.giz105_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giz105_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giz105_Response_to_Reviewer_Comments_Revision_2Click here for additional data file.giz105_Reviewer_1_Report_Original_SubmissionOlivier Poirion, Ph.D. -- 1/22/2019 ReviewedClick here for additional data file.giz105_Reviewer_1_Report_Revision_1Olivier Poirion, Ph.D. -- 4/30/2019 ReviewedClick here for additional data file.giz105_Reviewer_1_Report_Revision_2Olivier Poirion, Ph.D. -- 7/25/2019 ReviewedClick here for additional data file.giz105_Reviewer_2_Report_Original_SubmissionNils Eling -- 1/31/2019 ReviewedClick here for additional data file.giz105_Reviewer_2_Report_Revision_1Nils Eling -- 4/29/2019 ReviewedClick here for additional data file.giz105_Reviewer_2_Report_Revision_2Nils Eling -- 7/15/2019 ReviewedClick here for additional data file.giz105_Supplemental_FileClick here for additional data file.ANOVA: analysis of variance; ATAC-seq: Assay for Transposase-Accessible Chromatin using sequencing; CPU: central processing unit; CSS: cell stability score; griph: Graph Inference of Population Heterogeneity; GUI: graphical user interface; PBMC: peripheral blood mononuclear cell; PCA: principal componet analysis; RAM: random access memory; rCASC: reproducible Classification Analysis of Single Cell sequencing data; RNA-seq: RNA sequencing; SATA: Serial Advanced Technology Attachment; scanpy: Single-Cell Analysis in Python; SIMLR: Single-cell Interpretation via Multi-kernel LeaRning; SS: silhouette score; SSD: solid-state drive; t-SNE: T-distributed Stochastic Neighbor Embedding; UMI: unique molecular identifier.L.A. and F.C. equally participated to write R scripts, to create the majority of Docker images, to package the workflow and release code. M.B. wrote the Java and C++ code and acted as corresponding author. N.L. implemented scanpy and extended the Java GUI. M.A. and M.O. prepared the single-cell data to be used as examples of the workflow functionality. G.R. prepared the Dockers for fastq to count table conversion. S.R. revised all packages and generated the Docker files for Docker image maintenance and further development. G.D.L. gave scientific advice and provided an unpublished dataset for MAIT resting and activated T-cells (generated with Fluidigm C1 platform) to investigate gene detection limits in 3\u2032-end sequencing technologies and whole-transcript sequencing. R.A.C. and L.P. equally oversaw the project and gave scientific advice. All authors read, contributed to, and approved the final manuscript."}
+{"text": "Vigna vexillata (zombi pea) is an underutilized legume crop considered to be a potential gene source in breeding for abiotic stress tolerance. This study focuses on the molecular characterization of mechanisms controlling waterlogging tolerance using two zombi pea varieties with contrasting waterlogging tolerance. Morphological examination revealed that in contrast to the sensitive variety, the tolerant variety was able to grow, maintain chlorophyll, form lateral roots, and develop aerenchyma in hypocotyl and taproots under waterlogging. To find the mechanism controlling waterlogging tolerance in zombi pea, comparative transcriptome analysis was performed using roots subjected to short-term waterlogging. Functional analysis indicated that glycolysis and fermentative genes were strongly upregulated in the sensitive variety, but not in the tolerant one. In contrast, the genes involved in auxin-regulated lateral root initiation and formation were expressed only in the tolerant variety. In addition, cell wall modification, aquaporin, and peroxidase genes were highly induced in the tolerant variety under waterlogging. Our findings suggest that energy management and root plasticity play important roles in mitigating the impact of waterlogging in zombi pea. The basic knowledge obtained from this study can be used in the molecular breeding of waterlogging-tolerant legume crops in the future. Flooding is one of the most significant problems facing global agriculture today. It can be categorized as waterlogging, i.e., when the height of the water column covers only the root-zone, or as submergence, when the aerial plant tissues are fully covered . WaterloArabidopsis [Brachypodium distachyon, transcriptomic analysis of submergence-tolerant and sensitive natural genetic variations revealed the oxidative stress pathway as a significant tolerance factor [The characterization of the molecular mechanisms for submergence tolerance has been extensively studied in model plants. Functional characterization of group VII of ethylene response factor (ERF) genes revealed their functional role as critical players regulating submergence tolerance in rice and bidopsis ,4,5. In bidopsis ,7. In ane factor . Most ofe factor .Fabaceae) is one of the most important food crops for human nutritional needs. However, molecular characterization of the mechanisms controlling waterlogging tolerance in the legume family is uneven. Most existing studies on the molecular basis of waterlogging tolerance in legumse were focused on soybean. A key component associated with waterlogging stress in soybean is an energy crisis in root-zone resulting from low oxygen conditions, with the meristem showing particular susceptibility. Waterlogging-tolerant soybean varieties were found to develop more aerenchyma and promote more root growth than the sensitive varieties under waterlogging stress [qWT_Gm_03, controlling root plasticity under waterlogging was identified in soybean and proposed to be involved in the auxin pathways regulating secondary root development and root plasticity [Trifolium [Pisum sativum) [Lens culinaris) [The legume family  , and lenlinaris) .Vigna is a particularly important legume crop, comprising more than 1000 species and distributed in extensive and diverse areas of Africa, America, Australia, and Asia [Vigna species including cowpea (V. unguiculata), zombi pea (V. vexillata), Bambara groundnut (V. subterranean), mungbean (V. radiata), azuki bean (V. angularis), rice bean (V. umbellata), black gram (V. mungo), moth bean (V. aconitifolia), and cr\u00e9ole bean (V. reflexo-pilosa) are grown mainly for dry seeds by small farmers in several cropping systems of tropical and sub-tropical regions [Vigna species are particularly sensitive to waterlogging, resulting in poor seed quality and significant yield reduction. In the case of mungbean, waterlogging at the vegetative stage results in decreased leaf area, growth rate, root growth, photosynthesis rate, chlorophyll and carotenoid contents, flowering rate, pod setting, yield, and altered dry matter partitioning [Vigna. Therefore, to improve Vigna waterlogging tolerance, mechanisms of waterlogging tolerance must be understood. It has been proposed that stress-resistant plant species closely related to the crop of interest could be used for the molecular analysis of the stress adaptation mechanisms [Vigna crops.The genus and Asia ,21. Dome regions ,23. Mostitioning . In contchanisms . Thus, dVigna vexillata (common name: zombi pea) is an underutilized legume crop that can be found in diverse areas of Africa, America, Australia, and Asia [and Asia . It is cand Asia . Previouand Asia , alkalinand Asia , droughtand Asia , and watand Asia . TherefoVigna.In this work, we investigated the changes in anatomy, morphology, and molecular responses to waterlogging with the assistance of RNA-sequencing (RNA-seq) of the waterlogged roots of two zombi pea varieties with contrasting waterlogging-tolerant phenotypes. We hypothesized that the natural genetic diversity of the zombi pea would allow us to find the molecular mechanism of waterlogging tolerance in the genus 2 at day zero and 0.017 mg/cm2 at day seven (2 at day zero and 0.018 mg/cm2 at day seven under NS (2) and day seven (0.008 mg/cm2) of WS  of WS D, suggesTrifolium, higher root porosity and the ability to form lateral roots contributed to waterlogging tolerance [qWT_Gm_03, enhanced waterlogging tolerance through controlling secondary root growth in a waterlogging-tolerant cultivar [Root architecture and plasticity play a vital role in the adaptation of plants to WS . Therefoolerance . Second,olerance . Lastly,cultivar . Since lTo examine the molecular mechanisms controlling waterlogging tolerance in zombi pea, we performed de novo transcriptome analysis by RNA-seq using WS and NS root samples derived from both \u201cA408\u201d and \u201cBali\u201d varieties. Twenty-two to twenty-six million reads were obtained for each RNA-seq library . To consArabidopsis were identified by the OrthoVenn2 web tool. Transcript expression, as represented by count per million (CPM) expression values can be found in To perform functional characterization of the de novo assembled transcriptomes, the candidate open reading frames of each transcript  were annotated using BLASTP to plant UniprotPK database to obtain the associated gene ontology (GO) terms and assigned to functional bins by Mercator pipeline . TranscrFor differential gene expression analysis, reads were mapped back to the assembled transcriptome. The majority of reads (96\u201397%) from each RNA-seq library could be mapped to the reference transcriptome , suggestArabidopsis [Arabidopsis (sucrose synthase (cluster 56), alcohol dehydrogenase (cluster 3967), similar to RCD one 5 , and wound-responsive family protein (cluster 8884), were induced in both \u201cA408\u201d and \u201cBali\u201d (Non-symbiotic hemoglobin 1 (cluster 13294) was induced only in \u201cA408\u201d. In contrast, 1-aminocyclopropane-1-carboxylate oxidase 1 , haloacid dehalogenase-like hydrolase (HAD) superfamily protein (cluster 6574), and LOB domain-containing protein 41  were specifically induced in \u201cBali\u201d.Using a list of core hypoxia-induced genes in bidopsis , we werebidopsis . Of thesd \u201cBali\u201d . Non-symWe took two contemporary approaches to identify differentially-expressed molecular mechanisms controlling waterlogging tolerance; GO enrichment analysis of co-expressed genes B and comTo compare the changes in WS transcriptome in the two zombi pea varieties with contrasting WS responses, comparative transcriptome analysis was analyzed by over-representation analysis (ORA) using Fisher\u2019s exact test with a cut-off of two. The results from the ORA analysis demonstrated that glycolysis, stress, MYB-related transcription factor family, and protein functional bins were overrepresented in the upregulated DEGs of \u201cBali\u201d . In contbeta-amylase (A_DN40578_c6_g3_i1), starch phosphorylase , fructokinase (A_DN41293_c1_g8_i1), and invertase (A_DN40864_C7_g2_i1) were downregulated in \u201cA408\u201d. However, the expression of sucrose synthase (SUSY) was upregulated in both \u201cA408\u201d (A_DN40966_c1_g1_i8) and \u201cBali\u201d (B_DN52186_c2_g2_i4 and i11). Several genes encoded for glycolysis enzymes were strongly upregulated in \u201cBali\u201d, including aldolase (B_DN50672_c0_g4_i1 and i2), enolase (B_DN51208_c1_g1_i4 and i9), glucose 6 phosphate (G6P) isomerase (B_DN50580_c2_g7_i2 and i8), GAP-DH (B_DN51637_c1_g4_i1 and i2), phosphofrucktokinases , phosphoglycerate mutase , and pyruvate kinases .Since WS creates a low oxygen environment that could promote glycolysis and fermentation and the GO enrichment and ORA analyses suggested differential expressions of glycolysis and fermentative genes in both varieties, we then examined changes in the expression of major carbohydrate metabolic, glycolysis and fermentative genes . Starch phospho-enol-pyruvate carboxylase kinase , which was induced by WS. Interestingly, PFK (A_DN40730_c0_g2_i10), encoded for one of the most important regulatory enzymes of glycolysis, was strongly downregulated under WS in \u201cA408\u201d. Several fermentative genes were strongly upregulated in \u201cBali\u201d, particularly alcohol dehydrogenases , aldehyde dehydrogenase (B_DN50511_c1_g7_i2), lactate dehydrogenase  and pyruvate decarboxylase . In contrast, only two genes encoding for ADH (A_DN39747_c0_g4_i3 and A_DN40875_c0_g1_i2) were upregulated in \u201cA408\u201d. Our results demonstrated that genes involved in starch degradation, glycolysis, and fermentation are differentially expressed at a significantly higher level under WS in \u201cBali\u201d than in \u201cA408\u201d, suggesting that \u201cA408\u201d could have a slower glycolytic process and a better ability to maintain carbohydrate reserves than \u201cBali\u201d.On the other hand, the analysis of \u201cA408\u201d DEGs revealed only one glycolysis gene, Arabidopsis in the genus Rorippa, showing that starch degradation, glycolysis, and fermentative genes are more strongly induced in the less flooding-tolerant R. amphibiathan than in R. sylvestris [Analysis of total soluble carbohydrates in the roots of both varieties confirmed that WS resulted in a greater reduction of the total soluble carbohydrate in \u201cBali\u201d than that of \u201cA408\u201d . Our reslvestris , thus suACC oxidase; A_DN37162_c1_g2_i3), a key-enzymatic gene controlling ethylene synthesis, was upregulated and three were downregulated . On the contrary, two ACC oxidase genes (B_DN48281_c0_g1_i1 and B_DN48281_c0_g2_i1 and i2) were strongly upregulated, and one was downregulated (B_DN51722_c0_g2_i2) in \u201cBali\u201d. For ethylene signaling and perception, ERF95 (A_DN40876_c5_g6_i1) and ERF106 (A_DN34489_c0_g2_i1) were upregulated in \u201cA408\u201d and ERF2 (B_DN48139_c0_g1_i1) and ERF106 (B_DN51699_c3_g8_i1) were upregulated in \u201cBali\u201d. The down-regulation of ERF13 genes was observed in both varieties . Interestingly, the down-regulation of ERF109 (A_DN39318_c1_g3_i2), a redox responsive transcription factor 1, was observed only in \u201cA408\u201d. In Arabidopsis, ERF109 is highly responsive to jasmonic acid and functions in the regulation of lateral root formation by mediating cross-talk between jasmonic acid signaling and auxin biosynthesis [ERF110 (B_DN49322_c2_g10_i2), DREB2C (B_DN44544_c1_g1_i1 and i2) and Ethylene Response DNA binding factor 1 . In \u201cA408\u201d, ERF subfamily B4 (A_DN39655_c3_g1_i1), ERF114 (A_DN39818_c4_g4_i1), RAP2.7 (A_DN39022_c4_g2_i1) and RAP2.3 (A_DN39022_c4_g2_i1) were upregulated by WS. On the other hand, DREB transcription factors  were downregulated in \u201cA408\u201d.During soil waterlogging, ethylene acts as a primary signal controlling morphological and metabolic adjustments in plant roots. Therefore, we examined the changes in gene expression of ethylene synthesis, perception, and responsive transcriptional regulator genes. In both varieties, changes in the expression of ethylene synthesis, perception, and responsive transcriptional regulatory genes were observed . In \u201cA40ACC oxidase could result in higher ethylene production in \u201cBali\u201d than in \u201cA408\u201d. Ethylene has an important role during lateral root initiation as treatment of ethylene reduces lateral root initiation in Arabidopsis seedlings [Arabidopsis mutants with enhanced ethylene synthesis or perception decreased lateral root formation, while ethylene-insensitive mutants increased the number of lateral roots [The stronger up-regulation of eedlings . Moreoveal roots . Furtheral roots  discusseAUX/IAA family is overrepresented in the upregulated DEGs of \u201cA408\u201d based on Fisher\u2019s exact test for over-representation analysis  were induced in \u201cA408\u201d . In contrast, only one SAUR (B_DN52605_c0_g2_i1) was induced in \u201cBali\u201d. Evidently, SAURs can regulate auxin-induced acid growth as defined by the loosening of cell walls at low pH which promotes cell wall extensibility and rapid cell elongation [A_DN40413_c3_g11_i1. The best BLAST hit of A_DN40413_c3_g11_i1 protein is the Arabidopsis SAUR51 /Indole-3-acetic acid-inducible (IAA) 3 (A_DN38749_c3_g4_i1) and IAA14 (A_DN39227_c1_g4_i1 and i2) only in the DEGs of \u201cA408\u201d. SHY2/IAA3 and IAA14 are auxin-inducible transcriptional repressors that regulate auxin-mediated gene expression by controlling the activity of auxin response factors (ARFs) by protein-protein interaction [Aux/IAA\u2013ARF modules cooperatively regulate the developmental steps during lateral root formation. Therefore, we speculated that SHY2/IAA3 and IAA14 could specifically regulate zombi pea lateral root formation under WS. An in-depth analysis of the WS-induced SAURs and Aux/IAAs is required to further provide candidate genes for improving waterlogging tolerance in Vigna crops.Additionally, we observed the up-regulation of two key regulators in auxin-regulated lateral root development, eraction ,46,47. Geraction  proposedfarnesyl transferase is a key regulator of ABA signal transduction in Arabidopsis. Interestingly, the down-regulation of farnesyl transferase increases the ABA response and drought tolerance in Brassica napa [genenylgeranyl pyrophosphate synthase 1  was observed in WS \u201cBali\u201d roots (Arabidopsis GGPS1 (encoded by At4g36810) has farnesyl transferase activity and functions as a key enzyme in the chloroplast isoprenoid biosynthetic pathway. GGPS1 catalyzes the formation of geranylgeranyl diphosphate, a precursor molecule of carotenoids, ABA, and GA [GA 3-oxidase 1 (GA3OX1: B_DN46875_c0_g1_i1) was also found in WS \u201cBali\u201d roots  and gibberellic acid (GA) play antagonistic roles to control plant development and response to environmental stresses. GO enrichment analysis also suggests the down-regulation of farnesyltranstransferase activity and gibberellin 3-beta-dioxygenase activity in WS \u201cBali\u201d roots . Additioica napa . In thisi\u201d roots . Arabido, and GA . Moreovei\u201d roots . Arabidot growth . Altogetplasma membrane intrinsic proteins (PIPs)  were specifically induced in \u201cA408\u201d , SUSY (A_DN40966_c1_g1_i8), ADH (A_DN39747_c0_g4_i3), aquaporin tonoplast intrinsic protein , and IAA14 (A_DN39227_c1_g4_i1) and one DEGs, WRKY transcription factor (A_DN40719_c0_g11_i2) was downregulated (ADH (B_DN50984_c1_g3_i5), GAP-DH (B_DN51637_c1_g4_i2), glucose-6-phosphate isomerase (B_DN50580_c2_g4_i2), aldolase (B_DN50672_c0_g4_i1) and two DEGs, TIP (B_DN50494_c3_g2_i1) and auxin-induced protein PCNT115 (B_DN50148_c6_g3_i4) were downregulated , was used as a reference for the relative gene expression calculation. Our qRT-PCR results demonstrate the reliability of the RNA-seq data.To validate the transcriptome results, for each variety we selected six DEGs and one non-DEG based on their function and expression level for quantitative real-time PCR analysis (qRT-PCR). For \u201cA408\u201d, of the six DEGs, five genes were upregulated including egulated . For \u201cBaegulated . The expVigna vexillata seeds  and AusTRCF320047 (\u201cA408\u201d) varieties) were germinated in soil and grown outdoors between April and June of 2016 and 2017 at Kasetsart University, Bang Khen campus. Fifteen-day-old, five-leaf-stage plants were used in the WS treatment. In brief, plant pots were placed in plastic containers filled with tap water. The level of water was set at 3 cm above the soil. Waterlogging stress began at midday and continued for 24 h. For the control, non-treated plants were placed in a container with no water. For each sample, the root tissue of five plants was harvested at the end of the treatment; it was immediately placed in liquid nitrogen, ground into a fine powder, and kept at \u221280 \u00b0C. For long term WS, plants were subjected to WS for up to 10 days.http://www.atleaf.com/SPAD.aspx.Adhering to the method described by Juntawong et al. , chloropFor analyses of root morphology, underground roots were collected and photographed after seven days of WS. Roots of NS plants grown side by side were used as controls. For the anatomical study, taproots and hypocotyls were immediately fixed in formaldehyde-acetic acid-alcohol (FAA) solution. Permanent slides for microscopic observation were prepared by standard microtechnique procedures . The emb2CO3 was performed to neutralize the extract. The anthrone method was used to determine total carbohydrate content relative to a standard series of glucose. In brief, the extract (300 \u03bcL) and distilled water (700 \u03bcL) were mixed with 4 mL of 0.14% (w/v) anthrone solution in 95% H2SO4; it was then incubated in a boiling water bath for 8 min, and rapidly cooled on ice. The absorbance was quantified at 630 nm.One hundred milligrams of frozen root tissue was used to quantify the total soluble carbohydrate content using a method described by Juntawong et al. . In briehttps://genome.ucsc.edu/ENCODE/protocols/dataStandards/ENCODE_RNAseq_Standards_V1.0.pdf). The integrity of the RNA samples (RIN) was evaluated on an RNA 6000 Nano LapChiprun on Agilent2100 Bioanalyzer . Samples with a RIN > 7 were used in RNA-seq library preparation. One\u2009\u03bcg of total RNAs were used to generate a sequencing library using a NEBNext\u00ae Ultra\u2122 RNA Library Prep Kit for Illumina\u00ae following the manufacturer\u2019s instructions. The mRNA fragmentation and priming were performed using NEBNext First Strand Synthesis Reaction Buffer and NEBNext Random Primers. First-strand cDNA was synthesized using ProtoScript II Reverse Transcriptase and the second-strand cDNA was synthesized using Second Strand Synthesis Enzyme Mix. The purified (via AxyPrep Mag PCR Clean-up (Axygen)) double-stranded cDNA was then treated with End Prep Enzyme Mix to repair both ends and added a dA-tailing in one reaction, followed by a T-A ligation to add adaptors to both ends. Size selection of Adaptor-ligated DNA was then performed using AxyPrep Mag PCR Clean-up (Axygen) and fragments of ~360 bp (with the approximate insert size of 300 bp) were recovered. Each sample was then amplified by PCR for 11 cycles using P5 and P7 primers, with both primers carrying sequences that could anneal with flow cell to perform bridge PCR and P7 primer carrying a six-base index allowing for multiplexing. The PCR products were cleaned using AxyPrep Mag PCR Clean-up (Axygen), validated using an Agilent 2100 Bioanalyzer , and quantified using Qubit 2.0 Fluorometer . Then libraries with different indices were multiplexed and loaded on an Illumina HiSeq 4000 instrument according to the manufacturer\u2019s instructions . Sequencing was carried out using a 2 \u00d7 150 bp paired-end (PE) configuration; image analysis and base calling were conducted by the HiSeq Control Software (HCS) + RTA 2.7 (Illumina) on the HiSeq 4000 instrument. The raw read files were deposited in the NCBI SRA database under the accession numbers SRR9214917- SRR9214924. Quality control filtering and 3\u2032 end trimming were analyzed using the FASTX-toolkit (http://hannonlab.cshl.edu/fastx_toolkit/index.html) and Trimmomatic software [Total RNA was extracted with TRIzol reagent (Invitrogen), according to the manufacturer\u2019s protocol. Total RNA samples were subjected to DNase treatment and RNA cleanup using an RNA-mini kit (Qiagen). Two replicates of total RNA samples were used for transcriptome analysis according to the ENCODE recommended RNA-seq standards (https://github.com/trinityrnaseq/trinityrnaseq) [http://agbase.arizona.edu) and the Mercator annotation pipeline with a blast cut-off score of 80 . The annotation information can be found in The transcriptome was assembled using the Trinity software (yrnaseq) . The asshttp://bowtie-bio.sourceforge.net/bowtie2/index.shtml). A binary format of sequence alignment files (BAM) was generated and used to create read count tables using the HTseq python library (https://htseq.readthedocs.io/). Differentially-expressed genes were calculated using the edgeR program (https://bioconductor.org/packages/release/bioc/html/edgeR.html) with an FDR cutoff of < 0.05.Differential gene expression analysis was performed according to Sirikhachornkit et al. . The FASp-value of < 0.05.Gene ontology enrichment analysis was performed in the R environment using the GOHyperGAll function . Gene anFor PAGEMAN analysis, the mapping file was generated from the protein sequences derived from the assembled transcriptomes using the Mercator pipeline. Over-representation analysis (ORA) was performed using the PAGEMAN  program A. thaliana protein sequences (TAIR10) by OrthoVenn2 [Homolog identification was performed using translated amino acid sequences (>100 amino acids) derived from the transcriptomes of \u201cA408\u201d and \u201cBali\u201d and thoVenn2 . The hom4 . For each sample, the PCR reaction was performed in triplicate. Each reaction contained 1 \u2009\u03bcL of diluted cDNA, 0.5\u2009 \u03bcM of each primer, and 10 \u2009\u03bcL of QPCR Green Master Mix, giving a final volume of 20 \u2009\u03bcL. The PCR cycle was 95 \u2009\u00b0C for 2\u2009 min, followed by 45 cycles of 95 \u2009\u00b0C for 15 \u2009s and 60\u2009 \u00b0C for 30 s. Amplification specificity was validated by melt-curve analysis at the end of each PCR experiment. Relative gene expression was calculated using the 2 \u2212\u2206\u2206CT method. The genes and primers used are shown in Three replicates of total RNA samples were used. Total RNAs were treated with DNase I  to eliminate contaminated genomic DNA. One microgram of total RNAs were used to construct cDNA using MMuLv reverse transcriptase  in a final volume of 20 \u03bcL. The cDNA was diluted five times. Quantitative-realtime PCR (qPCR) reaction was performed according to Sirikhachornkit et al. . FurtherVigna crops in the future.In this research, we aimed to discover the molecular mechanisms controlling waterlogging tolerance by constructing de novo transcriptomes and comparing the transcriptomes of two zombi pea varieties with contrasting waterlogging tolerance. Our results demonstrated that root plasticity could be an important determinant factor controlling waterlogging tolerance in zombi pea. Moreover, differential expressions of multiple genes encoding for energy production pathways, auxin-regulated lateral root initiation and formation, hormones, cell wall modification, membrane transporter, and peroxidase could contribute to waterlogging tolerance in zombi pea. Functional characterization of the WS-induced candidate genes is required to further identify candidate genes controlling waterlogging-tolerant traits. Additionally, recent studies demonstrated that differentially-regulated genes controlling for the traits of interest could be accurately identified using comparative transcriptome RNA-seq analysis of near-isogenic lines (NILs) ,67. Clea"}
+{"text": "PLP_deC) genes produce secondary metabolites and flavor volatiles in plants, and TDC (tryptophan decarboxylase), a member of the PLP_deC family, plays an important role in the biosynthesis of terpenoid indole alkaloids (TIAs). In this study, we identified eight PLP_deC genes in Dendrobium officinale  and six in Phalaenopsis equestris (P. equestris), and their structures, physicochemical properties, response elements, evolutionary relationships, and expression patterns were preliminarily predicted and analyzed. The results showed that PLP_deC genes play important roles in D. officinale and respond to different exogenous hormone treatments; additionally, the results support the selection of appropriate candidates for further functional characterization of PLP_deC genes in D. officinale.Studies have shown that the type II pyridoxal phosphate-dependent decarboxylase ( Dendrobium officinale Kimura et Migo  is a perennial herb that is commonly used as a valuable Chinese herbal medicine and has a long evolutionary history among orchids. D. officinale is rich in alkaloids , an, anPLP_ddu.cn /) .http://www.bork.embl.de/pal2nal/) [http://www.ub.edu/dnasp/) [The protein sequences of the gene pairs were first aligned using Clustal X 2.0, and then the multiple sequence alignments of proteins and the corresponding cDNA sequences were converted to codon alignments using PAL2NAL  . Finally/dnasp/) .PLP_deC gene was obtained, and an analysis of the upstream regulatory promoter elements was performed using the online tool PlantCARE (http://bioinformatics.psb.ugent.be/webtools/plantcare/html/) [A 2000 bp sequence upstream of the translation start site (ATG) of each e/html/) .D. officinale, we searched the NCBI SRA database (PRJNA348403) for RNA-sequence data from different tissues [D. officinale genome using Hisat2 [PLP_deC genes expression profiles was obtained by TBtools software (https://github.com/CJ-Chen/TBtools/releases).To obtain expression data for  tissues   to perform a quantitative fluorescence analysis of the PLP_deC genes in the cDNA samples from D. officinale protocorms with three replicates. \u03b2-actin [\u2212\u0394\u0394CT method [2O to a final volume of 20 \u03bcL. The reaction conditions were as follows: 95 \u00b0C for 3 min followed by 40 cycles of 95 \u00b0C for 10 s, 52 \u00b0C for 15 s, and 72 \u00b0C for 30 s.We used the CFX96 Touch \u03b2-actin ,46,47 waT method , and thet-test in SPSS 25.0 software [To determine whether the differential expression was significant, the difference in the relative expression of each target gene in the different treatment groups was analyzed by Student\u2019s software .PLP_deC genes in both D. officinale and P. equestris. Comparative analysis has shown that eight PLP_deC genes from D. officinale could be divided into three subfamilies: GAD, HDC, and AAD. Most genes have an acidic pI. Purifying selection may have played a key role in the evolution of this PLP_deC genes in D. officinale based on the Ka/Ks value. Among them, both DoAAD1 and DoAAD2 were highly expressed in the column, flower bud, and lip. Under three hormone treatments, MeJA, ABA, and SA, the PLP_deC genes responded to abiotic stresses. These results provide preliminary biological information for further studies of the evolution of PLP_deC genes in Orchidaceae.Overall, we conducted a comprehensive analysis of"}
+{"text": "Abundant evidence shows that triple-negative breast cancer (TNBC) is heterogeneous, and many efforts have been devoted to identifying TNBC subtypes on the basis of genomic profiling. However, few studies have explored the classification of TNBC specifically based on immune signatures that may facilitate the optimal stratification of TNBC patients responsive to immunotherapy.Using four publicly available TNBC genomics datasets, we classified TNBC on the basis of the immunogenomic profiling of 29 immune signatures. Unsupervised and supervised machine learning methods were used to perform the classification.CORO1A, STAT4, BCL11B, ZNF831, and EOMES) specifically significant in the Immunity_H subtype and a subnetwork centered around two TF genes (IRF8 and SPI1) characteristic of the Immunity_L subtype.We identified three TNBC subtypes that we named Immunity High (Immunity_H), Immunity Medium (Immunity_M), and Immunity Low (Immunity_L) and demonstrated that this classification was reliable and predictable by analyzing multiple different datasets. Immunity_H was characterized by greater immune cell infiltration and anti-tumor immune activities, as well as better survival prognosis compared to the other subtypes. Besides the immune signatures, some cancer-associated pathways were hyperactivated in Immunity_H, including apoptosis, calcium signaling, MAPK signaling, PI3K\u2013Akt signaling, and RAS signaling. In contrast, Immunity_L presented depressed immune signatures and increased activation of cell cycle, Hippo signaling, DNA replication, mismatch repair, cell adhesion molecule binding, spliceosome, adherens junction function, pyrimidine metabolism, glycosylphosphatidylinositol (GPI)-anchor biosynthesis, and RNA polymerase pathways. Furthermore, we identified a gene co-expression subnetwork centered around five transcription factor (TF) genes  contains supplementary material, which is available to authorized users. Triple-negative breast cancer (TNBC) is a breast cancer subtype that lacks the expression of hormone receptors (estrogen receptor (ER) and progesterone receptor (PR)) and human epidermal growth factor receptor 2 (HER2). TNBC is associated with a high risk of mortality for its aggressiveness and the lack of effective targeted therapies. Moreover, abundant evidence shows that TNBC is very heterogeneous \u20134. Lehma(BC) subtypes, which may warrant an immunotherapeutic approach for TNBC [Recently, cancer immunotherapy has been successful in treating many refractory malignancies . Thus, ifor TNBC , 13. Howfor TNBC \u201318.), and Immunity Low (Immunity_L). We demonstrated the stability and reproducibility of this classification in four independent datasets by a machine learning approach. Furthermore, we identified the subtype-specific molecular features, including genes, gene ontology, pathways, and networks. The identification of immune signature-associated TNBC subtypes may facilitate the optimal selection of TNBC patients responsive to immunotherapy.In this study, we classified TNBC into three distinct subtypes by immunogenomic profiling: Immunity High (Immunity_H), Immunity Medium  score , 20. BasESTIMATE  was usedWe performed gene-set enrichment analysis of the METABRIC and TCGA datasets by GSEA (R implementation) \u201324. ThisWe quantified the activity of a pathway with the ssGSEA score of the set of genes included in the pathway, and the immune cell infiltration level with the immune score. The Spearman correlation of the ssGSEA score and the immune score were used to evaluate the correlation of pathway activities with immune cell infiltration levels in TNBC.We used WGCNA  to identP-value <\u20090.05. Kaplan\u2013Meier curves were plotted to show the survival time differences. We performed the survival analyses using the METABRIC, TCGA, and GSE103091 datasets, where the survival data were available.We compared the survival prognosis , disease-free survival (DFS), and metastasis-free survival (MFS) of TNBC patients considering tumor subtype and the expression level of the identified genes, i.e., higher expression level (expression levels > median) versus lower expression level (expression levels < median). The log-rank test was used to calculate the significance of survival time differences using a threshold of xi into xi\u2032 by the equation xi\u2032\u2009=\u2009(xi\u2009\u2212\u2009xmin)/(xmax\u2009\u2212\u2009xmin), where xmin and xmax represent the minimum and maximum of the ssGSEA scores for the gene set across all TNBC samples, respectively. The Random Forest (RF) classifier was used to classify the TNBC subtypes. We set the number of trees to 100 and all 29 immune signatures as features for the RF classifier. The classification performance was evaluated by the accuracy and the weighted F-score. We carried out the classification in Weka [We transformed each attribute (immune signature or gene set) value (ssGSEA score)  in Weka .P\u2009<\u20090.05 as the criteria for the successful deconvolution of a sample. We compared the proportions of the immune cell subsets between TNBC subtypes using the Mann\u2013Whitney U test.CIBERSORT  was usedWe used the ABSOLUTE algorithm  to assesABCA8 and ALDH1A1), proliferation (MKI67), and epithelial-to-mesenchymal transition (EMT)  biological processes between the TNBC subtypes. The Kruskal\u2013Wallis test was used to determine the statistical significance of the results.We compared the activities (ssGSEA scores) of stem cell-associated (marker genes We applied GISTIC2  to the SP\u2009<\u20090.001) . These features directed the classification. In addition, when comparing the tumor purity and stromal score of the three TNBC subtypes, we obtained opposite trends, with tumor purity increasing from Immunity_H to Immunity_L (Immunity_H\u2009<\u2009Immunity_M\u2009<\u2009Immunity_L) and stromal score decreasing from Immunity_H to Immunity_L (Immunity_H\u2009>\u2009Immunity_M\u2009>\u2009Immunity_L)   , CD45RO (memory T cell), CD20 (B cell), CXCR5 (Tfh cell), FOXP3 (Treg), IL-17 (Th17 cell), CD1A (iDC), and IL3RA (pDC)  in the three TNBC subtypes and found that Immunity_H had the highest PD-L1 expression levels and Immunity_L had the lowest PD-L1 expression levels   Fig. c. This ssiveness .Survival analyses showed that these TNBC subtypes had distinct clinical outcomes. The Immunity_H subtype likely had a better survival prognosis than the Immunity_M and Immunity_L subtypes, but there was no significant survival difference between the Immunity_M and the Immunity_L subtypes Fig. d. This iP\u2009<\u20090.001)    Fig. b.Fig. 4IP\u2009=\u20094.0*10\u2212\u200954), while was depressed in Immunity_L (P\u2009=\u20091.0*10\u2212\u200932). Moreover, a high immune response was associated with a better survival prognosis in TNBC patients (P\u2009=\u20095.0*10\u2212\u20094). This finding is in line with the previous observation that the subtype Immunity_H is associated with better clinical outcomes than the other subtypes. Similar results were observed for the TCGA dataset , and were reduced in Immunity_L . In contrast, cell adhesion molecule (CAMD) binding activity was significantly increased in Immunity_L (P\u2009=\u20091.0*10\u2212\u200930) and decreased in Immunity_H (P\u2009=\u20092.0*10\u2212\u200935). This suggests that CAMD activity has a strong inverse correlation with tumor immunity in TNBC. Interestingly, CAMD activity correlated with reduced survival . Cell cycle process was also increased in Immunity_L (P\u2009=\u20090.04), suggesting that the cell cycle signature correlates with reduced tumor immunity. This finding is consistent with results from previous studies [We performed a weighted gene co-expression network analysis of the METRABRIC dataset by WGCNA  and iden studies , 39.CORO1A, STAT4, BCL11B, ZNF831, and EOMES. The five TFs interact with each other and form a subnetwork with diverse immune and cancer-related genes that they regulate  was regulated by all these TFs, and the cytotoxic T cell marker gene CD8A was co-regulated by CORO1A, STAT4, and EOMES. MAP4K1 (Mitogen-Activated Protein Kinase Kinase Kinase Kinase 1), which is involved in multiple immune and cancer-related pathways including B cell receptor signaling, JNK, EGF/EGFR, TGF-\u03b2, and MAPK signaling, was also regulated by the five TFs. CORO1A encodes a member of the WD repeat protein family which is involved in diverse cellular processes including cell cycle, apoptosis, signal transduction, and gene regulation. The main pathways related to CORO1A include cytoskeletal signaling and phagosome function, and its relatedness with immune regulation has been revealed [WGCNA generated a gene module  encodes a transcription factor that activates gene expression during immune cell development. As a result, the deregulation of SPI1 may affect immunity. In fact, SPI1 showed significantly lower expression levels in Immunity_L than in Immunity_H . Therefore, the down regulation of SPI1 may contribute to the decreased immunity of the Immunity_L subtype. The contribution of the IRF8- and SPI1-centered regulatory network to the depressed immunity of Immunity_L is evidenced by a previous study showing that IRF8 and SPI1 together negatively regulated immune cell differentiation [WGCNA also generated a gene module  were consistently associated with better survival prognosis in TNBC  to evaluate the classification performance in METABRIC and then predicted the TNBC subtypes in the other three datasets using the METABRIC dataset as the training set. The 10-fold CV accuracy was 89% in classifying the METABRIC dataset. The classification accuracies were 70, 84, and 63% in TCGA, GSE75688, and GSE103091, respectively. The weighted F-scores in these classifications were 89, 71, 83, and 63% for METABRIC, TCGA, GSE75688, and GSE103091, respectively   Fig.\u00a0. This an05) Fig.\u00a0d. In facP\u00a0=\u20090.04, 0.001, 0.0006 for comparisons of amplification, deletion, and total alteration frequencies, respectively)   Fig.\u00a0a. Moreovly) Fig. b. These PD-L1 is more highly expressed in Immunity_H TNBC, and PD-L1 expression is a predictive biomarker for the response to PD-1/PD-L1-directed immunotherapy [Currently, immunotherapy for TNBC is an active field of investigation , and theotherapy , 54.The identification of TNBC subtypes based on immune signatures has potential clinical implications for TNBC treatment.Additional file 1:Table S1. The 29 immune signatures represented by 29 different gene sets. (XLSX 41 kb)Additional file 2:Figure S1. Comparisons of the stromal content and tumor purity between TNBC subtypes (Mann\u2013Whitney U test). (PDF 368 kb)Additional file 3:Figure S2. Comparisons of the expression levels of immune-related genes between TNBC subtypes. A. Comparisons of the expression levels of HLA genes between TNBC subtypes. B. Comparisons of the expression levels of immune cell subpopulation marker genes between TNBC subtypes. ANOVA test. *P\u00a0<\u20090.05, **P\u00a0<\u20090.01, ***P\u00a0<\u20090.001. (PDF 168 kb)Additional file 4:Figure S3. Identification of TNBC subtype-specific pathways and gene ontology. A. KEGG pathways enriched in Immunity_H and Immunity_L. B. Gene modules significantly differentiating TNBC by subtype, survival time, or survival status. (PDF 164 kb)Additional file 5:Figure S4. Stem cell-associated activity is higher in Immunity_H than in the other subtypes. (PDF 110 kb)"}
+{"text": "Increasing studies have indicated that circular RNAs (circRNAs) are important in cancer progression. However, few circRNAs associated with epithelial-mesenchymal transition (EMT) have been elucidated in esophageal squamous cell carcinoma (ESCC). In this study, we aimed to identify whether hsa_circ_0006948 promotes ESCC cell EMT and explore its biological mechanisms. We first screened circRNA expression profiles using a circRNA microarray, and found that the expression of a novel circRNA, hsa_circ_0006948, is increased in 153 ESCC tissues and cell lines compared with noncancerous tissues and cell lines. Additionally, high hsa_circ_0006948 levels were positively associated with lymphatic metastasis and poor prognosis. Functionally, the assays indicated that cell proliferation, migration and invasion were promoted by hsa_circ_0006948 both in vitro and in vivo. Furthermore, we analyzed the relationship between hsa_circ_0006948 and miR-490-3p through bioinformatics, luciferase reporter assays, RNA immunoprecipitation and qRT-PCR. We found that hsa_circ_0006948 could bind directly to miR-490-3p which targets the 3\u2019UTR of the oncogene HMGA2 to induce EMT. In conclusion, hsa_circ_0006948 was overexpressed in ESCC tissues and promoted cancer progression, and it could induce EMT by enhancing HMGA2 by sponging miR-490-3p, suggesting that hsa_circ_0006948 could be a biomarker for ESCC. Esophageal cancer is the sixth leading cause of cancer deaths and the eighth most common cancers worldwide, with over 400,000 deaths annually , 2. Of tAs a novel non-coding RNA, circular RNA (circRNA) regulates eukaryote gene expression , 5. CircIn this study, using a circRNA microarray profiling, we identified that hsa_circ_0006948, which originates from exons 2, 3 and 4 of the FNDC3B gene, was up-regulated in ESCC tissues and cell lines. Next we found that high expression of hsa_circ_0006948 was associated with lymphatic metastasis and poor prognosis. Further studies suggested that hsa_circ_0006948 promoted proliferation, migration and invasion, and induced EMT in ESCC cells by sponging miR-490-3p. In summary, this study indicated that hsa_circ_0006948 may play an important regulatory role in the EMT process of ESCC cells.Briefly, the circRNA expression profiles of three paired ESCC tissue samples were analyzed using a microarray we deposited at Gene Expression Omnibus previously. Distinct circRNA expression profiles were shown in the hierarchical clustering . A volcaTwo sets of primers were designed. Divergent primers were used to amplify only the circular form, hsa_circ_0006948, while convergent primers were used to amplify only the linear form, FNDC3B mRNA. The PCR products were validated by electrophoresis using cDNA and genomic DNA (gDNA) as templates. The results revealed that the single product of the expected size was amplified distinctly, with the divergent primers for cDNA but not gDNA . The SanWe examined hsa_circ_0006948 expression in 153 pairs of ESCC tissues and adjacent normal tissues using qRT-PCR analysis and found that hsa_circ_0006948 was significantly higher in cancer tissues than in normal tissues . AdditioAfter transfection with siRNAs (siRNA1 and siRNA2) targeting the back-splice region, hsa_circ_0006948 expression in TE-1 and KYSE30 cells was knocked down, and there was no significant change in its linear counterpart, FNDC3B mRNA . HoweverThe transwell assay results showed that hsa_circ_0006948 downregulation inhibited ESCC cell migratory and invasion abilities. . In contEpithelial-mesenchymal transition (EMT) is an important process in tumor aggressiveness. Therefore, we assessed whether hsa_circ_0006948 could induce EMT in ESCC cells, and EMT markers were assessed by Western blot in the present study. Western blot analysis demonstrated that knockdown of hsa_circ_0006948 led to the upregulated expression of the epithelial marker E-cadherin and downregulated expression of the mesenchymal markers vimentin and N-cadherin. On the other hand, the expression of E-cadherin was decreased, whereas that of vimentin and N-cadherin was increased in ESCC cells overexpressing hsa_circ_0006948 . These rFor circRNAs, acting as miRNA sponges is the most commonly reported function pattern, and circRNAs in the cytoplasm may act as completing endogenous RNAs to bind miRNAs , 18. GivA previous study has verified that miR-490-3p inhibited the proliferation and metastasis of ESCC through experiments using miR-490-3p upregulation in ESCC cells . To furtHMGA2 have been reported to promote EMT, which was further verified in this study . AdditioNext, we tried to analyze whether hsa_circ_0006948 exerts its cancerous effect on ESCC by sponging miR-490-3p, and a \u201crescue\u201d experiment was performed to assess the functional interaction of \u201chsa_circ_0006948/miR-490-3p\u201d. Compared with the migratory and invasive abilities of ESCC cells transfected with empty vector and miR-490-3p mimics, those of upregulated hsa_circ_0006948 cells transfected with miR-490-3p mimics significantly increased, suggesting that hsa_circ0006948 promotes ESCC progression and partly reverses the tumor suppressive effect of miR-490-3p  6B. FurtTo investigate whether hsa_circ_0006948 could promote cancer progression and induce EMT by enhancing HMGA2, we transfected ESCC cells with HMGA2 siRNA and hsa_circ_0006948 overexpression plasmid. Cell proliferation assay indicated that hsa_circ_0006948 promoted cell viability of ESCC cells, while downregulation of HMGA2 reversed this effect , 7B. AddTo further assess whether hsa_circ_0006948 exerts a tumor-promoting effect in vivo, a xenograft mouse model was established by subcutaneously injecting TE-1 cells (n=5 for each group). After 12 days, negative control, si-hsa_circ_0006948 and combined si-hsa_circ_0006948 and agomiR-490-3p were injected intratumorally every two days for two weeks. The results indicated that the tumor weight and growth rates were significantly lower in the si-hsa_circ_0006948 group than in the control group. Importantly, the tumor volume and weight were lower in the combined si-hsa_circ_0006948 and agomiR-490-3p group than in the si-hsa_circ_0006948 group alone. We further used IHC to evaluate the tumor tissues. The IHC results showed that the expression levels of HMGA2, N-cadherin and vimentin were significantly inhibited in the combined si-hsa_circ_0006948 and agomiR-490-3p group compared with those in control group or si- hsa_circ_0006948 group alone, which implied that silencing hsa_circ_0006948 combined with miR-490-3p overexpression exhibits an additive inhibitory effect on ESCC growth in xenograft animal models .CircRNAs played a crucial role in carcinogenesis and cancer progression, and their high conservation and relative stability are two important properties . Thus, cAt present, the role of EMT in tumor progression has become a hotspot. EMT is a key participant in the biological processes such as the formation, invasion and metastasis of multiple cancers. The invasion and metastasis abilities of tumor cells may become stronger due to intercellular connecting substances loss, intercellular polar complex disappearance and structural changes in extracellular matrices . TherefoDue to their posttranscriptional function, circRNAs are considered crucial gene regulators. Because circRNAs contain multiple miRNA-binding sites or miRNA response elements, they have the potential to be miRNA sponges. For circRNAs, acting as a miRNA sponge is the most reported commonly function pattern , 26. In miRNAs have been reported to bind the 3\u2032UTR of target mRNAs and regulate tumor progression. To date, several studies have reported that miR-490-3p is a tumor suppressor in multiple types of cancer. For example, in colorectal cancer, miR-490-3p inhibits cancer progression through activating the Wnt/\u03b2-catenin signaling pathway . ImportaAdhesion factor, such as E-cadherin, vimentin and N-cadherin, plays important roles in EMT process. HMGA2 have been reported to regulate E-cadherin, vimentin and N-cadherin , 29. As In summary, hsa_circ_0006948 was up-regulated in ESCC tissues and cells, and we found that increased hsa_circ_0006948 was associated with poor survival in ESCC patients. Furthermore, our data indicated that overexpression of hsa_circ_0006948 promotes the cancer progression and could induce EMT by enhancing HMGA2 by sponging miR-490-3p. Our results suggested that hsa_circ_0006948 could be a vital biomarker for ESCC progression.This study was approved by the Ethics Committee of Sun Yat-sen Memorial Hospital. All clinical data were collected after each surgery. The esophageal cancer tissues and adjacent normal samples were obtained from patients who had undergone surgery at the Sun Yat-sen Memorial Hospital between January 1, 2014 and December 31, 2016. These 153 pairs of tumor and adjacent tissue specimens were frozen immediately and stored at \u221280 \u00b0C. After being confirmed by experienced clinical pathologists, the tumor and adjacent normal tissues were subjected to further analysis. We obtained written informed consent from all patients before their participation in this research.http://www.ncbi.nlm.nih.gov/geo/) previously, was used for the global profiling of human circRNAs. Differentially expressed circRNAs with statistical significance between the two groups were identified through volcano plot filtering. Differentially expressed circRNAs between the two sample types were identified through fold Change filtering. Distinguishable circRNA expression patterns among the samples were shown through hierarchical clustering.circRNA Microarray (GSE131969), which have been deposited at Gene Expression Omnibus  in accordance with the manufacturer\u2019s instructions. The purity and concentration of the total RNA samples were measured with a NanoDrop 2000 . All cDNA was generated with PrimeScript RT Master Mix .qRT-PCR for circRNAs was performed on a LightCycler\u00ae 96 System . The relative circRNA expression was calculated using:relative expression=22.ESCC cells  and normal epithelial esophageal cells (HEEC cell line) were purchased from the Shanghai Institutes for Biological Science, China. Briefly, esophageal cells were cultured in DMEM  with 10% fetal bovine plasma at 37 \u00b0C in a humidified atmosphere with 5% COBefore transfection, TE-1 and KYSE30 cells were seeded in 6-well plates to approximately 60% confluence. siRNA, miRNA mimics and inhibitors were transfected using a lipofectamine RNAiMAX transfection kit , and overexpression plasmids were transfected using a lipofectamine3000 transfection kit  following the manufacturer\u2019s instructions.3 /well and cultured for 24, 48, 72, 96, 120 and 144 h. Cell proliferation was determined using a Cell Counting Kit-8 . OD450 values were then determined. Three biological repeats were performed for the statistical analysis. We used colony-forming assays to evaluate the clonogenic ability of transfected TE-1 and KYSE30 cells. Cells were seeded into 6-well plates (1000/well) and incubated for approximately 2 weeks. The visible colonies were counted after staining with crystal violet.To determine whether hsa_circ_0006948 is involved in ESCC cell proliferation, CCK8 assays were performed. TE-1 and KYSE30 cells transfected with si_circ_0006948 and overexpression vectors were seeded into 96-well plates at 5\u00d7105 cells in serum-free medium were added to the upper chambers . DMEM with 10% fetal bovine serum was added to the lower chambers. After incubation for 24 h, the esophageal cancer cells migrated into the lower chambers were fixed in 4% paraformaldehyde and stained with a crystal violet staining solution. Random fields were digitally imaged and counted.Transwell assays were used to evaluate the migration and invasion of ESCC cells using transwell chambers  precoated with or without Matrigel. For the assay, 2\u00d710To study the location of hsa_circ_0006948, RNA fluorescence in situ hybridization (RNA-FISH) was performed using a Fluorescent in Situ Hybridization Kit  according to the manufacturer\u2019s guidelines. Cy3-labeled hsa_circ_0006948 probes were measured by the Fluorescent in Situ Hybridization Kit, followed by visualization with confocal microscopy.RNA immunoprecipitation (RIP) assays were performed using an anti-AGO2 antibody with the transfection of miR-490-3p mimics (miR-490-3p) or miR-NC in TE-1 and KYSE30 cells to detect hsa_circ_0006948 expression according to qRT-PCR.4 cells per well. After co-transfection with miR-490-3p mimics and constructed luciferase plasmids for 48 h, we measured luciferase activity using a dual-luciferase reporter assay system  according to the manufacturer\u2032s protocol. Relative luciferase activity was normalized to the Renilla luciferase internal control. Three independent experiments were performed in triplicate.Hsa_circ_0006948 sequences (or HMGA2-3\u2019UTR sequences) containing wild-type or mutated miR-490-3p binding sites were synthesized and inserted into luciferase vectors respectively. HEK-293T cells were seeded into 24-well plates at a density of 3 \u00d7 10Proteins were extracted using RIPA buffer . After being electrophoresed by SDS-PAGE, the samples were transferred to nitrocellulose membranes and incubated with primary antibodies specific for E-cadherin , vimentin , N-cadherin , HMGA2  and GAPDH  at 4 \u00b0C overnight. Then the membranes were incubated with secondary antibodies at room temperature for 1 h. Signals were detected with images acquisition using Immobilon ECL substrate  and Optimax X-ray Film Processor .6/0.2 ml PBS) were injected subcutaneously into the right backs of 4-week-old BALB/c nude mice. After 12 days, the mice were treated with intertumoral injection of negative control, si-hsa_circ_0006948 and combined si-hsa_circ_0006948 and agomiR-490-3p every two days, respectively. The tumors were measured every three days and their volumes were calculated according to the following formula: tumor volume = (length \u00d7 width2)/2. Thirty days later, the mice were sacrificed, and the tumors were excised for further immunohistochemistry. Primary antibodies against E-cadherin , vimentin , N-cadherin  and HMGA2  were used. We captured images using a Nikon Eclipse 80i system with NIS-Elements software .Ethical approval was obtained from the Institutional Research Ethics Committee of Sun Yat-sen University. All animal care and procedures were performed in accordance with institutional guidelines. To assess the effect of hsa_circ_0006948 on tumor growth in xenograft models, TE-1 cells  was assessed by Kaplan-Meier analysis and compared by log-rank test. Univariate and multivariate Cox proportional hazards regression analyses were used to analyze the independent prognosis factors. ROC curve were drawn to assess the diagnosis value and prognosis value of hsa_circ_0006948. P <0.05 was considered statistically significant. Data analyses were performed using PRISM , and Stata version 13.1 .Supplementary Figure 1"}
+{"text": "Circular RNAs (circRNAs) have been reported to be widely involved in pathological processes of various cancers. However, little is known about their diagnostic values in early gastric cancer (EGC). This study is aimed at exploring whether circulating circRNAs in plasma could act as biomarkers for EGC diagnosis.  Mass spectrometry (MS) was performed to identify the proteins that at significantly aberrantly levels in gastric cancer (GC) tissues. The target circRNA was identified by bioinformatics analysis. A receiver operating characteristic (ROC) curve was generated to evaluate the diagnostic utility. n = 30, p = 0.0094). Bioinformatics analysis predicted that there is a hsa_circ_0006848/hsa_miR-329-5p/RPL6 axis in GC progression. The hsa_circ_0006848 expression was significantly downregulated in EGC tissues  and plasma samples from EGC patients . In addition, the hsa_circ_0006848 plasma level in postoperative patients was significantly higher than that of preoperative patients . Furthermore, the decreased hsa_circ_0006848 expression in plasma was negatively correlated with poor differentiation (p = 0.037) and tumor size (p = 0.046). The area under the ROC curve (AUC) of hsa_circ_0006848 in plasma was 0.733, suggesting a good diagnostic value. The plasma hsa_circ_0006848 level combined with the carcinoembryonic antigen (CEA), carbohydrate-associated antigen 19-9 (CA19-9), and carbohydrate-associated antigen 72-4 (CA72-4) level increased the AUC to 0.825.  MS revealed that the ribosomal protein L6 (RPL6) expression was significantly downregulated only in EGC tissues vs. nontumorous tissues; this was validated by western blotting ( Our results indicated that plasma hsa_circ_0006848 may be a novel noninvasive biomarker in EGC diagnosis. Gastric cancer (GC) is one of the major causes of cancer-associated mortality worldwide . The procircRNAs are a new type of endogenous noncoding RNA (ncRNA), which is produced by posterior grafting and is characterized by a covalent closed-loop free of 3\u2032 and 5\u2032 ends . Due to http://www.circbase.org/cgi-bin/simplesearch.cgi). Its gene is located at chr1:42730785-42744343; its relative gene symbol is FOXJ3 (forkhead box J3). We first found that plasma hsa_circ_0006848 may be a new kind of potential circulating biomarker for diagnosing EGC.In this study, hsa_circ_0006848 was investigated , which as early detection marker of cancer , was fouA total of 46 pairs of gastric cancer tissues and adjacent nontumor tissues were obtained from patients with early gastric cancer at Fujian Medical University Union Hospital between May 2015 and May 2016, 30 pairs of which for RT-PCR and 16 of which for LC-MS/MS (Mass Spectrometric). These tissue specimens were immediately conserved in RNA-fixer Reagent  after removal from the patients' stomach and were stored at -80\u00b0C until use. All patients underwent radical gastrectomy. None of the patients underwent preoperative chemotherapy or radiotherapy. Postoperative adjuvant chemotherapy was performed with 5-fluorouracil-based drugs plus oxaliplatin in advanced cases. Besides, 30 paired plasma samples of preoperative and postoperative from 30 early gastric cancer patients, and the control fasting plasma from 30 healthy volunteers, gender and age matched to patients, were obtained from Fujian Medical University Union Hospital, between March 2018 and May 2018. The peripheral blood samples (5\u2009ml) obtained from preoperative and postoperative patients (10 days after surgery) and controls were collected in BD Vacutainer tubes  and then centrifuged (3000\u00d7g for 10\u2009min). Subsequently, the plasma samples were kept at -80\u00b0C. The pathologic stage of the tumor was reassessed according to the 2010 International Union Against Cancer (UICC) TNM classification on gastric cancer (seventh edition). This study was approved by the ethics committee of Fujian Medical University Union Hospital, and written consent was obtained from all patients involved.\u03bcl trypsin, 50\u2009mM ammonium bicarbonate, pH\u20098.0) at 37\u00b0C. The peptides were extracted with 100% acetonitrile followed by 5% formic acid/100% acetonitrile and then concentrated to a volume of \u223c20\u00a0\u03bcl. The extracted peptides were separated in an analytical capillary column (75\u2009\u03bcm \u00d7 30\u2009cm) that was packed with a 1.7\u2009\u03bcm spherical C18 reverse phase material . A NanoLC-1000 (ThermoFisher) binary pump was used to generate the HPLC gradient as follows: 0-5% B for 4\u2009min, 5-40% B for 20\u2009min, and 40-100% B for 8\u2009min . The eluted peptides were sprayed into a Q mass spectrometer that was equipped with a nano-ESI ion source. The mass spectrometer was operated in a data-dependent acquisition (DDA) mode with one MS scan followed by three MS/MS scans for each cycle. The data were processed using Proteome Discoverer software  with 1% False Discovery Rate (FDR) at both peptide and protein level. Search criteria included carbamidomethylation of cysteine as a fixed modification and oxidation of methionine and acetyl (protein N terminus) as variable modifications.Protein bands were excised from the SDS-PAGE gel, destained, and then reduced and blocked in 10\u2009mM TCEP and 40\u2009mM 2-chloroacetamide at 37\u00b0C for 30\u2009min. The protein bands were then digested in-gel with sequencing-grade trypsin  using the Thunderbird SYBR qPCR Mix (Toyobo) according to the manufacturer's recommendations. The primers' sequences of GAPDH were the control in these experiments. Primer sequences were as follows: hsa_circ_0006848: 5\u2032-TGCCTCGATCTAAGGATGACC-3\u2032 (forward) and 5\u2032-TGAGCTGTGGTAACCAGTCC-3\u2032 (reverse); and GAPDH: 5\u2032-GGTCGGAGTCAACGGATTTG-3\u2032 (forward) and 5\u2032-ATGAGCCCCAGCCTTCTCCAT-3\u2032 (reverse).RNA isolation was performed using the Trizol reagent according to the manufacturer's manual. Reverse transcription was performed with the ReverTra Ace qPCR RT kit  using random primers and 1\u00a0The methods were described as we previously described . Anti-RPt-test. The correlation between hsa_circ_0006848 expression levels and clinicopathological factors was analyzed by one-way analysis of variance (ANOVA) [p < 0.05 was considered to be statistically significant, and all p values were two-sided. All statistical analyses were performed using SPSS software 22.0  and GraphPad Prism 5.0 .Differences of hsa_circ_0006848 levels between GC tissues and paired adjacent nontumor tissues and between plasma samples from GC patients and healthy controls were analyzed using Student's  (ANOVA) . The recp < 0.05) . The result of 6 paired representative samples is shown in http://mirwalk.umm.uni-heidelberg.de). We found that hsa_miR-329-5p was one of them. We predicted the circRNA-miRNA-mRNA network using Arraystar's homemade miRNA target prediction software and TargetScan and miRanda. Then, we found hsa_miR-329-5p was the top related miRNA. In 30 paired EGC and their normal tissues, the expression of RPL6 was dramatically lower in cancer than in the normal tissues (p = 0.0073). The exact value of the relative expression of has-circ-0006848 in EGC tissues and adjacent nontumor tissue has been shown in p = 0.0065) and p = 0.0089, We tested hsa_circ_0006848 levels in EGC tissues and plasma samples of patients with EGC. As shown in p = 0.047) (We selected 30 cases of EGC patients and detected the plasma levels of hsa_circ_0006848 at preoperative and postoperative stage. The plasma levels of hsa_circ_0006848 in postoperative patients were significantly increased compared to preoperative patients (= 0.047) .p = 0.037 and p = 0.046, respectively).Since we found that hsa_circ_0006848 expression levels were lower in EGC tissues as well as in plasma, we further analyzed its association with clinicopathological features of patients with EGC. As shown in To evaluate the potential diagnostic value, a ROC curve was generated for hsa_circ_0006848 levels in plasma. We found that the area under the ROC curve (AUC) was 0.733 . When thIn the present study, the positive rate of CEA, CA19-9, and CA72-4 was 3.3% (1/30), 3.3% (1/30), and 6.7% (2/30), respectively. Of the 30 EGC patients, 26 patient CEA, CA19-9, and CA 72-4 are all in a normal level. Interestingly, the area under the ROC curve (AUC) of hsa_circ_0006848 in plasma of EGC patients with normal CEA, CA19-9, and CA72-4 level was 0.692 suggesting a good diagnostic value, which further confirm the superiority of the diagnostic value of hsa_circ_0006848 .The poor prognosis of gastric cancer (GC) is usually due to delayed diagnosis. Gastroscopy is a standard screening tool for gastric lesions; however, its results may be mainly related to the endoscopist's skill, awareness, and ability to identify EGC. Not to mention, patients consider it so uncomfortable. Previous study showed that the detection rate of EGC by intensive gastroscopy was 3.3%, whereas the detection rate of EGC only by white light endoscopy was 0.5% . AnotherIt has been proposed that many ribosomal proteins (RPs) may act as cancer genes in human . RecentlUp to date, a series of studies have explored the diagnostic value of various plasma tumor markers for gastric cancer , 11, 15.Nowadays, circRNA is one of the newest types of noncoding RNAs. There are increasing evidences that circRNAs are involved in the development and progression of diseases, especially cancer. Recently, more attention was focused on the clinical cancer diagnostic value of circRNAs , 24. Zhap < 0.01). This phenomenon may be mainly due to the reduction in the release of tumor-derived nucleic acid after tumor resection [In the present study, we calculated and analyzed the relevance of clinicopathological parameters and plasma hsa_circ_0006848 expression level in 30 EGC patients, indicating the close relation with histological grade and tumor size. Besides, comparing with CEA (0.529), CA19-9 (0.562), and CA72-4 (0.594), hsa_circ_0006848 had higher AUC value (0.733). Moreover, the combination of hsa_circ_0006848, CEA, CA19-9, and CA72-4 showed the highest AUC value of 0.825. However, the positive rates of CEA, CA19-9, and other regular tumor markers were relatively low for early gastric cancer . On the esection .There are some limitations in our study. Firstly, it is the retrospective study with a relative small sample. Thus, it would be better if initial or external validation. Secondly, we did not assess the predictive value of postoperative hsa_circ_0006848 on EGC recurrence patterns and prognosis. Thirdly, the molecular mechanisms of how hsa_circ_0006848 works are still largely unknown.In conclusion, our data suggest that the expression of RPL6 and its relative hsa_circ_0006848 was significantly downregulated in patients with EGC compared with that in healthy subjects, and hsa_circ_0006848 in plasma may be served as a promising diagnostic biomarker for EGC."}
+{"text": "The abovementioned methods coupled with Western blotting were used to investigate the molecular mechanisms. The current study showed that hsa_circ_0000673 was significantly down-regulated in GC. Overexpression of hsa_circ_0000673 inhibited the proliferation and invasion of GC cells. In contrast, hsa_circ_0000673 down-regulation promoted the proliferation and invasion of GC cells. Further studies revealed that hsa_circ_0000673 targetted miR-532-5p and up-regulated the expression of RUNX3. The present study showed that hsa_circ_0000673 was decreased in GC and it exerted tumor-suppressing effects by targetting miR-532-5p and up-regulating RUNX3 expression level. Hsa_circ_0000673 may be a promising diagnosis biomarker and therapeutic target in GC.Circular RNAs (circRNAs), a new class of endogenous non-coding RNAs, have recently been known to play critical roles in various cellular biological processes, including tumorigenesis, in which they act as an miRNA sponge that regulates gene expression. Thus, revealing the functions of circRNAs in carcinogenesis and cancer development has been of great interest. However, their expression and functions in gastric cancer (GC) development are still largely unknown. Therefore, the present study aimed to identify novel deregulated circRNAs in GC and reveal their biological functions and molecular mechanisms in GC. Quantitative real-time PCR (qRT-PCR) was performed to measure the expression levels of circRNAs in GC tissues, cell lines, and plasma. The MTT assay, colony formation assay, transwell assay, and tumor xenografts  Gastric cancer (GC) is a common malignant tumor with the fourth highest occurrence amongst all cancers and is the second leading cause of cancer-related deaths worldwide ,2. Many Circular RNAs (circRNAs), closed-loop RNAs produced through end-to-end joining of RNA transcription fragments during transcription, are a new class of endogenous non-coding RNAs ,12. AlthIn the present study, we analyzed circRNAs expression level in GC tissues and plasma to confirm hsa_circ_0000673 circRNA down-regulation in GC cell lines, and also examined its correlation with GC staging. We also performed further studies to confirm whether hsa_circ_0000673 can target miR-532-5p and up-regulate RUNX3, thus suppressing GC proliferation and invasion. In the current study, we proposed hsa_circ_0000673 as a biomarker for diagnosis and as a potential therapeutic target for GC treatment.All patients with GC in the present study were histopathologically and clinically diagnosed at the Lanzhou University Second Hospital  from 2015 to 2017. Patients\u2019 age ranged from 25 to 60 years. The ratio of male to female patients was 1:1. All patients were of Han ethnicity from Gansu Province. No patient received pre-operative chemotherapy, radiotherapy, or target therapy. All tumor tissues were histologically confirmed as GC using Hematoxylin and Eosin (H&E) staining after surgical resection. The clinicopathologic staging of the patients were in accordance with the manual by the American Joint Committee on Cancer. Normal gastric tissues 3 cm from the margin of resected neoplastic tissues of patients with GC were isolated and confirmed by pathological evaluation. We also collected 38 plasma samples from 14 healthy donors and 24 patients with GC. Consistent with the inclusion criteria of patients with GC, healthy donors were aged 25\u201360 years and of Han ethnicity from Gansu Province, with a male to female ratio of 1:1. They had no history of stomach or other systematic diseases and they were recruited from the hospital at the same time as patients with GC. We obtained approval from the Institutional Research Ethics Committee and written informed consent from all participants for use of tissue and blood specimens in our study.http://www.circbase.org/) to unify all circRNAs\u2019 names. Then, we used Venny 2.1 (http://bioinfogp.cnb.csic.es/tools/venny/index.html) to filter out circRNAs with the same trend in GSE83521 and GSE78092. The sequence of circRNAs can be obtained in CircBase.Two human circRNA microarray data (GSE83521 and GSE78092) were downloaded from NCBI GEO DataSets. circRNAs with more than two-folds change in expression level in tumor and normal counterparts were chosen for further investigation. We used CircBase . These cell lines were cultured in RPMI-1640 medium  supplemented with 10% FBS  and 1% penicillin/streptomycin (Invitrogen). The cells were incubated at 37\u00b0C and 5% COTotal RNA was isolated from the tissue specimens and cultured cells using an RNeasy Plus Mini Kit . Plasma RNA was isolated with a miRNeasy Serum/Plasma Kit  according to the manufacturer\u2019s instructions. cDNA was synthesized by reverse transcription using an MMLV transcriptase (Promega) using random primers. For gene detection, real-time RT-PCR was performed on a LC480 real-time PCR detection system (Bio-Rad) using a Roche SYBR FAST Universal qPCR Kit . GAPDH was used as an internal control. The sequences of primers are presented in The complete sequence of hsa_circ_0000673 was obtained from circBase. We purchased the retroviral transfer plasmid pLCDH-ciR-puro from Geneseed . Then, we designed circRNA primers based on the pLCDH-ciR-puro manual. The PCR product was inserted into pLCDH-ciR-puro with ClonExpress\u00ae II purchased from Vazyme . Sequencing and identification were needed to design two primers in the middle of the target circRNA fragment, bidirectional cross-sequencing, and the interval between the two primers should not be shorter than 90 bp. siRNAs of hsa_circ_0000673 were synthesized by RiboBio , designed to target the junction region of hsa_circ_0000673 sequence. miR-532-5p mimics, anti-miR-532-5p oligonucleotides, and their corresponding control oligonucleotides were also purchased from RiboBio . Transfection of plasmids or oligonucleotides was performed using Lipofectamine\u00ae 3000 reagent  according to the manufacturer\u2019s instructions. The sequences of clone primers and siRNA are presented in 4) were seeded in 48-well plates and cultured for 24 h. Then, miR-532-5p mimics, LUC-hsa_circ_0000673 and LUC-hsa_circ_0000673-mutant reporter plasmid and pRL-TK plasmid (Promega) were co-transfected in HEK-293 T cells using Lipofectamine\u00ae 3000 reagent  according to the manufacturer\u2019s instructions. After 48 h, we detected the luciferase activities of these reporters in whole cell lysate and normalized to corresponding luciferase activities of pRL-TK through the Dual-Luciferase Reporter Assay System (Promega) according to the manufacturer\u2019s protocols.HEK-293 T cells . Cell proliferation was detected every 24 h according to the manufacturer\u2019s protocol. Briefly, 20 \u00b5l MTT solution  was added to each well, which was incubated at 37\u00b0C for 4 h. Next, the supernatant was discarded and 150 \u00b5l DMSO was added to each well. The mixture was then gently shaken at room temperature for 15 min. The solution was measured spectrophotometrically at 450 nm. For clone formation assay, the cells were seeded in six-well plates (400 cells/well) and cultured for 10 days at 37\u00b0C and 5% COCell invasion ability was assessed using the transwell assay. The transwell assay was performed according to a previously described standard method .6 cells) were subcutaneously implanted in the inguinal folds of 7-week-old BALB/c nude mice (n=3 per group). Tumor lengths (L) and widths (W) were measured every 5\u20137 days. Tumor volumes were calculated using the following equation: volume (mm3) = L*W2/2. At the experimental end point, mice were anesthetized and killed. Tumors were then removed and photographed.The cells . P-values <0.05 were considered statistically significant.Statistical analysis was conducted using the Student\u2019s in vitro and in vivo, there was no significant difference in the expression level of hsa_circ_0009172 between GC and normal cells . We selected hsa_circ_0000673 for further investigations, whereas hsa_circ_0074854 was studied by other researchers in our facility.To identify novel circRNAs involved in the development of GC, we integrated two human circRNA microarray data  to screen common differentially expressed genes. We found that hsa_circ_0000673, hsa_circ_0009172, and hsa_circ_0074854 were all significantly down-regulated in GC tissues, compared with those in non-tumor tissues A. FurtheP<0.001). Taken together, the above data suggested that hsa_circ_0000673 expression was significantly down-regulated in GC.To determine the clinical significance of hsa_circ_0000673 expression level in patients with GC, we measured the expression level of hsa_circ_0000673 in 64 human GC specimens by using qRT-PCR. The specimens comprised 25 cases of stage I, 23 cases of stage II, and 16 cases of stages III and IV. As shown in RSL1D1. We subsequently measured the mRNA level of RSL1D1 by using qRT-PCR to evaluate the effect of hsa_circ_0000673 overexpression on RSL1D1. As shown in Supplementary Figure S2A, hsa_circ_0000673 overexpression did not affect RSL1D1 mRNA level.To address the role of hsa_circ_0000673 in GC pathogenesis, we established the GC cell lines AGS and BGC823, which exhibited low endogenous hsa_circ_0000673 expression levels, as cell lines that stably overexpress hsa_circ_0000673. As shown in Supplementary Figure S2A, the expression levels of hsa_circ_0000673 were markedly increased in hsa_circ_0000673-overexpressing GC cells compared with the corresponding vector control cells. On the genome, hsa_circ_0000673 is located in chr16:11940357-11940700, which is also a part of the gene Furthermore, using the abovementioned established cell lines, we examined the effect of hsa_circ_0000673 on GC cell proliferation and invasion. As shown in We performed hsa_circ_0000673 silencing by RNAi-mediated knockdown in MGC803 cells, which exhibited relatively high endogenous hsa_circ_0000673 expression, to evaluate whether endogenous hsa_circ_0000673 inhibit the proliferation and invasion of GC cells. As shown in Supplementary Figure S2B, hsa_circ_0000673 expression was significantly down-regulated by siRNAs compared with that of the negative control group. However, hsa_circ_0000673 silencing did not affect the expression level of RSL1D1 .As shown in in vivo, we established a line of stable hsa_circ_0000673-silenced GC cells, namely MGC803-circ-0000673 sh3 cells. . Next, we subcutaneously implanted AGS-vector, AGS-circ-0000673, MGC803-scramble, or MGC803-circ-0000673 sh3 cells into the inguinal fold of athymic mice. As shown in in vivo.To examine the effect of hsa_circ_0000673 on GC growth Evidence has shown that circRNAs sequester miRNAs to terminate the regulation of their target genes. To investigate the mechanism underlying the effect of hsa_circ_0000673 on the inhibition of proliferation and invasion in GC cells, we searched for miRNAs associated with hsa_circ_0000673. By combining the prediction from the databases TargetScan and MiRanda, we selected miR-532-5p as a potential target of hsa_circ_0000673 A. To figTo ascertain the role of miR-532-5p in the hsa_circ_0000673-induced inhibition of GC cell proliferation and invasion, we transiently transfected miR-532-5p mimics into the hsa_circ_0000673-overexpressed AGS and BGC823 cells, and miR-532-5p inhibitors into the hsa_circ_0000673-silenced MGC803 cells. As shown in Recently, the function of circRNAs in carcinogenesis and cancer development has garnered much attention. However, their expression level and function in GC development are still largely unknown, with only a few circRNAs reported to be involved in the development of GC . In our Despite suggesting tumor-suppressing effects of hsa_circ_0000673 in GC, the present study did not investigate other important deregulated circRNAs involved in the development of GC due to the screening we conducted at the beginning of the study. At the same time, interestingly, we found that almost all circRNAs reported in GC are decreased. Hence, in future studies, we will focus on identifying other highly expressed circRNAs in GC or determining the mechanism underlying the down-regulation of most circRNAs in GC.A recent study has shown that circRNAs exert their functions through multiple ways, including miRNA sponge, RBP sponge, and mRNA regulator . In our P<0.001), suggesting that hsa_circ_0000673 may be useful as diagnosis biomarkers and therapeutic target in GC.Compared with other non-coding RNAs, such as miRNAs and long non-coding RNAs (lncRNAs), circRNAs are highly conserved and stable. These two important properties of circRNAs were possibly responsible for their potential as ideal biomarkers in the diagnosis and therapy of cancers. In the present study, we collected 38 plasma samples, including 14 from healthy people and 24 from patients with GC, and detected the plasma level of hsa_circ_0000673 in these samples. The result showed that plasma hsa_circ_0000673 level was significantly down-regulated in patients with GC (In summary, hsa_circ_0000673 expression was significantly decreased in GC and negatively correlated with GC stage. Functionally and mechanistically, hsa_circ_0000673 inhibited GC proliferation and invasion through the sponge effect on miR-532-5p and up-regulation of RUNX3 expression, indicating its role as a tumor suppressor in GC development. Furthermore, plasma hsa_circ_0000673 level was significantly down-regulated in patients with GC, suggesting the potential roles of hsa_circ_0000673 as a diagnosis biomarker and therapeutic target in GC."}
+{"text": "Cellular signaling, predominantly mediated by phosphorylation through protein kinases, is found to be deregulated in most cancers. Accordingly, protein kinases have been subject to intense investigations in cancer research, to understand their role in oncogenesis and to discover new therapeutic targets. Despite great advances, an understanding of kinase dysfunction in cancer is far from complete.A powerful tool to investigate phosphorylation is mass-spectrometry (MS)-based phosphoproteomics, which enables the identification of thousands of phosphorylated peptides in a single experiment. Since every phosphorylation event results from the activity of a protein kinase, high-coverage phosphoproteomics data should indirectly contain comprehensive information about the activity of protein kinases.http://github.com/saezlab/kinact/.In this chapter, we discuss the use of computational methods to predict kinase activity scores from MS-based phosphoproteomics data. We start with a short explanation of the fundamental features of the phosphoproteomics data acquisition process from the perspective of the computational analysis. Next, we briefly review the existing databases with experimentally verified kinase-substrate relationships and present a set of bioinformatic tools to discover novel kinase targets. We then introduce different methods to infer kinase activities from phosphoproteomics data and these kinase-substrate relationships. We illustrate their application with a detailed protocol of one of the methods, KSEA . This method is implemented in Python within the framework of the open-source Kinase Activity Toolbox (kinact), which is freely available at  BCR and ABL genes, can give rise to and sustain chronic myeloid leukemia ) and kinase-substrate interactions (kin_sub_interactions), the variables \u2018mP\u2019 and \u2018delta\u2019 are needed to determine the rocedure .2, are shown together with the log2 fold change values of all phosphosites that are known to be targeted by this kinase. For methods, which use the mean, the median as more robust measure can be calculated alternatively. The qualitative changes of the kinase activities In the DataFrame object \u2018data_raw\u2019, the columns represent the different experimental conditions or additional information and the row\u2019s unique phosphosites. A good way to gain an overview about the data stored in a DataFrame and to keep track of changes are the following functions:print data_raw.head to show the first five rows of the DataFrame or print data_raw.shape in order to show the dimensions of the DataFrame.Phosphosites that can be matched to different proteins or several positions within the same protein are excluded from the analysis. In this example, ambiguous matching is indicated by the presence of a semicolon that separates multiple possible identifiers, and can be removed like this:data_reduced = data_raw[~data_raw[\u2018Proteins\u2019].str.contains]For more convenient data handling, we will index each phosphosite with an unambiguous identifier comprising the UniProt accession number, the type of the modified residue, and the position within the protein. For the example of a phosphorylation of the serine 59 in the Tyrosine-protein kinase Lck, the identifier would be P06239_S59. The identifier can be constructed by concatenating the information that should be provided in the dataset. In the example of de Graaf et al., the UniProt accession number can be found in the column \u2018Proteins\u2019, the modified residue in \u2018Amino acid\u2019, and the position in \u2018Positions within proteins\u2019.The index is used to access the rows in a DataFrame and will later be needed to construct the kinase-substrate sets. After the creation of the identifier, the DataFrame is indexed by calling the function \u2018set_index\u2019.data_reduced[\u2018ID\u2019] = data_reduced[\u2018Proteins\u2019] + \u2018_\u2019 +data_reduced[\u2018Amino acid\u2019] +data_reduced[\u2018Positions within proteins\u2019]data_indexed = data_reduced.set_index(data_reduced[\u2018ID\u2019])seeNote 2)Mass spectrometry data is usually accompanied by several columns containing additional information about the phosphosite  or statistics of the database search (for example the posterior error probability), which are not necessarily needed for KSEA. We therefore extract only the columns of interest containing the processed data. In the example dataset, the names of the crucial columns start with \u2018Average\u2019, enabling selection by a simple \u2018if\u2019 statement. Generally, more complex selection of column names can be achieved by regular expressions with the python module \u2018re\u2019.data_intensity = data_indexed[[x for x in data_indexed if x.startswith(\u2018Average\u2019)]] # (a/b)\u2009=\u2009log(a)\u2009\u2212\u2009log(b), we obtain the fold changes by subtracting the column with the control values from the rest using the \u2018sub\u2019 function of Pandas  results in the final dataset:data_fc.columns = [x.split[-1] for x in data_fc] # Rename columnsdata_fc.drop # Delete control columnprint data_fc.head>>> 5min 10min 20min 30min 60min>>> ID>>> A0AVK6_S71 -0.319306 -0.484960 -0.798082 -0.856103 -0.928753>>> A0FGR8_S743 -0.856661 -0.981951 -1.500412 -1.441868 -0.861470>>> A0FGR8_S758 -1.445386 -2.397915 -2.692994 -2.794762 -1.553398>>> A0FGR8_S691 0.271458 0.264596 0.501685 0.461984 0.655501>>> A0JLT2_S226 -0.080786 1.069710 0.519780 0.520883 -0.296040p-value. The p-value will be needed to perform KSEA using the \u2018Delta count\u2019 approach but may be dispensable for the mean methods. The example dataset contains a p-value (transformed as negative logarithm with base 10) in selected columns and can be extracted using:data_p_value = data_indexed[[x for x in data_indexedif x.startswith]]data_p_value = data_p_value.astype(\u2018float\u2019) # (seeNote 4)If the experiments have been performed with several replicates, statistical analysis enables estimation of the significance of the fold change compared to a control expressed by a seeNoteseeNote 6)Now, we load the prior knowledge about kinase-substrate relationships. In this example, we use the information provided in the PhosphoSitePlus database  & (ks_rel[\u2018SUB_ORGANISM\u2019] == \u2018human\u2019)]Next, we again construct unique identifiers for each phosphosite using the information provided in the dataset. The modified residue and its position are already combined in the provided data.ks_rel_human[\u2018psite\u2019] = ks_rel_human[\u2018SUB_ACC_ID\u2019] + \u2018_\u2019 + ks_rel_human[\u2018SUB_MOD_RSD\u2019]1, all other fields are filled with a 0. For this, the Pandas function \u2018pivot_table\u2019 can be used:ks_rel_human[\u2018value\u2019] = 1 # (seeNote 7)adj_matrix = pd.pivot_tableNow, we construct an adjacency matrix for the phosphosites and the kinases. In this matrix, an interaction between a kinase and a phosphosite is denoted with a m\u00a0\u00d7\u00a0n with m being the number of phosphosites and n the number of kinases. If a kinase is known to phosphorylate a given phosphosite, the corresponding entry in this matrix will be a 1, otherwise a 0. A 0 does not mean that there cannot be an interaction between the kinase and the respective phosphosite, but rather that this specific interaction has not been reported in the literature. As sanity check, we can print the number of known kinase-substrate interactions for each kinase saved in the adjacency matrix:print adj_matrix.sum(axis=0).sort_values.head>>> GENE>>> CDK2 541>>> CDK1 458>>> PRKACA 440>>> CSNK2A1 437>>> SRC 391>>> dtype: int64The result is an adjacency matrix of the form seeNoteIn the accompanying toolbox, we provide for each method of KSEA a custom python function that automates the analysis for all kinases in a given condition. Here, however, we demonstrate the principle of KSEA by computing the different activity scores for a single kinase and a single condition. As an example, the Cyclin-dependent kinase 1 detected_p_sites = data_condition.indexintersect=list(set(substrate_set).intersection(detected_p_sites))print len(intersect)>>> 114First, we determine the overlap between the known targets of the kinase and the detected phosphosites in this condition, because we need it for every method of KSEA. Now, we benefit from having the same format for the index of the dataset and the adjacency matrix. We can use the Python function \u2018intersection\u2019 to determine the overlap between two sets.substrate_set = adj_matrix[kinase].replace.dropna.index # set_alt = data_condition.ix[intersect].where.dropnamS_alt = set_alt.meanz_score_alt =  * np.sqrt) * 1/deltap_value_mean_alt = norm.sf)print mS_alt, p_value_mean_alt>>> -0.680835732551 1.26298232031e-13Alternatively, only the phosphosites in the substrate set that change significantly between conditions can be considered when computing the mean of the fold changes in the substrate set. Therefore, we need a cutoff, determining a significant increase or decrease, respectively, which can be a user-supplied parameter. Here, we use a standard level to define a significant change with a cutoff of 0.05. The significance of the KSEA score is tested as before with the seeNote 10)In the \u201cDelta count\u201d method, we count the number of phosphosites in the substrate set that are significantly increased in the condition versus the control and subtract the number of phosphosites that are significantly decreased.cut_off = -np.log10(0.05)score_delta = len(data_condition.ix[intersect].where((data_condition.ix[intersect] > 0) &).dropna) -len(data_condition.ix[intersect].where((data_condition.ix[intersect] < 0) &).dropna) # n = len(intersect)N = len[0])hypergeom_dist = hypergeomp_value_delta = hypergeom_dist.pmf.dropna))print score_delta, p_value_delta>>> -58 8.42823410966e-119The In summary, the methods described in this review use different approaches to calculate kinase activities or to relate kinases to activity profiles from phosphoproteomics datasets. All of them utilize prior knowledge about kinase-substrate relationships, either from curated databases or from computational prediction tools. Using these methods, the noisy and complex information from the vast amount of detected phosphorylation sites can be condensed into a much smaller set of kinase activities that is easier to interpret. Modeling of signaling pathways or prediction of drug responses can be performed in a straightforward way with these kinase activities as shown in the study by Casado et al. .The power of the described methods strongly depends on the available prior knowledge about kinase-substrate relationships. As our knowledge increases due to experimental methods like in vitro kinase selectivity studies  or the CPhosphoproteomic data is not only valuable for the analysis of kinase activities: for example, PTMfunc is a computational resource that predicts the functional impact of posttranslational modifications based on structural and domain information , and PHOFor the analysis of deregulated signaling in cancer, mutations in key signaling molecules can be of crucial importance. Recently, Creixell and colleagues presented a systematic classification of genomic variants that can perturb signaling, either by rewiring of the signaling network or by the destruction of phosphorylation sites . Anotherk-means clustering algorithm together with Fisher\u2019s exact test for enrichment. In a recent publication by Hernandez-Armenta et al. 4.p-values as string variables, not as floating point numbers. Therefore, this line is needed to convert the type of the p-values.Due to a compatibility problem with the output of Excel, Python recognizes the 5.The adjacency matrix can also be constructed based on kinase recognition motifs or kinase prediction scores and the amino acid sequence surrounding the phosphosite. To use NetworKIN scores for the creation of the adjacency matrix, kinact will provide dedicated functions. In the presented example, however, we focus on the curated kinase-substrate relationships from PhosphoSitePlus.6.The file from PhosphoSitePlus is provided as text file in which a tab (\u2018\\t\u2019) delimits the individual fields, not a comma. The file contains a disclaimer at the top, which has to be removed first. Alternatively, the option \u2018skiprows\u2019 in the function \u2018read_csv\u2019 can be set in order to ignore the disclaimer.7.This column is needed, so that in the matrix resulting from pd.pivot_table the value from this column will be entered.8.If necessary, mapping between protein names, gene names, and UniProt-Accession numbers can easily be performed with the Python module \u2018bioservices\u2019, to the documentation of which we want the refer the reader .9.0 values with NAs ), which are then deleted with dropna. Therefore, only those interactions remain, for which a 1 had been entered in the matrix. Of these interactions, we extract the index, which is a list of the phosphosites known to be targeted by the kinase of interest.In this statement, we first select the relevant columns of the kinase from the connectivity matrix (adj_matrix[kinase]). In this column, we replace all 10.The where method will return a copy of the DataFrame, in which for cases where the condition is not true, NA is returned. dropna will therefore delete all those occurrences, so that len will count how often the condition is true.data_log2 = np.log2(data_intensity)"}
+{"text": "The fragmented nature of most draft plant genomes has hindered downstream gene discovery, trait mapping for breeding, and other functional genomics applications. There is a pressing need to improve or finish draft plant genome assemblies.Fragaria vesca) revealed near-perfect 1:1 synteny with dramatic divergence in tandem gene array composition. Lineage-specific tandem gene arrays in black raspberry are related to agronomic traits such as disease resistance and secondary metabolite biosynthesis.Here, we present a chromosome-scale assembly of the black raspberry genome using single-molecule real-time Pacific Biosciences sequencing and high-throughput chromatin conformation capture (Hi-C) genome scaffolding. The updated V3 assembly has a contig N50 of 5.1 Mb, representing an \u223c200-fold improvement over the previous Illumina-based version. Each of the 235 contigs was anchored and oriented into seven chromosomes, correcting several major misassemblies. Black raspberry V3 contains 47 Mb of new sequences including large pericentromeric regions and thousands of previously unannotated protein-coding genes. Among the new genes are hundreds of expanded tandem gene arrays that were collapsed in the Illumina-based assembly. Detailed comparative genomics with the high-quality V4 woodland strawberry genome (Rubus.The improved resolution of tandem gene arrays highlights the need to reassemble these highly complex and biologically important regions in draft plant genomes. The updated, high-quality black raspberry reference genome will be useful for comparative genomics across the horticulturally important Rosaceae family and enable the development of marker assisted breeding in  To date, more than 200 plant genomes have been sequenced, including most plants with agronomic value. Notable exceptions include large, polyploid, or otherwise complex genomes and many horticultural, medicinal, or orphan crop species . Most plRubus occidentalis L.) is an important specialty fruit crop in the US Pacific Northwest that is closely related to the globally commercialized red raspberry (Rubus idaeus L.). Black raspberry has undergone little improvement since its domestication in the late 1800s [Rubus are needed to accelerate marker-assisted selection and improvement. The black raspberry genome was sequenced using an NGS-based approach, yielding a fragmented but much needed draft assembly [Rubus breeding communities.Black raspberry  contains 10 terminal telomeric tracks at both ends of chromosomes Ro02, Ro03, Ro05, and Ro07 and at one end of Ro01 and Ro04, validating the accuracy and quality of our assembly Fig.. The preWe aligned the V3 black raspberry assembly to the V1 pseudomolecules to assess genome collinearity. We identified numerous misassembles in V1 spanning most of the genome Fig.\u00a0. Misasseab initio using the MAKER-P pipeline [Fragaria vesca) [The V3 black raspberry assembly includes 43 Mb of new sequences that were unassembled in the V1 reference. We re-annotated the V3 assembly pipeline . Ten RNApipeline  and useda vesca)  and Araba vesca)  genomes a vesca)  pipelinea vesca) , 5, 20 aThe V3 black raspberry annotation has a striking increase in the size and number of tandem gene arrays. Tandem gene duplicates (TDs) with high sequence homology often collapse into single gene copies during the assembly of NGS data and are likely underrepresented in most genomes. We identified 7,453 TDs in the V3 assembly compared to 4,333 in V1. Tandem arrays range in size from 2 to 26 copies, with an average size of 4. Large tandem arrays show the greatest improvement in assembly accuracy, with the most dramatic increase from four copies in V1 to 26 in V3 , with the most common recent ancestor of these two species having diverged \u223c75 million years ago [F. vesca) and black raspberry have the same karyotype (2n = 14), and previous genetic map and genomic analyses suggest a high degree of collinearity [F. vesca assembly [F. vesca genomes are largely collinear , and each genome has unique patterns of expansion/deletion based on gene-level microsynteny  [RRID:SCR_014731) [RRID:SCR_001876) [High-molecular-weight (HMW) genomic DNA (gDNA) was isolated from young leaf tissue of black raspberry selection ORUS 4115-3 using a modified nuclei preparation method . A 20-kb_015880) . The fol_014731)  using \u223c8_014731) . Quality_014731)  with def_001876) . The parRRID:SCR_010910) [RRID:SCR_012091) [Hi-C library construction and sequencing was previously reported . In tota_010910)  with str_010910) . Briefly_010910) . Gaps in_012091)  using deCentromeres were identified using three lines of evidence: reduced intrachromosomal interactions in the Hi-C heat map, increased density of LTR retrotransposons, and presence of centromere-specific tandem repeat arrays (317 bp). First, the intrachromosomal interactions were used to locate the putative centromere locations. Centromere locations were validated by overlap with centromere-specific tandem repeat arrays. The estimated borders of centromeres were identified by the presence of centromere-specific tandem repeats and LTR retrotransposon density >85%. Putative centromeres were identified for six of the seven black raspberry chromosomes, and no enrichment of centromere tandem repeats was found in Ro06. Centromere-specific repeat arrays were found in only nine reads in the unassembled read file from Canu, suggesting the centromeres are well assembled in black raspberry.RRID:SCR_005309) [F. vesca) [Ab initio gene prediction was performed using SNAP and Augustus , with three and two rounds of reiterative training, respectively. The resulting gene set was filtered to remove gene models containing Pfam domains related to transposable elements, resulting in an annotation of 33-286 gene models. Annotation quality was assessed using the Benchmarking Universal Single-Copy Orthologs V3  [The MAKER-P pipeline   was used. vesca)  genomes . vesca) . A custo. vesca) . This cu_015008)  pipelineRRID:SCR_011848) [To build a gene expression atlas, RNA was collected from 10 diverse black raspberry tissues. This includes green berries, red berries, ripe berries, flowers, canes, roots, leaves, and methyl jasmonate-treated leaf tissue. Fresh tissue was flash-frozen in liquid nitrogen, and total RNA was extracted using KingFisher Pure RNA Plant kit , according to the manufacturer's instructions. Two micrograms of total RNA was used to construct stranded mRNA libraries . Multiplexed, pooled libraries were sequenced on the Illumina HiSeq4000 under paired-end \u2009150-nt mode in the genomics core at Michigan State University. Raw reads were trimmed using Trimmomatic V 0.33   and alig_011848) . Reads w_011848)  and outp_011848)  and visuF. vesca V4 [e-5 and maximum gene distance of 10 genes. Pair-wise, macrosynteny, and microsynteny plots were constructed using the python version of MCScan [The black raspberry V3 genome was compared to the black raspberry V1  and F. vvesca V4  genomes vesca V4 . Syntenif MCScan .GIGA-D-18-00032_Original_Submission.pdfClick here for additional data file.GIGA-D-18-00032_Revision_1.pdfClick here for additional data file.Response_to_Reviewer_Comments_Original_Submission.pdfClick here for additional data file.Reviewer_1_Report_ -- Felix Bemm2/26/2018 ReviewedClick here for additional data file.Reviewer_1_Report_(Revision_1) -- Felix Bemm5/15/2018 ReviewedClick here for additional data file.Reviewer_2_Original_Submission_(attachment).pdfClick here for additional data file.Reviewer_2_Report_ -- Susan Strickler3/14/2018 ReviewedClick here for additional data file.Supplemental Tables and FiguresClick here for additional data file."}
+{"text": "This article contains the set-up and input files of the implementation of Delft3D model to determine extreme hydrodynamic forces performed in Rueda-Bayona et\u00a0al. [1]. The model was configured with a multidomain grid using double-way communication between the hydrodynamic and wave module. The multidomain grids solve faster than single and nested grids because require less grid points to calculate. Also, the double-way communication between the hydrodynamic and wave modules allows to consider the non-linear interactions of wind, waves, tides and currents. Because there are no modelling examples related to multidomain grids in the open access official web site of Delft3d model, this data contributes to increase the availability information of this necessity. Finally, the files of this article are ready to be run in the Delft3D model to perform a sensitivity test recommended in Rueda-Bayona et\u00a0al. [1]. The data allows to model a multidomain grid with double-way communication in an offshore location of the Guajira \u2013 Colombia . Also, twww.nodc.noaa.gov) [The input data contains information of atmosphere  extracted from NARR-NOAA database , water loaa.gov) . The setoaa.gov)  and relaoaa.gov) .The data is gathered and stored within a compressed folder named as Multi_domain_2004_all_forces.zip. The Multi_domain_2004_all_forces folder contains the input and setup files of the Delf3d model mentioned above; the guajira.ddb file allows to connect the outer and inner grid. The dataset can be downloaded directly from the online version of this data article.2The study area of the multidomain modelling  is shownThe study area is considered as strategic  because The dataset of this article is in ASCII file format, and is organized and described as follows:2.1\u2022Outer bathymetry: outside.dep.\u2022Inner bathymetry: inside.dep.\u2022Boundary definition file (Flow module): 2004.bnd\u2022Time-series flow conditions (Flow module): 2004.bct.\u2022Transport conditions (Flow module): 2004.bcc.\u2022Bottom roughness file (Flow module): Chezy_5_60.rgh\u2022Heat flux model data (Flow module): 2004.tem.\u2022Wind data (Flow module): 2004.wnd.\u2022Wave boundary condition: TPAR.bnd.2.2\u2022Outer Grid (Flow module): outside_Guajira_2004.grd.\u2022Inner Grid (Flow module): inside_Guajira_2004.grd.\u2022Wave module grid: outside_swan.grd.\u2022Outer enclosure grid: outside_Guajira_2004.enc.\u2022Inner enclosure grid: inside_Guajira_2004.enc.\u2022Outer grid observation points: outside_puntos.obs.\u2022Inner grid observation points: windmill.obs.www.mathworks.com) language with the same methodology recommended in the data article of Rueda-Bayona et\u00a0al. [The data related to the atmosphere information  were processed through MATLAB (a et\u00a0al. . The bat"}
+{"text": "Displaying data onto anatomical structures is a convenient technique to quickly observe tissue related information. However, drawing tissues is a complex task that requires both expertise in anatomy and the arts. While web based applications exist for displaying gene expression on anatograms, other non-genetic disciplines lack similar tools. Moreover, web based tools often lack the modularity associated with packages in programming languages, such as R. Here I present gganatogram, an R package used to plot modular species anatograms based on a combination of the graphical grammar of ggplot2 and the publicly available anatograms from the Expression Atlas. This combination allows for quick and easy, modular, and reproducible generation of anatograms. Using only one command and a data frame with tissue name, group, colour, and \u00a0value, this tool enables the user to visualise specific human and mouse tissues with desired colours, grouped by a variable, or displaying a desired value, such as gene-expression, pharmacokinetics, or bacterial load across selected tissues. gganatogram consists of 5 highly annotated organisms, male/female human/mouse, and a cell anatogram. It further consists of 24 other less annotated organisms from the animal and plant kingdom. I hope that this tool will be useful by the wider community in biological sciences. Community members are welcome to submit additional anatograms, which can be incorporated into the package.neuroconductor, and a development version can be found on\u00a0github/jespermaag/gganatogram. An interactive shiny app of gganatogram can be found on\u00a0https://jespermaag.shinyapps.io/gganatogram/, which allows for non-R users to create anatograms. A stable version gganatogram has been deposited to Efficiently displaying tissue information in multicellular organisms can be a laborious and time consuming process. Often researchers want to showcase differences in values, such as gene expression or pharmacokinetics between tissues in one organism, or between similar tissues in different groups.Whereas bar charts and heatmaps provide an informative view of the differences between groups, it can be difficult to immediately observe the biological significance . As comp4. Although these tools provide important information regarding gene expression in various tissues and organisms, other disciplines besides genetics are unable to utilise these applications due to the focus on genes. Moreover, these tools often only include a predefined set of experiments that can be visualised, leading to difficulties in presenting your own data. Other caveats with these tools are that it can be laborious to recreate the plot or automatically create plots from results.Several online tools to display gene expression in different tissues already exist5 utilising 28 publicly available anatograms from the Expression Atlas2, and a cellular anatogram from The Protein Atlas6. With this package it is easy for any R user to quickly visualise anatograms with specified colours, groups, and values. Using the familiar grammar from ggplot25, this program allows for modular anatograms to be generated.Here I present gganatogram, an open source R package based on ggplot2neuroconductor7, an open-source platform for rapid testing and dissemination of reproducible computational imaging software. A development version can be found ongithub/jespermaag/gganatogram, which allows for the community to post issues with the package, submit requests, or add anatograms by creating coordinate files.gganatogram is stored onsource(\"https://neuroconductor.org/neurocLite.R\")neuro_install(\"gganatogram\")The development version can be installed from github:devtools::install_github(\"jespermaag/gganatogram\")SVG files present here2. (and processed them using a custom python script (available fromGitHub). The script scraped through the SVG files to extract the name, coordinates, and SVG transformations. These were then post-processed in R to create the rda files that make up the tissue coordinates. For the cell, the SVG was downloaded from The Protein Atlas6. Here, I converted the relative coordinates in the SVG to absolute using Inkscape. I then processed the absolute coordinate SVG using python.Briefly, to generate the main list objects that contain all tissue coordinates, I downloaded SVG files from the Expression Atlas             organ  colour      type     value1        pancreas  orange digestion 10.3731462           liver  orange digestion 19.7231723           colon  orange digestion 14.8533354     bone_marrow #41ab5d     other 19.6815875 urinary_bladder  orange digestion 14.9142736         stomach  orange digestion  2.667599The main function is called gganatogram. By default, and without any arguments, it plots the outline of a male human with standard ggplot2 parameters. By adding just a few options, it is possible to quickly change to female, fill specified organs by selected colour, or fill the organs based on a value .library(gganatogram)library(gridExtra)organPlot <- data.frame,type = c,colour = c,value = c,stringsAsFactors=F)A <- gganatogram + ggtitle(\"A\")B <- gganatogram + theme_void + ggtitle(\"B\")C <- gganatogram+ theme_void + ggtitle(\"C\")D <- gganatogram + theme_void +scale_fill_distiller + ggtitle(\"D\")grid.arrangeThis section provides additional plotting examples.To plot all tissues per organism, use the provided key files that exist per organism and sex. This displays all tissues in the order of each data frame. To change the order in which organs are layered on top of each other, reorder the data frame to have those tissues at the bottom .library(gganatogram)library(gridExtra)hgMale <- gganatogram + theme_void+ coord_fixedhgFemale <- gganatogram + theme_void+ coord_fixedmmMale <- gganatogram + theme_void+ coord_fixedmmFemale <- gganatogram + theme_void+ coord_fixedcell <- gganatogram + theme_void+ coord_fixed  lay <- rbind, c)grid.arrangeTo compare anatograms, e.g. draw one specific anatogram side by side and compare values, a long table has to be created with the type column changed to the variables to compare. The following code recreates .normal <- data.frame,value = c, type = rep,stringsAsFactors=F)cancer <- data.frame,value = c,  type = rep,stringsAsFactors=F)compareGroups <- rbindgganatogram +              theme_void +              facet_wrap(~type) +              scale_fill_distillerTo change the order of how organs are layered on top of each other, change the order of the data frame. The organs to plot on the top layer should be on the end of the data frame .organPlot <- data.frame,value = c,stringsAsFactors=F)A <- gganatogram + theme_void +scale_fill_distillerorganReorder <- corganPlotReorder <- organPlotB <- gganatogram + theme_void +scale_fill_distillergrid.arrangeOrgans can also be separated by faceting, as per standard ggplot2 using facet_wrap . This calibrary(gganatogram)gganatogram +theme_void +facet_wrap(~type)The cell diagram will be useful for users to plot cellular sub-locations of proteins, mRNAs, or other molecules.library(viridis)library(dplyr)normal <- cell_key[[\"cell\"]]normal$type <- \"Normal\"cancer <- cell_key[[\"cell\"]]cellCompartments <- ccancer$value <- c)cancer$type <- \"Cancer\"plotCell <- rbindplotCell %>%mutate))%>%gganatogram + theme_void +coord_fixed +  scale_fill_viridis +facet_wrap(~type)5 for the package, the user can add additional layers from standard plots. This can be useful to show highlight features, such as metastasis, location of tissue biopsies, or gene expression of specific biopsies (Because I elected to use ggplot2iopsies .library(gganatogram)library(dplyr)library(gridExtra)biopsies <- data.frame, x = c, y = c, value = c)p <- hgMale_key %>% dplyr::filter) %>% gganatogram + theme_void + ggtitle(\"Position\u2423of\u2423biopsies\")        p <- p + geom_point)p2 <- ggplot) + geom_bar + theme_minimal + theme(legend.position= \"none\")+ theme)+ ggtitle(\"Gene1\u2423expression\")        p3 <- hgMale_key%>% dplyr::filter)%>% gganatogram + theme_void + ggtitle+ geom_point, colour=\"red\")        lay <- rbind, c, c)grid.arrangeOther than human, mouse, and the cell diagram, gganatogram consists of 24 other organisms which can be called with the other_key . These clibrary(gridExtra)plotList <- listfor (organism in names(other_key)) {    plotList[[organism]] <- gganatogram  +               theme_void +               ggtitle(organism)+                theme) +                coord_fixed}do.call)https://jespermaag.shinyapps.io/gganatogram/.Furthermore, gganantogram has an online shiny app which can be used without any R installation. This app let people can select organisms, colour palette, select tissues and adjust values for the tissues. This should allow for researchers without any experience in R to be able to use gganatogram. The online shiny app is located atFor R users, the app can easily be run locally with the following command:library(shiny)runGitHubThis command checks for all required packages and installs them if needed.5 and the anatograms from Expression Atlas2, which when combined create a powerful tool to plot and display tissue information.In summary, I have designed and implemented an R package to easily visualise anatograms based on ggplot2The one line command to generate these plots should allow for users with even limited R knowledge to create informative anatograms for publications or presentations.1. Link to version control repository containing the source code:http://neuroconductor.org:8080/package/gganatogram2. Link to development version:github/jespermaag/gganatogram3. Link to archived source code as at time of revision:https://zenodo.org/record/14774749GPL-2Software license: Really nice update to the article!I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. This article describes a new R package, which allows easy plotting of discrete and continuous measurements onto human and mouse anatomy. This is a really valuable R package contribution, as it fills a real void in the current infrastructure. I found the code examples in the manuscript intuitive and easy to run. It is great that the author adopts the popular ggplot2 grammar as well as a tidy data structures.It would be useful to know how many tissues (and which) can be plotted using this package.An example of changing the order of the data frame to change the layering should be added.I encountered the following error when trying to install the package through neuroconductor: Error in latest_neuroc_release(release = \"stable\") : unused argument (release = \"stable\") Thank you for making all code (even for processing) publicly available. Minor comments:I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. The author describes an R package that displays discrete and continuous data onto anatomical structures. The structures are based on\u00a0mouse and human anatograms from the Expression Atlas project and the grammar from ggplot2 R library. The code example was easy to run and the necessary input data was intuitive and simple.\u00a0 The author can increase its usage by providing a webtool (even one based on shiny) that takes as input a csv or tsv table with predefined columns. This would allow physicians and scientists with no background in bioinformatics to easily display their data.\u00a0 Also, it would be useful to create an anatogram for the human brain and one for the different compartments of a cell .I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard."}
+{"text": "Breast cancer is a most common type of cancer in women. Circular RNAs (circRNAs) are involved in cancer development and progression, but their roles and regulatory mechanisms are unclear in breast cancer. Our previous study indicated that has_circ_0001667 (circ_0001667) was up-regulated in breast cancer from the array and might play an oncogenic role, however, the roles of circ_0001667 were not known. This study was aimed to investigate the role and the underlying molecular mechanism of circ_0001667 in breast cancer.The real-time PCR result showed that circ_0001667 was overexpressed in breast cancer tissues or cell lines compared to the adjacent normal tissues or normal cells. There was a negative relationship between circ_0001667 levels and the life time of breast cancer patients. Meanwhile, the inhibition of circ_0001667 suppressed the proliferation and metastasis of human breast cancer cells. Further bioinformatical analysis indicated that circ_0001667 sponged miR-125a-5p to regulate TAZ expression by Targetscan and miRanda. Dual luciferase reporter assay and western blotting experiments revealed that circ_0001667 negatively regulated miR-125a-5p expression leading to promoting TAZ expression through Hippo signal pathway in breast cancer cells.This study uncovered that circ_0001667 was a potential breast cancer prognostic marker, as well as a potential therapeutic target to inhibit breast cancer metastasis by circ_0001667/miR-125a-5p/TAZ axis. Breast cancer is by far the most common malignant neoplasm in women worldwide . At the Circular RNAs (circRNAs), a type of non-coding RNA, have no 5\u2032-3\u2032 polarity and polyA tail and play an important role in regulating gene expression through competitive binding miRNAs . Many stThe Hippo pathway, highly conserved from drosophila to mammals, regulates cell proliferation, growth and apoptosis , 9. MoreOur previous study indicated that hsa_circ_0001667 (circ_0001667) was up-regulated in breast cancer from the array and might play an oncogenic role, however, the regulatory roles of circ_0001667 were not known. The study will verify the expression of circ_0001667 and investigate the molecular role and mechanism of circ_0001667 in breast cancer. Our study demonstrated that the circ_0001667 levels was significantly higher in breast cancer tissues than adjacent normal tissues. Conversely, an inverse expression of miR-125a-5p sponged with circ_0001667 was observed. Moreover, we revealed that circ_0001667 inhibition decreased breast cancer cell proliferation and metastasis and activated the Hippo pathway by regulating TAZ.To investigate the role of circ_0001667 in breast cancer, the expression of circ_0001667 in 32 pairs of human breast cancer tissues from the patients were measured. The age range of the patients were 32\u201376 ages. 21 patients were ductal breast cancer and 11 patients were invasive ductal breast cancer without chemotherapy and radiotherapy. Our data showed that circ_0001667 expression was up-regulated significantly in breast cancer tissues when compared to non-tumor tissues  and normal human breast epithelial cells (MCF-10A) were primarily purchased from ATCC. Respectively, MCF-10A was cultured in RPMI1640 growth medium supplemented with 10% fetal bovine serum (Gibco), 50\u00a0U/ml of penicillin and 50\u00a0\u03bcg/ml of streptomycin, EGF and insulin. Other human breast cancer cells were maintained in High-DMEM completed growth medium. All cells were cultured at 37\u00a0\u00b0C and 5% COFor lentivirus and plasmid DNA transfection, cells were transfected with specific shRNA duplexes targeting circ_0001667 construct using Lipofectamine 2000 (Invitrogen) according to the manufacturer\u2019s instruction. To confirm the cellular function of miR-125a-5p, cells was transfected with miR-125a-5p mimics or miR-125a-5p inhibitor using Lipofectamine 2000 (Invitrogen). TAZ siRNAs and its control were obtained from RiboBio  and also transfected to cell using Lipofectamine 2000 (Invitrogen).3 cells were plated per well in 96-well plates with high DMEM completed medium. The absorbance was measured at 450\u00a0nm through microplate reader (BioTek) from 0 to 5\u00a0days. All experiments were repeated at least three times independently.CCK-8  was used to detect the cell proliferation according to the manufacturer\u2019s protocol. About 5\u2009\u00d7\u2009104/well cells were seeded into the upper chamber of transwell filter on a 24-well plate in which the upper chamber medium contained 1% FBS and 10% FBS medium was added to the lower well of the plate [Transwell plates  precoated with Matrigel Basement Membrane Matrix  were used to perform the cell invasion assay (MDA-MB-468 and BT459). 1\u2009\u00d7\u200910he plate . After 7For luciferase reporter assay, circ_0001667 Dual-luciferase vectors (Gene Pharma) were used to construct dual luciferase reporter plasmids. Sequences of miR-125a-5p and circ_0001667 were separately cloned into the vectors. MDA-468 cells were co-transfected with wild-type circ_0001667 or mutated type and miR-125a-5p mimics or negative control using Lipofectamine 2000 (Invitrogen). After induction for 48\u00a0h, luciferase activity was assessed using the dual-luciferase reporter kit . The relative firefly luciferase activity was normalized to Renilla luciferase activity.Total RNA was extracted from cells and tissues using TRIzol regent  according to the manufacturer\u2019s specification. cDNA was reversely transcribed from total RNA by Prime Script TM RT reagent Kit with gDNA Eraser . The qRT-PCR was performed in triplicate using the SYBR- Green PCR Master Mix kit  on a Step One Plus real time PCR system . The analyses were based on the comparative Ct method (2\u2212\u0394\u0394Ct) with GAPDH or 18SRNA as the reference genes for microRNA and other genes, respectively. All the primers used in the study were ordered from Sangon .Cells were lysed in cold RIPA lysis buffer including a protease inhibitor cocktail . The protein concentrations were measured by a bicinchoninic acid (BCA) protein assay kit . Equal protein concentrations were loaded and separated by SDS-PAGE and then electrophoretically transferred to PVDF membranes . After blocking in 5% skimmed milk powder in Tris-based saline with Tween 20 for 1\u00a0h at room temperature, the targeted primary antibodies  were incubated in the PVDF membranes at 4\u00a0\u00b0C overnight. The membranes were further incubated with a secondary antibody for 2\u00a0h, after washed in Tris-based saline with Tween20. At last, the membranes were scanned with Odyssey infrared imaging system. All primary antibodies come from cell signaling technology, USA. The secondary antibodies and B-actin antibodies come from Kangcheng Bio-tech, China.All statistical analysis was performed using the SPSS 15.0 statistical software."}
+{"text": "Three-party authentication key exchange (3PAKE) is a protocol that allows two users to set up a common session key with the help of a trusted remote server, which is effective for secret communication between clients in a large-scale network environment. Since chaotic maps have superior characteristics, researchers have recently presented some of the studies that apply it to authentication key exchange and cryptography. Providing user anonymity in the authentication key exchange is one of the important security requirements to protect users' personal secrets. We analyse Lu et al.'s scheme which attempts to provide user anonymity and we prove that his scheme has errors in the key exchange phase and password change phase. We propose a round-effective three-party authentication key exchange (3PAKE) protocol that provides user anonymity and we analyse its security properties based on BAN logic and AVISPA tool. Along with the rapid development of the information technology and computer network, user authentication plays an important role in protecting resources, service and user\u2019s personal information in the computer network. The authentication key exchange protocol is one of the important mechanisms of network security aimed at setting a session key for secret communication between users via an open network. The authentication key exchange protocol is keys exchange for the secret communication based on authentication between the communicating parties in essence. The authentication key exchange protocol can be classified into Two-Party Authentication Key Exchange (2PAKE), Three-Party Authentication Key Exchange (3PAKE), and Multi-Party Authentication Key Exchange (MPAKE) depending on the number of participating in the key exchange. The key point of the 3PAKE protocol is that it does not need to remember various passwords for each user, and can establish secret communication between users with the help of a trusted remote server.Since the authentication key exchange protocol was proposed by Bellovin and Merritt  in 1992,In 2008, in order to enhance the property of the Chebyshev chaotic maps, Zhang  proved tIn 3PAKE, the authentication server authenticates users and exchanges session key between users. In order for server to authenticate users in the 3PAKE protocol, researchers applied user password scheme , 27, 48,The user password scheme without public key and shared secret key is easily revealed by password guessing attack as the information entropy of the password is low . For exaThe server public key scheme has to construct key management mechanism, so the protocol design is relatively complex and computational complexity is increased. But, using this scheme in the 3PAKE can provide user anonymity by encrypting the message exchanged between the user and the server. In 2014, Xie et al.  proposedIn the shared secret key scheme, the server authenticates users by sharing his secret key with them. This scheme is safer than the password based scheme, because there is no user's private information in the server side. For example, it is resistant to privileged insider attack and stolen verifier attack. Tan  proposedMeanwhile, in order to improve the effectiveness and safety of the authentication, there have been studies to implement the 3PAKE protocol by using devices such as smart cards \u201354. In aThe user\u2019s identifier is a very important personal secret. If user anonymity is not provided, the attacker will know who is currently in the network conversation, and will be able to track the user\u2019s subscription history and current location. Chebyshev chaotic maps based authentication and key exchange scheme is suitable for the authentication system using smart card or the wireless sensor network, which requires low computational cost, simple encryption, small memory size, and low bandwidth. Based on such studies, we analyse the Lu et al.\u2019s scheme  and poinIn Section 2, we describe the theory of chaotic maps, one-way function and Bio-hashing function, and In Section 3 we review Lu et al.\u2019s scheme. Section 4 presents the proposed scheme, and Section 5 describes the security analysis of the proposed scheme. And Section 6 compares the proposed scheme with the previous schemes in terms of performance.This section describes Chebyshev chaotic maps and their computational problems, and Bio-hashing functions.Tn(x) is defined as follows, n\u2208NChebyshev polynomials satisfy the following recursive relationship.Tn(x) = 2x\u00b7Tn-1(x)\u2013Tn-2(x) (n>2),T0(x) = 1, T1(x) = xChebyshev polynomials have the following two properties, 46.n>1, Chebyshev polynomial map Tn(x):\u2192 of degree n is a chaotic map with its invariant density ln(n) > 0.Chaotic property: When r,s\u2208N and any x\u2208,Tr(Ts(x)) = Trs(x) = Ts(Tr(x)).Semi-group property: For The semi-group property holds for Chebyshev polynomials on the interval , which can enhance the property as follows , 43:Tn(x) = 2x\u00b7Tn-1(x)\u2013Tn-2(x) mod p, p is a large prime number),Tr(Ts(x)) \u2261 Trs(x) \u2261 Ts(Tr(x)) mod p .x and y, it is infeasible to find the integer r by any polynomial time bounded algorithm, where y = Tr(x) mod p , computes public key Tk(x) mod p and publishes {p, x, Tk(x) mod p, h(\u2219)}.The server selects random number A submits {IDA, gA = h1 } to S, where rA is random number.User S computes VPWA = h1\u2295gA. Next S randomly chooses a secret key q for A and sends it to A via the secure channel. Note that q is kept securely by A and is different for each user A. Finally, S stores k\u2295q and VPWA into its memory.Upon receiving the registration request, q, user A computes his own version of CA = EKAS, FA) and sends them to S, where KAS = Tq(Tk(x)), FA = h, gA), a \u2208  is a random number.Step 1: Using the stored shared secret key S first derives q by computing k\u2295q\u2295k and derives {IDA, IDB, Ta(x), FA} by decrypting CA with computed symmetric key KAS = Tk(Tq(x)). The next steps are omitted here.Step 2: Once receiving the message, A selects a new password pwdA* and computes RA = ETq(x), h, ZAS), ZAS = h, KAS) and sends them to S.Step 1: S decrypts RA to retrieve {IDA, h, h, ZAS} using the shared secret key q. The next steps are omitted here.Step 2: q is kept securely by A and is different for each user A, and S stores k\u2295q into its memory. Therefore, S must keep k\u2295q for each user and can obtain it by user identifier. In the step2 of session key exchange phase, Lu et al. pointed that S derives q by computing k\u2295q\u2295k and derives {IDA, IDB, Ta(x), FA} by decrypting CA with computed symmetric key KAS = Tk(Tq(x)). In order for S to retrieve k\u2295q of A, the A\u2019s identifier must be present, but A\u2019s message CA is encrypted for providing user anonymity and has not yet been decrypted. Therefore, S cannot know user A\u2019s identifier, and cannot compute q = (k\u2295q)\u2295k. If S stores a single k\u2295q for all users, S can decrypt the A\u2019s message CA as in the protocol. But, in this case, other users can also decrypt A\u2019s message because they also have q, so user anonymity cannot be provided in his scheme.In the registration phase, Lu et al. pointed that S does not obtain the key KSA = Tk(Tq(x)) to decrypt the message RA or cannot update password.In the password change step, the same defects exist as seen in the session key exchange step. That is, This section describes an improved 3PAKE protocol using smart card that overcomes the limitations of the Lu et al.'s scheme. The proposed scheme consists of four steps: system initialization phase, registration phase, authentication and session key exchange phase, and password change phase. The notation presented in S selects a large prime number p and x \u2208 Zp for Chebyshev polynomials Tn(x).S selects secure one-way hash function H(\u2219) and a symmetric encryption/decryption algorithm EK(\u2219)/DK(\u2219).S selects s \u2208  and keeps it as his secret key, and then computes public-key KS = Ts(x) mod p.S publishes {p, x, KS, H(\u2219), EK(\u2219), DK(\u2219)} as system\u2019s parameters.All users who want to exchange session keys using the proposed scheme must register on S.A sends his/her identifier IDA to S via secure channel. S checks whether user A has already been registered, otherwise it computes XA = H(IDA||s) and stores {p, x, XA, KS, H(\u2219), EK(\u2219), DK(\u2219)} in SCA and delivers it to user A via secure channel.User A, which receives SCA from S, inputs password pwA and biometric bmA to access SCA. The SCA that receives the user input computes GA = H(IDA||pwA||h(bmA)) \u2295 XA, FA = H(IDA||pwA|| h(bmA)||XA) and stores {p, x, GA, FA, KS, H(\u2219), EK(\u2219), DK(\u2219)} in his memory.User A connects his smart card SCA to the terminal and inputs his identifier IDA, password and biometrics bmA. SCA computesUser XA* = GA \u2295 H(IDA||pwA||h(bmA)), FA* = H(IDA||pwA||h(bmA)||XA*).FA \u2260 FA*, SCA aborts the process. Otherwise SCA selects any a\u2208  and computesIf KA = Ta(x) mod p, KAS = Ta(KS) = Tas(x) mod p, ZAS = H(IDA||IDB||KA ||XA), MAS = EKAS.A sends M1 = {MAS, KA} to B.MAS, KA} from A, B connects his smart card SCB to the terminal and inputs his identifier IDB, password and biometrics pwB. SCB computesAfter receiving {XB* = GB \u2295H(IDB||pwB||h(bmB)), FB* = H(IDB||pwB||h(bmB)||XB*).FB \u2260 FB*, SCB aborts the process. Otherwise SCB selects any b\u2208  and computesIf KB = Tb(x) mod p, KBS = Tb(KS) = Tbs(x) mod p, KAB = Tb(KA) = Tba(x) mod p,ZBA = H(IDB||KAB), ZBS = H(IDB||KB ||KA||XB), MBS = EKBS.B sends M2 = {MAS, KA, MBS, KB} to S.MAS, KA, MBS, KB} from B, S computesAfter receiving {KAS = Ts(KA) = Tsa(x) mod p, {IDA, IDB*, ZAS*} = DKAS (MAS), XA = H(IDA||s), ZAS = H(IDA||IDB*||KA||XA).S checks whether ZAS and ZAS* are same. If ZAS \u2260 ZAS*, S aborts the process. S also computesKBS = Ts(KB) = Tsb(x) mod p, {IDB, ZBS*, ZBA} = DKBS (MBS), XB = H(IDB||s), ZBS = H(IDB||KB||KA||XB).S checks whether ZBS and ZBS* are same. If ZBS \u2260 ZBS*, S aborts the process. S also checks whether IDB* of A\u2019s message and IDB of B\u2019s message are same. If not, S aborts the process.S computesAfter that, ZSA = H(IDA||IDB||KA||KB||XA), ZSB = H(IDB||IDA||KB||KA||XB), MSA = EKAS, MSB = EKBS.S sends M3 = {MSA, MSB} to A.MSA, MSB} from S, A computesAfter receiving {IDB, KB, ZBA*, ZSA*} = DKAS (MSA), ZSA = H(IDA||IDB||KA||KB||XA).{ZSA \u2260 ZSA*, A aborts the process. A also computesIf KAB = Ta(KB) = Tab(x) mod p, ZBA = H(IDB||KAB).ZBA \u2260 ZBA*, A aborts the process, otherwise A sets KAB as a session key. A also computesIf ZAB = H(IDA||IDB||KAB).A sends M5 = {MSB, ZAB} to B.MSB, ZAB*} from A, B computesAfter receiving {IDA, KA, ZSB*} = DKBS (MSB), ZSB = H(IDB||IDA||KB||KA||XB).{ZSB \u2260 ZSB*, B aborts the process. B also computesIf ZAB = H(IDA||IDB||KAB).ZAB \u2260 ZAB*, B aborts the process. Otherwise B sets KAB as a session key.If A connects his smart card SCA to the terminal and inputs his identifier A, password and biometrics bmA. SCA computes XA = GA \u2295 H(IDA||pwA||h(bmA)) and FA* = H(IDA||pwA||h(bmA)||XA), and checks whether FA and FA* are same. If FA \u2260 FA*, SCA aborts the process. Otherwise SCA requests the user to input a new password newpwA. SCA computes GAnew = H(IDA||newpwA||h(bmA)) \u2295 XA and FAnew = H(IDA||newpwA||h(bmA)||XA), and replaces <GA, FA> of his memory with <GAnew, FAnew>.User In this section, we analyse the security properties of the proposed scheme. First, we prove the correctness of the session key between users by using BAN logic . Next, wP and Q as the specific participators, S is the trusted server, and X is the formula (statement). Some notations and rules of BAN logic are as follows [We define  follows .P |\u2261 X: P believes X.P\u22b2X: P sees X.P |\u223c X: P once said X.P |\u21d2 X: P has jurisdiction over X.X): X is fresh.# and X is fresh, message  is also fresh.P believes Q believes the message set , P also believes Q believes the message X.P believes the message X and Y, P also believes the message set .P believes that the key K is shared with Q and receives a message containing X encrypted under K, then P sees X.The session key exchange protocol should achieve the following goals:We idealize the communication messages of the proposed scheme as follows:The initial assumptions of the proposed scheme are as follows:M3 and A5, we apply the message meaning rule (R1) and the See rule (R7), we can obtain:According to ZSA = H(IDA||IDB||Ta(x)||Tb(x)||XA), A2 and M3, we apply the Freshness rule (R4), we can obtain:According to S1 and S2, we apply the Nonce-verification rule (R2) and Belief rule 1(R5), we can obtain:According to S3 and A7, we apply the Jurisdiction rule (R3), we can obtain:According to S4, A1 and KAB = Ta(Tb(x)) = ), we apply the Belief rule 2(R6), we can obtain:According to M5 and A6, we apply the message meaning rule (R1), we can obtain:According to ZSB = H(IDB||IDA||Tb(x)||Ta(x)||XB), A4 and M5, we apply the Freshness rule(R4), we can obtain:According to S6 and S7, we apply the Nonce-verification rule (R2) and the Belief rule 1(R5), we can obtain:According to S8 and A8, we apply the Jurisdiction rule (R3), we can obtain:According to S9, A3 and KAB = Tb(Ta(x)) = ), we apply the Belief rule 2 (R6), we can obtain:According to M4, S1 and S5, we apply the message meaning rule (R1), we can obtain:According to A2 and KAB = Tb(Ta(x)) = ), we apply the Freshness rule (R4), we can obtain:According to S11 and S12, we apply the Nonce-verification rule (R2), we can obtain:According to M6 and S10, we apply the message meaning rule (R1), we can obtain:According to A4 and KAB = Ta(Tb(x)) = ), we apply the Freshness rule(R4), we can obtain:According to S14 and S15, we apply the Nonce-verification rule (R2), we can obtain:According to In this section, we simulate the proposed scheme for the formal security analysis using AVISPA, which is widely used to verify the security properties of designed protocol such as resistance against replay attack and man-in-the-middle attack. This tool implements four back-ends: On-the-Fly-Model-Check(OFMC), Constraint Logic based Attack Searcher(CL-AtSe), SAT-based Model-Checker(SATMC) and Three Automata based on Automatic Approximations for the Analysis of Security Protocols(TA4SP), which are given in details in . In ordeA, B, and server S. Figs A, B, and server S.In our HLPSL implementation, we define three basic roles for users In In our implementation, we verified the following five secrecy goals and six authentication properties.A's identifier IDA is kept secret to the user A, B and server S only.secrecy_of sec_ida: It represents that user B's identifier IDB is kept secret to the user A, B and server S only.secrecy_of sec_idb: It represents that user A's secret key XA is kept secret to the user A and server S only.secrecy_of sec_xa: It represents that user B's secret key XB is kept secret to the user B and server S only.secrecy_of sec_xb: It represents that user KAB is kept secret to the user A and B only.secrecy_of sec_kab: It represents that session key A receives the messages from server S and decrypts the message with KAS, A authenticates S based on KAS.authentication_on auth_a_s_kas: When user A receives ZBA from the messages from B, A authenticates B based on ZBA.authentication_on auth_a_b_zba: When user B receives the messages from server S and decrypts the message with KBS, B authenticates S based on KBS.authentication_on auth_b_s_kbs: When user B receives ZAB from the messages from A, B authenticates A based on ZAB.authentication_on auth_b_a_zab: When user S receives XA from the messages from A, S authenticates A based on XA.authentication_on auth_s_a_xa: When server S receives XB from the messages from B, S authenticates B based on XB.authentication_on auth_s_b_xb: When server We have simulated the proposed scheme using FMC and CL-AtSe back-ends of AVISPA. The simulation results for the security verification is shown in Figs The results ensure that the proposed scheme is secure under the test of AVISPA using OFMC and CL-AtSe back-ends, and guarantees user anonymity, and it is also secure against the passive attacks and the active attacks, such as the replay attack and man-in-the-middle attack.In this part, we demonstrate the proposed scheme can resist various kinds of attacks.MAS, MBS, MSA and MSB) associated with the user\u2019s identifier is encrypted with the shared secret key KXS between the server S and the user X. The shared secret key KAS is calculated from the random number a of the user A and the secret key s of the server S as follows: KAS = Ta(Ts(x)) = Ts(Ta(x)).The proposed scheme provides user anonymity for key exchange. All message (Ta(x) and Ts(x) is exposed, it is impossible to calculate KAS or a, s according to CDLP and CDHP assumptions. Therefore, a third party cannot know the user\u2019s identifier except user and server.Even if A\u2019s smart card is {GA, FA, p, x, KS, RS, H(\u2219), EK(\u2219), DK(\u2219)}, and the information that can be used for guessing password is GA = H(IDA||pwA||h(bmA))\u2295XA and FA = H(IDA||pwA||h(bmA) ||XA). Suppose that an attacker steals user A\u2019s smart card SCA and knows his identifier IDA. Then the attacker must compute PWA* = H(IDA||pwA*||h(bmA)), XA* = GA \u2295 PWA* and FA* = H(IDA||pwA*||h(bmA)||XA*) by using IDA and any password pwA* to compare FA* and FA stored in SCA. However, PWA* cannot be calculated without knowing h(bmA) which is related A\u2019s biometrics. Therefore, the attacker cannot guess the user\u2019s password.The proposed scheme resists the password guessing attack. The proposed scheme does not use passwords during the authentication process but only uses passwords when accessing the smart card. The information registered on the user The proposed scheme is secure against the privileged-insider attack. In the registration phase of the proposed scheme, only the user\u2019s identifier is transmitted to the server through a secure channel and the user\u2019s password is not transmitted to the server. Therefore, the privilege insider of the server cannot know the user\u2019s password. Therefore, the proposed scheme is secure against this attack.The proposed scheme is secure against stolen verifier attack. In the proposed scheme, there is no user registration table to authenticate user in the server. Therefore, the proposed scheme is secure against stolen verifier attack.The proposed scheme is secure against the user impersonate attack and the forgery attack.A, the attacker C changes KA to KC, and sends a message {MAS* ), KC} to the server. The server receiving the message from attacker C computes KSC from KC and decrypts MAS* using KSC to obtain IDA, IDB and ZAS*. Next, server computes XA = H(IDA||s) and ZAS = H(IDA||IDB||KA||XA), and compares it with ZAS*. Therefore, the attacker has to know XA = H(IDA||s) or s.In order to impersonate as user s is a secret key of the server and XA is a secret data that only user A has, the attacker C cannot know it, and thus the impersonate attack is impossible. Also, even if an attacker attempts to impersonate as the user B, he does not know XB or s, so he cannot achieve the attack as before.However, since C cannot know XA = H(IDA||s), XB = H(IDB||s) or s, so he cannot modify the sender\u2019s message or cannot change KA and KB, and cannot achieve the man-in-the-middle attack.As above, since an attacker C sends the previous message {MAS*, Ta*(x)} of the user A, according to CDLP and CDHP assumptions, he cannot know a*, so he does not calculate ZAB in the fourth message of the proposed scheme.If an attacker C sends the previous message {MBS*, Tb*(x)} of the user B, ZBS* is calculated as ZBS* = H(IDB||RA*||RB*||XB). Since ZBS is related to RA and the server verifies the correctness of ZBS, it is impossible for the attacker C to achieve the replay attack.If an attacker KAB is calculated as KAB = Ta(KB) = Tab(x) mod p. It contains the random numbers a and b that are generated for each session.In the proposed scheme, the session key Therefore, the proposed scheme provides the perfect forward secrecy of session key.KAB is calculated as KAB = Ta(KB) = Tab(x) mod p. It contains the random numbers a and b that are generated for each session. Even if an attacker knows previous session key, he cannot calculate a new session key.In the proposed scheme, the session key This section compares the computational cost and security performance of the proposed scheme with the recent similar 3PAKE techniques , 49, 50,c: time needed for Chebyshev polynomial operationte: time needed for a scalar multiplication on elliptic curvets: time needed for symmetric encryption/decryption operationtm: time needed for a modular squaring operationtq: time needed for a square root modulo N operationth: time needed for one-way hash function operationtAs shown in In this paper, we analyse the Lu et al.\u2019s scheme and point out its weakness, and propose a round-effective 3PAKE protocol based on chaotic maps using smart card to provide with user anonymity. In the proposed scheme, there is no information related to the user\u2019s password at the server side and users share the secret key with the server, which is derived by the server\u2019s secret key and his identifier. The proposed scheme is more efficient than other schemes in terms of number of rounds and computational cost, and it is formally analysed based on BAN logic and AVISPA tool, and can protect against various attacks as shown through informal security analysis. The proposed scheme is suitable for authentication and key agreement in a wireless network environment."}
+{"text": "With recent improvements in human magnetic resonance imaging (MRI) at ultra-high fields, the amount of data collected per subject in a given MRI experiment has increased considerably. Standard image processing packages are often challenged by the size of these data. Dedicated methods are needed to leverage their extraordinary spatial resolution. Here, we introduce a flexible Python toolbox that implements a set of advanced techniques for high-resolution neuroimaging. With these tools, segmentation and laminar analysis of cortical MRI data can be performed at resolutions up to 500 \u03bcm in reasonable times. Comprehensive online documentation makes the toolbox easy to use and install. An extensive developer\u2019s guide encourages contributions from other researchers that will help to accelerate progress in the promising field of high-resolution neuroimaging. This information allows researchers to ask new questions about the human brain. Examples include investigation of intracortical myelin  magnetic resonance imaging (MRI) make it possible to image the entire human brain at an unprecedented level of detail . Submilln , the lan , feedfon  to O(N2). Therefore, a change in spatial resolution from 1\u00a0mm to 0.5\u00a0mm easily entails an increase in computational requirements by a factor of 15 to 60, depending on the methods used. Moreover, new applications such as laminar analysis have only become possible with higher resolutions and are not implemented in many existing software packages.While ultra-high field scanners have become increasingly available and the first open 7T MRI datasets have been released , softwarCBS High-Res Brain Processing Tools (CBS Tools) is a software suite that addresses this gap by providing cutting-edge methods for efficient processing of MR images at submillimeter resolution . For exaMeanwhile, a range of versatile, interoperable open-source packages for the analysis of neuroscientific data has been developed using the increasingly popular programming language Python . For exa1, a new toolbox that makes the quantitative and high-resolution image-processing capabilities of CBS Tools available in Python. Nighres is a user-friendly Python package that interfaces with CBS Tools while avoiding the JIST and MIPAV dependency tree. It facilitates integration with other Python-based neuroimaging tools and interactive data exploration, e.g., in Jupyter notebooks ).Most major neuroimaging packages are optimized for data with a maximum spatial resolution of 1 mm isotropic. Only recently have some extensions and new tools for processing of high-resolution data begun to emerge, which will be discussed in the following section .Freesurfer is a popular open-source package for analyzing cortical surface data , 59. It More generally, Freesurfer\u2019s robustness and ease of use come at the cost of strict requirements for data organization  and limited flexibility in the adaptation of individual processing steps for new applications. While it provides excellent pipelines for standard processing of T1- or T2-weighted whole brain scans, it is not optimized for processing nonstandard data such as quantitative T1 maps or images with partial brain coverage. At the same time, replacing individual processing steps with customized algorithms, combining Freesurfer with other tools, or applying manual corrections can be challenging even for experienced users. Therefore, while Freesurfer will likely play an important role, as ultra-high field imaging becomes more abundant, it currently lacks the flexibility required for the active and collaborative development of new techniques in this emerging field.Another software that has recently extended its functionality to the specific demands of high-resolution image processing is BrainVoyager . A new pLAYNII is a set of highly optimized C++ tools for laminar analysis of high-resolution fMRI data with partial brain coverage ,65. EquiLAYNII is a good example of an advanced toolbox that serves a specific purpose and could benefit from being combined with a more comprehensive and well-documented software framework for high-resolution image processing. It will be crucial in the future to synchronize Nighres with more specialized projects such as LAYNII and make their integration as easy as possible.O(Nlog\u2009N)) or even quadratic (O(N2)) rate with data size, CBS Tools\u2019 algorithms approach linear rates (O(N)) or use noniterative solutions . CBS ToAs described in the introduction, CBS Tools\u2019 complex design and heavy dependencies make installation and handling challenging and impede contributions from other researchers. For a previous project, we presented simple Python wrappers for selected CBS Tools functions . Here, wrecon-all command, probably the most common approach for whole brain tissue classification and cortical surface reconstruction. This command processes a whole brain image at 1\u00a0mm isotropic resolution within a few hours. In comparison, the Nighres pipeline presented above achieves tissue classification and segmentation plus cortical layering at 0.7\u00a0mm isotropic resolution (roughly corresponding to a 3-fold increase in data size compared to 1\u00a0mm isotropic) in less than 15 minutes.We gave an example of Nighres\u2019 performance in the previous section : LinuxProgramming language: Python, JavaOther requirements: Java\u22651.7, Python\u22652.7, Numpy\u22651.13, Nibabel\u22652.1.0License: Apache License 2.0RRID:SCR_016287https://www.nitrc.org/frs/?group_id=1205. Snapshots of the data and code are also available in the GigaScience GigaDB repository [The datasets that support the results of this article are available in the NITRC image repository  under htpository .CGB: cerebrospinal fluid-gray matter boundary; CRUISE: cortical reconstruction using implicit surface evolution; GWB: gray-white matter boundary; MGDM: multiple object geometric deformable model; MPM: quantitative multi-parameter mapping; MP2RAGE: magnetization prepared two rapid acquisition gradient echoes; MRI: magnetic resonance imaging; Nighres: NeuroImaginG at High RESolution; NITRC: the neuroimaging informatics tools and resources clearinghouse; T: Tesla.The authors declare that they have no competing interests.J.M.H. was partially funded by a stipend from Google via the Google Summer of Code 2017 Program, with the International Neuroinformatics Coordinating Facility (INCF) as the mentoring organization.J.M.H., C.J.S., and P.L.B. contributed equally to the conceptualization of the project and writing of the manuscript. J.M.H. led and C.J.S. and P.L.B. supported software development. All authors read and approved the final manuscript.GIGA-D-17-00325_Original_Submission.pdfClick here for additional data file.GIGA-D-17-00325_Revision_1.pdfClick here for additional data file.Response_to_Reviewer_Comments_Original_Submission.pdfClick here for additional data file.Reviewer_1_Report_ -- Laurentius Huber1/8/2018 ReviewedClick here for additional data file.Reviewer_2_Report_ -- Christin Y. Sander, Ph.D.2/15/2018 ReviewedClick here for additional data file."}
+{"text": "Circular RNAs (circRNAs), a new class of non-coding RNAs, have emerged as important regulators during tumorigenesis. However, the functions of circRNAs have not been completely clarified in the progression of cancers. In our study, a novel circRNA hsa_circ_0109291 was investigated in oral squamous cell carcinoma (OSCC) tissues and cell lines. The expression profile of circRNAs in OSCC tumor tissues was performed by high-throughput sequencing. The CCK-8 wound healing and apoptosis assay were measured in OSCC cell lines after transfection with si-0109291 or si-NC. in vitro. In addition, inhibition of hsa_circ_0109291 dramatically induced apoptosis of OSCC cells. We further found that high hsa_circ_0109291 levels in OSCC patients resulted in a poorer prognosis than in patients with low hsa_circ_0109291 levels. We discovered that hsa_circ_0109291 was significantly increased in OSCC tissues and cell lines compared with their corresponding control group. Knockdown of hsa_circ_0109291 inhibited proliferation and migration of OSCC cell lines These findings indicated that hsa_circ_0109291 correlated with the progression of OSCC and might be a new therapeutic target for the treatment of OSCC. Squamous cell carcinoma (OSCC) is one of the frequently occurring malignancies, with approximately 540,000 newly diagnosed cases annually and 5-year survival rate of less than 50% . The majCircular RNAs (circRNAs) as a new class of non-coding RNAs are characterized by a covalently closed continuous loop, without 5\u2019 to 3\u2019 polarity and polyadenylated tail that endow the stable structure for circRNAs . CircRNAIn our study, high-throughput sequencing analysis was carried out to elaborate the differentially expressed circRNAs in OSCC and normal tissues, and the results demonstrated that hsa_circ_0109291 was significantly higher in OSCC tissues than in adjacent normal tissues. In addition, we examined the correlation between hsa_circ_0109291 expression in OSCC tissues and clinical outcomes. Furthermore, the association of hsa_circ_0109291 and cell proliferation, migration, and apoptosis were performed in OSCC cell lines. Patients and specimensFifty-one pairs of OSCC tissues and adjacent normal tissues were collected from the Affiliated Stomatological Hospital of Nanchang University  between January 2014 and June 2017. All of OSCC patients were recruited according to the histopathological evaluation without radiotherapy or chemotherapy before a surgical operation. All OSCC tissues and normal tissues were immediately stored in liquid nitrogen after surgical operation. Written informed consent was obtained from the patients. This study was approved by the Ethics Committee of the Affiliated Stomatological Hospital of Nanchang University .Cell culture2, and 95% air atmosphere in a humidified incubator . Normal human oral keratinocyte (NHOK) and five OSCC cell lines  were purchased from the Cell Bank of Type Culture Collection of Chinese Academy of Sciences . Cells were cultured in Dulbecco\u2019s modified Eagle\u2019s medium  with 5% fetal bovine serum , 5% COHigh-throughput sequencing et. al. methods  was synthesized by GenePharma Co., Ltd.  to inhibit the expression of hsa_circ_0109291 in SCC4 and CAL27 cells. The targeted sequence of the functional si-0109291-1, -2, or -3 were 5\u2019-AATCCCCAGGAGACGTTGACA-3\u2019, 5\u2019-CCCCAGGAGACGTTGACATTT-3\u2019, or 5\u2019-ATGAATCCCC-AGGAGACGTTG-3\u2019, respectively. Three si-RNAs were transfected into SCC4 and CAL27 cells using Lipofectamine 2000 (Invitrogen) according to the manufacturer\u2019s protocol. Finally, si-0109291-1 was selected and was used in proliferation, migration, apoptosis, and apoptosis-related protein assays. All experiments were repeated three times.CCK-8 assay4) was detected using a CCK-8 assay kit . The absorbance was measured at 450 nm using a SpectraMax M5 plate reader . The CCK-8 proliferation assay was performed as previously described  were trypsinized and reseeded in new 6-well plates. With 24 hr incubation, the confluent cells monolayers were scratched with a 10 \u03bcl sterile pipette tip. The wound healing assay was determined as previously described  and preserved at -80 RT-qPCR-\u0394\u0394Ct method was used to calculate the expression of hsa_circ_0109291  was used to synthesize cDNA. Divergent primers were designed and used to measure circRNA expression using an ABI7300 System  with SYBR Select Master Mix (Applied Biosystems). Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) served as an internal control gene to normalize the expression of hsa_circ_0109291. The PCR primers were used as follows: hsa_circ_0109291, forward 5\u2019-TGCTGTCTCTAAGCAAGACCC-3\u2019 and reverse 5\u2019-AGGGTTCAGGCATTCCCACT-3\u2019; GAPDH, forward 5\u2019-GCACCGTCAAGCTGAGAAC-3\u2019, and reverse 5\u2019-TGGTGAAGACGCCAGTGGA-3\u2019. The 2Western blottingTotal protein in SCC4 and CAL27 cells was extracted using RIPA Lysis Buffer . 30 \u03bcg of total protein were separated by 10% SDS-PAGE gel and transferred to nitrocellulose membranes . Primary antibodies Bcl-2  and Bax  were purchased from Santa Cruz Biotechnology . Cleaved-Caspase3  was purchased from Cell Signaling Technology, Inc. . Then, the membranes were incubated with secondary antibody  at room temperature for 2 hr. GAPDH  served as an internal control gene to normalize the protein expression.Statistical analysist-test was used to analyze two-group differences. Inter-group differences were analyzed by one-way analysis of variance, followed by Tukey\u2019s post hoc analysis. Survival analysis was performed using the Kaplan-Meier method. P-values less than 0.05 was considered to indicate a statistically significant difference.Data were analyzed using GraphPad Prism Version 7.0  and presented as the mean \u00b1 standard deviation (SD). Student Expression pattern of circRNAs in OSCCThe expression profiles of circRNAs in 4 pairs of OSCC and corresponding adjacent non-cancerous tissues were detected using circRNAs high-throughput sequencing analysis. Based on FDR \u2264 0.001 and fold change \u2265 2 or fold change \u2264 0.5, the candidate circRNAs were filtered out, and the results demonstrated that 41 circRNAs were selected out, among which 17 circRNAs and 24 circRNAs were up-regulated and down-regulated, respectively . Among tTo evaluate the clinical significance of hsa_circ_0109291 in OSCC patients, we found that hsa_circ_0109291 was positively correlated with the TNM stage in OSCC patients . In addiInhibition of hsa_circ_0109291 suppresses OSCC cells growth in vitroThe expression levels of hsa_circ_0109291 in NHOK and OSCC cell lines  were detected by RT-qPCR. As expected, hsa_circ_0109291 was dramatically up-regulated in all OSCC cell lines as compared with NHOK cells. Intriguingly, hsa_circ_0109291 expression was markedly higher in SCC4 and CAL27 cells than in SCC1, SCC9, or TU183 . Hence, Inhibition of hsa_circ_0109291 suppresses OSCC cells migration and induces apoptosis in vitroThe effect of si-0109291-1 on OSCC cell migration was determined by wound healing assay. Compared with the si-NC group, the wound closing was significantly blunted in si-0109291-1 transfected SCC4  and CAL2The present study showed robust circRNA expression of hsa_circ_0109291 in OSCC, and silencing of hsa_circ_0109291 dramatically inhibited growth and migration and induced apoptosis in OSCC cells. These findings indicated that hsa_circ_0109291 might play an oncogenic role in the tumorigenesis of OSCC. Therefore, we deduced that hsa_circ_0109291 might be a potential therapeutic target for the clinical management of OSCC. CircRNAs as a subset of non-coding RNAs are abundant in human cells and have recently emerged as a novel regulator of gene expression in a variety of cancers, including oral cancer . Based oThe further investigation found that the genomic length of hsa_circ_0109291 is 726 bp, and the spliced length is 226 bp, which is located in chr19:21280990-21281716, and its associated-gene symbol is zinc finger protein 714 . ZNF proteins are a class of transcription factors that regulate multiple genes expression in transcriptional levels . Increasin vivo, due to their resistance to RNase activity (Recent studies suggest that circRNAs can function as potential molecular markers of cancer to support diagnosis, which are more stable than other non-coding RNAs activity , 30. In activity . Overexpactivity . Hsa_ciractivity -42. In oTaken together, both circRNA high-throughput sequencing and RT-qPCR showed hsa_circ_0109291 was markedly increased in OSCC tissues. Hsa_circ_0109291 might exert regulatory functions in OSCC cell growth, migration, and apoptosis indicating that hsa_circ_0109291 might play a crucial role in OSCC tumorigenesis. These findings suggest that hsa_circ_0109291 can serve as a potential therapeutic target for the treatment of OSCC and may be a potential biomarker for OSCC diagnosis and prognosis."}
+{"text": "Massive growth in the amount of research data and computational analysis has led to increased use of pipeline managers in biomedical computational research. However, each of the >100 such managers uses its own way to describe pipelines, leading to difficulty porting workflows to different environments and therefore poor reproducibility of computational studies. For this reason, the Common Workflow Language (CWL) was recently introduced as a specification for platform-independent workflow description, and work began to transition existing pipelines and workflow managers to CWL.Herein, we present CWL-Airflow, a package that adds support for CWL to the Apache Airflow pipeline manager. CWL-Airflow uses CWL version 1.0 specification and can run workflows on stand-alone MacOS/Linux servers, on clusters, or on a variety of cloud platforms. A sample CWL pipeline for processing of chromatin immunoprecipitation sequencing data is provided.https://barski-lab.github.io/cwl-airflow, https://scicrunch.org/resolver/RRID:SCR_017196.CWL-Airflow will provide users with the features of a fully fledged pipeline manager and the ability to execute CWL workflows anywhere Airflow can run\u2014from a laptop to a cluster or cloud environment. CWL-Airflow is available under Apache License, version 2.0 (Apache-2.0), and can be downloaded from  However, of the >100 computational workflow systems used in biomedical research, most define their own specifications for computational pipelines . FurtherAfter version 1.0 of the CWL standard  and the Airflow  is a ligThe CWL-Airflow package extends Airflow's functionality with the ability to parse and execute workflows written with the CWL version 1.0 (v1.0) specification . CWL-AirIn order to run a CWL workflow in Airflow, a file describing the job should be placed in the jobs folder Fig.\u00a0. The jobCWLDAG is a class for combining the tasks into a DAG that reflects the CWL workflow structure. Every CWLStepOperator task corresponds to a workflow step and depends on others on the basis of the workflow step inputs and outputs. This implements dataflow principles and architecture that are missing in Airflow. Additionally, the JobDispatcher and JobCleanup tasks are added to the DAG. JobDisptacher is used to serialize the input parameters from the job file and provide the pipeline with the input data; JobCleanup returns the calculated results to the output folder. When the Airflow scheduler executes the pipeline from the CWLDAG, it runs the workflow with the structure identical to the CWL descriptor file used to create this graph.Although running CWL-Airflow on a single node may be sufficient in most cases, it is worth switching to the multi-node configuration Fig.\u00a0 for compAn example of a CWL-Airflow Celery cluster of 4 nodes is shown in Fig.\u00a0As an example, we used a workflow for basic analysis of chromatin immunoprecipitation sequencing (ChIP-Seq) data Fig.\u00a0 \u00a0. The pipThe CWL-Airflow package includes 2 additional demonstration workflows: (i) an identification of super-enhancers  and (ii)The key promise of CWL is the portability of analyses. Portability refers to the ability to seamlessly run a containerized CWL pipeline developed for one CWL platform on another CWL platform, allowing users to easily share computational workflows. To check whether CWL-Airflow can use pipelines developed by others, we downloaded an alternative workflow for the analysis of ChIP-Seq data developed by the Encyclopedia of DNA Elements (ENCODE) Data Coordination Center , 27 usinTo demonstrate the use of CWL-Airflow in a multi-node configuration, we set up a Celery cluster of 3 nodes with 4 CPUs and 94 GB of RAM each, with each node running an instance of the Airflow Celery worker. Tasks were queued for execution by the Airflow scheduler that was launched on the first node. Communication between the Celery workers was managed by the message queueing service RabbitMQ. RabbitMQ, as well as the Airflow database and web server, were run on the first node. Executing the 2 tested pipelines on the Airflow Celery cluster demonstrated only a slight slowdown on a per-run basis Table\u00a0.CWL-Airflow is one of the first pipeline managers supporting version 1.0 of the CWL standard and provides a robust and user-friendly interface for executing CWL pipelines. Unlike more complicated pipeline managers, the installation of Airflow and the CWL-Airflow extension can be performed with a single pip install command. Compared to the competing pipeline managers, Airflow has multiple advantages Table\u00a0. SpecifiUnlike most of the other workflow managers, Airflow provides a convenient, web-based GUI that allows a user to monitor and control the pipeline execution. Within this web interface, a user can easily track the workflow execution history and collect and visualize statistics from multiple workflow runs. Similar to some of the other pipeline managers, Airflow provides a REST API that allows a user to access its functionality through the dedicated end points. The API can be used by other software to communicate with the Airflow system.Airflow supports parallel workflow step execution. Step parallelization can be convenient when the workflow complexity is not high and the computational resources are not limited. However, when running multiple workflows, especially on a multi-node system, it becomes reasonable to limit parallelism and balance load over the available computing resources. Besides the standard load-balancing algorithms provided by the computing environment, Airflow supports pools and queues that allow for even distribution of tasks among multiple nodes.Addition of the CWL capability to Airflow has made it more convenient for scientific computing, in which the users are more interested in the flow of data than the tasks being executed. Although Airflow itself (and most of the pipeline managers ) only deFurthermore, as one of the most lightweight pipeline managers, Airflow contributes only a small amount of overhead to the overall execution of a computational pipeline Table\u00a0. We beliAGPL: Affero General Public License; ATDP: Average Tag Density Profile; BSD: Berkely Source Distribution; CC-BY-SA: Creative Commons Attribution-Share-Alike; CEBPB: CCAAT/enhancer-binding protein \u03b2; ChIP-Seq: chromatin immunoprecipitation sequencing; CLI: command line interface; CPU: central processing unit; CWL: Common Workflow Language; DAG: directed acyclic graph; ENCODE: Encyclopedia of DNA Elements; GUI: graphical user interface; JSON: JavaScript Object Notation; MACS: Model-based Analysis of ChIP-Seq; MIT: Massachusetts Institute of Technology; RAM: random access memory; REST API: representational state transfer application program interface; RNA-Seq: RNA sequencing.https://barski-lab.github.io/cwl-airflow, http://doi.org/10.5281/zenodo.2852870, and RRID: SCR_01\u00a07196. Snapshots and Research Object bundles from the example workflow are also available in the GigaScience GigaDB repository [https://barski-lab.github.io/cwl-airflow/ Operating system: macOS/Linux Programming language: Python Other requirements: Docker License: Apache license v2.0 (Apache-2.0) RRID: SCR_017196, http://doi.org/10.5281/zenodo.2852870.No new datasets or materials were generated. The source code is available under Apache license v2.0 (Apache-2.0) and can be downloaded from pository . ProjectA.V.K. and A.B. are co-founders of Datirium, LLC. Datirium, LLC, provides bioinformatics software support services.The project was supported in part by the Center for Clinical & Translational Research and Training  and by the NIH NIGMS New Innovator Award to A.B. (DP2GM119134). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.A.V.K. and A.B. conceived the project; A.V.K. and M.K. wrote the software; and M.K., A.V.K., and A.B. wrote and reviewed the manuscript.giz084_GIGA-D-19-00044_Original_SubmissionClick here for additional data file.giz084_GIGA-D-19-00044_Revision_1Click here for additional data file.giz084_GIGA-D-19-00044_Revision_2Click here for additional data file.giz084_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giz084_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giz084_Reviewer_1_Report_Original_SubmissionNiels Drost -- 3/20/2019 ReviewedClick here for additional data file.giz084_Reviewer_2_Report_Original_SubmissionSamuel Lampa, PhD -- 4/4/2019 ReviewedClick here for additional data file.giz084_Reviewer_2_Report_Revision_1Samuel Lampa, PhD -- 6/10/2019 ReviewedClick here for additional data file."}
+{"text": "Interactions between microorganisms during specific steps of anaerobic digestion determine metabolic pathways in bioreactors and consequently the efficiency of fermentation processes. This study focuses on conversion of lactate and acetate to butyrate by bacteria of dark fermentation. The recently recognized flavin-based electron bifurcation as a mode of energy coupling by anaerobes increases our knowledge of anaerobic lactate oxidation and butyrate formation.Clostridium butyricum are able to convert lactate and acetate to butyrate in batch experiments. The ability of C. butyricum to transform lactate and acetate to butyrate was shown for the first time, with ethanol identified as an additional end product of this process. A search for genes encoding EtfAB complexes and their gene neighbourhood in C. butyricum and other bacteria capable of lactate and acetate conversion to butyrate as well as butyrate-producers only and the lactate oxidiser Acetobacterium woodii, revealed that the Etf complexes involved in (i) lactate oxidation and (ii) butyrate synthesis, form separate clusters. There is a more extent similarity between Etf subunits that are involved in lactate oxidation in various species (e.g. A. woodii and C. butyricum) than between the different etf gene products within the same species of butyrate producers. A scheme for the metabolic pathway of lactate and acetate transformation to butyrate in C. butyricum was constructed.Microbial communities from dark fermentation bioreactors or pure culture of Clostridium butyricum suggest that a phenomenon analogous to cross-feeding of lactate in gastrointestinal tract also occurs in hydrogen-yielding reactors. A scheme of lactate and acetate transformation pathway is proposed, based on the example of C. butyricum, which employs flavin-based electron bifurcation. This process utilizes electron-transferring flavoprotein (Etf) complexes specific for (i) lactate oxidation and (ii) butyrate formation. Phylogenetic analysis revealed that such complexes are encoded in the genomes of other bacteria capable of lactate and acetate conversion to butyrate. These findings contribute significantly to our understanding of the metabolic pathways and symbiotic interactions between bacteria during the acidogenic step of anaerobic digestion.Studies on the conversion of lactate and acetate to butyrate by microbial communities from dark fermentation bioreactors or  Bifidobacterium adolescentis and bacteria related to Eubacterium hallii and Anaerostipes caccae [The conversion of lactate and acetate to butyrate (cross-feeding of lactate) is a recognized nutritional interaction between lactate- and acetate-forming bacteria and butyrate producers. This process has been investigated in vitro using co-cultures of bacteria isolated from the human gut: s caccae \u20133.Clostridium and other hydrogen-producing bacteria capable of butyric acid fermentation of carbohydrates [Clostridium acetobutylicum strain P262 [Butyribacterium methylotrophicum [Clostridium diolis [Previously, we postulated that a phenomenon analogous to cross-feeding of lactate in the gastrointestinal tract also occurs in dark fermentation bioreactors, since the ability to produce butyrate from lactate and acetate seems to be shared by members of the genus hydrates . In the ain P262 , Butyribm diolis  can utilm diolis \u201311.Clostridium spp. in the most efficient bioreactors. These finding support our proposal that LAB perform an important function in hydrogen-yielding microbial communities as competitors or stimulators of hydrogen producers, and help to balance specific groups of bacteria in bioreactors [Etchebehere et al.  analysedreactors .Clostridium, e.g. C. butyricum. Biochemistry of butyrate formation is commonly accepted [ox\u2009+\u2009crotonyl-CoA\u2009\u2192\u20092 NAD\u2009+\u2009Fdred\u2009+\u2009butyryl-CoA. Biochemistry of the reaction was described for Clostridium kluyveri [Firmicutes [Firmicutes [Butyric acid fermentation is bacterial fermentation well-recognized for many saccharolytic species of accepted . The keykluyveri  and thenrmicutes , 16. Genrmicutes .Bacteria and Archaea). It involves coupling exergonic and endergonic electron transfer reactions to generate a net exergonic reaction with minimal negative free energy change and maximal energy conservation [Flavin-based electron bifurcation has been recognized as a third mode of energy coupling in anaerobes /electron transferring flavoprotein (EtfAB) complex that catalyzes ferredoxin dependent reduction of NAD by lactate: 2 NAD\u2009+\u2009Fdred\u2009+\u2009lactate\u2009\u2192\u20092NADH\u2009+\u2009Fdox\u2009+\u2009pyruvate [Bacteria [Flavin-based electron bifurcation has also explained biochemistry of lactate oxidation on the example of pyruvate . The genBacteria , 21.According to our thesis the discovery of flavin-based electron bifurcation helps to increase our understanding of metabolic pathways of lactate and acetate transformation to butyrate and fermentation gases that were proposed previously , 5, 6.Bacteria and Archaea. They distinguished five distinct Etf groups named G1-G5. The Etfs involved in butyrate and lactate metabolism are bifurcating enzymes and belong exclusively to group G2. Furthermore, group 2 is divided into two subgroups. Subgroup G2A includes Etfs involved in butyrate metabolism, while subgroup G2B contains those involved in lactate metabolism [Recent studies based on bioinformatic and structural analyses have revealed that Etf enzymes are phylogenetically diverse and widely distributed in the domains tabolism .Clostridium butyricum 2478. Phylogenetic analysis of the EtfAB complexes from Firmicutes species capable of lactate to butyrate transformation and Acetobacterium woodii as a lactate-oxidizer, was performed. An updated scheme of the metabolic pathway of lactate and acetate transformation is proposed.The aim of the study was to confirm that bacteria of dark fermentation are able to convert lactate and acetate to butyrate and to propose an enzymatic machinery involved in this process. The transformation of lactate to butyrate was studied in batch experiments using media containing molasses supplemented with lactate and acetate, or a mixture of lactate and acetate without added carbohydrates, inoculated with samples of microbial communities from dark fermentation bioreactors  or a purClostridium butyricum 2478 .The bacteria used in the tests of lactate and acetate to butyrate conversion were samples of microbial communities from dark fermentation bioreactors described previously  and ClosC. butyricum, clostridial differential medium (CDA) (Sigma Aldrich) was also used. Starting pH of all media was 7.0. Since the M9 medium contains phosphates and possesses buffer properties no additional pH control was used.The liquid growth medium was M9  without 600 nm measurements.All bacterial cultures were grown anaerobically in a Vinyl Anaerobic Chamber  without shaking at 30\u00a0\u00b0C. Bacterial growth was determined by ODTests on the transformation of lactate and acetate to butyrate by the microbial communities from dark fermentation bioreactors were conducted in batch experiments in 250-ml Erlenmayer flasks for 9\u00a0days. At the start of each experiment a single granitic stone, acting as packing material in the packed-bed bioreactor , 25 and C. butyricum to transform lactate and acetate to butyrate, tubes containing 15\u00a0ml of fresh M9 medium plus sodium acetate and sodium lactate without added carbohydrates and supplemented with yeast extract, were inoculated with C. butyricum. These cultures were incubated as described above for 9\u00a0days.To test the ability of All experimental variants are summarized in Table\u00a0The pH of the media and the cultures was measured using a standard pH meter . The concentration of carbohydrates in the molasses-containing media and culture supernatants was analyzed using high performance liquid chromatography (HPLC) with refractometric detection . Short-chain fatty acids were analyzed by HPLC with photometric detection . Ethanol was quantified by gas chromatography with flame-ionization detection . The HPLC conditions used for these analyses were as described previously , 25.Clostridium butyricum KNU-L09  with a custom made database. The etfA (locus_tag: AWO_RS04415) and etfB (AWO_RS0441) genes of Acetobacterium woodii DSM 1030 (NC_016894) [etf sequences from different clostridial species were also used as queries and they gave identical results.Searches using tBLAST  were per_016894)  were use_016894) . The etfA. woodii EtfA and EtfB and additionally with its l-lactate permease (AWO_RS04425), was performed for the genomes of Roseburia intestinalis L1-82 , Eubacterium rectale ATCC 33656 , and Faecalibacterium prausnitzii A2165 (RefSeq: NZ_CP022479) [Similar search, with P022479) .Acetobacterium woodii DSM1030 , Butyribacterium methylotrophicum DSM3468 , Clostridium acetobutylicum ATCC 824 , Clostridium butyricum KNU-L09 , Clostridium diolis NJP7 , Clostridium kluyveri DSM555 , Megasphaera elsdenii DSM20460 .The EtfA and EtfB protein sequences were searched in the genome sequences of the following species: A. woodii DSM 1030 EtfA (AWO_RS04415) and EtfB (AWO_RS04410), with default parameters .A custom BLAST database was prepared from the genome sequences of the above listed species using Geneious software , and tBLThe selected EtfA and EtfB proteins were subjected to phylogenetic analysis. The particular EtfA and EtfB proteins (locus tags in parenthesis) were named Fig.\u00a0 accordinFor EtfA: A_woodii_DSM1030_acyl-CoA (AWO_RS08105), A_woodii_DSM1030_GlcD_LldP (AWO_RS04415), B_methylotrophicum_DSM3468_acyl-CoA (BUME_07090), B_methylotrophicum_DSM3468_GlcD_1 (BUME_04260), B_methylotrophicum_DSM3468_GlcD_2 (BUME_04230), B_methylotrophicum_DSM3468_GlcD_LldP (BUME_24810), C_acetobutylicum_ATTC824_3-hydroxybutyryl-CoA (CA_C2709), C_acetobutylicum_ATTC824_GlcD (CA_C2543), C_butyricum_KNU-L09_3-hydroxybutyryl-CoA (ATN24_RS03165), C_butyricum_KNU-L09_GlcD (ATN24_RS03030), C_butyricum_KNU-L09_LldP_GlcD (ATN24_RS08885), C_diolis_NJP7_3-hydroxybutyryl-CoA (CCS79_RS24290), C_diolis_NJP7_GlcD (CCS79_RS18270), C_diolis_NJP7_LldP_GlcD (CCS79_RS09600), C_kluyveri_DSM555_3-hydroxybutyryl-CoA (CKL_RS02260), C_kluyveri_DSM555_GlcD (CKL_RS17115), M_elsdenii_DSM20460 (MELS_RS10255), and M_elsdenii_DSM20460_acyl-CoA (MELS_RS10960).For EtfB: A_woodii_DSM1030_acyl-CoA (AWO_RS08100), A_woodii_DSM1030_GlcD_LldP (AWO_RS04410), B_methylotrophicum_DSM3468_acyl-CoA (BUME_07100), B_methylotrophicum_DSM3468_GlcD_1 (BUME_04270), B_methylotrophicum_DSM3468_GlcD_2 (BUME_04240), B_methylotrophicum_DSM3468_GlcD_LldP (BUME_24820), C_acetobutylicum_ATTC824_3-hydroxybutyryl-CoA (CA_C2710), C_acetobutylicum_ATTC824_GlcD (CA_C2544), C_butyricum_KNU-L09_3-hydroxybutyryl-CoA (ATN24_RS03160), C_butyricum_KNU-L09_GlcD (ATN24_RS03025), C_butyricum_KNU-L09_LldP_GlcD (ATN24_RS08880), C_diolis_NJP7_3-hydroxybutyryl-CoA (CCS79_RS24285), C_diolis_NJP7_GlcD (CCS79_RS18265), C_diolis_NJP7_LldP_GlcD (CCS79_RS09595), C_kluyveri_DSM555_3-hydroxybutyryl-CoA (CKL_RS02255), C_kluyveri_DSM555_GlcD (CKL_RS17120), M_elsdenii_DSM20460 (MELS_RS10260), and M_elsdenii_DSM20460_acyl-CoA (MELS_RS10965).C. diolis NJP7 EtfB: CCS79_RS24285 is truncated at the N-terminus being found at the edge of the contig. The C. diolis NJP7 EtfA: CCS79_RS18965 and EtfBs: CCS79_RS25810 and CCS79_RS25815 were omitted from the analysis.The Three sets of proteins were subjected to phylogenetic analysis: the EtfA and EtfB proteins, and their concatenated counterparts prepared with a custom Python script. Protein sets were aligned using ClustalW  with defA critical review of studies on hydrogen production during the acidic step of anaerobic digestion led us to postulate that a phenomenon analogous to cross-feeding of lactate in the gastrointestinal tract occurs in dark fermentation bioreactors , 33\u201339. C. butyricum, summarized in Table\u00a0600nm and the pH inside the flasks during fermentation process. Figure\u00a0Here, we present the results of three series of batch experiments focused on the conversion of lactate and acetate to butyrate by microbial community from dark fermentation bioreactor and a pure culture of Butyrate is a typical product of hydrogen-yielding saccharolytic clostridial-type fermentation. Thus, butyrate was an abundant non-gaseous fermentation product when molasses was a component of the medium processed by the microbial community from dark fermentation bioreactors in the first series of experiments. Lactate was also found as a fermentation product. It is noteworthy that lactate, butyrate and acetate were also detected as components of the starting molasses-containing medium Fig.\u00a0a. When tClostridium propionicum [In the next experimental approach, the medium contained only sodium lactate and sodium acetate as carbon sources. The 77\u201394% of lactate was used by microbial communities. The main components of the post-culture fluids were butyrate and acetate Fig.\u00a0b. These pionicum  in theseC. butyricum on medium containing lactate and acetate supplemented with yeast extract. The results showed for the first time that, in the absence of carbohydrates, C. butyricum, similarly to other representatives of the Firmicutes  both lactate and acetate are required for bacterial growth; (ii) lactate cannot be transformed to propionate as in the case of C. propionicum [The final series of experiments examined the growth of a pure culture of utylicum , Butyribrophicum , C. diol. diolis ), utiliz.7 Table\u00a0. After tted Fig.\u00a0c. It shopionicum .C. butyricum experiments was based on the following reasoning involving concentration of acetate, lactate, propionate, butyrate and ethanol in the medium and the post-cultured fluids:The approximate balance of carbon in millimoles for the It was assumed that the excess of acetate in the medium and the yeast extract-derived butyrate and propionate were not metabolized, thus the approximate balance of carbon was as follows:C. butyricum; see the section on the enzymatic machinery of lactate and acetate transformation to butyrate.The fermentation balance was further used for the proposed scheme of lactate and acetate conversion to butyrate in C. butyricum is able to convert lactate and acetate to butyrate we selected the genome of C. butyricum KNU-L09 (completed genome) for the presence of sequences encoding EtfAB complexes. BLAST searches revealed the existence of three gene clusters for EtfA/B complexes in the genome of C. butyricum KNU-L09 , and l-lactate permease and acyl-CoA dehydrogenase, respectively. The C. butyricum KNU-L09 genome encodes one other FAD-binding oxidoreductase with potential lactate dehydrogenase activity, denoted as cluster 4 in Fig.\u00a0The mechanism of transformation of lactate and acetate to butyrate proposed for gastrointestinal tract bacteria  and bactL09 Fig.\u00a0, all witL09 Fig.\u00a0 compriseL09 Fig.\u00a0 contain Roseburia intestinalis L1-82, Eubacterium rectale ATCC 33656, and Faecalibacterium prausnitzii A2165 recognized as butyrate producers but incapable of lactate oxidation [etfA and etfB genes with acyl-CoA and butyryl-CoA dehydrogenases encoding genes was found  or able to synthesize butyrate . The genomes of the analysed species capable of transforming lactate into butyrate encode at least two different EtfA/EtfB proteins and these genes are found in different genetic contexts (data not shown), similarly to those shown for C butyricum in Fig.\u00a0l-lactate permease and lactate oxidase, or (ii) 3-hydroxybutyryl-CoA dehydrogenase. As shown in Fig.\u00a0C. butyricum KNU-L09 EtfA/B proteins with coding sequences in the vicinity of LldP and GlcD genes, and those of B. methylotrophicum DSM3468 encoded in the vicinity of the GlcD gene, appear to form outgroups. Topologies with the same tendencies were obtained for trees of the EtfA only, EtfB only, and concatenated EtfA and EtfB proteins  [Briefly, a FAD-dependent lactate dehydrogenase LDH, in a stable complex with an electron transfer flavoprotein (EtfA/B), catalyzes endergonic lactate oxidation using NADntation) . Pyruvatntation) , 5\u20137, 40The findings of this study have increased our understanding of metabolic pathways and the symbiotic relationships between bacteria during acidogenesis.C. butyricum was used as a new model to study the transformation of lactate and acetate to butyrate. Notably, ethanol was found among the non-gaseous fermentation products. The identification of etfA/B genes in the genomes of C. butyricum and other species capable of lactate and acetate to butyrate conversion indicates the reaction-specificity of different Etf complexes. Finally, we propose a metabolic pathway of lactate and acetate transformation by C. butyricum.The results of this study have confirmed that lactate and acetate is converted to butyrate by microbial communities from dark fermentation bioreactors."}
+{"text": "Tapeworms lack a canonical piRNA-pathway, raising the question of how they can silence existing mobile genetic elements (MGE). Investigation towards the underlying mechanisms requires information on tapeworm transposons which is, however, presently scarce.Echinococcus multilocularis genome to calculate expression levels of densovirus-related genes. Transcription of densovirus loci was further analyzed by sequencing and RT-qPCR.The presence of densovirus-related sequences in tapeworm genomes was studied by bioinformatic approaches. Available RNA-Seq datasets were mapped against the E. multilocularis we identified more than 20 individual densovirus integration loci which contain the information for non-structural and structural virus proteins. The majority of densovirus loci are present as head-to-tail concatemers in isolated repeat containing regions of the genome. In some cases, unique densovirus loci have integrated close to histone gene clusters. We show that some of the densovirus loci of E. multilocularis are actively transcribed, whereas the majority are transcriptionally silent. RT-qPCR data further indicate that densovirus expression mainly occurs in the E. multilocularis stem cell population, which probably forms the germline of this organism. Sequences similar to the non-structural densovirus genes present in E. multilocularis were also identified in the genomes of E. canadensis, E. granulosus, Hydatigera taeniaeformis, Hymenolepis diminuta, Hymenolepis microstoma, Hymenolepis nana, Taenia asiatica, Taenia multiceps, Taenia saginata and Taenia solium.We herein provide evidence for the presence of densovirus-related elements in a variety of tapeworm genomes. In the high-quality genome of E. multilocularis, our data are relevant for future studies into gene silencing mechanisms in tapeworms. Furthermore, they indicate that densovirus-based vectors might be suitable tools for genetic manipulation of cestodes.Our data indicate that densovirus integration has occurred in many tapeworm species. This is the first report on widespread integration of DNA viruses into cestode genomes. Since only few densovirus integration sites were transcriptionally active in  Echinococcus multilocularis (fox tapeworm), E. granulosus (dog tapeworm), and Taenia solium (pork tapeworm), are of particular medical and veterinary interest since their larval stages reside within the inner organs of humans and livestock animals, thus causing the diseases alveolar echinococcosis, cystic echinococcosis, and cysticercosis/neurocysticercosis, respectively [Tapeworms (cestodes) form a group of highly specialized, obligate endoparasites that display extreme features of adaptation to their hosts such as the complete loss of a gut and a highly modified, segmented, body plan . The strectively , 3. The ectively \u20136, with ectively . In geneectively .E. multilocularis serving as a high-resolution reference [Hymenolepis diminuta and a number of additional model cestodes such as Schistocephalus solidus and Mesocestoides corti are currently being genomically and transcriptomically characterized [piwi and vasa orthologues [Towards a closer understanding of cestode biology, we and others have previously characterized the genomes of several cestode species, with the genome of eference , 9. As acterized . A strikhologues , 11, indhologues  and is chologues . This rahologues , 15, inahologues , and stehologues ; howeverParvoviridae contains the two subfamilies, Parvovirinae and Densovirinae, which infect vertebrates and invertebrates, respectively [The virus family ectively . All parectively . Best stectively . The traectively , 22. Altectively . Interesectively \u201326.E. multilocularis, we herein carried out analyses on the presence of respective genes in the genome of this and other tapeworms. We provide evidence for densovirus sequences within the genomes of Dibothriocephalus latus, Echinococcus canadensis, E. granulosus, E. multilocularis, Hydatigera taeniaeformis, Hymenolepis diminuta, H. microstoma, H. nana, Mesocestoides corti, Schistocephalus solidus, Spirometra erinaceieuropaei, Taenia asiatica, T. multiceps, T. saginata, T. solium and Schistosoma mansoni. We also show that some of the integrated virus sequences are transcriptionally active in the E. multilocularis germinative cell population, which are mitotically active, pluripotent somatic stem cells that most probably form the germline of this organism. The majority of densovirus integration loci, however, is transcriptionally silenced. Our results are discussed in the background of future studies concerning gene silencing mechanisms in cestodes and the possible utilization of densovirus vectors for the development of transgenic methodology in these organisms.Apart from two anecdotal reports on the presence of parvovirus-like sequences in planarian and trematode genome assemblies , 28, no E. multilocularis genome, we found the gene EmuJ_000388600, annotated as \u2018non-capsid protein NS1\u2019, which we analyzed further. Protein sequences for EmuJ_000388600 and the downstream open reading frame EmuJ_000388500  searches against the protein predictions of E. multilocularis  were curated individually, determining start and stop positions for the gene copies as well as their completeness. Frame shift mutations were identified by analysis of open reading frames (ORFs) using BioEdit six-frame translation [EmuDNV-NS1 was detected. This ORF was presumed to be coding for a capsid protein (VP) and therefore designated EmuDNV-VP. The longest ORFs were used as query for BLASTN  searches against the E. multilocularis genome to find additional gene copies. Detected EmuDNV-VP gene copies were curated individually as described for EmuDNV-NS1 and frameshift mutations were analyzed.When mining the database WormBaseParaSite WBPS 10 \u201331 for v WBPS 10 \u201331) were WBPS 10 . A multi WBPS 10 , 34. To  WBPS 14 \u201331) usin WBPS 14 \u201331). Nonnslation . In manyEmuDNV-NS1 and EmuDNV-VP. Protein sequences were also used for BLASTP  searches against the SwissProt/UniProt database and non-redundant protein sequences (nr) database (organism viruses) at NCBI.Protein structure analyses were performed with pfam   using trEmuDNV-NS1 nucleotide sequences together with 5000\u00a0bp flanking regions on both sides as input. To also discover remnants of ITRs nearby densovirus genes, local BLASTN  searches against the E. multilocularis genome were performed with the longest identified ITR sequence. Densovirus loci were assessed by their genomic location using the genome browser Ensemble at WormBaseParaSite (WBPS10) [Penaeus stylirostris densovirus [EmuDNV-NS1 and EmuDNV-VP by individual inspection of their upstream regions. Alignment of promotor regions was performed with MUSCLE (4 iterations) [Inverted terminal repeats (ITRs) were identified with the computer program \u201ceinverted\u201d   using Em(WBPS10) \u201331. Prevnsovirus . We deterations) , 34.E. multilocularis genome  te WBPS7 . To discte WBPS7 . ExpressDibothriocephalus latus (D_latum_Geneva_0011_upd) [Echinococcus canadensis (ECANG7) [E. granulosus (EGRAN001 and ASM52419v1) [E. multilocularis (EMULTI002) [Hydatigera taeniaeformis (H_taeniaeformis_Canary_Islands_0011_upd) [Hymenolepis diminuta (H_diminuta_Denmark_0011_upd) [Hymenolepis microstoma (HMN_v3) [Hymenolepis nana (H_nana_Japan_0011_upd) [Mesocestoides corti (M_corti_Specht_Voge_0011_upd) [Schistocephalus solidus (S_solidus_NST_G2_0011_upd) [Spirometra erinaceieuropaei (S_erinaceieuropaei) [Taenia asiatica (Taenia_asiatica_TASYD01_v1 and T_asiatica_South_Korea_0011_upd) [Taenia multiceps (ASM192302v3) [Taenia saginata (ASM169307v2) [Taenia solium (Tsolium_Mexico_v1) [S. mansoni (Smansoni_v7) [To identify putative densovirus non capsid protein 1 gene sequences in other cestode genomes, we searched the genomes of 011_upd) , Echinoc(ECANG7) , E. gran52419v1) , 9, E. mULTI002) , Hydatig011_upd) , Hymenol011_upd) , Hymenol(HMN_v3) , Hymenol011_upd) , Mesoces011_upd) , Schisto011_upd) , Spirome011_upd) , 42, Tae92302v3) , Taenia xico_v1) , and as soni_v7) , 45 (dowsoni_v7) \u201331) by lsoni_v7) , 34, 46.soni_v7)  using 10soni_v7)  and pairMeriones unguiculatus) by serial peritoneal passage as previously described [Parasite material was maintained in Mongolian jirds  for 7\u00a0days as described previously [eviously . Subsequeviously . Fluoreseviously . SamplesFeeder cell-free primary cell cultures were set up and cultivated for 2\u00a0days essentially as described previously , 50. Prig for 1\u00a0min. PBS was removed and the material was resuspended in 500\u00a0\u00b5l (cells) or 1\u00a0ml (vesicles) Trizol\u00ae Reagent , vortexed briefly and incubated at room temperature for 5\u00a0min. RNA extraction was performed using Direct-zol\u2122 RNA MiniPrep  according the manufacturer\u2019s instructions (including DNase treatment).Metacestode vesicles from HU treatment  were opeg. The supernatant was removed, and the pellet was re-suspended in lysis buffer , 50\u00a0mM EDTA (pH 8.0), 0.5% SDS, 20\u00a0\u03bcg/ml RNase A, 0.1\u00a0mg/ml Proteinase K, 1.2\u00a0ml/100 mg pellet). After overnight incubation at 50\u00a0\u00b0C, a standard phenol-chloroform extraction was carried out, followed by an ethanol precipitation.Vesicles from feeder cell-free metacestode cultures were disrupted by pipetting, washed with PBS and centrifuged for 10\u00a0min at 5000\u00d722VX-3\u2032) or a combination of the Oligo-dT primer and a random octamer primer. An RT-neg control (no reverse transcriptase) was included for all samples.Reverse transcription was performed with Omniscript\u00ae RT Kit  or SuperScript\u00aeIII Reverse Transcriptase  according to the manufacturers\u02bc instructions using an Oligo-dT primer  with the primers 5\u2032-GGC GTT CCA CTA CAA G-3\u2032 and 5\u2032-GCC AAC AAT TCA TAA ATG G-3\u2032. RT-neg and gDNA controls were included. PCR products from cDNA were cloned into pJet1.2 using CloneJETTM PCR Cloning Kit  and sequenced. The sequence of EmuDNV-NS1 was deposited at the EMBL Nucleotide Sequence Database under the accession number LR029140. To confirm the genome assembly at densovirus integration sites we performed PCR analysis and sequencing choosing primers annealing to an EmuDNV-NS1 gene version and to a neighboring tapeworm gene with annotated function. PCR was performed on gDNA using Taq-Polymerase  with the primers 5\u2032-GAT AGT CTG CCA TTA GGC-3\u2032 and 5\u2032-GGA AAC CTC CTC CGA CA-3\u2032 for EmuJ_000013900; 5\u2032-GCT TAT TCA TTC TGC GGT TTT-3\u2032 and 5\u2032-GAT AGT TTG TTC CAC CAT TGA-3\u2032 for EmuJ_002195700; 5\u2032-GAT TTC ATT GGC TGA AAA CAT-3\u2032 and 5\u2032-GGT GCT TTT TCA TAT TCT CGT-3\u2032 for EmuJ_000388600; and 5\u2032-GGC TCG AGG AAG GTA GTT GTC-3\u2032 and 5\u2032-GGC TCA ACA ACC GAC GTA AT-3\u2032 for EmuJ_000329200. PCR products were cloned into pDrive Cloning Vector using the QIAGEN\u00ae PCR Cloning Kit and sequenced.For the amplification of EmuDNV-NS1 were based on the sequences of the gene versions EmuJ_000034800, EmuJ_000388600 and EmuJ_000329200: 5\u2032-CAA CCA GCA GGA TCT CAA GCA-3\u2032 and 5\u2032-CAT CTA CCC TCT ATG GCG GCT-3\u2032. As the primers did not span an intron, RT-neg controls were used. emelp served as reference gene (primers: 5\u2032-TGA TGA AAG TGA AGC CAA GGA ACT TGA G-3\u2032 and 5\u2032-TTC GTC TGG AGC GTC TCA TTC TTA GAG-5\u2032). The following reaction mixture was used: 2\u00a0\u00b5l of 1:5 diluted cDNA (or RT-neg), 200\u00a0nM each primer (300\u00a0nM for emelp) and the HOT FIREPol\u00aeEvaGreen\u00ae qPCR Mix (ROX) ; with the following program: 15\u00a0min at 95\u00a0\u00b0C, 40 cycles of: 15\u00a0s at 95\u00a0\u00b0C, 20\u00a0s at 60\u00a0\u00b0C, 20\u00a0s at 72\u00a0\u00b0C; fluorescence measurement at 72\u00a0\u00b0C. Amplification product specificity was assessed by melting curve analysis and sequencing of the PCR-products. Experiment was performed with three technical and three biological replicates. The efficiency of the amplification was computed with linREG [Quantitative real-time PCR was performed with StepOnePlus Real-Time PCR-Systems . Primers for h linREG , 53. Forh linREG . The perh linREG  with tecE. multilocularis metacestode stage is crucially driven by a population of pluripotent stem cells, called germinative cells, which are the only mitotically active cells in the metacestode [E. multilocularis [E. multilocularis genome we mined the database WormBaseParaSite WBPS 10 [E. multilocularis genome and designated the gene EmuDNV-NS1. Immediately downstream of EmuDNV-NS1 we identified another reading frame (EmuJ_000388500) encoding a protein with weak homologies  to the minor component of the viral capsid of the Pea enation mosaic virus, which further supported that we had identified a densovirus integration locus.We previously established that growth and proliferation of the acestode  We also ocularis . In orde WBPS 10 \u201331 and p WBPS 10  for geneE. multilocularis genome for further densovirus integration events and identified a total of 26 loci with high similarity to EmuDNV-NS1. All these putative densovirus gene sequences were curated individually and translated into amino acid sequences. BLASTP analyses of the predicted amino acid sequences indicated that all sequences referred to genes encoding full-length or truncated versions of EmuDNV-NS1. The longest versions of EmuDNV-NS1 (431 amino acids) were encoded by loci on the contigs 0155 (EmuJ_000368400), 0221 (EmuJ_000048100), 0266 (EmuJ_000369300 and EmuJ_000368900) and 0868 (EmuJ_000007400)  and the Aedes albopictus densovirus (24%/43%). In BLASTP searches against the nr database (organism: viruses), high overall homologies (26%/43%) were also found between EmuDNV-NS1 and the Non-structural protein 1 of the Infectious hypodermal and hematopoietic necrosis virus (IHHNV), which has been isolated from the blue shrimp, Penaeus stylirostris [Protein structure analyses of non-truncated versions revealed that in all cases a PPV_E1_C domain and an overlapping Parvo_NS1 domain were present at the C-terminus of the protein, whereas no clear protein domains were predicted within the N-terminal portions. We thus concluded that the predicted irostris .EmuDNV-NS1. We found an ORF 67 nucleotides downstream of many EmuDNV-NS1 gene copies encoding a 321 amino acid protein which we designated EmuDNV-VP. By BLAST searches we detected 26 versions of EmuDNV-VP, 13 of which were full-length  for the gene encoding the structural proteins of the capsid (VP), we performed BioEdit six-frame translations of neighboring regions of EmuDNV reading frames for inverted terminal repeats (ITRs), we detected ITR sequences of different length, with the longest sequence being located 37 nucleotides downstream of the EmuDNV-VP gene version EmuJ_000329300. This ITR sequence was 370\u00a0nt long, with a 165\u00a0nt stem (89% matches) and a 37\u00a0nt loop. BLAST searches revealed that the other identified ITR sequences were shorter, slightly different versions of the same sequence. Additionally, remnants of ITR sequences were detected near several virus genes .When searching neighboring regions of the EmuDNV-NS1 gene version and to a neighboring tapeworm gene, encoding a solute carrier in case of EmuJ_000013900 and EmuJ_000388600 as well as a transcriptional corepressor of histone genes in case of EmuJ_002195700 and EmuJ_000329200   or contained N-terminal frameshift mutations (EmuJ_000034800 and EmuJ_000388600). Of the expressed EmuDNV-VP versions, one was a full-length version (EmuJ_000034900) and one had an N-terminal frameshift mutation (EmuJ_000388500). These data indicated that the majority of EmuDNV loci were transcriptionally silenced.We detected putative TATA boxes and additional potential promoter elements upstream of all EmuDNV-NS1 was amplified from cDNA of 2-day-old E. multilocularis primary cell preparations using primers binding to four EmuDNV-NS1 gene versions without mismatches  and to further 8 gene versions with mismatches (0\u20135 mismatches per primer). As expected, no PCR products were obtained from RT-negative cDNA preparations. For RT-positive cDNA preparations, on the other hand, a clear band of the expected size (c.1100\u00a0bp) was obtained and cloned. Eight of the obtained clones were analyzed and six of them yielded identical sequences. The other 2 sequences differed in only 1 nucleotide from the 6 sequences and were considered variations of the same sequence. The 1103\u00a0bp long partial sequence (deposited at the EMBL Nucleotide Sequence Database under the accession number LR029140) showed 99.8% homologies (2 mismatches) to the EmuDNV-NS1 version EmuJ_000388600 whereas at least 16 mismatches were observed to all other DNV-NS1 loci on the genome. We therefore concluded that the obtained sequence originated from the EmuDNV-NS1 version EmuJ_000388600, confirming gene expression of EmuDNV-NS1 in E. multilocularis and indicating that the gene versions EmuJ_000034800, EmuJ_002195700, EmuJ_000329200 are not or very lowly expressed.To verify the transcriptomic data by RT-PCR, EmuDNV genes showed a transcription profile typical of germinative cell-specifically expressed genes with high expression in E. multilocularis primary cell preparations  on vesicles without or with germinative cells. As shown in Fig.\u00a0EmuDNV-NS1 was significantly reduced in vesicles after treatment with HU, indicating that densovirus genes are specifically or at least preferentially expressed in the parasite\u2019s germinative cell population.According to RNA-Seq data, all  content ) and lowtroduced . To thisD. latus, E. canadensis, E. granulosus, H. taeniaeformis, H. diminuta, H. microstoma, H. nana, M. corti, S. solidus, S. erinaceieuropaei, T. asiatica, T. multiceps, T. saginata and T. solium alongside with E. multilocularis, and included S. mansoni as a trematode example , E. canadensis (n\u2009=\u200924), E. multilocularis (n\u2009=\u200923) and T. asiatica (PRJNA299871) (n\u2009=\u200923). Further sequences were detected in the genomes of T. multiceps (n\u2009=\u200921), H. microstoma (n\u2009=\u200919), H. nana (n\u2009=\u200917), T. asiatica (PRJEB532) (n\u2009=\u200912), T. saginata (n\u2009=\u200912), E. granulosus (n = 6 each in PRJEB121 and PRJNA182977), H. taeniaeformis (n\u2009=\u20094), T. solium (4) and S. mansoni (n\u2009=\u20093)  when compared to free-living flatworm species and all other animals is the absence of true orthologues of the common stem cell markers and vasa , 11, 57,and vasa  and are and vasa .Circumstand vasa , 59. Thinsposons , 16. Furnsposons . Hence, In the present work, we provide evidence for the presence of densovirus genes in the genomes of cestodes. The elements we identified displayed clear structural homologies to parvo- and densovirus elements found in other organisms such as reading frames encoding proteins with similarity to non-structural (NS1) and virus capsid proteins which are flanked by ITR. The presence of densovirus sequences in the vicinity of histone clusters, together with confirmation of the genome assembly at selected integration sites by PCR analysis, clearly indicate true integration events during cestode genome evolution. The presence of densovirus-related sequences in 13 of 17 analyzed cestode genomes indicates widespread endogenization of densoviruses in cestodes. Strongly varying numbers of densoviral sequences detected in the analyzed species might not correspond to different numbers of integration events, but could be caused by the different qualities of the genome assemblies. Many identified densoviral sequences are located on small contigs or near repetitive sequences, such as histone clusters. As repetitive sequences are generally difficult to assemble and often collapsed in the genome assembly, it is likely that the number of detected densoviral sequences is influenced by the quality of the genome assembly and the real number of sequences in the genome and might be higher. Additionally, densovirus sequences could appear to be truncated because the contig does not continue at this position which would lead to an underestimation of the number of complete densoviral sequences.EmuDNV-NS1 version EmuJ_000388600. In contrast, we did not obtain sequences for three other EmuDNV-NS1 versions with equal primer binding properties suggesting that they are not or relatively lowly expressed. This is in accordance with the transcriptome data that show no expression for two of them and comparatively low expression levels for the third. The presence of intact promotor elements together with apparent silencing of most densovirus loci indicates a specific silencing mechanism. We propose that epigenetic silencing might be the underlying mechanism. DNA methylation was recently detected in cestodes [Although all densoviral genes with a complete 5\u2032-end have intact promotor elements, the majority of them appear to be transcriptionally silent. According to transcriptome data only three densovirus loci are transcriptionally active. RT-PCR confirms expression of the cestodes . FurtherE. multilocularis genome after the separation of taeniid cestode species or earlier, our phylogenetic analyses nevertheless indicate that densoviruses were still actively spreading after the separation of E. multilocularis and E. granulosus. To address the question if densoviruses in cestodes are still able to replicate and spread, we examined if densoviral genes are expressed in germinative cells of E. multilocularis. Transcriptome data and qRT-PCR strongly indicate specific or preferential expression in germinative cells which provides an explanation for maintenance of densoviral sequences in the parasite\u02bcs germline-like cell population. It is thus likely that the other cestodes also express densoviral genes in their germinative cells. Parvoviral NS1 activities, such as endonuclease and helicase activity, are required for parvoviral DNA replication [EmuDNV-NS1 gene versions contain a complete and intact N-terminal domain without truncation or frameshift mutation suggesting that no active NS1 protein is available for densovirus replication in E. multilocularis. It is therefore questionable whether contemporary horizontal transmission events of endogenous densoviruses are possible in cestodes.Phylogenetic analysis of NS1 sequences in cestodes indicates a spread of densoviral sequences within species. Although the current cestode genome assemblies did not allow us to specifically determine whether a given densovirus locus has integrated into the lication , 63. HowE. multilocularis in the near future. Experiments towards this aim are currently underway.Interestingly, densovirus-based vectors have already successfully been used for genetic manipulation of insect cells and mosquitoes , 26. TheE. multilocularis, whereas most remain transcriptionally silent. Further study of active and silent elements will provide first clues for transposon silencing mechanisms in E. multilocularis and other cestodes. Our results further point to the possibility of utilizing densovirus-based vectors for genetic manipulation of E. multilocularis and other cestodes.Although tapeworms lack a canonical piRNA-pathway, their germline has to be protected against the activities of transposons in their genomes. Investigating possible transposon silencing mechanisms first requires comprehensive information on mobile genetic elements in these organisms. The data presented herein show integration of densovirus-related elements in a large number of tapeworm species. Transcriptome data and RT-PCR further indicates active transcription of some densovirus gene versions in Additional file 1: Table S1. Overview of analyzed tapeworm genomes. Table S2. Densovirus sequences in E. multilocularis. Table S3. Densovirus NS1 gene sequences in tapeworm genomes.Additional file 2: Figure S1. Schematic overview of the bioinformatics workflow.Additional file 3: Figure S2. Alignment of densovirus NS1 sequences.Additional file 4: Figure S3. Densovirus integration sites in the E. multilocularis genome.Additional file 5: Figure S4. Promotor regions of EmuDNV-NS1."}
+{"text": "Paracoccus inhabit various pristine and anthropologically-shaped environments. Many Paracoccus spp. have biotechnological value and several are opportunistic human pathogens. Despite extensive knowledge of their metabolic potential and genome architecture, little is known about viruses of Paracoccus spp. So far, only three active phages infecting these bacteria have been identified. In this study, 16 Paracoccus strains were screened for the presence of active temperate phages, which resulted in the identification of five novel viruses. Mitomycin C-induced prophages were isolated, visualized and their genomes sequenced and thoroughly analyzed, including functional validation of their toxin-antitoxin systems. This led to the identification of the first active Myoviridae phage in Paracoccus spp. and four novel Siphoviridae phages. In addition, another 53 prophages were distinguished in silico within genomic sequences of Paracoccus spp. available in public databases. Thus, the Paracoccus virome was defined as being composed of 66 (pro)phages. Comparative analyses revealed the diversity and mosaicism of the (pro)phage genomes. Moreover, similarity networking analysis highlighted the uniqueness of Paracoccus (pro)phages among known bacterial viruses.Bacteria of the genus  Paracoccus spp. (Alphaproteobacteria) are metabolically versatile bacteria, that have been isolated from a wide range of environments in various geographical locations, e.g.: biofilters for the treatment of waste gases from an animal rendering plant in Germany , contaminated soil in Japan (P. aminophilus JCM 7686 and P. aminovorans JCM 7685), rhizospheric soil of an Indian tropical leguminous plant , sea water from South Korea (P. haeundaensis LGM P-21903), marine sediments of the South China Sea  and marine bryozoan Bugula plumosa from North Sea in Germany (P. seriniphilus DSM 14827)7. Some Paracoccus spp. have also been recognized as causative agents of human disease8. The metabolic flexibility of Paracoccus spp. relies mostly on the wide variety of respiratory processes employed by these bacteria, including the usage of nitrate, nitrite, nitrous oxide and nitric oxide as alternative electron acceptors in denitrification, and the ability to use substrates that lack carbon-carbon bonds (e.g. methylamine) as electron donors to respiratory chains1.Paracoccus spp. have substantial biotechnological potential, especially in bioremediation, since they can conduct denitrification (e.g. P. denitrificans)9 and utilize various toxic organic compounds, e.g. N,N-dimethylformamide3 and herbicides10.Paracoccus spp. have multipartite genomes composed of a chromosome plus extrachromosomal replicons, including essential chromids and diverse plasmids8. As of June 25th 2018, when data were retrieved for this study, the following DNA sequences had been submitted to NCBI databases: (i) nine complete genomes of Paracoccus spp., i.e. P. denitrificans PD1222 (GenBank acc. nos. CP000489-CP000491), P. aminophilus JCM 768611, P. aminovorans JCM 768512, P. contaminans RKI 16-01929T13, P. yeei FDAARGOS_252 (GenBank acc. nos. NZ_CP020440-NZ_CP020447), P. yeei TT1314, P. zhejiangensis J615, Paracoccus sp. BM15 (GenBank acc. nos. NZ_CP025408-NZ_CP025411) and Paracoccus sp. CBA4604 (GenBank acc. nos. NZ_CP025583-NZ_CP025585), (ii) 54 draft genome sequences, and (iii) 52 plasmids of Paracoccus spp.Paracoccus spp., there is very little information about phages of these bacteria. To date, only three active phages infecting Paracoccus spp. have been identified and described: two lytic phages vB_PmaS-R3 (vB_PmaS_IMEP1)16 and Shpa17, plus one temperate virus \u03d5Pam-6 of P. aminophilus JCM 768611. Moreover, five other prophages  were identified within the genome of P. aminophilus11.Although much is known about the metabolic properties and genome architecture of Paracoccus spp., and performed a thorough comparative analysis of the Paracoccus virome.In this study, we identified five novel active temperate phages and 53 prophages in the available genome sequences of Paracoccus: P. alcaliphilus JCM 7364, P. aminovorans JCM 7685, P. alkenifer DSM 11593, P. bengalensis DSM 17099, P. ferroxidans NCCB 1300066, P. haeudaensis LGM P-21903, P. halophilus JCM 14014T, P. homiensis DSM 17862, P. kondratievae NCIMB 13773T, P. pantotrophus DSM 11072, P. seriniphilus DSM 14827, P. solventivorans DSM 11592, P. sulfuroxidans JCM 14013, P. thiocyanatus JCM 20756, P. versutus UW1R and P. yeei CCUG 32053. In each case, an exponentially growing culture was exposed to mitomycin C and released phage particles were concentrated using PEG/NaCl solution. This approach resulted in the induction of five phages, named vB_PbeS_Pben1 , vB_PkoS_Pkon1 (P. kondratievae), vB_PsuS_Psul1 (P. sulfuroxidans), vB_PthS_Pthi1 (P. thiocyanatus) and vBPyeM_Pyei1 (P. yeei). It is important to mention, that the term \u201cactive\u201d is used in this work for describing mitomycin C-induced and lytic viruses of Paracoccus spp., while it is still possible that other, in silico distinguished, prophages may respond to another stimuli (e.g. temperature or nutrient deprivation/excess) and therefore they may also be in fact active.The occurrence of active temperate phages was examined in 16 species of the genus P. aminophilus JCM 7686, were then tested as potential hosts for the induced phages using a spot test. None of the tested strains supported detectable lytic growth of any phage. It was concluded that all of the identified phages are species-specific, with a narrow host range that is possibly confined to their natural host strain.All of the aforementioned strains, plus Siphoviridae family, while vB_PyeM_Pyei1 represents the Myoviridae family , which indicated that the ends of their genomes did not form complementary overhangs and the phage DNAs was packaged by a headful mechanism (pac type). The headful mechanism is characteristic for circularly permuted genomes18. General characteristics and features of the Paracoccus phage genomes are summarized in Table\u00a0The genomes of the identified active phages were sequenced. After digestion of the 19. The distinguished gene clusters determine functions crucial for the phage life cycle, such as integration/excision, DNA recombination, early transcriptional regulation, DNA replication, packaging, capsid and tail assembly, and lysis . No obvious biological function could be attributed to 62% of the predicted phage gene products, so these were assigned as hypothetical proteins.Thorough manual sequence annotation of the phage genomes revealed modular structures that are typical for temperate bacteriophagessis Fig.\u00a0. SpecifiAlthough the aforementioned genetic modules with predicted functions show conservation of their order within the analyzed genomes, only two regions of sequence similarity were found in the DNA sequences of phages vB_PbeS_Pben1 and vB_PsuS_Psul1. The first region contains 13 predicted genes encoding proteins  sharing at least 46% amino acid (aa) identity  adjacent to the identified prophages were screened for the presence of direct repeats. This analysis revealed that the vB_PsuS_Psul1, vB_PthS_Pthi1 and vB_PyeM_Pyei1 phages integrated at the 3\u2032 ends of tRNA genes , and their integration reconstituted an intact copy of the target genes. Introduction of vB_PkoS_Pkon1 into the host chromosome disrupted a gene encoding a putative OmpR transcriptional regulator, while vB_PbeS_Pben1 integrated within an intergenic region between genes encoding a putative oxidoreductase and formyl-CoA transferase  family, but they share little sequence similarity.Site-specific recombination between 21. In all analyzed Paracoccus phages, predicted CI- and Cro-like repressors, belonging to the XRE family of transcriptional regulators (COG2932), were identified , vB_PkoS_Pkon1 (pkon1_p28), vB_PthS_Pthi1 (pthi1_p19-p20) and vB_PyeM_Pyei1 (pyei1_p23)  Table\u00a0, Fig.\u00a01B22.Toxin-antitoxin (TA) operons are commonly found within bacterial genomes. They encode two components: a stable toxin, which recognizes a specific cellular target and evokes a bactericidal or bacteriostatic effect, and a labile antitoxin that counteracts the toxin. These loci play important roles in bacterial growth, physiology and pathogenicity. They can also stabilize mobile genetic elements (MGEs) by elimination of MGE-less cells from a bacterial populationpben1_p24-p25) and vB_PkoS_Pkon1 (pkon1_p43-p44). In both cases the TA system genes are oriented oppositely to the surrounding genes. The pben1_p24-p25 locus encodes a HicA-type toxin , possibly involved in mRNA cleavage23, while pkon1_p43-p44 encodes an mRNA-degrading toxin of the RelE/ParE family 24.TA systems were identified in two prophages \u2013 vB_PbeS_Pben1  and pABW3-TA_PKO (TA of vB_PkoS_Pkon1). Plasmid pABW3-TA_PBE was stably maintained in strain UW225 (no plasmid-less cells were detected after 30 generations of growth under non-selective conditions), but this was not the case for pABW3-TA_PKO (9% of cells carried the plasmid following growth without selection). The \u201cempty\u201d vector pABW3 was present in 4% of cells after the same period of non-selective growth. These results indicate that pben1_p24-p25 comprises an active stabilizing system, while pkon1_p43-p44 seems to be non-functional, at least in this host. However, this TA system might be active in other hosts, as was previously observed for tad-ata-type systems25.We tested the functionality of these systems in a heterologous host \u2013 26. Endolysins are responsible for the degradation of the bacterial cell wall, causing the release of newly formed viral particles. These enzymes are synthesized without a signal sequence and thus accumulate in the cytosol during the viral life cycle26. Holins accumulate in the cell membrane and then perforate it, causing lesions that allow endolysin to access the cell wall peptidoglycan27.Many dsDNA bacteriophages use a holin-endolysin system for host cell lysis to release progeny virionsN-acetylmuramoyl-L-alanine amidases, i.e. enzymes that cleave the amide bond between N-acetylmuramic acid (MurNAc) and the first highly conserved stem L-alanine residue26. Psul1_p45, the predicted endolysin of phage vB_PsuS_Psul1, is a glycosidase (or muramidase), that presumably cleaves the linkage between MurNAc and N-acetylglucosamine26.A BLASTp search indicated that predicted proteins Pben1_p67, Pkon1_p73, Psul1_p45, Pthi1_p48 and Pyei1_p72  share significant sequence similarities with known holins. In addition, membrane-spanning domains were detected in Psul1_p43 and Pyei1_p73 using the programs TMHMM and TMPRED. However, confirmation that these proteins are indeed holins will require further experimental study.The identification of holin-encoding genes within the genomes of 28. They may also have complete restriction-modification (RM) systems29.Lytic and lysogenic phages often encode multi- and monospecific solitary DNA methyltransferases (MTases), not associated with restriction endonucleases5C) MTases, e.g. JCM7686_0772 and JCM7686_2655 of the prophages \u0424Pam-1 and \u0424Pam-5 of P. aminophilus JCM768 (~69% aa identity), respectively11. It was previously demonstrated that DNA modified by m5C MTases homologous to Pyei1_p05 is protected from cleavage by a wide variety of cytosine methylation-sensitive restriction endonucleases11. Therefore, it may be assumed that the Pyei1_p05 MTase also has a relaxed substrate specificity. The predicted MTases of the phages vB_PbeS_Pben1 and vB_PkoS_Pkon1 exhibit similarity to N6-adenine (m6A) modification enzymes. Protein Pben1_p29 is similar to DNA MTases encoded by viruses infecting Alphaproteobacteria . This group of viral MTases targets a sequence (5\u2032-GANTC-3\u2032) that is also recognized by Alphaproteobacteria-specific cell cycle-regulated MTase CcrM31. The pben1_p29 gene is located adjacent to the predicted replication module of phage vB_PbeS_Pben1. A putative m6A MTase was also identified in phage vB_PkoS_Pkon1. The pkon1_p75 gene is located at the end of the right arm of this genome  possess genes encoding orphan DNA MTases. Protein Pyei1_p05 exhibits similarity to several well characterized C5-methylcytosine (mpyei1_p13 gene encodes a homolog of a tellurite-resistance protein TerB. TerB is encoded within a tellurite resistance operon (terZABCDEF) found in e.g. E. coli APEC O1 plasmid pAPEC-O1-R32. Homologous (56.6% aa identity with the Pyei1_p13 protein), TerB protein is encoded by Sinorhizobium phage \u0424M5 (GenBank acc. no. ARV77549).Temperate bacteriophages can contain auxiliary genes that modulate and augment host cell metabolism during infection and facilitate production of new viruses. A presumed auxiliary metabolic gene was found only within the genome of vB_PyeM_Pyei1. The Paracoccus spp. genomes (nine complete and 54 drafts) and 52 complete plasmid sequences  were inspected for the presence of prophage regions using the PhiSpy tool33. Obtained results were afterwards manually curated. Only the regions comprising complete prophage genomes were included in further analyses. This was determined based on the presence of phage integration, replication, packaging, structural and lysis modules. Additionally, boarders of prophages were indicated based on the presence of predicted attB and attP sequences or, when not distinguishable, differences in %GC content between the prophage region and the surrounding host genome. As a result, 53 novel prophages were identified  and only one as Myoviridae (Table\u00a0Met(CAT) used by 10 prophages and (ii) tRNAPro(TGG) by four prophages, including active phage vB_PyeM_Pye1. These observations corroborate previous findings regarding the preferential integration of phages (and other integrative elements) within tRNA genes35.The integration modules of the identified prophages encode tyrosine recombinases (38 prophages), serine recombinases (16) or Mu-like transposases (5) (Supplementary Table\u00a0Siphoviridae prophages (indicated as fused in Supplementary Table\u00a0Paracoccus (pro)phages evolved via fusion of genes encoding the protease and major capsid protein. This is also in accordance with previous reports, e.g. regarding Lactococcus phage c2 structural proteins36. Such protein products were also predicted in the genomes of two of the active phages identified in this study: vB_PbeS_Pben1 (pben1_p41) and vB_PsuS_Psul1 (psul1_p30).With regard to phage structural proteins, the presence of the coding sequence for a nearly 700-amino acid-long protein in 17 (34%) of the Paracoccus prophages encode endolysins which were classified as N-acetylmuramyol-L-alanine amidases (16 prophages), muramidases (18), peptidases M15 (15) and chitinases (4) (Supplementary Table\u00a0Paracoccus prophages encode an extensive repertoire of DNA modification proteins. In the genomes of 48 (out of 59) prophages, at least one DNA MTase gene was identified , based on its similarity (78% aa identity) to Pami1_p55 of vB_PamS_Pami111.It was also revealed that 6A/m4C MTases (36 examples) is comprised of enzymes containing the ParB domain within their N-terminal region  were found. Far fewer MTase genes were found at other locations, including downstream (12 genes) and upstream (2) of the integrase gene, or downstream of the lysis module (7) , whereas such genomic localization of these genes was common in previously analyzed Sinorhizobium  prophages31.The most numerous subgroup of DNA mion Fig.\u00a0. These gion Fig.\u00a0. The pre(7) Fig.\u00a0. Interes38. In the Paracoccus prophage genomes, 13 RM systems  were identified  and therefore acquisition of metal resistance genes may be beneficial for these bacteria42. Acquired (with a phage) resistance genes may modify bacterial host reaction to toxic elements and therefore enhance its overall fitness under detrimental, environmental conditions and, in a consequence, facilitates production of the virus progeny.As mentioned above, phages can carry auxiliary metabolic genes that may benefit their hosts. Interestingly, genes encoding proteins that potentially confer metal resistance were found in 10 phages. These are:  tellurium resistance proteins TerB (phage vB_PamS_Pami1) and TerC (vB_PspS_PD44),  arsenite resistance protein ArsB (vB_PcoS_PD6 and vB_PsaS_PD20),  zinc/cadmium/lead-transporting ATPase ZntA (vB_PhoS_PD13 and vB_PspS_PD34), (vii) multidrug efflux system AcrABCR (vB_PsaS_PD23), (viii) lead/cadmium/zinc/mercury transporter, copper transporting ATPase and a multi-copper oxidase (vB_PspS_PD33), (ix) cobalt transporter CorA (vB_PyeS_PD47) and (x) zinc transporter ZitB (vB_PyeS_PD49)  and two lytic phages (vB_PmaS_IMPE1 and vB_PmaS_Shpa), constitute the current virome of the genus Paraccocus, which consists of 66 (pro)phages in total. This provided the opportunity for comprehensive genomic studies to reveal the common and unique features of these (pro)phages.In this study, 5 novel active lysogenic Paracoccus (pro)phages was whole genome all-against-all BLASTn searches and their visualization with Circoletto phages was continued by constructing protein-based similarity networks phages can be grouped into three major clusters and three orphan nodes . The densest (i.e. the most similar to one another) cluster is composed of a set of viruses of the Siphoviridae, while the most numerous cluster (31 elements) is more relaxed, reflecting a lower number of reciprocally similar proteins of phages that comprise this group phages on the peripheries, while the core is built by representatives of Siphoviridae and Myoviridae. Interestingly, vB_PkoS_Pkon1 constitutes an internal linker within this cluster because it encodes proteins  whose homologues are present in proteomes of Paracoccus phages classified to the Siphoviridae and Podoviridae. The third cluster of similar phages consists of all five Mu-like Siphoviridae viruses identified within the opportunistic human pathogens P. sanguinis 39542 and DSM 29303.Comparison of the rks Fig.\u00a0. In totaoup Fig.\u00a0. It is imi4 Fig.\u00a0. The relP. aminophilus JCM 7686 and Paracoccus sp. BM15, all identified prophages  show similarity of their encoded proteins; they share between five and 30 highly similar proteins. It is also worth mentioning that P. aminophilus JCM 7686 contains the highest number of prophages11. Some prophages of the other polylysogenic host strains also exhibit similarities, including (i) three (out of four) prophages of P. contaminans RKI (vB_PcoS_PD5-PD7) that share between 14 and 29 common proteins, (ii) two (out of three) prophages of Paracoccus sp. CBA4604 (vB_PspS_PD34 and vB_PspS_PD35), sharing 19 proteins, (iii) two (out of three) prophages of P. yeei ATCC BAA-599 (vB_PyeS_PD47 and vB_PyeS_PD49), sharing 11 proteins and (iv) two (out of four) prophages of P. yeei TT13 (vB_PyeS_PD52 and vB_PyeS_PD53), sharing 16 proteins. In contrast, of the five prophages of P. sanguinis 5503, only vB_PsaS_PD23 shares a single protein with vB_PsaS_PD24 and vB_PsaS_PD25. Similarly, amongst four prophages of Paracoccus sp. SCN 68-21 only vB_PspS_42 and vB_PspS_43 encode a single similar protein.Several polylysogenic host strains were identified in this study and we checked the reciprocal similarity of their phage proteomes. Within Met gene11) for only one phage encoding serine recombinase \u2013 the active lysogenic phage vB_PamS_Pami6 phages , integrated into tRNAThr genes with various anticodons phage integration module sequences showed that they group into 10 clusters and 30 unique nodes Fig.\u00a0. The larSinorhizobium (pro)phages, indicates that terminase large subunits and major capsid proteins as markers representing congruent clustering are the most convenient tools for phylogenetic analyses of alphaproteobacterial viruses31.The other two networks of large terminase subunits and major capsid protein sequences show a higher level of conservation among these proteins than in the case of recombinases Fig.\u00a0. In bothParacoccus (pro)phages were subjected to comparative analyses with all bacteriophage genomes deposited in the NCBI Viruses database by constructing a complex protein similarity network composed of 6,126 nodes and 330,592 edges , Dinoroseobacter (two) and Sulfitobacter (two)  Fig.\u00a0.Alphaproteobacteria phages from other bacteriophages and hence, searching for potential links between these phages and other viruses, we have used the IMG/VR database resources43. Amongst over 700,000 viral contigs present within the IMG/VR database, less than 1%  encoded at least a single protein similar to those of Alphaproteobacteria phages. From these viral contigs only 212 with completeness parameter over 75% were overlaid onto the global network , while vB_PmaS_Shpa, vB_PsaS_PD19, vB_PsaS_PD26 and vB_PsaS_PD28 have been linked with other Alphaproteobacteria phages (via single-stranded DNA-binding protein and large terminase subunit protein). Interestingly, newly added contigs retrieved from the IMG/VR database extended many other clusters and linked Alphaprotoebacteria phages with viruses infecting Terrabacteria and Gammaproteobacteria  phages create separate groups.The network analysis Fig.\u00a0 showed tIt is important to mention that since the number of alphaproteobacterial virus genomes currently available for comparison is low , all performed analyses still have some limitations. Therefore, network analyses should be repeated once the database has been enriched in the future.Paracoccus spp. (Alphaproteobacteria) were identified and analyzed together with six previously identified prophages of P. aminophilus JCM 7686 and two lytic phages (vB_PmaS_IMEP1 and vB_PmaS_Shpa). Four of the newly discovered active phages represent the Siphoviridae family, while vB_PyeM_Pyei1 is the first active Myoviridae phage infecting Paracoccus spp. Moreover, amongst the identified prophages, the first Podoviridae viruses infecting Paracoccus spp. were distinguished. Several auxiliary metabolic genes were found within the genomes of the identified Paracoccus (pro)phages. These genes encode proteins that potentially confer metal resistance. This may be highly beneficial to bacterial hosts, as many Paracoccus spp. have been isolated from various contaminated environments. Amongst other genes found within analysed (pro)phages, these encoding DNA methyltransferases are very common. It was shown that, 58 of identified methylases were classified as m6A/m4C DNA MTases and 30 as m5C DNA MTases. In similarity network analysis, these MTases formed highly conserved clusters, possibly grouping enzymes with common specificities. Interestingly, 57 genes encoding MTases were localized in a common region, i.e. the ParB-Tls locus. This location was also shared by a large group of genes encoding MTases fused with ParB-like proteins, that may be involved in directing the DNA-modification apparatus during packaging. Finally, it was shown that Paracoccus (pro)phages form a separate group of viruses, that is not only distinct from other phages of Alphaproteobacteria, but also from all other bacterial viruses.In this study, five novel active temperate phages and 53 prophages of E. coli DH5\u03b144, P. alcaliphilus JCM 736445, P. aminophilus JCM 76863, P. aminovorans JCM 76853, P. alkenifer DSM 115932, P. bengalensis DSM 170994, P. ferroxidans NCCB 130006646, P. haeundaensis LGM P-219035, P. halophilus JCM 14014T6, P. homiensis DSM 1786247, P. kondratievae NCIMB 13773T48, P. pantotrophus DSM 1107249, P. seriniphilus DSM 148277, P. solventivorans DSM 115922, P. sulfuroxidans JCM 1401350, P. thiocyanatus JCM 2075651, P. versutus UW1R52 and UW225 (Rifr-derivative of a wild-type strain)53, and P. yeei CCUG 3205354. All strains were grown in lysogeny broth (LB) medium at 37\u2009\u00b0C (E. coli) and 30\u2009\u00b0C (Paracoccus spp.). Liquid cultures were incubated with shaking. When required, media were supplemented with kanamycin (50\u2009\u03bcg\u2009ml\u22121) and rifampin (50\u2009\u03bcg\u2009ml\u22121). Plasmid pABW3 was used for the stability testing52.The following strains were used in this study: 55. Transformation of E. coli strains and triparental mating of P. versutus were performed according to previously described methods56. The test for the presence of cohesive ends of the phage genome was performed as previously described57, using various restriction enzymes .Standard DNA manipulation methods were performed as described by Sambrook and Russell (2001)GGATCCATGATCTCGGCATCAGCAG-3\u2032 and TAEcoRr 5\u2032-GGTGGTGAATTCAACACATTGCAGCAATGCTC-3\u2032 for the TA module of vB_PbeS_Pben1, and PKTABamHI 5\u2032-TGCAGGATCCAATACCGCATCCGTTCG-3\u2032 and PKTAEcoRI 5\u2032-AGCTGAATTCCATGGCCGCCTCAATCC-3\u2032 for the TA module of vB_PkoS_Pkon1 (introduced restriction sites are underlined). The following PCR program was applied using a Mastercycler  to amplify the desired products: initial denaturation at 95\u2009\u00b0C for 3\u2009min followed by 35 cycles of denaturation at 98\u2009\u00b0C for 20\u2009s, annealing at 64\u2009\u00b0C for 1\u2009min, extension at 72\u2009\u00b0C for 1\u2009min/kb and then a final extension at 72\u2009\u00b0C for 1\u2009min/kb. The obtained PCR amplicons were analyzed by agarose gel electrophoresis and purified using a Gel Out kit . The DNA fragments were then digested with EcoRI and BamHI and cloned in the vector pABW3 cleaved with the same restriction endonucleases. The resulting plasmid constructs were named pABW3-TA_PBE and pABW3-TA_PKO, respectively.Toxin-antitoxin (TA) systems of vB_PbeS_Pben1 and vB_PkoS_Pkon1 were PCR amplified using Phusion High-Fidelity DNA Polymerase (Thermo Fisher Scientific) with appropriate primer pairs, i.e. TABamHf 5\u2032-GTTGTTParacoccus cultures were grown to an OD600 of 0.4, then mitomycin C  was added to 500\u2009\u00b5g\u2009ml\u22121 and incubation was continued for 6\u2009h. The cells were then pelleted by centrifugation and phage particles in the supernatant precipitated using PEG/NaCl55. Bacteriophage particles were collected by centrifugation and resuspended in SM buffer55. Phage DNA was isolated by phenol-chloroform extraction and isopropanol precipitation55, and analyzed by 0.7% agarose gel electrophoresis. For transmission electron microscopy (TEM) analysis, phage particles were purified on a 1\u2009ml Convective Interaction Media (CIM\u00ae) anion-exchange monolith column  using an \u00c4KTApurifier system  running UNICORN\u2122 software, according to a recently published protocol58. Briefly, phages from the initial purification were loaded on the column at a flow rate of 2\u2009ml/min. Impurities were then washed out by increasing the proportion of elution buffer  in the mobile phase from 0 to 10%, at a flow rate of 4\u2009ml/min. Elution of the phage particles was achieved by increasing the proportion of elution buffer to 35%.For TEM analysis, 10\u2009\u03bcl samples of purified phage were adsorbed onto carbon-coated grids (Sigma-Aldrich) for 3\u2009min, stained with 1.5% uranyl acetate (Sigma-Aldrich) and examined using a Tecnai Spirit BioTWIN transmission electron microscope . Images were collected using iTEM software (FEI Company). The visualization of phages was performed at the Laboratory of Electron Microscopy, Faculty of Biology, University of Gdansk, Gdansk, Poland.Paracoccus strains  were grown in liquid LB medium and plated onto LB agar plates. A drop of each phage suspension was spotted onto the bacterial lawns and the plates were\u00a0incubated at 30\u2009\u00b0C. The plates were examined for evidence of bacterial lysis after 72\u2009h.To determine bacterial susceptibility to phage-mediated lysis, 17 P. versutus cells was tested as described previously25. Briefly, P. versutus UW225 containing the introduced plasmids  were grown overnight at 30\u2009\u00b0C in LB medium supplemented with kanamycin. Stationary phase cultures were then diluted in fresh medium without added antibiotic and cultivated for approximately 10, 20 or 30 generations. Samples were diluted and plated onto solid medium lacking kanamycin. One hundred colonies from each plate were then tested for the presence of the selection marker by replica plating. The percentage of kanamycin resistant colonies was used as a measure of the retention of the different plasmids. All plasmid stability assays were performed in triplicate.The stability of plasmids in Paracoccus phages were determined in the DNA Sequencing and Oligonucleotide Synthesis Laboratory (oligo.pl) at the Institute of Biochemistry and Biophysics, Polish Academy of Sciences. The phage genomes were sequenced using an Illumina MiSeq instrument in paired-end mode with a v3 chemistry kit. The obtained sequence reads were filtered for quality with cutAdapt v1.15 trimming bases on 3\u2032 ends  and removing reads containing Ns or shorter than 50 bp59. Processed reads were afterwards assembled using Newbler v3.0 software  with default settings. Final gap closure was performed by capillary sequencing of PCR amplicons using an ABI3730xl DNA Analyser .The complete nucleotide sequences of th 2018, 9 complete and 55 draft Paracoccus species genomes were retrieved from the National Center for Biotechnology Information (NCBI) genome browser. The sequences of these genomes together with all complete plasmids of Paracoccus spp., were screened for the presence of prophages using PhiSpy33 and the results were verified by manual inspection. Assessment of the prophage genome completeness was based on the presence of modules responsible for phage integration, lysis/lysogeny switch, DNA packaging, head-tail assembly and lysis.On June 2534.Taxonomy assignment of all prophages was conducted using the VIRFAM service, which also allowed more precise identification of certain structural proteinsParacoccus phages, were manually annotated using Clone Manager (Sci-Ed8) and Artemis software60. Annotation was based on homology searches performed using BLAST programs, including domain searches with CD-Search61. Putative tRNA genes were identified with the tRNAScan-SE 2.0 and ARAGORN programs63. Methyltransferase classification was performed using the REBASE database64 and manual inspection. For the identification of transmembrane proteins, i.e. holins, TMHMM65 and TMPRED  were used. The annotation of the identified heavy metal resistance genes was assisted by searches against the BacMet and PRIAM databases67.The identified prophage sequences, as well as those of the mitomycin C-induced 68. The construction of similarity networks was based on all-against-all BLASTp comparisons of three sets of proteomes: (i) those derived from 66 Paracoccus (pro)phages, (ii) those of the Paracoccus (pro)phages combined with all 6,253 viruses infecting Bacteria available in the NCBI genome browser (as of August 3rd 2018), and (iii) the previous two datasets extended with the part of the IMG/VR database version 3 (as of July 1st 2018)43. From the set of bacteriophages deposited in the NCBI database, 191 not encoding any proteins based on their annotations were excluded from the analyses. For the construction of the network only the IMG/VR viral contigs encoding at least a single protein similar to proteins encoded by known Alphaproteobacteria phages were used. From 4,915 resulting viral contigs , these duplicating the nodes and with parameter of the genome completeness below 75% were excluded from further analysis. The following thresholds were used during the BLASTp searches: e-value 1e-10 , query coverage of HSP of at least 75% and sequence identity of 80%. Within the obtained networks, each node represents a single (pro)phage  and each edge corresponds to a common reciprocated similarity of at least one protein encoded by two connected (pro)phages or viral contigs. The thickness of the edge reflects the number of common proteins between two analyzed (pro)phages. These networks were created using self-written Python scripts and visualized in Gephi69 using ForceAtlas 2 layout70.Phage genome comparisons were performed with the Circoletto tool, using an e-value of 1e-100 as the thresholdThe nucleotide sequences of the vB_PbeS_Pben1, vB_PkoS_Pkon1, vB_PsuS_Psul1, vB_PthS_Pthi1 and vBPyeM_Pyei1 phages have been deposited in the GenBank (NCBI) database with the accession numbers MK291441, MK291442, MK291443, MK291444 and MK291445, respectively.Supplementary Information 1Supplementary Dataset 1"}
+{"text": "Quantitative trait locus (QTL) mapping using bulk segregants is an effective approach for identifying genetic variants associated with phenotypes of interest in model organisms. By exploiting next-generation sequencing technology, the QTL mapping accuracy can be improved significantly, providing a valuable means to annotate new genetic variants. However, setting up a comprehensive analysis framework for this purpose is a time-consuming and error-prone task, posing many challenges for scientists with limited experience in this domain.Here, we present BSA4Yeast, a comprehensive web application for QTL mapping via bulk segregant analysis of yeast sequencing data. The software provides an automated and efficiency-optimized data processing, up-to-date functional annotations, and an interactive web interface to explore identified QTLs.https://bsa4yeast.lcsb.uni.lu.BSA4Yeast enables researchers to identify plausible candidate genes in QTL regions efficiently in order to validate their genetic variations experimentally as causative for a phenotype of interest. BSA4Yeast is freely available at  Deciphering the genetic basis of diseases or complex traits is a major task in biomedical and basic biological research and is a key first step towards a better understanding of the molecular mechanisms behind disorders with genetic components. As a forward genetic approach, linkage analysis of quantitative trait loci (QTLs) using bulk segregant analysis (BSA) in model organisms, such as yeast, is an efficient method for identifying novel genetic variants responsible for heritable phenotypic variability , 2. By eIt enables efficient and fully automated web-based NGS-BSA without requiring prior domain expertise;It supports multiple input file types including .fastq, .bam, or .map ;It provides comprehensive annotations for the detected QTLs, using the latest version of the yeast reference genome. These annotations are regularly updated;It enables an interactive web-based exploration of the detected QTLs, their associated annotations, and statistical results.For this purpose, we have developed BSA4Yeast, a comprehensive web-based analysis software for QTL mapping via bulk segregant analysis of yeast sequencing data Fig.\u00a0. BSA4YeaTo the best of our knowledge, BSA4Yeast is the first comprehensive web-based software that integrates automated NGS data analysis with QTL mapping via bulk segregant analysis.Saccharomyces cerevisiae dedicated annotations are generated additionally. Thus, BSA4Yeast can be used for BSA-QTL analyses either if the sequencing reads of the parental lines are provided as input or if the user only provides map files without the original sequences . After the preprocessing and alignment computations in the first step of the workflow, genetic markers will be identified automatically in the second phase. Optionally, the user can adjust the trade-off between the stringency and coverage of the marker identification by specifying a custom DNA sequencing depth of coverage. For the QTL analyses in the third and final step, the user can adjust the type and width of the used smoothing kernel and has the option to download intermediate results, such as allele frequency files, bam files, or map files, for further independent analyses. The QTL peaks, QTL regions, and corresponding empirically estimated P-values are determined using the G\u2032 statistic [The BSA4Yeast framework for QTL mapping via bulk segregant analysis of yeast sequencing data is built on custom scripts and open-source bioinformatics software Fig.\u00a0. The softatistic . To facitatistic . AdditioThe BSA4Yeast web application has been developed in Python 2.7 using the Flask micro-framework Fig.\u00a0 [7]. FlaIn order to provide an interactive and intuitive exploration of genomic data in the web browser, the BSA4Yeast graphical interface uses the libraries Bootstrap, jQuery, DataFrame.js, and Highcharts.js . The visS. cerevisiae), detecting 2 significant QTLs associated with chronological lifespan regulation [In a first proof-of-concept study, the BSA4Yeast analysis framework was applied successfully to investigate cellular aging in baker\u2019s yeast . Moreover, the software can be interlinked with other public internet databases and repositories, which contain further information on identified genes with a phenotype association of interest. The BSA4Yeast source code has been made available on GitLab  to allowS. cerevisiae reference genome  [Mapping short reads  to the standard n 0.7.4)  and geneDefining genetic markers between 2 parental lines given a user-defined coverage threshold (default: 5\u00d7);Calculating G\u2032 statistical values  for eachAnnotating exonic variants between the 2 parental lines and within each QTL region with ANNOVAR  ;Scoring the functional impact of non-synonymous variants with SNAP (version: 2.0) .In summary, BSA4Yeast was designed and implemented to enable users to perform comprehensive NGS-BSA studies efficiently without requiring prior bioinformatics knowledge. The overall software workflow Fig.\u00a0 covers tThe final results generated by the software, including the annotations, variant functional impact predictions, and statistical results for each determined QTL region, can be explored interactively and downloaded using current standard web browsers .Project name: BSA4YeastRRID: SCR_017113https://git-r3lab.uni.lu/zhi.zhang/bsa4yeastProject home page:\u00a0Operating system(s): CentOS 7.2Programming language: Python 2.7Other requirements: Flask 0.12.2, Celery 4.1.0, Redis 2.10.6License: GNU GPLhttps://bsa4yeast.lcsb.uni.lu. All other supporting data are also available via the GigaScience GigaDB repository [The example datasets presented are available at pository . For morbp: base pair; BSA: bulk segregant analysis; GB: gigabytes; GFF: General Feature Format; GPL: General Public License; HPC: high-performance computing; NGS: next-generation sequencing; PE: paired end; RAM: random access memory; QTL: quantitative trait locus; SE: single end; SNV: single-nucleotide variant; SQL: Structured Query Language; UCSC: University of California Santa Cruz; VCF: Variant Call Format.The authors declare that they have no competing interests.Acknowledgement is made for support by the Fonds Nationale de la Recherche (FNR) Luxembourg, through the National Centre of Excellence in Research (NCER) on Parkinson\u2019s disease (I1R-BIC-PFN-15NCER), and as part of the grant project PD-Strat (INTER/11651464).Z.Z. and E.G. designed the project. Z.Z. wrote the code of the project. Z.Z., P.P.J., and E.G. wrote the manuscript. V.G. contributed his knowledge to the server infrastructure and deployment. P.M. provided the SNAP2.0 value for mutations. P.P.J. and C.L. contributed the sequencing datasets for testing. All authors read the manuscript and provided feedback.giz060_GIGA-D-18-00409_Original_SubmissionClick here for additional data file.giz060_GIGA-D-18-00409_Revision_1Click here for additional data file.giz060_GIGA-D-18-00409_Revision_2Click here for additional data file.giz060_GIGA-D-18-00409_Revision_3Click here for additional data file.giz060_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giz060_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giz060_Response_to_Reviewer_Comments_Revision_2Click here for additional data file.giz060_Reviewer_1_Report_Original_Submission -- Christian Brion, Ph.D,12/8/2018 ReviewedClick here for additional data file.giz060_Reviewer_1_Report_Revision_1 -- Christian Brion, Ph.D,3/4/2019 ReviewedClick here for additional data file.giz060_Reviewer_2_Report_Original_Submission -- Francisco Cubillos12/11/2018 ReviewedClick here for additional data file.giz060_Supplemental_FileClick here for additional data file."}
+{"text": "Metatranscriptomics typically uses short sequence reads, which can either be directly aligned to external reference databases (\u201cassembly-free approach\u201d) or first assembled into contigs before alignment (\u201cassembly-based approach\u201d). We also compare CoMW (assembly-based implementation) with an assembly-free alternative workflow, using simulated and real-world metatranscriptomes from Arctic and temperate terrestrial environments. We evaluate their accuracy in precision and recall using generic and specialized hierarchical protein databases.Metatranscriptomics has been used widely for investigation and quantification of microbial communities\u2019 activity in response to external stimuli. By assessing the genes expressed, metatranscriptomics provides an understanding of the interactions between different major functional guilds and the environment. Here, we present a CoMW provided significantly fewer false-positive results, resulting in more precise identification and quantification of functional genes in metatranscriptomes. Using the comprehensive database M5nr, the assembly-based approach identified genes with only 0.6% false-positive results at thresholds ranging from inclusive to stringent compared with the assembly-free approach, which yielded up to 15% false-positive results. Using specialized databases (carbohydrate-active enzyme and nitrogen cycle), the assembly-based approach identified and quantified genes with 3\u20135 times fewer false-positive results. We also evaluated the impact of both approaches on real-world datasets.de novo assembly-based CoMW. Our benchmarking findings support assembling short reads into contigs before alignment to a reference database because this provides higher precision and minimizes false-positive results.We present an open source  Metatranscriptomics provides an unprecedented insight to complex functional dynamics of microbial communities in various environments. The method has been applied to study the microbial activity in thawing permafrost and the related biogeochemical mechanisms contributing to greenhouse gas emissions , and GonUsing high-throughput sequencing techniques such as Illumina, metatranscriptomics offers a non-PCR\u2013biased method for looking at transcriptional activity occurring within a complex and diverse microbial population at a specific point in time . Howeverde novo assembly-based approach, standardized and validated for functional annotation and quantitative expression analysis. We validated the suitability of CoMW for functional analysis by comparing it with a typical assembly-free approach using simulated datasets and evaluated the accuracy of both approaches using precision, recall, and false discovery rates (FDRs). Three different protein databases were selected for this benchmarking in order to include a representative selection of 3 different degrees of specialization, on a range from a more inclusive database with wide coverage  and low degree of expert curation to a smaller, highly curated database, with more narrow coverage: (i) M5nr , medium [TM], and high [TH]; 5 thresholds/category) resulted in dissimilar performance for the 2 approaches. The precision and recall of CoMW did not decrease below 99.3% and 98.5%, respectively, throughout all categories whereas the assembly-free approach had a maximum precision of 96.3% at TM and decreased to 85% at TL and TH. CoMW also produced fewer (only 0.6%) FPs consistently compared to the assembly-free approach, in which FPs ranged from 14.9% to a minimum of 3.6% at highest precision. Based on F-score the most optimal alignment for each approach is given in Table\u00a0Full-length genes of the simulated community dataset were aligned and identified into 671 unique eggNOG orthologs, belonging to 19 distinct functional subsystems (Level II). At the default confidence threshold (bit score 50), the assembly-free approach produced alignments to 820 orthologs with a precision of 85% , whereas CoMW identified 665 orthologs with a precision of 99.3% (0.6% FPs) at the default confidence threshold of 1E\u22125. Repeating the alignments using a gradient of 15 varying confidence thresholds for each approach , the assembly-free approach identified 765 functional genes belonging to 112 unique families and 6 enzyme classes with a precision of 28.5% (71.4% FPs). CoMW identified 488 functional genes from the CAZy database that were classified into 147 gene families from 7 enzyme classes with a precision of 66.0% (FDR 33.9%) at the default confidence threshold. However, when we repeated the process with 15 various confidence thresholds, precision improved consistently and FPs decreased, whereas for the assembly-free approach, precision decreased significantly with increasing confidence threshold . CoMW identified 42 nitrogen cycle genes classified into 25 gene families from 6 pathways with a precision of 59.5% (40.4% FPs) at a default confidence threshold of 1E\u22125. As with the comparisons against M5nr and CAZy we repeated the process with 15 different confidence thresholds for each approach. Precision improved significantly for CoMW at stringent thresholds whereas for the assembly-free approach, the best precision achieved was 5.8% Table\u00a0 \u00a0.We also compared the ability of both approaches to quantify the expression of identified transcripts by performing differential expression analysis of 2 groups in simulated communities and compared against the full-length gene expression simulated. We selected the 3 best identification thresholds for both approaches based on highest F-score and performed differential expression analysis. This analysis for both approaches was carried out against all 3 databases using the most specific level of hierarchy in the respective databases in order to capture their ability to quantify expression levels of specific genes.2 transformed) was then calculated for each subsystem/gene family , 73 genes were upregulated  and 380  were downregulated, whereas using the assembly-based approach (CoMW), 99 genes were identified as upregulated  and 249 downregulated . For the CAZy database full-length genes, 81 and 189 genes were identified as significantly up- and downregulated, respectively. Using the assembly-free approach 31 upregulated  and 137 downregulated genes  were identified, whereas the CoMW identified 83  and 191 , respectively. In the NCycDB expression analysis, 3 and 14 genes were seen as significantly up- and downregulated, respectively, using full-length genes. According to the assembly-free approach, 26  and 107  genes were up- and downregulated, respectively, whereas according to CoMW, 3  genes were upregulated and 18  were downregulated. Precision, recall, and FDR for both approaches against all 3 databases are available in To evaluate the effect of the 2 approaches on real-world data, 2 metatranscriptomes from microbial communities were studied. In the first study we investigated the transcriptional response during warming from \u221210\u00b0C to 2\u00b0C and subsequent cooling from 2\u00b0C to \u221210\u00b0C of an Arctic tundra active layer soil from Svalbard, Norway. The aim of the study was to understand taxonomic and functional shifts in microbial communities caused by thawing and freezing of Arctic soil. A pronounced shift during the incubation period was noticed by Schostag et\u00a0al.  that was\u22121]) and the effect over time was analysed in soil communities at 0, 3, 30, and 100 days after ash addition. This resulted in strong effects on functional expression as seen in Fig.\u00a0In the second study, we investigated the effects of wood ash amendment on Danish forest soils . Ash wasde novo sequence assemblers including Trinity  rate), accounting for how many correct annotations are selected, defined as TP/(TP + FN), where TP indicates the number of orthologs that have been correctly annotated, FN indicates the number of orthologs/genes/functional subsystems that are in the simulated communities but were not found by a certain approach, and FP indicates the number of orthologs/genes/functional subsystems that have been wrongly annotated (because they do not appear in the simulated communities). The F-score is the harmonic mean of precision and recall, defined as /.Project name: Comparative Metatranscriptomics Workflow (CoMW)Project home page: https://github.com/anwarMZ/CoMWOperating system(s): Platform independentProgramming language: Python, R, and bashOther requirements: Requirements mentioned in detailed manual at GitHubLicense: GNU General Public License v3.0GigaScience database, GigaDB [An archival copy of the code and supporting data are available via the , GigaDB Raw sequence data generated using simulation of full-length genes were deposited in the NCBI SRA and are accessible through BioProject accession number PRJNA509064https://github.com/anwarMZ/CoMW_suppProject supplementary scripts: https://doi.org/10.24433/CO.1793842.v1CoMW is published as computational capsule on codeocean  and can SciCrunch.org with RRID:SCR_017109CoMW is registered at Supplementary File 1\u2013Precision recall analysis of both approaches.Supplementary File 2\u2013Differential expression analysis of all approaches using eggNOG database.Supplementary File 3\u2013Differential expression analysis of all approaches using CAZy database.Supplementary File 4\u2013Differential expression analysis of all approaches using NCyc database.giz096_GIGA-D-19-00009_Original_SubmissionClick here for additional data file.giz096_GIGA-D-19-00009_Revision_1Click here for additional data file.giz096_GIGA-D-19-00009_Revision_2Click here for additional data file.giz096_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giz096_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giz096_Reviewer_1_Report_Original_SubmissionPatrick May -- 2/10/2019 ReviewedClick here for additional data file.giz096_Reviewer_1_Report_Revision_1Patrick May -- 6/10/2019 ReviewedClick here for additional data file.giz096_Reviewer_2_Report_Original_SubmissionHarriet Alexander -- 2/24/2019 ReviewedClick here for additional data file.giz096_Supplemental_FilesClick here for additional data file.ABySS: Assembly By Short Sequences; BLAST: Basic Local Alignment Search Tool; BWA: Burrows-Wheeler Aligner; CAZy: Carbohydrate-Active EnZymes database; COMAN: Comprehensive Metatranscriptomics Analysis; eggNOG: Evolutionary Genealogy of Genes: Non-supervised Orthologous Groups; EMBOSS: European Molecular Biology Open Software Suite; FDR: false discovery rate; FN: false-negative result; FP: false-positive result; IMP: Integrated Meta-omic Pipeline; NCBI: National Center for Biotechnology Information; NCycDB: Nitrogen Cycling Database; ORF: open reading frame; SAMSA2: Simple Annotation of Metatranscriptomes by Sequence Analysis 2; SRA: Sequence Read Archive; TP: true-positive result; BTS: Bit-scoreThe authors declare that they have no competing interests.This work was supported by a grant from the European Commission's Marie Sklowdowska Curie Actions program under project number 675546 (MicroArctic).M.Z.A. and C.S.J. conceived and designed the study. M.Z.A., T.B.A., and A.L. carried out the data production. M.Z.A. and A.L. carried out analysis. M.Z.A. drafted the manuscript, and A.L., T.B.A., and C.S.J. revised and approved the final version."}
+{"text": "Gastric cancer (GC) has a poor prognosis due to the lack of ideal tumor markers. Circular RNAs (circRNAs) are a novel type of noncoding RNA related to the occurrence of GC. Among our research, we investigated the role of hsa_circ_0005556 in GC.  The expression of hsa_circ_0005556 of 100 paired GC tissues and adjacent normal tissues was detected using quantitative reverse transcription-polymerase chain reaction (qRT-PCR). A receiver operating characteristic (ROC) curve was established to evaluate the diagnostic value of hsa_circ_0005556. The correlation between the expression of hsa_circ_0005556 and corresponding clinicopathological characteristic was explored. n = 100, p < 0.001). The areas under the ROC curve (AUC) of hsa_circ_0005556 were up to 0.773, while 64% sensitivity and 82% specificity, respectively. Moreover, its expression levels were significantly associated with differentiation (p = 0.001), TNM stage (p = 0.013), and lymphatic metastasis (p = 0.039). GC patients of high hsa_circ_0005556 levels had a longer overall survival (OS) than those of the low group (p = 0.047).  hsa_circ_0005556 was significantly downregulated in GC tissues contrasted with adjacent normal tissues . Gastric cancer (GC), a serious global health risks, ranks as the fifth most common cancer and the third primary cause of cancer-related death globally. Every year, beyond 1000000 new cases and 783000 new deaths are reported . Most paWith the development of RNA high-throughput sequencing technology and advances in biophysical technology, an increasing number of noncoding RNAs have been discovered . Some sthttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE89143), the expression of hsa_circ_0005556 was quite different in GC tissue compare to paired normal tissue. So, we chose hsa_circ_0005556 as a research target to analyze its diagnostic values of GC. The gene of hsa_circ_0005556 is located at chr2:15693549-15698758. Its spliced sequence length is 218\u2009nt, and the related gene symbol is the neuroblastoma-amplified sequence (NBAS). To our knowledge, few studies have identified the functions of hsa_circ_0005556. Here, quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was utilized to explore the expression of hsa_circ_0005556 in GC patients. And the relationship with clinicopathological factors was also analyzed. We identified hsa_circ_0005556 as a diagnostic biomarker in GC.Based on our previous microarray analysis , then preserved at -80\u00b0C until RNA isolation.th edition). The histological grade of the tumor was assigned basing on the American Joint Committee on Cancer (AJCC) cancer staging manual (8th edition). Written informed consent was acquired from all individuals. This research acquired the approval from the Human Research Ethics Committee of Ningbo University (IRB No. 20100303).The clinical information of all samples was collected. The GC diagnosis was confirmed by histopathology. The pathologic stage of the tumor was assigned basing on the tumor-node metastasis (TNM) staging system of the International Union Against Cancer . Next, the DS-11 spectrophotometer machine  was used to verify purity and concentration of total RNA. The A260/A280 ratio was used to evaluate the RNA purity, which only ranged from 1.8 to 2.0 for the samples that were qualified and used.Total RNA was performed to synthesize cDNA using the GoScript RT System  with a random primer, following the manufacturer's instructions.\u0394Cq (quantification cycle) method. A larger \u0394Cq indicates a lower level. Through three independent experiments, all of the data were expressed as the mean \u00b1 SD.qRT-PCR analysis was implemented on an Mx3005P real-time PCR System  with GoTaq qPCR Master Mix (Promega). The sequences of divergent primers were designed and produced by BGI  Co., Ltd. The primer of hsa_circ_0005556 was 5\u2032-GTGTGTGGAAATCAGCCTAG-3\u2032 and 5\u2032-AACCAAGCGAACCAGTCCAT-3\u2032. The primer of glyceraldehyde 3-phosphate dehydrogenase  primers was 5\u2032-CTGCCAACGTGTCAGTGGTG-3\u2032 and 5\u2032-TCAGTGTAGCCCAGGATGCC-3\u2032. The conditions of thermal cycling were as follows: hot start at 95\u00b0C for 5 minutes, next 40 cycles at 95\u00b0C for 15 seconds, 56\u00b0C for 30 seconds, and 72\u00b0C for 30 seconds. The qRT-PCR products were confirmed by Sanger sequencing, which was completed by Geneseed . The relative quantification level of circRNA was calculated by the t-test, analysis of variance (ANOVA), receiver operating characteristic (ROC) curves, and Kaplan-Meier survival curves were properly used. A value of p < 0.05 (two sided) was considered to be statistically significant.Statistical data were analyzed by Statistical Product and Service Solutions (SPSS) 19.0 software . Figures and tables were produced by GraphPad Prism 5.0 . The data in this paper are in accordance with the normal distribution. The Shapiro-Wilk test, Student's \u0394Cq between cancer and adjacent normal tissues was in accordance with the normal distribution. The results indicated that hsa_circ_0005556 was significantly downregulated in GC tissues contrast to adjacent normal tissues  called microRNA response elements. Given that miRNAs function as downstream regulatory elements of circRNAs to affect the progression of GC, identification of the signal axis from circRNAs to miRNAs is important. We predicted the potential binding miRNAs for hsa_circ_0005556 using sequence analysis.The results showed there were 6 potential binding miRNAs . Accordip = 0.001), TNM stage (p = 0.013), and lymphatic metastasis (p = 0.039). Besides, no relationship was detected between its level and the rest of clinicopathological factors, like age, gender, tumor diameter, invasion, distal metastasis, carbohydrate antigen 19-9 (CA19-9), and carcinoembryonic antigen (CEA).Since we discovered hsa_circ_0005556 was significantly downregulated in GC tissues, we further analyzed the connection between the expression of hsa_circ_0005556 and some clinicopathological characteristics of patients with GC. As described in p = 0.047, Next, the Kaplan-Meier survival analysis was conducted to analyze the overall survival (OS) of GC patients based on hsa_circ_0005556 expression. GC patients were divided into two groups, the low or high group according to the mean value of hsa_circ_0005556 expression in GC tissues. The results showed that patients in the low group had shorter OS compared to those in the high group . These characteristics may help determine the diagnosis of EGC. As a result, hsa_circ_0005556 may contribute to the choice of treatment for GC, especially to determine whether the patient is suitable for endoscopic treatment instead of conventional surgery.The association between circRNAs and some clinicopathological features has a strong clinical value. Sufficient tumor resection is the key therapeutic factor for resectable GC , 38. Then = 38, mean \u00b1 SD = 12.33 \u00b1 1.47; group Her2-positive, n = 7, mean \u00b1 SD = 11.79 \u00b1 0.93; p = 0.35). However, the number of sample is too small, especially for the Her2-positive group due to the low-Her2 positive rate ranging from 10% to 20%. In the future, we need to expand the sample for further study.The developing standard of care in GC is evaluation of human epidermal growth factor receptor 2 (Her2), mismatch repair (MMR), and programmed cell death ligand 1 (PD-L1) status in tumors, with related targeted therapies informing medical and surgical management and studies for these proteins also being done on preoperative specimens , 46. To In summary, our data indicated that hsa_circ_0005556 expression is significantly downregulated in GC tissues. The downregulated expression level was correlated with differentiation, TNM stage, and lymphatic metastasis. Our research suggests that hsa_circ_0005556 may become a biomarker for GC. hsa_circ_0005556 may help to decide the method of treatment for EGC by judging the indications of endoscopic treatment."}
+{"text": "This article contains data on the effects of seagrass decline on wave energy along the shoreline of Barnegat Bay (USA) previously evaluated in Donatelli et\u00a0al., 2019. This study was carried out applying the Coupled-Ocean-Atmosphere-Wave-Sediment Transport (COAWST) numerical modelling framework to six historical maps of seagrass distribution. A new routine recently implemented in COAWST was used, which explicitly computes the wave thrust acting on salt marsh boundaries. The numerical modelling results are reported in terms of wind-wave heights for different seagrass coverages, wind speeds and directions. From a comparison with a numerical experiment without submerged aquatic vegetation, we show how the computed wave thrust on marsh boundaries can be reduced by seagrass beds. We eval2# define MARSH_WAVE_EROSION.# define MARSH_WAVE_THRUST.# undef MARSH_SED_EROSION.and activating the new vegetation module recently implemented in COAWST by Beudin et al. # define VEGETATION.# ifdef VEGETATION.# undef ANA_VEGETATION.# define VEG_DRAG.# ifdef VEG_DRAG.# define VEG_FLEX.# define VEG_TURB.# endif.# define VEG_SWAN_COUPLING.# ifdef VEG_SWAN_COUPLING.# define VEG_STREAMING.# endif.The hydrodynamics of the system was simulated using the COAWST (Coupled-Ocean-Atmosphere-Wave-Sediment Transport Modeling System) modeling framework In numerical models, the simplest method to simulate the influence of plants on the mean flow is to increase the bottom roughness coefficient NVEG == 1 ! Number of submerged aquatic vegetation types.CD_VEG == 1.0d0 ! Drag coefficient for each vegetation type.E_VEG == 1.0d9 ! Young's Modulus for each vegetation type.VEG_MASSDENS == 700.0d0 ! Mass density for each vegetation type.VEGHMIXCOEF == 0.1d0 ! Additional horizontal viscosity coefficient at the edge of a vegetation patch.KFAC_MARSH == 0.6d-5 ! Marsh erosion factor depends on sediment cohesive properties.SCARP_HGHT == 0.2d0.! Logical switches (TRUE/FALSE) to activate writing of vegetation fields.! into HISTORY output file: Hout(ipdens) == F ! Plant_density Density of the plant for each vegetation.Hout(iphght) == F ! Plant_height Height of the plant for each vegetation.Hout(ipdiam) == F ! Plant_diameter Diameter of the plant for each vegetation.Hout(ipthck) == F ! Plant_thickness Thickness of the plant for each vegetation.Hout(ipagbm) == F ! Plant_agb Above ground plant biomass.Hout(ipbgbm) == F ! Plant_bgb Below ground plant biomass.Hout(idWdvg) == F ! Dissip_veg Wave dissipation due to vegetation.Hout(idTims) == T ! marsh_mask masking for getting thrust due to waves.Hout(idTtot) == T ! Thrust_total Total thrust due to waves.Hout(idTmfo) == F ! marsh_flux_out Marsh flux out.Hout(idTmmr) == F ! marsh_retreat Amount of marsh retreat from all four directions.Hout(idTmsc) == F ! marsh_scrp_height Amount of marsh retreat from all four directions.The presence of marsh is felt by the wave thrust routine through the variable marsh_mask, which is specified in the initial condition file. The variable marsh_mask is defined by a matrix with 0 and 1, where marsh pixels have a value of 1. Finally, the user needs to create a vegetation input file where mass density, number of vegetation types and mechanical properties of plants are listed:Different scenarios were considered for the wind forcing characterized by winds of constant speed  blowing from south-west and south-east for the entire period of simulation. Seagrass aerial extent and vegetation parameters are listed in"}
+{"text": "The number of microbial genome sequences is increasing exponentially, especially thanks to recent advances in recovering complete or near-complete genomes from metagenomes and single cells. Assigning reliable taxon labels to genomes is key and often a prerequisite for downstream analyses.We introduce CAMITAX, a scalable and reproducible workflow for the taxonomic labelling of microbial genomes recovered from isolates, single cells, and metagenomes. CAMITAX combines genome distance\u2013, 16S ribosomal RNA gene\u2013, and gene homology\u2013based taxonomic assignments with phylogenetic placement. It uses Nextflow to orchestrate reference databases and software containers and thus combines ease of installation and use with computational reproducibility. We evaluated the method on several hundred metagenome-assembled genomes with high-quality taxonomic annotations from the TARA Oceans project, and we show that the ensemble classification method in CAMITAX improved on all individual methods across tested ranks.https://github.com/CAMI-challenge/CAMITAX.While we initially developed CAMITAX to aid the Critical Assessment of Metagenome Interpretation (CAMI) initiative, it evolved into a comprehensive software package to reliably assign taxon labels to microbial genomes. CAMITAX is available under Apache License 2.0 at  The direct costs for sequencing a microbial genome are at an all-time low: a high-quality draft now costs <$100, a \u201cfinished\u201d genome sequence <$500. This has resulted in many culture-dependent genome studies, in which thousands of isolates\u2014selected by, e.g., their distinct phylogeny , abundanSingle-cell genome and shotgun metagenome studies further contribute to this expansion in genome numbers by enabling access to the genome sequences of (as-yet) uncultured microbes . NotablyTypically, the sequencing and assembly of a new genome is merely a prerequisite for further bioinformatics analyses  to uncover novel biological insights by, e.g., functional annotation ,18 or phHistorically, a bacterial or archaeal species was defined as a collection of strains that share 1 (or more) trait(s) and show DNA-DNA reassociation values of \u226570% . HoweverToday, 16S ribosomal RNA (rRNA) gene similarity, average nucleotide identity (ANI), genome phylogeny, or gene-centric voting schemes are used for taxonomic assignments . These aIn the following, we describe CAMITAX\u2019s assignment strategies and its implementation Fig.\u00a0.An ANI value of 95% roughly corresponds to a 70% DNA-DNA reassociation value  . In otheCAMITAX uses Mash\u00a0 to rapidThis strategy works best if the query genome is >80% complete (Mash does not accurately estimate the genome-wide ANI of incomplete genomes ) and is The 16S rRNA gene is widely used for classification tasks because it is a universal marker gene likely present in all bacteria and archaea ,35.CAMITAX uses nhmmer  to identOf course, this strategy only is applicable if the genome assembly contains a copy of the 16S rRNA gene\u2014which is not always the case, particularly for genomes recovered from metagenomes or single cells.Metagenomics and single-cell genomics are complementary approaches providing access to the genomes of (as-yet) uncultured microbes, but both have strings attached: Single amplified genomes (SAGs) are hindered by amplification bias and, as a consequence, are often incomplete ,43. MetaTo overcome these problems, CAMITAX implements a gene-based voting scheme. It uses Prodigal  to prediInferring genome taxonomy from a set of gene-level assignments is not trivial, and\u2014inspired by procedures implemented in anvi\u2019o  and dRepCAMITAX uses CheckM  for a phLast, CAMITAX reports the query genome\u2019s completeness and contamination as estimated by CheckM using its lineage-specific marker genes .E.\u2009coli, 2\u00d7 Bacteria \u21a6 E.\u2009coli3\u00d7 E.\u2009coli, 2\u00d7 E.\u2009albertii \u21a6 Escherichia3\u00d7 E.\u2009coli, 2\u00d7 Archaea \u21a6 Root3\u00d7 CAMITAX considers the lowest consistent assignment as the longest unambiguous root-to-node path in the taxonomic tree spanned by the individual assignments; i.e., it retains the most specific, yet consistent taxonomic label among all tools. For example, CAMITAX would determine as \u201cconsistent\u201d assignments for the individual assignments (derived with the different assignment strategies) the following:This strategy is more robust than computing the LCA of individual assignments because outliers, e.g., missing predictions of conservative methods, do not affect the overall assignment.At the same time, requiring a consistent assignment is less error prone than, e.g., selecting the maximal root-to-leaf path, which would introduce many false-positive assignments especially on lower ranks.The trade-off is that incorrect individual assignments, e.g., due to potentially misassembled or misbinned 16S rRNA gene sequences in MAGs, can result in overly conservative assignments on high taxonomic ranks. CAMITAX therefore also reports the maximal root-to-leaf path as an alternative, and we suggest that the user investigate taxonomic discrepancies manually, taking individual assignments into account.CAMITAX incorporates many state-of-the-art pieces of software, and automatically resolves all software and database dependencies with Nextflow  in a conWe applied CAMITAX to real data not present in its databases, a recent collection of 885 bacterial and archaeal MAGs from Delmont et\u00a0al. , who useDelmont et\u00a0al.  used CheAs expected, CAMITAX outperformed CheckM, which is rather conservative in its assignments, by adding low-ranking annotations based on high-quality predictions of other tools, such as Kaiju Fig.\u00a0. NotablyTo quantify taxonomic assignment performance, we calculated precision, recall, and accuracy across all ranks with AMBER 2.0  Fig.\u00a03)3). As thWe thus propose CAMITAX as a reliable and reproducible taxonomic assignment workflow, ideally followed by a manual refinement step\u2014as always.CAMITAX was initially developed while preparing the second Critical Assessment of Metagenome Interpretation (CAMI) challenge . The chaCAMITAX combines different taxonomic assignment strategies into one unifying workflow implementation. It uses Nextflow to orchestrate reference databases and software containers. Therefore, both databases and software can be easily substituted, providing the flexibility to cope with rapid change of standards oftentimes observed in the field. For instance, Parks et\u00a0al. recently proposed a standardized bacterial taxonomy based on genome phylogeny, the so-called Genome Taxonomy Database (GTDB) . While Chttps://github.com/CAMI-challenge/CAMITAX.CAMITAX is implemented in Nextflow and Python\u00a03 and is freely available under Apache License 2.0 at Mash sketches for all bacterial and archaeal genomes in RefSeq, snapshots of the NCBI Taxonomy databases, and Centrifuge and Kaiju indices for the proGenomes genes and proteins datasets are collected in Zenodo , as are Dada2-formatted training fasta files, derived from SILVA (release 132) and RDP , are also available in Zenodo , 71.https://data.ace.uq.edu.au/public/CheckM_databases.The CheckM reference databases are available at Snapshots of our code and other data further supporting this work are available in the GigaScience respository, GigaDB .ANI: average nucleotide identity; BLAST: Basic Local Alignment Search Tool; CPR: Candidate Phyla Radiation; LCA: lowest common ancestor; MAG: metagenome-assembled genome; NCBI: National Center for Biotechnology Information; nr/nt: non-redundant nucleotide; RAST: Rapid Annotation using Subsystem Technology; RDP: Ribosomal Database Project; rRNA: ribosomal RNA; SAG: single amplified genome.The authors declare that they have no competing interests.A.B. implemented the software, performed experiments, and wrote the manuscript with comments from A.F. and A.C.M. A.F. thoroughly tested the software. A.B. and A.C.M. jointly conceived the project and evaluated results. All authors read and approved the final manuscript.giz154_GIGA-D-19-00212_Original_SubmissionClick here for additional data file.giz154_GIGA-D-19-00212_Revision_1Click here for additional data file.giz154_GIGA-D-19-00212_Revision_2Click here for additional data file.giz154_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giz154_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giz154_Reviewer_1_Report_Original_SubmissionBen Woodcroft -- 7/29/2019 ReviewedClick here for additional data file.giz154_Reviewer_2_Report_Original_SubmissionBruno Fosso -- 8/6/2019 ReviewedClick here for additional data file."}
+{"text": "Neurons in the supragranular layers of the somatosensory cortex integrate sensory (bottom-up) and cognitive/perceptual (top-down) information as they orchestrate communication across cortical columns. It has been inferred, based on intracellular recordings from juvenile animals, that supragranular neurons are electrically mature by the fourth postnatal week. However, the dynamics of the neuronal integration in adulthood is largely unknown. Electrophysiological characterization of the active properties of these neurons throughout adulthood will help to address the biophysical and computational principles of the neuronal integration.Here, we provide a database of whole-cell intracellular recordings from 315 neurons located in the supragranular layers (L2/3) of the primary somatosensory cortex in adult mice (9\u201345\u00a0weeks old) from both sexes . Data include 361 somatic current-clamp (CC) and 476 voltage-clamp (VC) experiments, recorded using a step-and-hold protocol , frozen noise injections  and triangular voltage sweeps , 50 (N\u00a0= 146) and 100\u00a0ms (N\u00a0= 152)), from regular spiking (N\u00a0= 169) and fast-spiking neurons (N\u00a0= 66).The data can be used to systematically study the properties of somatic integration and the principles of action potential generation across sexes and across electrically characterized neuronal classes in adulthood. Understanding the principles of the somatic transformation of postsynaptic potentials into action potentials will shed light onto the computational principles of intracellular information transfer in single neurons and information processing in neuronal networks, helping to recreate neuronal functions in artificial systems. The primary somatosensory cortex encodes time-varying but spatially well-defined haptic information  from theUnderstanding the principles of neuronal information transfer in the supragranular layers will require a systematic analysis of the integrative properties of these cortical neurons. Thus far, however, slice experiments primarily focused on juvenile animals as it is widely considered that the neurons mature anatomically and electrophysiologically by the fourth postnatal week , 19\u201323. RRID:MGI:5315557) or Ssttm2.1(cre)Zjh/J mice (RRID:IMSR_JAX:013044) from either sex  from the local breeding colonies were used.Experiments that involve animals were conducted in accordance with the European Directive 2010/63/EU, national regulations in the Netherlands, and international guidelines on animal care and use of animals. Pvalbtm1(cre)Arbr  in the same ice-cold perfusion medium. The slices were then transferred to a chamber containing artificial cerebrospinal fluid (aCSF) (in mM): 120 NaCl, 3.5 KCl, 10 glucose\u00b7H2O, 2.5 CaCl2\u00b72H2O, 1.3 MgSO4\u00b77H2O, 25 NaHCO3, and 1.25 NaH2PO4\u00b7H2O and aerated with 95% O2/5% CO2 at 37\u00b0C. After 30 minutes, the slices were transferred to room temperature before whole-cell electrophysiological recordings started.The mice were anesthetized with Isoflurane (1.5\u00a0mL/mouse) before the tissue was extracted and coronal slices of the primary somatosensory cortex, barrel subfield region, were prepared  noise (max peak-to-peak amplitude 0.2\u00a0mV) that exists in a subset (\u223c4%) of the recordings was not filtered. Patch-clamp electrodes were pulled from glass capillaries  with a P-2000 puller  and used if their initial resistance was between 5 and 10\u00a0MOhm. They were filled with intracellular solution containing (in mM) 130 K-Gluconate, 5 KCl, 1.5\u00a0MgCllsewhere , 33 and After establishing the CC configuration, the resting membrane potential was set to \u221270\u00a0mV by direct somatic current injections, as required. The step-and-hold stimulation protocol included 10 steps of 500\u00a0ms long depolarization pulses  with an inter-sweep interval of 6.5 s. The stimulus train was repeated 1\u20133 times with a 20 s interval. The drift, if any, in resting membrane potential during the recording was not corrected for. However, any neuron whose resting membrane potential varied more than 7\u00a0mV was not included in the database. The frozen-noise (FN) stimulation protocol involved somatic injection of the current that is the output of an artificial neural network of 1,000 neurons, each firing Poisson spike trains in response to a \u201chidden state\u201d (see  for detaThe VC stimulation protocols included step-and-hold and sawtooth (triangular) pulse injections Fig.\u00a0. In bothFiles in \u201c.mat\u201d (MATLAB) format containing the original traces from each experiment are organized in folders separated by the structure described in Fig.\u00a0The CC data (see \u201cCurrent Clamp\u201d folder) contain two subfolders, Step Protocol and Frozen Noise. Step Protocol data include two channels (voltage and current), each of which includes two columns  for each repetition. Users can visualize both the current injected to clamp the soma and the observed voltage response. Data from each stimulus condition are saved under a separate variable that starts with \u201cTrace_a_b_c_d\u201d and includes information about (a) the cell and experiment ID, (b) the data type, (c) the number of sweeps in each dataset, and (d) the channels.The Frozen Noise subfolder contains the voltage trace , hidden state  and VC Sawtooth, the latter containing three subfolders with recordings from experiments with triangular sweeps at three frequencies . Data in the Voltage Clamp folder is organized similar to data in the Current Clamp folder, and variable naming follows the formatting rules described above.K-means clustering  was performed to classify neurons into fast-spiking and regular-spiking neurons, using CC step-and-hold recordings. The clustering was based on the maximum firing rate reached during the current step injections and on the mean spike half-width across all stimulus steps during the CC step-and-hold protocol. Please note that the cell classification is solely provided to help the user navigate the data. We do not claim that neurons can be necessarily electrically classified in a binary fashion nor do we claim that commonly utilized clustering approaches are optimal for accurate  classification of excitatory (mostly regular spiking) and inhibitory (predominantly fast-spiking) neurons.The dataset is rich in information regarding current vs voltage dynamics in adult cortical neurons. The independent variables in the database are the sex and age of the animal. While CC experiments provide information about sub- and suprathreshold voltage dynamics, the VC experiments are informative about the ionic conductances that lead to activation or inactivation of neurons.In the step-and-hold CC experiments, the voltage responses can be quantified using subthreshold  and suprathreshold  responses to somatic current injection Fig.\u00a0. BecauseAction potentials can be studied both in terms of their shape  and temporal response properties . Since adaptation to a sustained current injection is commonly used as a criterion to classify neurons, the data provide an inclusive database for the electrical classification of adult neurons, creating synergy with other publicly available databases, e.g., Neurodata Without Borders  and the In addition to sustained somatic depolarization, the CC database also includes FN injections, during which a time-varying current was injected into the recorded neuron Fig.\u00a0. The injIn the database, experimental data recorded from our FN protocol include the recorded membrane potential voltage, the hidden state, and the current injected into the neurons Fig.\u00a0. Thus, tGoing beyond the voltage dynamics in the adult neurons, the database also provides insight into the ionic currents that flow through the membrane. With the triangle-shaped VC-Saw protocol Fig.\u00a0, it is pThe current-voltage relationship was measured with VC steps Fig.\u00a0, which cProject name: Rapid mutual information calculation using frozen noise injectionhttps://github.com/DepartmentofNeurophysiology/Analysis-tools-for-electrophysiological-somatosensory-cortex-databank[Project home page: -databankOperating system: Platform independentProgramming language: MATLABOther requirements: MATLAB version 2017a or higher.License: GNU GPLRRID:SCR_016558GigaScience repository, GigaDB [Snapshots of the database and code, including additional supporting data, are available in the , GigaDB .Lantyer_Supplemental Table_Metadata.xlsxFrom the recordings available in this database, it is possible to actively quantify the membrane properties of supragranular layer neurons to infer current and voltage dynamics during somatic depolarization.Network development is based on processes of self-organization that are highly dependent on sensory stimuli and experience . Such plThe focus on adult neurons brings a new perspective to the study of membrane properties, as data from this mature age are still scarce. The dynamics of the active electrical properties of the membrane can be accessed as a function of different developmental time points and/or sex, and the recorded data can be used as virtual neurons in dynamic-clamp experiments.In a computational approach, spiking properties described herein could be used for biomimetic modeling of diverse networks, facilitating the study of computational roles of circuit motives. Moreover, applying the principles of information transfer and recovery to the data might help recreate neuronal functions in artificial systems.Neurons in this dataset originate from regular-spiking and fast-spiking neurons; however, there is no anatomical characterization of the neuron type studied. The database is focused on layer 2/3 of the somatosensory cortex as a model region and does not allow the study of neuronal information processing across different cortical regions in isolation. However, the user might consider comparing data across different regions and species by utilizing the other publicly available databases, e.g., Neurodata Without Borders , the CelaCSF: artificial cerebrospinal fluid; AP: action potential; CC: current clamp; FN: frozen noise; I/V: current/voltage; L2/3: cortical layer 2/3; VC: voltage clampThe authors declare that they have no competing interests.This work was supported by a doctoral fellowship from the National Council for Scientific and Technological Development of Brazil (CNPq) to A.S.L.; grants from the European Commission , European Regional Development Fund , and the Netherlands Organisation for Scientific Research  to T.C.; and by the Netherlands Organisation for Scientific Research  to F.Z.GIGA-D-18-00318_Original_Submission.pdfClick here for additional data file.GIGA-D-18-00318_Revision_1.pdfClick here for additional data file.GIGA-D-18-00318_Revision_2.pdfClick here for additional data file.GIGA-D-18-00318_Revision_3.pdfClick here for additional data file.Response_to_Reviewer_Comments_Original_Submission.pdfClick here for additional data file.Response_to_Reviewer_Comments_Revision_1.pdfClick here for additional data file.Response_to_Reviewer_Comments_Revision_2.pdfClick here for additional data file.Reviewer_1_Report_ -- Janos Fuzik9/7/2018 ReviewedClick here for additional data file.Reviewer_1_Report_(Revision_1) -- Janos Fuzik10/29/2018 ReviewedClick here for additional data file.Reviewer_2_Report_ -- Hongdian Yang9/14/2018 ReviewedClick here for additional data file.Reviewer_3_Original_Submission_(Attachment).pdfClick here for additional data file.Reviewer_3_Report_ -- Suhasa Bangalore Kodandaramaiah10/2/2018 ReviewedClick here for additional data file.Supplemental FileClick here for additional data file."}
+{"text": "Depth of coverage calculation is an important and computationally intensive preprocessing step in a variety of next-generation sequencing pipelines, including the analysis of RNA-sequencing data, detection of copy number variants, or quality control procedures.Building upon big data technologies, we have developed SeQuiLa-cov, an extension to the recently released SeQuiLa platform, which provides efficient depth of coverage calculations, reaching >100\u00d7 speedup over the state-of-the-art tools. The performance and scalability of our solution allow for exome and genome-wide calculations running locally or on a cluster while hiding the complexity of the distributed computing with Structured Query Language Application Programming Interface.SeQuiLa-cov provides significant performance gain in depth of coverage calculations streamlining the widely used bioinformatic processing pipelines. Given a set of sequencing reads and a genomic contig, depth of coverage for a given position is defined as the total number of reads overlapping the locus.The coverage calculation is a frequently performed but time-consuming step in the analysis of next-generation sequencing (NGS) data. In particular, copy number variant detection pipelines require obtaining sufficient read depth of the analyzed samples . In otheA number of tools supporting this operation have been developed, with 22 of them specified in the Omictools catalog\u00a0. Well-knTraditionally, these methods calculate the depth of coverage using a pileup-based approach   Fig.\u00a0.ith partition containing the set of reads (read_seti), the set of eventsi,chr vectors (where chr is an index of genomic contig represented in read_set) is allocated and updated, based on the items from read_seti. For all reads, the algorithm parses the concise idiosyncratic gapped alignment report (CIGAR) string, and for each continuous alignment block characterized by start position and length len it increments by 1 the eventsi,chr(start) and decrements by 1 the value of eventsi,chr(start + len). To compute the partial coverage vector for partition i and contig chr, a vector value at the index j is calculated as follows: In the most general case, the algorithm can be used in a distributed environment where each cluster node computes the coverage for the subset of data slices using the event-based method. Specifically, for the i,chr vectors distributed among the computation nodes. To calculate the final coverage for the whole read_set, an additional step of correction for overlaps between the partitions is required. An overlap overlapi,chr of length l between vectors partial_coveragei,chr and partial_coveragei+1,chr may occur on the partition boundaries where l tailing genomic positions of partial_coveragei,chr are the same as l heading genomic positions of partial_coveragei+1,chr  among the available computation nodes. Moreover, instead of simply performing the full data reduction stage of the partial coverage vectors, our solution minimizes required data shuffling among cluster nodes by limiting it to the overlapping part of coverage vectors. Importantly, the SeQuiLa-cov computation model supports fine-grained parallelism at a user-defined partition size in contrast to the traditional, coarse-grained parallelization strategies that involve splitting input data at a contig level.We have implemented SeQuiLa-cov in Scala programming language using the Apache Spark framework. To efficiently access the data from a BAM file we have prepared a custom data source using Data Source API exposed by SparkSQL. Performance of the read operation benefits from the Intel Genomics Kernel Library (GKL)  used forThe implementation of the core coverage calculation algorithm aimed to minimize the memory footprint whenever possible by using parsimonious data types, e.g., \u201cShort\u201d type instead of \u201cInteger,\u201d and to implement an efficient memory allocation strategy for large data structures, e.g., favoring static Arrays over dynamic size ArrayBuffers. Additionally, to reduce the overhead of data shuffling between the worker nodes in the correction for overlap stage, we used Spark\u2019s shared variables  \u201caccumulSeQuiLa-cov features 3 distinct result types: \u201cper-base,\u201d \u201cblocks,\u201d and \u201cfixed-length windows\u201d coverage Fig.\u00a0. For perThe SeQuiLa-cov solution promotes SQL as a data query and manipulation language in genomic analysis. Data flows are performed in SQL-like manner through the custom data source, supporting the convenient Create Table as Select and Insert as Select methods. SeQuiLa-cov provides a table abstraction over existing alignment files, with no need of data conversion, which can be further queried and manipulated in a declarative way. The coverage calculation function bdg_coverage, as described in the Algorithm subsection, has been implemented as a table-valued function Fig.\u00a0.SeQuiLa-cov can be used as an extension to Apache Spark in the form of an external JAR dependency or can be executed from the command line as a Docker container. Both options can be run locally (on a single node) or on a Hadoop cluster using YARN (see project documentation for sample commands). The tool accepts BAM/CRAM files as input and supports processing of short and long reads. The tabular output of the coverage computations can be stored in various file formats, e.g., binary , as well as text . The tool can be integrated with state-of-the-art applications through text files or can be used directly as an additional library in bioinformatics pipelines implemented in Scala, R, or Python.http://biodatageeks.org/sequila/benchmarking/benchmarking.html#depth-of-coverage). The tests were performed on the aligned whole-exome sequencing (WES) and whole-genome sequencing (WGS) reads from the NA12878 sample (see Methods for details) and aimed at calculating blocks and window coverage. To compare the performance and scalability of each solution, we executed calculations for 1, 5, and 10 cores on a single computation node  against samtools when using a single thread. This performance gain increases to \u223c3.7\u00d7 when using 5 decompression threads; however, it does not benefit from adding additional CPU power. In the case of fixed-length window coverage mosdepth achieves more than \u223c1.3 speedup against sambamba.SeQuiLa-cov achieves performance similar to mosdepth when run using a single core. However, SeQuiLa-cov is \u223c1.3\u00d7 and \u223c2.5\u00d7 as fast as mosdepth when using 5 and 10 CPU cores, respectively, demonstrating its better scalability. Similar performance is observed for both block and fixed-length window methods.To fully assess the scalability profile of our solution, we performed additional tests in a cluster environment (see Methods for details). Our results show that when utilizing additional resources , SeQuiLa-cov is able to reduce the total computation time to 15 seconds for WES and <1 minute for WGS data Fig.\u00a0. The scaTo evaluate the impact of the Intel GKL library on the deflate operation (BAM bzgf block decompression), we performed block coverage calculations on WES data on 50 CPU cores. The results showed on average \u223c1.18\u00d7 speedup when running with the Intel GKL deflate implementation.Finally, our comprehensive functional unit testing showed that the results calculated by SeQuiLa-cov and samtools depth are identical.Recent advances in big data technologies and distributed computing can contribute to speeding up both genomic data processing and management. Analysis of large genomic data sets requires efficient, accurate, and scalable algorithms to perform calculations using the computing power of multiple cluster nodes. In this work, we show that with a sufficiently large cluster, genome-wide coverage calculations may last <1 minute and at the same time be >100\u00d7 faster than the best single-threaded solution.Although the tool can be integrated with non-distributed software, our primary aim is to support large-scale processing pipelines, and the full advantage of SeQuiLa-cov\u2019s scalability and performance will be available once it is deployed and executed in a distributed environment. We expect that there will be a growing number of scalable solutions  and WGS data included >2.6 billion reads (272\u00a0GB of disk space). Both BAM files were compressed at the default BAM compression level (5).To perform comprehensive performance evaluation, we set up a test cluster consisting of 28 Hadoop nodes  with Hortonworks Data Platform 3.0.1 installed. Each data node has 28 cores (56 with hyper-threading) and 512\u00a0GB of RAM; YARN resource pool has been configured with 2,640 virtual cores and 9,671\u00a0GB RAM.In our benchmark we used the most recent versions of the investigated tools, i.e., samtools version 1.9, bedtools 2.27.0, sambamba 0.6.8, mosdepth version 0.2.3, and SeQuiLa-cov version 0.5.1.\u2022 Project name: SeQuiLa-covhttp://biodatageeks.org/sequila/\u2022 Project home page: https://github.com/ZSI-Bio/bdg-sequila\u2022 Source code repository: \u2022 Operating system: Platform independent\u2022 Programming language: Scala\u2022 Other requirements: Docker\u2022 License: Apache License 2.0\u2022 RRID: SCR_017220https://hub.docker.com/r/biodatageeks/. Supplementary information on benchmarking procedure as well as test data are publicly accessible at the project documentation site http://biodatageeks.org/sequila/benchmarking/benchmarking.html#depth-of-coverage. An archival copy of the code and supporting data is also available via the GigaScience database GigaDB [The Docker image is available at e GigaDB .API: Application Programming Interface; BAM: Binary Alignment Map; CPU: central processing unit; CSV: comma-separated values; GKL: Genomics Kernel Library; NGS: next-generation sequencing; ORC: optimized row columnar; RAM: random access memory; SQL: Structured Query Language; TSV: tab-separated values; YARN: Yet Another Resource Negotiator; WES: whole-exome sequencing; WGS: whole-genome sequencing.The authors declare that they have no competing interests.This work has been supported by the Polish budget funds for science in years 2016\u20132019 (Iuventus Plus grant IP2015 019874), as well as Polish National Science Center grant Preludium 2014/13/N/ST6/01843.M.W.: conceptualization, formal analysis, investigation, software, and writing. A.S.: data curation, formal analysis, investigation, software, visualization, and writing. W.K.: formal analysis, investigation, writing. T.G.: formal analysis, supervision, investigation, visualization, and writing. All authors approved the final manuscript.giz094_GIGA-D-18-00504-Original-SubmissionClick here for additional data file.giz094_GIGA-D-18-00504_Revision-1Click here for additional data file.giz094_GIGA-D-18-00504_Revision-2Click here for additional data file.giz094_GIGA-D-18-00504_Revision-3Click here for additional data file.giz094_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giz094_Response_to_Reviewer_Comments_Revision-1Click here for additional data file.giz094_Response_to_Reviewer_Comments_Revision_2Click here for additional data file.giz094_Reviewer_1_Report_Original_SubmissionBrent Pedersen -- 1/22/2019 ReviewedClick here for additional data file.giz094_Reviewer_1_Report_Revision_1Brent Pedersen -- 6/4/2019 ReviewedClick here for additional data file.giz094_Reviewer_2_Report_Original_SubmissionGianluigi Zanetti -- 2/10/2019 ReviewedClick here for additional data file.giz094_Reviewer_3_Report_Original_SubmissionSara Goodwin -- 2/26/2019 ReviewedClick here for additional data file."}